How to update a particular cell value in pandas DataFrame

Retrieving a single cell value or setting up the single cell value of a row in pandas dataFrame is sometime required when you dont want to create a new Dataframe for just updating that single cell value. You can retrieve and updates values from DataFrame using the following methods.

.loc[]
.iloc[]
.at[]
.iat[]

Create a DataFrame with values

import pandas as pd import numpy as np df = pd.DataFrame() df['Name'] = ['John', 'Doe', 'Bill','Jim','Harry','Ben'] df['TotalMarks'] = [82, 38, 63,22,55,40] df['Grade'] = ['A', 'E', 'B','E','C','D'] df['Promoted'] = [True, False,True,False,True,True] df

Name TotalMarks Grade Promoted 0 John 82 A True 1 Doe 38 E False 2 Bill 63 B True 3 Jim 22 E False 4 Harry 55 C True 5 Ben 40 D True

Set a particular cell value using DataFrame.loc[]

DataFrame.loc[] - selects subsets of rows and columns by label only.

df.loc[2,'TotalMarks'] = 111

Above code update third row of 'TotalMarks' column to 111 using DataFrame.loc[] .

Set a particular cell value using DataFrame.loc[]

DataFrame.iloc - selects subsets of rows and columns by integer location only.

df.iloc[[2], [1]] = 222

Above code update third row of 'TotalMarks' column to 222 using DataFrame.iloc[] .

Set a particular cell value using DataFrame.iloc[]

Also, you can use .at[] or .iat[] for setting a particular cell value. But it is not recommended their usage unless you have a very time-sensitive application.

Set a particular cell value using DataFrame.at[]

DataFrame.at[ ] - selects a single scalar value in the DataFrame by label only.

df.at[2, 'TotalMarks'] = 333

Above code update third row of 'TotalMarks' column to 333 using DataFrame.at[] .

Set a particular cell value using DataFrame.at[]

Set a particular cell value using DataFrame.iat[]

DataFrame.iat[] - selects a single scalar value in the DataFrame by integer location only.

df.iat[2, 1] = 444

Above code update third row of 'TotalMarks' column to 444 using DataFrame.iat[] .
Set a particular cell value using DataFrame.iat[]

SettingWithCopy warning

The crucial problem with chained indexing is when you assigning new values to the subset, in which DataFrame will usually alert the SettingWithCopy warning . This warning alert you that your DataFrame operation might not have worked as expected and that you should check the result and to make sure that you haven't made a mistake. May be you are tempting to ignore this warning if your code still works as expected. Actually, this is not a good practice and never be ignored this warning. Take some time to review your code and clarify why you are getting this warning.

Next : Creating an empty Pandas DataFrame

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