Visualization of the Titanic's Voyage¶

Data Preparation

1.1 Travellers Survival Rates by the Port of Embarkation
Ports of Embarkation on Map

2.1 Spatial DataFrame (Pandas GeoDataFrame)

2.2 Markers on Map
The "Titanic's site" Marker
The New York City Marker
Connecting Markers on Map
Pie-chart Markers on Map

6.1 Travellers Survival Rates, Now with Pie

6.2 Spatial Pies

6.3 Adjusting the Map Zoom Level and Position

In [1]:

import pandas as pd

from lets_plot import *

In [2]:

LetsPlot.setup_html()

1. Data Preparation¶

The Titanic dataset is available at kaggle : "Titanic: cleaned data" dataset (train_clean.csv).

In [3]:

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/titanic.csv")
df.head(3)

Out[3]:

	Age	Cabin	Embarked	Fare	Name	PassengerId	Pclass	Sex	SibSp	Survived	Ticket	Title	Family_Size
0	22.0	NaN	S	7.2500	Braund, Mr. Owen Harris	1	3	male	1	0.0	A/5 21171	Mr	1
1	38.0	C85	C	71.2833	Cumings, Mrs. John Bradley (Florence Briggs Th...	2	1	female	1	1.0	PC 17599	Mrs	1
2	26.0	NaN	S	7.9250	Heikkinen, Miss. Laina	3	3	female	0	1.0	STON/O2. 3101282	Miss	0

In this Titanic dataset the column Embarkedcontains a single-letter codes of the Titanic's ports of embarkation:

S: Southampton (UK)
C: Cherbourg (France)
Q: Cobh (Ireland)

Let's add new colum "Port" to the data:

In [4]:

def to_port_name (row):
    if row['Embarked'] == 'S' :
        return 'Southampton'
    if row['Embarked'] == 'C' :
        return 'Cherbourg'
    if row['Embarked'] == 'Q' :
        return 'Cobh'
    return 'Other'

df['Port']=df.apply (lambda row: to_port_name(row), axis=1)
df.head(3)

Out[4]:

	Age	Cabin	Embarked	Fare	Name	PassengerId	Pclass	Sex	SibSp	Survived	Ticket	Title	Family_Size	Port
0	22.0	NaN	S	7.2500	Braund, Mr. Owen Harris	1	3	male	1	0.0	A/5 21171	Mr	1	Southampton
1	38.0	C85	C	71.2833	Cumings, Mrs. John Bradley (Florence Briggs Th...	2	1	female	1	1.0	PC 17599	Mrs	1	Cherbourg
2	26.0	NaN	S	7.9250	Heikkinen, Miss. Laina	3	3	female	0	1.0	STON/O2. 3101282	Miss	0	Southampton

1.1 Travellers Survival Rates by the Port of Embarkation¶

In [5]:

c_surv="#E1A439"
c_lost="#6B9993"

bars = (ggplot(df) +
        geom_bar(
            aes('Port', fill=as_discrete('Survived')), 
            tooltips=layer_tooltips()
                .line('@{..count..} (@{..prop..})')
                .format('@{..prop..}', '.0%'),
            position='dodge') +
        scale_fill_manual(values=[c_lost, c_surv], labels=['no', 'yes']) +
        scale_x_discrete(limits=['Cobh', 'Cherbourg', 'Southampton']) +
        labs(x="", y="Travellers count") + 
        ggtitle("Survival by the Port of Embarkation")
)        

bars + ggsize(800, 300)

Out[5]:

2. Ports of Embarkation on Map¶

Titanic's ports of of embarkation were:

Southampton (UK)
Cherbourg (France)
Cobh (Ireland)

Let's find geographical coordinates of these cities using Lets-Plot geocoding module.

In [6]:

from lets_plot.geo_data import *

ports = ['Southampton', 'Cherbourg', 'Cobh']

The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).

2.1 Spatial DataFrame (Pandas GeoDataFrame)¶

In [7]:

ports_gcoder = (geocode_cities(ports)
                .where(ports[0], scope='England')
                .where(ports[1], scope='France'))
ports_gdf = ports_gcoder.get_centroids()
ports_gdf

Out[7]:

	city	found name	geometry
0	Southampton	Southampton	POINT (-1.40254 50.91837)
1	Cherbourg	Cherbourg	POINT (-1.60901 49.62728)
2	Cobh	Cobh	POINT (-8.29428 51.85315)

2.2 Markers on Map¶

Lets-Plot API makes it easy to create an interactive basemap layer using either its own vector tiles service or by configuring a 3-rd party Z-X-Y raster tile providers.

