bitcoin price vs energy usage

chart.py

import pandas as pd

energy = pd.read_csv('energy.csv')
price = pd.read_csv('btcusd.csv', skiprows=1, header='infer')
day_seconds = 24*60*60
energy['Timestamp'] = (energy['Timestamp'] // day_seconds) * day_seconds
energy.set_index('Timestamp', drop=True, inplace=True)

# Some are in ms precision, others are not. Convert everything to seconds
price['Unix Timestamp'] = price['Unix Timestamp'].apply(lambda x: x if x < 1800_000_000 else x//1000)
price['Unix Timestamp'] = (price['Unix Timestamp'] // day_seconds) * day_seconds
price.set_index('Unix Timestamp', drop=True, inplace=True)

price.sort_index(inplace=True)
energy.sort_index(inplace=True)

combined = price.join(energy)

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

from matplotlib.dates import DateFormatter
import matplotlib.pyplot as plt

price_per_energy = combined['Close'] / combined['GUESS']
price_per_energy = price_per_energy.dropna()


plt.figure()
# Create figure and plot space
fig, ax = plt.subplots(figsize=(12, 12))

# Add x-axis and y-axis
ax.plot(pd.to_datetime(price_per_energy.index.values, unit='s'),
       price_per_energy,
       color='purple')

# Set title and labels for axes
ax.set(xlabel="Date",
       ylabel="BTCUSD Close / Est yearly TWh",
       title="Price/energy of Bitcoin over time")

# Define the date format
date_form = DateFormatter("%b %Y")
ax.xaxis.set_major_formatter(date_form)

plt.text(x=0.1, y=0.01, s="""
data:
BTCUSD Gemini - http://www.cryptodatadownload.com/data/gemini/
Cambridge Bitcoin Electricity Consumption Index - https://cbeci.org/
""", fontsize=13, ha="left", transform=fig.transFigure)

plt.text(x=0.98, y=0.02, s="<3 proof of stake, proto", fontsize=13, ha="right", transform=fig.transFigure)

fig.subplots_adjust(bottom=0.15, top=0.95, left=0.08, right=0.98)

plt.show()