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Commit 1c64aab

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‎Logic_functions.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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"metadata": {},
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"source": [
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"# Logic functions"
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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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"metadata": {
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"collapsed": true
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.11.2'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.__version__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Truth value testing\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q1. Let x be an arbitrary. Return True if none of the elements of x is zero. Remind that 0 evaluates to False in python.\n"
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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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"metadata": {
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"collapsed": false
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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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"True\n",
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"False\n"
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]
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}
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],
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"source": [
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"x = np.array([1,2,3])\n",
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"#\n",
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"\n",
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"x = np.array([1,0,3])\n",
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"#"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q2. Let x be an arbitrary. Return True if any of the elements of x is non-zero."
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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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"metadata": {
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"collapsed": false
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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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"True\n",
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"False\n"
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]
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}
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],
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"source": [
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"x = np.array([1,0,0])\n",
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"#\n",
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"\n",
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"x = np.array([0,0,0])\n",
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"#"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Array contents\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q3. Predict the result of the following code."
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"x = np.array([1, 0, np.nan, np.inf])\n",
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"#print np.isfinite(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q4. Predict the result of the following code."
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"x = np.array([1, 0, np.nan, np.inf])\n",
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"#print np.isinf(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q5. Predict the result of the following code."
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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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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"x = np.array([1, 0, np.nan, np.inf])\n",
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"#print np.isnan(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Array type testing"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q6. Predict the result of the following code."
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])\n",
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"#print np.iscomplex(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q7. Predict the result of the following code."
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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": 18,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"x = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j])\n",
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"#print np.isreal(x)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q8. Predict the result of the following code."
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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": 21,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#print np.isscalar(3)\n",
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"#print np.isscalar([3])\n",
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"#print np.isscalar(True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Logical operations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q9. Predict the result of the following code."
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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": 31,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#print np.logical_and([True, False], [False, False])\n",
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"#print np.logical_or([True, False, True], [True, False, False])\n",
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"#print np.logical_xor([True, False, True], [True, False, False])\n",
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"#print np.logical_not([True, False, 0, 1])\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Comparison"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q10. Predict the result of the following code."
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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": 42,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#print np.allclose([3], [2.999999])\n",
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"#print np.array_equal([3], [2.999999])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q11. Write numpy comparison functions such that they return the results as you see."
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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": 51,
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"metadata": {
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"collapsed": false
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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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"[ True False]\n",
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"[ True True]\n",
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"[False False]\n",
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"[False True]\n"
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]
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}
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],
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"source": [
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"x = np.array([4, 5])\n",
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"y = np.array([2, 5])\n",
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"#\n",
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"#\n",
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"#\n",
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"#"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Q12. Predict the result of the following code."
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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": 50,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#print np.equal([1, 2], [1, 2.000001])\n",
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"#print np.isclose([1, 2], [1, 2.000001])"
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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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"metadata": {
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"collapsed": true
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},
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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 2",
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"language": "python",
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"name": "python2"
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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": 2
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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": "ipython2",
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"version": "2.7.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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