analisis_de_ventas_2019.ipynb

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          "text": [
            "<ipython-input-1-e8f37dae5b85>:13: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.\n",
            "  plt.style.use(\"seaborn\")\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "import datetime as dt\n",
        "import seaborn as sns\n",
        "\n",
        "import itertools\n",
        "from itertools import combinations\n",
        "import collections\n",
        "from collections import Counter\n",
        "\n",
        "plt.style.use(\"seaborn\")\n",
        "sns.set_style(\"darkgrid\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "os.listdir(\"files\")"
      ],
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        "outputId": "629b8455-2739-40d3-b8d5-29b1fd391426"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sales_March_2019.csv',\n",
              " 'Sales_January_2019.csv',\n",
              " 'Sales_June_2019.csv',\n",
              " 'Sales_October_2019.csv',\n",
              " 'Sales_April_2019.csv',\n",
              " 'Sales_September_2019.csv',\n",
              " 'Sales_February_2019.csv',\n",
              " 'Sales_July_2019.csv',\n",
              " 'Sales_December_2019.csv',\n",
              " 'Sales_May_2019.csv',\n",
              " 'Sales_November_2019.csv',\n",
              " 'Sales_August_2019.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sales_april = pd.read_csv(\"/content/files/Sales_April_2019.csv\")"
      ],
      "metadata": {
        "id": "ybnYrjMSfeOF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame()"
      ],
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        "id": "r6mqDrgMf1dH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "files = os.listdir(\"files\")\n",
        "for i in files:\n",
        "  file=pd.read_csv(\"files/\"+i)\n",
        "  df = pd.concat([file,df])"
      ],
      "metadata": {
        "id": "lCEUeVOljbLU"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df.head()"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  Order ID                     Product Quantity Ordered Price Each  \\\n",
              "0   236670            Wired Headphones                2      11.99   \n",
              "1   236671  Bose SoundSport Headphones                1      99.99   \n",
              "2   236672                      iPhone                1      700.0   \n",
              "3   236673       AA Batteries (4-pack)                2       3.84   \n",
              "4   236674       AA Batteries (4-pack)                2       3.84   \n",
              "\n",
              "       Order Date                      Purchase Address  \n",
              "0  08/31/19 22:21      359 Spruce St, Seattle, WA 98101  \n",
              "1  08/15/19 15:11        492 Ridge St, Dallas, TX 75001  \n",
              "2  08/06/19 14:40        149 7th St, Portland, OR 97035  \n",
              "3  08/29/19 20:59     631 2nd St, Los Angeles, CA 90001  \n",
              "4  08/15/19 19:53  736 14th St, New York City, NY 10001  "
            ],
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              "      <td>08/15/19 19:53</td>\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analisis exploratorio inicial"
      ],
      "metadata": {
        "id": "m9daRP-4lvyP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKtY-bmSl1zf",
        "outputId": "c18d8dbb-1083-4935-89cd-3ed9914ac287"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 186850 entries, 0 to 15225\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count   Dtype \n",
            "---  ------            --------------   ----- \n",
            " 0   Order ID          186305 non-null  object\n",
            " 1   Product           186305 non-null  object\n",
            " 2   Quantity Ordered  186305 non-null  object\n",
            " 3   Price Each        186305 non-null  object\n",
            " 4   Order Date        186305 non-null  object\n",
            " 5   Purchase Address  186305 non-null  object\n",
            "dtypes: object(6)\n",
            "memory usage: 10.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6un8cbrRl-2o",
        "outputId": "611782f2-a00d-4378-a664-c84357cc308f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order ID            object\n",
              "Product             object\n",
              "Quantity Ordered    object\n",
              "Price Each          object\n",
              "Order Date          object\n",
              "Purchase Address    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lA0l4sJdmIBD",
        "outputId": "3c2660c5-b826-49a2-b758-847827d39bee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order ID  Product  Quantity Ordered  Price Each  Order Date  Purchase Address\n",
              "False     False    False             False       False       False               186305\n",
              "True      True     True              True        True        True                   545\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Order ID\"].isnull().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zI2ZryP2me-T",
        "outputId": "8890ca21-0b34-43e9-cee6-d59c754fb356"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    186305\n",
              "True        545\n",
              "Name: Order ID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df[df[\"Order ID\"].notnull()]"
      ],
      "metadata": {
        "id": "AXFPTTp5ney1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMpXZ8ybnz1z",
        "outputId": "024d32be-fbeb-4022-8480-254fc168c937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order ID  Product  Quantity Ordered  Price Each  Order Date  Purchase Address\n",
              "False     False    False             False       False       False               186305\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definir el tipo de cada variable"
      ],
      "metadata": {
        "id": "DQ_lhwr_n_fS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
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        "id": "2x6kV65_n-Gr",
        "outputId": "d134ad2a-995e-4cae-98ca-4cf3a3a1f1e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 186305 entries, 0 to 15225\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count   Dtype \n",
            "---  ------            --------------   ----- \n",
            " 0   Order ID          186305 non-null  object\n",
            " 1   Product           186305 non-null  object\n",
            " 2   Quantity Ordered  186305 non-null  object\n",
            " 3   Price Each        186305 non-null  object\n",
            " 4   Order Date        186305 non-null  object\n",
            " 5   Purchase Address  186305 non-null  object\n",
            "dtypes: object(6)\n",
            "memory usage: 9.9+ MB\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
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          "height": 112
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        "id": "Tsp1DJBcoJ28",
        "outputId": "29f58b54-c3fa-465e-82ff-2a633654b06e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product Quantity Ordered Price Each  \\\n",
              "0   236670            Wired Headphones                2      11.99   \n",
              "1   236671  Bose SoundSport Headphones                1      99.99   \n",
              "\n",
              "       Order Date                  Purchase Address  \n",
              "0  08/31/19 22:21  359 Spruce St, Seattle, WA 98101  \n",
              "1  08/15/19 15:11    492 Ridge St, Dallas, TX 75001  "
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df[df[\"Product\"] != \"Product\"]"
      ],
      "metadata": {
        "id": "wnpw5sItqpK3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Quantity Ordered\"]=pd.to_numeric(df[\"Quantity Ordered\"])\n",
        "df[\"Price Each\"]=pd.to_numeric(df[\"Price Each\"])\n",
        "df[\"Order Date\"]=pd.to_datetime(df[\"Order Date\"])"
      ],
      "metadata": {
        "id": "UUG7wYkqoaOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qG1ga8EXqnQs",
        "outputId": "ddfbf596-5f1e-4e9e-aa60-384b44df257b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 185950 entries, 0 to 15225\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count   Dtype         \n",
            "---  ------            --------------   -----         \n",
            " 0   Order ID          185950 non-null  object        \n",
            " 1   Product           185950 non-null  object        \n",
            " 2   Quantity Ordered  185950 non-null  int64         \n",
            " 3   Price Each        185950 non-null  float64       \n",
            " 4   Order Date        185950 non-null  datetime64[ns]\n",
            " 5   Purchase Address  185950 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), int64(1), object(3)\n",
            "memory usage: 9.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ¿Cuál fue el mes con la venta más alta?"
      ],
      "metadata": {
        "id": "cRJ3wZn3scy8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "MX3Elisysl6u",
        "outputId": "480a19bf-3f30-4871-c4a3-0b3b2f7a7c8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   236670            Wired Headphones                 2       11.99   \n",
              "1   236671  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "           Order Date                  Purchase Address  \n",
              "0 2019-08-31 22:21:00  359 Spruce St, Seattle, WA 98101  \n",
              "1 2019-08-15 15:11:00    492 Ridge St, Dallas, TX 75001  "
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-766ca859-fc3c-43e2-bdc5-2b7feb08cd90');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7bd07c30-2b16-4f42-b71d-bb61e21d80f7')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bd07c30-2b16-4f42-b71d-bb61e21d80f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#extraer el mes\n",
        "\n",
        "df[\"Mes\"]=df[\"Order Date\"].dt.month"
      ],
      "metadata": {
        "id": "DA3TaASCsxtZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Determinar el numero total de ventas\n",
        "\n",
        "df[\"Total_Ventas\"] = df[\"Quantity Ordered\"]*df[\"Price Each\"]"
      ],
      "metadata": {
        "id": "V6xvRGQwvLuV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#agrupación por mes\n",
        "\n",
        "df_mes = df.groupby(\"Mes\").sum()\n",
        "\n",
        "#resetear indice\n",
        "\n",
        "df_mes = df_mes.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DJDho8_nvw0H",
        "outputId": "c17b3430-2e95-47be-c512-5527f71001e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-76484b3bbe0b>:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_mes = df.groupby(\"Mes\").sum()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(df_mes[\"Mes\"], df_mes[\"Total_Ventas\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "mEARD21hxVyx",
        "outputId": "c104bdff-5d5e-4f23-a091-489223da14c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 12 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Agrupación por fechas\n",
        "\n",
        "df_fechas=df.groupby(\"Order Date\").sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c90oJR0ly4Pp",
        "outputId": "c3273cb6-5679-4a70-cc33-0b03b9f90f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-9627f4ea73fb>:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_fechas=df.groupby(\"Order Date\").sum()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_fechas=df_fechas.resample(\"w\").sum()\n",
        "df_fechas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oyhK1LDVzK6m",
        "outputId": "999f7081-672d-482e-becc-0c0452f6e1b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Quantity Ordered  Price Each    Mes  Total_Ventas\n",
              "Order Date                                                   \n",
              "2019-01-06              2045   343062.87   1815     344855.67\n",
              "2019-01-13              2457   407539.11   2199     409428.28\n",
              "2019-01-20              2462   392745.06   2200     394936.90\n",
              "2019-01-27              2525   424518.30   2237     426632.16\n",
              "2019-02-03              2780   456563.41   3728     460355.55\n",
              "2019-02-10              3299   563327.94   5934     566203.88\n",
              "2019-02-17              3435   565026.42   6114     568227.10\n",
              "2019-02-24              3397   530962.84   5976     535323.33\n",
              "2019-03-03              3543   575845.83   7832     578418.38\n",
              "2019-03-10              3843   637208.90  10299     640535.27\n",
              "2019-03-17              3865   593018.15  10308     596595.99\n",
              "2019-03-24              3867   626002.79  10338     629763.90\n",
              "2019-03-31              3798   667535.91  10104     671432.83\n",
              "2019-04-07              4761   769298.11  16860     774904.91\n",
              "2019-04-14              4891   757662.78  17408     761676.00\n",
              "2019-04-21              4837   801229.10  17332     805556.11\n",
              "2019-04-28              4697   801171.47  16644     808871.58\n",
              "2019-05-05              4272   761939.45  17962     764849.44\n",
              "2019-05-12              4245   726185.05  18840     729427.91\n",
              "2019-05-19              4278   690170.32  19050     695770.71\n",
              "2019-05-26              4265   707381.71  18700     711496.54\n",
              "2019-06-02              4017   664194.59  18592     668258.10\n",
              "2019-06-09              3537   611480.92  18840     616391.11\n",
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              "      <td>456563.41</td>\n",
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              "      <td>563327.94</td>\n",
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              "      <td>568227.10</td>\n",
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              "      <th>2019-02-24</th>\n",
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              "      <td>3543</td>\n",
              "      <td>575845.83</td>\n",
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              "      <td>578418.38</td>\n",
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              "      <th>2019-03-10</th>\n",
              "      <td>3843</td>\n",
              "      <td>637208.90</td>\n",
              "      <td>10299</td>\n",
              "      <td>640535.27</td>\n",
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              "      <th>2019-03-17</th>\n",
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              "      <td>593018.15</td>\n",
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              "      <td>596595.99</td>\n",
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              "      <th>2019-03-24</th>\n",
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              "      <th>2019-04-07</th>\n",
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              "      <td>769298.11</td>\n",
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              "      <th>2019-05-05</th>\n",
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              "      <th>2019-05-12</th>\n",
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              "      <th>2019-05-19</th>\n",
              "      <td>4278</td>\n",
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              "      <td>695770.71</td>\n",
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              "      <th>2019-05-26</th>\n",
              "      <td>4265</td>\n",
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              "      <td>610101.51</td>\n",
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              "      <td>559889.44</td>\n",
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              "      <th>2019-08-04</th>\n",
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              "      <td>21432</td>\n",
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              "      <th>2019-08-11</th>\n",
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              "      <td>550752.14</td>\n",
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              "      <th>2019-08-18</th>\n",
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              "      <th>2019-09-08</th>\n",
              "      <td>3008</td>\n",
              "      <td>476273.37</td>\n",
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              "      <td>478816.84</td>\n",
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              "      <th>2019-09-15</th>\n",
              "      <td>3132</td>\n",
              "      <td>533957.14</td>\n",
              "      <td>25191</td>\n",
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              "      <td>24165</td>\n",
              "      <td>434949.27</td>\n",
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              "      <th>2019-09-29</th>\n",
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              "      <td>503436.64</td>\n",
              "      <td>24363</td>\n",
              "      <td>506249.62</td>\n",
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              "      <th>2019-10-06</th>\n",
              "      <td>4836</td>\n",
              "      <td>794141.68</td>\n",
              "      <td>42520</td>\n",
              "      <td>799212.50</td>\n",
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              "      <th>2019-10-13</th>\n",
              "      <td>5028</td>\n",
              "      <td>821368.08</td>\n",
              "      <td>45150</td>\n",
              "      <td>826241.75</td>\n",
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              "      <th>2019-10-20</th>\n",
              "      <td>5118</td>\n",
              "      <td>842683.03</td>\n",
              "      <td>45550</td>\n",
              "      <td>846999.42</td>\n",
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              "      <th>2019-10-27</th>\n",
              "      <td>5261</td>\n",
              "      <td>843065.08</td>\n",
              "      <td>46990</td>\n",
              "      <td>847769.59</td>\n",
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              "      <th>2019-11-03</th>\n",
              "      <td>4862</td>\n",
              "      <td>807003.23</td>\n",
              "      <td>45291</td>\n",
              "      <td>811488.35</td>\n",
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              "      <th>2019-11-10</th>\n",
              "      <td>4699</td>\n",
              "      <td>742981.65</td>\n",
              "      <td>45232</td>\n",
              "      <td>747093.90</td>\n",
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              "      <td>44209</td>\n",
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              "      <th>2019-11-24</th>\n",
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              "      <td>46024</td>\n",
              "      <td>775799.61</td>\n",
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              "      <td>47961</td>\n",
              "      <td>788703.80</td>\n",
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              "      <td>1048487.00</td>\n",
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              "      <td>1070021.09</td>\n",
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              "      <td>6477</td>\n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dfc4f0f9-e507-4918-9ebb-9314890545c6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dfc4f0f9-e507-4918-9ebb-9314890545c6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dfc4f0f9-e507-4918-9ebb-9314890545c6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#contratste de graficos\n",
        "\n",
        "fig, axes=plt.subplots(1,2, figsize=(16,8))\n",
        "ax1=plt.subplot(1,2,1)\n",
        "ax2=plt.subplot(1,2,2)\n",
        "\n",
        "bars=ax1.bar(df_mes[\"Mes\"], df_mes[\"Total_Ventas\"])\n",
        "ax2.plot(df_fechas[\"Total_Ventas\"])\n",
        "\n",
        "ax1.set_xticks(df_mes[\"Mes\"])\n",
        "\n",
        "\n",
        "#estilos\n",
        "ax1.set_title(\"Ventas por mes\", size=18)\n",
        "ax2.set_title(\"Tendencia de ventas por semana\", size=18)\n",
        "\n",
        "ax1.set_xlabel(\"Meses\")\n",
        "ax1.set_ylabel(\"Ventas en millones\")\n",
        "bars[11].set_color(\"#D80032\")\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "6HZ7cIlhxsh2",
        "outputId": "7b34e699-9d4e-477e-f388-1c0a84747072"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "¿Cual es la ciudad con más ventas?"
