projekt2, projekt3

Prezentacja_wykresy.ipynb

@@ -1,19 +1,8 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c3968b76-b939-4c1c-9b84-29e51f8ae69f",
-   "metadata": {},
-   "source": [
-    "# Statystyki miliarderów\n",
-    "**Zbiór danych zawiera statystyki dotyczące miliarderów na świecie, zawarte są również ich dane osobowe, branże którymi się zajmują oraz firmy.**\n",
-    "\n",
-    "[Źródło](https://www.kaggle.com/datasets/nelgiriyewithana/billionaires-statistics-dataset)"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 1,
    "id": "cb8f7a05-40a5-46cf-8900-88da58e8690f",
    "metadata": {},
    "outputs": [],
@@ -28,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 2,
    "id": "5a5c0cd6-6606-4983-b945-eeed8b5afe24",
    "metadata": {},
    "outputs": [],
@@ -492,26 +481,21 @@
    ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "# Plot the birth month distribution\n",
+    "\n",
     "ax1.set_title('Miesiące urodzenia miliarderów', fontsize=16)\n",
     "sns.histplot(data=df['birthMonth'], bins=12, color='skyblue', kde=True, ax=ax1)\n",
     "ax1.set_xlabel('Miesiąc urodzenia', fontsize=12)\n",
     "ax1.set_ylabel('Liczba miliarderów', fontsize=12)\n",
     "\n",
-    "# Plot the birth day distribution\n",
     "ax2.set_title('Dni urodzenia miliarderów', fontsize=16)\n",
     "sns.histplot(data=df['birthDay'], bins=31, color='skyblue', kde=True, ax=ax2)\n",
     "ax2.set_xlabel('Dzień urodzenia', fontsize=12)\n",
     "ax2.set_ylabel('Liczba miliarderów', fontsize=12)\n",
     "\n",
-    "# Set the style and remove the left despine\n",
     "sns.set_style(\"whitegrid\")\n",
     "sns.despine(left=True)\n",
     "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
-    "\n",
-    "# Adjust the spacing between subplots\n",
     "plt.subplots_adjust(wspace=0.4)\n",
-    "\n",
     "plt.show()"
    ]
   },
@@ -831,7 +815,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,

dashboard/README.md

@@ -0,0 +1 @@
+This template gives you a more "complete" dashboard for exploring the tips dataset. For an overview of what's here, visit [this article](https://shiny.posit.co/py/docs/user-interfaces.html).

