通过pd.concat()方法合并几个DataFrame对象进行合并
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导入支持包
import pandas as pd import numpy as np -
axis属性——指定合并的方向
- 生成测试数据
df1 = pd.DataFrame(np.ones((1,4))*0, columns=['a','b','c','d']) # a b c d # 0 0.0 0.0 0.0 0.0 df2 = pd.DataFrame(np.ones((2,4))*1, columns=['a','b','c','d']) # a b c d # 0 1.0 1.0 1.0 1.0 # 1 1.0 1.0 1.0 1.0 df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d']) # a b c d # 0 2.0 2.0 2.0 2.0 # 1 2.0 2.0 2.0 2.0 # 2 2.0 2.0 2.0 2.0- 纵向合并
# axis=0在数值方向上合并 res1 = pd.concat([df1, df2, df3], axis=0) # a b c d # 0 0.0 0.0 0.0 0.0 # 0 1.0 1.0 1.0 1.0 # 1 1.0 1.0 1.0 1.0 # 0 2.0 2.0 2.0 2.0 # 1 2.0 2.0 2.0 2.0 # 2 2.0 2.0 2.0 2.0- 横向合并
res2 = pd.concat([df1, df2, df3], axis=1) # axis=1在行方向上合并，缺少的数据补nan # a b c d a b c d a b c d # 0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 # 1 NaN NaN NaN NaN 1.0 1.0 1.0 1.0 2.0 2.0 2.0 2.0 # 2 NaN NaN NaN NaN NaN NaN NaN NaN 2.0 2.0 2.0 2.0- index_ignore属性
# ignore_index说的是index不参与合并，合成的res自己生成新的索引 res3 = pd.concat([df1, df2, df3], axis=0, ignore_index=True) # a b c d # 0 0.0 0.0 0.0 0.0 # 1 1.0 1.0 1.0 1.0 # 2 1.0 1.0 1.0 1.0 # 3 2.0 2.0 2.0 2.0 # 4 2.0 2.0 2.0 2.0 # 5 2.0 2.0 2.0 2.0 -
join属性——指定合并交集（inner）还是合并并集（outer）
- 生成测试数据
df1 = pd.DataFrame(np.ones((2,4))*0, columns=['a','b','c','d'], index=[1,2]) # a b c d # 1 0.0 0.0 0.0 0.0 # 2 0.0 0.0 0.0 0.0 df2 = pd.DataFrame(np.ones((2,4))*1, columns=['b','c','d','e'], index=[3,4]) # b c d e # 3 1.0 1.0 1.0 1.0 # 4 1.0 1.0 1.0 1.0 # df1和df2的属性(列)是错开的- join=‘outer’
# 外合并，join=‘outer’，对几个单体的所有属性进行合并，（缺少的数据补nan） res1 = pd.concat([df1, df2], axis=0, join='outer', sort=False) # 这个sort不传会报警告 # a b c d e # 1 0.0 0.0 0.0 0.0 NaN # 2 0.0 0.0 0.0 0.0 NaN # 3 NaN 1.0 1.0 1.0 1.0 # 4 NaN 1.0 1.0 1.0 1.0- join=‘inner’
# 内合并，join='inner'，对几个单体的公共属性进行合并 res2 = pd.concat([df1, df2], axis=0, join='inner') # b c d # 1 0.0 0.0 0.0 # 2 0.0 0.0 0.0 # 3 1.0 1.0 1.0 # 4 1.0 1.0 1.0 -
join_axes属性——合并行，指定要合并的行的索引
- 生成测试数据
df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3]) # a b c d # 1 0.0 0.0 0.0 0.0 # 2 0.0 0.0 0.0 0.0 # 3 0.0 0.0 0.0 0.0 df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4]) # b c d e # 2 1.0 1.0 1.0 1.0 # 3 1.0 1.0 1.0 1.0 # 4 1.0 1.0 1.0 1.0- join_axes=[df1.index] df2没有的补空值（nan）
# 依照 df1.index 进行横向合并，有点儿像数据库连接的左外连接 res1 = pd.concat([df1, df2], axis=1, join_axes=[df1.index]) # a b c d b c d e # 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN # 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 # 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 -
append在竖直方向上追加
- 生成测试数据
# 生成测试数据 df1 = pd.DataFrame(np.ones((2,3))*0, columns=['a','b','c']) # a b c # 0 0.0 0.0 0.0 # 1 0.0 0.0 0.0 df2 = pd.DataFrame(np.ones((2,3))*1, columns=['a','b','c']) # a b c # 0 1.0 1.0 1.0 # 1 1.0 1.0 1.0 df3 = pd.DataFrame(np.ones((2,3))*1, columns=['a','b','c']) # a b c # 0 1.0 1.0 1.0 # 1 1.0 1.0 1.0 s1 = pd.Series([1,2,3], index=['a','b','c']) # a 1 # b 2 # c 3 # dtype: int64- 示例
# 将df2合并到df1的下面， # ignore_index=True，不用原来的索引，重新生成索引 res1 = df1.append(df2, ignore_index=True) # a b c # 0 0.0 0.0 0.0 # 1 0.0 0.0 0.0 # 2 1.0 1.0 1.0 # 3 1.0 1.0 1.0 # 合并多个df，将df2与df3合并至df1的下面，以及重置index，并打印出结果 res2 = df1.append([df2, df3], ignore_index=True) # a b c # 0 0.0 0.0 0.0 # 1 0.0 0.0 0.0 # 2 1.0 1.0 1.0 # 3 1.0 1.0 1.0 # 4 1.0 1.0 1.0 # 5 1.0 1.0 1.0 # 合并series，将s1合并至df1，以及重置index，并打印出结果 res3 = df1.append(s1, ignore_index=True) # a b c # 0 0.0 0.0 0.0 # 1 0.0 0.0 0.0 # 2 1.0 2.0 3.0 -
参考文献
代码主要来自 Pandas 合并 concat，略有改动
