Data Analysis with Python: Introducing NumPy, Pandas, Matplotlib, and Essential Elements of Python Programming
- Length: 276 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2022-08-20
- ISBN-10: 9355510659
- ISBN-13: 9789355510655
- Sales Rank: #0 (See Top 100 Books)
An Absolute Beginner’s Guide to Learning Data Analysis Using Python, a Demanding Skill for Today
Key Features
- Hands-on learning experience of Python’s fundamentals.
- Covers various examples of how to code end-to-end data analysis with easy illustrations.
- An excellent starting point to begin your data analysis journey with Python programming.
Description
In an effort to provide content for beginners, the book ‘Data Analysis with Python’ provides a concrete first step in learning data analysis. Written by a data professional with decades of experience, this book provides a solid foundation in data analysis and numerous data science processes. In doing so, readers become familiar with common Python libraries and straightforward scripting techniques.
Python and many of its well-known data analysis libraries, such as Pandas, NumPy, and Matplotlib, are utilized throughout this book to carry out various operations typical of data analysis projects.
Following an introduction to Python programming fundamentals, the book combines well-known numerical calculation and statistical libraries to demonstrate the fundamentals of programming, accompanied by many practical examples. This book provides a solid groundwork for data analysis by teaching Python programming as well as Python’s built-in data analysis capabilities.
What you will learn
- Learn the fundamentals of core Python programming for data analysis.
- Master Python’s most demanding data analysis and visualization libraries, including Pandas, NumPy, and Matplotlib.
- Refresh your step-by-step data analysis process with live examples.
- Extend your expertise to include real-time data analysis and the creation of simple Python scripts.
- Work with external files such as Excel, CSV, and others to clean them up for further analysis.
Who this book is for
This book is intended to help and teach college students and data professionals about Python’s data analysis capabilities while also allowing them to work with Python tools.
Before diving into this book, working knowledge of Python is a definite plus.
Cover Page Title Page Copyright Page Dedication Page About the Author About the Reviewer Acknowledgement Preface Errata Table of Contents 1. Introducing Python Structure Objectives A brief history of Python Versions of Python Features of Python General purpose Interpreted High level Multiparadigm Open source Portable Extensible Embeddable/Integrated Interactive Dynamically typed Python use cases Automation Web scraping Healthcare Finance and banking Data analytics AI/ML Conclusion Questions Points to remember 2. Environment Setup for Development Structure Objectives Downloading and installing the Anaconda package Testing the installation Testing Python in interactive shell Running and testing Jupyter Notebook Conclusion Questions 3. Operators and Built-in Data Types Structure Objectives Variables in Python? Rules for defining a variable name in Python Operators in Python Arithmetic operators Coding example(s) Relational operators Assignment operator Logical operators Bitwise operators Membership operators Identity operators Built-in data types in Python Numeric type Type conversion or type casting String Accessing string components String concatenation String operations and built-in methods List Tuples Sets Dictionaries Conclusion Questions 4. Conditional Expressions in Python Structure Objectives Indentation in Python Conditional expressions in Python ‘If’ statement If…else statement Nested if (if..elif or if…if statements) AND/OR condition with IF statements Conclusion Questions 5. Loops in Python Structure Objectives Loop construct in Python Types of loops in Python Else clause with loops Loop control statements Conclusion Questions 6. Functions and Modules in Python Structure Objectives Defining a function Parameter(s) and argument(s) in a function Types of arguments Lambda function/anonyms function in Python The map(), filter(), and reduce() functions in Python Python modules How to create and use Python modules Creating a Python module Conclusion Questions 7. Working with Files I/O in Python Structure Objectives Opening a file in Python Closing a file in Python Reading the content of a file in Python Writing the content into a file in Python Conclusion Questions 8. Introducing Data Analysis Structure Objectives What is data analysis Data analysis versus data analytics Why data analysis? Types of data analysis Descriptive data analysis Diagnostic data analysis – (Why something happened in the past?) Predictive data analysis – (What can happen in the future?) Prescriptive data analysis – (What actions should I take?) Process flow of data analysis Requirements: gathering and planning Data collection Data cleaning Data preparation Data analysis Data interpretation and result summarization Data visualization Type of data Structured data Semi-structured data Unstructured data Tools for data analysis in Python IPython Pandas NumPy Matplotlib Conclusion Questions 9. Introducing Pandas Structure Objectives Defining pandas library Why do we need pandas library? Pandas data structure Loading data from external files into DataFrame Exploring the data of a DataFrame Selecting data from DataFrame Data cleaning in pandas DataFrame Grouping and aggregation Grouping Aggregation Sorting and ranking Adding row into DataFrame Adding column into DataFrame Dropping the row/column from DataFrame Concatenating the dataframes Merging/joining the dataframes The merge() function The join() function Writing the DataFrame to external files Conclusion Questions 10. Introduction to NumPy Structure Objectives What is NumPy? NumPy array object Creating the NumPy array Creating NumPy arrays using the Python list and tuple Creating the array using numeric range series Indexing and slicing in NumPy array Data types in NumPy NumPy array shape manipulation Inserting and deleting array element(s) Joining and splitting NumPy arrays Statistical functions in NumPy Numeric operations in NumPy Sorting in NumPy Writing data into files Reading data from files Conclusion Questions 11. Introduction to Matplotlib Structure Objectives What is data visualization What is Matplotlib? Getting started with Matplotlib Simple line plot using Matplotlib Object-oriented API in matplotlib The subplot() function in matplotlib Example#1 (1 by 2 subplot) Example#2 (2 by 2 subplot) Customizing the plot Some basic types of plots in matplotlib Export the plot into a file Conclusion Questions 12. Connecting Dots – Step-by-step Data Analysis and Hands-on Use Case Structure Objectives Understanding the Dataset Problem statement Step by step example to perform the data analysis on a given dataset Conclusion Index
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