PY0101EN-5.1_notebook_quizz_numpy1d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Get to Know a numpy Array </h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cast the following list to a numpy array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=[1,2,3,4,5]\n",
    "\n",
    "a = np.array(a)\n",
    "\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) type using the function type \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) the shape of the array \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) the type of data in the array \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) find the mean of the array \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Creating and Plotting Functions  </h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) create the following functions using the numpy array <code> x </code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$y=sin(x)+2$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.linspace(0,2*np.pi,100)\n",
    "y=np.sin(x)+2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)  plot the function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcaf1a35748>]"
      ]
     },
     "execution_count": 26,
     "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",
    "\n",
    "plt.plot(x,y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "<h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
 "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": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}