| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": {}, |
| "outputs": [], |
| "source": [ |
| "# update the pandas package - optional\n", |
| "# !pip install --upgrade pandas\n" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:33.890006Z", |
| "start_time": "2023-09-17T05:43:33.866292Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "import sqlite3\n", |
| "import sys\n", |
| "import pandas as pd\n", |
| "\n", |
| "# Add the hamilton module to your path - optinal\n", |
| "# project_dir = \"### ADD PATH HERE ###\"\n", |
| "# sys.path.append(project_dir)\n", |
| "\n", |
| "from hamilton import base, driver\n", |
| "from hamilton.io.materialization import to" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:33.890336Z", |
| "start_time": "2023-09-17T05:43:33.869093Z" |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# We use the autoreload extension that comes with ipython to automatically reload modules when\n", |
| "# the code in them changes.\n", |
| "\n", |
| "# import the jupyter extension\n", |
| "%load_ext autoreload\n", |
| "# set it to only reload the modules imported\n", |
| "%autoreload 1" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:33.930823Z", |
| "start_time": "2023-09-17T05:43:33.875941Z" |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Overwriting spend_calculations.py\n" |
| ] |
| } |
| ], |
| "source": [ |
| "%%writefile spend_calculations.py\n", |
| "# Define your new Hamilton functions.\n", |
| "# The %%writefile magic command creates a new Python module with the functions below.\n", |
| "# We will import this later and pass it into our Driver.\n", |
| "\n", |
| "import pandas as pd\n", |
| " \n", |
| "# Look at `my_functions` to see how these functions connect.\n", |
| "def avg_3wk_spend(spend: pd.Series) -> pd.Series:\n", |
| " \"\"\"Rolling 3 week average spend.\"\"\"\n", |
| " return spend.rolling(3).mean()\n", |
| "\n", |
| "\n", |
| "def spend_per_signup(spend: pd.Series, signups: pd.Series) -> pd.Series:\n", |
| " \"\"\"The cost per signup in relation to spend.\"\"\"\n", |
| " return spend / signups\n", |
| "\n", |
| "\n", |
| "def spend_mean(spend: pd.Series) -> float:\n", |
| " \"\"\"Shows function creating a scalar. In this case it computes the mean of the entire column.\"\"\"\n", |
| " return spend.mean()\n", |
| "\n", |
| "\n", |
| "def spend_zero_mean(spend: pd.Series, spend_mean: float) -> pd.Series:\n", |
| " \"\"\"Shows function that takes a scalar. In this case to zero mean spend.\"\"\"\n", |
| " return spend - spend_mean\n", |
| "\n", |
| "\n", |
| "def spend_std_dev(spend: pd.Series) -> float:\n", |
| " \"\"\"Function that computes the standard deviation of the spend column.\"\"\"\n", |
| " return spend.std()\n", |
| "\n", |
| "\n", |
| "def spend_zero_mean_unit_variance(spend_zero_mean: pd.Series, spend_std_dev: float) -> pd.Series:\n", |
| " \"\"\"Function showing one way to make spend have zero mean and unit variance.\"\"\"\n", |
| " return spend_zero_mean / spend_std_dev" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 4, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:33.931096Z", |
| "start_time": "2023-09-17T05:43:33.881858Z" |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "initial_columns = { # load from actuals or wherever -- this is our initial data we use as input.\n", |
| " # Note: these values don't have to be all series, they could be a scalar.\n", |
| " \"signups\": pd.Series([1, 10, 50, 100, 200, 400]),\n", |
| " \"spend\": pd.Series([10, 10, 20, 40, 40, 50]),\n", |
| "}" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:33.932468Z", |
| "start_time": "2023-09-17T05:43:33.887774Z" |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "Note: Hamilton collects completely anonymous data about usage. This will help us improve Hamilton over time. See https://github.com/dagworks-inc/hamilton#usage-analytics--data-privacy for details.\n" |
| ] |
| } |
| ], |
| "source": [ |
| "%aimport spend_calculations\n", |
| "\n", |
| "df_builder = base.PandasDataFrameResult()\n", |
| "dr = driver.Driver({}, spend_calculations) # can pass in multiple modules" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:33.932900Z", |
| "start_time": "2023-09-17T05:43:33.892681Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# we need to specify what we want in the final dataframe. These can be string names, or function references.\n", |
| "output_columns = [\n", |
| " \"spend\",\n", |
| " \"signups\",\n", |
| " \"avg_3wk_spend\",\n", |
| " \"spend_per_signup\",\n", |
| " \"spend_zero_mean_unit_variance\",\n", |
| "]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:33.933121Z", |
| "start_time": "2023-09-17T05:43:33.896779Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# set up db connection for sql materializer below\n", |
| "conn = sqlite3.connect(\"df.db\")\n", |
| "\n", |
| "# remove an previous instances of the 'test' table that will be created next\n", |
| "conn.cursor().execute(\"DROP TABLE IF EXISTS test;\")\n", |
| "conn.commit()" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:34.014270Z", |
| "start_time": "2023-09-17T05:43:33.912979Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "More than one applicable adapter detected for <class 'pandas.core.frame.DataFrame'>. Using the last one registered <class 'hamilton.plugins.pandas_extensions.PandasPickleWriter'>.\n" |
| ] |
| }, |
| { |
| "data": { |
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| " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", |
| "<!-- Generated by graphviz version 2.43.0 (0)\n", |
| " -->\n", |
| "<!-- Title: %3 Pages: 1 -->\n", |
| "<svg width=\"2561pt\" height=\"404pt\"\n", |
| " viewBox=\"0.00 0.00 2561.23 404.00\" 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(4 400)\">\n", |
| "<title>%3</title>\n", |
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| "<!-- df_to_orc_build_result -->\n", |
| "<g id=\"node1\" class=\"node\">\n", |
| "<title>df_to_orc_build_result</title>\n", |
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| "<text text-anchor=\"middle\" x=\"112.44\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">df_to_orc_build_result</text>\n", |
| "</g>\n", |
| "<!-- df_to_orc -->\n", |
| "<g id=\"node19\" class=\"node\">\n", |
| "<title>df_to_orc</title>\n", |
| "<polygon fill=\"none\" stroke=\"black\" points=\"152.94,-36 71.94,-36 71.94,0 152.94,0 152.94,-36\"/>\n", |
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| "</g>\n", |
| "<!-- df_to_orc_build_result->df_to_orc -->\n", |
| "<g id=\"edge48\" class=\"edge\">\n", |
| "<title>df_to_orc_build_result->df_to_orc</title>\n", |
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| "<!-- df_to_html_build_result -->\n", |
| "<g id=\"node4\" class=\"node\">\n", |
| "<title>df_to_html_build_result</title>\n", |
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| "<text text-anchor=\"middle\" x=\"1114.44\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">df_to_html_build_result</text>\n", |
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| "<g id=\"edge11\" class=\"edge\">\n", |
| "<title>spend_per_signup->df_to_html_build_result</title>\n", |
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| "</g>\n", |
| "<!-- df_to_stata_build_result -->\n", |
| "<g id=\"node8\" class=\"node\">\n", |
| "<title>df_to_stata_build_result</title>\n", |
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| "<text text-anchor=\"middle\" x=\"363.44\" y=\"-86.3\" font-family=\"Times,serif\" font-size=\"14.00\">df_to_stata_build_result</text>\n", |
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| "<g id=\"edge19\" class=\"edge\">\n", |
| "<title>spend_per_signup->df_to_stata_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_json_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_csv_build_result</title>\n", |
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| "<title>df_to_feather_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_feather_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_sql_build_result</title>\n", |
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| "<title>df_to_parquet_build_result</title>\n", |
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| "<title>spend_zero_mean_unit_variance->df_to_csv_build_result</title>\n", |
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| "<title>spend_zero_mean_unit_variance->df_to_feather_build_result</title>\n", |
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| "<g id=\"edge46\" class=\"edge\">\n", |
| "<title>spend_zero_mean_unit_variance->df_to_sql_build_result</title>\n", |
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| "<!-- spend_zero_mean_unit_variance->df_to_parquet_build_result -->\n", |
| "<g id=\"edge53\" class=\"edge\">\n", |
| "<title>spend_zero_mean_unit_variance->df_to_parquet_build_result</title>\n", |
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| "<!-- spend_zero_mean_unit_variance->df_to_xml_build_result -->\n", |
| "<g id=\"edge60\" class=\"edge\">\n", |
| "<title>spend_zero_mean_unit_variance->df_to_xml_build_result</title>\n", |
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| "<!-- spend_zero_mean_unit_variance->df_to_pickle_build_result -->\n", |
| "<g id=\"edge65\" class=\"edge\">\n", |
| "<title>spend_zero_mean_unit_variance->df_to_pickle_build_result</title>\n", |
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| "<!-- df_to_csv_build_result->df_to_csv -->\n", |
| "<g id=\"edge21\" class=\"edge\">\n", |
| "<title>df_to_csv_build_result->df_to_csv</title>\n", |
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| "<!-- df_to_feather_build_result->df_to_feather -->\n", |
| "<g id=\"edge15\" class=\"edge\">\n", |
| "<title>df_to_feather_build_result->df_to_feather</title>\n", |
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| "<!-- df_to_sql_build_result->df_to_sql -->\n", |
| "<g id=\"edge13\" class=\"edge\">\n", |
| "<title>df_to_sql_build_result->df_to_sql</title>\n", |
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| "<!-- df_to_pickle -->\n", |
| "<g id=\"node18\" class=\"node\">\n", |
| "<title>df_to_pickle</title>\n", |
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| "<text text-anchor=\"middle\" x=\"2428.44\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">df_to_pickle</text>\n", |
| "</g>\n", |
| "<!-- df_to_parquet_build_result->df_to_parquet -->\n", |
| "<g id=\"edge24\" class=\"edge\">\n", |
| "<title>df_to_parquet_build_result->df_to_parquet</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1905.44,-71.7C1905.44,-63.98 1905.44,-54.71 1905.44,-46.11\"/>\n", |
| "<polygon fill=\"black\" stroke=\"black\" points=\"1908.94,-46.1 1905.44,-36.1 1901.94,-46.1 1908.94,-46.1\"/>\n", |
| "</g>\n", |
| "<!-- spend_zero_mean->spend_zero_mean_unit_variance -->\n", |
| "<g id=\"edge30\" class=\"edge\">\n", |
| "<title>spend_zero_mean->spend_zero_mean_unit_variance</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"1461.36,-189 1455.59,-180.1 1454.55,-190.65 1461.36,-189\"/>\n", |
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| "<!-- df_to_xml -->\n", |
| "<g id=\"node28\" class=\"node\">\n", |
| "<title>df_to_xml</title>\n", |
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| "<text text-anchor=\"middle\" x=\"2171.44\" y=\"-14.3\" font-family=\"Times,serif\" font-size=\"14.00\">df_to_xml</text>\n", |
| "</g>\n", |
| "<!-- df_to_xml_build_result->df_to_xml -->\n", |
| "<g id=\"edge69\" class=\"edge\">\n", |
| "<title>df_to_xml_build_result->df_to_xml</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M2171.44,-71.7C2171.44,-63.98 2171.44,-54.71 2171.44,-46.11\"/>\n", |
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| "</g>\n", |
| "<!-- df_to_pickle_build_result->df_to_pickle -->\n", |
| "<g id=\"edge47\" class=\"edge\">\n", |
| "<title>df_to_pickle_build_result->df_to_pickle</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M2428.44,-71.7C2428.44,-63.98 2428.44,-54.71 2428.44,-46.11\"/>\n", |
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| "</g>\n", |
| "<!-- signups -->\n", |
| "<g id=\"node24\" class=\"node\">\n", |
| "<title>signups</title>\n", |
| "<polygon fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" points=\"1167.94,-252 1048.94,-252 1048.94,-216 1167.94,-216 1167.94,-252\"/>\n", |
| "<text text-anchor=\"middle\" x=\"1108.44\" y=\"-230.3\" font-family=\"Times,serif\" font-size=\"14.00\">Input: signups</text>\n", |
| "</g>\n", |
| "<!-- signups->df_to_orc_build_result -->\n", |
| "<g id=\"edge2\" class=\"edge\">\n", |
| "<title>signups->df_to_orc_build_result</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1048.94,-228.25C954.03,-220.35 762.64,-203.06 601.44,-180 454.38,-158.96 283.97,-125.97 188.12,-106.62\"/>\n", |
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| "</g>\n", |
| "<!-- signups->spend_per_signup -->\n", |
| "<g id=\"edge7\" class=\"edge\">\n", |
| "<title>signups->spend_per_signup</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1085.45,-215.7C1073.63,-206.8 1059.05,-195.82 1046.26,-186.2\"/>\n", |
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| "<!-- signups->df_to_html_build_result -->\n", |
| "<g id=\"edge9\" class=\"edge\">\n", |
| "<title>signups->df_to_html_build_result</title>\n", |
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| "<!-- signups->df_to_stata_build_result -->\n", |
| "<g id=\"edge17\" class=\"edge\">\n", |
| "<title>signups->df_to_stata_build_result</title>\n", |
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| "<!-- signups->df_to_json_build_result -->\n", |
| "<g id=\"edge26\" class=\"edge\">\n", |
| "<title>signups->df_to_json_build_result</title>\n", |
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| "<!-- signups->df_to_csv_build_result -->\n", |
| "<g id=\"edge33\" class=\"edge\">\n", |
| "<title>signups->df_to_csv_build_result</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1048.84,-219.44C1005.53,-209.31 945.61,-194.73 893.44,-180 815.2,-157.91 725.85,-128.69 669.8,-109.87\"/>\n", |
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| "<!-- signups->df_to_feather_build_result -->\n", |
| "<g id=\"edge38\" class=\"edge\">\n", |
| "<title>signups->df_to_feather_build_result</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1116.39,-215.78C1126.67,-195.48 1146.49,-162.21 1173.44,-144 1202.8,-124.16 1239.02,-111.75 1273.02,-103.98\"/>\n", |
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| "<!-- signups->df_to_sql_build_result -->\n", |
| "<g id=\"edge43\" class=\"edge\">\n", |
| "<title>signups->df_to_sql_build_result</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1168.31,-229.15C1288.07,-221.05 1548.7,-201.22 1583.44,-180 1606.93,-165.65 1623.02,-138.01 1632.39,-117.32\"/>\n", |
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| "<!-- signups->df_to_parquet_build_result -->\n", |
| "<g id=\"edge50\" class=\"edge\">\n", |
| "<title>signups->df_to_parquet_build_result</title>\n", |
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| "<!-- signups->df_to_xml_build_result -->\n", |
| "<g id=\"edge57\" class=\"edge\">\n", |
| "<title>signups->df_to_xml_build_result</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M1168.23,-229.48C1274.61,-222.8 1503.62,-206.53 1695.44,-180 1838.66,-160.19 2004.28,-126.83 2097.56,-107.06\"/>\n", |
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| "</g>\n", |
| "<!-- signups->df_to_pickle_build_result -->\n", |
| "<g id=\"edge62\" class=\"edge\">\n", |
| "<title>signups->df_to_pickle_build_result</title>\n", |
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| "</g>\n", |
| "<!-- spend_mean->spend_zero_mean -->\n", |
| "<g id=\"edge55\" class=\"edge\">\n", |
| "<title>spend_mean->spend_zero_mean</title>\n", |
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| "<!-- spend_std_dev->spend_zero_mean_unit_variance -->\n", |
| "<g id=\"edge31\" class=\"edge\">\n", |
| "<title>spend_std_dev->spend_zero_mean_unit_variance</title>\n", |
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| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<graphviz.graphs.Digraph at 0x7f2ab46e3d60>" |
| ] |
| }, |
| "execution_count": 9, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "materializers = [\n", |
| " # materialize the dataframe to a pickle file\n", |
| " to.pickle(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_pickle\",\n", |
| " path=\"./df.pkl\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " # materialize the dataframe to a JSON file\n", |
| " to.json(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_json\",\n", |
| " filepath_or_buffer=\"./df.json\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " to.sql(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_sql\",\n", |
| " table_name=\"test\",\n", |
| " db_connection=conn,\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " # materialize the dataframe to a XML file\n", |
| " to.xml(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_xml\",\n", |
| " path_or_buffer=\"./df.xml\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " to.html(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_html\",\n", |
| " buf=\"./df.html\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " to.stata(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_stata\",\n", |
| " path=\"./df.dta\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " to.feather(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_feather\",\n", |
| " path=\"./df.feather\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " to.parquet(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_parquet\",\n", |
| " path=\"./df.parquet.gzip\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " to.csv(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_csv\",\n", |
| " path=\"./df.csv\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " to.orc(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_orc\",\n", |
| " path=\"./df.orc\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| "]\n", |
| "# Visualize what is happening\n", |
| "dr.visualize_materialization(\n", |
| " *materializers,\n", |
| " additional_vars=output_columns,\n", |
| " inputs=initial_columns,\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:34.026203Z", |
| "start_time": "2023-09-17T05:43:34.016610Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "More than one applicable adapter detected for <class 'pandas.core.frame.DataFrame'>. Using the last one registered <class 'hamilton.plugins.pandas_extensions.PandasPickleWriter'>.\n" |
| ] |
| } |
| ], |
| "source": [ |
| "# Materialize a result, i.e. execute the DAG!\n", |
| "materialization_results, additional_outputs = dr.materialize(\n", |
| " *materializers,\n", |
| " additional_vars=[\n", |
| " \"df_to_pickle_build_result\",\n", |
| " \"df_to_json_build_result\",\n", |
| " \"df_to_sql_build_result\",\n", |
| " \"df_to_xml_build_result\",\n", |
| " \"df_to_html_build_result\",\n", |
| " \"df_to_stata_build_result\",\n", |
| " \"df_to_feather_build_result\",\n", |
| " \"df_to_parquet_build_result\",\n", |
| " \"df_to_csv_build_result\",\n", |
| " \"df_to_orc_build_result\",\n", |
| " ], # because combine is used, we can get that result here.\n", |
| " inputs=initial_columns,\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:34.041466Z", |
| "start_time": "2023-09-17T05:43:34.028346Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "{'df_to_pickle': {'size': 1088,\n", |
| " 'path': './df.pkl',\n", |
| " 'last_modified': 1698716078.9096448,\n", |
| " 'timestamp': 1698741278.914183},\n", |
| " 'df_to_json': {'size': 428,\n", |
| " 'path': './df.json',\n", |
| " 'last_modified': 1698716078.9096448,\n", |
| " 'timestamp': 1698741278.916944},\n", |
| " 'df_to_sql': {'rows': 6,\n", |
| " 'query': None,\n", |
| " 'table_name': 'test',\n", |
| " 'timestamp': 1698741278.927032},\n", |
| " 'df_to_xml': {'size': 1622,\n", |
| " 'path': './df.xml',\n", |
| " 'last_modified': 1698716078.9616463,\n", |
| " 'timestamp': 1698741278.966294},\n", |
| " 'df_to_html': {'size': 1145,\n", |
| " 'path': './df.html',\n", |
| " 'last_modified': 1698716078.9696465,\n", |
| " 'timestamp': 1698741278.974224},\n", |
| " 'df_to_stata': {'size': 1526,\n", |
| " 'path': './df.dta',\n", |
| " 'last_modified': 1698716078.985647,\n", |
| " 'timestamp': 1698741278.992052},\n", |
| " 'df_to_feather': {'size': 3522,\n", |
| " 'path': './df.feather',\n", |
| " 'last_modified': 1698716079.021648,\n", |
| " 'timestamp': 1698741279.028492},\n", |
| " 'df_to_parquet': {'size': 4705,\n", |
| " 'path': './df.parquet.gzip',\n", |
| " 'last_modified': 1698716079.0456486,\n", |
| " 'timestamp': 1698741279.053797},\n", |
| " 'df_to_csv': {'size': 335,\n", |
| " 'path': './df.csv',\n", |
| " 'last_modified': 1698716079.0536487,\n", |
| " 'timestamp': 1698741279.059399},\n", |
| " 'df_to_orc': {'size': 954,\n", |
| " 'path': './df.orc',\n", |
| " 'last_modified': 1698716079.065649,\n", |
| " 'timestamp': 1698741279.07081}}" |
| ] |
| }, |
| "execution_count": 11, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "materialization_results" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:34.051151Z", |
| "start_time": "2023-09-17T05:43:34.043320Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div>\n", |
| "<style scoped>\n", |
| " .dataframe tbody tr th:only-of-type {\n", |
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| "<table border=\"1\" class=\"dataframe\">\n", |
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| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>spend</th>\n", |
| " <th>signups</th>\n", |
| " <th>avg_3wk_spend</th>\n", |
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| " <td>0.677349</td>\n", |
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| " <th>4</th>\n", |
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| " <td>0.200</td>\n", |
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| "0 10 1 NaN 10.000 \n", |
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| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
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| "3 0.677349 \n", |
| "4 0.677349 \n", |
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| }, |
| "execution_count": 12, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_pickle_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 13, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:34.058608Z", |
| "start_time": "2023-09-17T05:43:34.048662Z" |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div>\n", |
| "<style scoped>\n", |
| " .dataframe tbody tr th:only-of-type {\n", |
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| " .dataframe tbody tr th {\n", |
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| " .dataframe thead th {\n", |
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| " }\n", |
| "</style>\n", |
| "<table border=\"1\" class=\"dataframe\">\n", |
| " <thead>\n", |
| " <tr style=\"text-align: right;\">\n", |
| " <th></th>\n", |
| " <th>spend</th>\n", |
| " <th>signups</th>\n", |
| " <th>avg_3wk_spend</th>\n", |
| " <th>spend_per_signup</th>\n", |
| " <th>spend_zero_mean_unit_variance</th>\n", |
| " </tr>\n", |
| " </thead>\n", |
| " <tbody>\n", |
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| " <td>0.400</td>\n", |
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| ], |
| "text/plain": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 " |
| ] |
| }, |
| "execution_count": 13, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_json_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 14, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:34.089706Z", |
| "start_time": "2023-09-17T05:43:34.060251Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_sql_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 15, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_xml_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 16, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_html_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 17, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_stata_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 18, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_feather_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 19, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_parquet_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 20, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_csv_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 21, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| " spend signups avg_3wk_spend spend_per_signup \\\n", |
| "0 10 1 NaN 10.000 \n", |
| "1 10 10 NaN 1.000 \n", |
| "2 20 50 13.333333 0.400 \n", |
| "3 40 100 23.333333 0.400 \n", |
| "4 40 200 33.333333 0.200 \n", |
| "5 50 400 43.333333 0.125 \n", |
| "\n", |
| " spend_zero_mean_unit_variance \n", |
| "0 -1.064405 \n", |
| "1 -1.064405 \n", |
| "2 -0.483821 \n", |
| "3 0.677349 \n", |
| "4 0.677349 \n", |
| "5 1.257934 \n" |
| ] |
| } |
| ], |
| "source": [ |
| "print(additional_outputs[\"df_to_orc_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 21, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-09-17T05:43:34.090208Z", |
| "start_time": "2023-09-17T05:43:34.066483Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# closing out db connection\n", |
| "conn.close()" |
| ] |
| } |
| ], |
| "metadata": { |
| "kernelspec": { |
| "display_name": "Python 3", |
| "language": "python", |
| "name": "python3" |
| }, |
| "language_info": { |
| "codemirror_mode": { |
| "name": "ipython", |
| "version": 3 |
| }, |
| "file_extension": ".py", |
| "mimetype": "text/x-python", |
| "name": "python", |
| "nbconvert_exporter": "python", |
| "pygments_lexer": "ipython3", |
| "version": "3.10.12" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 4 |
| } |
