数据处理包Pandas--第三章

约定不等于承诺〃 2023-10-03 08:14 7阅读 0赞

## 序列： ##

## 1：系列的创建于访问 ##

### 创建 ###

序列：由**索引**和**对应值**组成

import pandas as pd
    import numpy as np
    #列表转序列
    s1=pd.Series([1,2,3,4,5])
    #元祖转序列
    s2=pd.Series((1,3,4))
    #数组转序列
    s3=pd.Series(np.array([1,2,3,5]))
    #指定下标
    s3=pd.Series([5,2,3],index=['a','b','c'])
    #字典转序列
    mydict={
        'red':2000,"bule":3000,"yellow":4000}
    ss=pd.Series(mydict)

**有默认的序列索引，也可以index自己指定索引**  
**集合不能被序列化**  
![数组里的内容可以修改][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70]

### 访问 ###

**通过索引下标进行访问**

print(s1[3])

## 2：序列属性 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 1]

## 3：序列方法 ##

![\*\*加粗样式\*\*][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 2]

## 4：空值处理方法 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 3]

## 5：序列切片 ##

打印的值：对于序列来说都是索引值

import pandas as pd
    s3=pd.Series([5,2,3,4],index=['a','b','c','d'])
    #取索引号为a,b的元素
    s22=s3[['a','b']]
    #索引为连续的数组，取左不取右
    s11=s3[0:2]
    #索引为不连续的数组
    s33=s3[[0,1,3]]
    #索引为逻辑数组
    s44=s3[s3>1]

## 6：序列聚合运算 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 4]

## 数据框 ##

### 1：数据框的创建： ###

s1.DataFrame(data)

数据框每个数组必须相同的Index，相同的长度

### 2:数据框属性 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 5]

### 3：数据框方法 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 6]

### 4：数据框切片 ###

**1：利用数据框中的iloc属性进行切片**  
利用数据框中的iloc\[①，②\]可以实现下标值或逻辑定位索引，并进行切片操作，①控制行，②控制列  
跟np.ix\_()切片类似

**2：利用数据框中的loc属性进行切片**  
通过列值进行行行筛选定位，在通过指定列进行切片操作

import pandas as pd
    import numpy as np
    data={
        "shimmer":[1,2,3,4],"recently":[5,6,7,8],"Trada":[9,9.1,'kl',np.nan],
          "A":['KL','KL','KL','kl'],'B':[1,2,np.nan,3]}
    
    data1=pd.DataFrame(data)
    c1=data1.loc[data1['A'] == 'kl',:]
    c2=data1.loc[data1['A'] == 'KL',['A','B']]
    c3=data1.loc[data1['A'] == 'KL',['A','B']].head(2)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 7]

### 5:外部文件读取 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 8]

# -*- coding: utf-8 -*-
    """
    Created on Tue May 11 10:38:40 2021
    
    @author: Administrator
    """
    
    import pandas as pd
    #excel文件读取
    path="一、车次上车人数统计表.xlsx"
    data=pd.read_excel(path)
    
    data1=pd.read_excel(path,"Sheet2")
    
    #data2=pd.read_excel('dta.xlsx',header=None)
    
    data3=pd.read_excel(path,"t3",header=None)
    
    
    # TXT文件读取
    dta1=pd.read_table("txt1.txt",header=None)
    dta11=pd.read_table("txt1.txt")
    
    dta2=pd.read_table("txt2.txt",sep='\s+')
    
    dta3=pd.read_table("txt3.txt",sep=",",header=None)

### 6：数据框合并 ###

concat: 水平连接行数相同，垂直连接列数相同

import pandas as pd
    import numpy as np
    
    
    dict1={
        'a':[2,2,'kt',6],'b':[4,6,7,8],'c':[6,5,np.nan,6]}
    dict2={
        'd':[8,9,10,11],'e':['p',16,10,8]}
    dict3={
        'a':[1,2],'b':[2,3],'c':[3,4],'d':[4,5],'e':[5,6]}
    
    df1=pd.DataFrame(dict1)
    df2=pd.DataFrame(dict2)
    df3=pd.DataFrame(dict3)
    
    
    del  dict1,dict2,dict3
    df4=pd.concat([df1,df2],axis=1)
    df5=pd.concat([df3,df4],axis=0)
    #重新设置index的值
    df5.index=range(6)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NjU2MDc3_size_16_color_FFFFFF_t_70 9]

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