def q3():
_GLOBAL_RECOVERIES_URL = 'https://data.humdata.org/hxlproxy/data/download' \
'/time_series_covid19_recovered_global_narrow.csv?dest=data_edit&filter01=merge&merge' \
'-url01=https%3A%2F%2Fdocs.google.com%2Fspreadsheets%2Fd%2Fe%2F2PACX' \
'-1vTglKQRXpkKSErDiWG6ycqEth32MY0reMuVGhaslImLjfuLU0EUgyyu2e-3vKDArjqGX7dXEBV8FJ4f%2Fpub' \
'%3Fgid%3D1326629740%26single%3Dtrue%26output%3Dcsv&merge-keys01=%23country%2Bname&merge' \
'-tags01=%23country%2Bcode%2C%23region%2Bmain%2Bcode%2C%23region%2Bsub%2Bcode%2C' \
'%23region%2Bintermediate%2Bcode&filter02=merge&merge-url02=https%3A%2F%2Fdocs.google' \
'.com%2Fspreadsheets%2Fd%2Fe%2F2PACX' \
'-1vTglKQRXpkKSErDiWG6ycqEth32MY0reMuVGhaslImLjfuLU0EUgyyu2e-3vKDArjqGX7dXEBV8FJ4f%2Fpub' \
'%3Fgid%3D398158223%26single%3Dtrue%26output%3Dcsv&merge-keys02=%23adm1%2Bname&merge' \
'-tags02=%23country%2Bcode%2C%23region%2Bmain%2Bcode%2C%23region%2Bsub%2Bcode%2C' \
'%23region%2Bintermediate%2Bcode&merge-replace02=on&merge-overwrite02=on&filter03' \
'=explode&explode-header-att03=date&explode-value-att03=value&filter04=rename&rename' \
'-oldtag04=%23affected%2Bdate&rename-newtag04=%23date&rename-header04=Date&filter05' \
'=rename&rename-oldtag05=%23affected%2Bvalue&rename-newtag05=%23affected%2Binfected' \
'%2Bvalue%2Bnum&rename-header05=Value&filter06=clean&clean-date-tags06=%23date&filter07' \
'=sort&sort-tags07=%23date&sort-reverse07=on&filter08=sort&sort-tags08=%23country%2Bname' \
'%2C%23adm1%2Bname&tagger-match-all=on&tagger-default-tag=%23affected%2Blabel&tagger-01' \
'-header=province%2Fstate&tagger-01-tag=%23adm1%2Bname&tagger-02-header=country%2Fregion' \
'&tagger-02-tag=%23country%2Bname&tagger-03-header=lat&tagger-03-tag=%23geo%2Blat&tagger' \
'-04-header=long&tagger-04-tag=%23geo%2Blon&header-row=1&url=https%3A%2F%2Fraw' \
'.githubusercontent.com%2FCSSEGISandData%2FCOVID-19%2Fmaster%2Fcsse_covid_19_data' \
'%2Fcsse_covid_19_time_series%2Ftime_series_covid19_recovered_global.csv'
global_recoveries_data = download_convert_csv(_GLOBAL_RECOVERIES_URL)
us_data = download_convert_csv(_US_DATA_URL)
# Joining/reformatting data
us_mask = global_recoveries_data['Country/Region'] == 'US'
global_recoveries_data = global_recoveries_data[us_mask]
global_recoveries_data = global_recoveries_data[['Value', 'Date']]
us_combined_data = us_data.merge(global_recoveries_data,
how='inner',
left_on='date',
right_on='Date')
us_combined_data.rename(columns={'Value': 'recoveries'}, inplace=True)
us_combined_data = us_combined_data.drop(columns='Date')
us_combined_data['recoveries'] = us_combined_data['recoveries'].astype(
'int64')
us_combined_data.index = pd.to_datetime(us_combined_data['date'])
us_combined_data.drop(['date'], axis=1, inplace=True)
us_combined_data['recovery rate'] = (us_combined_data['recoveries'] /
us_combined_data['deaths'])
us_combined_data = us_combined_data[
us_combined_data['recovery rate'].notna()]
fig, ax = plt.subplots(1)
us_combined_data['recovery rate'].plot(ax=ax)
ax.set_title('US Covid-19 Recovery Rate')
plt.ylabel('Recoveries / Deaths')
ax.legend(loc='upper left')
ax.set_xlim([datetime.date(2020, 2, 29), us_combined_data.index.max()])
fig.savefig('results/recovery_rates.png',
bbox_inches='tight',
pad_inches=0.2)
def q2():
# FOR TESTING
# Tests question with local copy of the data
try:
us_states_df = download_convert_csv(_US_STATES_DATA_URL)
except requests.exceptions.RequestException as e:
print(SystemExit(e))
print('Using local copy...')
