This page was generated from docs/user_guide/3-session.ipynb. Interactive online version: .

Investigating the historical running data to evaluate my performance (Session summaries)

Many runners use third-party apps to track running activities. These apps and its companion website provide many visual charts with analytical metrics to help runners review their running performances to set up new training plans or make adjustments. In this notebook, we will extract this running data and analyse it locally using runpandas. We also take the opportunity to illustrate the runpandas methods for summarizing the historical workouts and get some valuable insights.

Looking at the data

The example data set used in this tutorial contains 68 sessions of a single female runner from the period of 2020 until 2021.

The code chunk below loads the data using the method runpandas.read_directory_aggregate, which allows the user to read all the tracking files of a support format in a directory and combine them in a data frame split by sessions based on the timestamps of each activity. It means that for each workout file will be stored in separate lines in the dataframe.

import warnings
warnings.filterwarnings('ignore')

import runpandas
session = runpandas.read_dir_aggregate(dirname='session/')

In pandas we use the pandas.MultiIndex which alows the dataframe have multiple columns as a row identifier, while having each index column related to another through a parent/child relationship. In our scenario we have the start time from each activity as the first index level and the timestamps from the activity as the second index level.

session

		alt	hr	lon	lat
start	time
2020-08-30 09:08:51.012	00:00:00	NaN	NaN	-34.893609	-8.045055
	00:00:01.091000	NaN	NaN	-34.893624	-8.045054
	00:00:02.091000	NaN	NaN	-34.893641	-8.045061
	00:00:03.098000	NaN	NaN	-34.893655	-8.045063
	00:00:04.098000	NaN	NaN	-34.893655	-8.045065
...	...	...	...	...	...
2021-07-04 11:23:19.418	00:52:39.582000	0.050001	189.0	-34.894534	-8.046602
	00:52:43.582000	NaN	NaN	-34.894465	-8.046533
	00:52:44.582000	NaN	NaN	-34.894443	-8.046515
	00:52:45.582000	NaN	NaN	-34.894429	-8.046494
	00:52:49.582000	NaN	190.0	-34.894395	-8.046398

48794 rows × 4 columns

session.index  #MultiIndex (start, timestamp)

MultiIndex([('2020-08-30 09:08:51.012000',        '00:00:00'),
            ('2020-08-30 09:08:51.012000', '00:00:01.091000'),
            ('2020-08-30 09:08:51.012000', '00:00:02.091000'),
            ('2020-08-30 09:08:51.012000', '00:00:03.098000'),
            ('2020-08-30 09:08:51.012000', '00:00:04.098000'),
            ('2020-08-30 09:08:51.012000', '00:00:05.096000'),
            ('2020-08-30 09:08:51.012000', '00:00:06.096000'),
            ('2020-08-30 09:08:51.012000', '00:00:07.097000'),
            ('2020-08-30 09:08:51.012000', '00:00:08.097000'),
            ('2020-08-30 09:08:51.012000', '00:00:09.102000'),
            ...
            ('2021-07-04 11:23:19.418000', '00:52:18.584000'),
            ('2021-07-04 11:23:19.418000', '00:52:22.584000'),
            ('2021-07-04 11:23:19.418000', '00:52:26.582000'),
            ('2021-07-04 11:23:19.418000', '00:52:30.582000'),
            ('2021-07-04 11:23:19.418000', '00:52:35.582000'),
            ('2021-07-04 11:23:19.418000', '00:52:39.582000'),
            ('2021-07-04 11:23:19.418000', '00:52:43.582000'),
            ('2021-07-04 11:23:19.418000', '00:52:44.582000'),
            ('2021-07-04 11:23:19.418000', '00:52:45.582000'),
            ('2021-07-04 11:23:19.418000', '00:52:49.582000')],
           names=['start', 'time'], length=48794)

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 and some summary statistics.

#count the number of activities in the session
print ('Total Activities:', session.session.count())

Total Activities: 68

We can compute the main running metrics (speed, pace, moving, etc) using the session methods available 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.

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

		alt	hr	lon	lat	distpos	dist
start	time
2020-08-30 09:08:51.012	00:00:00	NaN	NaN	-34.893609	-8.045055	NaN	NaN
	00:00:01.091000	NaN	NaN	-34.893624	-8.045054	1.690587	1.690587
	00:00:02.091000	NaN	NaN	-34.893641	-8.045061	2.095596	3.786183
	00:00:03.098000	NaN	NaN	-34.893655	-8.045063	1.594298	5.380481
	00:00:04.098000	NaN	NaN	-34.893655	-8.045065	0.163334	5.543815
...	...	...	...	...	...	...	...
2021-07-04 11:23:19.418	00:52:39.582000	0.050001	189.0	-34.894534	-8.046602	12.015437	8220.018885
	00:52:43.582000	NaN	NaN	-34.894465	-8.046533	10.749779	8230.768664
	00:52:44.582000	NaN	NaN	-34.894443	-8.046515	3.163638	8233.932302
	00:52:45.582000	NaN	NaN	-34.894429	-8.046494	2.851535	8236.783837
	00:52:49.582000	NaN	190.0	-34.894395	-8.046398	11.300740	8248.084577

48794 rows × 6 columns

#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()

session

		alt	hr	lon	lat	distpos	dist	speed	pace	moving
start	time
2020-08-30 09:08:51.012	00:00:00	NaN	NaN	-34.893609	-8.045055	NaN	NaN	NaN	NaT	False
	00:00:01.091000	NaN	NaN	-34.893624	-8.045054	1.690587	1.690587	1.549576	00:00:00.645337	True
	00:00:02.091000	NaN	NaN	-34.893641	-8.045061	2.095596	3.786183	2.095596	00:00:00.477191	True
	00:00:03.098000	NaN	NaN	-34.893655	-8.045063	1.594298	5.380481	1.583216	00:00:00.631625	True
	00:00:04.098000	NaN	NaN	-34.893655	-8.045065	0.163334	5.543815	0.163334	00:00:06.122433	False
...	...	...	...	...	...	...	...	...	...	...
2021-07-04 11:23:19.418	00:52:39.582000	0.050001	189.0	-34.894534	-8.046602	12.015437	8220.018885	3.003859	00:00:00.332905	True
	00:52:43.582000	NaN	NaN	-34.894465	-8.046533	10.749779	8230.768664	2.687445	00:00:00.372100	True
	00:52:44.582000	NaN	NaN	-34.894443	-8.046515	3.163638	8233.932302	3.163638	00:00:00.316091	True
	00:52:45.582000	NaN	NaN	-34.894429	-8.046494	2.851535	8236.783837	2.851535	00:00:00.350688	True
	00:52:49.582000	NaN	190.0	-34.894395	-8.046398	11.300740	8248.084577	2.825185	00:00:00.353959	True

48794 rows × 9 columns

After all the computation done, let’s going to the next step: the exploration and get some descriptive statistics.

Exploring the data

After the loading and metrics computation for all the activities, now let’s look further the data and get the basic summaries about the sessions: 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.

