1.前言
pandas处理多组数据的时候往往会要用到数据的合并处理,使用 concat是一种基本的合并方式.而且concat中有很多参数可以调整,合并成你想要的数据形式.
2.垂直合并（axis = 0）
axis=0是预设值，因此未设定任何参数时，函数默认axis=0
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.ones((4,5))*6, columns = ['a','b','c','d','e'])
df2 = pd.DataFrame(np.ones((4,5))*7, columns = ['a','b','c','d','e'])
df3 = pd.DataFrame(np.ones((4,5))*8, columns = ['a','b','c','d','e'])
res = pd.concat([df1,df2,df3],axis = 0)
print(res)
#输出
a b c d e
0 6.0 6.0 6.0 6.0 6.0
1 6.0 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0 6.0
0 7.0 7.0 7.0 7.0 7.0
1 7.0 7.0 7.0 7.0 7.0
2 7.0 7.0 7.0 7.0 7.0
3 7.0 7.0 7.0 7.0 7.0
0 8.0 8.0 8.0 8.0 8.0
1 8.0 8.0 8.0 8.0 8.0
2 8.0 8.0 8.0 8.0 8.0
3 8.0 8.0 8.0 8.0 8.0
仔细观察会发现结果的index是0, 1, 2, 0, 1, 2, 0, 1, 2，若要将index重置，添加开关ignore_index = True：
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.ones((4,5))*6, columns = ['a','b','c','d','e'])
df2 = pd.DataFrame(np.ones((4,5))*7, columns = ['a','b','c','d','e'])
df3 = pd.DataFrame(np.ones((4,5))*8, columns = ['a','b','c','d','e'])
res = pd.concat([df1,df2,df3],axis = 0,ignore_index = True)
print(res)
#输出
a b c d e
0 6.0 6.0 6.0 6.0 6.0
1 6.0 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0 6.0
4 7.0 7.0 7.0 7.0 7.0
5 7.0 7.0 7.0 7.0 7.0
6 7.0 7.0 7.0 7.0 7.0
7 7.0 7.0 7.0 7.0 7.0
8 8.0 8.0 8.0 8.0 8.0
9 8.0 8.0 8.0 8.0 8.0
10 8.0 8.0 8.0 8.0 8.0
11 8.0 8.0 8.0 8.0 8.0
3.join(合并方式)
join='outer’为预设值，因此未设定任何参数时，函数默认join=‘outer’。此方式是依照column来做纵向合并，有相同的column上下合并在一起，其他独自的column个自成列，原本没有值的位置皆以NaN填充。
import pandas as pd
import numpy as np
df1 = pd.DataFrame(np.ones((4,5))*6, columns = ['a','b','c','d','e'])
df2 = pd.DataFrame(np.ones((4,5))*7, columns = ['b','c','d','e','f'])
print(df1)
print('\n')
print(df2)
res = pd.concat([df1,df2],join = 'outer') #纵向"外"合并df1与df2
print(res)
#输出
a b c d e
0 6.0 6.0 6.0 6.0 6.0
1 6.0 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0 6.0
b c d e f
0 7.0 7.0 7.0 7.0 7.0
1 7.0 7.0 7.0 7.0 7.0
2 7.0 7.0 7.0 7.0 7.0
3 7.0 7.0 7.0 7.0 7.0
a b c d e f
0 6.0 6.0 6.0 6.0 6.0 NaN
1 6.0 6.0 6.0 6.0 6.0 NaN
2 6.0 6.0 6.0 6.0 6.0 NaN
3 6.0 6.0 6.0 6.0 6.0 NaN
0 NaN 7.0 7.0 7.0 7.0 7.0
1 NaN 7.0 7.0 7.0 7.0 7.0
2 NaN 7.0 7.0 7.0 7.0 7.0
3 NaN 7.0 7.0 7.0 7.0 7.0
原理同上个例子的说明，但只有相同的column合并在一起，其他的会被抛弃。
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.ones((4,5))*6, columns = ['a','b','c','d','e'])
df2 = pd.DataFrame(np.ones((4,5))*7, columns = ['b','c','d','e','f'])
print(df1)
print('\n')
print(df2)
print('\n')
res = pd.concat([df1,df2],axis = 0, join = 'inner')
print(res)
print('\n')
res_sort = pd.concat([df1,df2], axis = 0, join = 'inner',ignore_index = True) #重置index并打印结果
print(res_sort)
#输出
a b c d e
0 6.0 6.0 6.0 6.0 6.0
1 6.0 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0 6.0
b c d e f
0 7.0 7.0 7.0 7.0 7.0
1 7.0 7.0 7.0 7.0 7.0
2 7.0 7.0 7.0 7.0 7.0
3 7.0 7.0 7.0 7.0 7.0
b c d e
0 6.0 6.0 6.0 6.0
1 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0
0 7.0 7.0 7.0 7.0
1 7.0 7.0 7.0 7.0
2 7.0 7.0 7.0 7.0
