pandas如何使用列表和字典创建 Series_开发_开发者

01 使用列表创建 Series
02 使用 name 参数创建 Series
03 使用简写的列表创建 Series
04 使用字典创建 Series
05 如何使用 Numpy 函数创建 Series
06 如何获取 Series 的索引和值
07 如何在创建 Series 时指定索引
08如何获取 Series 的大小和形状
09 如何获取 Series 开始或末尾几行数据
10 使用切片获取 Series 子集

前言：

Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。

为了让大家对pandas的操作更加熟练，我整理了一些关于pandas的小操作，会依次为大家展示

今天我将先为大家如何关于pandas如何使用列表和字典创建 Series。

01 使用列表创建 Series

import panda编程客栈s as pd
 
ser1 = pd.Series([1.5, 2.5, 3, 4.5, 5.0, 6])
print(ser1)

Output:

0 1.5
1 2.5
2 3.0
3 4.5
4 5.0
5 6.0
dtype: float64

02 使用 name 参数创建 Series

import pandas as pd
 
ser2 = pd.Series(["India", "Canada", "Germany"], name="Countries")
print(ser2)

Output:

0 India
1 Canada
2 Germany
Name: Countries, dtype: object

03 使用简写的列表创建 Series

import pandas as pd
 
ser3 = pd.Series(["A"]*4)
print(ser3)

Output:

0 A
1 A
2 A
3 A
dtype: object

04 使用字典创建 Series

import pandas as pd
 
ser4 = pd.Series({"India": "New Delhi",
                  "Japan": "Tokyo",
                  "UK": "London"})
print(ser4)

Output:

India New Delhi
Japan Tokyo
UK London
dtype: object

05 如何使用 Numpy 函数创建 Series

import pandas as pd
import numpy as np
 
ser1 = pd.Series(np.linspace(1, 10, 5))
print(ser1)
 
ser2 = pd.Series(np.random.normal(size=5))
print(ser2)

Output:

0 1.00
1 3.25
2 5.50
编程客栈3 7.75
4 10.00
dtype: float64
0 -1.694452
1 -1.570006
2 1.713794
3 0.338292
4 0.803511
dtype: float64

06 如何获取 Series 的索引和值

import pandas as pd
import numpy as np
 
ser1 = pd.Series({"India": "New Delhi",
                  "Japan": "Tokyo",
                  "UK": "London"})
 
print(ser1.values)
print(ser1.index)
 
print("\n")
 
ser2 = pd.编程客栈Series(np.random.normal(size=5))
print(ser2.index)
print(ser2.values)

Output:

['New Delhi' 'Tokyo' 'London']
Index(['India', 'Japan', 'UK'], dtype='object')

RangeIndex(start=0, stop=5, step=1)
[ 0.66265478 -0.72222211 0.3608642 1.40955436 1.3096732 ]

07 如何在创建 Series 时指定索引

import pandas as pd
 
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
 
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
 
ser1 = pd.Series(values, index=code)
 
print(ser1)

Output:

INDXxQMcFt India
CAN Canada
AUS Australia
JAP Japan
GER Germany
FRA France
dtype: object

08如何获取 Series 的大小和形状

import pandas as pd
 
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
 
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
 
ser1 = pd.Series(values, index=code)
 
print(len(ser1))
 
print(ser1.shape)
 
print(ser1.size)

Output:

6
(6,)
6

09 如何获取 Series 开始或末尾几行数据

Head()函数：

import pandas as pd
 
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
 
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
 
ser1 = pd.Series(values, index=code)
 
print("-----Head()-----")
print(ser1.head())
 
print("\n\n-----Head(2)-----")
print(ser1.head(2))

Output:

-----Head()-----
IND India
CAN Canada
AUS Australia
JAP Japan
GER Germany
dtype: object

-----Head(2)-----
IND India
CAN Canada
dtype: object

Tail()函数：

import pandas as pd
 
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
 
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
 
ser1 = pd.Series(values, index=code)
 
print("-----Tail()-----")
print(ser1.tail())
 
print("\n\n-----Tail(2)-----")
print(ser1.tail(2))

Output:

-----Tail()-----
CAN Canada
AUS Australia
JAP Japan
GER Germany
FRA France
dtype: object

-----Tail(2)-----
GER Germany
FRA France
dtype: object

Take()函数：

import pandas as pd
 
values = ["India", "Canada", "Australia",
          "Japan", "Germany", "France"]
 
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
 
ser1 = pd.Series(values, index=code)
 
print("-----Take()-----")
print(ser1.take([2, 4, 5]))

Output:

-----Take()-----
AUS Australia
GER Germany
FRA France
dtype: object

10 使用切片获取 Series 子集

import pandas as pd
 
num = [000, 100, 200, 300, 400, 500, 600, 700, 800, 900]
 
idx = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
 
series = pd.Series(num, index=idx)
 
print("\n [2:2] \n")
print(series[2:4])
 
print("\n [1:6:2] \n")
print(series[1:6:2])
 
print("\n [:6] \n")
print(series[:6])
 
print("\n [4:] \n")
print(series[4:])
 
print("\n [:4:2] \n")
print(series[:4:2www.cppcns.com])
 
print("\n [4::2] \n")
print(series[4::2])
 
print("\n [::-1] \n")
print(series[::-1])

Output：

[2:2]

C 200
D 300
dtype: int64

[1:6:2]

B 100
D 300
F 500
dtype: int64

[:6]

A 0
B 100
C 200
D 300
E 400
F 500
dtype: int64

[4:]

E 400
F 500
G 600
H 700
I 800
J 900
dtype: int64

[:4:2]

A 0
C 200
dtype: int64

[4::2]

E 400
G 600
I 800
dtype: int64

[::-1]

J 900
I 800
H 700
G 600
F 500
E 400
D 300
C 200
B 100
A 0
dtype: int64