5. Saving in csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "host =''\n",
    "port =0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pymongo.cursor.Cursor object at 0x0000000005AC5278>\n"
     ]
    }
   ],
   "source": [
    "import pymongo\n",
    "client= pymongo.MongoClient(host,port)\n",
    "dataBase=client['Labels']\n",
    "collection= dataBase['Shape_Labels_5_Classes']\n",
    "content=collection.find({},{'_id':1})\n",
    "print(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "queries = []\n",
    "for i in content:\n",
    "    queries.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10958207"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(queries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your list `x`\n",
    "x = queries\n",
    "\n",
    "# Split `x` up in chunks of 3\n",
    "y = zip(*[iter(x)]*100)\n",
    "\n",
    "# Use `list()` to print the result of `zip()`\n",
    "#list(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zip"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(list(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flatten_dict(d):\n",
    "    def items():\n",
    "        for key, value in d.items():\n",
    "            if isinstance(value, dict):\n",
    "                for subkey, subvalue in flatten_dict(value).items():\n",
    "                    yield key + \".\" + subkey, subvalue\n",
    "            else:\n",
    "                yield key, value\n",
    "\n",
    "    return dict(items())\n",
    "\n",
    "#OR\n",
    "\n",
    "import collections\n",
    "def flatten(d, parent_key='', sep='_'):\n",
    "    items = []\n",
    "    for k, v in d.items():\n",
    "        new_key = parent_key + sep + k if parent_key else k\n",
    "        if isinstance(v, collections.MutableMapping):\n",
    "            items.extend(flatten(v, new_key, sep=sep).items())\n",
    "        else:\n",
    "            items.append((new_key, v))\n",
    "    return dict(items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows = []\n",
    "for query in queries[0:1000000]:\n",
    "    for doc in collection.find(query):\n",
    "        l = flatten_dict(doc)\n",
    "        rows.append(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for query in queries[1000000:2000000]:\n",
    "    for doc in collection.find(query):\n",
    "        m = flatten_dict(doc)\n",
    "        rows.append(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for query in queries[3000000:4000000]:\n",
    "    for doc in collection.find(query):\n",
    "        n = flatten_dict(doc)\n",
    "        rows.append(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for query in queries[4000000:5000000]:\n",
    "    for doc in collection.find(query):\n",
    "        o = flatten_dict(doc)            \n",
    "        rows.append(o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for query in queries[5000000:6000000]:\n",
    "    for doc in collection.find(query):\n",
    "        p = flatten_dict(doc)\n",
    "        rows.append(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for query in queries[7000000:8000000]:\n",
    "    for doc in collection.find(query):\n",
    "        q = flatten_dict(doc)\n",
    "        rows.append(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for query in queries[9000000:10000000]:\n",
    "    for doc in collection.find(query):\n",
    "        r = flatten_dict(doc)\n",
    "        rows.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for query in queries[10000000:]:\n",
    "    for doc in collection.find(query):\n",
    "        s = flatten_dict(doc)\n",
    "        rows.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.DataFrame(z)\n",
    "        annotations_df = pd.DataFrame(list(df['annotations.boxlabel']))\n",
    "        new_df = pd.concat([df, annotations_df], axis=1)\n",
    "        attributes_df = pd.DataFrame(list(new_df['attributes']))\n",
    "        final_df = pd.concat([new_df, attributes_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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