4 posts tagged with "plotly"

Map of found items​

Following a previous article analyzing monthly evolution as well as concentration of found items, here is another perspective on these data made available by the SNCF. Obviously, the higher the number of passengers, the higher the number of found items. Now, let's look into the details!

On this map are represented the number of found items per 100k passengers between 2015 and 2018. Only stations with more than 10 found items are shown.

What can we conclude from this visualization ?​

Clearly, there is a significant difference between the area of Paris/north of France, and anywhere else in the country. Nonetheless, one should avoid easy conclusion: are people more careful about their belongings or less prone to hand found items back ? The two options are possibly and even likely connected, but additional data are required to investigate. Perhaps in another part :)

Data​

• SNCF : train station attendance
• SNCF: train station list
• SNCF: found items

Libraries​

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

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

These two dataframes contain contain the following data:

• df_frequentation: train station attendance data

• df_gares: train station data, including latitude and longitude.

• df_items: found item data, including date and location.

Sample data from df_frequentation

Sample data from df_gares

Sampel data from df_items

2. Merging dataframes​

The three dataframes have a column identifier: the UIC code ("Union Internationale des Chemins de fer", International Union of Railways in french). We can merge them based on this unique value for each station.

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

df = df.merge(df_items, on='Code UIC', how='right')

3. Conversion to datetime​

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

df.Date.dtype

>> dtype('O')

In order to have a standardized datetime, pandas.to_datetime can be applied to get a datetime, and then tz_convert to make it french time.

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

4. Filtering dates​

As I want to analyze the number of found items according to the number of passengers, I must filter out data which is not in the period 2015-2018 as these are the only years for which the number of passengers is available.

df = df.loc[(df.Date.dt.year >= 2015) & (df.Date.dt.year <= 2018)]

5. Total number of passengers per station​

df['Passengers'] =(df['Total Voyageurs 2018'] 
                            + df['Total Voyageurs 2017'] 
                            + df['Total Voyageurs 2016'] 
                            + df['Total Voyageurs 2015'])

6. Grouping data by train station​

Data is grouped by station name, geographical coordinates are kept, as well as the count of found items, and the number of passengers.

df = (df.groupby(by="Intitulé gare")
        .agg({'Longitude WGS84': 'first',
              'Latitude WGS84': 'first',
              'Nature d\'objets': len,
              'Passengers': 'first'})
        .rename(columns={'Nature d\'objets': 'item_count'})
)

7. Count per 100k​

In this blog post, the focus is put on the ratio item/pax. A straightforward division would lead to number hard to imagine. Therefore, using a ratio per 100k passengers is more interesting. I restricted the selection to stations having at least 10 found items .

df['count_per_100kpax'] = df.item_count / df.Passengers * 1e5
df = df.sort_values(by='count_per_100kpax', ascending=False)
df = df[df.item_count >= 10]

8. Creation of categories​

To group data by category on the map, we need to bin them. I chose a standard quantile binning with 20% of data in each bin.

df['group'] = pd.qcut(df['count_per_100kpax'], q=np.linspace(0, 1, 6))

9. What we have so far​

Visualization​

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

fig = go.Figure()
colors = plotly.colors.sequential.Inferno # Custom colormap

# One scattermapbox per group
for i, group in enumerate(df.group.cat.categories):
    df_sub = df[df.group == group]
    fig.add_trace(go.Scattermapbox(
            lat=df_sub['Latitude WGS84'], 
            lon=df_sub['Longitude WGS84'],
            text=df_sub.index,
            marker=dict(
                color=colors[2*i],
                size=df_sub['count_per_100kpax'],
                sizemin=3,
                sizeref=.35,
                sizemode='area',
                opacity=.8,
            ),
            meta=df_sub['item_count'],
            hovertemplate="%{text}" + "<br>" 
                            + "Found items: %{meta}" + "<br>" 
                            + "Per 100kPax: " + "%{marker.size:.1f}",
            name=f'> {cat.left:.0f} per 100kPax',          
    ))

# Defining map style, margins, and original position
fig.update_layout(
    mapbox_style="open-street-map",
    #title='Passengers per french train station in 2018',
    margin={'l': 0, 'r': 0, 't': 0, 'b': 0},
    mapbox=dict(
        center={'lon': 2.39, 'lat': 47.09},
        zoom=4
    ),
)
# Legend layout
fig.update_layout(legend={'orientation': 'h', 'y': 0})

Link to the Jupyter notebook.

USA Covid19 Cases vs. Google Trends​

Based on user searches, Google Flu tried to estimate flu prevalence among populations. In this simple visualization I try to use a similar, yet simplified approach. Below are compared USA reported covid19 cases against Google Trends popularity for "Coronavirus" and "Worldometer" (which is a website used worldwide to track this pandemic).

Timeline​

• January 2020: quick spread in China
• Mid-February : spread to Europe
• "Coronavirus" popularity peaks on 15th March, when USA cases growth rate is maximal.
• "Worldometer" popularity peaks two weeks later.
• 11th April: USA 7-day moving average peaks.
• 31th May: USA 7-day moving average bottoms to a 2-month low.
• Since June: cases are up to new daily records, but popularity of "Coronavirus" remains low.

Data​

• CDC USA
• Google Trends

Link to the Jupyter notebook.

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​

Data​

• SNCF: train station list
• SNCF: found items

Libraries​

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

Processing​

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

Sampel data from df_items

2. Conversion to datetime​

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

df.Date.dtype

>> 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.

df.Date.dt.year.value_counts().sort_index()

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 = df_months.index.map(lambda 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
    .groupby(by='Gare')
    .count()
    .Date
    .rename('Count')
    .sort_values(ascending=False)
)

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]

Visualization​

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()
fig.add_trace(
    go.Bar(
        x=df_months.index,
        y=df_months, 
        hoverinfo="x+y",
        marker_color="#007bff",  
))

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

fig.update_layout(
    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.

fig.add_trace(
    go.Bar(
        x=np.arange(1, len(df_grouped)), 
        y=df_grouped, 
        text=df_grouped.index,
        meta = df_grouped,
        hoverinfo="text+y",
        name="Found items",
        marker_color="#007bff"
))

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

fig.update_layout(
    margin={'l':20, 'r': 0, 't': 0, 'b': 250},
    xaxis=dict(tickangle=-90,
               tickfont={'size': 14}),
    yaxis=dict(title="Found items per station",
               type="log",
               titlefont=dict(size=16))
)

Link to the Jupyter notebook.

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
    ),
)

Link to the Jupyter notebook.