pandas的Series类型与基本操作详解_Python

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 pd

import numpy as np

s = 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 zheng

dtype: 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.index

Out[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 1.0

1 NaN

dtype: 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 a

Out[21]: True



In [22]: 1 in a

Out[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+b

Out[31]:

a  NaN

b  9.0

c  NaN

c, NaN

d  NaN

e  NaN

dtype: float64

Series对象可以随时修改并即刻生效


In [32]: a.index = ['c','d','e']



In [33]: a

Out[33]:

c 1

d 3

e 5

dtype: int64



In [34]: a+b

Out[34]:

b  NaN

c  NaN

c,  NaN

d  7.0

e  10.0

dtype: float64

1.2 指定index


import pandas as pd

import numpy as np

s = 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 pd

import numpy as np

s = {"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 pd

import numpy as np



num_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   -0.102860

1   -1.138242

2    1.408063

3   -0.893559

4    1.378845

dtype: float64

a   -0.658398

b    1.568236

c    0.535451

d    0.103117

e   -1.556231

dtype: float64

1.5 选择数据


import pandas as pd

import numpy as np



s = 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 beijing

print(s[[1,3]])  # 选择第1个和第3个, zheng 128

print(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 pd

import numpy as np

s = 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 pd

import 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])

pandas的Series类型与基本操作详解

中位数


import pandas as pd

import 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 pd

import 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())

pandas的Series类型与基本操作详解

找出大于中位数的数


import pandas as pd

import 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()])

pandas的Series类型与基本操作详解

中位数


import pandas as pd

import 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])

pandas的Series类型与基本操作详解

1.8 Series赋值


import pandas as pd

import 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)

pandas的Series类型与基本操作详解

1.9 满足条件的统一赋值


import pandas as pd

import 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

pandas的Series类型与基本操作详解