Ejercicio_2.ipynb

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        "<a href=\"https://colab.research.google.com/github/FerBarrey/An-lisis-cuantitativo-avanzados/blob/main/Ejercicio_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Ejercicio 2\n",
        "## Cargar la base\n",
        "\n",
        "Para este segundo ejercicio se usa una base de libros disponible en: https://www.kaggle.com/datasets/jealousleopard/goodreadsbooks"
      ],
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        "id": "Oc6e7HCgBSYq"
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        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "from google.colab import userdata\n",
        "import json\n",
        "\n",
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "api_token = {\n",
        "    'username': userdata.get('KAGGLE_USER'),\n",
        "    'key': userdata.get('KAGGLE_KEY')}\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle datasets download -d jealousleopard/goodreadsbooks"
      ],
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          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/jealousleopard/goodreadsbooks\n",
            "License(s): CC0-1.0\n",
            "Downloading goodreadsbooks.zip to /content\n",
            "  0% 0.00/622k [00:00<?, ?B/s]\n",
            "100% 622k/622k [00:00<00:00, 74.1MB/s]\n"
          ]
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      ]
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    {
      "cell_type": "code",
      "source": [
        "# Extraigo todos los archivos que están en el .zip que acabamos de \"bajar\".\n",
        "\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "os.listdir()\n",
        "\n",
        "for file in os.listdir():\n",
        "    if file.endswith('.zip'):\n",
        "      zip_ref = zipfile.ZipFile(file, 'r')\n",
        "      zip_ref.extractall()\n",
        "      zip_ref.close()"
      ],
      "metadata": {
        "id": "hup9PyUaB2CN"
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      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Cargamos la base\n",
        "df = pd.read_csv('/content/books.csv', on_bad_lines='skip')"
      ],
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        "id": "zfr0Y1avB6FJ"
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      "execution_count": 3,
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        "df.head(2)"
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      "execution_count": 4,
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              "   bookID                                              title  \\\n",
              "0       1  Harry Potter and the Half-Blood Prince (Harry ...   \n",
              "1       2  Harry Potter and the Order of the Phoenix (Har...   \n",
              "\n",
              "                      authors  average_rating        isbn         isbn13  \\\n",
              "0  J.K. Rowling/Mary GrandPré            4.57  0439785960  9780439785969   \n",
              "1  J.K. Rowling/Mary GrandPré            4.49  0439358078  9780439358071   \n",
              "\n",
              "  language_code    num_pages  ratings_count  text_reviews_count  \\\n",
              "0           eng          652        2095690               27591   \n",
              "1           eng          870        2153167               29221   \n",
              "\n",
              "  publication_date        publisher  \n",
              "0        9/16/2006  Scholastic Inc.  \n",
              "1         9/1/2004  Scholastic Inc.  "
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11123,\n  \"fields\": [\n    {\n      \"column\": \"bookID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13094,\n        \"min\": 1,\n        \"max\": 45641,\n        \"num_unique_values\": 11123,\n        \"samples\": [\n          33439,\n          1537,\n          32330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10348,\n        \"samples\": [\n          \"Street of the Five Moons (Vicky Bliss  #2)\",\n          \"The Gunslinger (The Dark Tower #1)\",\n          \"The Totem\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"authors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6639,\n        \"samples\": [\n          \"Jamie Oliver/Marion Deuchars/David Loftus/Chris Terry\",\n          \"John Patrick Diggins/Arthur M. Schlesinger Jr.\",\n          \"George Gamow/Roger Penrose\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3504853116512785,\n        \"min\": 0.0,\n        \"max\": 5.0,\n        \"num_unique_values\": 209,\n        \"samples\": [\n          4.06,\n          3.18,\n          3.96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isbn\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11123,\n        \"samples\": [\n          \"1575667215\",\n          \"0452011671\",\n          \"0345492781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isbn13\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 442975846058,\n        \"min\": 8987059752,\n        \"max\": 9790007672386,\n        \"num_unique_values\": 11123,\n        \"samples\": [\n          9781575667218,\n          9780452011670,\n          9780345492784\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"en-CA\",\n          \"zho\",\n          \"ger\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"  num_pages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 241,\n        \"min\": 0,\n        \"max\": 6576,\n        \"num_unique_values\": 997,\n        \"samples\": [\n          268,\n          795,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratings_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112499,\n        \"min\": 0,\n        \"max\": 4597666,\n        \"num_unique_values\": 5294,\n        \"samples\": [\n          2883,\n          40629,\n          3760\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_reviews_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2576,\n        \"min\": 0,\n        \"max\": 94265,\n        \"num_unique_values\": 1822,\n        \"samples\": [\n          1607,\n          17547,\n          99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"publication_date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3679,\n        \"samples\": [\n          \"8/30/1999\",\n          \"9/6/1997\",\n          \"4/24/1998\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"publisher\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2290,\n        \"samples\": [\n          \"Possibilities\",\n          \"Soft Skull Press\",\n          \"Pocket Books: Gallery Books\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "             bookID  average_rating        isbn13     num_pages  \\\n",
              "count  11123.000000    11123.000000  1.112300e+04  11123.000000   \n",
              "mean   21310.856963        3.934075  9.759880e+12    336.405556   \n",
              "std    13094.727252        0.350485  4.429758e+11    241.152626   \n",
              "min        1.000000        0.000000  8.987060e+09      0.000000   \n",
              "25%    10277.500000        3.770000  9.780345e+12    192.000000   \n",
              "50%    20287.000000        3.960000  9.780582e+12    299.000000   \n",
              "75%    32104.500000        4.140000  9.780872e+12    416.000000   \n",
              "max    45641.000000        5.000000  9.790008e+12   6576.000000   \n",
              "\n",
              "       ratings_count  text_reviews_count  \n",
              "count   1.112300e+04        11123.000000  \n",
              "mean    1.794285e+04          542.048099  \n",
              "std     1.124992e+05         2576.619589  \n",
              "min     0.000000e+00            0.000000  \n",
              "25%     1.040000e+02            9.000000  \n",
              "50%     7.450000e+02           47.000000  \n",
              "75%     5.000500e+03          238.000000  \n",
              "max     4.597666e+06        94265.000000  "
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              "      <th>bookID</th>\n",
              "      <th>average_rating</th>\n",
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              "      <th>ratings_count</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11123.000000</td>\n",
              "      <td>11123.000000</td>\n",
              "      <td>1.112300e+04</td>\n",
              "      <td>11123.000000</td>\n",
              "      <td>1.112300e+04</td>\n",
              "      <td>11123.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>21310.856963</td>\n",
              "      <td>3.934075</td>\n",
              "      <td>9.759880e+12</td>\n",
              "      <td>336.405556</td>\n",
              "      <td>1.794285e+04</td>\n",
              "      <td>542.048099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13094.727252</td>\n",
              "      <td>0.350485</td>\n",
              "      <td>4.429758e+11</td>\n",
              "      <td>241.152626</td>\n",
              "      <td>1.124992e+05</td>\n",
              "      <td>2576.619589</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.987060e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10277.500000</td>\n",
              "      <td>3.770000</td>\n",
              "      <td>9.780345e+12</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>1.040000e+02</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20287.000000</td>\n",
              "      <td>3.960000</td>\n",
              "      <td>9.780582e+12</td>\n",
              "      <td>299.000000</td>\n",
              "      <td>7.450000e+02</td>\n",
              "      <td>47.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>32104.500000</td>\n",
              "      <td>4.140000</td>\n",
              "      <td>9.780872e+12</td>\n",
              "      <td>416.000000</td>\n",
              "      <td>5.000500e+03</td>\n",
              "      <td>238.000000</td>\n",
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              "      <td>45641.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.790008e+12</td>\n",
              "      <td>6576.000000</td>\n",
              "      <td>4.597666e+06</td>\n",
              "      <td>94265.000000</td>\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"bookID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14252.85737701736,\n        \"min\": 1.0,\n        \"max\": 45641.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21310.856963049537,\n          20287.0,\n          11123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3931.5063275494367,\n        \"min\": 0.0,\n        \"max\": 11123.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.9340753393868564,\n          3.96,\n          11123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isbn13\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4984644225903.03,\n        \"min\": 11123.0,\n        \"max\": 9790007672386.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9759880247639.154,\n          9780582461536.0,\n          11123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"  num_pages\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4165.066218299191,\n        \"min\": 0.0,\n        \"max\": 11123.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          336.4055560550211,\n          299.0,\n          11123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ratings_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1618517.2369487006,\n        \"min\": 0.0,\n        \"max\": 4597666.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17942.848062573048,\n          745.0,\n          11123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_reviews_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32813.11782870056,\n        \"min\": 0.0,\n        \"max\": 94265.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          542.048098534568,\n          47.0,\n          11123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preguntas"
      ],
      "metadata": {
        "id": "kyV3MJhCCqbi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pregunta 1: ¿Cuántos libros hay en la base?\n",
        "\n",
        "Ayuda: la función len devuelve el largo de una lista o filas de una base.\n",
        "\n",
        "Otra manera sería con el método .shape\n",
        "\n",
        "Como sea, el resultado debería ser 11123"
      ],
      "metadata": {
        "id": "17_5oIOICsLj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "DFyVaNafDcSK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41ac6ac2-d776-4fb7-c0cc-de3393d510b2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11123, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLl9njjGdxch",
        "outputId": "332e3457-3436-4ace-e7db-aa8093ffb639"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11123"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pregunta 2: ¿Cuál es el rating promedio?\n",
        "\n",
        "Ayuda: `df['average_rating'].mean()` devuelve el promedio de la columna en cuestión. También podría chequear que ese resultado conincida con el obtenido al hacer un \"describe\" de la base."
      ],
      "metadata": {
        "id": "5tGBwNTCEY62"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['average_rating'].mean()"
      ],
      "metadata": {
        "id": "5ATvvf65FULG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "30029246-9b2e-435e-fc0c-8d587c842b1e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.9340753393868564"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pregunta 3: ¿Cuál es la editorial con más libros en la base?\n",
        "\n",
        "Ayuda: la respuesta es \"Vintage\". Una manera de saberlo es mediante `df['publisher'].value_counts()` ¿Pero de qué otras maneras se podría llegar al mismo resultado?"
      ],
      "metadata": {
        "id": "96spzVjDFqbV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['publisher'].value_counts()"
      ],
      "metadata": {
        "id": "62FjXL8gF2Tb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "117286d9-9328-4f49-ffb3-1214b43d5ace"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "publisher\n",
              "Vintage                        318\n",
              "Penguin Books                  261\n",
              "Penguin Classics               184\n",
              "Mariner Books                  150\n",
              "Ballantine Books               144\n",
              "                              ... \n",
              "University of Calgary Press      1\n",
              "Marlowe & Company                1\n",
              "University Press of America      1\n",
              "Abstract Studio                  1\n",
              "VeloPress                        1\n",
              "Name: count, Length: 2290, dtype: int64"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mariner Books</th>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ballantine Books</th>\n",
              "      <td>144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>University of Calgary Press</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marlowe &amp; Company</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>University Press of America</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Abstract Studio</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VeloPress</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2290 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " La moda es el valor o valores que aparecen con mayor frecuencia en un conjunto de datos , con esta funcion llego al mismo resultado"
      ],
      "metadata": {
        "id": "2uxE7GzmiOMB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['publisher'].mode()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "G_eQaHXsf2cI",
        "outputId": "cadc3b53-327c-4fd0-dcdb-dfb5ed9cbbd5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Vintage'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pregunta 4: ¿Cómo es la distribución de la cantidad de páginas de los libros en la base?\n",
        "\n",
        "Ayuda: Podría hacerlo con seaborn\n",
        "\n",
        "`sns.distplot(df['  num_pages'],bins = 40)`"
      ],
      "metadata": {
        "id": "V8YWoMkEGGPg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.distplot(df['  num_pages'],bins = 40)"
      ],
      "metadata": {
        "id": "zd4UWZrRHhK2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "outputId": "5a07f8f1-e52a-4f03-d6cb-d1dd0ea48884"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-3f479a7a65d9>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df['  num_pages'],bins = 40)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='  num_pages', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pregunta 5: ¿Existe una relación entre el número de páginas de un libro y su rating promedio? Muestre gráficamente y con una regresión lineal.\n",
        "\n",
        "Ayuda: El gráfico con seaborn se puede hacer con el siguiente código:\n",
        "`sns.scatterplot(y='ratings_count',x='  num_pages',data=df)`\n",
        "\n",
        "La regresión podría hacerse con statsmodels de la siguiente manera:\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "import statsmodels.api as sm\n",
        "df=df.dropna()\n",
        "mod = sm.OLS(df['average_rating'], df['  num_pages'])\n",
        "res = mod.fit()\n",
        "print(res.summary())\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "También podría hacerse con sklearn. ¿Cómo?\n",
        "\n"
      ],
      "metadata": {
        "id": "lE8V4jdXIYKB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "df=df.dropna()\n",
        "mod = sm.OLS(df['average_rating'], df['  num_pages'])\n",
        "res = mod.fit()\n",
        "print(res.summary())\n",
        "import seaborn as sns\n",
        "sns.scatterplot(x='  num_pages', y='average_rating', data=df)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.xlim(0, 7000)\n",
        "plt.xticks(np.arange(0, 7000, 500))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "amj4j9psK6r8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "outputId": "33f67b63-8c2f-4164-c313-1a456db0722f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:         average_rating   R-squared (uncentered):                   0.668\n",
            "Model:                            OLS   Adj. R-squared (uncentered):              0.668\n",
            "Method:                 Least Squares   F-statistic:                          2.238e+04\n",
            "Date:                Mon, 09 Sep 2024   Prob (F-statistic):                        0.00\n",
            "Time:                        18:26:48   Log-Likelihood:                         -24929.\n",
            "No. Observations:               11123   AIC:                                  4.986e+04\n",
            "Df Residuals:                   11122   BIC:                                  4.987e+04\n",
            "Df Model:                           1                                                  \n",
            "Covariance Type:            nonrobust                                                  \n",
            "===============================================================================\n",
            "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
            "-------------------------------------------------------------------------------\n",
            "  num_pages     0.0078   5.21e-05    149.603      0.000       0.008       0.008\n",
            "==============================================================================\n",
            "Omnibus:                    10492.228   Durbin-Watson:                   1.076\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1724029.239\n",
            "Skew:                          -4.067   Prob(JB):                         0.00\n",
            "Kurtosis:                      63.446   Cond. No.                         1.00\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
            "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El análisis de regresión lineal y el grafico muestra que existe una relacion entre la cantidad de paginas y el promedio de rating de los libros."
      ],
      "metadata": {
        "id": "_SKOrh4kEG3j"
      }
    }
  ]
}