From 48a2f6853da15812091c8a760113875e46030c93 Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Fri, 16 Sep 2022 10:14:13 +0000
Subject: [PATCH] Adapted Numpy Pandas Intro Notebook
---
.../Preliminaries_Numpy_Pandas.ipynb | 520 ++++++++++++++++++
1 file changed, 520 insertions(+)
create mode 100644 notebooks/Block_0/Examples script/Preliminaries_Numpy_Pandas.ipynb
diff --git a/notebooks/Block_0/Examples script/Preliminaries_Numpy_Pandas.ipynb b/notebooks/Block_0/Examples script/Preliminaries_Numpy_Pandas.ipynb
new file mode 100644
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+++ b/notebooks/Block_0/Examples script/Preliminaries_Numpy_Pandas.ipynb
@@ -0,0 +1,520 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.2 Funktions, Conditionals, and Iteration in Python\n",
+ "\n",
+ "Let us create a Python function, and call it from a loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def HelloWorldXY(x, y):\n",
+ " if (x < 10):\n",
+ " print(\"Hello World, x was < 10\")\n",
+ " elif (x < 20):\n",
+ " print(\"Hello World, x was >= 10 but < 20\")\n",
+ " else:\n",
+ " print(\"Hello World, x was >= 20\")\n",
+ " return x + y\n",
+ "\n",
+ "print(HelloWorldXY(1,2))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let us call the function `HelloWorldXY()` from a loop:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "--- Now running with i: 8\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'HelloWorldXY' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-1-7ea2350df544>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# i=8, 13, 18, 23 (start, stop, step)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n--- Now running with i: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHelloWorldXY\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Result from HelloWorld: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'HelloWorldXY' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(8, 25, 5): # i=8, 13, 18, 23 (start, stop, step)\n",
+ " print(\"\\n--- Now running with i: {}\".format(i))\n",
+ " r = HelloWorldXY(i,i)\n",
+ " print(\"Result from HelloWorld: {}\".format(r))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you want a loop starting at 0 to 2 (exclusive) you could do any of the following:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Iterate over the items. `range(2)` is like a list [0,1].\n",
+ "0\n",
+ "1\n",
+ "Iterate over an actual list.\n",
+ "0\n",
+ "1\n",
+ "While works\n",
+ "0\n",
+ "1\n",
+ "Python supports standard key words like continue and break\n",
+ "Entered while\n",
+ "while broken\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Iterate over the items. `range(2)` is like a list [0,1].\")\n",
+ "for i in range(2):\n",
+ " print(i)\n",
+ "\n",
+ "print(\"Iterate over an actual list.\")\n",
+ "for i in [0,1]:\n",
+ " print(i)\n",
+ "\n",
+ "print(\"While works\")\n",
+ "i = 0\n",
+ "while i < 2:\n",
+ " print(i)\n",
+ " i += 1\n",
+ " \n",
+ "print(\"Python supports standard key words like continue and break\")\n",
+ "while True:\n",
+ " print(\"Entered while\")\n",
+ " break\n",
+ "print(\"while broken\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.3 Data in Numpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Shape scaler () \n",
+ "Shape vector (3,) \n",
+ "Shape matrix (3, 3) \n",
+ "Shape tensor (3, 3, 2, 1)\n",
+ "Type scalar or array <class 'numpy.ndarray'> \n",
+ "Type after addition with integer <class 'numpy.int64'>\n",
+ "v[1:] = [ 2 10] \n",
+ "m[1:][2:] = \n",
+ " [[5 6]\n",
+ " [8 9]]\n",
+ "[ 1 2 10] [[ 1 2 10]] [[ 1 2 10]]\n",
