Stay on Target (or why my Python app still isn’t built)

One of the things I’m not doing well is focusing on one task at a time. As I continue to learn Python, every time I across a way to do something, I want to implement it right away without thinking ahead of how all the different things work together. Then I’ll get stuck, and frustrated, and my pace slows.

I need to find a task, stay on target, and just finish it, rather than jumping from feature to feature. With this in my mind, I’ve taken a step back to think about what needs to get done.

There are two major things that need to be built:

nflpool.xyz

Using Pyramid, I need to get the website up – even if this is just a skeleton. The Python for Entrepreneurs will get me there. I need to follow through and finish the course.

Major features for the website include:

  1. User creation / management: This includes creating an account, resetting passwords and login / logout. The course does an awesome job of how to properly hash and salt the passwords – I just watched and worked on this chapter yesterday.
  2. Yearly Player Picks: I will need to create a form for each player, after they have created an account, to submit their picks. This form will need to talk to the database to display the list of teams in each conference and the players available in each of the positions. I briefly looked at the Pyramid documentation Friday night and something like WTForms might work for this, but I really know nothing about it at this point. From there the player will need to hit submit, then review their picks or make changes, and then submit their picks which are stored in the database.
  3. Scoring: The last section of the website is the most important part to each player – how are their picks doing against everyone else? One of the reasons I’m using a database is that the cumulative player stats that MySportsFeeds provides are just that – cumulative through the season. There isn’t a way to just get the quarterback stats for week 5 of the 2016 season – so I need to store each weeks stats in the databases. This way a player can track their progress in nflpool through the year. Want to see where they stand right now? Check. Versus two weeks ago? Check. So the website will need to default to the latest week and then let the player choose the year and the week to see past history.

The only downside at this time for creating the website is if I want to use sqlite vs mongodb – I’d prefer to use mongodb as I’m stuck on how to create the individual player picks table and wanted to try it as a key / value store in a mongodb collection. The course is focused on SQLite with SQLAlachemy – something I’d like to learn but I think mongodb might also be easier for taking the JSON from MySportsFeeds and just sticking it right in the database.

nflpool app

The app has two major features that need to be completed:

  1. Import data via JSON from MySportsFeeds into the database: I had all of this done using SQLite. If I choose to switch databases, I’ll need to rewrite this this code.
  2. Scoring calculations: This isn’t done at all. This depends on the player picks table in the database, which is where I was stuck a few months ago when I took a break. I can’t figure out the data model for it no matter how many times my wife tries to explain it to me and I don’t know if I’m just being optimistic when I think a key value store in mongodb would work better. I’m going to give this a bit more thought and actually write out what the document would look like. This would probably be an embedded document in each user’s account.

Next Steps

With all that said, I think working on the website and getting a skeleton up is the best next step. If I can get the site up, then start to work on the submission form for the picks (which will require a bit of importing team data into a database, so I’ll have to make a decision there), I think I’ll feel a lot better. In a perfect world – and I know this isn’t going to happen in the next 30 – 45 days when I really need it, would be to have the submission form working prior to the 2017 season starting. Even if I have to still calculate the points weekly like I’ve been doing for the last two years, at least I’d have the picks in the database this time instead of having to work with the challenge of Google Sheets.

MongoDB for Python for Developers

I’m taking the latest training course that just launched a couple of weeks ago from Michael Kennedy at Talk Python: MongoDB for Python for Developers. This is my first exposure to NoSQL. Over the last year, I’ve searched Google a few different times trying to understand what NoSQL without any success – it always went over my head. Within ten minutes of starting this course, I think I might understand what a document database is.

I took a break from coding the nflpool app a few months ago after my wife gave me some feedback on how I was designing the data model for the SQLite database I was using. I was pretty frustrated, not with her, but just my lack of knowledge. I still hadn’t figured out how to import the individual player pick’s from the Google Sheet I was using, though I did find some open source code that did it perfectly. The challenge was if I changed the data model, the import functionality was going to change significantly and I couldn’t figure it out.