      ],
      "metadata": {
        "id": "KOAQ3i0MfXbo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJx-UcEWfi_e",
        "outputId": "e21b23a6-99d0-45f6-90df-6bf94d75cc66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Order ID', 'Product', 'Quantity Ordered', 'Price Each', 'Order Date',\n",
              "       'Purchase Address', 'Mes', 'Total_Ventas'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "fDBqonOrfxnp",
        "outputId": "31cf44da-f1e3-4502-8862-e802b10284a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   236670            Wired Headphones                 2       11.99   \n",
              "1   236671  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "           Order Date                  Purchase Address  Mes  Total_Ventas  \n",
              "0 2019-08-31 22:21:00  359 Spruce St, Seattle, WA 98101    8         23.98  \n",
              "1 2019-08-15 15:11:00    492 Ridge St, Dallas, TX 75001    8         99.99  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Order ID</th>\n",
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              "      <td>236670</td>\n",
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              "      <td>2</td>\n",
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              "      <td>359 Spruce St, Seattle, WA 98101</td>\n",
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              "      <td>492 Ridge St, Dallas, TX 75001</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-17999739-b81f-4dfd-87f5-c3cfe4ea0cb3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-17999739-b81f-4dfd-87f5-c3cfe4ea0cb3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a0861602-7319-4ccd-a2ca-fe21c376670e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a0861602-7319-4ccd-a2ca-fe21c376670e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a0861602-7319-4ccd-a2ca-fe21c376670e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dr=df.iloc[0,5]\n",
        "dr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "vmmkI5NvfdQW",
        "outputId": "ce229a42-1006-460b-bb49-d622556efe93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'359 Spruce St, Seattle, WA 98101'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dr.split(\",\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7OSjpnsgHmY",
        "outputId": "28f9997a-9e08-4be8-9b32-a62b8687816b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['359 Spruce St', ' Seattle', ' WA 98101']"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dr.split(\",\")[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "2pJpq1VdgNBh",
        "outputId": "3df84060-8925-4a1f-e2ad-f2b4ea9ae696"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Seattle'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Ciudad\"]=df['Purchase Address'].apply(lambda x: x.split(\",\")[1])\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "F7ycgD8YgqlG",
        "outputId": "de34e3d4-12db-4de0-cc7b-84e8b1a14af4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   236670            Wired Headphones                 2       11.99   \n",
              "1   236671  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "           Order Date                  Purchase Address  Mes  Total_Ventas  \\\n",
              "0 2019-08-31 22:21:00  359 Spruce St, Seattle, WA 98101    8         23.98   \n",
              "1 2019-08-15 15:11:00    492 Ridge St, Dallas, TX 75001    8         99.99   \n",
              "\n",
              "     Ciudad  \n",
              "0   Seattle  \n",
              "1    Dallas  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a408910e-f24c-45dd-b500-2a6a1dcf5f30\" class=\"colab-df-container\">\n",
              "    <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>Order ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Quantity Ordered</th>\n",
              "      <th>Price Each</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Purchase Address</th>\n",
              "      <th>Mes</th>\n",
              "      <th>Total_Ventas</th>\n",
              "      <th>Ciudad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>236670</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>2</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-08-31 22:21:00</td>\n",
              "      <td>359 Spruce St, Seattle, WA 98101</td>\n",
              "      <td>8</td>\n",
              "      <td>23.98</td>\n",
              "      <td>Seattle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>236671</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-08-15 15:11:00</td>\n",
              "      <td>492 Ridge St, Dallas, TX 75001</td>\n",
              "      <td>8</td>\n",
              "      <td>99.99</td>\n",
              "      <td>Dallas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a408910e-f24c-45dd-b500-2a6a1dcf5f30')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a408910e-f24c-45dd-b500-2a6a1dcf5f30 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a408910e-f24c-45dd-b500-2a6a1dcf5f30');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-195b01be-87d4-497b-bb7d-7e6b2495347a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-195b01be-87d4-497b-bb7d-7e6b2495347a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-195b01be-87d4-497b-bb7d-7e6b2495347a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   236670            Wired Headphones                 2       11.99   \n",
              "1   236671  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "           Order Date                  Purchase Address  Mes  Total_Ventas  \\\n",
              "0 2019-08-31 22:21:00  359 Spruce St, Seattle, WA 98101    8         23.98   \n",
              "1 2019-08-15 15:11:00    492 Ridge St, Dallas, TX 75001    8         99.99   \n",
              "\n",
              "     Ciudad  \n",
              "0   Seattle  \n",
              "1    Dallas  "
            ],
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              "      <td>359 Spruce St, Seattle, WA 98101</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-14e57411-bd40-4f78-84ca-16093e2e51eb');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c68dc09e-bc08-4501-9700-fb20e469854b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_ciudad=df.groupby(\"Ciudad\").sum()\n",
        "\n",
        "df_ciudad=df_ciudad.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hgqpkb6hTXp",
        "outputId": "5c747c1d-9b29-49a0-eafd-e5bdb37fae60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-4595e8850065>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_ciudad=df.groupby(\"Ciudad\").sum()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bars=plt.bar(df_ciudad[\"Ciudad\"], df_ciudad[\"Total_Ventas\"])\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "bars[7].set_color(\"#D80032\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "S9vtalOphqcG",
        "outputId": "2b94e997-65bc-4bed-8c45-0c88d4cd974e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ¿Cual es la hora en que se venden más productos?"
      ],
      "metadata": {
        "id": "M-r6bF5KtX9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "LwFh998dtXa2",
        "outputId": "0a991476-f5c5-43da-ef3f-f7ad68abc594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   236670            Wired Headphones                 2       11.99   \n",
              "1   236671  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "           Order Date                  Purchase Address  Mes  Total_Ventas  \\\n",
              "0 2019-08-31 22:21:00  359 Spruce St, Seattle, WA 98101    8         23.98   \n",
              "1 2019-08-15 15:11:00    492 Ridge St, Dallas, TX 75001    8         99.99   \n",
              "\n",
              "     Ciudad  \n",
              "0   Seattle  \n",
              "1    Dallas  "
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Quantity Ordered</th>\n",
              "      <th>Price Each</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Purchase Address</th>\n",
              "      <th>Mes</th>\n",
              "      <th>Total_Ventas</th>\n",
              "      <th>Ciudad</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>236670</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>2</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-08-31 22:21:00</td>\n",
              "      <td>359 Spruce St, Seattle, WA 98101</td>\n",
              "      <td>8</td>\n",
              "      <td>23.98</td>\n",
              "      <td>Seattle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>236671</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-08-15 15:11:00</td>\n",
              "      <td>492 Ridge St, Dallas, TX 75001</td>\n",
              "      <td>8</td>\n",
              "      <td>99.99</td>\n",
              "      <td>Dallas</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4147ce3-88a0-47bd-9a12-200e5967af43')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b4147ce3-88a0-47bd-9a12-200e5967af43 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b4147ce3-88a0-47bd-9a12-200e5967af43');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ef6960bf-1287-4743-b4c9-75da833e8f6c')\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ef6960bf-1287-4743-b4c9-75da833e8f6c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Hora\"]=df[\"Order Date\"].apply(lambda x: x.hour)\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "0JDFz0IGtmok",
        "outputId": "c95650a1-aca8-480d-da37-a5a964fa1658"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   236670            Wired Headphones                 2       11.99   \n",
              "1   236671  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "           Order Date                  Purchase Address  Mes  Total_Ventas  \\\n",
              "0 2019-08-31 22:21:00  359 Spruce St, Seattle, WA 98101    8         23.98   \n",
              "1 2019-08-15 15:11:00    492 Ridge St, Dallas, TX 75001    8         99.99   \n",
              "\n",
              "     Ciudad  Hora  \n",
              "0   Seattle    22  \n",
              "1    Dallas    15  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ab37186-499d-4ae1-84af-a7046d44a216\" class=\"colab-df-container\">\n",
              "    <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>Order ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Quantity Ordered</th>\n",
              "      <th>Price Each</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Purchase Address</th>\n",
              "      <th>Mes</th>\n",
              "      <th>Total_Ventas</th>\n",
              "      <th>Ciudad</th>\n",
              "      <th>Hora</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>236670</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>2</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-08-31 22:21:00</td>\n",
              "      <td>359 Spruce St, Seattle, WA 98101</td>\n",
              "      <td>8</td>\n",
              "      <td>23.98</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>236671</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-08-15 15:11:00</td>\n",
              "      <td>492 Ridge St, Dallas, TX 75001</td>\n",
              "      <td>8</td>\n",
              "      <td>99.99</td>\n",
              "      <td>Dallas</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ab37186-499d-4ae1-84af-a7046d44a216')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9ab37186-499d-4ae1-84af-a7046d44a216 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9ab37186-499d-4ae1-84af-a7046d44a216');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-84e78e97-3a2a-4ac8-bb78-031a512ba0c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-84e78e97-3a2a-4ac8-bb78-031a512ba0c9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-84e78e97-3a2a-4ac8-bb78-031a512ba0c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agrupacion por hora"
      ],
      "metadata": {
        "id": "IoPLPLHyt-sf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_hour= df.groupby(\"Hora\").sum()\n",
        "df_hour=df_hour.reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hud6ciE9t970",
        "outputId": "9243aebf-fb10-43bf-cbf7-9a617c18bfb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-37-e836cfd89256>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
            "  df_hour= df.groupby(\"Hora\").sum()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "crear grafica de lineas"
      ],
      "metadata": {
        "id": "w0b91t8suehz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df_hour[\"Total_Ventas\"], marker=\"o\")\n",
        "plt.xticks(df_hour[\"Hora\"])\n",
        "\n",
        "plt.title(\"Venta por hora del día\")\n",
        "\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "T3eXeoZyujPC",
        "outputId": "3bba0dd6-4b06-4af7-826a-dcf2ce135fc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ¿Cuáles son los productos que se venden juntos?"
      ],
      "metadata": {
        "id": "HTeZkYuRvYgX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "Xo8k-2MDuunM",
        "outputId": "da00967b-355b-426d-a615-85b03f7f348e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   236670            Wired Headphones                 2       11.99   \n",
              "1   236671  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "           Order Date                  Purchase Address  Mes  Total_Ventas  \\\n",
              "0 2019-08-31 22:21:00  359 Spruce St, Seattle, WA 98101    8         23.98   \n",
              "1 2019-08-15 15:11:00    492 Ridge St, Dallas, TX 75001    8         99.99   \n",
              "\n",
              "     Ciudad  Hora  \n",
              "0   Seattle    22  \n",
              "1    Dallas    15  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Order ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>236670</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>2</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-08-31 22:21:00</td>\n",
              "      <td>359 Spruce St, Seattle, WA 98101</td>\n",
              "      <td>8</td>\n",
              "      <td>23.98</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>236671</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-08-15 15:11:00</td>\n",
              "      <td>492 Ridge St, Dallas, TX 75001</td>\n",
              "      <td>8</td>\n",
              "      <td>99.99</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        document.querySelector('#df-efd3d431-548b-4e5f-b90e-ea85972be70d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "\n",
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              "        const element = document.querySelector('#df-efd3d431-548b-4e5f-b90e-ea85972be70d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --bg-color: #3B4455;\n",
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              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-67d38e9e-280a-4b8a-a712-e3181f72df42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=df[df[\"Order ID\"].duplicated(keep=False)]\n",
        "\n",
        "df[\"agrupado\"]=df.groupby([\"Order ID\"])[\"Product\"].transform(lambda x: \",\".join(x))\n",
        "\n",
        "df=df[[\"Order ID\", \"agrupado\"]].drop_duplicates()\n",
        "\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "yYCiD23uvi1x",
        "outputId": "4a540bee-91f3-49f7-d5d3-4c5ecb0b1864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Order ID                                    agrupado\n",
              "46   236716  AA Batteries (4-pack),USB-C Charging Cable\n",
              "60   236729             iPhone,Apple Airpods Headphones"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e59354d-4c58-45ed-a8dd-a3675866dabd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Order ID</th>\n",
              "      <th>agrupado</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>236716</td>\n",
              "      <td>AA Batteries (4-pack),USB-C Charging Cable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>236729</td>\n",
              "      <td>iPhone,Apple Airpods Headphones</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      display:flex;\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2e59354d-4c58-45ed-a8dd-a3675866dabd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2e59354d-4c58-45ed-a8dd-a3675866dabd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cea80619-aebe-42e7-bdd7-fb0895f22f4d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cea80619-aebe-42e7-bdd7-fb0895f22f4d')\"\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cea80619-aebe-42e7-bdd7-fb0895f22f4d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count=Counter()\n",
        "\n",
        "for i in df[\"agrupado\"]:\n",
        "  filas=i.split(\",\")\n",
        "  count.update(Counter(combinations(filas,2)))\n",
        "\n",
        "count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSOZDxa1yAIg",
        "outputId": "a462fb2f-207a-4f25-f38b-24f3e5ab627d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({('AA Batteries (4-pack)', 'USB-C Charging Cable'): 37,\n",
              "         ('iPhone', 'Apple Airpods Headphones'): 360,\n",
              "         ('Google Phone', 'Wired Headphones'): 414,\n",
              "         ('Vareebadd Phone', 'USB-C Charging Cable'): 361,\n",
              "         ('27in 4K Gaming Monitor', 'USB-C Charging Cable'): 12,\n",
              "         ('Google Phone', 'USB-C Charging Cable'): 987,\n",
              "         ('iPhone', 'Lightning Charging Cable'): 1005,\n",
              "         ('Bose SoundSport Headphones', 'Wired Headphones'): 53,\n",
              "         ('LG Dryer', 'AA Batteries (4-pack)'): 4,\n",
              "         ('Bose SoundSport Headphones', 'AAA Batteries (4-pack)'): 37,\n",
              "         ('AAA Batteries (4-pack)', 'AA Batteries (4-pack)'): 39,\n",
              "         ('iPhone', 'Wired Headphones'): 447,\n",
              "         ('USB-C Charging Cable', 'Apple Airpods Headphones'): 34,\n",
              "         ('Lightning Charging Cable', 'Lightning Charging Cable'): 54,\n",
              "         ('AAA Batteries (4-pack)', '27in FHD Monitor'): 22,\n",
              "         ('Flatscreen TV', 'USB-C Charging Cable'): 7,\n",
              "         ('USB-C Charging Cable', 'Wired Headphones'): 160,\n",
              "         ('AA Batteries (4-pack)', '27in FHD Monitor'): 11,\n",
              "         ('27in FHD Monitor', 'Bose SoundSport Headphones'): 12,\n",
              "         ('27in 4K Gaming Monitor', 'Lightning Charging Cable'): 16,\n",
              "         ('Vareebadd Phone', '34in Ultrawide Monitor'): 4,\n",
              "         ('Bose SoundSport Headphones', 'Lightning Charging Cable'): 33,\n",
              "         ('USB-C Charging Cable', 'USB-C Charging Cable'): 48,\n",
              "         ('AAA Batteries (4-pack)', 'USB-C Charging Cable'): 50,\n",
              "         ('Flatscreen TV', 'Macbook Pro Laptop'): 4,\n",
              "         ('Apple Airpods Headphones', 'AA Batteries (4-pack)'): 29,\n",
              "         ('Lightning Charging Cable', 'AAA Batteries (4-pack)'): 36,\n",
              "         ('Vareebadd Phone', 'Wired Headphones'): 143,\n",
              "         ('Macbook Pro Laptop', 'Lightning Charging Cable'): 11,\n",
              "         ('AA Batteries (4-pack)', 'Google Phone'): 11,\n",
              "         ('Lightning Charging Cable', 'AA Batteries (4-pack)'): 55,\n",
              "         ('USB-C Charging Cable', 'AAA Batteries (4-pack)'): 45,\n",
              "         ('Apple Airpods Headphones', 'Macbook Pro Laptop'): 12,\n",
              "         ('AA Batteries (4-pack)', 'Wired Headphones'): 44,\n",
              "         ('Vareebadd Phone', 'AA Batteries (4-pack)'): 9,\n",
              "         ('Lightning Charging Cable', 'Apple Airpods Headphones'): 81,\n",
              "         ('Bose SoundSport Headphones', 'AA Batteries (4-pack)'): 27,\n",
              "         ('AA Batteries (4-pack)', 'AAA Batteries (4-pack)'): 48,\n",
              "         ('Wired Headphones', '34in Ultrawide Monitor'): 17,\n",
              "         ('Lightning Charging Cable', 'USB-C Charging Cable'): 58,\n",
              "         ('Google Phone', 'Vareebadd Phone'): 1,\n",
              "         ('USB-C Charging Cable', 'Vareebadd Phone'): 7,\n",
              "         ('Wired Headphones', 'Wired Headphones'): 35,\n",
              "         ('Google Phone', 'Bose SoundSport Headphones'): 220,\n",
              "         ('Vareebadd Phone', 'Bose SoundSport Headphones'): 80,\n",
              "         ('ThinkPad Laptop', 'iPhone'): 3,\n",
              "         ('Google Phone', 'AAA Batteries (4-pack)'): 11,\n",
              "         ('27in FHD Monitor', 'AAA Batteries (4-pack)'): 21,\n",
              "         ('Apple Airpods Headphones', 'Lightning Charging Cable'): 35,\n",
              "         ('34in Ultrawide Monitor', 'Wired Headphones'): 11,\n",
              "         ('Vareebadd Phone', 'Flatscreen TV'): 3,\n",
              "         ('AAA Batteries (4-pack)', '20in Monitor'): 8,\n",
              "         ('AA Batteries (4-pack)', 'AA Batteries (4-pack)'): 35,\n",
              "         ('Apple Airpods Headphones', 'USB-C Charging Cable'): 29,\n",
              "         ('Lightning Charging Cable', 'iPhone'): 10,\n",
              "         ('Macbook Pro Laptop', 'USB-C Charging Cable'): 12,\n",
              "         ('AAA Batteries (4-pack)', 'AAA Batteries (4-pack)'): 48,\n",
              "         ('34in Ultrawide Monitor', 'AA Batteries (4-pack)'): 19,\n",
              "         ('AAA Batteries (4-pack)', 'Wired Headphones'): 43,\n",
              "         ('USB-C Charging Cable', 'Bose SoundSport Headphones'): 77,\n",
              "         ('27in 4K Gaming Monitor', 'AAA Batteries (4-pack)'): 15,\n",
              "         ('27in FHD Monitor', '27in FHD Monitor'): 9,\n",
              "         ('Apple Airpods Headphones', 'iPhone'): 13,\n",
              "         ('iPhone', 'Bose SoundSport Headphones'): 5,\n",
              "         ('Lightning Charging Cable', 'Google Phone'): 16,\n",
              "         ('USB-C Charging Cable', 'Lightning Charging Cable'): 42,\n",
              "         ('27in 4K Gaming Monitor', '27in 4K Gaming Monitor'): 5,\n",
              "         ('iPhone', 'USB-C Charging Cable'): 15,\n",
              "         ('AA Batteries (4-pack)', 'Bose SoundSport Headphones'): 28,\n",
              "         ('Apple Airpods Headphones', 'ThinkPad Laptop'): 9,\n",
              "         ('AAA Batteries (4-pack)', '34in Ultrawide Monitor'): 13,\n",
              "         ('Apple Airpods Headphones', 'Bose SoundSport Headphones'): 32,\n",
              "         ('Wired Headphones', 'USB-C Charging Cable'): 45,\n",
              "         ('Bose SoundSport Headphones', '20in Monitor'): 6,\n",
              "         ('AA Batteries (4-pack)', '20in Monitor'): 10,\n",
              "         ('Lightning Charging Cable', '27in FHD Monitor'): 18,\n",
              "         ('Apple Airpods Headphones', 'Flatscreen TV'): 8,\n",
              "         ('27in 4K Gaming Monitor', '34in Ultrawide Monitor'): 6,\n",
              "         ('27in FHD Monitor', '27in 4K Gaming Monitor'): 4,\n",
              "         ('Google Phone', '27in FHD Monitor'): 7,\n",
              "         ('AA Batteries (4-pack)', 'ThinkPad Laptop'): 9,\n",
              "         ('Wired Headphones', '27in FHD Monitor'): 12,\n",
              "         ('27in FHD Monitor', 'Lightning Charging Cable'): 18,\n",
              "         ('Bose SoundSport Headphones', 'iPhone'): 7,\n",
              "         ('Flatscreen TV', 'Wired Headphones'): 4,\n",
              "         ('Lightning Charging Cable', 'LG Dryer'): 2,\n",
