Programming_cheat_sheet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Programming_cheat_sheet.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMBR7+nM+ebxPvriSFHcxsy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Intertangler/bioinformatics_stockholm/blob/master/Programming_cheat_sheet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eqHd2V_FFRXd"
      },
      "source": [
        "# basics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNiL7oagFASl"
      },
      "source": [
        "Getting information about functions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpYVtHv7FEX4"
      },
      "source": [
        "help(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z598MSBQDunX"
      },
      "source": [
        "Declaring variables of different types"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t17rCtlqEjOw",
        "outputId": "f2264d51-e770-411d-bcd7-fe47f453da1c"
      },
      "source": [
        "# checking what type it is:\n",
        "mystery_variable = 3/6\n",
        "type(mystery_variable)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Q5EfrE5Dhd8",
        "outputId": "2c3b681a-9118-4f8f-a093-7de49dc9de5c"
      },
      "source": [
        "# basic variable types\n",
        "my_integer1 = int(4.)\n",
        "my_integer2 = 3\n",
        "my_float1 = 3.\n",
        "my_float2 = float(3)\n",
        "my_string1 = \"\"\n",
        "my_string2 = \"hello\"\n",
        "my_string3 = str(3)\n",
        "my_string4 = 'asdfsdf'\n",
        "print(my_integer1,my_integer2)\n",
        "print(my_float1, my_float2)\n",
        "print(my_string1, my_string2, my_string3, my_string4)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4 3\n",
            "3.0 3.0\n",
            " hello 3 asdfsdf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3AiH-nB4EPgM",
        "outputId": "f7ba677f-e471-4837-8f56-a7e58832348d"
      },
      "source": [
        "# lists\n",
        "my_list1 = []\n",
        "my_list2 = [1,2,3,4]\n",
        "my_list3 = [\"a\",\"b\", \"c\"]\n",
        "my_list4 = list([1,2,3])\n",
        "my_list5 = list(\"abc\")\n",
        "print(my_list1, my_list2, my_list3, my_list4, my_list5)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[] [1, 2, 3, 4] ['a', 'b', 'c'] [1, 2, 3] ['a', 'b', 'c']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3OoPoKVUBkIb",
        "outputId": "05399ca4-600d-400b-c273-02d86d84b8a4"
      },
      "source": [
        "# list of lists\n",
        "my_list1 = [ [1,2,3], [6,7,8]  ]\n",
        "my_list1[0]"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUqJwJ9-Eaww",
        "outputId": "05b5fd73-4dab-4af5-b415-9b27597c8bff"
      },
      "source": [
        "# numpy arrays\n",
        "import numpy as np\n",
        "my_arr1 = np.array([1,2,3])\n",
        "my_arr2 = np.array([\"a\",\"b\",\"c\"])\n",
        "my_arr3 = np.zeros(5)\n",
        "my_arr4 = np.ones(5)\n",
        "print(my_arr1, my_arr2, my_arr3, my_arr4)\n"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 2 3] ['a' 'b' 'c'] [0. 0. 0. 0. 0.] [1. 1. 1. 1. 1.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gGBChS8XFmZv",
        "outputId": "d0f3291c-1a68-4cad-f9de-93de2a99609c"
      },
      "source": [
        "# 2D numpy arrays\n",
        "my_mat1 = np.array([[1,2,3],[6,7,8]])\n",
        "my_mat2 = np.zeros((3,5))\n",
        "my_mat3 = np.ones((5,4))\n",
        "print(my_mat1)\n",
        "print(my_mat2)\n",
        "print(my_mat3)\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [6 7 8]]\n",
            "[[0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]]\n",
            "[[1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "O9ziiskmKn4X",
        "outputId": "96d77b1d-3929-40af-9810-fac3e4c3c4ad"
      },
      "source": [
        "# visualizing matrices\n",
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(my_mat1, cmap=\"Greys\")"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbf3575e7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQMsH4doGHqP"
      },
      "source": [
        "# conditionals (if-statements)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCbtY2gfGCqV",
