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Day76-90/02.Pandas的应用.md
浏览文件 @ 43c147da
## Pandas的应用
### 1、pandas入门
### 2、pandas索引
### 3、pandas数据清洗之空数据
[数据挖掘之空数据处理（有史以来最全面）]: https://blog.csdn.net/Soft_Po/article/details/89302887
### 4、pandas多层索引
### 5、pandas多层索引计算
### 6、pandas数据集成concat
### 7、pandas数据集成merge
### 8、pandas分组聚合操作
### 9、pandas数据集成实战
### 10、美国大选项目
[2012美国大选政治献金项目数据分析（有史以来最全面）]: https://blog.csdn.net/Soft_Po/article/details/89283382
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Day76-90/code/1-pandas入门.ipynb 0 → 100644
浏览文件 @ 43c147da
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from pandas import Series,DataFrame"
]
},
{
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"s"
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"source": [
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"df.mean()"
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"data": {
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"Math 132\n",
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Day76-90/code/2-pandas-索引.ipynb 0 → 100644
浏览文件 @ 43c147da
此差异已折叠。
Day76-90/code/3-pandas数据清洗之空数据.ipynb 0 → 100644
浏览文件 @ 43c147da
此差异已折叠。
Day76-90/code/4-pandas多层索引.ipynb 0 → 100644
浏览文件 @ 43c147da
{
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{
"cell_type": "code",
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"metadata": {},
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"source": [
"import numpy as np\n",
"\n",
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"# 数据分析BI-------->人工智能AI\n",
"# 数据分析和数据挖掘一个意思，\n",
"# 工具和软件：Excel 免费版\n",
"# SPSS（一人一年10000）、SAS（一人一年5000）、Matlab 收费\n",
"# R、Python（全方位语言，流行） 免费\n",
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"s"
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},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
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{
"data": {
"text/plain": [
"张三 期中 114\n",
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"s2 = Series(np.random.randint(0,150,size = 6),index = pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]))\n",
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"df = DataFrame(np.random.randint(0,150,size = (6,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]) )\n",
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{
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" <td>91</td>\n",
" <td>87</td>\n",
" <td>143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>120</td>\n",
" <td>118</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">期末</th>\n",
" <th>A</th>\n",
" <td>56</td>\n",
" <td>76</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>11</td>\n",
" <td>105</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">王五</th>\n",
" <th rowspan=\"2\" valign=\"top\">期中</th>\n",
" <th>A</th>\n",
" <td>147</td>\n",
" <td>78</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>128</td>\n",
" <td>126</td>\n",
" <td>146</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">期末</th>\n",
" <th>A</th>\n",
" <td>49</td>\n",
" <td>45</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>121</td>\n",
" <td>26</td>\n",
" <td>77</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Python En Math\n",
"张三 期中 A 15 31 17\n",
" B 82 56 123\n",
" 期末 A 14 2 78\n",
" B 69 50 17\n",
"李四 期中 A 91 87 143\n",
" B 120 118 39\n",
" 期末 A 56 76 55\n",
" B 11 105 121\n",
"王五 期中 A 147 78 1\n",
" B 128 126 146\n",
" 期末 A 49 45 114\n",
" B 121 26 77"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 三层索引\n",
"df3 = DataFrame(np.random.randint(0,150,size = (12,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末'],['A','B']]) )\n",
"\n",
"df3"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"73"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 先获取列后获取行\n",
"df['Python']['张三']['期中']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df2 = df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"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></th>\n",
