Data_Science_para_Marketing.ipynb

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        "2 + 2"
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        "3 * 4"
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      "source": [
        "120 / 6"
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              "20.0"
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    {
      "cell_type": "code",
      "source": [
        "nome = \"Dani\""
      ],
      "metadata": {
        "id": "i6gAvJkeX_A5"
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    {
      "cell_type": "code",
      "source": [
        "print(nome)"
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          "name": "stdout",
          "text": [
            "Dani\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "idade = 35\n",
        "idade"
      ],
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            "text/plain": [
              "35"
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    {
      "cell_type": "code",
      "source": [
        "formacao = \"Marketing\""
      ],
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    {
      "cell_type": "code",
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        "curso = \"Data Science para Marketing\""
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      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Olá, {nome}! Notamos que você é formado em {formacao}. Bem vindo(a) ao curso de {curso}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "JCyBPTHNY8rk",
        "outputId": "5ebf8c15-0133-4974-d55e-8a9d18b2652e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Olá, Dani! Notamos que você é formado em Marketing. Bem vindo(a) ao curso de Data Science para Marketing.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analisando o arquivo de campanha"
      ],
      "metadata": {
        "id": "3pFMrlG1bpGw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "BUTiKeaGZYEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "campanha = pd.read_csv(\"/content/campanha.csv\")"
      ],
      "metadata": {
        "id": "tCJoPOy6cE-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "campanha.head()"
      ],
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        },
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id_campanha  impressoes  cliques       canal  custo  receita estado    pais\n",
              "0            1      152431     9462       Email  13258    32309     MG  Brasil\n",
              "1            1       39688     3218         PPC  16391    16568     BA  Brasil\n",
              "2            1      104506     4756  Google Ads   6189    19029     AL  Brasil\n",
              "3            1       80768     3566  Google Ads   5215        0     PI  Brasil\n",
              "4            1       54932     3645  Google Ads  16767     8825     RJ  Brasil"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "campanha",
              "summary": "{\n  \"name\": \"campanha\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"Id_campanha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 578,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"impressoes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38217,\n        \"min\": 100,\n        \"max\": 200000,\n        \"num_unique_values\": 42072,\n        \"samples\": [\n          144427,\n          79136,\n          76659\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cliques\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2612,\n        \"min\": 1,\n        \"max\": 19484,\n        \"num_unique_values\": 10960,\n        \"samples\": [\n          1585,\n          7059,\n          12201\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"canal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"PPC\",\n          \"Social Media\",\n          \"Google Ads\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"custo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6072,\n        \"min\": 2723,\n        \"max\": 80000,\n        \"num_unique_values\": 19420,\n        \"samples\": [\n          21610,\n          4432,\n          13584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"receita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10365,\n        \"min\": 0,\n        \"max\": 134842,\n        \"num_unique_values\": 23135,\n        \"samples\": [\n          20524,\n          14915,\n          20474\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estado\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"MS\",\n          \"PA\",\n          \"DF\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pais\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Brasil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
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              "(50000, 8)"
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          "metadata": {},
          "execution_count": 13
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      "execution_count": null,
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            "text/plain": [
              "0               Email\n",
              "1                 PPC\n",
              "2          Google Ads\n",
              "3          Google Ads\n",
              "4          Google Ads\n",
              "             ...     \n",
              "49995    Social Media\n",
              "49996      Google Ads\n",
              "49997      Google Ads\n",
              "49998             PPC\n",
              "49999        Meta Ads\n",
              "Name: canal, Length: 50000, dtype: object"
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        "campanha['canal'].unique()"
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        },
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        "outputId": "0d96b90c-4e21-4332-f4de-cbdef76bc49b"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "array(['Email', 'PPC', 'Google Ads', 'Meta Ads', 'Social Media'],\n",
              "      dtype=object)"
            ]
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          "metadata": {},
          "execution_count": 15
        }
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              "canal\n",
              "Google Ads      25146\n",
              "Meta Ads        15009\n",
              "Social Media     4874\n",
              "PPC              2526\n",
              "Email            2445\n",
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        "CTR = (cliques / impressoes) * 100"
      ],
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        "id": "ocEhlgU2hwJC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['ctr'] = (campanha['cliques']/campanha['impressoes']) * 100"
      ],
      "metadata": {
        "id": "s65jSX7ze-oU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "campanha.head()"
      ],
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        "colab": {
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        "id": "xIlBXO2DiIAp",
        "outputId": "d117a002-0304-4429-cfbc-ef5473c76932"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Id_campanha  impressoes  cliques       canal  custo  receita estado  \\\n",
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              "1            1       39688     3218         PPC  16391    16568     BA   \n",
              "2            1      104506     4756  Google Ads   6189    19029     AL   \n",
              "3            1       80768     3566  Google Ads   5215        0     PI   \n",
              "4            1       54932     3645  Google Ads  16767     8825     RJ   \n",
              "\n",
              "     pais       ctr  \n",
              "0  Brasil  6.207399  \n",
              "1  Brasil  8.108244  \n",
              "2  Brasil  4.550935  \n",
              "3  Brasil  4.415115  \n",
              "4  Brasil  6.635477  "
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              "type": "dataframe",
              "variable_name": "campanha",
              "summary": "{\n  \"name\": \"campanha\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"Id_campanha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 578,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"impressoes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38217,\n        \"min\": 100,\n        \"max\": 200000,\n        \"num_unique_values\": 42072,\n        \"samples\": [\n          144427,\n          79136,\n          76659\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cliques\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2612,\n        \"min\": 1,\n        \"max\": 19484,\n        \"num_unique_values\": 10960,\n        \"samples\": [\n          1585,\n          7059,\n          12201\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"canal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"PPC\",\n          \"Social Media\",\n          \"Google Ads\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"custo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6072,\n        \"min\": 2723,\n        \"max\": 80000,\n        \"num_unique_values\": 19420,\n        \"samples\": [\n          21610,\n          4432,\n          13584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"receita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10365,\n        \"min\": 0,\n        \"max\": 134842,\n        \"num_unique_values\": 23135,\n        \"samples\": [\n          20524,\n          14915,\n          20474\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estado\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"MS\",\n          \"PA\",\n          \"DF\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pais\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Brasil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ctr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6041190438542303,\n        \"min\": 0.9926629261976695,\n        \"max\": 10.0,\n        \"num_unique_values\": 49874,\n        \"samples\": [\n          4.860034069817701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['ctr'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "LUeRCeFniKqe",
