Question

我必须清理CSV文件数据。我想要清理的数据如下。条件：我必须在缺少它的每个字符串的末尾添加@ myclinic.com.au。

douglas@myclinic.com.au
mildura
broadford@myclinic.com.au
officer@myclinic.com.au
nowa nowa@myclinic.com.au
langsborough@myclinic.com.au
brisbane@myclinic.com.au
robertson@myclinic.com.au
logan village
ipswich@myclinic.com.au

这个代码是

    DataFrame = pandas.read_csv(ClinicCSVFile)
    DataFrame['Email'] = DataFrame['Email'].apply(lambda x: x if '@' in str(x) else str(x)+'@myclinic.com.au')
    DataFrameToCSV = DataFrame.to_csv('Temporary.csv', index = False)   
    print(DataFrameToCSV)

但是我得到的输出是none，我无法处理问题的后半部分，因为它产生了下面的错误

TypeError: 'NoneType' object is not iterable

由上述数据框引起。请帮帮我。

Answer 1

使用endswith作为条件，使用~反转并将字符串添加到结尾：

df.loc[~df['Email'].str.endswith('@myclinic.com.au'), 'Email'] += '@myclinic.com.au'
#if need check only @
#df.loc[~df['Email'].str.contains('@'), 'Email'] += '@myclinic.com.au'
print (df)
                           Email
0        douglas@myclinic.com.au
1        mildura@myclinic.com.au
2      broadford@myclinic.com.au
3        officer@myclinic.com.au
4      nowa nowa@myclinic.com.au
5   langsborough@myclinic.com.au
6       brisbane@myclinic.com.au
7      robertson@myclinic.com.au
8  logan village@myclinic.com.au
9        ipswich@myclinic.com.au

对我来说它很好用：

df = pd.DataFrame({'Email': ['douglas@myclinic.com.au', 'mildura', 'broadford@myclinic.com.au', 'officer@myclinic.com.au', 'nowa nowa@myclinic.com.au', 'langsborough@myclinic.com.au', 'brisbane@myclinic.com.au', 'robertson@myclinic.com.au', 'logan village', 'ipswich@myclinic.com.au']})
df.loc[~df['Email'].str.contains('@'), 'Email'] += '@myclinic.com.au'
print (df)
                           Email
0        douglas@myclinic.com.au
1        mildura@myclinic.com.au
2      broadford@myclinic.com.au
3        officer@myclinic.com.au
4      nowa nowa@myclinic.com.au
5   langsborough@myclinic.com.au
6       brisbane@myclinic.com.au
7      robertson@myclinic.com.au
8  logan village@myclinic.com.au
9        ipswich@myclinic.com.au

Answer 2

<强>实施例

apply

<强>输出：

endswith

Pandas数据帧返回None值

2 个答案: