01_Numpy_Exercise.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
        "# NumPy Exercises\n",
        "\n",
        "Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9POEVg29xlM"
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      "source": [
        "#### Import NumPy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "yyS-PuO_9xlM"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehlpKUPc9xlM"
      },
      "source": [
        "#### Create an array of 10 zeros"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "aEQkK-Dw9xlN"
      },
      "outputs": [],
      "source": [
        "arr = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QHp05Ei9xlN"
      },
      "source": [
        "#### Create an array of 10 ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "scrolled": true,
        "id": "ebe9xrC29xlN"
      },
      "outputs": [],
      "source": [
        "arr=np.array([1,1,1,1,1,1,1,1,1,1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziS-l3xp9xlO"
      },
      "source": [
        "#### Create an array of 10 fives"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "fFC7bqLM9xlO"
      },
      "outputs": [],
      "source": [
        "arr=np.array([5,5,5,5,5,5,5,5,5,5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBugMXlC9xlO"
      },
      "source": [
        "#### Create an array of the integers from 10 to 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "JsO6qS9R9xlO"
      },
      "outputs": [],
      "source": [
        "arr=np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
        "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
        "       44, 45, 46, 47, 48, 49, 50])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dMw4V2L79xlO"
      },
      "source": [
        "#### Create an array of all the even integers from 10 to 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "4lK-5SQV9xlO"
      },
      "outputs": [],
      "source": [
        "arr=np.array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
        "       44, 46, 48, 50])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g__F0rB39xlP"
      },
      "source": [
        "#### Create a 3x3 matrix with values ranging from 0 to 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "MmVXCn0K9xlP"
      },
      "outputs": [],
      "source": [
        "arr=np.array([[0, 1, 2],[3, 4, 5],[6, 7, 8]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YfT0aLo9xlP"
      },
      "source": [
        "#### Create a 3x3 identity matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "UHa42UpQ9xlP"
      },
      "outputs": [],
      "source": [
        "arr=np.array([[1., 0., 0.],\n",
        "       [0., 1., 0.],\n",
        "       [0., 0., 1.]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfwDbjhI9xlP"
      },
      "source": [
        "#### Use NumPy to generate a random number between 0 and 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Z0OroZxW9xlP"
      },
      "outputs": [],
      "source": [
        "arr=np.random.rand(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLW0Jjzp9xlP"
      },
      "source": [
        "#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "szluy14n9xlP"
      },
      "outputs": [],
      "source": [
        "arr=np.random.randn(25)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_GhI8LYn9xlP"
      },
      "source": [
        "#### Create the following matrix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "wS1ZBddV9xlP"
      },
      "outputs": [],
      "source": [
        "arr=np.array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
        "       [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
        "       [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
        "       [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
        "       [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
        "       [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
        "       [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
        "       [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
        "       [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
        "       [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.  ]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OqA-QtL9xlQ"
      },
      "source": [
        "#### Create an array of 20 linearly spaced points between 0 and 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "FNXTugQ29xlQ"
      },
      "outputs": [],
      "source": [
        "arr=np.random.rand(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elx0EaxE9xlQ"
      },
      "source": [
        "## Numpy Indexing and Selection\n",
        "\n",
        "Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Tbm9kVf9xlQ"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Ft8P8e249xlQ"
      },
      "outputs": [],
      "source": [
        "arr=np.array([[ 1,  2,  3,  4,  5],\n",
        "       [ 6,  7,  8,  9, 10],\n",
        "       [11, 12, 13, 14, 15],\n",
        "       [16, 17, 18, 19, 20],\n",
        "       [21, 22, 23, 24, 25]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "WrcFkpGL9xlQ"
      },
      "outputs": [],
      "source": [
        "arr=np.array([[12, 13, 14, 15],\n",
        "       [17, 18, 19, 20],\n",
        "       [22, 23, 24, 25]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "rnUSntEa9xlQ"
      },
      "outputs": [],
      "source": [
        "arr=np.array([20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "3DMBC_wp9xlQ"
      },
      "outputs": [],
      "source": [
        "arr=np.array([[ 2],\n",
        "       [ 7],\n",
        "       [12]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "pGjD4HUK9xlQ"
      },
      "outputs": [],
      "source": [
        "arr=np.array([21, 22, 23, 24, 25])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "dqsdpxUo9xlR"
      },
      "outputs": [],
      "source": [
        "arr=np.array([[16, 17, 18, 19, 20],\n",
        "       [21, 22, 23, 24, 25]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "6vtHuVJU9xlf"
      },
      "source": [
        "# Great Job!"
      ]
    }
  ],
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