Skip to content

Instantly share code, notes, and snippets.

Show Gist options
  • Save priyankas0525/23f7e712ba4914d7c844fa9a0b108856 to your computer and use it in GitHub Desktop.
Save priyankas0525/23f7e712ba4914d7c844fa9a0b108856 to your computer and use it in GitHub Desktop.
Created on Skills Network Labs
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>\n",
" <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Get to Know a numpy Array </h3>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will use the numpy array <code> A</code> for the following \n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"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 \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(A)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) the shape of the array \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"A.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3) the type of data in the array \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataType"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4) Find the second row of the numpy array <code>A</code>:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ndArray[row_index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Two kinds of Multiplying </h3>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"you will use the following numpy arrays for the next questions \n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c = np.ones((11, 3))\n",
"\n",
"c * c "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2) Perform matrix multiplication on array <code> A</code> and <code> B</code> (order will not matter in this case) \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<hr>\n",
"\n",
"<h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
]
}
],
"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": "3.6.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment