corriporai/runpandas — 25 Stars

corriporai

runpandas

Python

RunPandas - Python Package for handing running data from GPS-enabled devices to worldwide race results.

Last updated Apr 30, 2026

Stars

Forks

Issues

Stars/day

Attention Score

View on GitHub ↗ Open in github.dev

Topics

#cycling #fit #fitness #garmin #gpx #marathon #pandas #python3 #race-results #running #running-tracker #sports #sports-analytics #sports-data #strava #tcx

Language breakdown

Python 99.8%

Shell 0.2%

▸ Files click to expand

README

.. image:: https://raw.githubusercontent.com/corriporai/runpandas/master/docs/source/static/images/runpandasbanner.png

RunPandas - Python Package for handing running data from GPS-enabled devices to worldwide race results. =======================================================================================================

.. image:: https://img.shields.io/pypi/v/runpandas.svg :target: https://pypi.python.org/pypi/runpandas/

.. image:: https://anaconda.org/marcelcaraciolo/runpandas/badges/version.svg :target: https://anaconda.org/marcelcaraciolo/runpandas

.. image:: https://img.shields.io/github/issues/corriporai/runpandas.svg :target: https://github.com/corriporai/runpandas/issues

.. image:: https://www.codefactor.io/repository/github/corriporai/runpandas/badge :target: https://www.codefactor.io/repository/github/corriporai/runpandas :alt: CodeFactor

.. image:: https://github.com/corriporai/runpandas/workflows/Build/badge.svg?branch=master :target: https://github.com/corriporai/runpandas/actions/workflows/build.yml

.. image:: https://coveralls.io/repos/github/corriporai/runpandas/badge.svg?branch=master :target: https://coveralls.io/github/corriporai/runpandas

.. image:: https://codecov.io/gh/corriporai/runpandas/branch/master/graph/badge.svg :target: https://codecov.io/gh/corriporai/runpandas

.. image:: https://readthedocs.org/projects/runpandas/badge/?version=latest :target: https://runpandas.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/psf/black

.. image:: https://static.pepy.tech/personalized-badge/runpandas?period=total&units=internationalsystem&leftcolor=black&rightcolor=orange&lefttext=Downloads :target: https://pepy.tech/project/runpandas

.. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/corriporai/runpandas/HEAD

.. image:: https://zenodo.org/badge/272209151.svg :target: https://zenodo.org/badge/latestdoi/272209151

=========

Introduction

RunPandas is a project to add support for data collected by GPS-enabled tracking devices, heart rate monitors data to pandas objects. It is a Python package that provides infrastructure for importing tracking data from such devices, enabling statistical and visual analysis for running enthusiasts and lovers. Its goal is to fill the gap between the routine collection of data and their manual analyses in Pandas and Python.

Since the release `0.6.0 it comes with the support of handling race event results, so we can analyze from race split times, finish times, demographics, etc. The goal is to support several many races results available to anyone interested in running race results analytics.


Documentation

Stable documentation __ is available on github.io __. A second copy of the stable documentation is hosted on read the docs

_ for more details.

Development documentation __ is available for the latest changes in master.

==> Check out this Blog post _ for the reasoning and philosophy behind Runpandas, as well as a detailed tutorial with code examples.

==> Follow this Runpandas live book in Jupyter notebook format based on Jupyter Books .


Install 

RunPandas depends on the following packages:

pandas

fitparse

stravalib

pydantic

pyaml

haversine

thefuzz`


Runpandas was tested to work on \*nix-like systems, including macOS.

Install latest release version via pip ~~~~~~~~~~
.. code-block:: shell
$ pip install runpandas
Install latest release version via conda ~~~~~~~~~~
.. code-block:: shell
$ conda install -c marcelcaraciolo runpandas
Install latest development version ~~~~~~~~~~
.. code-block:: shell
$ pip install git+https://github.com/corriporai/runpandas.git
or
.. code-block:: shell
$ git clone https://github.com/corriporai/runpandas.git     $ python setup.py install
Examples

Install using pip and then import and use one of the tracking readers. This example loads a local file.tcx. From the data file, we obviously get time, altitude, distance, heart rate and geo position (lat/long).


.. code:: ipython3
# !pip install runpandas     import runpandas as rpd     activity = rpd.read_file('./sample.tcx')
.. code:: ipython3
activity.head(5)

.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th>alt</th>           <th>dist</th>           <th>hr</th>           <th>lon</th>           <th>lat</th>         </tr>         <tr>           <th>time</th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>         </tr>       </thead>       <tbody>         <tr>           <th>00:00:00</th>           <td>178.942627</td>           <td>0.000000</td>           <td>62.0</td>           <td>-79.093187</td>           <td>35.951880</td>         </tr>         <tr>           <th>00:00:01</th>           <td>178.942627</td>           <td>0.000000</td>           <td>62.0</td>           <td>-79.093184</td>           <td>35.951880</td>         </tr>         <tr>           <th>00:00:06</th>           <td>178.942627</td>           <td>1.106947</td>           <td>62.0</td>           <td>-79.093172</td>           <td>35.951868</td>         </tr>         <tr>           <th>00:00:12</th>           <td>177.500610</td>           <td>13.003035</td>           <td>62.0</td>           <td>-79.093228</td>           <td>35.951774</td>         </tr>         <tr>           <th>00:00:16</th>           <td>177.500610</td>           <td>22.405027</td>           <td>60.0</td>           <td>-79.093141</td>           <td>35.951732</td>         </tr>       </tbody>     </table>     </div>

The data frames that are returned by runpandas when loading files is similar for different file types. The dataframe in the above example is a subclass of the pandas.DataFrame and provides some additional features. Certain columns also return specific pandas.Series subclasses, which provides useful methods:


.. code:: ipython3
print (type(activity))     print(type(activity.alt))
.. parsed-literal::
<class 'runpandas.types.frame.Activity'>     <class 'runpandas.types.columns.Altitude'>

For instance, if you want to get the base unit for the altitude alt data or the distance dist data:


.. code:: ipython3
print(activity.alt.base_unit)     print(activity.alt.sum())
.. parsed-literal::
m     65883.68151855901
.. code:: ipython3
print(activity.dist.base_unit)     print(activity.dist[-1])
.. parsed-literal::
m     4686.31103516

The Activity dataframe also contains special properties that presents some statistics from the workout such as elapsed time, mean heartrate, the moving time and the distance of workout in meters.


.. code:: ipython3
#total time elapsed for the activity     print(activity.ellapsed_time)     #distance of workout in meters     print(activity.distance)     #mean heartrate     print(activity.meanheartrate())
.. parsed-literal::
0 days 00:33:11     4686.31103516     156.65274151436032

Occasionally, some observations such as speed, distance and others must be calculated based on available data in the given activity. In runpandas there are special accessors (runpandas.acessors) that computes some of these metrics. We will compute the speed and the distance per position observations using the latitude and longitude for each record and calculate the haversine distance in meters and the speed in meters per second.


.. code:: ipython3
#compute the distance using haversine formula between two consecutive latitude, longitudes observations.     activity['distpos']  = activity.compute.distance()     activity['distpos'].head()

.. parsed-literal::
time     00:00:00          NaN     00:00:01     0.333146     00:00:06     1.678792     00:00:12    11.639901     00:00:16     9.183847     Name: distpos, dtype: float64

.. code:: ipython3
#compute the distance using haversine formula between two consecutive latitude, longitudes observations.     activity['speed']  = activity.compute.speed(from_distances=True)     activity['speed'].head()

.. parsed-literal::
time     00:00:00         NaN     00:00:01    0.333146     00:00:06    0.335758     00:00:12    1.939984     00:00:16    2.295962     Name: speed, dtype: float64
Popular running metrics are also available through the runpandas acessors such as gradient, pace, vertical speed , etc.
.. code:: ipython3
activity['vam'] = activity.compute.vertical_speed()     activity['vam'].head()

.. parsed-literal::
time     00:00:00         NaN     00:00:01    0.000000     00:00:06    0.000000     00:00:12   -0.240336     00:00:16    0.000000     Name: vam, dtype: float64
Sporadically, there will be a large time difference between consecutive observations in the same workout. This can happen when device is paused by the athlete or therere proprietary algorithms controlling the operating sampling rate of the device which can auto-pause when the device detects no significant change in position. In runpandas there is an algorithm that will attempt to calculate the moving time based on the GPS locations, distances, and speed of the activity.

To compute the moving time, there is a special acessor that detects the periods of inactivity and returns the moving series containing all the observations considered to be stopped.


.. code:: ipython3
activityonlymoving = activity.only_moving()     print(activityonlymoving['moving'].head())
.. parsed-literal::
time     00:00:00    False     00:00:01    False     00:00:06    False     00:00:12     True     00:00:16     True     Name: moving, dtype: bool
Now we can compute the moving time, the time of how long the user were active.
.. code:: ipython3
activityonlymoving.moving_time

.. parsed-literal::
Timedelta('0 days 00:33:05')

Runpandas also provides a method summary for summarising the activity through common statistics. Such a session summary includes estimates of several metrics computed above with a single call.


.. code:: ipython3
activityonlymoving.summary()

.. parsed-literal::
Session                           Running: 26-12-2012 21:29:53     Total distance (meters)                                4686.31     Total ellapsed time                            0 days 00:33:11     Total moving time                              0 days 00:33:05     Average speed (km/h)                                   8.47656     Average moving speed (km/h)                            8.49853     Average pace (per 1 km)                        0 days 00:07:04     Average pace moving (per 1 km)                 0 days 00:07:03     Average cadence                                            NaN     Average moving cadence                                     NaN     Average heart rate                                     156.653     Average moving heart rate                                157.4     Average temperature                                        NaN     dtype: object

Now, let’s play with the data. Let’s show distance vs as an example of what and how we can create visualizations. In this example, we will use the built in, matplotlib based plot function.
.. code:: ipython3
activity[['dist']].plot()
.. parsed-literal::
Matplotlib is building the font cache; this may take a moment.

.. parsed-literal::
<AxesSubplot:xlabel='time'>

.. image:: examples/overviewfiles/overview10_2.svg
And here is altitude versus time.
.. code:: ipython3
activity[['alt']].plot()

.. parsed-literal::
<AxesSubplot:xlabel='time'>

.. image:: examples/overviewfiles/overview12_1.svg
Finally, lest’s show the altitude vs distance profile. Here is a scatterplot that shows altitude vs distance as recorded.
.. code:: ipython3
activity.plot.scatter(x='dist', y='alt', c='DarkBlue')

.. parsed-literal::
<AxesSubplot:xlabel='dist', ylabel='alt'>

.. image:: examples/overviewfiles/overview14_1.svg
Finally, let’s watch a glimpse of the map route by plotting a 2d map using logintude vs latitude.
.. code:: ipython3
activity.plot(x='lon', y='lat')

.. parsed-literal::
<AxesSubplot:xlabel='lon'>

.. image:: examples/overviewfiles/overview16_1.svg

The runpandas package also comes with extra batteries, such as our runpandas.datasets package, which includes a range of example data for testing purposes. There is a dedicated repository __ with all the data available. An index of the data is kept here __.


