Book Image

Python Data Analysis - Third Edition

By : Avinash Navlani, Ivan Idris
5 (1)
Book Image

Python Data Analysis - Third Edition

5 (1)
By: Avinash Navlani, Ivan Idris

Overview of this book

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.
Table of Contents (20 chapters)
1
Section 1: Foundation for Data Analysis
6
Section 2: Exploratory Data Analysis and Data Cleaning
11
Section 3: Deep Dive into Machine Learning
15
Section 4: NLP, Image Analytics, and Parallel Computing

To get the most out of this book

The execution of the code examples provided in this book requires the installation of Python 3.5 or newer on Mac OS X, Linux, or Microsoft Windows. In this book, we will frequently use SciPy, NumPy, Pandas, scikit-learn, statsmodels, matplotlib, and seaborn. Chapter 1, Getting Started with Python Libraries, provides instructions for the installation and advanced tips so that you can work smoothly. Also, the process of installing specific and additional libraries is explained in the respective chapters. Installation of Bokeh is explained in Chapter 5, Data Visualization. Similarly, the installation of NLTK and SpaCy is explained in Chapter 12, Analyzing Textual Data.

We can also install any library or package that you want to explore using the pip command. We need to run the following command with admin privileges:

$ pip install <library name>

We can also install it from our Jupyter Notebook with ! (exclamation mark) before the pip command:

!pip install <library name>

To uninstall a Python library or package installed with pip, use the following command:

$ pip uninstall <library name>

If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Python-Data-Analysis-Third-Edition. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781789955248_ColorImages.pdf.

Conventions used

In this book, you will find a number of text styles and conventions used throughout this book. Here, we have shown some examples of these styles. Code words in the text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The other convention the pandas project insists on is the import pandas as pd import statement."

A block of code is set as follows:

# Creating an array
import numpy as np

a = np.array([2,4,6,8,10])

print(a)

Any command-line input or output is written as follows:

$ mkdir 
$ cd css

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.
Tips and tricks appear like this.