Convert DataFrame column type from string to datetime, dd/mm/yyyy format

The easiest way is to use to_datetime:

df['col'] = pd.to_datetime(df['col'])

It also offers a dayfirst argument for European times (but beware this isn't strict).

Here it is in action:

In [11]: pd.to_datetime(pd.Series(['05/23/2005']))
Out[11]:
0   2005-05-23 00:00:00
dtype: datetime64[ns]

You can pass a specific format:

In [12]: pd.to_datetime(pd.Series(['05/23/2005']), format="%m/%d/%Y")
Out[12]:
0   2005-05-23
dtype: datetime64[ns]

If your date column is a string of the format '2017-01-01' you can use pandas astype to convert it to datetime.

df['date'] = df['date'].astype('datetime64[ns]')

or use datetime64[D] if you want Day precision and not nanoseconds

print(type(df_launath['date'].iloc[0]))

yields

<class 'pandas._libs.tslib.Timestamp'> the same as when you use pandas.to_datetime

You can try it with other formats then '%Y-%m-%d' but at least this works.

You can use the following if you want to specify tricky formats:

df['date_col'] =  pd.to_datetime(df['date_col'], format='%d/%m/%Y')

More details on format here:

Python 2 https://docs.python.org/2/library/datetime.html#strftime-strptime-behavior
Python 3 https://docs.python.org/3.7/library/datetime.html#strftime-strptime-behavior

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