repaso/2_LibreriasDatosPython.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2.-LibreriasDatosPython.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8td8HGcm7Yt",
        "colab_type": "text"
      },
      "source": [
        "#Numpy\n",
        "- Crear vector\n",
        "- Crear matriz\n",
        "- Inizializar vector con valor\n",
        "- Dot product\n",
        "- Flat matriz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8HXM8F8ocRy",
        "colab_type": "code",
        "outputId": "83b703f6-e57c-4990-e015-b974409524e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "vector = np.array([0,1,2])\n",
        "print(vector)\n",
        "print(vector.shape)\n",
        "\n",
        "vector[0] = 6\n",
        "\n",
        "print(vector)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2]\n",
            "(3,)\n",
            "[6 1 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgsOktYNop_8",
        "colab_type": "code",
        "outputId": "4c1c2db8-9ffd-475c-95fc-b626ca0feacd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "vector2 = np.array([0,12.2, \"Hola\"])\n",
        "print(vector2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['0' '12.2' 'Hola']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AMUgLcCo3U8",
        "colab_type": "code",
        "outputId": "707d7733-7c19-4740-f7d6-d85a659ea118",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "zeros = np.zeros(3)\n",
        "zeros"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EaaHvXZUpEA4",
        "colab_type": "code",
        "outputId": "c549fddb-15fc-4113-80d1-bdd48fe3d3d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "ones = np.ones(3)\n",
        "ones"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QilxuNkEo1VA",
        "colab_type": "code",
        "outputId": "a50aa41b-9bdb-4b01-a164-6b4ca56639f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "full = np.full(3, 100)\n",
        "full"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([100, 100, 100])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eTvLpZSpK3o",
        "colab_type": "code",
        "outputId": "c86e81a5-bbe2-4635-ec53-2d8bc27dedde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "random = np.random.random(3)\n",
        "print(random)\n",
        "\n",
        "random = np.random.randint(1, 100, 3)\n",
        "random"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.70515463 0.95380006 0.25123844]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([62,  9, 28])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNtSDnM-pOpr",
        "colab_type": "code",
        "outputId": "443a75a7-ab0f-4c16-df49-4a63892bd62a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "ordered = np.arange(10)\n",
        "print(ordered)\n",
        "\n",
        "ordered = np.arange(2,3,0.1)\n",
        "ordered"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFrznSTmpWfR",
        "colab_type": "code",
        "outputId": "ac6f700e-d44c-4f17-dab7-634fb74f1f0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "matriz = np.ones((2,2))\n",
        "matriz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1.],\n",
              "       [1., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JkNpHC67qQfa",
        "colab_type": "code",
