Mastering Python for Data Science with NumPy and Pandas -PDF

, Copyright © 2024 Davix Tech
All rights reserved.
No part of this book may be reproduced or transmitted in any form or by any means,
electronic or mechanical, including photocopying, recording, or by any information
storage and retrieval system, without written permission from the author, except for the
inclusion of brief quotations in a review.
DISCLAIMER
While the publisher and author have used their best efforts in preparing this book, they
make no warranties or representations with respect to the accuracy or completeness of
the contents and specifically disclaim any implied warranties of merchantability or
fitness for a particular purpose. No warranty may be created or implied by statements
or information contained in this book. The publisher and author shall not be liable for
any damages arising out of or in connection with the use of this book.

, Table of Contents
CHAPTER 1

​● I​ ntroduction to Data Science and Python
​● W
​ hat is Data Science?
​● W
​ hy is Data Science Important?
​ T
● ​ he Role of Python in Data Science
​● W
​ hy Python for Data Science?
​● B
​ eyond Technical Advantages
​● S
​ etting Up Your Python Environment (Anaconda, Jupyter Notebooks)
​ B
● ​ asic Python Syntax and Data Types (Numbers, Strings, Booleans, Lists, Tuples,
Dictionaries)
​● C
​ ontrol Flow Statements (if, else, for, while)
​● F
​ unctions and Modules

CHAPTER 2
​ E
● ​ ssential Tools for Data Exploration and Analysis
​● T
​ he IPython Shell and Jupyter Notebooks for Interactive Computing
​● C
​ hoosing Between IPython Shell and Jupyter Notebooks
​● V
​ ersion Control with Git (Optional)
​● L
​ earning Resources
​ D
● ​ ata Visualization Libraries (Matplotlib, Seaborn) (Introduction only, detailed use
covered later)

CHAPTER 3
​● I​ ntermediate Python Programming for Data Science
​● O
​ bject-Oriented Programming (Classes and Objects)
​ I​ ntroduction to Object-Oriented Programming (OOP)
●
​● A
​ dvantages of OOP in Data Science
​● W
​ orking with Files and Exceptions
​● R
​ egular Expressions for Text Manipulation
​ N
● ​ umPy Fundamentals: Arrays and VectorizedOperations (Detailed coverage)
​● I​ ntroduction to NumPy Arrays

CHAPTER 4
​● D
​ eep Dive into NumPy Arrays

, ​● C
​ reating Arrays from Various DataStructures
​ C
● ​ reating Arrays from Various Data Structures
​● A
​ rray Attributes (Shape, Dtype, Indexing and Slicing)
​● M
​ athematical Operations on Arrays (Element-wise and Universal Functions)
​● A
​ rray Broadcasting for Efficient Calculations
​ L
● ​ inear Algebra with NumPy (Matrices, Vectors,
​● D
​ ot Product, Linear Systems)
​● R
​ andom Number Generation for Simulations

CHAPTER 5
​ A
● ​ dvanced NumPy Techniques
​● F
​ ancy Indexing and Selection for Complex Data Access
​● F
​ ancy Indexing: Fine-Grained Selection
​● A
​ rray Reshaping and Transpose Operations
​ W
● ​ orking with Multidimensional Data (NDArrays)
​● H
​ andling Missing Data with NumPy
​● (​ NA values)
​● F
​ ile I/O with NumPy (Loading and Saving Data)

CHAPTER 6
​ P
● ​ erformance Optimization with NumPy
​● V
​ ectorization vs. Loops for Efficiency
​● P
​ rofiling Code to Identify Bottlenecks
​● L
​ everaging NumPy with Other Powerful Libraries

CHAPTER 7
​● I​ ntroduction to Pandas Data Structures
​● S
​ eries: One-Dimensional Labeled Data
​● D
​ ataFrames: Two-Dimensional Labeled Data with Columns
​ A
● ​ ccessing Data within a DataFrame
​● C
​ reating DataFrames from Various Sources (Lists, Dictionaries, CSV Files)
​● I​ ndexing, Selection, and Accessing Data in DataFrames

CHAPTER 8
​ E
● ​ ssential Data Manipulation with Pandas
​● H
​ andling Missing Data Cleaning and Imputation Techniques
​● D
​ ata Transformation (Filtering, Sorting, Grouping)
​● M
​ erging and Joining DataFrames for Combining Datasets
​● R
​ eshaping and Pivoting Data for Different Views