Back to Basics - Pandas #02

The power of the 'locs'

One of the most common tasks we perform with Pandas is data indexing and selection. We do that pretty much daily.

Let's delve into the world of Pandas and explore the differences between loc() and iloc() operators. These two methods are essential for data manipulation in Python, especially when working with DataFrames. Knowing how to apply those two well is the key to filtering a DataFrame efficiently. Did you get it? loc and key? Nevermind! Let's jump in.

Selecting with loc() and iloc()

1. loc() - Label-Based Data Selection

The loc() function is a label-based data selection method. It allows us to select rows or columns based on their labels (i.e., row or column names) but may also be used with a boolean array with the same length as the row axis. Some key points about loc():

• We pass the name of the row or column we want to select.
• It includes the last element of the range specified.
• It can accept boolean data for filtering.
• It's useful for selecting data based on specific conditions.

2. iloc() - Integer-Based Data Selection

The iloc() function, on the other hand, is an integer-based data selection method. It uses integer positions to access data but may also be used with a boolean array. Here are some aspects of iloc() to keep in mind:

• We specify the integer index of the row or column we want to select.
• It excludes the endpoint when slicing (similar to the Python slicing convention).
• Like loc[], it also accepts boolean data for filtering.
• It's ideal for accessing data by position.

Examples

Let's demonstrate these concepts using a sample DataFrame containing information about cars:

import pandas as pd
data = pd.DataFrame({
    'Brand': [
        'Ford', 'Hyundai', 'VW', 'Vauxhall', 'Ford', 
        'Hyundai', 'Renault', 'VW', 'Ford
    '],
    'Year': [
        2012, 2014, 2011, 2015, 2012, 
        2016, 2014, 2018, 2019
    ],
    'Kms Driven': [
        50000, 30000, 60000, 25000, 10000, 
        46000, 31000, 15000, 12000
    ],
    'City': [
        'Manchester', 'London', 'Birmingham', 'London', 'Birmingham', 
        'London', 'Birmingham', 'Liverpool', 'Nottingham'],
    'Mileage': [
        28, 27, 25, 26, 28, 29, 24, 21, 24
    ]
})
# Displaying the DataFrame
data

Displaying the DataFrame above we get:

Brand	Year	Kms Driven	City	Mileage
Maruti	2012	50000	Manchester	28
Hyundai	2014	30000	London	27
VW	2011	60000	Birmingham	25
Vauxhall	2015	25000	London	26
Ford	2012	10000	Birmingham	28
Hyundai	2016	46000	London	29
Renault	2014	31000	Birmingham	24
VW	2018	15000	Liverpool	21
Ford	2019	12000	Nottingham	24

Example 1: Conditional Selection Data

Let's use loc() to find Ford cars with a mileage greater than 25:

display(data.loc[(data.Brand == 'Ford') & (data.Mileage > 25)])

Output:

Brand	Year	Kms Driven	City	Mileage
Ford	2012	50000	Manchester	28
Ford	2012	10000	Birmingham	28

Example 2: Row Selection using ranges

We'll use iloc() to extract rows with indices from 2 to 5 (inclusive):

display(data.iloc[2:6])

Output:

Brand	Year	Kms Driven	City	Mileage
VW	2011	60000	Birmingham	25
Vauxhall	2015	25000	London	26
Ford	2012	10000	Birmingham	28
Hyundai	2016	46000	London	29

Summary

The Pandas loc and iloc are powerful tools for selecting and manipulating data within Pandas DataFrames and Series. Its utility ranges from simple row-and-column selections to more complex operations combined with other Pandas features like groupby. They can be adapted to work with boolean conditions, thereby offering a flexible approach to data manipulation tasks. Mastering loc and iloc will add flexibility to any Data Analyst's toolbox.