cashless.python

# %%
import json
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime 

def datetime_from_js_isoformat(string: str) -> datetime:
    """Creates a datetime object from a JavaScript ISO format string."""

    if string.endswith('Z'):
        return datetime.fromisoformat(string[:-1])

    return datetime.fromisoformat(string)

with open('./cashless.json', 'r', encoding="utf-8") as f:
  json_data = json.load(f)

data = []

for t in json_data:
    if t['type'] == "transaction":
        date = datetime_from_js_isoformat(t['date'])
        location = t['location_name']

        for r in t['rows'] :
            if len(r['payments']) == 0:
                continue
            item_name = r['item']
            if item_name == "Frais d'activation":
                continue
            assert len(r['totals']) == 1
            price = r['totals'][0]['amount'] / -100
            quantity = r['quantity']
            assert r['totals'][0]['decimal_places'] == 2
            data.append((date, location, item_name, price, quantity))

df = pd.DataFrame(data, columns=['date', 'location', 'name', 'price', 'quantity'])

df['price'].sum()

# %%
df.groupby(pd.Grouper(key='date',freq='D')).sum().plot.bar()


# %%
df[(df['date'] > '2022-06-19 04:00') & (df['date'] < '2022-06-20 23:59')].sort_index(ascending=False)

# %%
df.groupby(['location']).sum().sort_values(by=['price'], ascending=False)

# %%
df.groupby(['name']).sum().sort_values(by=['price'], ascending=False)