{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial on Numpy for CS 132 and 237\n",
"\n",
"Numpy is a Python library which provides high-performance multi-dimension arrays for\n",
"scientific computing and data science. It also provides a full set of\n",
"mathematical, especially probability, functions, which can be\n",
"applied to single values or entire arrays. We will use it throughout the\n",
"course, and in fact I recommend that you use it in place of the standard math\n",
"library. \n",
"\n",
"Although numpy provides extensive functionality for multi-dimensional arrays, we\n",
"will only use 1-D arrays, which look pretty much list normal Python lists. \n",
"\n",
"For more information about Numpy (and Python), check out this notebook, from which\n",
"I draw some of the information in this notebook:\n",
"\n",
"https://github.com/kuleshov/cs228-material/blob/master/tutorials/python/cs228-python-tutorial.ipynb\n",
"\n",
"A more advanced tutorial on how to write more efficient code with Numpy is here:\n",
"\n",
"https://www.youtube.com/watch?v=EEUXKG97YRw\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Here are some imports which will be used in code that we write for CS 237\n",
"\n",
"# Imports used for the code in CS 237\n",
"\n",
"import numpy as np # arrays and functions which operate on array, plus math functions\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy Arrays\n",
"\n",
"Numpy provides an (multi-dimensional) array type, ndarray but we will only need them in the 1-D case,\n",
"so we just want to understand how normal Python lists and numpy arrays work, and how to\n",
"integrate numpy arrays into our Python programming. \n",
"\n",
"\n",
"### Basic One-Dimensional Arrays\n",
"\n",
"The numpy library functions return arrays instead of normal Python lists. Since\n",
"numpy automatically converts normal lists into numpy arrays, we can use lists\n",
"without any problems. \n",
"\n",
"However, the reverse is not true: normal Python functions which expect lists will NOT\n",
"work with numpy arrays, and you'll have to make adjustments. "
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[12 3 5]\n",
" [ 2 3 4]]\n",
"\n",
"[[4 2 3]\n",
" [2 3 4]]\n"
]
}
],
"source": [
"# Creating a numpy array from a list\n",
"\n",
"A = np.array( [ [12,3,5], [2,3,4] ] )\n",
"B = np.array( [[ 11,3,0 ],[1,2,3]] )\n",
"print(A)\n",
"print()\n",
"A[0] = np.array( [4,2,3] ) \n",
"print(A)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that numpy arrays are printed WITHOUT commas! That is not actually the internal representation,\n",
"which you can see if you simply type the variable on a line by itself. "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8632093666488738"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12, 3, 5]\n",
"[12, 3, 5]\n"
]
}
],
"source": [
"# Turning a numpy 1-D array into a list\n",
"\n",
"ex2 = list( ex1 )\n",
"print(ex2)\n",
"\n",
"# Or:\n",
"\n",
"e3 = ex1.tolist()\n",
"print(e3)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.7168631 , -0.77276449, 0.94328826, 0.86224488])"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All the normal slices and access methods from lists will work with numpy arrays, so you don't have to\n",
"care about whether they are arrays or lists:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex1[2]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([12, 3])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex1[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Useful Numpy functions to create useful lists"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0.])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a list of 0's\n",
"\n",
"np.zeros(5) # <- the size of the result must be given"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a list of 1's\n",
"np.ones(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14, 3.14])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a list of constant values\n",
"np.full(10,3.14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating lists in a given range"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"[1, 2, 3, 4, 5, 6]\n",
"[2, 4, 6, 8, 10]\n",
"[12, 9, 6, 3]\n"
]
}
],
"source": [
"# Normal Python ranges - they are evaluated lazily by Python, so must turn them into lists to see....\n",
"\n",
"print( list(range(10)) )\n",
"\n",
"print( list(range(1,7)) )\n",
"\n",
"print( list(range(2,12,2)) )\n",
"\n",
"print( list(range(12,2, -3)) )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"[1, 2, 3, 4, 5, 6]\n",
"[2, 4, 6, 8, 10]\n",
"[12, 9, 6, 3]\n"
]
}
],
"source": [
"# Numpy ranges -- they are evaluated lazily by Python, so must turn them into lists to see....\n",
"\n",
"# Note that numpy has similar functions as with lists, but begin with an \"a\" (for A rray)\n",
"\n",
"print( list( np.arange(10) ) )\n",
"\n",
"print( list( np.arange(1,7) ) )\n",
"\n",
"print( list( np.arange(2,12,2) ) )\n",
"\n",
"print( list( np.arange(12,2, -3) ) )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.0, 0.1, 0.2, 0.30000000000000004, 0.4, 0.5, 0.6000000000000001, 0.7000000000000001, 0.8, 0.9]\n",
"\n",
"[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]\n"
]
}
],
"source": [
"# BUT also work with floating point, but you'll get the usual weirdness with floating-point values\n",
"\n",
"ex4 = list( np.arange(0.0,1.0,0.1) ) \n",
"\n",
"print(ex4)\n",
"print()\n",
"# If you want to print this out with less precision, you can round \n",
"ex4b = np.around( ex4, 1 ) \n",
"\n",
"print( ex4b )"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0.11111111 0.22222222 0.33333333 0.44444444 0.55555556\n",
" 0.66666667 0.77777778 0.88888889 1. ]\n",
"\n",
"[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]\n"
]
}
],
"source": [
"# Linear spacing in a range\n",
"\n",
"# The linspace function does the same thing as the previous, but it allows you\n",
"# to specify HOW MANY values, not the increment\n",
"\n",
"# NOTE: Unlike range, the upper bound is inclusive (it will be one of the values)\n",
"\n",
"print( np.linspace(0,1,10) ) \n",
"print()\n",
"print( np.linspace(0,1,11) ) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Useful array functions returning a scalar"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n",
"7\n",
"2\n",
"4.0\n",
"32\n",
"30240\n",
"6\n",
"0\n",
"a 2\n"
]
}
],
"source": [
"# Here are functions that take a list or array as input and produce a scalar value\n",
"\n",
"ex6 = np.array( [ 2,4,3,6,5,3,7, 2 ])\n",
"\n",
"print( np.size(ex6) ) # Number of elements in array, same as len(...) on lists\n",
"\n",
"print( np.amax(ex6) ) # Note the 'a' added to front for \"array max\"\n",
"\n",
"print( np.amin(ex6) ) # Ditto \n",
"\n",
"print( np.mean(ex6) ) # Find the mean (average) of the array\n",
"\n",
"print( np.sum(ex6) ) # sum of all members of list or array\n",
"\n",
"print( np.prod(ex6) ) # product -- note that the input is a list\n",
"\n",
"print( np.argmax(ex6) ) # find the index of the max element\n",
"\n",
"print( np.argmin(ex6) ) # find the index of the min elemnt\n",
"\n",
"print('a', np.where(ex6 == 3)[0][0] ) # Finding the indices of a given element in the array,\n",
" # very awkward syntax. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Useful array functions to modify an array"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12 3 5]\n",
"[ 3 5 12]\n",
"\n",
"[ 2 3 4 5 7 13]\n"
]
}
],
"source": [
"\n",
"\n",
"# Sorting (in place -- changes the array permanently)\n",
"\n",
"print(ex1)\n",
"\n",
"ex1.sort()\n",
"\n",
"print(ex1)\n",
"print()\n",
"\n",
"# eliminate duplicate values (sorts it as well!)\n",
