Drop the largest-orders cell

The tutorial no longer walks through nlargest, so remove it from the
script and the notebook.

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

Author: lpozo

positron-ide/notebooks/explore_sales.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4c1e042e",
+   "id": "b2641d4c",
    "metadata": {},
    "source": [
     "# Explore Sales Orders\n",
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8ae0382c",
+   "id": "92d9e9b6",
    "metadata": {},
    "source": [
     "## Load the Raw Data\n",
@@ -23,7 +23,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25d214b8",
+   "id": "e9a1c254",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a470ad43",
+   "id": "708e7591",
    "metadata": {},
    "source": [
     "## Check Data Quality\n",
@@ -53,7 +53,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bb49f812",
+   "id": "fbc5d423",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,28 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efafe93e",
-   "metadata": {},
-   "source": [
-    "## Inspect the Largest Orders\n",
-    "\n",
-    "List the five highest sale prices to see what the top of the range looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fef73d5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "columns = [\"order_type\", \"quantity\", \"unit_price\", \"sale_price\"]\n",
-    "print(sales.nlargest(5, \"sale_price\")[columns].to_string(index=False))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b3f369d",
+   "id": "81fdf76e",
    "metadata": {},
    "source": [
     "## Clean the Data\n",
@@ -100,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19acc30c",
+   "id": "4be9da3c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59e0cee4",
+   "id": "d6bc4409",
    "metadata": {},
    "source": [
     "## Verify the Totals\n",
@@ -126,7 +105,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5dd5fe37",
+   "id": "ce17885f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b245ddc1",
+   "id": "1aead3f7",
    "metadata": {},
    "source": [
     "## Describe the Data\n",
@@ -148,7 +127,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "41aa6fb0",
+   "id": "91aa9d71",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +139,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "891db419",
+   "id": "103777e1",
    "metadata": {},
    "source": [
     "## Compare Typical Orders\n",
@@ -171,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25aa795d",
+   "id": "85b34fca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a5f5657",
+   "id": "61b5d1d8",
    "metadata": {},
    "source": [
     "## Visualize the Comparison\n",
@@ -199,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a604d65d",
+   "id": "9e3851b8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "220350eb",
+   "id": "e0bd88e9",
    "metadata": {},
    "source": [
     "## Add an Interactive Chart\n",
@@ -228,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a856547e",
+   "id": "eb9b26c0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc5e8033",
+   "id": "7fcde02e",
    "metadata": {},
    "source": [
     "## Optional Assistant Follow-Up\n",

positron-ide/src/prepare_sales.py

@@ -16,10 +16,6 @@
 print("Repeated order numbers:", sales["order_number"].duplicated().sum())
 print(sales.dtypes)
 
-# %%
-columns = ["order_type", "quantity", "unit_price", "sale_price"]
-print(sales.nlargest(5, "sale_price")[columns].to_string(index=False))
-
 # %%
 clean_sales = sales.rename(columns={"produce_name": "product_name"}).copy()
 clean_sales["order_date"] = pd.to_datetime(