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Python Data Cleaning and Preparation Best Practices

Python Data Cleaning and Preparation Best Practices

By : Maria Zervou
4.8 (6)
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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)
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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

Summary

In this chapter, we explored various aspects of DataFrame operations in pandas, focusing on concatenation, merging, and the importance of managing indexes.

We discussed merging, which is suited for complex combinations based on shared keys, offering flexibility through various join types such as inner, outer, left, and right joins. We also discussed how concatenation is used to combine DataFrames along a specific axis (either row-wise or column-wise) and is particularly useful for appending datasets or adding new dimensions to data. The performance implications of these operations were discussed, highlighting that proper index management can significantly enhance the efficiency of these operations, especially in large datasets.

In the upcoming chapter, we will deep dive into how the groupby function can be leveraged alongside various aggregation functions to extract meaningful insights from complex data structures.

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Python Data Cleaning and Preparation Best Practices
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