+ "(3,) (1, 3) (1, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# Scalar\n",
+ "s = np.array(5)\n",
+ "# Vector\n",
+ "v = np.array([1, 2, 10])\n",
+ "# Matrix\n",
+ "m = np.array([[1,2,3], \n",
+ " [4,5,6], \n",
+ " [7,8,9]])\n",
+ "# Tensor:\n",
+ "t = np.array([[[[1],[2]], [[3],[4]], [[5],[6]]],\n",
+ " [[[7],[8]], [[9],[10]], [[11],[12]]],\n",
+ " [[[13],[14]], [[15],[16]], [[17],[17]]]])\n",
+ "\n",
+ "# Shape\n",
+ "print(\"Shape scaler\", s.shape, \"\\nShape vector\", v.shape, \"\\nShape matrix\", m.shape, \"\\nShape tensor\", t.shape)\n",
+ "\n",
+ "# Type\n",
+ "print(\"Type scalar or array\", type(s), \"\\nType after addition with integer\", type(s + 3))\n",
+ "\n",
+ "# Slicing\n",
+ "print(\"v[1:] = \", v[1:], \"\\nm[1:][2:] = \\n\", m[1:,1:])\n",
+ "\n",
+ "# Reshape arrays\n",
+ "x = v.reshape(1, 3)\n",
+ "y = v[None, :]\n",
+ "print(v, x, y)\n",
+ "print(v.shape, x.shape, y.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## 3.4 Element-wise Operations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[6, 7, 8, 9, 10]\n",
+ "[ 6 7 8 9 10]\n",
+ "[30 35 40 45 50] \n",
+ " [30 35 40 45 50] \n",
+ "\n",
+ "a =\n",
+ " [[1 3]\n",
+ " [5 7]] \n",
+ "b =\n",
+ " [[2 4]\n",
+ " [6 8]]\n",
+ "a + b =\n",
+ " [[ 3 7]\n",
+ " [11 15]]\n",
+ "a * b =\n",
+ " [[ 2 12]\n",
+ " [30 56]]\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "operands could not be broadcast together with shapes (2,2) (5,) ",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-4-7ddd6b5f4e75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a * b =\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;31m# Shape mismatch:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a * values =\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,2) (5,) "
+ ]
+ }
+ ],
+ "source": [
+ "# The Python way:\n",
+ "values = [1, 2, 3, 4, 5]\n",
+ "for i in range(len(values)):\n",
+ " values[i] += 5\n",
+ " \n",
+ "print(values)\n",
+ "\n",
+ "# The Numpy way:\n",
+ "values = np.array([1, 2, 3, 4, 5])\n",
+ "values += 5\n",
+ "\n",
+ "print(values)\n",
+ "\n",
+ "# Multiplication\n",
+ "x = np.multiply(values, 5)\n",
+ "y = values * 5\n",
+ "print(x, \"\\n\", y, \"\\n\")\n",
+ "\n",
+ "# Element wise matrix operations\n",
+ "a = np.array([[1,3],[5,7]])\n",
+ "b = np.array([[2,4],[6,8]])\n",
+ "print(\"a =\\n\", a, \"\\nb =\\n\", b)\n",
+ "print(\"a + b =\\n\", a + b)\n",
+ "print(\"a * b =\\n\", a * b)\n",
+ "# Shape mismatch:\n",
+ "print(\"a * values =\\n\", a * values)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Numpy Matrix Multiplication\n",
+ "Recap element-wise multiplication:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "m =\n",
+ " [[1 2 3]\n",
+ " [4 5 6]] \n",
+ "n =\n",
+ " [[0.25 0.5 0.75]\n",
+ " [1. 1.25 1.5 ]]\n",
+ "x =\n",
+ " [[0.25 1. 2.25]\n",
+ " [4. 6.25 9. ]] \n",
+ "y =\n",
+ " [[0.25 1. 2.25]\n",
+ " [4. 6.25 9. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Elementwise recap:\n",
+ "m = np.array([[1,2,3],[4,5,6]])\n",
+ "# Scalar multiplication\n",
+ "n = m * 0.25\n",
+ "# Python Elementwise matrix multiplication\n",
+ "x = m * n\n",
+ "# Numpy Elementwise matrix multiplication\n",
+ "y = np.multiply(m, n)\n",
+ "\n",
+ "print(\"m =\\n\", m, \"\\nn =\\n\", n)\n",
+ "print(\"x =\\n\", x, \"\\ny =\\n\", y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Matrix Product:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "a =\n",
+ " [[1 2 3 4]\n",
+ " [5 6 7 8]] \n",
+ "a.shape =\n",
+ " (2, 4) \n",
+ "b =\n",
+ " [[ 1 2 3]\n",
+ " [ 4 5 6]\n",
+ " [ 7 8 9]\n",
