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Pandas DataFrame | drop_duplicates method

Pandas
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Documentation
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DataFrame
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Data Selection and Renaming
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas's DataFrame.drop_duplicates(~) method returns a DataFrame with duplicate rows removed.

Parameters

1. subset | string or list | optional

The columns used to identify duplicates. By default, all columns are used.

2. keep | string or boolean | optional

How to deal with duplicate rows:

Value

Meaning

"first"

Keep only the first occurrence, and drop the rest.

"last"

Keep only the last occurrence, and drop the first.

False

Drop all duplicates.

By default, keep="first".

3. inplace | boolean | optional

  • If True, then the method will directly modify the source DataFrame instead of creating a new DataFrame.

  • If False, then a new DataFrame will be created and returned.

By default, inplace=False.

4. ignore_index | boolean | optional

  • If True, then the returned DataFrame will have index labels 0, 1, ..., n-1, where n is the number of rows of the returned DataFrame.

  • If False, then the returned DataFrame will keep its original index.

By default, ignore_index=False.

Return Value

A DataFrame with duplicate rows removed according to the specified parameters. If inplace=True, then nothing is returned since the source DataFrame is directly modified.

Examples

Basic Usage

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,2,2], "B":[4,5,4], "C":[2,8,2]})
df
A B C
0 2 4 2
1 2 5 8
2 2 4 2

The 1st and 3rd rows are duplicate. To remove duplicate rows:

df.drop_duplicates()
A B C
0 2 4 2
1 2 5 8

Removing duplicate rows, with only certain columns considered

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,2], "B":[4,3], "C":[2,2]})
df
A B C
0 2 4 2
1 2 3 2

Here, the 1st and 2nd rows are not exactly a duplicate ([2,4,2] vs [2,3,2]). We can still declare them as a duplicate by specifying subset=["A","C"], which will only consider columns A and C during the check for duplicates:

df.drop_duplicates(subset=["A","C"])
A B C
0 2 4 2

Different ways of dealing with duplicates

There are three ways in which we can deal with duplicates, as specified by keep="first" || "last" || "False".

Consider the following DataFrame:

df = pd.DataFrame({"A":[2,9,2], "B":[4,9,4], "C":[2,9,2]})
df
A B C
0 2 4 2
1 9 9 9
2 2 4 2

Keeping only the first occurrence

df.drop_duplicates(keep="first") # This is the default behaviour
A B C
0 2 4 2
1 9 9 9

Notice how the first occurrence of the duplicate is left as is.

Keeping only the last occurrence

df.drop_duplicates(keep="last")
A B C
1 9 9 9
2 2 4 2

Notice how the last occurrence of the duplicate is left as is.

Removing all occurrences

df.drop_duplicates(keep=False)
A B C
1 9 9 9

Notice how the all the duplicates got removed.

Removing duplicate rows in-place

To remove duplicate rows in-place, set inplace=True. This will directly remove the duplicate rows from the source DataFrame instead of creating a new one.

Consider the following DataFrame;

df = pd.DataFrame({"A":[2,9,2], "B":[4,9,4], "C":[2,9,2]})
df
A B C
0 2 4 2
1 9 9 9
2 2 4 2

We remove all duplicate rows with inplace=True:

df.drop_duplicates(inplace=True)
df
A B C
0 2 4 2
1 9 9 9

As shown in the output, the source DataFrame has been modified.

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Published by Isshin Inada
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