Python pandas入门笔记

最新推荐文章于 2025-03-05 23:45:00 发布

yyly2012

最新推荐文章于 2025-03-05 23:45:00 发布

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文章标签： pandas

本文介绍了Pandas库中的主要数据结构，包括Series、DataFrame和Panel，并通过实例演示了如何创建这些数据结构，展示了如何利用Pandas进行高效的数据处理。

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Pandas的主要数据结构：

Dimensions	Name	Description
1	Series	1D labeled homogeneously-typed array
2	DataFrame	General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed columns
3	Panel	General 3D labeled, also size-mutable array

import pandas as pd

字典对象

pd.Series(data,index)

先看看官网参数解释如下，学一个简单赋值案例，操作跟

| Parameters
| ----------
| data : array-like, dict, or scalar value
|      Contains data stored in Series
| index : array-like or Index (1d)
|      Values must be hashable and have the same length as `data`.
|      Non-unique index values are allowed. Will default to
|      RangeIndex(len(data)) if not provided. If both a dict and index
|      sequence are used, the index will override the keys found in the
|      dict.
| dtype : numpy.dtype or None
|      If None, dtype will be inferred
| copy : boolean, default False
|      Copy input data

>>> s = pd.Series(data=[ 1 , 2 , 3 , 4 ], index = [ 'a' , 'b' , 'c' , 'd' ]) //传入键和值方式
>>> s
a    1
b    2
c    3
d    4
dtype: int64
>>> s. index     //获取键列表
Index ([ 'a' , 'b' , 'c' , 'd' ], dtype= 'object' )
>>> s.values    //获取值列表
array ([ 1 , 2 , 3 , 4 ], dtype=int64)

DataFrame表格对象

复制一下案例以后参考使用

In [ 10 ]: df2 = pd.DataFrame({ 'A' : 1. ,
                     'B' : pd.Timestamp( '20130102' ),
                     'C' : pd.Series( 1 ,index= list (range( 4 )),dtype= 'float32' ),   //生成Series对象,取的是value
                     'D' : np. array ([ 3 ] * 4 ,dtype= 'int32' ), //生成numpy对象
                     'E' : pd.Categorical([ "test" , "train" , "test" , "train" ]), 'F' : 'foo' })

In [ 11 ]: df2
Out[ 11 ]:          // 默认以数字从0开始作为行键,以字典键为列键
     A          B    C D      E    F
0 1.0 2013 - 01 - 02 1.0 3    test foo
1 1.0 2013 - 01 - 02 1.0 3 train foo
2 1.0 2013 - 01 - 02 1.0 3    test foo
3 1.0 2013 - 01 - 02 1.0 3 train foo

In [ 6 ]: dates = pd.date_range( '20130101' , periods= 6 )

In [ 7 ]: dates
Out [ 7 ]:
DatetimeIndex([ '2013-01-01' , '2013-01-02' , '2013-01-03' , '2013-01-04' ,
               '2013-01-05' , '2013-01-06' ],
              dtype= 'datetime64[ns]' , freq= 'D' )

In [ 8 ]: df = pd.DataFrame(np.random.randn( 6 , 4 ), index =dates, columns=list( 'ABCD' ))    //np.random.randn(6,4)返回一个样本，具有标准正态分布

In [ 9 ]: df
Out [ 9 ]:          // 指定dates为行键，columns为列键
                   A         B         C         D
2013 - 01 - 01 0.469112 - 0.282863 - 1.509059 - 1.135632
2013 - 01 - 02 1.212112 - 0.173215 0.119209 - 1.044236
2013 - 01 - 03 - 0.861849 - 2.104569 - 0.494929 1.071804
2013 - 01 - 04 0.721555 - 0.706771 - 1.039575 0.271860
2013 - 01 - 05 - 0.424972 0.567020 0.276232 - 1.087401
2013 - 01 - 06 - 0.673690 0.113648 - 1.478427 0.524988