"\n",
"ex5 = np.array( [ 13,7,5,3,4,5,2,3,4 ])\n",
"\n",
"print( np.unique( ex5 ))\n",
"\n",
"# "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 3 5 12]\n"
]
}
],
"source": [
"# It is permanently changed!\n",
"print(ex1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 5])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Finding the indices of a given element in the array\n",
"\n",
"ex5 = np.array( [2,4,3,5,4,3,4] )\n",
"np.where(ex5 == 3)[0] # This syntax is weird!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy Math\n",
"\n",
"Numpy provides a complete set of mathematical functions which you should get used\n",
"to using. Here is a catalog of useful functions with examples of use.\n",
"\n",
"One of the most useful features of Numpy functions is that they allow you to give\n",
"numpy arrays or just Python lists, and it applies the function to every member\n",
"of the list. Numpy will manage as gracefully as possible if you give it\n",
"normal lists instead of numpy arrays. For the math functions, we will simply\n",
"show how to use them with normal Python lists. \n",
"\n",
"For further information, see the manual page: https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.math.html\n",
"\n",
"### Basic Math Constants: Pi and E (base of natural algorithms)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pi: 3.141592653589793\n",
"e: 2.718281828459045\n"
]
}
],
"source": [
"print( 'Pi:', np.pi )\n",
"\n",
"print( 'e:', np.e )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Trig Functions: Sin and Cos"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8632093666488738\n",
"[ 0.7168631 -0.77276449 0.94328826 0.86224488]\n"
]
}
],
"source": [
"ex1 = np.sin( 2.1 )\n",
"\n",
"ex2 = np.sin( [ 2.3423, 5.4, 1.2324, -5.2435 ] )\n",
"\n",
"print( ex1 )\n",
"print( ex2 )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rounding, truncating, ceiling, floor, absolute value"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.0\n",
"[ 0.7 -0.8 0.9 0.9]\n",
"[ 0.717 -0.773 0.943 0.862]\n",
"\n",
"3.0\n",
"[ 0. -0. 0. 0.]\n",
"\n",
"4.0\n",
"[ 1. -0. 1. 1.]\n",
"\n",
"3.0\n",
"[ 0. -1. 0. 0.]\n",
"3.141592653589793\n",
"[0.7168631 0.77276449 0.94328826 0.86224488]\n"
]
}
],
"source": [
"# Rounding\n",
"\n",
"print(np.around(np.pi)) # Notice the spelling starting with 'a' for \"array round\"\n",
"print(np.around(ex2, 1))\n",
"print(np.around(ex2, 3))\n",
"print()\n",
"\n",
"# Truncation\n",
"\n",
"print(np.trunc(np.pi)) \n",
"print(np.trunc(ex2))\n",
"print()\n",
"\n",
"# Ceiling\n",
"\n",
"print(np.ceil(np.pi)) \n",
"print(np.ceil(ex2))\n",
"print()\n",
"\n",
"# Floor\n",
"\n",
"print(np.floor(np.pi)) \n",
"print(np.floor(ex2))\n",
"\n",
"# Absolute value\n",
"\n",
"print(np.absolute(np.pi)) \n",
"print(np.absolute(ex2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logs and Powers"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"93648.04747608298\n",
"\n",
"22026.465794806718\n",
"\n",
"1.4142135623730951\n",
"1156\n",
"\n",
"2.302585092994046\n",
"\n",
"3.321928094887362\n",
"\n",
"1.0\n",
"\n"
]
}
],
"source": [
"\n",
"# Raising to a power -- same as (np.pi ** 5)\n",
"\n",
"print( np.power(np.pi, 10) ) \n",
"print()\n",
"\n",
"# calculate exponential e^x -- same as np.e ** x\n",
"\n",
"print( np.exp(10) ) # (e^10) \n",
"print()\n",
"\n",
"# sqs and sqrts\n",
"\n",
"print( np.sqrt(2))\n",
"print( np.square(34))\n",
"print()\n",
"\n",
"# calculate logs to various bases\n",
"\n",
"print( np.log(10) ) # log to base e \n",
"print()\n",
"\n",
"# calculate natural log (to base e)\n",
"\n",
"print( np.log2(10) ) # log to base 2 \n",
"print()\n",
"\n",
"# calculate natural log (to base e)\n",
"\n",
"print( np.log10(10) ) # log to base 10 \n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Basic Statistical functions\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.0\n",
"\n",
"1.2909944487358056\n",
"\n",
"1.6666666666666667\n",
"\n",
"4.0\n",
"\n"
]
}
],
"source": [
"# Mean or average\n",
"\n",
"ex7 = [2,4,3,6,4,5]\n",
"\n",
"print( np.mean(ex7) ) \n",
"print()\n",
"\n",
"# Standard deviation\n",
"\n",
"print( np.std(ex7) ) \n",
"print()\n",
"\n",
"print( np.var(ex7) ) \n",
"print()\n",
"\n",
"print( np.median(ex7) ) \n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multidimensional Ndarrays\n",
"\n",
"Numpy is designed to provide high-speed access to multi-dimensional\n",
"arrays, for linear algebra and data science. These will be used extensively in CS 132. "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 2 3 4]\n",
" [ 5 6 7 8]\n",
" [ 9 10 11 12]]\n"
]
},
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8],\n",
" [ 9, 10, 11, 12]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 2D array\n",
"\n",
"X = np.array([[ 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8],\n",
" [ 9, 10, 11, 12]] )\n",
"\n",
"print(X)\n",
"X"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The size of the array along each dimension can be found using the `shape` function:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 4)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# X has 3 rows and 4 columns\n",
"np.shape(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy array types. \n",
"\n",
"Numpy assigns a type to each matrix – for example, float or int. If you construct a matrix\n",
"using the constructor `array`, and all of the entries are integers, then numpy will auto-detect this as an integer matrix. If even one of the values is float, then the whole matrix will be float. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [5, 6, 7]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array( [ [ 1, 2, 3], [5, 6, 7]])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 2., 3.],\n",
" [5., 6., 7.]])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array( [ [ 1, 2, 3], [5, 6, 7.0]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When you assign values to an integer matrix they will be rounded to the nearest integer. This is not what you\n",
"want. So you do not want to work with integer matrices in general.\n",
"\n",
"So it is a good idea to make sure that the inputs to your functions are floating point matrices. To convert an\n",
"integer matrix to a floating point matrix you can convert it using the function `astype`:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 2., 3., 4.],\n",
" [ 5., 6., 7., 8.],\n",
" [ 9., 10., 11., 12.]])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X1 = X.astype(float)\n",
"X1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reshaping an array\n",
"\n",
"Once you have an array, of any shape, you can *reshape* it into\n",
"another form, as long as the number of elements is the same. \n",
"The elements will be taken in *row-major order* (across the columns of the first row, then\n",
"the second row, etc.) and inserted into a new array of the appropriate shape. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0 1 2 3 4 5 6 7 8 9 10 11] \n",
"\n",
"[[ 0 1 2]\n",
" [ 3 4 5]\n",
" [ 6 7 8]\n",
" [ 9 10 11]] \n",
"\n",
"[0. 0.34906585 0.6981317 1.04719755 1.3962634 1.74532925\n",
" 2.0943951 2.44346095 2.7925268 3.14159265] \n",
"\n",
"[[0. 0.34906585]\n",
" [0.6981317 1.04719755]\n",
" [1.3962634 1.74532925]\n",
" [2.0943951 2.44346095]\n",
" [2.7925268 3.14159265]] \n",
"\n"
]
}
],
"source": [
"# create a 1D array\n",
"A = np.arange(12)\n",
"print(A,'\\n')\n",
"\n",
"B = A.reshape((4,3))\n",
"print(B,'\\n')\n",
"\n",
"C = np.linspace(0,np.pi,10)\n",
"print(C,'\\n')\n",
"\n",
"D = C.reshape((5,2))\n",
