| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:33.343763Z", |
| "start_time": "2023-10-05T16:36:33.337662Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# update the polars package - optional\n", |
| "# !pip install --upgrade polars" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 2, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:35.401649Z", |
| "start_time": "2023-10-05T16:36:33.838727Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "d:\\Turing\\codes\\hamilton\\venv\\lib\\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" |
| ] |
| } |
| ], |
| "source": [ |
| "import polars as pl\n", |
| "import sys\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 driver\n", |
| "from hamilton.io.materialization import to\n", |
| "from hamilton.plugins import h_polars" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 3, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:35.465307Z", |
| "start_time": "2023-10-05T16:36:35.406399Z" |
| }, |
| "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": 4, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:36.389233Z", |
| "start_time": "2023-10-05T16:36:36.380788Z" |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Writing 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 polars as pl\n", |
| "\n", |
| "# Look at `my_functions` to see how these functions connect.\n", |
| "def avg_3wk_spend(spend: pl.Series) -> pl.Series:\n", |
| " \"\"\"Rolling 3 week average spend.\"\"\"\n", |
| " return spend.rolling_mean(3)\n", |
| "\n", |
| "\n", |
| "def spend_per_signup(spend: pl.Series, signups: pl.Series) -> pl.Series:\n", |
| " \"\"\"The cost per signup in relation to spend.\"\"\"\n", |
| " return spend / signups\n", |
| "\n", |
| "\n", |
| "def spend_mean(spend: pl.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: pl.Series, spend_mean: float) -> pl.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: pl.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: pl.Series, spend_std_dev: float) -> pl.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": 5, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:36.947063Z", |
| "start_time": "2023-10-05T16:36:36.938015Z" |
| }, |
| "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\": pl.Series([1, 10, 50, 100, 200, 400]),\n", |
| " \"spend\": pl.Series([10, 10, 20, 40, 40, 50]),\n", |
| "}" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:37.496013Z", |
| "start_time": "2023-10-05T16:36:37.447521Z" |
| }, |
| "pycharm": { |
| "name": "#%%\n" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "%aimport spend_calculations\n", |
| "\n", |
| "df_builder = h_polars.PolarsDataFrameResult()\n", |
| "dr = driver.Driver({}, spend_calculations) # can pass in multiple modules" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:38.178430Z", |
| "start_time": "2023-10-05T16:36:38.162359Z" |
| } |
| }, |
| "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": 8, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:39.212897Z", |
| "start_time": "2023-10-05T16:36:39.202499Z" |
| }, |
| "collapsed": false |
| }, |
| "outputs": [], |
| "source": [ |
| "# pass" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:39.908587Z", |
| "start_time": "2023-10-05T16:36:39.637374Z" |
| } |
| }, |
| "outputs": [ |
| { |
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| "<title>avg_3wk_spend->df_to_avro_build_result</title>\n", |
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| "<title>df_to_parquet</title>\n", |
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| "<title>df_to_spreadsheet</title>\n", |
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| "<title>_spend_mean_inputs</title>\n", |
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| "<title>_spend_mean_inputs->spend_mean</title>\n", |
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| "<title>_avg_3wk_spend_inputs</title>\n", |
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| "<g id=\"edge3\" class=\"edge\">\n", |
| "<title>_avg_3wk_spend_inputs->avg_3wk_spend</title>\n", |
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| "<title>_df_to_json_build_result_inputs</title>\n", |
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| "<g id=\"edge7\" class=\"edge\">\n", |
| "<title>_df_to_json_build_result_inputs->df_to_json_build_result</title>\n", |
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| "<title>_df_to_database_build_result_inputs</title>\n", |
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| "<g id=\"edge12\" class=\"edge\">\n", |
