Python for Data Analysis: A Beginners Guide to Master the Fundamentals of Data Science and Data Analysis by Using Pandas, Numpy and Ipython

Length: 173 pages
Edition: 1
Language: English
Publisher: Independently published
Publication Date: 2021-08-26
ISBN-10: B09DMP86NY
ISBN-13: 9798463514271
Sales Rank: #234755 (See Top 100 Books)

Ready to learn Data Science through Python language?

Python for Data Analysis is a step-by-step guide for beginners and dabblers-alike.
This book is designed to offer working knowledge of Python and data science and some of the tools required to apply that knowledge. It’s possible that you have little experience with or knowledge of data analysis and are interested in it. You might have some experience in coding. You may have worked with data before and want to use Python. We have made this book in a way that will be helpful to all these groups and more besides in varying ways. This can serve as an introduction to the most current tools and functions of those tools used by data scientists.

In this book You will learn:

Data Science/Analysis and its applications
IPython and Jupyter – an introduction to the basic tools and how to navigate and use them. You will also learn about its importance in a data scientist’s ecosystem.
Pandas – a powerful data management Python library that lets you do interesting things with data. You will learn all the basics you need to get started.
NumPy – a powerful numerical library for Python. You will learn more about its advantages.

Introduction
What You Should Keep in Mind
All Tech Work Has A Creative Element
Some Things Will Be Harder at First
You Don’t Know Everything
You Won’t Work Alone
Some Rules
To Python Beginners
Chapter 1: What is Data Science/Analysis?
Data Science vs. Data Analysis
An Example
Data Life Cycle
Data Collection
Data Cleaning
Data Wrangling
Analysis
Application
Why Python?
Chapter 2: Setting Up Your Environment
Anaconda
Windows Anaconda Installation
macOS Anaconda Installation
Using the Installer
Using the Command-line
Linux Anaconda Installation
Chapter 3: iPython & Jupyter
iPython
iPython Installation & Getting Started
iPython Special Features
Getting Information About the Object
Magic Functions
List of Magic Functions
Running and Editing a Python Script
Running System Commands
Jupyter
What Does it Do?
A Quick Overview
Understanding Modality
Jupyter Cell Magic Functions
IPyWidgets
Interactives
Types of Widgets
Numeric Widgets
Boolean Widgets
Selection Widgets
Chapter 4: Pandas
Setting Up Your Environment
Pandas Data Structures
DataFrames & Series
Labelling Indexes In A Series
Converting Tuples & Dictionaries Into A Series
Accessing Data In A DataFrame
Deleting Columns
How to Read and Write Data in Pandas
Learning More About the Data
Writing A DataFrame to A File
Selecting Data
Creating Plots
Creating New Columns
Adding and Removing Columns
Doing Statistics
Combining Tables
Dealing With Textual Data
Find length
Resources
Table A : Reading and Writing data table
Table B:2019 Weekly Data
Table C: The second set of 2019 data for DataFrame combining exercises and others
Chapter 5: NumPy
Installation
The Importance of NumPy Arrays
What is a NumPy Array?
Creating Arrays
Learning About An Array
Basic Array Operations
Accessing Elements, Slicing and Iterating Arrays
Manipulating Shapes
Stacking Arrays
Splitting An Array
Final Words & FAQ
When Do I Know I Have Enough Projects in My Portfolio?
What Type of PC Do I Need for Data Science?
What Are Some of the Skills I Will Need?
Is There a Future in Data Science/Analytics?
What Will it Take for Me to Become a Data Analyst?
Other Books from the Author
References