Resetting Index in DataFrame

pandas
Published

July 2, 2023

Understanding the DataFrame Index

Before tackling index resetting, let’s clarify what a DataFrame index is. The index is a unique identifier for each row in the DataFrame. By default, Pandas assigns a numerical index starting from 0. However, you can also set a custom index using one of your DataFrame’s columns, or even create a hierarchical index (MultiIndex).

import pandas as pd

data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
df = pd.DataFrame(data)
print("Default Index:\n", df)

df = pd.DataFrame(data, index=['A', 'B', 'C'])
print("\nCustom Index:\n", df)

Resetting the Index: reset_index()

The reset_index() method is your primary tool for altering the DataFrame’s index. By default, it moves the existing index into a new column named ‘index’, and assigns a new default numerical index.

import pandas as pd

data = {'col1': [1, 2, 3], 'col2': [4, 5, 6], 'col3': [7,8,9]}
df = pd.DataFrame(data, index=['A', 'B', 'C'])
print("Original DataFrame:\n", df)

df_reset = df.reset_index()
print("\nDataFrame after reset_index():\n", df_reset)

Notice how the original index (‘A’, ‘B’, ‘C’) is now a column named ‘index’.

Controlling the Reset: drop and inplace parameters

The reset_index() method offers two key parameters to fine-tune its behavior:

  • drop=True: This removes the existing index completely, avoiding the creation of a new ‘index’ column.

  • inplace=True: This modifies the DataFrame directly, rather than returning a new DataFrame. Using inplace=True is generally more memory-efficient for large DataFrames.

import pandas as pd

data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
df = pd.DataFrame(data, index=['A', 'B', 'C'])

df_drop = df.reset_index(drop=True)
print("Index dropped:\n", df_drop)

df.reset_index(inplace=True)
print("\nDataFrame modified in place:\n", df)

Resetting with MultiIndex

Resetting the index also works seamlessly with hierarchical (MultiIndex) DataFrames.

import pandas as pd

arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
          ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(data, index=index)
print("Original MultiIndex DataFrame:\n",df)

df_reset = df.reset_index()
print("\nDataFrame after reset_index():\n",df_reset)

This demonstrates how reset_index() handles the MultiIndex, flattening it into regular columns.

Setting a New Index During the Reset

You can also specify a new index column during the reset process.

import pandas as pd
data = {'col1': [1, 2, 3], 'col2': [4, 5, 6], 'new_index':[10,20,30]}
df = pd.DataFrame(data)
df = df.set_index('new_index')
df = df.reset_index()
print(df)

This example shows how to use a column as a new index while resetting the index. This is particularly useful when you want to rearrange your DataFrame based on a specific column’s values.