| { |
| "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 - optional\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": [], |
| "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": 8, |
| "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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| " -->\n", |
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| "<title>spend_per_signup</title>\n", |
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| "<title>spend_per_signup->df_to_sql_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_stata_build_result</title>\n", |
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| "<title>df_to_pickle_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_pickle_build_result</title>\n", |
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| "<title>df_to_html_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_html_build_result</title>\n", |
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| "<title>df_to_parquet_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_parquet_build_result</title>\n", |
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| "<title>df_to_orc_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_orc_build_result</title>\n", |
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| "<title>spend_per_signup->df_to_feather_build_result</title>\n", |
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| "<title>df_to_csv_build_result</title>\n", |
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| "<title>df_to_feather</title>\n", |
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| "<title>df_to_html_build_result->df_to_html</title>\n", |
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| "<title>df_to_parquet</title>\n", |
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| "<title>df_to_parquet_build_result->df_to_parquet</title>\n", |
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| "<title>df_to_orc</title>\n", |
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| "<title>df_to_orc_build_result->df_to_orc</title>\n", |
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| "<title>spend_mean</title>\n", |
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| "<!-- spend_zero_mean -->\n", |
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| "<title>spend_zero_mean</title>\n", |
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| "<title>spend_mean->spend_zero_mean</title>\n", |
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| "<title>df_to_excel</title>\n", |
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| "<title>avg_3wk_spend</title>\n", |
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| "<title>avg_3wk_spend->df_to_sql_build_result</title>\n", |
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| "<title>avg_3wk_spend->df_to_json_build_result</title>\n", |
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| "<title>df_to_csv_build_result->df_to_csv</title>\n", |
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| "<title>df_to_xml_build_result->df_to_xml</title>\n", |
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| "<title>_spend_std_dev_inputs->spend_std_dev</title>\n", |
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| "<title>_df_to_sql_build_result_inputs->df_to_sql_build_result</title>\n", |
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| "<!-- _df_to_stata_build_result_inputs -->\n", |
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| "<title>_df_to_stata_build_result_inputs->df_to_stata_build_result</title>\n", |
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| "<!-- _spend_per_signup_inputs -->\n", |
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| "<!-- _df_to_pickle_build_result_inputs -->\n", |
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| "<!-- _df_to_html_build_result_inputs -->\n", |
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| "<!-- _df_to_parquet_build_result_inputs -->\n", |
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| "<!-- _spend_mean_inputs -->\n", |
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| "<title>_spend_mean_inputs</title>\n", |
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| "<polygon fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" points=\"632.15,-149.6 512.55,-149.6 512.55,-84 632.15,-84 632.15,-149.6\"/>\n", |
| "<text text-anchor=\"start\" x=\"531.97\" y=\"-121.5\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">spend</text>\n", |
| "<text text-anchor=\"start\" x=\"579.22\" y=\"-121.5\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Series</text>\n", |
| "<text text-anchor=\"start\" x=\"527.1\" y=\"-100.5\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">signups</text>\n", |
| "<text text-anchor=\"start\" x=\"579.22\" y=\"-100.5\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Series</text>\n", |
| "</g>\n", |
| "<!-- _df_to_csv_build_result_inputs->df_to_csv_build_result -->\n", |
| "<g id=\"edge57\" class=\"edge\">\n", |
| "<title>_df_to_csv_build_result_inputs->df_to_csv_build_result</title>\n", |
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| "<!-- _df_to_xml_build_result_inputs -->\n", |
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| "<title>_df_to_xml_build_result_inputs</title>\n", |
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| "</g>\n", |
| "<!-- _df_to_xml_build_result_inputs->df_to_xml_build_result -->\n", |
| "<g id=\"edge61\" class=\"edge\">\n", |
| "<title>_df_to_xml_build_result_inputs->df_to_xml_build_result</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"719.3,-563.07 729.74,-561.29 720.47,-556.17 719.3,-563.07\"/>\n", |
| "</g>\n", |
| "<!-- _spend_zero_mean_inputs -->\n", |
| "<g id=\"node44\" class=\"node\">\n", |
| "<title>_spend_zero_mean_inputs</title>\n", |
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| "<text text-anchor=\"start\" x=\"203.9\" y=\"-463\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Series</text>\n", |
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| "<!-- _spend_zero_mean_inputs->spend_zero_mean -->\n", |
| "<g id=\"edge63\" class=\"edge\">\n", |
| "<title>_spend_zero_mean_inputs->spend_zero_mean</title>\n", |
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| "<polygon fill=\"black\" stroke=\"black\" points=\"273.7,-503.87 284.26,-504.69 276.51,-497.46 273.7,-503.87\"/>\n", |
| "</g>\n", |
| "<!-- input -->\n", |
| "<g id=\"node45\" class=\"node\">\n", |
| "<title>input</title>\n", |
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| "<text text-anchor=\"middle\" x=\"62.05\" y=\"-763\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">input</text>\n", |
| "</g>\n", |
| "<!-- function -->\n", |
| "<g id=\"node46\" class=\"node\">\n", |
| "<title>function</title>\n", |
| "<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M84.47,-732.1C84.47,-732.1 39.62,-732.1 39.62,-732.1 33.62,-732.1 27.62,-726.1 27.62,-720.1 27.62,-720.1 27.62,-707.5 27.62,-707.5 27.62,-701.5 33.62,-695.5 39.62,-695.5 39.62,-695.5 84.47,-695.5 84.47,-695.5 90.47,-695.5 96.47,-701.5 96.47,-707.5 96.47,-707.5 96.47,-720.1 96.47,-720.1 96.47,-726.1 90.47,-732.1 84.47,-732.1\"/>\n", |
| "<text text-anchor=\"middle\" x=\"62.05\" y=\"-708\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">function</text>\n", |
| "</g>\n", |
| "<!-- output -->\n", |
| "<g id=\"node47\" class=\"node\">\n", |
| "<title>output</title>\n", |
| "<path fill=\"#ffc857\" stroke=\"black\" d=\"M79.6,-677.1C79.6,-677.1 44.5,-677.1 44.5,-677.1 38.5,-677.1 32.5,-671.1 32.5,-665.1 32.5,-665.1 32.5,-652.5 32.5,-652.5 32.5,-646.5 38.5,-640.5 44.5,-640.5 44.5,-640.5 79.6,-640.5 79.6,-640.5 85.6,-640.5 91.6,-646.5 91.6,-652.5 91.6,-652.5 91.6,-665.1 91.6,-665.1 91.6,-671.1 85.6,-677.1 79.6,-677.1\"/>\n", |
| "<text text-anchor=\"middle\" x=\"62.05\" y=\"-653\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">output</text>\n", |
| "</g>\n", |
| "<!-- materializer -->\n", |
| "<g id=\"node48\" class=\"node\">\n", |
| "<title>materializer</title>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M108.1,-618.34C108.1,-620.37 87.46,-622.01 62.05,-622.01 36.64,-622.01 16,-620.37 16,-618.34 16,-618.34 16,-585.26 16,-585.26 16,-583.23 36.64,-581.59 62.05,-581.59 87.46,-581.59 108.1,-583.23 108.1,-585.26 108.1,-585.26 108.1,-618.34 108.1,-618.34\"/>\n", |
| "<path fill=\"none\" stroke=\"black\" d=\"M108.1,-618.34C108.1,-616.31 87.46,-614.66 62.05,-614.66 36.64,-614.66 16,-616.31 16,-618.34\"/>\n", |
| "<text text-anchor=\"middle\" x=\"62.05\" y=\"-596\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">materializer</text>\n", |
| "</g>\n", |
| "</g>\n", |
| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<graphviz.graphs.Digraph at 0x15ff50d50>" |
| ] |
| }, |
| "execution_count": 8, |
| "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", |
| " to.excel(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_excel\",\n", |
| " path=\"./df.xlsx\",\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": 9, |
| "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", |
| " \"df_to_excel_build_result\",\n", |
| " ], # because combine is used, we can get that result here.\n", |
| " inputs=initial_columns,\n", |
| ")" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "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': {'file_metadata': {'size': 1088,\n", |
| " 'path': './df.pkl',\n", |
| " 'last_modified': 1707261374.0912328,\n", |
| " 'timestamp': 1707257774.091604},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_json': {'file_metadata': {'size': 428,\n", |
| " 'path': './df.json',\n", |
| " 'last_modified': 1707261374.0931628,\n", |
| " 'timestamp': 1707257774.093234},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_sql': {'sql_metadata': {'rows': 6,\n", |
| " 'query': None,\n", |
| " 'table_name': 'test',\n", |
| " 'timestamp': 1707257774.096384},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_xml': {'file_metadata': {'size': 1622,\n", |
| " 'path': './df.xml',\n", |
| " 'last_modified': 1707261374.1253755,\n", |
| " 'timestamp': 1707257774.125475},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_html': {'file_metadata': {'size': 1145,\n", |
| " 'path': './df.html',\n", |
| " 'last_modified': 1707261374.1274278,\n", |
| " 'timestamp': 1707257774.127508},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_stata': {'file_metadata': {'size': 1526,\n", |
| " 'path': './df.dta',\n", |
| " 'last_modified': 1707261374.1300728,\n", |
| " 'timestamp': 1707257774.13016},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_feather': {'file_metadata': {'size': 3514,\n", |
| " 'path': './df.feather',\n", |
| " 'last_modified': 1707261374.1384852,\n", |
| " 'timestamp': 1707257774.138582},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_parquet': {'file_metadata': {'size': 4705,\n", |
| " 'path': './df.parquet.gzip',\n", |
| " 'last_modified': 1707261374.14694,\n", |
| " 'timestamp': 1707257774.147069},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_csv': {'file_metadata': {'size': 335,\n", |
| " 'path': './df.csv',\n", |
| " 'last_modified': 1707261374.1481698,\n", |
| " 'timestamp': 1707257774.148234},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_orc': {'file_metadata': {'size': 954,\n", |
| " 'path': './df.orc',\n", |
| " 'last_modified': 1707261374.153472,\n", |
| " 'timestamp': 1707257774.15356},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n", |
