diff --git a/.ipynb_checkpoints/API - Web Project - Miguel-checkpoint.ipynb b/.ipynb_checkpoints/API - Web Project - Miguel-checkpoint.ipynb
new file mode 100644
index 0000000..687ffa9
--- /dev/null
+++ b/.ipynb_checkpoints/API - Web Project - Miguel-checkpoint.ipynb
@@ -0,0 +1,1306 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests as req\n",
+ "import json as js\n",
+ "import pandas as pd\n",
+ "from pandas.io.json import json_normalize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#datau = req.get('http://universities.hipolabs.com/search?')\n",
+ "#datau = datau.json()\n",
+ "#dfu = pd.DataFrame(datau)\n",
+ "#dfu\n",
+ "#información muy pequeña y simple. Sin keys de autentificación"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#datauk = req.get('https://api.carbonintensity.org.uk/intensity')\n",
+ "#datauk_2 = datauk.json()\n",
+ "#type(datauk_2)\n",
+ "#datauk_2\n",
+ "#funciona bien pero debe ir cambiando los valores dentro de la URL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " '2019-07-19 15:50:00': {'1. open': '137.0450',\n",
+ " '2. high': '137.0700',\n",
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+ " '3. low': '137.0500',\n",
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+ " '3. low': '136.7100',\n",
+ " '4. close': '136.7505',\n",
+ " '5. volume': '455275'},\n",
+ " '2019-07-19 14:30:00': {'1. open': '137.0300',\n",
+ " '2. high': '137.1700',\n",
+ " '3. low': '136.6900',\n",
+ " '4. close': '136.7180',\n",
+ " '5. volume': '612677'},\n",
+ " '2019-07-19 14:25:00': {'1. open': '137.1480',\n",
+ " '2. high': '137.3400',\n",
+ " '3. low': '137.0200',\n",
+ " '4. close': '137.0500',\n",
+ " '5. volume': '474916'},\n",
+ " '2019-07-19 14:20:00': {'1. open': '137.3300',\n",
+ " '2. high': '137.4100',\n",
+ " '3. low': '137.1300',\n",
+ " '4. close': '137.1425',\n",
+ " '5. volume': '351629'},\n",
+ " '2019-07-19 14:15:00': {'1. open': '137.3400',\n",
+ " '2. high': '137.4218',\n",
+ " '3. low': '137.1900',\n",
+ " '4. close': '137.3200',\n",
+ " '5. volume': '551611'}}}"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "url2 = 'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=MSFT&interval=5min&apikey=AEVIFBN3Y7WUWVL9'\n",
+ "alpha = req.get(url2)\n",
+ "alpha = alpha.json()\n",
+ "alpha\n",
+ "#AEVIFBN3Y7WUWVL9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(alpha)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['Meta Data', 'Time Series (5min)'])"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "alpha.keys()\n",
+ "#dict_keys(['symbol', 'financials'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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5 rows × 100 columns
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+ " 2019-07-22 15:15:00 ... 2019-07-19 15:00:00 2019-07-19 14:55:00 \\\n",
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+ "2. high 138.6850 ... 137.1300 137.2800 \n",
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+ "\n",
+ "[5 rows x 100 columns]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_df = pd.DataFrame(alpha[\"Time Series (5min)\"])\n",
+ "msft_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "msft_df.to_csv('msft_df.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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+ " 3. low \n",
+ " 4. close \n",
+ " 5. volume \n",
+ " \n",
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+ " \n",
+ " \n",
+ " 2019-07-22 16:00:00 \n",
+ " 138.4400 \n",
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\n",
+ "
"
+ ],
+ "text/plain": [
+ " 1. open 2. high 3. low 4. close 5. volume\n",
+ "2019-07-22 16:00:00 138.4400 138.5500 138.3400 138.4300 886466\n",
+ "2019-07-22 15:55:00 138.4400 138.4900 138.3500 138.4400 321964\n",
+ "2019-07-22 15:50:00 138.3750 138.4550 138.3400 138.4400 252689\n",
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+ "2019-07-22 15:40:00 138.3400 138.4000 138.3300 138.3650 249061"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#a partir de aqui se empieza a editar el dataframe. 1. Agregar cabeceras, 2. dividir columnas de fechas / horas\n",
+ "# 3. realizar analisis de maximo - minimo valor\n",
+ "#vamos volumen transado\n",
+ "msft_dfT2 = msft_df.transpose()\n",
+ "msft_dfT2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['1. open', '2. high', '3. low', '4. close', '5. volume'], dtype='object')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
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+ " \n",
+ " Open Price \n",
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+ " Lowest Price \n",
+ " Close Price \n",
+ " Volume_Ops \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
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+ " 138.4400 \n",
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+ "text/plain": [
+ " Open Price Highest Price Lowest Price Close Price \\\n",
+ "2019-07-22 16:00:00 138.4400 138.5500 138.3400 138.4300 \n",
+ "2019-07-22 15:55:00 138.4400 138.4900 138.3500 138.4400 \n",
+ "2019-07-22 15:50:00 138.3750 138.4550 138.3400 138.4400 \n",
+ "2019-07-22 15:45:00 138.3600 138.4700 138.3350 138.3850 \n",
+ "2019-07-22 15:40:00 138.3400 138.4000 138.3300 138.3650 \n",
+ "\n",
+ " Volume_Ops \n",
+ "2019-07-22 16:00:00 886466 \n",
+ "2019-07-22 15:55:00 321964 \n",
+ "2019-07-22 15:50:00 252689 \n",
+ "2019-07-22 15:45:00 233272 \n",
+ "2019-07-22 15:40:00 249061 "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2.columns = ['Open Price', 'Highest Price', 'Lowest Price', 'Close Price', 'Volume_Ops']\n",
+ "msft_dfT2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Open Price object\n",
+ "Highest Price object\n",
+ "Lowest Price object\n",
+ "Close Price object\n",
+ "Volume_Ops object\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " 2019-07-22 16:00:00 \n",
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+ " 138.550 \n",
+ " 138.340 \n",
+ " 138.430 \n",
+ " 886466.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:55:00 \n",
+ " 138.440 \n",
+ " 138.490 \n",
+ " 138.350 \n",
+ " 138.440 \n",
+ " 321964.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:50:00 \n",
+ " 138.375 \n",
+ " 138.455 \n",
+ " 138.340 \n",
+ " 138.440 \n",
+ " 252689.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:45:00 \n",
+ " 138.360 \n",
+ " 138.470 \n",
+ " 138.335 \n",
+ " 138.385 \n",
+ " 233272.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:40:00 \n",
+ " 138.340 \n",
+ " 138.400 \n",
+ " 138.330 \n",
+ " 138.365 \n",
+ " 249061.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Open Price Highest Price Lowest Price Close Price \\\n",
+ "2019-07-22 16:00:00 138.440 138.550 138.340 138.430 \n",
+ "2019-07-22 15:55:00 138.440 138.490 138.350 138.440 \n",
+ "2019-07-22 15:50:00 138.375 138.455 138.340 138.440 \n",
+ "2019-07-22 15:45:00 138.360 138.470 138.335 138.385 \n",
+ "2019-07-22 15:40:00 138.340 138.400 138.330 138.365 \n",
+ "\n",
+ " Volume_Ops \n",
+ "2019-07-22 16:00:00 886466.0 \n",
+ "2019-07-22 15:55:00 321964.0 \n",
+ "2019-07-22 15:50:00 252689.0 \n",
+ "2019-07-22 15:45:00 233272.0 \n",
+ "2019-07-22 15:40:00 249061.0 "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2 = msft_dfT2.astype(float).round(3)\n",
+ "msft_dfT2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
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\n",
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+ " Lowest Price \n",
+ " Close Price \n",
+ " Volume_Ops \n",
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+ " \n",
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+ " \n",
+ " 2019-07-22 10:10:00 \n",
+ " 138.78 \n",
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+ " Open Price Highest Price Lowest Price Close Price \\\n",
+ "2019-07-22 10:10:00 138.78 139.19 138.76 139.0 \n",
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+ " Volume_Ops \n",
+ "2019-07-22 10:10:00 792631.0 "
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "maxP = msft_dfT2['Highest Price'].max()\n",
+ "msft_dfT2[(msft_dfT2['Highest Price'] == maxP)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "msft_dfT2.to_csv('msft_dfT2.csv', index=False)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
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+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
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+ "pygments_lexer": "ipython3",
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+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/Web Scrapping - Miguel-checkpoint.ipynb b/.ipynb_checkpoints/Web Scrapping - Miguel-checkpoint.ipynb
new file mode 100644
index 0000000..1485c31
--- /dev/null
+++ b/.ipynb_checkpoints/Web Scrapping - Miguel-checkpoint.ipynb
@@ -0,0 +1,3600 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import bs4\n",
+ "from bs4 import BeautifulSoup\n",
+ "import requests\n",
+ "import pandas\n",
+ "import lxml\n",
+ "import html5lib\n",
+ "import re"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "b'\\n\\n\\n \\nAnexo:Aglomeraciones urbanas m\\xc3\\xa1s pobladas del mundo - Wikipedia, la enciclopedia libre \\n\\n\\n \\n\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n\\n\\n\\n
\\n
\\n\\n\\t
\\n\\t
\\n\\t
\\n
\\n\\n\\t
Anexo:Aglomeraciones urbanas m\\xc3\\xa1s pobladas del mundo \\n\\t\\n\\t
\\n\\t\\t
De Wikipedia, la enciclopedia libre
\\n\\t\\t
\\n\\t\\t\\n\\t\\t\\n\\t\\t\\n\\t\\t
\\n\\t\\t
Ir a la navegación \\n\\t\\t
Ir a la búsqueda \\n\\t\\t
Este anexo contiene los listados de las aglomeraciones urbanas (megaciudades ) m\\xc3\\xa1s pobladas del mundo las estimaciones publicadas por el informe de las Naciones Unidas y Demographia para el mismo a\\xc3\\xb1o 2018, y la poblaci\\xc3\\xb3n con la que contaban dichas aglomeraciones al momento de hacerse el \\xc3\\xbaltimo censo donde se encuentre disponible.\\n
\\n
\\n\\n
Las 100 aglomeraciones urbanas m\\xc3\\xa1s pobladas del mundo [ editar ] \\n
\\n
Las mayores aglomeraciones urbanas de \\xc3\\x81frica [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en \\xc3\\x81frica seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las 50 mayores aglomeraciones urbanas del continente americano :\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica del Norte [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Am\\xc3\\xa9rica del Norte seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica Central y del Caribe [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Am\\xc3\\xa9rica Central y del Caribe , seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica del Sur [ editar ] \\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Am\\xc3\\xa9rica del Sur , seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Asia [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las 50 mayores aglomeraciones urbanas del continente asi\\xc3\\xa1tico .\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Oriente Medio, Asia Central y Siberia [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Oriente Medio , Asia Central y Asia del Norte seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas del subcontinente indio [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en el subcontinente indio seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Asia Oriental [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Asia Oriental seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas del Sureste Asi\\xc3\\xa1tico [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en el Sureste Asi\\xc3\\xa1tico seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Europa [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las 50 mayores aglomeraciones urbanas del continente Europeo .\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Europa Occidental [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Europa Occidental seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Europa Oriental [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Europa Oriental seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Ocean\\xc3\\xada [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Ocean\\xc3\\xada seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n\\nPosici\\xc3\\xb3n\\n \\nCiudad\\n \\nPa\\xc3\\xads\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban Citypopulation (2015)\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban ONU (2015)\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban Demographia (2015)\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban \\xc3\\xbaltimo censo\\n \\nFecha y fuente\\n \\n\\n1 \\nS\\xc3\\xaddney \\nAustralia Australia \\n4.850.000 \\n4.505.000 \\n4.036.000 \\n4.028.525 \\n2011 \\n \\n\\n2 \\nMelbourne \\nAustralia Australia \\n4.350.000 \\n4.203.000 \\n3.906.000 \\n3.847.567 \\n2011 \\n \\n\\n3 \\nBrisbane \\nAustralia Australia \\n2.875.000 \\n2.202.000 \\n1.999.000 \\n1.977.316 \\n2011 \\n \\n\\n4 \\nPerth \\nAustralia Australia \\n2.025.000 \\n1.861.000 \\n1.751.000 \\n1.670.952 \\n2011 \\n \\n\\n5 \\nAuckland \\nNueva Zelanda Nueva Zelanda \\n1.404.000 \\n1.344.000 \\n1.356.000 \\n1.308.831 \\n2013 \\n \\n\\n6 \\nAdelaida \\nAustralia Australia \\n1.290.000 \\n1.256.000 \\n1.140.000 \\n1.198.467 \\n2011 \\n \\n\\n7 \\nHonolulu [ n 9] \\nEstados Unidos Estados Unidos \\n1.000.000 \\n848.000 \\n842.000 \\n953.207 \\n2010 \\n
\\n
V\\xc3\\xa9ase tambi\\xc3\\xa9n [ editar ] \\n
\\n
Referencias y notas [ editar ] \\n
\\n
\\n
Referencias [ editar ] \\n
\\n
Enlaces externos [ editar ] \\n
\\n\\n\\n\\n\\n
\\n\\t\\t\\n\\t\\t\\n\\t\\t\\n\\t\\t
\\n\\t\\t
\\n\\t\\t\\n\\t
\\n
\\n\\n\\n\\t\\t\\n\\t\\t\\t
Men\\xc3\\xba de navegaci\\xc3\\xb3n \\n\\t\\t\\t
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\\n\\t\\t\\t\\t\\t\\t
Herramientas personales \\n\\t\\t\\t\\t\\t\\t
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Espacios de nombres \\n\\t\\t\\t\\t\\t\\t
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Vistas \\n\\t\\t\\t\\t\\t\\t
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\\n\\t\\t\\t\\t\\t\\t\\tBuscar \\n\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t\\t\\t
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Navegaci\\xc3\\xb3n \\n\\t\\t\\t
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Imprimir/exportar \\n\\t\\t\\t
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Herramientas \\n\\t\\t\\t
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En otros idiomas \\n\\t\\t\\t
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\\n\\t\\t\\t\\t
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\\n\\t\\t\\t\\t\\n\\t\\t\\n\\n\\n\\n\\n'"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wiki = requests.get('https://es.wikipedia.org/wiki/Anexo:Aglomeraciones_urbanas_m%C3%A1s_pobladas_del_mundo').content\n",
+ "wiki"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#wiki_soup = BeautifulSoup(wiki,'html')\n",
+ "#table = wiki_soup.find_all(['tr'])\n",
+ "#table = [row.text.strip().split(\"\\n\") for row in table]\n",
+ "#table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wiki2 = pandas.read_html(wiki) #solo funciona con tablas. Punto clave\n",
+ "#len(wiki2) #list of 28 pandas DataFrame \n",
+ "#type(wiki2) #list\n",
+ "#type(wiki2[0]) #dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\52557\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+ "of pandas will change to not sort by default.\n",
+ "\n",
+ "To accept the future behavior, pass 'sort=False'.\n",
+ "\n",
+ "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
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+ " Fecha y fuente \n",
+ " País \n",
+ " Población según Citypopulation (2015) \n",
+ " Población según Citypopulation (2016) \n",
+ " Población según Citypopulation[1] \n",
+ " Población según Demographia (2015) \n",
+ " Población según Demographia[2] \n",
+ " Población según ONU (2015) \n",
+ " Población según ONU[3] \n",
+ " Población según último censo \n",
+ " Población según último censo oficial \n",
+ " Posición \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Cantón \n",
+ " 2010 \n",
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+ " \n",
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+ " 2020 \n",
+ " Japón \n",
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+ " NaN \n",
+ " 8 945 695 \n",
+ " 2 \n",
+ " \n",
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+ " 2 \n",
+ " Shanghái \n",
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+ " \n",
+ " 3 \n",
+ " Yakarta \n",
+ " 2010 \n",
+ " Indonesia \n",
+ " NaN \n",
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+ " 30 600 000 \n",
+ " NaN \n",
+ " 11 399 000 \n",
+ " NaN \n",
+ " 30 477 000 \n",
+ " NaN \n",
+ " 25 420 288 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " Delhi \n",
+ " 2011 \n",
+ " India \n",
+ " NaN \n",
+ " NaN \n",
+ " 29 400 000 \n",
+ " NaN \n",
+ " 25 703 000 \n",
+ " NaN \n",
+ " 24 998 000 \n",
+ " NaN \n",
+ " 16 349 831 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " Manila \n",
+ " 2010 \n",
+ " Filipinas \n",
+ " NaN \n",
+ " NaN \n",
+ " 25 200 000 \n",
+ " NaN \n",
+ " 12 946 000 \n",
+ " NaN \n",
+ " 24 123 000 \n",
+ " NaN \n",
+ " 1 652 171 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " Seúl \n",
+ " 2010 \n",
+ " Corea del Sur \n",
+ " NaN \n",
+ " NaN \n",
+ " 24 700 000 \n",
+ " NaN \n",
+ " 13 558 000 \n",
+ " NaN \n",
+ " 23 480 000 \n",
+ " NaN \n",
+ " 23 836 272 \n",
+ " 7 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " Bombay \n",
+ " 2011 \n",
+ " India \n",
+ " NaN \n",
+ " NaN \n",
+ " 24 700 000 \n",
+ " NaN \n",
+ " 21 043 000 \n",
+ " NaN \n",
+ " 21 732 000 \n",
+ " NaN \n",
+ " 19 617 302 \n",
+ " 8 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " Ciudad de México \n",
+ " 2015 \n",
+ " México \n",
+ " NaN \n",
+ " NaN \n",
+ " 22 800 000 \n",
+ " NaN \n",
+ " 22 452 000 \n",
+ " NaN \n",
+ " 20 063 000 \n",
+ " NaN \n",
+ " 20 892 724 \n",
+ " 9 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " Nueva York \n",
+ " 2010 \n",
+ " Estados Unidos \n",
+ " NaN \n",
+ " NaN \n",
+ " 22 400 000 \n",
+ " NaN \n",
+ " 19 532 000 \n",
+ " NaN \n",
+ " 20 630 000 \n",
+ " NaN \n",
+ " 19 556 440 \n",
+ " 10 \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " São Paulo \n",
+ " 2010 \n",
+ " Brasil \n",
+ " NaN \n",
+ " NaN \n",
+ " 22 200 000 \n",
+ " NaN \n",
+ " 21 066 000 \n",
+ " NaN \n",
+ " 20 365 000 \n",
+ " NaN \n",
+ " 19 683 975 \n",
+ " 11 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " El Cairo \n",
+ " 2006 \n",
+ " Egipto \n",
+ " NaN \n",
+ " NaN \n",
+ " 20 500 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 7 740 018 \n",
+ " 12 \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " Pekín \n",
+ " 2010 \n",
+ " China \n",
+ " NaN \n",
+ " NaN \n",
+ " 20 400 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 16 446 857 \n",
+ " 13 \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " Daca \n",
+ " 2011 \n",
+ " Bangladés \n",
+ " NaN \n",
+ " NaN \n",
+ " 19 500 000 \n",
+ " NaN \n",
+ " 17 598 000 \n",
+ " NaN \n",
+ " 15 669 000 \n",
+ " NaN \n",
+ " 14 543 124 \n",
+ " 14 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " Lagos \n",
+ " 1991 \n",
+ " Nigeria \n",
+ " NaN \n",
+ " NaN \n",
+ " 18 800 000 \n",
+ " NaN \n",
+ " 18 772 000 \n",
+ " NaN \n",
+ " 15 600 000 \n",
+ " NaN \n",
+ " 5 195 247 \n",
+ " 15 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " Bangkok \n",
+ " 2010 \n",
+ " Tailandia \n",
+ " NaN \n",
+ " NaN \n",
+ " 18 300 000 \n",
+ " NaN \n",
+ " 11 084 000 \n",
+ " NaN \n",
+ " 14 998 000 \n",
+ " NaN \n",
+ " 8 986 218 \n",
+ " 16 \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " Los Ángeles \n",
+ " 2010 \n",
+ " Estados Unidos \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 800 000 \n",
+ " NaN \n",
+ " 14 504 000 \n",
+ " NaN \n",
+ " 15 058 000 \n",
+ " NaN \n",
+ " 17 053 905 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " Osaka \n",
+ " 2010 \n",
+ " Japón \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 700 000 \n",
+ " NaN \n",
+ " 20 238 000 \n",
+ " NaN \n",
+ " 17 444 000 \n",
+ " NaN \n",
+ " 2 665 314 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " Karachi \n",
+ " 2011 \n",
+ " Pakistán \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 300 000 \n",
+ " NaN \n",
+ " 16 618 000 \n",
+ " NaN \n",
+ " 22 123 000 \n",
+ " NaN \n",
+ " 21 142 625 \n",
+ " 19 \n",
+ " \n",
+ " \n",
+ " 19 \n",
+ " Moscú \n",
+ " 2010 \n",
+ " Rusia \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 200 000 \n",
+ " NaN \n",
+ " 12 166 000 \n",
+ " NaN \n",
+ " 16 170 000 \n",
+ " NaN \n",
+ " 11 612 885 \n",
+ " 20 \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Calcuta \n",
+ " 2011 \n",
+ " India \n",
+ " NaN \n",
+ " NaN \n",
+ " 16 600 000 \n",
+ " NaN \n",
+ " 14 865 000 \n",
+ " NaN \n",
+ " 14 667 000 \n",
+ " NaN \n",
+ " 14 057 991 \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Buenos Aires \n",
+ " 2017 \n",
+ " Argentina \n",
+ " NaN \n",
+ " NaN \n",
+ " 16 300 000 \n",
+ " NaN \n",
+ " 18 086 000 \n",
+ " NaN \n",
+ " 14 122 000 \n",
+ " NaN \n",
+ " 13 588 171 \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Estambul \n",
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+ " Turquía \n",
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+ " NaN \n",
+ " 15 800 000 \n",
+ " NaN \n",
+ " 14 164 000 \n",
+ " NaN \n",
+ " 13 287 000 \n",
+ " NaN \n",
+ " 14 657 000 \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Teherán \n",
+ " 2011 \n",
+ " Irán \n",
+ " NaN \n",
+ " NaN \n",
+ " 15 000 000 \n",
+ " NaN \n",
+ " 10 239 000 \n",
+ " NaN \n",
+ " 13 532 000 \n",
+ " NaN \n",
+ " 9 768 677 \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Londres \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " NaN \n",
+ " NaN \n",
+ " 14 700 000 \n",
+ " NaN \n",
+ " 10 313 000 \n",
+ " NaN \n",
+ " 10 236 000 \n",
+ " NaN \n",
+ " 11 140 445 \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Johannesburgo \n",
+ " 2009 \n",
+ " Sudáfrica \n",
+ " NaN \n",
+ " NaN \n",
+ " 13 700 000 \n",
+ " NaN \n",
+ " 12 613 000 \n",
+ " NaN \n",
+ " 12 066 000 \n",
+ " NaN \n",
+ " 10 002 039 \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " Tianjin \n",
+ " 2010 \n",
+ " China \n",
+ " NaN \n",
+ " NaN \n",
+ " 13 200 000 \n",
+ " NaN \n",
+ " 11 210 000 \n",
+ " NaN \n",
+ " 10 920 000 \n",
+ " NaN \n",
+ " 9 290 263 \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Río de Janeiro \n",
+ " 2010 \n",
+ " Brasil \n",
+ " NaN \n",
+ " NaN \n",
+ " 13 100 000 \n",
+ " NaN \n",
+ " 12 902 000 \n",
+ " NaN \n",
+ " 11 727 000 \n",
+ " NaN \n",
+ " 11 835 708 \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Lahore \n",
+ " 1998 \n",
+ " Pakistán \n",
+ " NaN \n",
+ " NaN \n",
+ " 12 600 000 \n",
+ " NaN \n",
+ " 8 741 000 \n",
+ " NaN \n",
+ " 10 052 000 \n",
+ " NaN \n",
+ " 5 143 495 \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Kinsasa \n",
+ " 2004 \n",
+ " República Democrática del Congo \n",
+ " NaN \n",
+ " NaN \n",
+ " 12 000 000 \n",
+ " NaN \n",
+ " 11 587 000 \n",
+ " NaN \n",
+ " 11 587 000 \n",
+ " NaN \n",
+ " 7 273 947 \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Lisboa \n",
+ " 2001 \n",
+ " Portugal \n",
+ " 2.600.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 2.666.000 \n",
+ " NaN \n",
+ " 2.884.000 \n",
+ " NaN \n",
+ " 564.657 \n",
+ " NaN \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Budapest \n",
+ " 2011 \n",
+ " Hungría \n",
+ " 2.550.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.710.000 \n",
+ " NaN \n",
+ " 1.714.000 \n",
+ " NaN \n",
+ " 1.729.040 \n",
+ " NaN \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Katowice \n",
+ " 2011 \n",
+ " Polonia \n",
+ " 2.400.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 2.190.000 \n",
+ " NaN \n",
+ " 303.000 \n",
+ " NaN \n",
+ " 310.764 \n",
+ " NaN \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Ámsterdam \n",
+ " 2001 \n",
+ " Países Bajos \n",
+ " 2.375.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.624.000 \n",
+ " NaN \n",
+ " 1.091.000 \n",
+ " NaN \n",
+ " 734.533 \n",
+ " NaN \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Stuttgart \n",
+ " 2011 \n",
+ " Alemania \n",
+ " 2.300.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.379.000 \n",
+ " NaN \n",
+ " 626.000 \n",
+ " NaN \n",
+ " 585.890 \n",
+ " NaN \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Varsovia \n",
+ " 2011 \n",
+ " Polonia \n",
+ " 2.275.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.720.000 \n",
+ " NaN \n",
+ " 1.722.000 \n",
+ " NaN \n",
+ " 1.700.612 \n",
+ " NaN \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
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+ " Rumania \n",
+ " 2.175.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.860.000 \n",
+ " NaN \n",
+ " 1.868.000 \n",
+ " NaN \n",
+ " 1.883.425 \n",
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+ " 27 \n",
+ " \n",
+ " \n",
+ " 27 \n",
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+ " Alemania \n",
+ " 2.175.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.981.000 \n",
+ " NaN \n",
+ " 1.438.000 \n",
+ " NaN \n",
+ " 1.348.335 \n",
+ " NaN \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Viena \n",
+ " 2011 \n",
+ " Austria \n",
+ " 2.125.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.763.000 \n",
+ " NaN \n",
+ " 1.753.000 \n",
+ " NaN \n",
+ " 2.015.580 \n",
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+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Leeds \n",
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+ " Reino Unido \n",
+ " 2.125.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.893.000 \n",
+ " NaN \n",
+ " 1.912.000 \n",
+ " NaN \n",
+ " 2.058.861 \n",
+ " NaN \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " Estocolmo \n",
+ " NaN \n",
+ " Suecia \n",
+ " 2.075.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.484.000 \n",
+ " NaN \n",
+ " 1.486.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 31 \n",
+ " \n",
+ " \n",
+ " 31 \n",
+ " Bruselas \n",
+ " NaN \n",
+ " Bélgica \n",
+ " 2.000.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 2.089.000 \n",
+ " NaN \n",
+ " 2.045.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 32 \n",
+ " \n",
+ " \n",
+ " 32 \n",
+ " Minsk \n",
+ " 2009 \n",
+ " Bielorrusia \n",
+ " 1.950.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.910.000 \n",
+ " NaN \n",
+ " 1.915.000 \n",
+ " NaN \n",
+ " 1.836.808 \n",
+ " NaN \n",
+ " 33 \n",
+ " \n",
+ " \n",
+ " 33 \n",
+ " Lyon \n",
+ " 1999 \n",
+ " Francia \n",
+ " 1.920.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.583.000 \n",
+ " NaN \n",
+ " 1.609.000 \n",
+ " NaN \n",
+ " 1.428.998 \n",
+ " NaN \n",
+ " 34 \n",
+ " \n",
+ " \n",
+ " 34 \n",
+ " Liverpool \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 1.830.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 875.000 \n",
+ " NaN \n",
+ " 870.000 \n",
+ " NaN \n",
+ " 1.367.147 \n",
+ " NaN \n",
+ " 35 \n",
+ " \n",
+ " \n",
+ " 35 \n",
+ " Valencia \n",
+ " 2011 \n",
+ " España \n",
+ " 1.780.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.561.000 \n",
+ " NaN \n",
+ " 810.000 \n",
+ " NaN \n",
+ " 792.054 \n",
+ " NaN \n",
+ " 36 \n",
+ " \n",
+ " \n",
+ " 36 \n",
+ " Nizni Nóvgorod \n",
+ " 2010 \n",
+ " Rusia \n",
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+ " NaN \n",
+ " NaN \n",
+ " 1.201.000 \n",
+ " NaN \n",
+ " 1.212.000 \n",
+ " NaN \n",
+ " 1.250.619 \n",
+ " NaN \n",
+ " 37 \n",
+ " \n",
+ " \n",
+ " 37 \n",
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+ " Italia \n",
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+ " NaN \n",
+ " NaN \n",
+ " 1.521.000 \n",
+ " NaN \n",
+ " 1.765.000 \n",
+ " NaN \n",
+ " 872.367 \n",
+ " NaN \n",
+ " 38 \n",
+ " \n",
+ " \n",
+ " 38 \n",
+ " Járkov \n",
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+ " 1.650.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.440.000 \n",
+ " NaN \n",
+ " 1.441.000 \n",
+ " NaN \n",
+ " 1.470.902 \n",
+ " NaN \n",
+ " 39 \n",
+ " \n",
+ " \n",
+ " 39 \n",
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+ " NaN \n",
+ " NaN \n",
+ " 1.397.000 \n",
+ " NaN \n",
+ " 1.605.000 \n",
+ " NaN \n",
+ " 1.463.016 \n",
+ " NaN \n",
+ " 40 \n",
+ " \n",
+ " \n",
+ " 40 \n",
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+ " Reino Unido \n",
+ " 1.610.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.220.000 \n",
+ " NaN \n",
+ " 1.223.000 \n",
+ " NaN \n",
+ " 1.601.154 \n",
+ " NaN \n",
+ " 41 \n",
+ " \n",
+ " \n",
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+ " NaN \n",
+ " NaN \n",
+ " 1.248.000 \n",
+ " NaN \n",
+ " 1.268.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 42 \n",
+ " \n",
+ " \n",
+ " 42 \n",
+ " Sheffield \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 1.530.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 706.000 \n",
+ " NaN \n",
+ " 706.000 \n",
+ " NaN \n",
+ " 795.844 \n",
+ " NaN \n",
+ " 43 \n",
+ " \n",
+ " \n",
+ " 43 \n",
+ " Mannheim \n",
+ " 2011 \n",
+ " Alemania \n",
+ " 1.520.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 559.000 \n",
+ " NaN \n",
+ " 319.000 \n",
+ " NaN \n",
+ " 290.117 \n",
+ " NaN \n",
+ " 44 \n",
+ " \n",
+ " \n",
+ " 44 \n",
+ " Donetsk \n",
+ " 2001 \n",
+ " Ucrania \n",
+ " 1.480.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 930.000 \n",
+ " NaN \n",
+ " 934.000 \n",
+ " NaN \n",
+ " 1.016.194 \n",
+ " NaN \n",
+ " 45 \n",
+ " \n",
+ " \n",
+ " 45 \n",
+ " Newcastle upon Tyne \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 1.460.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 793.000 \n",
+ " NaN \n",
+ " 791.000 \n",
+ " NaN \n",
+ " 1.220.781 \n",
+ " NaN \n",
+ " 46 \n",
+ " \n",
+ " \n",
+ " 46 \n",
+ " Praga \n",
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+ " República Checa \n",
+ " 1.460.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.310.000 \n",
+ " NaN \n",
+ " 1.314.000 \n",
+ " NaN \n",
+ " 1.169.106 \n",
+ " NaN \n",
+ " 47 \n",
+ " \n",
+ " \n",
+ " 47 \n",
+ " Volgogrado \n",
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+ " Rusia \n",
+ " 1.410.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 999.000 \n",
+ " NaN \n",
+ " 1.022.000 \n",
+ " NaN \n",
+ " 1.021.215 \n",
+ " NaN \n",
+ " 48 \n",
+ " \n",
+ " \n",
+ " 48 \n",
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+ " 2011 \n",
+ " Serbia \n",
+ " 1.400.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.180.000 \n",
+ " NaN \n",
+ " 1.182.000 \n",
+ " NaN \n",
+ " 1.166.763 \n",