              "         ('20in Monitor', 'Wired Headphones'): 12,\n",
              "         ('Apple Airpods Headphones', 'Apple Airpods Headphones'): 24,\n",
              "         ('AAA Batteries (4-pack)', 'ThinkPad Laptop'): 11,\n",
              "         ('AA Batteries (4-pack)', 'Lightning Charging Cable'): 51,\n",
              "         ('Apple Airpods Headphones', '34in Ultrawide Monitor'): 6,\n",
              "         ('Lightning Charging Cable', 'Wired Headphones'): 92,\n",
              "         ('Apple Airpods Headphones', 'Wired Headphones'): 69,\n",
              "         ('ThinkPad Laptop', 'Apple Airpods Headphones'): 7,\n",
              "         ('27in FHD Monitor', 'AA Batteries (4-pack)'): 15,\n",
              "         ('Wired Headphones', 'Apple Airpods Headphones'): 31,\n",
              "         ('ThinkPad Laptop', 'USB-C Charging Cable'): 9,\n",
              "         ('Lightning Charging Cable', '34in Ultrawide Monitor'): 15,\n",
              "         ('34in Ultrawide Monitor', 'Flatscreen TV'): 3,\n",
              "         ('AAA Batteries (4-pack)', 'Apple Airpods Headphones'): 33,\n",
              "         ('20in Monitor', 'iPhone'): 1,\n",
              "         ('Wired Headphones', 'Lightning Charging Cable'): 37,\n",
              "         ('AA Batteries (4-pack)', 'Apple Airpods Headphones'): 41,\n",
              "         ('Lightning Charging Cable', '20in Monitor'): 15,\n",
              "         ('Macbook Pro Laptop', 'Macbook Pro Laptop'): 3,\n",
              "         ('20in Monitor', 'Macbook Pro Laptop'): 4,\n",
              "         ('Wired Headphones', 'AA Batteries (4-pack)'): 39,\n",
              "         ('Lightning Charging Cable', '27in 4K Gaming Monitor'): 18,\n",
              "         ('AAA Batteries (4-pack)', 'Bose SoundSport Headphones'): 20,\n",
              "         ('Lightning Charging Cable', 'Bose SoundSport Headphones'): 39,\n",
              "         ('34in Ultrawide Monitor', 'Lightning Charging Cable'): 18,\n",
              "         ('Macbook Pro Laptop', 'LG Washing Machine'): 2,\n",
              "         ('AAA Batteries (4-pack)', 'Lightning Charging Cable'): 44,\n",
              "         ('ThinkPad Laptop', 'AAA Batteries (4-pack)'): 16,\n",
              "         ('Apple Airpods Headphones', 'AAA Batteries (4-pack)'): 48,\n",
              "         ('Wired Headphones', 'ThinkPad Laptop'): 10,\n",
              "         ('ThinkPad Laptop', 'Flatscreen TV'): 4,\n",
              "         ('USB-C Charging Cable', 'AA Batteries (4-pack)'): 38,\n",
              "         ('Bose SoundSport Headphones', 'Flatscreen TV'): 10,\n",
              "         ('Flatscreen TV', 'Lightning Charging Cable'): 10,\n",
              "         ('Bose SoundSport Headphones', 'Bose SoundSport Headphones'): 27,\n",
              "         ('34in Ultrawide Monitor', 'ThinkPad Laptop'): 3,\n",
              "         ('iPhone', 'AA Batteries (4-pack)'): 14,\n",
              "         ('34in Ultrawide Monitor', '34in Ultrawide Monitor'): 7,\n",
              "         ('Lightning Charging Cable', 'Flatscreen TV'): 12,\n",
              "         ('AA Batteries (4-pack)', '34in Ultrawide Monitor'): 13,\n",
              "         ('Flatscreen TV', 'iPhone'): 4,\n",
              "         ('27in 4K Gaming Monitor', 'ThinkPad Laptop'): 4,\n",
              "         ('Macbook Pro Laptop', 'Bose SoundSport Headphones'): 11,\n",
              "         ('iPhone', 'Flatscreen TV'): 6,\n",
              "         ('27in 4K Gaming Monitor', 'Apple Airpods Headphones'): 10,\n",
              "         ('Wired Headphones', 'AAA Batteries (4-pack)'): 43,\n",
              "         ('USB-C Charging Cable', '34in Ultrawide Monitor'): 11,\n",
              "         ('Vareebadd Phone', 'Google Phone'): 4,\n",
              "         ('AA Batteries (4-pack)', 'iPhone'): 18,\n",
              "         ('Macbook Pro Laptop', 'Apple Airpods Headphones'): 7,\n",
              "         ('LG Dryer', 'Vareebadd Phone'): 1,\n",
              "         ('ThinkPad Laptop', 'Bose SoundSport Headphones'): 9,\n",
              "         ('Bose SoundSport Headphones', 'ThinkPad Laptop'): 4,\n",
              "         ('Bose SoundSport Headphones', '27in FHD Monitor'): 14,\n",
              "         ('Flatscreen TV', 'AAA Batteries (4-pack)'): 11,\n",
              "         ('Wired Headphones', 'Vareebadd Phone'): 6,\n",
              "         ('Google Phone', 'Google Phone'): 3,\n",
              "         ('Lightning Charging Cable', 'Macbook Pro Laptop'): 10,\n",
              "         ('Wired Headphones', '27in 4K Gaming Monitor'): 18,\n",
              "         ('20in Monitor', 'USB-C Charging Cable'): 15,\n",
              "         ('27in FHD Monitor', '20in Monitor'): 3,\n",
              "         ('27in FHD Monitor', 'Google Phone'): 1,\n",
              "         ('Bose SoundSport Headphones', 'Google Phone'): 8,\n",
              "         ('USB-C Charging Cable', 'iPhone'): 14,\n",
              "         ('iPhone', 'AAA Batteries (4-pack)'): 17,\n",
              "         ('AA Batteries (4-pack)', 'Flatscreen TV'): 13,\n",
              "         ('Wired Headphones', 'iPhone'): 15,\n",
              "         ('Google Phone', 'iPhone'): 6,\n",
              "         ('USB-C Charging Cable', '27in FHD Monitor'): 31,\n",
              "         ('Macbook Pro Laptop', '27in FHD Monitor'): 3,\n",
              "         ('34in Ultrawide Monitor', 'AAA Batteries (4-pack)'): 12,\n",
              "         ('Flatscreen TV', 'Google Phone'): 3,\n",
              "         ('27in FHD Monitor', 'ThinkPad Laptop'): 4,\n",
              "         ('20in Monitor', 'AAA Batteries (4-pack)'): 3,\n",
              "         ('AA Batteries (4-pack)', 'Macbook Pro Laptop'): 8,\n",
              "         ('USB-C Charging Cable', 'Flatscreen TV'): 10,\n",
              "         ('Wired Headphones', 'Macbook Pro Laptop'): 13,\n",
              "         ('34in Ultrawide Monitor', 'USB-C Charging Cable'): 15,\n",
              "         ('AAA Batteries (4-pack)', 'Macbook Pro Laptop'): 11,\n",
              "         ('20in Monitor', 'Apple Airpods Headphones'): 7,\n",
              "         ('Bose SoundSport Headphones', 'Apple Airpods Headphones'): 18,\n",
              "         ('27in 4K Gaming Monitor', 'Wired Headphones'): 11,\n",
              "         ('Lightning Charging Cable', 'ThinkPad Laptop'): 9,\n",
              "         ('27in FHD Monitor', 'USB-C Charging Cable'): 12,\n",
              "         ('Wired Headphones', 'Bose SoundSport Headphones'): 21,\n",
              "         ('Bose SoundSport Headphones', 'USB-C Charging Cable'): 25,\n",
              "         ('Lightning Charging Cable', 'LG Washing Machine'): 4,\n",
              "         ('Bose SoundSport Headphones', '27in 4K Gaming Monitor'): 10,\n",
              "         ('Google Phone', 'Macbook Pro Laptop'): 3,\n",
              "         ('iPhone', '27in 4K Gaming Monitor'): 6,\n",
              "         ('Bose SoundSport Headphones', 'Vareebadd Phone'): 2,\n",
              "         ('AA Batteries (4-pack)', '27in 4K Gaming Monitor'): 14,\n",
              "         ('Wired Headphones', 'Flatscreen TV'): 7,\n",
              "         ('AAA Batteries (4-pack)', '27in 4K Gaming Monitor'): 15,\n",
              "         ('Vareebadd Phone', 'iPhone'): 3,\n",
              "         ('Wired Headphones', 'Google Phone'): 9,\n",
              "         ('20in Monitor', 'Bose SoundSport Headphones'): 9,\n",
              "         ('Lightning Charging Cable', 'Vareebadd Phone'): 3,\n",
              "         ('Macbook Pro Laptop', 'AAA Batteries (4-pack)'): 7,\n",
              "         ('Apple Airpods Headphones', 'LG Dryer'): 3,\n",
              "         ('34in Ultrawide Monitor', '27in FHD Monitor'): 5,\n",
              "         ('iPhone', '27in FHD Monitor'): 1,\n",
              "         ('AAA Batteries (4-pack)', 'Vareebadd Phone'): 4,\n",
              "         ('Flatscreen TV', 'ThinkPad Laptop'): 2,\n",
              "         ('ThinkPad Laptop', '27in FHD Monitor'): 2,\n",
              "         ('Flatscreen TV', 'Flatscreen TV'): 6,\n",
              "         ('Google Phone', 'Lightning Charging Cable'): 12,\n",
              "         ('Flatscreen TV', '34in Ultrawide Monitor'): 5,\n",
              "         ('27in FHD Monitor', 'Wired Headphones'): 10,\n",
              "         ('27in 4K Gaming Monitor', 'Macbook Pro Laptop'): 6,\n",
              "         ('AAA Batteries (4-pack)', 'iPhone'): 14,\n",
              "         ('USB-C Charging Cable', '27in 4K Gaming Monitor'): 11,\n",
              "         ('AAA Batteries (4-pack)', 'Google Phone'): 11,\n",
              "         ('Apple Airpods Headphones', 'Google Phone'): 14,\n",
              "         ('Vareebadd Phone', 'Apple Airpods Headphones'): 6,\n",
              "         ('Macbook Pro Laptop', 'Google Phone'): 4,\n",
              "         ('34in Ultrawide Monitor', 'Bose SoundSport Headphones'): 6,\n",
              "         ('USB-C Charging Cable', '20in Monitor'): 10,\n",
              "         ('iPhone', 'ThinkPad Laptop'): 4,\n",
              "         ('Macbook Pro Laptop', '34in Ultrawide Monitor'): 4,\n",
              "         ('Apple Airpods Headphones', '20in Monitor'): 6,\n",
              "         ('Apple Airpods Headphones', '27in FHD Monitor'): 12,\n",
              "         ('Bose SoundSport Headphones', '34in Ultrawide Monitor'): 11,\n",
              "         ('Vareebadd Phone', '27in FHD Monitor'): 1,\n",
              "         ('Apple Airpods Headphones', '27in 4K Gaming Monitor'): 12,\n",
              "         ('Wired Headphones', '20in Monitor'): 7,\n",
              "         ('AA Batteries (4-pack)', 'Vareebadd Phone'): 3,\n",
              "         ('Macbook Pro Laptop', 'AA Batteries (4-pack)'): 9,\n",
              "         ('iPhone', 'Google Phone'): 3,\n",
              "         ('ThinkPad Laptop', '27in 4K Gaming Monitor'): 1,\n",
              "         ('34in Ultrawide Monitor', 'Apple Airpods Headphones'): 8,\n",
              "         ('Apple Airpods Headphones', 'Vareebadd Phone'): 6,\n",
              "         ('34in Ultrawide Monitor', 'Macbook Pro Laptop'): 3,\n",
              "         ('20in Monitor', 'Lightning Charging Cable'): 11,\n",
              "         ('USB-C Charging Cable', 'Google Phone'): 12,\n",
              "         ('iPhone', 'Vareebadd Phone'): 4,\n",
              "         ('Bose SoundSport Headphones', 'Macbook Pro Laptop'): 4,\n",
              "         ('20in Monitor', 'Flatscreen TV'): 1,\n",
              "         ('Flatscreen TV', 'AA Batteries (4-pack)'): 8,\n",
              "         ('20in Monitor', 'AA Batteries (4-pack)'): 6,\n",
              "         ('27in 4K Gaming Monitor', 'Google Phone'): 5,\n",
              "         ('27in 4K Gaming Monitor', 'iPhone'): 3,\n",
              "         ('Flatscreen TV', '27in FHD Monitor'): 4,\n",
              "         ('27in FHD Monitor', 'Macbook Pro Laptop'): 6,\n",
              "         ('Vareebadd Phone', 'AAA Batteries (4-pack)'): 2,\n",
              "         ('USB-C Charging Cable', 'LG Dryer'): 1,\n",
              "         ('34in Ultrawide Monitor', 'iPhone'): 7,\n",
              "         ('27in FHD Monitor', 'LG Dryer'): 2,\n",
              "         ('ThinkPad Laptop', 'Lightning Charging Cable'): 11,\n",
              "         ('Google Phone', 'Apple Airpods Headphones'): 7,\n",
              "         ('Macbook Pro Laptop', '27in 4K Gaming Monitor'): 7,\n",
              "         ('AAA Batteries (4-pack)', 'Flatscreen TV'): 10,\n",
              "         ('ThinkPad Laptop', 'Wired Headphones'): 4,\n",
              "         ('LG Washing Machine', '20in Monitor'): 1,\n",
              "         ('Flatscreen TV', '20in Monitor'): 1,\n",
              "         ('Google Phone', 'AA Batteries (4-pack)'): 14,\n",
              "         ('Macbook Pro Laptop', 'iPhone'): 3,\n",
              "         ('Flatscreen TV', 'Apple Airpods Headphones'): 5,\n",
              "         ('ThinkPad Laptop', 'AA Batteries (4-pack)'): 8,\n",
              "         ('27in FHD Monitor', 'Flatscreen TV'): 2,\n",
              "         ('27in 4K Gaming Monitor', '27in FHD Monitor'): 4,\n",
              "         ('27in FHD Monitor', 'Apple Airpods Headphones'): 12,\n",
              "         ('Macbook Pro Laptop', 'Flatscreen TV'): 1,\n",
              "         ('ThinkPad Laptop', 'Vareebadd Phone'): 1,\n",
              "         ('20in Monitor', '27in FHD Monitor'): 2,\n",
              "         ('USB-C Charging Cable', 'ThinkPad Laptop'): 11,\n",
              "         ('Wired Headphones', 'LG Washing Machine'): 3,\n",
              "         ('iPhone', '34in Ultrawide Monitor'): 6,\n",
              "         ('27in 4K Gaming Monitor', 'AA Batteries (4-pack)'): 12,\n",
              "         ('Google Phone', '34in Ultrawide Monitor'): 3,\n",
              "         ('ThinkPad Laptop', 'ThinkPad Laptop'): 2,\n",
              "         ('Google Phone', 'Flatscreen TV'): 1,\n",
              "         ('ThinkPad Laptop', 'Macbook Pro Laptop'): 2,\n",
              "         ('27in FHD Monitor', '34in Ultrawide Monitor'): 5,\n",
              "         ('Macbook Pro Laptop', 'Wired Headphones'): 7,\n",
              "         ('Vareebadd Phone', 'ThinkPad Laptop'): 2,\n",
              "         ('20in Monitor', 'ThinkPad Laptop'): 2,\n",
              "         ('USB-C Charging Cable', 'Macbook Pro Laptop'): 9,\n",
              "         ('27in 4K Gaming Monitor', 'Bose SoundSport Headphones'): 9,\n",
              "         ('iPhone', '20in Monitor'): 2,\n",
              "         ('27in FHD Monitor', 'LG Washing Machine'): 1,\n",
              "         ('LG Dryer', '27in FHD Monitor'): 1,\n",
              "         ('20in Monitor', '34in Ultrawide Monitor'): 1,\n",
              "         ('Macbook Pro Laptop', '20in Monitor'): 2,\n",
              "         ('34in Ultrawide Monitor', '20in Monitor'): 1,\n",
              "         ('20in Monitor', 'Google Phone'): 2,\n",
              "         ('ThinkPad Laptop', 'Google Phone'): 3,\n",
              "         ('LG Washing Machine', 'Lightning Charging Cable'): 2,\n",
              "         ('LG Washing Machine', 'Bose SoundSport Headphones'): 2,\n",
              "         ('iPhone', 'iPhone'): 2,\n",
              "         ('LG Washing Machine', 'Google Phone'): 1,\n",
              "         ('LG Washing Machine', 'Wired Headphones'): 1,\n",
              "         ('Flatscreen TV', 'Bose SoundSport Headphones'): 3,\n",
              "         ('Vareebadd Phone', '27in 4K Gaming Monitor'): 2,\n",
              "         ('27in 4K Gaming Monitor', '20in Monitor'): 2,\n",
              "         ('Google Phone', '20in Monitor'): 2,\n",
              "         ('LG Dryer', 'Lightning Charging Cable'): 1,\n",
              "         ('ThinkPad Laptop', 'LG Dryer'): 1,\n",
              "         ('LG Washing Machine', 'AA Batteries (4-pack)'): 1,\n",
              "         ('27in 4K Gaming Monitor', 'Flatscreen TV'): 3,\n",
              "         ('iPhone', 'Macbook Pro Laptop'): 3,\n",
              "         ('34in Ultrawide Monitor', 'Google Phone'): 2,\n",
              "         ('Macbook Pro Laptop', 'ThinkPad Laptop'): 3,\n",
              "         ('27in 4K Gaming Monitor', 'Vareebadd Phone'): 1,\n",
              "         ('Flatscreen TV', '27in 4K Gaming Monitor'): 2,\n",
              "         ('27in FHD Monitor', 'iPhone'): 1,\n",
              "         ('Vareebadd Phone', 'Lightning Charging Cable'): 1,\n",
              "         ('Google Phone', 'ThinkPad Laptop'): 3,\n",
              "         ('20in Monitor', '20in Monitor'): 3,\n",
              "         ('LG Dryer', 'AAA Batteries (4-pack)'): 1,\n",
              "         ('ThinkPad Laptop', '34in Ultrawide Monitor'): 1,\n",
              "         ('iPhone', 'LG Washing Machine'): 1,\n",
              "         ('AAA Batteries (4-pack)', 'LG Dryer'): 1,\n",
              "         ('LG Dryer', '27in 4K Gaming Monitor'): 1,\n",
              "         ('LG Washing Machine', 'AAA Batteries (4-pack)'): 4,\n",
              "         ('34in Ultrawide Monitor', 'LG Washing Machine'): 1,\n",
              "         ('Google Phone', '27in 4K Gaming Monitor'): 1,\n",
              "         ('LG Washing Machine', 'iPhone'): 1,\n",
              "         ('LG Dryer', 'Wired Headphones'): 1,\n",
              "         ('LG Dryer', 'Flatscreen TV'): 1,\n",
              "         ('AA Batteries (4-pack)', 'LG Dryer'): 2,\n",
              "         ('27in FHD Monitor', 'Vareebadd Phone'): 1,\n",
              "         ('LG Washing Machine', '27in 4K Gaming Monitor'): 1,\n",
              "         ('LG Washing Machine', 'Apple Airpods Headphones'): 1,\n",