        "outputId": "3ce58167-7a09-46db-f82d-13eead2d3564"
      },
      "source": [
        "# simple case\n",
        "a = 1\n",
        "if a == 1:\n",
        "  print(\"ok\")\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ok\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UeQb3rBHDx3P",
        "outputId": "86ecc97f-284d-4166-96b2-93b7dd695093"
      },
      "source": [
        "# using the not-equals operator\n",
        "if a != 10:\n",
        "  print(\"a is not equal to 10\")"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a is not equal to 10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LBd5LzcQGQnD"
      },
      "source": [
        "# simple case but with else clause"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdzpfREsGYmT",
        "outputId": "7cb1d6ca-0708-4654-d3e5-9485c96273f4"
      },
      "source": [
        "a = 1\n",
        "if a == 3:\n",
        "  print(b)\n",
        "else:\n",
        "  print(\"hello\")  "
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hello\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__L3u5OOH4Kn",
        "outputId": "f6614f59-631b-44f3-83f6-5155562e0397"
      },
      "source": [
        "# multiple elif clauses\n",
        "if a < 0:\n",
        "  print(\"a is negative\")\n",
        "elif a == 0:\n",
        "  print(\"a is zero\")\n",
        "elif a < 5:\n",
        "  print(\"a is between 2 and 5\")\n",
        "else:\n",
        "  print(\"a is something else\")"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a is between 2 and 5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8YAAPrNIPH1"
      },
      "source": [
        "# using <or> and <and>\n",
        "if a == 0 or a == 3:\n",
        "  print(\"a is either 0 or 3\")\n",
        "elif a == 1 and type(a) == int:\n",
        "  print(\"a is the integer 1\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnzgUfnyIpwk"
      },
      "source": [
        "# for-loops"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6B42IC6JIk00"
      },
      "source": [
        "# loop through elements in a list, string, or numpy array\n",
        "my_string = \"abcd\"\n",
        "for i in my_string:\n",
        "  print(i)\n",
        "\n",
        "my_list = [5,4,3]\n",
        "for i in my_list:\n",
        "  print(i)\n",
        "\n",
        "my_array = np.array([0,8,7])\n",
        "for i in my_array:\n",
        "  print(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIEX4tY_JeH2",
        "outputId": "8243d37b-4cc9-4eca-bc05-3c40b6580b5e"
      },
      "source": [
        "# loop through a range\n",
        "for i in range(0,4):\n",
        "  print(i)\n",
        "\n",
        "for i in [0,1,2,3,4]:\n",
        "  print(i)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaElsSLuJrKy"
      },
      "source": [
        "## * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * ##\n",
        "# loop through a range and use it to fetch values according to their positions in a sequential datatype\n",
        "my_string = \"abcd\"\n",
        "for i in range(0, len(my_string)):\n",
        "  print(my_string[i], i)\n",
        "\n",
        "my_list = [\"a\", \"b\", \"c\"]\n",
        "for i in range(0, len(my_list)):\n",
        "  print(my_list[i], i)\n",
        "\n",
        "my_arr = np.array([5, 8, 8])\n",
        "for i in range(0, len(my_arr)):\n",
        "  print(my_arr[i], i)  \n",
        "\n",
        "\n",
        "## * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * ##"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "q8m97taoKJJ4",
        "outputId": "a58732f2-b551-4d5a-aefd-7e5fde7de957"
      },
      "source": [
        "# loop through x values to generate y values and plot\n",
        "# compute square of x\n",
        "import matplotlib.pyplot as plt\n",
        "x = [0,1,2,3,4,5]\n",
        "y = [] \n",
        "for i in x:\n",
        "  y = y + [i**2]\n",
        "plt.plot(x,y)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fbf35b99e90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "rwJRihyLG4L4",
        "outputId": "7a51013c-5829-43bd-c204-0140c7744415"
