使用字符串中的数字替换数据帧列中的字符串

Question

我目前有一个数据框，如下所示，我只想用Maturity中的数字替换它们中的数字。例如，我想用0等替换FZCY0D。

  Date Maturity Yield_pct Currency 
0  2009-01-02  FZCY0D  4.25  AUS 
1  2009-01-05  FZCY0D  4.25  AUS 
2  2009-01-06  FZCY0D  4.25  AUS 

我的代码如下，我试图用数字替换这些字符串，但导致错误AttributeError: 'Series' object has no attribute 'split'在该行result.Maturity.replace(result['Maturity'], [int(s) for s in result['Maturity'].split() if s.isdigit()])。因此我很难理解如何做到这一点。

from pandas.io.excel import read_excel 
import pandas as pd 
import numpy as np 
import xlrd 

url = 'http://www.rba.gov.au/statistics/tables/xls/f17hist.xls' 
xls = pd.ExcelFile(url) 

#Gets rid of the information that I dont need in my dataframe 
df = xls.parse('Yields', skiprows=10, index_col=None, na_values=['NA']) 


df.rename(columns={'Series ID': 'Date'}, inplace=True) 

# This line assumes you want datetime, ignore if you don't 
#combined_data['Date'] = pd.to_datetime(combined_data['Date']) 

result = pd.melt(df, id_vars=['Date']) 

result['Currency'] = 'AUS' 
result.rename(columns={'value': 'Yield_pct'}, inplace=True) 
result.rename(columns={'variable': 'Maturity'}, inplace=True) 

result.Maturity.replace(result['Maturity'], [int(s) for s in result['Maturity'].split() if s.isdigit()]) 


print result 

Answer 1

您可以使用矢量化str方法，并通过一个正则表达式来提取号码：

In [15]: 

df['Maturity'] = df['Maturity'].str.extract('(\d+)') 
df 
Out[15]: 
     Date Maturity Yield_pct Currency 
0 2009-01-02  0  4.25  AUS 
1 2009-01-05  0  4.25  AUS 
2 2009-01-06  0  4.25  AUS 

您可以拨打astype(int)投系列为int：

In [17]: 
df['Maturity'] = df['Maturity'].str.extract('(\d+)').astype(int) 
df.info() 

<class 'pandas.core.frame.DataFrame'> 
Int64Index: 3 entries, 0 to 2 
Data columns (total 4 columns): 
Date   3 non-null object 
Maturity  3 non-null int32 
Yield_pct 3 non-null float64 
Currency  3 non-null object 
dtypes: float64(1), int32(1), object(2) 
memory usage: 108.0+ bytes