+ " NaN \n",
+ " 49 \n",
+ " \n",
+ " \n",
+ " 49 \n",
+ " Dnipropetrovsk \n",
+ " 2001 \n",
+ " Ucrania \n",
+ " 1.390.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 950.000 \n",
+ " NaN \n",
+ " 957.000 \n",
+ " NaN \n",
+ " 1.065.008 \n",
+ " NaN \n",
+ " 50 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
839 rows × 13 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Ciudad Fecha y fuente País \\\n",
+ "0 Cantón 2010 China \n",
+ "1 Tokio 2020 Japón \n",
+ "2 Shanghái 2010 China \n",
+ "3 Yakarta 2010 Indonesia \n",
+ "4 Delhi 2011 India \n",
+ "5 Manila 2010 Filipinas \n",
+ "6 Seúl 2010 Corea del Sur \n",
+ "7 Bombay 2011 India \n",
+ "8 Ciudad de México 2015 México \n",
+ "9 Nueva York 2010 Estados Unidos \n",
+ "10 São Paulo 2010 Brasil \n",
+ "11 El Cairo 2006 Egipto \n",
+ "12 Pekín 2010 China \n",
+ "13 Daca 2011 Bangladés \n",
+ "14 Lagos 1991 Nigeria \n",
+ "15 Bangkok 2010 Tailandia \n",
+ "16 Los Ángeles 2010 Estados Unidos \n",
+ "17 Osaka 2010 Japón \n",
+ "18 Karachi 2011 Pakistán \n",
+ "19 Moscú 2010 Rusia \n",
+ "20 Calcuta 2011 India \n",
+ "21 Buenos Aires 2017 Argentina \n",
+ "22 Estambul 2015 Turquía \n",
+ "23 Teherán 2011 Irán \n",
+ "24 Londres 2011 Reino Unido \n",
+ "25 Johannesburgo 2009 Sudáfrica \n",
+ "26 Tianjin 2010 China \n",
+ "27 Río de Janeiro 2010 Brasil \n",
+ "28 Lahore 1998 Pakistán \n",
+ "29 Kinsasa 2004 República Democrática del Congo \n",
+ ".. ... ... ... \n",
+ "20 Lisboa 2001 Portugal \n",
+ "21 Budapest 2011 Hungría \n",
+ "22 Katowice 2011 Polonia \n",
+ "23 Ámsterdam 2001 Países Bajos \n",
+ "24 Stuttgart 2011 Alemania \n",
+ "25 Varsovia 2011 Polonia \n",
+ "26 Bucarest 2011 Rumania \n",
+ "27 Múnich 2011 Alemania \n",
+ "28 Viena 2011 Austria \n",
+ "29 Leeds 2011 Reino Unido \n",
+ "30 Estocolmo NaN Suecia \n",
+ "31 Bruselas NaN Bélgica \n",
+ "32 Minsk 2009 Bielorrusia \n",
+ "33 Lyon 1999 Francia \n",
+ "34 Liverpool 2011 Reino Unido \n",
+ "35 Valencia 2011 España \n",
+ "36 Nizni Nóvgorod 2010 Rusia \n",
+ "37 Turín 2011 Italia \n",
+ "38 Járkov 2001 Ucrania \n",
+ "39 Marsella 1999 Francia \n",
+ "40 Glasgow 2011 Reino Unido \n",
+ "41 Copenhague NaN Dinamarca \n",
+ "42 Sheffield 2011 Reino Unido \n",
+ "43 Mannheim 2011 Alemania \n",
+ "44 Donetsk 2001 Ucrania \n",
+ "45 Newcastle upon Tyne 2011 Reino Unido \n",
+ "46 Praga 2001 República Checa \n",
+ "47 Volgogrado 2010 Rusia \n",
+ "48 Belgrado 2011 Serbia \n",
+ "49 Dnipropetrovsk 2001 Ucrania \n",
+ "\n",
+ " Población según Citypopulation (2015) \\\n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "5 NaN \n",
+ "6 NaN \n",
+ "7 NaN \n",
+ "8 NaN \n",
+ "9 NaN \n",
+ "10 NaN \n",
+ "11 NaN \n",
+ "12 NaN \n",
+ "13 NaN \n",
+ "14 NaN \n",
+ "15 NaN \n",
+ "16 NaN \n",
+ "17 NaN \n",
+ "18 NaN \n",
+ "19 NaN \n",
+ "20 NaN \n",
+ "21 NaN \n",
+ "22 NaN \n",
+ "23 NaN \n",
+ "24 NaN \n",
+ "25 NaN \n",
+ "26 NaN \n",
+ "27 NaN \n",
+ "28 NaN \n",
+ "29 NaN \n",
+ ".. ... \n",
+ "20 2.600.000 \n",
+ "21 2.550.000 \n",
+ "22 2.400.000 \n",
+ "23 2.375.000 \n",
+ "24 2.300.000 \n",
+ "25 2.275.000 \n",
+ "26 2.175.000 \n",
+ "27 2.175.000 \n",
+ "28 2.125.000 \n",
+ "29 2.125.000 \n",
+ "30 2.075.000 \n",
+ "31 2.000.000 \n",
+ "32 1.950.000 \n",
+ "33 1.920.000 \n",
+ "34 1.830.000 \n",
+ "35 1.780.000 \n",
+ "36 1.750.000 \n",
+ "37 1.670.000 \n",
+ "38 1.650.000 \n",
+ "39 1.640.000 \n",
+ "40 1.610.000 \n",
+ "41 1.600.000 \n",
+ "42 1.530.000 \n",
+ "43 1.520.000 \n",
+ "44 1.480.000 \n",
+ "45 1.460.000 \n",
+ "46 1.460.000 \n",
+ "47 1.410.000 \n",
+ "48 1.400.000 \n",
+ "49 1.390.000 \n",
+ "\n",
+ " Población según Citypopulation (2016) Población según Citypopulation[1] \\\n",
+ "0 NaN 45 600 000 \n",
+ "1 NaN 40 200 000 \n",
+ "2 NaN 35 900 000 \n",
+ "3 NaN 30 600 000 \n",
+ "4 NaN 29 400 000 \n",
+ "5 NaN 25 200 000 \n",
+ "6 NaN 24 700 000 \n",
+ "7 NaN 24 700 000 \n",
+ "8 NaN 22 800 000 \n",
+ "9 NaN 22 400 000 \n",
+ "10 NaN 22 200 000 \n",
+ "11 NaN 20 500 000 \n",
+ "12 NaN 20 400 000 \n",
+ "13 NaN 19 500 000 \n",
+ "14 NaN 18 800 000 \n",
+ "15 NaN 18 300 000 \n",
+ "16 NaN 17 800 000 \n",
+ "17 NaN 17 700 000 \n",
+ "18 NaN 17 300 000 \n",
+ "19 NaN 17 200 000 \n",
+ "20 NaN 16 600 000 \n",
+ "21 NaN 16 300 000 \n",
+ "22 NaN 15 800 000 \n",
+ "23 NaN 15 000 000 \n",
+ "24 NaN 14 700 000 \n",
+ "25 NaN 13 700 000 \n",
+ "26 NaN 13 200 000 \n",
+ "27 NaN 13 100 000 \n",
+ "28 NaN 12 600 000 \n",
+ "29 NaN 12 000 000 \n",
+ ".. ... ... \n",
+ "20 NaN NaN \n",
+ "21 NaN NaN \n",
+ "22 NaN NaN \n",
+ "23 NaN NaN \n",
+ "24 NaN NaN \n",
+ "25 NaN NaN \n",
+ "26 NaN NaN \n",
+ "27 NaN NaN \n",
+ "28 NaN NaN \n",
+ "29 NaN NaN \n",
+ "30 NaN NaN \n",
+ "31 NaN NaN \n",
+ "32 NaN NaN \n",
+ "33 NaN NaN \n",
+ "34 NaN NaN \n",
+ "35 NaN NaN \n",
+ "36 NaN NaN \n",
+ "37 NaN NaN \n",
+ "38 NaN NaN \n",
+ "39 NaN NaN \n",
+ "40 NaN NaN \n",
+ "41 NaN NaN \n",
+ "42 NaN NaN \n",
+ "43 NaN NaN \n",
+ "44 NaN NaN \n",
+ "45 NaN NaN \n",
+ "46 NaN NaN \n",
+ "47 NaN NaN \n",
+ "48 NaN NaN \n",
+ "49 NaN NaN \n",
+ "\n",
+ " Población según Demographia (2015) Población según Demographia[2] \\\n",
+ "0 NaN 42 941 000 \n",
+ "1 NaN 38 001 000 \n",
+ "2 NaN 29 213 000 \n",
+ "3 NaN 11 399 000 \n",
+ "4 NaN 25 703 000 \n",
+ "5 NaN 12 946 000 \n",
+ "6 NaN 13 558 000 \n",
+ "7 NaN 21 043 000 \n",
+ "8 NaN 22 452 000 \n",
+ "9 NaN 19 532 000 \n",
+ "10 NaN 21 066 000 \n",
+ "11 NaN 13 123 000 \n",
+ "12 NaN 13 123 000 \n",
+ "13 NaN 17 598 000 \n",
+ "14 NaN 18 772 000 \n",
+ "15 NaN 11 084 000 \n",
+ "16 NaN 14 504 000 \n",
+ "17 NaN 20 238 000 \n",
+ "18 NaN 16 618 000 \n",
+ "19 NaN 12 166 000 \n",
+ "20 NaN 14 865 000 \n",
+ "21 NaN 18 086 000 \n",
+ "22 NaN 14 164 000 \n",
+ "23 NaN 10 239 000 \n",
+ "24 NaN 10 313 000 \n",
+ "25 NaN 12 613 000 \n",
+ "26 NaN 11 210 000 \n",
+ "27 NaN 12 902 000 \n",
+ "28 NaN 8 741 000 \n",
+ "29 NaN 11 587 000 \n",
+ ".. ... ... \n",
+ "20 2.666.000 NaN \n",
+ "21 1.710.000 NaN \n",
+ "22 2.190.000 NaN \n",
+ "23 1.624.000 NaN \n",
+ "24 1.379.000 NaN \n",
+ "25 1.720.000 NaN \n",
+ "26 1.860.000 NaN \n",
+ "27 1.981.000 NaN \n",
+ "28 1.763.000 NaN \n",
+ "29 1.893.000 NaN \n",
+ "30 1.484.000 NaN \n",
+ "31 2.089.000 NaN \n",
+ "32 1.910.000 NaN \n",
+ "33 1.583.000 NaN \n",
+ "34 875.000 NaN \n",
+ "35 1.561.000 NaN \n",
+ "36 1.201.000 NaN \n",
+ "37 1.521.000 NaN \n",
+ "38 1.440.000 NaN \n",
+ "39 1.397.000 NaN \n",
+ "40 1.220.000 NaN \n",
+ "41 1.248.000 NaN \n",
+ "42 706.000 NaN \n",
+ "43 559.000 NaN \n",
+ "44 930.000 NaN \n",
+ "45 793.000 NaN \n",
+ "46 1.310.000 NaN \n",
+ "47 999.000 NaN \n",
+ "48 1.180.000 NaN \n",
+ "49 950.000 NaN \n",
+ "\n",
+ " Población según ONU (2015) Población según ONU[3] \\\n",
+ "0 NaN 45 553 000 \n",
+ "1 NaN 37 843 000 \n",
+ "2 NaN 30 539 000 \n",
+ "3 NaN 30 477 000 \n",
+ "4 NaN 24 998 000 \n",
+ "5 NaN 24 123 000 \n",
+ "6 NaN 23 480 000 \n",
+ "7 NaN 21 732 000 \n",
+ "8 NaN 20 063 000 \n",
+ "9 NaN 20 630 000 \n",
+ "10 NaN 20 365 000 \n",
+ "11 NaN 13 123 000 \n",
+ "12 NaN 13 123 000 \n",
+ "13 NaN 15 669 000 \n",
+ "14 NaN 15 600 000 \n",
+ "15 NaN 14 998 000 \n",
+ "16 NaN 15 058 000 \n",
+ "17 NaN 17 444 000 \n",
+ "18 NaN 22 123 000 \n",
+ "19 NaN 16 170 000 \n",
+ "20 NaN 14 667 000 \n",
+ "21 NaN 14 122 000 \n",
+ "22 NaN 13 287 000 \n",
+ "23 NaN 13 532 000 \n",
+ "24 NaN 10 236 000 \n",
+ "25 NaN 12 066 000 \n",
+ "26 NaN 10 920 000 \n",
+ "27 NaN 11 727 000 \n",
+ "28 NaN 10 052 000 \n",
+ "29 NaN 11 587 000 \n",
+ ".. ... ... \n",
+ "20 2.884.000 NaN \n",
+ "21 1.714.000 NaN \n",
+ "22 303.000 NaN \n",
+ "23 1.091.000 NaN \n",
+ "24 626.000 NaN \n",
+ "25 1.722.000 NaN \n",
+ "26 1.868.000 NaN \n",
+ "27 1.438.000 NaN \n",
+ "28 1.753.000 NaN \n",
+ "29 1.912.000 NaN \n",
+ "30 1.486.000 NaN \n",
+ "31 2.045.000 NaN \n",
+ "32 1.915.000 NaN \n",
+ "33 1.609.000 NaN \n",
+ "34 870.000 NaN \n",
+ "35 810.000 NaN \n",
+ "36 1.212.000 NaN \n",
+ "37 1.765.000 NaN \n",
+ "38 1.441.000 NaN \n",
+ "39 1.605.000 NaN \n",
+ "40 1.223.000 NaN \n",
+ "41 1.268.000 NaN \n",
+ "42 706.000 NaN \n",
+ "43 319.000 NaN \n",
+ "44 934.000 NaN \n",
+ "45 791.000 NaN \n",
+ "46 1.314.000 NaN \n",
+ "47 1.022.000 NaN \n",
+ "48 1.182.000 NaN \n",
+ "49 957.000 NaN \n",
+ "\n",
+ " Población según último censo Población según último censo oficial Posición \n",
+ "0 NaN 39 264 086 1 \n",
+ "1 NaN 8 945 695 2 \n",
+ "2 NaN 10 558 121 3 \n",
+ "3 NaN 25 420 288 4 \n",
+ "4 NaN 16 349 831 5 \n",
+ "5 NaN 1 652 171 6 \n",
+ "6 NaN 23 836 272 7 \n",
+ "7 NaN 19 617 302 8 \n",
+ "8 NaN 20 892 724 9 \n",
+ "9 NaN 19 556 440 10 \n",
+ "10 NaN 19 683 975 11 \n",
+ "11 NaN 7 740 018 12 \n",
+ "12 NaN 16 446 857 13 \n",
+ "13 NaN 14 543 124 14 \n",
+ "14 NaN 5 195 247 15 \n",
+ "15 NaN 8 986 218 16 \n",
+ "16 NaN 17 053 905 17 \n",
+ "17 NaN 2 665 314 18 \n",
+ "18 NaN 21 142 625 19 \n",
+ "19 NaN 11 612 885 20 \n",
+ "20 NaN 14 057 991 21 \n",
+ "21 NaN 13 588 171 22 \n",
+ "22 NaN 14 657 000 23 \n",
+ "23 NaN 9 768 677 24 \n",
+ "24 NaN 11 140 445 25 \n",
+ "25 NaN 10 002 039 26 \n",
+ "26 NaN 9 290 263 28 \n",
+ "27 NaN 11 835 708 27 \n",
+ "28 NaN 5 143 495 29 \n",
+ "29 NaN 7 273 947 30 \n",
+ ".. ... ... ... \n",
+ "20 564.657 NaN 21 \n",
+ "21 1.729.040 NaN 22 \n",
+ "22 310.764 NaN 23 \n",
+ "23 734.533 NaN 24 \n",
+ "24 585.890 NaN 25 \n",
+ "25 1.700.612 NaN 26 \n",
+ "26 1.883.425 NaN 27 \n",
+ "27 1.348.335 NaN 28 \n",
+ "28 2.015.580 NaN 29 \n",
+ "29 2.058.861 NaN 30 \n",
+ "30 NaN NaN 31 \n",
+ "31 NaN NaN 32 \n",
+ "32 1.836.808 NaN 33 \n",
+ "33 1.428.998 NaN 34 \n",
+ "34 1.367.147 NaN 35 \n",
+ "35 792.054 NaN 36 \n",
+ "36 1.250.619 NaN 37 \n",
+ "37 872.367 NaN 38 \n",
+ "38 1.470.902 NaN 39 \n",
+ "39 1.463.016 NaN 40 \n",
+ "40 1.601.154 NaN 41 \n",
+ "41 NaN NaN 42 \n",
+ "42 795.844 NaN 43 \n",
+ "43 290.117 NaN 44 \n",
+ "44 1.016.194 NaN 45 \n",
+ "45 1.220.781 NaN 46 \n",
+ "46 1.169.106 NaN 47 \n",
+ "47 1.021.215 NaN 48 \n",
+ "48 1.166.763 NaN 49 \n",
+ "49 1.065.008 NaN 50 \n",
+ "\n",
+ "[839 rows x 13 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#consolidar todos los datos de todas los dataframes\n",
+ "wikiWW = wiki2[0]\n",
+ "wikiAF = wiki2[2]\n",
+ "wikiAN = wiki2[6]\n",
+ "wikiAC = wiki2[8]\n",
+ "wikiAS = wiki2[9]\n",
+ "wikiOM = wiki2[13]\n",
+ "wikiIN = wiki2[15]\n",
+ "wikiAO = wiki2[17]\n",
+ "wikiSA = wiki2[19]\n",
+ "wikiEOC = wiki2[23]\n",
+ "wikiEOR = wiki2[25]\n",
+ "wikiOC = wiki2[27]\n",
+ "wikiAM = wiki2[4]\n",
+ "wikiAS = wiki2[11]\n",
+ "wikiEU = wiki2[21]\n",
+ "\n",
+ "wiki3_df = pandas.concat([wikiWW, wikiAF, wikiAN, wikiAC, wikiAS, wikiOM, wikiIN, wikiAO, wikiSA, wikiEOC, wikiEOR, wikiOC, wikiAM, wikiAS, wikiEU], axis=0)\n",
+ "wiki3_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Exportar hacia un archivo csv todo el dataframe duplicado\n",
+ "wiki3_df.to_csv('wiki3_df.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Ciudad', 'Fecha y fuente', 'País',\n",
+ " 'Población según Citypopulation (2015)',\n",
+ " 'Población según Citypopulation (2016)',\n",
+ " 'Población según Citypopulation[1]',\n",
+ " 'Población según Demographia (2015)', 'Población según Demographia[2]',\n",
+ " 'Población según ONU (2015)', 'Población según ONU[3]',\n",
+ " 'Población según último censo', 'Población según último censo oficial',\n",
+ " 'Posición'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#sin embargo existen columnas duplicadas. Por ejemplo Citpopulation tiene 3 columnas con fechas diferentes.\n",
+ "#debemos cambiar el nombre de las columnas (1) y convertir sus valores en números (2)\n",
+ "wiki3_df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Ciudad',\n",
+ " 'Fecha y fuente',\n",
+ " 'País',\n",
+ " 'Población según Citypopulation (2015)',\n",
+ " 'Población según Citypopulation (2016)',\n",
+ " 'Población según Citypopulation[1]\\u200b',\n",
+ " 'Población según Demographia (2015)',\n",
+ " 'Población según Demographia[2]\\u200b',\n",
+ " 'Población según ONU (2015)',\n",
+ " 'Población según ONU[3]\\u200b',\n",
+ " 'Población según último censo',\n",
+ " 'Población según último censo oficial',\n",
+ " 'Posición']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#determinar los nombres de las columnas cuyos nombres deberán cambiar\n",
+ "lista_columns = list(wiki3_df.columns)\n",
+ "lista_columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 45 600 000\n",
+ "1 40 200 000\n",
+ "2 35 900 000\n",
+ "3 30 600 000\n",
+ "4 29 400 000\n",
+ "Name: Población según Citypopulation[1], dtype: object"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wiki3_df['Población según Citypopulation[1]\\u200b'].head()\n",
+ "#esta es la columna cuyo nombre es el más complicado por los []\n",
+ "#Observar la diferencia entre \"Población según Citypopulation[1]\" y \"Población según Citypopulation[1]\\u200b\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Ciudad', 'Fecha', 'País', 'Citypopulation 2015', 'Citypopulation 2016',\n",
+ " 'Citypopulation Sin Fecha', 'Demographia 2015', 'Demographia Sin Fecha',\n",
+ " 'ONU 2015', 'ONU Sin Fecha', 'Ultimo Censo', 'Ultimo Censo Oficial',\n",
+ " 'Posición en Tabla Inicial'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wiki3_df.rename(columns = {'Población según Citypopulation (2015)': 'Citypopulation 2015',\n",
+ " 'Población según Citypopulation (2016)': 'Citypopulation 2016',\n",
+ " 'Población según Demographia (2015)': 'Demographia 2015',\n",
+ " 'Población según ONU (2015)': 'ONU 2015',\n",
+ " 'Población según último censo': 'Ultimo Censo',\n",
+ " 'Población según último censo oficial': 'Ultimo Censo Oficial',\n",
+ " 'Posición': 'Posición en Tabla Inicial', \n",
+ " 'Población según Citypopulation[1]\\u200b': 'Citypopulation Sin Fecha', 'Población según Demographia[2]\\u200b': 'Demographia Sin Fecha', 'Población según ONU[3]\\u200b': 'ONU Sin Fecha', 'Fecha y fuente': 'Fecha'},\n",
+ " inplace=True)\n",
+ "wiki3_df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Al existir múltiples fechas para una misma fuente, se consolidarán en nuevas columnas generadas:\n",
+ "#el criterio de selección será seleccionar fecha más reciente o valor disponible\n",
+ "#primer paso generar nuevas columnas\n",
+ "#wiki3_df['Citypopulation'] = ''\n",
+ "#wiki3_df['Demographia'] = ''\n",
+ "#wiki3_df['ONU'] = ''\n",
+ "#wiki3_df.columns\n",
+ "#se descarta esta opción debido que los inputs de las nuevas columnas generadas no provienen de una sola columna (no es copiar y pegar)\n",
+ "#se debe realizar una evaluación (if: existe numero o no estaba vacío) o unir las columnas de origen (Citypopulation 2015, 2016 y sin fecha)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Ciudad 0\n",
+ "Fecha 19\n",
+ "País 0\n",
+ "Citypopulation 2015 303\n",
+ "Citypopulation 2016 636\n",
+ "Citypopulation Sin Fecha 739\n",
+ "Demographia 2015 100\n",
+ "Demographia Sin Fecha 739\n",
+ "ONU 2015 100\n",
+ "ONU Sin Fecha 739\n",
+ "Ultimo Censo 119\n",
+ "Ultimo Censo Oficial 739\n",
+ "Posición en Tabla Inicial 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#primero validar qué columnas están vacias\n",
+ "null_cols = wiki3_df.isnull().sum()\n",
+ "null_cols\n",
+ "#null_cols[null_cols > 0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Ciudad 0\n",
+ "Fecha 19\n",
+ "País 0\n",
+ "Citypopulation 2015 303\n",
+ "Demographia 2015 100\n",
+ "ONU 2015 100\n",
+ "Ultimo Censo 119\n",
+ "Posición en Tabla Inicial 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#las columnas con el mayor numero de valores vacios se eliminarán finalmente existen\n",
+ "#Existen 9 columnas con diferentes fuentes sobre el censo en las ciudades\n",
+ "wiki3_df = wiki3_df.drop(['Citypopulation 2016', 'Citypopulation Sin Fecha', 'Demographia Sin Fecha', 'ONU Sin Fecha', 'Ultimo Censo Oficial'], axis=1)\n",
+ "null_cols = wiki3_df.isnull().sum()\n",
+ "null_cols"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Ciudad object\n",
+ "Fecha object\n",
+ "País object\n",
+ "Citypopulation 2015 object\n",
+ "Demographia 2015 object\n",
+ "ONU 2015 object\n",
+ "Ultimo Censo object\n",
+ "Posición en Tabla Inicial int64\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "#confirmar los tipos de valores que tienen las columnas para poder sumar\n",
+ "print(wiki3_df.dtypes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Antes de convertir a numeros, se debe limpiar la data de los puntos(.) de lo contrario ocurirá error en la conversión\n",
+ "wiki3_df['Citypopulation 2015']= wiki3_df['Citypopulation 2015'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','') \n",
+ "wiki3_df['Demographia 2015']= wiki3_df['Demographia 2015'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','') \n",
+ "wiki3_df['ONU 2015']= wiki3_df['ONU 2015'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','')\n",
+ "wiki3_df['Ultimo Censo']= wiki3_df['Ultimo Censo'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Ciudad object\n",
+ "Fecha float64\n",
+ "País object\n",
+ "Citypopulation 2015 float64\n",
+ "Demographia 2015 float64\n",
+ "ONU 2015 float64\n",
+ "Ultimo Censo float64\n",
+ "Posición en Tabla Inicial int64\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Convertir todos los valores de estimaciones de población a números\n",
+ "wiki3_df[\"Citypopulation 2015\"] = pandas.to_numeric(wiki3_df[\"Citypopulation 2015\"], errors='coerce')\n",
+ "wiki3_df[\"Demographia 2015\"] = pandas.to_numeric(wiki3_df[\"Demographia 2015\"], errors='coerce')\n",
+ "wiki3_df[\"ONU 2015\"] = pandas.to_numeric(wiki3_df[\"ONU 2015\"], errors='coerce')\n",
+ "wiki3_df[\"Fecha\"] = pandas.to_numeric(wiki3_df[\"Fecha\"], errors='coerce')\n",
+ "wiki3_df[\"Ultimo Censo\"] = pandas.to_numeric(wiki3_df[\"Ultimo Censo\"], errors='coerce')\n",
+ "#wiki3_df = wiki3_df.astype({\"Posición en la Tabla Inicial\": int})\n",
+ "print(wiki3_df.dtypes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Ciudad \n",
+ " Fecha \n",
+ " País \n",
+ " Citypopulation 2015 \n",
+ " Demographia 2015 \n",
+ " ONU 2015 \n",
+ " Ultimo Censo \n",
+ " Posición en Tabla Inicial \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Cantón (incluyendo Dongguan, Foshan, Jiangmen,... \n",
+ " 2010.0 \n",
+ " China \n",
+ " 46900000.0 \n",
+ " 45553000.0 \n",
+ " 42941000.0 \n",
+ " 39264086.0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " Tokio \n",
+ " 2010.0 \n",
+ " Japón \n",
+ " 39500000.0 \n",
+ " 37843000.0 \n",
+ " 38001000.0 \n",
+ " 8945695.0 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " Shanghái (incl. Suzhou, Kunshan) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 30400000.0 \n",
+ " 30477000.0 \n",
+ " 29213000.0 \n",
+ " 25420288.0 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " Yakarta (incluyendo Bogor) \n",
+ " 2010.0 \n",
+ " Indonesia \n",
+ " 30100000.0 \n",
+ " 30539000.0 \n",
+ " 11399000.0 \n",
+ " 10558121.0 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " Delhi \n",
+ " 2011.0 \n",
+ " India \n",
+ " 28400000.0 \n",
+ " 24998000.0 \n",
+ " 25703000.0 \n",
+ " 16349831.0 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " Karachi \n",
+ " 2011.0 \n",
+ " Pakistán \n",
+ " 25300000.0 \n",
+ " 22123000.0 \n",
+ " 16618000.0 \n",
+ " 21142625.0 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " Manila \n",
+ " 2010.0 \n",
+ " Filipinas \n",
+ " 24600000.0 \n",
+ " 24123000.0 \n",
+ " 12946000.0 \n",
+ " 1652171.0 \n",
+ " 7 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " Bombay (incluyendo Kalyan y Vasai-Virar) \n",
+ " 2011.0 \n",
+ " India \n",
+ " 24300000.0 \n",
+ " 21732000.0 \n",
+ " 21043000.0 \n",
+ " 19617302.0 \n",
+ " 8 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " Seúl (incluyendo Incheon y Suwon) \n",
+ " 2010.0 \n",
+ " Corea del Sur \n",
+ " 24100000.0 \n",
+ " 23480000.0 \n",
+ " 10558000.0 \n",
+ " 23836272.0 \n",
+ " 9 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " Daca \n",
+ " 2011.0 \n",
+ " Bangladés \n",
+ " 22300000.0 \n",
+ " 15669000.0 \n",
+ " 17598000.0 \n",
+ " 14543124.0 \n",
+ " 10 \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " Pekín \n",
+ " 2010.0 \n",
+ " China \n",
+ " 20700000.0 \n",
+ " 21009000.0 \n",
+ " 20384000.0 \n",
+ " 16446857.0 \n",
+ " 11 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " Osaka \n",
+ " 2010.0 \n",
+ " Japón \n",
+ " 19800000.0 \n",
+ " 17444000.0 \n",
+ " 20238000.0 \n",
+ " 2665314.0 \n",
+ " 12 \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " Bangkok (incluyendo Samut Prakan) \n",
+ " 2010.0 \n",
+ " Tailandia \n",
+ " 16700000.0 \n",
+ " 14998000.0 \n",
+ " 11084000.0 \n",
+ " 8986218.0 \n",
+ " 13 \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " Calcuta \n",
+ " 2011.0 \n",
+ " India \n",
+ " 15900000.0 \n",
+ " 14667000.0 \n",
+ " 14865000.0 \n",
+ " 14057991.0 \n",
+ " 14 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " Teherán (incluyendo Karaj) \n",
+ " 2011.0 \n",
+ " Irán \n",
+ " 13600000.0 \n",
+ " 13532000.0 \n",
+ " 10239000.0 \n",
+ " 9768677.0 \n",
+ " 15 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " Tianjin \n",
+ " 2010.0 \n",
+ " China \n",
+ " 11200000.0 \n",
+ " 10920000.0 \n",
+ " 11210000.0 \n",
+ " 9290263.0 \n",
+ " 16 \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " Nagoya \n",
+ " 2010.0 \n",
+ " Japón \n",
+ " 10400000.0 \n",
+ " 10177000.0 \n",
+ " 9406000.0 \n",
+ " 2263894.0 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " Bangalore \n",
+ " 2011.0 \n",
+ " India \n",
+ " 10300000.0 \n",
+ " 9807000.0 \n",
+ " 10087000.0 \n",
+ " 8520435.0 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " Lahore \n",
+ " 1998.0 \n",
+ " Pakistán \n",
+ " 9950000.0 \n",
+ " 10052000.0 \n",
+ " 8741000.0 \n",
+ " 5143495.0 \n",
+ " 19 \n",
+ " \n",
+ " \n",
+ " 19 \n",
+ " Madrás \n",
+ " 2011.0 \n",
+ " India \n",
+ " 9900000.0 \n",
+ " 9714000.0 \n",
+ " 9890000.0 \n",
+ " 8653521.0 \n",
+ " 20 \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Xiamen (incluyendl Quanzhou) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 9850000.0 \n",
+ " 11130000.0 \n",
+ " 5825000.0 \n",
+ " 4273841.0 \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Chengdu \n",
+ " 2010.0 \n",
+ " China \n",
+ " 9400000.0 \n",
+ " 10376000.0 \n",
+ " 7556000.0 \n",
+ " 6316922.0 \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Taipéi \n",
+ " NaN \n",
+ " Taiwán \n",
+ " 9000000.0 \n",
+ " 7438000.0 \n",
+ " 2666000.0 \n",
+ " NaN \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Hyderabad \n",
+ " 2011.0 \n",
+ " India \n",
+ " 8900000.0 \n",
+ " 8754000.0 \n",
+ " 8942000.0 \n",
+ " 7677018.0 \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Hangzhou (incluyendo Shaoxing) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 8150000.0 \n",
+ " 9625000.0 \n",
+ " 8467000.0 \n",
+ " 6887819.0 \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Ciudad Ho Chi Minh \n",
+ " 2009.0 \n",
+ " Vietnam \n",
+ " 8150000.0 \n",
+ " 8957000.0 \n",
+ " 7298000.0 \n",
+ " 5880615.0 \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " Wuhan \n",
+ " 2010.0 \n",
+ " China \n",
+ " 7950000.0 \n",
+ " 7509000.0 \n",
+ " 7906000.0 \n",
+ " 7541527.0 \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Shantou (incluyendo Chaozhou, Puning, Chaoyang... \n",
+ " 2010.0 \n",
+ " China \n",
+ " 7850000.0 \n",
+ " 6337000.0 \n",
+ " 6287000.0 \n",
+ " 5775239.0 \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Shenyang (incluyendo Fushun) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 7600000.0 \n",
+ " 7402000.0 \n",
+ " 7613000.0 \n",
+ " 7037040.0 \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Ahmedabad \n",
+ " 2011.0 \n",
+ " India \n",
+ " 7350000.0 \n",
+ " 7186000.0 \n",
+ " 7343000.0 \n",
+ " 6357693.0 \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Lisboa \n",
+ " 2001.0 \n",
+ " Portugal \n",
+ " 2600000.0 \n",
+ " 2666000.0 \n",
+ " 2884000.0 \n",
+ " 564657.0 \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Budapest \n",
+ " 2011.0 \n",
+ " Hungría \n",
+ " 2550000.0 \n",
+ " 1710000.0 \n",
+ " 1714000.0 \n",
+ " 1729040.0 \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Katowice \n",
+ " 2011.0 \n",
+ " Polonia \n",
+ " 2400000.0 \n",
+ " 2190000.0 \n",
+ " 303000.0 \n",
+ " 310764.0 \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Ámsterdam \n",
+ " 2001.0 \n",
+ " Países Bajos \n",
+ " 2375000.0 \n",
+ " 1624000.0 \n",
+ " 1091000.0 \n",
+ " 734533.0 \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Stuttgart \n",
+ " 2011.0 \n",
+ " Alemania \n",
+ " 2300000.0 \n",
+ " 1379000.0 \n",
+ " 626000.0 \n",
+ " 585890.0 \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Varsovia \n",
+ " 2011.0 \n",
+ " Polonia \n",
+ " 2275000.0 \n",
+ " 1720000.0 \n",
+ " 1722000.0 \n",
+ " 1700612.0 \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " Bucarest \n",
+ " 2011.0 \n",
+ " Rumania \n",
+ " 2175000.0 \n",
+ " 1860000.0 \n",
+ " 1868000.0 \n",
+ " 1883425.0 \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Múnich \n",
+ " 2011.0 \n",
+ " Alemania \n",
+ " 2175000.0 \n",
+ " 1981000.0 \n",
+ " 1438000.0 \n",
+ " 1348335.0 \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Viena \n",
+ " 2011.0 \n",
+ " Austria \n",
+ " 2125000.0 \n",
+ " 1763000.0 \n",
+ " 1753000.0 \n",
+ " 2015580.0 \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Leeds \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 2125000.0 \n",
+ " 1893000.0 \n",
+ " 1912000.0 \n",
+ " 2058861.0 \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " Estocolmo \n",
+ " NaN \n",
+ " Suecia \n",
+ " 2075000.0 \n",
+ " 1484000.0 \n",
+ " 1486000.0 \n",
+ " NaN \n",
+ " 31 \n",
+ " \n",
+ " \n",
+ " 31 \n",
+ " Bruselas \n",
+ " NaN \n",
+ " Bélgica \n",
+ " 2000000.0 \n",
+ " 2089000.0 \n",
+ " 2045000.0 \n",
+ " NaN \n",
+ " 32 \n",
+ " \n",
+ " \n",
+ " 32 \n",
+ " Minsk \n",
+ " 2009.0 \n",
+ " Bielorrusia \n",
+ " 1950000.0 \n",
+ " 1910000.0 \n",
+ " 1915000.0 \n",
+ " 1836808.0 \n",
+ " 33 \n",
+ " \n",
+ " \n",
+ " 33 \n",
+ " Lyon \n",
+ " 1999.0 \n",
+ " Francia \n",
+ " 1920000.0 \n",
+ " 1583000.0 \n",
+ " 1609000.0 \n",
+ " 1428998.0 \n",
+ " 34 \n",
+ " \n",
+ " \n",
+ " 34 \n",
+ " Liverpool \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1830000.0 \n",
+ " 875000.0 \n",
+ " 870000.0 \n",
+ " 1367147.0 \n",
+ " 35 \n",
+ " \n",
+ " \n",
+ " 35 \n",
+ " Valencia \n",
+ " 2011.0 \n",
+ " España \n",
+ " 1780000.0 \n",
+ " 1561000.0 \n",
+ " 810000.0 \n",
+ " 792054.0 \n",
+ " 36 \n",
+ " \n",
+ " \n",
+ " 36 \n",
+ " Nizni Nóvgorod \n",
+ " 2010.0 \n",
+ " Rusia \n",
+ " 1750000.0 \n",
+ " 1201000.0 \n",
+ " 1212000.0 \n",
+ " 1250619.0 \n",
+ " 37 \n",
+ " \n",
+ " \n",
+ " 37 \n",
+ " Turín \n",
+ " 2011.0 \n",
+ " Italia \n",
+ " 1670000.0 \n",
+ " 1521000.0 \n",
+ " 1765000.0 \n",
+ " 872367.0 \n",
+ " 38 \n",
+ " \n",
+ " \n",
+ " 38 \n",
+ " Járkov \n",
+ " 2001.0 \n",
+ " Ucrania \n",
+ " 1650000.0 \n",
+ " 1440000.0 \n",
+ " 1441000.0 \n",
+ " 1470902.0 \n",
+ " 39 \n",
+ " \n",
+ " \n",
+ " 39 \n",
+ " Marsella \n",
+ " 1999.0 \n",
+ " Francia \n",
+ " 1640000.0 \n",
+ " 1397000.0 \n",
+ " 1605000.0 \n",
+ " 1463016.0 \n",
+ " 40 \n",
+ " \n",
+ " \n",
+ " 40 \n",
+ " Glasgow \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1610000.0 \n",
+ " 1220000.0 \n",
+ " 1223000.0 \n",
+ " 1601154.0 \n",
+ " 41 \n",
+ " \n",
+ " \n",
+ " 41 \n",
+ " Copenhague \n",
+ " NaN \n",
+ " Dinamarca \n",
+ " 1600000.0 \n",
+ " 1248000.0 \n",
+ " 1268000.0 \n",
+ " NaN \n",
+ " 42 \n",
+ " \n",
+ " \n",
+ " 42 \n",
+ " Sheffield \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1530000.0 \n",
+ " 706000.0 \n",
+ " 706000.0 \n",
+ " 795844.0 \n",
+ " 43 \n",
+ " \n",
+ " \n",
+ " 43 \n",
+ " Mannheim \n",
+ " 2011.0 \n",
+ " Alemania \n",
+ " 1520000.0 \n",
+ " 559000.0 \n",
+ " 319000.0 \n",
+ " 290117.0 \n",
+ " 44 \n",
+ " \n",
+ " \n",
+ " 44 \n",
+ " Donetsk \n",
+ " 2001.0 \n",
+ " Ucrania \n",
+ " 1480000.0 \n",
+ " 930000.0 \n",
+ " 934000.0 \n",
+ " 1016194.0 \n",
+ " 45 \n",
+ " \n",
+ " \n",
+ " 45 \n",
+ " Newcastle upon Tyne \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1460000.0 \n",
+ " 793000.0 \n",
+ " 791000.0 \n",
+ " 1220781.0 \n",
+ " 46 \n",
+ " \n",
+ " \n",
+ " 46 \n",
+ " Praga \n",
+ " 2001.0 \n",
+ " República Checa \n",
+ " 1460000.0 \n",
+ " 1310000.0 \n",
+ " 1314000.0 \n",
+ " 1169106.0 \n",
+ " 47 \n",
+ " \n",
+ " \n",
+ " 47 \n",
+ " Volgogrado \n",
+ " 2010.0 \n",
+ " Rusia \n",
+ " 1410000.0 \n",
+ " 999000.0 \n",
+ " 1022000.0 \n",
+ " 1021215.0 \n",
+ " 48 \n",
+ " \n",
+ " \n",
+ " 48 \n",
+ " Belgrado \n",
+ " 2011.0 \n",
+ " Serbia \n",
+ " 1400000.0 \n",
+ " 1180000.0 \n",
+ " 1182000.0 \n",
+ " 1166763.0 \n",
+ " 49 \n",
+ " \n",
+ " \n",
+ " 49 \n",
+ " Dnipropetrovsk \n",
+ " 2001.0 \n",
+ " Ucrania \n",
+ " 1390000.0 \n",
+ " 950000.0 \n",
+ " 957000.0 \n",
+ " 1065008.0 \n",
+ " 50 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
491 rows × 8 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Ciudad Fecha \\\n",
+ "0 Cantón (incluyendo Dongguan, Foshan, Jiangmen,... 2010.0 \n",
+ "1 Tokio 2010.0 \n",
+ "2 Shanghái (incl. Suzhou, Kunshan) 2010.0 \n",
+ "3 Yakarta (incluyendo Bogor) 2010.0 \n",
+ "4 Delhi 2011.0 \n",
+ "5 Karachi 2011.0 \n",
+ "6 Manila 2010.0 \n",
+ "7 Bombay (incluyendo Kalyan y Vasai-Virar) 2011.0 \n",
+ "8 Seúl (incluyendo Incheon y Suwon) 2010.0 \n",
+ "9 Daca 2011.0 \n",
+ "10 Pekín 2010.0 \n",
+ "11 Osaka 2010.0 \n",
+ "12 Bangkok (incluyendo Samut Prakan) 2010.0 \n",
+ "13 Calcuta 2011.0 \n",
+ "14 Teherán (incluyendo Karaj) 2011.0 \n",
+ "15 Tianjin 2010.0 \n",
+ "16 Nagoya 2010.0 \n",
+ "17 Bangalore 2011.0 \n",
+ "18 Lahore 1998.0 \n",
+ "19 Madrás 2011.0 \n",
+ "20 Xiamen (incluyendl Quanzhou) 2010.0 \n",
+ "21 Chengdu 2010.0 \n",
+ "22 Taipéi NaN \n",
+ "23 Hyderabad 2011.0 \n",
+ "24 Hangzhou (incluyendo Shaoxing) 2010.0 \n",
+ "25 Ciudad Ho Chi Minh 2009.0 \n",
+ "26 Wuhan 2010.0 \n",
+ "27 Shantou (incluyendo Chaozhou, Puning, Chaoyang... 2010.0 \n",
+ "28 Shenyang (incluyendo Fushun) 2010.0 \n",
+ "29 Ahmedabad 2011.0 \n",
+ ".. ... ... \n",
+ "20 Lisboa 2001.0 \n",
+ "21 Budapest 2011.0 \n",
+ "22 Katowice 2011.0 \n",
+ "23 Ámsterdam 2001.0 \n",
+ "24 Stuttgart 2011.0 \n",
+ "25 Varsovia 2011.0 \n",
+ "26 Bucarest 2011.0 \n",
+ "27 Múnich 2011.0 \n",
+ "28 Viena 2011.0 \n",
+ "29 Leeds 2011.0 \n",
+ "30 Estocolmo NaN \n",
+ "31 Bruselas NaN \n",
+ "32 Minsk 2009.0 \n",
+ "33 Lyon 1999.0 \n",
+ "34 Liverpool 2011.0 \n",
+ "35 Valencia 2011.0 \n",
+ "36 Nizni Nóvgorod 2010.0 \n",
+ "37 Turín 2011.0 \n",
+ "38 Járkov 2001.0 \n",
+ "39 Marsella 1999.0 \n",
+ "40 Glasgow 2011.0 \n",
+ "41 Copenhague NaN \n",
