Logging in a Django App

Per the Django Documentation you can set up

A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle.

In order to set this up you need to include in your file something like:

	('John', ''), 
	('Mary', '')

The difficulties I always ran into were:

  1. How to set up the AdminEmailHandler
  2. How to set up a way to actually email from the Django Server

Again, per the Django Documentation:

Django provides one log handler in addition to those provided by the Python logging module

Reading through the documentation didn’t really help me all that much. The docs show the following example:

'handlers': {
    'mail_admins': {
        'level': 'ERROR',
        'class': 'django.utils.log.AdminEmailHandler',
        'include_html': True,

That’s great, but there’s not a direct link (that I could find) to the example of how to configure the logging in that section. It is instead at the VERY bottom of the documentation page in the Contents section in the Configured logging > Examples section … and you really need to know that you have to look for it!

The important thing to do is to include the above in the appropriate LOGGING setting, like this:

    'version': 1,
    'disable_existing_loggers': False,
    'handlers': {
	    'mail_admins': {
	        'level': 'ERROR',
	        'class': 'django.utils.log.AdminEmailHandler',
	        'include_html': True,

Sending an email with Logging information

We’ve got the logging and it will be sent via email, but there’s no way for the email to get sent out yet!

In order to accomplish this I use SendGrid. No real reason other than that’s what I’ve used in the past.

There are great tutorials online for how to get SendGrid integrated with Django, so I won’t rehash that here. I’ll just drop my the settings I used in my


EMAIL_HOST_USER = "apikey"

One final thing I needed to do was to update the email address that was being used to send the email. By default it uses root@localhost which isn’t ideal.

You can override this by setting

SERVER_EMAIL = myemail@mydomain.tld

With those three settings, everything should just work.


Writing a Raffle Script

Due to the COVID Pandemic, many things are … different. One thing that needed to be different this year was the way that students at my daughters middle school got to spend their ‘Hero Points’.

Hero Points are points earned for good behavior. In a typical year the students would get to spend them at the student store, but with all of the closures, this wasn’t possible. For the students in my daughter’s 8th grade this was a big deal as they’re going on to High School next year, so we can just roll them over to next year!

Instead of having the kids ‘spend’ their Hero Points the PTO offered up the solution of a raffle based on the number of Hero Points they had. But they weren’t sure how to do it.

I jumped at the chance to write something like this up (especially after all of my works on the PyBites platform) and so my wife volunteered me 😁

In order to really get my head wrapped around the problem, I wanted to treat my solution like a real world analog. For example, in a real work raffle, when you get your tickets, there are two tickets with the same number. One that you get to hold onto, and one that goes into a bowl (or other vessel) that is randomly drawn from.

How many tickets?

Each student had some number of Hero Points. The PTO decided that 10 Hero Points would equal 1 Raffle ticket. Further, it was decided that we would ALWAYS round up. This means that 1 Hero Point would equal 1 Raffle Ticket, but that 9 Hero Points would also equal 1 Raffle Ticket.

Create tickets

I decided to use a namedtuple to store the Raffle Tickets. Specifically, I store the student name, ticket numbers they drew, and the number of tickets they have

Raffle_Tickets = namedtuple('Raffle_Tickets', ['name', 'ticket_numbers', 'tickets'])

The list of student names and total Hero Points was stored in an Excel File (.xlsx) so I decided to use the Pandas Package to import it and manipulate it into a dataframe. The structure of the excel file is: Student Name, Grade, Available Points.

df = pd.read_excel (r'/Users/ryan/Documents/python-files/8th  Hero Points.xlsx')

After a bit of review it turned out that there were a couple of students with NEGATIVE Hero Points. I’m not really sure how that happened, but I was not properly accounting for that originally, so I had to update my dataframe.

The code below filters the dataframe to only return students with positive ‘Available Points’ and then reindex. Finally, it calculates the number of Raffle tickets by dividing by 10 and rounding up using Python’s ceil function. It puts all of this into a list called tickets. We append our tickets list to the original dataframe.

df = df[df['Available Points'] >0]
df.reset_index(inplace=True, drop=True)
tickets = []
for i in df['Available Points'] / 10:
df['Tickets'] = tickets

Our dataframe now looks like this: Student Name, Grade, Available Points, Tickets.

