pandas 第三節 (merge用法)

df1

 

df2

join是按照行索引來關聯操作,列名不能相同

df1.join(df2) #返回df1的行數，df1和df2的所有列名 值沒有的話用NaN

merge是按照列索引來關聯操作，和關係型數據庫很相近

1. df1.merge(df2,on=0) #等價於df1.0=df2.0
    0  1_x   2   3   4  1_y
0  18   15  19  15  12   10
1  18   15  19  15  12   11
2  16   13  11  15  18   13
SELECT * FROM df1
JOIN df2 ON df1.[0]=df2.[0]
2. df1.merge(df2,left_on=3,right_on=0) #默認是inner
   0_x  1_x   2   3   4  0_y  1_y
0   11   18  12  18  14   18   10
1   11   18  12  18  14   18   11
SELECT * FROM df1
JOIN df2 ON df1.[3]=df2.[0]

3. df1.merge(df2,left_on=3,right_on=0,how="outer")
    0_x   1_x     2   3     4   0_y   1_y
0  18.0  15.0  19.0  15  12.0   NaN   NaN
1  16.0  13.0  11.0  15  18.0   NaN   NaN
2  10.0  16.0  15.0  14  16.0   NaN   NaN
3  11.0  18.0  12.0  18  14.0  18.0  10.0
4  11.0  18.0  12.0  18  14.0  18.0  11.0
5   NaN   NaN   NaN  19   NaN  19.0  10.0
6   NaN   NaN   NaN  16   NaN  16.0  13.0
SELECT * FROM df1 
FULL OUTER JOIN df2 on df1.[3]=df2.[0]

4. df1.merge(df2,left_on=3,right_on=0,how="left")
   0_x  1_x   2   3   4   0_y   1_y
0   18   15  19  15  12   NaN   NaN
1   10   16  15  14  16   NaN   NaN
2   11   18  12  18  14  18.0  10.0
3   11   18  12  18  14  18.0  11.0
4   16   13  11  15  18   NaN   NaN
SELECT * FROM df1 
LEFT JOIN df2 on df1.[3]=df2.[0]

5. df1.merge(df2,left_on=3,right_on=0,how="right")
    0_x   1_x     2    3     4   0_y   1_y
0   11.0  18.0   12.0   18   14.0   18   10
1   11.0  18.0   12.0   18   14.0   18   11
2   NaN   NaN   NaN  19   NaN   19   10
3   NaN   NaN   NaN  16   NaN   16   13
SELECT * FROM df1 
RIGHT JOIN df2 on df1.[3]=df2.[0]

 分組 group

grouped=df2.groupby([0]) #pandas.core.groupby.generic.DataFrameGroupBy

遍歷分組
for i,j in grouped:
    print(type(i),type(j))
    print("$"*50)
    print(i)
    print("-"*50)
    print(j)
    print("*"*50

c=grouped.count()

select [0],count([1]),count([2]) 
from df2
group by [0]