| "<title>_df_to_database_build_result_inputs->df_to_database_build_result</title>\n", |
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| "<!-- _df_to_feather_build_result_inputs -->\n", |
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| "<title>_df_to_feather_build_result_inputs</title>\n", |
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| "<title>_df_to_feather_build_result_inputs->df_to_feather_build_result</title>\n", |
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| "<title>_df_to_parquet_build_result_inputs</title>\n", |
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| "<g id=\"edge22\" class=\"edge\">\n", |
| "<title>_df_to_parquet_build_result_inputs->df_to_parquet_build_result</title>\n", |
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| "</g>\n", |
| "<!-- _spend_std_dev_inputs -->\n", |
| "<g id=\"node25\" class=\"node\">\n", |
| "<title>_spend_std_dev_inputs</title>\n", |
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| "<!-- _spend_std_dev_inputs->spend_std_dev -->\n", |
| "<g id=\"edge24\" class=\"edge\">\n", |
| "<title>_spend_std_dev_inputs->spend_std_dev</title>\n", |
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| "<!-- _df_to_spreadsheet_build_result_inputs -->\n", |
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| "<title>_df_to_spreadsheet_build_result_inputs</title>\n", |
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| "<!-- _df_to_spreadsheet_build_result_inputs->df_to_spreadsheet_build_result -->\n", |
| "<g id=\"edge28\" class=\"edge\">\n", |
| "<title>_df_to_spreadsheet_build_result_inputs->df_to_spreadsheet_build_result</title>\n", |
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| "<!-- _df_to_avro_build_result_inputs -->\n", |
| "<g id=\"node27\" class=\"node\">\n", |
| "<title>_df_to_avro_build_result_inputs</title>\n", |
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| "<!-- _df_to_avro_build_result_inputs->df_to_avro_build_result -->\n", |
| "<g id=\"edge32\" class=\"edge\">\n", |
| "<title>_df_to_avro_build_result_inputs->df_to_avro_build_result</title>\n", |
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| "<!-- _spend_zero_mean_inputs -->\n", |
| "<g id=\"node28\" class=\"node\">\n", |
| "<title>_spend_zero_mean_inputs</title>\n", |
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| "<!-- _spend_zero_mean_inputs->spend_zero_mean -->\n", |
| "<g id=\"edge37\" class=\"edge\">\n", |
| "<title>_spend_zero_mean_inputs->spend_zero_mean</title>\n", |
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| "<!-- _spend_per_signup_inputs -->\n", |
| "<g id=\"node29\" class=\"node\">\n", |
| "<title>_spend_per_signup_inputs</title>\n", |
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| "<!-- _spend_per_signup_inputs->spend_per_signup -->\n", |
| "<g id=\"edge38\" class=\"edge\">\n", |
| "<title>_spend_per_signup_inputs->spend_per_signup</title>\n", |
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| "<!-- input -->\n", |
| "<g id=\"node30\" class=\"node\">\n", |
| "<title>input</title>\n", |
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| "</g>\n", |
| "<!-- function -->\n", |
| "<g id=\"node31\" class=\"node\">\n", |
| "<title>function</title>\n", |
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| "</g>\n", |
| "<!-- output -->\n", |
| "<g id=\"node32\" class=\"node\">\n", |
| "<title>output</title>\n", |
| "<path fill=\"#ffc857\" stroke=\"black\" d=\"M81.1,-461.85C81.1,-461.85 44.5,-461.85 44.5,-461.85 38.5,-461.85 32.5,-455.85 32.5,-449.85 32.5,-449.85 32.5,-435.75 32.5,-435.75 32.5,-429.75 38.5,-423.75 44.5,-423.75 44.5,-423.75 81.1,-423.75 81.1,-423.75 87.1,-423.75 93.1,-429.75 93.1,-435.75 93.1,-435.75 93.1,-449.85 93.1,-449.85 93.1,-455.85 87.1,-461.85 81.1,-461.85\"/>\n", |
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| "</g>\n", |
| "<!-- materializer -->\n", |
| "<g id=\"node33\" class=\"node\">\n", |
| "<title>materializer</title>\n", |
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| "</g>\n", |
| "</g>\n", |
| "</svg>\n" |
| ], |
| "text/plain": [ |
| "<graphviz.graphs.Digraph at 0x2b06a471e40>" |
| ] |
| }, |
| "execution_count": 9, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "materializers = [\n", |
| " # materialize the dataframe to a parquet file\n", |
| " to.parquet(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_parquet\",\n", |
| " file=\"./df.parquet\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " # materialize the dataframe to a feather file\n", |
| " to.feather(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_feather\",\n", |
| " file=\"./df.feather\",\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", |
| " file=\"./df.json\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " # materialize the dataframe to an avro file\n", |
| " to.avro(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_avro\",\n", |
| " file=\"./df.avro\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " # materialize the dataframe to a database\n", |
| " to.database(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_database\",\n", |
| " table_name=\"test\",\n", |
| " connection=\"sqlite:///df.db\",\n", |
| " if_table_exists=\"append\",\n", |
| " combine=df_builder,\n", |
| " ),\n", |
| " #materialize the dataframe to a spreadsheet file\n", |
| " to.spreadsheet(\n", |
| " dependencies=output_columns,\n", |
| " id=\"df_to_spreadsheet\",\n", |
| " workbook=\"./df.xlsx\",\n", |
| " worksheet=\"Sheet1\",\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": "2023-10-05T16:36:56.565384Z", |
| "start_time": "2023-10-05T16:36:56.531421Z" |
| } |
| }, |
| "outputs": [], |
| "source": [ |
| "# Materialize a result, i.e. execute the DAG!\n", |
| "materialization_results, additional_outputs = dr.materialize(\n", |
| " *materializers,\n", |
| " additional_vars=[\n", |
| " \"df_to_parquet_build_result\",\n", |
| " \"df_to_feather_build_result\",\n", |
| " \"df_to_json_build_result\",\n", |
| " \"df_to_avro_build_result\",\n", |
| " \"df_to_database_build_result\",\n", |
| " \"df_to_spreadsheet_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": "2023-10-05T16:36:57.122715Z", |
| "start_time": "2023-10-05T16:36:57.101770Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/plain": [ |
| "{'df_to_parquet': {'file_metadata': {'size': 1609,\n", |
| " 'path': './df.parquet',\n", |
| " 'last_modified': 1711363433.0819426,\n", |
| " 'timestamp': 1711343633.081942,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''},\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': 1696,\n", |
| " 'path': './df.feather',\n", |
| " 'last_modified': 1711363433.0829415,\n", |
| " 'timestamp': 1711343633.082941,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''},\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': 657,\n", |
| " 'path': './df.json',\n", |
| " 'last_modified': 1711363433.0839424,\n", |
| " 'timestamp': 1711343633.083942,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''},\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_avro': {'file_metadata': {'size': 517,\n", |
| " 'path': './df.avro',\n", |
| " 'last_modified': 1711363433.0849397,\n", |
| " 'timestamp': 1711343633.084939,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''},\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_database': {'file_metadata': {'size': None,\n", |
| " 'path': 'test',\n", |
| " 'last_modified': 1711363433.3330674,\n", |
| " 'timestamp': 1711343633.333067,\n", |
| " 'scheme': '',\n", |
| " 'notes': 'File metadata is unsupported for scheme: or path: test does not exist.'},\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_spreadsheet': {'file_metadata': {'size': 6503,\n", |
| " 'path': './df.xlsx',\n", |
| " 'last_modified': 1711363433.3686314,\n", |
| " 'timestamp': 1711343633.369631,\n", |
| " 'scheme': '',\n", |
| " 'notes': ''},\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": 11, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "materialization_results" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:57.826266Z", |
| "start_time": "2023-10-05T16:36:57.805459Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
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| ".dataframe > tbody > tr {\n", |
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| "┌───────┬─────────┬───────────────┬──────────────────┬───────────────────────────────┐\n", |
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| }, |
| "execution_count": 12, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_parquet_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 13, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:36:58.919203Z", |
| "start_time": "2023-10-05T16:36:58.869761Z" |
| } |
| }, |
| "outputs": [ |
| { |
| "data": { |
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| ] |
| }, |
| "execution_count": 13, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_feather_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 14, |
| "metadata": { |
| "ExecuteTime": { |
| "end_time": "2023-10-05T16:37:01.766669Z", |
