# automated script that grabs all performance measurements and averages them
# to be run on Mondays

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# read in the csv file and put into dataframe
df = pd.read_csv('/Users/joconnor/Desktop/Me/performance_february_14_2019.csv')

# convert date column to datetime object
df['date'] = pd.to_datetime(df['date'])

# set start date, last date, and derive number of weeks from there
start_date = df['date'].iloc[0]
last_date = df['date'].iloc[-1]
next_date = pd.date_range(start_date, last_date, freq='W-MON')

# give yourself an empty list to write all weekly calculations to
columns = []

# run calculation over all data
for index, date in enumerate(next_date):
	if index == len(next_date)-1:
		continue
	else:
		week = df['date'].between(date, (next_date[index+1] - pd.offsets.Day(1)))
		grouped_week = df[week].groupby(['action'], as_index=False).duration.mean()
		named_week = grouped_week.rename(columns={'duration': 'Week of ' + date.strftime('%b %d')})
		columns.append(named_week)

# combine all date into a pandas dataframe
df2 = pd.merge(*columns, on='action', how='right')

# export it to a CSV (change to sep='/t' if you want it tab deliniated)
df2.to_csv('auto_performance_calculator.csv', sep=',', encoding='utf-8', index=False)