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        "author": "Meng Pan",
        "author_email": "meng.pan95@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Libraries"
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        "description": "## FeatureTools for Spark (featuretools4s)\n\n### 1. What's FeatureTools? \nFeatureTools is a Python library open-sourced by MIT's \nFeatureLab aiming to automate \nthe process of feature engineering in Machine Learning \napplications. \n\nPlease visit the [official website](https://docs.featuretools.com/index.html)\nfor more details about FeatureTools. \n\n*FeatureTools4S* is a Python library written by me aiming to scale \nFeatureTools with **Spark**, making it capable of generating \nfeatures for billions of rows of data, which is usually \nconsidered impossible to process on single machine using \noriginal FeatureTools library with Pandas. \n\n*FeatureTools4S* provides **almost the same** API as original \nFeatureTools, which make its users completely free of transferring \nbetween FeatureTools and FeatureTools4S. **Hence we suggest the readers \nfirst to learn FeatureTools and then you can easily work on FeatureTools4S.**\n\n### 2. How to use FeatureTools4S? \nFirst install *featuretools4s* through pip: \n```bash\npip3 install featuretools4s \n```  \n\nThen a simple example of using *featuretools4s* is as follows: \n```python\nimport featuretools4s as fts\nfrom pyspark.sql import SparkSession\n\nimport os\nimport pandas as pd\n\nos.environ[\"SPARK_HOME\"] = \"C:\\Python36\\Lib\\site-packages\\pyspark\"\nos.environ[\"PATH\"] = \"C:\\Python36;\" + os.environ[\"PATH\"]\npd.set_option('display.expand_frame_repr', False)\nspark = SparkSession.builder.master(\"local[*]\").getOrCreate()\n\norder_df = spark.read.csv(\"resources/order.csv\", header=True, inferSchema=True).sort(\"sales_tax\")\ncustomer_df = spark.read.csv(\"resources/customer.csv\", header=True, inferSchema=True)\n\nes = fts.EntitySetSpark(id=\"test\")\nes.entity_from_dataframe(\"order\", order_df, index=\"order_num\", time_index=\"wo_timestamp\")\nes.entity_from_dataframe(\"customer\", customer_df, index=\"cust_num\")\nes.add_relationship(fts.Relationship(es[\"customer\"][\"cust_num\"], es[\"order\"][\"cust_num\"]))\n\nfeatures = fts.dfs(spark, entityset=es, target_entity=\"customer\", primary_col=\"cust_num\", num_partition=5)\nfeatures.show()\n```",
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        "license": "BSD License",
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