Drop duplicate rows in a dataframe of particular column

Question

I have a dataframe like the following:


    Districtname    pincode
0   central delhi   110001
1   central delhi   110002
2   central delhi   110003
3   central delhi   110004
4   central delhi   110005

How can I drop rows based on column DistrictName and select the first unique value

The output I want:

    Districtname    pincode
0   central delhi   110001

df.drop_duplicates('Districtname') ?

anky
– anky

2019-09-02 17:19:34 +00:00
Commented Sep 2, 2019 at 17:19 — anky
– anky, Commented Sep 2, 2019 at 17:19

Tarun Kolla · Accepted Answer · 2022-09-13 14:19:31Z

4

Data Frames can be dropped using pandas.DataFrame.drop_duplicates() and defaults to keeping the first occurrence. In your case DataFrame.drop_duplicates(subset = "Districtname") should work. If you would like to update the same DataFrame DataFrame.drop_duplicates(subset = "Districtname", inplace = True) will do the job. Docs: https://pandas.pydata.org/pandas-docs/version/0.17/generated/pandas.DataFrame.drop_duplicates.html

edited Sep 13, 2022 at 14:19

answered Sep 2, 2019 at 17:29

Tarun Kolla

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ansev · Accepted Answer · 2019-09-02 17:30:35Z

1

Use drop_duplicates with inplace=true:

df.drop_duplicates('Districtname',inplace=True)

answered Sep 2, 2019 at 17:30

ansev

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