NumPy/Speed-up-NumPy.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 NumPy 20x with Numexpr\n",
    "\n",
    "Post Link: [Substack](https://www.blog.dailydoseofds.com/p/a-major-limitation-of-numpy-which)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/feed/update/urn:li:share:7075778020199833600/)\n",
    "\n",
    "Twitter Post: [Twitter](https://twitter.com/_avichawla/status/1670012340073267202)"
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      "Requirement already satisfied: numexpr in /Users/avi/anaconda3/lib/python3.9/site-packages (2.8.1)\r\n",
      "Requirement already satisfied: packaging in /Users/avi/anaconda3/lib/python3.9/site-packages (from numexpr) (21.3)\r\n",
      "Requirement already satisfied: numpy>=1.13.3 in /Users/avi/anaconda3/lib/python3.9/site-packages (from numexpr) (1.21.5)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/avi/anaconda3/lib/python3.9/site-packages (from packaging->numexpr) (3.0.4)\r\n"
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   "source": [
    "!pip install numexpr"
   ]
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   "source": [
    "import numpy as np\n",
    "import numexpr as ne"
   ]
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   "source": [
    "a = np.random.random(10**7) \n",
    "b = np.random.random(10**7)"
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     "text": [
      "158 ms ± 2.69 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.cos(a) + np.sin(b)"
   ]
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     "text": [
      "26 ms ± 396 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit ne.evaluate(\"cos(a) + sin(b)\")"
   ]
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