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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "metadata": { |
| 6 | + "collapsed": true |
| 7 | + }, |
| 8 | + "source": [ |
| 9 | + "# LAMBDA, MAP, FILTER in PYTHON - source-nerd" |
| 10 | + ] |
| 11 | + }, |
| 12 | + { |
| 13 | + "cell_type": "markdown", |
| 14 | + "metadata": {}, |
| 15 | + "source": [ |
| 16 | + "<div class=\"alert alert-block alert-info\">\n", |
| 17 | + "<b>1. Lambda Function</b><br>\n", |
| 18 | + "It is a small anonymous function which has only one expression and can take n arguments.\n", |
| 19 | + "</div>\n", |
| 20 | + "> lambda arguments: expression\t" |
| 21 | + ] |
| 22 | + }, |
| 23 | + { |
| 24 | + "cell_type": "code", |
| 25 | + "execution_count": 3, |
| 26 | + "metadata": {}, |
| 27 | + "outputs": [ |
| 28 | + { |
| 29 | + "data": { |
| 30 | + "text/plain": [ |
| 31 | + "8" |
| 32 | + ] |
| 33 | + }, |
| 34 | + "execution_count": 3, |
| 35 | + "metadata": {}, |
| 36 | + "output_type": "execute_result" |
| 37 | + } |
| 38 | + ], |
| 39 | + "source": [ |
| 40 | + "# Typical Add Function in Python\n", |
| 41 | + "def add(x, y):\n", |
| 42 | + " return x + y\n", |
| 43 | + "\n", |
| 44 | + "\n", |
| 45 | + "# Call the function\n", |
| 46 | + "add(3, 5)" |
| 47 | + ] |
| 48 | + }, |
| 49 | + { |
| 50 | + "cell_type": "code", |
| 51 | + "execution_count": 5, |
| 52 | + "metadata": {}, |
| 53 | + "outputs": [ |
| 54 | + { |
| 55 | + "name": "stdout", |
| 56 | + "output_type": "stream", |
| 57 | + "text": [ |
| 58 | + "5\n" |
| 59 | + ] |
| 60 | + } |
| 61 | + ], |
| 62 | + "source": [ |
| 63 | + "# The above function converted to Lambda Function\n", |
| 64 | + "add_lambda = lambda x, y: x + y\n", |
| 65 | + "print(add_lambda(2, 3))\n" |
| 66 | + ] |
| 67 | + }, |
| 68 | + { |
| 69 | + "cell_type": "markdown", |
| 70 | + "metadata": {}, |
| 71 | + "source": [ |
| 72 | + "<div class=\"alert alert-block alert-info\">\n", |
| 73 | + "<b>2. Map Function</b><br>\n", |
| 74 | + "It applies a function to all items of the input.\n", |
| 75 | + "</div>\n", |
| 76 | + "> map(function_to_apply, list_of_inputs)" |
| 77 | + ] |
| 78 | + }, |
| 79 | + { |
| 80 | + "cell_type": "code", |
| 81 | + "execution_count": 7, |
| 82 | + "metadata": {}, |
| 83 | + "outputs": [ |
| 84 | + { |
| 85 | + "data": { |
| 86 | + "text/plain": [ |
| 87 | + "[1, 4, 9, 16, 25]" |
| 88 | + ] |
| 89 | + }, |
| 90 | + "execution_count": 7, |
| 91 | + "metadata": {}, |
| 92 | + "output_type": "execute_result" |
| 93 | + } |
| 94 | + ], |
| 95 | + "source": [ |
| 96 | + "# Typical function without map\n", |
| 97 | + "# In this example the square of items needs to be calculated and should be appended in a separate list\n", |
| 98 | + "items = [1, 2, 3, 4, 5]\n", |
| 99 | + "squared_items = []\n", |
| 100 | + "for x in items:\n", |
| 101 | + " squared_items.append(x**2)\n", |
| 102 | + "\n", |
| 103 | + "squared_items" |
| 104 | + ] |
| 105 | + }, |
| 106 | + { |
| 107 | + "cell_type": "code", |
| 108 | + "execution_count": 9, |
