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explorer.py
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import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from string import Template
# ===========================================================================
# GUARDINT asset URL and color scheme
# ===========================================================================
asset_url = "https://guardint-assets.sehn.dev"
colors = [
"#600b0c",
"#C01518",
"#ff1c1f",
"#ff5557",
"#ff8e8f",
"#ffc7c7",
"#ffe3e3",
"#efefef",
]
# ===========================================================================
# Utility Functions to be cached
# ===========================================================================
@st.cache
def gen_px_pie(df, values, names, color_discrete_sequence=colors, **kwargs):
fig = px.pie(
df,
values=values,
names=names,
color_discrete_sequence=color_discrete_sequence,
color=kwargs.get("color", None),
color_discrete_map=kwargs.get("color_discrete_map", None),
custom_data=kwargs.get("custom_data", None),
)
# Update what is shown on the slices (on hover)
fig.update_traces(
texttemplate="<b>%{value}</b><br>%{percent}",
hovertemplate="""<b>Answer</b> %{label}
<br><br>given by <b>%{value}</b> respondents or <b>%{percent}</b>
<br>of all who answered the question
<br>given the current filter.
""",
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 18, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 12)},
"orientation": kwargs.get("legend_orientation", "h"),
"bgcolor": "#efefef",
"x": -0.2,
"y": 1.1,
},
modebar={"orientation": "v"},
)
# Add logo
fig.add_layout_image(
dict(
source=f"{asset_url}/guardint_logo.png",
xref="paper",
yref="paper",
x=1.00,
y=0.00,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_go_pie(labels, values, marker_colors=colors, **kwargs):
fig = go.Figure(
data=[
go.Pie(
labels=labels, values=values, marker_colors=marker_colors, sort=False
)
]
)
# Update what is shown on the slices (on hover)
fig.update_traces(
texttemplate="<b>%{value}</b><br>%{percent}",
hovertemplate="""<b>Answer</b> %{label}
<br><br>given by <b>%{value}</b> respondents or <b>%{percent}</b>
<br>of all who answered the question
<br>given the current filter.<extra></extra>
""",
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=kwargs.get("height", 450),
margin=dict(l=0, r=0, b=50, t=30),
font={"size": kwargs.get("font_size", 18), "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 12)},
"orientation": "v",
"bgcolor": "#efefef",
"x": kwargs.get("legend_x", -0.2),
"y": kwargs.get("legend_y", 1.1),
},
modebar={"orientation": "v"},
)
# Add logo
fig.add_layout_image(
dict(
source=f"{asset_url}/guardint_logo.png",
xref="paper",
yref="paper",
x=1.00,
y=0.00,
sizex=kwargs.get("image_sizex", 0.15),
sizey=kwargs.get("image_sizey", 0.15),
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_px_histogram(
df, x, y, nbins, color, labels, color_discrete_map=colors, **kwargs
):
fig = px.histogram(
df,
x=x,
y=y,
nbins=nbins,
color=color,
color_discrete_map=color_discrete_map,
labels=labels,
marginal=kwargs.get("marginal", "rug"),
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": kwargs.get("font_size", 13), "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="{asset_url}/guardint_logo.png",
xref="paper",
yref="paper",
x=1.18,
y=-0.005,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_go_histogram_overlaid(traces, names, colors, **kwargs):
fig = go.Figure()
for trace, name, color in zip(traces, names, colors):
fig.add_trace(
go.Histogram(
x=trace,
name=name,
marker_color=color,
xbins={"size": 2},
cumulative_enabled=True,
)
)
fig.update_layout(barmode="overlay")
fig.update_traces(opacity=0.75)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": kwargs.get("font_size", 13), "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="{asset_url}/guardint_logo.png",
xref="paper",
yref="paper",
x=1.18,
y=-0.005,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_px_box(df, x, y, points, color, labels, color_discrete_map=colors, **kwargs):
fig = px.box(
df,
x=x,
y=y,
points=points,
color=color,
labels=labels,
color_discrete_map=color_discrete_map,
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 13, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="{asset_url}/guardint_logo.png",
xref="paper",
yref="paper",
x=1.18,
y=-0.005,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_go_bar_stack(data, **kwargs):
fig = go.Figure(data=data)
# Update layout
fig.update_layout(
barmode="stack",
autosize=False,
width=700,
height=700,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 13, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="{asset_url}/guardint_logo.png",
xref="paper",
yref="paper",
x=1.18,
y=-0.005,
sizex=0.15,
sizey=0.15,
xanchor="right",
yanchor="bottom",
)
)
return fig
@st.cache
def gen_rank_plt(input_col, options, **kwargs):
input_col_score = pd.Series(index=options)
