一、Series

Series是一种类似于一维数组的对象，由数据及与之相关的标签组成。
https://pandas.pydata.org/pandas-docs/stable/reference/series.html

1、Series的创建

1.1 使用列表或者np.array来创建
1.1.1 默认数值索引

> import numpy as np
> import pandas as pd
> array= np.array([1, 3, 5, 7, np.NaN]) 	
> series = pd.Series(array)
> print(series)
0    1.0
1    3.0
2    5.0
3    7.0
4    NaN
dtype: float64
> print(pd.Series([2, 4, 6, 8, np.NaN]))
0    2.0
1    4.0
2    6.0
3    8.0
4    NaN
dtype: float64

1.1.2 自定义索引

> series_index = pd.Series([0, 1, 2, 3, 4, 5],index=['a', 'b', 'c', 'd',
> 'e', 'f']) print(series_index)

a    0
b    1
c    2
d    3
e    4
f    5
dtype: int64``

1.2 使用字典来创建

> dict = {
    
    'a':10, 'b':20, 'c':30.0, 'd':40, 'e':50, 'f':60} s =
> pd.Series(dict)

a    0
b    1
c    2
d    3
e    4
f    5
dtype: float64

2、使用

2.1、获取数值
2.1.1、通过索引获取数值

> dict = {
    
    'a':10, 'b':20, 'c':30.0, 'd':40, 'e':50, 'f':60} 
> s = pd.Series(dict)
> print(s['a'], s['f'])
10.0 60.0

2.1.2、通过切片来获取

> s[0:len(s):2]
a    10.0
c    30.0
e    50.0
dtype: float64

2.2 判断Series是否包含索引

> print('a' in s) 
> print('z' in s)
True
False

2.2 属性
2.2.1

> print(s.index) 
> print('-'*66) 
> print(s.array) 
> print('-'*66)
> print(s.values) 
> print('-'*66) 
> print(s.dtype) 
> print('-'*66)
> print(s.shape) 
> print('-'*66) 
> print(s.size)

Index(['a', 'b', 'c', 'd', 'e', 'f'], dtype='object')
------------------------------------------------------------------
<PandasArray>
[10.0, 20.0, 30.0, 40.0, 50.0, 60.0]
Length: 6, dtype: float64
------------------------------------------------------------------
[10. 20. 30. 40. 50. 60.]
------------------------------------------------------------------
float64
------------------------------------------------------------------
(6,)
------------------------------------------------------------------
6

> dict = {
    
    'a':10, 'b':20, 'c':30.0, 'd':40, 'e':50, 'f':np.NaN} 
> s = pd.Series(dict) print(s.hasnans)
True

> s.name = 'my_Series' 
> s.index.name = 'my_Series_Index' 
> print(s)

my_Series_Index
a    10.0
b    20.0
c    30.0
d    40.0
e    50.0
f     NaN
Name: my_Series, dtype: float64

2.3 notes
可以使用s.str 来对转化为字符串从而使用字符串的方法

二、DataFrame

1、DataFrame的创建

DataFrame是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型(数值、字符串、布尔值等)。DataFrame既 有行索引也有列索引，它可以被看做由Series组成的字典(共用同一个索引)。跟其他类似的数据结构相比( 如R的data.frame)，DataFrame数据是以一个或多个二维块存放的(而不是列表、字典或别的一维数据结构)。有关中 面向行和面向列的操作基本.上是平衡的。
https://pandas.pydata.org/pandas-docs/stable/reference/frame.html
1.1 使用等长列表或Numpy数组组成的字典：
列名：[数据],
列名：[数据],
列名：[数据]

> data= {
    
    
>     'province':['shanxi', 'shanxi', 'beijing', 'shanghai', 'sichuan', 'jiangsu'],
>     'year':[2016, 2017, 2018, 2019, 2020, 2021],
>     'pop':[2.0, 2.5, 4.0, 7.0, 6.0, 7.0] } 
> frame = pd.DataFrame(data,columns=['year','province','pop']) 
> frame

扫描二维码关注公众号，回复： 12718405 查看本文章

frame = pd.DataFrame(data, columns=['year','province','pop', 'debt'], index = ['one', 'two', 'three', 'four', 'five', 'six'])
frame

notes：datafrme相当于具有相同索引的Series的组合，每一个列都是Series。如下frame[‘year’]的属性名和类型

> type(frame['year'])
pandas.core.series.Series

> frame['year']
one      2016
two      2017
three    2018
four     2019
five     2020
six      2021
Name: year, dtype: int64

1.2 字典套字典的形式创建：
{
列名：{行名：数据，行名：数据}，
列名：{行名：数据，行名：数据}，
列名：{行名：数据，行名：数据}
}

data2 = {
    
    'year':{
    
    'one':2016, 'two':2017, 'three':2018, 'four':2019, 'five':2020, 'six':202},
         'province':{
    
    'one':'shanxi', 'two':'shanxi', 'three':'beijing', 'four':'shanghai', 'five':'sichuan', 'six':'jiangsu'}}
frame = pd.DataFrame(data2)
frame

2、使用

2.1、获取数值
2.1.1 通过列名获取Series

frame['year']
one      2016
two      2017
three    2018
four     2019
five     2020
six       202
Name: year, dtype: int64

2.1.2 通过索引切片获得每个Series下的值

frame[:4]
		year	province
one		2016	shanxi
two		2017	shanxi
three	2018	beijing
four	2019	shanghai

2.1.3 获取指定数据

> frame['year']['one']
2016

2.2 属性

> print(frame.index) 
> print('-'*60) 
> print(frame.values) 
> print('-'*60)
> print(frame.columns) 
> print('-'*60) 
> print(frame.size) 
> print('-'*60)
> print(frame.shape) 
> print('-'*60)

Index(['one', 'two', 'three', 'four', 'five', 'six'], dtype='object')
------------------------------------------------------------
[[2016 'shanxi']
 [2017 'shanxi']
 [2018 'beijing']
 [2019 'shanghai']
 [2020 'sichuan']
 [202 'jiangsu']]
------------------------------------------------------------
Index(['year', 'province'], dtype='object')
------------------------------------------------------------
12
------------------------------------------------------------
(6, 2)

> print(frame.keys())
Index(['year', 'province'], dtype='object')

>for column_name,column_series in frame.items():
    print(column_name)
    print(column_series)
year
one      2016
two      2017
three    2018
four     2019
five     2020
six       202
Name: year, dtype: int64
province
one        shanxi
two        shanxi
three     beijing
four     shanghai
five      sichuan
six       jiangsu
Name: province, dtype: object

2.3 loc,iloc
https://www.jianshu.com/p/d6a9845a0a34