NYCTaxiDemo.ipynb

{
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install prerequisites\n",
    "!pip install pymongo\n",
    "!pip install --upgrade folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "from pymongo import MongoClient\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Database(MongoClient(host=['mongodb-1559242352-replica-set-0.mongodb-1559242352.mongodbn.svc.cluster.local:27017'], document_class=dict, tz_aware=False, connect=True), 'MongoDBWorld')\n"
     ]
    }
   ],
   "source": [
    "# Connect to the database\n",
    "client = MongoClient('mongodb://scientist:scientist@mongodb-1559242352-replica-set-0.mongodb-1559242352.mongodbn.svc.cluster.local:27017/MongoDBWorld')\n",
    "db = client.MongoDBWorld\n",
    "print(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NYCTaxi']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.list_collection_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-06-07 04:16:38--  https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2016-01.csv\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.144.29\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.144.29|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1708674492 (1.6G) [application/octet-stream]\n",
      "Saving to: ‘yellow_tripdata_2016-01.csv’\n",
      "\n",
      "100%[====================================>] 1,708,674,492 5.83MB/s   in 4m 54s \n",
      "\n",
      "2019-06-07 04:21:34 (5.53 MB/s) - ‘yellow_tripdata_2016-01.csv’ saved [1708674492/1708674492]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get the taxi data\n",
    "!wget https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2016-01.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use only the first 1M trips\n",
    "!head -1000000 yellow_tripdata_2016-01.csv > yellow_tripdata_2016-01-first1M.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data into the collection\n",
    "df = pd.read_csv(\"yellow_tripdata_2016-01-first1M.csv\")\n",
    "records_ = df.to_dict(orient = 'records')\n",
    "result = db.NYCTaxi.insert_many(records_ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the pickup data from the collection\n",
    "pickup_query = db.NYCTaxi.find({\"pickup_latitude\" : { \"$lt\" : 40.86, \"$gt\" : 40.70},\n",
    "                                 \"pickup_longitude\" : { \"$lt\" : -73.93, \"$gt\" : -74.02}},\n",
    "                                {\"pickup_latitude\" : 1, \"pickup_longitude\" : 1, \"_id\" : 0})\n",
    "pickup_df = pd.DataFrame(list(pickup_query))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the k-means clustering\n",
    "kmeans = KMeans(n_clusters=7).fit(pickup_df)\n",
    "pickup_centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
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