Pandas Dataframe Index

Index in pandas dataframe act as reference for each row in dataset. It can be numeric or based on specific column values. The default index is usually a RangeIndex starting from 0, but you can customize it for better data understanding. You can easily access the current index of a dataframe using the index attribute. Let's us understand with the help of an example:

1. Accessing and Modifying the Index

import pandas as pd

data = {'Name': ['John', 'Alice', 'Bob', 'Eve', 'Charlie'],
        'Age': [25, 30, 22, 35, 28],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
        'Salary': [50000, 55000, 40000, 70000, 48000]}

df = pd.DataFrame(data)
print(df.index)  # Accessing the index

RangeIndex(start=0, stop=5, step=1)

2. Setting a Custom Index

To set a custom index, you can use the set_index()  method, allowing you to set a custom index based on a column, such as Name or Age.

# Set 'Name' column as the index
df_with_index = df.set_index('Name')
print(df_with_index)

         Age  Gender  Salary
Name                        
John      25    Male   50000
Alice     30  Female   55000
Bob       22    Male   40000
Eve       35  Female   70000
Charlie   28    Male   480...

There are various operations you can perform with the DataFrame index, such as resetting it, changing it, or indexing with loc[]. Let's understand these as well:

3. Resetting the Index

If you need to reset the index back to default integer index, use reset_index() method. This will convert the current index into a regular column and create a new default index.

# Reset the index back to the default integer index
df_reset = df.reset_index()
print(df_reset)

      Name  Age  Gender  Salary
0     John   25    Male   50000
1    Alice   30  Female   55000
2      Bob   22    Male   40000
3      Eve   35  Female   70000
4  Charlie   28    Male   48000

4. Indexing with loc

The loc[] method in pandas allows to access rows and columns of a dataFrame using their labels, making it easy to retrieve specific data points.

row = df.loc['Alice']
print(row)

Age           30
Gender    Female
Salary     55000
Name: Alice, dtype: object

5. Changing the Index

Change the index of dataFrame, with help of set_index() method; allows to set one or more columns as the new index.

# Set 'Age' as the new index
df_with_new_index = df.set_index('Age')
print(df_with_new_index)

        Name  Gender  Salary
Age                         
25      John    Male   50000
30     Alice  Female   55000
22       Bob    Male   40000
35       Eve  Female   70000
28   Charlie    Male   480...

Here are some Key Takeaways:

• Use .loc[] for label-based row selection and set_index() to set custom indices.
• Access the index with .index and reset_index() restores the default index, with an option to drop the old index.