07 - Pandas Aggregate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 07 - Pandas Aggregate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We start with a refresher on Numpy\n",
    "# We have used rea-world data which is massive and messy.\n",
    "# Let's generate small and simple dataset today\n",
    "# create a list of 100 integers from 0 to 99 using Numpy arange \n",
    "# then reshape it to a 10 by 10 matrix (a list with 10 lists, each is a list of 10 integers)\n",
    "\n",
    "x = np.arange(100).reshape(10,10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a string of 26 alphabets (type str)\n",
    "# turn a str into a list of 26 alphabets\n",
    "# then only keep the fist 10 alphabets\n",
    "# We will use the 10 alphabets as the column names for our dataframe\n",
    "\n",
    "y = list(string.ascii_uppercase)[0:10]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
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       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>74</td>\n",
       "      <td>75</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>87</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>90</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "    A   B   C   D   E   F   G   H   I   J\n",
       "0   0   1   2   3   4   5   6   7   8   9\n",
       "1  10  11  12  13  14  15  16  17  18  19\n",
       "2  20  21  22  23  24  25  26  27  28  29\n",
       "3  30  31  32  33  34  35  36  37  38  39\n",
       "4  40  41  42  43  44  45  46  47  48  49\n",
       "5  50  51  52  53  54  55  56  57  58  59\n",
       "6  60  61  62  63  64  65  66  67  68  69\n",
       "7  70  71  72  73  74  75  76  77  78  79\n",
       "8  80  81  82  83  84  85  86  87  88  89\n",
       "9  90  91  92  93  94  95  96  97  98  99"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a data frame using x as the data and y as the column names\n",
    "df = pd.DataFrame(x, columns=y)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>25.500000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>67.500000</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>76.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>99.000000</td>\n",
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      "text/plain": [
       "               A          B          C          D          E          F  \\\n",
       "count  10.000000  10.000000  10.000000  10.000000  10.000000  10.000000   \n",
       "mean   45.000000  46.000000  47.000000  48.000000  49.000000  50.000000   \n",
       "std    30.276504  30.276504  30.276504  30.276504  30.276504  30.276504   \n",
       "min     0.000000   1.000000   2.000000   3.000000   4.000000   5.000000   \n",
       "25%    22.500000  23.500000  24.500000  25.500000  26.500000  27.500000   \n",
       "50%    45.000000  46.000000  47.000000  48.000000  49.000000  50.000000   \n",
       "75%    67.500000  68.500000  69.500000  70.500000  71.500000  72.500000   \n",
       "max    90.000000  91.000000  92.000000  93.000000  94.000000  95.000000   \n",
       "\n",
       "               G          H          I          J  \n",
       "count  10.000000  10.000000  10.000000  10.000000  \n",
       "mean   51.000000  52.000000  53.000000  54.000000  \n",
       "std    30.276504  30.276504  30.276504  30.276504  \n",
       "min     6.000000   7.000000   8.000000   9.000000  \n",
       "25%    28.500000  29.500000  30.500000  31.500000  \n",
       "50%    51.000000  52.000000  53.000000  54.000000  \n",
       "75%    73.500000  74.500000  75.500000  76.500000  \n",
       "max    96.000000  97.000000  98.000000  99.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Use describe() method to produce summary statistics \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The summary statistics does not include the sum of each column or each row \n",
    "# we would use aggregate() function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    450\n",
       "B    460\n",
       "C    470\n",
       "D    480\n",
       "E    490\n",
       "F    500\n",
       "G    510\n",
       "H    520\n",
       "I    530\n",
       "J    540\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By default, aggregate is over the rows \n",
    "\n",
    "df.aggregate(\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     45\n",
       "1    145\n",
       "2    245\n",
       "3    345\n",
       "4    445\n",
       "5    545\n",
       "6    645\n",
       "7    745\n",
       "8    845\n",
       "9    945\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also aggregate over columns\n",
    "# BTW, agg is an alias for aggregrate\n",
    "\n",
    "df.agg(\"sum\",axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
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       "      <th>sum</th>\n",
       "      <td>450.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>520.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
       "      <td>30.276504</td>\n",
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      "text/plain": [
       "                 A           B           C           D           E  \\\n",
       "sum     450.000000  460.000000  470.000000  480.000000  490.000000   \n",
       "median   45.000000   46.000000   47.000000   48.000000   49.000000   \n",
       "std      30.276504   30.276504   30.276504   30.276504   30.276504   \n",
       "\n",
       "                 F           G           H           I           J  \n",
       "sum     500.000000  510.000000  520.000000  530.000000  540.000000  \n",
       "median   50.000000   51.000000   52.000000   53.000000   54.000000  \n",
       "std      30.276504   30.276504   30.276504   30.276504   30.276504  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can aggregate using multiple functions\n",
    "\n",
    "df.agg([\"sum\", \"median\",\"std\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B    C\n",
       "0  1.0  2.0  3.0\n",
       "1  4.0  5.0  6.0\n",
       "2  7.0  8.0  9.0\n",
       "3  NaN  NaN  NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is another example with less data\n",
    "\n",
    "df = pd.DataFrame([[1,2,3],\n",
    "                  [4,5,6],\n",
