Replacing Few Values in a Pandas Dataframe Column with Another Value

Question

I have a pandas dataframe df as illustrated below:

BrandName Specialty
A          H
B          I
ABC        J
D          K
AB         L

I want to replace 'ABC' and 'AB' in column BrandName by 'A'. Can someone help with this?

Accepted Answer

The easiest way is to use the replace method on the column. The arguments are a list of the things you want to replace (here ['ABC', 'AB']) and what you want to replace them with (the string 'A' in this case):

>>> df['BrandName'].replace(['ABC', 'AB'], 'A')
0    A
1    B
2    A
3    D
4    A

This creates a new Series of values so you need to assign this new column to the correct column name:

df['BrandName'] = df['BrandName'].replace(['ABC', 'AB'], 'A')

Answer 2

Replace

DataFrame object has powerful and flexible replace method:

DataFrame.replace(
        to_replace=None,
        value=None,
        inplace=False,
        limit=None,
        regex=False, 
        method='pad',
        axis=None)

Note, if you need to make changes in place, use inplace boolean argument for replace method:

Inplace

inplace: boolean, default False If True, in place. Note: this will modify any other views on this object (e.g. a column form a DataFrame). Returns the caller if this is True.

Snippet

df['BrandName'].replace(
    to_replace=['ABC', 'AB'],
    value='A',
    inplace=True
)

Answer 3

loc method can be used to replace multiple values:

df.loc[df['BrandName'].isin(['ABC', 'AB'])] = 'A'

Answer 4

You could also pass a dict to the pandas.replace method:

data.replace({
    'column_name': {
        'value_to_replace': 'replace_value_with_this'
    }
})

This has the advantage that you can replace multiple values in multiple columns at once, like so:

data.replace({
    'column_name': {
        'value_to_replace': 'replace_value_with_this',
        'foo': 'bar',
        'spam': 'eggs'
    },
    'other_column_name': {
        'other_value_to_replace': 'other_replace_value_with_this'
    },
    ...
})

Answer 5

This solution will change the existing dataframe itself:

mydf = pd.DataFrame({"BrandName":["A", "B", "ABC", "D", "AB"], "Speciality":["H", "I", "J", "K", "L"]})
mydf["BrandName"].replace(["ABC", "AB"], "A", inplace=True)

Answer 6

Created the Data frame:

import pandas as pd
dk=pd.DataFrame({"BrandName":['A','B','ABC','D','AB'],"Specialty":['H','I','J','K','L']})

Now use DataFrame.replace() function:

dk.BrandName.replace(to_replace=['ABC','AB'],value='A')

Answer 7

Just wanted to show that there is no performance difference between the 2 main ways of doing it:

df = pd.DataFrame(np.random.randint(0,10,size=(100, 4)), columns=list('ABCD'))

def loc():
    df1.loc[df1["A"] == 2] = 5
%timeit loc
19.9 ns ± 0.0873 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)


def replace():
    df2['A'].replace(
        to_replace=2,
        value=5,
        inplace=True
    )
%timeit replace
19.6 ns ± 0.509 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

Answer 8

You can use loc for replacing based on condition and specifying the column name

df = pd.DataFrame([['A','H'],['B','I'],['ABC','ABC'],['D','K'],['AB','L']],columns=['BrandName','Col2'])
df.loc[df['BrandName'].isin(['ABC', 'AB']),'BrandName'] = 'A'

Output

Replacing Few Values in a Pandas Dataframe Column with Another Value

8 Answers

Replace

Inplace

Snippet

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Your Answer

David Miller

Replacing Few Values in a Pandas Dataframe Column with Another Value

8 Answers

Replace

Inplace

Snippet

Must Read

Your Answer

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David Miller