DataMining_week07.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.1"
    },
    "colab": {
      "name": "DataMining-week07.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MLRG-CEFET-RJ/ml-class/blob/master/DataMining_week07.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxd7RH5sXKPz",
        "colab_type": "text"
      },
      "source": [
        "# Origins"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXV-3r8EXKP1",
        "colab_type": "text"
      },
      "source": [
        "![texto alternativo](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e2/Guido-portrait-2014-drc.jpg/1280px-Guido-portrait-2014-drc.jpg)\n",
        "\n",
        "Guido van Rossum is a Dutch programmer who created the Python programming language.\n",
        "\n",
        "\"Over six years ago, in December 1989, I was looking for a 'hobby' programming project that would keep me occupied during the week around Christmas. My office ... would be closed, but I had a home computer, and not much else on my hands. I decided to write an interpreter for the new scripting language I had been thinking about lately: a descendant of ABC that would appeal to Unix/C hackers. I chose Python as a working title for the project, being in a slightly irreverent mood (and a big fan of Monty Python's Flying Circus).\"\n",
        "<cite>Guido van Rossum</cite>\n",
        "\n",
        "![texto alternativo](https://i.pinimg.com/564x/37/d8/81/37d8817293fe78aa82e6fadf32c8b7a0.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0r8p1kUB6pgu",
        "colab_type": "text"
      },
      "source": [
        "# Evolution in popularity\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArOZnznx7gt3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "outputId": "96ab4595-38b6-4ecf-cc3f-67229568db48"
      },
      "source": [
        "from IPython.display import HTML\n",
        "HTML('<iframe width=\"926\" height=\"521\" src=\"https://www.youtube.com/embed/pKPaHH7hnv8\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<iframe width=\"926\" height=\"521\" src=\"https://www.youtube.com/embed/pKPaHH7hnv8\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O3KMkC6CyIKo",
        "colab_type": "text"
      },
      "source": [
        "# The print command"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuNdTbUfXKP_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "2e8b0ecc-d2a2-4ac6-b48a-9661ea8c3319"
      },
      "source": [
        "# Example' source: https://learnpythonthehardway.org/book/ex5.html\n",
        "\n",
        "my_name = 'Zed A. Shaw'\n",
        "my_age = 35 # not a lie\n",
        "my_height = 74 # inches\n",
        "my_weight = 180 # lbs\n",
        "my_eyes = 'Blue'\n",
        "my_teeth = 'White'\n",
        "my_hair = 'Brown'\n",
        "\n",
        "print(\"Let us talk!\")\n",
        "print(\"Let's talk about %s.\" % my_name)\n",
        "print(\"He's %d inches tall.\" % my_height)\n",
        "print(\"He's %d pounds heavy.\" % my_weight)\n",
        "print(\"Actually that's not too heavy.\")\n",
        "print(\"He's got %s eyes and %s hair.\" % (my_eyes, my_hair))\n",
        "print(\"His teeth are usually %s depending on the coffee.\" % my_teeth)\n",
        "\n",
        "# this line is tricky, try to get it exactly right\n",
        "print(\"If I add %d, %d, and %d I get %d.\" % (\n",
        "    my_age, my_height, my_weight, my_age + my_height + my_weight))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Let us talk!\n",
            "Let's talk about Zed A. Shaw.\n",
            "He's 74 inches tall.\n",
            "He's 180 pounds heavy.\n",
            "Actually that's not too heavy.\n",
            "He's got Blue eyes and Brown hair.\n",
            "His teeth are usually White depending on the coffee.\n",
            "If I add 35, 74, and 180 I get 289.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wKWYg0kDXKQF",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# Character strings \n",
        "\n",
        "Character string in Python are enclosed in single brackets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trwJgqz5XKQP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d017fe9-572c-4afb-d5a4-59b8cc8d440d"
      },
      "source": [
        "some_string = 'Py'\n",
        "print(some_string)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALGz9hcDypaI",
        "colab_type": "text"
      },
      "source": [
        "Strings can be *concatenated* with the **+** operator."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOZcqWh7ynVt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "580c2601-919b-452f-8b1c-6e6b8773badf"
      },
      "source": [
        "some_other_string = some_string + 'thon'\n",
        "print(some_other_string)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Python\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V7iwdPs5y-NV",
        "colab_type": "text"
      },
      "source": [
        "The function (command) `len` can be used to inspect how many characters a string has."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeXYWqaIXKQU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "21b5410d-7ad1-4dd2-d8c7-26f896ad32b7"
      },
      "source": [
        "word = 'Python'\n",
        "print(len(word))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOIxFeJPXKQl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d85ecfd0-f9d2-4585-fb84-495e975bf8c3"
      },
      "source": [
        "# CSV\n",
        "line = 'Data Mining,Saint Martin'\n",
        "components = line.split(\",\")\n",
        "print(components)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Data Mining', 'Saint Martin']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GhYfac0XXKQq",
        "colab_type": "text"
      },
      "source": [
        "## String can be indexed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2OtEFlgXKQq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7dc438c6-018f-4ed7-b502-07cac6a77c26"
      },
      "source": [
        "word = 'Python Programming Language'\n",
        "word[0]  # character in position 0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'P'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prO9tMAVXKQx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4c6d3a5f-47ce-4d4f-e2f1-dd07a8199dc1"
      },
      "source": [
        "word[5]  # character in position 5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rh-6J3zdXKQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4f76e99d-dbf9-49e2-d938-30c2f9605718"
      },
      "source": [
        "word[-1]  # last character"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'e'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ku_EAnZDXKQ6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "549f5e58-a63b-49b3-cfc6-d25c3af3ccf8"
      },
      "source": [
        "word[-2]  # second-last character"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'g'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4XwDClwgXKQ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "05a0b096-59f5-442f-ce9d-260b4a05b0a5"
      },
      "source": [
        "word[-6]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U63GseRdXKRD",
        "colab_type": "text"
      },
      "source": [
        "## Strings can be sliced"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B6-P3gFd8zka",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "053ff54e-9e11-4fd1-d33c-c71331644f92"
      },
      "source": [
        "word"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Python'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjxaCyZ1XKRE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ef2db3ef-7aa4-4b03-e194-a7784a6d1785"
      },
      "source": [
        "word[0:2]  # characters from position 0 (included) to 2 (excluded)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Py'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZDWn3T7XKRN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f3beb77d-05cc-46a8-fdb3-5bbda98ac2d9"
      },
      "source": [
        "word[2:5]  # characters from position 2 (included) to 5 (excluded)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'tho'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FuEyhPltXKRT",
        "colab_type": "text"
      },
      "source": [
        "Slicing has default values "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-u0NGCv9DGK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5b802f57-20dc-494c-8941-bfafc6788641"
      },
      "source": [
        "word"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Python'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eB-BoSuSXKRU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f54bf65e-6546-4bc5-8040-0d3ef5571d38"
      },
      "source": [
        "word[:2] + word[2:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Python'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tp4X7KK_XKRX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3b6d1215-4dc6-4990-daab-c403abfd1e2a"
      },
      "source": [
        "word[:4] + word[4:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Python Programming Language'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WsEI4vvXKRb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f9618753-61aa-4e37-de03-e7bc65f94f1e"
      },
      "source": [
        "word[:2]   # character from the beginning to position 2 (excluded)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Py'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gmd0e7SiXKRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2bcf1806-de8a-49b7-9341-a1b3aebd08c2"
      },
      "source": [
        "word[4:]   # characters from position 4 (included) to the end"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'on Programming Language'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xoouhuv8XKRj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "65f5627a-969d-4c85-9a02-31c5571a420f"
      },
      "source": [
        "word[-2:]  # characters from the second-last (included) to the end"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'ge'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6OctD9txXKRo",
        "colab_type": "raw"
      },
      "source": [
        "One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n. \n",
        "\n",
        "---\n",
        "\n",
        "```\n",
        "+---+---+---+---+---+---+\n",
        " | P | y | t | h | o | n |\n",
        " +---+---+---+---+---+---+\n",
        " 0   1   2   3   4   5   6\n",
        "-6  -5  -4  -3  -2  -1\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lL72Ke2sXKRo",
        "colab_type": "text"
      },
      "source": [
        "Strings in Python are immutable "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SeDmv-4QXKRp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "d2904c58-0c9d-4f5f-c8ca-aab025ff63c8"
      },
      "source": [
        "word[0] = 'J' # generates an error"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-c6a45f350659>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'J'\u001b[0m \u001b[0;31m# generates an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAlIMhqZXKRt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "7124461c-d956-44d3-8094-a3b95e5a92b0"
      },
      "source": [
        "word[2:] = 'py' # generates an error"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-6488bbf78f5a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'py'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v2HjwR08XKR2",
        "colab_type": "text"
      },
      "source": [
        "## Formating strings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qBaUWYeKXKR2",
        "colab_type": "text"
      },
      "source": [
        "![texto alternativo](http://www.python-course.eu/images/format_method_positional_parameters.png)\n",
        "Source: http://www.python-course.eu/images/format_method_positional_parameters.png"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SR8LFGUtXKR3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "798ce792-0a4c-4a25-eed8-d91e7aa54867"
      },
      "source": [
        "\"Art: {a:1d},  Price: {p:8.2f}\".format(a=453, p=59.058)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Art: 453,  Price:    59.06'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ovoooGMTXKR7",
