doc: Updated example to include user-defined price thresholds

docs/example.ipynb

@@ -266,7 +266,7 @@
    "source": [
     "## Get Revenue Share for each Product Category\n",
     "\n",
-    "Another feature of `saleanalyzer`, the `segment_revenue_share()` function, segments products into three categories (cheap < medium < expensive) — based on their price, and calculates the respective share of total revenue contributed by each segment. This function is particularly useful for analyzing product sales data and understanding revenue distribution across different pricing tiers.\n",
+    "Another feature of `saleanalyzer`, the `segment_revenue_share()` function, segments products into three categories (cheap < medium < expensive) — based on their price, and calculates the respective share of total revenue contributed by each segment. By default, the price thresholds are set automatically, but users can define custom thresholds to categorize products according to their specific business needs. This function is particularly useful for analyzing product sales data and understanding revenue distribution across different pricing tiers.\n",
     "> Use help(sales_summary_statistics) for more information about the function"
    ]
   },
@@ -337,10 +337,83 @@
     }
    ],
    "source": [
+    "# Using default price thresholds\n",
     "revenue_share = segment_revenue_share(sample_data, price_col='UnitPrice', quantity_col='Quantity')\n",
     "revenue_share"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PriceSegment</th>\n",
+       "      <th>TotalRevenue</th>\n",
+       "      <th>RevenueShare (%)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cheap</td>\n",
+       "      <td>1150</td>\n",
+       "      <td>9.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>medium</td>\n",
+       "      <td>3600</td>\n",
+       "      <td>28.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>expensive</td>\n",
+       "      <td>7700</td>\n",
+       "      <td>61.85</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  PriceSegment  TotalRevenue  RevenueShare (%)\n",
+       "0        cheap          1150              9.24\n",
+       "1       medium          3600             28.92\n",
+       "2    expensive          7700             61.85"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Using user-defined price thresholds\n",
+    "revenue_share = segment_revenue_share(sample_data, price_col='UnitPrice', quantity_col='Quantity', price_thresholds=(300, 500))\n",
+    "revenue_share"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -356,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -409,7 +482,7 @@
        "1              1.33"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -494,7 +567,7 @@
        "1              1.88"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -509,16 +582,11 @@
    "source": [
     "This is the end of the tutorial, where you have seen how to get sales data insights using our package."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "salesanalyzser",
+   "display_name": "salesanalyzer",
    "language": "python",
    "name": "python3"
   },
@@ -532,7 +600,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,