Merge pull request #8 from Jake-Pullen/feature/add_visuals

Feature/add visuals

dash_app.py

@@ -0,0 +1,150 @@
+'''Module to create a Dash app that displays visualizations of YNAB data.'''
+
+import polars as pl
+import plotly.express as px
+from dash import Dash, html, dcc
+import dash_bootstrap_components as dbc
+import pandas as pd
+
+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')
+
+# Join transactions with accounts, categories, and payees to create a master DataFrame
+master_df = transactions.join(categories, left_on='category_id', right_on='id', suffix='_category')\
+                    .join(accounts, left_on='account_id', right_on='id', suffix='_account')\
+                    .join(payees, left_on='payee_id', right_on='id', suffix='_payee')\
+                    .join(dates, left_on='transaction_date', right_on='date_id', suffix='_date')\
+
+# Create aggregations
+spend_per_day = master_df.sql('''
+    SELECT 
+        date,
+        year,
+        month,
+        day,
+        ABS(SUM(transaction_amount)) as total
+    FROM self
+    WHERE category_name != 'Inflow: Ready to Assign'
+    GROUP BY date, year, month, day
+    ORDER BY date DESC
+    '''
+)
+
+spend_per_category = master_df.sql('''
+    SELECT
+        category_name,
+        ABS(SUM(transaction_amount)) as total
+    FROM self
+    WHERE category_name != 'Inflow: Ready to Assign'
+    GROUP BY category_name
+    ORDER BY total DESC
+    '''
+)
+
+spend_per_payee = master_df.sql('''
+    SELECT
+        payee_name,
+        ABS(SUM(transaction_amount)) as total
+    FROM self
+    WHERE payee_name != 'Starting Balance'
+        AND transaction_amount < 0
+    GROUP BY payee_name
+    ORDER BY total DESC
+    '''
+)
+
+# 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'
+)
+
+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'
+)
+
+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'
+)
+
+# Initialize the app with a dark theme
+app = Dash(external_stylesheets=[dbc.themes.DARKLY])
+
+# App layout
+app.layout = dbc.Container(
+    [
+        dbc.Row(
+            dbc.Col(
+                html.Div("Data Pipeline For YNAB, Preview Visualisations",
+                        className="text-center text-light"),
+                        width=12
+                )
+        ),
+        dbc.Row(
+            [
+                dbc.Col(
+                    dbc.Card(
+                        dbc.CardBody(
+                            [
+                                html.H4("Spend Per Day", className="card-title"),
+                                dcc.Graph(figure=spend_per_day_line)
+                            ]
+                        ),
+                        className="mb-4"
+                    ),
+                    width=12
+                )
+            ]
+        ),
+        dbc.Row(
+            [
+                dbc.Col(
+                    dbc.Card(
+                        dbc.CardBody(
+                            [
+                                html.H4("Spend Per Category", className="card-title"),
+                                dcc.Graph(figure=spend_per_category_bar)
+                            ]
+                        ),
+                        className="mb-4"
+                    ),
+                    width=6
+                ),
+                dbc.Col(
+                    dbc.Card(
+                        dbc.CardBody(
+                            [
+                                html.H4("Spend Per Payee", className="card-title"),
+                                dcc.Graph(figure=spend_per_payee_bar)
+                            ]
+                        ),
+                        className="mb-4"
+                    ),
+                    width=6
+                )
+            ]
+        )
+    ],
+    fluid=True
+)

main.py

@@ -8,10 +8,8 @@ import logging.config
 import logging.handlers
 
 import config.exit_codes as ec
-from pipeline.ingest import Ingest
+from dash_app import app
-from pipeline.raw_to_base import RawToBase
+from pipeline.pipeline_main import pipeline_main
-from pipeline.dimensions import DimAccounts, DimCategories, DimPayees, DimDate
-from pipeline.facts import FactTransactions, FactScheduledTransactions
 
 def set_up_logging():
     try:
@@ -37,7 +35,7 @@ dotenv.load_dotenv()
 API_TOKEN = os.getenv('API_TOKEN')
 BUDGET_ID = os.getenv('BUDGET_ID')
 
-def main():
+
 if not API_TOKEN or not BUDGET_ID:
     logging.error('API_TOKEN or BUDGET_ID is not set in .env file')
     sys.exit(ec.MISSING_ENV_VARS)
@@ -55,23 +53,12 @@ def main():
 config['API_TOKEN'] = API_TOKEN
 config['BUDGET_ID'] = BUDGET_ID
 