        "colab_type": "text"
      },
      "source": [
        "# Python operators (aritmetic, relacional, logic)\n",
        "\n",
        "Refer to https://www.w3schools.com/python/python_operators.asp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0KkSO48XKR8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8cc0430c-f149-4782-a390-89071443c504"
      },
      "source": [
        "tax = 12.5 / 100\n",
        "price = 100.50\n",
        "print(tax)\n",
        "print(price * tax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.125\n",
            "12.5625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnJD4N52Krhp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a72efb1d-7f90-4879-b444-bcc320ec1dfe"
      },
      "source": [
        "44 - 6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5csvLfRdXKSA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d7dc7569-d3bc-433c-b5da-67a64bad33c2"
      },
      "source": [
        "44 % 6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ITGFAxoXKSE",
        "colab_type": "code",
        "colab": {},
        "outputId": "a0affeeb-8a8a-461e-c80e-634a574df094"
      },
      "source": [
        "10  / 3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.3333333333333335"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tY-bRnZjXKSK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3295408b-e960-4ba3-93e2-b08b2aac7d80"
      },
      "source": [
        "10 // 3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9OZH6uOXKSO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6c4b87fc-e841-4305-957f-cdaeccc3b937"
      },
      "source": [
        "10.3 // 3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLWjZ-GgXKSS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f5a17424-640e-43e5-8455-9302b8e2738c"
      },
      "source": [
        "4 ** 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaTGwTleXKSW",
        "colab_type": "text"
      },
      "source": [
        "# Some math functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGSG8YVWXKSX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "outputId": "49d5b26a-6e79-4dfe-8d5a-ddd765512975"
      },
      "source": [
        "import math\n",
        "valor = 10.5\n",
        "print('abs(%d) = %8.2f' % (valor, abs(valor)))\n",
        "print('ceil(%d) = %d' % (valor, math.ceil(valor)))\n",
        "print('cos(%d) = %.2f' % (valor, math.cos(valor)))\n",
        "print('floor(%d) = %.2f' % (valor, math.floor(valor)))\n",
        "print('log(%d) = %.2f' % (valor, math.log(valor)))\n",
        "print('log10(%d) = %.2f' % (valor, math.log10(valor)))\n",
        "print('round(%d) = %.2f' % (valor, round(valor)))\n",
        "print('sin(%d) = %.2f' % (valor, math.sin(valor)))\n",
        "print('sqrt(%d) = %.2f' % (valor, math.sqrt(valor)))\n",
        "\n",
        "valor1, valor2 = 12, 23\n",
        "print('max(%.2f, %.2f) = %.2f' % (valor1, valor2, max(valor1, valor2)))\n",
        "print('min(%.2f, %.2f) = %.2f' % (valor1, valor2, min(valor1, valor2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "abs(10) \t=    10.50\n",
            "ceil(10) \t= 11\n",
            "cos(10) \t= -0.48\n",
            "floor(10) \t= 10.00\n",
            "log(10) \t= 2.35\n",
            "log10(10) \t= 1.02\n",
            "round(10) \t= 10.00\n",
            "sin(10) \t= -0.88\n",
            "sqrt(10) \t= 3.24\n",
            "max(12.00, 23.00) = 23.00\n",
            "min(12.00, 23.00) = 12.00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y7dF6ECuXKSo",
        "colab_type": "text"
      },
      "source": [
        "# Control structures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVe1j08PXKSp",
        "colab_type": "text"
      },
      "source": [
        "## Loops (repetition) "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q59cc_goXKSq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "b21c0f86-b113-4bc1-edd8-25f0dd572f76"
      },
      "source": [
        "campi_cefet = ['Maracanã', 'Maria da Graça', 'Nova Iguaçu', 'Seattle']\n",
        "\n",
        "for campi in campi_cefet:\n",
        "    print(campi, len(campi))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Maracanã 8\n",
            "Maria da Graça 14\n",
            "Nova Iguaçu 11\n",
            "Seattle 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENVNkmKtXKSt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "outputId": "dccf1e23-420b-43e5-fd45-834cd8b164aa"
      },
      "source": [
        "for x in range(2, 10):\n",
        "    print(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Z3BkdD28XKSz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "37ae4139-3ecc-479b-80c4-971c755da966"
      },
      "source": [
        "for x in range(5, 0, -1):\n",
        "    print(x)\n",
        "print(\"fim!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "4\n",
            "3\n",
            "2\n",
            "1\n",
            "fim!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "J24RnNsZXKS5",
        "colab_type": "code",
        "colab": {},
        "outputId": "0b891c9d-80b3-46cd-c605-b9dd1e6fe1e1"
      },
      "source": [
        "# imprime os fatores (quando existem) dos números naturais de 1 até 9\n",
        "# fonte do exemplo: https://docs.python.org/3/tutorial/controlflow.html\n",
        "for n in range(2, 10):\n",
        "     for x in range(2, n):\n",
        "         if n % x == 0:\n",
        "             print(n, 'é igual a', x, '*', n//x)\n",
        "             break\n",
        "     else:\n",
        "         # loop chegou ao fim sem a execução do bloco subordinado\n",
        "         print(n, 'é um número primo')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2 é um número primo\n",
            "3 é um número primo\n",
            "4 é igual a 2 * 2\n",
            "5 é um número primo\n",
            "6 é igual a 2 * 3\n",
            "7 é um número primo\n",
            "8 é igual a 2 * 4\n",
            "9 é igual a 3 * 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kY1Id8NoXKTB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "2d3287b1-3f88-46a4-a01a-8becdc7400ac"
      },
      "source": [
        "for letter in 'Python':     # 1st example\n",
        "   if letter == 'h':\n",
        "      continue\n",
        "   print('Current letter :', letter)\n",
        "\n",
        "var = 10                    # 2nd example\n",
        "while var > 0:              \n",
        "   var = var -1\n",
        "   if var == 5:\n",
        "      continue\n",
        "   print('Current value of variable :', var)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Current letter : P\n",
            "Current letter : y\n",
            "Current letter : t\n",
            "Current letter : o\n",
            "Current letter : n\n",
            "Current value of variable : 9\n",
            "Current value of variable : 8\n",
            "Current value of variable : 7\n",
            "Current value of variable : 6\n",
            "Current value of variable : 4\n",
            "Current value of variable : 3\n",
            "Current value of variable : 2\n",
            "Current value of variable : 1\n",
            "Current value of variable : 0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KD2ZzmEXKTG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        },
        "outputId": "c6b1638d-ebee-4176-fae6-4ba4701269f7"
      },
      "source": [
        "number = 1\n",
        "while number < 200:\n",
        "    print(number) \n",
        "    number = number * 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "2\n",
            "4\n",
            "8\n",
            "16\n",
            "32\n",
            "64\n",
            "128\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DvRfAqjXKTK",
        "colab_type": "text"
      },
      "source": [
        "## Condicional branchs "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABlygBXiXKTL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "the_world_is_flat = False\n",
        "if the_world_is_flat:\n",
        "  print(\"Be careful not to fall off!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVXqYF9ZXKTR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9c5e5228-5401-4407-d8e6-6a4a33a2f4bc"
      },
      "source": [
        "id = '0920783WEB'\n",
        "if id[0] == '0' or id[0] == '1':\n",
        "    year = 2000 + int(id[0:2])\n",
        "elif id[0] == '9':\n",
        "    year = 1900 + int(id[0:2])\n",
        "print(year)\n",
        "\n",
        "#else if"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2009\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcuwQNDkXKTV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "167d23ea-3d9b-414c-86fb-553b5dd25503"
      },
      "source": [
        "# an if statement can be followed by zero or more elif statements, and by zero or one else statement\n",
        "qtd = 3\n",
        "r = 2\n",
        "if r < qtd - 2:\n",
        "    criticity = 'BLUE'\n",
        "elif r == qtd - 2:\n",
        "    criticity = 'ORANGE'\n",
        "elif r == qtd - 1:\n",
        "    criticity = 'RED'\n",
        "else:\n",
        "    criticity = 'BLACK'\n",
        "print('Criticity: %s' % criticity)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Criticity: RED\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SKfQWuG7XKUN",
        "colab_type": "text"
      },
      "source": [
        "# Data Structures\n",
        "\n",
        "Python is full of data types for manipulating collections of items: lists, tuples, sets, dictionaries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "La3azoKAXKUO",
        "colab_type": "text"
      },
      "source": [
        "## Lists"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzLSiMFZXKUP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1f9d9541-81e0-4b35-8035-21409475ac09"
      },
      "source": [
        "cities = ['Rio de Janeiro', 'São Paulo', 'Porto Alegre']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Rio de Janeiro', 'São Paulo', 'Porto Alegre']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGtqMFlfzLoB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fbef376d-b0ad-45ea-e1d8-7f4449c4f036"
      },
      "source": [
        "cities"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Rio de Janeiro', 'São Paulo', 'Porto Alegre']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygDEypFyXKUW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "perfect_squares = [1, 2, 4, 9, 16, 25]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1E-s8OLkzPss",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0028c3f9-456c-4950-ebb2-cad54a1f95e0"
      },
      "source": [
        "perfect_squares"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 4, 9, 16, 25]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6WE5iO9XKUa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9d0c8e4c-0b82-4131-fbfc-9500307b966e"
      },
      "source": [
        "type(perfect_squares)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Du8B0gbXKUe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e9081498-9dd1-470f-8abe-19fdc6fb59bc"
      },
      "source": [
        "perfect_squares[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7d4LmAuSXKUi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "65a0aaa8-2c0c-4913-9cff-aa8c41ed0410"
      },
      "source": [
        "perfect_squares[-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVnbVGoBXKUk",
        "colab_type": "code",
        "colab": {},
        "outputId": "7bb99ad9-33b5-4793-ba4b-99170f180e27"
      },
      "source": [
        "periodos[-3:] # fatiamento produz uma lista nova"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[9, 16, 25]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kQXyL5FXKUn",
        "colab_type": "code",
        "colab": {},
        "outputId": "c260814f-f6b7-4ab8-eb11-01005e973201"
      },
      "source": [
