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‎labs/workshops/README.md‎

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2. **Local Jupyter (Local Mode)**
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```bash
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cd labs/workshops
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jupyter lab
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jupyter lab .
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```
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## 📚 Available Challenges

‎labs/workshops/listings-wizard/00-low-conversion-challenge.ipynb‎

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{
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"cell_type": "code",
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"id": "ef145f6d-ce8c-4c21-804e-254ca8190350",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-29T12:14:48.174450Z",
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"start_time": "2025-09-29T12:14:46.832477Z"
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}
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},
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"metadata": {},
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"source": [
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"pip install amzn-sp-api==1.2.0"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: amzn-sp-api==1.2.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (1.2.0)\r\n",
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"Requirement already satisfied: urllib3<3.0.0,>=2.1.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from amzn-sp-api==1.2.0) (2.3.0)\r\n",
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"Requirement already satisfied: certifi>=2025.1.31 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from amzn-sp-api==1.2.0) (2025.8.3)\r\n",
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"Requirement already satisfied: requests>=2.32.3 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from amzn-sp-api==1.2.0) (2.32.3)\r\n",
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"Requirement already satisfied: six>=1.17.0 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from amzn-sp-api==1.2.0) (1.17.0)\r\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from requests>=2.32.3->amzn-sp-api==1.2.0) (3.4.1)\r\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages (from requests>=2.32.3->amzn-sp-api==1.2.0) (3.10)\r\n",
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"\r\n",
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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m25.2\u001B[0m\r\n",
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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49m/usr/local/bin/python3 -m pip install --upgrade pip\u001B[0m\r\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"execution_count": 1
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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{
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"cell_type": "code",
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"id": "4b7d9770-ccdc-4edd-a910-242916c578f7",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-29T12:15:13.908703Z",
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"start_time": "2025-09-29T12:15:13.887970Z"
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}
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},
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"metadata": {},
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"source": [
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"from spapi.models.datakiosk_v2023_11_15 import GetDocumentResponse, CreateQueryResponse, GetQueriesResponse, Query\n",
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"from spapi import SPAPIConfig, SPAPIClient, ApiException, QueriesApi, ListingsApi\n",
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"listings_api = ListingsApi(client.api_client)\n",
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"print(\"💻 API Client initiated\")"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"💻 API Client initiated\n"
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]
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}
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],
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"execution_count": 5
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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"- Submit custom queries\n",
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"- Check their status\n",
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"- Download results\n",
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" \n",
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"\n",
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"🙌 Time to get seller data with the power of GraphQL!"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "pip install pandas",
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"id": "6cc8eb127ee0292b"
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},
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{
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"cell_type": "code",
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"id": "7250c7cd-62cd-4ce3-9c40-db746184a37f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-29T12:15:50.137187Z",
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"start_time": "2025-09-29T12:15:49.991204Z"
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}
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},
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"metadata": {},
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"source": [
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"graphql_query_str = (\"query { analytics_economics_2024_03_15 { economics( marketplaceIds: [\\\"ATVPDKIKX0DER\\\"], \"\n",
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" \"startDate: \\\"2025-06-01\\\", endDate: \\\"2025-06-05\\\", \"\n",
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"plt.tight_layout()\n",
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"plt.show()\n"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"📃 Query: \n",
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" query { analytics_economics_2024_03_15 { economics( marketplaceIds: [\"ATVPDKIKX0DER\"], startDate: \"2025-06-01\", endDate: \"2025-06-05\", aggregateBy: { date: DAY, productId: MSKU } ) { startDate endDate msku marketplaceId sales { unitsOrdered netUnitsSold netProductSales { amount currencyCode } } netProceeds { total { amount currencyCode } } } } }\n",
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"✅ Query submitted! Response: {'query_id': '1000001'}\n",
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"⏱️ Query status: {'created_time': datetime.datetime(2025, 7, 17, 11, 25, 13, 645000, tzinfo=tzutc()),\n",
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" 'data_document_id': 'usecase_1_document_id.amzn.100001',\n",
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" 'error_document_id': None,\n",
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" 'pagination': None,\n",
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" 'processing_end_time': datetime.datetime(2025, 7, 17, 11, 25, 13, 645000, tzinfo=tzutc()),\n",
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" 'processing_start_time': datetime.datetime(2025, 7, 17, 11, 25, 13, 645000, tzinfo=tzutc()),\n",
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" 'processing_status': 'DONE',\n",
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" 'query': 'query { analytics_economics_2024_03_15 { economics( marketplaceIds: '\n",
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" '[\"ATVPDKIKX0DER\"], startDate: \"2025-06-01\", endDate: \"2025-06-05\", '\n",
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" 'aggregateBy: { date: DAY, productId: MSKU } ) { startDate endDate '\n",
