finplot popularity

pypi store download statistics in Google's BigQuery. Seeing how many people download a package could work as an estimate of the popularity of a package. So how does finplot stack up to mplfinance? Let's check. Here's the data I downloaded for a few months back:

month	finplot	mplfinance
2020-02-01	912	4178
2020-03-01	1388	7883
2020-04-01	2341	9641
2020-05-01	2817	15191
2020-06-01	1404	17842
2020-07-01	1822	21510
2020-08-01	1817	15208
2020-09-01	3239	9701
2020-10-01	765	4034

To plot the data you first want to chuck it into a table of sorts, preferably with dates ready to go. Pandas to the rescue:

import pandas as pd
df = pd.DataFrame([[912, 4178], [1388, 7883], [2341, 9641], [2817, 15191], [1404, 17842], [1822, 21510], [1817, 15208], [3239, 9701], [765, 4034]], columns=['finplot', 'mplfinance'])
df.index = pd.to_datetime(['2020-02-01', '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01', '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01'])

Ok, let's plot. In finplot. Note that I use log scale here.

import finplot as fplt
fplt.create_plot('finplot & mplfinance popularity', yscale='log')
fplt.plot(df['finplot'], legend='finplot')
fplt.plot(df['mplfinance'], legend='mplfinance')
fplt.show()

Some of this, I'm sure, is driven by systems automatically downloading new packages.

But I put zero finplot updates out on pypi in February, June, August and October. This will be interesting to follow. Perhaps it's even possible to make some money off of this?