Learn Data Analysis with Python (eBook, PDF) - Henley, A. J.; Wolf, Dave
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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…mehr

Produktbeschreibung
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.

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  • Produktdetails
  • Verlag: Springer-Verlag GmbH
  • Erscheinungstermin: 22.02.2018
  • Englisch
  • ISBN-13: 9781484234860
  • Artikelnr.: 52945408
Autorenporträt
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.
Inhaltsangabe
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 Filtering Inappropriate Values 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 Binning Data 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 Removing and Adding Columns 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 Data Quality Report 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
Rezensionen
"The present book is built as an accessible, yet thorough introduction to data analysis using Python as programming environment. ... The style of the book and textbook-like presentation of concepts recommend it as a good starting point for novices who wish either to understand more about data analysis or wish to learn Python through meaningful examples." (Irina Ioana Mohorianu, zbMATH 1393.68002, 2018)