3 7.0 7.0 7.0 7.0
b c d e
0 6.0 6.0 6.0 6.0
1 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0
4 7.0 7.0 7.0 7.0
5 7.0 7.0 7.0 7.0
6 7.0 7.0 7.0 7.0
7 7.0 7.0 7.0 7.0
4.join_axes
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.ones((4,5))*6,columns = ['a','b','c','d','e'],index = [1,2,3,4])
df2 = pd.DataFrame(np.ones((4,5))*7,columns = ['b','c','d','e','f'],index = [2,3,4,5])
print(df1)
print('\n')
print(df2)
print('\n')
res = pd.concat([df1,df2],axis = 1) #原始合并，没有的行补充NaN
print(res)
print('\n')
res_sort = pd.concat([df1,df2],axis = 1,join_axes = [df1.index]) #按照df1的index合并
print(res_sort)
#输出
a b c d e
1 6.0 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0 6.0
4 6.0 6.0 6.0 6.0 6.0
b c d e f
2 7.0 7.0 7.0 7.0 7.0
3 7.0 7.0 7.0 7.0 7.0
4 7.0 7.0 7.0 7.0 7.0
5 7.0 7.0 7.0 7.0 7.0
a b c d e b c d e f
1 6.0 6.0 6.0 6.0 6.0 NaN NaN NaN NaN NaN
2 6.0 6.0 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0
3 6.0 6.0 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0
4 6.0 6.0 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0
5 NaN NaN NaN NaN NaN 7.0 7.0 7.0 7.0 7.0
a b c d e b c d e f
1 6.0 6.0 6.0 6.0 6.0 NaN NaN NaN NaN NaN
2 6.0 6.0 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0
3 6.0 6.0 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0
4 6.0 6.0 6.0 6.0 6.0 7.0 7.0 7.0 7.0 7.0
5.append (添加数据)
append只有纵向合并，没有横向合并。
import numpy as np
import pandas as pd
df1 = pd.DataFrame(np.ones((4,5))*6,columns = ['a','b','c','d','e'],index = [1,2,3,4])
df2 = pd.DataFrame(np.ones((4,5))*7,columns = ['b','c','d','e','f'],index = [2,3,4,5])
df3 = pd.DataFrame(np.ones((4,5))*8,columns = ['c','d','e','f','g'],index = [3,4,5,6])
s1 = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e'])
print(df1)
print(df2)
res_df1_df2 = df1.append(df2, ignore_index = True) #将df2合并到df1的下面，以及重置index，并打印出结果
print(res_df1_df2)
res_df1_df2_df3 = df1.append([df2,df3],ignore_index = True) #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
print(res_df1_df2_df3)
res_series = df1.append(s1,ignore_index = True)
print(res_series)
#输出
a b c d e
1 6.0 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0 6.0
4 6.0 6.0 6.0 6.0 6.0
b c d e f
2 7.0 7.0 7.0 7.0 7.0
3 7.0 7.0 7.0 7.0 7.0
4 7.0 7.0 7.0 7.0 7.0
5 7.0 7.0 7.0 7.0 7.0
a b c d e f
0 6.0 6.0 6.0 6.0 6.0 NaN
1 6.0 6.0 6.0 6.0 6.0 NaN
2 6.0 6.0 6.0 6.0 6.0 NaN
3 6.0 6.0 6.0 6.0 6.0 NaN
4 NaN 7.0 7.0 7.0 7.0 7.0
5 NaN 7.0 7.0 7.0 7.0 7.0
6 NaN 7.0 7.0 7.0 7.0 7.0
7 NaN 7.0 7.0 7.0 7.0 7.0
a b c d e f g
0 6.0 6.0 6.0 6.0 6.0 NaN NaN
1 6.0 6.0 6.0 6.0 6.0 NaN NaN
2 6.0 6.0 6.0 6.0 6.0 NaN NaN
3 6.0 6.0 6.0 6.0 6.0 NaN NaN
4 NaN 7.0 7.0 7.0 7.0 7.0 NaN
5 NaN 7.0 7.0 7.0 7.0 7.0 NaN
6 NaN 7.0 7.0 7.0 7.0 7.0 NaN
7 NaN 7.0 7.0 7.0 7.0 7.0 NaN
8 NaN NaN 8.0 8.0 8.0 8.0 8.0
9 NaN NaN 8.0 8.0 8.0 8.0 8.0
10 NaN NaN 8.0 8.0 8.0 8.0 8.0
11 NaN NaN 8.0 8.0 8.0 8.0 8.0
a b c d e
0 6.0 6.0 6.0 6.0 6.0
1 6.0 6.0 6.0 6.0 6.0
2 6.0 6.0 6.0 6.0 6.0
3 6.0 6.0 6.0 6.0 6.0
4 1.0 2.0 3.0 4.0 5.0