us_states_df = pd.read_csv('us_states_data.csv')
# Plotting
fig, axs = plt.subplots(2, figsize=(15, 8))
# Washington
q2_state_plotter(data=us_states_df,
state_name='Washington',
axs=axs,
subplot=0)
# Minnesota
q2_state_plotter(data=us_states_df,
state_name='Minnesota',
axs=axs,
subplot=1)
# Save
fig.tight_layout(pad=2.0)
fig.savefig('results/floyd_model_cases.png',
bbox_inches='tight',
pad_inches=0.2)
def q4():
df = download_convert_csv(_US_CASES_BY_AGE_URL)
df['Specimen Collection Date'] = df['Specimen Collection Date'].apply(
pd.to_datetime)
df.drop(columns=['Unnamed: 0', 'New Confirmed Cases'],
inplace=True,
errors='ignore')
fig, ax = plt.subplots(1)
all_ages = df.groupby('Specimen Collection Date').sum()
all_ages.rename(
columns={'Cumulative Confirmed Cases': 'Cum. Cases (All Ages)'},
inplace=True)
age_groups = [
'under 18', '18-30', '31-40', '41-50', '51-60', '61-70', '71-80', '81+'
]
for age_group in age_groups:
mask = df['Age Group'] == age_group
label = f'Cum. Cases ({age_group})'
age_group_df = df[mask].merge(all_ages, on='Specimen Collection Date')
age_group_df.rename(columns={'Cumulative Confirmed Cases': label},
inplace=True)
percentages = age_group_df[label] / age_group_df[
'Cum. Cases (All Ages)'] * 100
age_group_df[age_group] = percentages
age_group_df.plot(ax=ax, x='Specimen Collection Date', y=age_group)
ax.set_title('COVID-19 Cases (Percentage) by Age Group in the US')
ax.set_ylabel('Percentage of Total COVID-19 Cases')
fig.savefig('results/cases_by_age.png',
bbox_inches='tight',
pad_inches=0.2)
def q5():
df = download_convert_csv(_US_STATES_DATA_URL)
df['date'] = df['date'].apply(pd.to_datetime)
fig, ax = plt.subplots(1)
case_reductions = {}
new_cases_by_state = {}
for state in df['state'].unique():
mask = df['state'] == state
state_df = df[mask].copy()
new_cases = df[mask]['cases'].diff()
# diff() gives NaN for first row
new_cases.iloc[0] = state_df['cases'].iloc[0]
state_df['new cases'] = new_cases.astype(int)
case_reductions[state] = state_df['new cases'].diff().min()
new_cases_by_state[state] = state_df
top_5 = sorted(case_reductions, key=case_reductions.get)[:5]
for state in top_5:
state_df = new_cases_by_state[state]
state_df.rename(columns={'new cases': state}, inplace=True)
state_df.plot(ax=ax, x='date', y=state, legend=True)
ax.set_title('Largest reductions in COVID-19 cases per day (Top 5)')
ax.set_ylabel('New Cases Per Day')
fig.savefig('results/largest_reductions.png',
bbox_inches='tight',
pad_inches=0.2)
# Plotting on map
fig2, ax2 = plt.subplots(1)
case_reductions_mapping = {k: abs(v) for k, v in case_reductions.items()}
geo_states = combine_state_data(state_col='NAME',
data_col='state',
data=df)
geo_states = update_geo(geo_states, to_drop='state')
geo_states['date'] = pd.to_datetime(geo_states['date'])
for state, reductions in case_reductions_mapping.items():
geo_states.loc[geo_states['state'] == state, 'reductions'] = reductions
geo_states.plot(column='reductions',
linewidth=0.5,
edgecolor='black',
cmap='Reds',
legend=True,
legend_kwds={
'label': "Reductions",
'orientation': "horizontal"
},
ax=ax2)
ax2.set_title('Largest Covid-19 Case Reductions in One Day as of: ' +
str(geo_states['date'].max().date()))
ax2.axis('off')
fig2.savefig('results/reductions_map.png', bbox_inches='tight')
def q1():
import numpy as np
flu_hospitalizations2019 = 490561
us_data = download_convert_csv(_US_COMPREHENSIVE_URL)
# Convert to Time Series
us_data.index = pd.to_datetime(us_data['date'], format='%Y%m%d')
us_data.drop(['date'], axis=1, inplace=True)
# Replace dates with numeric counter for use in regression model training
us_numeric = us_data.assign(date=range(len(us_data)))
# Linear Regression
us_numeric = us_numeric[us_numeric['hospitalizedCumulative'].notna()]
x, y = us_numeric[['date']], us_numeric['hospitalizedCumulative']
model = LinearRegression()
model.fit(x, y)
pred = model.predict(us_numeric[['date']])
pred_df = pd.DataFrame({'linear predictions': pred})
pred_df.index = us_numeric.index
us_data['linear predictions'], us_numeric[
'linear predictions'] = pred_df, pred_df
# Plotting
fig, ax = plt.subplots(1)
us_data['hospitalizedCumulative'].plot(ax=ax, ylim=0, label='Hospitalized')
# us_data['linear predictions'].plot(ax=ax, ylim=0, label='Linear Predictions') # Regression line for testing
ax.set_xlim([datetime.date(2020, 3, 1), datetime.date(2020, 12, 2)])
ax.set_ylim([0, 600000])
print(us_numeric[['date', 'linear predictions']].to_string())
# Creates regression line on graph
ax.axhline(y=flu_hospitalizations2019,
color='r',
linestyle='-',
label='2019 Flu Hospitalizations')
ax.axvline(x=18552, color='r', linestyle='-')
# Plotting regression line with point-slope form
x = np.linspace(0, 19000, 50)
y = 2210.45379 * (x - 18325) - 11633.431643
ax.plot(x, y, label='Linear Predictions')
ax.legend(loc='upper left')
ax.set_title('US Hospitalizations')
fig.savefig('results/us_hospitalizations',
bbox_inches='tight',
pad_inches=0.2)