Pandas DataFrame.at

Pandas DataFrame.at

The DataFrame.at property in pandas provides a fast and label-based way to access or modify a single value in a DataFrame. It is primarily used for accessing or setting scalar values in a DataFrame based on row and column labels.

Syntax

The syntax for accessing or modifying a value using DataFrame.at is:

DataFrame.at[row_label, column_label]

Here, DataFrame refers to the pandas DataFrame, and row_label and column_label are the labels for the row and column, respectively.

Parameters

  • row_label: The label of the row to access or modify.
  • column_label: The label of the column to access or modify.

Returns

The value at the specified row and column in the DataFrame.

Examples

Accessing a Single Value

Use DataFrame.at to access a specific value in a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Access the age of Ram
print("Age of Ram:")
age_of_ram = df.at[1, 'Age']
print(age_of_ram)

Output

Age of Ram:
30

Modifying a Single Value

Use DataFrame.at to modify a specific value in a DataFrame.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)

# Update the salary of Priya
print("Updating Priya's Salary:")
df.at[2, 'Salary'] = 95000
print(df)

Output

Updating Priya's Salary:
    Name  Age  Salary
0  Arjun   25   70000
1    Ram   30   80000
2   Priya   35   95000

Using DataFrame.at with a Multi-Index

DataFrame.at also supports MultiIndex DataFrames.

Python Program

import pandas as pd

# Create a MultiIndex DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Department': ['HR', 'Finance', 'IT'],
    'Salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)
df.set_index(['Department', 'Name'], inplace=True)

# Access Priya's salary
print("Priya's Salary:")
salary_of_priya = df.at[('IT', 'Priya'), 'Salary']
print(salary_of_priya)

Output

Priya's Salary:
90000

Accessing a Value in a Non-Numeric Column

Use DataFrame.at to access a non-numeric value, such as a string.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35],
    'Department': ['HR', 'Finance', 'IT']
}
df = pd.DataFrame(data)

# Access the department of Arjun
print("Department of Arjun:")
department_of_arjun = df.at[0, 'Department']
print(department_of_arjun)

Output

Department of Arjun:
HR

Summary

In this tutorial, we explored the DataFrame.at property in pandas. Key takeaways include:

  • Using DataFrame.at for label-based access to scalar values.
  • Efficiently modifying specific values in a DataFrame.
  • Supporting MultiIndex operations with DataFrame.at.

The DataFrame.at property is an efficient and convenient way to access or update single values in a pandas DataFrame.

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