Created on Cognitive Class Labs

PY0101EN-5.1_notebook_quizz_numpy1d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Get to Know a numpy Array </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cast the following list to a numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a=[1,2,3,4,5]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) type using the function type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) the shape of the array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=np.array(a)\n",
    "b.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) the type of data in the array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) find the mean of the array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Creating and Plotting Functions  </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) create the following functions using the numpy array <code> x </code>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$y=sin(x)+2$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.06346652, 0.12693304, 0.19039955, 0.25386607,\n",
       "       0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,\n",
       "       0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,\n",
       "       0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,\n",
       "       1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,\n",
       "       1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,\n",
       "       1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,\n",
       "       2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,\n",
       "       2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,\n",
       "       2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,\n",
       "       3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,\n",
       "       3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,\n",
       "       3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,\n",
       "       4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,\n",
       "       4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,\n",
       "       4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,\n",
       "       5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,\n",
       "       5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,\n",
       "       5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,\n",
       "       6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.linspace(0,2*np.pi,100)\n",
    "y=np.sin(x)+2\n",
    "x\n",
    "#y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)  plot the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff900239710>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "plt.plot(x,y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<small>Copyright &copy; 2018 IBM Cognitive Class. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/).</small>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}