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Mastering pandas

Mastering pandas

By : Kumar
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Mastering pandas

Mastering pandas

By: Kumar

Overview of this book

pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process.
Table of Contents (21 chapters)
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1
Section 1: Overview of Data Analysis and pandas
4
Section 2: Data Structures and I/O in pandas
7
Section 3: Mastering Different Data Operations in pandas
12
Section 4: Going a Step Beyond with pandas

Practical applications of multidimensional arrays

Panel data (spreadsheet-like data with several distinguishable rows and columns; the kind of data we generally encounter) is best handled by the DataFrame data structure available in pandas and R. Arrays can be used too but it would be tedious.

So what is a good example of data in real life that can be best represented by an array? Images, which are generally represented as multidimensional arrays of pixels, are a good example. In this section, we will see examples of multidimensional representation of an image and why it makes sense.

Any object detection or image-processing algorithm performed on an image requires it to be represented in a numerical array format. For text data, term-document matrix and term frequency-inverse document frequency (TF-IDF) are used to vectorize (create numerical arrays) the data. In the case of an...

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