        "perfect_squares[:]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 4, 9, 16, 25]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOuvqoziXKUp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "number_of_days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxZarW2nXKUs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2d00f8de-48c0-4ffd-b77e-6930806da180"
      },
      "source": [
        "number_of_days"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaW_coLQXKUw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ca8afe45-4381-4382-a4be-f46255d040f7"
      },
      "source": [
        "number_of_days"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpkis3x5XKU1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "duration_months_first_semester = number_of_days[0:7]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXJspem4XKU3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f28505e3-02ee-4a11-e85a-72f0c9e5d5c2"
      },
      "source": [
        "duration_months_first_semester"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[31, 28, 31, 30, 31, 30, 31]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7oS0-6w7XKU-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "duration_months_second_semester = number_of_days[6:13]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QI5lCfD4XKVC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "af52289f-bad0-43f6-a1ea-4ff7d7181c1d"
      },
      "source": [
        "duration_months_second_semester"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[31, 31, 30, 31, 30, 31]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nD2L3CrfXKWC",
        "colab_type": "text"
      },
      "source": [
        "### List comprehensions\n",
        "\n",
        "Mechanism that provides a concise way to create lists. Syntactically, it consists of a pair of square brackets containing an expression followed by a for clause, which can be followed by zero or more for and if clauses."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omVNPGm3XKWD",
        "colab_type": "code",
        "colab": {},
        "outputId": "def82b60-d7e6-4eb6-e2a3-3e8c8b4c3edc"
      },
      "source": [
        "#fonte: https://docs.python.org/3/tutorial/datastructures.html\n",
        "\n",
        "# filter the list to exclude negative numbers\n",
        "[x for x in vec if x >= 0]\n",
        "\n",
        "# apply a function to all the elements\n",
        "[abs(x) for x in vec]\n",
        "\n",
        "# call a method on each element\n",
        "freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']\n",
        "[weapon.strip() for weapon in freshfruit]\n",
        "\n",
        "# create a list of 2-tuples like (number, square)\n",
        "[(x, x**2) for x in range(6)]\n",
        "\n",
        "# flatten a list using a listcomp with two 'for'\n",
        "vec = [[1,2,3], [4,5,6], [7,8,9]]\n",
        "[num for elem in vec for num in elem]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxqxIt9QGRHj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2bb161a7-9001-41aa-bd97-4c8eaf119e16"
      },
      "source": [
        "vec = [-4, -2, 0, 2, 4]\n",
        "# create a new list with the values doubled\n",
        "[x*2 for x in vec]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-8, -4, 0, 4, 8]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxarDtnJG3_G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6483be51-a1b5-4219-f47e-9979f994fed9"
      },
      "source": [
        "vec = [-4, -2, 0, 2, 4]\n",
        "[x**2 for x in vec]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[16, 4, 0, 4, 16]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoGsbEMAXKWG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2ef32a23-0ad9-4e91-9037-610d8d930052"
      },
      "source": [
        "# N.B.: the tuple must be parenthesized, otherwise an error is raised\n",
        "[(x, x**2) for x in range(6)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssLHCGLJXKWI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5bf94409-90de-4c2a-f545-64d67c2439e1"
      },
      "source": [
        "squares = []\n",
        "for x in range(10):\n",
        "    squares.append(x**2)\n",
        "\n",
        "squares"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aGHA907XKWK",
        "colab_type": "code",
        "colab": {},
        "outputId": "65483883-f793-4ba0-81e1-d0d498bde158"
      },
      "source": [
        "squares = list(map(lambda x: x**2, range(10)))\n",
        "\n",
        "squares"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD3-IHKJXKWM",
        "colab_type": "code",
        "colab": {},
        "outputId": "ca507155-4192-4a89-9009-c12f5a8b96dc"
      },
      "source": [
        "squares = [x**2 for x in range(10)]\n",
        "\n",
        "squares"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WULb4vczXKWO",
        "colab_type": "code",
        "colab": {},
        "outputId": "20b0d4f5-e30a-451b-f1ac-0a24faaea951"
      },
      "source": [
        "[(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UbE3U9m2XKWQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "6ab78b40-3b02-49c9-80ae-8984322ddfb4"
      },
      "source": [
        "# expressão acima é equivalente a este bloco:\n",
        "combs = []\n",
        "for x in [1,2,3]:\n",
        "    for y in [3,1,4]:\n",
        "        if x != y:\n",
        "            combs.append((x, y))\n",
        "\n",
        "combs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VImIYQiXKWc",
        "colab_type": "code",
        "colab": {},
        "outputId": "b040e670-59c3-4f4c-a0f4-7cbace8630fb"
      },
      "source": [
        "[2 * x for x in range(40) if x * x > 30]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[12,\n",
              " 14,\n",
              " 16,\n",
              " 18,\n",
              " 20,\n",
              " 22,\n",
              " 24,\n",
              " 26,\n",
              " 28,\n",
              " 30,\n",
              " 32,\n",
              " 34,\n",
              " 36,\n",
              " 38,\n",
              " 40,\n",
              " 42,\n",
              " 44,\n",
              " 46,\n",
              " 48,\n",
              " 50,\n",
              " 52,\n",
              " 54,\n",
              " 56,\n",
              " 58,\n",
              " 60,\n",
              " 62,\n",
              " 64,\n",
              " 66,\n",
              " 68,\n",
              " 70,\n",
              " 72,\n",
              " 74,\n",
              " 76,\n",
              " 78]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 193
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtPNcu1cIsrI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7cdbc353-0d89-4777-83ee-cf6e6106e77a"
      },
      "source": [
        "a_list = [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]\n",
        "a_list[2] = 42\n",
        "print(a_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 1, 42, 9, 16, 25, 36, 49, 64, 81]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGafvIHkXKWh",
        "colab_type": "text"
      },
      "source": [
        "## Tuples\n",
        "\n",
        "A tuple is an immutable list of values ​​separated by commas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9V7cqquGXKWi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5e949596-25df-4569-eaf0-415f22400084"
      },
      "source": [
        "t = 12345, 54321, 'hello!'\n",
        "t[0]\n",
        "\n",
        "print(t)\n",
        "\n",
        "# Tuples may be nested:\n",
        "u = t, (1, 2, 3, 4, 5)\n",
        "print(u)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12345, 54321, 'hello!')\n",
            "((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEUSQJPcXKWl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "a2895bc5-c614-4301-cad3-a0418800b308"
      },
      "source": [
        "# Tuples are immutable. The following command generates an error.\n",
        "t[0] = 88888 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-b8851d26c9f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Tuples are immutable. The following command generates an error.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m88888\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 't' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qF8efYAaXKWn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5f45b0f7-5eca-479b-830a-87caa9182dd4"
      },
      "source": [
        "# but they can contain mutable objects:\n",
        "v = ([1, 2, 3], [3, 2, 1])\n",
        "print(v)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "([1, 2, 3], [3, 2, 1])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQD90paxXKWp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "v[0][0] = 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCDAbIGmXKWs",
        "colab_type": "code",
        "colab": {},
        "outputId": "236c58d0-38b5-4dc3-f05f-6c694a42c7ab"
      },
      "source": [
        "v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([100, 2, 3], [3, 2, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xto5TnOXKWu",
        "colab_type": "code",
        "colab": {},
        "outputId": "23a1f290-13d0-4890-e2f8-31550c9f41a4"
      },
      "source": [
        "# tuple packing, sequence unpacking\n",
        "t = 12345, 54321, 'hello!'\n",
        "x, y, z = t\n",
        "print(x + y)\n",
        "print(z)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "66666\n",
            "hello!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuEABl5RXKWx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "89b5568e-8d88-4e09-df13-e68fee953412"
      },
      "source": [
        "# Source: https://wiki.python.org/moin/HowTo/Sorting\n",
        "student_tuples = [\n",
        "        ('john', 'A', 15),\n",
        "        ('jane', 'B', 12),\n",
        "        ('dave', 'B', 10),\n",
        "]\n",
        "# dataset\n",
        "\n",
        "print(lambda student: student[2])\n",
        "f = lambda student: student[2]\n",
        "\n",
        "sorted(student_tuples, key=lambda student: student[2])   # sort by age\n",
        "[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<function <lambda> at 0x7fc344f99ea0>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQGKPnxdJ8en",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c6103e71-f88e-4cf9-d728-156b89d0a78a"
      },
      "source": [
        "basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}\n",
        "print(basket)                      # show that duplicates have been removed"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'pear', 'orange', 'apple', 'banana'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTeFrxpaXKWz",
        "colab_type": "text"
      },
      "source": [
        "## Sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1Kd6b91XKWz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6cc36a28-681f-4a82-9114-eca76957ddfe"
      },
      "source": [
        "basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}\n",
        "print(basket)                      # show that duplicates have been removed\n",
        "\n",
        "'orange' in basket                 # fast membership testing\n",
        "\n",
        "'crabgrass' in basket\n",
        "\n",
        "\n",
        "# Demonstrate set operations on unique letters from two words\n",
        "\n",
        "a = set('abracadabra')\n",
        "b = set('alacazam')\n",
        "a                                  # unique letters in a\n",
        "\n",
        "a - b                              # letters in a but not in b\n",
        "\n",
        "a | b                              # letters in a or b or both\n",
        "\n",
        "a & b                              # letters in both a and b\n",
        "\n",
        "a ^ b                              # letters in a or b but not both"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'banana', 'orange', 'apple', 'pear'}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'b', 'd', 'l', 'm', 'r', 'z'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9T5b4grzXKW1",
        "colab_type": "code",
        "colab": {},
        "outputId": "dc08ceac-2bef-4e59-d3a5-beb7aa0dff74"
      },
      "source": [
        "a = {x for x in 'abracadabra' if x not in 'abc'}\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'d', 'r'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4lRNexprXKW3",
