import pandas as pd import matplotlib.pyplot as plt # Load data from CSV file covid_data = pd.read_csv('covid_data.csv') # Plot daily cases plt.plot(covid_data['date'], covid_data['cases']) plt.xlabel('Date') plt.ylabel('Cases') plt.show()
from fbprophet import Prophet # Load data from CSV file covid_data = pd.read_csv('covid_data.csv') covid_data = covid_data[['date', 'cases']].rename(columns={'date': 'ds', 'cases': 'y'}) # Create Prophet model model = Prophet() model.fit(covid_data) # Make future predictions future = model.make_future_dataframe(periods=30) forecast = model.predict(future) # Plot forecasted cases model.plot(forecast) plt.xlabel('Date') plt.ylabel('Cases') plt.show()
import plotly.express as px # Load data from CSV file covid_data = pd.read_csv('covid_data.csv') # Create country-level dataset country_cases = covid_data.groupby(['country'])['cases'].sum().reset_index() # Create heatmap fig = px.choropleth(country_cases, locations='country', locationmode='country names', color='cases', hover_name='country', title='COVID-19 Cases by Country', color_continuous_scale='reds') fig.show()Package library: Plotly (plotly.express) Overall, there are many package libraries available for analyzing and visualizing COVID-19 data in Python, including pandas, Prophet, Plotly, and others. The choice of package library depends on the specific analysis or visualization goals.