Set the First Column and Row as Index in Pandas

In Pandas, an index is a label that uniquely identifies each row or column in a DataFrame. It serves as a reference or identifier for the data, allowing for easy and efficient data retrieval and manipulation. One common operation when working with tabular data is setting a specific column or row as the index. In this article, we will focus on how to set the first column and row as the index in a Pandas DataFrame.

Set the First Column and Row as Index in Pandas

Setting the First Column as the Index

Suppose we have a DataFrame with data as given below in Python

Python3

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'San Francisco', 'Los Angeles']}
df = pd.DataFrame(data)

print(df)

Output

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles 

To set the ‘Name’ column as the index, you can use the set_index method:

Python3

df.set_index('Name', inplace=True)
print(df)

Output

         Age           City
Name                       
Alice     25       New York
Bob       30  San Francisco
Charlie   35    Los Angeles 

Setting the First Row as the Column Names

Suppose we have a DataFrame with data as given below.

Python3

import pandas as pd

data = [['Name', 'Age', 'City'],
        ['Alice', 25, 'New York'],
        ['Bob', 30, 'San Francisco'],
        ['Charlie', 35, 'Los Angeles']]
df = pd.DataFrame(data)

print(df)

Output

         0    1              2
0     Name  Age           City
1    Alice   25       New York
2   Bob   30  San Francisco
3  Charlie   35    Los Angeles

To set the first row as the column names, we can use the following code:

Python3

df.columns = df.iloc[0]
df = df[1:]
print(df)

Output

     Name  Age           City
1   Alice     25        New York
2  Bob      30     San Francisco
3  Charlie 35       Los Angeles

Setting both the row and column in a Pandas DataFrame

Suppose we have a DataFrame with data as given below.

Python3

import pandas as pd

data = [['Name', 'Alice', 'Bob', 'Charlie'],
        ['Age', 25, 30, 35],
        ['City', 'New York', 'San Francisco', 'Los Angeles']]
df = pd.DataFrame(data)
print(df)

Output

      0         1              2            3
0  Name     Alice            Bob      Charlie
1   Age        25             30           35
2  City  New York  San Francisco  Los Angeles

Now, let’s set the first row as column names and the first column as the index:

Python3

# Set the first row as column names
df.columns = df.iloc[0]
df = df[1:]

# Set the first column as the index
df.set_index('Name', inplace=True)

print(df)

Output

0        Alice            Bob      Charlie
Name                                      
Age         25             30           35
City  New York  San Francisco  Los Angeles

Set Index Using a List

In this example, we first create a DataFrame named df using sample data. We then define lists row_labels and col_labels that contain the labels we want to use for rows and columns, respectively. Finally, we set the index and column names of the DataFrame using these lists.

Python3

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6],
        'C': [7, 8, 9]}

df = pd.DataFrame(data)

# Define a list of row labels and column labels to use as the index
row_labels = ['Row1', 'Row2', 'Row3']
col_labels = ['ColA', 'ColB', 'ColC']

# Set the index using the lists
df.index = row_labels
df.columns = col_labels

# Display the DataFrame with the custom index
print(df)

Output

      ColA  ColB  ColC
Row1     1     4     7
Row2     2     5     8
Row3     3     6     9

Set Index Using a Range

In this example, we first create a DataFrame df using sample data. We then calculate the number of rows and columns in the DataFrame using the shape attribute. Next, we set the index using the range function to create a numeric range starting from 1.

Python3

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6],
        'C': [7, 8, 9]}

df = pd.DataFrame(data)

# Define the number of rows and columns
num_rows = df.shape[0]
num_cols = df.shape[1]

# Set the index using a range of numbers
# Start from 1
df.index = range(1, num_rows + 1)

# Display the DataFrame with the numeric index
print(df)

Output

   A  B  C
1  1  4  7
2  2  5  8
3  3  6  9