Code:
import pandas as pd
# merging two df by key/keys
# 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)
operation result:
A B key
0 A0 B0 K0
1 A1 B1 K1
2 A2 B2 K2
3 A3 B3 K3
C D key
0 C0 D0 K0
1 C1 D1 K1
2 C2 D2 K2
3 C3 D3 K3
A B key C D
0 A0 B0 K0 C0 D0
1 A1 B1 K1 C1 D1
2 A2 B2 K2 C2 D2
3 A3 B3 K3 C3 D3
Code:
# 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)
operation result:
A B key1 key2
0 A0 B0 K0 K0
1 A1 B1 K0 K1
2 A2 B2 K1 K0
3 A3 B3 K2 K1
C D key1 key2
0 C0 D0 K0 K0
1 C1 D1 K1 K0
2 C2 D2 K1 K0
3 C3 D3 K2 K0
Code:
# how = ['left','right','outer','inner']
# Only consider the same key:inner
res = pd.merge(left, right, on=['key1', 'key2'], how='inner')
print("Inner")
print(res)
# None are filled with NAN
res = pd.merge(left, right, on=['key1', 'key2'], how='outer')
print("Outer")
print(res)
# The left side does not move, the right side is filled with NAN
res = pd.merge(left, right, on=['key1', 'key2'], how='left')
print("Left")
print(res)
# The right side does not move, the left side is filled with NAN
res = pd.merge(left, right, on=['key1', 'key2'], how='right')
print("Right")
print(res)
operation result:
Inner
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A2 B2 K1 K0 C1 D1
2 A2 B2 K1 K0 C2 D2
Outer
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A1 B1 K0 K1 NaN NaN
2 A2 B2 K1 K0 C1 D1
3 A2 B2 K1 K0 C2 D2
4 A3 B3 K2 K1 NaN NaN
5 NaN NaN K2 K0 C3 D3
Left
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A1 B1 K0 K1 NaN NaN
2 A2 B2 K1 K0 C1 D1
3 A2 B2 K1 K0 C2 D2
4 A3 B3 K2 K1 NaN NaN
Right
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A2 B2 K1 K0 C1 D1
2 A2 B2 K1 K0 C2 D2
3 NaN NaN K2 K0 C3 D3
Code:
# indicator
df1 = pd.DataFrame({'col1': [0, 1],
'col_left': ['a', 'b']})
df2 = pd.DataFrame({'col1': [1, 2, 3],
'col_right': [2, 2, 2]})
print(df1)
print(df2)
res = pd.merge(df1, df2, on='col1', how='outer', indicator=True)
print(res)
res = pd.merge(df1, df2, on='col1', how='outer', indicator="AAA")
print(res)
operation result:
col1 col_left 0 0 a 1 1 b col1 col_right 0 1 2 1 2 2 2 3 2 col1 col_left col_right _merge 0 0 a NaN left_only 1 1 b 2.0 both 2 2 NaN 2.0 right_only 3 3 NaN 2.0 right_only col1 col_left col_right AAA 0 0 a NaN left_only 1 1 b 2.0 both 2 2 NaN 2.0 right_only 3 3 NaN 2.0 right_only
Code:
# merged by index
left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
'B': ['B0', 'B1', 'B2']},
index=['K0', 'K1', 'K2'])
right = pd.DataFrame({'C': ['C0', 'C1', 'C2'],
'D': ['D0', 'D1', 'D2']},
index=['K0', 'K2', 'K3'])
print(left)
print(right)
res1 = pd.merge(left, right, left_index=True, right_index=True, how='outer')
res2 = pd.merge(left, right, left_index=True, right_index=True, how='inner')
print(res1)
print(res2)
operation result:
A B
K0 A0 B0
K1 A1 B1
K2 A2 B2
C D
K0 C0 D0
K2 C1 D1
K3 C2 D2
A B C D
K0 A0 B0 C0 D0
K1 A1 B1 NaN NaN
K2 A2 B2 C1 D1
K3 NaN NaN C2 D2
A B C D
K0 A0 B0 C0 D0
K2 A2 B2 C1 D1
Code:
# handle overlapping
# handle duplicates
boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'],
'age': [1, 2, 3]})
girls = pd.DataFrame({'k': ['K0', 'K0', 'K2'],
'age': [4, 5, 6]})
print(boys)
print(girls)
res = pd.merge(boys, girls, on='k', suffixes=['_boys', '_girls'], how='inner')
print(res)
operation result:
age k 0 1 K0 1 2 K1 2 3 K2 age k 0 4 K0 1 5 K0 2 6 K2 age_boys k age_girls 0 1 K0 4 1 1 K0 5 2 3 K2 6