Analyzing Strava metadata sweatscience fitness
Backlinks: Strava Art
I love running, and I love stroller running with my son even more. Strava is my go-to fitness app and I've tagged all of my stroller runs with a searchable tag so I can count the miles we've logged together, mostly while he has slept!
The search functionality on Strava's site doesn't provide summaries like total miles, but I can search my own data easily using Python.
Request your archive
Go to your settings:
Account page:
Click the download button:
Click this button:
Then you'll get an email with a link to your data.
Load up your data.
This part is simple, and to make things even easier I'll use Pandas.
import pandas as pd
df = pd.read_csv("activities.csv")
df.head()
Then we can pull out all of the activies with type Run, that contain the stroller tag, and sum up the miles all like this:
run = df["Activity Type"].str.contains("Run")
stroller = df["Activity Name"].str.contains("#stroller")
miles = (df
.loc[run & stroller, :] # filter
.loc[:, 'Distance'] # select
.astype('float') # convert dtype
.sum()/1.609 # sum and convert to miles
)
miles
Let's break that down: first, we select out two filters (type Run and stroller in the title), then we convert the distance field to a float, then we sum it up. The last step converts km into miles. I had actually first written it in one line, as below, but thought the above would be clearer.
df.loc[df["Activity Type"].str.contains("Run") & df["Activity Name"].str.contains("#stroller"), :].Distance.astype('float').sum()/1.609
All told, by the end of 2019 I'll have logged 857 miles with my little man.