Merging pandas dataframe rows with similar values in more than one columns-CodePudding

I have a dataframe like this

ID	Performed Time	Reported Time
101	13:05.	15.02.
121	14.05.	16.10.
101	14.20.	15.02.

I want to filer rows if the ID and the Reported Time are the same. ie the resultant dataframe should be

ID	Reported Time
101	15.02.
121	16.10.

I tried using groupby to no avail.

CodePudding user response：

Please check this ticket:

CodePudding user response：

You just need distinct():

>>> from datar.all import f, tibble, distinct
>>> df = tibble(
...     ID=[101, 121, 101],
...     **{
...         "Performed Time": ["13:05.", "14.05.", "14.20."],
...         "Reported Time": ["15.02.", "16.10.", "15.02."]
...     }
... )
>>> 
>>> df >> distinct(f.ID, f["Reported Time"])
       ID Reported Time
  <int64>      <object>
0     101        15.02.
1     121        16.10.

I am the author of datar, the grammar of data manipulation in python, which wraps pandas APIs, and also with modin support now.

CodePudding user response：

df[["ID", "Reported Time"]].drop_duplicates()