Have you ever stopped to think about the amount of Data Processing Python generates?
Speed, distance, elevation, heart rate… these are valuable pieces of information that tell the story of your performance. What if you could transform all of this into visual insights with just a few lines of code?
Using Python Data Processing gives you exactly that advantage. With tools like Pandas and Matplotlib, you can analyze your workouts in a practical and fun way. The benefits?
You gain clarity about your progress, save time with automatic reports, and discover patterns you never knew existed.
In this article, you’ll learn how to use Python to process your own cycling data and turn numbers into insights. Get ready for a journey that goes beyond your rides, and that can improve your performance in surprising ways.
Why analyze cycling data with Python?
If you track your activities with apps like Strava or Garmin, you already have a gold mine at your fingertips. You just need to learn how to dig.
With Python Data Processing, you can transform those numbers into graphs that show exactly where and how to improve. It’s important to emphasize that this doesn’t require advanced knowledge. With a little practice, any curious cyclist can get started.
Another important point to consider: by understanding your own data, you’ll be able to make smarter training decisions.
Essential tools to get started
To begin your journey with data analysis in Python, you only need:
- Python (version 3.9 or higher)
- Pandas – to read, process, and organize data
- Matplotlib – to create simple and efficient graphs
- Jupyter Notebook – ideal environment for quick testing and interactive visualization
It’s also worth noting that using a virtual environment (virtualenv) helps keep everything organized and avoids conflicts between libraries.
Step by Step: From Data Collection to Visualization
Let’s now explore in more detail how to transform your data into insights:
Create a virtual environment
- bash
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python -m venv ciclismo-env
source ciclismo-env/bin/activate # Linux/macOS
.\ciclismo-env\Scripts\activate # Windows
Install the necessary libraries
- bash
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pip install pandas matplotlib jupyter
Open Jupyter Notebook
- bash
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jupyter notebook
Import your CSV file with the data
- python
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import pandas as pd
df = pd.read_csv(“my_data.csv”)
Visualize main data
- python
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print(df.head())
print(df.describe())
Clean columns Unnecessary
- Python
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df = df.drop(columns=[“unnecessary_column”])
Create a simple line graph
- Python
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import matplotlib.pyplot as plt
plt.plot(df[“Date”], df[“Distance”])
plt.title(“Distance Evolution by Day”)
plt.xlabel(“Date”)
plt.ylabel(“Distance (km)”)
plt.show()
Explore patterns such as pace, cadence, and heart rate variations
With these steps, you can now see trends, performance peaks, and even identify active recovery days.
How to import data from apps like Strava and Garmin
If you use Strava, Garmin Connect, or another app to record your workouts, the first step is to export your data. The good news? It’s quite simple.
In Strava, go to the desired activity, select the three-dot menu, and choose “Export as GPX file.” In Garmin, access the dashboard and export as a CSV or TCX file.
After downloading the file, save it to a dedicated folder on your computer. Ideally, you should always have a copy named with the date or workout type for easier organization.
Now, just load the file into your notebook with Pandas:
python
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import pandas as pd
df = pd.read_csv(“cycling_activity.csv”)
If the file is a .tcx or .gpx file, libraries like gpxpy or fitparse can be useful for converting the data before analysis.
Understanding the main training data
To better understand the data you’ll process, here are the most common ones:
- Distance (km): how far you pedaled
- Average speed (km/h): overall performance
- Heart rate (bpm): intensity of effort
- Accumulated elevation (m): altitude gain
- Cadence (rpm): average pedal rotation
- Power (watts): useful if you use a power meter
It’s also worth noting that many files present this data separated by time or lap. This allows for more specific analyses, such as: “How was my performance in the first hour of training?”
Transforming data into visual stories
Visualizing your data is like telling the story of each pedal stroke. You can, for example, plot a line graph showing distance over the weeks:
python
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df[“Data”] = pd.to_datetime(df[“Data”])
df.set_index(“Data”, inplace=True)
df[“Distance”].resample(“W”).sum().plot()
Another interesting point is to cross-reference variables. For example:
- Scatter plot between average speed and heart rate
- Double-line graph to compare distance and accumulated elevation
These visualizations help you notice trends, such as “the higher the elevation, the lower my average speed.”
How to interpret the generated graphs
Creating graphs is the first step. Knowing how to interpret them is what will help you evolve as a cyclist.
- A graph with irregular heart rate spikes can indicate overexertion in easy workouts.
- An upward curve in weekly distance shows progress as long as it’s accompanied by rest.
- Sudden drops in data can reveal periods of fatigue or inconsistency.
Use the graphs to adjust your planning, assess whether you’re getting enough rest, or whether it’s time to push harder.
Common mistakes when working with cycling data
Here are some mistakes you can easily avoid:
- Ignoring incomplete workouts or those with GPS errors – they distort your graphs.
- Using different units on the same graph – always standardize km, bpm, etc.
- Not handling missing data – if you have empty columns, clear them before plotting.
- Mixing workout types – separate by road, trail, indoor training…
- Forgetting to adjust the time zone – causes confusion in the order of the data.
How to use your data to set realistic goals
Using your training history to plan your next steps is one of the most useful parts of this journey.
Here’s how to get started:
- Calculate your average weekly distance for the last 2 months
- Set a goal of 5% improvement per week
- Use graphs to visualize your progress and adjust your plan
This makes your goals tangible, based on your reality, not on comparisons with others.
Making Analysis a Weekly Habit
Tracking your data every week creates awareness of your progress. Here’s a simple routine:
- Export this week’s workout on Sunday
- Run your Python script or notebook
- Analyze 3 metrics: distance, average heart rate, and elevation
- Note observations in a journal or app
- Set micro-goals for the following week
This turns raw data into smart decisions, and puts you in control of your training.
Practical and Little-Known Tips
- Use different colors by heart rate zone—helps visualize efforts over time.
- Apply filters to remove short or erratic workouts—cleans your database and improves graphs.
- Group data by week or month—ideal for long-term reports.
- Generate interactive charts with Plotly—to more easily explore data.
- Integrate with APIs like Strava—to automate collection and analysis.
Gist with base code + suggested virtual environment
To make things even easier, I’ve prepared a Gist with the initial code ready to use.
You can access it and copy the content to your own project.
We recommend using a virtual environment with the libraries mentioned above, which makes your setup clean and reusable.
Access the Gist with the base project:
gist.github.com/exemplo-ciclismo-python (replace with the actual link)
Conclusion: Data Processing Python
With this guide, you’ve seen how you can use Python Data Processing to better understand your cycling data, transform spreadsheets into graphs, and even discover patterns in your performance.
The benefits are clear: more control over your progress, better training planning, and insights that only data can reveal.
Now it’s your turn! Use Gist, set up your virtual environment, and try visualizing the data from your next ride.
Technology can be a powerful ally in your progress as a cyclist. Take the first step today and discover what your data has to say.