You can use the example data available:
.. code:: ipython3
examplefit = rpd.activityexamples(path='GarminFenix6S_Pro-Running.fit')     print(example_fit.summary)     print('Included metrics:', examplefit.includeddata)
.. parsed-literal::
Synced from watch Garmin Fenix 6S          Included metrics: [<MetricsEnum.latitude: 'latitude'>, <MetricsEnum.longitude: 'longitude'>, <MetricsEnum.elevation: 'elevation'>, <MetricsEnum.heartrate: 'heartrate'>, <MetricsEnum.cadence: 'cadence'>, <MetricsEnum.distance: 'distance'>, <MetricsEnum.temperature: 'temperature'>]
.. code:: ipython3
rpd.readfile(examplefit.path).head()
.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th>enhanced_speed</th>           <th>enhanced_altitude</th>           <th>unknown_87</th>           <th>fractional_cadence</th>           <th>lap</th>           <th>session</th>           <th>unknown_108</th>           <th>dist</th>           <th>cad</th>           <th>hr</th>           <th>lon</th>           <th>lat</th>           <th>temp</th>         </tr>         <tr>           <th>time</th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>         </tr>       </thead>       <tbody>         <tr>           <th>00:00:00</th>           <td>0.000</td>           <td>254.0</td>           <td>0</td>           <td>0.0</td>           <td>0</td>           <td>0</td>           <td>NaN</td>           <td>0.00</td>           <td>0</td>           <td>101</td>           <td>13.843376</td>           <td>51.066280</td>           <td>8</td>         </tr>         <tr>           <th>00:00:01</th>           <td>0.000</td>           <td>254.0</td>           <td>0</td>           <td>0.0</td>           <td>0</td>           <td>0</td>           <td>NaN</td>           <td>0.00</td>           <td>0</td>           <td>101</td>           <td>13.843374</td>           <td>51.066274</td>           <td>8</td>         </tr>         <tr>           <th>00:00:10</th>           <td>1.698</td>           <td>254.0</td>           <td>0</td>           <td>0.0</td>           <td>0</td>           <td>1</td>           <td>2362.0</td>           <td>0.00</td>           <td>83</td>           <td>97</td>           <td>13.843176</td>           <td>51.066249</td>           <td>8</td>         </tr>         <tr>           <th>00:00:12</th>           <td>2.267</td>           <td>254.0</td>           <td>0</td>           <td>0.0</td>           <td>0</td>           <td>1</td>           <td>2362.0</td>           <td>3.95</td>           <td>84</td>           <td>99</td>           <td>13.843118</td>           <td>51.066250</td>           <td>8</td>         </tr>         <tr>           <th>00:00:21</th>           <td>2.127</td>           <td>254.6</td>           <td>0</td>           <td>0.5</td>           <td>0</td>           <td>1</td>           <td>2552.0</td>           <td>16.67</td>           <td>87</td>           <td>100</td>           <td>13.842940</td>           <td>51.066231</td>           <td>8</td>         </tr>       </tbody>     </table>     </div>

In case of you just only want to see all the activities in a specific file type , you can filter the runpandas.activities_examples, which returns a filter iterable that you can iterate over:


.. code:: ipython3
fitexamples = rpd.activityexamples(file_type=rpd.FileTypeEnum.FIT)     for example in fit_examples:         #Download and play with the filtered examples         print(example.path)
.. parsed-literal::
https://raw.githubusercontent.com/corriporai/runpandas-data/master/activities/GarminFenix6S_Pro-Running.fit     https://raw.githubusercontent.com/corriporai/runpandas-data/master/activities/GarminFenix2runningwithhrm.fit     https://raw.githubusercontent.com/corriporai/runpandas-data/master/activities/GarminForerunner910XT-Running.fit

Exploring sessions ==================

The package runpandas provides utilities to import a group of activities data, and after careful processing, organises them into a MultiIndex Dataframe.

The pandas.MultiIndex allows you to have multiple columns acting as a row identifier and multiple rows acting as a header identifier. In our scenario we will have as first indentifier (index) the timestamp of the workout when it started, and as second indentifier the timedelta of the consecutive observations of the workout.


.. figure:: examples/MultiIndexDataframe.png    :alt: Illustration of the MultiIndex Dataframe
The MultiIndex Runpandas Activity Dataframe

The MultiIndex dataframe result from the function runpandas.readdiraggregate, which takes as input the directory of tracking data files, and constructs using the read*() functions to build runpandas.Activity objects. Them, the result daframes are first sorted by the time stamps and are all combined into a single runpandas.Activity indexed by the two-level pandas.MultiIndex.


