Notebook

Data Collection¶

In [3]:

import requests
import json
import pandas as pd
import numpy as np
from geopy import geocoders
from geopy.geocoders import Nominatim


r = requests.get('https://statsapi.web.nhl.com/api/v1/game/2019021017/feed/live')

def jprint(obj):
    # create a formatted string of the Python JSON object
    text = json.dumps(obj, sort_keys=True, indent=4)
    print(text)

def geo(location):
    g = geocoders.Nominatim(user_agent='MSGT_Lab5', timeout=None)
    try:
        loc = g.geocode(location)
        return loc.latitude, loc.longitude
    except:
        return 0,0

x = r.json()

df = pd.DataFrame(x['liveData']['plays']['allPlays'])
df

data = x['liveData']['plays']['allPlays']
data

results_df = pd.json_normalize(data)
results_df

coords = df.iloc[:,2]

events = df.iloc[:,0][3:]

coords_df = pd.json_normalize(coords)
coords_df


event_df = pd.json_normalize(events)

latitude = []
longitude = []

for i in coords:
    
    if coords_df['x'] is not None:
        
        lat = coords_df['y']
        lon = coords_df['x']
        for i in [lat]:
            latitude.append(i)
        for i in [lon]:
            longitude.append(i)

coordinates_df = pd.DataFrame(list(zip(results_df['result.event'],results_df['result.description'],lat,lon)), columns = ['Event','Description','Lat','Lon'])

coordinates_df = coordinates_df.dropna()

coordinates_df

coordinates_df.to_csv('events.csv', header = True, mode = 'a')

Mapbox & Plotly of Events¶

In [4]:

import plotly.graph_objects as go
import pandas as pd
import plotly.express as px
mapbox_access_token = open("mapbox_token.txt").read() # Create a local file with mapbox token


df = pd.read_csv('events.csv')
events_lat = df.Lat
events_lon = df.Lon
locations_name = df.Event

hits = df[df['Event'].str.contains('Hit')]
hits_lat = df[df['Event'].str.contains('Hit')]['Lat']
hits_lon = df[df['Event'].str.contains('Hit')]['Lon']
hits_text = df[df['Event'].str.contains('Hit')]['Event']

shots = df[df['Event'].str.contains('Shot')]
shots_lat = df[df['Event'].str.contains('Shot')]['Lat']
shots_lon = df[df['Event'].str.contains('Shot')]['Lon']
shots_text = df[df['Event'].str.contains('Shot')]['Event']

fig = go.Figure()

fig.add_trace(go.Scattermapbox(
        lat=events_lat,
        lon=events_lon,
        mode='markers',
        marker=go.scattermapbox.Marker(
            size=17,
            color='rgb(255, 0, 0)',
            opacity=0.7
        ),
        text=locations_name,
        hoverinfo='text',
        name="All Events"
    ))

fig.add_trace(go.Scattermapbox(
        lat=hits_lat,
        lon=hits_lon,
        mode='markers',
        marker=go.scattermapbox.Marker(
            size=10,
            color='rgb(255,255,0)',
            opacity=0.7
        ),
        text=hits_text,
        hoverinfo='text',
        name="Hits"
    ))

fig.add_trace(go.Scattermapbox(
        lat=shots_lat,
        lon=shots_lon,
        mode='markers',
        marker=go.scattermapbox.Marker(
            size=10,
            color='rgb(0, 0, 255)',
            opacity=0.7
        ),
        text=shots_text,
        hoverinfo='text',
        name="Shots"
    ))

fig.update_layout(
    title='Events',
    autosize=True,
    hovermode='closest',
    showlegend=True,
    legend_title='<b> Layers </b>',
    legend_orientation="h",
        legend=dict(
        x=.1,
        y=1.2,
        traceorder="normal",
        font=dict(
            family="sans-serif",
            size=12,
            color="black"
        ),
        bgcolor="LightSteelBlue",
        bordercolor="Black",
        borderwidth=4
    ),
    mapbox=dict(
        accesstoken=mapbox_access_token,
        bearing=0,
        center=dict(
            lat=0,
            lon=0
        ),
        pitch=0,
        zoom=1,
        style='mapbox://styles/isaacv/ck7aposk400za1ip6rwingtiz'
    ),
)

fig.show()

In [ ]:

There are a number of things I can do from here:
    
    -Can style each point based on their type of event
    -Can display a media item based on the event
    -Can group together the types of events and show them relative to time
    