        "outputId": "3460602e-164f-451f-c1fc-1165ae2a7903"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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xevTjHXpxyf4Wl6mqrXP3JPFXIBr8bjtcrH9tylZVgGa+/XxHrqWzJ2ce978zQyD4PcJptPnRrDUqqarVhkPBa0EfgaE3IoXiY5GWBOpC2VSZHz28QsmuHKf++NluSdKhGZODXBp7GGP8HjHYanvySjTn5KOqX1w6VAlxsc2Wuf9f2/RROxtJV/tw3P+jHfMl1dQF9iI9f/tRXTnK+8zw/np9ZYaeuPZsS9bVEdR8tKFh0qhQe2RhlzknZ/JEc+E0OVqwv1TCzfGy0Gkgmt/O+Uf88fh/duqi6Yt0woYG6VJ9Y/NPt+WouNyzsXPjx3f/NWuN19/1NXi4XEavLj/ocePoSzuuh9oxX1JFkKcvCEeEjwhk5fdMQ2v9SNB0v5woq1axjz09vBn16Bf6OD08ajv8fWZsRe8vY4zWHAz8bNH+hEBjjF/j7bTHuoPHLRtgTpKe+NTa8XgOFjSvdn9z1SHlOav01ppMWwaTe3r+bt357hb97M31LS7j/XGd7z7bflRPzd+tH73kPcRY6flF9g9vED63Pt4RPrywq0q6gaONJ/Ll1XVhMbpkOKmqcWnskws15vEv212DUVFTp3vmpVtbsAD5azsujjO/2qs73t7U7m1+sTO33b/rj8/8aJfxizkbdfajX7RrO74eJTe8srZd629JWwG5tePXGKP/W7hXH2w+7H7thUUtt6dwOKRnFuzxv5BNLN1zTB9taTmYf3jyva3ZRR3eVmNfNjrmMpqErEA+ArT7OyMSED68WNxGoyS7K7Af+miHRj/2pd+DTNlV1X60uCIsHkE07l2T26gqu8blkjFGu2wYnyLQWrrgt2cgsZlf7dOCdgSIhr289mDojTS6KCRGGLXOexuyde4TX7b4fnp2kf66aJ/ufW+rz+vcddSa82DaP9P9/p3KDs52fNvbm1q8TgajdqKpovJqTZ+/u8OTTuY6K7W/0VQdc1Yf0t8WB//v8wfh46RwaNR51M/ZKu3onrVod54mTF+sO/7R/jtkK/jyt76wuOU7vldXHNT3nl9hZZH88u9Nh/36gmhJey74vgqDUyTo7O4Sed+/t8nZyiOeovLwqjF9ZUXH5zCqqXPp3n+m6y8L93q8fqAdU1ZY7eGPd+rl5Qd11czlHV7X794/db149JOdeu7LvWHzGFiKwvCxNbtIj368o1lDp6aCMSGSVfUUBaXVevCD7X5POe+LptfWl09OeBZuM4k2bdPw0rLgTtz22/c7Hjzay9evy1ofW/SXV9W2OtV4JNub5/3vDqVxGuxsHOnvn53v7HhD36PFlfqglUc+TdnZFnt7gMYCaRAuj4GlKOxqe+2LqyRJ/958RD+dMFj3XXWm18cT767P0k8vGmx38SzT0hDrQHu97uNssQ2jjp43qGuLy5RWhXbvAJ+/NJtcOnydpK8xl8soJqZ+RXnOShkj9U1L9Hs9dgmhHOWVryHZX8v3HtPEM3oFZN1Zx8v1ydYj+umEIUpL6hSQbYSaqKv5aFBaVatZSw9o0W7vz4C3Zhfr+ln2DEfeXlW1dVE7ZbUUehdBfx+LWcWu3ZDrZ5fPzVlFLb73u/e36kCU1o409sdPd+nCpxfpWEmVaupcGv/0Il00fVHAJhEMRf/ckB3U7ft6HfnZG+t9vt4eLa7wmGZibQu9vowx2nGkWBOfXaLnvtwbsOH+Q1HUho8Gx0q9V/NV1ta1evFsjR3VeFW1dRrz+Je67Lmlgd9YAIRYbogqobLv5/o5p8/R4gr9+cs9yg1SyAuE11ZmqKC0Sm+uylB5o9qgovIa1dS5AjbxXUva+iIuLKvW17mnGqSWVdVq9irfasRaYvUj20Bef+t8TCpN29/f+Mpavb4yQ4eOl3u8/tqKDH2/0fQD6zNCr5F2oETdYxcrbDhUqC1ZJzR2UDeP16trXcooKLPljnxfXqkqa1w6fKIi8BsDQsDP39igPXklWrLH3h4rzsoapSb6VhX+r02H217IR79+Z7Olw2pb4bwnF3r8/4+f7eYRr4+e9DJey4tLW24E31gkDhIYVTUfR4qaf1G3Jyi4jPTDv692V8GVVNaozmX0i7c26qqZy/2unkbrKmvq9PaaQ8ouLG97YR+F2iMb1M858ejHO1r8nBtGp9xxxN4u0ec89qWeWfC1T8vO9mFE4OzCct3w8hot/rr1YBFqwcObFfuOBbsIYWvV/vbNT9NRoRJjoqrm4/5/bWv22rwNWXIZo7gY/z+SOmNU6KzUhU8v0ln9UjvcP/6NDlZfRqoXFu/Ti0sOKC5ml/Y//T2P9zKPl2n7kWK/HyV0tJ+9VRyO+scJHVqHRWUJtpteWauc4krNWZOpjOnfs+Vuz9fhaf6+9IAG9+jc/I12hNjfvb9V6zIKta6VKnYrRmFtz6zR8zZkqX/XpA5vu4G/JbBijBFvh01OUYWKK0Jriowpr61T187R0bjUm6gKH/klzWskth0u1rbD7R/Gd+HJKdQ7ctIUV9To0205OhgC/dBD0eoD9Y21al2m2R3ot55d2q51BnI8DH9UVLs0YfriDq3D33lIQqnbZ2M5jdpybM4q0vmDu7WytP+8tRXZlHnC0m20pHGQKvRh/pSOfkZZheUa/dgXOs3PIHG0uFL9LOxpc3mItEm7eEbHzrFACcQ4LJ9szVGnGIeuHt3P6/slJztbTB7dT4O8BWqbRNVjl1D1ydYc3fnulmAXwy//2Zqji55e5DE8srdRTic/v0Lv+dmavby6Vk9+ukubMpvfGf596QG/yxrKjpV2/BHdi0vCc5+0VrFRFYDeHj95fZ3Py4ZmPPPdC4v3qby6rs2xfjYeKtQT//FsixDuf3u0u3vuFv3qnc3a3sJN9Qebj+hPC77W1X/t+EBnHUH4CCOhVL1+19wtynVWesxpMPqxL/RakxEKd+Y4dd+/mz/uas1fF+3T6yszdH0Ls1paraMVAdW1Lv3dx4ZjgdDR7tbeGsJFov0tfBH7WssQSudfW3w9pv/rpTW2Pu6trXMFfJ6qtubKihbX/G1lq++XdXAo+46KqscuCKyy6jr98bPdLb7v60X+QH54PX56fWWGJZNxITj25ZfqjD4pQS1DKHVmqKrxDLPrDh7X+GE99BsLHlVOfn6lT9Pah7JQfWzZkmMlVbaOausrwkeYCrPjX0Me+CzYRQiYnTkdm/o72h04Vqqp72wO2varbZiRNJy+sJq2X9t2uFjjh/Vwz0TbmL9d/e0IHoEOcjN87PlkpY78SU/Pb/mGMJgIHy0IxYvFKysO6ukfjg52MQKu1uXbl0HmcWtqSByO0Py8vSmuqIm44Zd/9/5WfR0ivY9a03SAqPbyNhLui0sOqNzmanBngB9/RKJrXljZ4vw9Vsgv6fjcNk0VtDCQZrBFVZsPf75f5m/3bSpxO8cceNfPESHDla93onf8o/W75c1Z9vRksFOoXkjao6EHSGkrs7LaYVeOU2+uymhX19T2aKkb7ZurDtmy/QbT59t/Bx/uAhk8og01Hx305U7fQgrs9//+Htpz87RHUXnzbprhUWfjJxvbQDQ0iE7qFKsbLxxk34aDbH9+6Nc2FZRWqaSyVkN7dgl2UYKrneeDlQMzWi2qaj6AYHrsk5367szlHZo0bMbnze9WOzJOTUvC5CmUpXY3ausQLo/hIt24P36ly59b2uGB+FoTSo19W7L2gPeJ6dpy6TNLPCa4CyWEjw4IxvVp6rubdSwAzwVDWSR8D+Q5KzV79SF9nVuiX/1jU7vXE0lD91fXuvSjl1a3ORaFy2V0x9vt32fomGe/DH5Prp02D6kfanKKKyNu0jkeu3TAN/+0WMd9GK3QSp9tOyqHpNsnnm7rdtE+Gw8V6oEPtnuMMbFkT3Dnw+joiKpW8WUeFElac/C4FtjweHPOmkwVVdTorzeO9fl3yqoD016lqCJ0GoPa0RvISmFQkdEuGw5FVvig5qMDCkqDM1dATlGFTGQ+6Q+KfXmlAZtH5MZX1rY4uJUk5RT5V5NhRS1QdQcHJbNbRx5T+evj9By/lg9UG9VfvrUxMCsOU4Ecp6KmjmtpMBA+wlCtTa3yo8UcH+/AW9Lap9HWZ3X4hH8Nwg6fqGg2imwkmjYvPajbP+GlYa+dAjHnRzi7a+4Wvb6SiTcjSVSFj0j5yt52uNinyalCWUmQu1c2Fm7HRWujyEaK/JIqlQfokYYvfvPPrUHbNrzzdRqAcGhAiigLH5HE3+rhUBRKAQSeqFxDY1+EyJAC3rqaW2HIA5/pp35MPBhI0dLTivABv1h5WrTUBz3czj1vs/mGu4+8DKUdDBXVdT43TEXg3B4ivY3OfWKhD0u1r+pjxb4C5YdAb7KWajaf/SL4vY6sFFXhwxVu32pRas3B9vVpD5bPd4TGXaGVVu4PjbEBnv1ij+3jFFQEebZPO7y9NrPNLs7hasbn7X8sOWfNIesK0k7R0rYlqrraHjwWXrOlhqI6l0tVtZF1cd5wqDBobWjIw61bfcD+EBRuXUvb4+GPdgS7CAHz1e78dv/uW2syLSwJWhNV4SOSBOu54KS/LFdip8iqMMu0aMIwhKaDx0o1rFeyz8sXWzzGxqbMyJtjCOioyPoWgS0qayy6M/Tx0Wx6dpE120NYsWqitW//eZlfyz8137deFb66flbkzTEEdBThA0BIsrKBXU6R73OD7Mm1fsK1zVlFlq8TCGeEjygRyc94wxljEtjjkY99P/79HXUWkSMa2vuECsJHlHh7LQ2pEL38aYQYbsPPwzpVhA/bED4AAICtCB8Iae9tyA52EQAAFiN8hKloGR7ivn9vC3YRAmpvXmQO9GQVxkEBIhPhA0DIOh7mEygC8I7wEabynVXBLkKHvbws8qeGR8cUlIb/cQ6gOcJHmAq3+U8AAGhA+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVlETPmrqXMEuAgAAUADCR11dnR5++GENHTpUSUlJOv300/Xkk0/KGGP1pvyy+gCzwAIAEArirF7hn/70J82aNUtz5szRqFGjtHHjRt1yyy1KS0vT3XffbfXmfOYKcvgBAAD1LA8fq1ev1rXXXqvJkydLkoYMGaK5c+dq/fr1Vm8KAACEIcsfu1x88cVatGiR9u7dK0naunWrVq5cqauvvtrr8lVVVXI6nR4/AAAgclle8/HAAw/I6XRq5MiRio2NVV1dnZ566ilNmTLF6/LTp0/X448/bnUxAABAiLK85uO9997TO++8o3fffVebN2/WnDlz9Nxzz2nOnDlel3/wwQdVXFzs/snOzra6SAAAIIRYXvPx+9//Xg888IBuvPFGSdLo0aOVmZmp6dOn6+abb262fEJCghISEqwuBgAACFGW13yUl5crJsZztbGxsXK5GGcDAAAEoObjmmuu0VNPPaVBgwZp1KhR2rJli/7yl7/o1ltvtXpTAAAgDFkePl544QU9/PDD+vWvf638/Hz1799ft99+ux555BGrN+UXR1C3DgAAGlgePlJSUjRz5kzNnDnT6lUDAIAIEDVzuwAAgNBA+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsFXUhA+Hg2HGAAAIBVETPgAAQGggfAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbBU14YMhxgAACA1REz4AAEBoIHwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANgqasKHg4E+AAAICVETPowJdgkAAIAUReEDAACEBsIHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtoiZ8MMgYAAChIWrCBwAACA1REz5KKmuDXQQAAKAoCh81da5gFwEAACiKwgcAAAgNhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFZREz4czCwHAEBIiJrwAQAAQgPhAwAA2IrwAQAAbEX4AAAAtoqa8EFzUwAAQkPUhA8T7AIAAABJURQ+AABAaCB8AAAAWxE+AACAraImfNDgFACA0BA14QMAAIQGwgcAALBV1IQP5pUDACA0RE34AAAAoSFqwsfQnl2CXQQAAKAoCh8AACA0ED4AAICtAhI+jhw5op/85Cfq0aOHkpKSNHr0aG3cuDEQmwIAAGEmzuoVnjhxQpdccokuv/xyff755+rVq5f27dunbt26Wb0pAAAQhiwPH3/60580cOBAvfnmm+7Xhg4davVm/OZgjFMAAEKC5Y9dPvnkE40bN04/+tGP1Lt3b40dO1avvvpqi8tXVVXJ6XR6/AAAgMhlefg4ePCgZs2apREjRuiLL77Qr371K919992aM2eO1+WnT5+utLQ098/AgQOtLpIkBhkDACBUOIwxxsoVxsfHa9y4cVq9erX7tbvvvlsbNmzQmjVrmi1fVVWlqqoq9/+dTqcGDhyo4uJipaamWlauXTlOfe/5FZatDwCAcHZoxmRL1+d0OpWWlubT97flNR/9+vXTWWed5fHaN77xDWVlZXldPiEhQampqR4/AAAgclkePi655BLt2bPH47W9e/dq8ODBVm8KAACEIcvDx29+8xutXbtWTz/9tPbv3693331Xr7zyiqZOnWr1pgAAQBiyPHxccMEF+vDDDzV37lydffbZevLJJzVz5kxNmTLF6k0BAIAwZPk4H5L0/e9/X9///vcDsWoAABDmomZuF7raAgAQGqImfAAAgNAQNeGDmg8AAEJD1IQPAAAQGggfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsFTXhwyH62gIAEAqiJ3yQPQAACAlREz6MCXYJAACAFEXhAwAAhAbCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACAraImfDDIGAAAoSF6wkewCwAAACRFUfgAAAChgfABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANgqasIHw6sDABAaoiZ8AACA0ED4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgqygKH/S1BQAgFERR+AAAAKGA8AEAAGwVNeGDEU4BAAgNURM+AABAaCB8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2iprwwQCnAACEhqgJHwAAIDQQPgAAgK0IHwAAwFZREz4cTGsLAEBIiJrwAQAAQgPhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAW0VN+KCvCwAAoSFqwgcAAAgNhA8AAGCrgIePGTNmyOFwaNq0aYHeFAAACAMBDR8bNmzQyy+/rHPOOSeQmwEAAGEkYOGjtLRUU6ZM0auvvqpu3boFajMAACDMBCx8TJ06VZMnT9akSZNaXa6qqkpOp9PjBwAARK64QKx03rx52rx5szZs2NDmstOnT9fjjz8eiGIAAIAQZHnNR3Z2tu655x698847SkxMbHP5Bx98UMXFxe6f7Oxsq4sEAABCiOU1H5s2bVJ+fr7OO+8892t1dXVavny5/va3v6mqqkqxsbHu9xISEpSQkGB1MQAAQIiyPHxcccUV2r59u8drt9xyi0aOHKn777/fI3gAAIDoY3n4SElJ0dlnn+3xWpcuXdSjR49mr9vJwfjqAACEBEY4BQAAtgpIb5emli5dasdmAABAGKDmAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACAraImfHRJsKVjDwAAaEPUhI+eyQzhDgBAKIia8AEAAEID4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbEX4AAAAtiJ8AAAAWxE+AACArQgfAADAVoQPAABgK8IHAACwFeEDAADYivABAABsRfgAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsJXl4WP69Om64IILlJKSot69e+u6667Tnj17rN4MAAAIU5aHj2XLlmnq1Klau3atFi5cqJqaGl155ZUqKyuzelMAACAMxVm9wgULFnj8f/bs2erdu7c2bdqkiRMnWr05AADgp2vG9A/q9i0PH00VFxdLkrp37+71/aqqKlVVVbn/73Q6A10kAACi2qRv9A7q9gPa4NTlcmnatGm65JJLdPbZZ3tdZvr06UpLS3P/DBw4MJBFAgAAQRbQ8DF16lTt2LFD8+bNa3GZBx98UMXFxe6f7OzsQBYJAAAEWcAeu9x555369NNPtXz5cg0YMKDF5RISEpSQkBCoYoScnskJKiitantBAAAilOXhwxiju+66Sx9++KGWLl2qoUOHWr2JsJbYiaFVAADRzfLwMXXqVL377rv6+OOPlZKSotzcXElSWlqakpKSrN4cAAAIM5bfhs+aNUvFxcW67LLL1K9fP/fPP//5T6s3BQBAu215+DvBLkLUCshjFwAAQl1srCPYRYhaNECwmYNjHQAQ5QgfABAikjrFBrsIgC0IH4gaP794SLCL0GHxcZyykSyGmlFb+dpK4PReXQJbkCjElQxRo3N8+N9Vdu8cH+wi+ISLNSLJf48fHOwiRBzCh80c4tYG7RcubYZe/dm4YBcBQAgjfKBdLh3RM9hFQAgb1is52EUAEMIIHzaLlGe6A7t31q8vOz3YxUAQDePRiuUYqADRgvBhsxduOs/S9X17ZPCmRb7vuyP1zH+dE7TtR5r375gQ7CL4ZUiPLlr74BXBLkbE6NElPNrztOXWS5hSA20jfNhs9IA0S9f343EtT9qH8HLBkO5tLhNqFWd90xKDXYSIMefWC4NdhOhDVVPQED4i1MDuzKMTibhWRi4eY4UuRu62HuEjQvVMTgjo+kPtDhxAaAiXHlkILsJHBLjvu2c2e21Ij8DeRfXvGn41K9y7SKeF4edmtx+M6R/sIoQ1KgngC8JH2HOod4r35+5j/GxfMqRHZ5+XjY/l0AlHv7iUxoBtSWAUWbeLhrXdDikaOKjOsRxnWQQb0jM0niF3j5BW/JHgJxcxUiMQTOSYeoQPG00+p5+t26P6E011osbKL1PGD7J1e5yzp5w/uFtA1//FtIkBXX9L3rj5gqBsN9RwJbLRtCtGBLsIQUHQDy1WzXETqZ9r4+//cyzuGt8agqGnebddFND1n9k3RUlBmO+Jmo96HO02ig3Q8KYTTu/R7LVIOL6vPrtvsIsQcnqndnxcjS+mTVS3zp0sKA2sFOjw0b1LvP7r/FPjAu14/CqP9zvHx2rJ7y4LaBn8YUcYY5bo4GHPR4DTuiYFbaTJyaOtfZQ08Yxelq4v0CZ9o+MjzA7vney1x1JT3zqjl56zYETZgd076xYbRqFcef/lAd8GfDewe2eNGdjV/f/khDiP98/un6ahIdJODJGP8GGjQLaY7puWqLm/9Kym9Hdz7Slfl4Q4fXLnJX7/XkveCrNRHn0ZlbQtX937Lf36suFtLjfn1gt1Wrfw6So7oJvvvadCSeOzIDYmRg9ePTJoZbHaxSdrSb31VjN0RoeNCB828qcra3t4e/zij/ZGoxgeYiJCff+cfrr9W5EzgeLpvZK19HeXaePDk4JdlHYLRi1vOIxw+p2z+gS7CH4hfNgoUvuKByp8DLZ4oLQwuH60qaW/4YIhHesZ0Cc1sCPihqvETvY3SAy0IT27KDUxfNv8MJ+Qd6/+bJw++PXFwS6Gzwgf6LCRfVN06YieLb7f3u/8Hl3i9UsGxfKJIyKaGAORz+FwKClAofa8QYHtnmwlwgc6LCbGobf/Z3xA1j28d7Jfy192Zng1WA2E9++YEOwiWCY1MU6bHrL3EcEFQ4M3qmcg210QT0NHXIB6PkrSoO7h0daK8BGpovRKM/uW8Gqw6i9fnnCd0TulzWUaj/WR2Cm0LwM9AjxJYmOrHvi2BjD/DcLY5/dcGuwi+CS0rzpB0teCsRRaM22SdYON9Uhm6HIrBWosllAzZfxgDe3ZRYO6d9bmh78T7OK0aESftoPUHRY2CD2ta5LPwf2sfqkRPxbNDRcMDHYR4KcuTbpQhyrChxejbRzVsMEPx57Wrt8bF+AhiMNZe6o250RCzYkPf3bSyQGllt93uTrHt32xGuHn4y8rXH/eAL1w01iv713Y6NHI6NPsP1+l+qA66yfnB2XbLTmrX6ql67vu3NP06V3ftHSdLWl8vt5I6Il4hI8OsmLWx5k3nKuHJn+jXb8bCT1oXvlpyxdwfxpS3nLJEI//D/aja/PIvinqEh+rcR3sNRKunvvRmFbfb+0wGxaggan+/OMx6t/CI5DeKR1/FNPR2WtDcVyMCy1ur+JwOHS2TeGu8TF288VDbNlmKLju3P7ufz92zVlBLIm9CB8d1NZF2xcj+6VE9VgZyYkdryY8b1BXPTS5/Sfu/Lsv1ZZHrozIrpW+uHQEDXX9NTHE99mFQ7rrtonDWl3m/u9GzgBq4eSOy+ofFU4+p59O73WqVvGbrfQabPB/N3T8OycUED5OSgnwc7KbLqQaMZBGn5bWofYaMTEO5nlop2gNbAlxof13z73tIl3WxnQF4dIzwhcXDuluyXQHdvjVt07Xp3d9UzNvONfv3/3h2AHNXuvZjrZ/wQ7PXG1P+uI3E9WjS/0HGIgDuHG6bYu3oY/h3aj+9c+4f3he8xMyENrbNieSzbzx3A4/emk8hHmvlIRWH8WFixG9k5X+SOg25g2UG9t5o3XFSN9H6Ex/5Dv66t6JHq9dOaqPXrv5Ap9uJAPdqaAtMTH1j7OsmDxv5g3nav7d/vVwmX/3perWJbidFaLyW65pQ8RzB3ZV/65J+vI3EzX7lgv0o/MH6q5vN59r4+cnn0Oe0edUkOjpYzfAxuNPfLdJC/kB3Tp7PHp4+v+N9nj/4haGTffW0O4bJxucXX/eAI8ZLFvy9A9PbWvad85oddnGz5O/5eN4Gl07d9Kdlw93/7upiWf00hlNejSkJdUvd9mZvZoNGT+yr+eyH029RGse/LbObTRh1lWj6i9it00c5v631Hy/N0hp8tin8efb2M8vHqKpl5/qWXHRsO664hstXzAbtzl5tIVnuRc2mRumIdy09BSupbvtqSeP17SkTurSqBvtgEZzwbQ2EJwk/e5Kz8+/8Wny4Peat0m66cJBkqQz+qRo8e8uc/9+w+uN3TxhsK5sZfjnxo8Hlv7uMl05yvOzaih7jy7xeuGmsR69MM4b3NVj2abHyMDuzduN9E1N1O+u9D6Z35k+9LCRpEln1d+kXDOmv9f3p14+XF07x+v2b9X/bQ3787/HD9K9Tc61huPg7is8e8I1nZKhrZ49jc/5GIc0slED1Fu/2XzAvnMHdXX/+6cThkg69Vnc3ugz8Xbs3DxhsG6eMNj9/+SEOG195Eqd0SdFlwz3PG9bGvq7/8nRSh0O6U+NJk0c2EKNzHdPHhddO8dreJMu5Q2PLDY8NElTxjc/BiWp38ntTb/+1HWvrUdTvVMSdPlI/25IG8KNt++RhhumBpNO7pteKQnqm+Z5rH7/nPqJOxu+exoGJ7v75HpvmzhM1409ze/ZrkNhAkGHCbFB651Op9LS0lRcXKzUVGtbblfW1GnV/gJdOqKXSqtq5TJGReXVGti9c7OLujFGB46VanCPLtp2uEgOh0PnDeqmjIIy9UtLVEV1neqMUc/kBBVX1Ki0qlZxMQ6lZxdpRO9klVfX6ax+qdp11Km0pE7NTqYjRRU6cqJCA7snqd/JA+5YSZWMjHolJ2hnjlM9kuO1J7dEE0f00pGiCqV17qT9+aUqLq/R4B6ddVq3pGblrqqt09GiSg05eXBlFJSpf9dEZR0vl7OyVp1iHRrZN1WZx8sUHxejwT266GhxhZIT4pSS2EmbMgs1oFtnJXaK1ebMExrQLUlHiys1bkg3xcXEaG9eiVITO2mQl8acx0qqtP1IkVwu6ZwBaXJW1qpvWqK6xMfqwLEyDe3ZRYeOlynreLk6x8cqOTFOI3qnKD4upv61hFj1TE5QWVWtCsuq3fssu7BcxRU16pIQp35pieoUG6P524/q6rP7Ks7LnUNtnUuZheXuu/EV+wp0wZDuSoqPVUZBmfqkJuhocaWqa13qHB+rXikJHj0+KmvqlFVYriMnKjRuSDeVVtWqorpOQ3p0UUyMQ8XlNTrqrNCwnsmKj4tRTlGF6lxGCZ1iVF3r0t68Em0/7NRd3x6uoooaHTpepvMGdVN+SaXKquqUmhgnh8Oh/fmlGjuoq8fdT22dSweOlWlE72TN33FU5w/upliHQwmdYt2hrKC0Si6XUZeEOH21O0+jT0vTsF7J2p9fqj6pCUpJ7KTCsmq5jFFyQpyOlVSpzmV0WrekVu+0jDHam1equFiH6lxGw3sla09eibrEx2lQj87KKapQfkmVEuJilNQp1n2MNT1nhvVMVszJb9rjpVXaklWkb4/sLZcx2nq4WKd1TVJ69gkldorVgG6d1TctUckJcSquqFFNnctroK+sqdPOnGKNHdjNve5dOU51SYjV4B5dlOes1P8t3KtfXzZcvVMTlFtc6d5fXTt30n+2HdXwXsnqkRzv3kZCXIwOHS9XbZ1Luc5KjRtcf4xI0uET5eoSH+e+O8w8XqaC0ip17RyvnskJKiqvdg//X1Pn0p7cEo3sm6IV+ws0fmh35RZXatjJ2k5jjA4dL9eQHp21L79Uw3sly+GQ9uaVqnN8rPKclTr7tDQldop1fwaFZdU6q3+qEjvFKPN4uY4WV+qS03soxuHQ3vwSnd4rWV/uzNM3h/dUQVmV+qQm6sTJcybfWanYGId7fJTswnIVllV7zGjb2P78EhWV12jcyQDkchntP1ZfzoZ9XVPnUubxMknS0J7J+jrXqZF9U+WQdLCgTImdYtS9S7z7PKpfvlxpSZ20+6hTE07voYqaOs3fdlRjB3VTalKcjhZXalT/VB0vrVZKYv31p7iiRtW1LvU62aD4eGmViipqlJrYSRkFZfXnQ6NUXFpVq4W7cnVWvzSd2Sh0ulxGW7JPqLiiRsbUnzMThvVUv66JOlpU6XH9crmM9uaXKCWxk7p3jtfuXKd6JSeoX1qiVuwr0GVn9pLD4dDhE+Xq3iVe+/JKVVherbEDu6q8uk4HjpWqR5cE7T9WqvFDu8tZUaPTeyUrs7D+Mz90vFxbsk7o2yN7q7LG5XV4+OzCcvVMTlBSfKz255dqU2ahrh7dT5071V+zhvdO1uETFerWJV7JCXEyxiijoP6a6q3TQUFplfbklujcgV21KfOEhvTool4pCdp2uEiDe3QJ2BD1/nx/R1X4AAAAgeHP93dUPnYBAADBQ/gAAAC2InwAAABbET4AAICtCB8AAMBWhA8AAGArwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFZxbS9ir4ZJdp1OZ5BLAgAAfNXwvd3wPd6akAsfJSUlkqSBAwcGuSQAAMBfJSUlSktLa3UZh/ElotjI5XIpJydHKSkpcjgclq7b6XRq4MCBys7OVmpqqqXrxinsZ3uwn+3BfrYP+9oegdrPxhiVlJSof//+iolpvVVHyNV8xMTEaMCAAQHdRmpqKge2DdjP9mA/24P9bB/2tT0CsZ/bqvFoQINTAABgK8IHAACwVVSFj4SEBD366KNKSEgIdlEiGvvZHuxne7Cf7cO+tkco7OeQa3AKAAAiW1TVfAAAgOAjfAAAAFsRPgAAgK0IHwAAwFZREz5efPFFDRkyRImJiRo/frzWr18f7CKFlOXLl+uaa65R//795XA49NFHH3m8b4zRI488on79+ikpKUmTJk3Svn37PJYpLCzUlClTlJqaqq5du+p//ud/VFpa6rHMtm3bdOmllyoxMVEDBw7UM88806ws77//vkaOHKnExESNHj1a8+fPt/zvDYbp06frggsuUEpKinr37q3rrrtOe/bs8VimsrJSU6dOVY8ePZScnKzrr79eeXl5HstkZWVp8uTJ6ty5s3r37q3f//73qq2t9Vhm6dKlOu+885SQkKDhw4dr9uzZzcoTyefErFmzdM4557gHUZowYYI+//xz9/vsZ+vNmDFDDodD06ZNc7/GfrbGY489JofD4fEzcuRI9/thuZ9NFJg3b56Jj483b7zxhtm5c6f55S9/abp27Wry8vKCXbSQMX/+fPOHP/zBfPDBB0aS+fDDDz3enzFjhklLSzMfffSR2bp1q/nBD35ghg4daioqKtzLfPe73zVjxowxa9euNStWrDDDhw83N910k/v94uJi06dPHzNlyhSzY8cOM3fuXJOUlGRefvll9zKrVq0ysbGx5plnnjG7du0yDz30kOnUqZPZvn17wPdBoF111VXmzTffNDt27DDp6enme9/7nhk0aJApLS11L3PHHXeYgQMHmkWLFpmNGzeaiy66yFx88cXu92tra83ZZ59tJk2aZLZs2WLmz59vevbsaR588EH3MgcPHjSdO3c29957r9m1a5d54YUXTGxsrFmwYIF7mUg/Jz755BPz2Wefmb1795o9e/aY//3f/zWdOnUyO3bsMMawn622fv16M2TIEHPOOeeYe+65x/06+9kajz76qBk1apQ5evSo++fYsWPu98NxP0dF+LjwwgvN1KlT3f+vq6sz/fv3N9OnTw9iqUJX0/DhcrlM3759zbPPPut+raioyCQkJJi5c+caY4zZtWuXkWQ2bNjgXubzzz83DofDHDlyxBhjzN///nfTrVs3U1VV5V7m/vvvN2eeeab7/z/+8Y/N5MmTPcozfvx4c/vtt1v6N4aC/Px8I8ksW7bMGFO/Tzt16mTef/999zK7d+82ksyaNWuMMfUhMSYmxuTm5rqXmTVrlklNTXXv1/vuu8+MGjXKY1s33HCDueqqq9z/j8Zzolu3bua1115jP1uspKTEjBgxwixcuNB861vfcocP9rN1Hn30UTNmzBiv74Xrfo74xy7V1dXatGmTJk2a5H4tJiZGkyZN0po1a4JYsvCRkZGh3Nxcj32Ylpam8ePHu/fhmjVr1LVrV40bN869zKRJkxQTE6N169a5l5k4caLi4+Pdy1x11VXas2ePTpw44V6m8XYalonEz6q4uFiS1L17d0nSpk2bVFNT4/H3jxw5UoMGDfLYz6NHj1afPn3cy1x11VVyOp3auXOne5nW9mG0nRN1dXWaN2+eysrKNGHCBPazxaZOnarJkyc32xfsZ2vt27dP/fv317BhwzRlyhRlZWVJCt/9HPHho6CgQHV1dR47XZL69Omj3NzcIJUqvDTsp9b2YW5urnr37u3xflxcnLp37+6xjLd1NN5GS8tE2mflcrk0bdo0XXLJJTr77LMl1f/t8fHx6tq1q8eyTfdze/eh0+lURUVF1JwT27dvV3JyshISEnTHHXfoww8/1FlnncV+ttC8efO0efNmTZ8+vdl77GfrjB8/XrNnz9aCBQs0a9YsZWRk6NJLL1VJSUnY7ueQm9UWiAZTp07Vjh07tHLlymAXJWKdeeaZSk9PV3Fxsf71r3/p5ptv1rJly4JdrIiRnZ2te+65RwsXLlRiYmKwixPRrr76ave/zznnHI0fP16DBw/We++9p6SkpCCWrP0ivuajZ8+eio2NbdbyNy8vT3379g1SqcJLw35qbR/27dtX+fn5Hu/X1taqsLDQYxlv62i8jZaWiaTP6s4779Snn36qJUuWaMCAAe7X+/btq+rqahUVFXks33Q/t3cfpqamKikpKWrOifj4eA0fPlznn3++pk+frjFjxuivf/0r+9kimzZtUn5+vs477zzFxcUpLi5Oy5Yt0/PPP6+4uDj16dOH/RwgXbt21RlnnKH9+/eH7fEc8eEjPj5e559/vhYtWuR+zeVyadGiRZowYUIQSxY+hg4dqr59+3rsQ6fTqXXr1rn34YQJE1RUVKRNmza5l1m8eLFcLpfGjx/vXmb58uWqqalxL7Nw4UKdeeaZ6tatm3uZxttpWCYSPitjjO688059+OGHWrx4sYYOHerx/vnnn69OnTp5/P179uxRVlaWx37evn27R9BbuHChUlNTddZZZ7mXaW0fRus54XK5VFVVxX62yBVXXKHt27crPT3d/TNu3DhNmTLF/W/2c2CUlpbqwIED6tevX/gez343UQ1D8+bNMwkJCWb27Nlm165d5rbbbjNdu3b1aPkb7UpKSsyWLVvMli1bjCTzl7/8xWzZssVkZmYaY+q72nbt2tV8/PHHZtu2bebaa6/12tV27NixZt26dWblypVmxIgRHl1ti4qKTJ8+fcxPf/pTs2PHDjNv3jzTuXPnZl1t4+LizHPPPWd2795tHn300YjpavurX/3KpKWlmaVLl3p0mSsvL3cvc8cdd5hBgwaZxYsXm40bN5oJEyaYCRMmuN9v6DJ35ZVXmvT0dLNgwQLTq1cvr13mfv/735vdu3ebF1980WuXuUg+Jx544AGzbNkyk5GRYbZt22YeeOAB43A4zJdffmmMYT8HSuPeLsawn63y29/+1ixdutRkZGSYVatWmUmTJpmePXua/Px8Y0x47ueoCB/GGPPCCy+YQYMGmfj4eHPhhReatWvXBrtIIWXJkiVGUrOfm2++2RhT39324YcfNn369DEJCQnmiiuuMHv27PFYx/Hjx81NN91kkpOTTWpqqrnllltMSUmJxzJbt2413/zmN01CQoI57bTTzIwZM5qV5b333jNnnHGGiY+PN6NGjTKfffZZwP5uO3nbv5LMm2++6V6moqLC/PrXvzbdunUznTt3Nj/84Q/N0aNHPdZz6NAhc/XVV5ukpCTTs2dP89vf/tbU1NR4LLNkyRJz7rnnmvj4eDNs2DCPbTSI5HPi1ltvNYMHDzbx8fGmV69e5oorrnAHD2PYz4HSNHywn61xww03mH79+pn4+Hhz2mmnmRtuuMHs37/f/X447meHMcb4X18CAADQPhHf5gMAAIQWwgcAALAV4QMAANiK8AEAAGxF+AAAALYifAAAAFsRPgAAgK0IHwAAwFaEDwAAYCvCBwAAsBXhAwAA2IrwAQAAbPX/AcTvVN5hQKUNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['ctr'].plot(kind='hist')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "P5cQi3q3iZyP",
        "outputId": "0cfd09c9-1d74-4a92-d656-6a5efcaec849"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['ctr'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "NItXfcd8itSK",
        "outputId": "f814bfa5-523c-4920-92cb-f9e4f124a536"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    50000.000000\n",
              "mean         5.148482\n",
              "std          1.604119\n",
              "min          0.992663\n",
              "25%          4.050781\n",
              "50%          5.132384\n",
              "75%          6.241502\n",
              "max         10.000000\n",
              "Name: ctr, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ctr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.148482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.604119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.992663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.050781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.132384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.241502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['ctr'].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DrTuGt-0jF4O",
        "outputId": "dedc7f20-a380-4ea2-baed-b8cbdd86011b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.132384085295483"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['ctr'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCsoORMMjXH_",
        "outputId": "09d0bac1-5716-423b-a3d9-b7fc6123a904"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.1484815905088634"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "DPZv4g7yjafS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(campanha['ctr'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "vwZ_cY4rjtTC",
        "outputId": "28434860-87a1-4347-b275-3fc7c249246f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='ctr'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha.query('Id_campanha == 1')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MfRY2Qkqj0K6",
        "outputId": "ee66186f-a3da-41c1-8f0b-ef8d8d07b7d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Id_campanha  impressoes  cliques         canal  custo  receita estado  \\\n",
              "0             1      152431     9462         Email  13258    32309     MG   \n",
              "1             1       39688     3218           PPC  16391    16568     BA   \n",
              "2             1      104506     4756    Google Ads   6189    19029     AL   \n",
              "3             1       80768     3566    Google Ads   5215        0     PI   \n",
              "4             1       54932     3645    Google Ads  16767     8825     RJ   \n",
              "5             1       86472     3266    Google Ads  15870    12483     BA   \n",
              "6             1       63460     4869      Meta Ads  13769    10849     SP   \n",
              "7             1       45206     2231      Meta Ads  26270     8583     MA   \n",
              "8             1       85974     5340    Google Ads  42535       91     PE   \n",
              "9             1      128497     4717      Meta Ads  10310    19235     MS   \n",
              "10            1      103955     5454         Email  17591    29256     SP   \n",
              "11            1       82063     5585  Social Media   9910    12884     DF   \n",
              "12            1      122904     8441      Meta Ads   8107    10805     SP   \n",
              "13            1       87811     2517    Google Ads  11272     5717     MG   \n",
              "14            1      135775     8984    Google Ads  10944    14342     CE   \n",
              "15            1      118325     6944           PPC   5979    44053     MG   \n",
              "16            1      152660     6590    Google Ads  18576     9128     MG   \n",
              "17            1       32584     1214  Social Media  13377    54517     AM   \n",
              "18            1       96197     4361    Google Ads  15034     9391     MG   \n",
              "19            1       92587     6483      Meta Ads  17899     5253     BA   \n",
              "20            1      131817     7797         Email  23343    10703     SE   \n",
              "21            1      140411     2183    Google Ads   8569    16090     SP   \n",
              "22            1      111975     4070           PPC  13284    19859     PA   \n",
              "23            1      121385     4714      Meta Ads  11756     9432     BA   \n",
              "24            1       87129     5175      Meta Ads   7463     8447     PR   \n",
              "25            1       71381     4685    Google Ads   7550        0     BA   \n",
              "26            1      159400     8595    Google Ads  20898        0     SC   \n",
              "27            1         100        5    Google Ads   7781    18476     BA   \n",
              "28            1      117185     4760    Google Ads   9730     9071     RJ   \n",
              "29            1      118326     4332      Meta Ads  11466     6509     PA   \n",
              "30            1       68101     4006    Google Ads  12663    12120     BA   \n",
              "\n",
              "      pais       ctr  \n",
              "0   Brasil  6.207399  \n",
              "1   Brasil  8.108244  \n",
              "2   Brasil  4.550935  \n",
              "3   Brasil  4.415115  \n",
              "4   Brasil  6.635477  \n",
              "5   Brasil  3.776945  \n",
              "6   Brasil  7.672550  \n",
              "7   Brasil  4.935186  \n",
              "8   Brasil  6.211180  \n",
              "9   Brasil  3.670903  \n",
              "10  Brasil  5.246501  \n",
              "11  Brasil  6.805747  \n",
              "12  Brasil  6.867962  \n",
              "13  Brasil  2.866383  \n",
              "14  Brasil  6.616829  \n",
              "15  Brasil  5.868582  \n",
              "16  Brasil  4.316782  \n",
              "17  Brasil  3.725755  \n",
              "18  Brasil  4.533405  \n",
              "19  Brasil  7.002063  \n",
              "20  Brasil  5.915019  \n",
              "21  Brasil  1.554721  \n",
              "22  Brasil  3.634740  \n",
              "23  Brasil  3.883511  \n",
              "24  Brasil  5.939469  \n",
              "25  Brasil  6.563371  \n",
              "26  Brasil  5.392095  \n",
              "27  Brasil  5.000000  \n",
              "28  Brasil  4.061953  \n",
              "29  Brasil  3.661072  \n",
              "30  Brasil  5.882439  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e35e90f-86df-47a1-b877-0f425e4da625\" 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>Id_campanha</th>\n",
              "      <th>impressoes</th>\n",
              "      <th>cliques</th>\n",
              "      <th>canal</th>\n",
              "      <th>custo</th>\n",
              "      <th>receita</th>\n",
              "      <th>estado</th>\n",
              "      <th>pais</th>\n",
              "      <th>ctr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>152431</td>\n",
              "      <td>9462</td>\n",
              "      <td>Email</td>\n",
              "      <td>13258</td>\n",
              "      <td>32309</td>\n",
              "      <td>MG</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.207399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>39688</td>\n",
              "      <td>3218</td>\n",
              "      <td>PPC</td>\n",
              "      <td>16391</td>\n",
              "      <td>16568</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>8.108244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>104506</td>\n",
              "      <td>4756</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>6189</td>\n",
              "      <td>19029</td>\n",
              "      <td>AL</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.550935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>80768</td>\n",
              "      <td>3566</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>5215</td>\n",
              "      <td>0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.415115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>54932</td>\n",
              "      <td>3645</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>16767</td>\n",
              "      <td>8825</td>\n",
              "      <td>RJ</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.635477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>86472</td>\n",
              "      <td>3266</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>15870</td>\n",
              "      <td>12483</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>3.776945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1</td>\n",
              "      <td>63460</td>\n",
              "      <td>4869</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>13769</td>\n",
              "      <td>10849</td>\n",
              "      <td>SP</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>7.672550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>45206</td>\n",
              "      <td>2231</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>26270</td>\n",
              "      <td>8583</td>\n",
              "      <td>MA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.935186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1</td>\n",
              "      <td>85974</td>\n",
              "      <td>5340</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>42535</td>\n",
              "      <td>91</td>\n",
              "      <td>PE</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.211180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1</td>\n",
              "      <td>128497</td>\n",
              "      <td>4717</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>10310</td>\n",
              "      <td>19235</td>\n",
              "      <td>MS</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>3.670903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1</td>\n",
              "      <td>103955</td>\n",
              "      <td>5454</td>\n",
              "      <td>Email</td>\n",
              "      <td>17591</td>\n",
              "      <td>29256</td>\n",
              "      <td>SP</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>5.246501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
              "      <td>82063</td>\n",
              "      <td>5585</td>\n",
              "      <td>Social Media</td>\n",
              "      <td>9910</td>\n",
              "      <td>12884</td>\n",
              "      <td>DF</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.805747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>122904</td>\n",
              "      <td>8441</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>8107</td>\n",