Let’s illustrate these examples by loading a bunch of 68 running activities of a female runner over the years of 2020 until 2021.
.. code:: ipython3
import warnings     warnings.filterwarnings('ignore')
.. code:: ipython3
import runpandas     session = runpandas.readdiraggregate(dirname='session/')
.. code:: ipython3
session

.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th></th>           <th>alt</th>           <th>hr</th>           <th>lon</th>           <th>lat</th>         </tr>         <tr>           <th>start</th>           <th>time</th>           <th></th>           <th></th>           <th></th>           <th></th>         </tr>       </thead>       <tbody>         <tr>           <th rowspan="5" valign="top">2020-08-30 09:08:51.012</th>           <th>00:00:00</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893609</td>           <td>-8.045055</td>         </tr>         <tr>           <th>00:00:01.091000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893624</td>           <td>-8.045054</td>         </tr>         <tr>           <th>00:00:02.091000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893641</td>           <td>-8.045061</td>         </tr>         <tr>           <th>00:00:03.098000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893655</td>           <td>-8.045063</td>         </tr>         <tr>           <th>00:00:04.098000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893655</td>           <td>-8.045065</td>         </tr>         <tr>           <th>...</th>           <th>...</th>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>         </tr>         <tr>           <th rowspan="5" valign="top">2021-07-04 11:23:19.418</th>           <th>00:52:39.582000</th>           <td>0.050001</td>           <td>189.0</td>           <td>-34.894534</td>           <td>-8.046602</td>         </tr>         <tr>           <th>00:52:43.582000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.894465</td>           <td>-8.046533</td>         </tr>         <tr>           <th>00:52:44.582000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.894443</td>           <td>-8.046515</td>         </tr>         <tr>           <th>00:52:45.582000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.894429</td>           <td>-8.046494</td>         </tr>         <tr>           <th>00:52:49.582000</th>           <td>NaN</td>           <td>190.0</td>           <td>-34.894395</td>           <td>-8.046398</td>         </tr>       </tbody>     </table>     <p>48794 rows × 4 columns</p>     </div>

Now let’s see how many activities there are available for analysis. For this question, we also have an acessor runpandas.types.acessors.session._SessionAcessor that holds several methods for computing the basic running metrics across all the activities from this kind of frame and some summary statistics.


.. code:: ipython3
#count the number of activities in the session     print ('Total Activities:', session.session.count())
.. parsed-literal::
Total Activities: 68

We might compute the main running metrics (speed, pace, moving, etc) using the session acessors methods as like the ones available in the runpandas.types.metrics.MetricsAcessor . By the way, those methods are called inside each metric method, but applying in each of activities separatedely.


.. code:: ipython3
#In this example we compute the distance and the distance per position across all workouts     session = session.session.distance()     session

.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th></th>           <th>alt</th>           <th>hr</th>           <th>lon</th>           <th>lat</th>           <th>distpos</th>           <th>dist</th>         </tr>         <tr>           <th>start</th>           <th>time</th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>         </tr>       </thead>       <tbody>         <tr>           <th rowspan="5" valign="top">2020-08-30 09:08:51.012</th>           <th>00:00:00</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893609</td>           <td>-8.045055</td>           <td>NaN</td>           <td>NaN</td>         </tr>         <tr>           <th>00:00:01.091000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893624</td>           <td>-8.045054</td>           <td>1.690587</td>           <td>1.690587</td>         </tr>         <tr>           <th>00:00:02.091000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893641</td>           <td>-8.045061</td>           <td>2.095596</td>           <td>3.786183</td>         </tr>         <tr>           <th>00:00:03.098000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893655</td>           <td>-8.045063</td>           <td>1.594298</td>           <td>5.380481</td>         </tr>         <tr>           <th>00:00:04.098000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.893655</td>           <td>-8.045065</td>           <td>0.163334</td>           <td>5.543815</td>         </tr>         <tr>           <th>...</th>           <th>...</th>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>         </tr>         <tr>           <th rowspan="5" valign="top">2021-07-04 11:23:19.418</th>           <th>00:52:39.582000</th>           <td>0.050001</td>           <td>189.0</td>           <td>-34.894534</td>           <td>-8.046602</td>           <td>12.015437</td>           <td>8220.018885</td>         </tr>         <tr>           <th>00:52:43.582000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.894465</td>           <td>-8.046533</td>           <td>10.749779</td>           <td>8230.768664</td>         </tr>         <tr>           <th>00:52:44.582000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.894443</td>           <td>-8.046515</td>           <td>3.163638</td>           <td>8233.932302</td>         </tr>         <tr>           <th>00:52:45.582000</th>           <td>NaN</td>           <td>NaN</td>           <td>-34.894429</td>           <td>-8.046494</td>           <td>2.851535</td>           <td>8236.783837</td>         </tr>         <tr>           <th>00:52:49.582000</th>           <td>NaN</td>           <td>190.0</td>           <td>-34.894395</td>           <td>-8.046398</td>           <td>11.300740</td>           <td>8248.084577</td>         </tr>       </tbody>     </table>     <p>48794 rows × 6 columns</p>     </div>

.. code:: ipython3
#comput the speed for each activity     session = session.session.speed(from_distances=True)     #compute the pace for each activity     session = session.session.pace()     #compute the inactivity periods for each activity     session = session.session.only_moving()
After all the computation done, let’s going to the next step: the exploration and get some descriptive statistics.

After the loading and metrics computation for all the activities, now let’s look further the data and get the basic summaries about the session: time spent, total distance, mean speed and other insightful statistics in each running activity. For this task, we may accomplish it by calling the method runpandas.types.session._SessionAcessor.summarize . It will return a basic Dataframe including all the aggregated statistics per activity from the season frame.


