I can check column types using df.dtypes
, where df
is pandas DataFrame. However, my question is a bit different. I have the following DataFrame:
col1 col2
0 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
1 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
2 <class 'float'>
3 NaN
4 <class 'pandas._libs.tslibs.timestamps.Timestamp'>
The df["col2"].dtypes
returns object
.
I need to create a new column is_timestamp
that would check if col2
value is pandas timestamp. For testing, I tried this code:
isinstance(df_viz["col2"][0], pd._libs.tslibs.timestamps.Timestamp)
But it returns False
.
The expected output:
col1 col2 col3
0 <class 'pandas._libs.tslibs.timestamps.Timestamp'> Yes
1 <class 'pandas._libs.tslibs.timestamps.Timestamp'> Yes
2 <class 'float'> No
3 NaN No
4 <class 'pandas._libs.tslibs.timestamps.Timestamp'> Yes
Try with:
df_viz['col3']=(df_viz.col2.transform(lambda x:
np.where(x==pd._libs.tslibs.timestamps.Timestamp,'Yes','No')))
you can check for each row like this
df['check_datetime'] = [type(val) == datetime.datetime for val in df['datime_field']]
I'm not sure about your type you can find your type by (type(val)) and place them into code
if you want 'YES' and 'NO'
can using
df['my_col'] = np.where(df['my_col'] is True,"YES","NO)
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