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Introduction to Data Analysis

Before we can begin our hands-on introduction to data analysis with pandas, we need to learn about the fundamentals of data analysis. Those who have ever looked at the documentation for a software library know how overwhelming it can be if you have no clue what you are looking for. Therefore, it is essential that we not only master the coding aspect, but also the thought process and workflow required to analyze data, which will prove the most useful in augmenting our skill set in the future.

Much like the scientific method, data science has some common workflows that we can follow when we want to conduct an analysis and present the results. The backbone of this process is statistics, which gives us ways to describe our data, make predictions, and also draw conclusions about it. Since prior knowledge of statistics is not a prerequisite, this chapter...

Key benefits

Perform efficient data analysis and manipulation tasks using pandas

Apply pandas to different real-world domains with the help of step-by-step demonstrations

Get accustomed to using pandas as an effective data exploration tool.

Description

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

Who is this book for?

This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.

What you will learn

Understand how data analysts and scientists gather and analyze data

Perform data analysis and data wrangling using Python

Combine, group, and aggregate data from multiple sources

Create data visualizations with pandas, matplotlib, and seaborn

Apply machine learning (ML) algorithms to identify patterns and make predictions

Use Python data science libraries to analyze real-world datasets

Use pandas to solve common data representation and analysis problems

Build Python scripts, modules, and packages for reusable analysis code

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

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Ali Assad Apr 06, 2021

Excellent book! If you are looking to sharpen up on pandas and data analysis in python in general (not only pandas) this is a very solid. In my case I needed a strong refresher and this book was a great choice going beyond that. The accompanying notebooks are great too.

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Felipe M. Sep 21, 2019

If you are new to Pandas and looking for a way to get up to speed fast this is a great book. Lots of good examples to see how to apply the concepts in real life, which helps a lot to understand how the different dataframe methods work. You can see the author payed a lot of attention to the detail, from the way to breakdown the chapters to the choice of exercises at the end of each chapter.As a Python programmer Learning Pandas I also appreciate a lot the quality of the code. Usually the code you find in data science examples get things done but it’s far from being of good quality. It’s very refreshing to see good examples written in a very pythonic way.I keep this book next to my desk and I have the feeling I will be coming back to use it as a reference a lot in the coming weeks. The only way this could be improved would be to have the author sit next to you every time you need to use Pandas :)

Amazon Customer Nov 16, 2021

Having just started on my Pandas journey and coming from a non technical background, I find this book simple and the exercises relatable .

CryptoKnight Aug 30, 2019

I really enjoyed this book. This book is great for python programmers who want to learn/implement something cool with data using Pandas (of-course). With some programming background in python/R/C, this book is perfect for the data scientist wannabe. Every chapter has a very interesting exercise set. Easy to follow. I am glad that the author has provided the github repo for this too, way better than reading code from the book alone! The book starts from easy steps e.g., import data, transform, basic computations, more fancy topics like plotting, to more advanced and application-specific like finance, machine learning (yes the book also talked about this in practical aspect!, using sklearn), security analytics!I can't say enough good things about this book and about how effective it is. I started 10 days ago with a skill of python but no data science background. Now, nearly two weeks later, I'm making my scripts do all kinds of things that are complicated if you don't know Pandas e.g., aggregate statistics based on duration (day, week, ..). I genuinely found this book and the exercises a truly beneficial experience/resource. Honestly I usually don't trust this publisher. Most Packt books I own I think weren't worth their price. This book is surprisingly good quality. Finally they have a good author.

Adam Mar 23, 2021

Amazing read. Very thorough, covering a broad range of topics in the data science space. I would highly recommend starting with this book for anyone interested in building a foundation in Data Science.

Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python

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Hands-On Data Analysis with Pandas

Introduction to Data Analysis

Chapter materials

Fundamentals of data analysis

Data collection

Statistical foundations

Setting up a virtual environment

Summary

Exercises

Further reading

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Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python

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