In [23]:

# CrossCompute
'''https://nbviewer.jupyter.org/github/vincentropy/python_cartography_tutorial/blob/master/part1_basic_folium_maps.ipynb'''

Out[23]:

'https://nbviewer.jupyter.org/github/vincentropy/python_cartography_tutorial/blob/master/part1_basic_folium_maps.ipynb'

In [24]:

import pandas as pd
import folium

In [25]:

folium_map = folium.Map(location=[40.738, -73.98],
                        zoom_start=13,
                        tiles="CartoDB dark_matter")

folium.CircleMarker(location=[40.738, -73.98],fill=True).add_to(folium_map)
folium_map

Out[25]:

In [26]:

bike_data = pd.read_csv('201306-citibike-tripdata - Copy.csv', na_values = 'n/a')

bike_data["starttime"] = pd.to_datetime(bike_data["starttime"])
bike_data["stoptime"] = pd.to_datetime(bike_data["stoptime"])
bike_data["hour"] = bike_data["starttime"].map(lambda x: x.hour)

In [27]:

bike_data.head()

Out[27]:

	tripduration	starttime	stoptime	start station id	start station name	start station latitude	start station longitude	end station id	end station name	end station latitude	end station longitude	bikeid	usertype	birth year	gender
0	695	2013-06-01 00:00:00	2013-06-01 00:11:00	444	Broadway & W 24 St	40.742354	-73.989151	434.0	9 Ave & W 18 St	40.743174	-74.003664	19678	Subscriber	1983.0	1
1	693	2013-06-01 00:00:00	2013-06-01 00:11:00	444	Broadway & W 24 St	40.742354	-73.989151	434.0	9 Ave & W 18 St	40.743174	-74.003664	16649	Subscriber	1984.0	1
2	2059	2013-06-01 00:00:00	2013-06-01 00:35:00	406	Hicks St & Montague St	40.695128	-73.995951	406.0	Hicks St & Montague St	40.695128	-73.995951	19599	Customer	NaN	0
3	123	2013-06-01 00:01:00	2013-06-01 00:03:00	475	E 15 St & Irving Pl	40.735243	-73.987586	262.0	Washington Park	40.691782	-73.973730	16352	Subscriber	1960.0	1
4	1521	2013-06-01 00:01:00	2013-06-01 00:26:00	2008	Little West St & 1 Pl	40.705693	-74.016777	310.0	State St & Smith St	40.689269	-73.989129	15567	Subscriber	1983.0	1

In [28]:

def get_trip_counts_by_hour(selected_hour):
    # make a DataFrame with locations for each bike station
    locations = bike_data.groupby("start station id").first()
    locations = locations.loc[:, ["start station latitude",
                                 "start station longitude",
                                 "start station name"]]
    
    #select one time of day
    subset = bike_data[bike_data["hour"]==selected_hour]
    
    # count trips for each destination
    departure_counts =  subset.groupby("start station id").count()
    departure_counts = departure_counts.iloc[:,[0]]
    departure_counts.columns= ["departure Count"]
    
    # count trips for each origin
    arrival_counts =  subset.groupby("end station id").count().iloc[:,[0]]
    arrival_counts.columns= ["arrival count"]

    #join departure counts, arrival counts, and locations
    trip_counts = departure_counts.join(locations).join(arrival_counts)
    return trip_counts

# print a sample to check our code works
get_trip_counts_by_hour(6).head()

Out[28]:

	departure Count	start station latitude	start station longitude	start station name	arrival count
start station id
116	1	40.741776	-74.001497	W 17 St & 8 Ave	NaN
128	1	40.727103	-74.002971	MacDougal St & Prince St	NaN
173	1	40.760683	-73.984527	Broadway & W 49 St	NaN
233	1	40.694757	-73.990527	Cadman Plaza W & Pierrepont St	NaN
285	2	40.734546	-73.990741	Broadway & E 14 St	1.0

In [29]:

def plot_station_counts(trip_counts):
    # generate a new map
    folium_map = folium.Map(location=[40.738, -73.98],
                            zoom_start=13,
                            tiles="CartoDB dark_matter",
                            width='50%')

    # for each row in the data, add a cicle marker
    for index, row in trip_counts.iterrows():
        # calculate net departures
        net_departures = (row["departure count"]-row["arrival count"])
        
        # generate the popup message that is shown on click.
        popup_text = "{}<br> total departures: {}<br> total arrivals: {}<br> net departures: {}"
        popup_text = popup_text.format(row["start station name"],
                          row["arrival count"],
                          row["departure count"],
                          net_departures)
        
        # radius of circles
        radius = net_departures/20
        
        # choose the color of the marker
        if net_departures>0:
            # color="#FFCE00" # orange
            # color="#007849" # green
            color="#E37222" # tangerine
        else:
            # color="#0375B4" # blue
            # color="#FFCE00" # yellow            
            color="#0A8A9F" # teal
        
        # add marker to the map
        folium.CircleMarker(location=(row["start station latitude"],
                                      row["start station longitude"]),
                            radius=radius,
                            color=color,
                            popup=popup_text,
                            fill=True).add_to(folium_map)
    return folium_map

In [30]:

# plot net departures at 9AM

trip_counts = get_trip_counts_by_hour(9)
plot_station_counts(trip_counts)

Out[30]:

In [31]:

# plot net departures at 6PM

trip_counts = get_trip_counts_by_hour(18)
folium_map = plot_station_counts(trip_counts)
folium_map

Out[31]:

In [32]:

folium_map.save("part_1.html")

In [ ]:

Pay Notebook Creator: Haige Cui	0
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