Resolve PROJECT_ROOT from the parent of the running folder

Positron starts a session in the folder holding the file, so Path.cwd()
pointed at src/ or notebooks/ and the data path missed by one level.
Use Path.cwd().parent in both scripts and the notebook, and document
running them from their own folder.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

Author: lpozo

positron-ide/README.md

@@ -14,12 +14,15 @@ Select the project's `.venv` for both the Python Console and the notebook kernel
 
 ## Prepare and Analyze the Data
 
-Run `src/prepare_sales.py` cell by cell in Positron, or run it from this folder:
+Run `src/prepare_sales.py` cell by cell in Positron, or run it from the `src/` folder:
 
 ```console
-uv run src/prepare_sales.py
+cd src
+uv run prepare_sales.py
 ```
 
+The scripts and the notebook build their paths with `PROJECT_ROOT = Path.cwd().parent`, so run them from the folder that holds them.
+
 This creates `data/sales_clean.csv`. It checks for missing values, duplicates, and inconsistent totals, corrects a column name, parses dates, and normalizes category labels. It preserves all 5,130 source rows.
 
 Next, run the cells in `src/analyze_sales.py` to inspect descriptive statistics, compare median order values between retail and wholesale orders, and view a Matplotlib chart. Use Positron's **Run Cell** controls to keep the resulting objects available in the Console and Variables pane.

positron-ide/notebooks/explore_sales.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "007a2246",
+   "id": "4c1e042e",
    "metadata": {},
    "source": [
     "# Explore Sales Orders\n",
@@ -12,18 +12,18 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e775e704",
+   "id": "8ae0382c",
    "metadata": {},
    "source": [
     "## Load the Raw Data\n",
     "\n",
-    "Import the project's libraries and read `data/sales_data.csv`. Open the project folder in Positron so the session starts at the project root."
+    "Import the project's libraries and read `data/sales_data.csv`. The kernel starts in this `notebooks/` folder, so `PROJECT_ROOT` steps up one level to reach the project root."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "88383efa",
+   "id": "25d214b8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "\n",
-    "PROJECT_ROOT = Path.cwd()\n",
+    "PROJECT_ROOT = Path.cwd().parent\n",
     "\n",
     "sales = pd.read_csv(PROJECT_ROOT / \"data/sales_data.csv\")\n",
     "\n",
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be568194",
+   "id": "a470ad43",
    "metadata": {},
    "source": [
     "## Check Data Quality\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "374cce97",
+   "id": "bb49f812",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd02b9e0",
+   "id": "efafe93e",
    "metadata": {},
    "source": [
     "## Inspect the Largest Orders\n",
@@ -79,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3cd51e0d",
+   "id": "6fef73d5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39d16a2d",
+   "id": "2b3f369d",
    "metadata": {},
    "source": [
     "## Clean the Data\n",
@@ -100,7 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5de8943e",
+   "id": "19acc30c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68d59532",
+   "id": "59e0cee4",
    "metadata": {},
    "source": [
     "## Verify the Totals\n",
@@ -126,7 +126,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2d1feea1",
+   "id": "5dd5fe37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8af4e7c5",
+   "id": "b245ddc1",
    "metadata": {},
    "source": [
     "## Describe the Data\n",
@@ -148,7 +148,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a16889e4",
+   "id": "41aa6fb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9852e1d",
+   "id": "891db419",
    "metadata": {},
    "source": [
     "## Compare Typical Orders\n",
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4ded6526",
+   "id": "25aa795d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f8c2b68",
+   "id": "1a5f5657",
    "metadata": {},
    "source": [
     "## Visualize the Comparison\n",
@@ -199,7 +199,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1ac999fa",
+   "id": "a604d65d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +217,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0fe3723",
+   "id": "220350eb",
    "metadata": {},
    "source": [
     "## Add an Interactive Chart\n",
@@ -228,7 +228,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2b6e2e6a",
+   "id": "a856547e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "230e8d43",
+   "id": "dc5e8033",
    "metadata": {},
    "source": [
     "## Optional Assistant Follow-Up\n",

positron-ide/src/analyze_sales.py

@@ -4,7 +4,7 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-PROJECT_ROOT = Path.cwd()
+PROJECT_ROOT = Path.cwd().parent
 
 sales = pd.read_csv(
     PROJECT_ROOT / "data/sales_clean.csv",

positron-ide/src/prepare_sales.py

@@ -3,7 +3,7 @@
 
 import pandas as pd
 
-PROJECT_ROOT = Path.cwd()
+PROJECT_ROOT = Path.cwd().parent
 
 sales = pd.read_csv(PROJECT_ROOT / "data/sales_data.csv")