{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# 02.04 - PANDAS"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ["!wget --no-cache -O init.py -q https://raw.githubusercontent.com/rramosp/20201.xai4eng/master/content/init.py\n", "import init; init.init(force_download=False); init.get_weblink()"]}, {"cell_type": "code", "execution_count": 237, "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## `pandas` is mostly about manipulating tables of data\n", "\n", "see this cheat sheet: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## Pandas main object is a `DataFrame`\n", "\n", "- can read .csv, .excel, etc.\n"]}, {"cell_type": "code", "execution_count": 238, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["# Pais,Uso_Internet,Uso_Facebook\r\n", "Argentina,49.40,30.53\r\n", "Australia,80.60,46.01\r\n", "Belgium,67.30,36.98\r\n", "Brazil,37.76,4.39\r\n", "Canada,72.30,52.08\r\n", "Chile,50.90,46.14\r\n", "China,22.40,0.05\r\n", "Colombia,38.80,25.90\r\n", "Egypt,12.90,5.68\r\n"]}], "source": ["!head local/data/internet_facebook.dat"]}, {"cell_type": "code", "execution_count": 239, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["  8760  17519 254046 local/data/weather_data_austin_2010.csv\r\n"]}], "source": ["!wc local/data/weather_data_austin_2010.csv"]}, {"cell_type": "code", "execution_count": 240, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th># Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Argentina</th>\n", "      <td>49.40</td>\n", "      <td>30.53</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Australia</th>\n", "      <td>80.60</td>\n", "      <td>46.01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Belgium</th>\n", "      <td>67.30</td>\n", "      <td>36.98</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Brazil</th>\n", "      <td>37.76</td>\n", "      <td>4.39</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Canada</th>\n", "      <td>72.30</td>\n", "      <td>52.08</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Chile</th>\n", "      <td>50.90</td>\n", "      <td>46.14</td>\n", "    </tr>\n", "    <tr>\n", "      <th>China</th>\n", "      <td>22.40</td>\n", "      <td>0.05</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Colombia</th>\n", "      <td>38.80</td>\n", "      <td>25.90</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Egypt</th>\n", "      <td>12.90</td>\n", "      <td>5.68</td>\n", "    </tr>\n", "    <tr>\n", "      <th>France</th>\n", "      <td>65.70</td>\n", "      <td>32.91</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Germany</th>\n", "      <td>67.00</td>\n", "      <td>14.07</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Hong_Kong</th>\n", "      <td>69.50</td>\n", "      <td>52.33</td>\n", "    </tr>\n", "    <tr>\n", "      <th>India</th>\n", "      <td>7.10</td>\n", "      <td>1.52</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Indonesia</th>\n", "      <td>10.50</td>\n", "      <td>13.49</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Italy</th>\n", "      <td>48.80</td>\n", "      <td>30.62</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Japan</th>\n", "      <td>73.80</td>\n", "      <td>2.00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Malaysia</th>\n", "      <td>62.80</td>\n", "      <td>37.77</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Mexico</th>\n", "      <td>24.90</td>\n", "      <td>16.80</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Netherlands</th>\n", "      <td>82.90</td>\n", "      <td>20.54</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Peru</th>\n", "      <td>26.20</td>\n", "      <td>13.34</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Philippines</th>\n", "      <td>21.50</td>\n", "      <td>19.68</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Poland</th>\n", "      <td>52.00</td>\n", "      <td>11.79</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Russia</th>\n", "      <td>27.00</td>\n", "      <td>2.99</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Saudi_Arabia</th>\n", "      <td>22.70</td>\n", "      <td>11.65</td>\n", "    </tr>\n", "    <tr>\n", "      <th>South_Africa</th>\n", "      <td>10.50</td>\n", "      <td>7.83</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Spain</th>\n", "      <td>66.80</td>\n", "      <td>30.24</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Sweden</th>\n", "      <td>80.70</td>\n", "      <td>44.72</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Taiwan</th>\n", "      <td>66.10</td>\n", "      <td>38.21</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Thailand</th>\n", "      <td>20.50</td>\n", "      <td>10.29</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Turkey</th>\n", "      <td>35.00</td>\n", "      <td>31.91</td>\n", "    </tr>\n", "    <tr>\n", "      <th>USA</th>\n", "      <td>77.33</td>\n", "      <td>46.98</td>\n", "    </tr>\n", "    <tr>\n", "      <th>UK</th>\n", "      <td>70.18</td>\n", "      <td>45.97</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Venezuela</th>\n", "      <td>25.50</td>\n", "      <td>28.64</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["              Uso_Internet  Uso_Facebook\n", "# Pais                                  \n", "Argentina            49.40         30.53\n", "Australia            80.60         46.01\n", "Belgium              67.30         36.98\n", "Brazil               37.76          4.39\n", "Canada               72.30         52.08\n", "Chile                50.90         46.14\n", "China                22.40          0.05\n", "Colombia             38.80         25.90\n", "Egypt                12.90          5.68\n", "France               65.70         32.91\n", "Germany              67.00         14.07\n", "Hong_Kong            69.50         52.33\n", "India                 7.10          1.52\n", "Indonesia            10.50         13.49\n", "Italy                48.80         30.62\n", "Japan                73.80          2.00\n", "Malaysia             62.80         37.77\n", "Mexico               24.90         16.80\n", "Netherlands          82.90         20.54\n", "Peru                 26.20         13.34\n", "Philippines          21.50         19.68\n", "Poland               52.00         11.79\n", "Russia               27.00          2.99\n", "Saudi_Arabia         22.70         11.65\n", "South_Africa         10.50          7.83\n", "Spain                66.80         30.24\n", "Sweden               80.70         44.72\n", "Taiwan               66.10         38.21\n", "Thailand             20.50         10.29\n", "Turkey               35.00         31.91\n", "USA                  77.33         46.98\n", "UK                   70.18         45.97\n", "Venezuela            25.50         28.64"]}, "execution_count": 240, "metadata": {}, "output_type": "execute_result"}], "source": ["df = pd.read_csv('local/data/internet_facebook.dat', index_col='# Pais')\n", "df"]}, {"cell_type": "code", "execution_count": 241, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th># Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Argentina</th>\n", "      <td>49.40</td>\n", "      <td>30.53</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Australia</th>\n", "      <td>80.60</td>\n", "      <td>46.01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Belgium</th>\n", "      <td>67.30</td>\n", "      <td>36.98</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Brazil</th>\n", "      <td>37.76</td>\n", "      <td>4.39</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Canada</th>\n", "      <td>72.30</td>\n", "      <td>52.08</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["           Uso_Internet  Uso_Facebook\n", "# Pais                               \n", "Argentina         49.40         30.53\n", "Australia         80.60         46.01\n", "Belgium           67.30         36.98\n", "Brazil            37.76          4.39\n", "Canada            72.30         52.08"]}, "execution_count": 241, "metadata": {}, "output_type": "execute_result"}], "source": ["df.head()"]}, {"cell_type": "code", "execution_count": 242, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th># Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Thailand</th>\n", "      <td>20.50</td>\n", "      <td>10.29</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Turkey</th>\n", "      <td>35.00</td>\n", "      <td>31.91</td>\n", "    </tr>\n", "    <tr>\n", "      <th>USA</th>\n", "      <td>77.33</td>\n", "      <td>46.98</td>\n", "    </tr>\n", "    <tr>\n", "      <th>UK</th>\n", "      <td>70.18</td>\n", "      <td>45.97</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Venezuela</th>\n", "      <td>25.50</td>\n", "      <td>28.64</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["           Uso_Internet  Uso_Facebook\n", "# Pais                               \n", "Thailand          20.50         10.29\n", "Turkey            35.00         31.91\n", "USA               77.33         46.98\n", "UK                70.18         45.97\n", "Venezuela         25.50         28.64"]}, "execution_count": 242, "metadata": {}, "output_type": "execute_result"}], "source": ["df.tail()"]}, {"cell_type": "code", "execution_count": 243, "metadata": {}, "outputs": [{"data": {"text/plain": ["Index(['Uso_Internet', 'Uso_Facebook'], dtype='object')"]}, "execution_count": 243, "metadata": {}, "output_type": "execute_result"}], "source": ["df.columns"]}, {"cell_type": "code", "execution_count": 244, "metadata": {}, "outputs": [{"data": {"text/plain": ["Index(['Argentina', 'Australia', 'Belgium', 'Brazil', 'Canada', 'Chile',\n", "       'China', 'Colombia', 'Egypt', 'France', 'Germany', 'Hong_Kong', 'India',\n", "       'Indonesia', 'Italy', 'Japan', 'Malaysia', 'Mexico', 'Netherlands',\n", "       'Peru', 'Philippines', 'Poland', 'Russia', 'Saudi_Arabia',\n", "       'South_Africa', 'Spain', 'Sweden', 'Taiwan', 'Thailand', 'Turkey',\n", "       'USA', 'UK', 'Venezuela'],\n", "      dtype='object', name='# Pais')"]}, "execution_count": 244, "metadata": {}, "output_type": "execute_result"}], "source": ["df.index"]}, {"cell_type": "markdown", "metadata": {}, "source": ["**fix the index name**"]}, {"cell_type": "code", "execution_count": 245, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Argentina</th>\n", "      <td>49.40</td>\n", "      <td>30.53</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Australia</th>\n", "      <td>80.60</td>\n", "      <td>46.01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Belgium</th>\n", "      <td>67.30</td>\n", "      <td>36.98</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Brazil</th>\n", "      <td>37.76</td>\n", "      <td>4.39</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Canada</th>\n", "      <td>72.30</td>\n", "      <td>52.08</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["           Uso_Internet  Uso_Facebook\n", "Pais                                 \n", "Argentina         49.40         30.53\n", "Australia         80.60         46.01\n", "Belgium           67.30         36.98\n", "Brazil            37.76          4.39\n", "Canada            72.30         52.08"]}, "execution_count": 245, "metadata": {}, "output_type": "execute_result"}], "source": ["df.index.name=\"Pais\"\n", "df.head()"]}, {"cell_type": "code", "execution_count": 246, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>count</th>\n", "      <td>33.000000</td>\n", "      <td>33.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>mean</th>\n", "      <td>46.890000</td>\n", "      <td>24.668182</td>\n", "    </tr>\n", "    <tr>\n", "      <th>std</th>\n", "      <td>24.456421</td>\n", "      <td>16.511662</td>\n", "    </tr>\n", "    <tr>\n", "      <th>min</th>\n", "      <td>7.100000</td>\n", "      <td>0.050000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>25%</th>\n", "      <td>24.900000</td>\n", "      <td>11.650000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>50%</th>\n", "      <td>49.400000</td>\n", "      <td>25.900000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>75%</th>\n", "      <td>67.300000</td>\n", "      <td>37.770000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>max</th>\n", "      <td>82.900000</td>\n", "      <td>52.330000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["       Uso_Internet  Uso_Facebook\n", "count     33.000000     33.000000\n", "mean      46.890000     24.668182\n", "std       24.456421     16.511662\n", "min        7.100000      0.050000\n", "25%       24.900000     11.650000\n", "50%       49.400000     25.900000\n", "75%       67.300000     37.770000\n", "max       82.900000     52.330000"]}, "execution_count": 246, "metadata": {}, "output_type": "execute_result"}], "source": ["df.describe()"]}, {"cell_type": "code", "execution_count": 247, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["<class 'pandas.core.frame.DataFrame'>\n", "Index: 33 entries, Argentina to Venezuela\n", "Data columns (total 2 columns):\n", "Uso_Internet    33 non-null float64\n", "Uso_Facebook    33 non-null float64\n", "dtypes: float64(2)\n", "memory usage: 792.0+ bytes\n"]}], "source": ["df.info()"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["**a dataframe is made of `Series`**. Observe that each series has **its own type**"]}, {"cell_type": "code", "execution_count": 248, "metadata": {}, "outputs": [{"data": {"text/plain": ["pandas.core.series.Series"]}, "execution_count": 248, "metadata": {}, "output_type": "execute_result"}], "source": ["s1 = df[\"Uso_Internet\"]\n", "type(s1)"]}, {"cell_type": "code", "execution_count": 249, "metadata": {}, "outputs": [{"data": {"text/plain": ["Pais\n", "Argentina       49.40\n", "Australia       80.60\n", "Belgium         67.30\n", "Brazil          37.76\n", "Canada          72.30\n", "Chile           50.90\n", "China           22.40\n", "Colombia        38.80\n", "Egypt           12.90\n", "France          65.70\n", "Germany         67.00\n", "Hong_Kong       69.50\n", "India            7.10\n", "Indonesia       10.50\n", "Italy           48.80\n", "Japan           73.80\n", "Malaysia        62.80\n", "Mexico          24.90\n", "Netherlands     82.90\n", "Peru            26.20\n", "Philippines     21.50\n", "Poland          52.00\n", "Russia          27.00\n", "Saudi_Arabia    22.70\n", "South_Africa    10.50\n", "Spain           66.80\n", "Sweden          80.70\n", "Taiwan          66.10\n", "Thailand        20.50\n", "Turkey          35.00\n", "USA             77.33\n", "UK              70.18\n", "Venezuela       25.50\n", "Name: Uso_Internet, dtype: float64"]}, "execution_count": 249, "metadata": {}, "output_type": "execute_result"}], "source": ["s1"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["if the column name is not too fancy (empy spaces, accents, etc.) we can use columns names as python syntax."]}, {"cell_type": "code", "execution_count": 250, "metadata": {}, "outputs": [{"data": {"text/plain": ["Pais\n", "Argentina       30.53\n", "Australia       46.01\n", "Belgium         36.98\n", "Brazil           4.39\n", "Canada          52.08\n", "Chile           46.14\n", "China            0.05\n", "Colombia        25.90\n", "Egypt            5.68\n", "France          32.91\n", "Germany         14.07\n", "Hong_Kong       52.33\n", "India            1.52\n", "Indonesia       13.49\n", "Italy           30.62\n", "Japan            2.00\n", "Malaysia        37.77\n", "Mexico          16.80\n", "Netherlands     20.54\n", "Peru            13.34\n", "Philippines     19.68\n", "Poland          11.79\n", "Russia           2.99\n", "Saudi_Arabia    11.65\n", "South_Africa     7.83\n", "Spain           30.24\n", "Sweden          44.72\n", "Taiwan          38.21\n", "Thailand        10.29\n", "Turkey          31.91\n", "USA             46.98\n", "UK              45.97\n", "Venezuela       28.64\n", "Name: Uso_Facebook, dtype: float64"]}, "execution_count": 250, "metadata": {}, "output_type": "execute_result"}], "source": ["df.Uso_Facebook"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## DataFrame indexing\n", "\n", "is **NOT** exactly like numpy\n", "\n", "- first index\n", "    - if string refers to columns\n", "    - if `Series` of booleans is used as a filter\n", "    \n", "- for selecting columns:\n", "    - use `.loc` to select by Index\n", "    - use `.iloc` to select by position   "]}, {"cell_type": "code", "execution_count": 251, "metadata": {}, "outputs": [{"ename": "KeyError", "evalue": "'Colombia'", "output_type": "error", "traceback": ["\u001b[0;31m---------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m                            Traceback (most recent call last)", "\u001b[0;32m/opt/anaconda3/envs/p37/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'Colombia'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[0;31mKeyError\u001b[0m                            Traceback (most recent call last)", "\u001b[0;32m<ipython-input-251-5f701fabe22b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Colombia\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/opt/anaconda3/envs/p37/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2993\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2994\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2995\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2996\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2997\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/anaconda3/envs/p37/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'Colombia'"]}], "source": ["df[\"Colombia\"]"]}, {"cell_type": "code", "execution_count": 252, "metadata": {}, "outputs": [{"data": {"text/plain": ["Uso_Internet    38.8\n", "Uso_Facebook    25.9\n", "Name: Colombia, dtype: float64"]}, "execution_count": 252, "metadata": {}, "output_type": "execute_result"}], "source": ["df.loc[\"Colombia\"]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Index semantics is exact!!"]}, {"cell_type": "code", "execution_count": 253, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Colombia</th>\n", "      <td>38.8</td>\n", "      <td>25.90</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Egypt</th>\n", "      <td>12.9</td>\n", "      <td>5.68</td>\n", "    </tr>\n", "    <tr>\n", "      <th>France</th>\n", "      <td>65.7</td>\n", "      <td>32.91</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Germany</th>\n", "      <td>67.0</td>\n", "      <td>14.07</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Hong_Kong</th>\n", "      <td>69.5</td>\n", "      <td>52.33</td>\n", "    </tr>\n", "    <tr>\n", "      <th>India</th>\n", "      <td>7.1</td>\n", "      <td>1.52</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Indonesia</th>\n", "      <td>10.5</td>\n", "      <td>13.49</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Italy</th>\n", "      <td>48.8</td>\n", "      <td>30.62</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Japan</th>\n", "      <td>73.8</td>\n", "      <td>2.00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Malaysia</th>\n", "      <td>62.8</td>\n", "      <td>37.77</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Mexico</th>\n", "      <td>24.9</td>\n", "      <td>16.80</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Netherlands</th>\n", "      <td>82.9</td>\n", "      <td>20.54</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Peru</th>\n", "      <td>26.2</td>\n", "      <td>13.34</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Philippines</th>\n", "      <td>21.5</td>\n", "      <td>19.68</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Poland</th>\n", "      <td>52.0</td>\n", "      <td>11.79</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Russia</th>\n", "      <td>27.0</td>\n", "      <td>2.99</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Saudi_Arabia</th>\n", "      <td>22.7</td>\n", "      <td>11.65</td>\n", "    </tr>\n", "    <tr>\n", "      <th>South_Africa</th>\n", "      <td>10.5</td>\n", "      <td>7.83</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Spain</th>\n", "      <td>66.8</td>\n", "      <td>30.24</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["              Uso_Internet  Uso_Facebook\n", "Pais                                    \n", "Colombia              38.8         25.90\n", "Egypt                 12.9          5.68\n", "France                65.7         32.91\n", "Germany               67.0         14.07\n", "Hong_Kong             69.5         52.33\n", "India                  7.1          1.52\n", "Indonesia             10.5         13.49\n", "Italy                 48.8         30.62\n", "Japan                 73.8          2.00\n", "Malaysia              62.8         37.77\n", "Mexico                24.9         16.80\n", "Netherlands           82.9         20.54\n", "Peru                  26.2         13.34\n", "Philippines           21.5         19.68\n", "Poland                52.0         11.79\n", "Russia                27.0          2.99\n", "Saudi_Arabia          22.7         11.65\n", "South_Africa          10.5          7.83\n", "Spain                 66.8         30.24"]}, "execution_count": 253, "metadata": {}, "output_type": "execute_result"}], "source": ["df.loc[\"Colombia\":\"Spain\"]"]}, {"cell_type": "code", "execution_count": 254, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Germany</th>\n", "      <td>67.0</td>\n", "      <td>14.07</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Hong_Kong</th>\n", "      <td>69.5</td>\n", "      <td>52.33</td>\n", "    </tr>\n", "    <tr>\n", "      <th>India</th>\n", "      <td>7.1</td>\n", "      <td>1.52</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Indonesia</th>\n", "      <td>10.5</td>\n", "      <td>13.49</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Italy</th>\n", "      <td>48.8</td>\n", "      <td>30.62</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["           Uso_Internet  Uso_Facebook\n", "Pais                                 \n", "Germany            67.0         14.07\n", "Hong_Kong          69.5         52.33\n", "India               7.1          1.52\n", "Indonesia          10.5         13.49\n", "Italy              48.8         30.62"]}, "execution_count": 254, "metadata": {}, "output_type": "execute_result"}], "source": ["df.iloc[10:15]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["filtering"]}, {"cell_type": "code", "execution_count": 255, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Australia</th>\n", "      <td>80.6</td>\n", "      <td>46.01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Netherlands</th>\n", "      <td>82.9</td>\n", "      <td>20.54</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Sweden</th>\n", "      <td>80.7</td>\n", "      <td>44.72</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["             Uso_Internet  Uso_Facebook\n", "Pais                                   \n", "Australia            80.6         46.01\n", "Netherlands          82.9         20.54\n", "Sweden               80.7         44.72"]}, "execution_count": 255, "metadata": {}, "output_type": "execute_result"}], "source": ["df[df.Uso_Internet>80]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["combined conditions"]}, {"cell_type": "code", "execution_count": 256, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Canada</th>\n", "      <td>72.3</td>\n", "      <td>52.08</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Hong_Kong</th>\n", "      <td>69.5</td>\n", "      <td>52.33</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["           Uso_Internet  Uso_Facebook\n", "Pais                                 \n", "Canada             72.3         52.08\n", "Hong_Kong          69.5         52.33"]}, "execution_count": 256, "metadata": {}, "output_type": "execute_result"}], "source": ["df[(df.Uso_Internet>50)&(df.Uso_Facebook>50)]"]}, {"cell_type": "code", "execution_count": 257, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Uso_Internet</th>\n", "      <th>Uso_Facebook</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Pais</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>Australia</th>\n", "      <td>80.60</td>\n", "      <td>46.01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Belgium</th>\n", "      <td>67.30</td>\n", "      <td>36.98</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Canada</th>\n", "      <td>72.30</td>\n", "      <td>52.08</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Chile</th>\n", "      <td>50.90</td>\n", "      <td>46.14</td>\n", "    </tr>\n", "    <tr>\n", "      <th>France</th>\n", "      <td>65.70</td>\n", "      <td>32.91</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Germany</th>\n", "      <td>67.00</td>\n", "      <td>14.07</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Hong_Kong</th>\n", "      <td>69.50</td>\n", "      <td>52.33</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Japan</th>\n", "      <td>73.80</td>\n", "      <td>2.00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Malaysia</th>\n", "      <td>62.80</td>\n", "      <td>37.77</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Netherlands</th>\n", "      <td>82.90</td>\n", "      <td>20.54</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Poland</th>\n", "      <td>52.00</td>\n", "      <td>11.79</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Spain</th>\n", "      <td>66.80</td>\n", "      <td>30.24</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Sweden</th>\n", "      <td>80.70</td>\n", "      <td>44.72</td>\n", "    </tr>\n", "    <tr>\n", "      <th>Taiwan</th>\n", "      <td>66.10</td>\n", "      <td>38.21</td>\n", "    </tr>\n", "    <tr>\n", "      <th>USA</th>\n", "      <td>77.33</td>\n", "      <td>46.98</td>\n", "    </tr>\n", "    <tr>\n", "      <th>UK</th>\n", "      <td>70.18</td>\n", "      <td>45.97</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["             Uso_Internet  Uso_Facebook\n", "Pais                                   \n", "Australia           80.60         46.01\n", "Belgium             67.30         36.98\n", "Canada              72.30         52.08\n", "Chile               50.90         46.14\n", "France              65.70         32.91\n", "Germany             67.00         14.07\n", "Hong_Kong           69.50         52.33\n", "Japan               73.80          2.00\n", "Malaysia            62.80         37.77\n", "Netherlands         82.90         20.54\n", "Poland              52.00         11.79\n", "Spain               66.80         30.24\n", "Sweden              80.70         44.72\n", "Taiwan              66.10         38.21\n", "USA                 77.33         46.98\n", "UK                  70.18         45.97"]}, "execution_count": 257, "metadata": {}, "output_type": "execute_result"}], "source": ["df[(df.Uso_Internet>50)|(df.Uso_Facebook>50)]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Managing data"]}, {"cell_type": "markdown", "metadata": {}, "source": ["    \n", "    \n", "observe csv structure:\n", "- missing column name\n", "- missing data "]}, {"cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Date,,Berri1,Maisonneuve_1,Maisonneuve_2,Br\u00e9beuf\r\n", "01/01/2009,00:00,29,20,35,\r\n", "02/01/2009,00:00,19,3,22,\r\n", "03/01/2009,00:00,24,12,22,\r\n", "04/01/2009,00:00,24,8,15,\r\n", "05/01/2009,00:00,120,111,141,\r\n", "06/01/2009,00:00,261,146,236,\r\n", "07/01/2009,00:00,60,33,80,\r\n", "08/01/2009,00:00,24,14,14,\r\n", "09/01/2009,00:00,35,20,32,\r\n"]}], "source": ["!head local/data/comptagevelo2009.csv"]}, {"cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Date</th>\n", "      <th>Unnamed: 1</th>\n", "      <th>Berri1</th>\n", "      <th>Maisonneuve_1</th>\n", "      <th>Maisonneuve_2</th>\n", "      <th>Br\u00e9beuf</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>01/01/2009</td>\n", "      <td>00:00</td>\n", "      <td>29</td>\n", "      <td>20</td>\n", "      <td>35</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>02/01/2009</td>\n", "      <td>00:00</td>\n", "      <td>19</td>\n", "      <td>3</td>\n", "      <td>22</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>03/01/2009</td>\n", "      <td>00:00</td>\n", "      <td>24</td>\n", "      <td>12</td>\n", "      <td>22</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>04/01/2009</td>\n", "      <td>00:00</td>\n", "      <td>24</td>\n", "      <td>8</td>\n", "      <td>15</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>05/01/2009</td>\n", "      <td>00:00</td>\n", "      <td>120</td>\n", "      <td>111</td>\n", "      <td>141</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>360</th>\n", "      <td>27/12/2009</td>\n", "      <td>00:00</td>\n", "      <td>66</td>\n", "      <td>29</td>\n", "      <td>52</td>\n", "      <td>0.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>361</th>\n", "      <td>28/12/2009</td>\n", "      <td>00:00</td>\n", "      <td>61</td>\n", "      <td>41</td>\n", "      <td>99</td>\n", "      <td>0.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>362</th>\n", "      <td>29/12/2009</td>\n", "      <td>00:00</td>\n", "      <td>89</td>\n", "      <td>52</td>\n", "      <td>115</td>\n", "      <td>0.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>363</th>\n", "      <td>30/12/2009</td>\n", "      <td>00:00</td>\n", "      <td>76</td>\n", "      <td>43</td>\n", "      <td>115</td>\n", "      <td>0.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>364</th>\n", "      <td>31/12/2009</td>\n", "      <td>00:00</td>\n", "      <td>53</td>\n", "      <td>46</td>\n", "      <td>112</td>\n", "      <td>0.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>365 rows \u00d7 6 columns</p>\n", "</div>"], "text/plain": ["           Date Unnamed: 1  Berri1  Maisonneuve_1  Maisonneuve_2  Br\u00e9beuf\n", "0    01/01/2009      00:00      29             20             35      NaN\n", "1    02/01/2009      00:00      19              3             22      NaN\n", "2    03/01/2009      00:00      24             12             22      NaN\n", "3    04/01/2009      00:00      24              8             15      NaN\n", "4    05/01/2009      00:00     120            111            141      NaN\n", "..          ...        ...     ...            ...            ...      ...\n", "360  27/12/2009      00:00      66             29             52      0.0\n", "361  28/12/2009      00:00      61             41             99      0.0\n", "362  29/12/2009      00:00      89             52            115      0.0\n", "363  30/12/2009      00:00      76             43            115      0.0\n", "364  31/12/2009      00:00      53             46            112      0.0\n", "\n", "[365 rows x 6 columns]"]}, "execution_count": 116, "metadata": {}, "output_type": "execute_result"}], "source": ["d = pd.read_csv(\"local/data/comptagevelo2009.csv\")\n", "d"]}, {"cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [{"data": {"text/plain": ["(Index(['Date', 'Unnamed: 1', 'Berri1', 'Maisonneuve_1', 'Maisonneuve_2',\n", "        'Br\u00e9beuf'],\n", "       dtype='object'), (365, 6))"]}, "execution_count": 117, "metadata": {}, "output_type": "execute_result"}], "source": ["d.columns, d.shape\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["numerical features"]}, {"cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri1</th>\n", "      <th>Maisonneuve_1</th>\n", "      <th>Maisonneuve_2</th>\n", "      <th>Br\u00e9beuf</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>count</th>\n", "      <td>365.000000</td>\n", "      <td>365.000000</td>\n", "      <td>365.000000</td>\n", "      <td>178.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>mean</th>\n", "      <td>2032.200000</td>\n", "      <td>1060.252055</td>\n", "      <td>2093.169863</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>std</th>\n", "      <td>1878.879799</td>\n", "      <td>1079.533086</td>\n", "      <td>1854.368523</td>\n", "      <td>2484.004743</td>\n", "    </tr>\n", "    <tr>\n", "      <th>min</th>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>25%</th>\n", "      <td>194.000000</td>\n", "      <td>90.000000</td>\n", "      <td>228.000000</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>50%</th>\n", "      <td>1726.000000</td>\n", "      <td>678.000000</td>\n", "      <td>1686.000000</td>\n", "      <td>1443.500000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>75%</th>\n", "      <td>3540.000000</td>\n", "      <td>1882.000000</td>\n", "      <td>3520.000000</td>\n", "      <td>4638.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>max</th>\n", "      <td>6626.000000</td>\n", "      <td>4242.000000</td>\n", "      <td>6587.000000</td>\n", "      <td>7575.000000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Berri1  Maisonneuve_1  Maisonneuve_2      Br\u00e9beuf\n", "count   365.000000     365.000000     365.000000   178.000000\n", "mean   2032.200000    1060.252055    2093.169863  2576.359551\n", "std    1878.879799    1079.533086    1854.368523  2484.004743\n", "min       0.000000       0.000000       0.000000     0.000000\n", "25%     194.000000      90.000000     228.000000     0.000000\n", "50%    1726.000000     678.000000    1686.000000  1443.500000\n", "75%    3540.000000    1882.000000    3520.000000  4638.000000\n", "max    6626.000000    4242.000000    6587.000000  7575.000000"]}, "execution_count": 119, "metadata": {}, "output_type": "execute_result"}], "source": ["d.describe()"]}, {"cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [{"data": {"text/plain": ["0     29\n", "1     19\n", "2     24\n", "3     24\n", "4    120\n", "Name: Berri1, dtype: int64"]}, "execution_count": 120, "metadata": {}, "output_type": "execute_result"}], "source": ["d[\"Berri1\"].head()"]}, {"cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [{"data": {"text/plain": ["array(['00:00'], dtype=object)"]}, "execution_count": 121, "metadata": {}, "output_type": "execute_result"}], "source": ["d[\"Unnamed: 1\"].unique()\n"]}, {"cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([  29,   19,   24,  120,  261,   60,   35,   81,  318,  105,  168,\n", "        145,  131,   93,   25,   52,  136,  147,  109,  172,  148,   15,\n", "        209,   92,  110,   14,  158,  179,  122,   95,  185,   82,  190,\n", "        228,  306,  188,   98,  139,  258,  304,  326,  134,  125,   96,\n", "         65,  123,  129,  154,  239,  198,   32,   67,  157,  164,  300,\n", "        176,  195,  310,  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["d[\"Berri1\"].unique()\n"]}, {"cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [{"data": {"text/plain": ["(dtype('int64'), dtype('O'), dtype('O'))"]}, "execution_count": 123, "metadata": {}, "output_type": "execute_result"}], "source": ["d[\"Berri1\"].dtype, d[\"Date\"].dtype, d[\"Unnamed: 1\"].dtype\n"]}, {"cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [{"data": {"text/plain": ["RangeIndex(start=0, stop=365, step=1)"]}, "execution_count": 124, "metadata": {}, "output_type": "execute_result"}], "source": ["d.index\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Fixing data\n", "\n", "observe we set one column as the index one, and we **convert** it to date object type"]}, {"cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [{"data": {"text/plain": ["0      01/01/2009\n", "1      02/01/2009\n", "2      03/01/2009\n", "3      04/01/2009\n", "4      05/01/2009\n", "          ...    \n", "360    27/12/2009\n", "361    28/12/2009\n", "362    29/12/2009\n", "363    30/12/2009\n", "364    31/12/2009\n", "Name: Date, Length: 365, dtype: object"]}, "execution_count": 128, "metadata": {}, "output_type": "execute_result"}], "source": ["d.Date"]}, {"cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri1</th>\n", "      <th>Maisonneuve_1</th>\n", "      <th>Maisonneuve_2</th>\n", "      <th>Br\u00e9beuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01</th>\n", "      <td>29</td>\n", "      <td>20</td>\n", "      <td>35</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-02-01</th>\n", "      <td>19</td>\n", "      <td>3</td>\n", "      <td>22</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-03-01</th>\n", "      <td>24</td>\n", "      <td>12</td>\n", "      <td>22</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-04-01</th>\n", "      <td>24</td>\n", "      <td>8</td>\n", "      <td>15</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-05-01</th>\n", "      <td>120</td>\n", "      <td>111</td>\n", "      <td>141</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Berri1  Maisonneuve_1  Maisonneuve_2  Br\u00e9beuf\n", "Date                                                     \n", "2009-01-01      29             20             35      NaN\n", "2009-02-01      19              3             22      NaN\n", "2009-03-01      24             12             22      NaN\n", "2009-04-01      24              8             15      NaN\n", "2009-05-01     120            111            141      NaN"]}, "execution_count": 129, "metadata": {}, "output_type": "execute_result"}], "source": ["d.index = pd.to_datetime(d.Date)\n", "del(d[\"Date\"])\n", "del(d[\"Unnamed: 1\"])\n", "d.head()"]}, {"cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [{"data": {"text/plain": ["DatetimeIndex(['2009-01-01', '2009-02-01', '2009-03-01', '2009-04-01',\n", "               '2009-05-01', '2009-06-01', '2009-07-01', '2009-08-01',\n", "               '2009-09-01', '2009-10-01',\n", "               ...\n", "               '2009-12-22', '2009-12-23', '2009-12-24', '2009-12-25',\n", "               '2009-12-26', '2009-12-27', '2009-12-28', '2009-12-29',\n", "               '2009-12-30', '2009-12-31'],\n", "              dtype='datetime64[ns]', name='Date', length=365, freq=None)"]}, "execution_count": 130, "metadata": {}, "output_type": "execute_result"}], "source": ["d.index"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["let's fix columns names"]}, {"cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01</th>\n", "      <td>29</td>\n", "      <td>20</td>\n", "      <td>35</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-02-01</th>\n", "      <td>19</td>\n", "      <td>3</td>\n", "      <td>22</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-03-01</th>\n", "      <td>24</td>\n", "      <td>12</td>\n", "      <td>22</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-04-01</th>\n", "      <td>24</td>\n", "      <td>8</td>\n", "      <td>15</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-05-01</th>\n", "      <td>120</td>\n", "      <td>111</td>\n", "      <td>141</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Berri  Mneuve1  Mneuve2  Brebeuf\n", "Date                                        \n", "2009-01-01     29       20       35      NaN\n", "2009-02-01     19        3       22      NaN\n", "2009-03-01     24       12       22      NaN\n", "2009-04-01     24        8       15      NaN\n", "2009-05-01    120      111      141      NaN"]}, "execution_count": 131, "metadata": {}, "output_type": "execute_result"}], "source": ["d.columns=[\"Berri\", \"Mneuve1\", \"Mneuve2\", \"Brebeuf\"]\n", "d.head()"]}, {"cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Berri 0\n", "Mneuve1 0\n", "Mneuve2 0\n", "Brebeuf 187\n"]}], "source": ["for col in d.columns:\n", "    print (col, np.sum(pd.isnull(d[col])))"]}, {"cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [{"data": {"text/plain": ["(365, 4)"]}, "execution_count": 133, "metadata": {}, "output_type": "execute_result"}], "source": ["d.shape"]}, {"cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [{"data": {"text/plain": ["count     178.000000\n", "mean     2576.359551\n", "std      2484.004743\n", "min         0.000000\n", "25%         0.000000\n", "50%      1443.500000\n", "75%      4638.000000\n", "max      7575.000000\n", "Name: Brebeuf, dtype: float64"]}, "execution_count": 134, "metadata": {}, "output_type": "execute_result"}], "source": ["d['Brebeuf'].describe()"]}, {"cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [{"data": {"image/png": 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"text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.hist(d.Brebeuf, bins=30);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["**fix missing**!!!"]}, {"cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [], "source": ["d.Brebeuf.fillna(d.Brebeuf.mean(), inplace=True)\n"]}, {"cell_type": "code", "execution_count": 140, "metadata": {}, "outputs": [{"data": {"text/plain": ["count     365.000000\n", "mean     2576.359551\n", "std      1732.161423\n", "min         0.000000\n", "25%      1588.000000\n", "50%      2576.359551\n", "75%      2576.359551\n", "max      7575.000000\n", "Name: Brebeuf, dtype: float64"]}, "execution_count": 140, "metadata": {}, "output_type": "execute_result"}], "source": ["d['Brebeuf'].describe()"]}, {"cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.hist(d.Brebeuf, bins=30);"]}, {"cell_type": "code", "execution_count": 142, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01</th>\n", "      <td>29</td>\n", "      <td>20</td>\n", "      <td>35</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-02-01</th>\n", "      <td>19</td>\n", "      <td>3</td>\n", "      <td>22</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-03-01</th>\n", "      <td>24</td>\n", "      <td>12</td>\n", "      <td>22</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-04-01</th>\n", "      <td>24</td>\n", "      <td>8</td>\n", "      <td>15</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-05-01</th>\n", "      <td>120</td>\n", "      <td>111</td>\n", "      <td>141</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-12-27</th>\n", "      <td>66</td>\n", "      <td>29</td>\n", "      <td>52</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-12-28</th>\n", "      <td>61</td>\n", "      <td>41</td>\n", "      <td>99</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-12-29</th>\n", "      <td>89</td>\n", "      <td>52</td>\n", "      <td>115</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-12-30</th>\n", "      <td>76</td>\n", "      <td>43</td>\n", "      <td>115</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-12-31</th>\n", "      <td>53</td>\n", "      <td>46</td>\n", "      <td>112</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>365 rows \u00d7 4 columns</p>\n", "</div>"], "text/plain": ["            Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                            \n", "2009-01-01     29       20       35  2576.359551\n", "2009-02-01     19        3       22  2576.359551\n", "2009-03-01     24       12       22  2576.359551\n", "2009-04-01     24        8       15  2576.359551\n", "2009-05-01    120      111      141  2576.359551\n", "...           ...      ...      ...          ...\n", "2009-12-27     66       29       52     0.000000\n", "2009-12-28     61       41       99     0.000000\n", "2009-12-29     89       52      115     0.000000\n", "2009-12-30     76       43      115     0.000000\n", "2009-12-31     53       46      112     0.000000\n", "\n", "[365 rows x 4 columns]"]}, "execution_count": 142, "metadata": {}, "output_type": "execute_result"}], "source": ["d"]}, {"cell_type": "markdown", "metadata": {}, "source": ["let's make sure it is sorted"]}, {"cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01</th>\n", "      <td>29</td>\n", "      <td>20</td>\n", "      <td>35</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-02</th>\n", "      <td>14</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-03</th>\n", "      <td>67</td>\n", "      <td>30</td>\n", "      <td>80</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-04</th>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-05</th>\n", "      <td>1925</td>\n", "      <td>1256</td>\n", "      <td>1501</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                            \n", "2009-01-01     29       20       35  2576.359551\n", "2009-01-02     14        2        2  2576.359551\n", "2009-01-03     67       30       80  2576.359551\n", "2009-01-04      0        0        0  2576.359551\n", "2009-01-05   1925     1256     1501  2576.359551"]}, "execution_count": 144, "metadata": {}, "output_type": "execute_result"}], "source": ["d.sort_index(inplace=True)\n", "d.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Filtering"]}, {"cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-05-06</th>\n", "      <td>6028</td>\n", "      <td>4120</td>\n", "      <td>4223</td>\n", "      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</tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                            \n", "2009-07-04      0        0        0  2576.359551\n", "2009-03-30      0        0        0  2576.359551\n", "2009-04-04      0        0        0  2576.359551\n", "2009-04-13      0        0        0  2576.359551\n", "2009-04-14      0        0        0  2576.359551"]}, "execution_count": 150, "metadata": {}, "output_type": "execute_result"}], "source": ["d.sort_values(by=\"Berri\").head()\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["and chain operations"]}, {"cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-10-04</th>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-01</th>\n", "      <td>81</td>\n", "      <td>45</td>\n", "      <td>79</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-02</th>\n", "      <td>228</td>\n", "      <td>101</td>\n", "      <td>260</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-03</th>\n", "      <td>366</td>\n", "      <td>203</td>\n", "      <td>354</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-05</th>\n", "      <td>728</td>\n", "      <td>362</td>\n", "      <td>523</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-10</th>\n", "      <td>1407</td>\n", "      <td>725</td>\n", "      <td>1443</td>\n", "      <td>1003.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-08</th>\n", "      <td>2999</td>\n", "      <td>1545</td>\n", "      <td>3185</td>\n", "      <td>4187.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-06</th>\n", "      <td>3460</td>\n", "      <td>2354</td>\n", "      <td>3978</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-09</th>\n", "      <td>5496</td>\n", "      <td>2921</td>\n", "      <td>6587</td>\n", "      <td>6520.