Python_Series

Python_Series

Series介绍

Pandas模块的数据结构主要有两种：1、Series 2、DataFrame

Series是一维数组，基于Numpy的ndarray结构

Series创建：

pd.Series([list],index=[list]) 参数为list,index为可选参数，若不填写则默认index从0开始

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In ：指定索引

import pandas as pd

import numpy as np

s1 = pd.Series(['a','b','c','d','e'],index=[1,2,3,4,5])

s1

Out:

1    a
2    b
3    c
4    d
5    e
dtype: object

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In :默认索引

s2 = pd.Series(['a','b','c','d','e'])

s2

Out:

0    a
1    b
2    c
3    d
4    e
dtype: object
---------------------------------------------------------

In ：

s3 = pd.Series(np.arange(3),index=['a','b','c'])

s3

Out[4]:

a    0
b    1
c    2
dtype: int32
---------------------------------------------------------

s4 = pd.Series({dict}) 由字典创建Series

In [6]:

s4 = pd.Series({'a':1,'b':2,'c':3,'d':4,'e':5,'f':6})

s4

Out[6]:

a    1
b    2
c    3
d    4
e    5
f    6
dtype: int64
---------------------------------------------------------
In [7]:
s4.values

Out[7]:
array([1, 2, 3, 4, 5, 6], dtype=int64)
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In [8]:索引

s4.index

Out[8]:

Index(['a', 'b', 'c', 'd', 'e', 'f'], dtype='object')
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In [9]:切片

s4[:]

Out[9]:

a    1
b    2
c    3
d    4
e    5
f    6
dtype: int64
---------------------------------------------------------
In [10]:
s4[2]

Out[10]:
3
---------------------------------------------------------
In [11]:

s4[0:2]

Out[11]:

a    1
b    2
dtype: int64
---------------------------------------------------------
In [13]:

s4[3:]

Out[13]:

d    4
e    5
f    6
dtype: int64
---------------------------------------------------------
In [14]:指定索引值取数

s4['a']

Out[14]:

1

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In [19]:指定多个索引值取数


s4[['a','b','c']]

Out[19]:

a    1
b    2
c    3
dtype: int64

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In [21]:按照值的条件取数
s4[s4.values>3]


Out[21]:

d    4
e    5
f    6
dtype: int64

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In [23]:按照值的条件取数

s4[s4.index=='d']


Out[23]:

d    4
dtype: int64
---------------------------------------------------------
In [24]:

s4.mean()    #均值

Out[24]:

3.5
---------------------------------------------------------

In [27]:

s4.median()  #中位值


Out[27]:

3.5
---------------------------------------------------------

In [26]:

s4.sum()   #求和


Out[26]:

21

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In [28]:

s4.std()   #标准差

Out[28]:

1.8708286933869707
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In [30]:

s4.value_counts() #每个值的重复次数

Out[30]:

6    1
5    1
4    1
3    1
2    1
1    1
dtype: int64
---------------------------------------------------------

In [34]: 运算

s4/2  ,s4//2 ,s4%2 ,s4**2  #每个值除2  , 每个值除2后取整  ，取余 ， 求平方 
Out[34]:

(a    0.5
 b    1.0
 c    1.5
 d    2.0
 e    2.5
 f    3.0
 dtype: float64, a    0
 b    1
 c    1
 d    2
 e    2
 f    3
 dtype: int64, a    1
 b    0
 c    1
 d    0
 e    1
 f    0
 dtype: int64, a     1
 b     4
 c     9
 d    16
 e    25
 f    36
 dtype: int64)

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In [35]:

s4
Out[35]:

a    1
b    2
c    3
d    4
e    5
f    6
dtype: int64
---------------------------------------------------------

In [40]:

s5 = pd.Series({'a':1,'b':2,'g':7})

s5
Out[40]:

a    1
b    2
g    7
dtype: int64
---------------------------------------------------------

In [45]:

s6=s4+s5

s6
Out[45]:

a    2.0
b    4.0
c    NaN
d    NaN
e    NaN
f    NaN
g    NaN
dtype: float64
---------------------------------------------------------

In [44]:

s6[s6.notnull()]          #取非空值

Out[44]:

a    2.0
b    4.0
dtype: float64
. . .

In [46]:





s6[s6.isnull()]          #取空值
Out[46]:

c   NaN
d   NaN
e   NaN
f   NaN
g   NaN
dtype: float64
---------------------------------------------------------

In [48]:

s6=s6.fillna(2)         # 空值填充

In [49]:

s6
Out[49]:

a    2.0
b    4.0
c    2.0
d    2.0
e    2.0
f    2.0
g    2.0
dtype: float64
---------------------------------------------------------

In [52]:

s6.drop('g')           #删除
Out[52]:

a    2.0
b    4.0
c    2.0
d    2.0
e    2.0
f    2.0
dtype: float64
---------------------------------------------------------

In [53]:

s6

Out[53]:

a    2.0
b    4.0
c    2.0
d    2.0
e    2.0
f    2.0
g    2.0
dtype: float64

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