Pandas Set Value to Particular Cell in DataFrame Using Index

You can set/change the value to a particular cell in a pandas DataFrame for a given Index by using DataFrame.at[] and DataFrame.loc[] methods. Setting a value to a cell modifies the DataFrame with a new value at some index. In this article, I will explain how to set values for a particular cell in DataFrame using an index with examples.

Key Points –

Pandas provides the at method to set the value of a particular cell in a DataFrame using index and column labels.
at method in Pandas DataFrame offers a concise and efficient way to modify individual cell values by specifying the row index and column label.
Leveraging at enhances code readability and maintainability, particularly when targeting specific data points within large datasets.
The at method provides a more efficient alternative to loc for setting values in a single cell.
This method allows for precise manipulation of specific data points within the DataFrame.
It is particularly useful when you need to update or modify specific values in a DataFrame without affecting the rest of the data.

Quick Examples of Set Value for Particular Cell in DataFrame using Index

If you are in a hurry, below are some quick examples of how to set/change the value for a particular cell in DataFrame using an index.


# Below are the quick examples.

# Use DataFrame.at[] method 
# To set value for particular cell
df.at[0,'Courses'] = 'Java'
df.at[1,'Fee'] = 40000
df.at[2,'Duration'] = '55days'

# Use Dataframe.loc[] method 
# To set value for particular cell 
df.loc[0, 'Duration'] = "45days"
df.loc[1, 'Courses'] ="Python"
df.loc[3, 'Fee'] = 45000

Now, let's create a DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.


# Create DataFrame
import pandas as pd
technologies = [
            ("Spark", 22000,'40days',1500),
            ("PySpark",25000,'50days',3000),
            ("Hadoop",23000,'30days',2500),
            ("Pandas",30000,'60days',2800)
            ]
df = pd.DataFrame(technologies,columns = ['Courses','Fee','Duration','Discount'])
print(df)

Yields below output.


# Output:
   Courses    Fee Duration  Discount
0    Spark  22000   40days      1500
1  PySpark  25000   50days      3000
2   Hadoop  23000   30days      2500
3   Pandas  30000   60days      2800

Use DataFrame.at[] to Set Value for Particular Cell

pandas.DataFrame.at[] method is primarily used when you need to set a single value in pandas DataFrame. while accessing the cell you have to specify the index and column as DataFrame.at[0,'Courses']. This example changes the value of the Courses column of the first row. The first parameter is the index, and the second is the column.


# Use DataFrame.at[] method 
# To set value for particular cell
df.at[0,'Courses'] = 'Java'
df.at[1,'Fee'] = 40000
df.at[2,'Duration'] = '55days'
df.at[3,'Discount'] = 4000
print(df)

Yields below output.


# Output:
   Courses    Fee Duration  Discount
0     Java  22000   40days      1500
1  PySpark  40000   50days      3000
2   Hadoop  23000   55days      2500
3   Pandas  30000   60days      4000

Use Dataframe.loc[] Method to Set Value for Particular Cell using Index

You can also use DataFrame.loc[] method to set values for particular cells using the index. For instance, DataFrame.loc[row_index,col_indexer]=value.


# Use Dataframe.loc[] method 
# To set value for particular cell 
df.loc[1, 'Courses'] ="Python"
df.loc[3, 'Fee'] = 45000
df.loc[0, 'Duration'] = "45days"
df.loc[2, 'Discount'] = 2500
print(df)

The above program uses the DataFrame.loc[] method to set values for specific cells based on their index and column label. It first displays the original DataFrame and then modifies particular cells using the loc[] method. Finally, it prints out the DataFrame after the modifications. This example yields the below output.


# Output:
  Courses    Fee Duration  Discount
0   Spark  22000   45days      1500
1  Python  25000   50days      3000
2  Hadoop  23000   30days      2500
3  Pandas  45000   60days      2800

Complete Example


import pandas as pd
technologies = [
            ("Spark", 22000,'40days',1500),
            ("PySpark",25000,'50days',3000),
            ("Hadoop",23000,'30days',2500),
            ("Pandas",30000,'60days',2800)
            ]
df = pd.DataFrame(technologies,columns = ['Courses','Fee','Duration','Discount'])
print(df) 

# Use DataFrame.at[] method 
# To set value for particular cell
df.at[0,'Courses'] = 'Java'
df.at[1,'Fee'] = 40000
df.at[2,'Duration'] = '55days'
df.at[3,'Discount'] = 4000

# Use Dataframe.loc[] method 
# To set value for particular cell 
df.loc[1, 'Courses'] ="Python"
df.loc[3, 'Fee'] = 45000
df.loc[0, 'Duration'] = "45days"
df.loc[2, 'Discount'] = 2500
print(df)

Frequently Asked Questions on Set Value for Particular Cell in DataFrame Using Index

Setting a value to a particular cell allows you to modify specific data points within the DataFrame, which can be useful for data manipulation and analysis tasks.

You can set a value to a particular cell in a Pandas DataFrame using its index and column labels with either the DataFrame.at[] or DataFrame.loc[] method.

The DataFrame.at[] method is specifically designed for label-based indexing, meaning it's optimized for setting a single value using row and column labels.
The DataFrame.loc[] method is more versatile and can be used for label-based indexing as well as boolean array indexing.

You can use integer-based indexing with DataFrame.loc[] to set values in a Pandas DataFrame.While DataFrame.loc[] is commonly associated with label-based indexing, it also supports integer-based indexing. When using DataFrame.loc[] with integer-based indexing, it interprets the provided indexers as row labels rather than positional indices.

Generally, DataFrame.at[] is faster than DataFrame.loc[] when setting a single value to a particular cell because it's optimized for label-based indexing. However, for more complex indexing operations, DataFrame.loc[] might be more appropriate.

Conclusion

In this article, you have learned how to set/change/update a value for particular cell in pandas DataFrame using index by using DataFrame.at[] and DataFrame.loc[] methods with examples

Happy Learning !!

References

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.at.html