Simple code for processing BTC prices with dataframe and statistical normalization

import datetime
import matplotlib.pyplot as plt
import matplotlib.dates as dates
from pycoingecko import CoinGeckoAPI
import statistics
import pandas as nd

cg = CoinGeckoAPI()
btcprice = cg.get_price(ids='bitcoin', vs_currencies='usd')
mult = cg.get_price(ids='bitcoin,litecoin,ethereum', vs_currencies='usd')
history = cg.get_coin_market_chart_range_by_id(id='bitcoin', vs_currency='usd', from_timestamp='1604906000',
                                               to_timestamp='1605099600')

vmax = max(list(zip(*history['prices']))[1])
vmin = min(list(zip(*history['prices']))[1])
delta = vmax - vmin
# dfObj = nd.DataFrame(history['prices'], columns=['date', 'normalized'])
dfObj = nd.DataFrame.from_records(history['prices'],
                                  columns=['date', 'normalized'])

dfObj['normalized'] = dfObj['normalized'].apply(lambda x: (x - vmin) / delta)

dfObj['date'] = dfObj['date'].apply(lambda x: datetime.datetime.fromtimestamp(x / 1000).strftime("%d/%m-%H:%M"))

print(dfObj)

formatter = dates.DateFormatter('%H:%M')
dfObj.plot(style='k.', subplots=True)

plt.gcf().axes[0].xaxis.set_major_formatter(formatter)

lines = dfObj.plot.line(y='normalized', x='date')

plt.show()