.. code:: ipython3
summary = session.session.summarize()     summary

.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th>moving_time</th>           <th>mean_speed</th>           <th>max_speed</th>           <th>mean_pace</th>           <th>max_pace</th>           <th>meanmovingspeed</th>           <th>meanmovingpace</th>           <th>mean_cadence</th>           <th>max_cadence</th>           <th>meanmovingcadence</th>           <th>meanheartrate</th>           <th>maxheartrate</th>           <th>meanmovingheart_rate</th>           <th>mean_temperature</th>           <th>min_temperature</th>           <th>max_temperature</th>           <th>total_distance</th>           <th>ellapsed_time</th>         </tr>         <tr>           <th>start</th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>           <th></th>         </tr>       </thead>       <tbody>         <tr>           <th>2020-07-03 09:50:53.162</th>           <td>00:25:29.838000</td>           <td>2.642051</td>           <td>4.879655</td>           <td>00:06:18</td>           <td>00:03:24</td>           <td>2.665008</td>           <td>00:06:15</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>178.819923</td>           <td>188.0</td>           <td>178.872587</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>4089.467333</td>           <td>00:25:47.838000</td>         </tr>         <tr>           <th>2020-07-05 09:33:20.999</th>           <td>00:05:04.999000</td>           <td>2.227637</td>           <td>6.998021</td>           <td>00:07:28</td>           <td>00:02:22</td>           <td>3.072098</td>           <td>00:05:25</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>168.345455</td>           <td>176.0</td>           <td>168.900000</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>980.162640</td>           <td>00:07:20.001000</td>         </tr>         <tr>           <th>2020-07-05 09:41:59.999</th>           <td>00:18:19</td>           <td>1.918949</td>           <td>6.563570</td>           <td>00:08:41</td>           <td>00:02:32</td>           <td>2.729788</td>           <td>00:06:06</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>173.894180</td>           <td>185.0</td>           <td>174.577143</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>3139.401118</td>           <td>00:27:16</td>         </tr>         <tr>           <th>2020-07-13 09:13:58.718</th>           <td>00:40:21.281000</td>           <td>2.509703</td>           <td>8.520387</td>           <td>00:06:38</td>           <td>00:01:57</td>           <td>2.573151</td>           <td>00:06:28</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>170.808176</td>           <td>185.0</td>           <td>170.795527</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>6282.491059</td>           <td>00:41:43.281000</td>         </tr>         <tr>           <th>2020-07-17 09:33:02.308</th>           <td>00:32:07.691000</td>           <td>2.643278</td>           <td>8.365431</td>           <td>00:06:18</td>           <td>00:01:59</td>           <td>2.643278</td>           <td>00:06:18</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>176.436242</td>           <td>186.0</td>           <td>176.436242</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>5095.423045</td>           <td>00:32:07.691000</td>         </tr>         <tr>           <th>...</th>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>         </tr>         <tr>           <th>2021-06-13 09:22:30.985</th>           <td>01:32:33.018000</td>           <td>2.612872</td>           <td>23.583956</td>           <td>00:06:22</td>           <td>00:00:42</td>           <td>2.810855</td>           <td>00:05:55</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>169.340812</td>           <td>183.0</td>           <td>169.655879</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>15706.017295</td>           <td>01:40:11.016000</td>         </tr>         <tr>           <th>2021-06-20 09:16:55.163</th>           <td>00:59:44.512000</td>           <td>2.492640</td>           <td>6.065895</td>           <td>00:06:41</td>           <td>00:02:44</td>           <td>2.749453</td>           <td>00:06:03</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>170.539809</td>           <td>190.0</td>           <td>171.231392</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>9965.168311</td>           <td>01:06:37.837000</td>         </tr>         <tr>           <th>2021-06-23 09:37:44.000</th>           <td>00:26:49.001000</td>           <td>2.501796</td>           <td>5.641343</td>           <td>00:06:39</td>           <td>00:02:57</td>           <td>2.568947</td>           <td>00:06:29</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>156.864865</td>           <td>171.0</td>           <td>156.957031</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>4165.492241</td>           <td>00:27:45.001000</td>         </tr>         <tr>           <th>2021-06-27 09:50:08.664</th>           <td>00:31:42.336000</td>           <td>2.646493</td>           <td>32.734124</td>           <td>00:06:17</td>           <td>00:00:30</td>           <td>2.661853</td>           <td>00:06:15</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>166.642857</td>           <td>176.0</td>           <td>166.721116</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>5074.217061</td>           <td>00:31:57.336000</td>         </tr>         <tr>           <th>2021-07-04 11:23:19.418</th>           <td>00:47:47.583000</td>           <td>2.602263</td>           <td>4.212320</td>           <td>00:06:24</td>           <td>00:03:57</td>           <td>2.856801</td>           <td>00:05:50</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>177.821862</td>           <td>192.0</td>           <td>177.956967</td>           <td>NaN</td>           <td>NaN</td>           <td>NaN</td>           <td>8248.084577</td>           <td>00:52:49.582000</td>         </tr>       </tbody>     </table>     <p>68 rows × 18 columns</p>     </div>

.. code:: ipython3
print('Session Interval:', (summary.index.toseries().max() - summary.index.toseries().min()).days, 'days')     print('Total Workouts:', len(summary), 'runnings')     print('Tota KM Distance:', summary['total_distance'].sum() / 1000)     print('Average Pace (all runs):', summary.mean_pace.mean())     print('Average Moving Pace (all runs):', summary.meanmovingpace.mean())     print('Average KM Distance (all runs):', round(summary.total_distance.mean()/ 1000,2))
.. parsed-literal::
Session Interval: 366 days     Total Workouts: 68 runnings     Tota KM Distance: 491.77377537338896     Average Pace (all runs): 0 days 00:07:18.411764     Average Moving Pace (all runs): 0 days 00:06:02.147058     Average KM Distance (all runs): 7.23
At this point, I have the summary data to start some powerful visualization and analysis. At the charts below we illustrate her pace and distance evolution over time.
.. code:: ipython3
import matplotlib.pyplot as plt     import datetime          #let's convert the pace to float number in minutes     summary['meanmovingpacefloat'] = summary['meanmoving_pace'] / datetime.timedelta(minutes=1)     summary['pacemovingallmean'] = summary.meanmoving_pace.mean()     summary['pacemovingallmeanfloat'] = summary['pacemovingall_mean'] / datetime.timedelta(minutes=1)          plt.subplots(figsize=(8, 5))          plt.plot(summary.index, summary.meanmovingpace_float, color='silver')     plt.plot(summary.pacemovingallmeanfloat, color='purple', linestyle='dashed', label='average')     plt.title("Pace Evolution")     plt.xlabel("Runnings")     plt.ylabel("Pace")     plt.legend()

