stream_app.py

# -*- coding: utf-8 -*-
import streamlit as st

import plotly.express as px

from data_processing import load_data, process_data, get_monetary_dataframe, get_themes_per_year


def _max_width_():
    max_width_str = f"max-width: 1500px;"
    st.markdown(
        f"""
    <style>
    .reportview-container .main .block-container{{
        {max_width_str}
    }}
    </style>    
    """,
        unsafe_allow_html=True,
    )


# force screen width
_max_width_()

st.title("Data Analysis 🌎 📃")
st.write("by [Teolex](https://www.theolex.io/)")

# load and process data
data = load_data()
decisions, organizations, authorities = process_data(data)

st.sidebar.title("Parameters")
authorities_country = st.sidebar.selectbox('Authority country', authorities.country.unique())

select_auth = authorities[authorities.country == authorities_country].name.sort_values()
authority = st.sidebar.selectbox('Authority', ['All', *select_auth])

min_year, max_year = st.sidebar.slider('Decisions year', min_value=2001, max_value=2021, value=(2010, 2021))

# apply filters
authority_filter = True
if authority != 'All':
    authority_filter = decisions.authorities_name.apply(lambda a: authority in a)
else:
    authority_filter = decisions.authorities_name.apply(lambda a: bool(set(select_auth) & set(a)))
year_filter = (decisions.year >= min_year) & (decisions.year <= max_year)
decision_scope = decisions[authority_filter & year_filter]

# explore monetary sanctions
monetary_decision = get_monetary_dataframe(decision_scope)

##
# Plot Graphs
##

st.subheader("The organizations' sectors targeted by the sanctions: ")
st.markdown("The graph shows the cumulated monetary sanction for the current filters")

fig = px.treemap(monetary_decision,
                 path=['org_company_type'],
                 color='org_revenues',
                 color_continuous_scale='RdBu',
                 template="simple_white",
                 values='monetary_sanction',
                 width=1000, height=600)
st.plotly_chart(fig)

st.subheader("The organizations' regions targeted by the sanctions: ")
st.markdown("The graph shows the cumulated monetary sanction for the current filters")
fig = px.treemap(monetary_decision[~monetary_decision.org_continent.isnull()],
                 path=['org_continent', 'org_country'],
                 color_continuous_scale='RdBu',
                 template="simple_white",
                 values='monetary_sanction',
                 width=1000, height=600)
st.plotly_chart(fig)

st.subheader("Revenues vs monetary sanctions representation ")
st.markdown("The graph shows the cumulated monetary sanction for the current filters")
fig = px.scatter(monetary_decision,
                 x="org_revenues",
                 y="monetary_sanction",
                 log_x=True,
                 log_y=True,
                 template="simple_white",
                 color="same_country",
                 color_continuous_scale='RdBu',
                 hover_name="org_name",
                 width=1000, height=600)
st.plotly_chart(fig)

st.subheader("Sum of monetary sanctions over time ")
st.markdown("The graph shows the cumulated monetary sanction per year for each violation theme")
chart_data = get_themes_per_year(monetary_decision)
fig = px.area(chart_data, x="year",
              y="monetary_sanction",
              color="violation_theme",
              template="simple_white",
              # groupnorm="fraction",
              line_group="violation_theme",
              width=1000, height=600)
st.plotly_chart(fig)