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SNCF Open Data: found items - 1

· 7 min read
Hugo Le Moine

Monthly found items in french train stations

We can view found items as a proxy for passenger traffic. On this first chart, we can clearly identify a few traffic disruptions:

  • April 2018 to June 2018 : discontinuous strike against project to reform SNCF.
  • December 2019 : strike against pension reform.
  • March 2020 to June 2020 : Covid-19 restrictions.

We can also observe a peak in July each year, corresponding to summer vacations for most people.

18% of stations represent 80% of found items



The following libraries are imported:

  • pandas and numpy for data processing
  • plotly.colors to use a specific colorscale
  • plotly.graph_object for data visualization
import pandas as pd                     
import numpy as np
import plotly.colors
import plotly.graph_objects as go


1. Reading csv files

df_gares = pd.read_csv('data/referentiel-gares-voyageurs.csv', sep=';')
df_items = pd.read_csv('data/objets-trouves-restitution.csv', sep=';')

These two dataframes contain contain the following data:

  • df_gares: train station data, including latitude and longitude.
  • df_items: found item data, including date and location.

Sample data from df_gares

Code plate-formeIntitulé gareIntitulé fronton de gareGare DRGGare étrangèreAgence gareRégion SNCFUnité gareUTNbre plateformes...Longitude WGS84Latitude WGS84Code UICTVSSegment DRGNiveau de serviceSOPRGDate fin validité plateformeWGS 84
000007-1Bourg-MadameBourg-MadameTrueFalseAgence Grand SudREGION LANGUEDOC-ROUSSILLONUG Languedoc RoussillonBOURG MADAME GARE1...1.94867042.43240787784876BMDc1.0NaNGARES C LANGUEDOC ROUSSILLONNaN42.4324069,1.9486704
100014-1Bolquère - EyneBolquère - EyneTrueFalseAgence Grand SudREGION LANGUEDOC-ROUSSILLONUG Languedoc RoussillonBOLQUERE EYNE GARE1...2.08755942.49787387784801BQEc1.0NaNGARES C LANGUEDOC ROUSSILLONNaN42.4978734,2.0875591
200015-1Mont-Louis - La CabanasseMont-Louis - La CabanasseTrueFalseAgence Grand SudREGION LANGUEDOC-ROUSSILLONUG Languedoc RoussillonMONT LOUIS LA CABANASSE GARE1...2.11313842.50209087784793MTCc1.0NaNGARES C LANGUEDOC ROUSSILLONNaN42.5020902,2.1131379

Sampel data from df_items

DateDate et heure de restitutionGareCode UICNature d'objetsType d'objetsType d'enregistrement
02014-03-09T14:25:29+01:00NaNParis Montparnasse87391003.0Manteau, veste, blazer, parka, blouson, capeVêtements, chaussuresObjet trouvé
12018-01-23T15:07:32+01:00NaNSaint-Étienne Châteaucreux87726000.0MontreBijoux, montresObjet trouvé
22018-02-06T15:35:49+01:00NaNRennes87471003.0Clés, porte-clésClés, porte-clés, badge magnétiqueObjet trouvé

2. Conversion to datetime

For now, the DataFrame has a Date column, but was not assigned any particular format.

>> dtype('O')

In order to have a standardized datetime, pandas.to_datetimecan be applied to get a datetime, and then tz_convertto make it french time.

df['Date'] = pd.to_datetime(df.Date, utc=True).dt.tz_convert('Europe/Paris')

3. Filtering dates

To know when data collection has actually been put into place, let's analyse dates contained in the DataFrame.


To keep consistency, I decided to drop data from 2013: data collection may have not started everywhere, and had definitely not started in January 2013. If we want to dig deeper into the data and check correlations with passenger count, it would be difficult to achieve if data are not complete. Therefore only data collected from 2014 onwards will be kept:

df = df[df.Date.dt.year >= 2014]

5. Grouping data by year and month

In the first chart, I wanted to plot found item count for each month in the dataset. To do so, we need to group by year, then month.

df_months = df.groupby(by=[df.Date.dt.year, df.Date.dt.month]).Date.count()

The result is the following:

2014  1       5788
2 5815
3 7012
4 8543
5 8657
2020 2 9271
3 4473
4 72
5 1386
6 3649

Now we need to get the index back to datetime, combining years and months.

df_months.index = t: pd.to_datetime('-'.join(map(str, t))))

6. Grouping data by train station

The second chart was made after grouping by train station. It can be achieved by the following instructions:

df_grouped = (df

This will first group by train station name, then apply a count aggregation, keep the Date renamed as Count, and values will be sorted from greatest to lowest count.

7. Pareto

The df_groupedDataFrame allows us to demonstrate an application of the Pareto priciple. The following lines are doing:

  • Cumulative found item count of df_grouped
  • Sum of all items in df_grouped
  • Assigning booleans to cumulative counts below 80% of the total number of items, and counting them
pareto = (df_grouped.cumsum() < df_grouped.sum() * 0.8).sum()

The df_groupedDataFrame contains 181 train stations, and 32 (18%) of them account for 80% of the total count of found items.

8. Filtering grouped data

Using the Pareto principle described above, I filtered df_groupedto keep the first 32 rows for visualization purpose.

df_grouped = df_grouped.iloc[:pareto]


Now that all the processing part is completed, we can proceed to visualization.

1. Monthly found items

The first chart is simply a bar chart of the total count of found items per month. x will therefore be the index of the dataframe df_months, and y will be the sum of all found items found for the associated months.

fig = go.Figure()

Then, y axis title is added, as well as margins to enhance the result.

yaxis=dict(title="Found items in train stations", titlefont=dict(size=16)),
margin={'l': 30, 'r': 30, 't': 50, 'b': 0},

2. Found items per train station

The second chart is also a bar chart. The data plotted are the counts of found items in each train station. As mentioned in the processing part, only the first 32 stations are displayed, ordered by number of found items.

x=np.arange(1, len(df_grouped)),
meta = df_grouped,
name="Found items",

The final step is adjusting the layout: margins, axis titles and log scale for the y axis.

margin={'l':20, 'r': 0, 't': 0, 'b': 250},
tickfont={'size': 14}),
yaxis=dict(title="Found items per station",

Link to the Jupyter notebook.