Jake/data_pipeline_for_YNAB
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style: 🎨 Added UV and Ruff linting

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Jake Pullen
2025-05-30 11:43:47 +01:00
parent cb5c03ab74
commit 5da9ba4b0b
11 changed files with 618 additions and 56 deletions
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visuals/components.py
+50 -44
View File
@@ -2,22 +2,31 @@ import polars as pl
import plotly.express as px
import pandas as pd
import logging
import config.exit_codes as ec
# import datetime
class data_components():
accounts = pl.read_parquet('data/warehouse/accounts.parquet')
categories = pl.read_parquet('data/warehouse/categories.parquet')
dates = pl.read_parquet('data/warehouse/dates.parquet')
payees = pl.read_parquet('data/warehouse/payees.parquet')
scheduled_transactions = pl.read_parquet('data/warehouse/scheduled_transactions.parquet')
transactions = pl.read_parquet('data/warehouse/transactions.parquet')
master_transactions = transactions.join(categories, left_on='category_id', right_on='category_id', suffix='_category')\
.join(accounts, left_on='account_id', right_on='account_id', suffix='_account')\
.join(payees, left_on='payee_id', right_on='payee_id', suffix='_payee')\
.join(dates, left_on='transaction_date', right_on='date_id', suffix='_date')
class data_components:
accounts = pl.read_parquet("data/warehouse/accounts.parquet")
categories = pl.read_parquet("data/warehouse/categories.parquet")
dates = pl.read_parquet("data/warehouse/dates.parquet")
payees = pl.read_parquet("data/warehouse/payees.parquet")
scheduled_transactions = pl.read_parquet(
"data/warehouse/scheduled_transactions.parquet"
)
transactions = pl.read_parquet("data/warehouse/transactions.parquet")
master_transactions = (
transactions.join(
categories,
left_on="category_id",
right_on="category_id",
suffix="_category",
)
.join(accounts, left_on="account_id", right_on="account_id", suffix="_account")
.join(payees, left_on="payee_id", right_on="payee_id", suffix="_payee")
.join(dates, left_on="transaction_date", right_on="date_id", suffix="_date")
)
def __init__(self):
logging.info("Initializing data components")
pass
@@ -35,10 +44,10 @@ class data_components():
raise e
return master_data
def update_data(master_data,callback=0):
def update_data(master_data, callback=0):
# Create aggregations
spend_per_day = master_data.sql('''
SELECT
spend_per_day = master_data.sql("""
SELECT
date,
year,
month,
@@ -48,10 +57,9 @@ class data_components():
WHERE category_name != 'Inflow: Ready to Assign'
GROUP BY date, year, month, day
ORDER BY date DESC
'''
)
""")
spend_per_category = master_data.sql('''
spend_per_category = master_data.sql("""
SELECT
category_name,
ABS(SUM(transaction_amount)) as total
@@ -59,10 +67,9 @@ class data_components():
WHERE category_name != 'Inflow: Ready to Assign'
GROUP BY category_name
ORDER BY total DESC
'''
)
""")
spend_per_payee = master_data.sql('''
spend_per_payee = master_data.sql("""
SELECT
payee_name,
ABS(SUM(transaction_amount)) as total
@@ -71,57 +78,56 @@ class data_components():
AND transaction_amount < 0
GROUP BY payee_name
ORDER BY total DESC
'''
)
total_spend = master_data.sql('''
""")
total_spend = master_data.sql("""
SELECT ABS(SUM(transaction_amount)) AS total
FROM self
WHERE payee_name != 'Starting Balance'
AND transaction_amount < 0
''').item()
""").item()
# Convert DataFrame to list of dictionaries
spend_per_day_data = spend_per_day.to_dicts()
spend_per_category_data = spend_per_category.to_dicts()
spend_per_payee_data = spend_per_payee.to_dicts()
# Convert list of dictionaries to Pandas DataFrame
spend_per_day_df = pd.DataFrame(spend_per_day_data)
spend_per_category_df = pd.DataFrame(spend_per_category_data)
spend_per_payee_df = pd.DataFrame(spend_per_payee_data)
spend_per_day_line = px.line(spend_per_day_df, x="date", y="total")
spend_per_day_line.update_layout(
plot_bgcolor='black',
paper_bgcolor='black',
font_color='white'
plot_bgcolor="black", paper_bgcolor="black", font_color="white"
)
spend_per_category_bar = px.bar(spend_per_category_df, x="category_name", y="total")
spend_per_category_bar = px.bar(
spend_per_category_df, x="category_name", y="total"
)
spend_per_category_bar.update_layout(
plot_bgcolor='black',
paper_bgcolor='black',
font_color='white'
plot_bgcolor="black", paper_bgcolor="black", font_color="white"
)
spend_per_payee_bar = px.bar(spend_per_payee_df, x="payee_name", y="total")
spend_per_payee_bar.update_layout(
plot_bgcolor='black',
paper_bgcolor='black',
font_color='white'
plot_bgcolor="black", paper_bgcolor="black", font_color="white"
)
total_spend_line = f"### £{total_spend:,.2f}"
data = {"spend_per_day_line": spend_per_day_line,
"spend_per_category_bar": spend_per_category_bar,
"spend_per_payee_bar": spend_per_payee_bar,
"total_spend": total_spend_line}
data = {
"spend_per_day_line": spend_per_day_line,
"spend_per_category_bar": spend_per_category_bar,
"spend_per_payee_bar": spend_per_payee_bar,
"total_spend": total_spend_line,
}
if callback == 0:
return data
else:
return spend_per_day_line,spend_per_category_bar,spend_per_payee_bar,total_spend
return (
spend_per_day_line,
spend_per_category_bar,
spend_per_payee_bar,
total_spend,
)
visuals/dash_app.py
+1 -2
View File
@@ -1,7 +1,6 @@
'''Module to create a Dash app that displays visualizations of YNAB data.'''
import dash
from dash import dcc, html
from dash.dependencies import Input, Output, State
from dash.dependencies import Input, Output
import dash_bootstrap_components as dbc
from visuals.layout import create_layout
from visuals.components import data_components
visuals/layout.py
+1 -1
View File
@@ -1,7 +1,7 @@
from dash import html, dcc
from datetime import date
import dash_bootstrap_components as dbc
from datetime import date, timedelta
from datetime import timedelta
today = date.today()
one_year_ago = today - timedelta(days=365)