create pd 17

Author: Mofan Zhou

numpy&pandas/17_merge.py

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+# View more python tutorials on my Youtube and Youku channel!!!
+
+# Youtube video tutorial: https://www.youtube.com/channel/UCdyjiB5H8Pu7aDTNVXTTpcg
+# Youku video tutorial: http://i.youku.com/pythontutorial
+
+"""
+Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
+"""
+import pandas as pd
+
+# merging two df by key/keys. (may be used in database)
+# simple example
+left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
+                                  'A': ['A0', 'A1', 'A2', 'A3'],
+                                  'B': ['B0', 'B1', 'B2', 'B3']})
+right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
+                                    'C': ['C0', 'C1', 'C2', 'C3'],
+                                    'D': ['D0', 'D1', 'D2', 'D3']})
+print(left)
+print(right)
+res = pd.merge(left, right, on='key')
+print(res)
+
+# consider two keys
+left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
+                             'key2': ['K0', 'K1', 'K0', 'K1'],
+                             'A': ['A0', 'A1', 'A2', 'A3'],
+                             'B': ['B0', 'B1', 'B2', 'B3']})
+right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
+                              'key2': ['K0', 'K0', 'K0', 'K0'],
+                              'C': ['C0', 'C1', 'C2', 'C3'],
+                              'D': ['D0', 'D1', 'D2', 'D3']})
+print(left)
+print(right)
+res = pd.merge(left, right, on=['key1', 'key2'], how='inner')  # default for how='inner'
+# how = ['left', 'right', 'outer', 'inner']
+res = pd.merge(left, right, on=['key1', 'key2'], how='left')
+print(res)
+
+# indicator
+df1 = pd.DataFrame({'col1':[0,1], 'col_left':['a','b']})
+df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
+print(df1)
+print(df2)
+res = pd.merge(df1, df2, on='col1', how='outer', indicator=True)
+# give the indicator a custom name
+res = pd.merge(df1, df2, on='col1', how='outer', indicator='indicator_column')
+
+
+# merged by index
+left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
+                                  'B': ['B0', 'B1', 'B2']},
+                                  index=['K0', 'K1', 'K2'])
+right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
+                                     'D': ['D0', 'D2', 'D3']},
+                                      index=['K0', 'K2', 'K3'])
+print(left)
+print(right)
+# left_index and right_index
+res = pd.merge(left, right, left_index=True, right_index=True, how='outer')
+res = pd.merge(left, right, left_index=True, right_index=True, how='inner')
+
+# handle overlapping
+boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]})
+girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]})
+res = pd.merge(boys, girls, on='k', suffixes=['_boy', '_girl'], how='inner')
+print(res)
+
+# join function in pandas is similar with merge. If know merge, you will understand join