              "      <td>10805</td>\n",
              "      <td>SP</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.867962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1</td>\n",
              "      <td>87811</td>\n",
              "      <td>2517</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>11272</td>\n",
              "      <td>5717</td>\n",
              "      <td>MG</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>2.866383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1</td>\n",
              "      <td>135775</td>\n",
              "      <td>8984</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>10944</td>\n",
              "      <td>14342</td>\n",
              "      <td>CE</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.616829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1</td>\n",
              "      <td>118325</td>\n",
              "      <td>6944</td>\n",
              "      <td>PPC</td>\n",
              "      <td>5979</td>\n",
              "      <td>44053</td>\n",
              "      <td>MG</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>5.868582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1</td>\n",
              "      <td>152660</td>\n",
              "      <td>6590</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>18576</td>\n",
              "      <td>9128</td>\n",
              "      <td>MG</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.316782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>32584</td>\n",
              "      <td>1214</td>\n",
              "      <td>Social Media</td>\n",
              "      <td>13377</td>\n",
              "      <td>54517</td>\n",
              "      <td>AM</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>3.725755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1</td>\n",
              "      <td>96197</td>\n",
              "      <td>4361</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>15034</td>\n",
              "      <td>9391</td>\n",
              "      <td>MG</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.533405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1</td>\n",
              "      <td>92587</td>\n",
              "      <td>6483</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>17899</td>\n",
              "      <td>5253</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>7.002063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>131817</td>\n",
              "      <td>7797</td>\n",
              "      <td>Email</td>\n",
              "      <td>23343</td>\n",
              "      <td>10703</td>\n",
              "      <td>SE</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>5.915019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "      <td>140411</td>\n",
              "      <td>2183</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>8569</td>\n",
              "      <td>16090</td>\n",
              "      <td>SP</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>1.554721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1</td>\n",
              "      <td>111975</td>\n",
              "      <td>4070</td>\n",
              "      <td>PPC</td>\n",
              "      <td>13284</td>\n",
              "      <td>19859</td>\n",
              "      <td>PA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>3.634740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1</td>\n",
              "      <td>121385</td>\n",
              "      <td>4714</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>11756</td>\n",
              "      <td>9432</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>3.883511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1</td>\n",
              "      <td>87129</td>\n",
              "      <td>5175</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>7463</td>\n",
              "      <td>8447</td>\n",
              "      <td>PR</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>5.939469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>71381</td>\n",
              "      <td>4685</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>7550</td>\n",
              "      <td>0</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.563371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>159400</td>\n",
              "      <td>8595</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>20898</td>\n",
              "      <td>0</td>\n",
              "      <td>SC</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>5.392095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>5</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>7781</td>\n",
              "      <td>18476</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>117185</td>\n",
              "      <td>4760</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>9730</td>\n",
              "      <td>9071</td>\n",
              "      <td>RJ</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.061953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>118326</td>\n",
              "      <td>4332</td>\n",
              "      <td>Meta Ads</td>\n",
              "      <td>11466</td>\n",
              "      <td>6509</td>\n",
              "      <td>PA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>3.661072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1</td>\n",
              "      <td>68101</td>\n",
              "      <td>4006</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>12663</td>\n",
              "      <td>12120</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>5.882439</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-3e35e90f-86df-47a1-b877-0f425e4da625')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3e35e90f-86df-47a1-b877-0f425e4da625 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-3e35e90f-86df-47a1-b877-0f425e4da625');\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",
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              "\n",
              "\n",
              "<div id=\"df-42511ece-06a7-4323-a088-01a26000ba43\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-42511ece-06a7-4323-a088-01a26000ba43')\"\n",
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              "            style=\"display:none;\">\n",
              "\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",
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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",
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              "    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",
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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",
              "      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-42511ece-06a7-4323-a088-01a26000ba43 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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        "campanha.query('Id_campanha == 1').mean(numeric_only=True)"
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              "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7c1afc8006a0>"
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          "execution_count": 30
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      "source": [
        "medias_por_campanha = campanha.groupby('Id_campanha')['impressoes'].mean()\n",
        "medias_por_campanha.head()\n"
      ],
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              "Id_campanha\n",
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            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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hhZ39+/f/5BiXy6UxY8aEsnsAAICwCWnNzoIFC7R06dJ67UuXLtWiRYsaXBQAAEC4hBR28vLy1Lp163rtqampevzxxxtcFAAAQLiEFHaKiorUpUuXeu2dOnVSUVFRg4sCAAAIl5DCTmpqqnbt2lWvfefOnWrVqlWDiwIAAAiXkMLOyJEj9R//8R9at26dampqVFNTo7Vr12rChAm67bbbwl0jAABAyEK6G2vGjBk6ePCgBg0apPj4b3dRW1ur0aNHs2YHAABElZDCjsvl0uuvv64ZM2Zo586dSkpKUu/evdWpU6dw1wcAANAgIYWdOt27d1f37t3DVQsAAEDYhRR2ampqtHDhQuXn5+vIkSOqra0N6l+7dm1YigMAAGiokMLOhAkTtHDhQmVlZalXr16KiYkJd10AAABhEVLYWbJkid544w0NHTo03PUAAACEVUi3nrtcLnXt2jXctQAAAIRdSGHnvvvu07PPPitjTLjrAQAACKuQvsZ67733tG7dOq1cuVKXXHKJEhISgvqXLVsWluIAAAAaKqSwk5KSoptuuinctQAAAIRdSGFnwYIF4a4DAADgnAhpzY4kVVdXa82aNXrhhRd0/PhxSVJxcbFOnDgRtuIAAAAaKqQrO1988YWuu+46FRUVqaKiQr/+9a/VvHlz/fnPf1ZFRYXmz58f7joBAABCEtKVnQkTJqhfv3766quvlJSU5LTfdNNNys/PD1txAAAADRXSlZ1//OMf2rhxo1wuV1B7586d9c9//jMshQEAAIRDSFd2amtrVVNTU6/98OHDat68eYOLAgAACJeQws7gwYP1zDPPOK9jYmJ04sQJTZ8+nZ+QAAAAUSWkr7GeeuopZWZmKj09XadOndJvf/tb7d+/X61bt9Zrr70W7hoBAABCFlLYad++vXbu3KklS5Zo165dOnHihMaNG6fs7OygBcsAAACRFlLYkaT4+Hjdfvvt4awFAAAg7EIKOy+//PKP9o8ePTqkYgAAAMItpLAzYcKEoNdVVVX6+uuv5XK5lJycTNgBAABRI6S7sb766qug7cSJE9q3b58GDhzIAmUAABBVQv5trO/r1q2bZs6cWe+qDwAAQCSFLexI3y5aLi4uDucuAQAAGiSkNTv/93//F/TaGKOSkhL993//t371q1+FpTAAAIBwCCns3HjjjUGvY2Ji1KZNG1177bV66qmnwlEXAABAWIQUdmpra8NdBwAAwDkR1jU7AAAA0SakKzu5ublnPHb27NmhHAIAACAsQgo727dv1/bt21VVVaUePXpIkj755BPFxcXp8ssvd8bFxMSEp0oAAIAQhRR2brjhBjVv3lyLFi1SixYtJH37oMGxY8fqqquu0n333RfWIgEAAEIV0pqdp556Snl5eU7QkaQWLVroT3/6E3djAQCAqBJS2AkEAjp69Gi99qNHj+r48eMNLgoAACBcQgo7N910k8aOHatly5bp8OHDOnz4sP73f/9X48aN0/Dhw8NdIwAAQMhCWrMzf/583X///frtb3+rqqqqb3cUH69x48bpiSeeCGuBAAAADRFS2ElOTtbcuXP1xBNP6LPPPpMkXXTRRWratGlYiwMAAGioBj1UsKSkRCUlJerWrZuaNm0qY0y46gIAAAiLkMLOl19+qUGDBql79+4aOnSoSkpKJEnjxo3jtnMAABBVQgo7kyZNUkJCgoqKipScnOy033rrrVq1alXYigMAAGiokNbsvPvuu3rnnXfUvn37oPZu3brpiy++CEthAAAA4RDSlZ2TJ08GXdGpc+zYMSUmJja4KAAAgHAJKexcddVVevnll53XMTExqq2t1axZs3TNNdeErTgAAICGCulrrFmzZmnQoEHatm2bKisr9cADD2jPnj06duyY3n///XDXCAAAELKQruz06tVLn3zyiQYOHKhhw4bp5MmTGj58uLZv366LLroo3DUCAACE7Kyv7FRVVem6667T/Pnz9dBDD52LmgAAAMLmrK/sJCQkaNeuXeeiFgAAgLAL6Wus22+/XS+++GK4awEAAAi7kBYoV1dX66WXXtKaNWvUt2/fer+JNXv27LAUBwAA0FBnFXY+//xzde7cWR9++KEuv/xySdInn3wSNCYmJiZ81QEAADTQWYWdbt26qaSkROvWrZP07c9DPPfcc0pLSzsnxQEAADTUWa3Z+f6vmq9cuVInT54Ma0EAAADhFNIC5TrfDz8AAADR5qzCTkxMTL01OazRAQAA0eys1uwYY3THHXc4P/Z56tQp3X333fXuxlq2bFn4KgQAAGiAs7qyM2bMGKWmpsrj8cjj8ej2229Xu3btnNd125nKy8vTL3/5SzVv3lypqam68cYbtW/fvqAxp06dUk5Ojlq1aqVmzZppxIgRKi0tDRpTVFSkrKwsJScnKzU1VZMnT1Z1dfXZnBoAALDUWV3ZWbBgQVgPvn79euXk5OiXv/ylqqur9Z//+Z8aPHiw9u7d61wtmjRpklasWKGlS5fK4/Fo/PjxGj58uPODozU1NcrKypLX69XGjRtVUlKi0aNHKyEhQY8//nhY6wUAAOefGBNFq4yPHj2q1NRUrV+/XldffbXKy8vVpk0bLV68WDfffLMk6eOPP9bFF1+sgoICDRgwQCtXrtT111+v4uJi5xb4+fPna8qUKTp69KhcLtdPHjcQCMjj8ai8vFxut/ucniNQp/ODKyJdAhBWB2dmRboE/Myc6ed3g+7GCrfy8nJJUsuWLSVJhYWFqqqqUkZGhjOmZ8+e6tixowoKCiRJBQUF6t27d9CzfjIzMxUIBLRnz57THqeiokKBQCBoAwAAdoqasFNbW6uJEyfqV7/6lXr16iVJ8vv9crlcSklJCRqblpYmv9/vjPn+Qw3rXteN+b68vLygNUYdOnQI89kAAIBoETVhJycnRx9++KGWLFlyzo81depUlZeXO9uhQ4fO+TEBAEBkhPRDoOE2fvx4LV++XBs2bFD79u2ddq/Xq8rKSpWVlQVd3SktLZXX63XGbNmyJWh/dXdr1Y35vsTEROf2eQAAYLeIXtkxxmj8+PF68803tXbtWnXp0iWov2/fvkpISFB+fr7Ttm/fPhUVFcnn80mSfD6fdu/erSNHjjhjVq9eLbfbrfT09MY5EQAAELUiemUnJydHixcv1l//+lc1b97cWWPj8XiUlJQkj8ejcePGKTc3Vy1btpTb7da9994rn8+nAQMGSJIGDx6s9PR0jRo1SrNmzZLf79e0adOUk5PD1RsAABDZsDNv3jxJ0r/9278FtS9YsEB33HGHJOnpp59WbGysRowYoYqKCmVmZmru3LnO2Li4OC1fvlz33HOPfD6fmjZtqjFjxuixxx5rrNMAAABRLKqesxMpPGcHkcBzdmAbnrODxnZePmcHAAAg3Ag7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLX4SBcAALBD5wdXRLqEs3ZwZlakS0Aj4MoOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYLX4SBcAhEPnB1dEugQAQJSK6JWdDRs26IYbblC7du0UExOjt956K6jfGKNHHnlEbdu2VVJSkjIyMrR///6gMceOHVN2drbcbrdSUlI0btw4nThxohHPAgAARLOIhp2TJ0+qT58+mjNnzmn7Z82apeeee07z58/X5s2b1bRpU2VmZurUqVPOmOzsbO3Zs0erV6/W8uXLtWHDBt11112NdQoAACDKRfRrrCFDhmjIkCGn7TPG6JlnntG0adM0bNgwSdLLL7+stLQ0vfXWW7rtttv00UcfadWqVdq6dav69esnSXr++ec1dOhQPfnkk2rXrl2jnQsAAIhOUbtA+cCBA/L7/crIyHDaPB6P+vfvr4KCAklSQUGBUlJSnKAjSRkZGYqNjdXmzZt/cN8VFRUKBAJBGwAAsFPUhh2/3y9JSktLC2pPS0tz+vx+v1JTU4P64+Pj1bJlS2fM6eTl5cnj8Thbhw4dwlw9AACIFlEbds6lqVOnqry83NkOHToU6ZIAAMA5ErVhx+v1SpJKS0uD2ktLS50+r9erI0eOBPVXV1fr2LFjzpjTSUxMlNvtDtoAAICdojbsdOnSRV6vV/n5+U5bIBDQ5s2b5fP5JEk+n09lZWUqLCx0xqxdu1a1tbXq379/o9cMAACiT0Tvxjpx4oQ+/fRT5/WBAwe0Y8cOtWzZUh07dtTEiRP1pz/9Sd26dVOXLl308MMPq127drrxxhslSRdffLGuu+463XnnnZo/f76qqqo0fvx43XbbbdyJBQAAJEU47Gzbtk3XXHON8zo3N1eSNGbMGC1cuFAPPPCATp48qbvuuktlZWUaOHCgVq1apSZNmjjvefXVVzV+/HgNGjRIsbGxGjFihJ577rlGPxcAABCdYowxJtJFRFogEJDH41F5eTnrd85T/FwEgFAcnJkV6RLQAGf6+R21a3YAAADCgbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwWnykCwAAIFI6P7gi0iWctYMzsyJdwnmHKzsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1eIjXQCiT+cHV0S6BAAAwoYrOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWi490AQAA4Mx1fnBFpEs4awdnZkX0+FzZAQAAViPsAAAAqxF2AACA1Vizc46dj9+tAgBgE2uu7MyZM0edO3dWkyZN1L9/f23ZsiXSJQEAgChgRdh5/fXXlZubq+nTp+uDDz5Qnz59lJmZqSNHjkS6NAAAEGFWhJ3Zs2frzjvv1NixY5Wenq758+crOTlZL730UqRLAwAAEXber9mprKxUYWGhpk6d6rTFxsYqIyNDBQUFp31PRUWFKioqnNfl5eWSpEAgEPb6aiu+Dvs+AQA4n5yLz9fv7tcY86Pjzvuw869//Us1NTVKS0sLak9LS9PHH3982vfk5eXp0UcfrdfeoUOHc1IjAAA/Z55nzu3+jx8/Lo/H84P9533YCcXUqVOVm5vrvK6trdWxY8fUqlUrxcTERLCy6BYIBNShQwcdOnRIbrc70uVYi3luPMx142CeG8/Pba6NMTp+/LjatWv3o+PO+7DTunVrxcXFqbS0NKi9tLRUXq/3tO9JTExUYmJiUFtKSsq5KtE6brf7Z/EfUaQxz42HuW4czHPj+TnN9Y9d0alz3i9Qdrlc6tu3r/Lz85222tpa5efny+fzRbAyAAAQDc77KzuSlJubqzFjxqhfv3664oor9Mwzz+jkyZMaO3ZspEsDAAARZkXYufXWW3X06FE98sgj8vv9uuyyy7Rq1ap6i5bRMImJiZo+fXq9rwARXsxz42GuGwfz3HiY69OLMT91vxYAAMB57LxfswMAAPBjCDsAAMBqhB0AAGA1wg4AALAaYcdiNTU1evjhh9WlSxclJSXpoosu0owZM4J+Q8QYo0ceeURt27ZVUlKSMjIytH///qD9HDt2TNnZ2XK73UpJSdG4ceN04sSJoDG7du3SVVddpSZNmqhDhw6aNWtWvXqWLl2qnj17qkmTJurdu7fefvvtc3PiEXD8+HFNnDhRnTp1UlJSkq688kpt3brV6WeeQ7NhwwbdcMMNateunWJiYvTWW28F9UfTvJ5JLdHqp+Z52bJlGjx4sPOU+R07dtTbx6lTp5STk6NWrVqpWbNmGjFiRL2HvRYVFSkrK0vJyclKTU3V5MmTVV1dHTTm73//uy6//HIlJiaqa9euWrhwYb1jzZkzR507d1aTJk3Uv39/bdmypaFT0Gh+bK6rqqo0ZcoU9e7dW02bNlW7du00evRoFRcXB+2Dv+kQGFjrv/7rv0yrVq3M8uXLzYEDB8zSpUtNs2bNzLPPPuuMmTlzpvF4POatt94yO3fuNL/5zW9Mly5dzDfffOOMue6660yfPn3Mpk2bzD/+8Q/TtWtXM3LkSKe/vLzcpKWlmezsbPPhhx+a1157zSQlJZkXXnjBGfP++++buLg4M2vWLLN3714zbdo0k5CQYHbv3t04k3GO3XLLLSY9Pd2sX7/e7N+/30yfPt243W5z+PBhYwzzHKq3337bPPTQQ2bZsmVGknnzzTeD+qNpXs+klmj1U/P88ssvm0cffdT85S9/MZLM9u3b6+3j7rvvNh06dDD5+flm27ZtZsCAAebKK690+qurq02vXr1MRkaG2b59u3n77bdN69atzdSpU50xn3/+uUlOTja5ublm79695vnnnzdxcXFm1apVzpglS5YYl8tlXnrpJbNnzx5z5513mpSUFFNaWhr2eTkXfmyuy8rKTEZGhnn99dfNxx9/bAoKCswVV1xh+vbtG7QP/qbPHmHHYllZWeZ3v/tdUNvw4cNNdna2McaY2tpa4/V6zRNPPOH0l5WVmcTERPPaa68ZY4zZu3evkWS2bt3qjFm5cqWJiYkx//znP40xxsydO9e0aNHCVFRUOGOmTJlievTo4by+5ZZbTFZWVlAt/fv3N7///e/DdLaR8/XXX5u4uDizfPnyoPbLL7/cPPTQQ8xzmHz/gyGa5vVMajlfnC7s1Dlw4MBpw05ZWZlJSEgwS5cuddo++ugjI8kUFBQYY779kI+NjTV+v98ZM2/ePON2u525f+CBB8wll1wStO9bb73VZGZmOq+vuOIKk5OT47yuqakx7dq1M3l5eSGdbyT92FzX2bJli5FkvvjiC2MMf9Oh4mssi1155ZXKz8/XJ598IknauXOn3nvvPQ0ZMkSSdODAAfn9fmVkZDjv8Xg86t+/vwoKCiRJBQUFSklJUb9+/ZwxGRkZio2N1ebNm50xV199tVwulzMmMzNT+/bt01dffeWM+e5x6sbUHed8Vl1drZqaGjVp0iSoPSkpSe+99x7zfI5E07yeSS02KywsVFVVVdD59+zZUx07dgz6d9G7d++gh71mZmYqEAhoz549zpgfm+fKykoVFhYGjYmNjVVGRoa181xeXq6YmBjn9xv5mw4NYcdiDz74oG677Tb17NlTCQkJ+sUvfqGJEycqOztbkuT3+yWp3pOm09LSnD6/36/U1NSg/vj4eLVs2TJozOn28d1j/NCYuv7zWfPmzeXz+TRjxgwVFxerpqZGr7zyigoKClRSUsI8nyPRNK9nUovN/H6/XC5XvR9U/v4chTrPgUBA33zzjf71r3+ppqbmZzPPp06d0pQpUzRy5EjnRz35mw4NYcdib7zxhl599VUtXrxYH3zwgRYtWqQnn3xSixYtinRp1vmf//kfGWN0wQUXKDExUc8995xGjhyp2Fj+EwNw9qqqqnTLLbfIGKN58+ZFupzzHv8nttjkyZOdqzu9e/fWqFGjNGnSJOXl5UmSvF6vJNW7Y6K0tNTp83q9OnLkSFB/dXW1jh07FjTmdPv47jF+aExd//nuoosu0vr163XixAkdOnRIW7ZsUVVVlS688ELm+RyJpnk9k1ps5vV6VVlZqbKysqD2789RqPPsdruVlJSk1q1bKy4uzvp5rgs6X3zxhVavXu1c1ZH4mw4VYcdiX3/9db0rC3FxcaqtrZUkdenSRV6vV/n5+U5/IBDQ5s2b5fP5JEk+n09lZWUqLCx0xqxdu1a1tbXq37+/M2bDhg2qqqpyxqxevVo9evRQixYtnDHfPU7dmLrj2KJp06Zq27atvvrqK73zzjsaNmwY83yORNO8nkktNuvbt68SEhKCzn/fvn0qKioK+nexe/fuoA/qug/y9PR0Z8yPzbPL5VLfvn2DxtTW1io/P9+aea4LOvv379eaNWvUqlWroH7+pkMU6RXSOHfGjBljLrjgAufW82XLlpnWrVubBx54wBkzc+ZMk5KSYv7617+aXbt2mWHDhp321t1f/OIXZvPmzea9994z3bp1C7rNsayszKSlpZlRo0aZDz/80CxZssQkJyfXu80xPj7ePPnkk+ajjz4y06dPP69vif6+VatWmZUrV5rPP//cvPvuu6ZPnz6mf//+prKy0hjDPIfq+PHjZvv27Wb79u1Gkpk9e7bZvn27c2dKNM3rmdQSrX5qnr/88kuzfft2s2LFCiPJLFmyxGzfvt2UlJQ4+7j77rtNx44dzdq1a822bduMz+czPp/P6a+79Xzw4MFmx44dZtWqVaZNmzanvfV88uTJ5qOPPjJz5sw57a3niYmJZuHChWbv3r3mrrvuMikpKUF3eUWzH5vryspK85vf/Ma0b9/e7Nixw5SUlDjbd++s4m/67BF2LBYIBMyECRNMx44dTZMmTcyFF15oHnrooaD/aGpra83DDz9s0tLSTGJiohk0aJDZt29f0H6+/PJLM3LkSNOsWTPjdrvN2LFjzfHjx4PG7Ny50wwcONAkJiaaCy64wMycObNePW+88Ybp3r27cblc5pJLLjErVqw4NyceAa+//rq58MILjcvlMl6v1+Tk5JiysjKnn3kOzbp164yketuYMWOMMdE1r2dSS7T6qXlesGDBafunT5/u7OObb74xf/jDH0yLFi1McnKyuemmm4LCkDHGHDx40AwZMsQkJSWZ1q1bm/vuu89UVVXVq+Wyyy4zLpfLXHjhhWbBggX16n3++edNx44djcvlMldccYXZtGlTuKfknPmxua67tf9027p165x98Dd99mKM+c7jdAEAACzDmh0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArPb/AN+1XOCuw6KvAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metricas_por_tipo = campanha.groupby('canal')['ctr'].mean()\n",
        "metricas_por_tipo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "8mSQb438ncvK",
        "outputId": "142afd12-55e4-4cbf-cb24-17e7d27804f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "canal\n",
              "Email           5.109386\n",
              "Google Ads      5.152163\n",
              "Meta Ads        5.133841\n",
              "PPC             5.162208\n",
              "Social Media    5.187068\n",
              "Name: ctr, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ctr</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>canal</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Email</th>\n",
              "      <td>5.109386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Google Ads</th>\n",
              "      <td>5.152163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meta Ads</th>\n",
              "      <td>5.133841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PPC</th>\n",
              "      <td>5.162208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Social Media</th>\n",
              "      <td>5.187068</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(medias_por_campanha)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "GJCvcRsMnvJt",
        "outputId": "5777ca69-2ee0-46ce-e055-9fd7d73570d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='impressoes'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "medias_por_campanha.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "oOnfYX8CpuIr",
        "outputId": "ef03984d-bd34-4862-8eac-77ea11643428"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      2000.000000\n",
              "mean      99924.847399\n",
              "std        7651.690148\n",
              "min       74315.388889\n",
              "25%       94601.752717\n",
              "50%       99731.860577\n",
              "75%      105173.546176\n",
              "max      124154.071429\n",
              "Name: impressoes, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>impressoes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>99924.847399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7651.690148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>74315.388889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>94601.752717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>99731.860577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>105173.546176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>124154.071429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.displot(medias_por_campanha, kde = True)\n",
        "plt.title('Histograma das médias das campanhas')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "cM4_kslup5kR",
        "outputId": "d6522014-77dc-445c-e9c0-d31c940a41d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(campanha['receita'])\n",
        "plt.title('Distribuição da receita das campanhas')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "ljVgQvYjqLZf",
        "outputId": "fbc62db3-6891-4f5a-b022-4e7cb0ae0f0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: gráfico de distribuição do custo da campanha\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "sns.displot(campanha['custo'])\n",
        "plt.title('Distribuição do custo das campanhas')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "w0G0h_sWs9sC",
        "outputId": "d5a2b97d-e9e6-44b1-b6f5-deb6835679b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuQxcPihuA1M",
        "outputId": "531b4cc2-6ef8-491c-bf71-227874a04c38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Id_campanha  50000 non-null  int64  \n",
            " 1   impressoes   50000 non-null  int64  \n",
            " 2   cliques      50000 non-null  int64  \n",
            " 3   canal        50000 non-null  object \n",
            " 4   custo        50000 non-null  int64  \n",
            " 5   receita      50000 non-null  int64  \n",
            " 6   estado       50000 non-null  object \n",
            " 7   pais         50000 non-null  object \n",
            " 8   ctr          50000 non-null  float64\n",
            "dtypes: float64(1), int64(5), object(3)\n",
            "memory usage: 3.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "4S7k7N3wuJpy",
        "outputId": "845a4e88-db64-46d1-a5b8-ed513bfa2399"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id_campanha     impressoes      cliques         custo        receita  \\\n",
              "count  50000.000000   50000.000000  50000.00000  50000.000000   50000.000000   \n",
              "mean    1004.637160   99916.792660   5146.89304  14478.263920   14242.518860   \n",
              "std      578.014356   38217.517352   2612.96365   6072.944902   10365.240991   \n",
              "min        1.000000     100.000000      1.00000   2723.000000       0.000000   \n",
              "25%      504.000000   72238.000000   3209.00000  10164.000000    7757.000000   \n",
              "50%     1007.000000   99951.500000   4829.00000  13348.000000   11855.500000   \n",
              "75%     1506.000000  127436.000000   6741.25000  17513.000000   18070.250000   \n",
              "max     2000.000000  200000.000000  19484.00000  80000.000000  134842.000000   \n",
              "\n",
              "                ctr  \n",
              "count  50000.000000  \n",
              "mean       5.148482  \n",
              "std        1.604119  \n",
              "min        0.992663  \n",
              "25%        4.050781  \n",
              "50%        5.132384  \n",
              "75%        6.241502  \n",
              "max       10.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aaae0d94-4c28-43b3-87b3-50eaa3c9484e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id_campanha</th>\n",
              "      <th>impressoes</th>\n",
              "      <th>cliques</th>\n",
              "      <th>custo</th>\n",
              "      <th>receita</th>\n",
              "      <th>ctr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.00000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1004.637160</td>\n",
              "      <td>99916.792660</td>\n",
              "      <td>5146.89304</td>\n",
              "      <td>14478.263920</td>\n",
              "      <td>14242.518860</td>\n",
              "      <td>5.148482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>578.014356</td>\n",
              "      <td>38217.517352</td>\n",
              "      <td>2612.96365</td>\n",
              "      <td>6072.944902</td>\n",
              "      <td>10365.240991</td>\n",
              "      <td>1.604119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>2723.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.992663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>504.000000</td>\n",
              "      <td>72238.000000</td>\n",
              "      <td>3209.00000</td>\n",
              "      <td>10164.000000</td>\n",
              "      <td>7757.000000</td>\n",
              "      <td>4.050781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1007.000000</td>\n",
              "      <td>99951.500000</td>\n",
              "      <td>4829.00000</td>\n",
              "      <td>13348.000000</td>\n",
              "      <td>11855.500000</td>\n",
              "      <td>5.132384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1506.000000</td>\n",
              "      <td>127436.000000</td>\n",
              "      <td>6741.25000</td>\n",
              "      <td>17513.000000</td>\n",
              "      <td>18070.250000</td>\n",
              "      <td>6.241502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2000.000000</td>\n",
              "      <td>200000.000000</td>\n",
              "      <td>19484.00000</td>\n",
              "      <td>80000.000000</td>\n",
              "      <td>134842.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    0% {\n",
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              "\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"campanha\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Id_campanha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17355.218562295173,\n        \"min\": 1.0,\n        \"max\": 50000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1004.63716,\n          1007.0,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"impressoes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61235.64485016425,\n        \"min\": 100.0,\n        \"max\": 200000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          99916.79266,\n          99951.5,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cliques\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16619.485942972675,\n        \"min\": 1.0,\n        \"max\": 50000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5146.89304,\n          4829.0,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"custo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26787.720708576693,\n        \"min\": 2723.0,\n        \"max\": 80000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14478.26392,\n          13348.0,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"receita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44538.26719708733,\n        \"min\": 0.0,\n        \"max\": 134842.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14242.51886,\n          11855.5,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ctr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17675.994415801102,\n        \"min\": 0.9926629261976695,\n        \"max\": 50000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.1484815905088634,\n          5.132384085295483,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['estado']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "TwsrQHi2xQip",
        "outputId": "9dadb23b-5c57-47e5-ccda-0187359a144e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        MG\n",
              "1        BA\n",
              "2        AL\n",
              "3        PI\n",
              "4        RJ\n",
              "         ..\n",
              "49995    MG\n",
              "49996    MA\n",
              "49997    RS\n",
              "49998    DF\n",
              "49999    RJ\n",
              "Name: estado, Length: 50000, dtype: object"
            ],
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              "      <th>49996</th>\n",
              "      <td>MA</td>\n",
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              "      <th>49997</th>\n",
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              "      <th>49998</th>\n",
              "      <td>DF</td>\n",
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              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>RJ</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['estado'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8phnaZkx0SW",
        "outputId": "34d05517-bf4a-41ea-ce1c-456b93e71a32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['MG', 'BA', 'AL', 'PI', 'RJ', 'SP', 'MA', 'PE', 'MS', 'DF', 'CE',\n",
              "       'AM', 'SE', 'PA', 'PR', 'SC', 'GO', 'RN', 'RO', 'ES', 'MT', 'TO',\n",
              "       'PB', 'RS', 'AC', 'AP', 'RR'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['estado'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "_biRoVRGx8Aa",
        "outputId": "916f99ae-265f-4d13-f181-24eebf83bbd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "estado\n",
              "SP    11006\n",
              "MG     4902\n",
              "RJ     4221\n",
              "BA     3524\n",
              "RS     2701\n",
              "PR     2698\n",
              "PE     2255\n",
              "CE     2140\n",
              "PA     2024\n",
              "SC     1741\n",
              "GO     1715\n",
              "MA     1643\n",
              "AM      959\n",
              "PB      952\n",
              "ES      927\n",
              "MT      839\n",
              "RN      823\n",
              "PI      784\n",
              "AL      784\n",
              "DF      714\n",
              "MS      674\n",
              "SE      586\n",
              "RO      445\n",
