How do I convert dates in a Pandas data frame to a 'date' data type?

Question

I have a Pandas data frame, one of the column contains date strings in the format YYYY-MM-DD

For e.g. \'2013-10-28\'

At the moment th

Answer 1

It may be the case that dates need to be converted to a different frequency. In this case, I would suggest setting an index by dates.

#set an index by dates
df.set_index(['time'], drop=True, inplace=True)

After this, you can more easily convert to the type of date format you will need most. Below, I sequentially convert to a number of date formats, ultimately ending up with a set of daily dates at the beginning of the month.

#Convert to daily dates
df.index = pd.DatetimeIndex(data=df.index)

#Convert to monthly dates
df.index = df.index.to_period(freq='M')

#Convert to strings
df.index = df.index.strftime('%Y-%m')

#Convert to daily dates
df.index = pd.DatetimeIndex(data=df.index)

For brevity, I don't show that I run the following code after each line above:

print(df.index)
print(df.index.dtype)
print(type(df.index))

This gives me the following output:

Index(['2013-01-01', '2013-01-02', '2013-01-03'], dtype='object', name='time')
object


DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03'], dtype='datetime64[ns]', name='time', freq=None)
datetime64[ns]


PeriodIndex(['2013-01', '2013-01', '2013-01'], dtype='period[M]', name='time', freq='M')
period[M]


Index(['2013-01', '2013-01', '2013-01'], dtype='object')
object


DatetimeIndex(['2013-01-01', '2013-01-01', '2013-01-01'], dtype='datetime64[ns]', freq=None)
datetime64[ns]