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-10-07</th>\n", "      <td>6274</td>\n", "      <td>4242</td>\n", "      <td>5435</td>\n", "      <td>7268.000000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                            \n", "2009-10-04      0        0        0  2576.359551\n", "2009-10-01     81       45       79  2576.359551\n", "2009-10-02    228      101      260  2576.359551\n", "2009-10-03    366      203      354  2576.359551\n", "2009-10-05    728      362      523  2576.359551\n", "2009-10-10   1407      725     1443  1003.000000\n", "2009-10-08   2999     1545     3185  4187.000000\n", "2009-10-06   3460     2354     3978  2576.359551\n", "2009-10-09   5496     2921     6587  6520.000000\n", "2009-10-07   6274     4242     5435  7268.000000"]}, "execution_count": 151, "metadata": {}, "output_type": "execute_result"}], "source": ["d.sort_values(by=\"Berri\").loc[\"2009-10-01\":\"2009-10-10\"]\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Time series operations"]}, {"cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "   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      NaN          NaN\n", "2009-01-03    36.666667    17.333333    39.000000  2576.359551\n", "2009-01-04    27.000000    10.666667    27.333333  2576.359551\n", "2009-01-05   664.000000   428.666667   527.000000  2576.359551\n", "2009-01-06  1733.000000  1116.333333  1409.000000  2576.359551\n", "2009-01-07  3223.333333  1862.666667  2426.000000  2576.359551\n", "2009-01-08  2602.666667  1795.000000  3042.666667  3673.906367\n", "2009-01-09  3277.333333  2029.333333  4055.666667  5128.119850\n", "2009-01-10  2668.000000  1290.333333  4214.666667  4798.666667"]}, "execution_count": 152, "metadata": {}, "output_type": "execute_result"}], "source": ["d.rolling(3).mean().head(10)\n"]}, {"cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    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<td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-04 00:05:00</th>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-05 00:05:00</th>\n", "      <td>1925</td>\n", "      <td>1256</td>\n", "      <td>1501</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                                     \n", "2009-01-01 00:05:00     29       20       35  2576.359551\n", "2009-01-02 00:05:00     14        2        2  2576.359551\n", "2009-01-03 00:05:00     67       30       80  2576.359551\n", "2009-01-04 00:05:00      0        0        0  2576.359551\n", "2009-01-05 00:05:00   1925     1256     1501  2576.359551"]}, "execution_count": 153, "metadata": {}, "output_type": "execute_result"}], "source": ["d.index = d.index + pd.Timedelta(\"5m\")\n", "d.head()"]}, {"cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-01 00:05:00</th>\n", "      <td>29</td>\n", "      <td>20</td>\n", "      <td>35</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-02 00:05:00</th>\n", "      <td>14</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-03 00:05:00</th>\n", "      <td>67</td>\n", "      <td>30</td>\n", "      <td>80</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-04 00:05:00</th>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-05 00:05:00</th>\n", "      <td>1925</td>\n", "      <td>1256</td>\n", "      <td>1501</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                                     \n", "2010-01-01 00:05:00     29       20       35  2576.359551\n", "2010-01-02 00:05:00     14        2        2  2576.359551\n", "2010-01-03 00:05:00     67       30       80  2576.359551\n", "2010-01-04 00:05:00      0        0        0  2576.359551\n", "2010-01-05 00:05:00   1925     1256     1501  2576.359551"]}, "execution_count": 154, "metadata": {}, "output_type": "execute_result"}], "source": ["d.shift(freq=pd.Timedelta(days=365)).head()\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Downsampling"]}, {"cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      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<td>2796</td>\n", "      <td>5765</td>\n", "      <td>6939.000000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                            \n", "2009-01-01     29       20       35  2576.359551\n", "2009-01-03     67       30       80  2576.359551\n", "2009-01-05   1925     1256     1501  2576.359551\n", "2009-01-07   4471     2239     3051  2576.359551\n", "2009-01-09   5298     2796     5765  6939.000000"]}, "execution_count": 155, "metadata": {}, "output_type": "execute_result"}], "source": ["d.resample(pd.Timedelta(\"2d\")).first().head()\n"]}, {"cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe 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  <tr>\n", "      <th>2009-01-07</th>\n", "      <td>2267.0</td>\n", "      <td>1646.0</td>\n", "      <td>3201.0</td>\n", "      <td>4222.679775</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-09</th>\n", "      <td>3970.5</td>\n", "      <td>1409.0</td>\n", "      <td>4646.5</td>\n", "      <td>4263.500000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["             Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                             \n", "2009-01-01    21.5     11.0     18.5  2576.359551\n", "2009-01-03    33.5     15.0     40.0  2576.359551\n", "2009-01-05  2599.5   1674.5   2113.5  2576.359551\n", "2009-01-07  2267.0   1646.0   3201.0  4222.679775\n", "2009-01-09  3970.5   1409.0   4646.5  4263.500000"]}, "execution_count": 156, "metadata": {}, "output_type": "execute_result"}], "source": ["d.resample(pd.Timedelta(\"2d\")).mean().head()\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Upsampling"]}, {"cell_type": "code", "execution_count": 157, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01 00:00:00</th>\n", "      <td>29.0</td>\n", "      <td>20.0</td>\n", "      <td>35.0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-01 12:00:00</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-02 00:00:00</th>\n", "      <td>14.0</td>\n", "      <td>2.0</td>\n", "      <td>2.0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-02 12:00:00</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-03 00:00:00</th>\n", "      <td>67.0</td>\n", "      <td>30.0</td>\n", "      <td>80.0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                                     \n", "2009-01-01 00:00:00   29.0     20.0     35.0  2576.359551\n", "2009-01-01 12:00:00    NaN      NaN      NaN          NaN\n", "2009-01-02 00:00:00   14.0      2.0      2.0  2576.359551\n", "2009-01-02 12:00:00    NaN      NaN      NaN          NaN\n", "2009-01-03 00:00:00   67.0     30.0     80.0  2576.359551"]}, "execution_count": 157, "metadata": {}, "output_type": "execute_result"}], "source": ["d.resample(pd.Timedelta(\"12h\")).first().head()\n"]}, {"cell_type": "code", "execution_count": 158, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01 00:00:00</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-01 12:00:00</th>\n", "      <td>29.0</td>\n", "      <td>20.0</td>\n", "      <td>35.0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-02 00:00:00</th>\n", "      <td>29.0</td>\n", "      <td>20.0</td>\n", "      <td>35.0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-02 12:00:00</th>\n", "      <td>14.0</td>\n", "      <td>2.0</td>\n", "      <td>2.0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-03 00:00:00</th>\n", "      <td>14.0</td>\n", "      <td>2.0</td>\n", "      <td>2.0</td>\n", "      <td>2576.359551</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Berri  Mneuve1  Mneuve2      Brebeuf\n", "Date                                                     \n", "2009-01-01 00:00:00    NaN      NaN      NaN          NaN\n", "2009-01-01 12:00:00   29.0     20.0     35.0  2576.359551\n", "2009-01-02 00:00:00   29.0     20.0     35.0  2576.359551\n", "2009-01-02 12:00:00   14.0      2.0      2.0  2576.359551\n", "2009-01-03 00:00:00   14.0      2.0      2.0  2576.359551"]}, "execution_count": 158, "metadata": {}, "output_type": "execute_result"}], "source": ["d.resample(pd.Timedelta(\"12h\")).fillna(method=\"pad\").head()\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Building Dataframes from other structures"]}, {"cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[6, 4, 8, 2, 0],\n", "       [8, 9, 7, 4, 9],\n", "       [3, 3, 5, 0, 2],\n", "       [8, 9, 3, 9, 1],\n", "       [6, 9, 4, 5, 9],\n", "       [8, 6, 1, 0, 1],\n", "       [9, 6, 1, 7, 2],\n", "       [0, 7, 4, 6, 1],\n", "       [2, 2, 5, 3, 7],\n", "       [4, 9, 3, 3, 9],\n", "       [7, 3, 5, 6, 8],\n", "       [6, 7, 6, 2, 8],\n", "       [1, 2, 5, 2, 8],\n", "       [6, 7, 6, 9, 4],\n", "       [9, 4, 5, 5, 1],\n", "       [4, 4, 5, 9, 8],\n", "       [4, 4, 1, 1, 2],\n", "       [2, 8, 9, 7, 7],\n", "       [3, 4, 3, 0, 5],\n", "       [5, 8, 5, 6, 2]])"]}, "execution_count": 160, "metadata": {}, "output_type": "execute_result"}], "source": ["\n", "a = np.random.randint(10,size=(20,5))\n", "a"]}, {"cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>uno</th>\n", "      <th>dos</th>\n", "      <th>tres</th>\n", "      <th>cuatro</th>\n", "      <th>cinco</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>10</th>\n", "      <td>6</td>\n", "      <td>4</td>\n", "      <td>8</td>\n", "      <td>2</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>11</th>\n", "      <td>8</td>\n", "      <td>9</td>\n", "      <td>7</td>\n", "      <td>4</td>\n", "      <td>9</td>\n", "    </tr>\n", "    <tr>\n", "      <th>12</th>\n", "      <td>3</td>\n", "      <td>3</td>\n", "      <td>5</td>\n", "      <td>0</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>13</th>\n", "      <td>8</td>\n", "      <td>9</td>\n", "      <td>3</td>\n", "      <td>9</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>14</th>\n", "      <td>6</td>\n", "      <td>9</td>\n", "      <td>4</td>\n", "      <td>5</td>\n", "      <td>9</td>\n", "    </tr>\n", "    <tr>\n", "      <th>15</th>\n", "      <td>8</td>\n", "      <td>6</td>\n", "      <td>1</td>\n", "      <td>0</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>16</th>\n", "      <td>9</td>\n", "      <td>6</td>\n", "      <td>1</td>\n", "      <td>7</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>17</th>\n", "      <td>0</td>\n", "      <td>7</td>\n", "      <td>4</td>\n", "      <td>6</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>18</th>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>5</td>\n", "      <td>3</td>\n", "      <td>7</td>\n", "    </tr>\n", "    <tr>\n", "      <th>19</th>\n", "      <td>4</td>\n", "      <td>9</td>\n", "      <td>3</td>\n", "      <td>3</td>\n", "      <td>9</td>\n", "    </tr>\n", "    <tr>\n", "      <th>20</th>\n", "      <td>7</td>\n", "      <td>3</td>\n", "      <td>5</td>\n", "      <td>6</td>\n", "      <td>8</td>\n", "    </tr>\n", "    <tr>\n", "      <th>21</th>\n", "      <td>6</td>\n", "      <td>7</td>\n", "      <td>6</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "    </tr>\n", "    <tr>\n", "      <th>22</th>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>8</td>\n", "    </tr>\n", "    <tr>\n", "      <th>23</th>\n", "      <td>6</td>\n", "      <td>7</td>\n", "      <td>6</td>\n", "      <td>9</td>\n", "      <td>4</td>\n", "    </tr>\n", "    <tr>\n", "      <th>24</th>\n", "      <td>9</td>\n", "      <td>4</td>\n", "      <td>5</td>\n", "      <td>5</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>25</th>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>5</td>\n", "      <td>9</td>\n", "      <td>8</td>\n", "    </tr>\n", "    <tr>\n", "      <th>26</th>\n", "      <td>4</td>\n", "      <td>4</td>\n", "      <td>1</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "    </tr>\n", "    <tr>\n", "      <th>27</th>\n", "      <td>2</td>\n", "      <td>8</td>\n", "      <td>9</td>\n", "      <td>7</td>\n", "      <td>7</td>\n", "    </tr>\n", "    <tr>\n", "      <th>28</th>\n", "      <td>3</td>\n", "      <td>4</td>\n", "      <td>3</td>\n", "      <td>0</td>\n", "      <td>5</td>\n", "    </tr>\n", "    <tr>\n", "      <th>29</th>\n", "      <td>5</td>\n", "      <td>8</td>\n", "      <td>5</td>\n", "      <td>6</td>\n", "      <td>2</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["    uno  dos  tres  cuatro  cinco\n", "10    6    4     8       2      0\n", "11    8    9     7       4      9\n", "12    3    3     5       0      2\n", "13    8    9     3       9      1\n", "14    6    9     4       5      9\n", "15    8    6     1       0      1\n", "16    9    6     1       7      2\n", "17    0    7     4       6      1\n", "18    2    2     5       3      7\n", "19    4    9     3       3      9\n", "20    7    3     5       6      8\n", "21    6    7     6       2      8\n", "22    1    2     5       2      8\n", "23    6    7     6       9      4\n", "24    9    4     5       5      1\n", "25    4    4     5       9      8\n", "26    4    4     1       1      2\n", "27    2    8     9       7      7\n", "28    3    4     3       0      5\n", "29    5    8     5       6      2"]}, "execution_count": 161, "metadata": {}, "output_type": "execute_result"}], "source": ["k = pd.DataFrame(a, columns=[\"uno\", \"dos\", \"tres\", \"cuatro\", \"cinco\"], index=range(10,10+len(a)))\n", "k"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## `.values` access