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Delete new-york-taxi_scale-out.ipynb

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Shané Winner
2019-07-28 00:38:05 -07:00
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how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scale-Out Data Preparation\n",
"Copyright (c) Microsoft Corporation. All rights reserved.<br>\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we are done with preparing and featurizing the data locally, we can run the same steps on the full dataset in scale-out mode. The new york taxi cab data is about 300GB in total, which is perfect for scale-out. Let's start by downloading the package we saved earlier to disk. Feel free to run the `new_york_taxi_cab.ipynb` notebook to generate the package yourself, in which case you may comment out the download code and set the `package_path` to where the package is saved."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tempfile import mkdtemp\n",
"from os import path\n",
"from urllib.request import urlretrieve\n",
"\n",
"dflow_root = mkdtemp()\n",
"dflow_path = path.join(dflow_root, \"new_york_taxi.dprep\")\n",
"print(\"Downloading Dataflow to: {}\".format(dflow_path))\n",
"urlretrieve(\"https://dprepdata.blob.core.windows.net/demo/new_york_taxi_v2.dprep\", dflow_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load the package we just downloaded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"\n",
"df = dprep.Dataflow.open(dflow_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's replace the datasources with the full dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from uuid import uuid4\n",
"\n",
"other_step = df._get_steps()[7].arguments['dataflows'][0]['anonymousSteps'][0]\n",
"other_step['id'] = str(uuid4())\n",
"other_step['arguments']['path']['target'] = 1\n",
"other_step['arguments']['path']['resourceDetails'][0]['path'] = 'https://wranglewestus.blob.core.windows.net/nyctaxi/yellow_tripdata*'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"green_dsource = dprep.BlobDataSource(\"https://wranglewestus.blob.core.windows.net/nyctaxi/green_tripdata*\")\n",
"df = df.replace_datasource(green_dsource)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once we have replaced the datasource, we can now run the same steps on the full dataset. We will print the first 5 rows of the spark DataFrame. Since we are running on the full dataset, this might take a little while depending on your spark cluster's size."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"spark_df = df.to_spark_dataframe()\n",
"spark_df.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/work-with-data/dataprep/case-studies/new-york-taxi/new-york-taxi_scale-out.png)"
]
}
],
"metadata": {
"authors": [
{
"name": "sihhu"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
},
"skip_execute_as_test": true
},
"nbformat": 4,
"nbformat_minor": 2
}