" <th>Python</th>\n",
" <th>En</th>\n",
" <th>Math</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>73</td>\n",
" <td>5</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">李四</th>\n",
" <th>期中</th>\n",
" <td>149</td>\n",
" <td>81</td>\n",
" <td>142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>71</td>\n",
" <td>138</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">王五</th>\n",
" <th>期中</th>\n",
" <td>11</td>\n",
" <td>94</td>\n",
" <td>103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>25</td>\n",
" <td>121</td>\n",
" <td>83</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Python En Math\n",
"张三 期中 73 5 25\n",
" 期末 37 36 56\n",
"李四 期中 149 81 142\n",
" 期末 71 138 0\n",
"王五 期中 11 94 103\n",
" 期末 25 121 83"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"73"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 先获取行，后获取列\n",
"df.loc['张三'].loc['期中']['Python']"
]
},
{
"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></th>\n",
" <th>Python</th>\n",
" <th>En</th>\n",
" <th>Math</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">张三</th>\n",
" <th>期中</th>\n",
" <td>73</td>\n",
" <td>5</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>期末</th>\n",
" <td>37</td>\n",
" <td>36</td>\n",
" <td>56</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Python En Math\n",
"张三 期中 73 5 25\n",
" 期末 37 36 56"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[[0,1]]"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Day76-90/code/5-pandas多层索引计算.ipynb 0 → 100644
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Day76-90/code/6-pandas数据集成.ipynb 0 → 100644
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Day76-90/code/7-pandas数据集成merge.ipynb 0 → 100644
浏览文件 @ 43c147da
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Day76-90/code/8-pandas分组聚合操作.ipynb 0 → 100644
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Day76-90/code/9-pandas数据集成实战.ipynb 0 → 100644
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Day76-90/code/state-abbrevs.csv 0 → 100644
浏览文件 @ 43c147da
"state","abbreviation"
"Alabama","AL"
"Alaska","AK"
"Arizona","AZ"
"Arkansas","AR"
"California","CA"
"Colorado","CO"
"Connecticut","CT"
"Delaware","DE"
"District of Columbia","DC"
"Florida","FL"
"Georgia","GA"
"Hawaii","HI"
"Idaho","ID"
"Illinois","IL"
"Indiana","IN"
"Iowa","IA"
"Kansas","KS"
"Kentucky","KY"
"Louisiana","LA"
"Maine","ME"
"Montana","MT"
"Nebraska","NE"
"Nevada","NV"
"New Hampshire","NH"
"New Jersey","NJ"
"New Mexico","NM"
"New York","NY"
"North Carolina","NC"
"North Dakota","ND"
"Ohio","OH"
"Oklahoma","OK"
"Oregon","OR"
"Maryland","MD"
"Massachusetts","MA"
"Michigan","MI"
"Minnesota","MN"
"Mississippi","MS"
"Missouri","MO"
"Pennsylvania","PA"
"Rhode Island","RI"
"South Carolina","SC"
"South Dakota","SD"
"Tennessee","TN"
"Texas","TX"
"Utah","UT"
"Vermont","VT"
"Virginia","VA"
"Washington","WA"
"West Virginia","WV"
"Wisconsin","WI"
"Wyoming","WY"
\ No newline at end of file
Day76-90/code/state-areas.csv 0 → 100644
浏览文件 @ 43c147da
state,area (sq. mi)
Alabama,52423
Alaska,656425
Arizona,114006
Arkansas,53182
California,163707
Colorado,104100
Connecticut,5544
Delaware,1954
Florida,65758
Georgia,59441
Hawaii,10932
Idaho,83574
Illinois,57918
Indiana,36420
Iowa,56276
Kansas,82282
Kentucky,40411
Louisiana,51843
Maine,35387
Maryland,12407
Massachusetts,10555
Michigan,96810
Minnesota,86943
Mississippi,48434
Missouri,69709
Montana,147046
Nebraska,77358
Nevada,110567
New Hampshire,9351
New Jersey,8722
New Mexico,121593
New York,54475
North Carolina,53821
North Dakota,70704
Ohio,44828
Oklahoma,69903
Oregon,98386
Pennsylvania,46058
Rhode Island,1545
South Carolina,32007
South Dakota,77121
Tennessee,42146
Texas,268601
Utah,84904
Vermont,9615
Virginia,42769
Washington,71303
West Virginia,24231
Wisconsin,65503
Wyoming,97818
District of Columbia,68
Puerto Rico,3515
Day76-90/code/state-population.csv 0 → 100644
浏览文件 @ 43c147da
此差异已折叠。
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