      },
      "source": [
        "# another plotting example\n",
        "import math\n",
        "import numpy as np\n",
        "xs = np.zeros((100))\n",
        "ys = np.zeros((100))\n",
        "for i in range(0,100):\n",
        "  xs[i] = i\n",
        "  ys[i] = math.sqrt(i)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(xs,ys)  "
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fbf35670690>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INEwjLiXLLJf",
        "outputId": "e3cd6dd6-c0f3-4015-befc-2ce3aadc8999"
      },
      "source": [
        "# use a for-loop to build a string\n",
        "my_string = \"\"\n",
        "for i in range(0,50):\n",
        "  my_string = my_string + \"a\"\n",
        "print(my_string)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1Sd7FHrLiMb",
        "outputId": "2d724875-9363-4f5c-d782-258018f9a460"
      },
      "source": [
        "## * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * ##\n",
        "\n",
        "# use nested for-loops to update values in an empty matrix\n",
        "my_mat = np.zeros((5,5))\n",
        "print(my_mat)\n",
        "for i in range(0, len(my_mat)):\n",
        "  for j in range(0, len(my_mat)):\n",
        "    my_mat[i,j] = 4\n",
        "    # print(my_mat)\n",
        "print(my_mat)\n",
        "\n",
        "## * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * ##"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0.]]\n",
            "[[4. 4. 4. 4. 4.]\n",
            " [4. 4. 4. 4. 4.]\n",
            " [4. 4. 4. 4. 4.]\n",
            " [4. 4. 4. 4. 4.]\n",
            " [4. 4. 4. 4. 4.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTz8zFT0CkEn",
        "outputId": "89ad1b20-c015-455d-b06b-a4ed4efb0d2e"
      },
      "source": [
        "# using an array of zeros and a loop to add new data to array\n",
        "import random\n",
        "my_array = np.zeros(5)\n",
        "print(my_array)\n",
        "for i in range(0, len(my_array)):\n",
        "  my_array[i] = random.random()\n",
        "print(my_array)  "
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0. 0. 0. 0. 0.]\n",
            "[0.10923687 0.01895482 0.72485858 0.39289945 0.18156714]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1bFF7TltL2G7",
        "outputId": "850f1198-b110-4282-9442-04332f778b6d"
      },
      "source": [
        "## * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * ##\n",
        "\n",
        "# combine a for-loop with a conditional statement to search for things\n",
        "my_list = [1,3,5,7,4,3,7,4,2,5,7]\n",
        "for i in range(0, len(my_list)):\n",
        "  if my_list[i] == 7:\n",
        "    print(\"we found a 7 at position: \", i)\n",
        "\n",
        "## * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * #### * ##"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "we found a 7 at position:  3\n",
            "we found a 7 at position:  6\n",
            "we found a 7 at position:  10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCHK83S9MZF4"
      },
      "source": [
        "# indexing in sequential datatypes (strings, numpy arrays, and lists)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5G2AcEuLMIZi",
        "outputId": "ef6b0ebc-09f5-4b4b-8769-59da7e0ac21b"
      },
      "source": [
        "# fetch the value stored at a particular index\n",
        "my_list = [0,0,-1,0,0]\n",
        "print(my_list[2])\n",
        "\n",
        "my_array = np.array([0,0,-1,0,0])\n",
        "print(my_array[2])\n",
        "\n",
        "my_string = \"abcdef\"\n",
        "print(my_string[2])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-1\n",
            "-1\n",
            "c\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6vsK5U3ZMw-N",
        "outputId": "9707a03b-50f3-44de-f038-2afc59a2033f"
      },
      "source": [
        "# fetch a range of values by supplying the start and end indices\n",
        "my_list = [0,0,-1,0,0]\n",
        "print(my_list[2:4])\n",
        "\n",
        "my_array = np.array([0,0,-1,0,0])\n",
        "print(my_array[1:2])\n",
        "\n",
        "my_string = \"abcdef\"\n",
