Learn Data Analysis with Python (eBook)

Lessons in Coding

A.J. Henley, Dave Wolf (Autoren)

eBook Download: PDF

2018 | 1st ed.
IX, 97 Seiten
Apress (Verlag)
978-1-4842-3486-0 (ISBN)

Infos

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.

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.

Inhalt

Table of Contents1. IntroductionHow to use this bookInstalling iPython NotebookWhat is iPython notebook?What is Anaconda?Getting StartedGetting the datasets for the workbook’s exercises2. Getting Data into and out of PythonLoading Data from CSV FilesSaving Data to CSVLoading Data from Excel FilesSaving Data to Excel FilesCombining Data from Multiple Excel Files:Loading Data from SQLSaving Data to SQLRandom Numbers and Creating Random Data3. Preparing Data is Half the BattleCleaning DataCalculating and Removing OutliersMissing Data in Pandas DataframesFiltering Inappropriate ValuesFinding Duplicate RowsRemoving Punctuation from Column ContentsRemoving Whitespace from Column ContentsStandardizing DatesStandardizing Text like SSN’s, Phone #’s and Zip CodesCreating New VariablesBinning DataApplying Function to Groups, Bins and ColumnsRanking Rows of DataCreate a Column Based on a ConditionalMaking New Columns Using FunctionsConverting String Categories to Numeric VariablesOrganizing the DataRemoving and Adding ColumnsSelecting ColumnsChange Column NameSetting Column Names to Lower CaseFinding Matching RowsFilter Rows Based on Conditions:Selecting Rows Based on ConditionsRandom Sampling Dataframe4. Finding the MeaningComputing aggregate statisticsComputing Aggregate Statistics on Matching RowsSorting DataCorrelationRegressionRegression without InterceptBasic Pivot TableRandom Sampling DataframeSelecting Pandas DataFrame Rows Based on ConditionsDistribution AnalysisCategorical Variable AnalysisTime Series Analysis5. Visualizing DataData Quality ReportGraph a Dataset - Line PlotGraph a Dataset - Bar PlotGraph a Dataset - Box PlotGraph a Dataset - HistogramGraph a Dataset - Pie ChartGraph a Dataset - Scatter PlotPlotting w/ ImagePlotting Data on a Map with BasemapPlotting a Gantt ChartSetting ticks, labels & gridsAdding legends & annotationsMoving Spines to the Center6. Practice ProblemsPivot Exercise 1Pivot Exercise 2Pivot Exercise 2Pivot Exercise 3LegendRegression Exercise 1Regression Exercise 2Regression Exercise 3Analysis ProjectNotes

Produkt-Details

• Erscheint lt. Verlag: 22.2.2018
• Zusatzinfo: IX, 97 p. 15 illus. in color.
• Verlagsort: Berkeley
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Informatik ► Programmiersprachen / -werkzeuge ► Python
• Themenwelt: Wirtschaft
• Schlagworte: Analysis • application • Big Data • Code • Data • Data Science • learn • machine learning • Program • Python • Software • source
• ISBN-10: 1-4842-3486-3 / 1484234863
• ISBN-13: 978-1-4842-3486-0 / 9781484234860