Jake
124 lines
4.2 KiB
Python
124 lines
4.2 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 sys
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import config.exit_codes as ec
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import datetime
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try:
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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('data/warehouse/scheduled_transactions.parquet')
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transactions = pl.read_parquet('data/warehouse/transactions.parquet')
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except FileNotFoundError:
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logging.error('Data warehouse files not found. Run the data pipeline to create them.')
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sys.exit(ec.MISSING_DATA_FILES)
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try:
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# Join transactions with accounts, categories, and payees to create a master DataFrame
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master_transactions = transactions.join(categories, left_on='category_id', right_on='category_id', suffix='_category')\
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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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except Exception as e:
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logging.error(f'Error joining DataFrames: {e}')
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sys.exit(ec.BAD_JOIN)
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def update_dates(start_date, end_date):
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master_data = master_transactions.filter(
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(pl.col('year') >= start_date.year ) & (pl.col('year') <= end_date.year) &
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(pl.col('month') >= start_date.month ) & (pl.col('month') <= end_date.month) &
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(pl.col('day') >= start_date.day ) & (pl.col('day') <= end_date.day)
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)
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return master_data
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def update_data(master_data):
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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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)
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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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)
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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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)
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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',
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paper_bgcolor='black',
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font_color='white'
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)
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spend_per_category_bar = px.bar(spend_per_category_df, x="category_name", y="total")
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spend_per_category_bar.update_layout(
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plot_bgcolor='black',
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paper_bgcolor='black',
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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',
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paper_bgcolor='black',
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font_color='white'
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)
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data = {"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}
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print(data)
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return data
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