-    logging.info('Starting data pipeline')
+    #sys.exit(ec.SUCCESS)
-
-    Ingest(config)
-    RawToBase(config)
-    DimAccounts(config)
-    DimCategories(config)
-    DimPayees(config)
-    DimDate(config)
-    FactTransactions(config)
-    FactScheduledTransactions(config)
-
-    logging.info('Data pipeline completed successfully')
-    sys.exit(ec.SUCCESS)
 
 if __name__ == '__main__':
     try:
-        main()
+        pipeline_main(config)
+        app.run() #debug=True)
     except SystemExit as e:
         exit_code = e.code
         if exit_code == ec.SUCCESS:

pipeline/dimensions.py

@@ -191,6 +191,18 @@ class DimDate(Dimensions):
         except Exception as e:
             logging.error(f"Failed to create a new column to indicate if the date is a weekday or weekend: {e}")
             return
+        
+        # Create a primary key by concatenating year, month, and day with no separators
+        try:
+            dates_df = dates_df.with_columns([
+                (pl.col('year').cast(pl.Utf8) + 
+                 pl.col('month').cast(pl.Utf8).str.zfill(2) +
+                 pl.col('day').cast(pl.Utf8).str.zfill(2)
+                ).alias('date_id')
+            ])
+        except Exception as e:
+            logging.error(f"Failed to create the primary key column: {e}")
+            return
         # Write the DataFrame to a new parquet file
         logging.info("Writing the transformed dates DataFrame to parquet file")
         try:

pipeline/facts.py

@@ -27,12 +27,23 @@ class FactTransactions(Facts):
         
         # Transform the DataFrame
         logging.info("Transforming the transactions DataFrame")
+        try:
+            # Ensure the date column is in datetime format
+            transactions_df = transactions_df.with_columns([
+                pl.col("date").str.strptime(pl.Date, format="%Y-%m-%d").alias("date")
+            ])
+        except Exception as e:
+            logging.error(f"Failed to covert the date to date format: {e}")
+            return
+        
         try:
             transactions_df = (
                 transactions_df
                 .with_columns([
                     pl.col("id").alias("transaction_id"),
-                    pl.col("date").alias("transaction_date"),
+                    (pl.col("date").dt.year().cast(pl.Utf8) +
+                        pl.col("date").dt.month().cast(pl.Utf8).str.zfill(2) +
+                        pl.col("date").dt.day().cast(pl.Utf8).str.zfill(2)).alias("transaction_date"),
                     pl.col("amount").alias("transaction_amount"),
                     pl.col("memo").alias("transaction_memo"),
                     pl.col("cleared").alias("transaction_cleared"),
@@ -45,7 +56,7 @@ class FactTransactions(Facts):
                 ])
                 .with_columns([
                     pl.col("memo").fill_null("unknown"),
-                    (pl.col("amount") / 100).alias("transaction_amount"),
+                    (pl.col("amount") / 1000).alias("transaction_amount"),
                 ])
                 .drop([
                     "transfer_transaction_id", "matched_transaction_id", "import_id",
@@ -98,7 +109,7 @@ class FactScheduledTransactions(Facts):
                 ])
                 .with_columns([
                     pl.col("memo").fill_null("unknown"),
-                    (pl.col("amount") / 100).alias("scheduled_transaction_amount"),
+                    (pl.col("amount") / 1000).alias("scheduled_transaction_amount"),
                 ])
                 .drop([
                     "subtransactions", "deleted","flag_name","account_name",

pipeline/pipeline_main.py

@@ -0,0 +1,24 @@
+'''Module to run the data pipeline'''
+
+import logging
+
+from pipeline.ingest import Ingest
+from pipeline.raw_to_base import RawToBase
+from pipeline.dimensions import DimAccounts, DimCategories, DimPayees, DimDate
+from pipeline.facts import FactTransactions, FactScheduledTransactions
+
+
+def pipeline_main(config):
+    '''Run the data pipeline'''
+    logging.info('Starting data pipeline')
+
+    Ingest(config)
+    RawToBase(config)
+    DimAccounts(config)
+    DimCategories(config)
+    DimPayees(config)
+    DimDate(config)
+    FactTransactions(config)
+    FactScheduledTransactions(config)
+
+    logging.info('Data pipeline completed successfully')

pipeline/raw_to_base.py

@@ -130,7 +130,7 @@ Then move the files back in one at a time oldest to newest and run again for eac
                 df = df.with_columns(
                     pl.when(pl.col(col).is_null())
                     .then(pl.lit("null"))
-                    .otherwise(pl.col(col).map_elements(lambda x: str(x) if x is not None else "null"))
+                    .otherwise(pl.col(col).map_elements(lambda x: str(x) if x is not None else "null", return_dtype=pl.Utf8))
                     .alias(col)
                 )
         return df

requirements.txt

@@ -2,3 +2,8 @@ python-dotenv
 polars
 requests
 pyyaml
+#visualisation requirements below
+dash
+pandas
+pyarrow 
+dash-bootstrap-components