Tutorial: Interactive Maps with Python and Folium, Part 1¶

Basic Maps and Circle Markers¶

Using data from NYC CitiBike program¶

In [1]:

import pandas as pd
import folium

To test that everything is working, let’s pull up a map of New York City and add a circle marker.

In [2]:

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[2]:

Showing some real data, NYC bike trips¶

Next, we’ll load some data. The NYC bike share program makes its data public, you can download it here: https://www.citibikenyc.com/system-data. Just one month of data will do for this example.

We’ll use pandas to load the data into python, we’ll convert time strings into DateTime objects

In [3]:

bike_data = pd.read_csv("201610-citibike-tripdata.csv")
bike_data["Start Time"] = pd.to_datetime(bike_data["Start Time"])
bike_data["Stop Time"] = pd.to_datetime(bike_data["Stop Time"])
bike_data["hour"] = bike_data["Start Time"].map(lambda x: x.hour)

In [4]:

bike_data.head()

Out[4]:

	Trip Duration	Start Time	Stop Time	Start Station ID	Start Station Name	Start Station Latitude	Start Station Longitude	End Station ID	End Station Name	End Station Latitude	End Station Longitude	Bike ID	User Type	Birth Year	Gender
0	328	2016-10-01 00:00:07	2016-10-01 00:05:35	471	Grand St & Havemeyer St	40.712868	-73.956981	3077	Stagg St & Union Ave	40.708771	-73.950953	25254	Subscriber	1992.0	1
1	398	2016-10-01 00:00:11	2016-10-01 00:06:49	3147	E 85 St & 3 Ave	40.778012	-73.954071	3140	1 Ave & E 78 St	40.771404	-73.953517	17810	Subscriber	1988.0	2
2	430	2016-10-01 00:00:14	2016-10-01 00:07:25	345	W 13 St & 6 Ave	40.736494	-73.997044	470	W 20 St & 8 Ave	40.743453	-74.000040	20940	Subscriber	1965.0	1
3	351	2016-10-01 00:00:21	2016-10-01 00:06:12	3307	West End Ave & W 94 St	40.794165	-73.974124	3357	W 106 St & Amsterdam Ave	40.800836	-73.966449	19086	Subscriber	1993.0	1
4	2693	2016-10-01 00:00:21	2016-10-01 00:45:15	3428	8 Ave & W 16 St	40.740983	-74.001702	3323	W 106 St & Central Park West	40.798186	-73.960591	26502	Subscriber	1991.0	1

pre-processing data¶

We'll write a function that does the following:

generate a DataFrame containing locations of stations
generates a DataFrame containing the number of trips originating at each station.
generates a DataFrame containing the number of trips arriving at each station.
join the three dataframes into one.

In [5]:

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[5]:

	Departure Count	Start Station Latitude	Start Station Longitude	Start Station Name	Arrival Count
Start Station ID
72	42	40.767272	-73.993929	W 52 St & 11 Ave	96.0
79	29	40.719116	-74.006667	Franklin St & W Broadway	79.0
82	31	40.711174	-74.000165	St James Pl & Pearl St	13.0
83	32	40.683826	-73.976323	Atlantic Ave & Fort Greene Pl	35.0
116	64	40.741776	-74.001497	W 17 St & 8 Ave	85.0

Now we'll write a function that generates a new folium map and adds circle markers for each station.

In [6]:

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

Showing Real data¶

We'll make 2 maps to show the different patterns for bike migration at 9 am and 6pm.

In [7]:

# plot net departures at 9AM

trip_counts = get_trip_counts_by_hour(9)
plot_station_counts(trip_counts)

Out[7]:

In [8]:

# plot net departures at 6PM

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

Out[8]:

In [9]:

folium_map.save("part_1.html")