| " 'df_to_excel': {'file_metadata': {'size': 5218,\n", |
| " 'path': './df.xlsx',\n", |
| " 'last_modified': 1707261374.2337506,\n", |
| " 'timestamp': 1707257774.233816},\n", |
| " 'dataframe_metadata': {'rows': 6,\n", |
| " 'columns': 5,\n", |
| " 'column_names': ['spend',\n", |
| " 'signups',\n", |
| " 'avg_3wk_spend',\n", |
| " 'spend_per_signup',\n", |
| " 'spend_zero_mean_unit_variance'],\n", |
| " 'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}}}" |
| ] |
| }, |
| "execution_count": 10, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "materialization_results" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "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", |
| " 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>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", |
| " <tr>\n", |
| " <th>0</th>\n", |
| " <td>10</td>\n", |
| " <td>1</td>\n", |
| " <td>NaN</td>\n", |
| " <td>10.000</td>\n", |
| " <td>-1.064405</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1</th>\n", |
| " <td>10</td>\n", |
| " <td>10</td>\n", |
| " <td>NaN</td>\n", |
| " <td>1.000</td>\n", |
| " <td>-1.064405</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>2</th>\n", |
| " <td>20</td>\n", |
| " <td>50</td>\n", |
| " <td>13.333333</td>\n", |
| " <td>0.400</td>\n", |
| " <td>-0.483821</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>3</th>\n", |
| " <td>40</td>\n", |
| " <td>100</td>\n", |
| " <td>23.333333</td>\n", |
| " <td>0.400</td>\n", |
| " <td>0.677349</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>4</th>\n", |
| " <td>40</td>\n", |
| " <td>200</td>\n", |
| " <td>33.333333</td>\n", |
| " <td>0.200</td>\n", |
| " <td>0.677349</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>5</th>\n", |
| " <td>50</td>\n", |
| " <td>400</td>\n", |
| " <td>43.333333</td>\n", |
| " <td>0.125</td>\n", |
| " <td>1.257934</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "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": 11, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_pickle_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "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", |
| " 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>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", |
| " <tr>\n", |
| " <th>0</th>\n", |
| " <td>10</td>\n", |
| " <td>1</td>\n", |
| " <td>NaN</td>\n", |
| " <td>10.000</td>\n", |
| " <td>-1.064405</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1</th>\n", |
| " <td>10</td>\n", |
| " <td>10</td>\n", |
| " <td>NaN</td>\n", |
| " <td>1.000</td>\n", |
| " <td>-1.064405</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>2</th>\n", |
| " <td>20</td>\n", |
| " <td>50</td>\n", |
| " <td>13.333333</td>\n", |
| " <td>0.400</td>\n", |
| " <td>-0.483821</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>3</th>\n", |
| " <td>40</td>\n", |
| " <td>100</td>\n", |
| " <td>23.333333</td>\n", |
| " <td>0.400</td>\n", |
| " <td>0.677349</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>4</th>\n", |
| " <td>40</td>\n", |
| " <td>200</td>\n", |
| " <td>33.333333</td>\n", |
| " <td>0.200</td>\n", |
| " <td>0.677349</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>5</th>\n", |
| " <td>50</td>\n", |
| " <td>400</td>\n", |
| " <td>43.333333</td>\n", |
| " <td>0.125</td>\n", |
| " <td>1.257934</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "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": 12, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_json_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 13, |
| "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": 14, |
| "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": 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_html_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_stata_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_feather_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_parquet_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_csv_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_orc_build_result\"])" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 21, |
| "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>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", |
| " <tr>\n", |
| " <th>0</th>\n", |
| " <td>10</td>\n", |
| " <td>1</td>\n", |
| " <td>NaN</td>\n", |
| " <td>10.000</td>\n", |
| " <td>-1.064405</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>1</th>\n", |
| " <td>10</td>\n", |
| " <td>10</td>\n", |
| " <td>NaN</td>\n", |
| " <td>1.000</td>\n", |
| " <td>-1.064405</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>2</th>\n", |
| " <td>20</td>\n", |
| " <td>50</td>\n", |
| " <td>13.333333</td>\n", |
| " <td>0.400</td>\n", |
| " <td>-0.483821</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>3</th>\n", |
| " <td>40</td>\n", |
| " <td>100</td>\n", |
| " <td>23.333333</td>\n", |
| " <td>0.400</td>\n", |
| " <td>0.677349</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>4</th>\n", |
| " <td>40</td>\n", |
| " <td>200</td>\n", |
| " <td>33.333333</td>\n", |
| " <td>0.200</td>\n", |
| " <td>0.677349</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <th>5</th>\n", |
| " <td>50</td>\n", |
| " <td>400</td>\n", |
| " <td>43.333333</td>\n", |
| " <td>0.125</td>\n", |
| " <td>1.257934</td>\n", |
| " </tr>\n", |
| " </tbody>\n", |
| "</table>\n", |
| "</div>" |
| ], |
| "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": 21, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_excel_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 22, |
| "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": "3.11.7" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 4 |
| } |