Next, we need to figure out the Raffle ticket numbers. To do that I count the total number of Tickets available. I’m also using some extra features of the range function which allows me to set the start number of the Raffle.1

total_number_of_tickets = sum(df['Tickets'])
ticket_number_start = 1000000
ticket_number_list = []
for i in range(ticket_number_start, ticket_number_start+total_number_of_tickets):

Once we have the list of ticket numbers I want to make a copy of it … remember there are two tickets, one that goes in the bowl and one that the student ‘gets’. Extending the metaphor of having two different, but related, tickets, I decided to use the deepcopy function on the ticket_number_list to create a list called assigned_ticket_number_list.

For more on deepcopy versus (shallow) copy see the documentation

assigned_ticket_number_list = deepcopy(ticket_number_list)

Finally, I reindex the dataframe just to add a bit more randomness to the list

df = df.reindex(np.random.permutation(df.index))

Assign Tickets

Next we’ll assign the tickets randomly to the students.

raffle_list = []
for student in range(df.shape[0]):
    student_ticket_list = []
    for i in range(df.loc[student].Tickets):
        assigned_ticket_number = randint(0, len(assigned_ticket_number_list)-1)
    raffle_list.append(Raffle_Tickets(df.loc[student].Name, student_ticket_list, len(student_ticket_list)))

OK … the code above looks pretty dense, but basically all we’re doing is looping through the students to determine the number of tickets they each have. Once we have that we loop through the available ticket numbers and randomly assign it to the student. At the end we add a namedtuple object called Raffle_Tickets that we defined above to the raffle_list to store the student’s name, their ticket numbers, and the number of tickets that they received.

Draw Tickets

Now we want to ‘draw’ the tickets from the ‘bowl’. We want to select 25 winners, but we also don’t want to have any student win more than once. Honestly, the ’25 winning tickets with 25 distinct winners’ was the hardest part to get through.

selected_tickets = []
for i in range(25):
    selected_ticket_number_index = randint(0, len(ticket_number_list) - 1)
    selected_ticket_number = ticket_number_list[selected_ticket_number_index]
    for r in raffle_list:
        if selected_ticket_number in r.ticket_numbers:
            ticket_number_list = [x for x in ticket_number_list if x not in r.ticket_numbers]

We see above that we’ll select 25 items from the ‘bowl’ of tickets. We select the tickets one at a time. For each ticket we determine what set of tickets that selected ticket is in. Once we know that, we then remove all tickets associated with that winning ticket so that we can guarantee 25 unique winners.

Find the Winners

We now have 25 tickets with 25 winners. Now we just need to get their names!

for r in raffle_list:
    for t in r.ticket_numbers:
        student_winning_list = []
        if t in selected_tickets:
            winners_list.append((Raffle_Tickets(, student_winning_list, len(student_winning_list))))

Again, we construct a list of namedtuple Raffle\_Tickets only this time it’s just the winners.

Output winners

Whew! Now that we have the results we want to write them to a file.

with open('/Users/ryan/PyBites/Raffle/winners_new.txt', 'w+') as f:
    for winner in winners_list:
        tickets = ticket_count(
        percent_chance_of_winning = tickets / total_number_of_tickets * 100
        percent_chance_of_winning_string = "{:.2f}".format(percent_chance_of_winning)
        f.write(f'{} with winning ticket {winner.ticket_numbers[0]}. They had {tickets} tickets and a {percent_chance_of_winning_string}% chance of winning.\n')

One of the reasons that I stored the number of tickets above was so that we could see what the chance was of a student winning given the number of tickets they started with.

For each student we output to a line to a file with the student’s name, the winning tickets number, the number of tickets they started with and their chance of winning (the ratio of tickets the student had to the total number of starting tickets)


This was a fun project for me because it was needed for a real world application, allowed me to use MANY of the concepts I learned at PyBites AND helped my daughter’s school.