summary = session.session.summarize()
summary

	moving_time	mean_speed	max_speed	mean_pace	max_pace	mean_moving_speed	mean_moving_pace	mean_cadence	max_cadence	mean_moving_cadence	mean_heart_rate	max_heart_rate	mean_moving_heart_rate	mean_temperature	min_temperature	max_temperature	total_distance	ellapsed_time
start
2020-07-03 09:50:53.162	00:25:29.838000	2.642051	4.879655	00:06:18	00:03:24	2.665008	00:06:15	NaN	NaN	NaN	178.819923	188.0	178.872587	NaN	NaN	NaN	4089.467333	00:25:47.838000
2020-07-05 09:33:20.999	00:05:04.999000	2.227637	6.998021	00:07:28	00:02:22	3.072098	00:05:25	NaN	NaN	NaN	168.345455	176.0	168.900000	NaN	NaN	NaN	980.162640	00:07:20.001000
2020-07-05 09:41:59.999	00:18:19	1.918949	6.563570	00:08:41	00:02:32	2.729788	00:06:06	NaN	NaN	NaN	173.894180	185.0	174.577143	NaN	NaN	NaN	3139.401118	00:27:16
2020-07-13 09:13:58.718	00:40:21.281000	2.509703	8.520387	00:06:38	00:01:57	2.573151	00:06:28	NaN	NaN	NaN	170.808176	185.0	170.795527	NaN	NaN	NaN	6282.491059	00:41:43.281000
2020-07-17 09:33:02.308	00:32:07.691000	2.643278	8.365431	00:06:18	00:01:59	2.643278	00:06:18	NaN	NaN	NaN	176.436242	186.0	176.436242	NaN	NaN	NaN	5095.423045	00:32:07.691000
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2021-06-13 09:22:30.985	01:32:33.018000	2.612872	23.583956	00:06:22	00:00:42	2.810855	00:05:55	NaN	NaN	NaN	169.340812	183.0	169.655879	NaN	NaN	NaN	15706.017295	01:40:11.016000
2021-06-20 09:16:55.163	00:59:44.512000	2.492640	6.065895	00:06:41	00:02:44	2.749453	00:06:03	NaN	NaN	NaN	170.539809	190.0	171.231392	NaN	NaN	NaN	9965.168311	01:06:37.837000
2021-06-23 09:37:44.000	00:26:49.001000	2.501796	5.641343	00:06:39	00:02:57	2.568947	00:06:29	NaN	NaN	NaN	156.864865	171.0	156.957031	NaN	NaN	NaN	4165.492241	00:27:45.001000
2021-06-27 09:50:08.664	00:31:42.336000	2.646493	32.734124	00:06:17	00:00:30	2.661853	00:06:15	NaN	NaN	NaN	166.642857	176.0	166.721116	NaN	NaN	NaN	5074.217061	00:31:57.336000
2021-07-04 11:23:19.418	00:47:47.583000	2.602263	4.212320	00:06:24	00:03:57	2.856801	00:05:50	NaN	NaN	NaN	177.821862	192.0	177.956967	NaN	NaN	NaN	8248.084577	00:52:49.582000

68 rows × 18 columns

Here, some descriptive statistics:

summary['day_diff'] = summary.index.to_series().diff().astype('timedelta64[D]').astype('Int64')
summary['pace_moving_all_mean'] = summary.mean_moving_pace.mean()
summary['distance_all_mean'] = round(summary.total_distance.mean()/1000,2)
summary['mean_speed'] = summary['mean_speed'] * 3.6 #convert from m/s  to km/h
summary['max_speed'] = summary['max_speed'] * 3.6 #convert from m/s  to km/h
summary['mean_moving_speed'] = summary['mean_moving_speed'] * 3.6 #convert from m/s  to km/h

print('Session Interval:', (summary.index.to_series().max() - summary.index.to_series().min()).days, 'days')
print('Total Workouts:', len(summary), 'runnings')
print('Tota KM Distance:', summary['total_distance'].sum() / 1000)
print('Running Intervals (average):' , round(summary.day_diff.mean(), 2), 'days')
print('Average Pace (all runs):', summary.mean_pace.mean())
print('Average Moving Pace (all runs):', summary.mean_moving_pace.mean())
print('Average KM Distance (all runs):', round(summary.total_distance.mean()/ 1000,2))

Session Interval: 366 days
Total Workouts: 68 runnings
Tota KM Distance: 491.77377537338896
Running Intervals (average): 5.0 days
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

As we can see above, we analyzed the period of 366 days (one year) of running workouts. In this period, she ran 68 times which achieved the total distance of 491 km! The average interval in days between two actitivies is 5 days, with average moving pace of 06’02” per km and average distance of 7.23km! Great numbers for a starter runner!

Exploring the data with some visualizations

At this point, I have the data to start some visualization and analysis. The first question that popped was : How was the evolution of my average moving pace through all the runs and the corresponding average? Let’s use the matplotlib package to illustrate the possible answer.

#let's convert the pace to float number in minutes
import datetime
summary['mean_moving_pace_float'] = summary['mean_moving_pace'] / datetime.timedelta(minutes=1)
summary['pace_moving_all_mean_float'] = summary['pace_moving_all_mean'] / datetime.timedelta(minutes=1)
#replace NA values with 0
summary['day_diff'].fillna(0, inplace=True)

import matplotlib.pyplot as plt

plt.subplots(figsize=(8, 5))

plt.plot(summary.index, summary.mean_moving_pace_float, color='silver')
plt.plot(summary.pace_moving_all_mean_float, color='purple', linestyle='dashed', label='average')
plt.title("Pace Evolution")
plt.xlabel("Runnings")
plt.ylabel("Pace")
plt.legend()

