Skip to content

Instantly share code, notes, and snippets.

@theLivin
Created February 19, 2020 01:40
Show Gist options
  • Save theLivin/4db68da3d62951a7d79425337d52800c to your computer and use it in GitHub Desktop.
Save theLivin/4db68da3d62951a7d79425337d52800c to your computer and use it in GitHub Desktop.
Created on Cognitive Class Labs
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Get to Know a numpy Array </h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will use the numpy array <code> A</code> for the following "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"A=np.array([[11,12],[21,22],[31,32]])\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": 5,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(3, 2)"
]
},
"execution_count": 5,
"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": 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": [
"4) Find the second row of the numpy array <code>A</code>:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([21, 22])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Two kinds of Multiplying </h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"you will use the following numpy arrays for the next questions "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"A=np.array([[11,12],[21,22]])\n",
"B=np.array([[1, 0],[0,1]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1) multiply array <code> A </code> and <code>B</code>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[11, 0],\n",
" [ 0, 22]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A*B"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) plot the function"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[11, 12],\n",
" [21, 22]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.dot(A,B)\n",
"np.dot(B,A)\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
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment