How to update a particular cell value in pandas DataFrame

By: Rajesh P.S.

Retrieving a specific cell value or modifying the value of a single cell in a Pandas DataFrame becomes necessary when you wish to avoid the creation of a new DataFrame solely for updating that particular cell. This is a common scenario in data manipulation tasks, where precision and efficiency are crucial.

To accomplish this, Pandas provides several methods that enable you to access and update individual cell values within the DataFrame without the need for unnecessary data duplication or manipulation. These methods, such as df.at, df.loc, and df.iat, empower users to interact with DataFrame data in a granular and efficient manner, making data exploration and modification seamless. You can retrieve and updates values from DataFrame using the following methods.

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

Create a DataFrame with values

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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[]

The DataFrame.loc[] method in Pandas is primarily used to select subsets of rows and columns from a DataFrame using labels. It allows you to access data based on the labels of rows and columns, making it a powerful tool for data indexing and selection in a DataFrame. By using labels, you can easily retrieve specific rows or columns from the DataFrame, or even subsets of data that meet certain conditions, simplifying data analysis and manipulation tasks.

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[]

The DataFrame.iloc[] method in Pandas is used for selecting subsets of rows and columns from a DataFrame based on their integer locations rather than labels. It allows you to access data using numerical indices, making it a useful tool for retrieving data based on position within the DataFrame. With DataFrame.iloc[], you can specify the row and column indices to extract specific data points or slices of the DataFrame, providing more flexibility in data manipulation and analysis tasks.

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[]

The DataFrame.at[] method in Pandas is used for selecting a single scalar value from a DataFrame based on its label. It allows you to access and retrieve a specific data point in the DataFrame by providing the row and column labels corresponding to that value. Since it is designed for accessing individual elements, DataFrame.at[] is particularly useful when you need to retrieve or modify a single cell value in a DataFrame without creating a new DataFrame. Using this method, you can efficiently extract or update specific data points by directly specifying their row and column labels.

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[]

The DataFrame.iat[] method in Pandas is used to select a single scalar value from a DataFrame based on its integer location (row and column index) only. Similar to DataFrame.at[], this method is designed for retrieving or modifying a specific data point in the DataFrame. However, unlike DataFrame.at[], which uses labels for indexing, DataFrame.iat[] relies on integer positions to access the data. It is particularly useful when you need to work with large DataFrames and want to efficiently retrieve or update individual elements by their integer row and column indices.

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 SettingWithCopy warning in Pandas is an important alert that should never be ignored. It is triggered when there is a potential ambiguity in the DataFrame operation, specifically when using chained indexing (e.g., using multiple indexing methods in a single line). This ambiguity may lead to unintended behavior and incorrect results, and ignoring the warning can result in data integrity issues.

Conclusion

The warning serves as a reminder to review the code carefully, understand the underlying data manipulations, and ensure that the DataFrame is modified as intended. To avoid the SettingWithCopy warning, it is recommended to use explicit and unambiguous DataFrame operations, such as loc or iloc, when assigning or modifying values in the DataFrame. By doing so, you can ensure that your code behaves as expected and avoid any potential pitfalls related to chained indexing.

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