From 2b9df5c297968e00ff9dcdd18f94107a0da5d411 Mon Sep 17 00:00:00 2001 From: Melissa Badrudin Date: Sun, 2 Aug 2020 20:31:11 +0100 Subject: [PATCH] commit --- your-code/main.ipynb | 618 ++++++++++++++++++++++++++++++++++++------- 1 file changed, 521 insertions(+), 97 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..796550e 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "# Libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import random\n", + "import matplotlib.pyplot as plt" ] }, { @@ -29,11 +33,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "#note to self: o for loop tem de ficar em list comprehension porque senão a data frame so nos retorna 1 valor e não 10\n", + "def roll(n):\n", + " return pd.DataFrame([random.randint(1, 6) for i in range(n)])" ] }, { @@ -45,11 +52,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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7valjNO06JnkAoi0ni/rlXUd1uLFZy5jkAcgAOVnUFbG4Jo4apLtvZJIHIPpyrqh3HWrUxvhJLV1QyiQPQEbIuaKu6JrkfY5JHoAMkVNFffJcq36y9ZDunz1BwwuZ5AHIDDlV1Gs31aqlPcF1PQBklJwp6vaOhJ7dUKNFU0ZrOpM8ABkkZ4r6lbeP6lBjM0fTADJOzhR1eSyu4pGDdM9N14WOAgA9khNFvfvwGVUeOMlV8gBkpJwo6opYXAX9+zHJA5CRsr6oT51r1fotB3X/rGKNKBwQOg4A9FjWF/UPqurU0s4L1wLIXFld1O0dCT2zoUYLJ4/WjdcPCx0HAHolq4v61d3HdPD0Ba6SByCjZXVRl8cOaMKIQbr3Jq6SByBzZW1R7zlyRm/tP6lHF05Sfl7W/mcCyAFZ22AXJ3mfn8skD0Bmy8qiPn2+c5L3mdsnMMkDkPGysqh/sKlOzW0JTiICyArdFrWZTTSz181st5ntMrMn+iJYb3UkXKs21Gj+DaN00zgmeQAyXzJH1O2SvuHuN0laIOlxM7s5vbF679XdR3Xw9AU9trg0dBQASIlui9rdD7v75q63z0raLWlCuoP1VkUsrvHDC3QvV8kDkCV69Bi1mZVKmiWp8jKfW2FmVWZW1dDQkJp0PfSbI2cVe/eEHl1YyiQPQNZIus3MbIik5yV9zd3PfPDz7r7S3cvcvayoqCiVGZNWsSGugflM8gBkl6SK2sz6q7OkV7v7C+mN1DuN59u0fnPnJG/kYCZ5ALJHMqsPk/Rvkna7+z+kP1Lv/LCqThfaOpjkAcg6yRxRL5b0qKS7zWxr159PpDlXj3QkXBUb4pp3wyjdPJ5JHoDskt/dDdz915Ii/fpVr+05pvpTF/TNT9wUOgoApFxWTCPKYwc0bniBPnYzkzwA2Sfji3rv0bN6c98JLVnAVfIAZKeMb7byWFwD8vvpkXkloaMAQFpkdFE3XmjTC5sP6g9mjtcoJnkAslRGF/WPmOQByAEZW9QXr5I3t3SkbpkwPHQcAEibjC3q1/ccU+3J8xxNA8h6GVvUFRviun5Ygf7Lh64PHQUA0ioji3rfsbP6f3uPa8mCEvVnkgcgy2Vky1XEapjkAcgZGVfUZ5rb9Pzmev3+beM1esjA0HEAIO0yrqh/VFWv860dWs5JRAA5IqOKOpFwrdoQ15xJI3VrMZM8ALkho4r6l+8cU82J8xxNA8gpGVXUT78Z13XDBuq+W5jkAcgdGVPU7zY0dU7y5k9ikgcgp2RM462KxTUgr58emc8kD0BuyYiiPtvcpnXV9frUzHEawyQPQI7JiKJeV12vc0zyAOSoyBd1IuGqiMU1u2SEbiseEToOAPS5yBf1r/Y2KH6Cq+QByF2RL+ryN+MaO3SgPn7LuNBRACCISBf1/oYm/eqdBn1h/iQNyI90VABIm0i336oNNeqfZ/qvTPIA5LDIFvVvJ3m3jVfRUCZ5AHJXZIv6+ep6NbW0M8kDkPMiWdSJrheuvX3iCM2cyCQPQG6LZFG/sbdB+4+f02OLS0NHAYDgIlnUFbG4ipjkAYCkCBb1gePn9PpvGvSF+SVM8gBAESzqVRviTPIA4H26LWoz+76ZHTOznekO09TSrh9V1euTt47T2KEF6f7rACAjJHNEXS7pvjTnkCS9sLlzksd1PQDgPd0Wtbu/IelkuoMkEq7yWFwzJ47QrJKR6f7rACBj5KfqC5nZCkkrJKmkpOePL19o69C80lH6vWljUhUJALKCuXv3NzIrlfSiu9+SzBctKyvzqqqqa0sGADnEzKrdvexyn4vc6gMAcCmKGgAiLpl53hpJGyTNMLN6M/tS+mMBAC7q9mSiuz/SF0EAAJfHQx8AEHEUNQBEHEUNABFHUQNAxCX1hJcef1GzBkk1vfzXx0g6nsI4mYz74lLcH5fi/nhPNtwXk9y96HKfSEtRXwszq7rSs3NyDffFpbg/LsX98Z5svy946AMAIo6iBoCIi2JRrwwdIEK4Ly7F/XEp7o/3ZPV9EbnHqAEAl4riETUA4H0oagCIuMgUtZndZ2a/MbN9ZvZU6DwhmdlEM3vdzHab2S4zeyJ0ptDMLM/MtpjZi6GzhGZmI8xsnZnt6foeWRg6U0hm9vWun5OdZrbGzLLulbEjUdRmlifpnyR9XNLNkh4xs5vDpgqqXdI33P0mSQskPZ7j94ckPSFpd+gQEfFdSS+5+42SZiqH7xczmyDpTySVdb0CVZ6kz4dNlXqRKGpJ8yTtc/f97t4qaa2kPwicKRh3P+zum7vePqvOH8QJYVOFY2bFkj4p6V9DZwnNzIZJukPSv0mSu7e6++mwqYLLlzTIzPIlFUo6FDhPykWlqCdIqnvf+/XK4WJ6v67Xq5wlqTJskqC+I+lJSYnQQSJgsqQGSU93PRT0r2Y2OHSoUNz9oKS/l1Qr6bCkRnd/OWyq1ItKUdtlPpbzu0EzGyLpeUlfc/czofOEYGafknTM3atDZ4mIfEmzJX3P3WdJOicpZ8/pmNlIdf72fYOk8ZIGm9mSsKlSLypFXS9p4vveL1YW/vrSE2bWX50lvdrdXwidJ6DFkj5tZnF1PiR2t5k9GzZSUPWS6t394m9Y69RZ3LnqXkkH3L3B3dskvSBpUeBMKReVot4kaZqZ3WBmA9R5MuCngTMFY2amzscgd7v7P4TOE5K7/5m7F7t7qTq/L15z96w7YkqWux+RVGdmM7o+dI+ktwNGCq1W0gIzK+z6ublHWXhytdvXTOwL7t5uZn8s6T/Uedb2++6+K3CskBZLelTSDjPb2vWxb7r7zwNmQnR8VdLqroOa/ZIeC5wnGHevNLN1kjarcy21RVn4dHKeQg4AEReVhz4AAFdAUQNAxFHUABBxFDUARBxFDQARR1EDQMRR1AAQcf8fQQmGoYfFP/8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "dado_df = roll(10)\n", + "\n", + "#sort the values by index (se fosse column teria de por 1)\n", + "dado_df.sort_values(by=0,inplace=True)\n", + "\n", + "#agora os valores estão ordered mas o index não por isso temos de fazer o reset e aloca-lo à coluna 0 porque senao ele adiciona um novo index e deixa lá o antigo\n", + "dado_df2= dado_df.reset_index()[0]\n", + "\n", + "# now plot\n", + "\n", + "plt.plot(dado_df2)\n", + "plt.show()\n", + "#plt.plot(range(len(dice)), dice, color= 'gray')" ] }, { @@ -61,21 +94,58 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# when we want frequencies we can do histograms\n", + "\n", + "plt.hist(dado_df2, bins= 6)\n", + "plt.show" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nIn the first plot the frequency is on the x axis and on the second is on the y axis\\n'" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "In the first plot the frequency is on the x axis and on the second is on the y axis\n", "\"\"\"" ] }, @@ -91,11 +161,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "4.2" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "def f_mean(data):\n", + " m= sum(data)/len(data)\n", + " return m\n", + "\n", + "f_mean(dado_df2)" ] }, { @@ -107,11 +193,39 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1\n", + "4 2\n", + "3 3\n", + "6 4\n", + "Name: result, dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "2.5" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "newdf=pd.DataFrame(dado_df2)\n", + "newdf.rename(columns={0: 'result'}, inplace = True)\n", + "frequencies = newdf['result'].value_counts(ascending=True)\n", + "print(frequencies)\n", + "\n", + "f_mean(frequencies)" ] }, { @@ -124,11 +238,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "def calc_median(data):\n", + " data.sort_values()\n", + " if len(data) %2 == 0:\n", + " return sum(data[int(len(data)/2)-1:int(len(data)/2)+1])/2\n", + " else:\n", + " data[int(len(data)/2)]\n", + " \n" ] }, { @@ -140,11 +260,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "def quartiles(data):\n", + " \n", + " half = calc_median(data)\n", + " \n", + " if len(data) % 2 == 0:\n", + " fst_quartile = median(data[:int(len(data)/2)])\n", + " trd_quartile = median(data[int(len(data)/2):])\n", + " \n", + " else:\n", + " fst_quartile = median(data[:int(len(data)/2)-1])\n", + " trd_quartile = median(data[int(len(data)/2)+1:])" ] }, { @@ -158,18 +289,57 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "hundred_df= pd.read_csv('../data/roll_the_dice_hundred.csv')\n", + "hundred_df\n", + "hundred_df.drop(columns='Unnamed: 0', inplace = True)\n", + "hundred_df.sort_values(by=['value'], inplace = True)\n", + "\n", + "hundred_df['value'].plot.bar()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", @@ -185,11 +355,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "f_mean(hundred_df['value'])" ] }, { @@ -201,11 +383,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "freq_dist = hundred_df['value'].value_counts()" ] }, { @@ -217,18 +400,54 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "#freq_dist.plot.bar()\n", + "hundred_df['value'].plot.hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", @@ -244,18 +463,58 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 125, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "ugh=pd.read_csv('../data/roll_the_dice_thousand.csv')\n", + "\n", + "ugh.drop(columns='Unnamed: 