"print(D,'\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives us a nice way to create arrays with given elements, or with random elements:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2]\n",
" [ 3 4 5]\n",
" [ 6 7 8]\n",
" [ 9 10 11]\n",
" [12 13 14]\n",
" [15 16 17]] \n",
"\n",
"[[0. 0.13089969 0.26179939 0.39269908 0.52359878]\n",
" [0.65449847 0.78539816 0.91629786 1.04719755 1.17809725]\n",
" [1.30899694 1.43989663 1.57079633 1.70169602 1.83259571]\n",
" [1.96349541 2.0943951 2.2252948 2.35619449 2.48709418]\n",
" [2.61799388 2.74889357 2.87979327 3.01069296 3.14159265]] \n",
"\n"
]
}
],
"source": [
"# creating a new array from an enumeration by reshaping\n",
"\n",
"X = np.arange(18).reshape((6,3)) # note: 18 = 6 * 3\n",
"print(X,'\\n')\n",
"\n",
"Y = np.linspace(0,np.pi,25).reshape((5,5)) # note: 25 = 5 * 5\n",
"print(Y,'\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create random arrays, the following three functions are useful; each takes\n",
"a shape tuple and `uniform` and `randint` take a lower and (non-inclusive) upper bound. "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.7082424 0.30264922 0.41288602 0.8171613 0.40376055]\n",
" [0.01836922 0.6748002 0.96498414 0.61027238 0.63361108]\n",
" [0.64705751 0.25037382 0.26695584 0.67790125 0.59280213]\n",
" [0.19652579 0.58766959 0.46327854 0.84143452 0.27678226]] \n",
"\n",
"[[29.61048533 23.89588319 24.18716578]\n",
" [22.67756205 20.26039133 29.07199451]] \n",
"\n",
"[[5 9]\n",
" [8 7]\n",
" [7 3]\n",
" [0 8]] \n",
"\n"
]
}
],
"source": [
"print( np.random.random((4,5)), '\\n') # random floats in range [0..1)\n",
"\n",
"print( np.random.uniform(20,30,(2,3)), '\\n') # random floats in range [20..30)\n",
"\n",
"print( np.random.randint(0,10,(4,2)), '\\n') # random ints in range [0..9]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Indexing and Slicing \n",
"\n",
"Indexing and slicing can be done as you would expect from using\n",
"Python lists, but numpy has a number of other sophisticated access methods.\n",
"For a complete tutorial, see https://numpy.org/doc/stable/reference/arrays.indexing.html"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]]\n",
"\n",
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]]\n",
"\n",
"[4 5 6 7]\n",
"\n",
"[[ 2 3]\n",
" [ 6 7]\n",
" [10 11]]\n",
"\n",
"[[ 5 6]\n",
" [ 9 10]]\n",
"\n"
]
}
],
"source": [
"# Using a comma, you can slice/access rows and columns separately\n",
"# The result will be formatted appropriately\n",
"\n",
"print(X)\n",
"print()\n",
"print(X[:,:]) # all rows and all columns\n",
"print()\n",
"print(X[1,:]) # row 1 and all columns; result has 1D, so printed as 1D array\n",
"print()\n",
"print(X[:,2:]) # all rows, and columns >= 2\n",
"print()\n",
"print(X[1:3,1:3]) # rows 1 & 2 and columns 1 & 2\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X:\n",
"[[ 0 1 2]\n",
" [ 3 4 5]\n",
" [ 6 7 8]\n",
" [ 9 10 11]\n",
" [12 13 14]\n",
" [15 16 17]] \n",
"\n",
"X[0]:\n",
"[0 1 2] \n",
"\n",
"X[1:]:\n",
"[[ 3 4 5]\n",
" [ 6 7 8]\n",
" [ 9 10 11]\n",
" [12 13 14]\n",
" [15 16 17]] \n",
"\n",
"X[1:,:2]:\n",
"[[ 3 4]\n",
" [ 6 7]\n",
" [ 9 10]\n",
" [12 13]\n",
" [15 16]] \n",
"\n",
"X[:,2] # note that if result is 1D then will be presented as 1D array\n",
"\n",
"[ 2 5 8 11 14 17] \n",
"\n",
"X[[1,3,2],:]: # just select rows 1, 3, and 2, in that order\n",
"\n",
"[[ 3 4 5]\n",
" [ 9 10 11]\n",
" [ 6 7 8]] \n",
"\n",
"X[[3,2]]: # Can leave out column range\n",
"\n",
"[[ 9 10 11]\n",
" [ 6 7 8]] \n",
"\n",
"X[:,[2,1]]: # just select columns 2 and 1, in that order\n",
"\n",
"[[ 2 1]\n",
" [ 5 4]\n",
" [ 8 7]\n",
" [11 10]\n",
" [14 13]\n",
" [17 16]] \n",
"\n",
"X[[3,2],2]: # just select rows 3 and 2, and column 2, is presented in 1D\n",
"\n",
"[11 8] \n",
"\n",
"X[[3,2],1:3]: # just select rows 3 and 2, and columns 1,2\n",
"\n",
"[[10 11]\n",
" [ 7 8]] \n",
"\n"
]
}
],
"source": [
"# Some more complicated examples.....\n",
"\n",
"X = np.arange(18).reshape((6,3))\n",
"\n",
"print('X:')\n",
"print(X,'\\n')\n",
"\n",
"print('X[0]:')\n",
"print(X[0],'\\n')\n",
"\n",
"print('X[1:]:')\n",
"print(X[1:],'\\n')\n",
"\n",
"print('X[1:,:2]:')\n",
"print(X[1:,:2],'\\n')\n",
"\n",
"print('X[:,2] # note that if result is 1D then will be presented as 1D array\\n')\n",
"print(X[:,2],'\\n')\n",
"\n",
"print('X[[1,3,2],:]: # just select rows 1, 3, and 2, in that order\\n')\n",
"print(X[[1,3,2],:],'\\n') \n",
"\n",
"print('X[[3,2]]: # Can leave out column range\\n')\n",
"print(X[[3,2]],'\\n')\n",
"\n",
"print('X[:,[2,1]]: # just select columns 2 and 1, in that order\\n')\n",
"print(X[:,[2,1]],'\\n') \n",
"\n",
"print('X[[3,2],2]: # just select rows 3 and 2, and column 2, is presented in 1D\\n')\n",
"print(X[[3,2],2],'\\n') \n",
"\n",
"print('X[[3,2],1:3]: # just select rows 3 and 2, and columns 1,2\\n')\n",
"print(X[[3,2],1:3],'\\n') \n",
" \n",
"#print(X[[3,2,1],[1,0]],'\\n') # This doesn't do what I expected!\n",
"\n",
"#print((X[[3,2,1]])[:,[1,0]],'\\n') # this rearranges rows and columns, what I intended \n",
" # by the previous\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 1 2 3 4]\n",
" [ 5 6 7 8]\n",
" [ 9 10 11 12]]\n",
"\n",
" [[ 2 3 4 5]\n",
" [ 6 7 8 9]\n",
" [10 11 12 13]]]\n"
]
},
{
"data": {
"text/plain": [
"(2, 3, 4)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 3D array\n",
"\n",
"Y = np.array([ [[ 1, 2, 3, 4],\n",
" [ 5, 6, 7, 8],\n",
" [ 9, 10, 11, 12]],\n",
" [[ 2, 3, 4, 5],\n",
" [ 6, 7, 8, 9],\n",
" [ 10, 11, 12, 13]] ] )\n",
"\n",
"print(Y)\n",
"\n",
"np.shape(Y)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 3 7 11]\n",
" [ 4 8 12]]\n",
"\n",
"[[ 2 3]\n",
" [ 6 7]\n",
" [10 11]]\n",
"\n"
]
}
],
"source": [
"print(Y[:,:,2]) \n",
"print()\n",
"print(Y[1,:,:2]) \n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rearranging rows and columns"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]]\n",
"\n",
"[[ 4 5 6 7]\n",
" [ 0 1 2 3]\n",
" [ 8 9 10 11]]\n",
"\n",
"[[ 0 1 2 3]\n",
" [ 8 9 10 11]\n",
" [ 0 1 2 3]]\n",
"\n",
"[[ 1 0 2 3]\n",
" [ 9 8 10 11]\n",
" [ 1 0 2 3]]\n",
"\n"
]
}
],
"source": [
"## Modifying Numpy Arrays\n",
"\n",
"T1 = np.arange(12).reshape((3,4))\n",
"print(T1)\n",
"print()\n",
"\n",
"T1[[0,1],:] = T1[[1,0],:] #row exchange\n",
"print(T1)\n",
"print()\n",
"\n",
"T1[[0,1,2],:] = T1[[1,2,1],:] # rearrange all the rows, and even repeat....\n",
"print(T1)\n",
"print()\n",
" \n",
"T1[:, [0,1]] = T1[:, [1,0]] # column exchange\n",
"print(T1)\n",
"print()\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]] \n",
"\n",
"[[ 0 4 8]\n",
" [ 1 5 9]\n",
" [ 2 6 10]\n",
" [ 3 7 11]] \n",
"\n"
]
}
],
"source": [
"# Transposing an array\n",
"X = np.arange(12).reshape((3,4))\n",
"\n",
"print(X,'\\n')\n",
"print(X.T,'\\n')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4 5 6 7 8 9] \n",
"\n",
"[0 1 2 3 4 5 6 7 8 9] \n",
"\n"
]
}
],
"source": [
"# Watch it, though, transposing a 1D array does nothing\n",
"\n",
"Y = np.arange(10)\n",
"print(Y,'\\n')\n",
"print(Y.T,'\\n')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0]\n",
" [1]\n",
" [2]\n",
" [3]\n",
" [4]] \n",
"\n",
"[[0]\n",
" [1]\n",
" [2]\n",
" [3]\n",
" [4]] \n",
"\n"
]
}
],
"source": [
"# to create a column vector, you can do it explicitly (a pain) or create\n",
"# a 2D array with one column, then transpose:\n",
"\n",
"Z = np.array( [ [0], [1], [2], [3], [4] ] )\n",
"print(Z,'\\n')\n",
"\n",
"Z = np.array( [ range(5) ] )\n",
"print(Z.T,'\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Array Stacking\n",
"\n",
"Creating new arrays out of existing arrays is a basic\n",
"procedure in linear algebra, and `numpy` makes this\n",
"easy!\n",
"\n",
