First of all need to convert DataFrame Series to datetime object. You can start by importing the datetime module.
import datetime
You can check the column data type of any Pandas DF columns
df['col_name'].dtype
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:
http://strftime.org/
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:
df['dt_parsed'].dt.day
Your byte size news and commentary from Silicon Valley the land of startup vanities, coding, learn-to-code and unicorn billionaire stories.
Subscribe to:
Post Comments (Atom)
Developing apps for airtable using Airtable Blocks
The airtable smart sheets now has an app platform called Airtable Blocks, which allows developers to add custom code, and build apps quickly...


-
In this downtown startup work space design the designers used fat boy bean bags and an extra wide step tiered staircase to create work space...
-
Can hack schools solve Silicon Valley's talent crunch? The truth about coding bootcamps and the students left behind http://t.co/xXNfqN...
-
This review is updated continuously throughout the program. Yay I just joined the Udacity Nanodegree for Digital Marketing! I am such an Uda...

No comments:
Post a Comment