+ "42 Sheffield 2011.0 \n",
+ "43 Mannheim 2011.0 \n",
+ "44 Donetsk 2001.0 \n",
+ "45 Newcastle upon Tyne 2011.0 \n",
+ "46 Praga 2001.0 \n",
+ "47 Volgogrado 2010.0 \n",
+ "48 Belgrado 2011.0 \n",
+ "49 Dnipropetrovsk 2001.0 \n",
+ "\n",
+ " País Citypopulation 2015 Demographia 2015 ONU 2015 \\\n",
+ "0 China 46900000.0 45553000.0 42941000.0 \n",
+ "1 Japón 39500000.0 37843000.0 38001000.0 \n",
+ "2 China 30400000.0 30477000.0 29213000.0 \n",
+ "3 Indonesia 30100000.0 30539000.0 11399000.0 \n",
+ "4 India 28400000.0 24998000.0 25703000.0 \n",
+ "5 Pakistán 25300000.0 22123000.0 16618000.0 \n",
+ "6 Filipinas 24600000.0 24123000.0 12946000.0 \n",
+ "7 India 24300000.0 21732000.0 21043000.0 \n",
+ "8 Corea del Sur 24100000.0 23480000.0 10558000.0 \n",
+ "9 Bangladés 22300000.0 15669000.0 17598000.0 \n",
+ "10 China 20700000.0 21009000.0 20384000.0 \n",
+ "11 Japón 19800000.0 17444000.0 20238000.0 \n",
+ "12 Tailandia 16700000.0 14998000.0 11084000.0 \n",
+ "13 India 15900000.0 14667000.0 14865000.0 \n",
+ "14 Irán 13600000.0 13532000.0 10239000.0 \n",
+ "15 China 11200000.0 10920000.0 11210000.0 \n",
+ "16 Japón 10400000.0 10177000.0 9406000.0 \n",
+ "17 India 10300000.0 9807000.0 10087000.0 \n",
+ "18 Pakistán 9950000.0 10052000.0 8741000.0 \n",
+ "19 India 9900000.0 9714000.0 9890000.0 \n",
+ "20 China 9850000.0 11130000.0 5825000.0 \n",
+ "21 China 9400000.0 10376000.0 7556000.0 \n",
+ "22 Taiwán 9000000.0 7438000.0 2666000.0 \n",
+ "23 India 8900000.0 8754000.0 8942000.0 \n",
+ "24 China 8150000.0 9625000.0 8467000.0 \n",
+ "25 Vietnam 8150000.0 8957000.0 7298000.0 \n",
+ "26 China 7950000.0 7509000.0 7906000.0 \n",
+ "27 China 7850000.0 6337000.0 6287000.0 \n",
+ "28 China 7600000.0 7402000.0 7613000.0 \n",
+ "29 India 7350000.0 7186000.0 7343000.0 \n",
+ ".. ... ... ... ... \n",
+ "20 Portugal 2600000.0 2666000.0 2884000.0 \n",
+ "21 Hungría 2550000.0 1710000.0 1714000.0 \n",
+ "22 Polonia 2400000.0 2190000.0 303000.0 \n",
+ "23 Países Bajos 2375000.0 1624000.0 1091000.0 \n",
+ "24 Alemania 2300000.0 1379000.0 626000.0 \n",
+ "25 Polonia 2275000.0 1720000.0 1722000.0 \n",
+ "26 Rumania 2175000.0 1860000.0 1868000.0 \n",
+ "27 Alemania 2175000.0 1981000.0 1438000.0 \n",
+ "28 Austria 2125000.0 1763000.0 1753000.0 \n",
+ "29 Reino Unido 2125000.0 1893000.0 1912000.0 \n",
+ "30 Suecia 2075000.0 1484000.0 1486000.0 \n",
+ "31 Bélgica 2000000.0 2089000.0 2045000.0 \n",
+ "32 Bielorrusia 1950000.0 1910000.0 1915000.0 \n",
+ "33 Francia 1920000.0 1583000.0 1609000.0 \n",
+ "34 Reino Unido 1830000.0 875000.0 870000.0 \n",
+ "35 España 1780000.0 1561000.0 810000.0 \n",
+ "36 Rusia 1750000.0 1201000.0 1212000.0 \n",
+ "37 Italia 1670000.0 1521000.0 1765000.0 \n",
+ "38 Ucrania 1650000.0 1440000.0 1441000.0 \n",
+ "39 Francia 1640000.0 1397000.0 1605000.0 \n",
+ "40 Reino Unido 1610000.0 1220000.0 1223000.0 \n",
+ "41 Dinamarca 1600000.0 1248000.0 1268000.0 \n",
+ "42 Reino Unido 1530000.0 706000.0 706000.0 \n",
+ "43 Alemania 1520000.0 559000.0 319000.0 \n",
+ "44 Ucrania 1480000.0 930000.0 934000.0 \n",
+ "45 Reino Unido 1460000.0 793000.0 791000.0 \n",
+ "46 República Checa 1460000.0 1310000.0 1314000.0 \n",
+ "47 Rusia 1410000.0 999000.0 1022000.0 \n",
+ "48 Serbia 1400000.0 1180000.0 1182000.0 \n",
+ "49 Ucrania 1390000.0 950000.0 957000.0 \n",
+ "\n",
+ " Ultimo Censo Posición en Tabla Inicial \n",
+ "0 39264086.0 1 \n",
+ "1 8945695.0 2 \n",
+ "2 25420288.0 3 \n",
+ "3 10558121.0 4 \n",
+ "4 16349831.0 5 \n",
+ "5 21142625.0 6 \n",
+ "6 1652171.0 7 \n",
+ "7 19617302.0 8 \n",
+ "8 23836272.0 9 \n",
+ "9 14543124.0 10 \n",
+ "10 16446857.0 11 \n",
+ "11 2665314.0 12 \n",
+ "12 8986218.0 13 \n",
+ "13 14057991.0 14 \n",
+ "14 9768677.0 15 \n",
+ "15 9290263.0 16 \n",
+ "16 2263894.0 17 \n",
+ "17 8520435.0 18 \n",
+ "18 5143495.0 19 \n",
+ "19 8653521.0 20 \n",
+ "20 4273841.0 21 \n",
+ "21 6316922.0 22 \n",
+ "22 NaN 23 \n",
+ "23 7677018.0 24 \n",
+ "24 6887819.0 25 \n",
+ "25 5880615.0 26 \n",
+ "26 7541527.0 27 \n",
+ "27 5775239.0 28 \n",
+ "28 7037040.0 29 \n",
+ "29 6357693.0 30 \n",
+ ".. ... ... \n",
+ "20 564657.0 21 \n",
+ "21 1729040.0 22 \n",
+ "22 310764.0 23 \n",
+ "23 734533.0 24 \n",
+ "24 585890.0 25 \n",
+ "25 1700612.0 26 \n",
+ "26 1883425.0 27 \n",
+ "27 1348335.0 28 \n",
+ "28 2015580.0 29 \n",
+ "29 2058861.0 30 \n",
+ "30 NaN 31 \n",
+ "31 NaN 32 \n",
+ "32 1836808.0 33 \n",
+ "33 1428998.0 34 \n",
+ "34 1367147.0 35 \n",
+ "35 792054.0 36 \n",
+ "36 1250619.0 37 \n",
+ "37 872367.0 38 \n",
+ "38 1470902.0 39 \n",
+ "39 1463016.0 40 \n",
+ "40 1601154.0 41 \n",
+ "41 NaN 42 \n",
+ "42 795844.0 43 \n",
+ "43 290117.0 44 \n",
+ "44 1016194.0 45 \n",
+ "45 1220781.0 46 \n",
+ "46 1169106.0 47 \n",
+ "47 1021215.0 48 \n",
+ "48 1166763.0 49 \n",
+ "49 1065008.0 50 \n",
+ "\n",
+ "[491 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Eliminar los rows que contengan valores \"0\" o \"NaN\": ¿Cómo debe resultar?\n",
+ "wiki3_df.dropna(subset=['Citypopulation 2015'], how = 'all', inplace = True)\n",
+ "wiki3_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of duplicate records dropped: 50\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Empezar a trabajar en limpiar la data:\n",
+ "#1. Eliminar registros duplicados\n",
+ "before = len(wiki3_df)\n",
+ "wiki3_df = wiki3_df.drop_duplicates()\n",
+ "after = len(wiki3_df)\n",
+ "print('Number of duplicate records dropped: ', str(before - after))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Ciudad Fecha País Citypopulation 2015 Demographia 2015 ONU 2015 \\\n",
+ "27 Beirut 1970.0 Líbano 1630000.0 2200000.0 2226000.0 \n",
+ "\n",
+ " Ultimo Censo Posición en Tabla Inicial \n",
+ "27 474870.0 28 \n"
+ ]
+ }
+ ],
+ "source": [
+ "#2. ¿Cuál es el censo más antiguo?\n",
+ "print(wiki3_df[wiki3_df.Fecha== wiki3_df.Fecha.min()])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Ciudad \n",
+ " Fecha \n",
+ " Citypopulation 2015 \n",
+ " Demographia 2015 \n",
+ " ONU 2015 \n",
+ " Ultimo Censo \n",
+ " Posición en Tabla Inicial \n",
+ " \n",
+ " \n",
+ " País \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Afganistán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Alemania \n",
+ " 17 \n",
+ " 16 \n",
+ " 17 \n",
+ " 15 \n",
+ " 15 \n",
+ " 7 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " Arabia Saudita \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " Armenia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Australia \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " Austria \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Azerbaiyán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Bangladés \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " Bielorrusia \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Birmania \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Bulgaria \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Bélgica \n",
+ " 3 \n",
+ " 0 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 0 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Camboya \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " China \n",
+ " 116 \n",
+ " 116 \n",
+ " 116 \n",
+ " 107 \n",
+ " 108 \n",
+ " 109 \n",
+ " 116 \n",
+ " \n",
+ " \n",
+ " Corea del Norte \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Corea del Sur \n",
+ " 8 \n",
+ " 8 \n",
+ " 8 \n",
+ " 8 \n",
+ " 7 \n",
+ " 8 \n",
+ " 8 \n",
+ " \n",
+ " \n",
+ " Dinamarca \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Emiratos Árabes Unidos \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " España \n",
+ " 7 \n",
+ " 7 \n",
+ " 7 \n",
+ " 7 \n",
+ " 7 \n",
+ " 4 \n",
+ " 7 \n",
+ " \n",
+ " \n",
+ " Estados Unidos \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Filipinas \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 1 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " Finlandia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Francia \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " Georgia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Grecia \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Hong Kong \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Hungría \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " India \n",
+ " 65 \n",
+ " 64 \n",
+ " 65 \n",
+ " 63 \n",
+ " 64 \n",
+ " 63 \n",
+ " 65 \n",
+ " \n",
+ " \n",
+ " Indonesia \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 16 \n",
+ " 10 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " Irak \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " Kuwait \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Líbano \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Malasia \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 2 \n",
+ " 1 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Mongolia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Nepal \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Noruega \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Nueva Zelanda \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Pakistán \n",
+ " 11 \n",
+ " 11 \n",
+ " 11 \n",
+ " 11 \n",
+ " 10 \n",
+ " 10 \n",
+ " 11 \n",
+ " \n",
+ " \n",
+ " Palestina \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Países Bajos \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " Polonia \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " Portugal \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Reino Unido \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " República Checa \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Rumania \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Rusia \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 17 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " Serbia \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Singapur Malasia \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Siria \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Sri Lanka \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Suecia \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Suiza \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Tailandia \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Taiwán \n",
+ " 5 \n",
+ " 0 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 0 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " Turkmenistán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Turquía \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " \n",
+ " \n",
+ " Ucrania \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " \n",
+ " \n",
+ " Uzbekistán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Vietnam \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Yemen \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
67 rows × 7 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Ciudad Fecha Citypopulation 2015 Demographia 2015 \\\n",
+ "País \n",
+ "Afganistán 1 1 1 1 \n",
+ "Alemania 17 16 17 15 \n",
+ "Arabia Saudita 6 6 6 6 \n",
+ "Armenia 1 1 1 1 \n",
+ "Australia 5 5 5 5 \n",
+ "Austria 2 2 2 2 \n",
+ "Azerbaiyán 1 1 1 1 \n",
+ "Bangladés 4 4 4 4 \n",
+ "Bielorrusia 2 2 2 2 \n",
+ "Birmania 3 3 3 3 \n",
+ "Bulgaria 1 1 1 1 \n",
+ "Bélgica 3 0 3 3 \n",
+ "Camboya 1 1 1 1 \n",
+ "China 116 116 116 107 \n",
+ "Corea del Norte 1 1 1 1 \n",
+ "Corea del Sur 8 8 8 8 \n",
+ "Dinamarca 2 0 2 2 \n",
+ "Emiratos Árabes Unidos 2 2 2 2 \n",
+ "España 7 7 7 7 \n",
+ "Estados Unidos 1 1 1 1 \n",
+ "Filipinas 5 5 5 5 \n",
+ "Finlandia 1 1 1 1 \n",
+ "Francia 6 6 6 6 \n",
+ "Georgia 1 1 1 1 \n",
+ "Grecia 2 2 2 2 \n",
+ "Hong Kong 2 2 2 2 \n",
+ "Hungría 2 2 2 2 \n",
+ "India 65 64 65 63 \n",
+ "Indonesia 18 18 18 18 \n",
+ "Irak 6 6 6 6 \n",
+ "... ... ... ... ... \n",
+ "Kuwait 1 0 1 1 \n",
+ "Líbano 1 1 1 1 \n",
+ "Malasia 3 3 3 3 \n",
+ "Mongolia 1 1 1 1 \n",
+ "Nepal 1 1 1 1 \n",
+ "Noruega 1 1 1 1 \n",
+ "Nueva Zelanda 1 1 1 1 \n",
+ "Pakistán 11 11 11 11 \n",
+ "Palestina 1 1 1 1 \n",
+ "Países Bajos 4 4 4 4 \n",
+ "Polonia 4 4 4 4 \n",
+ "Portugal 3 3 3 3 \n",
+ "Reino Unido 17 17 17 17 \n",
+ "República Checa 2 2 2 2 \n",
+ "Rumania 2 2 2 2 \n",
+ "Rusia 18 18 18 18 \n",
+ "Serbia 2 2 2 2 \n",
+ "Singapur Malasia 2 0 2 1 \n",
+ "Siria 2 2 2 2 \n",
+ "Sri Lanka 2 2 2 2 \n",
+ "Suecia 2 0 2 2 \n",
+ "Suiza 1 1 1 1 \n",
+ "Tailandia 3 3 3 3 \n",
+ "Taiwán 5 0 5 5 \n",
+ "Turkmenistán 1 1 1 1 \n",
+ "Turquía 10 10 10 10 \n",
+ "Ucrania 9 9 9 9 \n",
+ "Uzbekistán 1 1 1 1 \n",
+ "Vietnam 3 3 3 3 \n",
+ "Yemen 1 1 1 1 \n",
+ "\n",
+ " ONU 2015 Ultimo Censo Posición en Tabla Inicial \n",
+ "País \n",
+ "Afganistán 1 1 1 \n",
+ "Alemania 15 7 17 \n",
+ "Arabia Saudita 6 6 6 \n",
+ "Armenia 1 1 1 \n",
+ "Australia 5 5 5 \n",
+ "Austria 2 2 2 \n",
+ "Azerbaiyán 1 1 1 \n",
+ "Bangladés 4 4 4 \n",
+ "Bielorrusia 2 2 2 \n",
+ "Birmania 3 3 3 \n",
+ "Bulgaria 1 1 1 \n",
+ "Bélgica 3 0 3 \n",
+ "Camboya 1 1 1 \n",
+ "China 108 109 116 \n",
+ "Corea del Norte 1 1 1 \n",
+ "Corea del Sur 7 8 8 \n",
+ "Dinamarca 2 0 2 \n",
+ "Emiratos Árabes Unidos 1 1 2 \n",
+ "España 7 4 7 \n",
+ "Estados Unidos 1 1 1 \n",
+ "Filipinas 5 1 5 \n",
+ "Finlandia 1 1 1 \n",
+ "Francia 6 6 6 \n",
+ "Georgia 1 1 1 \n",
+ "Grecia 2 2 2 \n",
+ "Hong Kong 2 2 2 \n",
+ "Hungría 2 2 2 \n",
+ "India 64 63 65 \n",
+ "Indonesia 16 10 18 \n",
+ "Irak 6 6 6 \n",
+ "... ... ... ... \n",
+ "Kuwait 1 0 1 \n",
+ "Líbano 1 1 1 \n",
+ "Malasia 2 1 3 \n",
+ "Mongolia 1 1 1 \n",
+ "Nepal 1 1 1 \n",
+ "Noruega 1 1 1 \n",
+ "Nueva Zelanda 1 1 1 \n",
+ "Pakistán 10 10 11 \n",
+ "Palestina 1 1 1 \n",
+ "Países Bajos 4 2 4 \n",
+ "Polonia 4 2 4 \n",
+ "Portugal 3 1 3 \n",
+ "Reino Unido 17 17 17 \n",
+ "República Checa 2 2 2 \n",
+ "Rumania 2 2 2 \n",
+ "Rusia 18 17 18 \n",
+ "Serbia 2 2 2 \n",
+ "Singapur Malasia 1 1 2 \n",
+ "Siria 2 2 2 \n",
+ "Sri Lanka 2 1 2 \n",
+ "Suecia 2 0 2 \n",
+ "Suiza 1 1 1 \n",
+ "Tailandia 3 1 3 \n",
+ "Taiwán 5 0 5 \n",
+ "Turkmenistán 1 1 1 \n",
+ "Turquía 10 10 10 \n",
+ "Ucrania 9 9 9 \n",
+ "Uzbekistán 1 1 1 \n",
+ "Vietnam 3 3 3 \n",
+ "Yemen 1 1 1 \n",
+ "\n",
+ "[67 rows x 7 columns]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#3. Agrupar por numero de paises y las ciudades que aparecen en la lista\n",
+ "wiki3_df.groupby('País').count()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wiki3_df.to_csv('wiki3_df2.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/API - Web Project - Miguel.ipynb b/API - Web Project - Miguel.ipynb
new file mode 100644
index 0000000..687ffa9
--- /dev/null
+++ b/API - Web Project - Miguel.ipynb
@@ -0,0 +1,1306 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests as req\n",
+ "import json as js\n",
+ "import pandas as pd\n",
+ "from pandas.io.json import json_normalize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#datau = req.get('http://universities.hipolabs.com/search?')\n",
+ "#datau = datau.json()\n",
+ "#dfu = pd.DataFrame(datau)\n",
+ "#dfu\n",
+ "#información muy pequeña y simple. Sin keys de autentificación"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#datauk = req.get('https://api.carbonintensity.org.uk/intensity')\n",
+ "#datauk_2 = datauk.json()\n",
+ "#type(datauk_2)\n",
+ "#datauk_2\n",
+ "#funciona bien pero debe ir cambiando los valores dentro de la URL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "url2 = 'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=MSFT&interval=5min&apikey=AEVIFBN3Y7WUWVL9'\n",
+ "alpha = req.get(url2)\n",
+ "alpha = alpha.json()\n",
+ "alpha\n",
+ "#AEVIFBN3Y7WUWVL9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(alpha)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['Meta Data', 'Time Series (5min)'])"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "alpha.keys()\n",
+ "#dict_keys(['symbol', 'financials'])"
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+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
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5 rows × 100 columns
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+ "metadata": {},
+ "output_type": "execute_result"
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+ "source": [
+ "msft_df = pd.DataFrame(alpha[\"Time Series (5min)\"])\n",
+ "msft_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
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+ " \n",
+ " \n",
+ " 1. open \n",
+ " 2. high \n",
+ " 3. low \n",
+ " 4. close \n",
+ " 5. volume \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2019-07-22 16:00:00 \n",
+ " 138.4400 \n",
+ " 138.5500 \n",
+ " 138.3400 \n",
+ " 138.4300 \n",
+ " 886466 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:55:00 \n",
+ " 138.4400 \n",
+ " 138.4900 \n",
+ " 138.3500 \n",
+ " 138.4400 \n",
+ " 321964 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:50:00 \n",
+ " 138.3750 \n",
+ " 138.4550 \n",
+ " 138.3400 \n",
+ " 138.4400 \n",
+ " 252689 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:45:00 \n",
+ " 138.3600 \n",
+ " 138.4700 \n",
+ " 138.3350 \n",
+ " 138.3850 \n",
+ " 233272 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:40:00 \n",
+ " 138.3400 \n",
+ " 138.4000 \n",
+ " 138.3300 \n",
+ " 138.3650 \n",
+ " 249061 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " 1. open 2. high 3. low 4. close 5. volume\n",
+ "2019-07-22 16:00:00 138.4400 138.5500 138.3400 138.4300 886466\n",
+ "2019-07-22 15:55:00 138.4400 138.4900 138.3500 138.4400 321964\n",
+ "2019-07-22 15:50:00 138.3750 138.4550 138.3400 138.4400 252689\n",
+ "2019-07-22 15:45:00 138.3600 138.4700 138.3350 138.3850 233272\n",
+ "2019-07-22 15:40:00 138.3400 138.4000 138.3300 138.3650 249061"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#a partir de aqui se empieza a editar el dataframe. 1. Agregar cabeceras, 2. dividir columnas de fechas / horas\n",
+ "# 3. realizar analisis de maximo - minimo valor\n",
+ "#vamos volumen transado\n",
+ "msft_dfT2 = msft_df.transpose()\n",
+ "msft_dfT2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['1. open', '2. high', '3. low', '4. close', '5. volume'], dtype='object')"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Open Price \n",
+ " Highest Price \n",
+ " Lowest Price \n",
+ " Close Price \n",
+ " Volume_Ops \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2019-07-22 16:00:00 \n",
+ " 138.4400 \n",
+ " 138.5500 \n",
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+ " 138.4300 \n",
+ " 886466 \n",
+ " \n",
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+ " 2019-07-22 15:50:00 \n",
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+ " 138.4400 \n",
+ " 252689 \n",
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+ " \n",
+ " 2019-07-22 15:45:00 \n",
+ " 138.3600 \n",
+ " 138.4700 \n",
+ " 138.3350 \n",
+ " 138.3850 \n",
+ " 233272 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:40:00 \n",
+ " 138.3400 \n",
+ " 138.4000 \n",
+ " 138.3300 \n",
+ " 138.3650 \n",
+ " 249061 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Open Price Highest Price Lowest Price Close Price \\\n",
+ "2019-07-22 16:00:00 138.4400 138.5500 138.3400 138.4300 \n",
+ "2019-07-22 15:55:00 138.4400 138.4900 138.3500 138.4400 \n",
+ "2019-07-22 15:50:00 138.3750 138.4550 138.3400 138.4400 \n",
+ "2019-07-22 15:45:00 138.3600 138.4700 138.3350 138.3850 \n",
+ "2019-07-22 15:40:00 138.3400 138.4000 138.3300 138.3650 \n",
+ "\n",
+ " Volume_Ops \n",
+ "2019-07-22 16:00:00 886466 \n",
+ "2019-07-22 15:55:00 321964 \n",
+ "2019-07-22 15:50:00 252689 \n",
+ "2019-07-22 15:45:00 233272 \n",
+ "2019-07-22 15:40:00 249061 "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2.columns = ['Open Price', 'Highest Price', 'Lowest Price', 'Close Price', 'Volume_Ops']\n",
+ "msft_dfT2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Open Price object\n",
+ "Highest Price object\n",
+ "Lowest Price object\n",
+ "Close Price object\n",
+ "Volume_Ops object\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2.dtypes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " Lowest Price \n",
+ " Close Price \n",
+ " Volume_Ops \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2019-07-22 16:00:00 \n",
+ " 138.440 \n",
+ " 138.550 \n",
+ " 138.340 \n",
+ " 138.430 \n",
+ " 886466.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:55:00 \n",
+ " 138.440 \n",
+ " 138.490 \n",
+ " 138.350 \n",
+ " 138.440 \n",
+ " 321964.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:50:00 \n",
+ " 138.375 \n",
+ " 138.455 \n",
+ " 138.340 \n",
+ " 138.440 \n",
+ " 252689.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:45:00 \n",
+ " 138.360 \n",
+ " 138.470 \n",
+ " 138.335 \n",
+ " 138.385 \n",
+ " 233272.0 \n",
+ " \n",
+ " \n",
+ " 2019-07-22 15:40:00 \n",
+ " 138.340 \n",
+ " 138.400 \n",
+ " 138.330 \n",
+ " 138.365 \n",
+ " 249061.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Open Price Highest Price Lowest Price Close Price \\\n",
+ "2019-07-22 16:00:00 138.440 138.550 138.340 138.430 \n",
+ "2019-07-22 15:55:00 138.440 138.490 138.350 138.440 \n",
+ "2019-07-22 15:50:00 138.375 138.455 138.340 138.440 \n",
+ "2019-07-22 15:45:00 138.360 138.470 138.335 138.385 \n",
+ "2019-07-22 15:40:00 138.340 138.400 138.330 138.365 \n",
+ "\n",
+ " Volume_Ops \n",
+ "2019-07-22 16:00:00 886466.0 \n",
+ "2019-07-22 15:55:00 321964.0 \n",
+ "2019-07-22 15:50:00 252689.0 \n",
+ "2019-07-22 15:45:00 233272.0 \n",
+ "2019-07-22 15:40:00 249061.0 "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "msft_dfT2 = msft_dfT2.astype(float).round(3)\n",
+ "msft_dfT2.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Open Price \n",
+ " Highest Price \n",
+ " Lowest Price \n",
+ " Close Price \n",
+ " Volume_Ops \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 2019-07-22 10:10:00 \n",
+ " 138.78 \n",
+ " 139.19 \n",
+ " 138.76 \n",
+ " 139.0 \n",
+ " 792631.0 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Open Price Highest Price Lowest Price Close Price \\\n",
+ "2019-07-22 10:10:00 138.78 139.19 138.76 139.0 \n",
+ "\n",
+ " Volume_Ops \n",
+ "2019-07-22 10:10:00 792631.0 "
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "maxP = msft_dfT2['Highest Price'].max()\n",
+ "msft_dfT2[(msft_dfT2['Highest Price'] == maxP)]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "msft_dfT2.to_csv('msft_dfT2.csv', index=False)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Web Scrapping - Miguel.ipynb b/Web Scrapping - Miguel.ipynb
new file mode 100644
index 0000000..1485c31
--- /dev/null
+++ b/Web Scrapping - Miguel.ipynb
@@ -0,0 +1,3600 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import bs4\n",
+ "from bs4 import BeautifulSoup\n",
+ "import requests\n",
+ "import pandas\n",
+ "import lxml\n",
+ "import html5lib\n",
+ "import re"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "b'\\n\\n\\n \\nAnexo:Aglomeraciones urbanas m\\xc3\\xa1s pobladas del mundo - Wikipedia, la enciclopedia libre \\n\\n\\n \\n\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n\\n\\n\\n
\\n
\\n\\n\\t
\\n\\t
\\n\\t
\\n
\\n\\n\\t
Anexo:Aglomeraciones urbanas m\\xc3\\xa1s pobladas del mundo \\n\\t\\n\\t
\\n\\t\\t
De Wikipedia, la enciclopedia libre
\\n\\t\\t
\\n\\t\\t\\n\\t\\t\\n\\t\\t\\n\\t\\t
\\n\\t\\t
Ir a la navegación \\n\\t\\t
Ir a la búsqueda \\n\\t\\t
Este anexo contiene los listados de las aglomeraciones urbanas (megaciudades ) m\\xc3\\xa1s pobladas del mundo las estimaciones publicadas por el informe de las Naciones Unidas y Demographia para el mismo a\\xc3\\xb1o 2018, y la poblaci\\xc3\\xb3n con la que contaban dichas aglomeraciones al momento de hacerse el \\xc3\\xbaltimo censo donde se encuentre disponible.\\n
\\n
\\n\\n
Las 100 aglomeraciones urbanas m\\xc3\\xa1s pobladas del mundo [ editar ] \\n
\\n
Las mayores aglomeraciones urbanas de \\xc3\\x81frica [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en \\xc3\\x81frica seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las 50 mayores aglomeraciones urbanas del continente americano :\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica del Norte [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Am\\xc3\\xa9rica del Norte seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica Central y del Caribe [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Am\\xc3\\xa9rica Central y del Caribe , seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Am\\xc3\\xa9rica del Sur [ editar ] \\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Am\\xc3\\xa9rica del Sur , seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Asia [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las 50 mayores aglomeraciones urbanas del continente asi\\xc3\\xa1tico .\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Oriente Medio, Asia Central y Siberia [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Oriente Medio , Asia Central y Asia del Norte seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas del subcontinente indio [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en el subcontinente indio seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Asia Oriental [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Asia Oriental seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas del Sureste Asi\\xc3\\xa1tico [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en el Sureste Asi\\xc3\\xa1tico seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Europa [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las 50 mayores aglomeraciones urbanas del continente Europeo .\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Europa Occidental [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Europa Occidental seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Europa Oriental [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Europa Oriental seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n
Las mayores aglomeraciones urbanas de Ocean\\xc3\\xada [ editar ] \\n
\\n\\n\\n \\nEste art\\xc3\\xadculo o secci\\xc3\\xb3n se encuentra desactualizado. La informaci\\xc3\\xb3n suministrada ha quedado obsoleta o es insuficiente. Uso de esta plantilla: {{sust:Desactualizado|tema del art\\xc3\\xadculo }} \\n \\n
\\n
Las aglomeraciones urbanas que superar\\xc3\\xadan el mill\\xc3\\xb3n de habitantes en Ocean\\xc3\\xada seg\\xc3\\xban estimaciones recientes y los datos de los \\xc3\\xbaltimos censos oficiales donde existan datos disponibles, ordenadas seg\\xc3\\xban las estimaciones de Citypopulation:\\n \\n
\\n
\\n\\nPosici\\xc3\\xb3n\\n \\nCiudad\\n \\nPa\\xc3\\xads\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban Citypopulation (2015)\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban ONU (2015)\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban Demographia (2015)\\n \\nPoblaci\\xc3\\xb3n seg\\xc3\\xban \\xc3\\xbaltimo censo\\n \\nFecha y fuente\\n \\n\\n1 \\nS\\xc3\\xaddney \\nAustralia Australia \\n4.850.000 \\n4.505.000 \\n4.036.000 \\n4.028.525 \\n2011 \\n \\n\\n2 \\nMelbourne \\nAustralia Australia \\n4.350.000 \\n4.203.000 \\n3.906.000 \\n3.847.567 \\n2011 \\n \\n\\n3 \\nBrisbane \\nAustralia Australia \\n2.875.000 \\n2.202.000 \\n1.999.000 \\n1.977.316 \\n2011 \\n \\n\\n4 \\nPerth \\nAustralia Australia \\n2.025.000 \\n1.861.000 \\n1.751.000 \\n1.670.952 \\n2011 \\n \\n\\n5 \\nAuckland \\nNueva Zelanda Nueva Zelanda \\n1.404.000 \\n1.344.000 \\n1.356.000 \\n1.308.831 \\n2013 \\n \\n\\n6 \\nAdelaida \\nAustralia Australia \\n1.290.000 \\n1.256.000 \\n1.140.000 \\n1.198.467 \\n2011 \\n \\n\\n7 \\nHonolulu [ n 9] \\nEstados Unidos Estados Unidos \\n1.000.000 \\n848.000 \\n842.000 \\n953.207 \\n2010 \\n
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+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wiki = requests.get('https://es.wikipedia.org/wiki/Anexo:Aglomeraciones_urbanas_m%C3%A1s_pobladas_del_mundo').content\n",
+ "wiki"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#wiki_soup = BeautifulSoup(wiki,'html')\n",
+ "#table = wiki_soup.find_all(['tr'])\n",
+ "#table = [row.text.strip().split(\"\\n\") for row in table]\n",
+ "#table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wiki2 = pandas.read_html(wiki) #solo funciona con tablas. Punto clave\n",
+ "#len(wiki2) #list of 28 pandas DataFrame \n",
+ "#type(wiki2) #list\n",
+ "#type(wiki2[0]) #dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\52557\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+ "of pandas will change to not sort by default.\n",
+ "\n",
+ "To accept the future behavior, pass 'sort=False'.\n",
+ "\n",
+ "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Ciudad \n",
+ " Fecha y fuente \n",
+ " País \n",
+ " Población según Citypopulation (2015) \n",
+ " Población según Citypopulation (2016) \n",
+ " Población según Citypopulation[1] \n",
+ " Población según Demographia (2015) \n",
+ " Población según Demographia[2] \n",
+ " Población según ONU (2015) \n",
+ " Población según ONU[3] \n",
+ " Población según último censo \n",
+ " Población según último censo oficial \n",
+ " Posición \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Cantón \n",
+ " 2010 \n",
+ " China \n",
+ " NaN \n",
+ " NaN \n",
+ " 45 600 000 \n",
+ " NaN \n",
+ " 42 941 000 \n",
+ " NaN \n",
+ " 45 553 000 \n",
+ " NaN \n",
+ " 39 264 086 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " Tokio \n",
+ " 2020 \n",
+ " Japón \n",
+ " NaN \n",
+ " NaN \n",
+ " 40 200 000 \n",
+ " NaN \n",
+ " 38 001 000 \n",
+ " NaN \n",
+ " 37 843 000 \n",
+ " NaN \n",
+ " 8 945 695 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " Shanghái \n",
+ " 2010 \n",
+ " China \n",
+ " NaN \n",
+ " NaN \n",
+ " 35 900 000 \n",
+ " NaN \n",
+ " 29 213 000 \n",
+ " NaN \n",
+ " 30 539 000 \n",
+ " NaN \n",
+ " 10 558 121 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " Yakarta \n",
+ " 2010 \n",
+ " Indonesia \n",
+ " NaN \n",
+ " NaN \n",
+ " 30 600 000 \n",
+ " NaN \n",
+ " 11 399 000 \n",
+ " NaN \n",
+ " 30 477 000 \n",
+ " NaN \n",
+ " 25 420 288 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " Delhi \n",
+ " 2011 \n",
+ " India \n",
+ " NaN \n",
+ " NaN \n",
+ " 29 400 000 \n",
+ " NaN \n",
+ " 25 703 000 \n",
+ " NaN \n",
+ " 24 998 000 \n",
+ " NaN \n",
+ " 16 349 831 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " Manila \n",
+ " 2010 \n",
+ " Filipinas \n",
+ " NaN \n",
+ " NaN \n",
+ " 25 200 000 \n",
+ " NaN \n",
+ " 12 946 000 \n",
+ " NaN \n",
+ " 24 123 000 \n",
+ " NaN \n",
+ " 1 652 171 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " Seúl \n",
+ " 2010 \n",
+ " Corea del Sur \n",
+ " NaN \n",
+ " NaN \n",
+ " 24 700 000 \n",
+ " NaN \n",
+ " 13 558 000 \n",
+ " NaN \n",
+ " 23 480 000 \n",
+ " NaN \n",
+ " 23 836 272 \n",
+ " 7 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " Bombay \n",
+ " 2011 \n",
