Upload dashboard_covid.py

dashboard_covid.py

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+# -*- coding: utf-8 -*-
+"""Dashboard covid.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1pg2FFvoMPYu0fPgxf4luzAkLoYiFDgJs
+"""
+
+!pip install hvplot
+!pip install jupyter_bokeh
+!pip install panel
+!pip install bokeh
+!pip install holoviews
+!pip install plotly --upgrade
+!pip install pandas numpy plotly ipywidgets
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+!gdown 'https://drive.google.com/uc?export=download&id=1L42Fjkh8-cPIwswMHK5GtyecYdF-_Xjz'
+
+df = pd.read_csv('owid-covid-data.csv')
+
+import pandas as pd
+import plotly.express as px
+import panel as pn
+
+# Enable Panel Plotly extension
+pn.extension('plotly')
+
+# Read the dataset
+df = pd.read_csv("owid-covid-data.csv")
+
+# Convert 'date' column to datetime
+df['date'] = pd.to_datetime(df['date'])
+
+# Add a year column
+df['year'] = df['date'].dt.year
+
+# Drop rows with NaN in 'continent'
+df = df.dropna(subset=['continent'])
+
+# Get unique continents
+continents_list = sorted(df['continent'].unique().tolist())
+
+# Create Panel widgets for interactivity
+continents_dropdown = pn.widgets.Select(name='Select Continent', options=continents_list)
+countries_dropdown = pn.widgets.Select(name='Select Country', options=[])
+
+# Create a year slider from 2020 to 2023
+year_slider = pn.widgets.IntSlider(name='Select Year', start=2020, end=2023, step=1, value=2020)
+
+# Callback to update the country dropdown based on the selected continent
+def update_countries(event):
+    selected_continent = event.new
+    filtered_countries = (
+        df[df['continent'] == selected_continent]['location']
+        .dropna()
+        .unique()
+        .tolist()
+    )
+    filtered_countries.sort()
+    countries_dropdown.options = filtered_countries
+
+# Attach the callback
+continents_dropdown.param.watch(update_countries, 'value')
+
+# Callback for Total Cases line chart, updated with the year slider
+@pn.depends(countries_dropdown, year_slider)
+def total_cases_plot(selected_country, selected_year):
+    if not selected_country:
+        return pn.pane.Markdown("**Please select a country.**")
+
+    filtered_data = df[(df['location'] == selected_country) & (df['year'] == selected_year)]
+    if filtered_data.empty:
+        return pn.pane.Markdown("**No data available for the selected country and year.**")
+
+    fig = px.line(
+        filtered_data,
+        x='date',
+        y='total_cases',
+        title=f"Total Cases in {selected_country} ({selected_year})",
+        labels={'total_cases': 'Total Cases'}
+    )
+    return pn.pane.Plotly(fig)
+
+# Callback for Total Deaths bar plot, updated with the year slider
+@pn.depends(countries_dropdown, year_slider)
+def total_deaths_plot(selected_country, selected_year):
+    if not selected_country:
+        return pn.pane.Markdown("**Please select a country.**")
+
+    filtered_data = df[(df['location'] == selected_country) & (df['year'] == selected_year)]
+    if filtered_data.empty:
+        return pn.pane.Markdown("**No data available for the selected country and year.**")
+
+    fig = px.bar(
+        filtered_data,
+        x='date',
+        y='total_deaths',
+        title=f"Total Deaths in {selected_country} ({selected_year})",
+        labels={'total_deaths': 'Total Deaths'}
+    )
+    return pn.pane.Plotly(fig)
+
+# Global vaccination trend plot, updated with the year slider
+@pn.depends(year_slider)
+def vaccination_trend_plot(selected_year):
+    filtered_data = df[(df['year'] == selected_year) & df['total_vaccinations'].notna()]
+    fig = px.line(
+        filtered_data,
+        x='date',
+        y='total_vaccinations',
+        color='continent',
+        title=f"Global Vaccination Trend in {selected_year}",
+        labels={'total_vaccinations': 'Total Vaccinations'}
+    )
+    return pn.pane.Plotly(fig)
+
+# Correlation heatmap between total cases and total deaths, updated with the year slider
+@pn.depends(year_slider)
+def correlation_heatmap(selected_year):
+    filtered_data = df[(df['year'] == selected_year) & df['total_cases'].notna() & df['total_deaths'].notna()]
+    correlation_data = filtered_data[['total_cases', 'total_deaths']].dropna()
+    correlation_matrix = correlation_data.corr()
+
+    fig = px.imshow(
+        correlation_matrix,
+        text_auto=True,
+        color_continuous_scale='Blues',
+        title=f"Correlation Between Total Cases and Total Deaths ({selected_year})",
+        labels={'color': 'Correlation Coefficient'}
+    )
+    return pn.pane.Plotly(fig)
+
+# Layout the dashboard
+dashboard = pn.Column(
+    pn.pane.Markdown("# COVID-19 Interactive Dashboard"),
+    pn.pane.Markdown("### Select a Continent, Country, and Year to View Data"),
+    continents_dropdown,
+    countries_dropdown,
+    year_slider,
+    pn.Row(total_cases_plot, total_deaths_plot),  # First row: Line + Bar plot
+    pn.Row(vaccination_trend_plot, correlation_heatmap)  # Second row: Line + Heatmap
+)
+
+# Display the dashboard
+dashboard.servable()
+