{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyze Your Runkeeper Fitness Data\n", "> Import, clean, and analyze seven years worth of training data tracked on the Runkeeper app. This is the Result of Project \"Analyze Your Runkeeper Fitness Data\", via datacamp.\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Datacamp, Data_Science, Time_Series_Analysis]\n", "- image: images/runner_in_blue.jpg" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "4" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 1. Obtain and review raw data\n", "

One day, my old running friend and I were chatting about our running styles, training habits, and achievements, when I suddenly realized that I could take an in-depth analytical look at my training. I have been using a popular GPS fitness tracker called Runkeeper for years and decided it was time to analyze my running data to see how I was doing.

\n", "

Since 2012, I've been using the Runkeeper app, and it's great. One key feature: its excellent data export. Anyone who has a smartphone can download the app and analyze their data like we will in this notebook.

\n", "

\"Runner

\n", "

After logging your run, the first step is to export the data from Runkeeper (which I've done already). Then import the data and start exploring to find potential problems. After that, create data cleaning strategies to fix the issues. Finally, analyze and visualize the clean time-series data.

\n", "

I exported seven years worth of my training data, from 2012 through 2018. The data is a CSV file where each row is a single training activity. Let's load and inspect it.

" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "dc": { "key": "4" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Activity IdTypeRoute NameDistance (km)DurationAverage PaceAverage Speed (km/h)Calories BurnedClimb (m)Average Heart Rate (bpm)Friend's TaggedNotesGPX File
Date
2018-01-14 12:48:41039df468-14d0-4fdd-8496-c9f06f5a5d47RunningNaN13.011:12:445:3510.73910.0180153.0NaNTomTom MySports Watch2018-01-14-124841.gpx
2017-05-28 16:42:07524ed1b8-7056-4455-be45-331b472c21f7RunningNaN21.111:58:505:3810.661477.0337143.0NaNTomTom MySports Watch2017-05-28-164207.gpx
2016-07-12 18:20:28d1b3933f-2b58-4158-b2e7-b2385682da43RunningNaN9.7150:455:1411.47696.052NaNNaNNaN2016-07-12-182028.gpx
\n", "
" ], "text/plain": [ " Activity Id Type Route Name \\\n", "Date \n", "2018-01-14 12:48:41 039df468-14d0-4fdd-8496-c9f06f5a5d47 Running NaN \n", "2017-05-28 16:42:07 524ed1b8-7056-4455-be45-331b472c21f7 Running NaN \n", "2016-07-12 18:20:28 d1b3933f-2b58-4158-b2e7-b2385682da43 Running NaN \n", "\n", " Distance (km) Duration Average Pace \\\n", "Date \n", "2018-01-14 12:48:41 13.01 1:12:44 5:35 \n", "2017-05-28 16:42:07 21.11 1:58:50 5:38 \n", "2016-07-12 18:20:28 9.71 50:45 5:14 \n", "\n", " Average Speed (km/h) Calories Burned Climb (m) \\\n", "Date \n", "2018-01-14 12:48:41 10.73 910.0 180 \n", "2017-05-28 16:42:07 10.66 1477.0 337 \n", "2016-07-12 18:20:28 11.47 696.0 52 \n", "\n", " Average Heart Rate (bpm) Friend's Tagged \\\n", "Date \n", "2018-01-14 12:48:41 153.0 NaN \n", "2017-05-28 16:42:07 143.0 NaN \n", "2016-07-12 18:20:28 NaN NaN \n", "\n", " Notes GPX File \n", "Date \n", "2018-01-14 12:48:41 TomTom MySports Watch 2018-01-14-124841.gpx \n", "2017-05-28 16:42:07 TomTom MySports Watch 2017-05-28-164207.gpx \n", "2016-07-12 18:20:28 NaN 2016-07-12-182028.gpx " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "DatetimeIndex: 508 entries, 2018-11-11 14:05:12 to 2012-08-22 18:53:54\n", "Data columns (total 13 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Activity Id 508 non-null object \n", " 1 Type 508 non-null object \n", " 2 Route Name 1 non-null object \n", " 3 Distance (km) 508 non-null float64\n", " 4 Duration 508 non-null object \n", " 5 Average Pace 508 non-null object \n", " 6 Average Speed (km/h) 508 non-null float64\n", " 7 Calories Burned 508 non-null float64\n", " 8 Climb (m) 508 non-null int64 \n", " 9 Average Heart Rate (bpm) 294 non-null float64\n", " 10 Friend's Tagged 0 non-null float64\n", " 11 Notes 231 non-null object \n", " 12 GPX File 504 non-null object \n", "dtypes: float64(5), int64(1), object(7)\n", "memory usage: 55.6+ KB\n" ] }, { "data": { "text/plain": [ "None" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import pandas\n", "import pandas as pd\n", "\n", "# Define file containing dataset\n", "runkeeper_file = 'dataset/cardioActivities.csv'\n", "\n", "# Create DataFrame with parse_dates and index_col parameters \n", "df_activities = pd.read_csv(runkeeper_file, parse_dates=True, index_col='Date')\n", "\n", "# First look at exported data: select sample of 3 random rows \n", "display(df_activities.sample(3))\n", "\n", "# Print DataFrame summary\n", "display(df_activities.info())" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "12" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 2. Data preprocessing\n", "