+ " [10 11 12]] \n",
+ "b.shape =\n",
+ " (4, 3)\n",
+ "c = \n",
+ " [[ 70 80 90]\n",
+ " [158 184 210]] \n",
+ "c.shape =\n",
+ " (2, 3)\n",
+ "d = \n",
+ " [[ 70 80 90]\n",
+ " [158 184 210]] \n",
+ "d.shape =\n",
+ " (2, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "\"\"\" Using np.matmul \"\"\"\n",
+ "a = np.array([[1,2,3,4],[5,6,7,8]])\n",
+ "b = np.array([[1,2,3],[4,5,6],[7,8,9],[10,11,12]])\n",
+ "\n",
+ "print(\"a =\\n\", a, \"\\na.shape =\\n\", a.shape, \"\\nb =\\n\", b, \"\\nb.shape =\\n\", b.shape)\n",
+ "\n",
+ "# Matrix product\n",
+ "c = np.matmul(a, b)\n",
+ "print(\"c = \\n\", c, \"\\nc.shape =\\n\", c.shape)\n",
+ "\n",
+ "# Dimension mismatch:\n",
+ "# print(np.matmul(b, a))\n",
+ "\"\"\" Using np.dot \"\"\"\n",
+ "d = np.dot(a, b)\n",
+ "print(\"d = \\n\", d, \"\\nd.shape =\\n\", d.shape)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Transpose"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "m = \n",
+ " [[ 1 2 3 4]\n",
+ " [ 5 6 7 8]\n",
+ " [ 9 10 11 12]] \n",
+ "m.T = \n",
+ " [[ 1 5 9]\n",
+ " [ 2 6 10]\n",
+ " [ 3 7 11]\n",
+ " [ 4 8 12]]\n",
+ "m = \n",
+ " [[ 1 2 3 4]\n",
+ " [ 5 6 7 200]\n",
+ " [ 9 10 11 12]] \n",
+ "m_t = \n",
+ " [[ 1 5 9]\n",
+ " [ 2 6 10]\n",
+ " [ 3 7 11]\n",
+ " [ 4 200 12]]\n",
+ "entries [3][1], [1][3], respectively are edited in both matrices\n"
+ ]
+ }
+ ],
+ "source": [
+ "m = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+ "print(\"m = \\n\", m,\"\\nm.T = \\n\", m.T)\n",
+ "\n",
+ "# note how the transposed matrix is not a copy of the original:\n",
+ "m_t = m.T\n",
+ "m_t[3][1] = 200\n",
+ "print(\"m = \\n\", m, \"\\nm_t = \\n\", m_t)\n",
+ "print(\"entries [3][1], [1][3], respectively are edited in both matrices\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## A real use case"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[-0.27 0.45 0.64 0.31]] (1, 4)\n",
+ "[[ 0.02 0.001 -0.03 0.036]\n",
+ " [ 0.04 -0.003 0.025 0.009]\n",
+ " [ 0.012 -0.045 0.28 -0.067]] (3, 4)\n",
+ "Matrix multiplication gives:\n",
+ " [[-0.01299 0.00664 0.13494]] \n",
+ "or, equivalently:\n",
+ " [[-0.01299]\n",
+ " [ 0.00664]\n",
+ " [ 0.13494]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "inputs = np.array([[-0.27, 0.45, 0.64, 0.31]])\n",
+ "print(inputs, inputs.shape)\n",
+ "\n",
+ "weights = np.array([[0.02, 0.001, -0.03, 0.036], \n",
+ " [0.04, -0.003, 0.025, 0.009], \n",
+ " [0.012, -0.045, 0.28, -0.067]])\n",
+ "print(weights, weights.shape)\n",
+ "\n",
+ "print(\"Matrix multiplication gives:\\n\", np.matmul(inputs, weights.T), \"\\nor, equivalently:\\n\", np.matmul(weights, inputs.T))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Some more useful Numpy methods"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Showing some basic math on arrays\n",
+ "Max: 4\n",
+ "Average: 2.0\n",
+ "Max index: 2\n",
+ "\n",
+ "Use numpy to create a [3,3] dimension array with random number\n",
+ "[[0.92371879 0.58999086 0.76979433]\n",
+ " [0.48733651 0.44698554 0.91494542]\n",
+ " [0.59130531 0.69632003 0.32785335]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"\\nShowing some basic math on arrays\")\n",
+ "\n",
+ "b = np.array([0,1,4,3,2])\n",
+ "print(\"Max: {}\".format(np.max(b)))\n",
+ "print(\"Average: {}\".format(np.average(b)))\n",
+ "print(\"Max index: {}\".format(np.argmax(b)))\n",
+ "\n",
+ "print(\"\\nUse numpy to create a [3,3] dimension array with random number\")\n",
+ "c = np.random.rand(3, 3)\n",
+ "print(c)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--
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