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导入支持包
import pandas as pd -
on属性
- 生成测试数据
# 生成测试数据 left1 = pd.DataFrame({ 'key': ['K0', 'K1', 'K2', 'K3'], 'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3'] }) # key A B # 0 K0 A0 B0 # 1 K1 A1 B1 # 2 K2 A2 B2 # 3 K3 A3 B3 right1 = pd.DataFrame({ 'key': ['K0', 'K1', 'K2', 'K3'], 'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3'] }) # key C D # 0 K0 C0 D0 # 1 K1 C1 D1 # 2 K2 C2 D2 # 3 K3 C3 D3- 根据一列
# 依据key column合并，并打印出 res = pd.merge(left1, right1, on='key') # key A B C D # 0 K0 A0 B0 C0 D0 # 1 K1 A1 B1 C1 D1 # 2 K2 A2 B2 C2 D2 # 3 K3 A3 B3 C3 D3- 根据两列
# 依据key1与key2 columns进行合并，并打印出四种结果['left', 'right', 'outer', 'inner'] # 默认how=inner res = pd.merge(left2, right2, on=['key1', 'key2'], how='inner') # how=‘inner’，取key1、key2有交集的行 # key1 key2 A B C D # 0 K0 K0 A0 B0 C0 D0 # 1 K1 K0 A2 B2 C1 D1 # 2 K1 K0 A2 B2 C2 D2 res = pd.merge(left2, right2, on=['key1', 'key2'], how='outer') # how='outer',取key1，key2的并集行 # key1 key2 A B C D # 0 K0 K0 A0 B0 C0 D0 # 1 K0 K1 A1 B1 NaN NaN # 2 K1 K0 A2 B2 C1 D1 # 3 K1 K0 A2 B2 C2 D2 # 4 K2 K1 A3 B3 NaN NaN # 5 K2 K0 NaN NaN C3 D3 res = pd.merge(left2, right2, on=['key1', 'key2'], how='left') # how='left',即以左边为主，取左边的全部，与右边的交集行，左边有而右边对应行没有的补nan # key1 key2 A B C D # 0 K0 K0 A0 B0 C0 D0 # 1 K0 K1 A1 B1 NaN NaN # 2 K1 K0 A2 B2 C1 D1 # 3 K1 K0 A2 B2 C2 D2 # 4 K2 K1 A3 B3 NaN NaN res = pd.merge(left2, right2, on=['key1', 'key2'], how='right') # how='right',即以右边为主，取右边的全部，与左边的交集行，右边有而左边对应行没有的补nan # key1 key2 A B C D # 0 K0 K0 A0 B0 C0 D0 # 1 K1 K0 A2 B2 C1 D1 # 2 K1 K0 A2 B2 C2 D2 # 3 K2 K0 NaN NaN C3 D3 -
indicator属性
作用是增加一列说明列，每个数据指明本行是左边的数据（left_only）、右边的数据（right_only） 还是 两边共有的数据（both）
- 生成测试数据
# 生成测试数据 left3 = pd.DataFrame({'col1': [0, 1], 'col_left': ['a', 'b']}) # col1 col_left # 0 0 a # 1 1 b right3 = pd.DataFrame({'col1': [1, 2, 2], 'col_right': [2, 2, 2]}) # col1 col_right # 0 1 2 # 1 2 2 # 2 2 2- 示例
# 默认值是False，即不追加这么一列 # 如果指定为True，添加说明列，默认标题为‘_merge’ res = pd.merge(left3, right3, on='col1', how='outer', indicator=True) # 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 2 NaN 2.0 right_only # 如果为indicator传入一个str类型的值，则追加说明列，并以该str变量为列名 res = pd.merge(left3, right3, on='col1', how='outer', indicator='indicator_column') # col1 col_left col_right indicator_column # 0 0 a NaN left_only # 1 1 b 2.0 both # 2 2 NaN 2.0 right_only # 3 2 NaN 2.0 right_only -
根据index属性进行合并
- 生成测试数据
# 生成测试数据 left4 = pd.DataFrame({ 'A': ['A0', 'A1', 'A2'], 'B': ['B0', 'B1', 'B2']}, index=['K0', 'K1', 'K2']) # A B # K0 A0 B0 # K1 A1 B1 # K2 A2 B2 right4 = pd.DataFrame({ 'C': ['C0', 'C2', 'C3'], 'D': ['D0', 'D2', 'D3']}, index=['K0', 'K2', 'K3']) # C D # K0 C0 D0 # K2 C2 D2 # K3 C3 D3- 示例
根据index合并主要根据left_index、right_index两个属性来指定，left_index、right_index两个属性必须同时指定
# 依据左右资料集的index进行合并，how='outer',并打印出 res = pd.merge(left4, right4, left_index=True, right_index=True, how='outer') # A B C D # K0 A0 B0 C0 D0 # K1 A1 B1 NaN NaN # K2 A2 B2 C2 D2 # K3 NaN NaN C3 D3 # 依据左右资料集的index进行合并，how='inner',并打印出 res = pd.merge(left4, right4, left_index=True, right_index=True, how='inner') # A B C D # K0 A0 B0 C0 D0 # K2 A2 B2 C2 D2 -
参考文献
代码主要来自 Pandas 合并 merge