for i in range(1, 7):
input_col_counts = df[f"{input_col}[{i}]"].value_counts()
scores = input_col_counts.multiply(scoring[i])
input_col_score = input_col_score.add(scores, fill_value=0)
input_col_score = input_col_score.sort_values(ascending=False)
if i == 1:
ranked_first = df[f"{input_col}[1]"].value_counts()
ranked_first_clean = pd.DataFrame(
{
"institution": ranked_first.index,
"No of times<br>ranked first": ranked_first.values,
}
)
input_col_df = pd.DataFrame(
{
"institution": input_col_score.index,
"score": input_col_score.values,
}
)
input_col_df = input_col_df.merge(
ranked_first_clean, on="institution", how="left"
).fillna(0)
input_col_df = input_col_df.sort_values(["score", "No of times<br>ranked first"])
fig = px.bar(
input_col_df.sort_values(by="score"),
y="institution",
x="score",
color="No of times<br>ranked first",
color_continuous_scale=[colors[5], colors[2]],
orientation="h",
)
# Update layout
fig.update_layout(
autosize=False,
width=700,
height=450,
margin=dict(l=0, r=0, b=100, t=30),
font={"size": 13, "family": "Roboto Mono, monospace"},
legend={
"font": {"size": kwargs.get("legend_font_size", 10)},
},
modebar={"orientation": "h"},
)
# Add logo
fig.add_layout_image(
dict(
source="{asset_url}/guardint_logo.png",
xref="paper",
yref="paper",
x=1.0,
y=0.0,
sizex=0.25,
sizey=0.25,
xanchor="right",
yanchor="bottom",
)
)
return fig
def print_total(number):
st.write(f"**{number}** respondents answered the question with the current filter")
def answered_by(group):
if group == "cso":
text = "CSO professionals"
else:
text = "journalists"
st.caption(f"This question was only answered by {text}")
chart_config = {
"displaylogo": False,
"modeBarButtonsToRemove": ["hoverClosestPie"],
"toImageButtonOptions": {
"width": 700,
"height": 450,
"scale": (210 / 25.4) / (700 / 300),
},
}
# ===========================================================================
# Import data from stored pickle
# ===========================================================================
df = pd.read_pickle("data/guardint_survey.pkl")
# ===========================================================================
# General configuration
# ===========================================================================
st.set_page_config(
page_title="GUARDINT Survey Data Explorer",
page_icon=f"{asset_url}/guardint_favicon.png",
)
def callback():
st.experimental_set_query_params(section=st.session_state.section)
sections = [
"Overview",
"Resources > HR",
"Resources > Expertise",
"Resources > Finance",
"Resources > FOI",
"Resources > Appreciation",
"Media Reporting",
"Public Campaigning",
"Policy Advocacy",
"Strategic Litigation",
"Protection",
"Constraints",
"Attitudes",
]
try:
query_params = st.experimental_get_query_params()
query_section = query_params["section"][0]
if "section" not in st.session_state:
st.session_state.section = query_section
except KeyError:
st.experimental_set_query_params(section=sections[0])
query_params = st.experimental_get_query_params()
query_section = query_params["section"][0]
if "section" not in st.session_state:
st.session_state.section = query_section
selected_section = st.sidebar.radio(
"Choose section",
sections,
index=sections.index(query_section),
key="section",
on_change=callback,
)
st.caption("GUARD//INT Survey > " + selected_section)
filters = {
"country": st.sidebar.selectbox(
"Country", ["All", "United Kingdom", "Germany", "France"]
),
"field": st.sidebar.selectbox("Field", ["All", "CSO Professionals", "Journalists"]),
}
filter = np.full(len(df.index), True)
for column_name, selectbox in filters.items():
if selectbox == "All":
continue
else:
filter = filter & (df[column_name] == selectbox)
# ===========================================================================
# Custom JS/CSS
# ===========================================================================
# This causes the page to scroll to top when section is changed
components.html(
f"""
<!--{st.session_state.section}-->
<script>
window.parent.document.querySelector('section.main').scrollTo(0, 0);
</script>
""",
height=0,
)
# Here, a custom font is loaded from the GitHub repo
css = Template(
""" <style>
@font-face {
font-family: 'Roboto Mono';
font-style: normal;
font-weight: 400;
font-display: swap;
src: url($asset_url/roboto_mono.woff2) format('woff2');
unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
}
html, body, [class*="css"] {
font-family: 'Roboto Mono';
}
.st-bx {
font-family: 'Roboto Mono' !important;
}
.st-ae {
font-family: 'Roboto Mono' !important;
}
.css-1rh8hwn {
font-size: 0.8rem;
}
button {
border-width: 3px 3px 3px 3px !important;
border-radius: 0 !important;
}
h1 {
margin-top: -1em;
}
h3 {line-height: 1.3}
footer {
visibility: hidden;
}
.e8zbici2 {
visibility: hidden;
}
.custom-footer {
display: block;
padding-top: 150px;
margin-bottom: -400px;
color: rgba(38, 39, 48, 0.4);
flex: 0 1 0%;
font-size: 0.8rem !important;
max-width: 730px;
width: 100%;
}
strong {
font-style: bold;
font-weight: 700;
color: #000;
}
code {
color: #ff1c1f;
}
a {
color: #ff1c1f !important;
}
a:hover {
color: #ff5557 !important;
}
a:visited {
color: #600b0c !important;
}
</style>
"""
)
css = css.substitute({"asset_url": asset_url})
st.markdown(css, unsafe_allow_html=True)
# ===========================================================================
# Overview
# ===========================================================================
if selected_section == "Overview":
st.write("# Civic Intelligence Oversight Survey Data Explorer")
st.write(
"""
A better understanding of the democratic governance of intelligence
requires more detailed information about an increasingly important group
of actors outside the traditional corridors of power: Civic intelligence
oversight practitioners. As part of the GUARD//INT research project, we
conducted online surveys with representatives of civil society
organisations (CSOs) and journalists in France, Germany and the United
Kingdom.
This site provides open access to the anonymized data we gathered. It visualises
the first empirical investigation of the perceptions of media and CSO
professionals specialised in intelligence and surveillance. The data enables us
to better understand the potential and limitations of civic intelligence
oversight as an increasingly important part of how democratic societies respond
to and oversee digital surveillance.
"""
)
col1, col2 = st.columns(2)
col1.metric("Respondents", len(df[filter].index))
col2.metric(
"Cumulative years spent working on SBIA",
int(df[filter]["expertise1"].sum()),
)
col1, col2 = st.columns(2)
col1.metric(
"Avg. years spent working on SBIA†",
"%.1f" % df[filter]["expertise1"].mean(),
)
col2.metric(
"Avg. No. of FOI requests in the past 5 years",
int(df[filter]["foi2"].mean()),
)
col1, col2 = st.columns(2)
col1.metric(
"Journalists",
len(df[filter & (df.field == "Journalists")].index),
)
col2.metric(
"Civil Society Organisation professionals",
len(df[filter & (df.field == "CSO Professionals")].index),
)
st.caption(
"""† For the calculation of the mean only valid numerical answers were
counted, excluding those who haven't specified their experience in years.
"""
)
st.write("## Understanding the data")
st.write(
"""The individuals we surveyed mostly engage in civic oversight
practices in their professional capacity and were invited to complete
the survey based on their experience in working on surveillance by
intelligence agencies. We designed two different but overlapping
surveys: One for journalists and one for CSO professionals. When we
posed a question only to journalists, the question code is prepended
with `MS` (media scrutiny). When we put the question only to CSO
professionals, the code is prepended with `CS` (civil society scrutiny).
If the question was put to both groups, the prefix was removed.
"""
)
st.write(
"""The questions and response options are structured in different
sections ranging from different kinds of resources (human resources,
expertise, use of freedom of information rights, financial resources,
journalistic appreciation) to different civic oversight activities
(media reporting, public campaigning, policy advocacy, strategic
litigation) as well as protections, constraints and attitudes. A
comprehensive overview of all questions and answer options can be found
in our two codebooks:
"""
)
with open("codebooks/guardint_survey_codebook_media.pdf", "rb") as file:
st.download_button(
label="Codebook Media Scrutiny (PDF)",
data=file,
file_name="guardint_survey_codebook_media.pdf",
)
with open("codebooks/guardint_survey_codebook_civil_society.pdf", "rb") as file:
st.download_button(
label="Codebook Civil Society Scrutiny (PDF)",
data=file,
file_name="guardint_survey_codebook_civil_society.pdf",
)
st.write(
"""If you want to have a look at the data yourself, you can also
download the entire dataset that this website is built on
below:
"""
)
with open("data/guardint_survey.csv", "rb") as file:
st.download_button(
label="Survey data (CSV)", data=file, file_name="guardint_survey_data.csv"
)
with open("data/guardint_survey.xlsx", "rb") as file:
st.download_button(
label="Survey data (Excel)",
data=file,
file_name="guardint_survey_data.xlsx",
)
st.write("## About this website")
st.write(
"""We initially built this website to give ourselves an easy way to
peruse and analyse the data. Since we found it helpful, we thought you
might find it helpful, too. Using the controls located in the sidebar on
the left you can filter by country (United Kingdom, France or Germany)
and by field (Journalists, CSO Professionals). For every question, the
data explorer indicates the total number of responses according to the
currently applied filter. By hoovering over the charts, further details
on the respective answer option appear. All charts can be downloaded as
.png files.