+ " India \n",
+ " NaN \n",
+ " NaN \n",
+ " 24 700 000 \n",
+ " NaN \n",
+ " 21 043 000 \n",
+ " NaN \n",
+ " 21 732 000 \n",
+ " NaN \n",
+ " 19 617 302 \n",
+ " 8 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " Ciudad de México \n",
+ " 2015 \n",
+ " México \n",
+ " NaN \n",
+ " NaN \n",
+ " 22 800 000 \n",
+ " NaN \n",
+ " 22 452 000 \n",
+ " NaN \n",
+ " 20 063 000 \n",
+ " NaN \n",
+ " 20 892 724 \n",
+ " 9 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " Nueva York \n",
+ " 2010 \n",
+ " Estados Unidos \n",
+ " NaN \n",
+ " NaN \n",
+ " 22 400 000 \n",
+ " NaN \n",
+ " 19 532 000 \n",
+ " NaN \n",
+ " 20 630 000 \n",
+ " NaN \n",
+ " 19 556 440 \n",
+ " 10 \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " São Paulo \n",
+ " 2010 \n",
+ " Brasil \n",
+ " NaN \n",
+ " NaN \n",
+ " 22 200 000 \n",
+ " NaN \n",
+ " 21 066 000 \n",
+ " NaN \n",
+ " 20 365 000 \n",
+ " NaN \n",
+ " 19 683 975 \n",
+ " 11 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " El Cairo \n",
+ " 2006 \n",
+ " Egipto \n",
+ " NaN \n",
+ " NaN \n",
+ " 20 500 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 7 740 018 \n",
+ " 12 \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " Pekín \n",
+ " 2010 \n",
+ " China \n",
+ " NaN \n",
+ " NaN \n",
+ " 20 400 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 13 123 000 \n",
+ " NaN \n",
+ " 16 446 857 \n",
+ " 13 \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " Daca \n",
+ " 2011 \n",
+ " Bangladés \n",
+ " NaN \n",
+ " NaN \n",
+ " 19 500 000 \n",
+ " NaN \n",
+ " 17 598 000 \n",
+ " NaN \n",
+ " 15 669 000 \n",
+ " NaN \n",
+ " 14 543 124 \n",
+ " 14 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " Lagos \n",
+ " 1991 \n",
+ " Nigeria \n",
+ " NaN \n",
+ " NaN \n",
+ " 18 800 000 \n",
+ " NaN \n",
+ " 18 772 000 \n",
+ " NaN \n",
+ " 15 600 000 \n",
+ " NaN \n",
+ " 5 195 247 \n",
+ " 15 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " Bangkok \n",
+ " 2010 \n",
+ " Tailandia \n",
+ " NaN \n",
+ " NaN \n",
+ " 18 300 000 \n",
+ " NaN \n",
+ " 11 084 000 \n",
+ " NaN \n",
+ " 14 998 000 \n",
+ " NaN \n",
+ " 8 986 218 \n",
+ " 16 \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " Los Ángeles \n",
+ " 2010 \n",
+ " Estados Unidos \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 800 000 \n",
+ " NaN \n",
+ " 14 504 000 \n",
+ " NaN \n",
+ " 15 058 000 \n",
+ " NaN \n",
+ " 17 053 905 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " Osaka \n",
+ " 2010 \n",
+ " Japón \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 700 000 \n",
+ " NaN \n",
+ " 20 238 000 \n",
+ " NaN \n",
+ " 17 444 000 \n",
+ " NaN \n",
+ " 2 665 314 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " Karachi \n",
+ " 2011 \n",
+ " Pakistán \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 300 000 \n",
+ " NaN \n",
+ " 16 618 000 \n",
+ " NaN \n",
+ " 22 123 000 \n",
+ " NaN \n",
+ " 21 142 625 \n",
+ " 19 \n",
+ " \n",
+ " \n",
+ " 19 \n",
+ " Moscú \n",
+ " 2010 \n",
+ " Rusia \n",
+ " NaN \n",
+ " NaN \n",
+ " 17 200 000 \n",
+ " NaN \n",
+ " 12 166 000 \n",
+ " NaN \n",
+ " 16 170 000 \n",
+ " NaN \n",
+ " 11 612 885 \n",
+ " 20 \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Calcuta \n",
+ " 2011 \n",
+ " India \n",
+ " NaN \n",
+ " NaN \n",
+ " 16 600 000 \n",
+ " NaN \n",
+ " 14 865 000 \n",
+ " NaN \n",
+ " 14 667 000 \n",
+ " NaN \n",
+ " 14 057 991 \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Buenos Aires \n",
+ " 2017 \n",
+ " Argentina \n",
+ " NaN \n",
+ " NaN \n",
+ " 16 300 000 \n",
+ " NaN \n",
+ " 18 086 000 \n",
+ " NaN \n",
+ " 14 122 000 \n",
+ " NaN \n",
+ " 13 588 171 \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Estambul \n",
+ " 2015 \n",
+ " Turquía \n",
+ " NaN \n",
+ " NaN \n",
+ " 15 800 000 \n",
+ " NaN \n",
+ " 14 164 000 \n",
+ " NaN \n",
+ " 13 287 000 \n",
+ " NaN \n",
+ " 14 657 000 \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Teherán \n",
+ " 2011 \n",
+ " Irán \n",
+ " NaN \n",
+ " NaN \n",
+ " 15 000 000 \n",
+ " NaN \n",
+ " 10 239 000 \n",
+ " NaN \n",
+ " 13 532 000 \n",
+ " NaN \n",
+ " 9 768 677 \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Londres \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " NaN \n",
+ " NaN \n",
+ " 14 700 000 \n",
+ " NaN \n",
+ " 10 313 000 \n",
+ " NaN \n",
+ " 10 236 000 \n",
+ " NaN \n",
+ " 11 140 445 \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Johannesburgo \n",
+ " 2009 \n",
+ " Sudáfrica \n",
+ " NaN \n",
+ " NaN \n",
+ " 13 700 000 \n",
+ " NaN \n",
+ " 12 613 000 \n",
+ " NaN \n",
+ " 12 066 000 \n",
+ " NaN \n",
+ " 10 002 039 \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " Tianjin \n",
+ " 2010 \n",
+ " China \n",
+ " NaN \n",
+ " NaN \n",
+ " 13 200 000 \n",
+ " NaN \n",
+ " 11 210 000 \n",
+ " NaN \n",
+ " 10 920 000 \n",
+ " NaN \n",
+ " 9 290 263 \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Río de Janeiro \n",
+ " 2010 \n",
+ " Brasil \n",
+ " NaN \n",
+ " NaN \n",
+ " 13 100 000 \n",
+ " NaN \n",
+ " 12 902 000 \n",
+ " NaN \n",
+ " 11 727 000 \n",
+ " NaN \n",
+ " 11 835 708 \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Lahore \n",
+ " 1998 \n",
+ " Pakistán \n",
+ " NaN \n",
+ " NaN \n",
+ " 12 600 000 \n",
+ " NaN \n",
+ " 8 741 000 \n",
+ " NaN \n",
+ " 10 052 000 \n",
+ " NaN \n",
+ " 5 143 495 \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Kinsasa \n",
+ " 2004 \n",
+ " República Democrática del Congo \n",
+ " NaN \n",
+ " NaN \n",
+ " 12 000 000 \n",
+ " NaN \n",
+ " 11 587 000 \n",
+ " NaN \n",
+ " 11 587 000 \n",
+ " NaN \n",
+ " 7 273 947 \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Lisboa \n",
+ " 2001 \n",
+ " Portugal \n",
+ " 2.600.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 2.666.000 \n",
+ " NaN \n",
+ " 2.884.000 \n",
+ " NaN \n",
+ " 564.657 \n",
+ " NaN \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Budapest \n",
+ " 2011 \n",
+ " Hungría \n",
+ " 2.550.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.710.000 \n",
+ " NaN \n",
+ " 1.714.000 \n",
+ " NaN \n",
+ " 1.729.040 \n",
+ " NaN \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Katowice \n",
+ " 2011 \n",
+ " Polonia \n",
+ " 2.400.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 2.190.000 \n",
+ " NaN \n",
+ " 303.000 \n",
+ " NaN \n",
+ " 310.764 \n",
+ " NaN \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Ámsterdam \n",
+ " 2001 \n",
+ " Países Bajos \n",
+ " 2.375.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.624.000 \n",
+ " NaN \n",
+ " 1.091.000 \n",
+ " NaN \n",
+ " 734.533 \n",
+ " NaN \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Stuttgart \n",
+ " 2011 \n",
+ " Alemania \n",
+ " 2.300.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.379.000 \n",
+ " NaN \n",
+ " 626.000 \n",
+ " NaN \n",
+ " 585.890 \n",
+ " NaN \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Varsovia \n",
+ " 2011 \n",
+ " Polonia \n",
+ " 2.275.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.720.000 \n",
+ " NaN \n",
+ " 1.722.000 \n",
+ " NaN \n",
+ " 1.700.612 \n",
+ " NaN \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " Bucarest \n",
+ " 2011 \n",
+ " Rumania \n",
+ " 2.175.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.860.000 \n",
+ " NaN \n",
+ " 1.868.000 \n",
+ " NaN \n",
+ " 1.883.425 \n",
+ " NaN \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Múnich \n",
+ " 2011 \n",
+ " Alemania \n",
+ " 2.175.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.981.000 \n",
+ " NaN \n",
+ " 1.438.000 \n",
+ " NaN \n",
+ " 1.348.335 \n",
+ " NaN \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Viena \n",
+ " 2011 \n",
+ " Austria \n",
+ " 2.125.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.763.000 \n",
+ " NaN \n",
+ " 1.753.000 \n",
+ " NaN \n",
+ " 2.015.580 \n",
+ " NaN \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Leeds \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 2.125.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.893.000 \n",
+ " NaN \n",
+ " 1.912.000 \n",
+ " NaN \n",
+ " 2.058.861 \n",
+ " NaN \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " Estocolmo \n",
+ " NaN \n",
+ " Suecia \n",
+ " 2.075.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.484.000 \n",
+ " NaN \n",
+ " 1.486.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 31 \n",
+ " \n",
+ " \n",
+ " 31 \n",
+ " Bruselas \n",
+ " NaN \n",
+ " Bélgica \n",
+ " 2.000.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 2.089.000 \n",
+ " NaN \n",
+ " 2.045.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 32 \n",
+ " \n",
+ " \n",
+ " 32 \n",
+ " Minsk \n",
+ " 2009 \n",
+ " Bielorrusia \n",
+ " 1.950.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.910.000 \n",
+ " NaN \n",
+ " 1.915.000 \n",
+ " NaN \n",
+ " 1.836.808 \n",
+ " NaN \n",
+ " 33 \n",
+ " \n",
+ " \n",
+ " 33 \n",
+ " Lyon \n",
+ " 1999 \n",
+ " Francia \n",
+ " 1.920.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.583.000 \n",
+ " NaN \n",
+ " 1.609.000 \n",
+ " NaN \n",
+ " 1.428.998 \n",
+ " NaN \n",
+ " 34 \n",
+ " \n",
+ " \n",
+ " 34 \n",
+ " Liverpool \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 1.830.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 875.000 \n",
+ " NaN \n",
+ " 870.000 \n",
+ " NaN \n",
+ " 1.367.147 \n",
+ " NaN \n",
+ " 35 \n",
+ " \n",
+ " \n",
+ " 35 \n",
+ " Valencia \n",
+ " 2011 \n",
+ " España \n",
+ " 1.780.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.561.000 \n",
+ " NaN \n",
+ " 810.000 \n",
+ " NaN \n",
+ " 792.054 \n",
+ " NaN \n",
+ " 36 \n",
+ " \n",
+ " \n",
+ " 36 \n",
+ " Nizni Nóvgorod \n",
+ " 2010 \n",
+ " Rusia \n",
+ " 1.750.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.201.000 \n",
+ " NaN \n",
+ " 1.212.000 \n",
+ " NaN \n",
+ " 1.250.619 \n",
+ " NaN \n",
+ " 37 \n",
+ " \n",
+ " \n",
+ " 37 \n",
+ " Turín \n",
+ " 2011 \n",
+ " Italia \n",
+ " 1.670.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.521.000 \n",
+ " NaN \n",
+ " 1.765.000 \n",
+ " NaN \n",
+ " 872.367 \n",
+ " NaN \n",
+ " 38 \n",
+ " \n",
+ " \n",
+ " 38 \n",
+ " Járkov \n",
+ " 2001 \n",
+ " Ucrania \n",
+ " 1.650.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.440.000 \n",
+ " NaN \n",
+ " 1.441.000 \n",
+ " NaN \n",
+ " 1.470.902 \n",
+ " NaN \n",
+ " 39 \n",
+ " \n",
+ " \n",
+ " 39 \n",
+ " Marsella \n",
+ " 1999 \n",
+ " Francia \n",
+ " 1.640.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.397.000 \n",
+ " NaN \n",
+ " 1.605.000 \n",
+ " NaN \n",
+ " 1.463.016 \n",
+ " NaN \n",
+ " 40 \n",
+ " \n",
+ " \n",
+ " 40 \n",
+ " Glasgow \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 1.610.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.220.000 \n",
+ " NaN \n",
+ " 1.223.000 \n",
+ " NaN \n",
+ " 1.601.154 \n",
+ " NaN \n",
+ " 41 \n",
+ " \n",
+ " \n",
+ " 41 \n",
+ " Copenhague \n",
+ " NaN \n",
+ " Dinamarca \n",
+ " 1.600.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.248.000 \n",
+ " NaN \n",
+ " 1.268.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " NaN \n",
+ " 42 \n",
+ " \n",
+ " \n",
+ " 42 \n",
+ " Sheffield \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 1.530.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 706.000 \n",
+ " NaN \n",
+ " 706.000 \n",
+ " NaN \n",
+ " 795.844 \n",
+ " NaN \n",
+ " 43 \n",
+ " \n",
+ " \n",
+ " 43 \n",
+ " Mannheim \n",
+ " 2011 \n",
+ " Alemania \n",
+ " 1.520.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 559.000 \n",
+ " NaN \n",
+ " 319.000 \n",
+ " NaN \n",
+ " 290.117 \n",
+ " NaN \n",
+ " 44 \n",
+ " \n",
+ " \n",
+ " 44 \n",
+ " Donetsk \n",
+ " 2001 \n",
+ " Ucrania \n",
+ " 1.480.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 930.000 \n",
+ " NaN \n",
+ " 934.000 \n",
+ " NaN \n",
+ " 1.016.194 \n",
+ " NaN \n",
+ " 45 \n",
+ " \n",
+ " \n",
+ " 45 \n",
+ " Newcastle upon Tyne \n",
+ " 2011 \n",
+ " Reino Unido \n",
+ " 1.460.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 793.000 \n",
+ " NaN \n",
+ " 791.000 \n",
+ " NaN \n",
+ " 1.220.781 \n",
+ " NaN \n",
+ " 46 \n",
+ " \n",
+ " \n",
+ " 46 \n",
+ " Praga \n",
+ " 2001 \n",
+ " República Checa \n",
+ " 1.460.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.310.000 \n",
+ " NaN \n",
+ " 1.314.000 \n",
+ " NaN \n",
+ " 1.169.106 \n",
+ " NaN \n",
+ " 47 \n",
+ " \n",
+ " \n",
+ " 47 \n",
+ " Volgogrado \n",
+ " 2010 \n",
+ " Rusia \n",
+ " 1.410.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 999.000 \n",
+ " NaN \n",
+ " 1.022.000 \n",
+ " NaN \n",
+ " 1.021.215 \n",
+ " NaN \n",
+ " 48 \n",
+ " \n",
+ " \n",
+ " 48 \n",
+ " Belgrado \n",
+ " 2011 \n",
+ " Serbia \n",
+ " 1.400.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 1.180.000 \n",
+ " NaN \n",
+ " 1.182.000 \n",
+ " NaN \n",
+ " 1.166.763 \n",
+ " NaN \n",
+ " 49 \n",
+ " \n",
+ " \n",
+ " 49 \n",
+ " Dnipropetrovsk \n",
+ " 2001 \n",
+ " Ucrania \n",
+ " 1.390.000 \n",
+ " NaN \n",
+ " NaN \n",
+ " 950.000 \n",
+ " NaN \n",
+ " 957.000 \n",
+ " NaN \n",
+ " 1.065.008 \n",
+ " NaN \n",
+ " 50 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
839 rows × 13 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Ciudad Fecha y fuente País \\\n",
+ "0 Cantón 2010 China \n",
+ "1 Tokio 2020 Japón \n",
+ "2 Shanghái 2010 China \n",
+ "3 Yakarta 2010 Indonesia \n",
+ "4 Delhi 2011 India \n",
+ "5 Manila 2010 Filipinas \n",
+ "6 Seúl 2010 Corea del Sur \n",
+ "7 Bombay 2011 India \n",
+ "8 Ciudad de México 2015 México \n",
+ "9 Nueva York 2010 Estados Unidos \n",
+ "10 São Paulo 2010 Brasil \n",
+ "11 El Cairo 2006 Egipto \n",
+ "12 Pekín 2010 China \n",
+ "13 Daca 2011 Bangladés \n",
+ "14 Lagos 1991 Nigeria \n",
+ "15 Bangkok 2010 Tailandia \n",
+ "16 Los Ángeles 2010 Estados Unidos \n",
+ "17 Osaka 2010 Japón \n",
+ "18 Karachi 2011 Pakistán \n",
+ "19 Moscú 2010 Rusia \n",
+ "20 Calcuta 2011 India \n",
+ "21 Buenos Aires 2017 Argentina \n",
+ "22 Estambul 2015 Turquía \n",
+ "23 Teherán 2011 Irán \n",
+ "24 Londres 2011 Reino Unido \n",
+ "25 Johannesburgo 2009 Sudáfrica \n",
+ "26 Tianjin 2010 China \n",
+ "27 Río de Janeiro 2010 Brasil \n",
+ "28 Lahore 1998 Pakistán \n",
+ "29 Kinsasa 2004 República Democrática del Congo \n",
+ ".. ... ... ... \n",
+ "20 Lisboa 2001 Portugal \n",
+ "21 Budapest 2011 Hungría \n",
+ "22 Katowice 2011 Polonia \n",
+ "23 Ámsterdam 2001 Países Bajos \n",
+ "24 Stuttgart 2011 Alemania \n",
+ "25 Varsovia 2011 Polonia \n",
+ "26 Bucarest 2011 Rumania \n",
+ "27 Múnich 2011 Alemania \n",
+ "28 Viena 2011 Austria \n",
+ "29 Leeds 2011 Reino Unido \n",
+ "30 Estocolmo NaN Suecia \n",
+ "31 Bruselas NaN Bélgica \n",
+ "32 Minsk 2009 Bielorrusia \n",
+ "33 Lyon 1999 Francia \n",
+ "34 Liverpool 2011 Reino Unido \n",
+ "35 Valencia 2011 España \n",
+ "36 Nizni Nóvgorod 2010 Rusia \n",
+ "37 Turín 2011 Italia \n",
+ "38 Járkov 2001 Ucrania \n",
+ "39 Marsella 1999 Francia \n",
+ "40 Glasgow 2011 Reino Unido \n",
+ "41 Copenhague NaN Dinamarca \n",
+ "42 Sheffield 2011 Reino Unido \n",
+ "43 Mannheim 2011 Alemania \n",
+ "44 Donetsk 2001 Ucrania \n",
+ "45 Newcastle upon Tyne 2011 Reino Unido \n",
+ "46 Praga 2001 República Checa \n",
+ "47 Volgogrado 2010 Rusia \n",
+ "48 Belgrado 2011 Serbia \n",
+ "49 Dnipropetrovsk 2001 Ucrania \n",
+ "\n",
+ " Población según Citypopulation (2015) \\\n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "5 NaN \n",
+ "6 NaN \n",
+ "7 NaN \n",
+ "8 NaN \n",
+ "9 NaN \n",
+ "10 NaN \n",
+ "11 NaN \n",
+ "12 NaN \n",
+ "13 NaN \n",
+ "14 NaN \n",
+ "15 NaN \n",
+ "16 NaN \n",
+ "17 NaN \n",
+ "18 NaN \n",
+ "19 NaN \n",
+ "20 NaN \n",
+ "21 NaN \n",
+ "22 NaN \n",
+ "23 NaN \n",
+ "24 NaN \n",
+ "25 NaN \n",
+ "26 NaN \n",
+ "27 NaN \n",
+ "28 NaN \n",
+ "29 NaN \n",
+ ".. ... \n",
+ "20 2.600.000 \n",
+ "21 2.550.000 \n",
+ "22 2.400.000 \n",
+ "23 2.375.000 \n",
+ "24 2.300.000 \n",
+ "25 2.275.000 \n",
+ "26 2.175.000 \n",
+ "27 2.175.000 \n",
+ "28 2.125.000 \n",
+ "29 2.125.000 \n",
+ "30 2.075.000 \n",
+ "31 2.000.000 \n",
+ "32 1.950.000 \n",
+ "33 1.920.000 \n",
+ "34 1.830.000 \n",
+ "35 1.780.000 \n",
+ "36 1.750.000 \n",
+ "37 1.670.000 \n",
+ "38 1.650.000 \n",
+ "39 1.640.000 \n",
+ "40 1.610.000 \n",
+ "41 1.600.000 \n",
+ "42 1.530.000 \n",
+ "43 1.520.000 \n",
+ "44 1.480.000 \n",
+ "45 1.460.000 \n",
+ "46 1.460.000 \n",
+ "47 1.410.000 \n",
+ "48 1.400.000 \n",
+ "49 1.390.000 \n",
+ "\n",
+ " Población según Citypopulation (2016) Población según Citypopulation[1] \\\n",
+ "0 NaN 45 600 000 \n",
+ "1 NaN 40 200 000 \n",
+ "2 NaN 35 900 000 \n",
+ "3 NaN 30 600 000 \n",
+ "4 NaN 29 400 000 \n",
+ "5 NaN 25 200 000 \n",
+ "6 NaN 24 700 000 \n",
+ "7 NaN 24 700 000 \n",
+ "8 NaN 22 800 000 \n",
+ "9 NaN 22 400 000 \n",
+ "10 NaN 22 200 000 \n",
+ "11 NaN 20 500 000 \n",
+ "12 NaN 20 400 000 \n",
+ "13 NaN 19 500 000 \n",
+ "14 NaN 18 800 000 \n",
+ "15 NaN 18 300 000 \n",
+ "16 NaN 17 800 000 \n",
+ "17 NaN 17 700 000 \n",
+ "18 NaN 17 300 000 \n",
+ "19 NaN 17 200 000 \n",
+ "20 NaN 16 600 000 \n",
+ "21 NaN 16 300 000 \n",
+ "22 NaN 15 800 000 \n",
+ "23 NaN 15 000 000 \n",
+ "24 NaN 14 700 000 \n",
+ "25 NaN 13 700 000 \n",
+ "26 NaN 13 200 000 \n",
+ "27 NaN 13 100 000 \n",
+ "28 NaN 12 600 000 \n",
+ "29 NaN 12 000 000 \n",
+ ".. ... ... \n",
+ "20 NaN NaN \n",
+ "21 NaN NaN \n",
+ "22 NaN NaN \n",
+ "23 NaN NaN \n",
+ "24 NaN NaN \n",
+ "25 NaN NaN \n",
+ "26 NaN NaN \n",
+ "27 NaN NaN \n",
+ "28 NaN NaN \n",
+ "29 NaN NaN \n",
+ "30 NaN NaN \n",
+ "31 NaN NaN \n",
+ "32 NaN NaN \n",
+ "33 NaN NaN \n",
+ "34 NaN NaN \n",
+ "35 NaN NaN \n",
+ "36 NaN NaN \n",
+ "37 NaN NaN \n",
+ "38 NaN NaN \n",
+ "39 NaN NaN \n",
+ "40 NaN NaN \n",
+ "41 NaN NaN \n",
+ "42 NaN NaN \n",
+ "43 NaN NaN \n",
+ "44 NaN NaN \n",
+ "45 NaN NaN \n",
+ "46 NaN NaN \n",
+ "47 NaN NaN \n",
+ "48 NaN NaN \n",
+ "49 NaN NaN \n",
+ "\n",
+ " Población según Demographia (2015) Población según Demographia[2] \\\n",
+ "0 NaN 42 941 000 \n",
+ "1 NaN 38 001 000 \n",
+ "2 NaN 29 213 000 \n",
+ "3 NaN 11 399 000 \n",
+ "4 NaN 25 703 000 \n",
+ "5 NaN 12 946 000 \n",
+ "6 NaN 13 558 000 \n",
+ "7 NaN 21 043 000 \n",
+ "8 NaN 22 452 000 \n",
+ "9 NaN 19 532 000 \n",
+ "10 NaN 21 066 000 \n",
+ "11 NaN 13 123 000 \n",
+ "12 NaN 13 123 000 \n",
+ "13 NaN 17 598 000 \n",
+ "14 NaN 18 772 000 \n",
+ "15 NaN 11 084 000 \n",
+ "16 NaN 14 504 000 \n",
+ "17 NaN 20 238 000 \n",
+ "18 NaN 16 618 000 \n",
+ "19 NaN 12 166 000 \n",
+ "20 NaN 14 865 000 \n",
+ "21 NaN 18 086 000 \n",
+ "22 NaN 14 164 000 \n",
+ "23 NaN 10 239 000 \n",
+ "24 NaN 10 313 000 \n",
+ "25 NaN 12 613 000 \n",
+ "26 NaN 11 210 000 \n",
+ "27 NaN 12 902 000 \n",
+ "28 NaN 8 741 000 \n",
+ "29 NaN 11 587 000 \n",
+ ".. ... ... \n",
+ "20 2.666.000 NaN \n",
+ "21 1.710.000 NaN \n",
+ "22 2.190.000 NaN \n",
+ "23 1.624.000 NaN \n",
+ "24 1.379.000 NaN \n",
+ "25 1.720.000 NaN \n",
+ "26 1.860.000 NaN \n",
+ "27 1.981.000 NaN \n",
+ "28 1.763.000 NaN \n",
+ "29 1.893.000 NaN \n",
+ "30 1.484.000 NaN \n",
+ "31 2.089.000 NaN \n",
+ "32 1.910.000 NaN \n",
+ "33 1.583.000 NaN \n",
+ "34 875.000 NaN \n",
+ "35 1.561.000 NaN \n",
+ "36 1.201.000 NaN \n",
+ "37 1.521.000 NaN \n",
+ "38 1.440.000 NaN \n",
+ "39 1.397.000 NaN \n",
+ "40 1.220.000 NaN \n",
+ "41 1.248.000 NaN \n",
+ "42 706.000 NaN \n",
+ "43 559.000 NaN \n",
+ "44 930.000 NaN \n",
+ "45 793.000 NaN \n",
+ "46 1.310.000 NaN \n",
+ "47 999.000 NaN \n",
+ "48 1.180.000 NaN \n",
+ "49 950.000 NaN \n",
+ "\n",
+ " Población según ONU (2015) Población según ONU[3] \\\n",
+ "0 NaN 45 553 000 \n",
+ "1 NaN 37 843 000 \n",
+ "2 NaN 30 539 000 \n",
+ "3 NaN 30 477 000 \n",
+ "4 NaN 24 998 000 \n",
+ "5 NaN 24 123 000 \n",
+ "6 NaN 23 480 000 \n",
+ "7 NaN 21 732 000 \n",
+ "8 NaN 20 063 000 \n",
+ "9 NaN 20 630 000 \n",
+ "10 NaN 20 365 000 \n",
+ "11 NaN 13 123 000 \n",
+ "12 NaN 13 123 000 \n",
+ "13 NaN 15 669 000 \n",
+ "14 NaN 15 600 000 \n",
+ "15 NaN 14 998 000 \n",
+ "16 NaN 15 058 000 \n",
+ "17 NaN 17 444 000 \n",
+ "18 NaN 22 123 000 \n",
+ "19 NaN 16 170 000 \n",
+ "20 NaN 14 667 000 \n",
+ "21 NaN 14 122 000 \n",
+ "22 NaN 13 287 000 \n",
+ "23 NaN 13 532 000 \n",
+ "24 NaN 10 236 000 \n",
+ "25 NaN 12 066 000 \n",
+ "26 NaN 10 920 000 \n",
+ "27 NaN 11 727 000 \n",
+ "28 NaN 10 052 000 \n",
+ "29 NaN 11 587 000 \n",
+ ".. ... ... \n",
+ "20 2.884.000 NaN \n",
+ "21 1.714.000 NaN \n",
+ "22 303.000 NaN \n",
+ "23 1.091.000 NaN \n",
+ "24 626.000 NaN \n",
+ "25 1.722.000 NaN \n",
+ "26 1.868.000 NaN \n",
+ "27 1.438.000 NaN \n",
+ "28 1.753.000 NaN \n",
+ "29 1.912.000 NaN \n",
+ "30 1.486.000 NaN \n",
+ "31 2.045.000 NaN \n",
+ "32 1.915.000 NaN \n",
+ "33 1.609.000 NaN \n",
+ "34 870.000 NaN \n",
+ "35 810.000 NaN \n",
+ "36 1.212.000 NaN \n",
+ "37 1.765.000 NaN \n",
+ "38 1.441.000 NaN \n",
+ "39 1.605.000 NaN \n",
+ "40 1.223.000 NaN \n",
+ "41 1.268.000 NaN \n",
+ "42 706.000 NaN \n",
+ "43 319.000 NaN \n",
+ "44 934.000 NaN \n",
+ "45 791.000 NaN \n",
+ "46 1.314.000 NaN \n",
+ "47 1.022.000 NaN \n",
+ "48 1.182.000 NaN \n",
+ "49 957.000 NaN \n",
+ "\n",
+ " Población según último censo Población según último censo oficial Posición \n",
+ "0 NaN 39 264 086 1 \n",
+ "1 NaN 8 945 695 2 \n",
+ "2 NaN 10 558 121 3 \n",
+ "3 NaN 25 420 288 4 \n",
+ "4 NaN 16 349 831 5 \n",
+ "5 NaN 1 652 171 6 \n",
+ "6 NaN 23 836 272 7 \n",
+ "7 NaN 19 617 302 8 \n",
+ "8 NaN 20 892 724 9 \n",
+ "9 NaN 19 556 440 10 \n",
+ "10 NaN 19 683 975 11 \n",
+ "11 NaN 7 740 018 12 \n",
+ "12 NaN 16 446 857 13 \n",
+ "13 NaN 14 543 124 14 \n",
+ "14 NaN 5 195 247 15 \n",
+ "15 NaN 8 986 218 16 \n",
+ "16 NaN 17 053 905 17 \n",
+ "17 NaN 2 665 314 18 \n",
+ "18 NaN 21 142 625 19 \n",
+ "19 NaN 11 612 885 20 \n",
+ "20 NaN 14 057 991 21 \n",
+ "21 NaN 13 588 171 22 \n",
+ "22 NaN 14 657 000 23 \n",
+ "23 NaN 9 768 677 24 \n",
+ "24 NaN 11 140 445 25 \n",
+ "25 NaN 10 002 039 26 \n",
+ "26 NaN 9 290 263 28 \n",
+ "27 NaN 11 835 708 27 \n",
+ "28 NaN 5 143 495 29 \n",
+ "29 NaN 7 273 947 30 \n",
+ ".. ... ... ... \n",
+ "20 564.657 NaN 21 \n",
+ "21 1.729.040 NaN 22 \n",
+ "22 310.764 NaN 23 \n",
+ "23 734.533 NaN 24 \n",
+ "24 585.890 NaN 25 \n",
+ "25 1.700.612 NaN 26 \n",
+ "26 1.883.425 NaN 27 \n",
+ "27 1.348.335 NaN 28 \n",
+ "28 2.015.580 NaN 29 \n",
+ "29 2.058.861 NaN 30 \n",
+ "30 NaN NaN 31 \n",
+ "31 NaN NaN 32 \n",
+ "32 1.836.808 NaN 33 \n",
+ "33 1.428.998 NaN 34 \n",
+ "34 1.367.147 NaN 35 \n",
+ "35 792.054 NaN 36 \n",
+ "36 1.250.619 NaN 37 \n",
+ "37 872.367 NaN 38 \n",
+ "38 1.470.902 NaN 39 \n",
+ "39 1.463.016 NaN 40 \n",
+ "40 1.601.154 NaN 41 \n",
+ "41 NaN NaN 42 \n",
+ "42 795.844 NaN 43 \n",
+ "43 290.117 NaN 44 \n",
+ "44 1.016.194 NaN 45 \n",
+ "45 1.220.781 NaN 46 \n",
+ "46 1.169.106 NaN 47 \n",
+ "47 1.021.215 NaN 48 \n",
+ "48 1.166.763 NaN 49 \n",
+ "49 1.065.008 NaN 50 \n",
+ "\n",
+ "[839 rows x 13 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#consolidar todos los datos de todas los dataframes\n",
+ "wikiWW = wiki2[0]\n",
+ "wikiAF = wiki2[2]\n",
+ "wikiAN = wiki2[6]\n",
+ "wikiAC = wiki2[8]\n",
+ "wikiAS = wiki2[9]\n",
+ "wikiOM = wiki2[13]\n",
+ "wikiIN = wiki2[15]\n",
+ "wikiAO = wiki2[17]\n",
+ "wikiSA = wiki2[19]\n",
+ "wikiEOC = wiki2[23]\n",
+ "wikiEOR = wiki2[25]\n",
+ "wikiOC = wiki2[27]\n",
+ "wikiAM = wiki2[4]\n",
+ "wikiAS = wiki2[11]\n",
+ "wikiEU = wiki2[21]\n",
+ "\n",
+ "wiki3_df = pandas.concat([wikiWW, wikiAF, wikiAN, wikiAC, wikiAS, wikiOM, wikiIN, wikiAO, wikiSA, wikiEOC, wikiEOR, wikiOC, wikiAM, wikiAS, wikiEU], axis=0)\n",
+ "wiki3_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Exportar hacia un archivo csv todo el dataframe duplicado\n",
+ "wiki3_df.to_csv('wiki3_df.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Ciudad', 'Fecha y fuente', 'País',\n",
+ " 'Población según Citypopulation (2015)',\n",
+ " 'Población según Citypopulation (2016)',\n",
+ " 'Población según Citypopulation[1]',\n",
+ " 'Población según Demographia (2015)', 'Población según Demographia[2]',\n",
+ " 'Población según ONU (2015)', 'Población según ONU[3]',\n",
+ " 'Población según último censo', 'Población según último censo oficial',\n",
+ " 'Posición'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#sin embargo existen columnas duplicadas. Por ejemplo Citpopulation tiene 3 columnas con fechas diferentes.\n",
+ "#debemos cambiar el nombre de las columnas (1) y convertir sus valores en números (2)\n",
+ "wiki3_df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Ciudad',\n",
+ " 'Fecha y fuente',\n",
+ " 'País',\n",
+ " 'Población según Citypopulation (2015)',\n",
+ " 'Población según Citypopulation (2016)',\n",
+ " 'Población según Citypopulation[1]\\u200b',\n",
+ " 'Población según Demographia (2015)',\n",
+ " 'Población según Demographia[2]\\u200b',\n",
+ " 'Población según ONU (2015)',\n",
+ " 'Población según ONU[3]\\u200b',\n",
+ " 'Población según último censo',\n",
+ " 'Población según último censo oficial',\n",
+ " 'Posición']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#determinar los nombres de las columnas cuyos nombres deberán cambiar\n",
+ "lista_columns = list(wiki3_df.columns)\n",
+ "lista_columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 45 600 000\n",
+ "1 40 200 000\n",
+ "2 35 900 000\n",
+ "3 30 600 000\n",
+ "4 29 400 000\n",
+ "Name: Población según Citypopulation[1], dtype: object"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wiki3_df['Población según Citypopulation[1]\\u200b'].head()\n",
+ "#esta es la columna cuyo nombre es el más complicado por los []\n",
+ "#Observar la diferencia entre \"Población según Citypopulation[1]\" y \"Población según Citypopulation[1]\\u200b\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Ciudad', 'Fecha', 'País', 'Citypopulation 2015', 'Citypopulation 2016',\n",
+ " 'Citypopulation Sin Fecha', 'Demographia 2015', 'Demographia Sin Fecha',\n",
+ " 'ONU 2015', 'ONU Sin Fecha', 'Ultimo Censo', 'Ultimo Censo Oficial',\n",
+ " 'Posición en Tabla Inicial'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wiki3_df.rename(columns = {'Población según Citypopulation (2015)': 'Citypopulation 2015',\n",
+ " 'Población según Citypopulation (2016)': 'Citypopulation 2016',\n",
+ " 'Población según Demographia (2015)': 'Demographia 2015',\n",
+ " 'Población según ONU (2015)': 'ONU 2015',\n",
+ " 'Población según último censo': 'Ultimo Censo',\n",
+ " 'Población según último censo oficial': 'Ultimo Censo Oficial',\n",
+ " 'Posición': 'Posición en Tabla Inicial', \n",
+ " 'Población según Citypopulation[1]\\u200b': 'Citypopulation Sin Fecha', 'Población según Demographia[2]\\u200b': 'Demographia Sin Fecha', 'Población según ONU[3]\\u200b': 'ONU Sin Fecha', 'Fecha y fuente': 'Fecha'},\n",
+ " inplace=True)\n",
+ "wiki3_df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Al existir múltiples fechas para una misma fuente, se consolidarán en nuevas columnas generadas:\n",
+ "#el criterio de selección será seleccionar fecha más reciente o valor disponible\n",
+ "#primer paso generar nuevas columnas\n",
+ "#wiki3_df['Citypopulation'] = ''\n",
+ "#wiki3_df['Demographia'] = ''\n",
+ "#wiki3_df['ONU'] = ''\n",
+ "#wiki3_df.columns\n",
+ "#se descarta esta opción debido que los inputs de las nuevas columnas generadas no provienen de una sola columna (no es copiar y pegar)\n",
+ "#se debe realizar una evaluación (if: existe numero o no estaba vacío) o unir las columnas de origen (Citypopulation 2015, 2016 y sin fecha)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Ciudad 0\n",
+ "Fecha 19\n",
+ "País 0\n",
+ "Citypopulation 2015 303\n",
+ "Citypopulation 2016 636\n",
+ "Citypopulation Sin Fecha 739\n",
+ "Demographia 2015 100\n",
+ "Demographia Sin Fecha 739\n",
+ "ONU 2015 100\n",
+ "ONU Sin Fecha 739\n",
+ "Ultimo Censo 119\n",
+ "Ultimo Censo Oficial 739\n",
+ "Posición en Tabla Inicial 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#primero validar qué columnas están vacias\n",
+ "null_cols = wiki3_df.isnull().sum()\n",
+ "null_cols\n",
+ "#null_cols[null_cols > 0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Ciudad 0\n",
+ "Fecha 19\n",
+ "País 0\n",
+ "Citypopulation 2015 303\n",
+ "Demographia 2015 100\n",
+ "ONU 2015 100\n",
+ "Ultimo Censo 119\n",
+ "Posición en Tabla Inicial 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#las columnas con el mayor numero de valores vacios se eliminarán finalmente existen\n",
+ "#Existen 9 columnas con diferentes fuentes sobre el censo en las ciudades\n",
+ "wiki3_df = wiki3_df.drop(['Citypopulation 2016', 'Citypopulation Sin Fecha', 'Demographia Sin Fecha', 'ONU Sin Fecha', 'Ultimo Censo Oficial'], axis=1)\n",
+ "null_cols = wiki3_df.isnull().sum()\n",
+ "null_cols"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Ciudad object\n",
+ "Fecha object\n",
+ "País object\n",
+ "Citypopulation 2015 object\n",
+ "Demographia 2015 object\n",
+ "ONU 2015 object\n",
+ "Ultimo Censo object\n",
+ "Posición en Tabla Inicial int64\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "#confirmar los tipos de valores que tienen las columnas para poder sumar\n",
+ "print(wiki3_df.dtypes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Antes de convertir a numeros, se debe limpiar la data de los puntos(.) de lo contrario ocurirá error en la conversión\n",
+ "wiki3_df['Citypopulation 2015']= wiki3_df['Citypopulation 2015'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','') \n",
+ "wiki3_df['Demographia 2015']= wiki3_df['Demographia 2015'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','') \n",
+ "wiki3_df['ONU 2015']= wiki3_df['ONU 2015'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','')\n",
+ "wiki3_df['Ultimo Censo']= wiki3_df['Ultimo Censo'].str.replace('.', '').replace('NaN', '0').replace('---','').replace(' ','')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Ciudad object\n",
+ "Fecha float64\n",
+ "País object\n",
+ "Citypopulation 2015 float64\n",
+ "Demographia 2015 float64\n",
+ "ONU 2015 float64\n",
+ "Ultimo Censo float64\n",
+ "Posición en Tabla Inicial int64\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Convertir todos los valores de estimaciones de población a números\n",
+ "wiki3_df[\"Citypopulation 2015\"] = pandas.to_numeric(wiki3_df[\"Citypopulation 2015\"], errors='coerce')\n",
+ "wiki3_df[\"Demographia 2015\"] = pandas.to_numeric(wiki3_df[\"Demographia 2015\"], errors='coerce')\n",
+ "wiki3_df[\"ONU 2015\"] = pandas.to_numeric(wiki3_df[\"ONU 2015\"], errors='coerce')\n",
+ "wiki3_df[\"Fecha\"] = pandas.to_numeric(wiki3_df[\"Fecha\"], errors='coerce')\n",
+ "wiki3_df[\"Ultimo Censo\"] = pandas.to_numeric(wiki3_df[\"Ultimo Censo\"], errors='coerce')\n",
+ "#wiki3_df = wiki3_df.astype({\"Posición en la Tabla Inicial\": int})\n",
