Pandas合并之Concat合并
1.Concat语法
pd.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,keys=None, levels=None, names=None, verify_integrity=False)参数说明
objs:series或者dataframe对象构成的序列
asix:需要合并连接的轴,0是行,1是列
join:连接的方式,inner内连接,outer外连接
其它不常用参数......
2.Concat范例
(1)Series对象
1>Series对象的合并,横向合并纵向合并
#创建两个对象
ser_1 = pd.Series(np.random.randint(0,5,5),index=np.arange(5))
print(ser_1)
ser_2 = pd.Series(np.random.randint(5,10,5),index=np.arange(5))
print(ser_2)
'''
0 1
1 0
2 4
3 0
4 4
dtype: int32
0 7
1 9
2 6
3 5
4 5
dtype: int32
'''
#axis默认为0,是横向连接,返回一个series对象
ser = pd.concat([ser_1,ser_2])
print(ser)
'''
0 1
1 0
2 4
3 0
4 4
0 7
1 9
2 6
3 5
4 5
dtype: int32
'''2>Series对象的合并,纵向合并,使用内连接
ser_1 = pd.Series(np.random.randint(0,10,5),index=range(5))
ser_2 = pd.Series(np.random.randint(0,10,4),index=range(4))
ser_3 = pd.Series(np.random.randint(0,10,3),index=range(3))
print(ser_1)
'''
0 4
1 3
2 6
3 4
4 6
dtype: int32
'''
print(ser_2)
'''
0 8
1 9
2 9
3 5
dtype: int32
'''
print(ser_3)
'''
0 6
1 0
2 3
dtype: int32
'''
ser = pd.concat([ser_1,ser_2,ser_3],axis=1,join='inner')
print(ser)
'''
dtype: int32
0 1 2
0 4 8 6
1 3 9 0
2 6 9 3
'''3>Series对象的合并,纵向合并,使用外连接
ser_1 = pd.Series(np.random.randint(0,10,5),index=range(5))
ser_2 = pd.Series(np.random.randint(0,10,4),index=range(4))
ser_3 = pd.Series(np.random.randint(0,10,3),index=range(3))
print(ser_1)
'''
0 2
1 7
2 6
3 7
4 3
dtype: int32
'''
print(ser_2)
'''
0 7
1 4
2 7
3 0
dtype: int32
'''
print(ser_3)
'''
0 3
1 3
2 5
dtype: int32
'''
ser = pd.concat([ser_1,ser_2,ser_3],axis=1,join='outer')
print(ser)
#返回一个DataFrame对象,并且取并集,对应位置没有数据自动填充Nan
'''
0 1 2
0 2 7.0 3.0
1 7 4.0 3.0
2 6 7.0 5.0
3 7 0.0 NaN
4 3 NaN NaN
'''(2)DataFrame对象
1>DataFrame对象的合并,axis=0
# 创建两个DataFrame对象
df_1 = pd.DataFrame(np.random.randint(0,10,(3,2)),index=['a','b','c'],columns=['A','B'])
df_2 = pd.DataFrame(np.random.randint(0,10,(2,2)),index=['a','b'],columns=['C','D'])
print(df_1)
'''
A B
a 7 0
b 0 8
c 9 3
'''
print(df_2)
'''
C D
a 7 5
b 4 0
'''
df = pd.concat([df_1,df_2],axis=0)
print(df)
'''
A B C D
a 9.0 9.0 NaN NaN
b 0.0 3.0 NaN NaN
c 4.0 7.0 NaN NaN
a NaN NaN 1.0 3.0
b NaN NaN 2.0 8.0
'''1>DataFrame对象的合并,axis=1
# 创建两个DataFrame对象
df_1 = pd.DataFrame(np.random.randint(0,10,(3,2)),index=['a','b','c'],columns=['A','B'])
df_2 = pd.DataFrame(np.random.randint(0,10,(2,2)),index=['a','b'],columns=['C','D'])
print(df_1)
'''
A B
a 5 6
b 8 9
c 4 7
'''
print(df_2)
'''
C D
a 5 4
b 2 0
'''
df = pd.concat([df_1,df_2],axis=1)
print(df)
'''
A B C D
a 5 6 5.0 4.0
b 8 9 2.0 0.0
c 4 7 NaN NaN
'''(3)ndarray对象
import numpy as np
import pandas as pd
arr_1 = np.random.randint(0,10,(3,4))
arr_2 = np.random.randint(0,10,(3,4))
print(arr_1)
'''
[[1 8 7 9]
[7 5 5 3]
[0 7 5 7]]
'''
print(arr_2)
'''
[[8 8 2 9]
[5 9 5 8]
[3 1 6 5]]
'''
# concatenate 函数 合并的时候有轴向
arr = np.concatenate([arr_1,arr_2],axis=0)
print(arr)
'''
[[1 8 7 9]
[7 5 5 3]
[0 7 5 7]
[8 8 2 9]
[5 9 5 8]
[3 1 6 5]]
'''
arr = np.concatenate([arr_1,arr_2],axis=1)
print(arr)
'''
[[1 8 7 9 8 8 2 9]
[7 5 5 3 5 9 5 8]
[0 7 5 7 3 1 6 5]]
'''