Pandas/Never-Iterate-Over-Pandas.ipynb

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    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# Why Are We Typically Advised To Never Iterate Over A DataFrame?\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/why-are-we-typically-advised-to-never)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/feed/update/urn:li:share:7040278032325959680/)"
   ]
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   "source": [
    "import pandas as pd"
   ]
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   "source": [
    "df = pd.read_parquet(\"../_Datasets/employee_dataset.parquet\")"
   ]
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   "source": [
    "df.shape"
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   "source": [
    "### Access column"
   ]
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     "text": [
      "1.95 µs ± 913 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n10000 -r7 df[\"Name\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db4280d2",
   "metadata": {},
   "source": [
    "### Access row"
   ]
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   "execution_count": 5,
   "id": "f9a9f427",
   "metadata": {
    "ExecuteTime": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38.9 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n10000 -r7 df.iloc[0]"
   ]
  }
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