| "start_time": "2023-10-05T16:37:01.705286Z" |
| }, |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
| "<div><style>\n", |
| ".dataframe > thead > tr,\n", |
| ".dataframe > tbody > tr {\n", |
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| ], |
| "text/plain": [ |
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| "┌───────┬─────────┬───────────────┬──────────────────┬───────────────────────────────┐\n", |
| "│ spend ┆ signups ┆ avg_3wk_spend ┆ spend_per_signup ┆ spend_zero_mean_unit_variance │\n", |
| "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", |
| "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 │\n", |
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| ] |
| }, |
| "execution_count": 14, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_json_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 15, |
| "metadata": { |
| "collapsed": false |
| }, |
| "outputs": [ |
| { |
| "data": { |
| "text/html": [ |
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| "<small>shape: (6, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>spend</th><th>signups</th><th>avg_3wk_spend</th><th>spend_per_signup</th><th>spend_zero_mean_unit_variance</th></tr><tr><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>10</td><td>1</td><td>null</td><td>10.0</td><td>-1.064405</td></tr><tr><td>10</td><td>10</td><td>null</td><td>1.0</td><td>-1.064405</td></tr><tr><td>20</td><td>50</td><td>13.333333</td><td>0.4</td><td>-0.483821</td></tr><tr><td>40</td><td>100</td><td>23.333333</td><td>0.4</td><td>0.677349</td></tr><tr><td>40</td><td>200</td><td>33.333333</td><td>0.2</td><td>0.677349</td></tr><tr><td>50</td><td>400</td><td>43.333333</td><td>0.125</td><td>1.257934</td></tr></tbody></table></div>" |
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| "┌───────┬─────────┬───────────────┬──────────────────┬───────────────────────────────┐\n", |
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| }, |
| "execution_count": 15, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_avro_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 16, |
| "metadata": {}, |
| "outputs": [ |
| { |
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| "<small>shape: (6, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>spend</th><th>signups</th><th>avg_3wk_spend</th><th>spend_per_signup</th><th>spend_zero_mean_unit_variance</th></tr><tr><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>10</td><td>1</td><td>null</td><td>10.0</td><td>-1.064405</td></tr><tr><td>10</td><td>10</td><td>null</td><td>1.0</td><td>-1.064405</td></tr><tr><td>20</td><td>50</td><td>13.333333</td><td>0.4</td><td>-0.483821</td></tr><tr><td>40</td><td>100</td><td>23.333333</td><td>0.4</td><td>0.677349</td></tr><tr><td>40</td><td>200</td><td>33.333333</td><td>0.2</td><td>0.677349</td></tr><tr><td>50</td><td>400</td><td>43.333333</td><td>0.125</td><td>1.257934</td></tr></tbody></table></div>" |
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| }, |
| "execution_count": 16, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_database_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 17, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "data": { |
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| "│ spend ┆ signups ┆ avg_3wk_spend ┆ spend_per_signup ┆ spend_zero_mean_unit_variance │\n", |
| "│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", |
| "│ i64 ┆ i64 ┆ f64 ┆ f64 ┆ f64 │\n", |
| "╞═══════╪═════════╪═══════════════╪══════════════════╪═══════════════════════════════╡\n", |
| "│ 10 ┆ 1 ┆ null ┆ 10.0 ┆ -1.064405 │\n", |
| "│ 10 ┆ 10 ┆ null ┆ 1.0 ┆ -1.064405 │\n", |
| "│ 20 ┆ 50 ┆ 13.333333 ┆ 0.4 ┆ -0.483821 │\n", |
| "│ 40 ┆ 100 ┆ 23.333333 ┆ 0.4 ┆ 0.677349 │\n", |
| "│ 40 ┆ 200 ┆ 33.333333 ┆ 0.2 ┆ 0.677349 │\n", |
| "│ 50 ┆ 400 ┆ 43.333333 ┆ 0.125 ┆ 1.257934 │\n", |
| "└───────┴─────────┴───────────────┴──────────────────┴───────────────────────────────┘" |
| ] |
| }, |
| "execution_count": 17, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "additional_outputs[\"df_to_spreadsheet_build_result\"]" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": {}, |
| "outputs": [], |
| "source": [] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": null, |
| "metadata": {}, |
| "outputs": [], |
| "source": [] |
| } |
| ], |
| "metadata": { |
| "kernelspec": { |
| "display_name": "Python 3 (ipykernel)", |
| "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": "3.10.5" |
| } |
| }, |
| "nbformat": 4, |
| "nbformat_minor": 4 |
| } |
