Lesson_1/using-built-in-functions-to-create-ndarrays.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "\n",
    "my_list = [1,2,3,4,5]\n",
    "np.array(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 1. Create a Numpy array of zeros with a desired shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a 3 x 4 ndarray full of zeros. \n",
    "X = np.zeros((3,4), dtype=int)\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in X are of type:', X.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 2. Create a Numpy array of ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a 3 x 2 ndarray full of ones. \n",
    "X = np.ones((3,2))\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in X are of type:', X.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 3. Create a Numpy array of constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a 2 x 3 ndarray full of fives. \n",
    "X = np.full((2,3), 5) \n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in X are of type:', X.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 4. Create a Numpy array of Identity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a 5 x 5 Identity matrix. \n",
    "X = np.eye(5)\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in X are of type:', X.dtype)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 5. Create a Numpy array of constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a 4 x 4 diagonal matrix that contains the numbers 10,20,30, and 50\n",
    "# on its main diagonal\n",
    "X = np.diag([10,20,30,50])\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 5. Create a Numpy array of evenly spaced values in a given range, using `arange(stop_val)`\n",
    "See the complete syntax of `NumPy.arange()` [here](https://numpy.org/doc/stable/reference/generated/numpy.arange.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a rank 1 ndarray that has sequential integers from 0 to 9\n",
    "x = np.arange(10)\n",
    "\n",
    "# We print the ndarray\n",
    "print()\n",
    "print('x = ', x)\n",
    "print()\n",
    "\n",
    "# We print information about the ndarray\n",
    "print('x has dimensions:', x.shape)\n",
    "print('x is an object of type:', type(x))\n",
    "print('The elements in x are of type:', x.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 6. Create a Numpy array using `arange(start_val, stop_val)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a rank 1 ndarray that has sequential integers from 4 to 9. \n",
    "x = np.arange(4,10)\n",
    "\n",
    "# We print the ndarray\n",
    "print()\n",
    "print('x = ', x)\n",
    "print()\n",
    "\n",
    "# We print information about the ndarray\n",
    "print('x has dimensions:', x.shape)\n",
    "print('x is an object of type:', type(x))\n",
    "print('The elements in x are of type:', x.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 7. Create a Numpy array using `arange(start_val, stop_val, step_size)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a rank 1 ndarray that has evenly spaced integers from 1 to 13 in steps of 3.\n",
    "x = np.arange(1,14,3)\n",
    "\n",
    "# We print the ndarray\n",
    "print()\n",
    "print('x = ', x)\n",
    "print()\n",
    "\n",
    "# We print information about the ndarray\n",
    "print('x has dimensions:', x.shape)\n",
    "print('x is an object of type:', type(x))\n",
    "print('The elements in x are of type:', x.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 8. Create a Numpy array using `linspace(start, stop, n)`, with `stop` inclusive.\n",
    "See the all possible arguments in the syntax [here](https://numpy.org/doc/stable/reference/generated/numpy.linspace.html) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a rank 1 ndarray that has 10 integers evenly spaced between 0 and 25.\n",
    "x = np.linspace(0,25,10)\n",
    "\n",
    "# We print the ndarray\n",
    "print()\n",
    "print('x = \\n', x)\n",
    "print()\n",
    "\n",
    "# We print information about the ndarray\n",
    "print('x has dimensions:', x.shape)\n",
    "print('x is an object of type:', type(x))\n",
    "print('The elements in x are of type:', x.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 9. Create a Numpy array using `linspace(start, stop, n)`, with `stop` excluded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a rank 1 ndarray that has 10 integers evenly spaced between 0 and 25,\n",
    "# with 25 excluded.\n",
    "x = np.linspace(0,25,10, endpoint = False)\n",
    "\n",
    "# We print the ndarray\n",
    "print()\n",
    "print('x = ', x)\n",
    "print()\n",
    "\n",
    "# We print information about the ndarray\n",
    "print('x has dimensions:', x.shape)\n",
    "print('x is an object of type:', type(x))\n",
    "print('The elements in x are of type:', x.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 10. Create a Numpy array by feeding the output of `arange()` function to the  `reshape()` function.\n",
    "Here, `arange()` function will give you a 1-D array, whereas the `reshape()` function will convert that 1-D array into a  desired shape. **Remember, that the  `size` of the final output must be as same as the `size` of the initial  1-D array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a rank 1 ndarray with sequential integers from 0 to 19\n",
    "x = np.arange(20)\n",
    "\n",
    "# We print x\n",
    "print()\n",
    "print('Original x = ', x)\n",
    "print()\n",
    "\n",
    "# We reshape x into a 4 x 5 ndarray \n",
    "x = np.reshape(x, (4,5))\n",
    "\n",
    "# We print the reshaped x\n",
    "print()\n",
    "print('Reshaped x = \\n', x)\n",
    "print()\n",
    "\n",
    "# We print information about the reshaped x\n",
    "print('x has dimensions:', x.shape)\n",
    "print('x is an object of type:', type(x))\n",
    "print('The elements in x are of type:', x.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 11. Create a Numpy array by calling the `reshape()` function from the output of `arange()` function.\n",
    "Notice the change in the arguments of `reshape()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a a rank 1 ndarray with sequential integers from 0 to 19 and\n",
    "# reshape it to a 4 x 5 array \n",
    "Y = np.arange(20).reshape(4, 5)\n",
    "\n",
    "# We print Y\n",
    "print()\n",
    "print('Y = \\n', Y)\n",
    "print()\n",
    "\n",
    "# We print information about Y\n",
    "print('Y has dimensions:', Y.shape)\n",
    "print('Y is an object of type:', type(Y))\n",
    "print('The elements in Y are of type:', Y.dtype) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 12. Create a rank 2 Numpy array by using the `reshape()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a rank 1 ndarray with 10 integers evenly spaced between 0 and 50,\n",
    "# with 50 excluded. We then reshape it to a 5 x 2 ndarray\n",
    "X = np.linspace(0,50,10, endpoint=False).reshape(5,2)\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in X are of type:', X.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 13. Create a Numpy array using the `numpy.random.random()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a 3 x 3 ndarray with random floats in the half-open interval [0.0, 1.0).\n",
    "X = np.random.random((3,3))\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in x are of type:', X.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 14. Create a Numpy array  using the `numpy.random.randint()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a 3 x 2 ndarray with random integers in the half-open interval [4, 15).\n",
    "X = np.random.randint(4,15,size=(3,2))\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in X are of type:', X.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 15. Create a Numpy array of \"Normal\" distributed random numbers, using the `numpy.random.normal()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We create a 1000 x 1000 ndarray of random floats drawn from normal (Gaussian) distribution\n",
    "# with a mean of zero and a standard deviation of 0.1.\n",
    "X = np.random.normal(0, 0.1, size=(1000,1000))\n",
    "\n",
    "# We print X\n",
    "print()\n",
    "print('X = \\n', X)\n",
    "print()\n",
    "\n",
    "# We print information about X\n",
    "print('X has dimensions:', X.shape)\n",
    "print('X is an object of type:', type(X))\n",
    "print('The elements in X are of type:', X.dtype)\n",
    "print('The elements in X have a mean of:', X.mean())\n",
    "print('The maximum value in X is:', X.max())\n",
    "print('The minimum value in X is:', X.min())\n",
    "print('X has', (X < 0).sum(), 'negative numbers')\n",
    "print('X has', (X > 0).sum(), 'positive numbers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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