How to Get Cell Value from Pandas DataFrame?

Last Updated : 06 Dec, 2024

In this article, we will explore various methods to retrieve cell values from a Pandas DataFrame in Python. Pandas provides several functions to access specific cell values, either by label or by position.

Get value from a cell of Dataframe using loc() function

The .loc[] function in Pandas allows you to access a group of rows and columns by labels. It is commonly used when you know the exact row and column labels.

Python

import pandas as pd

# Create DataFrame
data = pd.DataFrame({
    "id": [7058, 7059, 7072, 7054],
    "name": ['sravan', 'jyothika', 'harsha', 'ramya'],
    "subjects": ['java', 'python', 'html/php', 'php/js']
})

# Accessing cell value using loc[]
print(data['id'].loc[data.index[0]])        # 7058
print(data['name'].loc[data.index[1]])      # jyothika
print(data['subjects'].loc[data.index[3]])  # php/js

Output

7058
jyothika
php/js

Using iloc[] for Position-based Access

If you don’t know the index labels or if the DataFrame's index is not numeric, .iloc[] is used to select data by integer position.

Python

import pandas as pd

# Create DataFrame
data = pd.DataFrame({
    "id": [7058, 7059, 7072, 7054],
    "name": ['sravan', 'jyothika', 'harsha', 'ramya'],
    "subjects": ['java', 'python', 'html/php', 'php/js']
})

# Accessing cell value using iloc[]
print(data['id'].iloc[0])        # 7058
print(data['name'].iloc[0])      # sravan
print(data['subjects'].iloc[0])  # java

Using values[] for Accessing Data as an Array

The .values[] function returns the DataFrame’s data as a NumPy array, allowing direct indexing to retrieve cell values.

Python

# import pandas module
import pandas as pd

# get the cell value using values() function
# in id column and 1 row
print(data['name'].values[0])

# get the cell value using values() function
# in id column and 2 row
print(data['id'].values[1])

# get the cell value using values() function
# in subjects column and 4 row
print(data['subjects'].values[3])

Output:

sravan
7059
php/js

Using at[] for Fast Scalar Access

The .at[] function allows you to access a single value at a specific row and column. This is similar to .loc[], but it is optimized for quick access to individual cell values.

Python

# import pandas module
import pandas as pd

# get the cell value using at() function
# in id column and 2 row
print(data.at[1, 'id'])

# get the cell value using at() function
# in id column and 4 row
print(data.at[3, 'name'])

# get the cell value using at() function
# in subjects column and 1 row
print(data.at[0, 'subjects'])

Output:

7059
ramya
java

Using iat[] for Fast Position-based Access

The .iat[] function is used for accessing a specific cell by row and column index position. It is similar to .at[] but uses integer positions.

Python

import pandas as pd

# Create DataFrame
data = pd.DataFrame({
    "id": [7058, 7059, 7072, 7054],
    "name": ['sravan', 'jyothika', 'harsha', 'ramya'],
    "subjects": ['java', 'python', 'html/php', 'php/js']
})

# Accessing cell value using iat[]
print(data.iat[0, 0])   # 7058
print(data.iat[0, 1])   # sravan
print(data.iat[1, 0])   # 7059

Output:

7058
sravan
7059

Each of the methods listed above provides a way to access specific values in a Pandas DataFrame, with different strengths based on your needs: