数据规整化：清理、转换、合并、重塑

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<pre class="blockcode">你也可以来看我的Github上的原文，欢迎交流：</pre>
<pre class="blockcode">https://github.com/AsuraDong/Blog/blob/master/Articles/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/%E6%95%B0%E6%8D%AE%E8%A7%84%E6%95%B4%E5%8C%96%EF%BC%9A%E6%B8%85%E7%90%86%E3%80%81%E8%BD%AC%E6%8D%A2%E3%80%81%E5%90%88%E5%B9%B6%E3%80%81%E9%87%8D%E5%A1%91.md</pre>
<pre class="blockcode">import pandas as pd
import numpy as np
from pandas import DataFrame from pandas import Series</pre>
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<h2>1.合并数据集</h2>
<ul><li>pd.merge():各种参数的使用 </li></ul>
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<pre class="blockcode">df1 = DataFrame({'key':['b','b','a','c','a','a','b'],\ 'data1':[i for i in range(7)]}) df2 = DataFrame({'key':['a','b','d'],\ 'data2':[i for i in range(3)]}) print(df1)</pre>
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<pre class="blockcode"><code> data1 key
0 0 b
1 1 b
2 2 a
3 3 c
4 4 a
5 5 a
6 6 b
</code></pre>
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<pre class="blockcode">print(df2)</pre>
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<pre class="blockcode"><code> data2 key
0 0 a
1 1 b
2 2 d
</code></pre>
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<pre class="blockcode">print(pd.merge(df1,df2,on='key') ) #pd1 和 pd2 进行inner联结 #on ：指明将列当做键。默认是重叠的。</pre>
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<pre class="blockcode"><code> data1 key data2
0 0 b 1
1 1 b 1
2 6 b 1
3 2 a 0
4 4 a 0
5 5 a 0
</code></pre>
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<pre class="blockcode">df3 = DataFrame({'key1':['b','b','a','c','a','a','b'],\ 'data1':[i for i in range(7)]}) df4 = DataFrame({'key2':['a','b','d'],\ 'data2':[i for i in range(3)]}) # 如果没有重叠的列名 # 分别指定 print(pd.merge(df3,df4,left_on="key1<span class

数据规整化：清理、转换、合并、重塑

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