Python Data Analyst's Toolkit - Learn Python and Python-Based Libraries with Applications in Data Analysis and Statistics

Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted...

Ausführliche Beschreibung

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Bibliographische Detailangaben
1. Verfasser: Rajagopalan, Gayathri
Format: E-Book Buch
Sprache:Englisch
Veröffentlicht: Berkeley, CA Apress, an imprint of Springer Nature 2021
Apress
Apress L. P
Ausgabe:1
Schlagworte:
Computer games
Computer programming
Computer Science
Data mining
Decision Making & Business Analysis
Engineering Management & Leadership
General Engineering & Project Administration
General References
Professional and Applied Computing
Professional Computing
Programming
Python
Python (Computer program language)
Statistics
Statistics > Data processing
Statistics, general
ISBN:9781484263983, 1484263987, 9781484263990, 1484263995
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can further be adapted and extended. This book is divided into three parts - programming with Python, data analysis and visualization, and statistics. You'll start with an introduction to Python - the syntax, functions, conditional statements, data types, and different types of containers. You'll then review more advanced concepts like regular expressions, handling of files, and solving mathematical problems with Python. The second part of the book, will cover Python libraries used for data analysis. There will be an introductory chapter covering basic concepts and terminology, and one chapter each on NumPy(the scientific computation library), Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn. Case studies will be included as examples to help readers understand some real-world applications of data analysis.
Bibliographie:Includes index
ISBN:9781484263983
1484263987
9781484263990
1484263995
DOI:10.1007/978-1-4842-6399-0