Code: USD75OFF
This is the code repository for Exploratory Data Analysis with Python Cookbook, published by Packt.
Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data
Exploratory data analysis (EDA) is a crucial step in data analysis and machine learning projects as it helps in uncovering relationships and patterns and provides insights into structured and unstructured datasets. With various techniques and libraries available for performing EDA, choosing the right approach can sometimes bechallenging. This hands-on guide provides you with practical steps and ready-to-use code for conducting exploratory analysis on tabular, time series, and textual data.
This book covers the following exciting features:
- Perform EDA with leading Python data visualization libraries
- Execute univariate, bivariate, and multivariate analyses on tabular data
- Uncover patterns and relationships within time series data
- Identify hidden patterns within textual data
- Discover different techniques to prepare data for analysis
- Overcome the challenge of outliers and missing values during data analysis
- Leverage automated EDA for fast and efficient analysis
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import numpy as np
import pandas as pd
import seaborn as sns
Following is what you need for this book: If you are a data analyst interested in the practical application of exploratory data analysis in Python, then this book is for you. This book will also benefit data scientists, researchers, and statisticians who are looking for hands-on instructions on how to apply EDA techniques using Python libraries. Basic knowledge of Python programming and a basic understanding of fundamental statistical concepts is a prerequisite.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
Basic knowledge of Python and statistical concepts is all that is needed to get the best out of this book. System requirements are mentioned in the following table:
| Software/Hardware | Operating System requirements |
|---|---|
| Python 3.6+ | Windows, Mac OS X, and Linux (Any) |
| 512GB, 8GB RAM, i5 processor(Preferred specs) | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Ayodele Oluleye is a certified data professional with a rich cross functional background that spans across strategy, data management, analytics, and data science. He currently leads a team of data professionals that spearheads data science and analytics initiatives across a leading African non-banking financial services group. Prior to this role, he spent over 8 years at a big four consulting firm working on strategy, data science and automation projects for clients across various industries. In that capacity, he was a key member of the data science and automation team which developed a proprietary big data fraud detection solution used by many Nigerian financial institutions today. To learn more about him, visit his LinkedIn profile.