如何使用 Pandas/in Python 重新格式化索引中的日期

Question

My variable dates_city stores this:

Index(['2020-11-17T00:00:00', '2020-11-18T00:00:00', '2020-11-19T00:00:00',
       '2020-11-20T00:00:00', '2020-11-21T00:00:00', '2020-11-22T00:00:00',
       '2020-11-23T00:00:00', '2020-11-24T00:00:00', '2020-11-25T00:00:00',
       '2020-11-26T00:00:00', '2020-11-27T00:00:00', '2020-11-28T00:00:00'])

I want it to be stored as:

Index(['2020-11-17', '2020-11-18', '2020-11-19',
       '2020-11-20', '2020-11-21', '2020-11-22',
       '2020-11-23', '2020-11-24', '2020-11-25',
       '2020-11-26', '2020-11-27', '2020-11-28'])

So, basically with just the date in yyyy-mm-dd format. I was trying to use datetime but I can't seem to get it to work, possibly because this variable is an index, not an array. How do I reformat this?

Answer 1

You could change the index of your dataframe using pandas reset_index() method. Note that this will rename the date column to 'index', so you may want to rename it using pandas rename() method.

Then you can use pandas strftime() method to reformat your dates. After reformatting, if you still want to use the date column as the index, you can do that by changing the index attribute of the dataframe (see code below):

df.index = df['Date']

Answer 2

pandas.to_datetime worked for me:

pd.to_datetime(dates_city)
#DatetimeIndex(['2020-11-17', '2020-11-18', '2020-11-19', '2020-11-20',
#               '2020-11-21', '2020-11-22', '2020-11-23', '2020-11-24',
#               '2020-11-25', '2020-11-26', '2020-11-27', '2020-11-28'],
#              dtype='datetime64[ns]', freq=None)

If you want to keep it as pandas.Index , you can add the method pandas.DatetimeIndex.strftime :

pd.to_datetime(dates_city).strftime("%Y-%m-%d")
#Index(['2020-11-17', '2020-11-18', '2020-11-19', '2020-11-20', '2020-11-21',
#       '2020-11-22', '2020-11-23', '2020-11-24', '2020-11-25', '2020-11-26',
#       '2020-11-27', '2020-11-28'],
#      dtype='object')

You can find the datetime format codes here .