        "print(my_string[3:5])"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1, 0]\n",
            "[0]\n",
            "de\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1S3TktNNE3_",
        "outputId": "9dbe33c5-28aa-4675-92fe-b353e1dc8863"
      },
      "source": [
        "# fetch a range of values by just supplying the stopping point (beginning assumed to be 0)\n",
        "my_list = [0,0,-1,0,0]\n",
        "print(my_list[:4])\n",
        "print(my_list[2:])\n",
        "print(my_list[:])\n",
        "\n",
        "my_array = np.array([0,0,-1,0,0])\n",
        "print(my_array[:2])\n",
        "\n",
        "my_string = \"abcdef\"\n",
        "print(my_string[:5])"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 0, -1, 0]\n",
            "[-1, 0, 0]\n",
            "[0, 0, -1, 0, 0]\n",
            "[0 0]\n",
            "abcde\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mJcmsdlNLVk",
        "outputId": "717aae16-7464-4c1c-d27d-6a2bfadf25f7"
      },
      "source": [
        "# index from the end or get the last value using a minus sign\n",
        "my_list = [0,0,-1,0,0]\n",
        "print(my_list[-1])\n",
        "\n",
        "my_array = np.array([0,0,-1,0,0])\n",
        "print(my_array[-3])\n",
        "\n",
        "my_string = \"abcdef\"\n",
        "print(my_string[0:-2])"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "-1\n",
            "abcd\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Tdbxy41NWGD",
        "outputId": "fc939e03-703c-4c24-9a97-9255d5b29dac"
      },
      "source": [
        "# reverse the order\n",
        "my_list = [0,0,-1,0,0,9]\n",
        "print(my_list[::-1])\n",
        "\n",
        "my_array = np.array([0,0,-1,0,0,9])\n",
        "print(my_array[::-1])\n",
        "\n",
        "my_string = \"abcdef\"\n",
        "print(my_string[::-1])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9, 0, 0, -1, 0, 0]\n",
            "[ 9  0  0 -1  0  0]\n",
            "fedcba\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyMb8vSDPB1m"
      },
      "source": [
        "# indexing with higher dimensional datatypes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aegERjjLPucW",
        "outputId": "56de6968-af75-4362-9e84-7e713149a458"
      },
      "source": [
        "my_mat = np.array([[15,26,34,35],[10,12,15,28], [99,88,77,100]])\n",
        "print(my_mat)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 15  26  34  35]\n",
            " [ 10  12  15  28]\n",
            " [ 99  88  77 100]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RqZ-GNFgPgQq",
        "outputId": "1d7e2e7a-3245-4f33-b465-960346584534"
      },
      "source": [
        "# fetching the item stored in row 1 column 3\n",
        "my_mat = np.array([[15,26,34,35],[10,12,15,28], [99,88,77,100]])\n",
        "my_mat[1,3]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqupMEP_NkQh",
        "outputId": "50f174a1-3c64-40bb-d29f-6632a1d9626b"
      },
      "source": [
        "# fetch all items in row 1\n",
        "# numparray[r,c] Roman Centurion\n",
        "my_mat = np.array([[15,26,34,35],[10,12,15,28], [99,88,77,100]])\n",
        "my_mat[1,:]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10, 12, 15, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rbv8ZqruPbEi",
        "outputId": "04207f5a-b48f-41e5-acdc-f5b082561bd6"
      },
      "source": [
        "# fetch all items in column 1\n",
        "my_mat = np.array([[15,26,34,35],[10,12,15,28], [99,88,77,100]])\n",
        "my_mat[:,1]"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([26, 12, 88])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DnVvHMUbPyFV",
        "outputId": "dd2a9e3e-29c2-4b76-d582-d6a714bd2ed1"
      },
      "source": [
        "# fetching an item from a list within a list\n",
        "my_list = [[11,44,66],[77,55,33]]\n",
        "my_sub_list = my_list[1]\n",
        "my_element = my_sub_list[2]\n",
        "print(my_list[1][2], my_element)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "33 33\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eBk2rVPQAd5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}