How to change the datetime format in pandas
Question
My dataframe has a DOB column (example format 1/1/2016) which by default gets converted to pandas dtype 'object': DOB object
Converting this to date format with df['DOB'] = pd.to_datetime(df['DOB']), the date gets converted to: 2016-01-26 and its dtype is: DOB datetime64[ns].
Now I want to convert this date format to 01/26/2016 or in any other general date formats. How do I do it?
Whatever the method I try, it always shows the date in 2016-01-26 format.
Accepted Answer
You can use dt.strftime if you need to convert datetime to other formats (but note that then dtype of column will be object (string)):
import pandas as pd
df = pd.DataFrame({'DOB': {0: '26/1/2016', 1: '26/1/2016'}})
print (df)
DOB
0 26/1/2016
1 26/1/2016
df['DOB'] = pd.to_datetime(df.DOB)
print (df)
DOB
0 2016-01-26
1 2016-01-26
df['DOB1'] = df['DOB'].dt.strftime('%m/%d/%Y')
print (df)
DOB DOB1
0 2016-01-26 01/26/2016
1 2016-01-26 01/26/2016
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Changing the format but not changing the type:
df['date'] = pd.to_datetime(df["date"].dt.strftime('%Y-%m'))
The below code worked for me instead of the previous one - try it out !
df['DOB']=pd.to_datetime(df['DOB'].astype(str), format='%m/%d/%Y')
Compared to the first answer, I will recommend to use dt.strftime() first, then pd.to_datetime(). In this way, it will still result in the datetime data type.
For example,
import pandas as pd
df = pd.DataFrame({'DOB': {0: '26/1/2016 ', 1: '26/1/2016 '})
print(df.dtypes)
df['DOB1'] = df['DOB'].dt.strftime('%m/%d/%Y')
print(df.dtypes)
df['DOB1'] = pd.to_datetime(df['DOB1'])
print(df.dtypes)
There is a difference between
- the content of a dataframe cell (a binary value) and
- its presentation (displaying it) for us, humans.
So the question is: How to reach the appropriate presentation of my datas without changing the data / data types themselves?
Here is the answer:
- If you use the Jupyter notebook for displaying your dataframe, or
- if you want to reach a presentation in the form of an HTML file (even with many prepared superfluous
idandclassattributes for further CSS styling — you may or you may not use them),
use styling. Styling don't change data / data types of columns of your dataframe.
Now I show you how to reach it in the Jupyter notebook — for a presentation in the form of HTML file see the note near the end of the question.
I will suppose that your column DOB already has the type datetime64 (you shown that you know how to reach it). I prepared a simple dataframe (with only one column) to show you some basic styling:
Not styled:
df
DOB 0 2019-07-03 1 2019-08-03 2 2019-09-03 3 2019-10-03
Styling it as
mm/dd/yyyy:df.style.format({"DOB": lambda t: t.strftime("%m/%d/%Y")})
DOB 0 07/03/2019 1 08/03/2019 2 09/03/2019 3 10/03/2019
Styling it as
dd-mm-yyyy:df.style.format({"DOB": lambda t: t.strftime("%d-%m-%Y")})
DOB 0 03-07-2019 1 03-08-2019 2 03-09-2019 3 03-10-2019
Be careful!
The returning object is NOT a dataframe — it is an object of the class Styler, so don't assign it back to df:
Don´t do this:
df = df.style.format({"DOB": lambda t: t.strftime("%m/%d/%Y")}) # Don´t do this!
(Every dataframe has its Styler object accessible by its .style property, and we changed this df.style object, not the dataframe itself.)
Questions and Answers:
Q: Why your Styler object (or an expression returning it) used as the last command in a Jupyter notebook cell displays your (styled) table, and not the Styler object itself?
A: Because every Styler object has a callback method
._repr_html_()which returns an HTML code for rendering your dataframe (as a nice HTML table).Jupyter Notebook IDE calls this method automatically to render objects which have it.
Note:
You don't need the Jupyter notebook for styling (i.e. for nice outputting a dataframe without changing its data / data types).
A Styler object has a method render(), too, if you want to obtain a string with the HTML code (e.g. for publishing your formatted dataframe to the Web, or simply present your table in the HTML format):
df_styler = df.style.format({"DOB": lambda t: t.strftime("%m/%d/%Y")})
HTML_string = df_styler.render()
You can try this it'll convert the date format to DD-MM-YYYY:
df['DOB'] = pd.to_datetime(df['DOB'], dayfirst = True)