rainfall.py

import pandas as pd

filenames = ['observatory-hill','parramatta','wollongong']
# filenames = ['observatory-hill']
# filename = 'lismore'

for filename in filenames:

	df = pd.read_csv(f'{filename}.csv')
	
	df['date'] = df.Year.map(str) + "-" + df.Month.map(str) + "-" + df.Day.map(str)
	
	df['short_date'] = "1900" + "-" + df.Month.map(str) + "-" + df.Day.map(str)
	
	df['date'] = pd.to_datetime(df['date'], format="%Y-%m-%d")
	
	df['quarter'] = df['date'].dt.quarter
	
	df['fy'] = df['date'].dt.to_period('Q-JUN').dt.qyear.apply(lambda x: str(x-1) + "-" + str(x))
	df['season'] = df['date'].dt.month%12 // 3 + 1
	
	df2 = df[df['short_date'] != "1900-2-29"].copy()
	
	# df2 = df
	df2['rainfall'] = df2['Rainfall amount (millimetres)']/df2['Period over which rainfall was measured (days)']
	df2['rainfall'] = df2['rainfall'].fillna(0)
	
	df2['rainfall_cum'] = df2.groupby(['Year'])['rainfall'].apply(lambda x: x.cumsum())
	
	df2 = df2.rename(columns={"Year": "year"})
	
	df2['year'] = pd.to_numeric(df2['year'])
	
# 	def makeShortDate(row):
# 		if row['quarter'] == 1 or row['quarter'] == 2:
# 			return "1901" + "-" + row['short_date']
# 		else:
# 			return "1900" + "-" + row['short_date']
# 	
# 	
# 	df2['short_date'] = df2.apply(makeShortDate, axis=1)
	
	df2['short_date'] = pd.to_datetime(df2['short_date'], format="%Y-%m-%d")
	
	df2 = df2[df2['year'] >= 1900]
	
	summer = df2[df2['season'] == 1]
	
	summer_totals = summer[['fy','rainfall']].groupby(['fy']).sum()
	
	short = df2[['rainfall_cum','date','short_date','year','fy']]

	totals = short[short['year'] != 2022].groupby(['short_date']).median()
	
	totals = totals.rename(columns={'rainfall_cum': "Median"})
	
	totals['Very dry'] = short[short['year'] != 2022].groupby(['short_date']).quantile(0.10)['rainfall_cum']
	
	totals['Very wet'] = short[short['year'] != 2022].groupby(['short_date']).quantile(0.90)['rainfall_cum']
	totals['Extremely dry'] = short[short['year'] != 2022].groupby(['short_date']).quantile(0.01)['rainfall_cum']

	totals['Extremely wet'] = short[short['year'] != 2022].groupby(['short_date']).quantile(0.99)['rainfall_cum']
    
    
	current_rainfall = df2[df2['year'] == 2022].copy().set_index('short_date')
	
	totals['2022 to date'] = current_rainfall['rainfall_cum']
	
	# totals = totals[:"1900-06-30"]
	
	totals = totals.reset_index()
	totals = totals.rename(columns={"short_date":"date"})
	totals[['date','Very dry','Extremely dry', 'Very wet', 'Extremely wet','Median','2022 to date']].to_csv(f'../cumulative-chart/assets/{filename}-year.csv', index=False)
	
	df3 = df2.copy()
	df3 = df3.drop(['date'], axis=1)
	
	df3 = df3.rename(columns={"short_date":"date"}) 
	df3[['year','date','rainfall']].to_csv(f'../charts/assets/{filename}-year.csv', index=False)