create jupyter notebook for map()

Author: rmccorm4

built_in_functions/map.ipynb

@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# map()\n",
+    "Map applies a function to all the items in an input_list. Here is the \n",
+    "blueprint:\n",
+    "\n",
+    "```python\n",
+    "map(function_to_apply, list_of_inputs)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the times we want to pass all the list elements to a function \n",
+    "one-by-one and then collect the output. For instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n",
+      "[1, 4, 9, 16, 25]\n"
+     ]
+    }
+   ],
+   "source": [
+    "items = [1, 2, 3, 4, 5]\n",
+    "squared_items = []\n",
+    "for i in items:\n",
+    "    squared_items.append(i**2)\n",
+    "    \n",
+    "print(items)\n",
+    "print(squared_items)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Map allows us to implement this in a much simpler and nicer way. Here you go:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n",
+      "[1, 4, 9, 16, 25]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def square(x):\n",
+    "    return x**2\n",
+    "\n",
+    "items = [1, 2, 3, 4, 5]\n",
+    "squared_items = list(map(square, items))\n",
+    "\n",
+    "print(items)\n",
+    "print(squared_items)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most of the times we use lambdas with `map`, so I did the same. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5]\n",
+      "[1, 4, 9, 16, 25]\n"
+     ]
+    }
+   ],
+   "source": [
+    "items = [1, 2, 3, 4, 5]\n",
+    "squared = list(map(lambda x: x**2, items))\n",
+    "\n",
+    "print(items)\n",
+    "print(squared)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of a list of inputs, we can even apply a list of functions!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 0]\n",
+      "[1, 2]\n",
+      "[4, 4]\n",
+      "[9, 6]\n",
+      "[16, 8]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def square(x):\n",
+    "\treturn (x*x)\n",
+    "def double(x):\n",
+    "\treturn (2*x)\n",
+    "\n",
+    "# For the numbers from 0 to 4, square it and double it\n",
+    "funcs = [square, double]\n",
+    "for i in range(5):\n",
+    "    # value will be a list containing two elements [i^2, 2*i], one for each function in funcs\n",
+    "    value = list(map(lambda x: x(i), funcs))\n",
+    "    print(value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}