From c94adf5f0064b1cd24e0a11d3f55a6f5fa4027c1 Mon Sep 17 00:00:00 2001 From: diogomerces <52171353+diogomerces@users.noreply.github.com> Date: Mon, 7 Sep 2020 12:00:06 +0100 Subject: [PATCH] Solutions_created --- your-code/ages_population.csv | 1001 ++++++++++++++++ your-code/ages_population2.csv | 1001 ++++++++++++++++ your-code/ages_population3.csv | 1001 ++++++++++++++++ your-code/main_Solutions.ipynb | 1574 ++++++++++++++++++++++++++ your-code/roll_the_dice_hundred.csv | 101 ++ your-code/roll_the_dice_thousand.csv | 1001 ++++++++++++++++ 6 files changed, 5679 insertions(+) create mode 100644 your-code/ages_population.csv create mode 100644 your-code/ages_population2.csv create mode 100644 your-code/ages_population3.csv create mode 100644 your-code/main_Solutions.ipynb create mode 100644 your-code/roll_the_dice_hundred.csv create mode 100644 your-code/roll_the_dice_thousand.csv diff --git a/your-code/ages_population.csv b/your-code/ages_population.csv new file mode 100644 index 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Libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import random\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 1\n", + "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n", + "**Hint**: you can use the *choices* function from module *random* to help you with the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "def dice(x):\n", + " list = []\n", + " for i in range(x):\n", + " list.append(random.randint(1, 6))\n", + " return list\n", + "\n", + "dice10 = pd.DataFrame(dice(10),columns={\"values\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " values\n", + "0 5\n", + "1 5\n", + "2 6\n", + "3 3\n", + "4 5\n", + "5 6\n", + "6 4\n", + "7 1\n", + "8 3\n", + "9 4" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dice10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Plot the results sorted by value." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "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", + "diceplot = dice10.sort_values(by='values').reset_index(drop=True).plot(kind = 'bar')\n", + "\n", + "plt.show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "dice_freq = dice10.groupby('values')['values'].count()\n", + "plt.bar(dice_freq.index, dice_freq.values)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nyour comments here\\n\\nThe first plot, it's the frequency of dice\\nThe second plot, it's the frequency of each side dice\\n\"" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "The first plot, it's the frequency of dice\n", + "The second plot, it's the frequency of each side dice\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 2\n", + "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n", + "\n", + "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "def mean(x):\n", + " return sum(x) / len(x)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. " + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "mean(dice_freq)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n", + "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.5" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "def median(x):\n", + " if len(x)%2 == 0:\n", + " median = (len(x)/2 + len(x)/2-1)/2\n", + " else:\n", + " median = int(len(x)/2) \n", + " return median\n", + "\n", + "median(dice10)\n", + " \n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.5, 4.5, 7.5, 10)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "def dice_quartiles(x):\n", + " q1 = (len(x))*0.25\n", + " q2 = dice_median(x)\n", + " q3 = (len(x))*0.75\n", + " q4 = int(len(x))\n", + " return q1,q2,q3,q4\n", + "\n", + "dice_quartiles(dice10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 3\n", + "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n", + "#### 1.- Sort the values and plot them. What do you see?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 1\n", + "47 47 47 1\n", + "56 56 56 1\n", + "9 9 9 1\n", + "73 73 73 1\n", + ".. ... ... ...