def _get_currency_price(self, bot, update, groups):
currency = groups[0]
info = self._get_info(currency.replace("_", "-"))
text = "Current {} price - ${}".format(info["name"], format_thousands(info["price_usd"], sep=" "))
bot.send_message(chat_id=update.message.chat_id, text=text)
# Look at only observations with revenue per retailer.
stats = retailer_stats[~retailer_stats['revenue_per_retailer'].isnull()]
# Run a regression of sales per retailer on retailers per 100,000 adults.
Y = stats['revenue_per_retailer']
X = stats['retailers_per_100_000']
X = sm.add_constant(X)
regression = sm.OLS(Y, X).fit()
print(regression.summary())
# Interpret the relationship.
beta = regression.params.values[1]
statement = """If retailers per 100,000 adults increases by 1,
then everything else held constant one would expect
revenue per retailer to change by {}.
""".format(format_thousands(beta))
print(statement)
# Visualize the regression.
ax = stats.plot(x='retailers_per_100_000',
y='revenue_per_retailer',
kind='scatter')
abline_plot(model_results=regression, ax=ax)
plt.show()
#--------------------------------------------------------------------------
# Create a beautiful visualization.
#--------------------------------------------------------------------------
# Set chart defaults.
plt.style.use('fivethirtyeight')
try:
sample_forecast = forecasts[lab_id]
timeseries = sample_forecast.resample('Y').sum()
timeseries = timeseries.loc[
timeseries.index >= pd.to_datetime('2022-01-01')
]
code = lab_id.replace('WA', '')
price = labs_data.loc[code]['panel_price']
revenue = timeseries * price
total_revenue = revenue.sum()
total_samples = timeseries.sum()
five_year_forecasts[lab_id] = {
'revenue': revenue.sum(),
'formatted_revenue': format_millions(total_revenue),
'total_samples': total_samples,
'formatted_total_samples': format_thousands(total_samples),
'price': price,
}
except KeyError:
pass
# Print 5 year forecasted revenue for each lab.
five_year_forecast_data = pd.DataFrame.from_dict(five_year_forecasts, orient='index')
five_year_forecast_data.sort_values(by='revenue', ascending=False, inplace=True)
print('5 Year Projected Revenue')
print(five_year_forecast_data[['formatted_revenue', 'formatted_total_samples', 'price']])
#------------------------------------------------------------------------------
# Future work: What are the prices of required instruments?
#------------------------------------------------------------------------------
# Notes and data source.
plt.text(
-125,
44.25,
'Data Source: Washington State Leaf Traceability Data',
ha='left',
va='bottom',
size=20,
color='#000000',
)
# Annotate statistics
# stats = monthly_plants.loc[monthly_plants.month == date].iloc[0]
total_plants = data['total_plants'].sum()
if total_plants > 1_000:
total_plants = format_thousands(total_plants)
total_cultivators = len(data['mme_id'].unique())
plt.text(
-124.875,
49.125,
month,
ha='left',
va='bottom',
size=22,
color='#000000'
)
plt.text(
-124.875,
48.875,
'Cultivators: %i' % total_cultivators,
ha='left',
