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 ofPandas and explore the differences betweenloc() andiloc() operators. These two methods are essential for data manipulation in Python, especially when working withDataFrames. Knowing how to apply those two well is thekey to filtering a DataFrame efficiently. Did you get it?loc andkey? Nevermind! Let's jump in.

Selecting withloc() andiloc()

1.loc() - Label-Based Data Selection

Theloc() function is a label-based data selection method. It allows us to select rows or columns based on theirlabels (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 aboutloc():

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

2.iloc() - Integer-Based Data Selection

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

• We specify theinteger index of the row or column we want to select.
• Itexcludes the endpoint when slicing (similar to the Python slicing convention).
• Likeloc[], it also acceptsboolean 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:

importpandasaspddata=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 DataFramedata

Displaying the DataFrame above we get:

Example 1: Conditional Selection Data

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

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

Output:

Example 2: Row Selection using ranges

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

display(data.iloc[2:6])

Output:

Summary

The Pandasloc andiloc 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 likegroupby. They can be adapted to work with boolean conditions, thereby offering a flexible approach to data manipulation tasks. Masteringloc andiloc will add flexibility to any Data Analyst's toolbox.