Original: https: //morvanzhou.github.io/tutorials/data-manipulation/np-pd/3-6-pd-concat/ This article has deletion
Concat
pandas
When dealing with multiple sets of data tend to use the combined processing of data, using concat
a basic merger. And concat
there are many parameters can be adjusted, merged into the form of data you want.
Axis (merging direction)
axis = 0 is the default value, when any parameter is not set, the default function axis = 0.
import pandas as pd
import numpy as np
#定义资料集
df1 = 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纵向合并
res = pd.concat([df1, df2, df3], axis=0)
#打印结果
print(res)
"""
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 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
2 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
"""
Careful observation will find that the results index
are 0, 1, 2, 0, 1, 2, 0, 1, 2, To index
reset, see below.
ignore_index (it resets index)
#承上一个例子,并将index_ignore设定为True
res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)
#打印结果
print(res)
"""
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
6 2.0 2.0 2.0 2.0
7 2.0 2.0 2.0 2.0
8 2.0 2.0 2.0 2.0
"""
Variable results index 0, 1, 2, 3, 4, 5, 6, 7, 8.
join (merger)
join='outer'
When a preset value, so no parameter is not set, the default function join = 'outer'. This embodiment is done in accordance with the column longitudinal combined, have the same combined vertical column, a column alone from other columns, had no value NaN begin filling position.
import pandas as pd
import numpy as np
#定义资料集
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=[2,3,4])
#纵向"外"合并df1与df2
res = pd.concat([df1, df2], axis=0, join='outer')
print(res)
"""
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 0.0 0.0 0.0 0.0 NaN
2 NaN 1.0 1.0 1.0 1.0
3 NaN 1.0 1.0 1.0 1.0
4 NaN 1.0 1.0 1.0 1.0
"""
Principle above explained example, but only the same column merge together, the other will be abandoned.
#承上一个例子
#纵向"内"合并df1与df2
res = pd.concat([df1, df2], axis=0, join='inner')
#打印结果
print(res)
"""
b c d
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 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
"""
#重置index并打印结果
res = pd.concat([df1, df2], axis=0, join='inner', ignore_index=True)
print(res)
"""
b c d
0 0.0 0.0 0.0
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
5 1.0 1.0 1.0
"""
join_axes (combined axes pursuant)
import pandas as pd
import numpy as np
#定义资料集
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=[2,3,4])
#依照`df1.index`进行横向合并
res = pd.concat([df1, df2], axis=1, join_axes=[df1.index])
#打印结果
print(res)
"""
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
"""
#移除join_axes,并打印结果
res = pd.concat([df1, df2], axis=1)
print(res)
"""
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
4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0
"""
append (add data)
append only vertical integration, no lateral combined.
import pandas as pd
import numpy as np
#定义资料集
df1 = 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))*1, columns=['a','b','c','d'])
s1 = pd.Series([1,2,3,4], index=['a','b','c','d'])
#将df2合并到df1的下面,以及重置index,并打印出结果
res = df1.append(df2, ignore_index=True)
print(res)
"""
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 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
res = df1.append([df2, df3], ignore_index=True)
print(res)
"""
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
6 1.0 1.0 1.0 1.0
7 1.0 1.0 1.0 1.0
8 1.0 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
res = df1.append(s1, ignore_index=True)
print(res)
"""
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 2.0 3.0 4.0
"""