01. 推荐系统开发环境检测.ipynb

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   "source": [
    "## 推荐系统开发环境检测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python数据科学相关包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import sklearn\n",
    "import tensorflow as tf\n",
    "import flask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy\t1.18.1\n",
      "pandas\t1.0.1\n",
      "matplotlib\t3.1.3\n",
      "sklearn\t0.22.1\n",
      "tensorflow\t2.2.0\n",
      "flask\t1.1.1\n"
     ]
    }
   ],
   "source": [
    "for module in (np, pd, mpl, sklearn, tf, flask):\n",
    "    print(f\"{module.__name__}\\t{module.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PySpark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "spark = SparkSession \\\n",
    "    .builder \\\n",
    "    .appName(\"test pyspark\") \\\n",
    "    .getOrCreate()\n",
    "\n",
    "sc = spark.sparkContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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     "data": {
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       "4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sc.textFile(\"./README.md\")\n",
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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