the underlying `numpy` structure"]}, {"cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([[2.90000000e+01, 2.00000000e+01, 3.50000000e+01, 2.57635955e+03],\n", "       [1.40000000e+01, 2.00000000e+00, 2.00000000e+00, 2.57635955e+03],\n", "       [6.70000000e+01, 3.00000000e+01, 8.00000000e+01, 2.57635955e+03],\n", "       ...,\n", "       [8.90000000e+01, 5.20000000e+01, 1.15000000e+02, 0.00000000e+00],\n", "       [7.60000000e+01, 4.30000000e+01, 1.15000000e+02, 0.00000000e+00],\n", "       [5.30000000e+01, 4.60000000e+01, 1.12000000e+02, 0.00000000e+00]])"]}, "execution_count": 162, "metadata": {}, "output_type": "execute_result"}], "source": ["d.values"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## some out-of-the-box plotting\n", "\n", "but recall that we always can do custom plotting"]}, {"cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x7fb9f2a9ac10>"]}, "execution_count": 163, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 1080x216 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["d.plot(figsize=(15,3))\n"]}, {"cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [{"data": {"text/plain": ["[<matplotlib.lines.Line2D at 0x7fb9f29f2250>]"]}, "execution_count": 165, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 1080x216 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(15,3))\n", "plt.plot(d.Berri)"]}, {"cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x7fb9f294c450>"]}, "execution_count": 166, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["d.Berri.cumsum().plot()\n"]}, {"cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.collections.PathCollection at 0x7fb9f360fb50>"]}, "execution_count": 167, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.scatter(d.Berri, d.Brebeuf)\n"]}, {"cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 720x720 with 16 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["pd.plotting.scatter_matrix(d, figsize=(10,10));"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Grouping"]}, {"cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "      <th>month</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2009-01-01 00:05:00</th>\n", "      <td>29</td>\n", "      <td>20</td>\n", "      <td>35</td>\n", "      <td>2576.359551</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-02 00:05:00</th>\n", "      <td>14</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>2576.359551</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-03 00:05:00</th>\n", "      <td>67</td>\n", "      <td>30</td>\n", "      <td>80</td>\n", "      <td>2576.359551</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-04 00:05:00</th>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>2576.359551</td>\n", "      <td>1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2009-01-05 00:05:00</th>\n", "      <td>1925</td>\n", "      <td>1256</td>\n", "      <td>1501</td>\n", "      <td>2576.359551</td>\n", "      <td>1</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Berri  Mneuve1  Mneuve2      Brebeuf  month\n", "Date                                                            \n", "2009-01-01 00:05:00     29       20       35  2576.359551      1\n", "2009-01-02 00:05:00     14        2        2  2576.359551      1\n", "2009-01-03 00:05:00     67       30       80  2576.359551      1\n", "2009-01-04 00:05:00      0        0        0  2576.359551      1\n", "2009-01-05 00:05:00   1925     1256     1501  2576.359551      1"]}, "execution_count": 169, "metadata": {}, "output_type": "execute_result"}], "source": ["\n", "d[\"month\"] = [i.month for i in d.index]\n", "d.head()"]}, {"cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>month</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>5298</td>\n", "      <td>2796</td>\n", "      <td>5765</td>\n", "      <td>6939.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>5451</td>\n", "      <td>2868</td>\n", "      <td>5517</td>\n", "      <td>7052.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>5904</td>\n", "      <td>3523</td>\n", "      <td>5762</td>\n", "      <td>7194.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>5278</td>\n", "      <td>3499</td>\n", "      <td>5327</td>\n", "      <td>5837.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5</th>\n", "      <td>6028</td>\n", "      <td>4120</td>\n", "      <td>5397</td>\n", "      <td>7121.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>6</th>\n", "      <td>6320</td>\n", "      <td>3499</td>\n", "      <td>6047</td>\n", "      <td>5259.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>7</th>\n", "      <td>6100</td>\n", "      <td>3825</td>\n", "      <td>5536</td>\n", "      <td>7219.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>5452</td>\n", "      <td>2865</td>\n", "      <td>6379</td>\n", "      <td>7044.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>9</th>\n", "      <td>6626</td>\n", "      <td>4227</td>\n", "      <td>6535</td>\n", "      <td>7575.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>10</th>\n", "      <td>6274</td>\n", "      <td>4242</td>\n", "      <td>6587</td>\n", "      <td>7268.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>11</th>\n", "      <td>4864</td>\n", "      <td>2648</td>\n", "      <td>5895</td>\n", "      <td>6044.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>12</th>\n", "      <td>5538</td>\n", "      <td>2983</td>\n", "      <td>5107</td>\n", "      <td>7127.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["       Berri  Mneuve1  Mneuve2  Brebeuf\n", "month                                  \n", "1       5298     2796     5765   6939.0\n", "2       5451     2868     5517   7052.0\n", "3       5904     3523     5762   7194.0\n", "4       5278     3499     5327   5837.0\n", "5       6028     4120     5397   7121.0\n", "6       6320     3499     6047   5259.0\n", "7       6100     3825     5536   7219.0\n", "8       5452     2865     6379   7044.0\n", "9       6626     4227     6535   7575.0\n", "10      6274     4242     6587   7268.0\n", "11      4864     2648     5895   6044.0\n", "12      5538     2983     5107   7127.0"]}, "execution_count": 170, "metadata": {}, "output_type": "execute_result"}], "source": ["d.groupby(\"month\").max()\n"]}, {"cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Berri</th>\n", "      <th>Mneuve1</th>\n", "      <th>Mneuve2</th>\n", "      <th>Brebeuf</th>\n", "    </tr>\n", "    <tr>\n", "      <th>month</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>28</td>\n", "      <td>28</td>\n", "      <td>28</td>\n", "      <td>28</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5</th>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "    </tr>\n", "    <tr>\n", "      <th>6</th>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "    </tr>\n", "    <tr>\n", "      <th>7</th>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "    </tr>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "    </tr>\n", "    <tr>\n", "      <th>9</th>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "    </tr>\n", "    <tr>\n", "      <th>10</th>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "    </tr>\n", "    <tr>\n", "      <th>11</th>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "      <td>30</td>\n", "    </tr>\n", "    <tr>\n", "      <th>12</th>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "      <td>31</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["       Berri  Mneuve1  Mneuve2  Brebeuf\n", "month                                  \n", "1         31       31       31       31\n", "2         28       28       28       28\n", "3         31       31       31       31\n", "4         30       30       30       30\n", "5         31       31       31       31\n", "6         30       30       30       30\n", "7         31       31       31       31\n", "8         31       31       31       31\n", "9         30       30       30       30\n", "10        31       31       31       31\n", "11        30       30       30       30\n", "12        31       31       31       31"]}, "execution_count": 171, "metadata": {}, "output_type": "execute_result"}], "source": ["d.groupby(\"month\").count()\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Time series\n", "\n", "observe we can **establish at load time** many thing if the dataset is relatively clean"]}, {"cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-01 00:00:00</th>\n", "      <td>46.2</td>\n", "      <td>37.5</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 01:00:00</th>\n", "      <td>44.6</td>\n", "      <td>37.1</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 02:00:00</th>\n", "      <td>44.1</td>\n", "      <td>36.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 03:00:00</th>\n", "      <td>43.8</td>\n", "      <td>36.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 04:00:00</th>\n", "      <td>43.5</td>\n", "      <td>36.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 19:00:00</th>\n", "      <td>51.1</td>\n", "      <td>38.1</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 20:00:00</th>\n", "      <td>49.0</td>\n", "      <td>37.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 21:00:00</th>\n", "      <td>47.9</td>\n", "      <td>37.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 22:00:00</th>\n", "      <td>46.9</td>\n", "      <td>37.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 23:00:00</th>\n", "      <td>46.2</td>\n", "      <td>37.7</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>8759 rows \u00d7 3 columns</p>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-01-01 00:00:00         46.2      37.5       1.0\n", "2010-01-01 01:00:00         44.6      37.1       1.0\n", "2010-01-01 02:00:00         44.1      36.9       1.0\n", "2010-01-01 03:00:00         43.8      36.9       1.0\n", "2010-01-01 04:00:00         43.5      36.8       1.0\n", "...                          ...       ...       ...\n", "2010-12-31 19:00:00         51.1      38.1       1.0\n", "2010-12-31 20:00:00         49.0      37.9       1.0\n", "2010-12-31 21:00:00         47.9      37.9       1.0\n", "2010-12-31 22:00:00         46.9      37.9       1.0\n", "2010-12-31 23:00:00         46.2      37.7       1.0\n", "\n", "[8759 rows x 3 columns]"]}, "execution_count": 179, "metadata": {}, "output_type": "execute_result"}], "source": ["\n", "tiempo=pd.read_csv('local/data/weather_data_austin_2010.csv',parse_dates=['Date'], dayfirst=True ,index_col='Date')\n", "tiempo"]}, {"cell_type": "code", "execution_count": 178, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-08-01 00:00:00</th>\n", "      <td>79.0</td>\n", "      <td>70.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-08-01 01:00:00</th>\n", "      <td>77.4</td>\n", "      <td>71.2</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-08-01 02:00:00</th>\n", "      <td>76.4</td>\n", "      <td>71.3</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-08-01 03:00:00</th>\n", "      <td>75.7</td>\n", "      <td>71.4</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-08-01 04:00:00</th>\n", "      <td>75.1</td>\n", "      <td>71.4</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-30 19:00:00</th>\n", "      <td>65.4</td>\n", "      <td>53.6</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-30 20:00:00</th>\n", "      <td>63.6</td>\n", "      <td>53.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-30 21:00:00</th>\n", "      <td>62.2</td>\n", "      <td>53.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-30 22:00:00</th>\n", "      <td>61.4</td>\n", "      <td>54.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-30 23:00:00</th>\n", "      <td>60.3</td>\n", "      <td>53.