<matplotlib.legend.Legend at 0x7fa8d8d28b50>

We can see some outliers at the running workouts at september 2020. Let’s do some filtering (excluding those with “unreasonable” paces that might be mislabelled)

summary_without_outliers = summary[summary['mean_moving_pace_float'].between(5,10)]

plt.plot(summary_without_outliers.index, summary_without_outliers.mean_moving_pace_float, color='silver')
plt.plot(summary_without_outliers.pace_moving_all_mean_float, color='purple', linestyle='dashed', label='average')
plt.title("Pace Evolution")
plt.xlabel("Runnings")
plt.xticks(rotation=90)
plt.ylabel("Pace")
plt.legend()

<matplotlib.legend.Legend at 0x7fa8d916ac10>

That looks much better now. Now we can see clearly that her pace kept floating between 6’50 min and 6’00 min over all this year. I also created a new variable at my summary dataframe called day_diff, which means the interval in day between consecutive workouts. This variable helped me to see if her pace grew up as the number of the days without running also increased.

#recompute the day_diff removing the outliers
summary_without_outliers['day_diff'] = summary_without_outliers.index.to_series().diff().astype('timedelta64[D]').astype('Int64')
summary_without_outliers['day_diff'].fillna(0, inplace=True)

fig,ax = plt.subplots(figsize=(8, 5))
ax.plot(summary_without_outliers.index, summary_without_outliers.mean_moving_pace_float, color='silver')
ax.set_xlabel('Runnings')
ax.set_ylabel('Pace',color='silver')
ax2=ax.twinx()
ax2.plot(summary_without_outliers.index, summary_without_outliers.day_diff.astype('int'),color='purple')
ax2.set_ylabel('Days Without Running',color='indigo', rotation=270)
plt.title('Pace vs Days without Running')
plt.show()

As we can see at the chart above, there isn’t a linear correlation between the number of the days without running and her growing pace. In her case it might be the period of rest helped her to a better recovery. Can we have a statistical evidence ?

The following code will create a regression plot of her interval of days vs pace. I will use Seaborn to create plot. As we can see there is negative correlation between the two variables, although, it’s not strong.

import seaborn as sns
sns.set(style="ticks", context="talk")
sns.regplot(x=summary_without_outliers.day_diff.astype('int'), y=summary_without_outliers.mean_moving_pace_float).set_title("Day Diff vs Pace")

Text(0.5, 1.0, 'Day Diff vs Pace')

Let’s explore now the relationship between the mean pace and the average speed. It is obvious that the bigger the average speed the pace is lower. More fast means less time to cover the distance.

plt.subplots(figsize=(8, 5))

plt.plot(summary_without_outliers.index, summary_without_outliers.mean_moving_pace_float, color='silver', label='Average Pace')
plt.plot(summary_without_outliers.mean_moving_speed, color='purple', label = 'Average Speed')
plt.title("Average Speed x Average Pace")
plt.xlabel("Runnings")
plt.legend()
plt.show()

The chart below illustrates the evolution of the total distance covered at her runnings workouts. There is a higher variance of her run distances. The average distance for most of the runs is close to 7km. The second chart illustrating the histogram of the run distances show that most of her usual weekday runs are around 4-7km and my longer weekend runs (she started to run longer distances above 7kms very recently).

plt.subplots(figsize=(8, 5))

plt.plot(summary_without_outliers.index, summary_without_outliers.total_distance / 1000, color='silver')
plt.plot(summary_without_outliers.distance_all_mean, color='purple', linestyle='dashed', label='average')
plt.title("Distance Evolution")
plt.xlabel("Runs")
plt.ylabel("distance")
plt.legend()


plt.show()

(summary_without_outliers['total_distance'] / 1000.0).hist(bins=30)

<AxesSubplot:>

Now let’s see how faster she was and if there is a distinction between longer and shorter runs related to the speed (slower vs faster). Let’s try plotting distance against speed to get a better idea.

plt.subplots(figsize=(8, 5))

plt.scatter(summary_without_outliers.total_distance/ 1000, summary_without_outliers.mean_moving_pace_float, color='purple', marker='s')
plt.title("Distance vs. pace")
plt.xlabel("Distance")
plt.ylabel("Pace")
plt.legend()

No handles with labels found to put in legend.

