Merge, Join, Concat

1. Merge
   首先merge的操作非常类似sql里面的join，实现将两个Dataframe根据一些共有的列连接起来，当然，在实际场景中，这些共有列一般是Id，可以选择inner(默认)，left,right,outer 这几种模式，分别对应的是内连接，左连接，右连接

import numpy as np
import pandas as pd
dframe1 = pd.DataFrame({'key':['X','Z','Y','Z','X','X'],'value_df1': np.arange(6)})
print(dframe1)
dframe2 = pd.DataFrame({'key':['Q','Y','Z'],'value_df2':[1,2,3]})
print(dframe2)

• InnerMerge (内连接)

pd.merge(dframe1,dframe2,on='key',how='inner') #how值默认inner

• LeftMerge (左连接)

pd.merge(dframe1,dframe2,on='key',how='left')

• RightMerge (右连接)

pd.merge(dframe1,dframe2,on='key',how='right')

• OuterMerge (全连接)

pd.merge(dframe1,dframe2,on='key',how='outer')

• MultipleKey Merge (基于多个key上的merge)

df_left = pd.DataFrame({'key1': ['SF', 'SF', 'LA'],
                  'key2': ['one', 'two', 'one'],
                  'left_data': [10,20,30]})
df_right = pd.DataFrame({'key1': ['SF', 'SF', 'LA', 'LA'],
                   'key2': ['one', 'one', 'one', 'two'],
                   'right_data': [40,50,60,70]})
print(df_left)
print(df_right)
pd.merge(df_left,df_right,on='key1')

• Merge on Index (基于index上的merge)

df_left = pd.DataFrame({'key': ['X','Y','Z','X','Y'],
                  'data': range(5)})
df_right = pd.DataFrame({'group_data': [10, 20]}, index=['X', 'Y'])
print(df_left)
print(df_right)
pd.merge(df_left,df_right,left_on='key',right_index=True,how='outer')

2、join

left = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 
                    'B': ['B0', 'B1', 'B2', 'B3']}, 
                    index = ['K0', 'K1', 'K2', 'K3']) 
  
right = pd.DataFrame({'C': ['C0', 'C1', 'C2', 'C3'], 
                      'D': ['D0', 'D1', 'D2', 'D3']}, 
                      index = ['K0', 'K1', 'K2', 'K3']) 
print(left)
print(right)
left.join(right)

3、Concat

pd.concat(['df1','df2'],axis = 1/0)