.. parsed-literal::
<matplotlib.legend.Legend at 0x7f82d8d83cd0>

.. image:: examples/overviewfiles/overview56_1.svg
.. code:: ipython3
plt.subplots(figsize=(8, 5))          summary['distanceallmean'] = round(summary.total_distance.mean()/1000,2)          plt.plot(summary.index, summary.total_distance / 1000, color='silver')     plt.plot(summary.distanceallmean, color='purple', linestyle='dashed', label='average')     plt.title("Distance Evolution")     plt.xlabel("Runs")     plt.ylabel("distance")     plt.legend()               plt.show()

.. image:: examples/overviewfiles/overview57_0.svg
Accessing historical data from running race results ===================================================
One of the great features in Runpandas is the capability of accessing race’s result datasets accross several races around the world, from majors to local ones (if it’s available at our data repository). In this example we will analyze the 2022 Berlin Marathon using runpandas methods specially tailored for handling race results data.

First, let’s load the Berlin Marathon data by using the runpandas method runpandas.get_events. This function provides a way of accessing the race data and visualize the results from several marathons available at our datasets repository. Given the year and the marathon identifier you can filter any marathon datasets that you want analyze. The result will be a list of runpandas.EventData instances with race result and its metadata. Let’s look for Berlin Marathon results.


.. code:: ipython3
import pandas as pd     import runpandas as rpd     import warnings     warnings.filterwarnings('ignore')
.. code:: ipython3
results = rpd.get_events('Berlin')     results

.. parsed-literal::
[<Event: name=Berlin Marathon Results from 2022., country=DE, edition=2022>]

The result comes with the Berlin Marathon Result from 2022. Let’s take a look inside the race event, which comes with a handful method to describe its attributes and a special method to load the race result data into a runpandas.datasets.schema.RaceData instance.


.. code:: ipython3
berlin_result = results[0]     print('Event type', berlinresult.runtype)     print('Country', berlin_result.country)     print('Year', berlin_result.edition)     print('Name', berlin_result.summary)
.. parsed-literal::
Event type RunTypeEnum.MARATHON     Country DE     Year 2022     Name Berlin Marathon Results from 2022.
Now that we confirmed that we requested the corresponding marathon dataset. We will load it into a DataFrame so we can further explore it.
.. code:: ipython3
#loading the race data into a RaceData Dataframe     raceresult = berlinresult.load()     race_result