<matplotlib.legend.Legend at 0x7fa8da85bf40>

The distribution of her pace across the distance runs is very dispersed. It means a no clear trend there. But what if we restrict it just to this year ?

summary_without_outliers_2021 = summary_without_outliers[summary_without_outliers.index > '2021-01-01']

summary_without_outliers_2021

	moving_time	mean_speed	max_speed	mean_pace	max_pace	mean_moving_speed	mean_moving_pace	mean_cadence	max_cadence	mean_moving_cadence	...	mean_temperature	min_temperature	max_temperature	total_distance	ellapsed_time	day_diff	pace_moving_all_mean	distance_all_mean	mean_moving_pace_float	pace_moving_all_mean_float
start
2021-01-02 09:07:36.900	01:04:45	9.244853	68.900544	00:06:29	00:00:52	9.351208	00:06:24	NaN	NaN	NaN	...	NaN	NaN	NaN	10113.098986	01:05:38.100000	5	00:06:02.147058	7.23	6.400000	6.035784
2021-01-15 09:08:54.810	00:37:22.191000	10.048012	152.739973	00:05:58	00:00:23	10.106837	00:05:56	NaN	NaN	NaN	...	NaN	NaN	NaN	6305.660653	00:37:39.191000	13	00:06:02.147058	7.23	5.933333	6.035784
2021-01-17 09:41:25.270	00:38:41.730000	9.321227	23.778071	00:06:26	00:02:31	9.464527	00:06:20	NaN	NaN	NaN	...	NaN	NaN	NaN	6135.777736	00:39:29.731000	2	00:06:02.147058	7.23	6.333333	6.035784
2021-01-27 18:24:14.001	00:23:40	10.432233	20.445663	00:05:45	00:02:56	10.432233	00:05:45	NaN	NaN	NaN	...	NaN	NaN	NaN	4114.936468	00:23:40	10	00:06:02.147058	7.23	5.750000	6.035784
2021-01-31 09:18:46.676	00:53:54.324000	8.830295	49.801374	00:06:47	00:01:12	9.416889	00:06:22	NaN	NaN	NaN	...	NaN	NaN	NaN	8502.408803	00:57:46.325000	3	00:06:02.147058	7.23	6.366667	6.035784
2021-02-05 08:58:10.295	00:24:56.707000	9.690825	25.378717	00:06:11	00:02:21	9.690825	00:06:11	NaN	NaN	NaN	...	NaN	NaN	NaN	4028.979227	00:24:56.707000	4	00:06:02.147058	7.23	6.183333	6.035784
2021-02-07 09:15:40.107	01:19:07.893000	8.925130	17.529999	00:06:43	00:03:25	9.314387	00:06:26	NaN	NaN	NaN	...	NaN	NaN	NaN	12351.125311	01:23:01.894000	2	00:06:02.147058	7.23	6.433333	6.035784
2021-02-12 09:05:13.211	00:30:39.790000	10.035336	22.995251	00:05:58	00:02:36	10.035336	00:05:58	NaN	NaN	NaN	...	NaN	NaN	NaN	5128.586271	00:30:39.790000	4	00:06:02.147058	7.23	5.966667	6.035784
2021-02-19 09:12:26.076	00:45:10.925000	9.489594	54.967249	00:06:19	00:01:05	9.564430	00:06:16	NaN	NaN	NaN	...	NaN	NaN	NaN	7214.529809	00:45:36.925000	7	00:06:02.147058	7.23	6.266667	6.035784
2021-02-21 09:47:38.109	00:43:32.892000	9.840626	23.993080	00:06:05	00:02:30	9.840626	00:06:05	NaN	NaN	NaN	...	NaN	NaN	NaN	7142.359084	00:43:32.892000	2	00:06:02.147058	7.23	6.083333	6.035784
2021-03-14 10:06:16.609	00:32:01.391000	9.619669	256.195692	00:06:14	00:00:14	9.668954	00:06:12	NaN	NaN	NaN	...	NaN	NaN	NaN	5171.616917	00:32:15.391000	21	00:06:02.147058	7.23	6.200000	6.035784
2021-03-20 09:28:54.161	00:32:54.843000	8.040413	12.058690	00:07:27	00:04:58	9.370022	00:06:24	NaN	NaN	NaN	...	NaN	NaN	NaN	5214.743810	00:38:54.840000	5	00:06:02.147058	7.23	6.400000	6.035784
2021-03-21 09:47:35.279	00:47:52.721000	8.391221	12.847088	00:07:09	00:04:40	8.892325	00:06:44	NaN	NaN	NaN	...	NaN	NaN	NaN	7127.228493	00:50:57.722000	1	00:06:02.147058	7.23	6.733333	6.035784
2021-03-28 09:31:37.000	00:55:37.999000	8.365421	20.043178	00:07:10	00:02:59	9.415942	00:06:22	NaN	NaN	NaN	...	NaN	NaN	NaN	8862.698326	01:03:34	6	00:06:02.147058	7.23	6.366667	6.035784