        "outputId": "3ebb9080-6ce3-4f02-ca7b-ce848d8024b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "unit = np.eye(3)\n",
        "unit"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfVnGxH7qTZn",
        "colab_type": "code",
        "outputId": "d440c317-9a33-4dd9-b0dc-acde1069d439",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "ordered[:3]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2. , 2.1, 2.2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UNhYtjqkqdLn",
        "colab_type": "code",
        "outputId": "abe2a4e4-0f50-44af-de37-758a3749d39a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "number = np.arange(10)\n",
        "mult2 = number %2 == 0\n",
        "print(mult2)\n",
        "\n",
        "number * 2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ True False  True False  True False  True False  True False]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzt6leYmq0MH",
        "colab_type": "code",
        "outputId": "a73ceae1-d019-43b8-a540-7bcec9424840",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "source": [
        "x = np.array([[1,2], [3,4]])\n",
        "y = np.array([[5,6], [7,8]])\n",
        "\n",
        "v = np.array([9,10])\n",
        "\n",
        "print(x.dot(y))\n",
        "print(np.dot(x,y))\n",
        "\n",
        "x.dot(v)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[19 22]\n",
            " [43 50]]\n",
            "[[19 22]\n",
            " [43 50]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([29, 67])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qo0mwWPdrVnV",
        "colab_type": "code",
        "outputId": "8f532b07-4d02-43d0-84f0-02a1a3eacc51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "x.flatten()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CXGO-4xrm9Wb",
        "colab_type": "text"
      },
      "source": [
        "# Pandas\n",
        "- Cargar CSV\n",
        "- Ver resumen del dataset ( columnas, estadisticos )\n",
        "- Agrupar por columnas\n",
        "- Filtrar por valores\n",
        "- Ordenar\n",
        "- Sacar operaciones ( sum, avg, mean )"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmB7dHZNrfAo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3j_cWpl6UPmR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uj_baD3sc64",
        "colab_type": "code",
        "outputId": "af567fa2-dd28-4d3b-bfe0-720ad5d43242",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df = pd.read_csv(\"./satisfaction.csv\")\n",
        "df.head()"
      ],
      "execution_count": 0,
      "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>Satisfaction</th>\n",
              "      <th>Loyalty</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>-1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>-0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>-0.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>-0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1.06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Satisfaction  Loyalty\n",
              "0             4    -1.33\n",
              "1             6    -0.28\n",
              "2             5    -0.99\n",
              "3             7    -0.29\n",
              "4             4     1.06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPBLc9lTshVt",
        "colab_type": "code",
        "outputId": "2a69b994-0e5f-4174-fb71-092662186bf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "print(df.dtypes)\n",
        "print(df.index)\n",
        "print(df.describe())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Satisfaction      int64\n",
            "Loyalty         float64\n",
            "dtype: object\n",
            "RangeIndex(start=0, stop=30, step=1)\n",
            "       Satisfaction    Loyalty\n",
            "count     30.000000  30.000000\n",
            "mean       6.400000   0.001000\n",
            "std        2.620871   1.016476\n",
            "min        1.000000  -1.690000\n",
            "25%        4.250000  -0.967500\n",
            "50%        7.000000   0.150000\n",
            "75%        8.750000   0.947500\n",
            "max       10.000000   1.380000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9aYTDOos66s",
        "colab_type": "code",
        "outputId": "942c058a-54f5-4317-ac91-b1811c98af4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "print(df.iloc[1])\n",
        "print(df.iloc[:, 1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Satisfaction    6.00\n",
            "Loyalty        -0.28\n",
            "Name: 1, dtype: float64\n",
            "0    -1.33\n",
            "1    -0.28\n",
            "2    -0.99\n",
            "3    -0.29\n",
            "4     1.06\n",
            "5    -1.66\n",
            "6    -0.97\n",
            "7    -0.32\n",
            "8     1.02\n",
            "9     0.68\n",
            "10   -0.34\n",
            "11    0.39\n",
            "12   -1.69\n",
            "13    0.67\n",
            "14    0.27\n",
            "15    1.36\n",
            "16    1.38\n",
            "17    1.36\n",
            "18   -0.34\n",
            "19    0.67\n",
            "20    1.18\n",
            "21   -1.69\n",
            "22    1.04\n",
            "23   -0.96\n",
            "24    1.03\n",
            "25   -0.99\n",
            "26    0.37\n",
            "27    0.03\n",
            "28   -1.36\n",
            "29    0.73\n",
            "Name: Loyalty, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIZmmPAhtfyf",
        "colab_type": "code",
        "outputId": "d01d4c48-eac6-4893-b9e6-9dd44321cfec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df2 = df[df[\"Satisfaction\"] > 5]\n",
        "df2.head()"
      ],
      "execution_count": 0,
      "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>Satisfaction</th>\n",
              "      <th>Loyalty</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>-0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>-0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10</td>\n",
              "      <td>-0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>-0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Satisfaction  Loyalty\n",
              "1             6    -0.28\n",
              "3             7    -0.29\n",
              "6            10    -0.97\n",
              "7             8    -0.32\n",
              "8             8     1.02"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IC4PJxPithEx",
        "colab_type": "code",
        "outputId": "0afa86c8-74b6-4c5b-d1e2-88d66e532c16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        }
      },
      "source": [
        "df.sort_values(by=\"Satisfaction\")"
      ],
      "execution_count": 0,