        "colab_type": "text"
      },
      "source": [
        "## Dictonaries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9S07WPqXKW3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f02f5c59-a5c8-4f74-8c42-893c9f2daab8"
      },
      "source": [
        "tel = {'jack': 4098, 'sape': 4139}\n",
        "tel"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'jack': 4098, 'sape': 4139}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5matqOlbXKW8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6c8d9c74-3d52-47cc-e307-6c4b27c1fdae"
      },
      "source": [
        "tel['guido'] = 4127\n",
        "tel"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'guido': 4127, 'jack': 4098, 'sape': 4139}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPt8Pcq4XKXA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "58fcba50-6a6c-43f0-d3f9-91538f37ae70"
      },
      "source": [
        "tel['jack']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4098"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XS3Vk-kBXKXH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "10416519-79fd-4c0b-ba8e-0abf3500f752"
      },
      "source": [
        "del tel['sape']\n",
        "tel['irv'] = 4127\n",
        "tel"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'guido': 4127, 'irv': 4127, 'jack': 4098}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7c4qYEQjXKXK",
        "colab_type": "code",
        "colab": {},
        "outputId": "51a51579-8537-448d-934f-cdbab16f81be"
      },
      "source": [
        "list(tel.items())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('jack', 4098), ('guido', 4127), ('irv', 4127)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v2OOtYkVXKXM",
        "colab_type": "code",
        "colab": {},
        "outputId": "cb5ce8fc-c958-4f11-bab8-d2efd5ee1a61"
      },
      "source": [
        "list(tel.values())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4098, 4127, 4127]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4mUS6XjtXKXP",
        "colab_type": "code",
        "colab": {},
        "outputId": "6b9b8089-c4ed-4429-f1af-510fabe4829c"
      },
      "source": [
        "list(tel.keys())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['jack', 'guido', 'irv']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0E2YIPWXKXR",
        "colab_type": "code",
        "colab": {},
        "outputId": "6886cc07-929b-4f23-d81e-4b860810400f"
      },
      "source": [
        "sorted(tel.keys())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['guido', 'irv', 'jack']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8EVEE7lXKXT",
        "colab_type": "code",
        "colab": {},
        "outputId": "19424159-e22c-4646-df77-d51376ea27be"
      },
      "source": [
        "('guido' in tel) and ('jack' not in tel)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xqmrzw-MXKXV",
        "colab_type": "code",
        "colab": {},
        "outputId": "af67c355-d38a-488d-c099-379759ad6a5c"
      },
      "source": [
        "#O construtor dict constrói dicionários a partir de pares chave/valor.\n",
        "dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'guido': 4127, 'jack': 4098, 'sape': 4139}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xHheWYicXKYD",
        "colab_type": "text"
      },
      "source": [
        "# Objects and Classes \n",
        "\n",
        "Taken from https://www.learnpython.org/en/Classes_and_Objects: \n",
        "> *Objects are an encapsulation of variables and functions into a single entity. Objects get their variables and functions (methods) from classes. Classes are essentially a template to create your objects.*\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsxFDmLf6CG1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MyClass:\n",
        "    variable = \"Saint Martin\"\n",
        "\n",
        "    def function(self):\n",
        "        print(\"This is a message inside the class.\")\n",
        "\n",
        "    def add(self, a,b):\n",
        "      return a + b"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELLilvJI6N6e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "myobjectx = MyClass()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKAr0Vxq6RAm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0a92dffb-dd8c-4323-80cf-ebb6aa31f5b6"
      },
      "source": [
        "print(myobjectx.variable)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saint Martin\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCCPaJ2w6Y-q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fbdfb294-dd29-4e59-d968-fbd2ef91ee35"
      },
      "source": [
        "myobjectx.function()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is a message inside the class.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZN5ZNXRfHwLz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d505c402-5729-4ad2-e134-0842703945c1"
      },
      "source": [
        "myobjectx.add(42, 12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9BSCMGX-XKYT",
        "colab_type": "text"
      },
      "source": [
        "# Manipulating Data with Python libraries\n",
        "\n",
        "There are several libraries (packages) in Python suited for conducting data analisys tasks.\n",
        "\n",
        "*   [numpy](https://numpy.org)\n",
        "*   [Scikit-Learn](https://scikit-learn.org/stable/)\n",
        "*   [Pandas](https://pandas.pydata.org)\n",
        "*   [Seaborn](https://seaborn.pydata.org)\n",
        "\n",
        "Besides many other functionalities, these libraries provide several alternative ways to represent a dataset. Let us see how this can be done.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzFiSeptP7uG",
        "colab_type": "text"
      },
      "source": [
        "## NumPy arrays\n",
        "\n",
        "NumPy arrays are the basis of many Python packages to represent datasets.\n",
        "\n",
        "(The text below was taken from the numpy [Quick Start](https://numpy.org/devdocs/user/quickstart.html))\n",
        "\n",
        "> NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes.\n",
        "\n",
        "> For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3.\n",
        "\n",
        "> [[ 1., 0., 0.],\n",
        " [ 0., 1., 2.]]\n",
        "\n",
        "> NumPy’s array class is called ndarray. It is also known by the alias array. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYCTfRwEP6zg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "22c899c8-2cff-4117-9f3b-b96bcc87d3a3"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# numpy array - vector - tensor\n",
        "some_array = np.array([1,2,1])\n",
        "\n",
        "print(some_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 2 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0b9b3oHdQ8Wn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "8a64d0eb-9c67-4d1c-bd3d-cf4ec9abd021"
      },
      "source": [
        "another_array = np.array([[1., 0., 0.], [0., 1., 2.]])\n",
        "\n",
        "print(another_array)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0. 0.]\n",
            " [0. 1. 2.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aoUUP21IKIUG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "537dd2d9-d032-4f7a-fc7b-f0eeb467c91c"
      },
      "source": [
        "print(type(another_array))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znS80u7WRMyD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f4398283-d845-48a5-8397-a417af20a73e"
      },
      "source": [
        "print(some_array.shape)\n",
        "print(another_array.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3,)\n",
            "(2, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wWPdM0JFSaui",
        "colab_type": "text"
      },
      "source": [
        "Also from the numpy [Quick Start](https://numpy.org/devdocs/user/quickstart.html):\n",
        "\n",
        "- `ndarray.ndim`\n",
        "the number of axes (dimensions) of the array.\n",
        "\n",
        "- `ndarray.shape`\n",
        "the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim.\n",
        "\n",
        "- `ndarray.size`\n",
        "the total number of elements of the array. This is equal to the product of the elements of shape.\n",
        "\n",
        "- `ndarray.dtype`\n",
        "an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Ja9AMa3TLqH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "6681d0db-034c-49e3-b649-2648bd0830a6"
      },
      "source": [
        "print(another_array.ndim)\n",
        "print(another_array.shape)\n",
        "print(another_array.size)\n",
        "print(another_array.dtype)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n",
            "(2, 3)\n",
            "6\n",
            "float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5K-AwKIURbVZ",
        "colab_type": "text"
      },
      "source": [
        "### Example: Iris dataset\n",
        "\n",
        "Consider the dataset [Iris](https://en.wikipedia.org/wiki/Iris_flower_data_set). This dataset is to Data Mining as the Drosophila fly is to Genetics! You will see this dataset being used in a lot of Data Mining related tutorials.\n",
        "\n",
        "![texto alternativo](https://upload.wikimedia.org/wikipedia/commons/thumb/9/95/Drosophila_melanogaster_Proboscis.jpg/1280px-Drosophila_melanogaster_Proboscis.jpg)\n",
        "\n",
        "Let us see how we can load the iris dataset using Python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QsmEHpHTQlze",
        "colab_type": "text"
      },
      "source": [
        "One of the most popular packages for doing Data Mining in Python is Scikit-learn. This package provides the implementation of several Data Mining methods, as we shall see in future classes.\n",
        "\n",
        "For now, let us see how Scikit-Learn builds upon Numpy arrays to provide a way to represent a dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3rYfigfNI1O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import datasets\n",
        "\n",
        "# import Iris data to play with\n",
        "iris = datasets.load_iris()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ml18ilU-NaFi",
        "colab_type": "text"
      },
      "source": [
        "The object `iris` in the above code has several attributes that represent the information contained in the Iris dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VU7oEwJdOXPj",
        "colab_type": "text"
      },
      "source": [
        "The first attribute is `data`, a bidimentional NumPy array containing the measurements for each flower."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AnEvM-VNPVH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "393e920d-ec8e-4d61-acc2-be1bd881caf8"
      },
      "source": [
        "print(iris.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]\n",
            " [5.4 3.9 1.7 0.4]\n",
            " [4.6 3.4 1.4 0.3]\n",
            " [5.  3.4 1.5 0.2]\n",
            " [4.4 2.9 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.4 3.7 1.5 0.2]\n",
            " [4.8 3.4 1.6 0.2]\n",
            " [4.8 3.  1.4 0.1]\n",
            " [4.3 3.  1.1 0.1]\n",
            " [5.8 4.  1.2 0.2]\n",
            " [5.7 4.4 1.5 0.4]\n",
            " [5.4 3.9 1.3 0.4]\n",
            " [5.1 3.5 1.4 0.3]\n",
            " [5.7 3.8 1.7 0.3]\n",
            " [5.1 3.8 1.5 0.3]\n",
            " [5.4 3.4 1.7 0.2]\n",
            " [5.1 3.7 1.5 0.4]\n",
            " [4.6 3.6 1.  0.2]\n",
            " [5.1 3.3 1.7 0.5]\n",
            " [4.8 3.4 1.9 0.2]\n",
            " [5.  3.  1.6 0.2]\n",
            " [5.  3.4 1.6 0.4]\n",
            " [5.2 3.5 1.5 0.2]\n",
            " [5.2 3.4 1.4 0.2]\n",
            " [4.7 3.2 1.6 0.2]\n",
            " [4.8 3.1 1.6 0.2]\n",
            " [5.4 3.4 1.5 0.4]\n",
            " [5.2 4.1 1.5 0.1]\n",
            " [5.5 4.2 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.2]\n",
            " [5.  3.2 1.2 0.2]\n",
            " [5.5 3.5 1.3 0.2]\n",
            " [4.9 3.6 1.4 0.1]\n",
            " [4.4 3.  1.3 0.2]\n",
            " [5.1 3.4 1.5 0.2]\n",
            " [5.  3.5 1.3 0.3]\n",
            " [4.5 2.3 1.3 0.3]\n",
            " [4.4 3.2 1.3 0.2]\n",
            " [5.  3.5 1.6 0.6]\n",
            " [5.1 3.8 1.9 0.4]\n",
            " [4.8 3.  1.4 0.3]\n",
            " [5.1 3.8 1.6 0.2]\n",
            " [4.6 3.2 1.4 0.2]\n",
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            " [5.  3.3 1.4 0.2]\n",
            " [7.  3.2 4.7 1.4]\n",
            " [6.4 3.2 4.5 1.5]\n",
            " [6.9 3.1 4.9 1.5]\n",
            " [5.5 2.3 4.  1.3]\n",
            " [6.5 2.8 4.6 1.5]\n",
            " [5.7 2.8 4.5 1.3]\n",
            " [6.3 3.3 4.7 1.6]\n",
            " [4.9 2.4 3.3 1. ]\n",
            " [6.6 2.9 4.6 1.3]\n",
            " [5.2 2.7 3.9 1.4]\n",
            " [5.  2.  3.5 1. ]\n",
            " [5.9 3.  4.2 1.5]\n",
            " [6.  2.2 4.  1. ]\n",
            " [6.1 2.9 4.7 1.4]\n",
            " [5.6 2.9 3.6 1.3]\n",
            " [6.7 3.1 4.4 1.4]\n",
            " [5.6 3.  4.5 1.5]\n",
            " [5.8 2.7 4.1 1. ]\n",
            " [6.2 2.2 4.5 1.5]\n",
            " [5.6 2.5 3.9 1.1]\n",
            " [5.9 3.2 4.8 1.8]\n",
            " [6.1 2.8 4.  1.3]\n",
            " [6.3 2.5 4.9 1.5]\n",
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            " [6.4 2.9 4.3 1.3]\n",
            " [6.6 3.  4.4 1.4]\n",
            " [6.8 2.8 4.8 1.4]\n",
            " [6.7 3.  5.  1.7]\n",
            " [6.  2.9 4.5 1.5]\n",
            " [5.7 2.6 3.5 1. ]\n",
            " [5.5 2.4 3.8 1.1]\n",
            " [5.5 2.4 3.7 1. ]\n",
            " [5.8 2.7 3.9 1.2]\n",
            " [6.  2.7 5.1 1.6]\n",
            " [5.4 3.  4.5 1.5]\n",
            " [6.  3.4 4.5 1.6]\n",
            " [6.7 3.1 4.7 1.5]\n",
            " [6.3 2.3 4.4 1.3]\n",
            " [5.6 3.  4.1 1.3]\n",
            " [5.5 2.5 4.  1.3]\n",
            " [5.5 2.6 4.4 1.2]\n",
            " [6.1 3.  4.6 1.4]\n",
            " [5.8 2.6 4.  1.2]\n",
            " [5.  2.3 3.3 1. ]\n",
            " [5.6 2.7 4.2 1.3]\n",
            " [5.7 3.  4.2 1.2]\n",
            " [5.7 2.9 4.2 1.3]\n",
            " [6.2 2.9 4.3 1.3]\n",
            " [5.1 2.5 3.  1.1]\n",
            " [5.7 2.8 4.1 1.3]\n",
            " [6.3 3.3 6.  2.5]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [7.1 3.  5.9 2.1]\n",
            " [6.3 2.9 5.6 1.8]\n",
            " [6.5 3.  5.8 2.2]\n",
            " [7.6 3.  6.6 2.1]\n",
            " [4.9 2.5 4.5 1.7]\n",
            " [7.3 2.9 6.3 1.8]\n",
            " [6.7 2.5 5.8 1.8]\n",
            " [7.2 3.6 6.1 2.5]\n",
            " [6.5 3.2 5.1 2. ]\n",
            " [6.4 2.7 5.3 1.9]\n",
            " [6.8 3.  5.5 2.1]\n",
            " [5.7 2.5 5.  2. ]\n",
            " [5.8 2.8 5.1 2.4]\n",
            " [6.4 3.2 5.3 2.3]\n",
            " [6.5 3.  5.5 1.8]\n",
            " [7.7 3.8 6.7 2.2]\n",
            " [7.7 2.6 6.9 2.3]\n",
            " [6.  2.2 5.  1.5]\n",
            " [6.9 3.2 5.7 2.3]\n",
            " [5.6 2.8 4.9 2. ]\n",
            " [7.7 2.8 6.7 2. ]\n",
            " [6.3 2.7 4.9 1.8]\n",
            " [6.7 3.3 5.7 2.1]\n",
            " [7.2 3.2 6.  1.8]\n",
            " [6.2 2.8 4.8 1.8]\n",
            " [6.1 3.  4.9 1.8]\n",
            " [6.4 2.8 5.6 2.1]\n",
            " [7.2 3.  5.8 1.6]\n",
            " [7.4 2.8 6.1 1.9]\n",
            " [7.9 3.8 6.4 2. ]\n",
            " [6.4 2.8 5.6 2.2]\n",
            " [6.3 2.8 5.1 1.5]\n",
            " [6.1 2.6 5.6 1.4]\n",
            " [7.7 3.  6.1 2.3]\n",
            " [6.3 3.4 5.6 2.4]\n",
            " [6.4 3.1 5.5 1.8]\n",
            " [6.  3.  4.8 1.8]\n",
            " [6.9 3.1 5.4 2.1]\n",
            " [6.7 3.1 5.6 2.4]\n",
            " [6.9 3.1 5.1 2.3]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [6.8 3.2 5.9 2.3]\n",
            " [6.7 3.3 5.7 2.5]\n",
            " [6.7 3.  5.2 2.3]\n",
            " [6.3 2.5 5.  1.9]\n",
            " [6.5 3.  5.2 2. ]\n",
            " [6.2 3.4 5.4 2.3]\n",
            " [5.9 3.  5.1 1.8]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWP8fHyGOnLp",
        "colab_type": "text"
      },
      "source": [
        "Notice that the data attribute is realy a NumPy array. So, we can index it. See the next example."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zlvBzWojObYm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0d4ff121-3b70-4f94-abb8-48fda482c6c7"
      },
      "source": [
        "print(iris.data[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5.1 3.5 1.4 0.2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4T4IHOz2Oy0P",
        "colab_type": "text"
      },
      "source": [
        "We can also slice the attribute `data`. For example, the code below print the first 5 row of the iris dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k94dd0jzOqhS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "5bbd40da-2b75-4dc8-f6ab-43b0739516eb"
      },
      "source": [
        "print(iris.data[0:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Yi38B4WPBl8",
        "colab_type": "text"
      },
      "source": [
        "The code below prints the value of the first features in the first row."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCKDaURiOtyd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c3ac1e70-ee0d-4c7c-9dce-c43be7b3223c"
      },
      "source": [
        "print(iris.data[0][1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BLNZwKXPK6o",
        "colab_type": "text"
      },
      "source": [
        "The `target` attribute store the species of each flower. These values are coded: 0 --> setosa; 1 --> versicolor; 2 -> virginica."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXU4c44iNlVw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "3494bfbe-026c-444a-a7de-faa9dc0fe2f7"
      },
      "source": [
        "print(iris.target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uPdU_JcNosJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "563cfbb7-6775-4c9d-9aed-9084ee31cfc4"
      },
      "source": [
        "print(iris.target_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['setosa' 'versicolor' 'virginica']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K04NDMsG84cV",
        "colab_type": "text"
      },
      "source": [
        "Another popular way of representing a dataset in Python is by using the [Pandas](https://pandas.pydata.org/pandas-docs/stable/) package. Pandas is actually more user-friendly thatn Scikit-Learn when it comes to represent and deal with a dataset.\n",
        "\n",
        "Pandas is an open source library, that provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\n",
        "\n",
        "The two main data structures (classes) provided by the Pandas package are `Series` and` DataFrame`.\n",
        "\n",
        "* `DataFrame`: a two-dimensional data structure (columns and rows). A dataset can be represented as a ** DataFrame ** object: observations are stored as rows in the DataFrame. A DataFrame is more user-friendly than the NumPy array.\n",
        "\n",
        "* `Series`: a 1-dimension matrix; can be interpreted as a column in a DataFrame. A DataFrame object is composed of several Series objects. Each attribute in a dataset is represented as a Series object.\n",
        "\n",
        "![alt text](https://i.stack.imgur.com/DL0iQ.jpg)\n",
        "\n",
        "(source: [StackOverflow](https://stackoverflow.com/questions/25773245/ambiguity-in-pandas-dataframe-numpy-array-axis-definition))\n",
        "\n",
        "A specific cell in a DataFrame can be located once a column and row are fixed.\n",
        "\n",
        "![alt text](https://bookdata.readthedocs.io/en/latest/_images/base_01_pandas_5_0.png)\n",
        "(source: https://bookdata.readthedocs.io)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n36ZPobxtZVW",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "from sklearn import datasets\n",
        "\n",
        "# import the Iris dataset to play with\n",
        "iris = datasets.load_iris()\n",
        "\n",
        "# Here, we create a dataset as object of the class DataFrame.\n",
        "df_iris = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
        "\n",
        "# And here we add a column to this dataset.\n",
        "df_iris['species'] = iris.target"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D2qCw11VUQiB",
        "colab_type": "text"
      },
      "source": [
        "By invoking the function **head()** on a DataFrame, we can visualize the first lines of the data set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P1Jp1TsnUQ4c",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "eb0f52df-a641-4466-95a2-098d20ff27ba"
      },
      "source": [
        "df_iris.head(15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>species</th>\n",
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              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
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              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>4.8</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal length (cm)  sepal width (cm)  ...  petal width (cm)  species\n",
              "0                 5.1               3.5  ...               0.2        0\n",
              "1                 4.9               3.0  ...               0.2        0\n",
              "2                 4.7               3.2  ...               0.2        0\n",
              "3                 4.6               3.1  ...               0.2        0\n",
              "4                 5.0               3.6  ...               0.2        0\n",
              "5                 5.4               3.9  ...               0.4        0\n",
              "6                 4.6               3.4  ...               0.3        0\n",
              "7                 5.0               3.4  ...               0.2        0\n",
              "8                 4.4               2.9  ...               0.2        0\n",
              "9                 4.9               3.1  ...               0.1        0\n",
              "10                5.4               3.7  ...               0.2        0\n",
              "11                4.8               3.4  ...               0.2        0\n",
              "12                4.8               3.0  ...               0.1        0\n",
              "13                4.3               3.0  ...               0.1        0\n",
              "14                5.8               4.0  ...               0.2        0\n",
              "\n",
              "[15 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQPJRRVTA5Zr",
        "colab_type": "text"
      },
      "source": [
        "We can also inspect the last rows of a DataFrame:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcEJwrDx3WaC",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "b90279f2-8f00-40d5-d508-f539222ede30"
      },
      "source": [
        "df_iris.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  ...  petal width (cm)  species\n",
              "145                6.7               3.0  ...               2.3        2\n",
              "146                6.3               2.5  ...               1.9        2\n",
              "147                6.5               3.0  ...               2.0        2\n",
              "148                6.2               3.4  ...               2.3        2\n",
              "149                5.9               3.0  ...               1.8        2\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "abT_GDncuDxH",
        "colab_type": "text"
      },
      "source": [
        "In the above DataFrame object, each line refers to a single observed flower. The number of rows is the total number of flowers in the data set.\n",
        "\n",
        "Remember that (in the Data Mining terminology):\n",
        "* each row in the dataset is also called *sample*;\n",
        "* the columns of the dataset are also called *features* or *attributes*;\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHcr_rXsvLeW",
        "colab_type": "text"
      },
      "source": [
        "As we shall see in future classe, in the contexto of the Iris dataset, we may wish to build a **predictive model**, i.e., a model that can predict the flower species based on the measurements (sepal width, petal length, etc.). In this case, the column `species` would be considered the **target feature**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rwmae1FBECh",
        "colab_type": "text"
      },
      "source": [
        "By calling `value_counts()`, we can produce a histogram for a given column (feature). This is useful for inspecting discrete features. See the example below:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrp1H5rFEd6e",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "cf042dc6-e83f-4a3f-a10b-57d1d7f98bbf"
      },
      "source": [
        "df_iris[\"species\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    50\n",
              "1    50\n",
              "0    50\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "862U5UVIBtUi",
        "colab_type": "text"
      },
      "source": [
        "We can inspect the list of column names in a DataFrame:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbaDJK5v0YEY",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "3b257786-1cee-4c43-f1b9-74daec9ee18e"
      },
      "source": [
        "df_iris.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
              "       'petal width (cm)', 'species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K-Nz57bsHMC8",
        "colab_type": "text"
      },
      "source": [
        "The shape of a DataFrame is a tuple containg the numbers of rows and columns in it. \n",
        "\n",
        "We can inspect the *shape* of a DataFrame by simple using its `shape` attribute:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9wGTCV8HQ8P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "36b50036-e7c0-4dba-f573-86d0d62c8e7c"
      },
      "source": [
        "df_iris.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgY_H5ZcdGdB",
        "colab_type": "text"
      },
      "source": [
        "It is also possible to inspecto the values in a given column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZj2jxh419B6",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "2e0839db-412a-4249-80a0-e08da07238a6"
      },
      "source": [
        "df_iris['species']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0\n",
              "      ..\n",
              "145    2\n",
              "146    2\n",
              "147    2\n",
              "148    2\n",
              "149    2\n",
              "Name: species, Length: 150, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jO2SBYxTdNYw",
        "colab_type": "text"
      },
      "source": [
        "We can also filter some rows in a DataFrame by using sobre predicate (i.e, some condition the selected rows shuold meet). Se the example below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuymQhDb2QDA",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "891fa893-1991-4c0a-f66e-91287d927d01"
      },
      "source": [
        "df_iris[df_iris['species'] == 1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>5.5</td>\n",
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              "      <td>4.0</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>6.5</td>\n",
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              "      <td>4.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.5</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>6.3</td>\n",
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              "      <td>4.7</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>4.9</td>\n",
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              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>6.6</td>\n",
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              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>5.2</td>\n",
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              "      <td>3.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.9</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>6.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>6.8</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.8</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.6</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.4</td>\n",
              "      <td>3.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.7</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>6.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>5.4</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>6.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.3</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>5.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>5.5</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>6.1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>5.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>5.6</td>\n",
              "      <td>2.7</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>5.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>6.2</td>\n",
              "      <td>2.9</td>\n",
              "      <td>4.3</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>5.1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>5.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal length (cm)  sepal width (cm)  ...  petal width (cm)  species\n",
              "50                7.0               3.2  ...               1.4        1\n",
              "51                6.4               3.2  ...               1.5        1\n",
              "52                6.9               3.1  ...               1.5        1\n",
              "53                5.5               2.3  ...               1.3        1\n",
              "54                6.5               2.8  ...               1.5        1\n",
              "55                5.7               2.8  ...               1.3        1\n",
              "56                6.3               3.3  ...               1.6        1\n",
              "57                4.9               2.4  ...               1.0        1\n",
              "58                6.6               2.9  ...               1.3        1\n",
              "59                5.2               2.7  ...               1.4        1\n",
              "60                5.0               2.0  ...               1.0        1\n",
              "61                5.9               3.0  ...               1.5        1\n",
              "62                6.0               2.2  ...               1.0        1\n",
              "63                6.1               2.9  ...               1.4        1\n",
              "64                5.6               2.9  ...               1.3        1\n",
              "65                6.7               3.1  ...               1.4        1\n",
              "66                5.6               3.0  ...               1.5        1\n",
              "67                5.8               2.7  ...               1.0        1\n",
              "68                6.2               2.2  ...               1.5        1\n",
              "69                5.6               2.5  ...               1.1        1\n",
              "70                5.9               3.2  ...               1.8        1\n",
              "71                6.1               2.8  ...               1.3        1\n",
              "72                6.3               2.5  ...               1.5        1\n",
              "73                6.1               2.8  ...               1.2        1\n",
              "74                6.4               2.9  ...               1.3        1\n",
              "75                6.6               3.0  ...               1.4        1\n",
              "76                6.8               2.8  ...               1.4        1\n",
              "77                6.7               3.0  ...               1.7        1\n",
              "78                6.0               2.9  ...               1.5        1\n",
              "79                5.7               2.6  ...               1.0        1\n",
              "80                5.5               2.4  ...               1.1        1\n",
              "81                5.5               2.4  ...               1.0        1\n",
              "82                5.8               2.7  ...               1.2        1\n",
              "83                6.0               2.7  ...               1.6        1\n",
              "84                5.4               3.0  ...               1.5        1\n",
              "85                6.0               3.4  ...               1.6        1\n",
              "86                6.7               3.1  ...               1.5        1\n",
              "87                6.3               2.3  ...               1.3        1\n",
              "88                5.6               3.0  ...               1.3        1\n",
              "89                5.5               2.5  ...               1.3        1\n",
              "90                5.5               2.6  ...               1.2        1\n",
              "91                6.1               3.0  ...               1.4        1\n",
              "92                5.8               2.6  ...               1.2        1\n",
              "93                5.0               2.3  ...               1.0        1\n",
              "94                5.6               2.7  ...               1.3        1\n",
              "95                5.7               3.0  ...               1.2        1\n",
              "96                5.7               2.9  ...               1.3        1\n",
              "97                6.2               2.9  ...               1.3        1\n",
              "98                5.1               2.5  ...               1.1        1\n",
              "99                5.7               2.8  ...               1.3        1\n",
              "\n",
              "[50 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Pe6v_20CFdA",
        "colab_type": "text"
      },
      "source": [
        "We can read CSV-formatted data from a file (local or remote) using `pd.read_csv()`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O10XrYqs2_i0",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "7cb5d4ca-3726-43c1-c75f-b91588025486"
      },
      "source": [
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', \n",
        "                 header=None)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     0    1    2    3            4\n",
              "0  5.1  3.5  1.4  0.2  Iris-setosa\n",
              "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
              "2  4.7  3.2  1.3  0.2  Iris-setosa\n",
              "3  4.6  3.1  1.5  0.2  Iris-setosa\n",
              "4  5.0  3.6  1.4  0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uv9mTWlNXEp",
        "colab_type": "text"
      },
      "source": [
        "# Exploratory data analysis\n",
        "\n",
        "Exploratory data analysis (EDA) is an approach to analyze data sets to summarize their main characteristics, often with visual methods. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data and possibly formulate hypotheses that could lead to new data collections and experiments.\n",
        "\n",
        "The Pandas, Seaborn and Matplotlib packages are useful for carrying out exploratory analysis of a set of data through statistical summaries and visualizations.\n",
        "\n",
        "Useful DataFrame methods for initial exploration are `describe()` and `info()`.\n",
        "\n",
        "- describe: presents a statistical summary of the numeric attributes contained in the DataFrame.\n",
        "\n",
        "- info: produces a quick description of the data, in particular the total number of lines and the type and number of non-null values ​​of each attribute.\n",
        "\n",
        "The Pandas Dataframe also allows us to easily produce box plots, scatter plots, and histograms to visualy inspect a dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLEtZUyKoGbk",
        "colab_type": "text"
      },
      "source": [
        "#### Histograms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fz3w1jwuUTNu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f51e12fe-8ea6-4c1b-e6d5-875a4cff1bd0"
      },
      "source": [
        "df_iris['sepal length (cm)'].hist(bins=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f50d0716f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "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": "1rttOB1-na8y",
        "colab_type": "text"
      },
      "source": [
        "#### Box plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7txxaWjPcPB",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "6f2ce0b6-7eb0-4f2b-b30d-fd996d68cd0a"
      },
      "source": [
        "df_iris.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
              "       'petal width (cm)', 'species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkccFodj6t_V",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a5de3d4f-89ba-467e-b22f-1f038c497415"
      },
      "source": [
        "import seaborn as sns \n",
        "c_palette = ['tab:blue', 'tab:orange', 'tab:green']\n",
        "sns.boxplot(y='sepal length (cm)', data=df_iris, palette=c_palette)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d6a1afd30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "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": {
        "id": "lroQ3mys8vII",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "0d048b44-3de9-40ef-a10a-96345934ab73"
      },
      "source": [
        "sns.boxplot(x='species', y='sepal length (cm)', data=df_iris, palette=c_palette)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d6a20e320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "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": "eSK2BEHaXM9w",
        "colab_type": "text"
      },
      "source": [
        "### Case study: *California housing prices* dataset\n",
        "\n",
        "Lets practice EDA through Pandas by exploring the housing dataset (details about this datset can be found [here](https://www.kaggle.com/camnugent/california-housing-prices))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eO8_0v5XGuRT",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "52256fd7-69d1-4caf-b49c-effbab10d1b4"
      },
      "source": [
        "import pandas as pd\n",
        "df_housing = pd.read_csv('housing.csv')\n",
        "df_housing.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "      <th>ocean_proximity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  median_house_value  ocean_proximity\n",
              "0    -122.23     37.88  ...            452600.0         NEAR BAY\n",
              "1    -122.22     37.86  ...            358500.0         NEAR BAY\n",
              "2    -122.24     37.85  ...            352100.0         NEAR BAY\n",
              "3    -122.25     37.85  ...            341300.0         NEAR BAY\n",
              "4    -122.25     37.85  ...            342200.0         NEAR BAY\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-eVLEfz_PjZ",
        "colab_type": "text"
      },
      "source": [
        "We can view the number of rows and features in this dataset by inpecting the attribute `shape`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K19mQdf5LFjq",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "774893f6-c7ec-490a-95a6-9a879a70dd61"
      },
      "source": [
        "print(df_housing.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20640, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIJ21kKG_bI3",
        "colab_type": "text"
      },
      "source": [
        "By calling the method `describe`, it is possible to get descriptive statistics for all the features in the dataset.\n",
        "\n",
        "From the [Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html):\n",
        "\n",
        "> Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-6B89KzR4Um",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "76e8a06d-2329-41dd-9ed5-c5a006b5a491"
      },
      "source": [
        "df_housing.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20433.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.569704</td>\n",
              "      <td>35.631861</td>\n",
              "      <td>28.639486</td>\n",
              "      <td>2635.763081</td>\n",
              "      <td>537.870553</td>\n",
              "      <td>1425.476744</td>\n",
              "      <td>499.539680</td>\n",
              "      <td>3.870671</td>\n",
              "      <td>206855.816909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.003532</td>\n",
              "      <td>2.135952</td>\n",
              "      <td>12.585558</td>\n",
              "      <td>2181.615252</td>\n",
              "      <td>421.385070</td>\n",
              "      <td>1132.462122</td>\n",
              "      <td>382.329753</td>\n",
              "      <td>1.899822</td>\n",
              "      <td>115395.615874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.800000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1447.750000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>787.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>2.563400</td>\n",
              "      <td>119600.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.534800</td>\n",
              "      <td>179700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.010000</td>\n",
              "      <td>37.710000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>647.000000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>605.000000</td>\n",
              "      <td>4.743250</td>\n",
              "      <td>264725.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>39320.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          longitude      latitude  ...  median_income  median_house_value\n",
              "count  20640.000000  20640.000000  ...   20640.000000        20640.000000\n",
              "mean    -119.569704     35.631861  ...       3.870671       206855.816909\n",
              "std        2.003532      2.135952  ...       1.899822       115395.615874\n",
              "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
              "25%     -121.800000     33.930000  ...       2.563400       119600.000000\n",
              "50%     -118.490000     34.260000  ...       3.534800       179700.000000\n",
              "75%     -118.010000     37.710000  ...       4.743250       264725.000000\n",
              "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pV2qTPiJGNav",
        "colab_type": "text"
      },
      "source": [
        "The count, mean, min and max lines are self-explanatory.\n",
        "\n",
        "Note that null values ​​are ignored in count (for example, the total_bedrooms count is 20,433, not 20,640).\n",
        "\n",
        "The std line shows the standard deviation (which measures the dispersion of attribute values).\n",
        "\n",
        "The 25%, 50% and 75% lines show the corresponding percentiles: a percentile indicates the value below which a given percentage of observations is found. For example, 25% of the districts have a housing_median_age of less than 18, while 50% are less than 29 and 75% are less than 37. These are often called the 25th percentile (or 1st quartile), the median and the 75th percentile (or 3rd quartile)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09uwhsbVAwY1",
        "colab_type": "text"
      },
      "source": [
        "By calling the method `info`, we can get more information about the dataset's structure.\n",
        "\n",
        "From the [Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html):\n",
        "\n",
        "> This method prints information about a DataFrame including the index dtype and column dtypes, non-null values and memory usage."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6acCOvvhyTY2",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "d3124c4f-b9a1-46c9-be98-456c90945e65"
      },
      "source": [
        "df_housing.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   longitude           20640 non-null  float64\n",
            " 1   latitude            20640 non-null  float64\n",
            " 2   housing_median_age  20640 non-null  float64\n",
            " 3   total_rooms         20640 non-null  float64\n",
            " 4   total_bedrooms      20433 non-null  float64\n",
            " 5   population          20640 non-null  float64\n",
            " 6   households          20640 non-null  float64\n",
            " 7   median_income       20640 non-null  float64\n",
            " 8   median_house_value  20640 non-null  float64\n",
            " 9   ocean_proximity     20640 non-null  object \n",
            "dtypes: float64(9), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDZ6W9et-VZs",
        "colab_type": "text"
      },
      "source": [
        "Another Python package that integrates with Pandas is [Seaborn](https://seaborn.pydata.org). In the example below, we use Seaborn to plot a nice histogram showing the amount of houses grouped by the feature `ocean_proximity`. In particular, the method count_plot is used. \n",
        "\n",
        "From the Seaborn documentation:\n",
        "> A count plot can be thought of as a histogram across a categorical, instead of quantitative, variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TwUGXYakOmKG",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "521c8142-aa11-4193-f052-1eb635e084f5"
      },
      "source": [
        "import seaborn as sns\n",
        "c_palette = ['tab:blue', 'tab:orange', 'tab:green']\n",
        "sns.countplot(y='ocean_proximity', data=df_housing, palette=c_palette)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3d1eaf7588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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": "u8kROPHVBlYJ",
        "colab_type": "text"
      },
      "source": [
        "Sse other examples of the `countplot` function [here](https://seaborn.pydata.org/generated/seaborn.countplot.html)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--UY17_JJM0T",
        "colab_type": "text"
      },
      "source": [
        "Another quick way to get an idea of ​​how the data is distributed in a dataset is to plot a histogram for each quantitative (numeric) feature.\n",
        "\n",
        "A histogram shows the number of instances (on the vertical axis) that have a certain range of values ​​(on the horizontal axis).\n",
        "\n",
        "The method [`hist`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.hist.html) of a DataFrame can be invoked on a single feature or on the whole set of data. In the latter case, the method will produce a histogram for each numeric attribute.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7T6sN88CXBl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "outputId": "eb8d3473-4e85-471e-88ff-679efc13efe9"
      },
      "source": [
        "df_housing.households.hist(bins=50, figsize=(20,15))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f50d21c0b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oj7TIhnoJO9N",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "outputId": "686ef37f-17ba-4b3e-b320-7807496ee1a3"
      },
      "source": [
        "df_housing.hist(bins=50, figsize=(20,15))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f50d06a5b38>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f50d0646c88>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f50d0675ef0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f50d0633198>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f50d05e1400>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f50d0595668>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f50d05498d0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f50d057db00>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f50d057db70>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6X0Od0QTIqnx",
        "colab_type": "text"
      },
      "source": [
        "It is also possible to plot *scatter plots*, as shown in the example below."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMzCAAd0XtWM",
        "colab_type": "text"
      },
      "source": [
        "![alt text](https://upload.wikimedia.org/wikipedia/commons/thumb/9/94/California_in_United_States.svg/1280px-California_in_United_States.svg.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYssfEx0Pu5P",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "d2f576ab-0da9-4e71-d761-e4e6f54f1167"
