Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Python Data Mining Quick Start Guide
  • Toc
  • feedback
Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

By : Greeneltch
5 (10)
close
Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

5 (10)
By: Greeneltch

Overview of this book

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.
Table of Contents (9 chapters)
close

Access, search, and sanity checks with pandas

Pandas includes some built-in access functions and search/filter functions to make life easier for users. Pandas also has some sanity checks that are available for you to quickly view your data and ensure that you have the correct batch loaded. For example, we've used the head() method, which displays the first five rows with column names, as a way to check which data we loaded in the beginning of this chapter. Don't by shy about sanity checks; if your company has a lot of money riding on the outcome of your analysis, then the last thing you want to do is to mistakenly work with the wrong data loaded.

For ad hoc work in the IPython console, you don't have to include print statements in order to send your output to a console. For example, you can simply pass df.head() into the IPython console and return the first five...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete