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"# Data Loaders Overview\n",
"\n",
"This example demonstrates how to build scalable and maintainable Extract-Transform-Load (ETL) pipelines using Hamilton, focusing on the Extract phase. Hamilton allows for easy switching between different data sources without the need for cluttered if-else statements, making the dataflow modular and easier to maintain.\n",
"\n",
"The goal is to show:\n",
"1. How to load data from various sources.\n",
"2. How to switch between different data sources using polymorphism.\n",
"\n",
"Three data loaders are used in this example:\n",
"\n",
"- Mock Data Loader: Generates mock data on the fly, useful for unit testing or quick iterations.\n",
"- CSV Data Loader: Reads data from CSV files, useful for ad-hoc research.\n",
"- DuckDB Data Loader: Loads data from a DuckDB database, representing more production-ready data pipelines.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pre-requisites"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You need to have the following installed and set up:\n",
"- Hamilton\n",
"- pandas\n",
"- duckdb\n",
"\n",
"Execute the code commented below to install the above if you don't have them already installed."
]
},
{
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"source": [
"# %pip install sf-hamilton[visualization] pandas duckdb==0.5.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing all the things you need\n"
]
},
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"cell_type": "code",
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"metadata": {},
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"name": "stderr",
"output_type": "stream",
"text": [
"/Users/stefankrawczyk/.pyenv/versions/knowledge_retrieval-py39/lib/python3.9/site-packages/pyspark/pandas/__init__.py:50: UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from hamilton import base\n",
"import hamilton.driver\n",
"%load_ext hamilton.plugins.jupyter_magic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating the data loaders modules"
]
},
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"metadata": {},
"source": [
"### `load_data_duckdb` module"
]
},
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"%%cell_to_module load_data_duckdb --display\n",
"# Loading duckdb data\n",
"import duckdb\n",
"import pandas as pd\n",
"\n",
"def connection(db_path: str) -> duckdb.DuckDBPyConnection:\n",
" return duckdb.connect(database=db_path)\n",
"\n",
"\n",
"def spend(connection: duckdb.DuckDBPyConnection) -> pd.DataFrame:\n",
" return connection.execute(\"select * from marketing_spend\").fetchdf()\n",
"\n",
"\n",
"def churn(connection: duckdb.DuckDBPyConnection) -> pd.DataFrame:\n",
" return connection.execute(\"select * from churn\").fetchdf()\n",
"\n",
"\n",
"def signups(connection: duckdb.DuckDBPyConnection) -> pd.DataFrame:\n",
" return connection.execute(\"select * from signups\").fetchdf()\n"
]
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"metadata": {},
"source": [
"### `load_data_csv` module"
]
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"%%cell_to_module load_data_csv --display\n",
"# Loading csv data\n",
"import pandas as pd\n",
"from hamilton.function_modifiers import load_from, value\n",
"\n",
"\n",
"@load_from.csv(path=value(\"test_data/marketing_spend.csv\"))\n",
"def spend(data: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"Takes in the dataframe and then generates a date index column to it,\n",
" where each row is a day starting from 2020-01-01\"\"\"\n",
" data[\"date\"] = pd.date_range(start=\"2020-01-01\", periods=len(data), freq=\"D\")\n",
" return data\n",
"\n",
"\n",
"@load_from.csv(path=value(\"test_data/churn.csv\"))\n",
"def churn(data: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"Takes in the dataframe and then generates a date index column to it,\n",
" where each row is a day starting from 2020-01-01\n",
" \"\"\"\n",
" data[\"date\"] = pd.date_range(start=\"2020-01-01\", periods=len(data), freq=\"D\")\n",
" return data\n",
"\n",
"\n",
"@load_from.csv(path=value(\"test_data/signups.csv\"))\n",
"def signups(data: pd.DataFrame) -> pd.DataFrame:\n",
" \"\"\"Takes in the dataframe and then generates a date index column to it,\n",
" where each row is a day starting from 2020-01-01\n",
" \"\"\"\n",
" data[\"date\"] = pd.date_range(start=\"2020-01-01\", periods=len(data), freq=\"D\")\n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `load_data_mock` module"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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"source": [
"%%cell_to_module load_data_mock --display\n",
"# loading mock data\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"def spend() -> pd.DataFrame:\n",
" data = np.array(\n",
" [\n",
" (\n",
" \"2022-08-03T00:00:00.000000000\",\n",
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" ),\n",
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" ),\n",
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" 11536.41253561,\n",
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" \"2022-08-05T00:00:00.000000000\",\n",
" ),\n",
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" 102263.53043232,\n",
" 115601.2888751,\n",
" 71474.76280964,\n",
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" 11652.28867968,\n",
" 1046.83170946,\n",
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" (\n",
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" 103271.09660695,\n",
" 115306.96341012,\n",
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" ),\n",
" (\n",
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" 100775.86701231,\n",
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" ),\n",
" (\n",
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" ),\n",
" (\n",
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" 100317.64365959,\n",
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" ),\n",
" (\n",
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" 102024.067597,\n",
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" ),\n",
" ],\n",
" dtype=[\n",
" (\"index\", \"<M8[ns]\"),\n",
" (\"facebook\", \"<f8\"),\n",
" (\"twitter\", \"<f8\"),\n",
" (\"tv\", \"<f8\"),\n",
" (\"youtube\", \"<f8\"),\n",
" (\"radio\", \"<f8\"),\n",
" (\"billboards\", \"<f8\"),\n",
" (\"date\", \"<M8[ns]\"),\n",
" ],\n",
" )\n",
" return pd.DataFrame.from_records(data)\n",
"\n",
"\n",
"def churn() -> pd.DataFrame:\n",
" data = np.array(\n",
" [\n",
" (\"2022-08-03T00:00:00.000000000\", 160, 53, \"2022-08-03T00:00:00.000000000\"),\n",
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" (\"2022-08-10T00:00:00.000000000\", 160, 57, \"2022-08-10T00:00:00.000000000\"),\n",
" (\"2022-08-11T00:00:00.000000000\", 156, 56, \"2022-08-11T00:00:00.000000000\"),\n",
" (\"2022-08-12T00:00:00.000000000\", 148, 58, \"2022-08-12T00:00:00.000000000\"),\n",
" ],\n",
" dtype=[(\"index\", \"<M8[ns]\"), (\"womens\", \"<i8\"), (\"mens\", \"<i8\"), (\"date\", \"<M8[ns]\")],\n",
" )\n",
" return pd.DataFrame.from_records(data)\n",
"\n",
"\n",
"def signups() -> pd.DataFrame:\n",
" data = np.array(\n",
" [\n",
" (\"2022-08-03T00:00:00.000000000\", 2184, 429, \"2022-08-03T00:00:00.000000000\"),\n",
" (\"2022-08-04T00:00:00.000000000\", 2164, 461, \"2022-08-04T00:00:00.000000000\"),\n",
" (\"2022-08-05T00:00:00.000000000\", 2159, 454, \"2022-08-05T00:00:00.000000000\"),\n",
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" (\"2022-08-07T00:00:00.000000000\", 2121, 478, \"2022-08-07T00:00:00.000000000\"),\n",
" (\"2022-08-08T00:00:00.000000000\", 2151, 517, \"2022-08-08T00:00:00.000000000\"),\n",
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" (\"2022-08-10T00:00:00.000000000\", 2160, 565, \"2022-08-10T00:00:00.000000000\"),\n",
" (\"2022-08-11T00:00:00.000000000\", 2135, 609, \"2022-08-11T00:00:00.000000000\"),\n",
" (\"2022-08-12T00:00:00.000000000\", 2116, 633, \"2022-08-12T00:00:00.000000000\"),\n",
" ],\n",
" dtype=[(\"index\", \"<M8[ns]\"), (\"womens\", \"<i8\"), (\"mens\", \"<i8\"), (\"date\", \"<M8[ns]\")],\n",
" )\n",
" return pd.DataFrame.from_records(data)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `prep_data` module"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"<title>tv_spend</title>\n",
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"<title>tv_spend_smoothed</title>\n",
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"<title>facebook_spend</title>\n",
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"<title>facebook_spend_smoothed</title>\n",
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"<title>radio_spend</title>\n",
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"<title>radio_spend&#45;&gt;total_marketing_spend</title>\n",
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"<title>total_churn&#45;&gt;total_customers</title>\n",
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"<title>mens_churn</title>\n",
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"<title>joined_data</title>\n",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%cell_to_module prep_data --display\n",
"# Preparing the data\n",
"import pandas as pd\n",
"from hamilton.function_modifiers import does, extract_columns, parameterize, source, value\n",
"\n",
"def _sum_series(**series):\n",
" return sum(series.values())\n",
"\n",
"\n",
"@extract_columns(\n",
" \"facebook_spend\",\n",
" \"twitter_spend\",\n",
" \"tv_spend\",\n",
" \"youtube_spend\",\n",
" \"radio_spend\",\n",
" \"billboards_spend\",\n",
" \"womens_churn\",\n",
" \"mens_churn\",\n",
" \"womens_signups\",\n",
" \"mens_signups\",\n",
")\n",
"def joined_data(spend: pd.DataFrame, signups: pd.DataFrame, churn: pd.DataFrame) -> pd.DataFrame:\n",
" spend = spend.set_index(\"date\").rename(columns=lambda col: col + \"_spend\")\n",
" churn = churn.set_index(\"date\").rename(columns=lambda col: col + \"_churn\")\n",
" signups = signups.set_index(\"date\").rename(columns=lambda col: col + \"_signups\")\n",
" return pd.concat([spend, churn, signups], axis=1)\n",
"\n",
"\n",
"@does(_sum_series)\n",
"def total_marketing_spend(\n",
" facebook_spend: pd.Series,\n",
" twitter_spend: pd.Series,\n",
" tv_spend: pd.Series,\n",
" youtube_spend: pd.Series,\n",
" radio_spend: pd.Series,\n",
" billboards_spend: pd.Series,\n",
") -> pd.Series:\n",
" pass\n",
"\n",
"\n",
"@does(_sum_series)\n",
"def total_signups(mens_signups: pd.Series, womens_signups: pd.Series) -> pd.Series:\n",
" pass\n",
"\n",
"\n",
"@does(_sum_series)\n",
"def total_churn(mens_churn: pd.Series, womens_churn: pd.Series) -> pd.Series:\n",
" pass\n",
"\n",
"\n",
"def total_customers(total_signups: pd.Series, total_churn: pd.Series) -> pd.Series:\n",
" customer_deltas = total_signups + total_churn\n",
" return customer_deltas.cumsum()\n",
"\n",
"\n",
"def acquisition_cost(total_marketing_spend: pd.Series, total_signups: pd.Series) -> pd.Series:\n",
" return total_marketing_spend / total_signups\n",
"\n",
"\n",
"@parameterize(\n",
" twitter_spend_smoothed={\"lookback_days\": value(7), \"spend\": source(\"twitter_spend\")},\n",
" facebook_spend_smoothed={\"lookback_days\": value(7), \"spend\": source(\"facebook_spend\")},\n",
" radio_spend_smoothed={\"lookback_days\": value(21), \"spend\": source(\"radio_spend\")},\n",
" tv_spend_smoothed={\"lookback_days\": value(21), \"spend\": source(\"tv_spend\")},\n",
" billboards_spend_smoothed={\"lookback_days\": value(7), \"spend\": source(\"billboards_spend\")},\n",
" youtube_spend_smoothed={\"lookback_days\": value(7), \"spend\": source(\"twitter_spend\")},\n",
")\n",
"def spend_smoothed(lookback_days: int, spend: pd.Series) -> pd.Series:\n",
" \"\"\"{spend} smoothed by {lookback_days}. Might want to smooth different ad spends differently,\n",
" figuring that it takes different amounts of time to get to the customer. A cheap hack at determining\n",
" auto-correlation of a series -- this should be a parameter in a model,\n",
" but this is to demonstrate the framework\n",
"\n",
" :param lookback_days: Days to smooth over\n",
" :param spend: Spend source\n",
" :return:\n",
" \"\"\"\n",
" return spend.rolling(window=lookback_days).mean().fillna(0)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Execution for the three data loaders"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"VARS = [\n",
" \"total_signups\",\n",
" \"total_churn\",\n",
" \"total_marketing_spend\",\n",
" \"acquisition_cost\",\n",
" \"twitter_spend_smoothed\",\n",
" \"facebook_spend_smoothed\",\n",
" \"radio_spend_smoothed\",\n",
" \"tv_spend_smoothed\",\n",
" \"billboards_spend_smoothed\",\n",
" \"youtube_spend_smoothed\",\n",
"]"
]
},
{
"cell_type": "code",
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"metadata": {},
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"source": [
"# Execution for duckdb data\n",
"\n",
"driver = (hamilton.driver.Builder()\n",
" .with_modules(load_data_duckdb, prep_data)\n",
" .with_adapters(base.PandasDataFrameResult())\n",
" .build()\n",
")\n",
"# Uncomment the print function below to display the execution result when have manually handled connections/whatnot for duckdb\n",
"# print(driver.execute(VARS)) \n",
"\n",
"duckdb_execution_graph = driver.visualize_execution(VARS, inputs={\"db_path\": \"./test_data/database.duckdb\"})\n",
"display(duckdb_execution_graph)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'keep_date_col' keyword in pd.read_csv is deprecated and will be removed in a future version. Explicitly remove unwanted columns after parsing instead.\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version.\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'keep_date_col' keyword in pd.read_csv is deprecated and will be removed in a future version. Explicitly remove unwanted columns after parsing instead.\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version.\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'keep_date_col' keyword in pd.read_csv is deprecated and will be removed in a future version. Explicitly remove unwanted columns after parsing instead.\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\\s+'`` instead\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n",
"/Users/stefankrawczyk/dagworks/hamilton/hamilton/plugins/pandas_extensions.py:249: FutureWarning: The 'verbose' keyword in pd.read_csv is deprecated and will be removed in a future version.\n",
" df = pd.read_csv(self.path, **self._get_loading_kwargs())\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" total_signups total_churn total_marketing_spend \\\n",
"date \n",
"2020-01-01 1947 203 52730.560003 \n",
"2020-01-02 1910 197 49279.312391 \n",
"2020-01-03 1899 203 51302.996119 \n",
"2020-01-04 1930 197 56406.767429 \n",
"2020-01-05 1893 200 62571.780395 \n",
"... ... ... ... \n",
"2022-08-28 2827 191 363933.708589 \n",
"2022-08-29 2843 192 368187.139781 \n",
"2022-08-30 2822 192 364502.996497 \n",
"2022-08-31 2870 184 361548.842342 \n",
"2022-09-01 2853 185 357315.802822 \n",
"\n",
" acquisition_cost twitter_spend_smoothed facebook_spend_smoothed \\\n",
"date \n",
"2020-01-01 27.082979 0.000000 0.000000 \n",
"2020-01-02 25.800687 0.000000 0.000000 \n",
"2020-01-03 27.015796 0.000000 0.000000 \n",
"2020-01-04 29.226304 0.000000 0.000000 \n",
"2020-01-05 33.054295 0.000000 0.000000 \n",
"... ... ... ... \n",
"2022-08-28 128.734952 119720.253147 96123.914351 \n",
"2022-08-29 129.506556 120202.788392 95909.302910 \n",
"2022-08-30 129.164776 120732.077431 96035.431620 \n",
"2022-08-31 125.975206 121276.011602 96364.649955 \n",
"2022-09-01 125.242132 121910.888572 96362.987882 \n",
"\n",
" radio_spend_smoothed tv_spend_smoothed \\\n",
"date \n",
"2020-01-01 0.000000 0.000000 \n",
"2020-01-02 0.000000 0.000000 \n",
"2020-01-03 0.000000 0.000000 \n",
"2020-01-04 0.000000 0.000000 \n",
"2020-01-05 0.000000 0.000000 \n",
"... ... ... \n",
"2022-08-28 13640.112643 67931.752455 \n",
"2022-08-29 13731.030787 67883.385906 \n",
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"2022-08-31 13815.628266 67796.627318 \n",
"2022-09-01 13826.582827 67695.406002 \n",
"\n",
" billboards_spend_smoothed youtube_spend_smoothed \n",
"date \n",
"2020-01-01 0.000000 0.000000 \n",
"2020-01-02 0.000000 0.000000 \n",
"2020-01-03 0.000000 0.000000 \n",
"2020-01-04 0.000000 0.000000 \n",
"2020-01-05 0.000000 0.000000 \n",
"... ... ... \n",
"2022-08-28 2370.658271 119720.253147 \n",
"2022-08-29 2088.525551 120202.788392 \n",
"2022-08-30 1649.969593 120732.077431 \n",
"2022-08-31 1337.251853 121276.011602 \n",
"2022-09-01 997.866137 121910.888572 \n",
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"<title>facebook_spend&#45;&gt;facebook_spend_smoothed</title>\n",
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"<!-- spend -->\n",
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"<title>spend</title>\n",
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"<!-- joined_data -->\n",
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"<!-- womens_signups&#45;&gt;total_signups -->\n",
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"<title>billboards_spend_smoothed</title>\n",
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"</g>\n",
"<!-- spend.select_data.data -->\n",
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"<title>spend.select_data.data</title>\n",
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"<!-- spend.select_data.data&#45;&gt;spend -->\n",
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"<!-- radio_spend -->\n",
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"</g>\n",
"<!-- joined_data&#45;&gt;tv_spend -->\n",
"<g id=\"edge13\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;tv_spend</title>\n",
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"</g>\n",
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"<title>joined_data&#45;&gt;mens_churn</title>\n",
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"<!-- joined_data&#45;&gt;mens_signups -->\n",
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"<text text-anchor=\"start\" x=\"26.92\" y=\"-568.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">PandasCSVReader</text>\n",
"</g>\n",
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"</g>\n",
"<!-- twitter_spend&#45;&gt;total_marketing_spend -->\n",
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"<title>twitter_spend&#45;&gt;youtube_spend_smoothed</title>\n",
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}
],
"source": [
"# Execution for csv data\n",
"\n",
"driver = (hamilton.driver.Builder()\n",
" .with_modules(load_data_csv, prep_data)\n",
" .with_adapters(base.PandasDataFrameResult())\n",
" .build()\n",
")\n",
"print(driver.execute(VARS))\n",
"\n",
"csv_execution_graph = driver.visualize_execution(VARS, inputs={\"db_path\": \"./test_data\"})\n",
"display(csv_execution_graph)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" total_signups total_churn total_marketing_spend \\\n",
"date \n",
"2022-08-03 2613 213 352562.280870 \n",
"2022-08-04 2625 216 355564.902400 \n",
"2022-08-05 2613 212 353811.045380 \n",
"2022-08-06 2606 214 354899.924090 \n",
"2022-08-07 2599 209 354891.300149 \n",
"2022-08-08 2668 212 357514.248166 \n",
"2022-08-09 2674 214 351627.165746 \n",
"2022-08-10 2725 217 350941.982457 \n",
"2022-08-11 2744 212 350736.588485 \n",
"2022-08-12 2749 206 350944.306317 \n",
"\n",
" acquisition_cost twitter_spend_smoothed facebook_spend_smoothed \\\n",
"date \n",
"2022-08-03 134.926246 0.000000 0.000000 \n",
"2022-08-04 135.453296 0.000000 0.000000 \n",
"2022-08-05 135.404151 0.000000 0.000000 \n",
"2022-08-06 136.185696 0.000000 0.000000 \n",
"2022-08-07 136.549173 0.000000 0.000000 \n",
"2022-08-08 134.000843 0.000000 0.000000 \n",
"2022-08-09 131.498566 115461.750752 102420.254555 \n",
"2022-08-10 128.786049 115410.481211 102005.647728 \n",
"2022-08-11 127.819456 115461.364832 101589.002831 \n",
"2022-08-12 127.662534 115686.617590 101618.669361 \n",
"\n",
" radio_spend_smoothed tv_spend_smoothed \\\n",
"date \n",
"2022-08-03 0.0 0.0 \n",
"2022-08-04 0.0 0.0 \n",
"2022-08-05 0.0 0.0 \n",
"2022-08-06 0.0 0.0 \n",
"2022-08-07 0.0 0.0 \n",
"2022-08-08 0.0 0.0 \n",
"2022-08-09 0.0 0.0 \n",
"2022-08-10 0.0 0.0 \n",
"2022-08-11 0.0 0.0 \n",
"2022-08-12 0.0 0.0 \n",
"\n",
" billboards_spend_smoothed youtube_spend_smoothed \n",
"date \n",
"2022-08-03 0.000000 0.000000 \n",
"2022-08-04 0.000000 0.000000 \n",
"2022-08-05 0.000000 0.000000 \n",
"2022-08-06 0.000000 0.000000 \n",
"2022-08-07 0.000000 0.000000 \n",
"2022-08-08 0.000000 0.000000 \n",
"2022-08-09 1943.742110 115461.750752 \n",
"2022-08-10 2205.101803 115410.481211 \n",
"2022-08-11 2394.622409 115461.364832 \n",
"2022-08-12 2361.296779 115686.617590 \n"
]
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"</g>\n",
"<!-- mens_churn -->\n",
"<g id=\"node11\" class=\"node\">\n",
"<title>mens_churn</title>\n",
"<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M357.43,-801.6C357.43,-801.6 278.82,-801.6 278.82,-801.6 272.82,-801.6 266.82,-795.6 266.82,-789.6 266.82,-789.6 266.82,-750 266.82,-750 266.82,-744 272.82,-738 278.82,-738 278.82,-738 357.43,-738 357.43,-738 363.43,-738 369.43,-744 369.43,-750 369.43,-750 369.43,-789.6 369.43,-789.6 369.43,-795.6 363.43,-801.6 357.43,-801.6\"/>\n",
"<text text-anchor=\"start\" x=\"277.62\" y=\"-778.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">mens_churn</text>\n",
"<text text-anchor=\"start\" x=\"299\" y=\"-750.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n",
"</g>\n",
"<!-- mens_churn&#45;&gt;total_churn -->\n",
"<g id=\"edge9\" class=\"edge\">\n",
"<title>mens_churn&#45;&gt;total_churn</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M369.92,-780.31C397.26,-785.96 431.1,-792.96 459.46,-798.83\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"458.45,-802.19 468.95,-800.79 459.87,-795.34 458.45,-802.19\"/>\n",
"</g>\n",
"<!-- total_marketing_spend&#45;&gt;acquisition_cost -->\n",
"<g id=\"edge29\" class=\"edge\">\n",
"<title>total_marketing_spend&#45;&gt;acquisition_cost</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M603.84,-307.32C610.01,-310.79 615.95,-314.61 621.4,-318.8 649.91,-340.7 673.91,-373.64 690.25,-399.92\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"687.17,-401.59 695.35,-408.33 693.16,-397.96 687.17,-401.59\"/>\n",
"</g>\n",
"<!-- signups -->\n",
"<g id=\"node14\" class=\"node\">\n",
"<title>signups</title>\n",
"<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M83.35,-514.6C83.35,-514.6 17.5,-514.6 17.5,-514.6 11.5,-514.6 5.5,-508.6 5.5,-502.6 5.5,-502.6 5.5,-463 5.5,-463 5.5,-457 11.5,-451 17.5,-451 17.5,-451 83.35,-451 83.35,-451 89.35,-451 95.35,-457 95.35,-463 95.35,-463 95.35,-502.6 95.35,-502.6 95.35,-508.6 89.35,-514.6 83.35,-514.6\"/>\n",
"<text text-anchor=\"start\" x=\"24.55\" y=\"-491.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">signups</text>\n",
"<text text-anchor=\"start\" x=\"16.3\" y=\"-463.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">DataFrame</text>\n",
"</g>\n",
"<!-- signups&#45;&gt;joined_data -->\n",
"<g id=\"edge22\" class=\"edge\">\n",
"<title>signups&#45;&gt;joined_data</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M95.51,-482.8C101.15,-482.8 106.98,-482.8 112.81,-482.8\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"112.41,-486.3 122.41,-482.8 112.41,-479.3 112.41,-486.3\"/>\n",
"</g>\n",
"<!-- billboards_spend -->\n",
"<g id=\"node15\" class=\"node\">\n",
"<title>billboards_spend</title>\n",
"<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M372.8,-227.6C372.8,-227.6 263.45,-227.6 263.45,-227.6 257.45,-227.6 251.45,-221.6 251.45,-215.6 251.45,-215.6 251.45,-176 251.45,-176 251.45,-170 257.45,-164 263.45,-164 263.45,-164 372.8,-164 372.8,-164 378.8,-164 384.8,-170 384.8,-176 384.8,-176 384.8,-215.6 384.8,-215.6 384.8,-221.6 378.8,-227.6 372.8,-227.6\"/>\n",
"<text text-anchor=\"start\" x=\"262.25\" y=\"-204.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">billboards_spend</text>\n",
"<text text-anchor=\"start\" x=\"299\" y=\"-176.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n",
"</g>\n",
"<!-- billboards_spend&#45;&gt;billboards_spend_smoothed -->\n",
"<g id=\"edge7\" class=\"edge\">\n",
"<title>billboards_spend&#45;&gt;billboards_spend_smoothed</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M385.13,-195.8C391.11,-195.8 397.28,-195.8 403.52,-195.8\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"403.32,-199.3 413.31,-195.8 403.31,-192.3 403.32,-199.3\"/>\n",
"</g>\n",
"<!-- billboards_spend&#45;&gt;total_marketing_spend -->\n",
"<g id=\"edge19\" class=\"edge\">\n",
"<title>billboards_spend&#45;&gt;total_marketing_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M385.27,-224.21C395.32,-228.47 405.57,-232.77 415.3,-236.8 418.85,-238.27 422.47,-239.76 426.13,-241.26\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"424.53,-244.39 435.11,-244.93 427.18,-237.91 424.53,-244.39\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;youtube_spend -->\n",
"<g id=\"edge1\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;youtube_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M182.43,-450.66C193.45,-415.09 215.22,-357.68 249.95,-318.8 250.21,-318.51 250.46,-318.23 250.73,-317.94\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"253.01,-320.6 257.8,-311.15 248.16,-315.55 253.01,-320.6\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;womens_churn -->\n",
"<g id=\"edge4\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;womens_churn</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M173.22,-515.02C173.96,-577.9 183.52,-718.86 249.95,-810.8 250.02,-810.9 250.09,-810.99 250.16,-811.09\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"247.32,-813.16 256.52,-818.4 252.6,-808.56 247.32,-813.16\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;facebook_spend -->\n",
"<g id=\"edge5\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;facebook_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M221.43,-496.45C228.3,-498.42 235.51,-500.48 242.75,-502.55\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"241.7,-505.89 252.27,-505.27 243.62,-499.16 241.7,-505.89\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;womens_signups -->\n",
"<g id=\"edge6\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;womens_signups</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M182.43,-514.94C193.45,-550.51 215.22,-607.92 249.95,-646.8 250.21,-647.09 250.46,-647.37 250.73,-647.66\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"248.16,-650.05 257.8,-654.45 253.01,-645 248.16,-650.05\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;radio_spend -->\n",
"<g id=\"edge8\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;radio_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M221.43,-469.15C232.54,-465.97 244.53,-462.54 256.14,-459.23\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"256.99,-462.62 265.65,-456.51 255.07,-455.89 256.99,-462.62\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;tv_spend -->\n",
"<g id=\"edge11\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;tv_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M199.72,-450.85C213.68,-434.81 231.72,-415.68 249.95,-400.8 255.64,-396.16 261.9,-391.65 268.24,-387.44\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"269.73,-390.65 276.26,-382.3 265.95,-384.75 269.73,-390.65\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;mens_churn -->\n",
