我的CSV文件有电子邮件地址。其地址不完整。使用python完全识别它们
问题说明
我是使用Python进行数据科学的初学者。
我正在处理一个数据集，在其中必须执行以下任务：
使用Python petl：
I'm a begineer in data science with python. I'm working on a Dataset in which i've to do following tasks: Using the Python petl:
a。 清除 clinics.csv 中的数据。这涉及使用python和Regex对
的电子邮件地址进行标准化，以便它们可用作html链接，并且
a. clean the data in the clinics.csv. This involves using python and Regex to standardise email addresses so they are usable as a html link, and
b。合并并清理的数据到名为 clinic_locations.csv 的CSV文件中。
b. output the merged and cleaned data into a CSV file with the name clinic_locations.csv.
到目前为止我能够处理点（ b ）的一部分，即，我已经轻松地从xml文件中提取了数据并将其与csv文件结合在一起。
但这是问题
So, far i'm able to do handle a part of point (b) i.e. i've easily extracted data from the xml file and combined it with the csv file. But the problem with this is
我无法清除CSV文件的数据
这是我的CSV文件：
ID Name Suburb State Postcode Email
1 Hurstville Clinic Hurstville NSW 1493 hurstville
2 Sydney Centre Clinic Sydney NSW 2000 sydney@myclinic.com.au
3 Auburn Clinic Auburn NSW 2144 auburn@myclinic.com.au
4 Riverwood Clinic Riverwood NSW 2210 riverwood@myclinic.com.au
因为您可以看到电子邮件列中的数据不完整且整个链接无法使用。
谁能从一开始就帮助我。
as you can see the data in email column is incomplete and whole links are unusable. Can anyone help me from starting.
已更新：
我得到的输出是
updated: the output that I'm getting is
ID Name Suburb State Postcode \
0 1 Hurstville Clinic Hurstville NSW 1493
1 2 Sydney Centre Clinic Sydney NSW 2000
2 3 Auburn Clinic Auburn NSW 2144
3 4 Riverwood Clinic Riverwood NSW 2210
4 5 Fingal Bay Clinic Fingal Bay NSW 2315
5 6 Harrington Clinic Harrington NSW 2427
6 7 Back Forest Clinic Back Forest NSW 2535
7 8 Jindabyne Clinic Jindabyne NSW 2627
8 9 Benolong Clinic Benolong NSW 2830
9 10 Melbourne Centre Clinic Melbourne VIC 3000
Email
0 hurstville@myclinic.com.au
1 sydney@myclinic.com.au
2 auburn@myclinic.com.au
3 riverwood@myclinic.com.au
4 fingal bay@myclinic.com.au
5 harrington@myclinic.com.au
6 back forest@myclinic.com.au
7 jindabyne @myclinic.com.au
8 benolong@myclinic.com.au
9 melbourne@myclinic.com.au
正确答案
我希望这对假设您所有电子邮件ID都具有相似域名的人有帮助：
I hope this will help assuming you have similar domain for all email ids:
import pandas as pd
df=pd.read_csv("clinic_locations.csv") #Provide complete path to your file
df['Email']=df['Email'].apply(lambda x: x if '@' in str(x) else str(x) '@myclinic.com.au')
#To see data frame
print(df.head(10))
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