[Python] How to visualize recent orders for a crypto exchange

I would like to visualize the recent trades from a crypto exchange. I use ccxt to get the recent trades:

import ccxt
binance = ccxt.binance({
'apiKey': 'KEY',
'secret': 'SECRET'
})

the_tape = binance.fetch_trades('ETHBUSD')


here is what my API call returns:

[
{
'info': {
'a': '157594085',
'p': '3224.42000000',
'q': '0.00820000',
'f': '198701298',
'l': '198701298',
'T': '1631528034145',
'm': False,
'M': True
},
'timestamp': 1631528034145,
'datetime': '2021-09-13T10:13:54.145Z',
'symbol': 'ETH/BUSD',
'id': '157594085',
'order': None,
'type': None,
'side': 'buy',
'takerOrMaker': None,
'price': 3224.42,
'amount': 0.0082,
'cost': 26.440244,
'fee': None
},
{
'info': {
'a': '157594086',
'p': '3224.48000000',
'q': '0.10080000',
'f': '198701299',
'l': '198701302',
'T': '1631528034545',
'm': False,
'M': True
},
'timestamp': 1631528034545,
'datetime': '2021-09-13T10:13:54.545Z',
'symbol': 'ETH/BUSD',
'id': '157594086',
'order': None,
'type': None,
'side': 'buy',
'takerOrMaker': None,
'price': 3224.48,
'amount': 0.1008,
'cost': 325.027584,
'fee': None
},


And I make it into a DataFrame:

new_tape = [{'datetime': el['datetime'], 'side': el['side'], 'price': el['price'],
'amount': el['amount'], 'total': el['cost']} for el in the_tape]


This is my pandas DataFrame:

datetime side price amount total
0 2021-09-13 09:34:01.042 buy 3208.97 0.0234 75.089898
1 2021-09-13 09:34:01.091 buy 3208.97 0.0200 64.179400
2 2021-09-13 09:34:01.148 buy 3208.97 0.1444 463.375268
3 2021-09-13 09:34:01.242 buy 3209.16 0.6205 1991.283780
4 2021-09-13 09:34:01.420 sell 3209.00 0.2500 802.250000


I would like to get two histograms: (1) cumulated histogram with two bars: total bought and total sold (2) weighted histogram with the amounts bought and sold, something that would look like this:

I would like to use seaborn in order to be able to save the images and maybe upload them to a website or word document.

I found this code but I don't know how to adapt it and what "binwidth" is:

fig, ax = plt.subplots()

ax.set_title(f"Last update: {t} (ID: {last_update_id})")

sns.histplot(x="price", weights="quantity", hue="side", binwidth=binwidth, data=data, ax=ax)
sns.scatterplot(x="price", y="quantity", hue="side", data=data, ax=ax)

ax.set_xlabel("Price")
ax.set_ylabel("Quantity")

plt.show()


Any help or suggestion as to how to best represent this data would be deeply appreciated.

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