2021-04-02 09:39:34.000	00:30:31	10.005436	37.905503	00:05:59	00:01:34	10.081349	00:05:57	NaN	NaN	NaN	...	NaN	NaN	NaN	5138.905933	00:30:49.001000	5	00:06:02.147058	7.23	5.950000	6.035784
2021-04-04 09:16:15.798	01:03:41.203000	9.525359	352.418093	00:06:17	00:00:10	9.939046	00:06:02	NaN	NaN	NaN	...	NaN	NaN	NaN	10623.961318	01:06:55.204000	1	00:06:02.147058	7.23	6.033333	6.035784
2021-04-09 09:39:12.563	00:19:11.437000	10.108209	33.004691	00:05:56	00:01:49	10.108209	00:05:56	NaN	NaN	NaN	...	NaN	NaN	NaN	3233.046143	00:19:11.437000	5	00:06:02.147058	7.23	5.933333	6.035784
2021-04-11 09:36:44.033	01:03:52.970000	8.931733	62.591395	00:06:43	00:00:57	9.801456	00:06:07	NaN	NaN	NaN	...	NaN	NaN	NaN	10544.330137	01:10:49.969000	1	00:06:02.147058	7.23	6.116667	6.035784
2021-04-21 09:05:33.139	00:36:30.861000	10.122163	20.957374	00:05:55	00:02:51	10.122163	00:05:55	NaN	NaN	NaN	...	NaN	NaN	NaN	6160.070149	00:36:30.861000	9	00:06:02.147058	7.23	5.916667	6.035784
2021-04-25 09:38:37.511	01:15:46.633000	9.651477	49.152245	00:06:13	00:01:13	10.000173	00:05:59	NaN	NaN	NaN	...	NaN	NaN	NaN	12719.815370	01:19:04.490000	4	00:06:02.147058	7.23	5.983333	6.035784
2021-05-02 09:39:39.002	00:28:44.998000	9.902854	13.701415	00:06:03	00:04:22	10.494083	00:05:43	NaN	NaN	NaN	...	NaN	NaN	NaN	5050.450029	00:30:35.998000	7	00:06:02.147058	7.23	5.716667	6.035784
2021-05-21 09:17:20.000	00:25:12	9.362450	17.238005	00:06:24	00:03:28	9.782870	00:06:07	NaN	NaN	NaN	...	NaN	NaN	NaN	4142.884173	00:26:33	18	00:06:02.147058	7.23	6.116667	6.035784
2021-05-28 19:37:03.392	00:43:42.408000	9.546533	32.766574	00:06:17	00:01:49	9.997542	00:06:00	NaN	NaN	NaN	...	NaN	NaN	NaN	7349.790670	00:46:11.608000	7	00:06:02.147058	7.23	6.000000	6.035784
2021-05-30 09:58:12.455	01:02:05.546000	9.230633	26.180357	00:06:30	00:02:17	9.762058	00:06:08	NaN	NaN	NaN	...	NaN	NaN	NaN	10147.404038	01:05:57.546000	1	00:06:02.147058	7.23	6.133333	6.035784
2021-06-06 09:33:11.000	00:50:08.002000	8.863385	24.451184	00:06:46	00:02:27	9.663379	00:06:12	NaN	NaN	NaN	...	NaN	NaN	NaN	8196.171447	00:55:29.001000	6	00:06:02.147058	7.23	6.200000	6.035784
2021-06-11 09:30:44.000	00:25:50	9.470173	21.739674	00:06:20	00:02:45	9.694837	00:06:11	NaN	NaN	NaN	...	NaN	NaN	NaN	4206.337676	00:26:39.001000	4	00:06:02.147058	7.23	6.183333	6.035784
2021-06-13 09:22:30.985	01:32:33.018000	9.406340	84.902240	00:06:22	00:00:42	10.119079	00:05:55	NaN	NaN	NaN	...	NaN	NaN	NaN	15706.017295	01:40:11.016000	1	00:06:02.147058	7.23	5.916667	6.035784
2021-06-20 09:16:55.163	00:59:44.512000	8.973504	21.837222	00:06:41	00:02:44	9.898030	00:06:03	NaN	NaN	NaN	...	NaN	NaN	NaN	9965.168311	01:06:37.837000	6	00:06:02.147058	7.23	6.050000	6.035784
2021-06-23 09:37:44.000	00:26:49.001000	9.006464	20.308834	00:06:39	00:02:57	9.248208	00:06:29	NaN	NaN	NaN	...	NaN	NaN	NaN	4165.492241	00:27:45.001000	3	00:06:02.147058	7.23	6.483333	6.035784
2021-06-27 09:50:08.664	00:31:42.336000	9.527376	117.842846	00:06:17	00:00:30	9.582671	00:06:15	NaN	NaN	NaN	...	NaN	NaN	NaN	5074.217061	00:31:57.336000	4	00:06:02.147058	7.23	6.250000	6.035784
2021-07-04 11:23:19.418	00:47:47.583000	9.368145	15.164352	00:06:24	00:03:57	10.284485	00:05:50	NaN	NaN	NaN	...	NaN	NaN	NaN	8248.084577	00:52:49.582000	7	00:06:02.147058	7.23	5.833333	6.035784