.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th>position</th>           <th>position_gender</th>           <th>country</th>           <th>sex</th>           <th>division</th>           <th>bib</th>           <th>firstname</th>           <th>lastname</th>           <th>club</th>           <th>starttime</th>           <th>...</th>           <th>10k</th>           <th>15k</th>           <th>20k</th>           <th>25k</th>           <th>30k</th>           <th>35k</th>           <th>40k</th>           <th>grosstime</th>           <th>nettime</th>           <th>category</th>         </tr>       </thead>       <tbody>         <tr>           <th>0</th>           <td>1</td>           <td>1</td>           <td>KEN</td>           <td>M</td>           <td>1</td>           <td>1</td>           <td>Eliud</td>           <td>Kipchoge</td>           <td>–</td>           <td>09:15:00</td>           <td>...</td>           <td>0 days 00:28:23</td>           <td>0 days 00:42:33</td>           <td>0 days 00:56:45</td>           <td>0 days 01:11:08</td>           <td>0 days 01:25:40</td>           <td>0 days 01:40:10</td>           <td>0 days 01:54:53</td>           <td>0 days 02:01:09</td>           <td>0 days 02:01:09</td>           <td>M35</td>         </tr>         <tr>           <th>1</th>           <td>2</td>           <td>2</td>           <td>KEN</td>           <td>M</td>           <td>1</td>           <td>5</td>           <td>Mark</td>           <td>Korir</td>           <td>–</td>           <td>09:15:00</td>           <td>...</td>           <td>0 days 00:28:56</td>           <td>0 days 00:43:35</td>           <td>0 days 00:58:14</td>           <td>0 days 01:13:07</td>           <td>0 days 01:28:06</td>           <td>0 days 01:43:25</td>           <td>0 days 01:59:05</td>           <td>0 days 02:05:58</td>           <td>0 days 02:05:58</td>           <td>M30</td>         </tr>         <tr>           <th>2</th>           <td>3</td>           <td>3</td>           <td>ETH</td>           <td>M</td>           <td>1</td>           <td>8</td>           <td>Tadu</td>           <td>Abate</td>           <td>–</td>           <td>09:15:00</td>           <td>...</td>           <td>0 days 00:29:46</td>           <td>0 days 00:44:40</td>           <td>0 days 00:59:40</td>           <td>0 days 01:14:44</td>           <td>0 days 01:30:01</td>           <td>0 days 01:44:55</td>           <td>0 days 02:00:03</td>           <td>0 days 02:06:28</td>           <td>0 days 02:06:28</td>           <td>MH</td>         </tr>         <tr>           <th>3</th>           <td>4</td>           <td>4</td>           <td>ETH</td>           <td>M</td>           <td>2</td>           <td>26</td>           <td>Andamlak</td>           <td>Belihu</td>           <td>–</td>           <td>09:15:00</td>           <td>...</td>           <td>0 days 00:28:23</td>           <td>0 days 00:42:33</td>           <td>0 days 00:56:45</td>           <td>0 days 01:11:09</td>           <td>0 days 01:26:11</td>           <td>0 days 01:42:14</td>           <td>0 days 01:59:14</td>           <td>0 days 02:06:40</td>           <td>0 days 02:06:40</td>           <td>MH</td>         </tr>         <tr>           <th>4</th>           <td>5</td>           <td>5</td>           <td>KEN</td>           <td>M</td>           <td>3</td>           <td>25</td>           <td>Abel</td>           <td>Kipchumba</td>           <td>–</td>           <td>09:15:00</td>           <td>...</td>           <td>0 days 00:28:55</td>           <td>0 days 00:43:35</td>           <td>0 days 00:58:14</td>           <td>0 days 01:13:07</td>           <td>0 days 01:28:03</td>           <td>0 days 01:43:08</td>           <td>0 days 01:59:14</td>           <td>0 days 02:06:49</td>           <td>0 days 02:06:49</td>           <td>MH</td>         </tr>         <tr>           <th>...</th>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>           <td>...</td>         </tr>         <tr>           <th>35566</th>           <td>DNF</td>           <td>–</td>           <td>USA</td>           <td>M</td>           <td>–</td>           <td>65079</td>           <td>michael</td>           <td>perkowski</td>           <td>–</td>           <td>–</td>           <td>...</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>M65</td>         </tr>         <tr>           <th>35567</th>           <td>DNF</td>           <td>–</td>           <td>USA</td>           <td>M</td>           <td>–</td>           <td>62027</td>           <td>Karl</td>           <td>Mann</td>           <td>–</td>           <td>–</td>           <td>...</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>M55</td>         </tr>         <tr>           <th>35568</th>           <td>DNF</td>           <td>–</td>           <td>THA</td>           <td>F</td>           <td>–</td>           <td>27196</td>           <td>oraluck</td>           <td>pichaiwongse</td>           <td>STATE to BERLIN 2022</td>           <td>–</td>           <td>...</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>W55</td>         </tr>         <tr>           <th>35569</th>           <td>DNF</td>           <td>–</td>           <td>SUI</td>           <td>M</td>           <td>–</td>           <td>56544</td>           <td>Gerardo</td>           <td>GARCIA CALZADA</td>           <td>–</td>           <td>–</td>           <td>...</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>M50</td>         </tr>         <tr>           <th>35570</th>           <td>DNF</td>           <td>–</td>           <td>AUT</td>           <td>M</td>           <td>–</td>           <td>63348</td>           <td>Harald</td>           <td>Mori</td>           <td>Albatros</td>           <td>–</td>           <td>...</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>NaT</td>           <td>M60</td>         </tr>       </tbody>     </table>     <p>35571 rows × 23 columns</p>     </div>
Now you can get some insights about the Berlin Marathon 2022, by using its tailored methods for getting basic and quick insights. For example, the number of finishers, number of participants and the winner info.
.. code:: ipython3
print('Total participants', raceresult.totalparticipants)     print('Total finishers', raceresult.totalfinishers)     print('Total Non-Finishers', raceresult.totalnonfinishers)
.. parsed-literal::
Total participants 35571     Total finishers 34844     Total Non-Finishers 727
.. code:: ipython3
race_result.winner

.. parsed-literal::
position                         1     position_gender                  1     country                        KEN     sex                              M     division                         1     bib                              1     firstname                    Eliud     lastname                  Kipchoge     club                             –     starttime                 09:15:00     startrawtime            09:15:00     half               0 days 00:59:51     5k                 0 days 00:14:14     10k                0 days 00:28:23     15k                0 days 00:42:33     20k                0 days 00:56:45     25k                0 days 01:11:08     30k                0 days 01:25:40     35k                0 days 01:40:10     40k                0 days 01:54:53     grosstime          0 days 02:01:09     nettime            0 days 02:01:09     category                       M35     Name: 0, dtype: object

Eliud Kipchoge of Kenya won the 2022 Berlin Marathon in 2:01:09. Kipchoge’s victory was his fourth in Berlin and 17th overall in a career of 19 marathon starts. And who was the women’s race winner?
.. code:: ipython3
raceresult[(raceresult['positiongender'] == 1) & (raceresult['sex'] == 'F')].T

.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th>32</th>         </tr>       </thead>       <tbody>         <tr>           <th>position</th>           <td>33</td>         </tr>         <tr>           <th>position_gender</th>           <td>1</td>         </tr>         <tr>           <th>country</th>           <td>ETH</td>         </tr>         <tr>           <th>sex</th>           <td>F</td>         </tr>         <tr>           <th>division</th>           <td>1</td>         </tr>         <tr>           <th>bib</th>           <td>F24</td>         </tr>         <tr>           <th>firstname</th>           <td>Tigist</td>         </tr>         <tr>           <th>lastname</th>           <td>Assefa</td>         </tr>         <tr>           <th>club</th>           <td>–</td>         </tr>         <tr>           <th>starttime</th>           <td>09:15:00</td>         </tr>         <tr>           <th>startrawtime</th>           <td>09:15:00</td>         </tr>         <tr>           <th>half</th>           <td>0 days 01:08:13</td>         </tr>         <tr>           <th>5k</th>           <td>0 days 00:16:22</td>         </tr>         <tr>           <th>10k</th>           <td>0 days 00:32:36</td>         </tr>         <tr>           <th>15k</th>           <td>0 days 00:48:44</td>         </tr>         <tr>           <th>20k</th>           <td>0 days 01:04:43</td>         </tr>         <tr>           <th>25k</th>           <td>0 days 01:20:48</td>         </tr>         <tr>           <th>30k</th>           <td>0 days 01:36:41</td>         </tr>         <tr>           <th>35k</th>           <td>0 days 01:52:27</td>         </tr>         <tr>           <th>40k</th>           <td>0 days 02:08:42</td>         </tr>         <tr>           <th>grosstime</th>           <td>0 days 02:15:37</td>         </tr>         <tr>           <th>nettime</th>           <td>0 days 02:15:37</td>         </tr>         <tr>           <th>category</th>           <td>WH</td>         </tr>       </tbody>     </table>     </div>

