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Home > Data > Data Analysis > Hands-On Data Analysis with Pandas
Hands-On Data Analysis with Pandas
Hands-On Data Analysis with Pandas

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

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Profile Icon Stefanie Molin
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€8.99 €29.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (11 Ratings)
eBook Jul 2019 740 pages 1st Edition
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Arrow left icon
Profile Icon Stefanie Molin
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€8.99 €29.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (11 Ratings)
eBook Jul 2019 740 pages 1st Edition
eBook
€8.99 €29.99
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€36.99
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Hands-On Data Analysis with Pandas

Section 1: Getting Started with Pandas

Our journey begins with an introduction to data analysis and statistics, which will lay a strong foundation for the concepts we will cover throughout the book. Then, we will set up our Python data science environment, which contains everything we will need to work through the examples, and get started with learning the basics of pandas.

The following chapters are included in this section:

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Page 1 of 1

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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 26, 2019
Length: 740 pages
Edition : 1st
Language : English
ISBN-13 : 9781789612806
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Pandas

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Publication date : Jul 26, 2019
Length: 740 pages
Edition : 1st
Language : English
ISBN-13 : 9781789612806
Category :
Data
Languages :
Python
Concepts :
Data Analysis
Tools :
Pandas

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Table of Contents

19 Chapters
Section 1: Getting Started with Pandas Chevron down icon Chevron up icon
Section 1: Getting Started with Pandas
Introduction to Data Analysis Chevron down icon Chevron up icon
Introduction to Data Analysis
Chapter materials
Fundamentals of data analysis
Statistical foundations
Setting up a virtual environment
Summary
Exercises
Further reading
Working with Pandas DataFrames Chevron down icon Chevron up icon
Working with Pandas DataFrames
Chapter materials
Pandas data structures
Bringing data into a pandas DataFrame
Inspecting a DataFrame object
Grabbing subsets of the data
Adding and removing data
Summary
Exercises
Further reading
Section 2: Using Pandas for Data Analysis Chevron down icon Chevron up icon
Section 2: Using Pandas for Data Analysis
Data Wrangling with Pandas Chevron down icon Chevron up icon
Data Wrangling with Pandas
Chapter materials
What is data wrangling?
Collecting temperature data
Cleaning up the data
Restructuring the data
Handling duplicate, missing, or invalid data
Summary
Exercises
Further reading
Aggregating Pandas DataFrames Chevron down icon Chevron up icon
Aggregating Pandas DataFrames
Chapter materials
Database-style operations on DataFrames
DataFrame operations
Aggregations with pandas and numpy
Time series
Summary
Exercises
Further reading
Visualizing Data with Pandas and Matplotlib Chevron down icon Chevron up icon
Visualizing Data with Pandas and Matplotlib
Chapter materials
An introduction to matplotlib
Plotting with pandas
The pandas.plotting subpackage
Summary
Exercises
Further reading
Plotting with Seaborn and Customization Techniques Chevron down icon Chevron up icon
Plotting with Seaborn and Customization Techniques
Chapter materials
Utilizing seaborn for advanced plotting
Formatting
Customizing visualizations
Summary
Exercises
Further reading
Section 3: Applications - Real-World Analyses Using Pandas Chevron down icon Chevron up icon
Section 3: Applications - Real-World Analyses Using Pandas
Financial Analysis - Bitcoin and the Stock Market Chevron down icon Chevron up icon
Financial Analysis - Bitcoin and the Stock Market
Chapter materials
Building a Python package
Data extraction with pandas
Exploratory data analysis
Technical analysis of financial instruments
Modeling performance
Summary
Exercises
Further reading
Rule-Based Anomaly Detection Chevron down icon Chevron up icon
Rule-Based Anomaly Detection
Chapter materials
Simulating login attempts
Exploratory data analysis
Rule-based anomaly detection
Summary
Exercises
Further reading
Section 4: Introduction to Machine Learning with Scikit-Learn Chevron down icon Chevron up icon
Section 4: Introduction to Machine Learning with Scikit-Learn
Getting Started with Machine Learning in Python Chevron down icon Chevron up icon
Getting Started with Machine Learning in Python
Chapter materials
Learning the lingo
Exploratory data analysis
Preprocessing data
Clustering
Regression
Classification
Summary
Exercises
Further reading
Making Better Predictions - Optimizing Models Chevron down icon Chevron up icon
Making Better Predictions - Optimizing Models
Chapter materials
Hyperparameter tuning with grid search
Feature engineering
Ensemble methods
Inspecting classification prediction confidence
Addressing class imbalance
Regularization
Summary
Exercises
Further reading
Machine Learning Anomaly Detection Chevron down icon Chevron up icon
Machine Learning Anomaly Detection
Chapter materials
Exploring the data
Unsupervised methods
Supervised methods
Online learning
Summary
Exercises
Further reading
Section 5: Additional Resources Chevron down icon Chevron up icon
Section 5: Additional Resources
The Road Ahead Chevron down icon Chevron up icon
The Road Ahead
Data resources
Practicing working with data
Python practice
Summary
Exercises
Further reading
Solutions Chevron down icon Chevron up icon
Solutions
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Customer reviews

Top Reviews
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  • Most Recent
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
(11 Ratings)
5 star 90.9%
4 star 0%
3 star 0%
2 star 9.1%
1 star 0%
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Top Reviews
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Ali Assad Apr 06, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Felipe M. Sep 21, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Verified review Amazon
Amazon Customer Nov 16, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Having just started on my Pandas journey and coming from a non technical background, I find this book simple and the exercises relatable .
Amazon Verified review Amazon
CryptoKnight Aug 30, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Adam Mar 23, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
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About the author

Profile icon Stefanie Molin
Stefanie Molin
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Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
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