dashboard/app.py

@@ -0,0 +1,231 @@
+import faicons as fa
+import plotly.express as px
+from shared import app_dir, billionaires
+from shinywidgets import render_plotly
+import pandas as pd
+from shiny import reactive, render
+from shiny.express import input, ui
+
+
+worth_rng = (min(billionaires.finalWorth), max(billionaires.finalWorth))
+
+ui.page_opts(title="Billionaires dataset", fillable=False)
+
+with ui.sidebar(open="desktop"):
+    ui.input_slider(
+        "net_worth",
+        "Net worth range in billions",
+        min=worth_rng[0],
+        max=worth_rng[1],
+        value=worth_rng,
+        pre="$",
+        post="B",
+    )
+    ui.input_selectize(
+        "categories",
+        "Busniess categories",
+        billionaires['category'].unique().tolist(),
+        selected="Technology",
+    )
+    ui.input_action_button("reset", "Reset filter")
+
+# Add main content
+ICONS = {
+    "user": fa.icon_svg("user", "regular"),
+    "currency-dollar": fa.icon_svg("dollar-sign"),
+    "ellipsis": fa.icon_svg("ellipsis"),
+    "age": fa.icon_svg("hourglass-half")
+}
+
+with ui.layout_columns(fill=False):
+    with ui.value_box(showcase=ICONS["user"]):
+        "Total billionaires"
+
+        @render.express
+        def total_billionaires():
+            billionaires_data().shape[0]
+
+    with ui.value_box(showcase=ICONS["currency-dollar"]):
+        "Average net worth"
+
+        @render.express
+        def average_net_worth():
+            d = billionaires_data()
+            if d.shape[0] > 0:
+                avg_worth = d['finalWorth'].mean()
+                f"${avg_worth:,.2f}B"
+
+    with ui.value_box(showcase=ICONS["age"]):
+        "Average age"
+
+        @render.express
+        def average_bill():
+            d = billionaires_data()
+            if d.shape[0] > 0:
+                avg_age = d['age'].mean()
+                f"{avg_age:.1f}"
+
+
+with ui.layout_columns(col_widths=[6, 6, 12]):
+    with ui.card(full_screen=True):
+        ui.card_header("Billionaires data")
+
+        @render.data_frame
+        def table():
+            return render.DataGrid(billionaires_data())
+
+    with ui.card(full_screen=True):
+        ui.card_header("Billionaires by Country")
+
+        @render_plotly
+        def billionaires_map():
+            country_counts = billionaires_data()['country'].value_counts()
+            country_data = pd.DataFrame({
+                'country': country_counts.index,
+                'billionaires': country_counts.values
+            })
+
+            fig = px.choropleth(
+                country_data,
+                locations="country",
+                locationmode='country names',
+                color="billionaires",
+                hover_name="country",
+                color_continuous_scale="Plasma",
+                projection="orthographic",
+                labels={'billionaires': 'Number of Billionaires'},
+            )
+            fig.update_layout(
+                geo=dict(
+                    showframe=False,
+                    projection_type="orthographic",
+                    showcoastlines=True,
+                    showocean=True,
+                    showland=True,
+                    showcountries=True,
+                    landcolor='rgb(243, 243, 243)',
+                    countrycolor='rgb(204, 204, 204)'
+                ),
+                showlegend=False,
+            )
+            return fig
+
+    with ui.card(full_screen=True):
+        with ui.card_header(class_="d-flex justify-content-between align-items-center"):
+            "Top Billionaires by Net Worth"
+            with ui.popover(title=None):
+                ICONS["ellipsis"]
+                ui.input_radio_buttons(
+                    "gender_split",
+                    "Choose gender",
+                    ["M", "F"],
+                    selected="M",
+                    inline=True,
+                )
+
+        @render_plotly
+        def top_billionaires_plot():
+            gender_filter = input.gender_split()
+            filtered_data = billionaires_data()[billionaires_data()["gender"] == gender_filter]
+            top_10 = filtered_data.nlargest(10, 'finalWorth')
+            
+            fig = px.bar(
+                top_10,
+                x='finalWorth',
+                y='personName',
+                color='personName',
+                orientation='h',
+                labels={'finalWorth': 'Net Worth (Billion $)', 'personName': 'Name'},
+                facet_row_spacing=0.1,
+            )
+            fig.update_layout(
+                xaxis_title='Net Worth (Billion $)',
+                yaxis_title='Name',
+                showlegend=False,
+            )
+            return fig
+    
+with ui.layout_columns(col_widths=[6, 6]):
+    with ui.card(full_screen=True):
+        ui.card_header("Number of Billionaires and Total Tax Rate by Country'")
+
+        @render_plotly
+        def tabl():
+            billionaires_per_country = billionaires_data()['country'].value_counts().reset_index()
+            billionaires_per_country.columns = ['country', 'number_of_billionaires']
+
+            tax_rate_df = billionaires_data()[['country', 'total_tax_rate_country']].drop_duplicates()
+
+            merged_df = pd.merge(billionaires_per_country, tax_rate_df, on='country')
+
+            fig = px.scatter(merged_df, 
+                 x='number_of_billionaires', 
+                 y='total_tax_rate_country', 
+                 hover_name='country',
+                 labels={
+                     'number_of_billionaires': 'Number of Billionaires',
+                     'total_tax_rate_country': 'Total Tax Rate (%)'
+                 },
+                 size='number_of_billionaires', 
+                 size_max=60,
+                 color='total_tax_rate_country', 
+                 color_continuous_scale=px.colors.sequential.Plasma) 
+
+            fig.update_layout(
+                title_font_size=20,
+                xaxis_title_font_size=16,
+                yaxis_title_font_size=16,
+                plot_bgcolor='rgba(240, 240, 240, 0.8)',
+                paper_bgcolor='rgba(0, 0, 0, 0)',
+                xaxis=dict(showgrid=True, gridcolor='lightgrey', type='log'),
+                yaxis=dict(showgrid=True, gridcolor='lightgrey')
+                
+            )
+
+            return fig
+
+
+    with ui.card(full_screen=True):
+        ui.card_header("Billionaires by City")
+
+        @render_plotly
+        def billionaire():
+            top_birth_cities = billionaires_data()['city'].value_counts().head(20)
+
+            fig = px.bar(
+                top_birth_cities,
+                x=top_birth_cities.index,
+                y=top_birth_cities.values,
+                orientation='v',
+                labels={'index': 'City', 'values': 'Number of Billionaires'},
+                color=top_birth_cities.values,
+                color_continuous_scale='Viridis',
+            )
+
+            fig.update_traces(
+                customdata=top_birth_cities.values,
+                hovertemplate='<br>Number of Billionaires: %{customdata}'
+            )
+            fig.update_layout(
+                xaxis_title='Number of Billionaires',
+                yaxis_title='City',
+                showlegend=False,
+            )
+
+            return fig
+    
+ui.include_css(app_dir / "styles.css")
+
+@reactive.calc
+def billionaires_data():
+    net_worth = input.net_worth()
+    idx1 = billionaires['finalWorth'].between(net_worth[0], net_worth[1])
+    idx2 = billionaires['category'].isin([input.categories()])
+    return billionaires[idx1 & idx2]
+
+
+@reactive.effect
+@reactive.event(input.reset)
+def _():
+    ui.update_slider("net_worth", value=worth_rng)
+    ui.update_checkbox_group("categories", selected=[billionaires['category'].unique().tolist()])

dashboard/requirements.txt

@@ -0,0 +1,6 @@
+faicons
+shiny
+shinywidgets
+plotly
+pandas
+ridgeplot

dashboard/shared.py

@@ -0,0 +1,7 @@
+from pathlib import Path
+
+import pandas as pd
+
+app_dir = Path(__file__).parent
+billionaires = pd.read_csv(app_dir / "data.csv")
+billionaires["finalWorth"] = billionaires["finalWorth"] / 1000

dashboard/styles.css

@@ -0,0 +1,12 @@
+:root {
+  --bslib-sidebar-main-bg: #f8f8f8;
+}
+
+.popover {
+  --bs-popover-header-bg: #222;
+  --bs-popover-header-color: #fff;
+}
+
+.popover .btn-close {
+  filter: var(--bs-btn-close-white-filter);
+}