Jake Pullen
151 lines
4.6 KiB
Python
151 lines
4.6 KiB
Python
'''Module to create a Dash app that displays visualizations of YNAB data.'''
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import polars as pl
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import plotly.express as px
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from dash import Dash, html, dcc
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import dash_bootstrap_components as dbc
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import pandas as pd
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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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# Join transactions with accounts, categories, and payees to create a master DataFrame
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master_df = transactions.join(categories, left_on='category_id', right_on='id', suffix='_category')\
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.join(accounts, left_on='account_id', right_on='id', suffix='_account')\
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.join(payees, left_on='payee_id', right_on='id', suffix='_payee')\
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.join(dates, left_on='transaction_date', right_on='date_id', suffix='_date')\
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# Create aggregations
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spend_per_day = master_df.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_df.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_df.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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# 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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# Initialize the app with a dark theme
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app = Dash(external_stylesheets=[dbc.themes.DARKLY])
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# App layout
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app.layout = dbc.Container(
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[
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dbc.Row(
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dbc.Col(
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html.Div("Data Pipeline For YNAB, Preview Visualisations",
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className="text-center text-light"),
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width=12
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)
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),
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dbc.Row(
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[
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dbc.Col(
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dbc.Card(
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dbc.CardBody(
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[
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html.H4("Spend Per Day", className="card-title"),
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dcc.Graph(figure=spend_per_day_line)
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]
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),
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className="mb-4"
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),
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width=12
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)
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]
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),
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dbc.Row(
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[
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dbc.Col(
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dbc.Card(
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dbc.CardBody(
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[
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html.H4("Spend Per Category", className="card-title"),
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dcc.Graph(figure=spend_per_category_bar)
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]
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),
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className="mb-4"
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),
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width=6
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),
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dbc.Col(
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dbc.Card(
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dbc.CardBody(
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[
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html.H4("Spend Per Payee", className="card-title"),
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dcc.Graph(figure=spend_per_payee_bar)
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]
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),
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className="mb-4"
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),
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width=6
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)
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]
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)
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],
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fluid=True
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)
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