First of all need to convert DataFrame Series to datetime object. You can start by importing the datetime module.
You can check the column data type of any Pandas DF columns
It likely returns 'O' for object
In order to convert a column of type object to datetime use the pd.to_datetime function. You must also pass in a format specifying the year, month, day
df['col_name'] = pd.to_datetime(df['col_name'], format="%m/%d/%y")
This results in a datetime64 type column.
Formatting cheatsheet can be found here:
For more advanced datetime formatting, also refer to the official document above
Pandas can sometimes infer or guess the datetime without receiving format instructions
use this flag below
df['my_col'] = pd.to_datetime(df['my_col'], infer_datetime_format=True)
Once converted to datetime, we can access year month day data:
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Thursday, March 29, 2018
Handling Date and Time in Pandas
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