\n", + "17 17 17 6\n", + "11 11 11 6\n", + "24 24 24 6\n", + "21 21 21 6\n", + "99 99 99 6\n", + "\n", + "[100 rows x 3 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "df = pd.read_csv('roll_the_dice_hundred.csv')\n", + "df.sort_values(by='value')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAK/UlEQVR4nO3dUYil91nH8d/TJqKmLW3JNCxNcFVKMAgmYYlKIERjS2qKiaBgwBCksl400qIgsTfVu9xYvZFCTGIjpimlaWgwpTbESixodTZGk5qWlLJqbMxOCNLEm5L28WJPZNnsZiZzzuzZZ/bzgeWc886ZeZ/3Yr/77n/ec051dwCY503rHgCA3RFwgKEEHGAoAQcYSsABhjrvTO7swgsv7IMHD57JXQKMd+TIkRe6e+Pk7Wc04AcPHszm5uaZ3CXAeFX176fabgkFYCgBBxhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYKgz+kpMgFU7ePvD6x5hR47eccPKf6YzcIChBBxgKAEHGErAAYYScIChBBxgKAEHGErAAYbyQh54Hefyi0Q4+zkDBxhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYCgBBxhKwAGGEnCAoQQcYCgBBxhKwAGG2jbgVXVJVX25qp6uqq9V1YcX299ZVY9U1TOL23fs/bgAvGonZ+CvJPnd7v6JJD+T5ENVdVmS25M82t3vSfLo4jEAZ8i2Ae/u57r78cX9l5I8neTdSW5Mcu/iafcmuWmvhgTgtd7QGnhVHUxyRZKvJrmou59Ljkc+ybtO8z2Hq2qzqja3traWmxaA/7fjgFfVW5I8kOQj3f2dnX5fd9/Z3Ye6+9DGxsZuZgTgFHYU8Ko6P8fjfV93f26x+fmqOrD4+oEkx/ZmRABOZSdXoVSSu5M83d0fP+FLDyW5dXH/1iSfX/14AJzOTj6V/uoktyR5sqqeWGz7aJI7knymqj6Y5D+S/OrejAjAqWwb8O7+SpI6zZevW+04AOyUV2ICDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDLXtp9Kzdw7e/vC6R9iRo3fcsO4RgFNwBg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDCXgAEMJOMBQAg4wlIADDLVtwKvqnqo6VlVPnbDtD6rqv6rqicWfX9zbMQE42U7OwD+Z5PpTbP/j7r588ecLqx0LgO1sG/DufizJi2dgFgDegGXWwG+rqn9dLLG843RPqqrDVbVZVZtbW1tL7A6AE+024J9I8uNJLk/yXJI/Ot0Tu/vO7j7U3Yc2NjZ2uTsATrargHf38939ve7+fpI/S3LVascCYDu7CnhVHTjh4S8neep0zwVgb2z7qfRVdX+Sa5NcWFXPJvlYkmur6vIkneRokt/awxkBOIVtA97dN59i8917MAsAb4BXYgIMJeAAQwk4wFDbroHDTh28/eF1j7AjR++4Yd0jwEo4AwcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYaswn8vi0F1iev0f7izNwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGCobQNeVfdU1bGqeuqEbe+sqkeq6pnF7Tv2dkwATraTM/BPJrn+pG23J3m0u9+T5NHFYwDOoG0D3t2PJXnxpM03Jrl3cf/eJDeteC4AtrHbNfCLuvu5JFncvut0T6yqw1W1WVWbW1tbu9wdACfb819idved3X2ouw9tbGzs9e4Azhm7DfjzVXUgSRa3x1Y3EgA7sduAP5Tk1sX9W5N8fjXjALBTO7mM8P4kf5/k0qp6tqo+mOSOJO+tqmeSvHfxGIAz6LztntDdN5/mS9eteBYA3gCvxAQYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGAoAQcYSsABhhJwgKEEHGCo85b55qo6muSlJN9L8kp3H1rFUABsb6mAL/xcd7+wgp8DwBtgCQVgqGUD3km+VFVHqurwqZ5QVYerarOqNre2tpbcHQCvWjbgV3f3lUnen+RDVXXNyU/o7ju7+1B3H9rY2FhydwC8aqmAd/e3F7fHkjyY5KpVDAXA9nYd8Kq6oKre+ur9JO9L8tSqBgPg9S1zFcpFSR6sqld/zqe6+4srmQqAbe064N39rSQ/tcJZAHgDXEYIMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUAIOMJSAAwwl4ABDCTjAUEsFvKqur6pvVNU3q+r2VQ0FwPZ2HfCqenOSP03y/iSXJbm5qi5b1WAAvL5lzsCvSvLN7v5Wd383yaeT3LiasQDYTnX37r6x6leSXN/dv7l4fEuSn+7u20563uEkhxcPL03yjd2Pu3IXJnlh3UOs2H47pv12PMn+O6b9djzJ2XdMP9LdGydvPG+JH1in2Paafw26+84kdy6xnz1TVZvdfWjdc6zSfjum/XY8yf47pv12PMmcY1pmCeXZJJec8PjiJN9ebhwAdmqZgP9TkvdU1Y9W1Q8k+bUkD61mLAC2s+sllO5+papuS/LXSd6c5J7u/trKJjszzsqlnSXtt2Pab8eT7L9j2m/Hkww5pl3/EhOA9fJKTIChBBxgqHMy4FV1T1Udq6qn1j3LKlTVJVX15ap6uqq+VlUfXvdMy6qqH6yqf6yqf1kc0x+ue6ZVqKo3V9U/V9VfrXuWVaiqo1X1ZFU9UVWb655nWVX19qr6bFV9ffH36WfXPdPrOSfXwKvqmiQvJ/mL7v7Jdc+zrKo6kORAdz9eVW9NciTJTd39b2sebdeqqpJc0N0vV9X5Sb6S5MPd/Q9rHm0pVfU7SQ4leVt3f2Dd8yyrqo4mOdTdZ9OLXnatqu5N8nfdfdfi6rof7u7/Wfdcp3NOnoF392NJXlz3HKvS3c919+OL+y8leTrJu9c71XL6uJcXD89f/Bl9tlFVFye5Icld656F16qqtyW5JsndSdLd3z2b452cowHfz6rqYJIrknx1vZMsb7Hc8ESSY0ke6e7px/QnSX4vyffXPcgKdZIvVdWRxdtmTPZjSbaS/Plimeuuqrpg3UO9HgHfR6rqLUkeSPKR7v7OuudZVnd/r7svz/FX+V5VVWOXu6rqA0mOdfeRdc+yYld395U5/q6kH1osT051XpIrk3yiu69I8r9Jzuq3yRbwfWKxTvxAkvu6+3PrnmeVFv+N/dsk1695lGVcneSXFmvGn07y81X1l+sdaXnd/e3F7bEkD+b4u5RO9WySZ0/4n95nczzoZy0B3wcWv/C7O8nT3f3xdc+zClW1UVVvX9z/oSS/kOTr651q97r797v74u4+mONvO/E33f3rax5rKVV1weKX5lksNbwvydgru7r7v5P8Z1Vduth0XZKz+kKAZd6NcKyquj/JtUkurKpnk3ysu+9e71RLuTrJLUmeXKwZJ8lHu/sLa5xpWQeS3Lv44JA3JflMd++LS+/2kYuSPHj8/CHnJflUd39xvSMt7beT3Le4AuVbSX5jzfO8rnPyMkKA/cASCsBQAg4wlIADDCXgAEMJOMBQAg4wlIADDPV/KY5WCk+exsIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "dice100 = df.groupby('value')['value'].count()\n", + "\n", + "plt.bar(dice100.index,dice100.values)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.74" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "mean(df['value'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now, calculate the frequency distribution.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6 23\n", + "4 22\n", + "2 17\n", + "3 14\n", + "5 12\n", + "1 12\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "df['value'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "plt.hist(df['value'])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "The plot confirms the average to be between 3 and 4.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Unnamed: 0 roll value\n", + "0 0 0 5\n", + "1 1 1 6\n", + "2 2 2 1\n", + "3 3 3 6\n", + "4 4 4 5\n", + ".. ... ... ...\n", + "995 995 995 1\n", + "996 996 996 4\n", + "997 997 997 4\n", + "998 998 998 3\n", + "999 999 999 6\n", + "\n", + "[1000 rows x 3 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "data = pd.read_csv('roll_the_dice_thousand.csv')\n", + "data.sort_values(by='value')\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_hist=data.groupby('value')['value'].count()\n", + "plt.bar(data_hist.index,data_hist.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3 175\n", + "1 175\n", + "4 168\n", + "2 167\n", + "6 166\n", + "5 149\n", + "Name: value, dtype: int64" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['value'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "Values are more distributed.\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 4\n", + "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n", + "\n", + "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 68.0\n", + "1 12.0\n", + "2 45.0\n", + "3 38.0\n", + "4 49.0\n", + ".. ...\n", + "995 27.0\n", + "996 47.0\n", + "997 53.0\n", + "998 33.0\n", + "999 31.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "data = pd.read_csv('ages_population.csv')\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "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\n", + "mean = data.mean()\n", + "std = data.std()\n", + "\n", + "print(mean)\n", + "print(std)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "Yes, viewing the plot they fall inside the ranges.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 25.0\n", + "1 31.0\n", + "2 29.0\n", + "3 31.0\n", + "4 29.0\n", + ".. ...\n", + "995 26.0\n", + "996 22.0\n", + "997 21.0\n", + "998 19.0\n", + "999 28.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "age_pop = pd.read_csv('ages_population2.csv')\n", + "age_pop" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# your code here\n", + "age_pop.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- What do you see? Is there any difference with the frequency distribution in step 1?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "There was a change in the average.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "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\n", + "mean2 = age_pop.mean()\n", + "std2 = age_pop.std()\n", + "print(mean2)\n", + "print(std2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "The value of the std is smaller, so the values are closer.\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Challenge 5\n", + "Now is the turn of `ages_population3.csv`.\n", + "\n", + "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " observation\n", + "0 21.0\n", + "1 21.0\n", + "2 24.0\n", + "3 31.0\n", + "4 54.0\n", + ".. ...\n", + "995 16.0\n", + "996 55.0\n", + "997 30.0\n", + "998 35.0\n", + "999 43.0\n", + "\n", + "[1000 rows x 1 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "age_pop2 = pd.read_csv('ages_population3.csv')\n", + "\n", + "age_pop2" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "age_pop2.hist()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "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\n", + "mean3 = age_pop2.mean()\n", + "std3 = age_pop2.std()\n", + "print(mean3)\n", + "print(std3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "there is the oldest 70 years old, this increases the average.\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30.0\n", + "40.0\n", + "53.0\n", + "77.0\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "q1 = np.quantile(age_pop2, 0.25)\n", + "q2 = np.quantile(age_pop2, 0.50)\n", + "q3 = np.quantile(age_pop2, 0.75)\n", + "q4 = np.quantile(age_pop2, 1)\n", + "print(q1)\n", + "print(q2)\n", + "print(q3)\n", + "print(q4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\n", + "The meddian is very close to the average.\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 22., 28., 32., 36., 40., 45., 50., 57., 67., 77.])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "np.percentile(age_pop2['observation'],[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "I can't see anything different with other percentages.\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bonus challenge\n", + "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "your comments here\n", + "\"\"\"" + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv new file mode 100644 index 0000000..50975a2 --- /dev/null +++ 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