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olivx/escola.xlsx

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Created December 5, 2018 21:29
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Annotations of Pandas DataFrame
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gen_names_xlsx.ipynb
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gen_random_values.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import string\n",
"# https://www.ritchieng.com/creating-dataframe-from-objects/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'ABCDEFGHIJKLMNOPQRSTUVWXYZ'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"string.ascii_uppercase"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>91</td>\n",
" <td>81</td>\n",
" <td>76</td>\n",
" <td>30</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>28</td>\n",
" <td>30</td>\n",
" <td>49</td>\n",
" <td>71</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>69</td>\n",
" <td>77</td>\n",
" <td>33</td>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>34</td>\n",
" <td>59</td>\n",
" <td>62</td>\n",
" <td>87</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>64</td>\n",
" <td>50</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>46</td>\n",
" <td>71</td>\n",
" <td>35</td>\n",
" <td>33</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3</td>\n",
" <td>69</td>\n",
" <td>65</td>\n",
" <td>30</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>33</td>\n",
" <td>46</td>\n",
" <td>64</td>\n",
" <td>16</td>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>26</td>\n",
" <td>58</td>\n",
" <td>78</td>\n",
" <td>19</td>\n",
" <td>87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>16</td>\n",
" <td>70</td>\n",
" <td>68</td>\n",
" <td>23</td>\n",
" <td>11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"0 91 81 76 30 19\n",
"1 28 30 49 71 32\n",
"2 69 77 33 16 17\n",
"3 34 59 62 87 57\n",
"4 0 93 64 50 33\n",
"5 46 71 35 33 31\n",
"6 3 69 65 30 92\n",
"7 33 46 64 16 82\n",
"8 26 58 78 19 87\n",
"9 16 70 68 23 11"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"size_columns = 5\n",
"size_rows = 10\n",
"max_value = 100\n",
"_string = string.ascii_uppercase[:size_columns]\n",
"df_int = pd.DataFrame(np.random.randint(0,max_value,size=(size_rows, size_columns)), columns=list(_string))\n",
"df_int"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>-0.556790</td>\n",
" <td>0.650430</td>\n",
" <td>-1.894784</td>\n",
" <td>-1.382816</td>\n",
" <td>1.808950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.297879</td>\n",
" <td>0.361739</td>\n",
" <td>-1.239575</td>\n",
" <td>0.769614</td>\n",
" <td>0.574830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.590421</td>\n",
" <td>1.447465</td>\n",
" <td>-0.868681</td>\n",
" <td>-0.105521</td>\n",
" <td>-1.796299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.035956</td>\n",
" <td>2.398848</td>\n",
" <td>0.889333</td>\n",
" <td>1.445073</td>\n",
" <td>0.310777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.035894</td>\n",
" <td>-0.801347</td>\n",
" <td>1.406680</td>\n",
" <td>1.230958</td>\n",
" <td>0.049223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.338208</td>\n",
" <td>0.073445</td>\n",
" <td>1.074535</td>\n",
" <td>0.448073</td>\n",
" <td>-0.361343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.282894</td>\n",
" <td>-1.440926</td>\n",
" <td>-1.309745</td>\n",
" <td>-0.264459</td>\n",
" <td>-0.335074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.853490</td>\n",
" <td>1.519119</td>\n",
" <td>0.574555</td>\n",
" <td>1.323457</td>\n",
" <td>0.727536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.823434</td>\n",
" <td>0.010703</td>\n",
" <td>-0.550191</td>\n",
" <td>-0.639050</td>\n",
" <td>1.143485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.713230</td>\n",
" <td>-0.980415</td>\n",
" <td>-1.481190</td>\n",
" <td>3.381801</td>\n",
" <td>-0.971942</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D E\n",
"0 -0.556790 0.650430 -1.894784 -1.382816 1.808950\n",
"1 1.297879 0.361739 -1.239575 0.769614 0.574830\n",
"2 0.590421 1.447465 -0.868681 -0.105521 -1.796299\n",
"3 0.035956 2.398848 0.889333 1.445073 0.310777\n",
"4 -0.035894 -0.801347 1.406680 1.230958 0.049223\n",
"5 0.338208 0.073445 1.074535 0.448073 -0.361343\n",
"6 0.282894 -1.440926 -1.309745 -0.264459 -0.335074\n",
"7 0.853490 1.519119 0.574555 1.323457 0.727536\n",
"8 -1.823434 0.010703 -0.550191 -0.639050 1.143485\n",
"9 0.713230 -0.980415 -1.481190 3.381801 -0.971942"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(np.random.randn(size_rows,size_columns), columns=list(_string))\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>city_id</th>\n",
" <th>population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>101</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>102</td>\n",
" <td>43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>103</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>104</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>105</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>106</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>107</td>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>108</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>109</td>\n",
" <td>62</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city_id population\n",
"0 100 23\n",
"1 101 14\n",
"2 102 43\n",
"3 103 4\n",
"4 104 12\n",
"5 105 92\n",
"6 106 46\n",
"7 107 53\n",
"8 108 25\n",
"9 109 62"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_dict = {\n",
" 'city_id': np.arange(100,110,1),\n",
" 'population': np.random.randint(0,101,10)\n",
"}\n",
"df_pop = pd.DataFrame(new_dict)\n",
"df_pop"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>city_id</th>\n",
" <th>population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10.00000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>104.50000</td>\n",
" <td>37.400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.02765</td>\n",
" <td>27.089153</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>100.00000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>102.25000</td>\n",
" <td>16.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>104.50000</td>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>106.75000</td>\n",
" <td>51.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>109.00000</td>\n",
" <td>92.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" city_id population\n",
"count 10.00000 10.000000\n",
"mean 104.50000 37.400000\n",
"std 3.02765 27.089153\n",
"min 100.00000 4.000000\n",
"25% 102.25000 16.250000\n",
"50% 104.50000 34.000000\n",
"75% 106.75000 51.250000\n",
"max 109.00000 92.000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_pop.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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