+ "print(wiki3_df.dtypes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Ciudad \n",
+ " Fecha \n",
+ " País \n",
+ " Citypopulation 2015 \n",
+ " Demographia 2015 \n",
+ " ONU 2015 \n",
+ " Ultimo Censo \n",
+ " Posición en Tabla Inicial \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " Cantón (incluyendo Dongguan, Foshan, Jiangmen,... \n",
+ " 2010.0 \n",
+ " China \n",
+ " 46900000.0 \n",
+ " 45553000.0 \n",
+ " 42941000.0 \n",
+ " 39264086.0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " Tokio \n",
+ " 2010.0 \n",
+ " Japón \n",
+ " 39500000.0 \n",
+ " 37843000.0 \n",
+ " 38001000.0 \n",
+ " 8945695.0 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " Shanghái (incl. Suzhou, Kunshan) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 30400000.0 \n",
+ " 30477000.0 \n",
+ " 29213000.0 \n",
+ " 25420288.0 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " Yakarta (incluyendo Bogor) \n",
+ " 2010.0 \n",
+ " Indonesia \n",
+ " 30100000.0 \n",
+ " 30539000.0 \n",
+ " 11399000.0 \n",
+ " 10558121.0 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " Delhi \n",
+ " 2011.0 \n",
+ " India \n",
+ " 28400000.0 \n",
+ " 24998000.0 \n",
+ " 25703000.0 \n",
+ " 16349831.0 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " Karachi \n",
+ " 2011.0 \n",
+ " Pakistán \n",
+ " 25300000.0 \n",
+ " 22123000.0 \n",
+ " 16618000.0 \n",
+ " 21142625.0 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " Manila \n",
+ " 2010.0 \n",
+ " Filipinas \n",
+ " 24600000.0 \n",
+ " 24123000.0 \n",
+ " 12946000.0 \n",
+ " 1652171.0 \n",
+ " 7 \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " Bombay (incluyendo Kalyan y Vasai-Virar) \n",
+ " 2011.0 \n",
+ " India \n",
+ " 24300000.0 \n",
+ " 21732000.0 \n",
+ " 21043000.0 \n",
+ " 19617302.0 \n",
+ " 8 \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " Seúl (incluyendo Incheon y Suwon) \n",
+ " 2010.0 \n",
+ " Corea del Sur \n",
+ " 24100000.0 \n",
+ " 23480000.0 \n",
+ " 10558000.0 \n",
+ " 23836272.0 \n",
+ " 9 \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " Daca \n",
+ " 2011.0 \n",
+ " Bangladés \n",
+ " 22300000.0 \n",
+ " 15669000.0 \n",
+ " 17598000.0 \n",
+ " 14543124.0 \n",
+ " 10 \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " Pekín \n",
+ " 2010.0 \n",
+ " China \n",
+ " 20700000.0 \n",
+ " 21009000.0 \n",
+ " 20384000.0 \n",
+ " 16446857.0 \n",
+ " 11 \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " Osaka \n",
+ " 2010.0 \n",
+ " Japón \n",
+ " 19800000.0 \n",
+ " 17444000.0 \n",
+ " 20238000.0 \n",
+ " 2665314.0 \n",
+ " 12 \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " Bangkok (incluyendo Samut Prakan) \n",
+ " 2010.0 \n",
+ " Tailandia \n",
+ " 16700000.0 \n",
+ " 14998000.0 \n",
+ " 11084000.0 \n",
+ " 8986218.0 \n",
+ " 13 \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " Calcuta \n",
+ " 2011.0 \n",
+ " India \n",
+ " 15900000.0 \n",
+ " 14667000.0 \n",
+ " 14865000.0 \n",
+ " 14057991.0 \n",
+ " 14 \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " Teherán (incluyendo Karaj) \n",
+ " 2011.0 \n",
+ " Irán \n",
+ " 13600000.0 \n",
+ " 13532000.0 \n",
+ " 10239000.0 \n",
+ " 9768677.0 \n",
+ " 15 \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " Tianjin \n",
+ " 2010.0 \n",
+ " China \n",
+ " 11200000.0 \n",
+ " 10920000.0 \n",
+ " 11210000.0 \n",
+ " 9290263.0 \n",
+ " 16 \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " Nagoya \n",
+ " 2010.0 \n",
+ " Japón \n",
+ " 10400000.0 \n",
+ " 10177000.0 \n",
+ " 9406000.0 \n",
+ " 2263894.0 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " Bangalore \n",
+ " 2011.0 \n",
+ " India \n",
+ " 10300000.0 \n",
+ " 9807000.0 \n",
+ " 10087000.0 \n",
+ " 8520435.0 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " Lahore \n",
+ " 1998.0 \n",
+ " Pakistán \n",
+ " 9950000.0 \n",
+ " 10052000.0 \n",
+ " 8741000.0 \n",
+ " 5143495.0 \n",
+ " 19 \n",
+ " \n",
+ " \n",
+ " 19 \n",
+ " Madrás \n",
+ " 2011.0 \n",
+ " India \n",
+ " 9900000.0 \n",
+ " 9714000.0 \n",
+ " 9890000.0 \n",
+ " 8653521.0 \n",
+ " 20 \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Xiamen (incluyendl Quanzhou) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 9850000.0 \n",
+ " 11130000.0 \n",
+ " 5825000.0 \n",
+ " 4273841.0 \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Chengdu \n",
+ " 2010.0 \n",
+ " China \n",
+ " 9400000.0 \n",
+ " 10376000.0 \n",
+ " 7556000.0 \n",
+ " 6316922.0 \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Taipéi \n",
+ " NaN \n",
+ " Taiwán \n",
+ " 9000000.0 \n",
+ " 7438000.0 \n",
+ " 2666000.0 \n",
+ " NaN \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Hyderabad \n",
+ " 2011.0 \n",
+ " India \n",
+ " 8900000.0 \n",
+ " 8754000.0 \n",
+ " 8942000.0 \n",
+ " 7677018.0 \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Hangzhou (incluyendo Shaoxing) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 8150000.0 \n",
+ " 9625000.0 \n",
+ " 8467000.0 \n",
+ " 6887819.0 \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Ciudad Ho Chi Minh \n",
+ " 2009.0 \n",
+ " Vietnam \n",
+ " 8150000.0 \n",
+ " 8957000.0 \n",
+ " 7298000.0 \n",
+ " 5880615.0 \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " Wuhan \n",
+ " 2010.0 \n",
+ " China \n",
+ " 7950000.0 \n",
+ " 7509000.0 \n",
+ " 7906000.0 \n",
+ " 7541527.0 \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Shantou (incluyendo Chaozhou, Puning, Chaoyang... \n",
+ " 2010.0 \n",
+ " China \n",
+ " 7850000.0 \n",
+ " 6337000.0 \n",
+ " 6287000.0 \n",
+ " 5775239.0 \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Shenyang (incluyendo Fushun) \n",
+ " 2010.0 \n",
+ " China \n",
+ " 7600000.0 \n",
+ " 7402000.0 \n",
+ " 7613000.0 \n",
+ " 7037040.0 \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Ahmedabad \n",
+ " 2011.0 \n",
+ " India \n",
+ " 7350000.0 \n",
+ " 7186000.0 \n",
+ " 7343000.0 \n",
+ " 6357693.0 \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " Lisboa \n",
+ " 2001.0 \n",
+ " Portugal \n",
+ " 2600000.0 \n",
+ " 2666000.0 \n",
+ " 2884000.0 \n",
+ " 564657.0 \n",
+ " 21 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " Budapest \n",
+ " 2011.0 \n",
+ " Hungría \n",
+ " 2550000.0 \n",
+ " 1710000.0 \n",
+ " 1714000.0 \n",
+ " 1729040.0 \n",
+ " 22 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " Katowice \n",
+ " 2011.0 \n",
+ " Polonia \n",
+ " 2400000.0 \n",
+ " 2190000.0 \n",
+ " 303000.0 \n",
+ " 310764.0 \n",
+ " 23 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " Ámsterdam \n",
+ " 2001.0 \n",
+ " Países Bajos \n",
+ " 2375000.0 \n",
+ " 1624000.0 \n",
+ " 1091000.0 \n",
+ " 734533.0 \n",
+ " 24 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " Stuttgart \n",
+ " 2011.0 \n",
+ " Alemania \n",
+ " 2300000.0 \n",
+ " 1379000.0 \n",
+ " 626000.0 \n",
+ " 585890.0 \n",
+ " 25 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " Varsovia \n",
+ " 2011.0 \n",
+ " Polonia \n",
+ " 2275000.0 \n",
+ " 1720000.0 \n",
+ " 1722000.0 \n",
+ " 1700612.0 \n",
+ " 26 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " Bucarest \n",
+ " 2011.0 \n",
+ " Rumania \n",
+ " 2175000.0 \n",
+ " 1860000.0 \n",
+ " 1868000.0 \n",
+ " 1883425.0 \n",
+ " 27 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " Múnich \n",
+ " 2011.0 \n",
+ " Alemania \n",
+ " 2175000.0 \n",
+ " 1981000.0 \n",
+ " 1438000.0 \n",
+ " 1348335.0 \n",
+ " 28 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " Viena \n",
+ " 2011.0 \n",
+ " Austria \n",
+ " 2125000.0 \n",
+ " 1763000.0 \n",
+ " 1753000.0 \n",
+ " 2015580.0 \n",
+ " 29 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " Leeds \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 2125000.0 \n",
+ " 1893000.0 \n",
+ " 1912000.0 \n",
+ " 2058861.0 \n",
+ " 30 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " Estocolmo \n",
+ " NaN \n",
+ " Suecia \n",
+ " 2075000.0 \n",
+ " 1484000.0 \n",
+ " 1486000.0 \n",
+ " NaN \n",
+ " 31 \n",
+ " \n",
+ " \n",
+ " 31 \n",
+ " Bruselas \n",
+ " NaN \n",
+ " Bélgica \n",
+ " 2000000.0 \n",
+ " 2089000.0 \n",
+ " 2045000.0 \n",
+ " NaN \n",
+ " 32 \n",
+ " \n",
+ " \n",
+ " 32 \n",
+ " Minsk \n",
+ " 2009.0 \n",
+ " Bielorrusia \n",
+ " 1950000.0 \n",
+ " 1910000.0 \n",
+ " 1915000.0 \n",
+ " 1836808.0 \n",
+ " 33 \n",
+ " \n",
+ " \n",
+ " 33 \n",
+ " Lyon \n",
+ " 1999.0 \n",
+ " Francia \n",
+ " 1920000.0 \n",
+ " 1583000.0 \n",
+ " 1609000.0 \n",
+ " 1428998.0 \n",
+ " 34 \n",
+ " \n",
+ " \n",
+ " 34 \n",
+ " Liverpool \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1830000.0 \n",
+ " 875000.0 \n",
+ " 870000.0 \n",
+ " 1367147.0 \n",
+ " 35 \n",
+ " \n",
+ " \n",
+ " 35 \n",
+ " Valencia \n",
+ " 2011.0 \n",
+ " España \n",
+ " 1780000.0 \n",
+ " 1561000.0 \n",
+ " 810000.0 \n",
+ " 792054.0 \n",
+ " 36 \n",
+ " \n",
+ " \n",
+ " 36 \n",
+ " Nizni Nóvgorod \n",
+ " 2010.0 \n",
+ " Rusia \n",
+ " 1750000.0 \n",
+ " 1201000.0 \n",
+ " 1212000.0 \n",
+ " 1250619.0 \n",
+ " 37 \n",
+ " \n",
+ " \n",
+ " 37 \n",
+ " Turín \n",
+ " 2011.0 \n",
+ " Italia \n",
+ " 1670000.0 \n",
+ " 1521000.0 \n",
+ " 1765000.0 \n",
+ " 872367.0 \n",
+ " 38 \n",
+ " \n",
+ " \n",
+ " 38 \n",
+ " Járkov \n",
+ " 2001.0 \n",
+ " Ucrania \n",
+ " 1650000.0 \n",
+ " 1440000.0 \n",
+ " 1441000.0 \n",
+ " 1470902.0 \n",
+ " 39 \n",
+ " \n",
+ " \n",
+ " 39 \n",
+ " Marsella \n",
+ " 1999.0 \n",
+ " Francia \n",
+ " 1640000.0 \n",
+ " 1397000.0 \n",
+ " 1605000.0 \n",
+ " 1463016.0 \n",
+ " 40 \n",
+ " \n",
+ " \n",
+ " 40 \n",
+ " Glasgow \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1610000.0 \n",
+ " 1220000.0 \n",
+ " 1223000.0 \n",
+ " 1601154.0 \n",
+ " 41 \n",
+ " \n",
+ " \n",
+ " 41 \n",
+ " Copenhague \n",
+ " NaN \n",
+ " Dinamarca \n",
+ " 1600000.0 \n",
+ " 1248000.0 \n",
+ " 1268000.0 \n",
+ " NaN \n",
+ " 42 \n",
+ " \n",
+ " \n",
+ " 42 \n",
+ " Sheffield \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1530000.0 \n",
+ " 706000.0 \n",
+ " 706000.0 \n",
+ " 795844.0 \n",
+ " 43 \n",
+ " \n",
+ " \n",
+ " 43 \n",
+ " Mannheim \n",
+ " 2011.0 \n",
+ " Alemania \n",
+ " 1520000.0 \n",
+ " 559000.0 \n",
+ " 319000.0 \n",
+ " 290117.0 \n",
+ " 44 \n",
+ " \n",
+ " \n",
+ " 44 \n",
+ " Donetsk \n",
+ " 2001.0 \n",
+ " Ucrania \n",
+ " 1480000.0 \n",
+ " 930000.0 \n",
+ " 934000.0 \n",
+ " 1016194.0 \n",
+ " 45 \n",
+ " \n",
+ " \n",
+ " 45 \n",
+ " Newcastle upon Tyne \n",
+ " 2011.0 \n",
+ " Reino Unido \n",
+ " 1460000.0 \n",
+ " 793000.0 \n",
+ " 791000.0 \n",
+ " 1220781.0 \n",
+ " 46 \n",
+ " \n",
+ " \n",
+ " 46 \n",
+ " Praga \n",
+ " 2001.0 \n",
+ " República Checa \n",
+ " 1460000.0 \n",
+ " 1310000.0 \n",
+ " 1314000.0 \n",
+ " 1169106.0 \n",
+ " 47 \n",
+ " \n",
+ " \n",
+ " 47 \n",
+ " Volgogrado \n",
+ " 2010.0 \n",
+ " Rusia \n",
+ " 1410000.0 \n",
+ " 999000.0 \n",
+ " 1022000.0 \n",
+ " 1021215.0 \n",
+ " 48 \n",
+ " \n",
+ " \n",
+ " 48 \n",
+ " Belgrado \n",
+ " 2011.0 \n",
+ " Serbia \n",
+ " 1400000.0 \n",
+ " 1180000.0 \n",
+ " 1182000.0 \n",
+ " 1166763.0 \n",
+ " 49 \n",
+ " \n",
+ " \n",
+ " 49 \n",
+ " Dnipropetrovsk \n",
+ " 2001.0 \n",
+ " Ucrania \n",
+ " 1390000.0 \n",
+ " 950000.0 \n",
+ " 957000.0 \n",
+ " 1065008.0 \n",
+ " 50 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
491 rows × 8 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Ciudad Fecha \\\n",
+ "0 Cantón (incluyendo Dongguan, Foshan, Jiangmen,... 2010.0 \n",
+ "1 Tokio 2010.0 \n",
+ "2 Shanghái (incl. Suzhou, Kunshan) 2010.0 \n",
+ "3 Yakarta (incluyendo Bogor) 2010.0 \n",
+ "4 Delhi 2011.0 \n",
+ "5 Karachi 2011.0 \n",
+ "6 Manila 2010.0 \n",
+ "7 Bombay (incluyendo Kalyan y Vasai-Virar) 2011.0 \n",
+ "8 Seúl (incluyendo Incheon y Suwon) 2010.0 \n",
+ "9 Daca 2011.0 \n",
+ "10 Pekín 2010.0 \n",
+ "11 Osaka 2010.0 \n",
+ "12 Bangkok (incluyendo Samut Prakan) 2010.0 \n",
+ "13 Calcuta 2011.0 \n",
+ "14 Teherán (incluyendo Karaj) 2011.0 \n",
+ "15 Tianjin 2010.0 \n",
+ "16 Nagoya 2010.0 \n",
+ "17 Bangalore 2011.0 \n",
+ "18 Lahore 1998.0 \n",
+ "19 Madrás 2011.0 \n",
+ "20 Xiamen (incluyendl Quanzhou) 2010.0 \n",
+ "21 Chengdu 2010.0 \n",
+ "22 Taipéi NaN \n",
+ "23 Hyderabad 2011.0 \n",
+ "24 Hangzhou (incluyendo Shaoxing) 2010.0 \n",
+ "25 Ciudad Ho Chi Minh 2009.0 \n",
+ "26 Wuhan 2010.0 \n",
+ "27 Shantou (incluyendo Chaozhou, Puning, Chaoyang... 2010.0 \n",
+ "28 Shenyang (incluyendo Fushun) 2010.0 \n",
+ "29 Ahmedabad 2011.0 \n",
+ ".. ... ... \n",
+ "20 Lisboa 2001.0 \n",
+ "21 Budapest 2011.0 \n",
+ "22 Katowice 2011.0 \n",
+ "23 Ámsterdam 2001.0 \n",
+ "24 Stuttgart 2011.0 \n",
+ "25 Varsovia 2011.0 \n",
+ "26 Bucarest 2011.0 \n",
+ "27 Múnich 2011.0 \n",
+ "28 Viena 2011.0 \n",
+ "29 Leeds 2011.0 \n",
+ "30 Estocolmo NaN \n",
+ "31 Bruselas NaN \n",
+ "32 Minsk 2009.0 \n",
+ "33 Lyon 1999.0 \n",
+ "34 Liverpool 2011.0 \n",
+ "35 Valencia 2011.0 \n",
+ "36 Nizni Nóvgorod 2010.0 \n",
+ "37 Turín 2011.0 \n",
+ "38 Járkov 2001.0 \n",
+ "39 Marsella 1999.0 \n",
+ "40 Glasgow 2011.0 \n",
+ "41 Copenhague NaN \n",
+ "42 Sheffield 2011.0 \n",
+ "43 Mannheim 2011.0 \n",
+ "44 Donetsk 2001.0 \n",
+ "45 Newcastle upon Tyne 2011.0 \n",
+ "46 Praga 2001.0 \n",
+ "47 Volgogrado 2010.0 \n",
+ "48 Belgrado 2011.0 \n",
+ "49 Dnipropetrovsk 2001.0 \n",
+ "\n",
+ " País Citypopulation 2015 Demographia 2015 ONU 2015 \\\n",
+ "0 China 46900000.0 45553000.0 42941000.0 \n",
+ "1 Japón 39500000.0 37843000.0 38001000.0 \n",
+ "2 China 30400000.0 30477000.0 29213000.0 \n",
+ "3 Indonesia 30100000.0 30539000.0 11399000.0 \n",
+ "4 India 28400000.0 24998000.0 25703000.0 \n",
+ "5 Pakistán 25300000.0 22123000.0 16618000.0 \n",
+ "6 Filipinas 24600000.0 24123000.0 12946000.0 \n",
+ "7 India 24300000.0 21732000.0 21043000.0 \n",
+ "8 Corea del Sur 24100000.0 23480000.0 10558000.0 \n",
+ "9 Bangladés 22300000.0 15669000.0 17598000.0 \n",
+ "10 China 20700000.0 21009000.0 20384000.0 \n",
+ "11 Japón 19800000.0 17444000.0 20238000.0 \n",
+ "12 Tailandia 16700000.0 14998000.0 11084000.0 \n",
+ "13 India 15900000.0 14667000.0 14865000.0 \n",
+ "14 Irán 13600000.0 13532000.0 10239000.0 \n",
+ "15 China 11200000.0 10920000.0 11210000.0 \n",
+ "16 Japón 10400000.0 10177000.0 9406000.0 \n",
+ "17 India 10300000.0 9807000.0 10087000.0 \n",
+ "18 Pakistán 9950000.0 10052000.0 8741000.0 \n",
+ "19 India 9900000.0 9714000.0 9890000.0 \n",
+ "20 China 9850000.0 11130000.0 5825000.0 \n",
+ "21 China 9400000.0 10376000.0 7556000.0 \n",
+ "22 Taiwán 9000000.0 7438000.0 2666000.0 \n",
+ "23 India 8900000.0 8754000.0 8942000.0 \n",
+ "24 China 8150000.0 9625000.0 8467000.0 \n",
+ "25 Vietnam 8150000.0 8957000.0 7298000.0 \n",
+ "26 China 7950000.0 7509000.0 7906000.0 \n",
+ "27 China 7850000.0 6337000.0 6287000.0 \n",
+ "28 China 7600000.0 7402000.0 7613000.0 \n",
+ "29 India 7350000.0 7186000.0 7343000.0 \n",
+ ".. ... ... ... ... \n",
+ "20 Portugal 2600000.0 2666000.0 2884000.0 \n",
+ "21 Hungría 2550000.0 1710000.0 1714000.0 \n",
+ "22 Polonia 2400000.0 2190000.0 303000.0 \n",
+ "23 Países Bajos 2375000.0 1624000.0 1091000.0 \n",
+ "24 Alemania 2300000.0 1379000.0 626000.0 \n",
+ "25 Polonia 2275000.0 1720000.0 1722000.0 \n",
+ "26 Rumania 2175000.0 1860000.0 1868000.0 \n",
+ "27 Alemania 2175000.0 1981000.0 1438000.0 \n",
+ "28 Austria 2125000.0 1763000.0 1753000.0 \n",
+ "29 Reino Unido 2125000.0 1893000.0 1912000.0 \n",
+ "30 Suecia 2075000.0 1484000.0 1486000.0 \n",
+ "31 Bélgica 2000000.0 2089000.0 2045000.0 \n",
+ "32 Bielorrusia 1950000.0 1910000.0 1915000.0 \n",
+ "33 Francia 1920000.0 1583000.0 1609000.0 \n",
+ "34 Reino Unido 1830000.0 875000.0 870000.0 \n",
+ "35 España 1780000.0 1561000.0 810000.0 \n",
+ "36 Rusia 1750000.0 1201000.0 1212000.0 \n",
+ "37 Italia 1670000.0 1521000.0 1765000.0 \n",
+ "38 Ucrania 1650000.0 1440000.0 1441000.0 \n",
+ "39 Francia 1640000.0 1397000.0 1605000.0 \n",
+ "40 Reino Unido 1610000.0 1220000.0 1223000.0 \n",
+ "41 Dinamarca 1600000.0 1248000.0 1268000.0 \n",
+ "42 Reino Unido 1530000.0 706000.0 706000.0 \n",
+ "43 Alemania 1520000.0 559000.0 319000.0 \n",
+ "44 Ucrania 1480000.0 930000.0 934000.0 \n",
+ "45 Reino Unido 1460000.0 793000.0 791000.0 \n",
+ "46 República Checa 1460000.0 1310000.0 1314000.0 \n",
+ "47 Rusia 1410000.0 999000.0 1022000.0 \n",
+ "48 Serbia 1400000.0 1180000.0 1182000.0 \n",
+ "49 Ucrania 1390000.0 950000.0 957000.0 \n",
+ "\n",
+ " Ultimo Censo Posición en Tabla Inicial \n",
+ "0 39264086.0 1 \n",
+ "1 8945695.0 2 \n",
+ "2 25420288.0 3 \n",
+ "3 10558121.0 4 \n",
+ "4 16349831.0 5 \n",
+ "5 21142625.0 6 \n",
+ "6 1652171.0 7 \n",
+ "7 19617302.0 8 \n",
+ "8 23836272.0 9 \n",
+ "9 14543124.0 10 \n",
+ "10 16446857.0 11 \n",
+ "11 2665314.0 12 \n",
+ "12 8986218.0 13 \n",
+ "13 14057991.0 14 \n",
+ "14 9768677.0 15 \n",
+ "15 9290263.0 16 \n",
+ "16 2263894.0 17 \n",
+ "17 8520435.0 18 \n",
+ "18 5143495.0 19 \n",
+ "19 8653521.0 20 \n",
+ "20 4273841.0 21 \n",
+ "21 6316922.0 22 \n",
+ "22 NaN 23 \n",
+ "23 7677018.0 24 \n",
+ "24 6887819.0 25 \n",
+ "25 5880615.0 26 \n",
+ "26 7541527.0 27 \n",
+ "27 5775239.0 28 \n",
+ "28 7037040.0 29 \n",
+ "29 6357693.0 30 \n",
+ ".. ... ... \n",
+ "20 564657.0 21 \n",
+ "21 1729040.0 22 \n",
+ "22 310764.0 23 \n",
+ "23 734533.0 24 \n",
+ "24 585890.0 25 \n",
+ "25 1700612.0 26 \n",
+ "26 1883425.0 27 \n",
+ "27 1348335.0 28 \n",
+ "28 2015580.0 29 \n",
+ "29 2058861.0 30 \n",
+ "30 NaN 31 \n",
+ "31 NaN 32 \n",
+ "32 1836808.0 33 \n",
+ "33 1428998.0 34 \n",
+ "34 1367147.0 35 \n",
+ "35 792054.0 36 \n",
+ "36 1250619.0 37 \n",
+ "37 872367.0 38 \n",
+ "38 1470902.0 39 \n",
+ "39 1463016.0 40 \n",
+ "40 1601154.0 41 \n",
+ "41 NaN 42 \n",
+ "42 795844.0 43 \n",
+ "43 290117.0 44 \n",
+ "44 1016194.0 45 \n",
+ "45 1220781.0 46 \n",
+ "46 1169106.0 47 \n",
+ "47 1021215.0 48 \n",
+ "48 1166763.0 49 \n",
+ "49 1065008.0 50 \n",
+ "\n",
+ "[491 rows x 8 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Eliminar los rows que contengan valores \"0\" o \"NaN\": ¿Cómo debe resultar?\n",
+ "wiki3_df.dropna(subset=['Citypopulation 2015'], how = 'all', inplace = True)\n",
+ "wiki3_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of duplicate records dropped: 50\n"
+ ]
+ }
+ ],
+ "source": [
+ "#Empezar a trabajar en limpiar la data:\n",
+ "#1. Eliminar registros duplicados\n",
+ "before = len(wiki3_df)\n",
+ "wiki3_df = wiki3_df.drop_duplicates()\n",
+ "after = len(wiki3_df)\n",
+ "print('Number of duplicate records dropped: ', str(before - after))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Ciudad Fecha País Citypopulation 2015 Demographia 2015 ONU 2015 \\\n",
+ "27 Beirut 1970.0 Líbano 1630000.0 2200000.0 2226000.0 \n",
+ "\n",
+ " Ultimo Censo Posición en Tabla Inicial \n",
+ "27 474870.0 28 \n"
+ ]
+ }
+ ],
+ "source": [
+ "#2. ¿Cuál es el censo más antiguo?\n",
+ "print(wiki3_df[wiki3_df.Fecha== wiki3_df.Fecha.min()])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Ciudad \n",
+ " Fecha \n",
+ " Citypopulation 2015 \n",
+ " Demographia 2015 \n",
+ " ONU 2015 \n",
+ " Ultimo Censo \n",
+ " Posición en Tabla Inicial \n",
+ " \n",
+ " \n",
+ " País \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Afganistán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Alemania \n",
+ " 17 \n",
+ " 16 \n",
+ " 17 \n",
+ " 15 \n",
+ " 15 \n",
+ " 7 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " Arabia Saudita \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " Armenia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Australia \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " Austria \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Azerbaiyán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Bangladés \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " Bielorrusia \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Birmania \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Bulgaria \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Bélgica \n",
+ " 3 \n",
+ " 0 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 0 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Camboya \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " China \n",
+ " 116 \n",
+ " 116 \n",
+ " 116 \n",
+ " 107 \n",
+ " 108 \n",
+ " 109 \n",
+ " 116 \n",
+ " \n",
+ " \n",
+ " Corea del Norte \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Corea del Sur \n",
+ " 8 \n",
+ " 8 \n",
+ " 8 \n",
+ " 8 \n",
+ " 7 \n",
+ " 8 \n",
+ " 8 \n",
+ " \n",
+ " \n",
+ " Dinamarca \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Emiratos Árabes Unidos \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " España \n",
+ " 7 \n",
+ " 7 \n",
+ " 7 \n",
+ " 7 \n",
+ " 7 \n",
+ " 4 \n",
+ " 7 \n",
+ " \n",
+ " \n",
+ " Estados Unidos \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Filipinas \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 1 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " Finlandia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Francia \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " Georgia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Grecia \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Hong Kong \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Hungría \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " India \n",
+ " 65 \n",
+ " 64 \n",
+ " 65 \n",
+ " 63 \n",
+ " 64 \n",
+ " 63 \n",
+ " 65 \n",
+ " \n",
+ " \n",
+ " Indonesia \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 16 \n",
+ " 10 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " Irak \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " 6 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " Kuwait \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 0 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Líbano \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Malasia \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 2 \n",
+ " 1 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Mongolia \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Nepal \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Noruega \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Nueva Zelanda \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Pakistán \n",
+ " 11 \n",
+ " 11 \n",
+ " 11 \n",
+ " 11 \n",
+ " 10 \n",
+ " 10 \n",
+ " 11 \n",
+ " \n",
+ " \n",
+ " Palestina \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Países Bajos \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " Polonia \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 4 \n",
+ " 2 \n",
+ " 4 \n",
+ " \n",
+ " \n",
+ " Portugal \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Reino Unido \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " 17 \n",
+ " \n",
+ " \n",
+ " República Checa \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Rumania \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Rusia \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 18 \n",
+ " 17 \n",
+ " 18 \n",
+ " \n",
+ " \n",
+ " Serbia \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Singapur Malasia \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Siria \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Sri Lanka \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 1 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Suecia \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " 2 \n",
+ " 2 \n",
+ " 0 \n",
+ " 2 \n",
+ " \n",
+ " \n",
+ " Suiza \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Tailandia \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 1 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Taiwán \n",
+ " 5 \n",
+ " 0 \n",
+ " 5 \n",
+ " 5 \n",
+ " 5 \n",
+ " 0 \n",
+ " 5 \n",
+ " \n",
+ " \n",
+ " Turkmenistán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Turquía \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " 10 \n",
+ " \n",
+ " \n",
+ " Ucrania \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " 9 \n",
+ " \n",
+ " \n",
+ " Uzbekistán \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ " Vietnam \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " 3 \n",
+ " \n",
+ " \n",
+ " Yemen \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " 1 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
67 rows × 7 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Ciudad Fecha Citypopulation 2015 Demographia 2015 \\\n",
+ "País \n",
+ "Afganistán 1 1 1 1 \n",
+ "Alemania 17 16 17 15 \n",
+ "Arabia Saudita 6 6 6 6 \n",
+ "Armenia 1 1 1 1 \n",
+ "Australia 5 5 5 5 \n",
+ "Austria 2 2 2 2 \n",
+ "Azerbaiyán 1 1 1 1 \n",
+ "Bangladés 4 4 4 4 \n",
+ "Bielorrusia 2 2 2 2 \n",
+ "Birmania 3 3 3 3 \n",
+ "Bulgaria 1 1 1 1 \n",
+ "Bélgica 3 0 3 3 \n",
+ "Camboya 1 1 1 1 \n",
+ "China 116 116 116 107 \n",
+ "Corea del Norte 1 1 1 1 \n",
+ "Corea del Sur 8 8 8 8 \n",
+ "Dinamarca 2 0 2 2 \n",
+ "Emiratos Árabes Unidos 2 2 2 2 \n",
+ "España 7 7 7 7 \n",
+ "Estados Unidos 1 1 1 1 \n",
+ "Filipinas 5 5 5 5 \n",
+ "Finlandia 1 1 1 1 \n",
+ "Francia 6 6 6 6 \n",
+ "Georgia 1 1 1 1 \n",
+ "Grecia 2 2 2 2 \n",
+ "Hong Kong 2 2 2 2 \n",
+ "Hungría 2 2 2 2 \n",
+ "India 65 64 65 63 \n",
+ "Indonesia 18 18 18 18 \n",
+ "Irak 6 6 6 6 \n",
+ "... ... ... ... ... \n",
+ "Kuwait 1 0 1 1 \n",
+ "Líbano 1 1 1 1 \n",
+ "Malasia 3 3 3 3 \n",
+ "Mongolia 1 1 1 1 \n",
+ "Nepal 1 1 1 1 \n",
+ "Noruega 1 1 1 1 \n",
+ "Nueva Zelanda 1 1 1 1 \n",
+ "Pakistán 11 11 11 11 \n",
+ "Palestina 1 1 1 1 \n",
+ "Países Bajos 4 4 4 4 \n",
+ "Polonia 4 4 4 4 \n",
+ "Portugal 3 3 3 3 \n",
+ "Reino Unido 17 17 17 17 \n",
+ "República Checa 2 2 2 2 \n",
+ "Rumania 2 2 2 2 \n",
+ "Rusia 18 18 18 18 \n",
+ "Serbia 2 2 2 2 \n",
+ "Singapur Malasia 2 0 2 1 \n",
+ "Siria 2 2 2 2 \n",
+ "Sri Lanka 2 2 2 2 \n",
+ "Suecia 2 0 2 2 \n",
+ "Suiza 1 1 1 1 \n",
+ "Tailandia 3 3 3 3 \n",
+ "Taiwán 5 0 5 5 \n",
+ "Turkmenistán 1 1 1 1 \n",
+ "Turquía 10 10 10 10 \n",
+ "Ucrania 9 9 9 9 \n",
+ "Uzbekistán 1 1 1 1 \n",
+ "Vietnam 3 3 3 3 \n",
+ "Yemen 1 1 1 1 \n",
+ "\n",
+ " ONU 2015 Ultimo Censo Posición en Tabla Inicial \n",
+ "País \n",
+ "Afganistán 1 1 1 \n",
+ "Alemania 15 7 17 \n",
+ "Arabia Saudita 6 6 6 \n",
+ "Armenia 1 1 1 \n",
+ "Australia 5 5 5 \n",
+ "Austria 2 2 2 \n",
+ "Azerbaiyán 1 1 1 \n",
+ "Bangladés 4 4 4 \n",
+ "Bielorrusia 2 2 2 \n",
+ "Birmania 3 3 3 \n",
+ "Bulgaria 1 1 1 \n",
+ "Bélgica 3 0 3 \n",
+ "Camboya 1 1 1 \n",
+ "China 108 109 116 \n",
+ "Corea del Norte 1 1 1 \n",
+ "Corea del Sur 7 8 8 \n",
+ "Dinamarca 2 0 2 \n",
+ "Emiratos Árabes Unidos 1 1 2 \n",
+ "España 7 4 7 \n",
+ "Estados Unidos 1 1 1 \n",
+ "Filipinas 5 1 5 \n",
+ "Finlandia 1 1 1 \n",
+ "Francia 6 6 6 \n",
+ "Georgia 1 1 1 \n",
+ "Grecia 2 2 2 \n",
+ "Hong Kong 2 2 2 \n",
+ "Hungría 2 2 2 \n",
+ "India 64 63 65 \n",
+ "Indonesia 16 10 18 \n",
+ "Irak 6 6 6 \n",
+ "... ... ... ... \n",
+ "Kuwait 1 0 1 \n",
+ "Líbano 1 1 1 \n",
+ "Malasia 2 1 3 \n",
+ "Mongolia 1 1 1 \n",
+ "Nepal 1 1 1 \n",
+ "Noruega 1 1 1 \n",
+ "Nueva Zelanda 1 1 1 \n",
+ "Pakistán 10 10 11 \n",
+ "Palestina 1 1 1 \n",
+ "Países Bajos 4 2 4 \n",
+ "Polonia 4 2 4 \n",
+ "Portugal 3 1 3 \n",
+ "Reino Unido 17 17 17 \n",
+ "República Checa 2 2 2 \n",
+ "Rumania 2 2 2 \n",
+ "Rusia 18 17 18 \n",
+ "Serbia 2 2 2 \n",
+ "Singapur Malasia 1 1 2 \n",
+ "Siria 2 2 2 \n",
+ "Sri Lanka 2 1 2 \n",
+ "Suecia 2 0 2 \n",
+ "Suiza 1 1 1 \n",
+ "Tailandia 3 1 3 \n",
+ "Taiwán 5 0 5 \n",
+ "Turkmenistán 1 1 1 \n",
+ "Turquía 10 10 10 \n",
+ "Ucrania 9 9 9 \n",
+ "Uzbekistán 1 1 1 \n",
+ "Vietnam 3 3 3 \n",
+ "Yemen 1 1 1 \n",
+ "\n",
+ "[67 rows x 7 columns]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#3. Agrupar por numero de paises y las ciudades que aparecen en la lista\n",
+ "wiki3_df.groupby('País').count()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wiki3_df.to_csv('wiki3_df2.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/msft_df.csv b/msft_df.csv
new file mode 100644
index 0000000..9205edb
--- /dev/null
+++ b/msft_df.csv
@@ -0,0 +1,6 @@
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diff --git a/msft_dfT2.csv b/msft_dfT2.csv
new file mode 100644
index 0000000..b4523d0
--- /dev/null
+++ b/msft_dfT2.csv
@@ -0,0 +1,101 @@
+Open Price,Highest Price,Lowest Price,Close Price,Volume_Ops
+138.44,138.55,138.34,138.43,886466.0
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+138.375,138.455,138.34,138.44,252689.0
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+138.63,138.65,138.41,138.46,183601.0
+138.57,138.67,138.55,138.63,161121.0
+138.68,138.685,138.53,138.57,169380.0
+138.705,138.79,138.665,138.68,166921.0
+138.445,138.8,138.435,138.705,404897.0
+138.41,138.47,138.39,138.45,115974.0
+138.35,138.42,138.34,138.41,118987.0
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+138.25,138.275,138.12,138.15,115094.0
+138.185,138.28,138.16,138.26,152923.0
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+138.23,138.26,138.18,138.195,100154.0
+138.255,138.27,138.16,138.237,172939.0
+138.215,138.33,138.2,138.25,183011.0
+138.175,138.25,138.175,138.215,147785.0
+138.145,138.22,138.14,138.17,132920.0
+138.09,138.157,138.08,138.15,99802.0
+138.146,138.155,138.04,138.09,142886.0
+138.17,138.226,138.14,138.145,122524.0
+138.18,138.2,138.12,138.165,136384.0
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+138.18,138.25,138.16,138.184,67901.0
+138.23,138.24,138.17,138.18,78538.0
+138.275,138.293,138.23,138.23,77151.0
+138.218,138.285,138.19,138.275,118863.0
+138.18,138.23,138.15,138.215,104196.0
+138.375,138.42,138.17,138.19,154095.0
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+138.439,138.445,138.36,138.36,105301.0
+138.505,138.53,138.42,138.433,118735.0
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+138.414,138.55,138.4,138.46,143815.0
+138.468,138.475,138.37,138.41,70224.0
+138.53,138.56,138.435,138.45,105264.0
+138.52,138.58,138.46,138.535,166159.0
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+138.62,138.64,138.53,138.595,166528.0
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+138.285,138.409,138.215,138.354,194263.0
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+138.4,138.5,138.22,138.3,267282.0
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+138.887,139.065,138.81,138.83,467613.0
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+138.04,138.201,137.914,138.155,470234.0
+138.085,138.36,138.0,138.03,705766.0
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diff --git a/wiki3_df.csv b/wiki3_df.csv
new file mode 100644
index 0000000..86ea667
--- /dev/null
+++ b/wiki3_df.csv
@@ -0,0 +1,840 @@
+Ciudad,Fecha y fuente,País,Población según Citypopulation (2015),Población según Citypopulation (2016),Población según Citypopulation[1],Población según Demographia (2015),Población según Demographia[2],Población según ONU (2015),Población según ONU[3],Población según último censo,Población según último censo oficial,Posición
+Cantón,2010,China,,,45 600 000,,42 941 000,,45 553 000,,39 264 086,1
+Tokio,2020,Japón,,,40 200 000,,38 001 000,,37 843 000,,8 945 695,2
+Shanghái,2010,China,,,35 900 000,,29 213 000,,30 539 000,,10 558 121,3
+Yakarta,2010,Indonesia,,,30 600 000,,11 399 000,,30 477 000,,25 420 288,4
+Delhi,2011,India,,,29 400 000,,25 703 000,,24 998 000,,16 349 831,5
+Manila,2010,Filipinas,,,25 200 000,,12 946 000,,24 123 000,,1 652 171,6
+Seúl,2010,Corea del Sur,,,24 700 000,,13 558 000,,23 480 000,,23 836 272,7
+Bombay,2011,India,,,24 700 000,,21 043 000,,21 732 000,,19 617 302,8
+Ciudad de México,2015,México,,,22 800 000,,22 452 000,,20 063 000,,20 892 724,9
+Nueva York,2010,Estados Unidos,,,22 400 000,,19 532 000,,20 630 000,,19 556 440,10
+São Paulo,2010,Brasil,,,22 200 000,,21 066 000,,20 365 000,,19 683 975,11
+El Cairo,2006,Egipto,,,20 500 000,,13 123 000,,13 123 000,,7 740 018,12
+Pekín,2010,China,,,20 400 000,,13 123 000,,13 123 000,,16 446 857,13
+Daca,2011,Bangladés,,,19 500 000,,17 598 000,,15 669 000,,14 543 124,14
+Lagos,1991,Nigeria,,,18 800 000,,18 772 000,,15 600 000,,5 195 247,15
+Bangkok,2010,Tailandia,,,18 300 000,,11 084 000,,14 998 000,,8 986 218,16
+Los Ángeles,2010,Estados Unidos,,,17 800 000,,14 504 000,,15 058 000,,17 053 905,17
+Osaka,2010,Japón,,,17 700 000,,20 238 000,,17 444 000,,2 665 314,18
+Karachi,2011,Pakistán,,,17 300 000,,16 618 000,,22 123 000,,21 142 625,19
+Moscú,2010,Rusia,,,17 200 000,,12 166 000,,16 170 000,,11 612 885,20
+Calcuta,2011,India,,,16 600 000,,14 865 000,,14 667 000,,14 057 991,21
+Buenos Aires,2017,Argentina,,,16 300 000,,18 086 000,,14 122 000,,13 588 171,22
+Estambul,2015,Turquía,,,15 800 000,,14 164 000,,13 287 000,,14 657 000,23
+Teherán,2011,Irán,,,15 000 000,,10 239 000,,13 532 000,,9 768 677,24
+Londres,2011,Reino Unido,,,14 700 000,,10 313 000,,10 236 000,,11 140 445,25
+Johannesburgo,2009,Sudáfrica,,,13 700 000,,12 613 000,,12 066 000,,10 002 039,26
+Tianjin,2010,China,,,13 200 000,,11 210 000,,10 920 000,,9 290 263,28
+Río de Janeiro,2010,Brasil,,,13 100 000,,12 902 000,,11 727 000,,11 835 708,27
+Lahore,1998,Pakistán,,,12 600 000,,8 741 000,,10 052 000,,5 143 495,29
+Kinsasa,2004,República Democrática del Congo,,,12 000 000,,11 587 000,,11 587 000,,7 273 947,30
+Bangalore,2011,India,,,11 800 000,,10 087 000,,9 807 000,,8 520 435,31
+París,1999,Francia,,,11 400 000,,10 843 000,,10 858 000,,9 738 809,32
+Madrás,2011,India,,,11 000 000,,9 890 000,,9 714 000,,8 653 521,33
+Nagoya,2010,Japón,,,11 500 000,,9 406 000,,10 177 000,,2 263 894,34
+Lima,2007,Perú,,,9 900 000,,10 247 000,,11 150 000,,9 789 000,35
+Xiamen,2018,China,,,9 900 000,,5 825 000,,11 130 000,,4 273 841,36
+Hyderabad,2011,India,,,9 850 000,,8 942 000,,8 754 000,,7 677 018,37
+Bogotá,2017,Colombia,,,9 800 000,,8 197 000,,8 950 932,,8 950 000,38
+Chengdu,2010,China,,,9 800 000,,7 556 000,,10 376 000,,6 316 922,39
+Chicago,2010,Estados Unidos,,,9 750 000,,8 745 000,,9 156 000,,9 461 537,40
+Taipéi,2017,Taiwán,,,9 100 000,,2 666 000,,7 438 000,,---,41
+Wuhan,2010,China,,,8 850 000,,8 467 000,,8 625 000,,6 787 819,42
+Kuala Lumpur,2002,Malasia,,,8 700 000,,5 507 000,,5 225 000,,4 656 690,43
+Ciudad Ho Chi Minh,2009,Vietnam,,,8 600 000,,7 298 000,,8 957 000,,5 880 615,44
+Washington D. C.,2010,Estados Unidos,,,8 550 000,,7 222 000,,7 152 000,,8 347 003,45
+Hangzhou,2010,China,,,8 300 000,,8 467 000,,9 625 000,,6 887 819,46
+Ahmedabad,2011,India,,,8 250 000,,7 343 000,,7 186 000,,6 357 693,47
+Chongqing,2010,China,,,8 050 000,,13 332 000,,7 217 000,,6 263 790,48
+Luanda,2014,Angola,,,7 900 000,,5 506 000,,5 899 000,,6 377 246,49
+Santiago de Chile,2017,Chile,,,7 960 000,,6 837 000,,7 288 000,,7 306 944,50
+Shenyang,2010,China,,,7 900 000,,7 613 000,,7 402 000,,7 037 040,51
+San Francisco-San José,2010,Estados Unidos,,,7 850 000,,5 030 000,,5 929 000,,6 172 501,52
+Singapur - Johor Bahru,2010 2000,Singapur Malasia,,,7 800 000,,6 531 000,,7 312 000,,5 719 644,53
+Riad,2010,Arabia Saudita,,,7 750 000,,6 370 000,,5 666 000,,5 188 286,54
+Shantou,2010,China,,,7 700 000,,6 287 000,,6 337 000,,5 775 239,55
+Boston (incluyendo Providence),2010,Estados Unidos,,,7 650 000,,5 445 000,,5 679 000,,6 153 628,56
+Hong Kong,2011,China,,,7 450 000,,7 314 000,,7 246 000,,7 071 576,57
+Filadelfia,2010,Estados Unidos,,,7 350 000,,5 585 000,,5 570 000,,5 965 368,58
+Toronto,2011,Canadá,,,7 350 000,,5 993 000,,6 456 000,,5 583 064,59
+Dallas,2010,Estados Unidos,,,7 100 000,,5 703 000,,6 174 000,,6 426 210,60
+Bagdad,1987,Irak,,,6 850 000,,6 643 000,,6 625 000,,3 841 268,61
+Bandung,2010,Indonesia,,,6 850 000,,2 544 000,,5 695 000,,2 394 873,62
+Xi'an,2010,China,,,6 800 000,,6 044 000,,5 977 000,,5 206 253,63
+Nankín,2010,China,,,6 700 000,,6 369 000,,6 155 000,,5 827 888,64
+Pune,2011,India,,,6 700 000,,5 728 000,,5 631 000,,5 057 709,65
+Houston,2010,Estados Unidos,,,6 600 000,,5 636 000,,5 764 000,,5 920 490,66
+Madrid,2011,España,,,6 450 000,,6 199 000,,6 171 000,,3 198 645,67
+Miami,2010,Estados Unidos,,,6 350 000,,5 817 000,,5 764 000,,5 566 299,68
+Surat,2011,India,,,6 350 000,,5 650 000,,5 447 000,,4 591 246,69
+Jartum,2008,Sudán,,,6 150 000,,5 126 000,,5 125 000,,4 272 728,70
+Dar es-Salam,2012,Tanzania,,,6 150 000,,5 116 000,,4 219 000,,4 364 541,71
+Nairobi,2009,Kenia,,,5 950 000,,3 915 000,,4 738 000,,3 133 518,72
+Qingdao,2010,China,,,5 950 000,,4 566 000,,5 816 000,,3 990 942,73
+Atlanta,2010,Estados Unidos,,,5 800 000,,5 142 000,,5 015 000,,5 286 727,74
+Alejandría,2006,Egipto,,,5 700 000,,4 778 000,,4 689 000,,4 028 028,75
+Detroit - Windsor,2010 2011,Estados Unidos Canadá,,,5 700 000,,3 954 000,,3 947 000,,4 615 559,76
+Región del Ruhr,2011,Alemania,,,5 700 000,,---,,---,,---,77
+San Petersburgo,2010,Rusia,,,5 600 000,,4 993 000,,5 126 000,,4 879 566,78
+Rangún,2014,Birmania,,,5 500 000,,4 802 000,,4 800 000,,4 728 524,79
+Abiyán,2014,Costa de Marfil,,,5 450 000,,4 860 000,,4 800 000,,4 395 243,80
+Amán,2015,Jordania,,,5 450 000,,4 778 000,,4 689 000,,4 028 028,81
+Zhengzhou,2010,China,,,5 350 000,,4 387 000,,4 942 000,,3 677 032,82
+Guadalajara,2010,México,,,5 250 000,,4 843 000,,4 603 000,,4 495 182,83
+Wenzhou,2010,China,,,5 250 000,,3 208 000,,4 303 000,,3 614 208,84
+Milán,2011,Italia,,,5 200 000,,3 099 000,,5 257 000,,1 242 123,85
+Sídney,2011,Australia,,,5 200 000,,4 505 000,,4 036 000,,4 028 525,86
+Harbin,2010,China,,,5 150 000,,5 457 000,,4 815 000,,4 596 313,86
+Colonia - Düsseldorf,2011,Alemania,,,5 000 000,,1 640 000,,8 783 000,,1 591 866,88
+Ankara,2000,Turquía,,,4 975 000,,4 750 000,,4 538 000,,3 203 362,89
+Belo Horizonte,2010,Brasil,,,4 975 000,,5 716 000,,4 517 000,,5 414 701,90
+Acra,2010,Ghana,,,4 950 000,,2 277 000,,4 145 000,,2 070 463,91
+Monterrey,2010,México,,,4 925 000,,4 513 000,,4 083 000,,1 135512,92
+Dubai,2016,Emiratos Árabes Unidos,,,4 900 000,,4 161 000,,4 000 000,,3 900 390,93
+Melbourne,2010,Australia,,,4 850 000,,5 258 000,,4 693 000,,1 611 013,94
+Chittagong,2011,Bangladés,,,4 825 000,,4 539 000,,3 176 000,,4 009 423,95
+Hefei,2010,China,,,4 825 000,,3 348 000,,3 665 000,,3 098 727,96
+Jeddah,2010,Arabia Saudita,,,4 775 000,,4 161 000,,4 000 000,,3 900 390,97
+Berlín,2011,Alemania,,,4 750 000,,3 563 000,,4 069 000,,3 292 365,98
+Changsha,2012,China,,,4 750 000,,707 000,,2 180 000,,561 314,99
+Barcelona,2011,España,,,4 725 000,,5 258 000,,4 693 000,,1 611 013,100
+Lagos,1991.0,Nigeria,,17.100.000,,13.123.000,,13.123.000,,5.195.247,,1
+El Cairo,2006.0,Egipto,,16.800.000,,15.600.000,,18.772.000,,7.740.018[n 1],,2
+Johannesburgo (incl. Pretoria - Vereeniging),2009.0,Sudáfrica,,13.400.000,,12.066.000[n 3],,12.613.000[n 2],,10.002.039[n 4],,3
+Kinsasa,2004.0,República Democrática del Congo,,10.600.000,,11.587.000,,11.587.000,,7.273.947,,4
+Casablanca,2014.0,Marruecos,,10.150.000,,5.899.000,,5.506.000,,6.377.246,,5
+Jartum,2008.0,Sudán,,5.550.000,,5.125.000,,5.126.000,,4.272.728,,6
+Dar es-Salam,2012.0,Tanzania,,5.300.000,,4.219.000,,5.116.000,,4.364.541,,7
+Alejandría,2006.0,Egipto,,5.150.000,,4.689.000,,4.778.000,,4.028.028,,8
+Nairobi,2009.0,Kenia,,5.200.000,,4.738.000,,3.915.000,,3.133.518,,9
+Abiyán,2014.0,Costa de Marfil,,5.050.000,,4.800.000,,4.860.000,,4.395.243,,10
+Acra,2010.0,Ghana,,4.575.000,,4.145.000,,2.277.000,,2.070.463[n 1],,11
+Luanda,2014.0,Angola,,4.175.000,,3.211.000,,3.515.000,,3.359.818,,12
+Ciudad del Cabo,2009.0,Sudáfrica,,4.125.000,,3.812.000,,3.660.000,,3.430.992,,13
+Kano,1991.0,Nigeria,,4.125.000,,3.555.000,,3.587.000,,2.166.554,,14
+Argel,2008.0,Argelia,,3.675.000,,2.590.000,,2.594.000,,2.364.230[n 1],,15
+Adís Abeba,2007.0,Etiopía,,3.475.000,,3.376.000,,3.238.000,,2.739.551,,16
+Dakar,2013.0,Senegal,,3.300.000,,3.520.000,,3.520.000,,3.026.316,,17
+Durban,2009.0,Sudáfrica,,3.225.000,,3.421.000,,2.901.000,,2.786.046,,18
+Ibadán,1991.0,Nigeria,,3.150.000,,3.160.000,,3.375.000,,1.835.300,,19
+Kampala,2014.0,Uganda,,3.025.000,,1.930.000,,1.936.000,,1.516.210[n 1],,20
+Bamako,2009.0,Malí,,2.950.000,,2.500.000,,2.515.000,,1.810.366,,21
+Duala,2005.0,Camerún,,2.825.000,,2.940.000,,2.943.000,,1.906.962,,22
+Abuya,1991.0,Nigeria,,2.825.000,,2.440.000,,2.440.000,,107.069,,23
+Yaundé,2005.0,Camerún,,2.725.000,,3.060.000,,3.066.000,,1.817.524,,24
+Kumasi,2010.0,Ghana,,2.675.000,,2.500.000,,2.599.000,,2.035.064,,25
+Túnez,2014.0,Túnez,,2.500.000,,1.990.000,,1.993.000,,2.359.721,,26
+Harare,2012.0,Zimbabue,,2.325.000,,2.203.000,,1.501.000,,1.485.231,,27
+Lusaka,2010.0,Zambia,,2.275.000,,2.190.000,,2.179.000,,1.747.152,,28
+Antananarivo,1993.0,Madagascar,,2.225.000,,2.398.000,,2.610.000,,710.236,,29
+Conakri,2014.0,Guinea,,2.225.000,,1.930.000,,1.936.000,,1.667.864,,30
+Maputo,2007.0,Mozambique,,2.200.000,,2.615.000,,1.187.000,,1.094.628[n 1],,31
+Uagadugú,2006.0,Burkina Faso,,2.100.000,,2.700.000,,2.741.000,,1.475.223,,32
+Port Harcourt,1991.0,Nigeria,,2.075.000,,2.340.000,,2.343.000,,703.421,,33
+Rabat,2014.0,Marruecos,,1.920.000,,1.845.000,,1.967.000,,577.827[n 1],,34
+Brazzaville,2007.0,República del Congo,,1.900.000,,1.850.000,,1.888.000,,1.373.382,,35
+Lubumbashi,2004.0,República Democrática del Congo,,1.870.000,,2.000.000,,2.015.000,,1.273.380,,36
+Mbuji-Mayi,2004.0,República Democrática del Congo,,1.860.000,,2.000.000,,2.007.000,,1.213.726,,37
+Lomé,2010.0,Togo,,1.820.000,,1.941.000,,956.000,,1.477.658,,38
+Mogadiscio,,Somalia,,1.720.000,,2.120.000,,2.138.000,,,,39
+Kaduna,1991.0,Nigeria,,1.680.000,,1.020.000,,1.048.000,,993.642,,40
+Cotonú,2013.0,Benín,,1.600.000,,871.000,,682.000,,679.012[n 1],,41
+Benin City,1991.0,Nigeria,,1.480.000,,1.490.000,,1.496.000,,762.719,,42
+Freetown,2004.0,Sierra Leona,,1.440.000,,1.000.000,,1.007.000,,772.873,,43
+Monrovia,2008.0,Liberia,,1.340.000,,1.100.000,,1.264.000,,1.021.762,,44
+Orán,2008.0,Argelia,,1.340.000,,850.000,,858.000,,803.329,,45
+Yamena,2009.0,Chad,,1.210.000,,1.260.000,,1.260.000,,951.418,,46
+Port Elizabeth,2011.0,Sudáfrica,,1.210.000,,1.212.000,,1.179.000,,876.436,,47
+Fez,2014.0,Marruecos,,1.200.000,,1.193.000,,1.172.000,,1.120.072,,48
+Mombasa,2009.0,Kenia,,1.120.000,,1.116.000,,1.104.000,,915.101,,49
+Niamey,2012.0,Níger,,1.120.000,,1.090.000,,1.090.000,,978.029,,50
+Trípoli,1984.0,Libia,,1.110.000,,1.110.000,,1.126.000,,591.062,,51
+Agadir,2014.0,Marruecos,,1.090.000,,608.000,,590.000,,421.844[n 1],,52
+Onitsha,1991.0,Nigeria,,1.070.000,,1.100.000,,1.109.000,,350.280,,53
+Lilongüe,2008.0,Malaui,,1.070.000,,900.000,,905.000,,674.448,,54
+Marrakech,2014.0,Marruecos,,1.060.000,,1.173.000,,1.134.000,,928.850,,55
+Nuakchot,2013.0,Mauritania,,1.060.000,,950.000,,968.000,,958.399,,56
+Bangui,2003.0,República Centroafricana,,1.060.000,,790.000,,794.000,,622.771,,57
+Maiduguri,1991.0,Nigeria,,1.060.000,,925.000,,728.000,,618.278,,58
+Aba,1991.0,Nigeria,,1.040.000,,940.000,,944.000,,500.183,,59
+Susa,2014.0,Túnez,,1.040.000,,---,,---,,978.968,,60
+Kigali,2012.0,Ruanda,,1.000.000,,1.121.000,,1.257.000,,859.332,,61
+Huambo,1970.0,Angola,,---,,1.260.000,,1.269.000,,61.885,,62
+Kananga,2004.0,República Democrática del Congo,,---,,1.150.000,,1.169.000,,720.362,,63
+Kisangani,2004.0,República Democrática del Congo,,---,,1.000.000,,1.040.000,,682.599,,64
+Zaria,1991.0,Nigeria,,---,,1.025.000,,703.000,,612.257,,65
+Ciudad de México (incluyendo la zona metropolitana del valle de México),2010,México,,22.100.000,,20.063.000,,22.452.000,,8.555.272[n 1],,1
+Nueva York,2010,Estados Unidos,,22.000.000,,20.630.000,,19.532.000,,19.556.440,,2
+Los Ángeles (incluyendo Riverside y San Bernardino),2010,Estados Unidos,,17.600.000,,15.058.000[n 3],,14.504.000[n 2],,17.053.905[n 4],,3
+Chicago,2010,Estados Unidos,,9.800.000,,9.156.000,,8.745.000,,9.461.537,,4
+Washington D. C. (incluyendo Baltimore),2010,Estados Unidos,,8.350.000,,7.152.000[n 3],,7.222.000[n 2],,8.347.003[n 4],,5
+San Francisco (incluyendo San José),2010,Estados Unidos,,7.600.000,,5.929.000,,5.030.000 [n 2],,6.172.501[n 4],,6
+Boston (incluyendo Providence),2010,Estados Unidos,,7.350.000,,5.679.000[n 3],,5.445.000[n 2],,6.153.628[n 4],,7
+Filadelfia,2010,Estados Unidos,,7.300.000,,5.570.000,,5.585.000,,5.965.368,,8
+Toronto,2011,Canadá,,7.100.000,,6.456.000,,5.993.000,,5.583.064,,9
+Dallas,2010,Estados Unidos,,6.550.000,,6.174.000,,5.703.000,,6.426.210,,10
+Houston,2010,Estados Unidos,,6.200.000,,5.764.000,,5.636.000,,5.920.490,,11
+Miami,2010,Estados Unidos,,6.100.000,,5.764.000,,5.817.000,,5.566.299,,12
+Detroit - Windsor,2010 2011,Estados Unidos Canadá,,5.700.000,,3.947.000[n 3],,3.954.000[n 2],,4.615.559[n 4],,13
+Atlanta,2010,Estados Unidos,,5.500.000,,5.015.000,,5.142.000,,5.286.727,,14
+Guadalajara,2010,México,,4.975.000,,4.603.000,,4.843.000,,1.495.182[n 1],,15
+Monterrey,2010,México,,4.650.000,,4.083.000,,4.513.000,,1.135.512[n 1],,16
+Phoenix,2010,Estados Unidos,,4.325.000,,4.194.000,,4.063.000,,4.193.127,,17
+Montreal,2011,Canadá,,4.100.000,,3.536.000,,3.981.000,,3.824.221,,18
+Seattle,2010,Estados Unidos,,4.075.000,,3.218.000,,3.249.000,,3.439.815,,19
+Tampa,2010,Estados Unidos,,4.025.000,,2.621.000,,2.659.000,,2.783.514,,20
+Denver,2010,Estados Unidos,,3.525.000,,2.559.000,,2.599.000,,2.543.594,,21
+San Diego,2010,Estados Unidos,,3.275.000,,3.086.000,,3.107.000,,3.095.308,,22
+Cleveland,2010,Estados Unidos,,3.075.000,,1.783.000,,1.773.000,,2.077.246,,23
+Orlando,2010,Estados Unidos,,3.075.000,,2.040.000,,1.731.000,,2.134.418,,24
+Minneapolis,2010,Estados Unidos,,3.050.000,,2.771.000,,2.791.000,,3.348.857,,25
+Puebla de Zaragoza,2010,México,,2.975.000,,2.088.000,,2.984.000,,1.434.062[n 1],,26
+Cincinnati,2010,Estados Unidos,,2.725.000,,1.682.000,,1.688.000,,2.114.755,,27
+Vancouver,2011,Canadá,,2.500.000,,2.273.000,,2.485.000,,2.313.328,,28
+Saint Louis,2010,Estados Unidos,,2.350.000,,2.186.000,,2.184.000,,2.787.752,,29
+Salt Lake City,2010,Estados Unidos,,2.300.000,,1.085.000,,1.096.000,,1.087.873,,30
+Portland,2010,Estados Unidos,,2.275.000,,1.976.000,,2.001.000,,2.226.011,,31
+Charlotte,2010,Estados Unidos,,2.275.000,,1.535.000,,1.616.000,,2.217.248,,32
+Toluca de Lerdo,2010,México,,2.150.000,,1.878.000,,2.164.000,,489.333[n 1],,33
+Las Vegas,2010,Estados Unidos,,2.075.000,,2.191.000,,2.270.000,,1.951.269,,34
+Pittsburgh,2010,Estados Unidos,,2.075.000,,1.730.000,,1.719.000,,2.356.285,,35
+San Antonio,2010,Estados Unidos,,2.050.000,,1.976.000,,2.030.000,,2.142.518,,36
+Sacramento,2010,Estados Unidos,,1.980.000,,1.885.000,,1.920.000,,2.149.143,,37
+Kansas City,2010,Estados Unidos,,1.920.000,,1.593.000,,1.604.000,,2.009.338,,38
+Indianápolis,2010,Estados Unidos,,1.910.000,,1.617.000,,1.646.000,,1.888.082,,39
+Tijuana,2010,México,,1.880.000,,1.968.000,,1.987.000,,1.300.983[n 1],,40
+León,2010,México,,1.800.000,,1.469.000,,1.807.000,,1.238.962[n 1],,41
+Austin,2010,Estados Unidos,,1.740.000,,1.616.000,,1.684.000,,1.716.303,,42
+Hartford,2010,Estados Unidos,,1.700.000,,960.000,,963.000,,1.212.387,,43
+Columbus,2010,Estados Unidos,,1.640.000,,1.481.000,,1.505.000,,1.902.015,,44
+Virginia Beach,2010,Estados Unidos,,1.610.000,,1.463.000,,1.460.000,,1.676.817,,45
+Milwaukee,2010,Estados Unidos,,1.540.000,,1.408.000,,1.409.000,,1.555.954,,46
+Raleigh,2010,Estados Unidos,,1.524.000,,1.085.000,,1.140.000,,1.130.490,,47
+Ciudad Juárez,2010,México,,1.513.000,,1.391.000,,1.440.000,,1.321.004[n 1],,48
+Calgary,2011,Canadá,,1.470.000,,1.189.000,,1.397.000,,1.214.839,,49
+Búfalo - St. Catharines,2010 2011,Estados Unidos Canadá,,1.450.000,,1.232.000 [n 3],,1.396.000 [n 2],,1.527.725 [n 4],,50
+Nashville,2010,Estados Unidos,,1.430.000,,1.081.000,,1.255.000,,1.670.900,,51
+Jacksonville,2010,Estados Unidos,,1.390.000,,1.154.000,,1.272.000,,1.345.596,,52
+Torreón,2010,México,,1.373.000,,1.327.000,,1.332.000,,608.836[n 1],,53
+Harrisburg,2010,Estados Unidos,,1.370.000,,484.000,,493.000,,549.473,,54
+Santiago de Querétaro,2010,México,,1.280.000,,1.249.000,,1.267.000,,626.495[n 1],,55
+Edmonton,2011,Canadá,,1.270.000,,1.040.000,,1.272.000,,1.159.869,,56
+McAllen,2010,Estados Unidos,,1.270.000,,838.000,,864.000,,774.773,,57
+Stockton,2010,Estados Unidos,,1.200.000,,371.000,,403.000,,685.308,,58
+Ottawa,2011,Canadá,,1.180.000,,994.000,,1.326.000,,1.236.324,,59
+San Luis Potosí,2010,México,,1.150.000,,1.137.000,,1.147.000,,722.772[n 1],,60
+Memphis,2010,Estados Unidos,,1.150.000,,1.102.000,,1.106.000,,1.324.829,,61
+Melbourne,2010,Estados Unidos,,1.110.000,,482.000,,486.000,,543.378,,62
+Oklahoma City,2010,Estados Unidos,,1.090.000,,917.000,,926.000,,1.252.992,,63
+Greensboro,2010,Estados Unidos,,1.090.000,,334.000,,337.000,,723.798,,64
+Mérida,2010,México,,1.070.000,,1.111.000,,1.068.000,,777.615[n 1],,65
+Aguascalientes,2010,México,,1.060.000,,1.020.000,,1.031.000,,722.250[n 1],,66
+Louisville,2010,Estados Unidos,,1.040.000,,1.025.000,,1.032.000,,1.235.710,,67
+Richmond,2010,Estados Unidos,,1.030.000,,1.018.000,,1.030.000,,1.208.080,,68
+El Paso,2010,Estados Unidos,,1.020.000,,865.000,,877.000,,804.123,,69
+Mexicali,2010,México,,1.010.000,,1.018.000,,1.034.000,,689.775[n 1],,70
+Nueva Orleans,2010,Estados Unidos,,1.010.000,,922.000,,921.000,,1.189.863,,71
+Cuernavaca,2010,México,,1.010.000,,990.000,,993.000,,338.650[n 1],,72
+Chihuahua,2010,México,,1.002.000,,940.000,,941.000,,809.232,,73
+Saltillo,2010,México,,994.000,,917.000,,932.000,,709.671,,74
+Acapulco,2010,México,,977.000,,812.000,,920.000,,297.284 [n 1],,75
+Santo Domingo,2010.0,República Dominicana,,3.650.000,,2.925.000,,2.945.000,,2.581.827[n 1],,1
+Ciudad de Guatemala,2002.0,Guatemala,,3.000.000,,1.289.000,,2.918.000,,942.348[n 1],,2
+Puerto Príncipe,2003.0,Haití,,2.850.000,,2.440.000,,2.440.000,,703.023[n 1],,3
+La Habana,2012.0,Cuba,,2.225.000,,2.130.000,,2.137.000,,2.106.146,,4
+San Juan,2010.0,Puerto Rico,,2.150.000,,2.139.000,,2.463.000,,2.350.306,,5
+San José,,Costa Rica,,1.840.000,,1.170.000,,1.170.000,,,,6
+San Salvador,2007.0,El Salvador,,1.820.000,,1.100.000,,1.098.000,,316.090[n 1],,7
+Panamá,2010.0,Panamá,,1.460.000,,1.498.000,,1.673.000,,430.299[n 1],,8
+Managua,2005.0,Nicaragua,,1.340.000,,980.000,,956.000,,908.892,,9
+San Pedro Sula,2001.0,Honduras,,1.110.000,,---,,852.000,,483.384,,10
+Tegucigalpa,2001.0,Honduras,,1.090.000,,1.120.000,,1.123.000,,819.867,,11
+"Cantón (incluyendo Dongguan, Foshan, Jiangmen, Shenzhen y Zhongshan)",2010,China,46.900.000,,,45.553.000,,42.941.000,,39.264.086,,1
+Tokio,2010,Japón,39.500.000,,,37.843.000,,38.001.000,,8.945.695,,2
+"Shanghái (incl. Suzhou, Kunshan)",2010,China,30.400.000,,,30.477.000,,29.213.000,,25.420.288,,3
+Yakarta (incluyendo Bogor),2010,Indonesia,30.100.000,,,30.539.000,,11.399.000,,10.558.121,,4
+Delhi,2011,India,28.400.000,,,24.998.000,,25.703.000,,16.349.831,,5
+Karachi,2011,Pakistán,25.300.000,,,22.123.000,,16.618.000,,21.142.625,,6
+Manila,2010,Filipinas,24.600.000,,,24.123.000,,12.946.000,,1.652.171,,7
+Bombay (incluyendo Kalyan y Vasai-Virar),2011,India,24.300.000,,,21.732.000,,21.043.000,,19.617.302,,8
+Seúl (incluyendo Incheon y Suwon),2010,Corea del Sur,24.100.000,,,23.480.000,,10.558.000,,23.836.272,,9
+Daca,2011,Bangladés,22.300.000,,,15.669.000,,17.598.000,,14.543.124,,10
+Pekín,2010,China,20.700.000,,,21.009.000,,20.384.000,,16.446.857,,11
+Osaka,2010,Japón,19.800.000,,,17.444.000,,20.238.000,,2.665.314,,12
+Bangkok (incluyendo Samut Prakan),2010,Tailandia,16.700.000,,,14.998.000,,11.084.000,,8.986.218,,13
+Calcuta,2011,India,15.900.000,,,14.667.000,,14.865.000,,14.057.991,,14
+Teherán (incluyendo Karaj),2011,Irán,13.600.000,,,13.532.000,,10.239.000,,9.768.677,,15
+Tianjin,2010,China,11.200.000,,,10.920.000,,11.210.000,,9.290.263,,16
+Nagoya,2010,Japón,10.400.000,,,10.177.000,,9.406.000,,2.263.894,,17
+Bangalore,2011,India,10.300.000,,,9.807.000,,10.087.000,,8.520.435,,18
+Lahore,1998,Pakistán,9.950.000,,,10.052.000,,8.741.000,,5.143.495,,19
+Madrás,2011,India,9.900.000,,,9.714.000,,9.890.000,,8.653.521,,20
+Xiamen (incluyendl Quanzhou),2010,China,9.850.000,,,11.130.000,,5.825.000,,4.273.841,,21
+Chengdu,2010,China,9.400.000,,,10.376.000,,7.556.000,,6.316.922,,22
+Taipéi,,Taiwán,9.000.000,,,7.438.000,,2.666.000,,,,23
+Hyderabad,2011,India,8.900.000,,,8.754.000,,8.942.000,,7.677.018,,24
+Hangzhou (incluyendo Shaoxing),2010,China,8.150.000,,,9.625.000,,8.467.000,,6.887.819,,25
+Ciudad Ho Chi Minh,2009,Vietnam,8.150.000,,,8.957.000,,7.298.000,,5.880.615,,26
+Wuhan,2010,China,7.950.000,,,7.509.000,,7.906.000,,7.541.527,,27
+"Shantou (incluyendo Chaozhou, Puning, Chaoyang y Chaonan)",2010,China,7.850.000,,,6.337.000,,6.287.000,,5.775.239,,28
+Shenyang (incluyendo Fushun),2010,China,7.600.000,,,7.402.000,,7.613.000,,7.037.040,,29
+Ahmedabad,2011,India,7.350.000,,,7.186.000,,7.343.000,,6.357.693,,30
+Hong Kong,2011,Hong Kong,7.200.000,,,7.246.000,,7.314.000,,7.071.576,,31
+Chongqing,2010,China,6.950.000,,,7.217.000,,13.332.000,,6.263.790,,32
+Kuala Lumpur,2000,Malasia,6.950.000,,,7.088.000,,6.837.000,,1.305.792,,33
+Singapur - Johor Bahru,2010 2000,Singapur Malasia,6.900.000,,,7.312.000,,6.531.000,,5.719.644,,34
+Nankín,2010,China,6.750.000,,,6.155.000,,7.369.000,,5.827.888,,35
+Bagdad,1987,Irak,6.750.000,,,6.625.000,,6.643.000,,3.841.268,,36
+Riad,2010,Arabia Saudita,6.550.000,,,5.666.000,,6.370.000,,5.188.286,,37
+Xi'an,2010,China,6.550.000,,,5.977.000,,6.044.000,,5.206.253,,38
+Pune,2011,India,6.000.000,,,5.631.000,,5.728.000,,5.057.709,,39
+Bandung,2010,Indonesia,5.900.000,,,5.695.000,,2.544.000,,2.394.873,,40
+Wenzhou (incluyendo Rui'an),2010,China,5.800.000,,,4.303.000,,3.208.000,,3.614.208,,41
+Qingdao,2010,China,5.650.000,,,5.816.000,,4.566.000,,3.990.942,,42
+Surat,2011,India,5.600.000,,,5.447.000,,5.650.000,,4.591.246,,43
+Harbin,2010,China,5.100.000,,,4.815.000,,5.457.000,,4.596.313,,44
+Rangún,2014,Birmania,5.100.000,,,4.800.000,,4.802.000,,4.728.524,,45
+Kitakyushu - Fukuoka,2010,Japón,4.725.000,,,4.505.000,,5.510.000,,2.440.589,,46
+Surabaya,2010,Indonesia,4.675.000,,,4.881.000,,2.853.000,,2.765.487,,47
+Colombo,2012,Sri Lanka,4.650.000,,,2.180.000,,707.000,,561.314,,48
+Ankara,2000,Turquía,4.625.000,,,4.538.000,,4.750.000,,3.203.362,,49
+Zhengzhou,2010,China,4.600.000,,,4.942.000,,4.387.000,,3.677.032,,50
+Teherán (incluyendo Karaj),2011.0,Irán,13.600.000,,,13.532.000,,10.239.000[n 2],,9.768.677[n 4],,1
+Bagdad,1987.0,Irak,6.750.000,,,6.625.000,,6.643.000,,3.841.268,,2
+Riad,2010.0,Arabia Saudita,6.550.000,,,5.666.000,,6.370.000,,5.188.286,,3
+Ankara,2000.0,Turquía,4.625.000,,,4.538.000,,4.750.000,,3.203.362,,4
+Yida,2010.0,Arabia Saudita,4.175.000,,,3.677.000,,4.076.000,,3.430.697,,5
+Kuwait,,Kuwait,4.075.000,,,4.283.000,,2.779.000,,,,6
+Dubái (incluyendo Sarja),1995.0,Emiratos Árabes Unidos,3.800.000,,,3.933.000,,3.694.000[n 2],,989.276[n 4],,7
+Damasco,2004.0,Siria,3.650.000,,,2.560.000,,2.566.000,,1.414.913,,8
+Kabul,1979.0,Afganistán,3.600.000,,,4.635.000,,4.635.000,,913.164,,9
+Amán,2004.0,Jordania,3.325.000,,,2.468.000,,1.155.000,,1.036.330,,10
+Alepo,2004.0,Siria,3.050.000,,,3.560.000,,3.562.000,,2.132.100,,11
+Mashhad,2011.0,Irán,3.050.000,,,3.294.000,,3.014.000,,2.749.374,,12
+Esmirna,2000.0,Turquía,2.925.000,,,3.112.000,,3.040.000,,2.232.265,,13
+Isfahán,2011.0,Irán,2.725.000,,,2.392.000,,1.880.000,,1.756.126,,14
+Taskent,1989.0,Uzbekistán,2.625.000,,,2.250.000,,2.251.000,,2.072.459,,15
+Tel Aviv,1995.0,Israel,2.475.000,,,2.979.000,,3.608.000,,348.245,,16
+Saná,2004.0,Yemen,2.425.000,,,2.980.000,,2.962.000,,1.707.531,,17
+Bakú,1989.0,Azerbaiyán,2.425.000,,,2.661.000,,2.374.000,,1.150.055,,18
+Dammam,2010.0,Arabia Saudita,2.350.000,,,1.019.000,,1.064.000,,903.312,,19
+Bursa,2000.0,Turquía,1.930.000,,,1.839.000,,1.923.000,,1.194.687,,20
+La Meca,2010.0,Arabia Saudita,1.840.000,,,1.647.000,,1.771.000,,1.534.731,,21
+Franja de Gaza,2007.0,Palestina,1.760.000,,,620.000,,624.000,,483.869,,22
+Almaty,1999.0,Kazajistán,1.750.000,,,1.500.000,,1.523.000,,1.365.632,,23
+Mosul,1987.0,Irak,1.680.000,,,1.675.000,,1.694.000,,664.221,,24
+Shiraz,2011.0,Irán,1.680.000,,,1.873.000,,1.661.000,,1.460.665,,25
+Adana,2000.0,Turquía,1.670.000,,,1.830.000,,1.830.000,,1.130.710,,26
+Novosibirsk [n 5],2010.0,Rusia,1.640.000,,,1.486.000,,1.497.000,,,,27
+Beirut,1970.0,Líbano,1.630.000,,,2.200.000,,2.226.000,,474.870,,28
+Tabriz,2011.0,Irán,1.610.000,,,1.693.000,,1.572.000,,1.494.998,,29
+Ekaterimburgo [n 5],2010.0,Rusia,1.590.000,,,1.361.000,,1.379.000,,1.473.754,,30
+Gaziantep,2000.0,Turquía,1.530.000,,,1.394.000,,1.528.000,,853.513,,31
+Ereván,2011.0,Armenia,1.480.000,,,1.274.000,,1.044.000,,1.060.138,,32
+Cheliábinsk [n 5],2010.0,Rusia,1.390.000,,,1.150.000,,1.157.000,,1.130.132,,33
+Basora,1987.0,Irak,1.390.000,,,1.000.000,,1.019.000,,406.296,,34
+Medina,2010.0,Arabia Saudita,1.320.000,,,1.233.000,,1.280.000,,1.100.093,,35
+Ahvaz,2011.0,Irán,1.240.000,,,1.315.000,,1.060.000,,1.112.021,,36
+Tiflis,2002.0,Georgia,1.230.000,,,1.125.000,,1.147.000,,1.073.345,,37
+Konya,2000.0,Turquía,1.190.000,,,1.190.000,,1.194.000,,742.690,,38
+Omsk [n 5],2010.0,Rusia,1.180.000,,,1.154.000,,1.162.000,,1.154.116,,39
+Qom,2011.0,Irán,1.160.000,,,1.101.000,,1.204.000,,1.074.036,,40
+Erbil,1987.0,Irak,1.150.000,,,1.150.000,,1.166.000,,485.968,,41
+Antalya,2000.0,Turquía,1.140.000,,,1.070.000,,1.072.000,,603.190,,42
+Asjabad,1989.0,Turkmenistán,1.140.000,,,740.000,,746.000,,401.135,,43
+Abu Dabi,1995.0,Emiratos Árabes Unidos,1.120.000,,,982.000,,1.145.000,,398.695,,44
+Kirkuk,1987.0,Irak,1.110.000,,,650.000,,650.000,,418.624,,45
+Krasnoyarsk [n 5],2010.0,Rusia,1.080.000,,,998.000,,1.008.000,,973.826,,46
+Kayseri,2000.0,Turquía,1.050.000,,,900.000,,904.000,,536.392,,47
+Homs,2004.0,Siria,---,,,1.640.000,,1.641.000,,652.609,,48
+Hama,2004.0,Siria,---,,,1.230.000,,1.237.000,,312.994,,49
+Haifa,1995.0,Israel,---,,,1.090.000,,1.097.000,,255.914,,50
+Solimania,1987.0,Irak,---,,,1.000.000,,1.004.000,,364.096,,51
+Diyarbakir,2000.0,Turquía,---,,,920.000,,926.000,,545.983,,52
+Nayaf,2000.0,Irak,---,,,880.000,,889.000,,309.010,,53
+Adén,2004.0,Yemen,---,,,880.000,,882.000,,588.938,,54
+Biskek,2009.0,Kirguistán,---,,,850.000,,865.000,,821.915,,55
+Delhi,2011.0,India,26.000.000,,,24.998.000,,25.703.000,,16.349.831,,1
+Karachi,2011.0,Pakistán,24.000.000,,,22.123.000,,16.618.000,,21.142.625,,2
+Bombay (incluyendo Kalyan y Vasai-Virar),2011.0,India,23.000.000,,,21.732.000 [n 3],,21.043.000,,19.617.302[n 4],,3
+Daca,2011.0,Bangladés,17.300.000,,,15.669.000,,17.598.000,,14.543.124,,4
+Calcuta,2011.0,India,15.900.000,,,14.667.000,,14.865.000,,14.057.991,,5
+Bangalore,2011.0,India,10.300.000,,,9.807.000,,10.087.000,,8.520.435,,6
+Lahore,1998.0,Pakistán,9.950.000,,,10.052.000,,8.741.000,,5.143.495,,7
+Madrás,2011.0,India,9.900.000,,,9.714.000,,9.890.000,,8.653.521,,8
+Hyderabad,2011.0,India,8.900.000,,,8.754.000,,8.942.000,,7.677.018,,9
+Ahmedabad,2011.0,India,7.350.000,,,7.186.000,,7.343.000,,6.357.693,,10
+Pune,2011.0,India,6.000.000,,,5.631.000,,5.728.000,,5.057.709,,11
+Surat,2011.0,India,5.600.000,,,5.447.000,,5.650.000,,4.591.246,,12
+Colombo,2012.0,Sri Lanka,4.650.000,,,2.180.000,,707.000,,561.314[n 1],,13
+Chittagong,2011.0,Bangladés,4.475.000,,,3.176.000,,4.539.000,,4.009.423,,14
+Faisalabad,1998.0,Pakistán,3.900.000,,,3.560.000,,3.567.000,,2.008.861,,15
+Rawalpindi (incluyendo Islamabad),1998.0,Pakistán,3.800.000,,,2.510.000,,3.871.000[n 2],,1.938.948[n 4],,16
+Jaipur,2011.0,India,3.475.000,,,3.409.000,,3.461.000,,3.046.163,,17
+Lucknow,2011.0,India,3.300.000,,,3.184.000,,3.222.000,,2.902.920,,18
+Kanpur,,India,3.275.000,,,3.037.000,,3.021.000,,2011,,19
+Nagpur,2011.0,India,3.000.000,,,2.668.000,,2.675.000,,2.497.870,,20
+Katmandú,2011.0,Nepal,2.875.000,,,1.180.000,,1.183.000,,1.003.285,,21
+Indore,2011.0,India,2.725.000,,,2.405.000,,2.441.000,,2.170.295,,22
+Bhilai (incluyendo Raipur),2011.0,India,2.500.000,,,2.564.000[n 3],,2.503.000[n 2],,2.187.780[n 4],,23
+Patna,2011.0,India,2.450.000,,,2.200.000,,2.210.000,,2.049.156,,24
+Coimbatore,2011.0,India,2.425.000,,,2.481.000,,2.549.000,,2.136.916,,25
+Gujranwala,1998.0,Pakistán,2.400.000,,,2.120.000,,2.122.000,,1.132.509,,26
+Hyderabad,1998.0,Pakistán,2.400.000,,,2.920.000,,1.772.000,,1.166.894,,27
+Bhopal,2011.0,India,2.150.000,,,2.075.000,,2.102.000,,1.886.100,,28
+Multan,1998.0,Pakistán,2.125.000,,,1.900.000,,1.921.000,,1.197.384,,29
+Vadodara,2011.0,India,2.025.000,,,1.963.000,,1.975.000,,1.822.221,,30
+Agra,2011.0,India,2.025.000,,,1.938.000,,1.966.000,,1.760.285,,31
+Chandigarh,2011.0,India,2.000.000,,,1.124.000,,1.134.000,,1.026.459,,32
+Visakhapatnam,2011.0,India,1.950.000,,,1.910.000,,1.935.000,,1.728.128,,33
+Peshawar,1998.0,Pakistán,1.870.000,,,1.730.000,,1.736.000,,982.816,,34
+Ludhiāna,2011.0,India,1.830.000,,,1.714.000,,1.716.000,,1.618.879,,35
+Nashik,2011.0,India,1.810.000,,,1.749.000,,1.779.000,,1.561.809,,36
+Benarés,2011.0,India,1.770.000,,,1.536.000,,1.541.000,,1.432.280,,37
+Vijayawada,2011.0,India,1.740.000,,,1.715.000,,1.760.000,,1.476.931,,38
+Bhubaneswar,2011.0,India,1.720.000,,,984.000,,999.000,,885.363,,39
+Rajkot,2011.0,India,1.620.000,,,1.568.000,,1.599.000,,1.390.640,,40
+Madurai,2011.0,India,1.620.000,,,1.582.000,,1.593.000,,1.465.625,,41
+Meerut,2011.0,India,1.580.000,,,1.541.000,,1.550.000,,1.420.902,,42
+Aurangabad,2011.0,India,1.570.000,,,1.324.000,,1.344.000,,1.193.167,,43
+Cochín,2011.0,India,1.530.000,,,2.374.000,,2.416.000,,2.119.742,,44
+Jamshedpur,2011.0,India,1.530.000,,,1.443.000,,1.451.000,,1.339.438,,45
+Kolhapur,2011.0,India,1.520.000,,,593.000,,591.000,,561.837,,46
+Asansol,2011.0,India,1.490.000,,,1.315.000,,1.313.000,,1.243.414,,47
+Srinagar,2011.0,India,1.430.000,,,1.409.000,,1.429.000,,1.264.202,,48
+Jabalpur,2011.0,India,1.380.000,,,1.339.000,,1.367.000,,1.268.848,,49
+Allahabad,2011.0,India,1.360.000,,,1.294.000,,1.295.000,,1.212.395,,50
+Jodhpur,2011.0,India,1.300.000,,,1.266.000,,1.284.000,,1.138.300,,51
+Amritsar,2011.0,India,1.300.000,,,1.264.000,,1.265.000,,1.183.549,,52
+Dhanbad,2011.0,India,1.290.000,,,1.258.000,,1.255.000,,1.196.214,,53
+Ranchi,2011.0,India,1.270.000,,,1.246.000,,1.262.000,,1.120.374,,54
+Tirupur,2011.0,India,1.260.000,,,1.177.000,,1.230.000,,963.173,,55
+Gwalior,2011.0,India,1.260.000,,,1.208.000,,1.221.000,,1.117.740,,56
+Kotah,2011.0,India,1.180.000,,,1.138.000,,1.163.000,,1.001.964,,57
+Quetta,1998.0,Pakistán,1.160.000,,,1.100.000,,1.109.000,,565.137,,58
+Bareilly,2011.0,India,1.150.000,,,1.094.000,,1.111.000,,985.752,,59
+Thiruvananthapuram,2011.0,India,1.120.000,,,1.921.000,,1.965.000,,1.679.754,,60
+Tiruchirappalli,2011.0,India,1.120.000,,,1.101.000,,1.106.000,,1.022.518,,61
+Mysore,2011.0,India,1.110.000,,,1.078.000,,1.082.000,,990.900,,62
+Aligarh,2011.0,India,1.080.000,,,1.020.000,,1.037.000,,911.223,,63
+Moradabad,2011.0,India,1.080.000,,,1.004.000,,1.023.000,,887.871,,64
+Khulna,2011.0,Bangladés,1.070.000,,,1.000.000,,1.022.000,,1.046.341,,65
+Guwahati,2011.0,India,1.050.000,,,1.039.000,,1.042.000,,962.334,,66
+Hubli - Dharwad,2011.0,India,1.040.000,,,613.000,,1.020.000,,943.788,,67
+Solapur,2011.0,India,1.030.000,,,991.000,,986.000,,951.558,,68
+Salem,2011.0,India,1.020.000,,,996.000,,1.003.000,,917.414,,69
+Jalandhar,2011.0,India,1.020.000,,,948.000,,954.000,,874.412,,70
+Kozhikode,2011.0,India,---,,,2.394.000,,2.476.000,,2.028.399,,71
+Thrissur,2011.0,India,---,,,2.236.000,,2.329.000,,1.861.269,,72
+Malappuram,2011.0,India,---,,,2.108.000,,2.216.000,,1.699.060,,73
+Cananor,2011.0,India,---,,,2.047.000,,2.153.000,,1.640.986,,74
+Kollam,2011.0,India,---,,,1.351.000,,1.410.000,,1.110.668,,75
+"Cantón (incluyendo Dongguan, Foshan, Jiangmen, Shenzhen y Zhongshan)",2010.0,China,46.900.000,,,45.553.000[n 3],,42.941.000[n 2],,39.264.086 [n 4],,1
+Tokio,2010.0,Japón,39.500.000,,,37.843.000,,38.001.000,,8.945.695[n 1],,2
+Shanghái (incluyendo Suzhou y Kunshan),2010.0,China,30.400.000,,,30.477.000[n 3],,29.213.000[n 2],,25.420.288 [n 4],,3
+Seúl (incluyendo Incheon y Suwon),2010.0,Corea del Sur,24.300.000,,,23.480.000,,13.558.000[n 2],,23.836.272,,4
+Pekín,2010.0,China,20.700.000,,,21.009.000,,20.384.000,,16.446.857,,5
+Osaka,2010.0,Japón,17.800.000,,,17.444.000,,20.238.000,,2.665.314[n 1],,6
+Tianjin,2010.0,China,11.200.000,,,10.920.000,,11.210.000,,9.290.263,,7
+Nagoya,2010.0,Japón,10.400.000,,,10.177.000,,9.406.000,,2.263.894[n 1],,8
+Xiamen (incluyendo Quanzhou),2010.0,China,9.850.000,,,11.130.000[n 3],,5.825.000[n 2],,4.273.841 [n 4],,9
+Chengdu,2010.0,China,9.400.000,,,10.376.000,,7.556.000,,6.316.922,,10
+Taipéi,,Taiwán,9.000.000,,,7.438.000,,2.666.000,,,,11
+Hangzhou (incluyendo Shaoxing),2010.0,China,8.150.000,,,9.625.000[n 3],,8.467.000[n 2],,6.887.819,,12
+Wuhan,2010.0,China,7.950.000,,,7.509.000,,7.906.000,,7.541.527,,13
+"Shantou (incluyendo Chaozhou, Puning, Chaoyang y Chaonan)",2010.0,China,7.850.000,,,6.337.000[n 3],,6.287.000[n 2],,5.775.239 [n 4],,14
+Shenyang (incluyendo Fushun),2010.0,China,7.600.000,,,7.402.000[n 3],,7.613.000[n 2],,7.037.040,,15
+Hong Kong,2011.0,Hong Kong,7.200.000,,,7.246.000,,7.314.000,,7.071.576,,16
+Chongqing,2010.0,China,6.950.000,,,7.217.000,,13.332.000,,6.263.790,,17
+Nankín,2010.0,China,6.750.000,,,6.155.000,,7.369.000,,5.827.888,,18
+Xi'an,2010.0,China,6.550.000,,,5.977.000,,6.044.000,,5.206.253,,19
+Wenzhou (incluyendo Rui'an),2010.0,China,5.800.000,,,4.303.000[n 3],,3.208.000,,3.614.208 [n 4],,20
+Qingdao,2010.0,China,5.650.000,,,5.816.000,,4.566.000,,3.990.942,,21
+Harbin,2010.0,China,5.100.000,,,4.815.000,,5.457.000,,4.596.313,,22
+Kitakyushu - Fukuoka,2010.0,Japón,4.725.000[n 6],,,4.505.000[n 3],,5.510.000,,2.440.589 [n 4],,23
+Zhengzhou,2010.0,China,4.600.000,,,4.942.000,,4.387.000,,3.677.032,,24
+Hefei,2010.0,China,4.475.000,,,3.665.000,,3.348.000,,3.098.727,,25
+Dalian,2010.0,China,4.425.000,,,4.183.000,,4.489.000,,3.902.467,,26
+Changsha,2010.0,China,4.375.000,,,3.657.000,,3.761.000,,3.193.354,,27
+Busán,2010.0,Corea del Sur,4.250.000,,,3.906.000,,3.216.000,,3.414.950,,28
+Taiyuan,2010.0,China,4.150.000,,,3.702.000,,3.482.000,,3.154.157,,29
+Kunming,2010.0,China,3.925.000,,,3.649.000,,3.780.000,,3.278.777,,30
+Jinan,2010.0,China,3.900.000,,,3.789.000,,4.032.000,,3.527.566,,31
+Fuzhou,2010.0,China,3.875.000,,,3.962.000,,3.283.000,,2.824.414,,32
+Shijiazhuang,2010.0,China,3.775.000,,,3.367.000,,3.264.000,,2.770.344,,33
+Changchun,2010.0,China,3.675.000,,,3.368.000,,3.762.000,,3.411.209,,34
+Nanchang,2010.0,China,3.600.000,,,2.637.000,,2.527.000,,2.223.661,,35
+Ürümqi,2010.0,China,3.550.000,,,3.184.000,,3.499.000,,2.853.398,,36
+Ningbo,2010.0,China,3.300.000,,,3.753.000,,3.132.000,,2.580.073,,37
+Zibo,2010.0,China,3.300.000,,,1.646.000,,2.430.000,,2.261.717,,38
+Wuxi,2010.0,China,3.225.000,,,3.597.000,,3.049.000,,2.757.736,,39
+Nanning,2010.0,China,3.150.000,,,2.590.000,,3.234.000,,2.660.833,,40
+Guiyang,2010.0,China,2.850.000,,,2.955.000,,2.871.000,,2.520.061,,41
+Lanzhou,2010.0,China,2.825.000,,,2.703.000,,2.723.000,,2.438.595,,42
+Pionyang,2008.0,Corea del Norte,2.800.000,,,2.850.000,,2.863.000,,2.581.076,,43
+Kaohsiung,,Taiwán,2.775.000,,,2.599.000,,1.523.000,,,,44
+Huizhou,2010.0,China,2.750.000,,,1.763.000,,2.312.000,,1.807.858,,45
+Daegu,2010.0,Corea del Sur,2.750.000,,,2.382.000,,2.244.000,,2.446.418,,46
+Changzhou,2010.0,China,2.625.000,,,3.425.000,,2.584.000,,2.257.376,,47
+Jiangyin,2010.0,China,2.625.000,,,3.056.000,,686.000,,1.013.670,,48
+Xuzhou,2010.0,China,2.525.000,,,1.301.000,,1.918.000,,2.214.795,,49
+Anshan,2010.0,China,2.500.000,,,1.516.000,,1.559.000,,1.504.996,,50
+Sapporo,2010.0,Japón,2.475.000,,,2.570.000,,2.571.000,,1.913.545[n 1],,51
+Shizuoka - Hamamatsu,2010.0,Japón,2.470.000[n 6],,,2.018.000[n 3],,3.369.000,,1.517.063 [n 4],,52
+Tangshan,2010.0,China,2.425.000,,,2.378.000,,2.743.000,,2.128.191,,53
+Taichung,,Taiwán,2.350.000,,,2.935.000,,1.225.000,,,,54
+Okayama,2010.0,Japón,2.200.000,,,707.000,,502.000,,709.584[n 1],,55
+Baotou,2010.0,China,2.125.000,,,2.159.000,,1.957.000,,1.900.373,,56
+Yantai,2010.0,China,2.075.000,,,1.520.000,,2.114.000,,1.797.871,,57
+Taizhou (incluyendo Wenling),2010.0,China,2.050.000,,,2.835.000[n 3],,1.648.000,,1.938.289 [n 4],,58
+Cixi,2010.0,China,2.050.000,,,1.490.000,,1.303.000,,1.059.942,,59
+Luoyang,2010.0,China,1.940.000,,,1.939.000,,2.015.000,,1.584.463,,60
+Nantong,2010.0,China,1.910.000,,,1.184.000,,1.978.000,,1.612.385,,61
+Liuzhou,2010.0,China,1.890.000,,,1.574.000,,1.619.000,,1.410.712,,62
+Hiroshima,2010.0,Japón,1.870.000,,,1.377.000,,2.173.000,,1.173.843 [n 1],,63
+Huai'an,2010.0,China,1.840.000,,,2.282.000,,2.000.000,,1.523.655,,64
+Haikou,2010.0,China,1.770.000,,,1.981.000,,1.903.000,,1.517.410,,65
+Yangzhou,2010.0,China,1.760.000,,,1.561.000,,1.765.000,,1.077.531,,66
+Hohhot,2010.0,China,1.750.000,,,2.219.000,,1.785.000,,1.497.110,,67
+Huainan,2010.0,China,1.740.000,,,1.142.000,,1.327.000,,1.238.488,,68
+Linyi,2010.0,China,1.700.000,,,2.465.000,,1.706.000,,1.522.488,,69
+Hengyang,2010.0,China,1.680.000,,,987.000,,1.301.000,,1.115.645,,70
+Daejeon,2010.0,Corea del Sur,1.600.000,,,1.564.000,,1.564.000,,1.501.859,,71
+Weifang (incluyendo Zhucheng),2010.0,China,1.590.000,,,2.636.000[n 3],,2.195.000,,1.848.234 [n 4],,72
+Baoding,2010.0,China,1.590.000,,,1.297.000,,1.106.000,,1.038.195,,73
+Gwangju,2010.0,Corea del Sur,1.580.000,,,1.601.000,,1.536.000,,1.475.745,,74
+Daqing,2010.0,China,1.550.000,,,983.000,,1.621.000,,1.433.698,,75
+Xiangyang,2010.0,China,1.550.000,,,1.183.000,,1.533.000,,1.433.057,,76
+Yiwu,2010.0,China,1.550.000,,,1.704.000,,1.080.000,,878.973,,77
+Zhuhai,2010.0,China,1.540.000,,,1.547.000,,1.542.000,,1.369.538,,78
+Datong,2010.0,China,1.510.000,,,1.709.000,,1.532.000,,1.362.314,,79
+Yinchuan,2010.0,China,1.500.000,,,1.614.000,,1.596.000,,1.159.457,,80
+Jilin,2010.0,China,1.500.000,,,1.633.000,,1.520.000,,1.469.722,,81
+Sendai,2010.0,Japón,1.480.000,,,1.277.000,,2.091.000,,1.045.986 [n 1],,82
+Jiaozuo,2010.0,China,1.350.000,,,809.000,,732.000,,702.527,,83
+Handan,2010.0,China,1.340.000,,,2.000.000,,1.634.000,,919.295,,84
+Putian,2010.0,China,1.340.000,,,1.468.000,,1.438.000,,1.107.199,,85
+Xiangtan,2010.0,China,1.320.000,,,1.007.000,,1.010.000,,903.287,,86
+Xining,2010.0,China,1.310.000,,,1.345.000,,1.323.000,,1.153.417,,87
+Huaibei,2010.0,China,1.300.000,,,1.116.000,,981.000,,854.696,,88
+Tainan,,Taiwán,1.300.000,,,1.216.000,,815.000,,,,89
+Xinxiang,2010.0,China,1.290.000,,,1.074.000,,991.000,,918.078,,90
+Wuhu,2010.0,China,1.280.000,,,1.456.000,,1.424.000,,1.108.087,,91
+Ulán Bator,2010.0,Mongolia,1.280.000,,,1.237.000,,1.377.000,,1.144.954,,92
+Xingtai,2010.0,China,1.280.000,,,749.000,,742.000,,668.765,,93
+Yancheng,2010.0,China,1.240.000,,,935.000,,1.436.000,,1.136.826,,94
+Taian,2010.0,China,1.220.000,,,817.000,,1.220.000,,1.123.541,,95
+Guilin,2010.0,China,1.190.000,,,949.000,,1.040.000,,963.629,,96
+Zhangjiakou,2010.0,China,1.180.000,,,1.156.000,,983.000,,924.628,,97
+Naha,2010.0,Japón,1.180.000,,,1.007.000,,321.000,,315.954 [n 1],,98
+Mianyang,2010.0,China,1.160.000,,,585.000,,1.065.000,,967.006,,99
+Zhanjiang,2010.0,China,1.150.000,,,1.042.000,,1.149.000,,1.038.762,,100
+Bengbu,2010.0,China,1.150.000,,,961.000,,842.000,,793.866,,101
+Kumamoto,2010.0,Japón,1.150.000,,,697.000,,601.000,,734.474 [n 1],,102
+Yichang,2010.0,China,1.140.000,,,1.039.000,,1.264.000,,1.049.363,,103
+Qingyuan,2010.0,China,1.130.000,,,588.000,,694.000,,916.453,,104
+Ulsan,2010.0,Corea del Sur,1.120.000,,,900.000,,904.000,,1.082.567,,105
+Zunyi,2010.0,China,1.120.000,,,108.000,,803.000,,715.148,,106
+Maanshan,2010.0,China,1.110.000,,,827.000,,858.000,,657.847,,107
+Qinhuangdao,2010.0,China,1.100.000,,,1.041.000,,1.109.000,,967.877,,108
+Changshu,2010.0,China,1.100.000,,,1.344.000,,726.000,,929.124,,109
+Changwon,2010.0,Corea del Sur,1.090.000,,,990.000,,1.039.000,,1.058.021,,110
+Cangnan,2010.0,China,1.090.000,,,823.000,,---,,648.219,,111
+Zhuzhou,2010.0,China,1.080.000,,,1.007.000,,1.083.000,,999.404,,112
+Maoming,2010.0,China,1.080.000,,,619.000,,609.000,,1.033.196,,113
+Benxi,2010.0,China,1.070.000,,,888.000,,1.070.000,,1.000.128,,114
+Qiqihar,2010.0,China,1.060.000,,,1.241.000,,1.452.000,,1.314.720,,115
+Lianyungang,2010.0,China,1.060.000,,,1.128.000,,1.099.000,,897.393,,116
+Zhenjiang,2010.0,China,1.050.000,,,969.000,,1.050.000,,950.516,,117
+Kaifeng,2010.0,China,1.040.000,,,633.000,,804.000,,725.573,,118
+Rizhao,2010.0,China,1.040.000,,,937.000,,1.062.000,,902.272,,119
+Nanchong,2010.0,China,1.030.000,,,692.000,,1.050.000,,890.402,,120
+Jinzhou,2010.0,China,1.030.000,,,922.000,,1.035.000,,946.098,,121
+Chifeng,2010.0,China,1.020.000,,,1.230.000,,1.018.000,,902.285,,122
+Fuji,2010.0,Japón,1.010.000,,,718.000,,---,,254.027 [n 1],,123
+Nanyang,2010.0,China,1.000.000,,,731.000,,1.011.000,,899.899,,124
+Wanzhou,2010.0,China,1.000.000,,,582.000,,---,,849.662,,125
+Jining,2010.0,China,---,,,623.000,,1.385.000,,939.034,,126
+Taizhou,2010.0,China,---,,,562.000,,1.184.000,,676.877,,127
+Anyang,2010.0,China,---,,,1.401.000,,1.140.000,,908.129,,128
+Suqian,2010.0,China,---,,,539.000,,1.050.000,,783.376,,129
+Yongin,2010.0,Corea del Sur,---,,,---,,1.048.000,,856.765,,130
+Zaozhuang (incluyendo Tengzhou),2010.0,China,---,,,1.481.000[n 3],,1.028.000,,1.764.366 [n 4],,131
+Yingkou,2010.0,China,---,,,708.000,,1.026.000,,880.412,,132
+Baoji,2010.0,China,---,,,933.000,,1.001.000,,871.940,,133
+Zhangzhou,2010.0,China,---,,,1.410.000,,---,,614.700,,134
+Weihai,2010.0,China,---,,,1.208.000,,---,,698.863,,135
+Dongying,2010.0,China,---,,,1.206.000,,---,,848.958,,136
+Jiaxing,2010.0,China,---,,,1.192.000,,---,,762.643,,137
+Jiamusi,2010.0,China,---,,,1.089.000,,---,,631.357,,138
+Fuzhou,2010.0,China,---,,,1.052.000,,---,,482.940,,139
+Huzhou,2010.0,China,---,,,1.021.000,,---,,748.471,,140
+Yakarta (incluyendo Bogor),2010,Indonesia,27.700.000,,,30.539.000,,11.399.000 [n 2],,10.558.121 [n 4],,1
+Manila,2010,Filipinas,23.100.000,,,24.123.000,,12.946.000,,1.652.171 [n 1],,2
+Bangkok (incluyendo Samut Prakan),2010,Tailandia,16.700.000,,,14.998.000,,11.084.000,,8.986.218 [n 4],,3
+Ciudad Ho Chi Minh,2009,Vietnam,8.150.000,,,8.957.000,,7.298.000,,5.880.615,,4
+Kuala Lumpur,2000,Malasia,6.950.000,,,7.088.000,,6.837.000,,1.305.792 [n 1],,5
+Singapur - Johor Bahru,2010 2000,Singapur Malasia,6.900.000,,,7.312.000 [n 3],,6.531.000 [n 2],,5.719.644 [n 4],,6
+Bandung,2010,Indonesia,5.900.000,,,5.695.000,,2.544.000,,2.394.873 [n 1],,7
+Rangún,2014,Birmania,5.100.000,,,4.800.000,,4.802.000,,4.728.524,,8
+Surabaya,2010,Indonesia,4.675.000,,,4.881.000,,2.853.000,,2.765.487 [n 1],,9
+Medan,2010,Indonesia,3.400.000,,,3.942.000,,2.204.000,,2.097.610 [n 1],,10
+Hanói,2009,Vietnam,2.925.000,,,3.715.000,,3.629.000,,2.316.772,,11
+Cebú,2010,Filipinas,2.250.000,,,2.535.000,,951.000,,866.171 [n 1],,12
+Semarang,2010,Indonesia,2.025.000,,,1.630.000,,1.630.000,,1.520.481,,13
+Nom Pen,2008,Camboya,1.830.000,,,1.729.000,,1.731.000,,1.416.582,,14
+Makasar,2010,Indonesia,1.760.000,,,1.484.000,,1.489.000,,1.331.391,,15
+Palembang,2010,Indonesia,1.680.000,,,1.434.000,,1.455.000,,1.440.678,,16
+George Town,2000,Malasia,1.530.000,,,1.336.000,,---,,181.380 [n 1],,17
+Denpasar,2010,Indonesia,1.470.000,,,1.175.000,,1.107.000,,788.589 [n 1],,18
+Malang,2010,Indonesia,1.410.000,,,1.114.000,,856.000,,820.243,,19
+Mandalay,2014,Birmania,1.390.000,,,1.160.000,,1.167.000,,1.225.546,,20
+Davao,2010,Filipinas,1.330.000,,,1.630.000,,1.630.000,,1.176.586 [n 1],,21
+Yogyakarta,2010,Indonesia,1.270.000,,,1.831.000,,385.000,,388.627 [n 1],,22
+Chonburi,2010,Tailandia,1.230.000,,,665.000,,518.000,,321.149 [n 1],,23
+Surakarta,2010,Indonesia,1.210.000,,,1.318.000,,504.000,,499.337 [n 1],,24
+Batam,2010,Indonesia,1.160.000,,,1.142.000,,1.391.000,,917.998,,25
+Pekanbaru,2010,Indonesia,1.160.000,,,1.100.000,,1.121.000,,882.045,,26
+Serang,2010,Indonesia,1.090.000,,,564.000,,---,,428.484 [n 1],,27
+Bandar Lampung,2010,Indonesia,1.080.000,,,909.000,,965.000,,873.007,,28
+Ángeles,2010,Filipinas,1.060.000,,,883.000,,363.000,,326.336 [n 1],,29
+Can Tho,2009,Vietnam,---,,,769.000,,1.175.000,,731.545,,30
+Hai Phong,2009,Vietnam,---,,,983.000,,1.075.000,,769.736,,31
+Naipyidó,2014,Birmania,---,,,1.030.000,,1.030.000,,333.506,,32
+General Santos,2010,Filipinas,---,,,1.579.000,,---,,444.116 [n 1],,33
+Cirebon,2010,Indonesia,---,,,1.143.000,,---,,296.389 [n 1],,34
+Vientián,2005,Laos,---,,,975.000,,997.000,,569.729,,35
+Londres,2011.0,Reino Unido,14.300.000,,,10.236.000,,10.313.000,,11.140.445,,1
+París,1999.0,Francia,11.200.000,,,10.858.000,,10.843.000,,9.738.809,,2
+Madrid,2011.0,España,6.400.000,,,6.171.000,,6.199.000,,3.198.645 [n 1],,3
+Región del Ruhr [n 7],,Alemania,5.600.000,,,---,,---,,,,4
+Milán,2011.0,Italia,5.150.000,,,5.257.000,,3.099.000,,1.242.123 [n 1],,5
+Colonia - Düsseldorf,2011.0,Alemania,4.825.000,,,8.783.000 [n 3],,1.640.000 [n 2],,1.591.866 [n 4],,6
+Barcelona,2011.0,España,4.700.000,,,4.693.000,,5.258.000,,1.611.013 [n 1],,7
+Berlín,2011.0,Alemania,4.450.000,,,4.069.000,,3.563.000,,3.292.365 [n 1],,8
+Nápoles,2011.0,Italia,4.225.000,,,3.706.000,,2.202.000,,962.003 [n 1],,9
+Atenas,2011.0,Grecia,3.600.000,,,3.484.000,,3.052.000,,3.168.846,,10
+Roma,2011.0,Italia,3.550.000,,,3.906.000,,3.718.000,,2.617.175 [n 1],,11
+Birmingham,2011.0,Reino Unido,3.100.000,,,2.512.000,,2.515.000,,2.697.168,,12
+Róterdam,2001.0,Países Bajos,3.100.000,,,2.660.000,,993.000,,608.422 [n 1],,13
+Fráncfort del Meno,2011.0,Alemania,3.100.000,,,1.915.000,,715.000,,667.925 [n 1],,14
+Mánchester,2011.0,Reino Unido,3.000.000,,,2.639.000,,2.646.000,,2.637.335,,15
+Hamburgo,2011.0,Alemania,2.750.000,,,2.087.000,,1.831.000,,1.706.696 [n 1],,16
+Lisboa,2001.0,Portugal,2.600.000,,,2.666.000,,2.884.000,,564.657 [n 1],,17
+Ámsterdam,2001.0,Países Bajos,2.375.000,,,1.624.000,,1.091.000,,734.533 [n 1],,18
+Stuttgart,2011.0,Alemania,2.300.000,,,1.379.000,,626.000,,585.890 [n 1],,19
+Múnich,2011.0,Alemania,2.175.000,,,1.981.000,,1.438.000,,1.348.335 [n 1],,20
+Viena,2011.0,Austria,2.125.000,,,1.763.000,,1.753.000,,2.015.580,,21
+Leeds,2011.0,Reino Unido,2.125.000,,,1.893.000,,1.912.000,,2.058.861,,22
+Estocolmo,,Suecia,2.075.000,,,1.484.000,,1.486.000,,,,23
+Bruselas,,Bélgica,2.000.000,,,2.089.000,,2.045.000,,,,24
+Lyon,1999.0,Francia,1.920.000,,,1.583.000,,1.609.000,,1.428.998,,25
+Liverpool,2011.0,Reino Unido,1.830.000,,,875.000,,870.000,,1.367.147,,26
+Valencia,2011.0,España,1.780.000,,,1.561.000,,810.000,,792.054 [n 1],,27
+Turín,2011.0,Italia,1.670.000,,,1.521.000,,1.765.000,,872.367 [n 1],,28
+Marsella,1999.0,Francia,1.640.000,,,1.397.000,,1.605.000,,1.463.016,,29
+Glasgow,2011.0,Reino Unido,1.610.000,,,1.220.000,,1.223.000,,1.601.154,,30
+Copenhague,,Dinamarca,1.600.000,,,1.248.000,,1.268.000,,,,31
+Sheffield,2011.0,Reino Unido,1.530.000,,,706.000,,706.000,,795.844,,32
+Mannheim,2011.0,Alemania,1.520.000,,,559.000,,319.000,,290.117 [n 1],,33
+Newcastle upon Tyne,2011.0,Reino Unido,1.460.000,,,793.000,,791.000,,1.220.781,,34
+Zúrich,2010.0,Suiza,1.350.000,,,785.000,,1.246.000,,1.249.750,,35
+Nottingham,2011.0,Reino Unido,1.350.000,,,755.000,,755.000,,754.789,,36
+Sevilla,2011.0,España,1.340.000,,,1.107.000,,701.000,,698.042,,37
+Dublín,2011.0,Irlanda,1.320.000,,,1.160.000,,1.169.000,,1.110.627,,38
+Lille,1999.0,Francia,1.270.000,,,1.018.000,,1.027.000,,999.797,,39
+Helsinki,2000.0,Finlandia,1.220.000,,,1.208.000,,1.180.000,,1.027.305,,40
+Oporto,2001.0,Portugal,1.190.000,,,1.474.000,,1.299.000,,263.131 [n 1],,41
+Núremberg,2011.0,Alemania,1.160.000,,,670.000,,517.000,,486.314 [n 1],,42
+Oslo,1990.0,Noruega,1.130.000,,,975.000,,986.000,,685.530,,43
+Southampton,2011.0,Reino Unido,1.130.000,,,883.000,,882.000,,1.060.326,,44
+Hannover,2011.0,Alemania,1.120.000,,,711.000,,533.000,,506.416 [n 1],,45
+Amberes,,Bélgica,1.020.000,,,1.008.000,,994.000,,,,46
+Málaga,2011.0,España,1.010.000,,,716.000,,574.000,,561.435,,47
+Niza - Cannes,1999.0,Francia,---,,,978.000,,967.000,,889.163,,48
+Toulouse,1999.0,Francia,---,,,922.000,,938.000,,761.963,,49
+Moscú,2010,Rusia,16.800.000,,,16.170.000,,12.166.000,,11.612.885,,1
+Estambul [n 8],2000,Turquía,14.200.000,,,13.287.000,,14.164.000,,8.803.468,,2
+San Petersburgo,2010,Rusia,5.400.000,,,5.126.000,,4.993.000,,4.879.566,,3
+Kiev,2001,Ucrania,3.375.000,,,2.241.000,,2.942.000,,2.611.327,,4
+Budapest,2011,Hungría,2.550.000,,,1.710.000,,1.714.000,,1.729.040,,5
+Katowice,2011,Polonia,2.400.000,,,2.190.000,,303.000,,310.764 [n 1],,6
+Varsovia,2011,Polonia,2.275.000,,,1.720.000,,1.722.000,,1.700.612 [n 1],,7
+Bucarest,2011,Rumania,2.175.000,,,1.860.000,,1.868.000,,1.883.425,,8
+Minsk,2009,Bielorrusia,1.950.000,,,1.910.000,,1.915.000,,1.836.808,,9
+Nizni Nóvgorod,2010,Rusia,1.750.000,,,1.201.000,,1.212.000,,1.250.619,,10
+Járkov,2001,Ucrania,1.650.000,,,1.440.000,,1.441.000,,1.470.902,,11
+Donetsk,2001,Ucrania,1.480.000,,,930.000,,934.000,,1.016.194,,12
+Praga,2001,República Checa,1.460.000,,,1.310.000,,1.314.000,,1.169.106,,13
+Volgogrado,2010,Rusia,1.410.000,,,999.000,,1.022.000,,1.021.215,,14
+Belgrado,2011,Serbia,1.400.000,,,1.180.000,,1.182.000,,1.166.763,,15
+Dnipropetrovsk,2001,Ucrania,1.390.000,,,950.000,,957.000,,1.065.008,,16
+Sofía,2011,Bulgaria,1.320.000,,,1.195.000,,1.226.000,,1.202.761,,17
+Samara,2010,Rusia,1.320.000,,,1.162.000,,1.164.000,,1.164.685,,18
+Rostov del Don,2010,Rusia,1.280.000,,,1.090.000,,1.097.000,,1.089.261,,19
+Kazán,2010,Rusia,1.210.000,,,1.160.000,,1.162.000,,1.143.535,,20
+Ufá,2010,Rusia,1.110.000,,,1.024.000,,1.070.000,,1.062.319,,21
+Odesa,2001,Ucrania,1.110.000,,,1.010.000,,1.010.000,,1.029.049,,22
+Perm,2010,Rusia,1.100.000,,,974.000,,982.000,,991.162,,23
+Sarátov,2010,Rusia,1.090.000,,,772.000,,815.000,,837.900,,24
+Vorónezh,2010,Rusia,1.030.000,,,897.000,,911.000,,975.373,,25
+Sídney,2011,Australia,4.850.000,,,4.036.000,,4.505.000,,4.028.525,,1
+Melbourne,2011,Australia,4.350.000,,,3.906.000,,4.203.000,,3.847.567,,2
+Brisbane,2011,Australia,2.875.000,,,1.999.000,,2.202.000,,1.977.316,,3
+Perth,2011,Australia,2.025.000,,,1.751.000,,1.861.000,,1.670.952,,4
+Auckland,2013,Nueva Zelanda,1.404.000,,,1.356.000,,1.344.000,,1.308.831,,5
+Adelaida,2011,Australia,1.290.000,,,1.140.000,,1.256.000,,1.198.467,,6
+Honolulu[n 9],2010,Estados Unidos,1.000.000,,,842.000,,848.000,,953.207,,7
+Ciudad de México,2010,México,,22.300.000,,22.063.000,,22.452.000,,20.555.272,,1
+Nueva York,2010,Estados Unidos,,22.200.000,,20.630.000,,21.900.000,,19.556.440,,2
+São Paulo,2010,Brasil,,21.900.000,,20.365.000,,21.600.000,,19.683.975,,3
+Los Ángeles (incluyendo Riverside y San Bernardino),2010,Estados Unidos,,17.600.000,,15.058.000,,14.504.000,,17.053.905,,4
+Buenos Aires,2010,Argentina,,15.800.000,,14.122.000,,15.180.000,,13.588.171,,5
+Río de Janeiro,2010,Brasil,,12.700.000,,11.727.000,,12.902.000,,11.835.708,,6
+Lima,2007,Perú,,10.300.000,,10.500.000,,10.600.000,,8.324.510,,7
+Chicago,2010,Estados Unidos,,9.800.000,,9.156.000,,8.745.000,,9.461.537,,8
+Bogotá (incl. Chía - Soacha - Mosquera - La Calera - Funza - Madrid),2005,Colombia,,9.550.000,,8.950.000,,8.197.000,,6.472.935,,9
+Washington D. C. (incluyendo Baltimore),2010,Estados Unidos,,8.350.000,,7.152.000,,7.222.000,,8.347.003,,10
+San Francisco (incluyendo San José),2010,Estados Unidos,,7.600.000,,5.929.000,,5.030.000,,6.172.501,,11
+Boston (incluyendo Providence),2010,Estados Unidos,,7.350.000,,5.679.000,,5.445.000,,6.153.628,,12
+Filadelfia,2010,Estados Unidos,,7.300.000,,5.570.000,,5.585.000,,5.965.368,,13
+Santiago de Chile,2010,Chile,,7.150.000,,6.174.000,,5.703.000,,6.426.210,,14
+Toronto,2011,Canadá,,7.100.000,,6.456.000,,5.993.000,,5.583.064,,15
+Houston,2010,Estados Unidos,,6.200.000,,5.764.000,,5.636.000,,5.920.490,,16
+Miami,2010,Estados Unidos,,6.100.000,,5.764.000,,5.817.000,,5.566.299,,17
+Dallas,2002,Estados Unidos,,5.985.000,,5.225.000,,5.507.000,,4.656.690,,18
+Detroit - Windsor,2010 2011,Estados Unidos Canadá,,5.700.000,,3.947.000,,3.954.000,,4.615.559,,19
+Caracas,2011,Venezuela,,5.690.000,,4.083.000,,4.513.000,,2.904.376,,20
+Atlanta,2010,Estados Unidos,,5.500.000,,5.015.000,,5.142.000,,5.286.727,,21
+Guadalajara,2010,México,,5.007 756,,4.603.000,,4.843.000,,4.495.182,,22
+Belo Horizonte,2010,Brasil,,4.925.000,,4.517.000,,5.716.000,,5.414.701,,23
+Monterrey,2010,México,,4.456.000,,4.513.000,,4.810.000,,1.135.512,,24
+Phoenix,2010,Estados Unidos,,4.325.000,,4.194.000,,4.063.000,,4.193.127,,24
+Montreal,2011,Canadá,,4.100.000,,3.536.000,,3.981.000,,3.824.221,,25
+Porto Alegre,2010,Brasil,,4.075.000,,3.413.000,,3.603.000,,3.958.985,,26
+Seattle,2010,Estados Unidos,,4.075.000,,3.218.000,,3.249.000,,3.439.815,,27
+Tampa,2010,Estados Unidos,,4.025.000,,2.621.000,,2.659.000,,2.783.514,,28
+Brasilia,2010,Brasil,,3.925.000,,2.536.000,,4.155.000,,3.717.728,,29
+Medellín,2005,Colombia,,3.900.000,,3.568.000,,3.911.000,,2.175.681,,30
+Recife,2010,Brasil,,3.775.000,,3.347.000,,3.739.000,,3.690.547,,31
+Salvador de Bahía,2010,Brasil,,3.650.000,,3.190.000,,3.583.000,,3.573.973,,32
+Santo Domingo,2010,República Dominicana,,3.650.000,,2.925.000,,2.945.000,,2.581.827,,33
+Fortaleza,2010,Brasil,,3.575.000,,3.401.000,,3.880.000,,3.615.767,,34
+Denver,2010,Estados Unidos,,3.525.000,,2.559.000,,2.599.000,,2.543.594,,35
+Maracaibo,2011,Venezuela,,3.400.000,,2.861.000,,2.916.000,,2.904.376,,36
+Curitiba,2010,Brasil,,3.275.000,,3.102.000,,3.474.000,,3.174.201,,37
+San Diego,2010,Estados Unidos,,3.275.000,,3.086.000,,3.107.000,,3.095.308,,38
+Cali,2005,Colombia,,3.250.000,,2.357.000,,2.646.000,,2.083.171,,39
+Cleveland,2010,Estados Unidos,,3.075.000,,1.783.000,,1.773.000,,2.077.246,,40
+Orlando,2010,Estados Unidos,,3.075.000,,2.040.000,,1.731.000,,2.134.418,,41
+Campinas,2010,Brasil,,3.050.000,,2.645.000,,3.047.000,,2.797.137,,42
+Minneapolis,2010,Estados Unidos,,3.050.000,,2.771.000,,2.791.000,,3.348.857,,43
+Ciudad de Guatemala,2002,Guatemala,,3.000.000,,1.289.000,,2.918.000,,942.348,,44
+Guayaquil,2010,Ecuador,,3.000.000,,2.700.000,,2.709.000,,2.278.691,,45
+Puebla de Zaragoza,2010,México,,2.975.000,,2.088.000,,2.984.000,,1.434.062,,46
+Puerto Príncipe,2003,Haití,,2.850.000,,2.440.000,,2.440.000,,703.023,,47
+Cincinnati,2010,Estados Unidos,,2.725.000,,1.682.000,,1.688.000,,2.114.755,,48
+Quito,2010,Ecuador,,2.550.000,,1.720.000,,1.726.000,,1.607.734,,49
+Vancouver,2011,Canadá,,2.500.000,,2.273.000,,2.485.000,,2.313.328,,50
+Barranquilla,2005,Colombia,,2.450.000,,1.218.000,,1.500.000,,1.967.000,,51
+"Cantón (incluyendo Dongguan, Foshan, Jiangmen, Shenzhen y Zhongshan)",2010,China,46.900.000,,,45.553.000,,42.941.000,,39.264.086,,1
+Tokio,2010,Japón,39.500.000,,,37.843.000,,38.001.000,,8.945.695,,2
+"Shanghái (incl. Suzhou, Kunshan)",2010,China,30.400.000,,,30.477.000,,29.213.000,,25.420.288,,3
+Yakarta (incluyendo Bogor),2010,Indonesia,30.100.000,,,30.539.000,,11.399.000,,10.558.121,,4
+Delhi,2011,India,28.400.000,,,24.998.000,,25.703.000,,16.349.831,,5
+Karachi,2011,Pakistán,25.300.000,,,22.123.000,,16.618.000,,21.142.625,,6
+Manila,2010,Filipinas,24.600.000,,,24.123.000,,12.946.000,,1.652.171,,7
+Bombay (incluyendo Kalyan y Vasai-Virar),2011,India,24.300.000,,,21.732.000,,21.043.000,,19.617.302,,8
+Seúl (incluyendo Incheon y Suwon),2010,Corea del Sur,24.100.000,,,23.480.000,,10.558.000,,23.836.272,,9
+Daca,2011,Bangladés,22.300.000,,,15.669.000,,17.598.000,,14.543.124,,10
+Pekín,2010,China,20.700.000,,,21.009.000,,20.384.000,,16.446.857,,11
+Osaka,2010,Japón,19.800.000,,,17.444.000,,20.238.000,,2.665.314,,12
+Bangkok (incluyendo Samut Prakan),2010,Tailandia,16.700.000,,,14.998.000,,11.084.000,,8.986.218,,13
+Calcuta,2011,India,15.900.000,,,14.667.000,,14.865.000,,14.057.991,,14
+Teherán (incluyendo Karaj),2011,Irán,13.600.000,,,13.532.000,,10.239.000,,9.768.677,,15
+Tianjin,2010,China,11.200.000,,,10.920.000,,11.210.000,,9.290.263,,16
+Nagoya,2010,Japón,10.400.000,,,10.177.000,,9.406.000,,2.263.894,,17
+Bangalore,2011,India,10.300.000,,,9.807.000,,10.087.000,,8.520.435,,18
+Lahore,1998,Pakistán,9.950.000,,,10.052.000,,8.741.000,,5.143.495,,19
+Madrás,2011,India,9.900.000,,,9.714.000,,9.890.000,,8.653.521,,20
+Xiamen (incluyendl Quanzhou),2010,China,9.850.000,,,11.130.000,,5.825.000,,4.273.841,,21
+Chengdu,2010,China,9.400.000,,,10.376.000,,7.556.000,,6.316.922,,22
+Taipéi,,Taiwán,9.000.000,,,7.438.000,,2.666.000,,,,23
+Hyderabad,2011,India,8.900.000,,,8.754.000,,8.942.000,,7.677.018,,24
+Hangzhou (incluyendo Shaoxing),2010,China,8.150.000,,,9.625.000,,8.467.000,,6.887.819,,25
+Ciudad Ho Chi Minh,2009,Vietnam,8.150.000,,,8.957.000,,7.298.000,,5.880.615,,26
+Wuhan,2010,China,7.950.000,,,7.509.000,,7.906.000,,7.541.527,,27
+"Shantou (incluyendo Chaozhou, Puning, Chaoyang y Chaonan)",2010,China,7.850.000,,,6.337.000,,6.287.000,,5.775.239,,28
+Shenyang (incluyendo Fushun),2010,China,7.600.000,,,7.402.000,,7.613.000,,7.037.040,,29
+Ahmedabad,2011,India,7.350.000,,,7.186.000,,7.343.000,,6.357.693,,30
+Hong Kong,2011,Hong Kong,7.200.000,,,7.246.000,,7.314.000,,7.071.576,,31
+Chongqing,2010,China,6.950.000,,,7.217.000,,13.332.000,,6.263.790,,32
+Kuala Lumpur,2000,Malasia,6.950.000,,,7.088.000,,6.837.000,,1.305.792,,33
+Singapur - Johor Bahru,2010 2000,Singapur Malasia,6.900.000,,,7.312.000,,6.531.000,,5.719.644,,34
+Nankín,2010,China,6.750.000,,,6.155.000,,7.369.000,,5.827.888,,35
+Bagdad,1987,Irak,6.750.000,,,6.625.000,,6.643.000,,3.841.268,,36
+Riad,2010,Arabia Saudita,6.550.000,,,5.666.000,,6.370.000,,5.188.286,,37
+Xi'an,2010,China,6.550.000,,,5.977.000,,6.044.000,,5.206.253,,38
+Pune,2011,India,6.000.000,,,5.631.000,,5.728.000,,5.057.709,,39
+Bandung,2010,Indonesia,5.900.000,,,5.695.000,,2.544.000,,2.394.873,,40
+Wenzhou (incluyendo Rui'an),2010,China,5.800.000,,,4.303.000,,3.208.000,,3.614.208,,41
+Qingdao,2010,China,5.650.000,,,5.816.000,,4.566.000,,3.990.942,,42
+Surat,2011,India,5.600.000,,,5.447.000,,5.650.000,,4.591.246,,43
+Harbin,2010,China,5.100.000,,,4.815.000,,5.457.000,,4.596.313,,44
+Rangún,2014,Birmania,5.100.000,,,4.800.000,,4.802.000,,4.728.524,,45
+Kitakyushu - Fukuoka,2010,Japón,4.725.000,,,4.505.000,,5.510.000,,2.440.589,,46
+Surabaya,2010,Indonesia,4.675.000,,,4.881.000,,2.853.000,,2.765.487,,47
+Colombo,2012,Sri Lanka,4.650.000,,,2.180.000,,707.000,,561.314,,48
+Ankara,2000,Turquía,4.625.000,,,4.538.000,,4.750.000,,3.203.362,,49
+Zhengzhou,2010,China,4.600.000,,,4.942.000,,4.387.000,,3.677.032,,50
+Moscú,2010.0,Rusia,16 800 000???,,,16 170 000???,,12 166 000???,,11 612 885???,,1
+Londres,2011.0,Reino Unido,14 300 000???,,,10 236 000???,,10 313 000???,,11 140 445???,,2
+Estambul,2000.0,Turquía,14 200 000,,,13.287 000,,14 164 000,,8 803 468,,3
+París,1999.0,Francia,11 200 000,,,10.858.000,,10.843.000,,9.738 809,,4
+Madrid,2011.0,España,6 400 000,,,6 171 000,,6 199 000,,3 198 645,,5
+Región del Ruhr,,Alemania,5 600 000,,,---,,---,,,,6
+San Petersburgo,2010.0,Rusia,5 400 000,,,5 126 000,,4 993 000,,4 879 566,,7
+Milán,2011.0,Italia,5.150.000,,,5.257.000,,3.099.000,,1.242.123,,8
+Colonia - Düsseldorf,2011.0,Alemania,4.825.000,,,8.783.000,,1.640.000,,1.591.866,,9
+Barcelona,2011.0,España,4.700.000,,,4.693.000,,5.258.000,,1.611.013,,10
+Berlín,2011.0,Alemania,4.450.000,,,4.069.000,,3.563.000,,3.292.365,,11
+Nápoles,2011.0,Italia,4.225.000,,,3.706.000,,2.202.000,,962.003,,12
+Atenas,2011.0,Grecia,3.600.000,,,3.484.000,,3.052.000,,3.168.846,,13
+Roma,2011.0,Italia,3.550.000,,,3.906.000,,3.718.000,,2.617.175,,14
+Kiev,2001.0,Ucrania,3.375.000,,,2.241.000,,2.942.000,,2.611.327,,15
+Birmingham,2011.0,Reino Unido,3.100.000,,,2.512.000,,2.515.000,,2.697.168,,16
+Róterdam,2001.0,Países Bajos,3.100.000,,,2.660.000,,993.000,,608.422,,17
+Fráncfort del Meno,2011.0,Alemania,3.100.000,,,1.915.000,,715.000,,667.925,,18
+Mánchester,2011.0,Reino Unido,3.000.000,,,2.639.000,,2.646.000,,2.637.335,,19
+Hamburgo,2011.0,Alemania,2.750.000,,,2.087.000,,1.831.000,,1.706.696,,20
+Lisboa,2001.0,Portugal,2.600.000,,,2.666.000,,2.884.000,,564.657,,21
+Budapest,2011.0,Hungría,2.550.000,,,1.710.000,,1.714.000,,1.729.040,,22
+Katowice,2011.0,Polonia,2.400.000,,,2.190.000,,303.000,,310.764,,23
+Ámsterdam,2001.0,Países Bajos,2.375.000,,,1.624.000,,1.091.000,,734.533,,24
+Stuttgart,2011.0,Alemania,2.300.000,,,1.379.000,,626.000,,585.890,,25
+Varsovia,2011.0,Polonia,2.275.000,,,1.720.000,,1.722.000,,1.700.612,,26
+Bucarest,2011.0,Rumania,2.175.000,,,1.860.000,,1.868.000,,1.883.425,,27
+Múnich,2011.0,Alemania,2.175.000,,,1.981.000,,1.438.000,,1.348.335,,28
+Viena,2011.0,Austria,2.125.000,,,1.763.000,,1.753.000,,2.015.580,,29
+Leeds,2011.0,Reino Unido,2.125.000,,,1.893.000,,1.912.000,,2.058.861,,30
+Estocolmo,,Suecia,2.075.000,,,1.484.000,,1.486.000,,,,31
+Bruselas,,Bélgica,2.000.000,,,2.089.000,,2.045.000,,,,32
+Minsk,2009.0,Bielorrusia,1.950.000,,,1.910.000,,1.915.000,,1.836.808,,33
+Lyon,1999.0,Francia,1.920.000,,,1.583.000,,1.609.000,,1.428.998,,34