Lucky for us, the column names Runkeeper provides are informative, and we don't need to rename any columns.

\n", "

But, we do notice missing values using the info() method. What are the reasons for these missing values? It depends. Some heart rate information is missing because I didn't always use a cardio sensor. In the case of the Notes column, it is an optional field that I sometimes left blank. Also, I only used the Route Name column once, and never used the Friend's Tagged column.

\n", "

We'll fill in missing values in the heart rate column to avoid misleading results later, but right now, our first data preprocessing steps will be to:

\n", "" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "dc": { "key": "12" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "text/plain": [ "Running 459\n", "Cycling 29\n", "Walking 18\n", "Other 2\n", "Name: Type, dtype: int64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Type 0\n", "Distance (km) 0\n", "Duration 0\n", "Average Pace 0\n", "Average Speed (km/h) 0\n", "Climb (m) 0\n", "Average Heart Rate (bpm) 214\n", "dtype: int64" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define list of columns to be deleted\n", "cols_to_drop = ['Friend\\'s Tagged','Route Name','GPX File','Activity Id','Calories Burned', 'Notes']\n", "\n", "# Delete unnecessary columns\n", "df_activities.drop(columns=cols_to_drop, inplace=True)\n", "\n", "# Count types of training activities\n", "display(df_activities['Type'].value_counts())\n", "\n", "# Rename 'Other' type to 'Unicycling'\n", "df_activities['Type'] = df_activities['Type'].str.replace('Other', 'Unicycling')\n", "\n", "# Count missing values for each column\n", "df_activities.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "19" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 3. Dealing with missing values\n", "

As we can see from the last output, there are 214 missing entries for my average heart rate.

\n", "

We can't go back in time to get those data, but we can fill in the missing values with an average value. This process is called mean imputation. When imputing the mean to fill in missing data, we need to consider that the average heart rate varies for different activities (e.g., walking vs. running). We'll filter the DataFrames by activity type (Type) and calculate each activity's mean heart rate, then fill in the missing values with those means.

" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "dc": { "key": "19" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "text/plain": [ "Type 0\n", "Distance (km) 0\n", "Duration 0\n", "Average Pace 0\n", "Average Speed (km/h) 0\n", "Climb (m) 0\n", "Average Heart Rate (bpm) 0\n", "dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calculate sample means for heart rate for each training activity type \n", "avg_hr_run = df_activities[df_activities['Type'] == 'Running']['Average Heart Rate (bpm)'].mean()\n", "avg_hr_cycle = df_activities[df_activities['Type'] == 'Cycling']['Average Heart Rate (bpm)'].mean()\n", "\n", "# Split whole DataFrame into several, specific for different activities\n", "df_run = df_activities[df_activities['Type'] == 'Running'].copy()\n", "df_walk = df_activities[df_activities['Type'] == 'Walking'].copy()\n", "df_cycle = df_activities[df_activities['Type'] == 'Cycling'].copy()\n", "\n", "# Filling missing values with counted means \n", "df_walk['Average Heart Rate (bpm)'].fillna(110, inplace=True)\n", "df_run['Average Heart Rate (bpm)'].fillna(int(avg_hr_run), inplace=True)\n", "df_cycle['Average Heart Rate (bpm)'].fillna(int(avg_hr_cycle), inplace=True)\n", "\n", "# Count missing values for each column in running data\n", "df_run.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "26" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 4. Plot running data\n", "

Now we can create our first plot! As we found earlier, most of the activities in my data were running (459 of them to be exact). There are only 29, 18, and two instances for cycling, walking, and unicycling, respectively. So for now, let's focus on plotting the different running metrics.

