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Added NumPy notebooks #2

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497 changes: 497 additions & 0 deletions NumPy/Lesson 1 - Introduction.ipynb
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634 changes: 634 additions & 0 deletions NumPy/Lesson 2 - Dimensions.ipynb
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1,279 changes: 1,279 additions & 0 deletions NumPy/Lesson 3 - Basic Operations.ipynb
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1,383 changes: 1,383 additions & 0 deletions NumPy/Lesson 4 - Indexing and Slicing.ipynb
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325 changes: 325 additions & 0 deletions NumPy/Lesson 5 - Universal Functions.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Universal Functions\n",
"\n",
"NumPy provides some useful out of the box functions to perform operations on matrices. These functions are divided into 2 different segments:\n",
"1. Unary Functions\n",
"2. Binary Functions\n",
"\n",
"A Unary function operates on a single matrix by itself, while a binary function requires 2 matrices. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Unary Functions\n",
"\n",
"## np.sqrt\n",
"This function simply returns square root of all the elements in an array."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2., 3., 4.],\n",
" [5., 6., 7.]])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"a = np.array([[4,9,16],\n",
" [25,36,49]])\n",
"\n",
"np.sqrt(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## np.exp\n",
"This function computes the exponent of each element in an array."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[5.45981500e+01, 8.10308393e+03, 8.88611052e+06],\n",
" [7.20048993e+10, 4.31123155e+15, 1.90734657e+21]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## np.log\n",
"This function will compute the natural logarithm (base e) of each element in an array. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.38629436, 2.19722458, 2.77258872],\n",
" [3.21887582, 3.58351894, 3.8918203 ]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, we can also use **log10, log2, logp**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## np.rint\n",
"This function rounds elements to their nearest integer. The function `rint` can be read as `Round to Integer`"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 2., 3.],\n",
" [3., 4., 4.]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1.38629436, 2.19722458, 2.77258872],\n",
" [3.21887582, 3.58351894, 3.8918203 ]])\n",
"np.rint(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](./images/ufuncs.JPG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Binary Functions\n",
"\n",
"Lets start with 2 matrices and perform some basic arithmatic operations on them. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"a = np.array([[1,2,3],\n",
" [4,5,6]])\n",
"\n",
"b = np.array([[2,4,8],\n",
" [16,32,64]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Addition"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 3, 6, 11],\n",
" [20, 37, 70]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a+b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Subtraction"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ -1, -2, -5],\n",
" [-12, -27, -58]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a-b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiplication"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2, 8, 24],\n",
" [ 64, 160, 384]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a*b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Division"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.5 , 0.5 , 0.375 ],\n",
" [0.25 , 0.15625, 0.09375]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a/b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several binary functions that one can choose from. The table below shows some of the binary function operations available.\n",
"\n",
"> Keep in mind that the matrices have to always be of appropriate size for the binary operation to work. We cannot add a (2,2) matrix to a (3x3) matrix. If you can perform the matrix operation mathematically, then the system can also perform it for you. But it will fail on attempting an invalid matrix operation.\n",
"\n",
"\n",
"![](./images/binary_funcs.JPG)"
]
}
],
"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": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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