              "         ('27in 4K Gaming Monitor', 'LG Dryer'): 1,\n",
              "         ('20in Monitor', 'LG Washing Machine'): 1,\n",
              "         ('LG Dryer', 'Google Phone'): 1})"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comunes=count.most_common(10)\n",
        "comunes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKtizKIMy3s5",
        "outputId": "18561be4-3d04-451b-d604-4af7fac5794a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(('iPhone', 'Lightning Charging Cable'), 1005),\n",
              " (('Google Phone', 'USB-C Charging Cable'), 987),\n",
              " (('iPhone', 'Wired Headphones'), 447),\n",
              " (('Google Phone', 'Wired Headphones'), 414),\n",
              " (('Vareebadd Phone', 'USB-C Charging Cable'), 361),\n",
              " (('iPhone', 'Apple Airpods Headphones'), 360),\n",
              " (('Google Phone', 'Bose SoundSport Headphones'), 220),\n",
              " (('USB-C Charging Cable', 'Wired Headphones'), 160),\n",
              " (('Vareebadd Phone', 'Wired Headphones'), 143),\n",
              " (('Lightning Charging Cable', 'Wired Headphones'), 92)]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ¿Cuál es el producto más vendido?"
      ],
      "metadata": {
        "id": "FPZb2m9izNYd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "Ro29Ey84QLTW",
        "outputId": "037b3594-9bfb-45b9-b824-81ad13561392"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Order ID                                    agrupado\n",
              "46   236716  AA Batteries (4-pack),USB-C Charging Cable\n",
              "60   236729             iPhone,Apple Airpods Headphones"
            ],
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              "      <th>agrupado</th>\n",
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              "        const element = document.querySelector('#df-f6f13c56-40c7-457c-85fc-1ff7a9ee7b11');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
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              "      --bg-color: #3B4455;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4ce52adf-7b8c-4f8d-8394-f1e33134b91d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"agrupado\"]=df[\"agrupado\"].str.split(\",\")"
      ],
      "metadata": {
        "id": "3eiB4fqpW8yT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.explode(\"agrupado\")"
      ],
      "metadata": {
        "id": "hu2rDDCZXkZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "cN5OnCBIXMjK",
        "outputId": "fdf01501-f834-4a85-cc90-44b193fe59ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Order ID               agrupado\n",
              "46   236716  AA Batteries (4-pack)\n",
              "46   236716   USB-C Charging Cable"
            ],
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              "      <th>Order ID</th>\n",
              "      <th>agrupado</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>236716</td>\n",
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              "      <td>236716</td>\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      const buttonEl =\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-91b9a2c1-7080-44de-943b-750155155c75');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-86d1f6d5-957b-4fae-8b1f-edb398cf9ef3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"agrupado\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1oCL1WTYS2J",
        "outputId": "54867f44-42dc-4e98-af21-c53d8e01c4e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['AA Batteries (4-pack)', 'USB-C Charging Cable', 'iPhone',\n",
              "       'Apple Airpods Headphones', 'Google Phone', 'Wired Headphones',\n",
              "       'Vareebadd Phone', '27in 4K Gaming Monitor',\n",
              "       'Lightning Charging Cable', 'Bose SoundSport Headphones',\n",
              "       'LG Dryer', 'AAA Batteries (4-pack)', '27in FHD Monitor',\n",
              "       'Flatscreen TV', '34in Ultrawide Monitor', 'Macbook Pro Laptop',\n",
              "       'ThinkPad Laptop', '20in Monitor', 'LG Washing Machine'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conteo = df[\"agrupado\"].value_counts()"
      ],
      "metadata": {
        "id": "u_9D3_YfcBtg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_pv = pd.DataFrame({\"Productos\":conteo.index, \"Ventas\": conteo.values})\n",
        "df_pv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "SqhGPg6_eVVa",
        "outputId": "4f696afb-b7ea-47f0-89df-43760c0056d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Productos  Ventas\n",
              "0         USB-C Charging Cable    2111\n",
              "1                       iPhone    1867\n",
              "2     Lightning Charging Cable    1827\n",
              "3             Wired Headphones    1674\n",
              "4                 Google Phone    1639\n",
              "5     Apple Airpods Headphones     974\n",
              "6   Bose SoundSport Headphones     820\n",
              "7       AAA Batteries (4-pack)     815\n",
              "8        AA Batteries (4-pack)     768\n",
              "9              Vareebadd Phone     601\n",
              "10            27in FHD Monitor     294\n",
              "11      27in 4K Gaming Monitor     251\n",
              "12      34in Ultrawide Monitor     246\n",
              "13          Macbook Pro Laptop     196\n",
              "14               Flatscreen TV     178\n",
              "15             ThinkPad Laptop     176\n",
              "16                20in Monitor     159\n",
              "17          LG Washing Machine      28\n",
              "18                    LG Dryer      25"
            ],
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              "      <th>3</th>\n",
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              "      <th>5</th>\n",
              "      <td>Apple Airpods Headphones</td>\n",
              "      <td>974</td>\n",
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              "      <th>6</th>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>820</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>AAA Batteries (4-pack)</td>\n",
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              "      <td>AA Batteries (4-pack)</td>\n",
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              "      <th>9</th>\n",
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              "      <th>10</th>\n",
              "      <td>27in FHD Monitor</td>\n",
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              "    </tr>\n",
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              "      <th>11</th>\n",
              "      <td>27in 4K Gaming Monitor</td>\n",
              "      <td>251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>34in Ultrawide Monitor</td>\n",
              "      <td>246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Macbook Pro Laptop</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Flatscreen TV</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>ThinkPad Laptop</td>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>20in Monitor</td>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LG Washing Machine</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LG Dryer</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c73f4085-c0a6-43ae-8e6b-306a24fda701')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c73f4085-c0a6-43ae-8e6b-306a24fda701 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c73f4085-c0a6-43ae-8e6b-306a24fda701');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-24f2fb70-8b87-4770-bd18-3daa7b85f357\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-24f2fb70-8b87-4770-bd18-3daa7b85f357')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-24f2fb70-8b87-4770-bd18-3daa7b85f357 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bars_1=plt.bar(df_pv[\"Productos\"], df_pv[\"Ventas\"])\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.title(\"Articulos vendidos\", size=18)\n",
        "\n",
        "plt.ylabel(\"Unidades vendidas\")\n",
        "plt.xlabel(\"Articulo\")\n",
        "\n",
        "bars_1[0].set_color(\"#D80032\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "id": "q3ig5kiYiwFy",
        "outputId": "b219c3a5-42f2-4b90-ebcc-10a92e5f93f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}