kimbosliceit · June 25, 2019 19:28
diff --git a/PY0101EN-5.1_notebook_quizz_numpy1d.ipynb b/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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "a=np.array([1,2,3,4,5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) type using the function type "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) the shape of the array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) the type of data in the array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) find the mean of the array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.mean()"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.linspace(0,2*np.pi,100)\n",
    "𝑦=np.𝑠𝑖𝑛(𝑥)+2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)  plot the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd20bb9f2e8>]"
      ]
     },
     "execution_count": 19,
     "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",
    "\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 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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
	{
	"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": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"a=np.array([1,2,3,4,5])\n"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"1) type using the function type "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"numpy.ndarray"
	]
	},
	"execution_count": 4,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"type(a)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"2) the shape of the array "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"(5,)"
	]
	},
	"execution_count": 7,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"a.shape"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"3) the type of data in the array "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"dtype('int64')"
	]
	},
	"execution_count": 11,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"a.dtype"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"4) find the mean of the array "
	]
	},
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"3.0"
	]
	},
	"execution_count": 12,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"a.mean()"
	]
	},
	{
	"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": 16,
	"metadata": {},
	"outputs": [],
	"source": [
	"x=np.linspace(0,2*np.pi,100)\n",
	"𝑦=np.𝑠𝑖𝑛(𝑥)+2\n"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"2) plot the function"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 19,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x7fd20bb9f2e8>]"
	]
	},
	"execution_count": 19,
	"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",
	"\n"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"<hr>\n",
	"<small>Copyright © 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 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": "3.6.8"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}