      "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>Satisfaction</th>\n",
              "      <th>Loyalty</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>-1.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2</td>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>3</td>\n",
              "      <td>-0.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>3</td>\n",
              "      <td>-1.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3</td>\n",
              "      <td>-1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4</td>\n",
              "      <td>-1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>4</td>\n",
              "      <td>1.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5</td>\n",
              "      <td>-1.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5</td>\n",
              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>5</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>-0.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>6</td>\n",
              "      <td>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>-0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>7</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>-0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>7</td>\n",
              "      <td>-0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>7</td>\n",
              "      <td>1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>-0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>8</td>\n",
              "      <td>1.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>9</td>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>9</td>\n",
              "      <td>1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>9</td>\n",
              "      <td>-0.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>9</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>-0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10</td>\n",
              "      <td>-0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>10</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>10</td>\n",
              "      <td>1.18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Satisfaction  Loyalty\n",
              "5              1    -1.66\n",
              "13             2     0.67\n",
              "23             3    -0.96\n",
              "21             3    -1.69\n",
              "28             3    -1.36\n",
              "0              4    -1.33\n",
              "22             4     1.04\n",
              "4              4     1.06\n",
              "12             5    -1.69\n",
              "11             5     0.39\n",
              "29             5     0.73\n",
              "2              5    -0.99\n",
              "24             6     1.03\n",
              "1              6    -0.28\n",
              "14             7     0.27\n",
              "3              7    -0.29\n",
              "18             7    -0.34\n",
              "17             7     1.36\n",
              "9              8     0.68\n",
              "7              8    -0.32\n",
              "8              8     1.02\n",
              "16             8     1.38\n",
              "19             9     0.67\n",
              "15             9     1.36\n",
              "25             9    -0.99\n",
