fork-of-strava-data-retrieval-and-analysis (2).ipynb

{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import requests\nimport urllib3\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"auth_url = \"https://www.strava.com/oauth/token\"\nactivites_url = \"https://www.strava.com/api/v3/athlete/activities\"\n\n#replace variables with values for your account\npayload = {\n    'client_id': \"my_client_id\",\n    'client_secret': 'my_client_secret',\n    'refresh_token': 'my_refresh_token',\n    'grant_type': \"refresh_token\",\n    'f': 'json'\n}","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Requesting Token...\\n\")\nres = requests.post(auth_url, data=payload, verify=False)\naccess_token = res.json()['access_token']\nprint(\"Access Token = {}\\n\".format(access_token))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"header = {'Authorization': 'Bearer ' + access_token}\nparam = {'per_page': 200, 'page': 1}\nmy_dataset = requests.get(activites_url, headers=header, params=param).json()\n\nprint(my_dataset[0][\"name\"])\nprint(my_dataset[0][\"map\"][\"summary_polyline\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom pandas.io.json import json_normalize","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"activities = json_normalize(my_dataset)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Explore dataset\n\n#activities.columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Create new dataframe with only columns I care about\ncols = ['name', 'upload_id', 'type', 'distance', 'moving_time',   \n         'average_speed', 'max_speed','total_elevation_gain',\n         'start_date_local'\n       ]\n\nactivities = activities[cols]\n\n#Break date into start time and date\nactivities['start_date_local'] = pd.to_datetime(activities['start_date_local'])\nactivities['start_time'] = activities['start_date_local'].dt.time\nactivities['start_date_local'] = activities['start_date_local'].dt.date\n\nactivities.head(5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"activities['type'].value_counts()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#download data\n#activities.to_csv('activities.csv',index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#graphs","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import seaborn as sns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"runs = activities.loc[activities['type'] == 'Run'] \nruns.head(5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.set(style=\"ticks\", context=\"talk\")\nsns.scatterplot(x='distance', y = 'average_speed', data = runs).set_title(\"Average Speed vs Distance\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.scatterplot(x='distance', y = 'max_speed', data = runs).set_title(\"Max Speed vs Distance\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#max speed over time\nfig = plt.figure()\nax = fig.add_subplot(111)\nx = np.asarray(runs.start_date_local)\ny = np.asarray(runs.max_speed)\nax.plot_date(x, y)\nax.set_title('Max Speed over Time')\nfig.autofmt_xdate(rotation=45)\nfig.tight_layout()\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fig = plt.figure()\nax1 = fig.add_subplot(111)\nx = np.asarray(runs.start_date_local)\ny = np.asarray(runs.average_speed)\nax1.plot_date(x, y)\nax1.set_title('Average Speed over Time')\nfig.autofmt_xdate(rotation=45)\nfig.tight_layout()\nfig.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Bike rides faster in Rockport or DC?","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"rides = activities.loc[activities['type'] == 'Ride'] \nrides.head(5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from datetime import datetime","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#add home / dc classification 2020-06-27 is date switch\ndc_date= '2020-06-27'\ndc_date = datetime.strptime(dc_date, '%Y-%m-%d').date()\nrides['start_date_local'] = pd.to_datetime(rides['start_date_local']).dt.date\nrides['dc'] = np.where(rides['start_date_local'] < dc_date, 'true', 'false')\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dc = rides.loc[rides['dc'] == 'true']\nhome = rides.loc[rides['dc'] == 'false']\ndc_speed = round(dc['average_speed'].mean() * 2.237, 2)\nhome_speed = round(home['average_speed'].mean() * 2.237, 2)\ndc_max_speed = round(dc['max_speed'].mean()* 2.237, 2)\nhome_max_speed = round(home['max_speed'].mean()* 2.237, 2)\nprint(\"Average DC Speed: \" + str(dc_speed) + \" | Average DC Max Speed: \" + str(dc_max_speed) + '\\n'\n      + \"Average Home Speed: \" + str(home_speed) + \" | Average Home Max Speed: \" + str(home_max_speed))\n\n\n#time is in seconds, distance is in meters\n#m/s to mph 1m/s to 2.237 mph","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"percent_increase_average = round((home_speed - dc_speed) * 100 / dc_speed,2)\npercent_increase_average\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"percent_increase_average_max = round((home_max_speed - dc_max_speed) * 100 / dc_max_speed,2)\npercent_increase_average_max ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#elevation change vs average speed","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.scatterplot(x='total_elevation_gain', y = 'average_speed', data = rides).set_title(\"Speed vs Elevation Gain\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}