      },
      "source": [
        "df_housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d6a256240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "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": "fGl2DgseQo1S",
        "colab_type": "text"
      },
      "source": [
        "For not very large data sets, it is possible to calculate the [correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) (also called Pearson's coefficient) between each pair of features using the method [`corr()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html) defined in DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBR99zHYQoBF",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "corr_matrix = df_housing.corr()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0_ujxK_hGkw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "c514a556-2444-4f9e-fc92-380c226c2a5c"
      },
      "source": [
        "corr_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.924664</td>\n",
              "      <td>-0.108197</td>\n",
              "      <td>0.044568</td>\n",
              "      <td>0.069608</td>\n",
              "      <td>0.099773</td>\n",
              "      <td>0.055310</td>\n",
              "      <td>-0.015176</td>\n",
              "      <td>-0.045967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>-0.924664</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011173</td>\n",
              "      <td>-0.036100</td>\n",
              "      <td>-0.066983</td>\n",
              "      <td>-0.108785</td>\n",
              "      <td>-0.071035</td>\n",
              "      <td>-0.079809</td>\n",
              "      <td>-0.144160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</th>\n",
              "      <td>-0.108197</td>\n",
              "      <td>0.011173</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.361262</td>\n",
              "      <td>-0.320451</td>\n",
              "      <td>-0.296244</td>\n",
              "      <td>-0.302916</td>\n",
              "      <td>-0.119034</td>\n",
              "      <td>0.105623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rooms</th>\n",
              "      <td>0.044568</td>\n",
              "      <td>-0.036100</td>\n",
              "      <td>-0.361262</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.930380</td>\n",
              "      <td>0.857126</td>\n",
              "      <td>0.918484</td>\n",
              "      <td>0.198050</td>\n",
              "      <td>0.134153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bedrooms</th>\n",
              "      <td>0.069608</td>\n",
              "      <td>-0.066983</td>\n",
              "      <td>-0.320451</td>\n",
              "      <td>0.930380</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.877747</td>\n",
              "      <td>0.979728</td>\n",
              "      <td>-0.007723</td>\n",
              "      <td>0.049686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>0.099773</td>\n",
              "      <td>-0.108785</td>\n",
              "      <td>-0.296244</td>\n",
              "      <td>0.857126</td>\n",
              "      <td>0.877747</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.907222</td>\n",
              "      <td>0.004834</td>\n",
              "      <td>-0.024650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>households</th>\n",
              "      <td>0.055310</td>\n",
              "      <td>-0.071035</td>\n",
              "      <td>-0.302916</td>\n",
              "      <td>0.918484</td>\n",
              "      <td>0.979728</td>\n",
              "      <td>0.907222</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013033</td>\n",
              "      <td>0.065843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_income</th>\n",
              "      <td>-0.015176</td>\n",
              "      <td>-0.079809</td>\n",
              "      <td>-0.119034</td>\n",
              "      <td>0.198050</td>\n",
              "      <td>-0.007723</td>\n",
              "      <td>0.004834</td>\n",
              "      <td>0.013033</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.688075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_house_value</th>\n",
              "      <td>-0.045967</td>\n",
              "      <td>-0.144160</td>\n",
              "      <td>0.105623</td>\n",
              "      <td>0.134153</td>\n",
              "      <td>0.049686</td>\n",
              "      <td>-0.024650</td>\n",
              "      <td>0.065843</td>\n",
              "      <td>0.688075</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    longitude  latitude  ...  median_income  median_house_value\n",
              "longitude            1.000000 -0.924664  ...      -0.015176           -0.045967\n",
              "latitude            -0.924664  1.000000  ...      -0.079809           -0.144160\n",
              "housing_median_age  -0.108197  0.011173  ...      -0.119034            0.105623\n",
              "total_rooms          0.044568 -0.036100  ...       0.198050            0.134153\n",
              "total_bedrooms       0.069608 -0.066983  ...      -0.007723            0.049686\n",
              "population           0.099773 -0.108785  ...       0.004834           -0.024650\n",
              "households           0.055310 -0.071035  ...       0.013033            0.065843\n",
              "median_income       -0.015176 -0.079809  ...       1.000000            0.688075\n",
              "median_house_value  -0.045967 -0.144160  ...       0.688075            1.000000\n",
              "\n",
              "[9 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N-nM1l_thNSB",
        "colab_type": "text"
      },
      "source": [
        "It also possible to compare a specific feature to all others. See the example below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekpPpcafQx73",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "aa20dbd6-e0e7-4120-883c-5a05983a24d8"
      },
      "source": [
        "corr_matrix[\"median_house_value\"].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "median_house_value    1.000000\n",
              "median_income         0.688075\n",
              "total_rooms           0.134153\n",
              "housing_median_age    0.105623\n",
              "households            0.065843\n",
              "total_bedrooms        0.049686\n",
              "population           -0.024650\n",
              "longitude            -0.045967\n",
              "latitude             -0.144160\n",
              "Name: median_house_value, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekAnTAmIDKr_",
        "colab_type": "text"
      },
      "source": [
        "From the above correlation values, what can you infer the feature `median_house_value`?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cKyp4IYuQEx",
        "colab_type": "text"
      },
      "source": [
        "# Exercises\n",
        "\n",
        "1) Using some of the exploratoty analysis functions presented on this notebook, answer to the following about the dataset adult.data (downloadable from [here](https://www.kaggle.com/wenruliu/adult-income-dataset)):\n",
        "\n",
        "a. How many rows and features are there?\n",
        "\n",
        "b. Does this dataset contain any non-numeric features? \n",
        "\n",
        "c. Does this dataset have missing values ​​in any features? How do you know?\n",
        "\n",
        "d. What is the average age of individuals recorded in this dataset?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "drzJqMw3HIfy",
        "colab_type": "text"
      },
      "source": [
        "## Answers\n",
        "\n",
        "Let us first load this dataset by using Pandas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9z9cgjqYpVgx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "fc9fe809-d485-4a2e-a822-baed8b741117"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# The file has no headers naming the columns, so we pass header=None\n",
        "# and provide the column names explicitly in \"names\"\n",
        "df_adult = pd.read_csv(\n",
        "    'adult.data', header=None, index_col=False,\n",
        "    names=['age', 'workclass', 'fnlwgt', 'education',  'education-num',\n",
        "           'marital-status', 'occupation', 'relationship', 'race', 'gender',\n",
        "           'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
        "           'income'])\n",
        "\n",
        "df_adult.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education-num</th>\n",
              "      <th>marital-status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>gender</th>\n",
              "      <th>capital-gain</th>\n",
              "      <th>capital-loss</th>\n",
              "      <th>hours-per-week</th>\n",
              "      <th>native-country</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>77516</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>2174</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age          workclass  fnlwgt  ... hours-per-week  native-country  income\n",
              "0   39          State-gov   77516  ...             40   United-States   <=50K\n",
              "1   50   Self-emp-not-inc   83311  ...             13   United-States   <=50K\n",
              "2   38            Private  215646  ...             40   United-States   <=50K\n",
              "3   53            Private  234721  ...             40   United-States   <=50K\n",
              "4   28            Private  338409  ...             40            Cuba   <=50K\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q0MxNHZZHMTy",
        "colab_type": "text"
      },
      "source": [
        "a) The amount of rows and columns in a DataFrame can be inspected by using the `shape` attribute."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lT-HONlTcBK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0c32e562-ea37-473f-cf7c-7cc36b4d2296"
      },
      "source": [
        "df_adult.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32561, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LoMCO3hHfjj",
        "colab_type": "text"
      },
      "source": [
        "b) The type of attribute in a DataFrame can be inspected by using the `info` method. From the result below, we can see the the Adult dataset has several non-numeric columns: 1, 3, 5-9, 13, 14."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6vAK5v_UT3r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "5243a1ce-c4cb-4617-eabd-22763009429a"
      },
      "source": [
        "df_adult.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32561 entries, 0 to 32560\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   age             32561 non-null  int64 \n",
            " 1   workclass       32561 non-null  object\n",
            " 2   fnlwgt          32561 non-null  int64 \n",
            " 3   education       32561 non-null  object\n",
            " 4   education-num   32561 non-null  int64 \n",
            " 5   marital-status  32561 non-null  object\n",
            " 6   occupation      32561 non-null  object\n",
            " 7   relationship    32561 non-null  object\n",
            " 8   race            32561 non-null  object\n",
            " 9   gender          32561 non-null  object\n",
            " 10  capital-gain    32561 non-null  int64 \n",
            " 11  capital-loss    32561 non-null  int64 \n",
            " 12  hours-per-week  32561 non-null  int64 \n",
            " 13  native-country  32561 non-null  object\n",
            " 14  income          32561 non-null  object\n",
            "dtypes: int64(6), object(9)\n",
            "memory usage: 3.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoV-RdzvIQCm",
        "colab_type": "text"
      },
      "source": [
        "c) The output of the info method (above) also allows us to conclude that there are no missing values in this version of the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8k7MpbHIrVa9",
        "colab_type": "text"
      },
      "source": [
        "d) TBD"
      ]
    }
  ]
}