\n", "

An excellent first visualization is a figure with four subplots, one for each running metric (each numerical column). Each subplot will have a different y-axis, which is explained in each legend. The x-axis, Date, is shared among all subplots.

" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "dc": { "key": "26" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TypeDistance (km)DurationAverage PaceAverage Speed (km/h)Climb (m)Average Heart Rate (bpm)
Date
2018-11-11 14:05:12Running10.4458:405:3710.68130159.0
2018-11-09 15:02:35Running12.841:14:125:4710.39168159.0
2018-11-04 16:05:00Running13.011:15:165:4710.37171155.0
2018-11-01 14:03:58Running12.981:14:255:4410.47169158.0
2018-10-27 17:01:36Running13.021:12:505:3610.73170154.0
........................
2012-09-08 08:35:02Running3.2715:554:5212.3215144.0
2012-09-04 19:12:17Running6.2632:355:1211.5334144.0
2012-09-02 08:41:31Running3.1416:165:1111.5618144.0
2012-08-24 08:13:12Running3.1516:005:0511.8217144.0
2012-08-22 18:53:54Running5.6931:085:2910.9532144.0
\n", "

459 rows × 7 columns

\n", "
" ], "text/plain": [ " Type Distance (km) Duration Average Pace \\\n", "Date \n", "2018-11-11 14:05:12 Running 10.44 58:40 5:37 \n", "2018-11-09 15:02:35 Running 12.84 1:14:12 5:47 \n", "2018-11-04 16:05:00 Running 13.01 1:15:16 5:47 \n", "2018-11-01 14:03:58 Running 12.98 1:14:25 5:44 \n", "2018-10-27 17:01:36 Running 13.02 1:12:50 5:36 \n", "... ... ... ... ... \n", "2012-09-08 08:35:02 Running 3.27 15:55 4:52 \n", "2012-09-04 19:12:17 Running 6.26 32:35 5:12 \n", "2012-09-02 08:41:31 Running 3.14 16:16 5:11 \n", "2012-08-24 08:13:12 Running 3.15 16:00 5:05 \n", "2012-08-22 18:53:54 Running 5.69 31:08 5:29 \n", "\n", " Average Speed (km/h) Climb (m) Average Heart Rate (bpm) \n", "Date \n", "2018-11-11 14:05:12 10.68 130 159.0 \n", "2018-11-09 15:02:35 10.39 168 159.0 \n", "2018-11-04 16:05:00 10.37 171 155.0 \n", "2018-11-01 14:03:58 10.47 169 158.0 \n", "2018-10-27 17:01:36 10.73 170 154.0 \n", "... ... ... ... \n", "2012-09-08 08:35:02 12.32 15 144.0 \n", "2012-09-04 19:12:17 11.53 34 144.0 \n", "2012-09-02 08:41:31 11.56 18 144.0 \n", "2012-08-24 08:13:12 11.82 17 144.0 \n", "2012-08-22 18:53:54 10.95 32 144.0 \n", "\n", "[459 rows x 7 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_run" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "dc": { "key": "26" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "# Import matplotlib, set style and ignore warning\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import warnings\n", "plt.style.use('ggplot')\n", "warnings.filterwarnings(\n", " action='ignore', module='matplotlib.figure', category=UserWarning,\n", " message=('This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.')\n", ")\n", "\n", "# Prepare data subsetting period from 2013 till 2018\n", "runs_subset_2013_2018 = df_run['2018':'2013']\n", "\n", "# Create, plot and customize in one step\n", "runs_subset_2013_2018.plot(subplots=True,\n", " sharex=False,\n", " figsize=(12,16),\n", " linestyle='none',\n", " marker='o',\n", " markersize=3,\n", " )\n", "\n", "# Show plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "33" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 5. Running statistics\n", "

No doubt, running helps people stay mentally and physically healthy and productive at any age. And it is great fun! When runners talk to each other about their hobby, we not only discuss our results, but we also discuss different training strategies.

\n", "

You'll know you're with a group of runners if you commonly hear questions like:

\n", "\n", "

Let's find the answers to these questions in my data. If you look back at plots in Task 4, you can see the answer to, Do you measure your heart rate? Before 2015: no. To look at the averages, let's only use the data from 2015 through 2018.