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>2184 rows \u00d7 3 columns</p>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-08-01 00:00:00         79.0      70.8       1.0\n", "2010-08-01 01:00:00         77.4      71.2       1.0\n", "2010-08-01 02:00:00         76.4      71.3       1.0\n", "2010-08-01 03:00:00         75.7      71.4       1.0\n", "2010-08-01 04:00:00         75.1      71.4       1.0\n", "...                          ...       ...       ...\n", "2010-10-30 19:00:00         65.4      53.6       1.0\n", "2010-10-30 20:00:00         63.6      53.9       1.0\n", "2010-10-30 21:00:00         62.2      53.8       1.0\n", "2010-10-30 22:00:00         61.4      54.0       1.0\n", "2010-10-30 23:00:00         60.3      53.9       1.0\n", "\n", "[2184 rows x 3 columns]"]}, "execution_count": 178, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.loc['2010-08-01':'2010-10-30']\n"]}, {"cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-06-01 00:00:00</th>\n", "      <td>74.0</td>\n", "      <td>67.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-06-01 01:00:00</th>\n", "      <td>72.6</td>\n", "      <td>68.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-06-01 02:00:00</th>\n", "      <td>72.0</td>\n", "      <td>67.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-06-01 03:00:00</th>\n", "      <td>71.6</td>\n", "      <td>67.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-06-01 04:00:00</th>\n", "      <td>71.1</td>\n", "      <td>67.7</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-06-01 00:00:00         74.0      67.9       1.0\n", "2010-06-01 01:00:00         72.6      68.0       1.0\n", "2010-06-01 02:00:00         72.0      67.9       1.0\n", "2010-06-01 03:00:00         71.6      67.9       1.0\n", "2010-06-01 04:00:00         71.1      67.7       1.0"]}, "execution_count": 181, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.loc['2010-06'].head()\n"]}, {"cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-12-24 20:00:00</th>\n", "      <td>48.9</td>\n", "      <td>38.6</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-12 01:00:00</th>\n", "      <td>64.7</td>\n", "      <td>59.3</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-02-21 03:00:00</th>\n", "      <td>49.3</td>\n", "      <td>42.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-07-20 16:00:00</th>\n", "      <td>93.5</td>\n", "      <td>67.1</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-11-04 12:00:00</th>\n", "      <td>70.4</td>\n", "      <td>53.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-29 15:00:00</th>\n", "      <td>75.4</td>\n", "      <td>53.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-07-18 17:00:00</th>\n", "      <td>92.5</td>\n", "      <td>67.3</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-04-16 13:00:00</th>\n", "      <td>76.4</td>\n", "      <td>56.7</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-07-05 06:00:00</th>\n", "      <td>74.4</td>\n", "      <td>71.2</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-07-03 16:00:00</th>\n", "      <td>91.4</td>\n", "      <td>68.6</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-12-24 20:00:00         48.9      38.6       1.0\n", "2010-10-12 01:00:00         64.7      59.3       1.0\n", "2010-02-21 03:00:00         49.3      42.8       1.0\n", "2010-07-20 16:00:00         93.5      67.1       1.0\n", "2010-11-04 12:00:00         70.4      53.0       1.0\n", "2010-10-29 15:00:00         75.4      53.0       1.0\n", "2010-07-18 17:00:00         92.5      67.3       1.0\n", "2010-04-16 13:00:00         76.4      56.7       1.0\n", "2010-07-05 06:00:00         74.4      71.2       1.0\n", "2010-07-03 16:00:00         91.4      68.6       1.0"]}, "execution_count": 182, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.sample(10)\n"]}, {"cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-12-22 23:00:00</th>\n", "      <td>45.9</td>\n", "      <td>37.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-05-19 15:00:00</th>\n", "      <td>84.9</td>\n", "      <td>64.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-02-24 12:00:00</th>\n", "      <td>61.9</td>\n", "      <td>44.5</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-10 00:00:00</th>\n", "      <td>46.2</td>\n", "      <td>37.4</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-21 19:00:00</th>\n", "      <td>50.7</td>\n", "      <td>38.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-05-27 11:00:00</th>\n", "      <td>81.7</td>\n", "      <td>67.3</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-08-11 03:00:00</th>\n", "      <td>75.8</td>\n", "      <td>71.3</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-03-17 13:00:00</th>\n", "      <td>68.5</td>\n", "      <td>49.6</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-10-28 14:00:00</th>\n", "      <td>75.2</td>\n", "      <td>53.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-08-21 03:00:00</th>\n", "      <td>75.9</td>\n", "      <td>71.1</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>88 rows \u00d7 3 columns</p>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-12-22 23:00:00         45.9      37.8       1.0\n", "2010-05-19 15:00:00         84.9      64.8       1.0\n", "2010-02-24 12:00:00         61.9      44.5       1.0\n", "2010-01-10 00:00:00         46.2      37.4       1.0\n", "2010-12-21 19:00:00         50.7      38.8       1.0\n", "...                          ...       ...       ...\n", "2010-05-27 11:00:00         81.7      67.3       1.0\n", "2010-08-11 03:00:00         75.8      71.3       1.0\n", "2010-03-17 13:00:00         68.5      49.6       1.0\n", "2010-10-28 14:00:00         75.2      53.9       1.0\n", "2010-08-21 03:00:00         75.9      71.1       1.0\n", "\n", "[88 rows x 3 columns]"]}, "execution_count": 183, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.sample(frac=0.01)\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Resampling"]}, {"cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-01 00:00:00</th>\n", "      <td>46.2</td>\n", "      <td>37.5</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 01:00:00</th>\n", "      <td>44.6</td>\n", "      <td>37.1</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 02:00:00</th>\n", "      <td>44.1</td>\n", "      <td>36.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 03:00:00</th>\n", "      <td>43.8</td>\n", "      <td>36.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 04:00:00</th>\n", "      <td>43.5</td>\n", "      <td>36.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-01-01 00:00:00         46.2      37.5       1.0\n", "2010-01-01 01:00:00         44.6      37.1       1.0\n", "2010-01-01 02:00:00         44.1      36.9       1.0\n", "2010-01-01 03:00:00         43.8      36.9       1.0\n", "2010-01-01 04:00:00         43.5      36.8       1.0"]}, "execution_count": 184, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.head()\n"]}, {"cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-01</th>\n", "      <td>49.720833</td>\n", "      <td>38.092500</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-06</th>\n", "      <td>49.449167</td>\n", "      <td>37.575000</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-11</th>\n", "      <td>49.222500</td>\n", "      <td>37.603333</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-16</th>\n", "      <td>49.441667</td>\n", "      <td>37.650000</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-21</th>\n", "      <td>50.683333</td>\n", "      <td>39.309167</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Temperature   DewPoint  Pressure\n", "Date                                        \n", "2010-01-01    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 <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-01</th>\n", "      <td>49.720833</td>\n", "      <td>38.092500</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-06</th>\n", "      <td>49.449167</td>\n", "      <td>37.575000</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-11</th>\n", "      <td>49.222500</td>\n", "      <td>37.603333</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-16</th>\n", "      <td>49.441667</td>\n", "      <td>37.650000</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-21</th>\n", "      <td>50.683333</td>\n", "      <td>39.309167</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            Temperature   DewPoint  Pressure\n", "Date                                        \n", "2010-01-01    49.720833  38.092500       1.0\n", "2010-01-06    49.449167  37.575000       1.0\n", "2010-01-11    49.222500  37.603333       1.0\n", "2010-01-16    49.441667  37.650000       1.0\n", "2010-01-21    50.683333  39.309167       1.0"]}, "execution_count": 186, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.resample(\"5d\").mean().head()\n"]}, {"cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      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Pressure\n", "Date                                        \n", "2010-01-01    49.720833  38.092500       1.0\n", "2010-01-06    49.449167  37.575000       1.0\n", "2010-01-11    49.222500  37.603333       1.0\n", "2010-01-16    49.441667  37.650000       1.0\n", "2010-01-21    50.683333  39.309167       1.0"]}, "execution_count": 187, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.resample(\"5d\").mean().head()\n"]}, {"cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      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06:00:00</th>\n", "      <td>43.1</td>\n", "      <td>36.3</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 06:30:00</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 07:00:00</th>\n", "      <td>42.3</td>\n", "      <td>35.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-01-01 00:00:00         46.2      37.5       1.0\n", "2010-01-01 00:30:00          NaN       NaN       NaN\n", "2010-01-01 01:00:00         44.6      37.1       1.0\n", "2010-01-01 01:30:00          NaN       NaN       NaN\n", "2010-01-01 02:00:00         44.1      36.9       1.0\n", "2010-01-01 02:30:00          NaN       NaN       NaN\n", "2010-01-01 03:00:00         43.8      36.9       1.0\n", "2010-01-01 03:30:00          NaN       NaN       NaN\n", "2010-01-01 04:00:00         43.5      36.8       1.0\n", "2010-01-01 04:30:00          NaN       NaN       NaN\n", "2010-01-01 05:00:00         43.0      36.5       1.0\n", "2010-01-01 05:30:00          NaN       NaN       NaN\n", "2010-01-01 06:00:00         43.1      36.3       1.0\n", "2010-01-01 06:30:00          NaN       NaN       NaN\n", "2010-01-01 07:00:00         42.3      35.9       1.0"]}, "execution_count": 191, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.resample(\"30min\").mean()[:15]\n"]}, {"cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Temperature</th>\n", "      <th>DewPoint</th>\n", "      <th>Pressure</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-01 01:00:00</th>\n", "      <td>44.6</td>\n", "      <td>37.1</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 02:00:00</th>\n", "      <td>44.1</td>\n", "      <td>36.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 03:00:00</th>\n", "      <td>43.8</td>\n", "      <td>36.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 04:00:00</th>\n", "      <td>43.5</td>\n", "      <td>36.8</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-01 05:00:00</th>\n", "      <td>43.0</td>\n", "      <td>36.5</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 08:00:00</th>\n", "      <td>42.5</td>\n", "      <td>36.1</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 09:00:00</th>\n", "      <td>46.0</td>\n", "      <td>37.7</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 10:00:00</th>\n", "      <td>49.4</td>\n", "      <td>38.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 11:00:00</th>\n", "      <td>52.4</td>\n", "      <td>38.0</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-12-31 12:00:00</th>\n", "      <td>54.7</td>\n", "      <td>37.9</td>\n", "      <td>1.