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AshutoshDash1999/5.1_notebook_quizz_numpy1d.ipynb

Created November 20, 2019 16:46
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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": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"a=[1,2,3,4,5]\n",
"a = np.array(a)\n",
"a\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1) type using the function type "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) the shape of the array "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5,)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.shape(a)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3) the type of data in the array "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4) find the mean of the array "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.mean(a)\n",
"a"
]
},
{
"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": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.00000000e+00, 6.34239197e-02, 1.26592454e-01, 1.89251244e-01,\n",
" 2.51147987e-01, 3.12033446e-01, 3.71662456e-01, 4.29794912e-01,\n",
" 4.86196736e-01, 5.40640817e-01, 5.92907929e-01, 6.42787610e-01,\n",
" 6.90079011e-01, 7.34591709e-01, 7.76146464e-01, 8.14575952e-01,\n",
" 8.49725430e-01, 8.81453363e-01, 9.09631995e-01, 9.34147860e-01,\n",
" 9.54902241e-01, 9.71811568e-01, 9.84807753e-01, 9.93838464e-01,\n",
" 9.98867339e-01, 9.99874128e-01, 9.96854776e-01, 9.89821442e-01,\n",
" 9.78802446e-01, 9.63842159e-01, 9.45000819e-01, 9.22354294e-01,\n",
" 8.95993774e-01, 8.66025404e-01, 8.32569855e-01, 7.95761841e-01,\n",
" 7.55749574e-01, 7.12694171e-01, 6.66769001e-01, 6.18158986e-01,\n",
" 5.67059864e-01, 5.13677392e-01, 4.58226522e-01, 4.00930535e-01,\n",
" 3.42020143e-01, 2.81732557e-01, 2.20310533e-01, 1.58001396e-01,\n",
" 9.50560433e-02, 3.17279335e-02, -3.17279335e-02, -9.50560433e-02,\n",
" -1.58001396e-01, -2.20310533e-01, -2.81732557e-01, -3.42020143e-01,\n",
" -4.00930535e-01, -4.58226522e-01, -5.13677392e-01, -5.67059864e-01,\n",
" -6.18158986e-01, -6.66769001e-01, -7.12694171e-01, -7.55749574e-01,\n",
" -7.95761841e-01, -8.32569855e-01, -8.66025404e-01, -8.95993774e-01,\n",
" -9.22354294e-01, -9.45000819e-01, -9.63842159e-01, -9.78802446e-01,\n",
" -9.89821442e-01, -9.96854776e-01, -9.99874128e-01, -9.98867339e-01,\n",
" -9.93838464e-01, -9.84807753e-01, -9.71811568e-01, -9.54902241e-01,\n",
" -9.34147860e-01, -9.09631995e-01, -8.81453363e-01, -8.49725430e-01,\n",
" -8.14575952e-01, -7.76146464e-01, -7.34591709e-01, -6.90079011e-01,\n",
" -6.42787610e-01, -5.92907929e-01, -5.40640817e-01, -4.86196736e-01,\n",
" -4.29794912e-01, -3.71662456e-01, -3.12033446e-01, -2.51147987e-01,\n",
" -1.89251244e-01, -1.26592454e-01, -6.34239197e-02, -2.44929360e-16])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x=np.linspace(0,2*np.pi,100)\n",
"x\n",
"y = np.sin(x)\n",
"y\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) plot the function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \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",
"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
}
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