Lessons in Coding
Buch, Englisch, 97 Seiten, Format (B × H): 151 mm x 236 mm, Gewicht: 1825 g
ISBN: 978-1-4842-3485-3
Verlag: Apress
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
Weitere Infos & Material
Table of Contents
1. 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
