Pandas DataFrame is a two-dimensional labeled data structure with rows and columns labels, it is looks and works similar to a table in a database or a spreadsheet. To work with the DataFrame labels, pandas provides simple tools to access and modify the rows and columns using index the index and columns attributes of a DataFrame.
In this tutorial, we will learn about how to access and modify rows and columns in a Pandas DataFrame using the index and columns attributes of the DataFrame.
The index attribute in Pandas is used to access row labels in a DataFrame. It returns an index object containing the series of labels corresponding to the data represented in the each row of the DataFrame. These labels can be integers, strings, or other hashable types.
The following example access the DataFrame row labels using the pd.index attribute.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
'Name': ['Steve', 'Lia', 'Vin', 'Katie'],
'Age': [32, 28, 45, 38],
'Gender': ['Male', 'Female', 'Male', 'Female'],
'Rating': [3.45, 4.6, 3.9, 2.78]},
index=['r1', 'r2', 'r3', 'r4'])
# Access the rows of the DataFrame
result = df.index
print('Output Accessed Row Labels:', result)Output Accessed Row Labels: Index(['r1', 'r2', 'r3', 'r4'], dtype='object')
With the index attribute you can also modify the row labels of a DataFrame.
Here is an example that demonstrates accessing and modifying the row labels of the Pandas DataFrame using the index attribute.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
'Name': ['Steve', 'Lia', 'Vin', 'Katie'],
'Age': [32, 28, 45, 38],
'Gender': ['Male', 'Female', 'Male', 'Female'],
'Rating': [3.45, 4.6, 3.9, 2.78]},
index=['r1', 'r2', 'r3', 'r4'])
# Display the Input DataFrame
print('Input DataFrame:\n', df)
# Modify the Row labels of the DataFrame
df.index = [100, 200, 300, 400]
print('Output Modified DataFrame with the updated index labels:\n', df)
Input DataFrame:
Name Age Gender Rating
r1 Steve 32 Male 3.45
r2 Lia 28 Female 4.60
r3 Vin 45 Male 3.90
r4 Katie 38 Female 2.78
Output Modified DataFrame with the updated index labels:
Name Age Gender Rating
100 Steve 32 Male 3.45
200 Lia 28 Female 4.60
300 Vin 45 Male 3.90
400 Katie 38 Female 2.78
The Pandas pd.columns attribute is used to access the labels of the columns in the DataFrame. You can access and modify these column labels similarly to how we work with row labels.
The following example demonstrates how to access the DataFrame column labels using the pd.columns attribute.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
'Name': ['Steve', 'Lia', 'Vin', 'Katie'],
'Age': [32, 28, 45, 38],
'Gender': ['Male', 'Female', 'Male', 'Female'],
'Rating': [3.45, 4.6, 3.9, 2.78]},
index=['r1', 'r2', 'r3', 'r4'])
# Access the column labels of the DataFrame
result = df.columns
print('Output Accessed column Labels:', result)Output Accessed column Labels: Index(['Name', 'Age', 'Gender', 'Rating'], dtype='object')
Column labels can be modified using the columns attribute.
This example demonstrates how to access and modify the DataFrame column labels using the pd.columns attribute.
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
'Name': ['Steve', 'Lia', 'Vin', 'Katie'],
'Age': [32, 28, 45, 38],
'Gender': ['Male', 'Female', 'Male', 'Female'],
'Rating': [3.45, 4.6, 3.9, 2.78]},
index=['r1', 'r2', 'r3', 'r4'])
# Display the Input DataFrame
print('Input DataFrame:\n', df)
# Modify the Column labels of the DataFrame
df.columns = ['Col1', 'Col2', 'Col3', 'Col4']
print('Output Modified DataFrame with the updated Column Labels\n:', df)
Input DataFrame:
Name Age Gender Rating
r1 Steve 32 Male 3.45
r2 Lia 28 Female 4.60
r3 Vin 45 Male 3.90
r4 Katie 38 Female 2.78
Output Modified DataFrame with the updated Column Labels
Col1 Col2 Col3 Col4
r1 Steve 32 Male 3.45
r2 Lia 28 Female 4.60
r3 Vin 45 Male 3.90
r4 Katie 38 Female 2.78