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. What You Will Learn Get data into and out of Python code Prepare the data and its format Find the meaning of the data Visualize the data using iPython Who This Book Is For 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.
Author(s): A.J. Henley; Dave Wolf
Publisher: Apress
Year: 2018
Language: English
Pages: 97
Table of Contents
About the Authors
About the Technical Reviewer
Chapter 1: How to Use This Book
Installing Jupyter Notebook
What Is Jupyter Notebook?
What Is Anaconda?
Getting Started
Getting the Datasets for the Workbook’s Exercises
Chapter 2: Getting Data Into and Out of Python
Loading Data from CSV Files
Your Turn
Saving Data to CSV
Your Turn
Loading Data from Excel Files
Your Turn
Saving Data to Excel Files
Your Turn
Combining Data from Multiple Excel Files
Your Turn
Loading Data from SQL
Your Turn
Saving Data to SQL
Your Turn
Random Numbers and Creating Random Data
Your Turn
Chapter 3: Preparing Data Is Half the Battle
Cleaning Data
Calculating and Removing Outliers
Your Turn
Missing Data in Pandas Dataframes
Your Turn
Filtering Inappropriate Values
Your Turn
Finding Duplicate Rows
Your Turn
Removing Punctuation from Column Contents
Removing Whitespace from Column Contents
Standardizing Dates
Standardizing Text like SSNs, Phone Numbers, and Zip Codes
Creating New Variables
Binning Data
Your Turn
Applying Functions to Groups, Bins, and Columns
Your Turn
Ranking Rows of Data
Your Turn
Create a Column Based on a Conditional
Your Turn
Making New Columns Using Functions
Your Turn
Converting String Categories to Numeric Variables
Your Turn
Organizing the Data
Removing and Adding Columns
Your Turn
Selecting Columns
Your Turn
Change Column Name
Your Turn
Setting Column Names to Lower Case
Your Turn
Finding Matching Rows
Your Turn
Filter Rows Based on Conditions
Your Turn
Selecting Rows Based on Conditions
Your Turn
Random Sampling Dataframe
Your Turn
Chapter 4: Finding the Meaning
Computing Aggregate Statistics
Your Turn
Computing Aggregate Statistics on Matching Rows
Your Turn
Sorting Data
Your Turn
Correlation
Your Turn
Regression
Your Turn
Regression without Intercept
Your Turn
Basic Pivot Table
Your Turn
Chapter 5: Visualizing Data
Data Quality Report
Your Turn
Graph a Dataset: Line Plot
Your Turn
Graph a Dataset: Bar Plot
Your Turn
Graph a Dataset: Box Plot
Your Turn
Graph a Dataset: Histogram
Your Turn
Graph a Dataset: Pie Chart
Your Turn
Graph a Dataset: Scatter Plot
Your Turn
Chapter 6: Practice Problems
Analysis Exercise 1
Analysis Exercise 2
Analysis Exercise 3
Analysis Exercise 4
Analysis Project
Required Deliverables
Index
