1. axis（合并方向）

import pandas as pdimport numpy as npdf1 = pd.DataFrame(np.ones((3, 4)) * 0, columns = ['a', 'b', 'c', 'd'])df2 = pd.DataFrame(np.ones((3, 4)) * 1, columns = ['a', 'b', 'c', 'd'])df3 = pd.DataFrame(np.ones((3, 4)) * 2, columns = ['a', 'b', 'c', 'd'])# concat# 合并df1、df2、df3# axis=0 纵向合并，axis=1 横向合并# ignore_index=True 表示忽略以前的index，index重新生成res =pd.concat([df1, df2, df3], axis = 0, ignore_index = True)print(res)

2. join, ['inner', 'outer'] （合并方式）

import pandas as pdimport numpy as np# join, ['inner', 'outer']df1 = pd.DataFrame(np.ones((3, 4)) * 0, columns = ['a', 'b', 'c', 'd'], index = [1, 2, 3])df2 = pd.DataFrame(np.ones((3, 4)) * 1, columns = ['b', 'c', 'd', 'e'], index = [1, 2, 3])print(df1)print(df2)# join默认outer模式，会将没有数据的位置使用NaN填充，类似于字段并集res = pd.concat([df1, df2], join = 'outer')print(res)# join='inner'，会将相同的部分进行合并，不同的部分被抛弃掉，类似于字段交集res2 = pd.concat([df1, df2], join = 'inner', ignore_index = True)print(res2)

3. join_axes（依照 axes 合并）

import pandas as pdimport numpy as np# join_axesdf1 = pd.DataFrame(np.ones((3, 4)) * 0, columns = ['a', 'b', 'c', 'd'], index = [1, 2, 3])df2 = pd.DataFrame(np.ones((3, 4)) * 1, columns = ['b', 'c', 'd', 'e'], index = [2, 3, 4])# 以df1的index为准，df2没有的index填充NaN，df2有但df1没有的index直接抛弃res = pd.concat([df1, df2], axis = 1, join_axes = [df1.index])print(res)

4. append（添加数据）

import pandas as pdimport numpy as np# appenddf1 = pd.DataFrame(np.ones((3, 4)) * 0, columns = ['a', 'b', 'c', 'd'])df2 = pd.DataFrame(np.ones((3, 4)) * 1, columns = ['a', 'b', 'c', 'd'])df3 = pd.DataFrame(np.ones((3, 4)) * 2, columns = ['a', 'b', 'c', 'd'])# 追加记录，添加记录res = df1.append([df2, df3], ignore_index = True)print(res)# 在df1后添加一条记录s1 = pd.Series([1, 2, 3, 4], index = ['a', 'b', 'c', 'd'])print(s1)res = df1.append(s1, ignore_index = True)print(res)