"<g id=\"edge12\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;mens_churn</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M176.36,-514.83C182.09,-565.21 199.39,-663.89 249.95,-728.8 252.33,-731.86 255.01,-734.74 257.89,-737.45\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"255.39,-739.92 265.3,-743.66 259.88,-734.55 255.39,-739.92\"/>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;billboards_spend -->\n",
"<g id=\"edge20\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;billboards_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M176.36,-450.77C182.09,-400.39 199.39,-301.71 249.95,-236.8 250.1,-236.6 250.26,-236.41 250.41,-236.22\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"252.8,-238.78 257.05,-229.08 247.68,-234.01 252.8,-238.78\"/>\n",
"</g>\n",
"<!-- mens_signups -->\n",
"<g id=\"node17\" class=\"node\">\n",
"<title>mens_signups</title>\n",
"<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M364.55,-637.6C364.55,-637.6 271.7,-637.6 271.7,-637.6 265.7,-637.6 259.7,-631.6 259.7,-625.6 259.7,-625.6 259.7,-586 259.7,-586 259.7,-580 265.7,-574 271.7,-574 271.7,-574 364.55,-574 364.55,-574 370.55,-574 376.55,-580 376.55,-586 376.55,-586 376.55,-625.6 376.55,-625.6 376.55,-631.6 370.55,-637.6 364.55,-637.6\"/>\n",
"<text text-anchor=\"start\" x=\"270.5\" y=\"-614.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">mens_signups</text>\n",
"<text text-anchor=\"start\" x=\"299\" y=\"-586.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;mens_signups -->\n",
"<g id=\"edge24\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;mens_signups</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M199.72,-514.75C213.68,-530.79 231.72,-549.92 249.95,-564.8 250.88,-565.56 251.82,-566.31 252.77,-567.06\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"250.32,-569.6 260.44,-572.75 254.5,-563.98 250.32,-569.6\"/>\n",
"</g>\n",
"<!-- twitter_spend -->\n",
"<g id=\"node21\" class=\"node\">\n",
"<title>twitter_spend</title>\n",
"<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M361.18,-145.6C361.18,-145.6 275.07,-145.6 275.07,-145.6 269.07,-145.6 263.07,-139.6 263.07,-133.6 263.07,-133.6 263.07,-94 263.07,-94 263.07,-88 269.07,-82 275.07,-82 275.07,-82 361.18,-82 361.18,-82 367.18,-82 373.18,-88 373.18,-94 373.18,-94 373.18,-133.6 373.18,-133.6 373.18,-139.6 367.18,-145.6 361.18,-145.6\"/>\n",
"<text text-anchor=\"start\" x=\"273.88\" y=\"-122.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">twitter_spend</text>\n",
"<text text-anchor=\"start\" x=\"299\" y=\"-94.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n",
"</g>\n",
"<!-- joined_data&#45;&gt;twitter_spend -->\n",
"<g id=\"edge27\" class=\"edge\">\n",
"<title>joined_data&#45;&gt;twitter_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M173.22,-450.58C173.96,-387.7 183.52,-246.74 249.95,-154.8 251.41,-152.78 253.01,-150.83 254.71,-148.97\"/>\n",
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"</g>\n",
"<!-- mens_signups&#45;&gt;total_signups -->\n",
"<g id=\"edge2\" class=\"edge\">\n",
"<title>mens_signups&#45;&gt;total_signups</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M376.99,-605.8C400.57,-605.8 427.94,-605.8 452.21,-605.8\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"451.97,-609.3 461.97,-605.8 451.97,-602.3 451.97,-609.3\"/>\n",
"</g>\n",
"<!-- twitter_spend_smoothed -->\n",
"<g id=\"node18\" class=\"node\">\n",
"<title>twitter_spend_smoothed</title>\n",
"<path fill=\"#ffc857\" stroke=\"black\" d=\"M597.77,-63.6C597.77,-63.6 438.93,-63.6 438.93,-63.6 432.93,-63.6 426.93,-57.6 426.93,-51.6 426.93,-51.6 426.93,-12 426.93,-12 426.93,-6 432.93,0 438.93,0 438.93,0 597.77,0 597.77,0 603.77,0 609.77,-6 609.77,-12 609.77,-12 609.77,-51.6 609.77,-51.6 609.77,-57.6 603.77,-63.6 597.77,-63.6\"/>\n",
"<text text-anchor=\"start\" x=\"437.73\" y=\"-40.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">twitter_spend_smoothed</text>\n",
"<text text-anchor=\"start\" x=\"499.23\" y=\"-12.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n",
"</g>\n",
"<!-- churn -->\n",
"<g id=\"node19\" class=\"node\">\n",
"<title>churn</title>\n",
"<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M83.35,-432.6C83.35,-432.6 17.5,-432.6 17.5,-432.6 11.5,-432.6 5.5,-426.6 5.5,-420.6 5.5,-420.6 5.5,-381 5.5,-381 5.5,-375 11.5,-369 17.5,-369 17.5,-369 83.35,-369 83.35,-369 89.35,-369 95.35,-375 95.35,-381 95.35,-381 95.35,-420.6 95.35,-420.6 95.35,-426.6 89.35,-432.6 83.35,-432.6\"/>\n",