\n", "

In pandas, the resample() method is similar to the groupby() method - with resample() you group by a specific time span. We'll use resample() to group the time series data by a sampling period and apply several methods to each sampling period. In our case, we'll resample annually and weekly.

" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "dc": { "key": "33" }, "tags": [ "sample_code" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "How my average run looks in last 4 years:\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Distance (km)Average Speed (km/h)Climb (m)Average Heart Rate (bpm)
Date
2015-12-3113.60280510.998902160.170732143.353659
2016-12-3111.41166710.837778133.194444143.388889
2017-12-3112.93517610.959059169.376471145.247059
2018-12-3113.33906310.777969191.218750148.125000
\n", "
" ], "text/plain": [ " Distance (km) Average Speed (km/h) Climb (m) \\\n", "Date \n", "2015-12-31 13.602805 10.998902 160.170732 \n", "2016-12-31 11.411667 10.837778 133.194444 \n", "2017-12-31 12.935176 10.959059 169.376471 \n", "2018-12-31 13.339063 10.777969 191.218750 \n", "\n", " Average Heart Rate (bpm) \n", "Date \n", "2015-12-31 143.353659 \n", "2016-12-31 143.388889 \n", "2017-12-31 145.247059 \n", "2018-12-31 148.125000 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Weekly averages of last 4 years:\n" ] }, { "data": { "text/plain": [ "Distance (km) 12.518176\n", "Average Speed (km/h) 10.835473\n", "Climb (m) 158.325444\n", "Average Heart Rate (bpm) 144.801775\n", "dtype: float64" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "How many trainings per week I had on average: 1.5\n" ] } ], "source": [ "# Prepare running data for the last 4 years\n", "runs_subset_2015_2018 = df_run['2018':'2015']\n", "\n", "# Calculate annual statistics\n", "print('How my average run looks in last 4 years:')\n", "display(runs_subset_2015_2018.resample('A').mean())\n", "\n", "# Calculate weekly statistics\n", "print('Weekly averages of last 4 years:')\n", "display(runs_subset_2015_2018.resample('W').mean().mean())\n", "\n", "# Mean weekly counts\n", "weekly_counts_average = runs_subset_2015_2018['Distance (km)'].resample('W').count().mean()\n", "print('How many trainings per week I had on average:', weekly_counts_average)" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "40" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 6. Visualization with averages\n", "

Let's plot the long term averages of my distance run and my heart rate with their raw data to visually compare the averages to each training session. Again, we'll use the data from 2015 through 2018.

\n", "

In this task, we will use matplotlib functionality for plot creation and customization.

" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "dc": { "key": "40" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Prepare data\n", "runs_subset_2015_2018 = df_run['2018':'2015']\n", "runs_distance = runs_subset_2015_2018['Distance (km)']\n", "runs_hr = runs_subset_2015_2018['Average Heart Rate (bpm)']\n", "\n", "# Create plot\n", "fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12, 8))\n", "\n", "# Plot and customize first subplot\n", "runs_distance.plot(ax=ax1)\n", "ax1.set(ylabel='Distance (km)', title='Historical data with averages')\n", "ax1.axhline(runs_distance.mean(), color='blue', linewidth=1, linestyle='-.')\n", "\n", "# Plot and customize second subplot\n", "runs_hr.plot(ax=ax2, color='gray')\n", "ax2.set(xlabel='Date', ylabel='Average Heart Rate (bpm)')\n", "ax2.axhline(runs_hr.mean(), color='blue', linewidth=1, linestyle='-.')\n", "\n", "# Show plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "47" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 7. Did I reach my goals?\n", "

To motivate myself to run regularly, I set a target goal of running 1000 km per year. Let's visualize my annual running distance (km) from 2013 through 2018 to see if I reached my goal each year. Only stars in the green region indicate success.

" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "dc": { "key": "47" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Prepare data\n", "df_run_dist_annual = df_run['2018':'2013']['Distance (km)'].resample('A').sum()\n", "\n", "# Create plot\n", "fig = plt.figure(figsize=(8, 5))\n", "\n", "# Plot and customize\n", "ax = df_run_dist_annual.plot(marker='*', markersize=14, linewidth=0, color='blue')\n", "ax.set(ylim=[0, 1210], \n", " xlim=['2012','2019'],\n", " ylabel='Distance (km)',\n", " xlabel='Years',\n", " title='Annual totals for distance')\n", "\n", "ax.axhspan(1000, 1210, color='green', alpha=0.4)\n", "ax.axhspan(800, 1000, color='yellow', alpha=0.3)\n", "ax.axhspan(0, 800, color='red', alpha=0.2)\n", "\n", "# Show plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "54" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 8. Am I progressing?\n", "

Let's dive a little deeper into the data to answer a tricky question: am I progressing in terms of my running skills?