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>4379 rows \u00d7 3 columns</p>\n", "</div>"], "text/plain": ["                     Temperature  DewPoint  Pressure\n", "Date                                                \n", "2010-01-01 01:00:00         44.6      37.1       1.0\n", "2010-01-01 02:00:00         44.1      36.9       1.0\n", "2010-01-01 03:00:00         43.8      36.9       1.0\n", "2010-01-01 04:00:00         43.5      36.8       1.0\n", "2010-01-01 05:00:00         43.0      36.5       1.0\n", "...                          ...       ...       ...\n", "2010-12-31 08:00:00         42.5      36.1       1.0\n", "2010-12-31 09:00:00         46.0      37.7       1.0\n", "2010-12-31 10:00:00         49.4      38.0       1.0\n", "2010-12-31 11:00:00         52.4      38.0       1.0\n", "2010-12-31 12:00:00         54.7      37.9       1.0\n", "\n", "[4379 rows x 3 columns]"]}, "execution_count": 192, "metadata": {}, "output_type": "execute_result"}], "source": ["\n", "subt=tiempo.between_time(start_time='1:00',end_time='12:00')\n", "subt"]}, {"cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [{"data": {"text/plain": ["Int64Index([4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", "            ...\n", "            4, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n", "           dtype='int64', name='Date', length=8759)"]}, "execution_count": 193, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.index.weekday\n"]}, {"cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [{"data": {"text/plain": ["Int64Index([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n", "            ...\n", "            12, 12, 12, 12, 12, 12, 12, 12, 12, 12],\n", "           dtype='int64', name='Date', length=8759)"]}, "execution_count": 195, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.index.month\n"]}, {"cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [{"data": {"text/plain": ["Int64Index([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n", "            ...\n", "            31, 31, 31, 31, 31, 31, 31, 31, 31, 31],\n", "           dtype='int64', name='Date', length=8759)"]}, "execution_count": 196, "metadata": {}, "output_type": "execute_result"}], "source": ["tiempo.index.day\n"]}, {"cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x7fb9e70c8250>"]}, "execution_count": 197, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["tiempo.plot(style='.')\n"]}, {"cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x7fb9f2af66d0>"]}, "execution_count": 201, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["tiempo['2010-01'].plot()"]}, {"cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x7fb9f2b8eed0>"]}, "execution_count": 202, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["tiempo['2010-01-04'].plot()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Rolling operations"]}, {"cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Collecting yfinance\n", "  Downloading https://files.pythonhosted.org/packages/c2/31/8b374a12b90def92a4e27d0fc595fc43635f395984e36a075244d98bd265/yfinance-0.1.54.tar.gz\n", "Collecting pandas>=0.24\n", "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4a/6a/94b219b8ea0f2d580169e85ed1edc0163743f55aaeca8a44c2e8fc1e344e/pandas-1.0.3-cp37-cp37m-manylinux1_x86_64.whl (10.0MB)\n", "\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10.0MB 453kB/s eta 0:00:01\n", "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /opt/anaconda3/lib/python3.7/site-packages (from yfinance) (1.15.1)\n", "Requirement already satisfied: requests>=2.20 in /opt/anaconda3/lib/python3.7/site-packages (from yfinance) (2.22.0)\n", "Collecting multitasking>=0.0.7\n", "  Downloading https://files.pythonhosted.org/packages/69/e7/e9f1661c28f7b87abfa08cb0e8f51dad2240a9f4f741f02ea839835e6d18/multitasking-0.0.9.tar.gz\n", "Requirement already satisfied: pytz>=2017.2 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.24->yfinance) (2018.5)\n", "Requirement already satisfied: python-dateutil>=2.6.1 in /opt/anaconda3/lib/python3.7/site-packages (from pandas>=0.24->yfinance) (2.7.3)\n", "Requirement already satisfied: idna<2.9,>=2.5 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.20->yfinance) (2.8)\n", "Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.20->yfinance) (2019.9.11)\n", "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.20->yfinance) (3.0.4)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/anaconda3/lib/python3.7/site-packages (from requests>=2.20->yfinance) (1.24.2)\n", "Requirement already satisfied: six>=1.5 in /opt/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas>=0.24->yfinance) (1.13.0)\n", "Building wheels for collected packages: yfinance, multitasking\n", "  Building wheel for yfinance (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h  Created wheel for yfinance: filename=yfinance-0.1.54-py2.py3-none-any.whl size=22411 sha256=d937a7a089b0883844df4d4f388c8bcc4dc145446c43e698a5181e6ebd099621\n", "  Stored in directory: /home/rlx/.cache/pip/wheels/f9/e3/5b/ec24dd2984b12d61e0abf26289746c2436a0e7844f26f2515c\n", "  Building wheel for multitasking (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h  Created wheel for multitasking: filename=multitasking-0.0.9-cp37-none-any.whl size=8368 sha256=bfe866419b7d2ac5e39bab8a20d2378ac727b56d6dc6e3f248e2a4115ea368bb\n", "  Stored in directory: /home/rlx/.cache/pip/wheels/37/fa/73/d492849e319038eb4d986f5152e4b19ffb1bc0639da84d2677\n", "Successfully built yfinance multitasking\n", "Installing collected packages: pandas, multitasking, yfinance\n", "  Found existing installation: pandas 0.23.4\n", "    Uninstalling pandas-0.23.4:\n", "      Successfully uninstalled pandas-0.23.4\n", "Successfully installed multitasking-0.0.9 pandas-1.0.3 yfinance-0.1.54\n"]}], "source": ["import pandas as pd\n", "### permite obtener data frames directamente de internet\n", "!pip install yfinance"]}, {"cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [], "source": ["import yfinance as yf\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>Open</th>\n", "      <th>High</th>\n", "      <th>Low</th>\n", "      <th>Close</th>\n", "      <th>Volume</th>\n", "      <th>Dividends</th>\n", "      <th>Stock Splits</th>\n", "    </tr>\n", "    <tr>\n", "      <th>Date</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2010-01-04</th>\n", "      <td>24.04</td>\n", "      <td>24.41</td>\n", "      <td>24.01</td>\n", "      <td>24.29</td>\n", "      <td>38409100</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-05</th>\n", "      <td>24.22</td>\n", "      <td>24.41</td>\n", "      <td>24.05</td>\n", "      <td>24.30</td>\n", "      <td>49749600</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-06</th>\n", "      <td>24.24</td>\n", "      <td>24.40</td>\n", "      <td>23.96</td>\n", "      <td>24.15</td>\n", "      <td>58182400</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-07</th>\n", "      <td>24.04</td>\n", "      <td>24.10</td>\n", "      <td>23.70</td>\n", "      <td>23.90</td>\n", "      <td>50559700</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2010-01-08</th>\n", "      <td>23.77</td>\n", "      <td>24.24</td>\n", "      <td>23.74</td>\n", "      <td>24.07</td>\n", "      <td>51197400</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-17</th>\n", "      <td>166.96</td>\n", "      <td>167.01</td>\n", "      <td>164.98</td>\n", "      <td>166.64</td>\n", "      <td>34371700</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-21</th>\n", "      <td>166.23</td>\n", "      <td>167.73</td>\n", "      <td>165.98</td>\n", "      <td>166.05</td>\n", "      <td>29517200</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-22</th>\n", "      <td>166.94</td>\n", "      <td>167.03</td>\n", "      <td>165.23</td>\n", "      <td>165.25</td>\n", "      <td>24138800</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-23</th>\n", "      <td>165.74</td>\n", "      <td>166.35</td>\n", "      <td>164.82</td>\n", "      <td>166.27</td>\n", "      <td>19680800</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-01-24</th>\n", "      <td>167.05</td>\n", "      <td>167.07</td>\n", "      <td>164.00</td>\n", "      <td>164.59</td>\n", "      <td>24918100</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>2532 rows \u00d7 7 columns</p>\n", "</div>"], "text/plain": ["              Open    High     Low   Close    Volume  Dividends  Stock Splits\n", "Date                                                                         \n", "2010-01-04   24.04   24.41   24.01   24.29  38409100        0.0             0\n", "2010-01-05   24.22   24.41   24.05   24.30  49749600        0.0             0\n", "2010-01-06   24.24   24.40   23.96   24.15  58182400        0.0             0\n", "2010-01-07   24.04   24.10   23.70   23.90  50559700        0.0             0\n", "2010-01-08   23.77   24.24   23.74   24.07  51197400        0.0             0\n", "...            ...     ...     ...     ...       ...        ...           ...\n", "2020-01-17  166.96  167.01  164.98  166.64  34371700        0.0             0\n", "2020-01-21  166.23  167.73  165.98  166.05  29517200        0.0             0\n", "2020-01-22  166.94  167.03  165.23  165.25  24138800        0.0             0\n", "2020-01-23  165.74  166.35  164.82  166.27  19680800        0.0             0\n", "2020-01-24  167.05  167.07  164.00  164.59  24918100        0.0             0\n", "\n", "[2532 rows x 7 columns]"]}, "execution_count": 220, "metadata": {}, "output_type": "execute_result"}], "source": ["#define the ticker symbol\n", "tickerSymbol = 'MSFT'\n", "\n", "#get data on this ticker\n", "tickerData = yf.Ticker(tickerSymbol)\n", "\n", "#get the historical prices for this ticker\n", "gs = tickerData.history(period='1d', start='2010-1-1', end='2020-1-25')\n", "\n", "#see your data\n", "gs"]}, {"cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [{"data": {"text/plain": ["Date\n", "2010-01-04       NaN\n", "2010-01-05       NaN\n", "2010-01-06       NaN\n", "2010-01-07       NaN\n", "2010-01-08       NaN\n", "2010-01-11       NaN\n", "2010-01-12       NaN\n", "2010-01-13       NaN\n", "2010-01-14       NaN\n", "2010-01-15    24.041\n", "2010-01-19    24.053\n", "2010-01-20    24.024\n", "2010-01-21    23.965\n", "2010-01-22    23.848\n", "2010-01-25    23.742\n", "2010-01-26    23.682\n", "2010-01-27    23.651\n", "2010-01-28    23.558\n", "2010-01-29    23.340\n", "2010-02-01    23.148\n", "Name: Close, dtype: float64"]}, "execution_count": 224, "metadata": {}, "output_type": "execute_result"}], "source": ["gs.Close.rolling(10).mean().head(20)\n"]}, {"cell_type": "code", "execution_count": 232, "metadata": {}, "outputs": [{"data": {"text/plain": ["[<matplotlib.lines.Line2D at 0x7fb9f289bc90>]"]}, "execution_count": 232, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 1440x216 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(20,3))\n", "plt.plot(gs.Close)\n", "plt.plot(gs.Close.rolling(50).mean())\n"]}, {"cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1440x216 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(20,3))\n", "plt.plot(gs.iloc[:400].Close, label=\"original\")\n", "plt.plot(gs.iloc[:400].Close.rolling(50).mean(), label=\"rolling\")\n", "plt.plot(gs.iloc[:400].Close.rolling(50, center=True).mean(), label=\"center\")\n", "plt.legend();"]}, {"cell_type": "code", "execution_count": 236, "metadata": {}, "outputs": [{"data": {"text/plain": ["[<matplotlib.lines.Line2D at 0x7fb9f26eff50>]"]}, "execution_count": 236, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 1440x216 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plt.figure(figsize=(20,3))\n", "plt.plot(gs.iloc[:400].Close.rolling(10).mean())\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "p37", "language": "python", "name": "p37"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6"}}, "nbformat": 4, "nbformat_minor": 2}