【452】pandas筛选出表中满足另一个表所有条件的数据

参考：pandas筛选出表中满足另一个表所有条件的数据

参考：pandas:匹配两个dataframe

使用 pd.merge 来实现

on 表示查询的 columns，如果都有 id，那么这是很好的区别项，找到 id 相同的进行merge。

>>> import numpy as np
	       
>>> import pandas as pd
	       
>>> data1 = {
	'one': pd.Series([1,2,3]),
	'two': pd.Series([11,22,33])
	}
	       
>>> df1 = pd.DataFrame(data = data1)
	       
>>> df1
	       
   one  two
0    1   11
1    2   22
2    3   33
>>> data2 = {
	'one': pd.Series([1,2,3,4,5,6]),
	'two': pd.Series([11,22,33]),
	'three': pd.Series([111,222,333]),
	'four': pd.Series([1111,2222,3333,4444,5555,6666])
	}
	              
>>> df2 = pd.DataFrame(data = data2)
	       
>>> df2
	       
   one   two  three  four
0    1  11.0  111.0  1111
1    2  22.0  222.0  2222
2    3  33.0  333.0  3333
3    4   NaN    NaN  4444
4    5   NaN    NaN  5555
5    6   NaN    NaN  6666
>>> df2[df2['one']<3]
	       
   one   two  three  four
0    1  11.0  111.0  1111
1    2  22.0  222.0  2222

>>> df = pd.merge(df1, df2, how='inner')
		     
>>> df
		     
   one  two  three  four
0    1   11  111.0  1111
1    2   22  222.0  2222
2    3   33  333.0  3333
>>> df1
		     
   one  two
0    1   11
1    2   22
2    3   33
>>> df2
		     
   one   two  three  four
0    1  11.0  111.0  1111
1    2  22.0  222.0  2222
2    3  33.0  333.0  3333
3    4   NaN    NaN  4444
4    5   NaN    NaN  5555
5    6   NaN    NaN  6666
>>> pd.merge(df1, df2, how='inner')
		     
   one  two  three  four
0    1   11  111.0  1111
1    2   22  222.0  2222
2    3   33  333.0  3333
>>> pd.merge(df2, df1, how='inner')
		     
   one   two  three  four
0    1  11.0  111.0  1111
1    2  22.0  222.0  2222
2    3  33.0  333.0  3333
>>> five = pd.Series([1,2,3,4,5,6])
		     
>>> df2['five'] = five
		     
>>> df2
		     
   one   two  three  four  five
0    1  11.0  111.0  1111     1
1    2  22.0  222.0  2222     2
2    3  33.0  333.0  3333     3
3    4   NaN    NaN  4444     4
4    5   NaN    NaN  5555     5
5    6   NaN    NaN  6666     6
>>> df1
		     
   one  two
0    1   11
1    2   22
2    3   33
>>> pd.merge(df2, df1, how='inner')
		     
   one   two  three  four  five
0    1  11.0  111.0  1111     1
1    2  22.0  222.0  2222     2
2    3  33.0  333.0  3333     3
>>> pd.merge(df1, df2, how='inner')
		     
   one  two  three  four  five
0    1   11  111.0  1111     1
1    2   22  222.0  2222     2
2    3   33  333.0  3333     3
>>> df1
		     
   one  two
0    1   11
1    2   22
2    3   33
>>> df2
		     
   one   two  three  four  five
0    1  11.0  111.0  1111     1
1    2  22.0  222.0  2222     2
2    3  33.0  333.0  3333     3
3    4   NaN    NaN  4444     4
4    5   NaN    NaN  5555     5
5    6   NaN    NaN  6666     6
>>> six = pd.Series([-1, -2, -3])
		     
>>> df1['six'] = six
		     
>>> df1
		     
   one  two  six
0    1   11   -1
1    2   22   -2
2    3   33   -3
>>> df2
		     
   one   two  three  four  five
0    1  11.0  111.0  1111     1
1    2  22.0  222.0  2222     2
2    3  33.0  333.0  3333     3
3    4   NaN    NaN  4444     4
4    5   NaN    NaN  5555     5
5    6   NaN    NaN  6666     6
>>> pd.merge(df1, df2, how='inner')
		     
   one  two  six  three  four  five
0    1   11   -1  111.0  1111     1
1    2   22   -2  222.0  2222     2
2    3   33   -3  333.0  3333     3
>>> pd.merge(df2, df1, how='inner')
		     
   one   two  three  four  five  six
0    1  11.0  111.0  1111     1   -1
1    2  22.0  222.0  2222     2   -2
2    3  33.0  333.0  3333     3   -3