| 109 | + "metadata": {}, |
| 110 | + "outputs": [ |
| 111 | + { |
| 112 | + "data": { |
| 113 | + "text/plain": [ |
| 114 | + "[1, 4, 9, 16, 25]" |
| 115 | + ] |
| 116 | + }, |
| 117 | + "execution_count": 9, |
| 118 | + "metadata": {}, |
| 119 | + "output_type": "execute_result" |
| 120 | + } |
| 121 | + ], |
| 122 | + "source": [ |
| 123 | + "# The above function using map\n", |
| 124 | + "squared_using_map = list(map(lambda x: x**2, items))\n", |
| 125 | + "squared_using_map" |
| 126 | + ] |
| 127 | + }, |
| 128 | + { |
| 129 | + "cell_type": "markdown", |
| 130 | + "metadata": {}, |
| 131 | + "source": [ |
| 132 | + "<div class=\"alert alert-block alert-info\">\n", |
| 133 | + "<b>3. Filter Function</b><br>\n", |
| 134 | + "Returns a list of elements for which the function returns True.\n", |
| 135 | + "</div>\n", |
| 136 | + "> filter(function_to_apply, list_of_inputs)" |
| 137 | + ] |
| 138 | + }, |
| 139 | + { |
| 140 | + "cell_type": "code", |
| 141 | + "execution_count": 10, |
| 142 | + "metadata": {}, |
| 143 | + "outputs": [ |
| 144 | + { |
| 145 | + "data": { |
| 146 | + "text/plain": [ |
| 147 | + "[2, 4]" |
| 148 | + ] |
| 149 | + }, |
| 150 | + "execution_count": 10, |
| 151 | + "metadata": {}, |
| 152 | + "output_type": "execute_result" |
| 153 | + } |
| 154 | + ], |
| 155 | + "source": [ |
| 156 | + "# Typical function without filter\n", |
| 157 | + "# In this example, we only need the values from the list whose remainder is 0 when divided by 2\n", |
| 158 | + "filtered_items = []\n", |
| 159 | + "for item in items:\n", |
| 160 | + " if item % 2 == 0:\n", |
| 161 | + " filtered_items.append(item)\n", |
| 162 | + " \n", |
| 163 | + "filtered_items" |
| 164 | + ] |
| 165 | + }, |
| 166 | + { |
| 167 | + "cell_type": "code", |
| 168 | + "execution_count": 14, |
| 169 | + "metadata": {}, |
| 170 | + "outputs": [ |
| 171 | + { |
| 172 | + "data": { |
| 173 | + "text/plain": [ |
| 174 | + "[2, 4]" |
| 175 | + ] |
| 176 | + }, |
| 177 | + "execution_count": 14, |
| 178 | + "metadata": {}, |
| 179 | + "output_type": "execute_result" |
| 180 | + } |
| 181 | + ], |
| 182 | + "source": [ |
| 183 | + "# The above example using filter\n", |
| 184 | + "filtered_using_filter = list(filter(lambda x: x % 2 == 0, items))\n", |
| 185 | + "filtered_using_filter" |
| 186 | + ] |
| 187 | + } |
| 188 | + ], |
| 189 | + "metadata": { |
| 190 | + "kernelspec": { |
| 191 | + "display_name": "Python 2", |
| 192 | + "language": "python", |
| 193 | + "name": "python2" |
| 194 | + }, |
| 195 | + "language_info": { |
| 196 | + "codemirror_mode": { |
| 197 | + "name": "ipython", |
| 198 | + "version": 2 |
| 199 | + }, |
| 200 | + "file_extension": ".py", |
| 201 | + "mimetype": "text/x-python", |
| 202 | + "name": "python", |
| 203 | + "nbconvert_exporter": "python", |
| 204 | + "pygments_lexer": "ipython2", |
| 205 | + "version": "2.7.6" |
| 206 | + } |
| 207 | + }, |
| 208 | + "nbformat": 4, |
| 209 | + "nbformat_minor": 0 |
| 210 | +} |
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