Pandas/Pandas-Apply-With-Numpy-Select.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",
    "# Speed-up Pandas Apply 5x with NumPy\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/speed-up-pandas-apply-5x-with-numpy)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/posts/activity-7015258640626421760-gDpY?utm_source=post_nba&utm_medium=member_desktop&utm_campaign=copy)"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "b97cfd3f",
   "metadata": {},
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       "    A   B   C   D\n",
       "0  73  81  92  54\n",
       "1   7  98  60  70\n",
       "2  52  75  40  31\n",
       "3  82  50  85  89\n",
       "4  40  57   3   7"
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   "source": [
    "df = pd.DataFrame(np.random.randint(1, 100, (10**7, 4)), columns = list(\"ABCD\"))\n",
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3cfec40",
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   "source": [
    "def assign_class(num):\n",
    "    if num<10:\n",
    "        return \"Class A\"\n",
    "    if num<50:\n",
    "        return \"Class B\"\n",
    "    return \"Class C\""
   ]
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  {
   "cell_type": "markdown",
   "id": "08f8218e",
   "metadata": {},
   "source": [
    "### Pandas Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb655ffa",
   "metadata": {
    "scrolled": true
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.06 s ± 77.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit \n",
    "\n",
    "a = df.A.apply(assign_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71443d2a",
   "metadata": {},
   "source": [
    "### NumPy Select"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd001be0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "266 ms ± 39.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "\n",
    "condlist   = [df[\"A\"]<10, df[\"A\"]<50]\n",
    "choicelist = [\"Class A\" , \"Class B\" ]\n",
    "\n",
    "np.select(condlist, choicelist, \"Class C\")"
   ]
  }
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