Pandas DataFrame.reset_index()

In Pandas, reset_index() method is used to reset the index of a DataFrame. By default, it creates a new integer-based index starting from 0, making the DataFrame easier to work with in various scenarios, especially after performing operations like filtering, grouping or multi-level indexing. Example:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Max'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data, index=['a', 'b', 'c'])

print("Original DataFrame:")
print(df)

# Resetting the index
df_reset = df.reset_index()

print("\nDataFrame after reset_index():")
print(df_reset)

Original DataFrame:
    Name  Age
a  Alice   25
b    Bob   30
c    Max   35

DataFrame after reset_index():
  index   Name  Age
0     a  Alice   25
1     b    Bob   30
2     c    Max   35

Explanation: This code creates a DataFrame with 'Name' and 'Age' columns and a custom index ('a', 'b', 'c'). The reset_index() function moves these index labels into a new 'index' column and replaces them with default row numbers (0, 1, 2).

Syntax

DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')

Parameters:

• level (optional): Specifies the index level(s) to remove or reset. It can be an integer, string, or a list of levels.
• drop (default: False): If set to True, the index column is removed entirely instead of being added as a column in the DataFrame.
• inplace (default: False): If True, modifies the original DataFrame in place. If False, returns a new DataFrame.
• col_level (default: 0): Used to select the level of the column to insert the index labels.
• col_fill (default: ''): If the DataFrame has multiple column levels, this determines how missing levels are filled in the column headers.

Returns: It returns a new DataFrame with the index reset unless inplace=True is used, in which case the original DataFrame is modified.

Examples of Pandas DataFrame.reset_index()

Example 1: Resetting Index of a Pandas DataFrame

In this example, we will set the "First Name" column as the index and then reset the index using reset_index(). Dataset Link: Employee.csv

import pandas as pd

data = pd.read_csv("employees.csv")
print("Original Employee Dataset:")
display(data.head())

# Set and reset "First Name" as index
data.set_index("First Name", inplace=True)
print("\nAfter Setting 'First Name' as Index:")
display(data.head())

data.reset_index(inplace=True)
print("\nAfter Resetting Index:")
display(data.head())

Output:

Explanation: We set the "First Name" column as the index using set_index(), replacing the default integer index. After displaying the modified dataset, we use reset_index() to restore the integer index and move "First Name" back as a regular column, then display the final dataset again.

Example 2: Resetting Index After Filtering Data

When filtering a DataFrame, the original row index is retained, which may cause inconsistencies when performing further operations. Using reset_index() helps maintain a clean index.

import pandas as pd

data = {'ID': [101, 102, 103, 104, 105],
        'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma'],
        'Dept': ['HR', 'IT', 'IT', 'Finance', 'HR'],
        'Salary': [50000, 60000, 65000, 70000, 55000]}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

# Filtering IT employees
df_it = df[df['Dept'] == 'IT']
print("\nFiltered DataFrame:")
print(df_it)

# Resetting index
df_it = df_it.reset_index(drop=True)
print("\nFiltered DataFrame after reset_index():")
print(df_it)

Output:

Explanation: The filtered DataFrame (df_filtered) is created by selecting only employees from the IT department while retaining the original row indices (1 and 2). Applying reset_index(drop=True) removes the old indices and replaces them with a default integer index (0,1), making the DataFrame more structured and easier to analyze.

Key Use Cases of reset_index()

• Simplifying Data Manipulation: After performing operations like filtering or sorting, you might want to reset the index to have a clean, sequential index.
• Handling Multi-Level Indexes: When working with multi-level indexes, reset_index() can be used to remove specific index levels without affecting the rest of the structure.
• Restoring Default Index: If you've set a custom index using the set_index() method, reset_index() restores the default integer-based index.