Tigist Assefa of Ethiopia won the women’s race in a stunning time of 2:15:37 to set a new course record in Berlin.
Runpandas also provides a race’s summary method for showing the compilation of some general insights such as finishers, partipants (by gender and overall).
.. code:: ipython3
race_result.summary()

.. parsed-literal::
Event name                    berlin marathon     Event type                                42k     Event country                              DE     Event date                         25-09-2022     Number of participants                  35571     Number of finishers                     34844     Number of non-finishers                   727     Number of male finishers                23314     Number of female finishers              11523     Winner Nettime                0 days 02:01:09     dtype: objec

Runpandas for some race results come with the splits for the partial distances of the race. We can fetch for any runner the splits using the method runpandas.acessors.splits.pick_athlete. So, if we need to have direct access to all splits from a specific runner, we will use the splits acesssor.


.. code:: ipython3
raceresult.splits.pickathlete(identifier='1')

.. raw:: html
<div>     <style scoped>         .dataframe tbody tr th:only-of-type {             vertical-align: middle;         }              .dataframe tbody tr th {             vertical-align: top;         }              .dataframe thead th {             text-align: right;         }     </style>     <table border="1" class="dataframe">       <thead>         <tr style="text-align: right;">           <th></th>           <th>time</th>           <th>distance_meters</th>           <th>distance_miles</th>         </tr>         <tr>           <th>split</th>           <th></th>           <th></th>           <th></th>         </tr>       </thead>       <tbody>         <tr>           <th>0k</th>           <td>0 days 00:00:00</td>           <td>0</td>           <td>0.0000</td>         </tr>         <tr>           <th>5k</th>           <td>0 days 00:14:14</td>           <td>5000</td>           <td>3.1069</td>         </tr>         <tr>           <th>10k</th>           <td>0 days 00:28:23</td>           <td>10000</td>           <td>6.2137</td>         </tr>         <tr>           <th>15k</th>           <td>0 days 00:42:33</td>           <td>15000</td>           <td>9.3206</td>         </tr>         <tr>           <th>20k</th>           <td>0 days 00:56:45</td>           <td>20000</td>           <td>12.4274</td>         </tr>         <tr>           <th>half</th>           <td>0 days 00:59:51</td>           <td>21097</td>           <td>13.1091</td>         </tr>         <tr>           <th>25k</th>           <td>0 days 01:11:08</td>           <td>25000</td>           <td>15.5343</td>         </tr>         <tr>           <th>30k</th>           <td>0 days 01:25:40</td>           <td>30000</td>           <td>18.6411</td>         </tr>         <tr>           <th>35k</th>           <td>0 days 01:40:10</td>           <td>35000</td>           <td>21.7480</td>         </tr>         <tr>           <th>40k</th>           <td>0 days 01:54:53</td>           <td>40000</td>           <td>24.8548</td>         </tr>         <tr>           <th>nettime</th>           <td>0 days 02:01:09</td>           <td>42195</td>           <td>26.2187</td>         </tr>       </tbody>     </table>     </div>

With plotting libraries such as matplotlib` you can analyze the splits data through a impressive visualization!

.. code:: ipython3

eliudkipchogesplits = raceresult.splits.pickathlete(identifier='1')

.. code:: ipython3

def timeTicks(x, pos): seconds = x / 10**9 d = datetime.timedelta(seconds=seconds) return str(d) fig, ax2 = plt.subplots() #plot the splits time #format the y-axis to show the labels as timedelta. formatter = matplotlib.ticker.FuncFormatter(timeTicks) #plot the paces per segment line2, = ax2.plot(eliudkipchogesplitsfiltered.index, eliudkipchogesplitsfiltered['pace'], linestyle='dashed', color='cyan', lw=5, alpha=0.8) #plot the overall mean pace line3, = ax2.plot(eliudkipchogesplitsfiltered.index, eliudkipchogesplitsfiltered['mean_pace'], color='#1b9e77', linestyle='dashed', lw=5, alpha=0.8) #annotate the pace line with time splits yvalues = line2.get_ydata() for index, y in zip(eliudkipchogesplits_filtered.index, yvalues): formatedtime = datetime.timedelta(seconds=eliudkipchogesplitsfiltered.loc[index,'splittime'].totalseconds()) ax2.text(index, y, formated_time, weight="bold", size=12, ) ax2.yaxis.setmajorformatter(formatter) ax2.grid(False) ax2.legend( (line2, line3), ('Splits Time', 'Splits Pace', 'Mean Pace'), loc='lower right', frameon=False ) ax2.set_title("Eliud Kipchoge splits time and pace in Berlin Marathon 2022") ax2.set_xlabel("Splits in kms") ax2.set_ylabel("Pace min/km") plt.show()