import warnings
from statsmodels.tools.sm_exceptions import ConvergenceWarning, ValueWarning
warnings.filterwarnings('ignore', category=ValueWarning)
warnings.filterwarnings('ignore', category=ConvergenceWarning)
warnings.filterwarnings('ignore', category=RuntimeWarning)
warnings.filterwarnings('ignore')
# Let us import basic packages we need for this lesson
import plotly.express as px
import pandas as pd

# Data setup
covid_url = "https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/jhu/new_cases.csv"
df = pd.read_csv(covid_url)
print(df.shape)
df.head()

# Looking at the columns
print(df.columns.tolist())

# Data preparation
latest = df.tail(1).T.copy()
latest.columns = ['new_cases']
latest = latest.reset_index()
latest = latest.rename(columns={'index':'country'})
latest = latest[latest['country']!='date']  # Skip the 'date' row if present
latest['new_cases'] = pd.to_numeric(latest['new_cases'], errors='coerce')
latest

# Rename and check for missing values
latest = latest.dropna()
latest = latest[latest['new_cases'] >= 0]
latest

# Simple visualization of new cases by country
latest.sort_values('new_cases', ascending=False).head(10).plot.barh(x='country', y='new_cases', figsize=(8,5), legend=False, color='skyblue', title='Top 10 Countries: New Daily COVID-19 Cases')

# Making our first choropleth world map!
fig = px.choropleth(latest, locations='country', locationmode='country names', color='new_cases',
                  color_continuous_scale='Reds', title='New COVID-19 Cases by Country (latest)',
                  height=500)
fig.show()

# Try custom colors: green to blue
fig2 = px.choropleth(latest, locations='country', locationmode='country names', color='new_cases',
                    color_continuous_scale='GnBu', title='COVID-19 New Cases (Green to Blue)', height=500)
fig2.show()

# Highlighting certain countries only
countries_of_interest = ['United States', 'India', 'Brazil', 'France', 'Russia']
focus = latest[latest['country'].isin(countries_of_interest)]
fig3 = px.choropleth(focus, locations='country', locationmode='country names', color='new_cases',
                     color_continuous_scale='Oranges', title='Select Countries: New COVID-19 Cases', height=400)
fig3.show()

# Customizing the color bar and map style
fig4 = px.choropleth(latest, locations='country', locationmode='country names', color='new_cases',
                    color_continuous_scale='Blues', title='Custom Color Bar and Style', height=500)
fig4.update_layout(coloraxis_colorbar=dict(title='New Cases', tickprefix='', ticks='outside'))
fig4.update_geos(projection_type='natural earth', showland=True, landcolor='lightgray')
fig4.show()

# Making country codes work with Plotly
gapminder = px.data.gapminder().drop_duplicates('country')
gapminder = gapminder.set_index('country', drop=False)
# Some countries in latest may not be in gapminder; get ISO codes stringently
latest['code'] = latest['country'].map(gapminder['iso_alpha'])
latest_with_code = latest.dropna(subset=['code']).copy()
latest_with_code = latest_with_code[latest_with_code['code'].str.len() == 3]
fig5 = px.choropleth(latest_with_code, locations='code', locationmode='ISO-3', color='new_cases',
                    color_continuous_scale='PuRd', title='Choropleth with ISO-3 Codes', height=500)
fig5.show()

# Making a map for US states (mini-project part 1)
df_states = pd.DataFrame({
    'state': ['California', 'Texas', 'Florida', 'New York', 'Illinois'],
    'cases': [40000, 32000, 28000, 25000, 22000]
})
fig6 = px.choropleth(df_states, locations='state', locationmode='USA-states', color='cases',
                    color_continuous_scale='OrRd', title='New Cases: Top 5 US States', scope='usa', height=400)
fig6.show()

# Mini-project part 2: Input your own data
states = []
case_counts = []
for i in range(3):
    state = input("Enter a US state name: ")
    cases = int(input("Enter new case count: "))
    states.append(state)
    case_counts.append(cases)
df_input = pd.DataFrame({'state': states, 'cases': case_counts})
fig7 = px.choropleth(df_input, locations='state', locationmode='USA-states', color='cases',
                    color_continuous_scale='BuPu', title='Your Own US States Map', scope='usa', height=380)
fig7.show()

# Optimizing data for mapping: Standardizing names
latest['country'] = latest['country'].str.strip()
latest['country'] = latest['country'].replace({'United States of America': 'United States'})

# Troubleshooting: What if nothing shows up?
if latest.empty: print("No data in the table! Check your previous steps.")
elif latest['country'].nunique() < 2: print("Not enough unique countries to plot.")
else: print("Data looks fine!")

# Extra tip: Save your map to a file
fig.write_html('choropleth_map.html')

# Challenge: Try a new color scale and only display countries with over 1000 new cases
high_cases = latest[latest['new_cases'] > 1000]
fig8 = px.choropleth(high_cases, locations='country', locationmode='country names', color='new_cases',
                    color_continuous_scale='aggrnyl', title='Countries with over 1000 New Cases', height=400)
fig8.show()