              "27             9     0.03\n",
              "10            10    -0.34\n",
              "6             10    -0.97\n",
              "26            10     0.37\n",
              "20            10     1.18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5B4LHOz-t4qM",
        "colab_type": "code",
        "outputId": "a61a214e-f49c-433b-cd7b-63b44a406197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        }
      },
      "source": [
        "df.groupby(\"Satisfaction\").size()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Satisfaction\n",
              "1     1\n",
              "2     1\n",
              "3     3\n",
              "4     3\n",
              "5     4\n",
              "6     2\n",
              "7     4\n",
              "8     4\n",
              "9     4\n",
              "10    4\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6W7z9zFuDa_",
        "colab_type": "code",
        "outputId": "affdcdf9-2d83-4605-9098-53d8f4ee47b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "print(df.Satisfaction.mean())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPSjegQunA0N",
        "colab_type": "text"
      },
      "source": [
        "# MatPlotLib\n",
        "- Visualizar graficas\n",
        "- Lineal\n",
        "- Scater\n",
        "- Bar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfshjaoGuY6a",
        "colab_type": "code",
        "outputId": "7d6074e0-80fa-487b-d046-57f3e25b3429",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "plt.plot([1,2,3,4], [1,4,9,16])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUBkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCmnn8ezuhwJ",
        "colab_type": "code",
        "outputId": "0dc094a5-4e6b-4317-d4e5-3e4575b0b90f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.plot([1,2,3,4],[1,4,9,16])\n",
        "plt.title(\"First plot\")\n",
        "plt.xlabel(\"X label\")\n",
        "plt.ylabel(\"Y label\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4sDJdpYu2Id",
        "colab_type": "code",
        "outputId": "90501dc4-0f6c-471c-8726-a3ca46f3359b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "divisions = [\"DivA\", \"DivB\", \"DivC\", \"DivD\"]\n",
        "averages = [70,82,73, 65]\n",
        "\n",
        "plt.bar(divisions, averages, color=\"green\")\n",
        "plt.title(\"Bar Graph\")\n",
        "plt.xlabel(\"Divisions\")\n",
        "plt.ylabel(\"Marks\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEWCAYAAABhffzLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9dgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLjaXzSAvGwl",
        "colab_type": "code",
        "outputId": "f17f0157-8b86-43a2-9d08-5f6a097672c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "height = np.array([167,170,149,165,184,169])\n",
        "width = np.array([84,74,66,89,77,40])\n",
        "plt.scatter(height, width)\n",
        "plt.title(\"Scatter Plot\")\n",
        "plt.xlabel(\"Height\")\n",
