Tag: Wrangling

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From the Publisher

From the Forward

The phrase data wrangling, born in the modern context of agile analytics, is meant to describe the lion’s share of the time people spend working with data. There is a common misperception that data analysis is mostly a process of running statistical algorithms on high-performance data engines. In practice, this is just the final step of a longer and more complex process; 50 to 80 percent of an analyst’s time is spent wrangling data to get it to the point at which this kind of analysis is possible. Not only does data wrangling consume most of an analyst’s workday, it also represents much of the analyst’s professional process: it captures activities like understanding what data is available; choosing what data to use and at what level of detail; understanding how to meaningfully combine multiple sources of data; and deciding how to distill the results to a size and shape that can drive downstream analysis. These activities represent the hard work that goes into both traditional data ‘curation’ and modern data analysis. And in the context of agile analytics, these activities also capture the creative and scientific intuition of the analyst, which can dictate different decisions for each use case and data source.

We have been working on these issues with data-centric folks of various stripes—from the IT professionals who fuel data infrastructure in large organizations, to professional data analysts, to data-savvy ‘enthusiasts’ in roles from marketing to journalism to science and social causes. Much is changing across the board here. This book is our effort to wrangle the lessons we have learned in this context into a coherent overview, with a specific focus on the more recent and quickly growing agile analytic processes in data-driven organizations. Hopefully, some of these lessons will help to clarify the importance—and yes, the satisfaction—of data wrangling done well.

ASIN ‏ : ‎ B073HMH8XG
Publisher ‏ : ‎ O’Reilly Media; 1st edition (June 29, 2017)
Publication date ‏ : ‎ June 29, 2017
Language ‏ : ‎ English
File size ‏ : ‎ 2846 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 96 pages

Data wrangling is a crucial step in the data analysis process, as it involves cleaning, transforming, and organizing raw data into a format that is suitable for analysis. In this post, we will discuss the principles of data wrangling and provide practical techniques for data preparation.

1. Understand the data: Before starting the data wrangling process, it is important to have a clear understanding of the data you are working with. This includes understanding the data sources, data types, and any potential data quality issues.

2. Clean the data: One of the first steps in data wrangling is cleaning the data. This involves handling missing values, removing duplicates, and correcting any errors in the data. There are various tools and techniques available for cleaning data, such as using data cleaning functions in programming languages like Python or R.

3. Transform the data: Once the data is cleaned, the next step is to transform the data into a format that is suitable for analysis. This may involve reshaping the data, creating new variables, or aggregating data at different levels. Techniques such as data normalization, standardization, and encoding can be used to prepare the data for analysis.

4. Handle outliers: Outliers are data points that are significantly different from the rest of the data. It is important to identify and handle outliers appropriately, as they can skew the results of the analysis. Techniques such as filtering, transforming, or imputing outliers can be used to handle them effectively.

5. Merge and join data: Data wrangling often involves combining data from multiple sources. This can be done using techniques such as merging, joining, or concatenating datasets. It is important to carefully match and combine datasets to ensure that the resulting dataset is accurate and complete.

6. Document the data wrangling process: It is important to document the data wrangling process, including the steps taken and any decisions made during the process. This documentation can help ensure reproducibility and transparency in the analysis.

By following these principles of data wrangling and using practical techniques for data preparation, you can ensure that your data is clean, organized, and ready for analysis. Data wrangling is a time-consuming process, but it is essential for obtaining accurate and reliable results in data analysis.
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