Python Data Analysis Third Edition

Perform data collection, data processing, wrangling, visualization, and model building using Python

Avinash Navlani , Armando Fandango , Ivan Idris 지음

Packt(GCO Science) 2021년 02월 05일 출간

작품소개

책소개

Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide

▶What You Will Learn
？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
？Perform time series analysis and signal processing using sunspot cycle data
？Analyze textual data and image data to perform advanced analysis
？Get up to speed with parallel computing using Dask

▶Key Features
？Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling
？Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods
？Get to grips with graph processing and sentiment analysis

▶Who This Book Is For
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.

목차

▶TABLE of CONTENTS
▷ Section 1: Foundation for Data Analysis
？Chapter 1: Getting Started with Python Libraries
？Chapter 2: NumPy and pandas
？Chapter 3: Statistics
？Chapter 4: Linear Algebra

▷ Section 2: Exploratory Data Analysis and Data Cleaning
？Chapter 5: Data Visualization
？Chapter 6: Retrieving, Processing, and Storing Data
？Chapter 7: Cleaning Messy Data
？Chapter 8: Signal Processing and Time Series

▷ Section 3: Deep Dive into Machine Learning
？Chapter 9: Supervised Learning - Regression Analysis
？Chapter 10: Supervised Learning - Classification Techniques
？Chapter 11: Unsupervised Learning - PCA and Clustering

▷ Section 4: NLP, Image Analytics, and Parallel Computing
？Chapter 12: Analyzing Textual Data
？Chapter 13: Analyzing Image Data
？Chapter 14: Parallel Computing Using Dask

책 속으로

▶What this book covers
？ Chapter 1, Getting Started with Python Libraries, explains the data analyst process and the successful installation of Python libraries and Anaconda. Also, we will discuss Jupyter Notebook and its advanced features.

？ Chapter 2, NumPy and Pandas, introduces NumPy and Pandas. This chapter provides a basic overview of NumPy arrays, Pandas DataFrames, and their associated functions.

？ Chapter 3, Statistics, gives a quick overview of descriptive and inferential statistics.

？ Chapter 4, Linear Algebra, gives a quick overview of linear algebra and its associated NumPy and SciPy functions.

？ Chapter 5, Data Visualization, introduces us to the matplotlib, seaborn, Pandas plotting, and bokeh visualization libraries.

？ Chapter 6, Retrieving, Processing, and Storing Data, explains how to read and write various data formats, such as CSV, Excel, JSON, HTML, and Parquet. Also, we will discuss how to acquire data from relational and NoSQL databases.

？ Chapter 7, Cleaning Messy Data, explains how to preprocess raw data and perform feature engineering.

？ Chapter 8, Signal Processing and Time Series, contains time series and signal processing examples using sales, beer production, and sunspot cycle dataset. In this chapter, we will mostly use NumPy, SciPy, and statsmodels.

？ Chapter 9, Supervised Learning ？ Regression Analysis, explains linear regression and logistic regression in detail with suitable examples using the scikit-learn library.

？ Chapter 10, Supervised Learning ？ Classification Techniques, explains various classification techniques, such as naive Bayes, decision tree, K-nearest neighbors, and SVM. Also, we will discuss model performance evaluation measures.

？ Chapter 11, Unsupervised Learning ？ PCA and Clustering, gives a detailed discussion on dimensionality reduction and clustering techniques. Also, we will evaluate the clustering performance.

？ Chapter 12, Analyzing Textual Data, gives a quick overview of text preprocessing, feature engineering, sentiment analysis, and text similarity. This chapter mostly uses the NLTK, SpaCy, and scikit-learn libraries.

？ Chapter 13, Analyzing Image Data, gives a quick overview of image processing operations using OpenCV. Also, we will discuss face detection.

？ Chapter 14, Parallel Computing Using Dask, explains how to perform data preprocessing and machine learning modeling in parallel using Dask.

출판사 서평

▶ Preface
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.

Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.

By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.

작가정보

저자(글) Avinash Navlani

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.

저자(글) Armando Fandango

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.

저자(글) Ivan Idris

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.