    "                  [7,8,9],\n",
    "                  [np.nan, np.nan, np.nan]],\n",
    "                  columns=[\"A\", \"B\", \"C\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A    B    C\n",
       "count  3.0  3.0  3.0\n",
       "mean   4.0  5.0  6.0\n",
       "std    3.0  3.0  3.0\n",
       "min    1.0  2.0  3.0\n",
       "25%    2.5  3.5  4.5\n",
       "50%    4.0  5.0  6.0\n",
       "75%    5.5  6.5  7.5\n",
       "max    7.0  8.0  9.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A     B     C\n",
       "min    1.0   2.0   3.0\n",
       "max    7.0   8.0   9.0\n",
       "sum   12.0  15.0  18.0\n",
       "mean   4.0   5.0   6.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.aggregate([\"min\", \"max\", \"sum\", \"mean\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    4.0\n",
       "2    7.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg(\"min\", axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[79, 80, 24, 28, 57],\n",
       "       [ 1, 31, 37, 97,  7],\n",
       "       [27, 64, 58, 65, 35],\n",
       "       [ 1, 25, 15, 73, 60],\n",
       "       [ 2, 86, 35, 47, 19],\n",
       "       [44, 84, 56, 53, 66],\n",
       "       [13, 72, 32, 29, 94],\n",
       "       [11, 17, 33, 74, 65],\n",
       "       [87, 75, 44,  4, 95],\n",
       "       [16, 34, 46, 72, 16],\n",
       "       [34,  6, 85,  9,  0],\n",
       "       [87, 60, 72, 42, 60],\n",
       "       [28, 56, 53,  6, 40],\n",
       "       [11, 72, 29, 40, 90],\n",
       "       [97, 20, 98, 77,  8],\n",
       "       [98,  3, 20, 55, 80],\n",
       "       [21, 23, 26,  0, 48],\n",
       "       [39, 79, 76,  1, 62],\n",
       "       [75,  8, 31, 35, 62],\n",
       "       [44, 80, 60, 70, 61]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is another example using random numbers\n",
    "# Generate 100 random integers ranging from 0 and 99\n",
    "# reshape the one-dimensional numpy array into a 20 by 5 matrix (20 rows and 5 columns)\n",
    "\n",
    "x = np.random.randint(0,100,100).reshape(20,5)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ABCDEFGHIJKLMNOPQRSTUVWXYZ'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the 26 alphabets using string library\n",
    "\n",
    "y = string.ascii_uppercase\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B', 'C', 'D', 'E']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the str object into a list and keep only the first five \n",
    "\n",
    "y = list(y)[0:5]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79</td>\n",
       "      <td>80</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "      <td>65</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>73</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>44</td>\n",
       "      <td>84</td>\n",
       "      <td>56</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "      <td>33</td>\n",
       "      <td>74</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>87</td>\n",
       "      <td>75</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "      <td>46</td>\n",
       "      <td>72</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>87</td>\n",
       "      <td>60</td>\n",
       "      <td>72</td>\n",
       "      <td>42</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>56</td>\n",
       "      <td>53</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>97</td>\n",
       "      <td>20</td>\n",
       "      <td>98</td>\n",
       "      <td>77</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>98</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>39</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>75</td>\n",
       "      <td>8</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>44</td>\n",
       "      <td>80</td>\n",
       "      <td>60</td>\n",
       "      <td>70</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A   B   C   D   E\n",
       "0   79  80  24  28  57\n",
       "1    1  31  37  97   7\n",
       "2   27  64  58  65  35\n",
       "3    1  25  15  73  60\n",
       "4    2  86  35  47  19\n",
       "5   44  84  56  53  66\n",
       "6   13  72  32  29  94\n",
       "7   11  17  33  74  65\n",
       "8   87  75  44   4  95\n",
       "9   16  34  46  72  16\n",
       "10  34   6  85   9   0\n",
       "11  87  60  72  42  60\n",
       "12  28  56  53   6  40\n",
       "13  11  72  29  40  90\n",
       "14  97  20  98  77   8\n",
       "15  98   3  20  55  80\n",
       "16  21  23  26   0  48\n",
       "17  39  79  76   1  62\n",
       "18  75   8  31  35  62\n",
       "19  44  80  60  70  61"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe using x and y\n",
    "\n",
    "df = pd.DataFrame(x,columns=y)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate summary statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.750000</td>\n",
       "      <td>48.750000</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>43.850000</td>\n",
       "      <td>51.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.013736</td>\n",
       "      <td>29.784975</td>\n",
       "      <td>22.816199</td>\n",
       "      <td>29.367499</td>\n",
       "      <td>29.134669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>22.250000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>40.500000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>65.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B          C          D          E\n",
       "count  20.000000  20.000000  20.000000  20.000000  20.000000\n",
       "mean   40.750000  48.750000  46.500000  43.850000  51.250000\n",
       "std    34.013736  29.784975  22.816199  29.367499  29.134669\n",
       "min     1.000000   3.000000  15.000000   0.000000   0.000000\n",
       "25%    12.500000  22.250000  30.500000  23.250000  31.000000\n",
       "50%    31.000000  58.000000  40.500000  44.500000  60.000000\n",
       "75%    76.000000  76.000000  58.500000  70.500000  65.250000\n",
       "max    98.000000  86.000000  98.000000  97.000000  95.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try pandas aggregate method here"
   ]
  }
 ],
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