        "plt.ylabel(\"Width\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEWCAYAAACAOivfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAXaklEQVR4nO3dfbRddX3n8ffHAPYqxfAQGQJiqA/x\nsRK4Up9oVexELUq0HQaXo9BhyuiaqohGyUxXdbqc5UN01Laz7Eq1I1MUBYwBndboULXjExoIEBQy\nIIpww0NAL6LeSgzf+ePsq5frfQq5+95zst+vte66Z//OPvt8ssXzufvh7J2qQpLUTQ9Z7ACSpMVj\nCUhSh1kCktRhloAkdZglIEkdZglIUodZAlIfSfL2JOcvdg51hyWggZTkOUm+luSeJD9M8tUkT9/L\nZZ6R5CuTxj6a5B17l/bX3uejSe5L8pMm+xeSPOFBLOf7SV4wn9nUPZaABk6Sg4DPAn8FHAIcCfxX\n4OeLmWsqSfab5qn3VNWBwFHAncBHFyyUNIEloEH0eICquqCqdlfVWFV9vqquGZ8hyZ8kuS7JvUm+\nk+S4ZvzcJN+dMP6yZvyJwN8Az2z+Qh9NchbwSuAtzdhnmnmXJ/lUkp1Jvpfk9RPe9+1JLk5yfpIf\nA2fM9A+pqp8BHweeMtXzSV6a5NtNni81OUny98DRwGeabG95cKtSXWcJaBD9P2B3kvOSvCjJwROf\nTPJvgLcDrwYOAl4K3N08/V3gROAR9LYezk9yRFVdB7wG+HpVHVhVS6tqA/Axmr/aq+olSR4CfAa4\nmt4WyEnA2UlWT4hwCnAxsLR5/bSSHEivaLZO8dzjgQuAs4FlwD/Q+9A/oKpeBfwAeEmT7T2zrzbp\n11kCGjhV9WPgOUABfwvsTHJpksObWf4DvQ/ub1XPjVV1c/Pai6pqR1XdX1WfBG4ATtiDt386sKyq\n/qKq7quqm5oMp02Y5+tVtal5j7FplvPmJKPAjcCBTL3F8G+B/11VX6iqXcB7gSHgWXuQV5rRdPsr\npb7W/OV+BkBzUPV84APAK4BH0fuL/9ckeTVwDrCiGToQOGwP3vrRwPLmA3zcEuD/Tpi+ZQ7LeW9V\n/dks8ywHbh6fqKr7k9xCbwtEmheWgAZeVV2f5KPAf2yGbgEeM3m+JI+m91f7SfT+Wt+d5Cog44ua\navGTpm8BvldVj5sp0h7En8kO4KnjE0lCr+BG5vl91GHuDtLASfKEJG9KclQz/Sh6WwDfaGb5ML3d\nLcen57FNATyc3gfnzuZ1f8wDD8jeARyV5IBJY781YfqbwL1J3ppkKMmSJE/Z29NTp3Eh8AdJTkqy\nP/AmemdAfW2abNIeswQ0iO4Ffge4PMlP6X34X0vvQ5Kqugj4b/TOurkX2AQcUlXfAd4HfJ3eB+hT\nga9OWO4/Ad8Gbk9yVzP2EeBJzdk5m6pqN3AycCzwPeAueqXziPn+R1bVduDf0TsV9i7gJfQOBN/X\nzPJO4M+abG+e7/dXN8SbykhSd7klIEkdZglIUodZApLUYZaAJHXYQHxP4LDDDqsVK1YsdgxJGihX\nXHHFXVW1bKZ5BqIEVqxYwZYtWxY7hiQNlCQ3zzaPu4MkqcNaLYEkb0hybXMp3LObsUOam2jc0Pw+\neLblSJLa0VoJJHkK8Cf0rtD4NODkJI8FzgUua669clkzLUlaBG1uCTwRuLyqflZVvwC+DLyc3rXW\nz2vmOQ9Y02IGSdIM2iyBa4ETkxya5GHAi+ldAfHwqrqtmed24PCpXpzkrCRbkmzZuXNnizElqbta\nOzuoqq5L8m7g88BPgauA3ZPmqSRTXryouavTBoDh4WEvcKTWbNo6wvrN29kxOsbypUOsXb2SNau8\nZL+6odUDw1X1kao6vqp+F/gRvdsC3pHkCIDm951tZpBmsmnrCOs2bmNkdIwCRkbHWLdxG5u2jsz6\nWmlf0PbZQY9sfh9N73jAx4FLgdObWU4HLmkzgzST9Zu3M7brARuojO3azfrN2xcpkbSw2v6y2KeS\nHArsAv5TVY0meRdwYZIz6d0679SWM0jT2jE69S2ApxuX9jWtlkBVnTjF2N30bu8nLbrlS4cYmeID\nf/nSoUVIIy08vzGsTlu7eiVD+y95wNjQ/ktYu3rlIiWSFtZAXDtIasv4WUCeHaSusgTUeWtWHemH\nvjrL3UGS1GGWgCR1mCUgSR1mCUhSh1kCktRhloAkdZglIEkdZglIUodZApLUYZaAJHWYJSBJHWYJ\nSFKHeQE5aZ54r2INIktAmgfj9yoev1Xl+L2KAYtAfc3dQdI88F7FGlSWgDQPvFexBpUlIM2D6e5J\n7L2K1e8sAWkeeK9iDSoPDEvzwHsVa1BZAtI88V7FGkTuDpKkDrMEJKnDLAFJ6jBLQJI6zBKQpA6z\nBCSpwywBSeowS0CSOqzVEkjyxiTfTnJtkguS/EaSY5JcnuTGJJ9MckCbGSRJ02utBJIcCbweGK6q\npwBLgNOAdwPvr6rHAj8CzmwrgyRpZm3vDtoPGEqyH/Aw4Dbg+cDFzfPnAWtaziBJmkZrJVBVI8B7\ngR/Q+/C/B7gCGK2qXzSz3Qp4sRVJWiRt7g46GDgFOAZYDjwceOEevP6sJFuSbNm5c2dLKSWp29rc\nHfQC4HtVtbOqdgEbgWcDS5vdQwBHASNTvbiqNlTVcFUNL1u2rMWYktRdbZbAD4BnJHlYkgAnAd8B\nvgj8UTPP6cAlLWaQJM2gzWMCl9M7AHwlsK15rw3AW4FzktwIHAp8pK0MkqSZtXpTmap6G/C2ScM3\nASe0+b6SpLnxG8OS1GGWgCR1mCUgSR1mCUhSh7V6YFiStOc2bR1h/ebt7BgdY/nSIdauXsmaVe1c\nXMESkKQ+smnrCOs2bmNs124ARkbHWLdxG0ArReDuIEnqI+s3b/9lAYwb27Wb9Zu3t/J+loAk9ZEd\no2N7NL63LAFJ6iPLlw7t0fjesgQkqY+sXb2Sof2XPGBsaP8lrF29spX388CwJPWR8YO/nh0kSR21\nZtWRrX3oT+buIEnqMEtAkjrMEpCkDrMEJKnDPDAsDZiFvK6M9n2WgDRAFvq6Mtr3uTtIGiALfV0Z\n7fssAWmALPR1ZbTvswSkAbLQ15XRvs8SkAbIQl9XRvs+DwxLA2ShryujfZ8lIA2YhbyujPZ97g6S\npA6zBCSpwywBSeowS0CSOswSkKQOswQkqcMsAUnqMEtAkjqstRJIsjLJVRN+fpzk7CSHJPlCkhua\n3we3lUGSNLPWSqCqtlfVsVV1LHA88DPg08C5wGVV9TjgsmZakrQIFmp30EnAd6vqZuAU4Lxm/Dxg\nzQJlkCRNslAlcBpwQfP48Kq6rXl8O3D4VC9IclaSLUm27Ny5cyEySlLntF4CSQ4AXgpcNPm5qiqg\npnpdVW2oquGqGl62bFnLKSWpmxZiS+BFwJVVdUczfUeSIwCa33cuQAZJ0hQWogRewa92BQFcCpze\nPD4duGQBMkiSptBqCSR5OPD7wMYJw+8Cfj/JDcALmmlJ0iJo9aYyVfVT4NBJY3fTO1tIkrTI/Maw\nJHWYJSBJHWYJSFKHWQKS1GGWgCR12JzODkryUOAPgRUTX1NVf9FOLEnSQpjrKaKXAPcAVwA/by+O\nJGkhzbUEjqqqF7aaZJ5t2jrC+s3b2TE6xvKlQ6xdvZI1q45c7FiS1Ffmekzga0me2mqSebRp6wjr\nNm5jZHSMAkZGx1i3cRubto4sdjRJ6iszlkCSbUmuAZ4DXJlke5JrJoz3pfWbtzO2a/cDxsZ27Wb9\n5u2LlEiS+tNsu4NOXpAU82zH6NgejUtSV824JVBVNzd3A3vH+OOJYwsTcc8tXzq0R+OS1FVzPSbw\n5IkTSZbQu29wX1q7eiVD+y95wNjQ/ktYu3rlIiWSpP404+6gJOuA/wwMJfnx+DBwH7Ch5WwP2vhZ\nQJ4dJEkzS+8Oj7PMlLyzqtYtQJ4pDQ8P15YtWxbr7SVpICW5oqqGZ5pnti2B45qHF014/EtVdeVe\n5JMkLbLZzg56X/P7N4Bh4Gp6u4N+G9gCPLO9aJKkts12dtDzqup5wG3AcVU1XFXHA6sAv3klSQNu\nrmcHrayqbeMTVXUt8MR2IkmSFspcrx10TZIPA+c3068E+vYbw5KkuZlrCfwx8FrgDc30PwMfaiWR\nJGnBzKkEqupfgPc3P5KkfcRsp4heWFWnJtkG/NoXCqrqt1tLJklq3WxbAluTnAC8DNi1AHkkSQto\nthI4FPgA8ARgG/BV4GvA16rqhy1nkyS1bMYSqKo3AyQ5gN6XxZ5F7yDxhiSjVfWk9iNKktoy17OD\nhoCDgEc0PzvobRlIkgbYbAeGN9C7jPS9wOX0dgX996r60QJkkyS1bLZvDB8NPBS4nd5lIm4FRtsO\nJUlaGLMdE3hhktDbGngW8CbgKUl+CHy9qt62ABklSS2Z9ZhA9W44cG2SUeCe5udk4ATAEpCkATbj\n7qAkr0/yiSQ/AL5M78P/euDlwCGzLTzJ0iQXJ7k+yXVJnpnkkCRfSHJD8/vgefmXSJL22GzHBFYA\nFwG/U1WPqapXVdWHqurqqrp/Dsv/IPC5qnoC8DTgOuBc4LKqehxwWTMtSVoEsx0TOOfBLjjJI4Df\nBc5olnUfcF+SU4DnNrOdB3wJeOuDfR9J0oM31/sJPBjHADuB/5lka5IPJ3k4cHhV3dbMcztw+FQv\nTnJWki1JtuzcubPFmJLUXW2WwH7AccCHqmoV8FMm7fppDjpPeaf7qtrQ3MlseNmyZS3GlKTuarME\nbgVurarLm+mL6ZXCHUmOAGh+39liBknSDForgaq6Hbglycpm6CTgO8ClwOnN2OnAJW1lkCTNbK7X\nDnqwXgd8rLkA3U30Lj73EODCJGcCNwOntpxBkjSNVkugqq6id/XRyU5q830lSXPT5jEBSVKfswQk\nqcMsAUnqMEtAkjrMEpCkDrMEJKnDLAFJ6jBLQJI6zBKQpA6zBCSpwywBSeowS0CSOswSkKQOswQk\nqcMsAUnqMEtAkjrMEpCkDrMEJKnDLAFJ6jBLQJI6zBKQpA6zBCSpwywBSeowS0CSOswSkKQOswQk\nqcMsAUnqMEtAkjrMEpCkDrMEJKnDLAFJ6rD92lx4ku8D9wK7gV9U1XCSQ4BPAiuA7wOnVtWP2swh\nSZraQmwJPK+qjq2q4Wb6XOCyqnoccFkzLUlaBIuxO+gU4Lzm8XnAmkXIIEmi/RIo4PNJrkhyVjN2\neFXd1jy+HTi85QySpGm0ekwAeE5VjSR5JPCFJNdPfLKqKklN9cKmNM4COProo1uOKUnd1OqWQFWN\nNL/vBD4NnADckeQIgOb3ndO8dkNVDVfV8LJly9qMKUmd1VoJJHl4kt8cfwz8a+Ba4FLg9Ga204FL\n2sogSZpZm7uDDgc+nWT8fT5eVZ9L8i3gwiRnAjcDp7aYQZI0g9ZKoKpuAp42xfjdwEltva8kae78\nxrAkdZglIEkdZglIUodZApLUYZaAJHWYJSBJHWYJSFKHWQKS1GGWgCR1mCUgSR1mCUhSh1kCktRh\nloAkdZglIEkdZglIUodZApLUYZaAJHWYJSBJHWYJSFKHWQKS1GGWgCR1mCUgSR1mCUhSh1kCktRh\nloAkdZglIEkdZglIUodZApLUYZaAJHWYJSBJHWYJSFKHWQKS1GH7tf0GSZYAW4CRqjo5yTHAJ4BD\ngSuAV1XVfW3nkAbBpq0jrN+8nR2jYyxfOsTa1StZs+rIxY6lfdhCbAm8AbhuwvS7gfdX1WOBHwFn\nLkAGqe9t2jrCuo3bGBkdo4CR0THWbdzGpq0jix1N+7BWSyDJUcAfAB9upgM8H7i4meU8YE2bGaRB\nsX7zdsZ27X7A2Niu3azfvH2REqkL2t4S+ADwFuD+ZvpQYLSqftFM3wpMua2b5KwkW5Js2blzZ8sx\npcW3Y3Rsj8al+dBaCSQ5Gbizqq54MK+vqg1VNVxVw8uWLZvndFL/Wb50aI/GpfnQ5pbAs4GXJvk+\nvQPBzwc+CCxNMn5A+ijAHZ4SsHb1Sob2X/KAsaH9l7B29cpFSqQuaK0EqmpdVR1VVSuA04B/qqpX\nAl8E/qiZ7XTgkrYySINkzaojeefLn8qRS4cIcOTSId758qd6dpBa1fopolN4K/CJJO8AtgIfWYQM\nUl9as+pIP/S1oBakBKrqS8CXmsc3AScsxPtKkmbmN4YlqcMsAUnqMEtAkjrMEpCkDktVLXaGWSXZ\nCdzc4lscBtzV4vLn0yBlhcHKO0hZYbDyDlJWGKy8M2V9dFXN+G3bgSiBtiXZUlXDi51jLgYpKwxW\n3kHKCoOVd5CywmDl3dus7g6SpA6zBCSpwyyBng2LHWAPDFJWGKy8g5QVBivvIGWFwcq7V1k9JiBJ\nHeaWgCR1mCUgSR22z5dAkr9LcmeSayeMvT3JSJKrmp8XT3huXZIbk2xPsrqf8yZZkWRswvjfLHbW\nZvx1Sa5P8u0k75kw3nfrdrq8/bhuk3xyQp7vJ7lqwnN9t26ny9un6/bYJN9o8mxJckIzniR/2azb\na5Ict5BZH0Te5ya5Z8K6/fNZ36Cq9ukf4HeB44BrJ4y9HXjzFPM+CbgaeChwDPBdYEkf510xcb4+\nWbfPA/4P8NBm+pF9vm6ny9t363bS8+8D/ryf1+0Meftu3QKfB17UPH4x8KUJj/8RCPAM4PI+z/tc\n4LN7svx9fkugqv4Z+OEcZz8F+ERV/byqvgfcyAJf9noP8y6qabK+FnhXVf28mefOZrxf1+10eRfV\nTP8dJAlwKnBBM9Sv6xaYMu+imiZrAQc1jx8B7GgenwL8r+r5Br07Ix6xMEmbYHuWd4/t8yUwgz9t\nNu/+LsnBzdiRwC0T5rm1GesHU+UFOCbJ1iRfTnLioqX7lccDJya5vMn09Ga8X9ftdHmh/9btuBOB\nO6rqhma6X9ftuMl5of/W7dnA+iS3AO8F1jXj/bpup8sL8MwkVyf5xyRPnm1BXS2BDwGPAY4FbqO3\nqdrPpst7G3B0Va0CzgE+nuSgqRexYPYDDqG36bwWuLD5S7BfTZe3H9ftuFfQJ39Vz9HkvP24bl8L\nvLGqHgW8kf6/4+F0ea+kd72gpwF/BWyabUGdLIGquqOqdlfV/cDf8qtN5xHgURNmPaoZW1TT5W02\n/+9uHl9Bb1/w4xcvKdD7S2ljs/n8TeB+ehe46st1yzR5+3TdkmQ/4OXAJycM9+u6nTJvn67b04GN\nzeOL6PPPBKbJW1U/rqqfNI//Adg/yWEzLaiTJTBpn97LgPGj7pcCpyV5aJJjgMcB31zofJNNlzfJ\nsiRLmse/RS/vTQuf8AE20TvYSpLHAwfQu8JhX65bpsnbp+sW4AXA9VV164Sxfl23MEXePl23O4Df\nax4/HxjfdXUp8OrmLKFnAPdU1W2LEXCSKfMm+VfjW97NGUMPAe6ecUkLfaR7oX/obYbeBuyi91ff\nmcDfA9uAa+j9j3zEhPn/C72/TLbTHH3v17zAHwLfBq6itxn4kj7IegBwPr2iuhJ4fp+v2ynz9uO6\nbcY/Crxmivn7bt1Ol7cf1y3wHOAKemdZXQ4c38wb4H8063YbMNwP63aGvH/arNurgW8Az5pt+V42\nQpI6rJO7gyRJPZaAJHWYJSBJHWYJSFKHWQKS1GGWgDovyU8mTZ+R5K9nec1Lk5w7yzzPTfLZaZ47\nO8nD9jytNL8sAelBqKpLq+pde7GIswFLQIvOEpBm0Hy79VNJvtX8PLsZ/+XWQpLHNNd235bkHZO2\nLA5McnF69yv4WPPN09cDy4EvJvniIvyzpF/ab7EDSH1gKBNu0ELvgnKXNo8/CLy/qr6S5GhgM/DE\nSa//IPDBqrogyWsmPbcKeDK9r/l/FXh2Vf1lknOA51XVXfP9j5H2hCUgwVhVHTs+keQMYLiZfAHw\npAkXQj0oyYGTXv9MYE3z+OP0Lu077pvVXDenKZoVwFfmM7y0NywBaWYPAZ5RVf8ycXAPro798wmP\nd+P/59RnPCYgzezzwOvGJ5IcO8U836B3UTSA0+a43HuB39y7aNLeswSkmb0eGG7u6vYdYPI+f+id\n6XNOkmuAxwL3zGG5G4DPeWBYi82riEp7qTnff6yqKslpwCuq6pTFziXNhfsnpb13PPDXzc08RoF/\nv8h5pDlzS0CSOsxjApLUYZaAJHWYJSBJHWYJSFKHWQKS1GH/H71rbCndqG8QAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usafvIOavFgH",
        "colab_type": "code",
        "outputId": "a1c4672c-21e8-4a75-8aee-340ce0511f5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "df.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc63a8a8e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+hJmKaDlAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJSAlkN7wLgH",
        "colab_type": "code",
        "outputId": "a5db60b1-aa75-45d8-e44b-e617dda3748b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "df.Satisfaction.plot(kind=\"bar\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc63d347898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD7CAYAAABzGc+QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zfu41kBzwQAA",
        "colab_type": "code",
        "outputId": "74c1909f-beb2-4381-ef7e-c1e50a08d86e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "df.plot.scatter(x=\"Satisfaction\", y=\"Loyalty\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc63d44a390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEHCAYAAACwUAEWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5XJQmJlZrv8PQwAuMrR+sUoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvV3RueZw83X",
        "colab_type": "text"
      },
      "source": [
        "# Ejercicios\n",
        "- Crea un vector \"v\" de 5 unidades con todo 0\n",
        "- Crea un vector \"w\" de 3 unidades con todo 9\n",
        "- Crea una matriz \"m\" 2,2 con cualquier valor\n",
        "- Crear una matriz unitaria\n",
        "- Haz el dot product de la matrix \"m\" con el vector \"a\"\n",
        "\n",
        "- Carga este CSV (https://drive.google.com/open?id=1uRynZPOrDfmzOjolJbLsLZ17KSFPEKew) en un dataframe\n",
        "- Has un resumen del dataframe\n",
        "- Selecciona solo la columna precio\n",
        "- Ordena la tabla por metros cuadrados (sqft)\n",
        "- Saca la media de precio de las casas\n",
        "\n",
        "- Crea una grafica lineal con cualquier dato\n",
        "- Muestra una grafica de barras con los precios de los primeros 10 casas\n"
      ]
    }
  ]
}