Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Python Data Cleaning and Preparation Best Practices
  • Table Of Contents Toc
Python Data Cleaning and Preparation Best Practices

Python Data Cleaning and Preparation Best Practices

By : Maria Zervou
4.8 (6)
close
close
Python Data Cleaning and Preparation Best Practices

Python Data Cleaning and Preparation Best Practices

4.8 (6)
By: Maria Zervou

Overview of this book

Professionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone. To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You’ll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You’ll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio. By the end of this book, you’ll be proficient in data cleaning and preparation techniques for both structured and unstructured data.
Table of Contents (19 chapters)
close
close
1
Part 1: Upstream Data Ingestion and Cleaning
9
Part 2: Downstream Data Cleaning – Consuming Structured Data
14
Part 3: Downstream Data Cleaning – Consuming Unstructured Data

Using the apply function on grouped data

The apply() function in Pandas is a powerful method used to apply a custom function along an axis of a DataFrame or Series. It is highly versatile and can be used in various scenarios to manipulate data, compute complex aggregations, or transform data based on custom logic. The apply() function can be used to do the following:

  • Apply functions row-wise or column-wise
  • Apply functions to groups of data when used in conjunction with groupby()

In the next section, we will focus on using the apply function on groups of data by first grouping on the column we want and then performing the apply operation.

Note

Using the apply function without groupby allows you to apply a function across either rows or columns of a DataFrame directly. This is useful when you need to perform row-wise or column-wise operations that don’t require grouping the data. Apply the same learnings and just skip the group by clause.

When using...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Python Data Cleaning and Preparation Best Practices
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon