pandas get cell value

2 min read 03-10-2024

Accessing Data with Ease: A Guide to Retrieving Cell Values in Pandas DataFrames

Pandas is a powerful Python library for data manipulation and analysis. Its core data structure, the DataFrame, is incredibly versatile and allows for efficient handling of tabular data. One common task is retrieving specific cell values from a DataFrame. Let's explore how to do this effectively.

The Problem:

Let's say you have a DataFrame like this:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 28],
        'City': ['New York', 'London', 'Paris']}

df = pd.DataFrame(data)
print(df)

Output:

      Name  Age      City
0    Alice   25  New York
1      Bob   30    London
2  Charlie   28     Paris

Now, you need to get the age of "Bob" from this DataFrame.

Solutions:

1. Using iloc (Position-Based Indexing):

iloc allows accessing rows and columns by their integer positions. Since "Bob" is in the second row (index 1) and "Age" is in the second column (index 1), you can use:

age_of_bob = df.iloc[1, 1]
print(age_of_bob)  # Output: 30

2. Using loc (Label-Based Indexing):

loc uses labels (row and column names) for indexing. You can retrieve the value based on the row label "Bob" and column label "Age":

age_of_bob = df.loc['Bob', 'Age']
print(age_of_bob)  # Output: 30

3. Using .at (Direct Access):

.at is a convenient method for directly accessing a single cell based on row and column labels:

age_of_bob = df.at['Bob', 'Age']
print(age_of_bob)  # Output: 30

4. Using .iat (Direct Access by Position):

.iat works like .at but uses integer positions instead of labels:

age_of_bob = df.iat[1, 1]
print(age_of_bob)  # Output: 30

Which Method to Choose?

iloc and iat: Prefer these methods when you know the exact integer positions of the desired row and column.
loc and .at: Use these methods when you know the row and column labels, making your code more readable.

Key Points to Remember:

iloc and iat use integer positions, starting from 0.
loc and .at use row and column labels.
For single-cell access, .at and .iat provide a concise and direct approach.

Conclusion:

Pandas offers multiple ways to retrieve specific cell values within DataFrames, each with its own advantages depending on your use case. Understanding these methods empowers you to work efficiently with tabular data and unlock the full potential of Pandas for data analysis.