This is the code repository for Python Data Analysis - Third Edition, published by Packt.
Perform data collection, data processing, wrangling, visualization, and model building using Python
Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines.
This book covers the following exciting features:
- Explore data science and its various process models
- Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values
- Create interactive visualizations using Matplotlib, Seaborn, and Bokeh
- Retrieve, process, and store data in a wide range of formats
- Understand data preprocessing and feature engineering using pandas and scikit-learn
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
# Creating an array
import numpy as np
a = np.array([2,4,6,8,10])
print(a)
Following is what you need for this book: This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-18).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | Install Python 3.7 with latest Anaconda environment | Windows, Mac OS X, and Linux (Any) |
| 2 | Install Python numerical computing library NumPy | Windows, Mac OS X, and Linux (Any) |
| 3 | Install Python data analysis library Pandas | Windows, Mac OS X, and Linux (Any) |
| 4 | Install Python scientific computing library SciPy | Windows, Mac OS X, and Linux (Any) |
| 5 | Install Python-MySQL connection library PyMySQ | Windows, Mac OS X, and Linux (Any) |
| 6 | Install Python-MySQL connection library mysql-connector | Windows, Mac OS X, and Linux (Any) |
| 7 | Install Python-MongoDB connection library PyMongo | Windows, Mac OS X, and Linux (Any) |
| 8 | Install Python-Cassandra connection library cassandra-driver | Windows, Mac OS X, and Linux (Any) |
| 9 | Install Python-Redis connection library redis | Windows, Mac OS X, and Linux (Any) |
| 10 | Install python visualization library matplotlib | Windows, Mac OS X, and Linux (Any) |
| 11 | Install python visualization library seaborn | Windows, Mac OS X, and Linux (Any) |
| 12 | Install python visualization library Bokeh | Windows, Mac OS X, and Linux (Any) |
| 13 | Install python natural language processing library Scikit-learn | Windows, Mac OS X, and Linux (Any) |
| 14 | Install python natural language processing library NLTK | Windows, Mac OS X, and Linux (Any) |
| 16 | Install natural language processing library SpaCy | Windows, Mac OS X, and Linux (Any) |
| 17 | Install image processing library OpenCV | Windows, Mac OS X, and Linux (Any) |
| 18 | Install parallel computing library Dask | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Practical Data Analysis Using Jupyter Notebook [Packt] [Amazon]
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Essential Statistics for Non-STEM Data Analysts [Packt] [Amazon]
Avinash Navlani has over 8 years of experience working in data science and AI. Currently, he is working as a senior data scientist, improving products and services for customers by using advanced analytics, deploying big data analytical tools, creating and maintaining models, and onboarding compelling new datasets. Previously, he was a university lecturer, where he trained and educated people in data science subjects such as Python for analytics, data mining, machine learning, database management, and NoSQL. Avinash has been involved in research activities in data science and has been a keynote speaker at many conferences in India.
Armando Fandango creates AI-empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership roles as the chief data scientist and director at start-ups and large enterprises. He has advised high-tech AI-based start-ups. Armando has authored books such as Python Data Analysis - Second Edition and Mastering TensorFlow, Packt Publishing. He has also published research in international journals and conferences.
Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing. You can find more information and a blog with a few NumPy examples at ivanidris.net.
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