"*Vertical stacking* collects the rows of two\n",
"arrays into a single array, and *Horizontal stacking* collects the columns\n",
"of two arrays into a single array. \n",
"\n",
"The following examples show how to use `vstack` and `hstack`. "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]]\n",
"\n",
"[[12 13 14 15]\n",
" [16 17 18 19]\n",
" [20 21 22 23]]\n"
]
}
],
"source": [
"# create two arrays for the example\n",
"\n",
"t1 = np.arange(12).reshape((3,4))\n",
"t2 = np.arange(12, 24).reshape((3,4))\n",
"print(t1)\n",
"print()\n",
"print(t2)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]\n",
" [12 13 14 15]\n",
" [16 17 18 19]\n",
" [20 21 22 23]]\n"
]
}
],
"source": [
" # Vertically stack the rows of t1 and t2\n",
"t3 = np.vstack((t1, t2))\n",
"print(t3)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[24 25 26 27]\n",
" [28 29 30 31]]\n",
"\n",
"[[ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]\n",
" [ 0 1 2 3]\n",
" [ 4 5 6 7]\n",
" [ 8 9 10 11]\n",
" [12 13 14 15]\n",
" [16 17 18 19]\n",
" [20 21 22 23]\n",
" [24 25 26 27]\n",
" [28 29 30 31]\n",
" [12 13 14 15]\n",
" [16 17 18 19]\n",
" [20 21 22 23]]\n"
]
}
],
"source": [
"# You can stack any number of arrays of the same type which have the\n",
"# same number of columns\n",
"\n",
"t4 = np.arange(24, 32).reshape((2,4))\n",
"\n",
"print(t4)\n",
"print()\n",
"\n",
"print( np.vstack( (t1,t3,t4,t2)) )"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 1 2 3 12 13 14 15]\n",
" [ 4 5 6 7 16 17 18 19]\n",
" [ 8 9 10 11 20 21 22 23]]\n",
"\n",
"[[ 0 1 2 3 0 1 2 3 12 13 14 15 12 13 14 15]\n",
" [ 4 5 6 7 4 5 6 7 16 17 18 19 16 17 18 19]\n",
" [ 8 9 10 11 8 9 10 11 20 21 22 23 20 21 22 23]]\n"
]
}
],
"source": [
" # Horizontal stacking is completely analogous:\n",
" \n",
"t5 = np.hstack((t1, t2))\n",
"print(t5)\n",
"print()\n",
"print( np.hstack( (t1,t5,t2)))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 0 -4]\n",
" [ 2 -1 4]\n",
" [ 1 0 2]\n",
" [ 5 4 2]]\n",
"\n",
"[[ 1]\n",
" [ 2]\n",
" [ -1]\n",
" [-19]]\n",
"\n",
"[[ 0 0 -4 1]\n",
" [ 2 -1 4 2]\n",
" [ 1 0 2 -1]\n",
" [ 5 4 2 -19]]\n"
]
}
],
"source": [
"# A typical application is the process of creating an augmented\n",
"# matrix to solve a linear algebra problem of the form Ax = b\n",
"\n",
"A = np.array([[0,0,-4], [2,-1,4],[1,0,2],[5,4,2]])\n",
"\n",
"# To create a vector (single column array), can do it explicitly:\n",
"b = np.array( [[1],[2],[-1],[-19]] )\n",
"\n",
"# but it is easier to transpose a 2D array with a single row:\n",
"b = np.array( [ [1,2,-1,-19] ] ).T\n",
"\n",
"print(A)\n",
"print()\n",
"print(b)\n",
"print()\n",
"\n",
"Aug = np.hstack( (A,b))\n",
"\n",
"print(Aug)"
]
},
{
"attachments": {
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we could run GaussJordan on the augmented array:\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparing Arrays\n",
"\n",
"We have shown above how you can take operations normally associated\n",
"with scalars and apply them to arrays: the operations are applied\n",
"pairwise to the individual elements, and an array is returned.\n",
"\n",
"When comparing arrays using Boolean operations, there are two issues,\n",
"one easy to fix, and one not so easy. \n",
"\n",
"The first is that you would expect to be able to use `==` and `!=`\n",
"on arrays to check if they are identical or not, but this does\n",
"not behave the way you expect, due to the pairwise nature of the\n",
"operators when they are applied to arrays:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True, True, True])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.array([1,2,3,4,5])\n",
"\n",
"B = np.array([1,2,3,4,5])\n",
"\n",
"A == B"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to return a single Boolean value, you have to use the\n",
"function `.all()` to find out if *all* of the Boolean values are\n",