+Liverpool,2011.0,Reino Unido,1.830.000,,,875.000,,870.000,,1.367.147,,35
+Valencia,2011.0,España,1.780.000,,,1.561.000,,810.000,,792.054,,36
+Nizni Nóvgorod,2010.0,Rusia,1.750.000,,,1.201.000,,1.212.000,,1.250.619,,37
+Turín,2011.0,Italia,1.670.000,,,1.521.000,,1.765.000,,872.367,,38
+Járkov,2001.0,Ucrania,1.650.000,,,1.440.000,,1.441.000,,1.470.902,,39
+Marsella,1999.0,Francia,1.640.000,,,1.397.000,,1.605.000,,1.463.016,,40
+Glasgow,2011.0,Reino Unido,1.610.000,,,1.220.000,,1.223.000,,1.601.154,,41
+Copenhague,,Dinamarca,1.600.000,,,1.248.000,,1.268.000,,,,42
+Sheffield,2011.0,Reino Unido,1.530.000,,,706.000,,706.000,,795.844,,43
+Mannheim,2011.0,Alemania,1.520.000,,,559.000,,319.000,,290.117,,44
+Donetsk,2001.0,Ucrania,1.480.000,,,930.000,,934.000,,1.016.194,,45
+Newcastle upon Tyne,2011.0,Reino Unido,1.460.000,,,793.000,,791.000,,1.220.781,,46
+Praga,2001.0,República Checa,1.460.000,,,1.310.000,,1.314.000,,1.169.106,,47
+Volgogrado,2010.0,Rusia,1.410.000,,,999.000,,1.022.000,,1.021.215,,48
+Belgrado,2011.0,Serbia,1.400.000,,,1.180.000,,1.182.000,,1.166.763,,49
+Dnipropetrovsk,2001.0,Ucrania,1.390.000,,,950.000,,957.000,,1.065.008,,50
diff --git a/wiki3_df2.csv b/wiki3_df2.csv
new file mode 100644
index 0000000..3186cbf
--- /dev/null
+++ b/wiki3_df2.csv
@@ -0,0 +1,442 @@
+Ciudad,Fecha,País,Citypopulation 2015,Demographia 2015,ONU 2015,Ultimo Censo,Posición en Tabla Inicial
+"Cantón (incluyendo Dongguan, Foshan, Jiangmen, Shenzhen y Zhongshan)",2010.0,China,46900000.0,45553000.0,42941000.0,39264086.0,1
+Tokio,2010.0,Japón,39500000.0,37843000.0,38001000.0,8945695.0,2
+"Shanghái (incl. Suzhou, Kunshan)",2010.0,China,30400000.0,30477000.0,29213000.0,25420288.0,3
+Yakarta (incluyendo Bogor),2010.0,Indonesia,30100000.0,30539000.0,11399000.0,10558121.0,4
+Delhi,2011.0,India,28400000.0,24998000.0,25703000.0,16349831.0,5
+Karachi,2011.0,Pakistán,25300000.0,22123000.0,16618000.0,21142625.0,6
+Manila,2010.0,Filipinas,24600000.0,24123000.0,12946000.0,1652171.0,7
+Bombay (incluyendo Kalyan y Vasai-Virar),2011.0,India,24300000.0,21732000.0,21043000.0,19617302.0,8
+Seúl (incluyendo Incheon y Suwon),2010.0,Corea del Sur,24100000.0,23480000.0,10558000.0,23836272.0,9
+Daca,2011.0,Bangladés,22300000.0,15669000.0,17598000.0,14543124.0,10
+Pekín,2010.0,China,20700000.0,21009000.0,20384000.0,16446857.0,11
+Osaka,2010.0,Japón,19800000.0,17444000.0,20238000.0,2665314.0,12
+Bangkok (incluyendo Samut Prakan),2010.0,Tailandia,16700000.0,14998000.0,11084000.0,8986218.0,13
+Calcuta,2011.0,India,15900000.0,14667000.0,14865000.0,14057991.0,14
+Teherán (incluyendo Karaj),2011.0,Irán,13600000.0,13532000.0,10239000.0,9768677.0,15
+Tianjin,2010.0,China,11200000.0,10920000.0,11210000.0,9290263.0,16
+Nagoya,2010.0,Japón,10400000.0,10177000.0,9406000.0,2263894.0,17
+Bangalore,2011.0,India,10300000.0,9807000.0,10087000.0,8520435.0,18
+Lahore,1998.0,Pakistán,9950000.0,10052000.0,8741000.0,5143495.0,19
+Madrás,2011.0,India,9900000.0,9714000.0,9890000.0,8653521.0,20
+Xiamen (incluyendl Quanzhou),2010.0,China,9850000.0,11130000.0,5825000.0,4273841.0,21
+Chengdu,2010.0,China,9400000.0,10376000.0,7556000.0,6316922.0,22
+Taipéi,,Taiwán,9000000.0,7438000.0,2666000.0,,23
+Hyderabad,2011.0,India,8900000.0,8754000.0,8942000.0,7677018.0,24
+Hangzhou (incluyendo Shaoxing),2010.0,China,8150000.0,9625000.0,8467000.0,6887819.0,25
+Ciudad Ho Chi Minh,2009.0,Vietnam,8150000.0,8957000.0,7298000.0,5880615.0,26
+Wuhan,2010.0,China,7950000.0,7509000.0,7906000.0,7541527.0,27
+"Shantou (incluyendo Chaozhou, Puning, Chaoyang y Chaonan)",2010.0,China,7850000.0,6337000.0,6287000.0,5775239.0,28
+Shenyang (incluyendo Fushun),2010.0,China,7600000.0,7402000.0,7613000.0,7037040.0,29
+Ahmedabad,2011.0,India,7350000.0,7186000.0,7343000.0,6357693.0,30
+Hong Kong,2011.0,Hong Kong,7200000.0,7246000.0,7314000.0,7071576.0,31
+Chongqing,2010.0,China,6950000.0,7217000.0,13332000.0,6263790.0,32
+Kuala Lumpur,2000.0,Malasia,6950000.0,7088000.0,6837000.0,1305792.0,33
+Singapur - Johor Bahru,,Singapur Malasia,6900000.0,7312000.0,6531000.0,5719644.0,34
+Nankín,2010.0,China,6750000.0,6155000.0,7369000.0,5827888.0,35
+Bagdad,1987.0,Irak,6750000.0,6625000.0,6643000.0,3841268.0,36
+Riad,2010.0,Arabia Saudita,6550000.0,5666000.0,6370000.0,5188286.0,37
+Xi'an,2010.0,China,6550000.0,5977000.0,6044000.0,5206253.0,38
+Pune,2011.0,India,6000000.0,5631000.0,5728000.0,5057709.0,39
+Bandung,2010.0,Indonesia,5900000.0,5695000.0,2544000.0,2394873.0,40
+Wenzhou (incluyendo Rui'an),2010.0,China,5800000.0,4303000.0,3208000.0,3614208.0,41
+Qingdao,2010.0,China,5650000.0,5816000.0,4566000.0,3990942.0,42
+Surat,2011.0,India,5600000.0,5447000.0,5650000.0,4591246.0,43
+Harbin,2010.0,China,5100000.0,4815000.0,5457000.0,4596313.0,44
+Rangún,2014.0,Birmania,5100000.0,4800000.0,4802000.0,4728524.0,45
+Kitakyushu - Fukuoka,2010.0,Japón,4725000.0,4505000.0,5510000.0,2440589.0,46
+Surabaya,2010.0,Indonesia,4675000.0,4881000.0,2853000.0,2765487.0,47
+Colombo,2012.0,Sri Lanka,4650000.0,2180000.0,707000.0,561314.0,48
+Ankara,2000.0,Turquía,4625000.0,4538000.0,4750000.0,3203362.0,49
+Zhengzhou,2010.0,China,4600000.0,4942000.0,4387000.0,3677032.0,50
+Teherán (incluyendo Karaj),2011.0,Irán,13600000.0,13532000.0,,,1
+Bagdad,1987.0,Irak,6750000.0,6625000.0,6643000.0,3841268.0,2
+Riad,2010.0,Arabia Saudita,6550000.0,5666000.0,6370000.0,5188286.0,3
+Ankara,2000.0,Turquía,4625000.0,4538000.0,4750000.0,3203362.0,4
+Yida,2010.0,Arabia Saudita,4175000.0,3677000.0,4076000.0,3430697.0,5
+Kuwait,,Kuwait,4075000.0,4283000.0,2779000.0,,6
+Dubái (incluyendo Sarja),1995.0,Emiratos Árabes Unidos,3800000.0,3933000.0,,,7
+Damasco,2004.0,Siria,3650000.0,2560000.0,2566000.0,1414913.0,8
+Kabul,1979.0,Afganistán,3600000.0,4635000.0,4635000.0,913164.0,9
+Amán,2004.0,Jordania,3325000.0,2468000.0,1155000.0,1036330.0,10
+Alepo,2004.0,Siria,3050000.0,3560000.0,3562000.0,2132100.0,11
+Mashhad,2011.0,Irán,3050000.0,3294000.0,3014000.0,2749374.0,12
+Esmirna,2000.0,Turquía,2925000.0,3112000.0,3040000.0,2232265.0,13
+Isfahán,2011.0,Irán,2725000.0,2392000.0,1880000.0,1756126.0,14
+Taskent,1989.0,Uzbekistán,2625000.0,2250000.0,2251000.0,2072459.0,15
+Tel Aviv,1995.0,Israel,2475000.0,2979000.0,3608000.0,348245.0,16
+Saná,2004.0,Yemen,2425000.0,2980000.0,2962000.0,1707531.0,17
+Bakú,1989.0,Azerbaiyán,2425000.0,2661000.0,2374000.0,1150055.0,18
+Dammam,2010.0,Arabia Saudita,2350000.0,1019000.0,1064000.0,903312.0,19
+Bursa,2000.0,Turquía,1930000.0,1839000.0,1923000.0,1194687.0,20
+La Meca,2010.0,Arabia Saudita,1840000.0,1647000.0,1771000.0,1534731.0,21
+Franja de Gaza,2007.0,Palestina,1760000.0,620000.0,624000.0,483869.0,22
+Almaty,1999.0,Kazajistán,1750000.0,1500000.0,1523000.0,1365632.0,23
+Mosul,1987.0,Irak,1680000.0,1675000.0,1694000.0,664221.0,24
+Shiraz,2011.0,Irán,1680000.0,1873000.0,1661000.0,1460665.0,25
+Adana,2000.0,Turquía,1670000.0,1830000.0,1830000.0,1130710.0,26
+Novosibirsk [n 5],2010.0,Rusia,1640000.0,1486000.0,1497000.0,,27
+Beirut,1970.0,Líbano,1630000.0,2200000.0,2226000.0,474870.0,28
+Tabriz,2011.0,Irán,1610000.0,1693000.0,1572000.0,1494998.0,29
+Ekaterimburgo [n 5],2010.0,Rusia,1590000.0,1361000.0,1379000.0,1473754.0,30
+Gaziantep,2000.0,Turquía,1530000.0,1394000.0,1528000.0,853513.0,31
+Ereván,2011.0,Armenia,1480000.0,1274000.0,1044000.0,1060138.0,32
+Cheliábinsk [n 5],2010.0,Rusia,1390000.0,1150000.0,1157000.0,1130132.0,33
+Basora,1987.0,Irak,1390000.0,1000000.0,1019000.0,406296.0,34
+Medina,2010.0,Arabia Saudita,1320000.0,1233000.0,1280000.0,1100093.0,35
+Ahvaz,2011.0,Irán,1240000.0,1315000.0,1060000.0,1112021.0,36
+Tiflis,2002.0,Georgia,1230000.0,1125000.0,1147000.0,1073345.0,37
+Konya,2000.0,Turquía,1190000.0,1190000.0,1194000.0,742690.0,38
+Omsk [n 5],2010.0,Rusia,1180000.0,1154000.0,1162000.0,1154116.0,39
+Qom,2011.0,Irán,1160000.0,1101000.0,1204000.0,1074036.0,40
+Erbil,1987.0,Irak,1150000.0,1150000.0,1166000.0,485968.0,41
+Antalya,2000.0,Turquía,1140000.0,1070000.0,1072000.0,603190.0,42
+Asjabad,1989.0,Turkmenistán,1140000.0,740000.0,746000.0,401135.0,43
+Abu Dabi,1995.0,Emiratos Árabes Unidos,1120000.0,982000.0,1145000.0,398695.0,44
+Kirkuk,1987.0,Irak,1110000.0,650000.0,650000.0,418624.0,45
+Krasnoyarsk [n 5],2010.0,Rusia,1080000.0,998000.0,1008000.0,973826.0,46
+Kayseri,2000.0,Turquía,1050000.0,900000.0,904000.0,536392.0,47
+Delhi,2011.0,India,26000000.0,24998000.0,25703000.0,16349831.0,1
+Karachi,2011.0,Pakistán,24000000.0,22123000.0,16618000.0,21142625.0,2
+Bombay (incluyendo Kalyan y Vasai-Virar),2011.0,India,23000000.0,,21043000.0,,3
+Daca,2011.0,Bangladés,17300000.0,15669000.0,17598000.0,14543124.0,4
+Calcuta,2011.0,India,15900000.0,14667000.0,14865000.0,14057991.0,5
+Bangalore,2011.0,India,10300000.0,9807000.0,10087000.0,8520435.0,6
+Lahore,1998.0,Pakistán,9950000.0,10052000.0,8741000.0,5143495.0,7
+Madrás,2011.0,India,9900000.0,9714000.0,9890000.0,8653521.0,8
+Hyderabad,2011.0,India,8900000.0,8754000.0,8942000.0,7677018.0,9
+Ahmedabad,2011.0,India,7350000.0,7186000.0,7343000.0,6357693.0,10
+Pune,2011.0,India,6000000.0,5631000.0,5728000.0,5057709.0,11
+Surat,2011.0,India,5600000.0,5447000.0,5650000.0,4591246.0,12
+Colombo,2012.0,Sri Lanka,4650000.0,2180000.0,707000.0,,13
+Chittagong,2011.0,Bangladés,4475000.0,3176000.0,4539000.0,4009423.0,14
+Faisalabad,1998.0,Pakistán,3900000.0,3560000.0,3567000.0,2008861.0,15
+Rawalpindi (incluyendo Islamabad),1998.0,Pakistán,3800000.0,2510000.0,,,16
+Jaipur,2011.0,India,3475000.0,3409000.0,3461000.0,3046163.0,17
+Lucknow,2011.0,India,3300000.0,3184000.0,3222000.0,2902920.0,18
+Kanpur,,India,3275000.0,3037000.0,3021000.0,2011.0,19
+Nagpur,2011.0,India,3000000.0,2668000.0,2675000.0,2497870.0,20
+Katmandú,2011.0,Nepal,2875000.0,1180000.0,1183000.0,1003285.0,21
+Indore,2011.0,India,2725000.0,2405000.0,2441000.0,2170295.0,22
+Bhilai (incluyendo Raipur),2011.0,India,2500000.0,,,,23
+Patna,2011.0,India,2450000.0,2200000.0,2210000.0,2049156.0,24
+Coimbatore,2011.0,India,2425000.0,2481000.0,2549000.0,2136916.0,25
+Gujranwala,1998.0,Pakistán,2400000.0,2120000.0,2122000.0,1132509.0,26
+Hyderabad,1998.0,Pakistán,2400000.0,2920000.0,1772000.0,1166894.0,27
+Bhopal,2011.0,India,2150000.0,2075000.0,2102000.0,1886100.0,28
+Multan,1998.0,Pakistán,2125000.0,1900000.0,1921000.0,1197384.0,29
+Vadodara,2011.0,India,2025000.0,1963000.0,1975000.0,1822221.0,30
+Agra,2011.0,India,2025000.0,1938000.0,1966000.0,1760285.0,31
+Chandigarh,2011.0,India,2000000.0,1124000.0,1134000.0,1026459.0,32
+Visakhapatnam,2011.0,India,1950000.0,1910000.0,1935000.0,1728128.0,33
+Peshawar,1998.0,Pakistán,1870000.0,1730000.0,1736000.0,982816.0,34
+Ludhiāna,2011.0,India,1830000.0,1714000.0,1716000.0,1618879.0,35
+Nashik,2011.0,India,1810000.0,1749000.0,1779000.0,1561809.0,36
+Benarés,2011.0,India,1770000.0,1536000.0,1541000.0,1432280.0,37
+Vijayawada,2011.0,India,1740000.0,1715000.0,1760000.0,1476931.0,38
+Bhubaneswar,2011.0,India,1720000.0,984000.0,999000.0,885363.0,39
+Rajkot,2011.0,India,1620000.0,1568000.0,1599000.0,1390640.0,40
+Madurai,2011.0,India,1620000.0,1582000.0,1593000.0,1465625.0,41
+Meerut,2011.0,India,1580000.0,1541000.0,1550000.0,1420902.0,42
+Aurangabad,2011.0,India,1570000.0,1324000.0,1344000.0,1193167.0,43
+Cochín,2011.0,India,1530000.0,2374000.0,2416000.0,2119742.0,44
+Jamshedpur,2011.0,India,1530000.0,1443000.0,1451000.0,1339438.0,45
+Kolhapur,2011.0,India,1520000.0,593000.0,591000.0,561837.0,46
+Asansol,2011.0,India,1490000.0,1315000.0,1313000.0,1243414.0,47
+Srinagar,2011.0,India,1430000.0,1409000.0,1429000.0,1264202.0,48
+Jabalpur,2011.0,India,1380000.0,1339000.0,1367000.0,1268848.0,49
+Allahabad,2011.0,India,1360000.0,1294000.0,1295000.0,1212395.0,50
+Jodhpur,2011.0,India,1300000.0,1266000.0,1284000.0,1138300.0,51
+Amritsar,2011.0,India,1300000.0,1264000.0,1265000.0,1183549.0,52
+Dhanbad,2011.0,India,1290000.0,1258000.0,1255000.0,1196214.0,53
+Ranchi,2011.0,India,1270000.0,1246000.0,1262000.0,1120374.0,54
+Tirupur,2011.0,India,1260000.0,1177000.0,1230000.0,963173.0,55
+Gwalior,2011.0,India,1260000.0,1208000.0,1221000.0,1117740.0,56
+Kotah,2011.0,India,1180000.0,1138000.0,1163000.0,1001964.0,57
+Quetta,1998.0,Pakistán,1160000.0,1100000.0,1109000.0,565137.0,58
+Bareilly,2011.0,India,1150000.0,1094000.0,1111000.0,985752.0,59
+Thiruvananthapuram,2011.0,India,1120000.0,1921000.0,1965000.0,1679754.0,60
+Tiruchirappalli,2011.0,India,1120000.0,1101000.0,1106000.0,1022518.0,61
+Mysore,2011.0,India,1110000.0,1078000.0,1082000.0,990900.0,62
+Aligarh,2011.0,India,1080000.0,1020000.0,1037000.0,911223.0,63
+Moradabad,2011.0,India,1080000.0,1004000.0,1023000.0,887871.0,64
+Khulna,2011.0,Bangladés,1070000.0,1000000.0,1022000.0,1046341.0,65
+Guwahati,2011.0,India,1050000.0,1039000.0,1042000.0,962334.0,66
+Hubli - Dharwad,2011.0,India,1040000.0,613000.0,1020000.0,943788.0,67
+Solapur,2011.0,India,1030000.0,991000.0,986000.0,951558.0,68
+Salem,2011.0,India,1020000.0,996000.0,1003000.0,917414.0,69
+Jalandhar,2011.0,India,1020000.0,948000.0,954000.0,874412.0,70
+"Cantón (incluyendo Dongguan, Foshan, Jiangmen, Shenzhen y Zhongshan)",2010.0,China,46900000.0,,,,1
+Tokio,2010.0,Japón,39500000.0,37843000.0,38001000.0,,2
+Shanghái (incluyendo Suzhou y Kunshan),2010.0,China,30400000.0,,,,3
+Seúl (incluyendo Incheon y Suwon),2010.0,Corea del Sur,24300000.0,23480000.0,,23836272.0,4
+Pekín,2010.0,China,20700000.0,21009000.0,20384000.0,16446857.0,5
+Osaka,2010.0,Japón,17800000.0,17444000.0,20238000.0,,6
+Tianjin,2010.0,China,11200000.0,10920000.0,11210000.0,9290263.0,7
+Nagoya,2010.0,Japón,10400000.0,10177000.0,9406000.0,,8
+Xiamen (incluyendo Quanzhou),2010.0,China,9850000.0,,,,9
+Chengdu,2010.0,China,9400000.0,10376000.0,7556000.0,6316922.0,10
+Taipéi,,Taiwán,9000000.0,7438000.0,2666000.0,,11
+Hangzhou (incluyendo Shaoxing),2010.0,China,8150000.0,,,6887819.0,12
+Wuhan,2010.0,China,7950000.0,7509000.0,7906000.0,7541527.0,13
+"Shantou (incluyendo Chaozhou, Puning, Chaoyang y Chaonan)",2010.0,China,7850000.0,,,,14
+Shenyang (incluyendo Fushun),2010.0,China,7600000.0,,,7037040.0,15
+Hong Kong,2011.0,Hong Kong,7200000.0,7246000.0,7314000.0,7071576.0,16
+Chongqing,2010.0,China,6950000.0,7217000.0,13332000.0,6263790.0,17
+Nankín,2010.0,China,6750000.0,6155000.0,7369000.0,5827888.0,18
+Xi'an,2010.0,China,6550000.0,5977000.0,6044000.0,5206253.0,19
+Wenzhou (incluyendo Rui'an),2010.0,China,5800000.0,,3208000.0,,20
+Qingdao,2010.0,China,5650000.0,5816000.0,4566000.0,3990942.0,21
+Harbin,2010.0,China,5100000.0,4815000.0,5457000.0,4596313.0,22
+Zhengzhou,2010.0,China,4600000.0,4942000.0,4387000.0,3677032.0,24
+Hefei,2010.0,China,4475000.0,3665000.0,3348000.0,3098727.0,25
+Dalian,2010.0,China,4425000.0,4183000.0,4489000.0,3902467.0,26
+Changsha,2010.0,China,4375000.0,3657000.0,3761000.0,3193354.0,27
+Busán,2010.0,Corea del Sur,4250000.0,3906000.0,3216000.0,3414950.0,28
+Taiyuan,2010.0,China,4150000.0,3702000.0,3482000.0,3154157.0,29
+Kunming,2010.0,China,3925000.0,3649000.0,3780000.0,3278777.0,30
+Jinan,2010.0,China,3900000.0,3789000.0,4032000.0,3527566.0,31
+Fuzhou,2010.0,China,3875000.0,3962000.0,3283000.0,2824414.0,32
+Shijiazhuang,2010.0,China,3775000.0,3367000.0,3264000.0,2770344.0,33
+Changchun,2010.0,China,3675000.0,3368000.0,3762000.0,3411209.0,34
+Nanchang,2010.0,China,3600000.0,2637000.0,2527000.0,2223661.0,35
+Ürümqi,2010.0,China,3550000.0,3184000.0,3499000.0,2853398.0,36
+Ningbo,2010.0,China,3300000.0,3753000.0,3132000.0,2580073.0,37
+Zibo,2010.0,China,3300000.0,1646000.0,2430000.0,2261717.0,38
+Wuxi,2010.0,China,3225000.0,3597000.0,3049000.0,2757736.0,39
+Nanning,2010.0,China,3150000.0,2590000.0,3234000.0,2660833.0,40
+Guiyang,2010.0,China,2850000.0,2955000.0,2871000.0,2520061.0,41
+Lanzhou,2010.0,China,2825000.0,2703000.0,2723000.0,2438595.0,42
+Pionyang,2008.0,Corea del Norte,2800000.0,2850000.0,2863000.0,2581076.0,43
+Kaohsiung,,Taiwán,2775000.0,2599000.0,1523000.0,,44
+Huizhou,2010.0,China,2750000.0,1763000.0,2312000.0,1807858.0,45
+Daegu,2010.0,Corea del Sur,2750000.0,2382000.0,2244000.0,2446418.0,46
+Changzhou,2010.0,China,2625000.0,3425000.0,2584000.0,2257376.0,47
+Jiangyin,2010.0,China,2625000.0,3056000.0,686000.0,1013670.0,48
+Xuzhou,2010.0,China,2525000.0,1301000.0,1918000.0,2214795.0,49
+Anshan,2010.0,China,2500000.0,1516000.0,1559000.0,1504996.0,50
+Sapporo,2010.0,Japón,2475000.0,2570000.0,2571000.0,,51
+Tangshan,2010.0,China,2425000.0,2378000.0,2743000.0,2128191.0,53
+Taichung,,Taiwán,2350000.0,2935000.0,1225000.0,,54
+Okayama,2010.0,Japón,2200000.0,707000.0,502000.0,,55
+Baotou,2010.0,China,2125000.0,2159000.0,1957000.0,1900373.0,56
+Yantai,2010.0,China,2075000.0,1520000.0,2114000.0,1797871.0,57
+Taizhou (incluyendo Wenling),2010.0,China,2050000.0,,1648000.0,,58
+Cixi,2010.0,China,2050000.0,1490000.0,1303000.0,1059942.0,59
+Luoyang,2010.0,China,1940000.0,1939000.0,2015000.0,1584463.0,60
+Nantong,2010.0,China,1910000.0,1184000.0,1978000.0,1612385.0,61
+Liuzhou,2010.0,China,1890000.0,1574000.0,1619000.0,1410712.0,62
+Hiroshima,2010.0,Japón,1870000.0,1377000.0,2173000.0,,63
+Huai'an,2010.0,China,1840000.0,2282000.0,2000000.0,1523655.0,64
+Haikou,2010.0,China,1770000.0,1981000.0,1903000.0,1517410.0,65
+Yangzhou,2010.0,China,1760000.0,1561000.0,1765000.0,1077531.0,66
+Hohhot,2010.0,China,1750000.0,2219000.0,1785000.0,1497110.0,67
+Huainan,2010.0,China,1740000.0,1142000.0,1327000.0,1238488.0,68
+Linyi,2010.0,China,1700000.0,2465000.0,1706000.0,1522488.0,69
+Hengyang,2010.0,China,1680000.0,987000.0,1301000.0,1115645.0,70
+Daejeon,2010.0,Corea del Sur,1600000.0,1564000.0,1564000.0,1501859.0,71
+Weifang (incluyendo Zhucheng),2010.0,China,1590000.0,,2195000.0,,72
+Baoding,2010.0,China,1590000.0,1297000.0,1106000.0,1038195.0,73
+Gwangju,2010.0,Corea del Sur,1580000.0,1601000.0,1536000.0,1475745.0,74
+Daqing,2010.0,China,1550000.0,983000.0,1621000.0,1433698.0,75
+Xiangyang,2010.0,China,1550000.0,1183000.0,1533000.0,1433057.0,76
+Yiwu,2010.0,China,1550000.0,1704000.0,1080000.0,878973.0,77
+Zhuhai,2010.0,China,1540000.0,1547000.0,1542000.0,1369538.0,78
+Datong,2010.0,China,1510000.0,1709000.0,1532000.0,1362314.0,79
+Yinchuan,2010.0,China,1500000.0,1614000.0,1596000.0,1159457.0,80
+Jilin,2010.0,China,1500000.0,1633000.0,1520000.0,1469722.0,81
+Sendai,2010.0,Japón,1480000.0,1277000.0,2091000.0,,82
+Jiaozuo,2010.0,China,1350000.0,809000.0,732000.0,702527.0,83
+Handan,2010.0,China,1340000.0,2000000.0,1634000.0,919295.0,84
+Putian,2010.0,China,1340000.0,1468000.0,1438000.0,1107199.0,85
+Xiangtan,2010.0,China,1320000.0,1007000.0,1010000.0,903287.0,86
+Xining,2010.0,China,1310000.0,1345000.0,1323000.0,1153417.0,87
+Huaibei,2010.0,China,1300000.0,1116000.0,981000.0,854696.0,88
+Tainan,,Taiwán,1300000.0,1216000.0,815000.0,,89
+Xinxiang,2010.0,China,1290000.0,1074000.0,991000.0,918078.0,90
+Wuhu,2010.0,China,1280000.0,1456000.0,1424000.0,1108087.0,91
+Ulán Bator,2010.0,Mongolia,1280000.0,1237000.0,1377000.0,1144954.0,92
+Xingtai,2010.0,China,1280000.0,749000.0,742000.0,668765.0,93
+Yancheng,2010.0,China,1240000.0,935000.0,1436000.0,1136826.0,94
+Taian,2010.0,China,1220000.0,817000.0,1220000.0,1123541.0,95
+Guilin,2010.0,China,1190000.0,949000.0,1040000.0,963629.0,96
+Zhangjiakou,2010.0,China,1180000.0,1156000.0,983000.0,924628.0,97
+Naha,2010.0,Japón,1180000.0,1007000.0,321000.0,,98
+Mianyang,2010.0,China,1160000.0,585000.0,1065000.0,967006.0,99
+Zhanjiang,2010.0,China,1150000.0,1042000.0,1149000.0,1038762.0,100
+Bengbu,2010.0,China,1150000.0,961000.0,842000.0,793866.0,101
+Kumamoto,2010.0,Japón,1150000.0,697000.0,601000.0,,102
+Yichang,2010.0,China,1140000.0,1039000.0,1264000.0,1049363.0,103
+Qingyuan,2010.0,China,1130000.0,588000.0,694000.0,916453.0,104
+Ulsan,2010.0,Corea del Sur,1120000.0,900000.0,904000.0,1082567.0,105
+Zunyi,2010.0,China,1120000.0,108000.0,803000.0,715148.0,106
+Maanshan,2010.0,China,1110000.0,827000.0,858000.0,657847.0,107
+Qinhuangdao,2010.0,China,1100000.0,1041000.0,1109000.0,967877.0,108
+Changshu,2010.0,China,1100000.0,1344000.0,726000.0,929124.0,109
+Changwon,2010.0,Corea del Sur,1090000.0,990000.0,1039000.0,1058021.0,110
+Cangnan,2010.0,China,1090000.0,823000.0,,648219.0,111
+Zhuzhou,2010.0,China,1080000.0,1007000.0,1083000.0,999404.0,112
+Maoming,2010.0,China,1080000.0,619000.0,609000.0,1033196.0,113
+Benxi,2010.0,China,1070000.0,888000.0,1070000.0,1000128.0,114
+Qiqihar,2010.0,China,1060000.0,1241000.0,1452000.0,1314720.0,115
+Lianyungang,2010.0,China,1060000.0,1128000.0,1099000.0,897393.0,116
+Zhenjiang,2010.0,China,1050000.0,969000.0,1050000.0,950516.0,117
+Kaifeng,2010.0,China,1040000.0,633000.0,804000.0,725573.0,118
+Rizhao,2010.0,China,1040000.0,937000.0,1062000.0,902272.0,119
+Nanchong,2010.0,China,1030000.0,692000.0,1050000.0,890402.0,120
+Jinzhou,2010.0,China,1030000.0,922000.0,1035000.0,946098.0,121
+Chifeng,2010.0,China,1020000.0,1230000.0,1018000.0,902285.0,122
+Fuji,2010.0,Japón,1010000.0,718000.0,,,123
+Nanyang,2010.0,China,1000000.0,731000.0,1011000.0,899899.0,124
+Wanzhou,2010.0,China,1000000.0,582000.0,,849662.0,125
+Yakarta (incluyendo Bogor),2010.0,Indonesia,27700000.0,30539000.0,,,1
+Manila,2010.0,Filipinas,23100000.0,24123000.0,12946000.0,,2
+Bangkok (incluyendo Samut Prakan),2010.0,Tailandia,16700000.0,14998000.0,11084000.0,,3
+Ciudad Ho Chi Minh,2009.0,Vietnam,8150000.0,8957000.0,7298000.0,5880615.0,4
+Kuala Lumpur,2000.0,Malasia,6950000.0,7088000.0,6837000.0,,5
+Singapur - Johor Bahru,,Singapur Malasia,6900000.0,,,,6
+Bandung,2010.0,Indonesia,5900000.0,5695000.0,2544000.0,,7
+Rangún,2014.0,Birmania,5100000.0,4800000.0,4802000.0,4728524.0,8
+Surabaya,2010.0,Indonesia,4675000.0,4881000.0,2853000.0,,9
+Medan,2010.0,Indonesia,3400000.0,3942000.0,2204000.0,,10
+Hanói,2009.0,Vietnam,2925000.0,3715000.0,3629000.0,2316772.0,11
+Cebú,2010.0,Filipinas,2250000.0,2535000.0,951000.0,,12
+Semarang,2010.0,Indonesia,2025000.0,1630000.0,1630000.0,1520481.0,13
+Nom Pen,2008.0,Camboya,1830000.0,1729000.0,1731000.0,1416582.0,14
+Makasar,2010.0,Indonesia,1760000.0,1484000.0,1489000.0,1331391.0,15
+Palembang,2010.0,Indonesia,1680000.0,1434000.0,1455000.0,1440678.0,16
+George Town,2000.0,Malasia,1530000.0,1336000.0,,,17
+Denpasar,2010.0,Indonesia,1470000.0,1175000.0,1107000.0,,18
+Malang,2010.0,Indonesia,1410000.0,1114000.0,856000.0,820243.0,19
+Mandalay,2014.0,Birmania,1390000.0,1160000.0,1167000.0,1225546.0,20
+Davao,2010.0,Filipinas,1330000.0,1630000.0,1630000.0,,21
+Yogyakarta,2010.0,Indonesia,1270000.0,1831000.0,385000.0,,22
+Chonburi,2010.0,Tailandia,1230000.0,665000.0,518000.0,,23
+Surakarta,2010.0,Indonesia,1210000.0,1318000.0,504000.0,,24
+Batam,2010.0,Indonesia,1160000.0,1142000.0,1391000.0,917998.0,25
+Pekanbaru,2010.0,Indonesia,1160000.0,1100000.0,1121000.0,882045.0,26
+Serang,2010.0,Indonesia,1090000.0,564000.0,,,27
+Bandar Lampung,2010.0,Indonesia,1080000.0,909000.0,965000.0,873007.0,28
+Ángeles,2010.0,Filipinas,1060000.0,883000.0,363000.0,,29
+Londres,2011.0,Reino Unido,14300000.0,10236000.0,10313000.0,11140445.0,1
+París,1999.0,Francia,11200000.0,10858000.0,10843000.0,9738809.0,2
+Madrid,2011.0,España,6400000.0,6171000.0,6199000.0,,3
+Región del Ruhr [n 7],,Alemania,5600000.0,,,,4
+Milán,2011.0,Italia,5150000.0,5257000.0,3099000.0,,5
+Colonia - Düsseldorf,2011.0,Alemania,4825000.0,,,,6
+Barcelona,2011.0,España,4700000.0,4693000.0,5258000.0,,7
+Berlín,2011.0,Alemania,4450000.0,4069000.0,3563000.0,,8
+Nápoles,2011.0,Italia,4225000.0,3706000.0,2202000.0,,9
+Atenas,2011.0,Grecia,3600000.0,3484000.0,3052000.0,3168846.0,10
+Roma,2011.0,Italia,3550000.0,3906000.0,3718000.0,,11
+Birmingham,2011.0,Reino Unido,3100000.0,2512000.0,2515000.0,2697168.0,12
+Róterdam,2001.0,Países Bajos,3100000.0,2660000.0,993000.0,,13
+Fráncfort del Meno,2011.0,Alemania,3100000.0,1915000.0,715000.0,,14
+Mánchester,2011.0,Reino Unido,3000000.0,2639000.0,2646000.0,2637335.0,15
+Hamburgo,2011.0,Alemania,2750000.0,2087000.0,1831000.0,,16
+Lisboa,2001.0,Portugal,2600000.0,2666000.0,2884000.0,,17
+Ámsterdam,2001.0,Países Bajos,2375000.0,1624000.0,1091000.0,,18
+Stuttgart,2011.0,Alemania,2300000.0,1379000.0,626000.0,,19
+Múnich,2011.0,Alemania,2175000.0,1981000.0,1438000.0,,20
+Viena,2011.0,Austria,2125000.0,1763000.0,1753000.0,2015580.0,21
+Leeds,2011.0,Reino Unido,2125000.0,1893000.0,1912000.0,2058861.0,22
+Estocolmo,,Suecia,2075000.0,1484000.0,1486000.0,,23
+Bruselas,,Bélgica,2000000.0,2089000.0,2045000.0,,24
+Lyon,1999.0,Francia,1920000.0,1583000.0,1609000.0,1428998.0,25
+Liverpool,2011.0,Reino Unido,1830000.0,875000.0,870000.0,1367147.0,26
+Valencia,2011.0,España,1780000.0,1561000.0,810000.0,,27
+Turín,2011.0,Italia,1670000.0,1521000.0,1765000.0,,28
+Marsella,1999.0,Francia,1640000.0,1397000.0,1605000.0,1463016.0,29
+Glasgow,2011.0,Reino Unido,1610000.0,1220000.0,1223000.0,1601154.0,30
+Copenhague,,Dinamarca,1600000.0,1248000.0,1268000.0,,31
+Sheffield,2011.0,Reino Unido,1530000.0,706000.0,706000.0,795844.0,32
+Mannheim,2011.0,Alemania,1520000.0,559000.0,319000.0,,33
+Newcastle upon Tyne,2011.0,Reino Unido,1460000.0,793000.0,791000.0,1220781.0,34
+Zúrich,2010.0,Suiza,1350000.0,785000.0,1246000.0,1249750.0,35
+Nottingham,2011.0,Reino Unido,1350000.0,755000.0,755000.0,754789.0,36
+Sevilla,2011.0,España,1340000.0,1107000.0,701000.0,698042.0,37
+Dublín,2011.0,Irlanda,1320000.0,1160000.0,1169000.0,1110627.0,38
+Lille,1999.0,Francia,1270000.0,1018000.0,1027000.0,999797.0,39
+Helsinki,2000.0,Finlandia,1220000.0,1208000.0,1180000.0,1027305.0,40
+Oporto,2001.0,Portugal,1190000.0,1474000.0,1299000.0,,41
+Núremberg,2011.0,Alemania,1160000.0,670000.0,517000.0,,42
+Oslo,1990.0,Noruega,1130000.0,975000.0,986000.0,685530.0,43
+Southampton,2011.0,Reino Unido,1130000.0,883000.0,882000.0,1060326.0,44
+Hannover,2011.0,Alemania,1120000.0,711000.0,533000.0,,45
+Amberes,,Bélgica,1020000.0,1008000.0,994000.0,,46
+Málaga,2011.0,España,1010000.0,716000.0,574000.0,561435.0,47
+Moscú,2010.0,Rusia,16800000.0,16170000.0,12166000.0,11612885.0,1
+Estambul [n 8],2000.0,Turquía,14200000.0,13287000.0,14164000.0,8803468.0,2
+San Petersburgo,2010.0,Rusia,5400000.0,5126000.0,4993000.0,4879566.0,3
+Kiev,2001.0,Ucrania,3375000.0,2241000.0,2942000.0,2611327.0,4
+Budapest,2011.0,Hungría,2550000.0,1710000.0,1714000.0,1729040.0,5
+Katowice,2011.0,Polonia,2400000.0,2190000.0,303000.0,,6
+Varsovia,2011.0,Polonia,2275000.0,1720000.0,1722000.0,,7
+Bucarest,2011.0,Rumania,2175000.0,1860000.0,1868000.0,1883425.0,8
+Minsk,2009.0,Bielorrusia,1950000.0,1910000.0,1915000.0,1836808.0,9
+Nizni Nóvgorod,2010.0,Rusia,1750000.0,1201000.0,1212000.0,1250619.0,10
+Járkov,2001.0,Ucrania,1650000.0,1440000.0,1441000.0,1470902.0,11
+Donetsk,2001.0,Ucrania,1480000.0,930000.0,934000.0,1016194.0,12
+Praga,2001.0,República Checa,1460000.0,1310000.0,1314000.0,1169106.0,13
+Volgogrado,2010.0,Rusia,1410000.0,999000.0,1022000.0,1021215.0,14
+Belgrado,2011.0,Serbia,1400000.0,1180000.0,1182000.0,1166763.0,15
+Dnipropetrovsk,2001.0,Ucrania,1390000.0,950000.0,957000.0,1065008.0,16
+Sofía,2011.0,Bulgaria,1320000.0,1195000.0,1226000.0,1202761.0,17
+Samara,2010.0,Rusia,1320000.0,1162000.0,1164000.0,1164685.0,18
+Rostov del Don,2010.0,Rusia,1280000.0,1090000.0,1097000.0,1089261.0,19
+Kazán,2010.0,Rusia,1210000.0,1160000.0,1162000.0,1143535.0,20
+Ufá,2010.0,Rusia,1110000.0,1024000.0,1070000.0,1062319.0,21
+Odesa,2001.0,Ucrania,1110000.0,1010000.0,1010000.0,1029049.0,22
+Perm,2010.0,Rusia,1100000.0,974000.0,982000.0,991162.0,23
+Sarátov,2010.0,Rusia,1090000.0,772000.0,815000.0,837900.0,24
+Vorónezh,2010.0,Rusia,1030000.0,897000.0,911000.0,975373.0,25
+Sídney,2011.0,Australia,4850000.0,4036000.0,4505000.0,4028525.0,1
+Melbourne,2011.0,Australia,4350000.0,3906000.0,4203000.0,3847567.0,2
+Brisbane,2011.0,Australia,2875000.0,1999000.0,2202000.0,1977316.0,3
+Perth,2011.0,Australia,2025000.0,1751000.0,1861000.0,1670952.0,4
+Auckland,2013.0,Nueva Zelanda,1404000.0,1356000.0,1344000.0,1308831.0,5
+Adelaida,2011.0,Australia,1290000.0,1140000.0,1256000.0,1198467.0,6
+Honolulu[n 9],2010.0,Estados Unidos,1000000.0,842000.0,848000.0,953207.0,7
+Milán,2011.0,Italia,5150000.0,5257000.0,3099000.0,1242123.0,8
+Colonia - Düsseldorf,2011.0,Alemania,4825000.0,8783000.0,1640000.0,1591866.0,9
+Barcelona,2011.0,España,4700000.0,4693000.0,5258000.0,1611013.0,10
+Berlín,2011.0,Alemania,4450000.0,4069000.0,3563000.0,3292365.0,11
+Nápoles,2011.0,Italia,4225000.0,3706000.0,2202000.0,962003.0,12
+Atenas,2011.0,Grecia,3600000.0,3484000.0,3052000.0,3168846.0,13
+Roma,2011.0,Italia,3550000.0,3906000.0,3718000.0,2617175.0,14
+Kiev,2001.0,Ucrania,3375000.0,2241000.0,2942000.0,2611327.0,15
+Birmingham,2011.0,Reino Unido,3100000.0,2512000.0,2515000.0,2697168.0,16
+Róterdam,2001.0,Países Bajos,3100000.0,2660000.0,993000.0,608422.0,17
+Fráncfort del Meno,2011.0,Alemania,3100000.0,1915000.0,715000.0,667925.0,18
+Mánchester,2011.0,Reino Unido,3000000.0,2639000.0,2646000.0,2637335.0,19
+Hamburgo,2011.0,Alemania,2750000.0,2087000.0,1831000.0,1706696.0,20
+Lisboa,2001.0,Portugal,2600000.0,2666000.0,2884000.0,564657.0,21
+Budapest,2011.0,Hungría,2550000.0,1710000.0,1714000.0,1729040.0,22
+Katowice,2011.0,Polonia,2400000.0,2190000.0,303000.0,310764.0,23
+Ámsterdam,2001.0,Países Bajos,2375000.0,1624000.0,1091000.0,734533.0,24
+Stuttgart,2011.0,Alemania,2300000.0,1379000.0,626000.0,585890.0,25
+Varsovia,2011.0,Polonia,2275000.0,1720000.0,1722000.0,1700612.0,26
+Bucarest,2011.0,Rumania,2175000.0,1860000.0,1868000.0,1883425.0,27
+Múnich,2011.0,Alemania,2175000.0,1981000.0,1438000.0,1348335.0,28
+Viena,2011.0,Austria,2125000.0,1763000.0,1753000.0,2015580.0,29
+Leeds,2011.0,Reino Unido,2125000.0,1893000.0,1912000.0,2058861.0,30
+Estocolmo,,Suecia,2075000.0,1484000.0,1486000.0,,31
+Bruselas,,Bélgica,2000000.0,2089000.0,2045000.0,,32
+Minsk,2009.0,Bielorrusia,1950000.0,1910000.0,1915000.0,1836808.0,33
+Lyon,1999.0,Francia,1920000.0,1583000.0,1609000.0,1428998.0,34
+Liverpool,2011.0,Reino Unido,1830000.0,875000.0,870000.0,1367147.0,35
+Valencia,2011.0,España,1780000.0,1561000.0,810000.0,792054.0,36
+Nizni Nóvgorod,2010.0,Rusia,1750000.0,1201000.0,1212000.0,1250619.0,37
+Turín,2011.0,Italia,1670000.0,1521000.0,1765000.0,872367.0,38
+Járkov,2001.0,Ucrania,1650000.0,1440000.0,1441000.0,1470902.0,39
+Marsella,1999.0,Francia,1640000.0,1397000.0,1605000.0,1463016.0,40
+Glasgow,2011.0,Reino Unido,1610000.0,1220000.0,1223000.0,1601154.0,41
+Copenhague,,Dinamarca,1600000.0,1248000.0,1268000.0,,42
+Sheffield,2011.0,Reino Unido,1530000.0,706000.0,706000.0,795844.0,43
+Mannheim,2011.0,Alemania,1520000.0,559000.0,319000.0,290117.0,44
+Donetsk,2001.0,Ucrania,1480000.0,930000.0,934000.0,1016194.0,45
+Newcastle upon Tyne,2011.0,Reino Unido,1460000.0,793000.0,791000.0,1220781.0,46
+Praga,2001.0,República Checa,1460000.0,1310000.0,1314000.0,1169106.0,47
+Volgogrado,2010.0,Rusia,1410000.0,999000.0,1022000.0,1021215.0,48
+Belgrado,2011.0,Serbia,1400000.0,1180000.0,1182000.0,1166763.0,49
+Dnipropetrovsk,2001.0,Ucrania,1390000.0,950000.0,957000.0,1065008.0,50