Replacing few values in a pandas dataframe column with another value

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?

8 Answers

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') 

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 ) 

loc method can be used to replace multiple values:

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

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' }, ... }) 

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) 

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') 

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) 

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