{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Data with Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Run these next few cells:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_1D = np.array([10,11,12,13, 14])\n",
    "array_1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_2D = np.array([[20,30,40,50,60], [43,54,65,76,87], [11,22,33,44,55]])\n",
    "array_2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_3D = np.array([[[1,2,3,4,5], [11,21,31,41,51]], [[11,12,13,14,15], [51,52,53,54,5]]])\n",
    "array_3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Generate 4 arrays of size 10:\n",
    "    A) The first one should be \"empty\"\n",
    "    B) The second one should be full of 0s\n",
    "    C) The third one should be full of 1s\n",
    "    D) The last one should be full of 2s\n",
    "   (<b>Hint</b>: Try to use 4 different functions here.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Generate 4 more arrays. This time, they should be 2 by 4 arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Use the <i> _like </i> functions to generate 4 more arrays:\n",
    "    A) An empty array with the same shape as array_1D. \n",
    "    B) An 2-D array of 0s with the same shape as array_2D.\n",
    "    C) A 3-D array of 1s with the same shappe as array_3D.\n",
    "    D) A 3-D array of 2s with the same shape as array_3D. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. With the help of the np.arange() function, generate several sequences of numbers:\n",
    "    A) The integers from 0 to 50, excluding 50. \n",
    "    B) The integers from 1 to 50, including 50. \n",
    "    C) The integers from 25 to 50, including 50. \n",
    "    D) Every 5-th integers from 25 to 50, including 50. \n",
    "    E) Every 5-th integers from 25 to 50, including 50, represented as decimals of up to 32 bits. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Import the following functions from the numpy.random module:\n",
    "    A) Generator as \"gen\".\n",
    "    B) PCG64 as \"pcg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Create a random generator object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Using the .random() method, generate the following pseudo-random values:\n",
    "    A) A single probability of an event occuring.\n",
    "    B) An array of size 10 with the probabilities of 10 events. \n",
    "    C) A 5 by 10 2-D array with the probabilities of 50 events.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Set the seed for the random generator to 123, and generate the same 3 sets of values:\n",
    "    A) A single probability of an event occuring.\n",
    "    B) An array of size 10 with the probabilities of 10 events. \n",
    "    C) A 5 by 10 2-D array with the probabilities of 50 events.  \n",
    "   <b> Note: </b> The seed only lasts for a single method before it gets reset. Hence, make sure you define the seed before calling the method every time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Set the seed for the random generator to 123, and generate the following arrays of integers:\n",
    "    A) A single integer between 0 and 10.\n",
    "    B) A 1-D array of 10 integers between 0 and 100.\n",
    "    C) A 5 by 10 array of two-digit integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. For this next bit, check the NumPy documentation and select 3 different probability distributions (e.g. normal, poisson, binomial, logistic) and generate the following:\n",
    "    A) One \"default\" value from one distribution (without specifying any non-mandatory arguments).\n",
    "    B) An array of 10 values from the second distribution. \n",
    "    C) A 5 by 10 2-D array with values from the third distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  }
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