Here it is, early July, and I feel the panic of not having the app built for the upcoming NFL season for the second year in a row. That’s ok – it’s a marathon, not a sprint, to learn Python and build the app. Everyone needs a hobby.

I’m going to create a new branch in nflpool and see if I can use MongoDB instead of SQLite. I need to sit down this weekend and give some thought and sketch out the data model for the Users collection, but I think it could (should?) work better than what I was planning. It’s already obvious that importing the the NFL statistics from MySportsFeeds via JSON directly into MongoDB should be a slam dunk.

The challenge in switching is twofold: First, I’ll need to understand how that changes the Python For Entrepreneurs course – as I’m going to use Pyramid for the web framework, I’ll need to understand how those will work together. This is especially true for the user accounts and database sections of the course.

The second risk is by switching to MongoDB from a SQL language, there will be no help available from my wife. I might drive her crazy with my questions and the way I ask them, but she has a lot of knowledge of SQL and it might be even more of challenge doing the database on my own, in addition to the Python.

I’m enjoying the MongoDB for Python for Developers course. To be fair, it’s definitely over my head – I’m not a real developer nor do I have any kind of database experience or know any Javascript, so I’m taking it slow and in chunks. I’m not coding the examples as I follow along yet – I’m going to audit the whole course, give some thought to confirm this is what I want to do, and then I’ll go through it again. It’s probably in my best interest to finish Python for Entrepreneurs and get the Pyramid web app up and running. I do enjoy Mr. Kennedy’s courses – the way the courses are structured, how each lecture builds on the others and his delivery makes them worth the money.  Even for some of the topics where I don’t have the prerequisite knowledge I probably should, I find myself learning.  I’m on vacation next week and plan to spend a good chunk of time going through both the Python for Entrepreneurs course and the new MongoDB course.

NFLPool 0.1 milestone completed

I followed through on my last blog post and made a lot of progress over the weekend – the best way to learn is by doing.  I’ve updated my roadmap for nflpool and broke the development of the nflpool app into chunks:

  • 0.1: Database creation complete – write the Python code and SQL statements to create all the needed database tables using sqlite3.  This includes using the requests module to import all players in the NFL into the database from MySportsFeeds.
  • 0.2: Import the 2016 statistics from MySportsFeeds into the database. This includes everything needed to calculate an NFLPool player’s score: individual player statistics, division standings, Wild Card seeds, etc.
  • 0.3: Scoring calculations are complete – the app works. The nflpool app can take every player’s picks, compare it to the final standings, and output everyone’s score for this past 2016 season.
  • 0.4: If 0.3 can calculate the final 2016 standings, 0.4 will add functionality to step through every week individually for 2016 from weeks 1 through 17. This will have to be different code as it won’t use the requests module to get real time data, it will use the JSON data I downloaded weekly last year. This will help me prepare for the 2017 season proving that it can calculate the score each week until the season ends.
  • 0.5: The nflpool app now lives on its website, nflpool.xyz. This will include an online form for the 2017 season where players can make their picks and these picks are inserted into the database. This will be built on Pyramid (after I complete the Python for Entrepreneurs course from Talk Python to do this.)
  • 1.0: Full nflpool.xyz integration. Players can browse by week for the current season and past seasons.

After this weekend, the 0.1 milestone is complete. I ran into a few challenges, but the database is complete and I even have cumulative NFL Player stats imported as part of the 0.2 milestone. The first challenge I ran into was I could not get the CSV file imported into the sqlite3 database. We originally used a Google Form to capture each player’s picks. I saved that in Google Docs as a CSV file to be imported. I kept getting a too many values to unpack error and no matter how many times I compared the CSV columns to the SQL statement – it was expecting 47 and no matter how many times I checked and re-checked, I couldn’t find my mistake. After doing some Google searches, I came across this Python script on Github to import a CSV into sqlite – and it worked!

