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app.py

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+import streamlit as st
+from gsheet_loader import get_data
+import pandas as pd
+import plotly.express as px
+import plotly.figure_factory as ff
+import plotly.graph_objects as go
+import datetime as dt
+
+st.set_page_config(
+    page_title="Catalog Data Dashboard",
+    layout="wide",
+    page_icon="📊",
+)
+
+st.title("📊 Catalog Data Dashboard")
+st.markdown(
+    """
+    This dashboard combines live [Google Sheets data](https://docs.google.com/spreadsheets/d/10nGgqXxunGXo_GI1LxybvsAr1TYSDdNiqqZX6DSTbDA) for:
+    - catalog onboarding  
+    - metadata completeness  
+    - mapping/scraping status  
+    """
+)
+
+cat_onboarding_df, cat_metadata_df, cat_status_df = get_data()
+
+tab0, tab1, tab2, tab3, tab4 = st.tabs(["Overview", "Static Data", "Onboarding Status", "Metadata Completeness", "Mapping Status"])
+
+# =========================================================================================================================
+# Tab 0 - Overview
+# =========================================================================================================================
+
+with tab0:
+    st.header("Overiew")
+    if st.button("🔄 Refresh Data"):
+        st.cache_data.clear()
+        st.toast("Refreshing data...", icon="🔄")
+        st.rerun()
+
+    st.markdown("---")
+    st.subheader("Quick Data Preview")
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.dataframe(cat_onboarding_df.head(5))
+    with col2:
+        st.dataframe(cat_metadata_df.head(5))
+    with col3:
+        st.dataframe(cat_status_df.head(5))
+
+# =========================================================================================================================
+# Tab 0 - Static stuff
+# =========================================================================================================================
+
+with tab1:
+    st.header("Static Data Preview")
+
+    full_countries_df = pd.read_csv('countries.csv')
+    full_languages_df = pd.read_csv('languages.csv')
+
+    # countries map 
+    fig = px.choropleth(
+    full_countries_df,
+    locations="country_name",
+    locationmode="country names",
+    color="log_count",
+    color_continuous_scale="Purples",
+    hover_name="country_name",
+    hover_data={"count": True, "log_count": False},
+    projection="natural earth",
+    title="Programs' availabilities by Country (Log Scale)"
+    )
+
+    fig.update_geos(showcountries=True, showcoastlines=True, showland=True, landcolor="white", projection_type="natural earth")
+    fig.update_layout(
+        width=1400,
+        height=700,
+        margin=dict(l=0, r=0, t=100, b=0),
+        title_y=0.95
+    )
+
+    st.plotly_chart(fig, use_container_width=True)
+
+    # languages map
+    fig1 = px.choropleth(
+    full_languages_df,
+    locations="country_name",
+    locationmode="country names",
+    color="log_count",
+    color_continuous_scale="Purples",
+    hover_name="country_name",
+    hover_data={"count": True, "log_count": False},
+    projection="natural earth",
+    title="Programs by Languages (Log Scale)"
+    )
+
+    fig1.update_geos(showcountries=True, showcoastlines=True, showland=True, landcolor="white", projection_type="natural earth")
+    fig1.update_layout(
+        width=1400,
+        height=700,
+        margin=dict(l=0, r=0, t=100, b=0),
+        title_y=0.95
+    )
+
+    st.plotly_chart(fig1, use_container_width=True)
+
+
+    # Completeness evaluation
+    catalog_scores = pd.read_csv("catalog_scores.csv")
+    colorscale = [
+    [0.0,  "#ffffff"],
+    [0.1,  "#dcd6f7"],
+    [0.3,  "#a29bfe"],
+    [0.6,  "#6c5ce7"],
+    [1.0,  "#341f97"]
+    ]
+
+    fig_completeness = px.bar(
+        catalog_scores,
+        x="Total",
+        y="Catalog",
+        orientation="h",
+        color="Total",
+        color_continuous_scale=colorscale,
+        title="Catalog Metadata Completeness Score",
+    )
+    fig_completeness.update_layout(yaxis={'categoryorder':'total ascending'}, template="plotly_dark", height=1000)
+    st.plotly_chart(fig_completeness, use_container_width=True)
+
+
+    # ### completeness score broken down
+    subcols = ["movie", "show", "season", "episode", "sport"]
+
+    # Compute sum of raw subscores
+    catalog_scores["raw_sum"] = catalog_scores[subcols].sum(axis=1)
+
+    # Build the figure
+    fig_completeness2 = go.Figure()
+
+    for col in subcols:
+
+        # normalized height of this bar segment
+        norm_vals = (catalog_scores[col] / catalog_scores["raw_sum"]) * catalog_scores["Total"]
+
+        fig_completeness2.add_trace(
+            go.Bar(
+                y=catalog_scores["Catalog"],
+                x=norm_vals,                  # BAR SIZE = normalized values
+                name=col.capitalize(),
+                orientation="h",
+                customdata=catalog_scores[col],    # RAW values for hover
+                hovertemplate=(
+                    "<b>%{y}</b><br>" +
+                    f"{col.capitalize()}: <b>%{{customdata}}</b><br>" +   # RAW value
+                    "Normalized: %{x:.2f}<extra></extra>"
+                )
+            )
+        )
+
+    fig_completeness2.update_layout(
+        barmode="stack",
+        title="Subscore Contribution per Catalog (Scaled to Total Score)",
+        xaxis_title="Total Score",
+        template="plotly_dark",
+        height=1200,
+        yaxis={'categoryorder':'total ascending'}
+    )
+
+    st.plotly_chart(fig_completeness2, use_container_width=True)
+
+
+    #scatter plot
+    fig_scatter = px.scatter(
+    catalog_scores,
+    x="Total",
+    y="Number of programs",
+    size="Number of programs",
+    color="Total",
+    hover_name="Catalog",
+    color_continuous_scale="Viridis",
+    size_max=50
+    )
+
+    st.plotly_chart(fig_scatter, use_container_width=True)
+
+
+
+# =========================================================================================================================
+# Tab 2 - Onboarding sheet
+# =========================================================================================================================
+
+with tab2:
+    st.header("Catalog Onboarding Status")
+
+        # Convert onboarding date to datetime (e.g., 21/11 → 2025-11-21)
+    cat_onboarding_df["Onboarding date"] = pd.to_datetime(
+        cat_onboarding_df["Onboarding date"], format="%d/%m", errors="coerce"
+    )
+    cat_onboarding_df["Onboarding date"] = cat_onboarding_df["Onboarding date"].apply(
+        lambda d: d.replace(year=2025) if pd.notna(d) else d
+    )
+
+    # Map textual months to end-of-month dates
+    month_map = {
+        "November 2025": dt.datetime(2025, 11, 30),
+        "December 2025": dt.datetime(2025, 12, 31),