\n", "

To answer this question, we'll decompose my weekly distance run and visually compare it to the raw data. A red trend line will represent the weekly distance run.

\n", "

We are going to use statsmodels library to decompose the weekly trend.

" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "dc": { "key": "54" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import required library\n", "import statsmodels.api as sm\n", "\n", "# Prepare data\n", "df_run_dist_wkly = df_run['2018':'2013']['Distance (km)'].resample('W').bfill()\n", "decomposed = sm.tsa.seasonal_decompose(df_run_dist_wkly, extrapolate_trend=1, period=52)\n", "\n", "# Create plot\n", "fig = plt.figure(figsize=(12, 5))\n", "\n", "# Plot and customize\n", "ax = decomposed.trend.plot(label='Trend', linewidth=2)\n", "ax = decomposed.observed.plot(label='Observed', linewidth=0.5)\n", "\n", "ax.legend()\n", "ax.set_title('Running distance trend')\n", "\n", "# Show plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "61" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 9. Training intensity\n", "

Heart rate is a popular metric used to measure training intensity. Depending on age and fitness level, heart rates are grouped into different zones that people can target depending on training goals. A target heart rate during moderate-intensity activities is about 50-70% of maximum heart rate, while during vigorous physical activity it’s about 70-85% of maximum.

\n", "

We'll create a distribution plot of my heart rate data by training intensity. It will be a visual presentation for the number of activities from predefined training zones.

" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "dc": { "key": "61" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Prepare data\n", "hr_zones = [100, 125, 133, 142, 151, 173]\n", "zone_names = ['Easy', 'Moderate', 'Hard', 'Very hard', 'Maximal']\n", "zone_colors = ['green', 'yellow', 'orange', 'tomato', 'red']\n", "df_run_hr_all = df_run['2018':'2015-03']['Average Heart Rate (bpm)']\n", "\n", "# Create plot\n", "fig, ax = plt.subplots(figsize=(8, 5))\n", "\n", "# Plot and customize\n", "n, bins, patches = ax.hist(df_run_hr_all, bins=hr_zones, alpha=0.5)\n", "for i in range(0, len(patches)):\n", " patches[i].set_facecolor(zone_colors[i])\n", "\n", "ax.set(title='Distribution of HR', ylabel='Number of runs')\n", "ax.xaxis.set(ticks=hr_zones)\n", "ax.set_xticklabels(labels=zone_names, rotation=-30, ha='left')\n", "\n", "# Show plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "68" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 10. Detailed summary report\n", "

With all this data cleaning, analysis, and visualization, let's create detailed summary tables of my training.

\n", "

To do this, we'll create two tables. The first table will be a summary of the distance (km) and climb (m) variables for each training activity. The second table will list the summary statistics for the average speed (km/hr), climb (m), and distance (km) variables for each training activity.

" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "dc": { "key": "68" }, "tags": [ "sample_code" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Totals for different training types:\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Distance (km)Climb (m)
Type
Cycling680.586976
Running5224.5057278
Walking33.45349
\n", "
" ], "text/plain": [ " Distance (km) Climb (m)\n", "Type \n", "Cycling 680.58 6976\n", "Running 5224.50 57278\n", "Walking 33.45 349" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Summary statistics for different training types:\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Average Speed (km/h)Climb (m)Distance (km)
Type
Cycling25%16.980000139.00000015.530000
50%19.500000199.00000020.300000
75%21.490000318.00000029.400000
count29.00000029.00000029.000000
max24.330000553.00000049.180000
mean19.125172240.55172423.468276
min11.38000058.00000011.410000
std3.257100128.9602899.451040
totalNaN6976.000000680.580000
Running25%10.49500054.0000007.415000
50%10.98000091.00000010.810000
75%11.520000171.00000013.190000
count459.000000459.000000459.000000
max20.720000982.00000038.320000
mean11.056296124.78867111.382353
min5.7700000.0000000.760000
std0.953273103.3821774.937853
totalNaN57278.0000005224.500000
Walking25%5.5550007.0000001.385000
50%5.97000010.0000001.485000
75%6.51250015.5000001.787500
count18.00000018.00000018.000000
max6.910000112.0000004.290000
mean5.54944419.3888891.858333
min1.0400005.0000001.220000
std1.45930927.1101000.880055
totalNaN349.00000033.450000
\n", "
" ], "text/plain": [ " Average Speed (km/h) Climb (m) Distance (km)\n", "Type \n", "Cycling 25% 16.980000 139.000000 15.530000\n", " 50% 19.500000 199.000000 20.300000\n", " 75% 21.490000 318.000000 29.400000\n", " count 29.000000 29.000000 29.000000\n", " max 24.330000 553.000000 49.180000\n", " mean 19.125172 240.551724 23.468276\n", " min 11.380000 58.000000 11.410000\n", " std 3.257100 128.960289 9.451040\n", " total NaN 6976.000000 680.580000\n", "Running 25% 10.495000 54.000000 7.415000\n", " 50% 10.980000 91.000000 10.810000\n", " 75% 11.520000 171.000000 13.190000\n", " count 459.000000 459.000000 459.000000\n", " max 20.720000 982.000000 38.320000\n", " mean 11.056296 124.788671 11.382353\n", " min 5.770000 0.000000 0.760000\n", " std 0.953273 103.382177 4.937853\n", " total NaN 57278.000000 5224.500000\n", "Walking 25% 5.555000 7.000000 1.385000\n", " 50% 5.970000 10.000000 1.485000\n", " 75% 6.512500 15.500000 1.787500\n", " count 18.000000 18.000000 18.000000\n", " max 6.910000 112.000000 4.290000\n", " mean 5.549444 19.388889 1.858333\n", " min 1.040000 5.000000 1.220000\n", " std 1.459309 27.110100 0.880055\n", " total NaN 349.000000 33.450000" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Concatenating three DataFrames\n", "df_run_walk_cycle = df_run.append([df_walk, df_cycle]).sort_index(ascending=False)\n", "\n", "dist_climb_cols, speed_col = ['Distance (km)', 'Climb (m)'], ['Average Speed (km/h)']\n", "\n", "# Calculating total distance and climb in each type of activities\n", "df_totals = df_run_walk_cycle.groupby('Type')[dist_climb_cols].sum()\n", "\n", "print('Totals for different training types:')\n", "display(df_totals)\n", "\n", "# Calculating summary statistics for each type of activities \n", "df_summary = df_run_walk_cycle.groupby('Type')[dist_climb_cols + speed_col].describe()\n", "\n", "# Combine totals with summary\n", "for i in dist_climb_cols:\n", " df_summary[i, 'total'] = df_totals[i]\n", "\n", "print('Summary statistics for different training types:')\n", "df_summary.stack()" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "75" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 11. Fun facts\n", "

To wrap up, let’s pick some fun facts out of the summary tables and solve the last exercise.

\n", "

These data (my running history) represent 6 years, 2 months and 21 days. And I remember how many running shoes I went through–7.

\n", "
FUN FACTS\n",
    "- Average distance: 11.38 km\n",
    "- Longest distance: 38.32 km\n",
    "- Highest climb: 982 m\n",
    "- Total climb: 57,278 m\n",
    "- Total number of km run: 5,224 km\n",
    "- Total runs: 459\n",
    "- Number of running shoes gone through: 7 pairs\n",
    "
\n", "

The story of Forrest Gump is well known–the man, who for no particular reason decided to go for a \"little run.\" His epic run duration was 3 years, 2 months and 14 days (1169 days). In the picture you can see Forrest’s route of 24,700 km.

\n", "
FORREST RUN FACTS\n",
    "- Average distance: 21.13 km\n",
    "- Total number of km run: 24,700 km\n",
    "- Total runs: 1169\n",
    "- Number of running shoes gone through: ...\n",
    "
\n", "

Assuming Forest and I go through running shoes at the same rate, figure out how many pairs of shoes Forrest needed for his run.

\n", "

\"Forrest's

" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "dc": { "key": "75" }, "tags": [ "sample_code" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forrest Gump would need 33 pairs of shoes!\n" ] } ], "source": [ "# Count average shoes per lifetime (as km per pair) using our fun facts\n", "average_shoes_lifetime = 5224 / 7\n", "\n", "# Count number of shoes for Forrest's run distance\n", "shoes_for_forrest_run = int(24700 / average_shoes_lifetime)\n", "\n", "print('Forrest Gump would need {} pairs of shoes!'.format(shoes_for_forrest_run))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }