In this post, I am going to explain how to set values to a specific columns using loc access specifier.
Example 1: Setting the value of a specific row and column
df.loc[row_label, column_to_update] = 'new_value'
Example 2: Updating multiple columns
df.loc[row_label, columns_to_update] = [new_val1, new_val2]
set_values_to_multiple_columns.py
import pandas as pd
# Create a sample DataFrame
data = {'Name': ['Krishna', 'Sailu', 'Joel', 'Chamu'],
'Age': [34, 35, 29, 35],
'City': ['Bangalore', 'Hyderabad', 'Hyderabad', 'Chennai'],
'Gender': ['Male', 'Female', 'Male', 'Female'],
'Rating': [81, 76, 67, 100]}
df = pd.DataFrame(data)
print('Original DataFrame')
print(df)
print('\nSet "Name" column as index column')
df.set_index('Name', inplace=True)
print(df)
# Setting the value of a specific row and column
row_label = 'Krishna'
column_to_update = 'City'
df.loc[row_label, column_to_update] = 'Chennai'
print('\nSetting the City of Krishna to "Chennai"')
print(df)
# Setting the value of a specific row and multiple columns
row_label = 'Krishna'
columns_to_update = ['Age', 'Rating']
df.loc[row_label, columns_to_update] = [45, 35]
print('\nUpdating the Age and Rating of Krishna')
print(df)
Output
Original DataFrame Name Age City Gender Rating 0 Krishna 34 Bangalore Male 81 1 Sailu 35 Hyderabad Female 76 2 Joel 29 Hyderabad Male 67 3 Chamu 35 Chennai Female 100 Set "Name" column as index column Age City Gender Rating Name Krishna 34 Bangalore Male 81 Sailu 35 Hyderabad Female 76 Joel 29 Hyderabad Male 67 Chamu 35 Chennai Female 100 Setting the City of Krishna to "Chennai" Age City Gender Rating Name Krishna 34 Chennai Male 81 Sailu 35 Hyderabad Female 76 Joel 29 Hyderabad Male 67 Chamu 35 Chennai Female 100 Updating the Age and Rating of Krishna Age City Gender Rating Name Krishna 45 Chennai Male 35 Sailu 35 Hyderabad Female 76 Joel 29 Hyderabad Male 67 Chamu 35 Chennai Female 100
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