+        "January 2026": dt.datetime(2026, 1, 31),
+        "February 2026": dt.datetime(2026, 2, 28),
+        "March 2026": dt.datetime(2026, 3, 31),
+        "April 2026": dt.datetime(2026, 4, 30),
+        "TBD": None,
+    }
+    cat_onboarding_df["Go live parsed"] = cat_onboarding_df["Go live (customer)"].map(month_map)
+
+    # Drop missing
+    timeline_df = cat_onboarding_df.dropna(subset=["Onboarding date", "Go live parsed"])
+
+    fig_timeline = px.timeline(
+    timeline_df,
+    x_start="Onboarding date",
+    x_end="Go live parsed",
+    y="NAME",
+    color="Onboarding Status",
+    hover_data=["Client", "Priority"],
+    title="Onboarding → Go-Live Timeline",
+    )
+    fig_timeline.update_yaxes(autorange="reversed")
+
+    st.plotly_chart(fig_timeline, use_container_width=True)
+
+    # bar chart 1
+    summary = (
+    cat_onboarding_df.groupby(["Client", "Onboarding Status"])
+    .size()
+    .reset_index(name="Count")
+    )
+
+    fig_client = px.bar(
+        summary,
+        x="Client",
+        y="Count",
+        color="Onboarding Status",
+        text_auto=True,
+        title="Catalogs per Client (by Onboarding Status)",
+    )
+    fig_client.update_layout(barmode="stack", xaxis_title="Client", yaxis_title="Catalog Count")
+
+    st.plotly_chart(fig_client, use_container_width=True)
+
+    # bar chart 2
+    summary = (
+    cat_onboarding_df.groupby(["Client", "Priority"])
+    .size()
+    .reset_index(name="Count")
+    )
+
+    fig_client1 = px.bar(
+        summary,
+        x="Client",
+        y="Count",
+        color="Priority",
+        text_auto=True,
+        title="Catalogs per Client (by Priority)",
+    )
+    fig_client1.update_layout(barmode="stack", xaxis_title="Client", yaxis_title="Catalog Count")
+
+    st.plotly_chart(fig_client1, use_container_width=True)
+
+    # bar chart 3
+
+    summary = (
+    cat_onboarding_df.groupby(["Onboarding Status", "Priority"])
+    .size()
+    .reset_index(name="Count")
+    )
+
+    fig_client2 = px.bar(
+        summary,
+        x="Onboarding Status",
+        y="Count",
+        color="Priority",
+        text_auto=True,
+        title="Catalogs per Onboarding Status (by Priority)",
+    )
+    fig_client2.update_layout(barmode="stack", xaxis_title="Onboarding Status", yaxis_title="Catalog Count")
+
+    st.plotly_chart(fig_client2, use_container_width=True)
+
+# =========================================================================================================================
+# Tab 3 - Metadata completeness
+# =========================================================================================================================
+
+with tab3:
+    st.header("Catalog Metadata Completeness")
+
+    cat_df = cat_metadata_df.copy()
+    meta_cols = [col for col in cat_df.columns if col not in ["Catalog name"]]
+
+    score_map = {"Yes": 1.0, "Some": 0.5, "No": 0.0, "None": 0.0, "": 0.0}
+
+    cat_df_numeric = cat_df.copy()
+    cat_df_numeric[meta_cols] = cat_df_numeric[meta_cols].replace(score_map)
+
+    # force conversion to numeric (anything else becomes NaN)
+    cat_df_numeric[meta_cols] = cat_df_numeric[meta_cols].apply(pd.to_numeric, errors="coerce")
+
+    cat_df_numeric["Completeness Score"] = cat_df_numeric[meta_cols].mean(axis=1)
+    cat_df_numeric_sorted = cat_df_numeric.sort_values("Completeness Score", ascending=False)
+
+    #graph 1
+    fig_completeness = px.bar(
+    cat_df_numeric_sorted,
+    x="Completeness Score",
+    y="Catalog name",
+    orientation="h",
+    color="Completeness Score",
+    color_continuous_scale="Greens",
+    title="Catalog Metadata Completeness Score",
+    )
+    fig_completeness.update_layout(yaxis={'categoryorder':'total ascending'})
+
+    st.plotly_chart(fig_completeness, use_container_width=True)
+
+    # graph 2
+    coverage = cat_df_numeric[meta_cols].mean().sort_values(ascending=False).reset_index()
+    coverage.columns = ["Metadata Field", "Average Score"]
+
+    fig_field_coverage = px.bar(
+        coverage,
+        x="Average Score",
+        y="Metadata Field",
+        orientation="h",
+        color="Average Score",
+        color_continuous_scale="Blues",
+        title="Metadata Field Coverage Across All Catalogs",
+    )
+    fig_field_coverage.update_layout(yaxis={'categoryorder':'total ascending'})
+
+    st.plotly_chart(fig_field_coverage, use_container_width=True)
+
+    # heatmap 1
+        # Prepare data
+    z = cat_df_numeric[meta_cols].astype(float).to_numpy()
+    x = list(meta_cols)
+    y = list(cat_df_numeric["Catalog name"].astype(str))
+
+    # Build the heatmap (no annotation_text)
+    fig_heatmap = ff.create_annotated_heatmap(
+        z=z,
+        x=x,
+        y=y,
+        showscale=True,
+        colorscale=[
+            [0.0, "rgb(255,77,77)"],     # red for 0 (No)
+            [0.5, "rgb(255,204,0)"],     # yellow for 0.5 (Some)
+            [1.0, "rgb(0,204,102)"]      # green for 1 (Yes)
+        ],
+        annotation_text=None   # removes numbers
+    )
+
+    # Layout adjustments
+    fig_heatmap.update_layout(
+        title="Metadata Completeness Heatmap (Catalog vs Field)",
+        xaxis_title="Metadata Field",
+        yaxis_title="Catalog Name",
+        width=1600,   # make it wide
+        height=1000,  # make it tall so names fit
+        margin=dict(l=200, r=50, t=80, b=150),  # spacing for labels
+    )
+
+    # Tweak label angles for readability
+    fig_heatmap.update_xaxes(tickangle=-45)
+    fig_heatmap.update_yaxes(automargin=True)
+
+    st.plotly_chart(fig_heatmap, use_container_width=True)
+
+
+    # heatmap 2
+
+    fig_heatmap1 = px.imshow(
+    cat_df_numeric[meta_cols],
+    labels=dict(x="Metadata Field", y="Catalog Name", color="Completeness"),
+    x=meta_cols,
+    y=cat_df_numeric["Catalog name"],
+    color_continuous_scale=[
+        [0.0, "rgb(255,77,77)"],
+        [0.5, "rgb(255,204,0)"],
+        [1.0, "rgb(0,204,102)"]
+    ],
+    )
+
+    fig_heatmap1.update_layout(
+        title="Metadata Completeness Heatmap (Catalog vs Field)",
+        width=1600,
+        height=1000,
+        margin=dict(l=200, r=50, t=80, b=150),
+    )
+    fig_heatmap1.update_xaxes(tickangle=-45)
+
+    st.plotly_chart(fig_heatmap1, use_container_width=True)
+
+
+
+with tab4:
+    st.header("Catalog Mapping status")
+

catalog_scores.csv

@@ -0,0 +1,52 @@
+,Catalog,Total,Number of programs,movie,show,season,episode,sport
+0,Netflix UK,78.37321487958448,53523,87.58,85.41,69.85,77.91,0.0
+1,Channel 5 UK,79.85897394440644,16081,77.0,76.99,72.99,81.0,0.0
+2,All Channel 4,81.06291694583658,37313,80.18,82.84,70.0,82.0,0.0
+3,ITVX UK,79.82202669166713,36266,83.99,81.78,68.0,80.81,0.0
+4,BBC iPlayer UK,79.5271696482445,61321,85.07,84.98,70.83,79.64,84.0
+5,Netflix Norway,76.15902045350585,45909,85.41,82.38,67.23,75.29,0.0
+6,SF Anytime Denmark,81.49234045053869,25525,84.84,84.38,72.86,75.91,0.0
+7,Netflix US,76.42054976420948,56406,85.38,82.93,67.97,76.01,0.0
+8,VG Plus Norway,78.30014328168117,9422,0.0,73.67,62.88,76.78,81.85
+9,Apple TV UK,83.0935448804564,213144,88.53,81.15,67.94,81.0,81.62
+10,SkyShowtime Norway,76.42113719636315,14738,81.84,77.55,73.0,76.0,0.0
+11,Filmoteket,86.29287764350454,2648,86.98,73.0,63.0,75.36,0.0
+12,MTV Katsomo,81.55067180179465,25186,89.06,85.32,74.42,81.59,81.28
+13,Pathè thuis Netherlands,85.45,6578,85.45,0.0,0.0,0.0,0.0
+14,Chili,93.06,12973,93.06,0.0,0.0,0.0,0.0
+15,Maxdome,79.75177596664139,42208,86.58,70.0,83.46,75.98,0.0
+16,Mejane Netherlands,84.48,4185,84.48,0.0,0.0,0.0,0.0
+17,Play Suisse,81.25779218148672,4937,84.28,84.73,69.99,79.0,0.0
+18,Britbox API,81.26944713870029,5155,90.0,89.0,79.0,81.0,0.0
+19,TV4 Play,85.69972552009584,18362,90.33,83.57,75.06,86.21,81.0
+20,Amazon Prime Sweden,91.68148708616673,31323,94.84,88.58,83.74,91.84,0.0
+21,Disney Global,90.0467131120896,67678,97.3,95.12,84.32,89.75,0.0
+22,TV 2 Play Norway,80.55460599334073,901,0.0,81.59,72.78,81.58,77.62
+23,SVT Sweden,82.10870164677989,38803,88.19,82.69,76.2,82.26,0.0
+24,Go Net TV,79.85426,500,80.69,79.32,0.0,0.0,75.09
+25,HBO Max Global,92.21603897591963,109093,95.8,92.63,80.89,92.88,0.0
+26,Canal plus,85.27148061104583,6808,92.64,87.93,83.07,84.7,83.49
+27,Rakuten API V2,88.98855841914173,33602,91.0,83.13,81.13,84.41,0.0
+28,Plex US,87.45695624312563,104555,92.84,91.22,83.13,85.25,0.0
+29,Viaplay Combined,88.41753728304577,39294,93.96,91.03,70.0,87.98,83.97
+30,Joyn DE,84.60275700046458,94708,87.06,84.79,79.17,84.82,0.0
+31,HBO Max Finland,91.38097137014316,35208,95.87,93.08,78.28,92.26,0.0
+32,Paramount Plus US,76.82886449111844,34566,87.52,81.93,68.0,76.96,76.12
+33,SF Anytime Norway,81.9365097173145,22640,82.0,81.0,81.85,81.84,0.0
+34,Apple TV AU,86.7086645785877,3512,94.84,91.62,76.95,87.0,95.33
+35,Amazon Prime South Africa,95.56407676425043,54244,97.08,95.51,90.63,95.65,0.0
+36,Paramount Plus UK,77.00172401001576,10783,88.86,81.05,69.99,76.99,0.0
+37,Apple TV+ US & CA,86.7542266695671,4597,94.81,91.55,76.78,87.0,95.33
+38,CBS,83.2792908047975,5753,0.0,77.71,70.0,84.0,0.0
+39,MGM Plus,81.89496840896038,1741,83.0,77.0,69.97,81.0,0.0
+40,Food network,78.14298572996707,5466,0.0,85.0,68.02,79.0,0.0
+41,Shudder,80.55810945273632,603,82.99,71.0,70.0,76.66,0.0
+42,Fox One,83.0974113225276,4257,88.39,86.86,81.59,82.74,84.2
+43,Hulu Plus,85.67620808839351,144083,90.36,89.39,69.99,86.54,89.77
+44,Apple TV+ US TVOD,85.97134842657486,364269,92.41,85.74,74.29,85.7,0.0
+45,NBC,76.50201542020046,19455,81.76,76.95,68.0,76.65,76.01
+46,Criterion Channel,84.56354499151104,2945,84.84,73.0,66.0,80.35,0.0
+47,Comedy Central,78.38826711749788,1183,89.0,84.88,70.0,77.88,0.0
+48,History,83.23552386412499,6977,80.35,81.83,69.98,84.32,0.0
+49,CW TV,84.78429994700583,9435,91.0,82.91,70.0,84.99,0.0
+50,Pluto TV - Scraping,78.29156520889642,153545,82.28,80.89,75.0,78.0,79.25

countries.csv

@@ -0,0 +1,72 @@
+,zone,count,country_name,log_count
+0,US,727975,United States,5.862117061676528
+1,GB,510425,United Kingdom,5.707932788301151
+2,CA,300189,Canada,5.477396220828122
+3,SE,222351,Sweden,5.34704104017154
+4,FI,202977,Finland,5.307448968965579
+5,NO,191194,Norway,5.28147653071831
+6,DK,169423,Denmark,5.228974930960943
+7,AT,146243,Austria,5.165078057179213
+8,DE,123501,Germany,5.091673990647756
+9,ES,113480,Spain,5.054923154164424
+10,FR,111854,France,5.048655402146333
+11,PL,108995,Poland,5.037410560236007
+12,RO,104160,Romania,5.017705140697976
+13,BG,104132,Bulgaria,5.017588380296173
+14,HU,103267,Hungary,5.01396576608478
+15,CZ,103076,Czechia,5.013161770158209
+16,PT,103032,Portugal,5.012976345312311
+17,IS,101007,Iceland,5.0043557719832785
+18,AR,99835,Argentina,4.999287172371123
+19,SV,99565,El Salvador,4.998111059977296
+20,PY,99545,Paraguay,4.998023813707183
+21,UY,99545,Uruguay,4.998023813707183
+22,CL,99542,Chile,4.998010725254825
+23,CR,99541,Costa Rica,4.998006362349715
+24,HN,99541,Honduras,4.998006362349715
+25,NI,99541,Nicaragua,4.998006362349715
+26,GT,99541,Guatemala,4.998006362349715
+27,EC,99540,Ecuador,4.998001999400775
+28,RS,98972,Serbia,4.995516734493555
+29,AL,98939,Albania,4.995371906028162
+30,ME,98900,Montenegro,4.9952006828235325
+31,HR,98628,Croatia,4.994004629831678
+32,SI,98574,Slovenia,4.993766785745261
+33,BA,98438,Bosnia and Herzegovina,4.99316719323995
+34,BR,97227,Brazil,4.987791352316747
+35,SK,95325,Slovakia,4.979211369856581
+36,IE,87810,Ireland,4.943548922968384
+37,CH,87222,Switzerland,4.940631019978309
+38,NL,86813,Netherlands,4.938589767025189
+39,MT,85033,Malta,4.929592608772758
+40,IT,83615,Italy,4.922289388047232
+41,EE,80972,Estonia,4.908340229918514
+42,LV,80965,Latvia,4.908302684158997
+43,LT,80940,Lithuania,4.908168565657217
+44,BE,73205,Belgium,4.864546677507894
+45,LU,67817,Luxembourg,4.8313449779845135
+46,MK,66323,North Macedonia,4.8216707106294
+47,GR,63778,Greece,4.804677705595455
+48,ZA,54234,South Africa,4.73427964449282
+49,TR,52842,Türkiye,4.72298746538574
+50,UA,39999,Ukraine,4.6020599913279625
+51,CO,37424,Colombia,4.57316180901509
+52,VE,37423,"Venezuela, Bolivarian Republic of",4.573150204465078
+53,BO,37423,"Bolivia, Plurinational State of",4.573150204465078
+54,PE,37423,Peru,4.573150204465078
+55,DO,37419,Dominican Republic,4.573103783163991
+56,PA,37419,Panama,4.573103783163991
+57,MX,37322,Mexico,4.571976544803343
+58,MC,26853,Monaco,4.42900898458089
+59,AD,26716,Andorra,4.426787690457996
+60,MD,26650,"Moldova, Republic of",4.4257135092850675
+61,BY,26617,Belarus,4.425175420725635
+62,LI,26310,Liechtenstein,4.420137354593826
+63,VA,26176,Holy See (Vatican City State),4.417919872997741
+64,SM,26175,San Marino,4.417903281991229
+65,GE,17087,Georgia,4.2326912353484625
+66,AM,17087,Armenia,4.2326912353484625
+67,KZ,17087,Kazakhstan,4.2326912353484625
+68,AU,7992,Australia,3.902709812969877
+69,RU,506,Russian Federation,2.705007959333336
+70,DA,37,,1.5797835966168101

gsheet_loader.py

@@ -0,0 +1,41 @@
+import time
+import gspread
+from google.oauth2.service_account import Credentials
+import pandas as pd
+import streamlit as st
+
+SCOPES = ["https://www.googleapis.com/auth/spreadsheets.readonly"]
+sheet_id = "10nGgqXxunGXo_GI1LxybvsAr1TYSDdNiqqZX6DSTbDA"
+key_path = "service_account_credentials.json"
+
+headers = ['Catalog', 'Mapping status', 'Priority', 'Program kinds', 'Customers', 'Size', 'Size Aprox', 'Needed by', 'Recommendations',	'Scraping?','Custom provider deeplinks', "Scraping link"]
+
+
+def load_gsheet(tab_name: str) -> pd.DataFrame:
+    creds = Credentials.from_service_account_file(key_path, scopes=SCOPES)
+    client = gspread.authorize(creds)
+    w = client.open_by_key(sheet_id)
+
+    for attempt in range(3):  # retry loop
+        try:
+            ws = w.worksheet(tab_name)
+            if tab_name == "Catalog Status":
+                df = pd.DataFrame(ws.get_all_records(expected_headers=headers))
+            else:
+                df= pd.DataFrame(ws.get_all_records())
+            return df
+        except gspread.exceptions.APIError as e:
+            if attempt < 2:
+                st.warning(f"Retrying Google API for {tab_name}... ({attempt+1}/3)")
+                time.sleep(2)  # avoid hammering API
+            else:
+                st.error(f"Failed to load '{tab_name}': {e}")
+                raise e
+            
+def get_data():
+    onboarding = load_gsheet("Catalog Onboarding")
+    time.sleep(1)
+    metadata = load_gsheet("NEW Catalog Data levels")
+    time.sleep(1)
+    mapping = load_gsheet("Catalog Status")
+    return onboarding, metadata, mapping

languages.csv

@@ -0,0 +1,61 @@
+,zone,count,country_name,log_count
+0,AL,559,Albania,2.7481880270062002
+1,AO,161399,Angola,5.2079035303860515
+2,AR,216955,Argentina,5.336371665208993
+3,AT,240338,Austria,5.380824249832158
+4,AU,1698308,Australia,6.230016711105746
+5,BA,558,Bosnia and Herzegovina,2.747411807886423
+6,BE,266294,Belgium,5.425363012128782
+7,BG,15717,Bulgaria,4.196397284437343
+8,BO,216955,"Bolivia, Plurinational State of",5.336371665208993
+9,BR,161399,Brazil,5.2079035303860515
+10,CA,1862685,Canada,6.270139650408456
+11,CH,506346,Switzerland,5.704448241219132
+12,CL,216955,Chile,5.336371665208993
+13,CO,216955,Colombia,5.336371665208993
+14,CY,15036,Cyprus,4.177161199726047
+15,CZ,122498,Czechia,5.0881325434250035
+16,DE,235459,Germany,5.371917139682522
+17,DK,205275,Denmark,5.312338176468242
+18,EE,15019,Estonia,4.176669932668149
+19,ES,216955,Spain,5.336371665208993
+20,FI,230232,Finland,5.362167572511623
+21,FR,164377,France,5.215843692048625
+22,GB,1709281,United Kingdom,6.232813719210268
+23,GR,15595,Greece,4.193013226515948
+24,HR,14617,Croatia,4.164887957547954
+25,HU,124328,Hungary,5.094572440556444
+26,IE,1711800,Ireland,6.233453275746968
+27,IN,51,India,1.7160033436347992
+28,IS,27722,Iceland,4.442840224963883
+29,IT,96014,Italy,4.982339086251471
+30,LT,14923,Lithuania,4.173885240368792
+31,LU,399836,Luxembourg,5.601882980258131
+32,LV,14644,Latvia,4.1656893760176175
+33,MD,123653,"Moldova, Republic of",5.092208169624367
+34,ME,559,Montenegro,2.7481880270062002
+35,MK,558,North Macedonia,2.747411807886423
+36,MT,559,Malta,2.7481880270062002
+37,MX,216955,Mexico,5.336371665208993
+38,MZ,161399,Mozambique,5.2079035303860515
+39,NA,14,Namibia,1.1760912590556813
+40,NL,97189,Netherlands,4.987621582125484
+41,NO,288672,Norway,5.460406165594033
+42,NZ,1698308,New Zealand,6.230016711105746
+43,PE,216955,Peru,5.336371665208993
+44,PL,124408,Poland,5.094851799237066
+45,PT,161399,Portugal,5.2079035303860515
+46,PY,216955,Paraguay,5.336371665208993
+47,RO,123653,Romania,5.092208169624367
+48,RS,558,Serbia,2.747411807886423
+49,SE,245320,Sweden,5.389734726330133
+50,SI,1451,Slovenia,3.161966616364075
+51,SK,111180,Slovakia,5.046030575913449
+52,SM,96014,San Marino,4.982339086251471
+53,TR,74927,Türkiye,4.874644140438004
+54,UA,2197,Ukraine,3.3420276880874717
+55,US,1698308,United States,6.230016711105746
+56,UY,216955,Uruguay,5.336371665208993
+57,VA,96014,Holy See (Vatican City State),4.982339086251471
+58,VE,216955,"Venezuela, Bolivarian Republic of",5.336371665208993
+59,ZA,1698322,South Africa,6.230020291194933