In this notebook we will use beautifull CARTO Voyager raster tiles by CARTO as our basemap.

In [8]:

from lets_plot import tilesets

LetsPlot.set(tilesets.CARTO_VOYAGER_HIRES)

In [9]:

basemap = ggplot() + geom_livemap() + ggsize(800, 350)


port_markers = geom_point(
                   map=ports_gdf, 
                   size=7, 
                   shape=21, 
                   color="black", 
                   fill="yellow")

basemap + port_markers

Out[9]:

3. The "Titanic's site" Marker¶

In [10]:

from shapely.geometry import Point, LineString
titanic_site = Point(-38.056641, 46.920255)

titanic_site_marker = geom_point(x=titanic_site.x, y = titanic_site.y, size=10, shape=9, color='red')

basemap + port_markers + titanic_site_marker

Out[10]:

4. The New York City Marker¶

New York City was the Titanic's destination.

In [11]:

NYC = geocode_cities(['New York']).get_centroids().geometry[0]
NYC_marker = geom_point(x=NYC.x, y=NYC.y, size=7, shape=21, color='black', fill='white')

(basemap + 
 port_markers +
 titanic_site_marker +
 NYC_marker
)

Out[11]:

5. Connecting Markers on Map¶

To connect markers on the map we will create a LineString object (from Shaply package).

In [12]:

from geopandas import GeoSeries
from geopandas import GeoDataFrame

# Points of embarkation (GeoSeries).
port_points = ports_gdf.geometry
path_points = pd.concat([port_points, GeoSeries([titanic_site, NYC], crs=ports_gdf.crs)], ignore_index=True)

# Create a new GeoDataFrame containing a `LineString` geometry.
path_gdf = GeoDataFrame(
    dict(geometry=[ LineString(path_points) ])
)

In [13]:

# Add "path" to the map.
titanic_path = geom_path(
    map=path_gdf, 
    color='dark-blue', 
    linetype='dotted', size=1.2)

(basemap +
 titanic_path +
 port_markers +
 titanic_site_marker +
 NYC_marker
)

Out[13]:

6. Pie-chart Markers on Map¶

6.1 Travellers Survival Rates, Now with Pie¶

In [14]:

pies = (ggplot(df) +
        geom_pie(
            aes(x='Port', y="..sum..", fill=as_discrete('Survived'), size="..sum.."), 
            labels=layer_labels()
                             .line('@{..count..}')
                             .line('(@{..prop..})').format('@{..prop..}', '.0%'),
            tooltips=layer_tooltips().title("@Port (@{..sum..})"),
            stroke=1.5, 
            hole=0.5) +
        scale_fill_manual(values=[c_lost, c_surv], labels=['no', 'yes']) +
        scale_x_discrete(limits=['Cobh', 'Cherbourg', 'Southampton'], expand=[0, 0.3]) +
        scale_size(range=[3, 10], guide="none") +
        ylim(0, 800) +
        labs(x="", y="Travellers count") + 
        ggtitle("Survival by the Port of Embarkation")
)

pies + ggsize(800, 300)

Out[14]:

6.2 Spatial Pies¶

In [15]:

spatial_pies = (
        geom_pie(
            aes(x='Port', fill=as_discrete('Survived'), size="..sum.."), 
            data=df,
            map=ports_gdf, 
            map_join=['Port','city'],
            tooltips=layer_tooltips()
                     .title("@Port (@{..sum..})")
                     .line('@{..count..} (@{..prop..})')
                     .format('@{..prop..}', '.0%'),
            stroke=1.5, 
            hole=0.5,
            color='white') +
        scale_fill_manual(values=[c_lost, c_surv], labels=['lost', 'survived']) +
        scale_size(range=[3, 10], guide="none") +
        theme(legend_position=[0.5, 1], 
              legend_justification=[0.5, 1], 
              legend_direction='horizontal',
              legend_title=element_blank())
)                

(basemap + 
 titanic_path +
 spatial_pies +
 titanic_site_marker +
 NYC_marker
)

Out[15]:

6.3 Adjusting the Map Zoom Level and Position¶

In [16]:

(ggplot() + ggsize(900, 600) + ggtitle("Titanic Survival Rate by Port of Embarkation") +
     geom_livemap(zoom=6, location=[-8.29, 51.85, -1.61, 49.63]) + 
     titanic_path +
     port_markers +
     spatial_pies +
     titanic_site_marker +
     NYC_marker +
     theme(text=element_text(family="Garamond"),
           plot_title=element_text(size=30))
)

Out[16]: