Review of Python Courses (Part 3)

In Part 2, I summarized the next three Datacamp courses I took. Today I will continue with courses 6-8.

As a reminder, I introduced you to my recent work learning Python here.

Course #6 was Python Data Science Toolbox, Part 2. This course covers:

• Iterators and iterables
• List and dictionary comprehensions
• Conditionals in comprehensions
• Generator expressions
• Using pandas read_csv iterator for streaming data

The next course I took was Merging Dataframes with pandas. Concepts covered in this course include:

• Reading multiple data files (including use of glob)
• Reindexing dataframes
• Arithmetic with series and dataframes
• Appending and concatenating series and dataframes
• Example of reading multiple files at once with a loop to build dataframe
• Concatenation, keys, and MultiIndexes
• Outer and inner joins
• Converting to, manipulating as, and stacking arrays
• Merging and joining dataframes
• Reshaping and plotting

My course #8 was Introduction to Importing Data in Python. Concepts covered here include:

• Importing text and tabular data
• Flat files
• Importing flat files using NumPy and pandas
• Importing pickled files and Excel spreadsheets
• Importing SAS/Stata files using pandas
• Importing HDF5 and MATLAB files
• Introduction to relational databases
• Creating a database engine in Python
• Querying relational databases in Python and directly with pandas
• Advanced querying: exploiting table relationships

As a bonus, let’s recap my class #9: Intermediate Importing Data with Python. Concepts covered here include:

• Importing flat files from the web with the urllib package
• HTTP requests to import files from the web
• Scraping the web in Python using BeautifulSoup
• Introduction to APIs and JSONs
• APIs and interacting with the world wide web
• The Twitter API and authentication

I will continue summarizing classes later.

Review of Python Courses (Part 2)

In Part 1, I summarized the first two Datacamp courses I took. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

Course #3 was Data Manipulation with pandas. This course started out with:

• Aspects of dataframes
• Sorting and subsetting
• Adding new columns
• Calculating summary statistics
• Counting (categorical data)

The course continues on with:

• Grouped Summary Statistics
• Pivot tables
• Explicit indexes
• Slicing and subsetting with .loc and .iloc accessors
• Data visualization
• Missing values
• Reading and writing .csv files

The next course I took was Data Types for Data Science in Python. Concepts covered in this class include:

• Lists (and iteration)
• Tuples (and iteration)
• Sets for unordered and unique data
• Using dictionaries
• Altering dictionaries
• Working with .csv files
• Counting with Collections module
• Dictionaries of unknown structure (defaultdict)
• Maintaining dictionary order with orderedDict
• Named tuples
• Working with datetime objects
• Adding and subtracting time
• Pendulum library
• Counting crimes case study

The next course I took was Python Data Science Toolbox, Part 1. Concepts covered in this class include:

• User-defined functions
• Multiple parameters and return values
• Object scope
• Nested functions
• Default and flexible arguments
• Lambda functions

I will continue summarizing classes later.

Review of Python Courses (Part 1)

In this post, I introduced you to my recent work learning Python. Although I’ve done all the exercises and taken lots of notes, I have zipped through tens of Python classes in less than four hours each (on average). For this reason, I certainly would not claim to be a Pythonista or a programming expert.

It’s a start, though, and as a way to help solidify my knowledge a bit, I’m going to go through the courses I have taken and provide brief summaries of them all. In doing this, I will go through my notes to aid with my own learning.

My first class was Introduction to Python. This starts by describing a brief history of Python and the DataCamp setup for programming and console (technical term) usage. The section continues with:

• Variables
• Data types
• Lists
• List slicing
• List manipulation

The course continues on with:

• Functions with round() as an exemplar
• Methods
• Packages
• Numpy
• Array types
• Array subsetting
• Data analysis

Class #2 for me was Intermediate Python. Concepts covered in this class include:

• Data visualization including scatter plots, histograms, and line plots (matplotlib)
• Customizing plots with axis labels, title, ticks, and formatting
• Dictionaries [vs. lists]
• [Pandas] Dataframes (Df)
• Df column/row access (.loc and .iloc accessors)
• Comparison operators
• Boolean operators
• If, elif, else
• Df filtering
• For and while loops
• .enumerate() method
• Loop data structures
• .apply() method
• Random numbers, generators, walks, distributions

Really good introductory stuff, here!

Attacking the Python

In 2019, I spent about four months taking some introductory online courses in Python, Excel VBA, and TradeStation EasyLanguage. This year, I’ve made a somewhat larger commitment to learn Python by subscribing to DataCamp (DC).

DC offers hundreds of online courses in Python, R, and other subjects pertaining to data science and programming. Classes include roughly 15 videos with each averaging around four minutes in length. The videos are punctuated with multiple-choice questions and fill-in-the-blank programming exercises. If I don’t know the answer then I can click to get a hint and, if necessary, click to get the answer. At any point, I can submit my answer to be checked. If correct, I gain points and move onto the next exercise or video (watched videos also accrue points).

One thing I found lacking at DC was a detailed explanation of solutions. I usually found provided solutions to be self-explanatory (especially in combination with the videos), but occasionally I was left scratching my head. To this end, DC has a Slack platform with multiple channels where questions can be asked or comments made. I have found this community to be of significant benefit thanks to a number of people eager to help.

DC bundles together classes into career tracks and skill tracks. The former encompass more classes and a broader education. I completed the “Data Science with Python” career track while going through 100+ classes. I was only a few courses away from completing a few other career tracks, but I took all the classes that captured my interest. I feel like I definitely gained a solid, introductory foundation for what programming in Python is all about.

In addition to the classes, DC offers additional practice exercises, assessments, and some open-ended (not fill-in-the-blank) projects. I plan to do some of these, but I have not yet started.

I have been doing supplemental practice exercises at w3resource.com. This is not the only website that offers such practice exercises, but it has a lot of them along with working solutions.

To aid with my review, I have also started my first bigger project: a futures backtester. This will take a lot of time (and require extra help since I have such limited programming experience) but should be very educational. I will write about this soon.