The second challenge I ran into today. I realized after importing the player’s picks and the NFL Player statistics that I was using NFL Player names in the CSV file but I was using the player_id, an integer, from MySportsFeeds for the database. Using the player_id is the correct way to do this, but I needed to modify the CSV and re-import. No problem, but after doing this, I realized I would need to do the same thing again for the Team picks – I need to use the team_id not the team name.

This is all now done and I can move on to the 0.2 milestone. Starting with the five picks for individual stats (passing yards, rushing yards, receiving yards, sacks and interceptions – all already imported using requests!), I’ll write a function that will compare a player’s picks to if the NFL player finished in the top three of that category and assign the correct points. I’ll then add an if statement to see if the nflpool player made a unique pick in that category, and if so, double the points earned.

From there I’ll move on to all the other categories such as Division Standings or Points For and use the same logic.

This is huge progress. The point calculations will be the hardest part of the app (outside of building the website) and now it’s time to see how much Python I’ve learned.

Writing Python to Learn

I’ve spent a lot of time on my Python journey watching videos, reading a lot of articles, reading Reddit and listening to podcasts trying to learn from osmosis. But everyone says the best way to learn is to have something you want to build and get to writing it.

I took a week of vacation in mid-February with a goal of buckling down and writing some code. That didn’t happen. I spent half a day getting my environment set up in Fedora; a half day researching Postgresql vs. MySQL and then getting MySQL set up on my development machine and on my server; a day of actual vacation (yay!), a day taking the latest Talk Python course (helpful – and cool!) and then a day spent trying to learn and figure out how to get MySQL working – which I was never able to.

Looking back, I wish I would have captured what worked well or wasn’t working in my journal at a minimum, so I could turn that into blog posts, or just blogged. When I started this journey to learn Python and build my two apps, I had every intention of doing exactly that. Everyone who has a blog has an intention to write in it – and how many actually do?

I find when sit down to code, one of two things happens. If things are going well, I lose track of time, and next thing I know I have to run the kids to hockey or basketball or it’s time for me to go to bed and I don’t recap what I’ve done. The other is I throw up my hands in frustration because it’s not working and I walk away – also not capturing where I’m stuck or why I’m frustrated.

So here we are again and I’m going to try harder to chronicle my journey. I had a good night last night in just sitting down and reviewing the nflpool code I had started. I’ve gone back to using SQLite as the SQL I had written to create the database tables works – making it work with MySQL wasn’t happening and I was sick of losing time and using it as an excuse.  Considering that there are less than 20 people in each of the two leagues, I ‘m not worried about performance right now.  The SQLite code works and I need to make some progress.

Three things I accomplished last night:

  • I created two additional branches in Git. I have a scratchpad branch – this is all my original code from six months ago. It’s terrible. I wasn’t writing functions, it’s not well organized, etc. This was my playground to experiment in trying to put the pieces together. I don’t want to lose these files, so I’ll store them in their own branch, but they won’t be used again. I created a develop branch – this is where I’m doing all my active development. When things are working as they should be, I’ll do a pull request and merge them into master. I don’t know if this is the “right” workflow, but it will work for me.
  • I had three or four different Python scripts to create the tables in SQLite. I created one Python file to create all of the tables I’ll need and created a function for each table. I tweaked some of the columns in a few of the tables after reviewing my data model, realizing that some tables didn’t capture the year or season. I added a main method to call all of these functions. I then deleted the Python scripts that did this individually and merged these changes into master.
  • Lastly, and maybe most important, when I was done for the night, I grabbed my notebook and made a to-do list of what to work on next. For example: one of the tables imports some information needed for the NFL Teams (their city, abbreviation, etc.) This data never changes, but I was importing it from JSON data I downloaded from MySportsFeeds. This needs to be re-written to make a request to the MySportsFeeds API to get the data rather than loading a file into memory. (Just in case anyway ever wants to re-use this code to run the same pool – I don’t ever see that happening, but it’s best to do it right the first time). This way I know where to pick up when I start again and should reduce the time reviewing the code to figure out what to work on next.

