Book Image

Numerical Computing with Python

By : Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou
Book Image

Numerical Computing with Python

By: Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, Theodore Petrou

Overview of this book

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: • Statistics for Machine Learning by Pratap Dangeti • Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim • Pandas Cookbook by Theodore Petrou
Table of Contents (21 chapters)
Title Page
Contributors
About Packt
Preface
Index

Selecting DataFrame rows and columns simultaneously


Directly using the indexing operator is the correct method to select one or more columns from a DataFrame. However, it does not allow you to select both rows and columns simultaneously. To select rows and columns simultaneously, you will need to pass both valid row and column selections separated by a comma to either the .iloc or .loc indexers.

 

Getting ready

The generic form to select rows and columns will look like the following code:

>>> df.iloc[rows, columns]
>>> df.loc[rows, columns]

The rows and columnsvariables may be scalar values, lists, slice objects, or boolean sequences.

Note

Passing a boolean sequence to the indexers is covered in Chapter 11, Boolean Indexing.

In this recipe, each step shows a simultaneous row and column selection using .iloc and its exact replication using .loc.

How to do it...

  1. Read in the college dataset, and set the index as the institution name. Select the first three rows and the first four columns...