"`True`:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(A == B).all()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second problem has to do with the accuracy of floating-point arithmetic: rounding in the least-significant digits may cause values to not be absolutely precise, so in general when doing floating-point\n",
"computations in which you need to check for equality, you should use\n",
"the function `isclose`. In the following, we show that when checking if a value is 0.0, `isclose` will return\n",
"true if the value is sufficiently close to 0.0. \n",
"\n",
"See the documentation for more details. "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.e+00 1.e-01 1.e-02 1.e-03 1.e-04 1.e-05 1.e-06 1.e-07 1.e-08 1.e-09]\n",
"[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
"[False False False False False False False False False False]\n",
"[False False False False False False False False True True]\n"
]
}
],
"source": [
"A = np.array( [ 10**(-k) for k in range(10)])\n",
"B = np.zeros(10)\n",
"\n",
"print(A)\n",
"\n",
"print(B)\n",
"\n",
"print((A == B))\n",
"\n",
"print( np.isclose(A,B) )"
]
},
{
"attachments": {
"Screen%20Shot%202021-05-24%20at%208.11.47%20PM.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading and writing matrices from text files\n",
"\n",
"Numpy provides a simple way to store matrices as text files, and to read them into your session. \n",
"You can read about the details in the documentation for these functions, but a few simple examples\n",
"will show the basic ideas. \n",
"\n",
"If you have a local file, created using a text editor such as Emacs or Textedit, you can simply\n",
"list the values of a 2D array as follows:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"ename": "OSError",
"evalue": "example1.txt not found.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Loading a simple text file into a 2D array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'example1.txt'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows)\u001b[0m\n\u001b[1;32m 979\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_is_string_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 982\u001b[0m \u001b[0mfencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'encoding'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'latin1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 983\u001b[0m \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataSource\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnewline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/_datasource.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m 621\u001b[0m encoding=encoding, newline=newline)\n\u001b[1;32m 622\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s not found.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mOSError\u001b[0m: example1.txt not found."
]
}
],
"source": [
"# Loading a simple text file into a 2D array\n",
"\n",
"X = np.loadtxt('example1.txt')\n",
"X"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also load such a file from a web server:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = np.loadtxt('https://www.cs.bu.edu/fac/snyder/cs237/tutorials/example1.txt')\n",
"X"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To write out such a matrix to a local file, you can do the following:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X[0][0] = 15\n",
"print(X)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('example2.txt', X)"
]
},
{
"attachments": {
"Screen%20Shot%202021-05-24%20at%208.18.55%20PM.png": {
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"This will be stored as follows:\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.loadtxt('example2.txt')"
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}