31 rows × 23 columns

plt.subplots(figsize=(8, 5))

plt.scatter(summary_without_outliers_2021.total_distance/ 1000, summary_without_outliers_2021.mean_moving_pace_float, color='purple', marker='s')
plt.title("Distance vs. pace")
plt.xlabel("Distance")
plt.ylabel("Pace")
plt.legend()

No handles with labels found to put in legend.

<matplotlib.legend.Legend at 0x7fa8dad6b0d0>

It still does not give us a clear trend, but it depicts that she really started to achieve better performance when she started to run longer distances (paces loweer then 6’20 when she started to run above 10km).

Finally, let’s see her performance evolution in pace over time in 5km, 10km and 15kms. For this analysis, I had to filter the session dataframe based on distances. In this tutorial, I will assume that the distances between 5km - 5.9km , 10km - 10.9km, 15km - 15.9km will be normalized as 5, 10 , 15km.

summary_without_outliers_5km = summary_without_outliers[summary_without_outliers['total_distance'].between(5000,5900)]
summary_without_outliers_10km = summary_without_outliers[summary_without_outliers['total_distance'].between(10000,10900)]
summary_without_outliers_15km = summary_without_outliers[summary_without_outliers['total_distance'].between(15000,15900)]

fig, axs = plt.subplots(3, sharex=True, figsize=(18, 16))
fig.suptitle('Average Moving Pace over time (5km, 10km, 15km)')

axs[0].plot(summary_without_outliers_5km.index, summary_without_outliers_5km.mean_moving_pace_float, marker='*')
axs[0].set_title('5km')

axs[1].plot(summary_without_outliers_10km.index, summary_without_outliers_10km.mean_moving_pace_float, marker='*')
axs[1].set_title('10km')


axs[2].plot(summary_without_outliers_15km.index, summary_without_outliers_15km.mean_moving_pace_float, marker='*')
axs[2].set_title('15km')

plt.xlabel('Date')

plt.show()

Her 5km got better until semptember 2020, but as she started to get greater distances, her performance got more slower. He 10km got better across 2021. Her 15km, since we only have one observation, there’s no insight/trend there.

Conclusions

• So, as expected, no major insight. However, always running help he to improve her performance, in order to maintain or lower her pace.

• She started to run greater distances with excelent paces, for instance the distance run of 15km with the moving pace 5’50.

• There is no trend about her run distance vs pace. We believe that we need to collect more data to might see any new insights.

In this tutorial, we showed the possibilities of using runpandas python package to perform several type of running analysis assisted by visualization and data handling packages such as Matplotlib and pandas. With the introduction of session feature, we now can analyse a group of activities and investigate new insights over time.