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Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python , Third Edition

Navlani

Idris

Free Trial

eBook Feb 2021 478 pages 3rd Edition

eBook

Can$13.99 ~~Can$39.99~~

Navlani

Idris

Free Trial

eBook Feb 2021 478 pages 3rd Edition

eBook

Can$13.99 ~~Can$39.99~~

Can$13.99 ~~Can$39.99~~

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Key benefits

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

Description

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.

Who is this book 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.

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

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Product Details

Publication date : Feb 05, 2021

Length: 478 pages

Edition : 3rd

Language : English

ISBN-13 : 9781789953459

Category :

Data

Languages :

Python

Concepts :

Machine Learning

Tools :

Pandas

19 Chapters

Section 1: Foundation for Data Analysis

Getting Started with Python Libraries

Understanding data analysis

The standard process of data analysis

The KDD process

SEMMA

CRISP-DM

Comparing data analysis and data science

The roles of data analysts and data scientists

The skillsets of data analysts and data scientists

Installing Python 3

Python installation and setup on Windows

Python installation and setup on Linux

Python installation and setup on Mac OS X with a GUI installer

Python installation and setup on Mac OS X with brew

Software used in this book

Using IPython as a shell

Reading manual pages

Where to find help and references to Python data analysis libraries

Using JupyterLab

Using Jupyter Notebooks

Advanced features of Jupyter Notebooks

Keyboard shortcuts

Installing other kernels

Running shell commands

Extensions for Notebook

Summary

NumPy and pandas

Technical requirements

Understanding NumPy arrays

Array features

Selecting array elements

NumPy array numerical data types

dtype objects

Data type character codes

dtype constructors

dtype attributes

Manipulating array shapes

The stacking of NumPy arrays

Partitioning NumPy arrays

Changing the data type of NumPy arrays

Creating NumPy views and copies

Slicing NumPy arrays

Boolean and fancy indexing

Broadcasting arrays

Creating pandas DataFrames

Understanding pandas Series

Reading and querying the Quandl data

Describing pandas DataFrames

Grouping and joining pandas DataFrame

Working with missing values

Creating pivot tables

Dealing with dates

Summary

References

Statistics

Technical requirements

Understanding attributes and their types

Types of attributes

Discrete and continuous attributes

Measuring central tendency

Skewness and kurtosis

Understanding relationships using covariance and correlation coefficients

Pearson's correlation coefficient

Spearman's rank correlation coefficient

Kendall's rank correlation coefficient

Central limit theorem

Collecting samples

Performing parametric tests

Performing non-parametric tests

Summary

Linear Algebra

Technical requirements

Fitting to polynomials with NumPy

Determinant

Finding the rank of a matrix

Matrix inverse using NumPy

Solving linear equations using NumPy

Decomposing a matrix using SVD

Eigenvectors and Eigenvalues using NumPy

Generating random numbers

Binomial distribution

Normal distribution

Testing normality of data using SciPy

Creating a masked array using the numpy.ma subpackage

Summary

Section 2: Exploratory Data Analysis and Data Cleaning

Data Visualization

Technical requirements

Visualization using Matplotlib

Accessories for charts

Advanced visualization using the Seaborn package

Interactive visualization with Bokeh

Plotting a simple graph

Glyphs

Layouts

Nested layout using row and column layouts

Retrieving, Processing, and Storing Data

Technical requirements

Reading and writing CSV files with NumPy

Reading and writing CSV files with pandas

Reading and writing data from Excel

Reading and writing data from JSON

Reading and writing data from HDF5

Reading and writing data from HTML tables

Reading and writing data from Parquet

Reading and writing data from a pickle pandas object

Lightweight access with sqllite3

Reading and writing data from MySQL

Inserting a whole DataFrame into the database

Reading and writing data from MongoDB

Reading and writing data from Cassandra

Reading and writing data from Redis

PonyORM

Summary

Cleaning Messy Data

Technical requirements

Exploring data

Filtering data to weed out the noise

Column-wise filtration

Row-wise filtration

Handling missing values

Dropping missing values

Filling in a missing value

Handling outliers

Feature encoding techniques

Methods for feature scaling

Feature transformation

Feature splitting

Summary

Signal Processing and Time Series

Technical requirements

The statsmodels modules

Moving averages

Window functions

Defining cointegration

STL decomposition

Autocorrelation

Autoregressive models

ARMA models

Generating periodic signals

Fourier analysis

Spectral analysis filtering

Summary

Section 3: Deep Dive into Machine Learning

Supervised Learning - Regression Analysis

Technical requirements

Linear regression

Multiple linear regression

Understanding multicollinearity

Removing multicollinearity

Dummy variables

Developing a linear regression model

Evaluating regression model performance

Fitting polynomial regression

Regression models for classification

Logistic regression

Characteristics of the logistic regression model

Types of logistic regression algorithms

Advantages and disadvantages of logistic regression

Implementing logistic regression using scikit-learn

Summary

Supervised Learning - Classification Techniques

Technical requirements

Classification

Naive Bayes classification

Decision tree classification

KNN classification

SVM classification

Terminology

Splitting training and testing sets

Holdout

K-fold cross-validation

Bootstrap method

Evaluating the classification model performance

Unsupervised Learning - PCA and Clustering

Technical requirements

Unsupervised learning

Reducing the dimensionality of data

PCA

Performing PCA

Clustering

Finding the number of clusters

The elbow method

The silhouette method

Partitioning data using k-means clustering

Hierarchical clustering

DBSCAN clustering

Spectral clustering

Evaluating clustering performance

Internal performance evaluation

The Davies-Bouldin index

The silhouette coefficient

External performance evaluation

The Rand score

The Jaccard score

F-Measure or F1-score

The Fowlkes-Mallows score

Summary

Section 4: NLP, Image Analytics, and Parallel Computing

Analyzing Textual Data

Technical requirements

Installing NLTK and SpaCy

Text normalization

Tokenization

Removing stopwords

Stemming and lemmatization

POS tagging

Recognizing entities

Dependency parsing

Creating a word cloud

Bag of Words

TF-IDF

Sentiment analysis using text classification

Classification using BoW

Classification using TF-IDF

Analyzing Image Data

Technical requirements

Installing OpenCV

Understanding image data

Changing the brightness

Blurring an image

Face detection

Summary

Parallel Computing Using Dask

Parallel computing using Dask

Converting a pandas DataFrame into a Dask DataFrame

Converting a Dask DataFrame into a pandas DataFrame

Dask Bags

Creating a Dask Bag using Python iterable items

Creating a Dask Bag using a text file

Storing a Dask Bag in a text file

Storing a Dask Bag in a DataFrame

Dask Delayed

Preprocessing data at scale

Feature scaling in Dask

Feature encoding in Dask

Machine learning at scale

Parallel computing using scikit-learn

Reimplementing ML algorithms for Dask

Logistic regression

Clustering

Summary

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About the authors

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.

See other products by Navlani

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.

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Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python , Third Edition

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