0', inplace = True)\n", + "ugh.sort_values(by=['value'], inplace = True)\n", + "freq_dist = ugh['value'].value_counts()\n", + "ugh['value'].plot.hist()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 126, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "your comments here\n", @@ -274,11 +533,39 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "\n", + "age_pop= pd.read_csv('../data/ages_population.csv')\n", + "\n", + "age_pop.plot.hist()\n", + "#mean should be between 40 " ] }, { @@ -290,21 +577,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 134, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 36.56\n", + "dtype: float64\n", + "observation 12.8165\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "print(age_pop.mean())\n", + "print(age_pop.std())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 129, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nyour comments here\\n'" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "they do\n", "\"\"\"" ] }, @@ -317,11 +627,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 139, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "age_pop2= pd.read_csv('../data/ages_population2.csv')\n", + "age_pop2.plot.hist()" ] }, { @@ -338,7 +673,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "The mean is lower and the std too.\n", "\"\"\"" ] }, @@ -351,11 +686,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 137, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 27.155\n", + "dtype: float64\n", + "observation 2.969814\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "print(age_pop2.mean())\n", + "print(age_pop2.std())" ] }, { @@ -365,7 +713,8 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "The mean is lower and the std too as predicted\n", + " \n", "\"\"\"" ] }, @@ -381,11 +730,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 138, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "# your code here" + "# your code here\n", + "age_pop3= pd.read_csv('../data/ages_population3.csv')\n", + "age_pop3.plot.hist()" ] }, { @@ -397,11 +771,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 140, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "observation 41.989\n", + "dtype: float64\n", + "observation 16.144706\n", + "dtype: float64\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "print(age_pop3.mean())\n", + "print(age_pop3.std())" ] }, { @@ -411,7 +798,7 @@ "outputs": [], "source": [ "\"\"\"\n", - "your comments here\n", + "The mean and the std are higher.\n", "\"\"\"" ] }, @@ -424,21 +811,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 147, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30.0 40.0 53.0\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "q1 = np.percentile(age_pop3, 25)\n", + "q2 = np.percentile(age_pop3, 50)\n", + "q3 = np.percentile(age_pop3, 75)\n", + "\n", + "print(q1,q2,q3)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 149, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\ntired. will do later\\n\\n'" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", - "your comments here\n", + "tired. will do later\n", + "\n", "\"\"\"" ] }, @@ -451,11 +863,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 30., 40., 53., 77.])" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "np.percentile(age_pop3, [0, 25, 50, 75, 100])" ] }, { @@ -500,9 +924,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "Python 3", "language": "python", - "name": "ironhack-3.7" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -514,9 +938,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 }