numpy_list_vs_ndarray.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy ndarray vs list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "usd = [10, 20, 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[350, 700, 3500]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = []\n",
    "for u in usd:\n",
    "    t.append(u * 35)\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### list comprehension "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[350, 700, 3500]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = [u * 35 for u in usd]\n",
    "t2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[350, 700, 3500]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = list(map(lambda u: u * 35, usd))\n",
    "t3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 350,  700, 3500])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t4 = np.array(usd) * 35\n",
    "t4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quadratic equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 12,  88, 150, 273, 943])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 5, 7, 10, 20])\n",
    "# a*x^2 + b*x + c\n",
    "a, b, c = (2, 7, 3)\n",
    "y = a * x**2 + b * x + c\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb8e91efa20>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}