Pymongo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymongo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymongo import MongoClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MongoClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MongoClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = MongoClient('localhost', 27017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = client.test_database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection = db.test_collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "post = {\"author\": \"Mike\",\n",
    "...         \"text\": \"My first blog post!\",\n",
    "...         \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n",
    "...         \"date\": datetime.datetime.utcnow()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "posts = db.posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_id = posts.insert_one(post).inserted_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectId('5ab89205bed18d1368f4db71')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "post_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['posts']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.collection_names(include_system_collections=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.io import sql\n",
    "\n",
    "df = pd.read_excel(open('Documents/mobile_city/xls/cars.xls','rb'), Sheetname='Sheet 1')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "import sys\n",
    "import pandas as pd\n",
    "import pymongo\n",
    "import json\n",
    "\n",
    "\n",
    "def import_content(filepath):\n",
    "    mng_client = pymongo.MongoClient('localhost', 27017)\n",
    "    mng_db = mng_client.exceltodb \n",
    "    \n",
    "    \n",
    "    \n",
    "    collection_name = 'sampleexcel' \n",
    "    \n",
    "    db_cm = mng_db['cars']\n",
    "    \n",
    "    data = pd.read_excel(open(filepath,'rb'), Sheetname='Sheet 1')\n",
    "    #pd.read_excel(file_res)\n",
    "    data_json = json.loads(data.to_json(orient='records'))\n",
    "    db_cm.delete_many( { } )\n",
    "    db_cm.insert_many(data_json)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "  filepath = 'Documents/mobile_city/xls/cars.xls'  \n",
    "  import_content(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'os' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-15f5e96fdb7b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'os' is not defined"
     ]
    }
   ],
   "source": [
    "os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cars.xls\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.path.basename(\"Documents/mobile_city/xls/cars.xls\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cars.xls'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ntpath\n",
    "ntpath.basename(\"Documents/mobile_city/xls/cars.xls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'caras'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os.path\n",
    "extension = os.path.splitext(\"Documents/mobile_city/xls/caras.xls\")[0]\n",
    "ntpath.basename(extension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ocuments/mobile_city/xls/cars'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "import sys\n",
    "import pandas as pd\n",
    "import pymongo\n",
    "import json\n",
    "import os.path\n",
    "\n",
    "\n",
    "def import_content(filepath):\n",
    "    mng_client = pymongo.MongoClient('localhost', 27017)\n",
    "    mng_db = mng_client.exceltodb \n",
    "    filename = os.path.splitext(filepath)[0]\n",
    "    filename = ntpath.basename(filename) \n",
    "    \n",
    "    db_cm = mng_db[filename]\n",
    "    \n",
    "    data = pd.read_excel(open(filepath,'rb'), Sheetname='Sheet 1')\n",
    "    #pd.read_excel(file_res)\n",
    "    data_json = json.loads(data.to_json(orient='records'))\n",
    "    db_cm.delete_many( { } )\n",
    "    db_cm.insert_many(data_json)\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "  filepath = 'Documents/mobile_city/xls/cola.xls'  \n",
    "  import_content(filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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