Loc and iloc tutorial

Introduction

In this tutorial, we will explore two important data selection methods in Python’s Pandas library: .loc and .iloc. Understanding when and how to use these methods is crucial for data manipulation and analysis in Python.

What are .loc and .iloc?

Pandas is an open-source library that provides data structures and data analysis tools for Python. The .loc and .iloc methods are used to access a group of rows and columns in Pandas DataFrame.

• .loc is label-based data selecting method which means that you have to pass the name of the row or column which you want to select. This method includes the last element of the range passed in it, unlike iloc.
• .iloc is integer index-based. So here, you have to pass the integer index in the method to select specific rows/columns. Unlike loc, it does not include the last element of the range passed in it.

Let’s create a DataFrame and use this for our examples.

# Import Pandas library
import pandas as pd

# Create a sample DataFrame
data = {
  'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
  'Age': [25, 30, 35, 40, 45],
  'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']
}
df = pd.DataFrame(data)
df

##       Name  Age         City
## 0    Alice   25     New York
## 1      Bob   30  Los Angeles
## 2  Charlie   35      Chicago
## 3    David   40      Houston
## 4      Eve   45      Phoenix

Using .loc

.loc is used for label-based indexing or selecting data based on labels. Let’s select the data of the person named ‘Charlie’.

# Selecting the data of 'Charlie' using .loc
charlie_data = df.loc[df['Name'] == 'Charlie']
charlie_data

##       Name  Age     City
## 2  Charlie   35  Chicago

We can also select specific columns along with the conditions.

# Select the Age and City of 'Charlie' using .loc
charlie_info = df.loc[df['Name'] == 'Charlie', ['Age', 'City']]
charlie_info

##    Age     City
## 2   35  Chicago

Using .iloc

.iloc is used for integer-based indexing. It is used to select elements by their position. Let’s select the data of the person who is in the third index position (fourth person in the DataFrame).

# Selecting the data of the person at index 3 using .iloc
person_at_index_3 = df.iloc[3]
person_at_index_3

## Name      David
## Age          40
## City    Houston
## Name: 3, dtype: object

We can also select specific columns by passing integers.

# Select the Name and City of the person at index 3 using .iloc
person_info = df.iloc[3, [0, 2]]
person_info

## Name      David
## City    Houston
## Name: 3, dtype: object

Summary

• Use .loc for label-based indexing.
• Use .iloc for integer-based indexing.
• .loc includes the last element in the range, while .iloc does not.