"""
)
st.write(
"""This website would not be remotely possible without the great
open-source software it's built with. We extend a big thank you to the
creators of the Streamlit library and all the other software libraries
that enable the functionality of this website. The code for this website
is freely available under a permissive license. If you find any errors
or room for improvement, don't hesitate to let us know.
"""
)
st.write("## Get in touch")
st.write(
"""If you have questions or comments about our research or the data
provided, you can contact the research team at the [Berlin Social Science
Center
(WZB)](https://www.wzb.eu/en/research/digitalization-and-societal-transformation/politics-of-digitalization/projects/oversight-and-intelligence-networks-who-guards-the-guardians-guardint)
and [Stiftung Neue Verantwortung (SNV)](https://www.stiftung-nv.de/en/project/digital-rights-surveillance-and-democracy) via email to
[[email protected]](mailto:[email protected]). You can also reach
out on Twitter [@guard_int](https://twitter.com/guard_int).
"""
)
st.write("## Summary statistics")
st.write(
"""Below you find some summary statistics, such as which countries the
respondents are primarily working in. You can always jump to a section
that interests you by using the outline in the left sidebar.
"""
)
country_counts = df[filter]["country"].value_counts()
st.write("### Country `[country]`")
print_total(country_counts.sum())
st.plotly_chart(
gen_px_pie(
df[filter],
values=country_counts,
names=country_counts.index,
color=country_counts.index,
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
),
use_container_width=True,
config=chart_config,
)
st.write("### Field `[field]`")
field_counts = df[filter]["field"].value_counts()
print_total(field_counts.sum())
st.plotly_chart(
gen_px_pie(
df[filter],
values=field_counts,
names=field_counts.index,
),
use_container_width=True,
config=chart_config,
)
st.write("### Predominant activity of CSO professionals `[CSpreselection]`")
CSpreselection_counts = df[filter]["CSpreselection"].value_counts()
print_total(CSpreselection_counts.sum())
st.plotly_chart(
gen_px_pie(
df[filter],
values=CSpreselection_counts,
names=CSpreselection_counts.index,
),
use_container_width=True,
config=chart_config,
)
st.write("### Gender `[gender]`")
gender_counts = df[filter]["gender"].value_counts()
print_total(gender_counts.sum())
st.plotly_chart(
gen_px_pie(
df[filter],
values=gender_counts,
names=gender_counts.index,
color=gender_counts.index,
color_discrete_map={
"Not specified": colors[0],
"Male": colors[1],
"Female": colors[2],
"Other": colors[3],
},
),
use_container_width=True,
config=chart_config,
)
# TODO Privacy notice
# ===========================================================================
# Resources > HR
# ===========================================================================
if selected_section == "Resources > HR":
st.write("# Resources > HR")
st.write("### What is your employment status? `[hr1]`")
hr1_counts = df[filter]["hr1"].value_counts()
print_total(hr1_counts.sum())
st.plotly_chart(
gen_px_pie(
hr1_counts,
values=hr1_counts,
names=hr1_counts.index,
color=hr1_counts.index,
color_discrete_map={
"Full-time": colors[0],
"Part-time (>50%)": colors[1],
"Part-time (<50%)": colors[2],
"Freelance": colors[3],
"Other": colors[4],
},
),
use_container_width=True,
config=chart_config,
)
# =======================================================================
st.write(
"### How many days per month do you work on surveillance by intelligence agencies? `[hr2]`"
)
hr2_counts = df[filter]["hr2"].value_counts()
print_total(hr2_counts.sum())
st.plotly_chart(
gen_px_histogram(
df=df[filter],
x="hr2",
y=None,
nbins=None,
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"hr2": "days per month"},
),
use_container_width=True,
config=chart_config,
)
st.plotly_chart(
gen_px_box(
df=df[filter],
points="all",
x="country",
y="hr2",
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"hr2": "days per month"},
),
use_container_width=True,
config=chart_config,
)
df["hr2_more_than_five"] = np.where(df["hr2"] > 5, True, False)
df["hr2_more_than_five"] = df["hr2_more_than_five"].replace(
{True: "more than 5 days", False: "5 days or less"}
)
hr2_more_than_five_counts = df[filter]["hr2_more_than_five"].value_counts()
