Task: write a script to get a nice CSV file of natural gas prices.

Challenge 1

# 1. Main data wanted is daily prices. (I downloaded the Henry_Hub_Natural_Gas_Spot_Price.csv with view history as daily)

import pandas as pd

dfg = pd.read_csv('Henry_Hub_Natural_Gas_Spot_Price.csv', skiprows=4)
dfg.index = pd.to_datetime(dfg["Day"],format='%m/%d/%Y')
dfg.to_csv('gas_byday.csv', index=False)  # => first output gas_byday.csv file

# 1.1 Bonus points for doing other granularities (e.g. month) - do them in separate CSV files with sensible naming Resulting CSV should have two columns: Date and Price. You may need to normalize the data to get this and/or work out dates. For months the Date should be the first date of the month.

dfg_month = dfg['Henry Hub Natural Gas Spot Price Dollars per Million Btu'].resample('M').sum()
df = pd.DataFrame(dfg_month, index=dfg_month.index.strftime("%d/%m/%Y"))
df.to_csv('gas_bymonth.csv', index=True)  # => second output gas_bymonth.csv file

# We want a script for this and we want this script to be in python (we'd allow node or bash or go script at a push but prefer python)
# Why a script? Ans: We'll want to run this again and again as they release new data. You could copy and paste data into Excel/Google Docs by hand, and then export the CSV. But that would be tedious, time consuming and error prone to do month after month
# Please use simple python libraries wherever possible rather than use a framework
# Bonus items (optional - extra kudos if you do either of these!):

# Make your repository into a Tabular Data Package - here's a guide
# Do a line graph visualization of the data in HTML + Javascript using e.g. vega or direct in D3
# Deploy your repo somewhere so this visualization is visitable online e.g. via github or gitlab pages