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How to select rows in pandas MultiIndex DataFrame?

Given a pandas MultiIndex DataFrame, we have to select rows.
Submitted by Pranit Sharma, on May 27, 2022

Columns are the different fields which contains their particular values when we create a DataFrame. We can perform certain operations on both rows & column values. In this article, we are going to learn how to drop a level from a multi-level column index.

Multilevel indexing is a type of indexing which include different levels of indexes or simply multiple indexes. The DataFrame is classified under multiple indexes and the topmost index layer is presented as level 0 of multilevel index followed by level 1, level 2 and so on.

To understand how to select a row from a multiindex DataFrame, we first need to create a multilevel index DataFrame.

To work with pandas, we need to import pandas package first, below is the syntax:

import pandas as pd

Example:

# Importing pandas package
import pandas as pd

# Creating multilevel index
index = pd.MultiIndex.from_tuples([('Vitamin A','Sources'),
                                   ('Vitamin C', 'Sources'),
                                   ('Vitamin D','Sources')])

# Creating a multilevel index DataFrame with 
# columns = multilevel indexes
df = pd.DataFrame([['Papaya','Orange','Oily Fish'],
                  ['Watermelon','Blackcurrent','Red meat'],
                   ['Mango','Kale','egg yolks']], columns=index)

# Display multilevel DataFrame
print("Multilevel DataFrame:\n",df)

Output:

Example 1: Select rows in pandas

Now we will select a row from this Multiindex DataFrame. We will use DataFrame.loc() for the purpose of selecting a row.

# Selecting row from this 
# multiindex DataFrame
result = df.loc[0,['Vitamin A']]

print("Selected Row:\n",result)

Output:

Example 2: Select rows in pandas

Python Pandas Programs »



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