Can be understood as a series of one-dimensional dataFrame composed of two-dimensional
import numpy as np
import pandas as pd
data = { 'name' : [ 'zhangsan' , 'lisi' , 'wangwu' , 'wangma' , 'zhaoliu' ] ,
'age' : [ 11 , 12 , 13 , 14 , 14 , ] ,
'tel' : [ 158 , 169 , 173 , 158 , 110 ] }
Series
s1 = pd. Series( data[ 'name' ] )
s1
0 zhangsan
1 lisi
2 wangwu
3 wangma
4 zhaoliu
dtype: object
s1. values
array(['zhangsan', 'lisi', 'wangwu', 'wangma', 'zhaoliu'], dtype=object)
s1. index
RangeIndex(start=0, stop=5, step=1)
s1 = pd. Series( data[ 'name' ] , index= [ 'A' , 'B' , 'C' , 'D' , 'E' ] )
s1
A zhangsan
B lisi
C wangwu
D wangma
E zhaoliu
dtype: object
s1. index
Index(['A', 'B', 'C', 'D', 'E'], dtype='object')
DataFrame
df1 = pd. DataFrame( data)
df1
name
age
tel
0
zhangsan
11
158
1
lysis
12
169
2
wangwu
13
173
3
wangma
14
158
4
zhaoliu
14
110
df1[ 'name' ]
0 zhangsan
1 lisi
2 wangwu
3 wangma
4 zhaoliu
Name: name, dtype: object
type ( df1[ 'name' ] )
pandas.core.series.Series
df1. iterrows( )
<generator object DataFrame.iterrows at 0x11757d950>
for row in df1. iterrows( ) :
print ( row) , print ( type ( row) ) , print ( len ( row) )
print ( row[ 0 ] , row[ 1 ] )
print ( type ( row[ 0 ] ) , type ( row[ 1 ] ) )
break
(0, name zhangsan
age 11
tel 158
Name: 0, dtype: object)
<class 'tuple'>
2
0 name zhangsan
age 11
tel 158
Name: 0, dtype: object
<class 'int'> <class 'pandas.core.series.Series'>
s1 = pd. Series( data[ 'name' ] )
s2 = pd. Series( data[ 'age' ] )
s3 = pd. Series( data[ 'tel' ] )
df_new = pd. DataFrame( [ s1, s2, s3] , index= [ 'name' , 'age' , 'tel' ] )
df_new
0
1
2
3
4
name
zhangsan
lysis
wangwu
wangma
zhaoliu
age
11
12
13
14
14
tel
158
169
173
158
110
df_new = df_new. T
df_new
name
age
tel
0
zhangsan
11
158
1
lysis
12
169
2
wangwu
13
173
3
wangma
14
158
4
zhaoliu
14
110
df1
name
age
tel
0
zhangsan
11
158
1
lysis
12
169
2
wangwu
13
173
3
wangma
14
158
4
zhaoliu
14
110