xdc_git_python_20210511/tutorials-master/numpy&pandas/17_merge.py

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"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
from __future__ import print_function
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