Learn Data Analysis with Python, 1st ed. Lessons in Coding

Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it.  

Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.

If you aren?t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.

Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.

Table of Contents

1. Introduction

How to use this book

Installing iPython Notebook

What is iPython notebook?

What is Anaconda?

Getting Started

Getting the datasets for the workbook’s exercises

2. Getting Data into and out of Python

Loading Data from CSV Files

Saving Data to CSV

Loading Data from Excel Files

Saving Data to Excel Files

Combining Data from Multiple Excel Files:

Loading Data from SQL

Saving Data to SQL

Random Numbers and Creating Random Data

3. Preparing Data is Half the Battle

Cleaning Data

Calculating and Removing Outliers

Missing Data in Pandas Dataframes

Finding Duplicate Rows

Removing Punctuation from Column Contents

Removing Whitespace from Column Contents

Standardizing Dates

Standardizing Text like SSN’s, Phone #’s and Zip Codes

Creating New Variables

Applying Function to Groups, Bins and Columns

Ranking Rows of Data

Create a Column Based on a Conditional

Making New Columns Using Functions

Converting String Categories to Numeric Variables

Organizing the Data

Selecting Columns

Change Column Name

Setting Column Names to Lower Case

Finding Matching Rows

Filter Rows Based on Conditions:

Selecting Rows Based on Conditions

Random Sampling Dataframe

4. Finding the Meaning

Computing aggregate statistics

Computing Aggregate Statistics on Matching Rows

Sorting Data

Correlation

Regression

Regression without Intercept

Basic Pivot Table

Random Sampling Dataframe

Selecting Pandas DataFrame Rows Based on Conditions

Distribution Analysis

Categorical Variable Analysis

Time Series Analysis

5. Visualizing Data

Graph a Dataset - Line Plot

Graph a Dataset - Bar Plot

Graph a Dataset - Box Plot

Graph a Dataset - Histogram

Graph a Dataset - Pie Chart

Graph a Dataset - Scatter Plot

Plotting w/ Image

Plotting Data on a Map with Basemap

Plotting a Gantt Chart

Setting ticks, labels & grids

Adding legends & annotations

Moving Spines to the Center

6. Practice Problems

Pivot Exercise 1

Pivot Exercise 2

Pivot Exercise 2

Pivot Exercise 3

Legend

Regression Exercise 1

Regression Exercise 2

Regression Exercise 3

Analysis Project

Notes

AJ Henley is teaching courses on data analysis using Python, Java and more.  He is a technology educator with over 20 years experience as a developer, designer and systems engineer. He is an instructor at Howard University and Montgomery College.

Dave Wolf is a certified Project Management Professional (PMP) with over twenty years' experience as a software developer, analyst and trainer.  His latest projects include collaboratively developing training materials and programming bootcamps for Java and Python.