pandas使用列表和字典创建 Series

时间:2022-05-23 迟业人气:0

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 pandas 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
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.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:

IND        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[:4:2])
 
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

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