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自学教程：pandas的Series类型与基本操作详解

51自学网 2021-10-30 22:48:49

python

这篇教程pandas的Series类型与基本操作详解写得很实用，希望能帮到您。

1 Series

线性的数据结构, series是一个一维数组

Pandas 会默然用0到n-1来作为series的index, 但也可以自己指定index( 可以把index理解为dict里面的key )

1.1创造一个serise数据

import pandas as pdimport numpy as nps = pd.Series([9, 'zheng', 'beijing', 128])print(s)

打印

0 9
1 zheng
2 beijing
3 128
dtype: object

访问其中某个数据

print(s[1:2])# 打印1 zhengdtype: object

Series类型的基本操作：

Series类型包括index和values两部分

In [14]: a = pd.Series({'a':1,'b':5})In [15]: a.indexOut[15]: Index(['a', 'b'], dtype='object')In [16]: a.values #返回一个多维数组numpy对象Out[16]: array([1, 5], dtype=int64)

Series类型的操作类似ndarray类型

#自动索引和自定义索引并存，但不能混用In [17]: a[0] #自动索引Out[17]: 1#自定义索引In [18]: a['a']Out[18]: 1#不能混用In [20]: a[['a',1]]Out[20]:a 1.01 NaNdtype: float64

Series类型的操作类似Python字典类型

#通过自定义索引访问#对索引保留字in操作，值不可以In [21]: 'a' in aOut[21]: TrueIn [22]: 1 in aOut[22]: False

Series类型在运算中会自动对齐不同索引的数据

In [29]: a = pd.Series([1,3,5],index = ['a','b','c'])In [30]: b = pd.Series([2,4,5,6],index = ['c,','d','e','b'])In [31]: a+bOut[31]:a  NaNb  9.0c  NaNc, NaNd  NaNe  NaNdtype: float64

Series对象可以随时修改并即刻生效

In [32]: a.index = ['c','d','e']In [33]: aOut[33]:c 1d 3e 5dtype: int64In [34]: a+bOut[34]:b  NaNc  NaNc,  NaNd  7.0e  10.0dtype: float64

1.2 指定index

import pandas as pdimport numpy as nps = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])print(s)

打印

1 9
2 zheng
3 beijing
e 128
f usa
g 990
dtype: object

根据索引找出值

print(s['f']) # usa

1.3 用dictionary构造一个series

import pandas as pdimport numpy as nps = {"ton": 20, "mary": 18, "jack": 19, "car": None}sa = pd.Series(s, name="age")print(sa)

打印

car NaN
jack 19.0
mary 18.0
ton 20.0
Name: age, dtype: float64

检测类型

print(type(sa)) # <class 'pandas.core.series.Series'>

1.4 用numpy ndarray构造一个Series

生成一个随机数

import pandas as pdimport numpy as npnum_abc = pd.Series(np.random.randn(5), index=list('abcde'))num = pd.Series(np.random.randn(5))print(num)print(num_abc)# 打印0   -0.1028601   -1.1382422    1.4080633   -0.8935594    1.378845dtype: float64a   -0.658398b    1.568236c    0.535451d    0.103117e   -1.556231dtype: float64

1.5 选择数据

import pandas as pdimport numpy as nps = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])print(s[1:3])  # 选择第1到3个, 包左不包右 zheng beijingprint(s[[1,3]])  # 选择第1个和第3个, zheng 128print(s[:-1]) # 选择第1个到倒数第1个, 9 zheng beijing 128 usa

1.6 操作数据

import pandas as pdimport numpy as nps = pd.Series([9, 'zheng', 'beijing', 128, 'usa', 990], index=[1,2,3,'e','f','g'])sum = s[1:3] + s[1:3]sum1 = s[1:4] + s[1:4]sum2 = s[1:3] + s[1:4]sum3 = s[:3] + s[1:]print(sum)print(sum1)print(sum2)print(sum3)

打印

2 zhengzheng
3 beijingbeijing
dtype: object
2 zhengzheng
3 beijingbeijing
e 256
dtype: object
2 zhengzheng
3 beijingbeijing
e NaN
dtype: object
1 NaN
2 zhengzheng
3 beijingbeijing
e NaN
f NaN
g NaN
dtype: object

1.7 查找

是否存在

USA in s # true

范围查找

import pandas as pdimport numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa[sa>19])

中位数

import pandas as pdimport numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa.median()) # 20

判断是否大于中位数

import pandas as pdimport numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa>sa.median())

找出大于中位数的数

import pandas as pdimport numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(sa[sa > sa.median()])

中位数

import pandas as pdimport numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") more_than_midian = sa>sa.median() print(more_than_midian) print('---------------------') print(sa[more_than_midian])

1.8 Series赋值

import pandas as pdimport numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(s) print('----------------') sa['ton'] = 99 print(sa)

1.9 满足条件的统一赋值

import pandas as pdimport numpy as np s = {"ton": 20, "mary": 18, "jack": 19, "jim": 22, "lj": 24, "car": None} sa = pd.Series(s, name="age") print(s) # 打印原字典 print('---------------------') # 分割线 sa[sa>19] = 88 # 将所有大于19的同一改为88 print(sa) # 打印更改之后的数据 print('---------------------') # 分割线 print(sa / 2) # 将所有数据除以2

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