熊猫read_csv解析国外日期

Question

I am trying to use read_csv on a .csv file that contains a date column. The problem is that the date column is in a foreign language (romanian), with entries like:

'26 septembrie 2017'

'13 iulie 2017'

etc. How can I parse this nicely into a pandas dataframe which has a US date format?

Answer 1

you can pass a converter for that column:

df = pd.read_csv(myfile, converters={'date_column': foreign_date_converter})

But first you have to define the converter to do what you want. This approach uses locale manipulation:

def foreign_date_converter(text):
    # Resets locale to "ro_RO" to parse romanian date properly
    # (non thread-safe code)
    loc = locale.getlocale(locale.LC_TIME)
    locale.setlocale(locale.LC_TIME, 'ro_RO')
    date = datetime.datetime.strptime(text '%d %b %Y').date()
    locale.setlocale(locale.LC_TIME, loc) # restores locale
    return date

Answer 2

Use dateparser module.

import dateparser
df = pd.read_csv('yourfile.csv', parse_dates=['date'], date_parser=dateparser.parse)

Enter your date column name in parse_dates parameter. I'm just assuming it as date

You may have output like this:

      date
0   2017-09-26    
1   2017-07-13      

If you want to change the format use strftime strftime

df['date'] = df.date.dt.strftime(date_format = '%d %B %Y')

output:

      date
0   26 September 2017
1        13 July 2017

Answer 3

The easiest solution would be to simply use 12 times the str.replace(old, new) function.

It is not pretty but if you just built the function:

def translater(date_string_with_exatly_one_date):
    date_str = date_string_with_exatly_one_date
    date_str = date_str.replace("iulie", "july")
    date_str = date_str.replace("septembrie", "september")
    #do this 10 more times with the right translation
    return date_str

Now you just have to call it for every entry. After that you can handle it like a US date string. This is not very efficient but it will get the job done and you do not have to search for special libraries.