{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "following-convergence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 5, 12]\n",
      "[6, 7]\n"
     ]
    }
   ],
   "source": [
    "# Merging Lists\n",
    "\n",
    "# Lists are created using square brackets.\n",
    "L1 = [1, 2, 5, 12]\n",
    "L2 = [6, 7]\n",
    "print(L1)\n",
    "print(L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "russian-champion",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 5, 12, 6, 7]\n"
     ]
    }
   ],
   "source": [
    "# You can merge the two lists using the addition notation.\n",
    "print(L1 + L2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "italic-allergy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  5 12  6  7]\n"
     ]
    }
   ],
   "source": [
    "# If you're using arrays made from the 'NumPy' module, merging, or concatenating\n",
    "# two arrays is a little different.\n",
    "import numpy as np \n",
    "A1 = np.array(L1)\n",
    "A2 = np.array(L2)\n",
    "A3 = np.concatenate((A1, A2))\n",
    "print(A3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "living-venezuela",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [-5 -6 -7 -8]]\n"
     ]
    }
   ],
   "source": [
    "# You can also build matrices from arrays (assuming the arrays have the same length)\n",
    "A1 = np.array([1, 2, 3, 4])\n",
    "A2 = np.array([-5, -6, -7, -8])\n",
    "M1 = np.matrix([A1, A2])\n",
    "print(M1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "parliamentary-imagination",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1 -5]\n",
      " [ 2 -6]\n",
      " [ 3 -7]\n",
      " [ 4 -8]]\n"
     ]
    }
   ],
   "source": [
    "# The matrix can then be transposed:\n",
    "M2 = np.transpose(M1)\n",
    "print(M2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bibliographic-conference",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 1.0,\n",
       " 5.656854249492381,\n",
       " 15.588457268119896,\n",
       " 32.0,\n",
       " 55.90169943749474,\n",
       " 88.18163074019441,\n",
       " 129.64181424216494,\n",
       " 181.01933598375618]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here's a way that a list can be buily using a loop.\n",
    "\n",
    "# First, define an empty list.\n",
    "listA = []\n",
    "\n",
    "# Next add to the list in successive iterations of a loop\n",
    "for i in range(9):\n",
    "    listA = listA + [i**2.5]\n",
    "\n",
    "# Now display the contents of the list.    \n",
    "listA"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}