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" 'msku marketplaceId sales { unitsOrdered netUnitsSold '\n",
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" 'netProductSales { amount currencyCode } } netProceeds { total { '\n",
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" 'amount currencyCode } } } } }',\n",
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" 'query_id': '1000001'}\n",
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"📊 Here's your data: {'document_id': 'usecase_1_document_id.amzn.100001',\n",
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" 'document_url': \"{'data': {'analytics_economics_2024_03_15': {'economics': \"\n",
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" \"[{'startDate': '2025-06-01', 'endDate': '2025-06-01', \"\n",
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" \"'msku': 'MOTOR-GEAR-US', 'marketplaceId': 'ATVPDKIKX0DER', \"\n",
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" \"'sales': {'unitsOrdered': 0, 'netUnitsSold': 0, \"\n",
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" \"'netProductSales': {'amount': 0.0, 'currencyCode': 'USD'}}, \"\n",
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" \"'netProceeds': {'total': {'amount': 0.0, 'currencyCode': \"\n",
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" \"'USD'}}}, {'startDate': '2025-06-01', 'endDate': \"\n",
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" \"'2025-06-01', 'msku': 'MOTOR-GEAR-CA', 'marketplaceId': \"\n",
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" \"'A2EUQ1WTGCTBG2', 'sales': {'unitsOrdered': 50, \"\n",
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" \"'netUnitsSold': 50, 'netProductSales': {'amount': 1000.0, \"\n",
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" \"'currencyCode': 'CAD'}}, 'netProceeds': {'total': {'amount': \"\n",
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" \"800.0, 'currencyCode': 'CAD'}}}, {'startDate': '2025-06-01', \"\n",
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" \"'endDate': '2025-06-01', 'msku': 'MOTOR-GEAR-MX', \"\n",
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" \"'marketplaceId': 'A1AM78C64UM0Y8', 'sales': {'unitsOrdered': \"\n",
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" \"50, 'netUnitsSold': 50, 'netProductSales': {'amount': \"\n",
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" \"1000.0, 'currencyCode': 'Mex'}}, 'netProceeds': {'total': \"\n",
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" \"{'amount': 800.0, 'currencyCode': 'Mex'}}}]}}}\"}\n"
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]
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},
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'pandas'",
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"output_type": "error",
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"traceback": [
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"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
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"\u001B[31mModuleNotFoundError\u001B[39m Traceback (most recent call last)",
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"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[6]\u001B[39m\u001B[32m, line 22\u001B[39m\n\u001B[32m 19\u001B[39m \u001B[38;5;28mprint\u001B[39m(\u001B[33m\"\u001B[39m\u001B[33m📊 Here\u001B[39m\u001B[33m'\u001B[39m\u001B[33ms your data:\u001B[39m\u001B[33m\"\u001B[39m, get_document)\n\u001B[32m 21\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mast\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m22\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mpandas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mpd\u001B[39;00m\n\u001B[32m 23\u001B[39m \u001B[38;5;28;01mimport\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mmatplotlib\u001B[39;00m\u001B[34;01m.\u001B[39;00m\u001B[34;01mpyplot\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mas\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34;01mplt\u001B[39;00m\n\u001B[32m 25\u001B[39m \u001B[38;5;66;03m# Safely convert the string to a dictionary\u001B[39;00m\n",
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"\u001B[31mModuleNotFoundError\u001B[39m: No module named 'pandas'"
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]
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}
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],
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"execution_count": 6
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b1b36cbd-ca0a-4e4f-8e9f-edfc49fa053a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"get_listing = listings_api.get_listings_item('AMY7FKRUBY7XV', \n",
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" included_data=['issues'])\n",
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"\n",
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"print(\"🛍️ Here's your listing: \\n\", json.dumps(get_listing.to_dict(), indent=4, default=str))"
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]
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],
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"outputs": [],
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"execution_count": null
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "37f273da-1ba6-4698-b4f1-7d1db92d2a26",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"body = {\n",
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"} \n",
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"patch_listing = listings_api.patch_listings_item('AMY7FKRUBY7XV', 'MOTOR-GEAR-US',['ATVPDKIKX0DER'], body)\n",
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"print(\"💾 Submission result: \\n\", json.dumps(patch_listing.to_dict(), indent=4, default=str))"
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]
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],
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"outputs": [],
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"execution_count": null
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},
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "",
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"id": "207ce43df479a203"
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"id": "207ce43df479a203",
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"outputs": [],
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"execution_count": null
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}
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],
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"metadata": {

‎labs/workshops/listings-wizard/02-high-refund-challenge.ipynb‎

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"\n"
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "pip install pandas",
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"id": "126758787e6eddb2"
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},
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{
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"cell_type": "code",
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"execution_count": null,

‎labs/workshops/listings-wizard/README.md‎

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**OR** run them locally in any Jupyter environment:
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```bash
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git clone https://github.com/amzn/sp-api-sample-solutions.git
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cd labs/workshops/listings-wizard
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python3 -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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jupyter lab
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jupyter lab .
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```
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---
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