TT VWAP in Python

data.csv

Date	Open	High	Low	Close	Volume
2020-05-06 18:15:00-04:00	2832	2838.5	2832	2838	3750
2020-05-06 18:30:00-04:00	2838	2838.5	2826	2827.75	6864
2020-05-06 18:45:00-04:00	2827.75	2830.5	2827	2829.5	2937
2020-05-06 19:00:00-04:00	2829.5	2830.5	2823	2826.5	4619
2020-05-06 19:15:00-04:00	2826.75	2829.5	2825.25	2827.75	3610
2020-05-06 19:30:00-04:00	2827.75	2829.5	2825.5	2826.75	2460
2020-05-06 19:45:00-04:00	2826.75	2830.75	2826.25	2830.5	2531
2020-05-06 20:00:00-04:00	2830.5	2833.5	2830.25	2833.5	2361

the codez

import pandas as pd
import mplfinance as mpf
import math

df = pd.read_csv('./data.csv', sep=',', quotechar='"')

df.set_index(['Date'], inplace=True)
df.index = pd.to_datetime(df.index)
df.index.name = 'Date'

# from here = https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/volume-weighted-average-price-vwap/
df['VWAP'] = (df.Volume * (df.High + df.Low) / 2).cumsum() / df.Volume.cumsum()
df['VWAP_MEAN_DIFF'] = ((df.High + df.Low) / 2) - df.VWAP
df['SQ_DIFF'] = df.VWAP_MEAN_DIFF.apply(lambda x: math.pow(x, 2))
df['SQ_DIFF_MEAN'] = df.SQ_DIFF.expanding().mean()
df['STDEV_TT'] = df.SQ_DIFF_MEAN.apply(math.sqrt)

stdev_multiple_1 = 1.28
stdev_multiple_2 = 2.01
stdev_multiple_3 = 2.51

df['STDEV_1'] = df.VWAP + stdev_multiple_1 * df['STDEV_TT']
df['STDEV_N1'] = df.VWAP - stdev_multiple_1 * df['STDEV_TT']

addplot  = [
    mpf.make_addplot(df['VWAP']),
    mpf.make_addplot(df['STDEV_1']),
    mpf.make_addplot(df['STDEV_N1']),
]

mpf.plot(df, type='candle', addplot=addplot)

Do you have a numpy version of vwap with bands?