  1. Why am I doing this, versus just stating a 0? Mostly because I wanted the Raffle Ticket numbers to look like real Raffle Ticket Numbers. How many times have you seen a raffle ticket with number 0 on it?
PyCharm Python

Issues with psycopg2 … again

In a previous post I had written about an issue I’d had with upgrading, installing, or just generally maintaining the python package psycopg2 (link).

I ran into that issue again today, and thought to myself, “Hey, I’ve had this problem before AND wrote something up about it. Let me go see what I did last time.”

I searched my site for psycopg2 and tried the solution, but I got the same forking error.

OK … let’s turn to the experts on the internet.

After a while I came across this article on StackOverflow but this specific answer helped get me up and running.

A side effect of all of this is that I upgraded from Python 3.7.5 to Python 3.8.1. I also updated all of my brew packages, and basically did a lot of cleaning up that I had neglected.

Not how I expected to spend my morning, but productive nonetheless.

Django Python

My First Django Project

I’ve been writing code for about 15 years (on and off) and Python for about 4 or 5 years. With Python it’s mostly small scripts and such. I’ve never considered myself a ‘real programmer’ (Python or otherwise).

About a year ago, I decided to change that (for Python at the very least) when I set out to do 100 Days Of Web in Python from Talk Python To Me. Part of that course were two sections taught by Bob regarding Django. I had tried learn Flask before and found it … overwhelming to say the least.

Sure, you could get a ‘hello world’ app in 5 lines of code, but then what? If you wanted to do just about anything it required ‘something’ else.

I had tried Django before, but wasn’t able to get over the ‘hump’ of deploying. Watching the Django section in the course made it just click for me. Finally, a tool to help me make AND deploy something! But what?

The Django App I wanted to create

A small project I had done previously was to write a short script for my Raspberry Pi to tell me when LA Dodger (Baseball) games were on (it also has beloved Dodger Announcer Vin Scully say his catch phrase, “It’s time for Dodger baseball!!!”).

I love the Dodgers. But I also love baseball. I love baseball so much I have on my bucket list a trip to visit all 30 MLB stadia. Given my love of baseball, and my new found fondness of Django, I thought I could write something to keep track of visited stadia. I mean, how hard could it really be?

What does it do?

My Django Site uses the MLB API to search for games and allows a user to indicate a game seen in person. This allows them to track which stadia you’ve been to. My site is composed of 4 apps:

  • Users
  • Content
  • API
  • Stadium Tracker

The API is written using Django Rest Framework (DRF) and is super simple to implement. It’s also really easy to changes to your models if you need to.

The Users app was inspired by Will S Vincent ( a member of the Django Software Foundation, author, and podcaster). He (and others) recommend creating a custom user model to more easily extend the User model later on. Almost all of what’s in my Users App is directly taken from his recommendations.

The Content App was created to allow me to update the home page, and about page (and any other content based page) using the database instead of updating html in a template.

The last App, and the reason for the site itself, is the Stadium Tracker! I created a search tool that allows a user to find a game on a specific day between two teams. Once found, the user can add that game to ‘Games Seen’. This will then update the list of games seen for that user AND mark the location of the game as a stadium visited. The best part is that because the game is from the MLB API I can do some interesting things:

  1. I can get the actual stadium from visited which allows the user to indicate historic (i.e. retired) stadia
  2. I can get details of the game (final score, hits, runs, errors, stories from MLB, etc) and display them on a details page.

That’s great and all, but what does it look like?

The Search Tool

Stadia Listing

National League West

American League West

What’s next?

I had created a roadmap at one point and was able to get through some (but not all) of those items. Items left to do:

  • Get Test coverage to at least 80% across the app (currently sits at 70%)
  • Allow users to be based on social networks (right now I’m looking at Twitter, and Instagram) probably with the Django Allauth Package
  • Add ability to for minor league team search and stadium tracking (this is already part of the MLB API, I just never implemented it)
  • Allow user to search for range of dates for teams
  • Update the theme … it’s the default MUI CSS which is nice, but I’d rather it was something a little bit different
  • Convert Swagger implementation from django-rest-swagger to drf-yasg

Final Thoughts

Writing this app did several things for me.

