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Create /autonotebook command for AI generated notebooks (#90)
* Initial autonotebook work. * Working autonotebook. * Adding first autogenerated notebook example. * Removing file. * Adding second autonotebook example. * Cleaning up code, renaming autonotebook to generate. * Minor fixes, adding new example notebook. * Renaming examples subdir.
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‎examples/generate/Creating Random Arrays with Numpy.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c46fd5c5",
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"metadata": {},
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"source": [
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"# Creating Random Arrays with Numpy"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a2a4149f",
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"metadata": {},
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"source": [
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"## Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4b488198",
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"metadata": {},
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"source": [
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"This notebook was created by [Jupyter AI](https://github.com/jupyterlab/jupyter-ai) with the following prompt:\n",
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"\n",
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"> /generate Create a Jupyter notebook that shows how to create a random array using numpy."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c6460605",
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"metadata": {},
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"source": [
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"This Jupyter notebook demonstrates how to create a random array using numpy. It covers topics such as importing necessary packages, creating a random array, setting the array size and shape, setting the data type of the array, and generating a random array with specified parameters. Each section includes sample code for creating a random array and printing the results. This notebook is useful for anyone looking to generate random arrays in their data analysis or machine learning projects."
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]
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},
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{
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"cell_type": "markdown",
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"id": "9cfcc84c",
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"metadata": {},
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"source": [
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"## Creating a random array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4c50ec33",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "02a9481d",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"np.random.seed(123)\n",
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"random_array = np.random.rand(3, 4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "5ed2a6c8",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Random array:\n",
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" [[0.69646919 0.28613933 0.22685145 0.55131477]\n",
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" [0.71946897 0.42310646 0.9807642 0.68482974]\n",
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" [0.4809319 0.39211752 0.34317802 0.72904971]]\n"
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]
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}
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],
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"source": [
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"print(\"Random array:\\n\", random_array)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e9d4225",
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"metadata": {},
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"source": [
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"## Setting the array size and shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "15bfe3cb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2aedfee5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Set the size and shape of the random array\n",
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"array_size = (3, 4) # number of rows and columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "6dc9a89a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Create the random array using the specified size and shape\n",
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"random_array = np.random.rand(*array_size) # *array_size unpacks the tuple"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "7b4b2ae5",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Random array:\n",
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" [[0.43857224 0.0596779 0.39804426 0.73799541]\n",
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" [0.18249173 0.17545176 0.53155137 0.53182759]\n",
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" [0.63440096 0.84943179 0.72445532 0.61102351]]\n"
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]
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}
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],
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"source": [
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"# Print the random array\n",
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"print(\"Random array:\\n\", random_array)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "863dd179",
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"metadata": {},
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"source": [
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"## Setting the data type of the array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "fed55a87",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "d2fa2a10",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Set the data type of the random array to be created\n",
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"dtype = np.int32"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "9c462fdb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Set the size and shape of the random array\n",
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"array_size = (3, 4) # number of rows and columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "ddcf206e",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Create the random array using the specified size, shape, and data type\n",
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"random_array = np.random.randint(low=0, high=10, size=array_size, dtype=dtype)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "fcc3d78c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Random array:\n",
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" [[4 6 1 5]\n",
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" [6 2 1 8]\n",
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" [3 5 0 2]]\n"
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]
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}
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],
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"source": [
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"# Print the random array\n",
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"print(\"Random array:\\n\", random_array)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f1c81186",
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"metadata": {},
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"source": [
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"## Generating a random array with specified parameters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "b0526789",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "9ebd784c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"array_size = (5, 7) \n",
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"min_val = -10\n",
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"max_val = 10"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "56567059",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def create_random_array(size, low, high):\n",
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" return np.random.randint(low=low, high=high, size=size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "57c8282d",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"random_array = create_random_array(array_size, min_val, max_val)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "a2c9d87f",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Random array:\n",
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" [[ 0 3 8 -6 5 1 2]\n",
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" [-4 3 9 6 -4 4 -3]\n",
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" [ 1 -3 -9 1 -5 8 7]\n",
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" [ 2 8 7 -9 9 2 -1]\n",
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" [ 6 7 -7 -7 1 -3 -1]]\n"
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]
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}
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],
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"source": [
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"print(\"Random array:\\n\", random_array)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0abd2b89-c2e1-4083-9d4a-29da5a2096c3",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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