Adds notebook -- proving it works

DAGWorks-Inc · Jul 10, 2024 · 984d6cb · 984d6cb
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diff --git a/examples/materialization/using_types/notebook.ipynb b/examples/materialization/using_types/notebook.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-06-25T00:00:13.662458Z",
+     "start_time": "2024-06-25T00:00:06.982077Z"
+    }
+   },
+   "source": "%load_ext hamilton.plugins.jupyter_magic\n",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/stefankrawczyk/.pyenv/versions/knowledge_retrieval-py39/lib/python3.9/site-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-25T00:00:25.003646Z",
+     "start_time": "2024-06-25T00:00:24.322577Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "%%cell_to_module simple_etl --display\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "from hamilton.htypes import DataLoaderMetadata, DataSaverMetadata\n",
+    "\n",
+    "\n",
+    "def raw_data() -> tuple[pd.DataFrame, DataLoaderMetadata]:\n",
+    "    data = datasets.load_digits()\n",
+    "    df = pd.DataFrame(data.data, columns=[f\"feature_{i}\" for i in range(data.data.shape[1])])\n",
+    "    return df, DataLoaderMetadata.from_dataframe(df)\n",
+    "\n",
+    "\n",
+    "def transformed_data(raw_data: pd.DataFrame) -> pd.DataFrame:\n",
+    "    return raw_data\n",
+    "\n",
+    "\n",
+    "def saved_data(transformed_data: pd.DataFrame, filepath: str) -> DataSaverMetadata:\n",
+    "    transformed_data.to_csv(filepath)\n",
+    "    return DataSaverMetadata.from_file_and_dataframe(filepath, transformed_data)\n"
+   ],
+   "id": "efd6c1b2417bb9cf",
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x152a40760>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-25T00:00:37.889540Z",
+     "start_time": "2024-06-25T00:00:35.994131Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from hamilton_sdk import adapters\n",
+    "\n",
+    "from hamilton import driver\n",
+    "\n",
+    "tracker = adapters.HamiltonTracker(\n",
+    "    project_id=7,  # modify this as needed\n",
+    "    username=\"[email protected]\",\n",
+    "    dag_name=\"my_version_of_the_dag\",\n",
+    "    tags={\"environment\": \"DEV\", \"team\": \"MY_TEAM\", \"version\": \"X\"},\n",
+    ")\n",
+    "dr = driver.Builder().with_config({}).with_modules(simple_etl).with_adapters(tracker).build()\n",
+    "dr.display_all_functions()"
+   ],
+   "id": "e9252f2a09228330",
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Generated by graphviz version 10.0.1 (20240210.2158)\n -->\n<!-- Pages: 1 -->\n<svg width=\"406pt\" height=\"341pt\"\n viewBox=\"0.00 0.00 405.55 341.30\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 337.3)\">\n<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-337.3 401.55,-337.3 401.55,4 -4,4\"/>\n<g id=\"clust1\" class=\"cluster\">\n<title>cluster__legend</title>\n<polygon fill=\"#ffffff\" stroke=\"black\" points=\"8,-136.3 8,-325.3 116.1,-325.3 116.1,-136.3 8,-136.3\"/>\n<text text-anchor=\"middle\" x=\"62.05\" y=\"-308\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Legend</text>\n</g>\n<!-- raw_data -->\n<g id=\"node1\" class=\"node\">\n<title>raw_data</title>\n<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M94.98,-126.1C94.98,-126.1 29.13,-126.1 29.13,-126.1 23.13,-126.1 17.13,-120.1 17.13,-114.1 17.13,-114.1 17.13,-74.5 17.13,-74.5 17.13,-68.5 23.13,-62.5 29.13,-62.5 29.13,-62.5 94.98,-62.5 94.98,-62.5 100.98,-62.5 106.98,-68.5 106.98,-74.5 106.98,-74.5 106.98,-114.1 106.98,-114.1 106.98,-120.1 100.98,-126.1 94.98,-126.1\"/>\n<text text-anchor=\"start\" x=\"32.43\" y=\"-103\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">raw_data</text>\n<text text-anchor=\"start\" x=\"27.93\" y=\"-75\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">DataFrame</text>\n</g>\n<!-- transformed_data -->\n<g id=\"node2\" class=\"node\">\n<title>transformed_data</title>\n<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M261.45,-126.1C261.45,-126.1 149.1,-126.1 149.1,-126.1 143.1,-126.1 137.1,-120.1 137.1,-114.1 137.1,-114.1 137.1,-74.5 137.1,-74.5 137.1,-68.5 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+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x1530081f0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-25T00:00:53.746596Z",
+     "start_time": "2024-06-25T00:00:52.320439Z"
+    }
+   },
+   "cell_type": "code",
+   "source": "dr.execute([\"saved_data\"], inputs={\"filepath\": \"data.csv\"})",
+   "id": "86c0d0f7da9a472b",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Capturing execution run. Results can be found at http://localhost:8241/dashboard/project/7/runs/25\n",
+      "\n",
+      "\n",
+      "Captured execution run. Results can be found at http://localhost:8241/dashboard/project/7/runs/25\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'saved_data': <hamilton.htypes.DataSaverMetadata at 0x1528de280>}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "e108601ca3a88aab"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/materialization/using_types/simple_etl.png b/examples/materialization/using_types/simple_etl.png
diff --git a/...rialization/using_types/def simple_etl.py → ...materialization/using_types/simple_etl.py b/...rialization/using_types/def simple_etl.py → ...materialization/using_types/simple_etl.py