Musings of a dad with too much time on his hands and not enough to do. Wait. Reverse that.
Dad. Technologist. Fan of English poet of John Lillison.
A recent episode of the Python Bytes podcast mentioned this cartoon about a robot uprising that should never have happened:
It should also remind us of the importance of unit tests and properly testing your code before deploying to Production, but so many of us are like the most interesting man in the world:
File this under: there-has-to-be-a-simpler-way-to-do-this-in-pandas-but-I-haven’t-found-what-that-is
Recently, I’ve been playing with some financial data to get a better understanding of the yield curve. Related to yield and inverted yield curves are the periods of recession in the US economy. In my work, I wanted to first build a chart that indicated the periods of recession and ultimately overlay that with yield curve data. Little did I realize the challenge of just coding that first part.
I downloaded a dataset of recession data, which contains a record for every calendar quarter from the 1960s to present day and a 0 or 1 to indicate whether the economy was in recession for that quarter–“1” indicating that it was. What I need to do was pull all the records with a “1” indicator and find the start and end times for each of those ranges so that I could paint them onto a chart.
I’ve heard it said before that any time you have to write a loop over your pandas dataframe, you’re probably doing it wrong. I’m certainly doing a loop here and I have a nagging suspicion there’s probably a more elegant way to achieve the solution. Nevertheless, here’s what I came up with to solve my recession chart problem:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline # for easy chart display in jupyter notebook
# recession dates: https://fred.stlouisfed.org/series/JHDUSRGDPBR
df_recessions = pd.read_csv('./data/JHDUSRGDPBR_20220327.csv')
df_recessions['DATE'] = pd.to_datetime(df_recessions.DATE)
df_recessions.head()
df_recessions[df_recessions.JHDUSRGDPBR==1.0].head()
So, now I’m asking myself, “how do I extract the start and stop dates for every period of recession identified in the dataset? Let’s start with first just finding the start dates of recessions.” That shouldn’t be too difficult. If I can filter in just the recession quarters and calculate the date differences from one row to the next, if the difference is greater than three months (I estimated 93 days as three months), then I know there was a gap in quarters prior to the current record indicating that current record is the start of a new recession. Here’s what I came up with [one further note: my yield curve data only starts in 1990, so I filtered the recession data for 1990 to present]:
df_spans = df_recessions[(df_recessions.DATE.dt.year>=1990) & (df_recessions.JHDUSRGDPBR==1.0)].copy()
df_spans['days_elapsed'] = df_spans.DATE - df_spans.shift(1).DATE
df_spans['ind'] = df_spans.days_elapsed.dt.days.apply(lambda d: 's' if d > 93 else '')
df_spans.iloc[0, 3] = 's' # mark first row as a recession start
df_spans
Here’s where my approach starts to go off the rails a little. The only way I could think to find the end dates of each recession is to:
Confusing? Here’s my code:
start_stop_dates = []
start_dates = df_spans.loc[df_spans.ind=='s', ].DATE.tolist()
for i, start_date in enumerate(start_dates):
if i < len(start_dates)-1:
stop_date = df_spans.loc[df_spans.DATE < start_dates[i+1]].iloc[-1].DATE
else:
stop_date = df_spans.iloc[-1].DATE
# add 3 months to the end of each stop date to stretch the value to the full quarter
start_stop_dates.append((start_date, stop_date + np.timedelta64(3,'M')))
start_stop_dates
With that start/stop list, I can build my underlying recession chart:
fig, ax = plt.subplots(figsize=(12,6))
_ = ax.plot()
_ = ax.set_xlim([date(1990, 1, 1), date(2022, 4, 1)])
_ = ax.set_ylim([0, 10])
for st, sp in start_stop_dates:
_ = ax.axvspan(st, sp, alpha=0.2, color='gray')
Phew. All that work and I’m only at the starting point of my yield curve exploration, but that will have to wait for a future post. However, if you can think of a more elegant way to identify these date ranges without having to resort to looping, I’d love to hear it!
I’ve been attending lots of high school sporting events of late–football games, basketball, wrestling, etc. Much of the time, the public announcement system will play random music prior to the start of the competition, but you’ll often know when the game’s about to kick-off when you hear that familiar riff from the band AC/DC:
I’m certainly happy to see the nods to songs of my youth–I’m always trying to get my children to appreciate the songs I grew up with. However, Thunderstruck can’t be the only song of its type to fire-up the fans for the upcoming contest. What are the attributes of the song that make it the go-to tune for these sort of events?
With these attributes in mind, what other song might be a decent substitute for this AC/DC classic? Perusing my own catalog, I’ve come up with several potential alternatives:
Let’s go ahead and get this one out of the way: Welcome to the Jungle is the common alternative you’ll hear at sporting events in place of AC/DC.
AC/DC has repeated their Thunderstruck formula multiple times throughout their career. Here are two examples from their 1995 album, Ballbreaker.
This Aerosmith classic doesn’t have the a very long lead-in time until the chorus kicks in, but the musical build-up is sure to energize the crowd.
Armored Saint provides some great options for these occasions, although several of their songs can be a little explicit, so do your homework.
I count Cinderella as one of my favorite 80s bands.
While the intro’s a little shorter than ideal, the guitar and drums are driving and sure to set a nice, competitive atmosphere.
Like Guns n’ Roses, I’d be remiss if I didn’t mention this Led Zeppelin classic. My guess is AC/DC has found new popularity due to the band’s inclusion in the various Marvel movies. The same could be said of Led Zeppelin. Immigrant Song would likely strike a chord in the hearts of both the young and not-so-young in attendance.
Like their name, Powerman 5000 delivers high octane tunes sure to invigorate sports fans and participants alike.
A great classic from the Iron Eagle soundtrack that needs mentioning.
Saxon is the author of several, hard, driving ditties sure to rile up the masses.
Another of my favorite 80s bands, Tesla has produced some heavy, inspirational tunes worthy of sports competitions. An interesting note is the band’s mention of “the U.S.S.R.” in Modern Day Cowboy.
So, these are my thoughts on potential Thunderstruck replacements from my own song collection. Thoughts? Other suggestions?
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