While working with data in Pandas in Python, we perform a vast array of operations on the data to get the data in the desired form. One of these operations could be that we want to remap the values of a specific column in the DataFrame. Let’s discuss several ways in which we can do that.
Creating Pandas DataFrame to remap values
Given a Dataframe containing data about an event, remap the values of a specific column to a new value.
Python3
# importing pandas as pdimport pandas as pd# Creating the DataFramedf = pd.DataFrame({'Date': ['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Event': ['Music', 'Poetry', 'Theatre', 'Comedy'], 'Cost': [10000, 5000, 15000, 2000]})# Print the dataframeprint(df) |
Output:
Remap values in Pandas columns using replace() function
Now we will remap the values of the ‘Event’ column by their respective codes using replace() function.
Python3
# Create a dictionary using which we# will remap the valuesdict = {'Music' : 'M', 'Poetry' : 'P', 'Theatre' : 'T', 'Comedy' : 'C'}# Print the dictionaryprint(dict)# Remap the values of the dataframedf.replace({"Event": dict}) |
Output :
Remap values in Pandas DataFrame columns using map() function
Now we will remap the values of the ‘Event’ column by their respective codes using map() function.
Python3
# Create a dictionary using which we# will remap the valuesdict = {'Music': 'M', 'Poetry': 'P', 'Theatre': 'T', 'Comedy': 'C'}# Print the dictionaryprint(dict)# Remap the values of the dataframedf['Event'] = df['Event'].map(dict)# Print the DataFrame after modificationprint(df) |
Output:
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