First, it removed some of the tutorial paralysis that I felt. Until I wrote this I didn’t think I was a web programmer (and I still don’t really), and therefore had no business writing a web app.

Second, it taught me how to use git more effectively. This directly lead to me contributing to Django itself (in a very small way via updates to documentation). It also allowed me to feel comfortable enough to write my first post on this very blog.

Finally, it introduced me to the wonderful ecosystem around Django. There is so much to learn, but the great thing is that EVERYONE is learning something. There isn’t anyone that knows it all which makes it easier to ask questions! And helps me in feeling more confident to answer questions when asked.

The site is deployed on Heroku and can be seen here. The code for the site can be seen here.

This article was also posted on the Blog


Using Python to Check for File Changes in Excel

The Problem

Data exchange in healthcare is … harder than it needs to be. Not all partners in the healthcare arena understand and use technology to its fullest benefit.

Take for example several health plans which want data reported to them for CMS (Centers for Medicare and Medicaid Services) regulations. They will ask their ‘delegated’ groups to fill out an excel file. As in, they expect you will actually fill out an excel file, either by manually entering the data OR by potentially copying and pasting your data into their excel file.

They will also, quite frequently, change their mind on what they want AND the order in which they want the data to appear in their excel file. But there’s no change log to tell you what (if anything has changed). All that you will get is an email which states, “Here’s the new template to be used for report XYZ” … even if this ‘new’ report is the same as the last one that was sent.

Some solutions might be to use versioning software (like Git) but all they will do is tell you that there is a difference, not what the difference is. For example, when looking at a simple excel file added to git and using git diff you see:

diff --git a/Book3.xlsx b/Book3.xlsx
index 05a8b41..e96cdb5 100644
Binary files a/Book3.xlsx and b/Book3.xlsx differ

This has been a giant pain in the butt for a while, but with the recent shelter-in-place directives, I have a bit more time on the weekends to solve these kinds of problems.

The Solution

Why Python of Course!

Only two libraries are needed to make the comparison: (1) os, (2) pandas

The basic idea is to:

  1. Load the files
  2. use pandas to compare the files
  3. write out the differences, if they exist

Load the Files

The code below loads the necessary libraries, and then loads the excel files into 2 pandas dataframes. One thing that my team has to watch out for are tab names that have leading spaces that aren’t easy to see inside of excel. This can cause all sorts of nightmares from a troubleshooting perspective.

import os
import pandas as pd

file_original = os.path.join(\\path\\to\\original\\file, original_file.xlsx)
file_new = os.path.join(\\path\\to\\new\\file, new_file.xlsx)

sheet_name_original = name_of_sheet_in_original_file
sheet_name_new = name_of_sheet_in_new_file

df1 = pd.read_excel(file_original, sheet_name_original)
df2 = pd.read_excel(file_new, sheet_name_new)

Use Pandas to compare

This is just a one liner, but is super powerful. Pandas DataFrames have a method to see if two frames are the same. So easy!

data_frame_same = df1.equals(df2)

Write out the differences if they exist:

First we specify where we’re going to write out the differences to. We use w+ because we’ll be writing out to a file AND potentially appending, depending on differences that are found. The f.truncate(0) will clear out the file so that we get just the differences on this run. If we don’t do this then we’ll just append to the file over and over again … and that can get confusing.\\path\\to\\file\\to\\write\\differences.txt, 'w+')

Next, we check to see if there are any differences and if they are, we write a simple message to our text file from above:

if data_frame_same:
	f.write('No differences detected')

If differences are found, then we loop through the lines of the file, finding the differences and and writing them to our file:

	f.write('*** WARNING *** Differences Found\n\n')
	for c in range(max(len(df1.columns), len(df2.columns))):
			header1 = df1.columns[c].strip().lower().replace('\n', '')
			header2 = df2.columns[c].strip().lower().replace('\n', '')
			if header1 == header2:
				f.write(f'Headers are the same: {header1}\n')
				f.write(f'Difference Found: {header1} -> {header2}\n')


The code above finds the largest column header list (the file may have had a new column added) and uses a try/except to let us get the max of that to loop over.

Next, we check for differences between header1 and header2. If they are the same, we just write that out, if they aren’t, we indicate that header1 was transformed to header2

A sample of the output when the column headers have changed is below:

*** WARNING *** Differences Found

Headers are the same: beneficiary first name
Difference Found: person who made the request -> who made the request?

Future Enhancements

In just using it a couple of times I’ve already spotted a couple of spots for enhancements:

  1. Use input to allow the user to enter the names/locations of the files
  2. Read the tab names and allow user to select from command line


I’m looking forward to implementing the enhancements mentioned above to make this even more user friendly. In the mean time, it’ll get the job done and allow someone on my team to work on something more interesting then comparing excel files to try (and hopefully find) differences.


Getting asked for Advice on being a Data Analyst

I got a message on LinkedIn from a former colleague of my from Arizona Priority Care asking me:

Wanted to pick your brain on something. what do you think the outlook is for a data analyst? Debating a masters program in that and covers a few things but also includes certifications in SAS. Trying to decide if that will “pay off” in the long run or if I should explore different disciplines.

This was a really good question and I thought about it a bit. My response was:

I think Data Analysis (or Data Science, or Analytics) are all going to play a huge role in business going forward and that it would be a smart move to get a masters degree in one of those. I would avoid any certification programs though, just because they can be less rigorous and don’t seem to have the same weight as a full degree.

SAS is an interesting language, but I’d investigate what companies use SAS and make sure that you’d like to work for them (or in the industry). Many companies are turning towards open source Data Analytics tools (like R and Python). But in general, don’t get too hung up on the tool (SAS, Python, R) but really understand what you’re doing with them. Why would I choose this Standard Regression over Two Stage Least Squares. When do I wan to use a Logistics regression model and why. What does the output tell me, and what is it missing.

Developing that understanding will allow you to really standout.

Good luck with your decision. Let me know which direction you decide to go in,



I hope that I was able to help my former colleague and was super happy that he reached out to me.

I wanted to write this into a more public form just in case in helps someone, or just in case I look back on it at some point and it helps me.

Django PyCharm Python

Mischief Managed

A few weeks back I decided to try and update my Python version with Homebrew. I had already been through an issue where the an update like this was going to cause an issue, but I also knew what the fix was.

With this knowledge in hand I happily performed the update. To my surprise, 2 things happened:

  1. The update seemed to have me go from Python 3.7.6 to 3.7.3
  2. When trying to reestablish my Virtual Environment two packages wouldn’t installed: psycopg2 and django-heroku

Now, the update/backdate isn’t the end of the world. Quite honestly, next weekend I’m going to just ditch homebrew and go with the standard download from because I’m hoping that this non-sense won’t be an issue anymore

The second issue was a bit more irritating though. I spent several hours trying to figure out what the problem was, only to find out, there wasn’t one really.

The ‘fix’ to the issue was to

  1. Open PyCharm
  2. Go to Setting
  3. Go to ‘Project Interpreter’
  4. Click the ‘+’ to add a package
  5. Look for the package that wouldn’t install
  6. Click ‘Install Package’
  7. Viola … mischief managed

The next time this happens I’m just buying a new computer

Class Based Views

CBV – PasswordChangeDoneView

From Classy Class Based Views PasswordChangeDoneView

Render a template. Pass keyword arguments from the URLconf to the context.


  • template_name: Much like the LogoutView the default view is the Django skin. Create your own password_change_done.html file to keep the user experience consistent across the site.
  • title: the default uses the function gettext_lazy() and passes the string ‘Password change successful’. The function gettext_lazy() will translate the text into the local language if a translation is available. I’d just keep the default on this.


class myPasswordChangeDoneView(PasswordChangeDoneView):

path('password_change_done_view/', views.myPasswordChangeDoneView.as_view(), name='password_change_done_view'),


{% extends "base.html" %}
{% load i18n %}

{% block content %}
    {% block title %}
        {{ title }}
    {% endblock %}
<p>{% trans "Password changed" %}</p>
{% endblock %}

LOGIN_URL = '/<app_name>/login_view/'

The above assumes that have this set up in your

Special Notes

You need to set the URL_LOGIN value in your It defaults to /accounts/login/. If that path isn’t valid you’ll get a 404 error.


A visual representation of how PasswordChangeDoneView is derived can be seen here:


Again, not much to do here. Let Django do all of the heavy lifting, but be mindful of the needed work in and the new template you’ll need/want to create

Class Based Views

CBV – PasswordChangeView

From Classy Class Based Views PasswordChangeView

A view for displaying a form and rendering a template response.


  • form_class: The form that will be used by the template created. Defaults to Django’s PasswordChangeForm
  • success_url: If you’ve created your own custom PasswordChangeDoneView then you’ll need to update this. The default is to use Django’s but unless you have a top level has the name of password_change_done you’ll get an error.
  • title: defaults to ‘Password Change’ and is translated into local language


class myPasswordChangeView(PasswordChangeView):
    success_url = reverse_lazy('rango:password_change_done_view')

path('password_change_view/', views.myPasswordChangeView.as_view(), name='password_change_view'),


{% extends "base.html" %}
{% load i18n %}

{% block content %}
    {% block title %}
        {{ title }}
    {% endblock %}
<p>{% trans "Password changed" %}</p>
{% endblock %}


A visual representation of how PasswordChangeView is derived can be seen here:


The only thing to keep in mind here is the success_url that will most likely need to be set based on the application you’ve written. If you get an error about not being able to use reverse to find your template, that’s the issue.


A beginners guide to Tableau Conference – 2019 edition

The Tableau Conference was held at the Mandalay Bay Convention Center this year (and will be again next year in 2020). I had the opportunity to attend (several weeks ago) and decided to write up my thoughts about it.

This is an introverted newbie’s guide navigating the conference.

The conference started on Tuesday with pre-conference sessions that you had to register (and pay for). I did not attend those.

Tuesday night there was a big welcome reception that I very nearly bailed on because of how many people there were, but I decided to give it a shot anyway. I’m glad I did.

The welcome reception (as well as all of the meals) were held in the data village (basically the convention show floor) which was a little weird but it worked.

In the reception they had industry specific areas (healthcare being one of them). I didn’t know this going in … I just kind of stumbled into it.

This was the luckiest break I could have had as I sat there there entire night and met about 10 people. Three of them (Josh, Kerry, and Molly) I spoke to the most, so much so that we decided that we’d go to the ‘ Data Night Out’ (the client party) together.

Being super introverted this was not my jam, but I’m glad I went, and I will go again next year.

Each day is jam packed full of sessions. I didn’t come across any sessions that were not worthwhile, although some were better than others.

You do have to register for the session in order to gain admittance to the room (they scan your badge to make sure you belong) but there seemed to be stand by room in most of the sessions I attended.

Keynote events

There are ‘Key Note’ events to kick off each day. They happen in the Mandalay Bay events center, but there is also an overflow room you can watch them from.

I would recommend going to at least one event in the events center, but as an introvert the overflow was really more my speed. A room that could sit 500 people with only 50 in it … yes please!

Iron Viz

A take on Iron Chef, Iron Viz was a chance for 3 Tableau wizards to showcase their skills with Tableau and a shared data set. It was really interesting to see the different ways that the data could be presented and the different stories that each competitor told for their visualizations.

Data Night Out

I didn’t do this, mostly because by Thursday I was pretty overwhelmed and just needed a quite night in. I don’t regret not going, but I think I will make myself go next year

Data Culture

I’m going to write more on this once I get my head really wrapped around it, but suffice it to say, this is something that I think is going to be very important going forward for the organization I work for.