SNCF Open Data train station attendance

01 September 2019 - 6 mins read time
Tags: Social Media Minimalism Technology

Passengers per french train station in 2018

Data

• SNCF : train station attendance
• SNCF : train station list

Libraries

The following libraries are imported:

• pandas and numpy for data processing
• plotly.colors to use a specific color scale
• plotly.graph_object for data visualization

import pandas as pd						
import numpy as np						
import plotly.colors
import plotly.graph_objects as go

Processing

1. Reading csv files

df_frequentation = pd.read_csv('data/frequentation-gares.csv', sep=';')
df_gares = pd.read_csv('data/referentiel-gares-voyageurs.csv', sep=';')

Sample data from df_frequentation

Sample data from df_gares

2. Merging dataframes

The UIC Code is a unique ID for train stations. However, the column names are different in both files, so it’s mandatory so specify the left_on and right_on arguments.

df = df_gares.merge(
    right=df_frequentation,
    left_on='Code UIC',
    right_on='Code UIC complet',
    how='inner')

3. Filtering

In order to avoid keeping small train stations, I chose to filter out stations with attendance below 1000 passengers in 2018. For visualization purpose, I added a column holding the square root of the number of passengers per station

df = df[df['Total Voyageurs 2018'] > 1000]

4. Adding a category column

By using pandas.cut data can be split into categories according to total number of passengers. This will allow to plot with a different color for each category.

df['category'] = pd.cut(df['Total Voyageurs 2018'], bins=[1e4, 1e5, 1e6, 1e7, np.inf])

Visualization

Plotly is a handy tool when it comes to creating interactive graphs and plots, that you can embed in other websites.

1. Scatter Mapbox

Data contain latitude and longitude: these will be used to plot train stations on the map. The size of the bubbles will depend on the square root of the number of passengers in 2018. A different trace is added for each of the categories defined above. Finally, information shown on mouse-hovering is defined using hovertemplate.

fig = go.Figure()
colors = plotly.colors.sequential.Viridis

for i, cat in enumerate(df.category.cat.categories):
    df_sub = df[df.category == cat]
    fig.add_trace(go.Scattermapbox(
        lat=df_sub['Latitude WGS84'], 
        lon=df_sub['Longitude WGS84'],
        text=df_sub['Intitulé gare'],
        marker=dict(
            color=colors[2*i+1],
            size=np.sqrt(df_sub['Total Voyageurs 2018 sqrt']),
            sizemin=1,
            sizeref=15,
            sizemode='area',
            opacity=.8,
        ),
        meta=df_sub['Total Voyageurs 2018'],
        hovertemplate="%{text}" + "<br>" + "Passengers: %{meta}",
        name=f'> {cat.left:1.0e} passengers',          
))

2. Layout

The last step is adding the background map, the title, margins around the plot, and the initial position & zoom.

fig.update_layout(
    mapbox_style="open-street-map",
    title='Passengers per french train station in 2018',
    margin={'l': 0, 'r': 0, 't': 50, 'b': 0},
    mapbox=dict(
        center={'lon': 2.39, 'lat': 47.09},
        zoom=4
    ),
)

Jupyter Notebook