examples/jupyter/Pandas_Taxi.ipynb

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   "source": [
    "# To run this notebook as done in the README GIFs, you must first locally download the 2015 NYC Taxi Trip Data.\n",
    "import urllib.request\n",
    "url_path = \"https://modin-datasets.intel.com/green-taxi/green_tripdata_2015-01.csv\"\n",
    "urllib.request.urlretrieve(url_path, \"taxi.csv\")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27f7321c",
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   "source": [
    "import pandas as pd"
   ]
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      "CPU times: user 30.7 s, sys: 4.25 s, total: 35 s\n",
      "Wall time: 35.3 s\n"
     ]
    }
   ],
   "source": [
    "%time df = pd.read_csv(\"taxi.csv\", parse_dates=[\"tpep_pickup_datetime\", \"tpep_dropoff_datetime\"], quoting=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14422c3f",
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.37 s, sys: 300 ms, total: 1.67 s\n",
      "Wall time: 1.67 s\n"
     ]
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   "source": [
    "%time isnull = df.isnull()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "f8f87974",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.07 s, sys: 305 ms, total: 3.37 s\n",
      "Wall time: 3.37 s\n"
     ]
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   ],
   "source": [
    "%time rounded_trip_distance = df[[\"pickup_longitude\"]].applymap(round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c7d62bf",
   "metadata": {},
   "outputs": [],
   "source": []
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