"<text text-anchor=\"start\" x=\"31.67\" y=\"-409.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">churn</text>\n",
"<text text-anchor=\"start\" x=\"16.3\" y=\"-381.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">DataFrame</text>\n",
"</g>\n",
"<!-- churn&#45;&gt;joined_data -->\n",
"<g id=\"edge23\" class=\"edge\">\n",
"<title>churn&#45;&gt;joined_data</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M95.51,-430.87C101.81,-435.17 108.37,-439.64 114.88,-444.08\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"112.85,-446.93 123.08,-449.67 116.79,-441.15 112.85,-446.93\"/>\n",
"</g>\n",
"<!-- youtube_spend_smoothed -->\n",
"<g id=\"node20\" class=\"node\">\n",
"<title>youtube_spend_smoothed</title>\n",
"<path fill=\"#ffc857\" stroke=\"black\" d=\"M603.4,-145.6C603.4,-145.6 433.3,-145.6 433.3,-145.6 427.3,-145.6 421.3,-139.6 421.3,-133.6 421.3,-133.6 421.3,-94 421.3,-94 421.3,-88 427.3,-82 433.3,-82 433.3,-82 603.4,-82 603.4,-82 609.4,-82 615.4,-88 615.4,-94 615.4,-94 615.4,-133.6 615.4,-133.6 615.4,-139.6 609.4,-145.6 603.4,-145.6\"/>\n",
"<text text-anchor=\"start\" x=\"432.1\" y=\"-122.5\" font-family=\"Helvetica,sans-Serif\" font-weight=\"bold\" font-size=\"14.00\">youtube_spend_smoothed</text>\n",
"<text text-anchor=\"start\" x=\"499.23\" y=\"-94.5\" font-family=\"Helvetica,sans-Serif\" font-style=\"italic\" font-size=\"14.00\">Series</text>\n",
"</g>\n",
"<!-- twitter_spend&#45;&gt;total_marketing_spend -->\n",
"<g id=\"edge15\" class=\"edge\">\n",
"<title>twitter_spend&#45;&gt;total_marketing_spend</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M373.57,-142.24C378.25,-146.05 382.6,-150.24 386.3,-154.8 410.65,-184.82 388.55,-208.9 415.3,-236.8 418.03,-239.64 420.97,-242.29 424.09,-244.76\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"421.86,-247.47 432.05,-250.39 425.9,-241.76 421.86,-247.47\"/>\n",
"</g>\n",
"<!-- twitter_spend&#45;&gt;twitter_spend_smoothed -->\n",
"<g id=\"edge25\" class=\"edge\">\n",
"<title>twitter_spend&#45;&gt;twitter_spend_smoothed</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M373.46,-90.4C387.1,-84.59 401.72,-78.42 415.3,-72.8 418.85,-71.33 422.47,-69.84 426.13,-68.34\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"427.18,-71.69 435.11,-64.67 424.53,-65.21 427.18,-71.69\"/>\n",
"</g>\n",
"<!-- twitter_spend&#45;&gt;youtube_spend_smoothed -->\n",
"<g id=\"edge26\" class=\"edge\">\n",
"<title>twitter_spend&#45;&gt;youtube_spend_smoothed</title>\n",
"<path fill=\"none\" stroke=\"black\" d=\"M373.55,-113.8C384.87,-113.8 397.18,-113.8 409.66,-113.8\"/>\n",
"<polygon fill=\"black\" stroke=\"black\" points=\"409.64,-117.3 419.64,-113.8 409.64,-110.3 409.64,-117.3\"/>\n",
"</g>\n",
"<!-- function -->\n",
"<g id=\"node25\" class=\"node\">\n",
"<title>function</title>\n",
"<path fill=\"#b4d8e4\" stroke=\"black\" d=\"M72.85,-651.1C72.85,-651.1 28,-651.1 28,-651.1 22,-651.1 16,-645.1 16,-639.1 16,-639.1 16,-626.5 16,-626.5 16,-620.5 22,-614.5 28,-614.5 28,-614.5 72.85,-614.5 72.85,-614.5 78.85,-614.5 84.85,-620.5 84.85,-626.5 84.85,-626.5 84.85,-639.1 84.85,-639.1 84.85,-645.1 78.85,-651.1 72.85,-651.1\"/>\n",
"<text text-anchor=\"middle\" x=\"50.42\" y=\"-627\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">function</text>\n",
"</g>\n",
"<!-- output -->\n",
"<g id=\"node26\" class=\"node\">\n",
"<title>output</title>\n",
"<path fill=\"#ffc857\" stroke=\"black\" d=\"M67.97,-706.1C67.97,-706.1 32.87,-706.1 32.87,-706.1 26.87,-706.1 20.87,-700.1 20.87,-694.1 20.87,-694.1 20.87,-681.5 20.87,-681.5 20.87,-675.5 26.87,-669.5 32.87,-669.5 32.87,-669.5 67.97,-669.5 67.97,-669.5 73.97,-669.5 79.97,-675.5 79.97,-681.5 79.97,-681.5 79.97,-694.1 79.97,-694.1 79.97,-700.1 73.97,-706.1 67.97,-706.1\"/>\n",
"<text text-anchor=\"middle\" x=\"50.42\" y=\"-682\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">output</text>\n",
"</g>\n",
"</g>\n",
"</svg>\n"
],
"text/plain": [
"<graphviz.graphs.Digraph at 0x14e22aee0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Execution for mock data\n",
"\n",
"driver = (hamilton.driver.Builder()\n",
" .with_modules(load_data_mock, prep_data)\n",
" .with_adapters(base.PandasDataFrameResult())\n",
" .build()\n",
")\n",
"print(driver.execute(VARS))\n",
"\n",
"mock_execution_graph = driver.visualize_execution(VARS)\n",
"display(mock_execution_graph)"
]
},
{
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}