Progress!

Talk Python Training: Consuming HTTP Services in Python Review

Summary / tl;dr: Consuming HTTP Services in Python is a great addition to the training courses from Talk Python and Michael Kennedy. You’ll come away with a thorough knowledge of the best way to get data from the internet using the requests module; you’ll use real world examples and APIs from Basecamp, Github and a custom API Michael built just from the course; Michael will explain and show the concepts in an easy to learn manner with a little humor and recap each concept to make sure you understand.

In addition to being host of the well known Talk Python podcast, Michael Kennedy has also created a number of Python training courses. The first, Python Jumpstart by Building 10 Apps, launched its Kickstarter exactly a year ago this month, and was quickly followed later in the year with Python for Entrepreneurs on Kickstarter and Write Pythonic Code Like a Seasoned Developer.

I started and finished Python Jumpstart by Building 10 Apps late last year and loved it. It was a very different learning experience than the University of Michigan’s Python for Everybody class on Coursera. There is an assumption with the Talk Python training courses that you have some basic understanding of computer science or programming. I don’t, so I typically go a little slower and take my time with the courses.

Looking back. there are a few things I liked about the Jumpstart by Building 10 Apps course and I was glad to see continue in this latest course:

  • Michael makes it very easy to follow along in the beginning of the courses. Everyone learns differently, but one of the ways I learn best is to follow along by typing the code as he does in the video, helping me commit it to memory.
  • After teaching you a core concept and coding it into one of the apps, Michael recaps what you’ve just learned in its own “Concept” video. This summarizes the concept you just put into practice and reinforces what you’ve learned.
  • Compared to some of the other online courses I’ve taken, I really like that I know I’m learning from someone well known in the community and I believe I’m not just learning how to code, but coding best practices. I don’t know if I’m explaining this right, but as an example: A few of the online classes I’ve taken haven’t had me put the code into functions and then call them in a main(): function, for example.
  • The source code to the examples Michael teaches you is on Github. You can download it, star it, fork it – but it’s available if you want to follow along, code along as the course goes, or just save it for reference for the future.

I’ve shared my enthusiasm for the Talk Python training courses here and on Twitter and when Michael reached out to me last week asking if I was interested in having a sneak peek at his latest course, Consuming HTTP Services in Python, I jumped at it (after making sure he knew I was still a novice early in my Python learning curve). I took a look at the course overview and this is right in my wheelhouse of what I need to learn. A core part of the app I want to build is exactly what this course is about – using the requests module to download at least a half dozen JSON feeds and then building my app around that. (My app is to build the scoring for a custom NFL Pool league – it’s not a fantasy league, it’s different. All of the data comes from MySportsFeeds, who provides sports data via JSON or XML which I will consume, store in a database, and then write a Python program to calculate the league and player scores to be displayed on the league website.)

What I really liked about this course was that it was focused on one thing: consuming services. I’ve taken a few different Python courses online as I try and learn Python, and most are throwing all the basics that you need to know – everything you’d expect in a beginner course, but it does get overwhelming. This was the first course I’ve taken that was focused on getting you really good at one thing, and in a few different ways that you might need to do it.

Immediately, I learn something new. I only knew of requests from I learned using Google and Stack Overflow. When I started playing around and putting together the building blocks of my app, I wrote the following code. MySportsFeed currently using HTTP Basic Authentication, so I have a separate file called secret.py that stores my username and password – I may be new to Python, but I’m smart enough to have created that, import it and add it to my .gitignore file!

