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pandas如何使用列表和字典创建 Series

这篇文章主要介绍了pandas如何使用列表和字典创建 Series，pandas 是基于NumPy的一种工具，该工具是为解决数据分析任务而创建的，下文我们就来看看文章是怎样介绍pandas,需要的朋友也可以参考一下

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

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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

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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

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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

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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

到此这篇关于pandas如何使用列表和字典创建 Series的文章就介绍到这了

原文链接：https://juejin.cn/post/7042124006333349902

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pandas如何使用列表和字典创建 Series

目录

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