st.plotly_chart(
gen_px_pie(
hr2_more_than_five_counts,
values=hr2_more_than_five_counts,
names=hr2_more_than_five_counts.index,
color=hr2_more_than_five_counts.index,
),
use_container_width=True,
config=chart_config,
)
# =======================================================================
st.write("### Which type of medium do you work for? `[MShr3]`")
MShr3_df = pd.DataFrame(columns=("option", "count", "country"))
answered_by("media")
MShr3_options = [
"daily_newspaper",
"weekly_newspaper",
"magazine",
"tv",
"radio",
"news_agency",
"online_stand_alone",
"online_of_offline",
]
MShr3_options_clean = [
"Daily newspaper",
"Weekly newspaper",
"Magazine",
"TV",
"Radio",
"News agency",
"Online outlet<br>(standalone)",
"Online outlet<br>(of an offline publication)",
]
for option, option_clean in zip(MShr3_options, MShr3_options_clean):
MShr3_data = df[filter]["country"][df[f"MShr3[{option}]"] == 1].tolist()
for i in MShr3_data:
MShr3_df = MShr3_df.append(
{"option": option_clean, "count": MShr3_data.count(i), "country": i},
ignore_index=True,
)
MShr3_df = MShr3_df.drop_duplicates()
if filters["field"] == "CSO Professionals":
print_total(0)
else:
# If one respondent chose at least one medium it counts towards the total
MShr3_col_list = [col for col in df[filter].columns if col.startswith("MShr3")]
MShr3_df_total = df[filter][MShr3_col_list]
MShr3_df_total["answered"] = [
"Y" if x > 0 else "N" for x in np.sum(MShr3_df_total.values == True, 1)
]
print_total(MShr3_df_total["answered"].value_counts().sort_index()[1])
st.plotly_chart(
gen_px_histogram(
MShr3_df,
x="option",
y="count",
nbins=None,
color="country",
color_discrete_map={
"Germany": colors[0],
"United Kingdom": colors[2],
"France": colors[5],
},
labels={"count": "people who work<br>for this medium"},
marginal=None,
),
use_container_width=True,
config=chart_config,
)
# =======================================================================
st.write(
"### Within the past year, did you have enough time to cover surveillance by intelligence agencies? `[MShr4]`"
)
answered_by("media")
MShr4_counts = df[filter]["MShr4"].value_counts().sort_index()
print_total(MShr4_counts.sum())
st.plotly_chart(
gen_go_pie(
labels=MShr4_counts.sort_index().index,
values=MShr4_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,
)
# ===========================================================================
# Resources > Expertise
# ===========================================================================
if selected_section == "Resources > Expertise":
st.write("# Resources > Expertise")
st.write(
"### How many years have you spent working on surveillance by intelligence agencies? `[expertise1]`"
)
expertise1_counts = df[filter]["expertise1"].value_counts()
print_total(expertise1_counts.sum())
st.plotly_chart(
gen_px_histogram(
df[filter],
x="expertise1",
y=None,
nbins=20,
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"expertise1": "years"},
),
use_container_width=True,
config=chart_config,
)
st.plotly_chart(
gen_px_box(
df=df[filter],
points="all",
x="country",
y="expertise1",
color="country",
color_discrete_map={
"Germany": colors[0],
"France": colors[2],
"United Kingdom": colors[5],
},
labels={"expertise1": "years"},
),
use_container_width=True,
config=chart_config,
)
# =======================================================================
st.write(
"### How do you assess your level of expertise concerning the **legal** aspects of surveillance by intelligence agencies? `[expertise2]`"
)
expertise2_counts = df[filter]["expertise2"].value_counts().sort_index()
print_total(expertise2_counts.sum())
st.plotly_chart(
gen_go_pie(
labels=expertise2_counts.sort_index().index,
values=expertise2_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,
)
# =======================================================================
st.write(
"### How do you assess your level of expertise concerning the **political** aspects of surveillance by intelligence agencies `[expertise3]`?"
)
expertise3_counts = df[filter]["expertise3"].value_counts().sort_index()
print_total(expertise3_counts.sum())
st.plotly_chart(
gen_go_pie(
labels=expertise3_counts.sort_index().index,
values=expertise3_counts.sort_index().values,
),
use_container_width=True,
config=chart_config,