字符串数据处理

• Pandas中提供了字符串的函数，但只能对字符型变量进行使用
• 通过str方法访问相关属性
• 可以使用字符串的相关方法进行数据处理

import pandas as pd
import numpy as np
import os

  

 

  
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os.getcwd()

  

 

  
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'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据清洗之数据转换'

  

 

  
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os.chdir('D:\\Jupyter\\notebook\\Python数据清洗实战\\数据')

  

 

  
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df = pd.read_csv('MotorcycleData.csv', encoding='gbk')

  

 

  
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df.head(5)

  

 

  
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5 rows × 22 columns

df.info()

  

 

  
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<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7493 entries, 0 to 7492
Data columns (total 22 columns):
Condition 7493 non-null object
Condition_Desc 1656 non-null object
Price 7493 non-null object
Location 7491 non-null object
Model_Year 7489 non-null float64
Mileage 7468 non-null object
Exterior_Color 6778 non-null object
Make 7489 non-null object
Warranty 5109 non-null object
Model 7370 non-null object
Sub_Model 2426 non-null object
Type 6011 non-null object
Vehicle_Title 268 non-null object
OBO 7427 non-null object
Feedback_Perc 6611 non-null object
Watch_Count 3517 non-null object
N_Reviews 7487 non-null object
Seller_Status 6868 non-null object
Vehicle_Tile 7439 non-null object
Auction 7476 non-null object
Buy_Now 7256 non-null object
Bid_Count 2190 non-null float64
dtypes: float64(2), object(20)
memory usage: 1.3+ MB

  

 

  
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# 里面有字符串，不能进行转换
# df['Price'].astype(float)

  

 

  
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# .str 方法可用于提取字符
df['Price'].str[1:3].head(5)

  

 

  
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0 11
1 17
2 3,
3 6,
4 10
Name: Price, dtype: object

  

 

  
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# 首先要对字符串进行相关处理
df['价格'] = df['Price'].str.strip('$')

  

 

  
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df['价格'].head(5)

  

 

  
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0 11,412 
1 17,200 
2 3,872 
3 6,575 
4 10,000 
Name: 价格, dtype: object

  

 

  
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df['价格'] = df['价格'].str.replace(',', '')

  

 

  
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df['价格'].head(5)

  

 

  
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0 11412 
1 17200 
2 3872 
3 6575 
4 10000 
Name: 价格, dtype: object

  

 

  
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df['价格'] = df['价格'].astype(float)

  

 

  
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df['价格'].head(5)

  

 

  
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0 11412.0
1 17200.0
2 3872.0
3 6575.0
4 10000.0
Name: 价格, dtype: float64

  

 

  
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df.dtypes

  

 

  
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Condition object
Condition_Desc object
Price object
Location object
Model_Year float64
Mileage object
Exterior_Color object
Make object
Warranty object
Model object
Sub_Model object
Type object
Vehicle_Title object
OBO object
Feedback_Perc object
Watch_Count object
N_Reviews object
Seller_Status object
Vehicle_Tile object
Auction object
Buy_Now object
Bid_Count float64
价格 float64
dtype: object

  

 

  
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# 字符串分割
df['Location'].str.split(',').str[0].head(5)

  

 

  
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0 McHenry
1 Fort Recovery
2 Chicago
3 Green Bay
4 West Bend
Name: Location, dtype: object

  

 

  
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# 计算字符串的长度
df['Location'].str.len().head(5)

  

 

  
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0 32.0
1 34.0
2 32.0
3 35.0
4 35.0
Name: Location, dtype: float64

  

 

  
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文章来源: ruochen.blog.csdn.net，作者：若尘，版权归原作者所有，如需转载，请联系作者。

原文链接：ruochen.blog.csdn.net/article/details/105565319