Introduction

September 17th is Practical Business Pythons anniversary. Last year, I reflected on 5 years of growth. This year, I wanted to take a step back and develop a guide to guide readers through the content on PBPython.

As of this writing, I have 84 articles on the site. They vary from fairly complex and lengthy to quick summaries. When I wrote them, I did it based on my interests at the time and without much thought on progression. Now that I have a decent volume of articles, I want to organize them in a more meaningfulway.

My ultimate goal for this site is that I want it to be a resource to help people use Python to automate away many of the repetitive tasks they do on a daily basis with tools like Excel. A secondary goal for is to cover more advanced Python topics that are difficult to do inExcel.

One reason for developing this guide is that at least 90% of my traffic comes from organic search. These users come to the site, read an article and move on. I hope those that find this guide will stay a little longer and find other relevant content and use this as a resource to navigate the Pythonecosystem.

Secondly, this guide will be useful to help me understand gaps in the content and keep a mental framework for continuing to develop content. My intent is to update the sections below as I add new content. I will also maintain a link at the top of the Archives page to point people in the rightdirection.

Table ofContents

• Getting Started withPython
• PandasFundamentals
• Advanced PandasConcepts
• Reporting
• PythonLibraries
• Working withWindows
• Data ScienceTopics
• DataVisualization
• SiteUpdates
• AdditionalResources

Getting Started withPython

Before you begin your Python journey, here are a couple of articles that are helpful for getting everything set up on yoursystem:

• Best Practices for Managing Your CodeLibrary
• Building a Repeatable Data Analysis Process with JupyterNotebooks
• Using Document Properties to Track Your ExcelReports
• Exploring an Alternative to Jupyter Notebooks for PythonDevelopment
• TalkPython Podcast - Escaping Excel Hell with Python andPandas
• TalkPython Podcast - 10 tips to move from Excel toPython

Just as importantly, you need to think about how long this journey will take and what you may need to do to spread the knowledge within yourorganization:

• Getting to the Plateau of Productivity withPython
• Evangelizing Python forBusiness
• Recorded webinar - Tips and techniques to move from Excel toPython

Case studies are also a great way to understand how the Python ecosytem can be used to solveproblems:

• Case Study Processing Historical WeatherPatterns
• Case Study Automating Excel File Creation and Distribution with Pandas andOutlook

PandasFundamentals

Pandas is a rich library with a lot of functionality. If you are new to pandas, this is the order I would recommend reading thearticles:

Basic pandasconcepts:

• Common Excel Tasks Demonstrated in Pandas and Part2
• Excel Filter and Edit - Demonstrated inPandas
• Tips for Selecting Columns in aDataFrame
• Overview of Pandas DataTypes
• Using The Pandas Category DataType
• Creating Pandas DataFrames from Lists andDictionaries
• Cleaning Up Currency Data withPandas
• Stylin withPandas

Grouping and summarizingdata:

• Comprehensive Guide to Grouping and Aggregating withPandas
• Pandas Pivot TableExplained
• Pandas CrosstabExplained
• Understanding the Transform Function inPandas
• Pandas Grouper and Agg FunctionsExplained
• Binning Data with Pandas qcut andcut
• Pandas GroupbyWarning

Data input andoutput:

• Combining Data From Multiple ExcelFiles
• Combine Multiple Excel Worksheets Into a Single PandasDataframe
• Reading HTML tables withPandas
• Reading Poorly Structured Excel Files withPandas

Advanced PandasConcepts

After you have experience with the basics of pandas, here are some articles that describe more complextopics:

• Exceldiff
• Learn More About Pandas By Building and Using a Weighted AverageFunction
• Building a Financial Model with Pandas and SecondTry
• Monte Carlo Simulation withPython
• Python Tools for Record Linking and FuzzyMatching
• Efficiently Cleaning Text withPandas

Reporting

One of the key challenges with moving to python is figuring out the best way to share your results with others. Here are several posts that describealternatives:

• Generating Excel Reports from a Pandas PivotTable
• Improving Pandas ExcelOutput
• Creating Advanced Excel Workbooks withPython
• Interactive Data Analysis with Python andExcel
• Creating PDF Reports with Pandas Jinja andWeasyPrint
• Automated Report Generation with Papermill and secondpart
• Creating Interactive Dashboards from JupyterNotebooks

PythonLibraries

There are several libraries that will be useful for this journey. Some are in the standard library and others are 3rd party applications. All can be useful for taskautomation:

• Using Pythons PathlibModule
• Web scraping withBeautifulsoup
• Collecting Data with Google Forms andPandas
• Adding a Simple GUI to Your PandasScript
• Building a PDF SplitterApplication
• Build a Celebrity Look-Alike Detector with Azures Face Detect andPython
• sidetable - Create Simple Summary Tables inPandas

Working withWindows

Windows has its own quirks. Several articles are helpful for working in a Windowsenvironment:

• Creating Powerpoint Presentations withPython
• Populating MS Word Templates withPython
• Automating Windows Applications Using COM
• Building a Windows Shortcut withPython
• Using WSL to Build a Python Development Environment onWindows

Data ScienceTopics

Data science is a broad category and many articles are related to various data science topics. These articles are more focused on specific data sciencetasks:

• Guide to Encoding Categorical Values inPython
• Introduction to Market Basket Analysis inPython
• Finding Natural Breaks in Data with the Fisher-JenksAlgorithm
• Forecasting Website Traffic Using Facebooks Prophet Library and followup

DataVisualization

Pythons data visualization landscape is complex and it can be difficult to determine the best tool to use. Here are some posts about the visualizationlandscape:

• Choosing a Python VisualizationTool
• Overview of Python VisualizationTools
• Overview of Pandas DataFrame VisualizationTools

Articles on some specificlibraries.

Matplotlib:

• Simple Graphing with IPython andPandas
• Effectively UsingMatplotlib
• Creating a Waterfall Chart inPython
• Building a Bullet Graph inPython

Altair:

• Introduction to Data Visualization withAltair
• Intro to pdvega - Plotting for Pandas usingVega-Lite

Bokeh:

• Interactive Visualization of Australian WineRatings
• Building Bullet Graphs and Waterfall Charts withBokeh

Plotly:

• Creating Interactive Visualizations with Plotlys DashFramework
• Taking Another Look atPlotly

Seaborn:

• Visualizing Google Forms Data withSeaborn
• New Plot Types in Seaborns LatestRelease

SiteUpdates

This section contains ad-hoc posts about the site and the technology behindit.

• The Technology Behind PBPython
• Configuring Pelican StaticBlog
• 5years
• Using Markdown to Create Responsive HTMLEmails
• NewsletterArchives

AdditionalResources

Book reviews and other recommended resources:

• Machine Learning with PythonCookbook
• Machine Learning PocketReference