This code polls the Playoff Team Standings feed on MySportsFeeds and then I have some (ugly) Python code that runs a for loop to rank each of the two NFL Conferences teams from 1 to 16.

response = requests.get(
    'https://www.mysportsfeeds.com/api/feed/pull/nfl/2016-2017-regular/playoff_team_standings.json?teamstats',
    auth=HTTPBasicAuth(secret.msf_username, secret.msf_pw))

rawdata = response.content
data = json.loads(rawdata.decode())

And what did I learn? As I tweeted last week:

Now my code looks like this:

response = requests.get(
    'https://www.mysportsfeeds.com/api/feed/pull/nfl/2016-2017-regular/playoff_team_standings.json?teamstats',
    auth=HTTPBasicAuth(secret.msf_username, secret.msf_pw))

data = response.json()

It’s not a lot, it’s just one line of code, but it’s these little things. I had no idea the power of requests – this is just one specific example of something I learned from this course. Another thing I learned? I should be taking the URL in the above eample, create a base_url variable and then append the feed name as another variable. This is covered in a later chapter of the course – Consuming RESTful HTTP services. This chapter has a ton of great examples I’m going to be referencing when writing my app and using.

The Consuming RESTful HTTP services chapter is where the course really starts to take off. I ran into this with the Jumpstart course as well – Michael does a great job in teaching you the building blocks and then the course seems to go from 0-60. This is where having previous programming experience is helpful as that jump from learning what each puzzle piece does to how you put the puzzle together clicks. For someone like me, without any programming experience, it’s a big jump, but possible.

With that said, this chapter is fantastic. While I had a cursory knowledge of HTTP commands like GET and PUT, the API Michael built for the course is awesome. You have the opportunity to create your own examples and interact with the API and blog explorer app – this isn’t something you see with most online courses out there.

I also learned that I only want to use requests, and not built-ins. Though I do now have an understanding of the urlib built-in for Python 3.x if I’m ever cornered and have to use it.

I will admit to skipping the chapter on SOAP. I’m a hobbyist, not an enterprise developer who may encounter SOAP. But it’s great this available for those who may need it as part of this course. This, combined with learning how to use JSON, XML, and screen scraping makes it a complete course.

The last chapter is on screen scraping. There are a ton of of tutorials and classes available on the web about screen scraping. I’ve taken a few of them – one of the challenges I have with my app is figuring out the playoff seeding and I thought about scraping NFL.com, but that’s a different story. This chapter kicks off with an example of using a site’s sitemap.xml – an example I’ve never seen before that makes so much sense once you learn about it. And if a website you want to scrape doesn’t have a sitemap.xml, shame on them for not being search engine friendly. But if they don’t, Michael goes through other ways to scrape a website using Beautiful Soup and does it in the most Pythonic way I’ve seen yet in a course.

I enjoyed Consuming HTTP Services in Python. With the requests module and JSON being a cornerstone of the app I hope to write, it was great to learn about everything I need to know to make that happen. Michael’s delivery is conversational and he makes it easy to follow along and do the code examples with him, if you choose to. If you have programming experience or are coming from a different language, the videos themselves will probably teach you what you need to do in Python. If you’re like me, a complete novice to Python, you’ll be able to follow along, but be prepared for the jump the course will make in the Consuming RESTful HTTP Services chapter – this moves pretty quickly, but if you’ve forked the Github repo you’ll have access to the program Michael has written and you can (and should) write your own examples to interact with the API on the blog explorer. For $39, you’re getting a well developed course from someone well known in the Python community teaching you the Pythonic way interact with services. While other online training sites might have “sales” that are cheaper, as someone new to Python who has taken some of those courses, trust me – the Talk Python courses are well worth the money.

I’m still early in my Python journey and the two courses I’ve finished from Talk Python have been the best learning resources I’ve used out of all the books and training I’ve purchased (and it’s a lot). I’m still working my way through Python for Entrepreneurs and am really looking forward to two of the upcoming courses using SQLAlchemy as this database stuff is way over my head right now. Thanks again to Michael for allowing me to have a preview of the Consuming HTTP Services course – now it’s time for me to take his advice from the last chapter of the course and write some code – the best way to actually learn.