Jake Pullen
134 lines
4.6 KiB
Python
134 lines
4.6 KiB
Python
import polars as pl
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import plotly.express as px
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import pandas as pd
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import logging
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# import datetime
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class data_components:
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accounts = pl.read_parquet("data/warehouse/accounts.parquet")
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categories = pl.read_parquet("data/warehouse/categories.parquet")
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dates = pl.read_parquet("data/warehouse/dates.parquet")
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payees = pl.read_parquet("data/warehouse/payees.parquet")
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scheduled_transactions = pl.read_parquet(
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"data/warehouse/scheduled_transactions.parquet"
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)
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transactions = pl.read_parquet("data/warehouse/transactions.parquet")
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master_transactions = (
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transactions.join(
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categories,
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left_on="category_id",
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right_on="category_id",
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suffix="_category",
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)
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.join(accounts, left_on="account_id", right_on="account_id", suffix="_account")
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.join(payees, left_on="payee_id", right_on="payee_id", suffix="_payee")
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.join(dates, left_on="transaction_date", right_on="date_id", suffix="_date")
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)
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def __init__(self):
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logging.info("Initializing data components")
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pass
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def update_dates(start_date, end_date):
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logging.info("Updating dates")
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logging.debug(f"start_date: {start_date}, end_date: {end_date}")
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logging.debug(data_components.master_transactions.columns)
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try:
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master_data = data_components.master_transactions.filter(
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pl.col("date").is_between(start_date, end_date)
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)
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except Exception as e:
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logging.error(f"Error updating dates: {e}")
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raise e
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return master_data
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def update_data(master_data, callback=0):
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# Create aggregations
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spend_per_day = master_data.sql("""
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SELECT
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date,
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year,
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month,
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day,
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ABS(SUM(transaction_amount)) as total
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FROM self
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WHERE category_name != 'Inflow: Ready to Assign'
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GROUP BY date, year, month, day
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ORDER BY date DESC
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""")
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spend_per_category = master_data.sql("""
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SELECT
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category_name,
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ABS(SUM(transaction_amount)) as total
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FROM self
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WHERE category_name != 'Inflow: Ready to Assign'
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GROUP BY category_name
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ORDER BY total DESC
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""")
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spend_per_payee = master_data.sql("""
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SELECT
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payee_name,
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ABS(SUM(transaction_amount)) as total
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FROM self
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WHERE payee_name != 'Starting Balance'
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AND transaction_amount < 0
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GROUP BY payee_name
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ORDER BY total DESC
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""")
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total_spend = master_data.sql("""
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SELECT ABS(SUM(transaction_amount)) AS total
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FROM self
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WHERE payee_name != 'Starting Balance'
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AND transaction_amount < 0
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""").item()
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# Convert DataFrame to list of dictionaries
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spend_per_day_data = spend_per_day.to_dicts()
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spend_per_category_data = spend_per_category.to_dicts()
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spend_per_payee_data = spend_per_payee.to_dicts()
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# Convert list of dictionaries to Pandas DataFrame
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spend_per_day_df = pd.DataFrame(spend_per_day_data)
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spend_per_category_df = pd.DataFrame(spend_per_category_data)
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spend_per_payee_df = pd.DataFrame(spend_per_payee_data)
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spend_per_day_line = px.line(spend_per_day_df, x="date", y="total")
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spend_per_day_line.update_layout(
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plot_bgcolor="black", paper_bgcolor="black", font_color="white"
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)
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spend_per_category_bar = px.bar(
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spend_per_category_df, x="category_name", y="total"
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)
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spend_per_category_bar.update_layout(
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plot_bgcolor="black", paper_bgcolor="black", font_color="white"
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)
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spend_per_payee_bar = px.bar(spend_per_payee_df, x="payee_name", y="total")
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spend_per_payee_bar.update_layout(
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plot_bgcolor="black", paper_bgcolor="black", font_color="white"
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)
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total_spend_line = f"### £{total_spend:,.2f}"
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data = {
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"spend_per_day_line": spend_per_day_line,
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"spend_per_category_bar": spend_per_category_bar,
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"spend_per_payee_bar": spend_per_payee_bar,
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"total_spend": total_spend_line,
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}
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if callback == 0:
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return data
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else:
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return (
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spend_per_day_line,
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spend_per_category_bar,
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spend_per_payee_bar,
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total_spend,
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)
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