              "TO      345\n",
              "AC      226\n",
              "AP      217\n",
              "RR      155\n",
              "Name: count, dtype: int64"
            ],
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              "      <th></th>\n",
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              "      <th>estado</th>\n",
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              "      <th>SP</th>\n",
              "      <td>11006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MG</th>\n",
              "      <td>4902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RJ</th>\n",
              "      <td>4221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BA</th>\n",
              "      <td>3524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RS</th>\n",
              "      <td>2701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PR</th>\n",
              "      <td>2698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PE</th>\n",
              "      <td>2255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CE</th>\n",
              "      <td>2140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PA</th>\n",
              "      <td>2024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SC</th>\n",
              "      <td>1741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GO</th>\n",
              "      <td>1715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MA</th>\n",
              "      <td>1643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AM</th>\n",
              "      <td>959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PB</th>\n",
              "      <td>952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ES</th>\n",
              "      <td>927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MT</th>\n",
              "      <td>839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RN</th>\n",
              "      <td>823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PI</th>\n",
              "      <td>784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AL</th>\n",
              "      <td>784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DF</th>\n",
              "      <td>714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MS</th>\n",
              "      <td>674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SE</th>\n",
              "      <td>586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RO</th>\n",
              "      <td>445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TO</th>\n",
              "      <td>345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AC</th>\n",
              "      <td>226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AP</th>\n",
              "      <td>217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RR</th>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha.head()"
      ],
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        "id": "xwytFlvRyDaj",
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Id_campanha  impressoes  cliques       canal  custo  receita estado  \\\n",
              "0            1      152431     9462       Email  13258    32309     MG   \n",
              "1            1       39688     3218         PPC  16391    16568     BA   \n",
              "2            1      104506     4756  Google Ads   6189    19029     AL   \n",
              "3            1       80768     3566  Google Ads   5215        0     PI   \n",
              "4            1       54932     3645  Google Ads  16767     8825     RJ   \n",
              "\n",
              "     pais       ctr  \n",
              "0  Brasil  6.207399  \n",
              "1  Brasil  8.108244  \n",
              "2  Brasil  4.550935  \n",
              "3  Brasil  4.415115  \n",
              "4  Brasil  6.635477  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "campanha",
              "summary": "{\n  \"name\": \"campanha\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"Id_campanha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 578,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"impressoes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38217,\n        \"min\": 100,\n        \"max\": 200000,\n        \"num_unique_values\": 42072,\n        \"samples\": [\n          144427,\n          79136,\n          76659\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cliques\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2612,\n        \"min\": 1,\n        \"max\": 19484,\n        \"num_unique_values\": 10960,\n        \"samples\": [\n          1585,\n          7059,\n          12201\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"canal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"PPC\",\n          \"Social Media\",\n          \"Google Ads\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"custo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6072,\n        \"min\": 2723,\n        \"max\": 80000,\n        \"num_unique_values\": 19420,\n        \"samples\": [\n          21610,\n          4432,\n          13584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"receita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10365,\n        \"min\": 0,\n        \"max\": 134842,\n        \"num_unique_values\": 23135,\n        \"samples\": [\n          20524,\n          14915,\n          20474\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estado\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"MS\",\n          \"PA\",\n          \"DF\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pais\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Brasil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ctr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6041190438542303,\n        \"min\": 0.9926629261976695,\n        \"max\": 10.0,\n        \"num_unique_values\": 49874,\n        \"samples\": [\n          4.860034069817701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['estado'].value_counts().index\n"
      ],
      "metadata": {
        "id": "bVVPNnTRyQst",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fa2b2ce-e44b-4323-dfc6-282650cd5181"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['SP', 'MG', 'RJ', 'BA', 'RS', 'PR', 'PE', 'CE', 'PA', 'SC', 'GO', 'MA',\n",
              "       'AM', 'PB', 'ES', 'MT', 'RN', 'PI', 'AL', 'DF', 'MS', 'SE', 'RO', 'TO',\n",
              "       'AC', 'AP', 'RR'],\n",
              "      dtype='object', name='estado')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['estado'].value_counts().values\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZeVP777Nl_R",
        "outputId": "7ebdaaf5-40b2-493d-8a7c-e779cc52425f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([11006,  4902,  4221,  3524,  2701,  2698,  2255,  2140,  2024,\n",
              "        1741,  1715,  1643,   959,   952,   927,   839,   823,   784,\n",
              "         784,   714,   674,   586,   445,   345,   226,   217,   155])"
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          "metadata": {},
          "execution_count": 46
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              "        count\n",
              "estado       \n",
              "SP      11006\n",
              "MG       4902\n",
              "RJ       4221\n",
              "BA       3524\n",
              "RS       2701\n",
              "PR       2698\n",
              "PE       2255\n",
              "CE       2140\n",
              "PA       2024\n",
              "SC       1741\n",
              "GO       1715\n",
              "MA       1643\n",
              "AM        959\n",
              "PB        952\n",
              "ES        927\n",
              "MT        839\n",
              "RN        823\n",
              "PI        784\n",
              "AL        784\n",
              "DF        714\n",
              "MS        674\n",
              "SE        586\n",
              "RO        445\n",
              "TO        345\n",
              "AC        226\n",
              "AP        217\n",
              "RR        155"
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              "\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "contagem_de_estados = campanha['estado'].value_counts().to_frame().reset_index()\n"
      ],
      "metadata": {
        "id": "USeZjM2GN5cS"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
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        "contagem_de_estados.columns = ['estado', 'total']"
      ],
      "metadata": {
        "id": "1r6qoDzBODza"
      },
      "execution_count": null,
      "outputs": []
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          "data": {
            "text/plain": [
              "  estado  total\n",
              "0     SP  11006\n",
              "1     MG   4902\n",
              "2     RJ   4221\n",
              "3     BA   3524\n",
              "4     RS   2701"
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da0d31f3-82b9-4a23-b4cf-4e90deaa70c3 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-da0d31f3-82b9-4a23-b4cf-4e90deaa70c3');\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-0d40ca7c-ac49-43da-81f8-05de1cf5ae0c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d40ca7c-ac49-43da-81f8-05de1cf5ae0c')\"\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-0d40ca7c-ac49-43da-81f8-05de1cf5ae0c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "contagem_de_estados",
              "summary": "{\n  \"name\": \"contagem_de_estados\",\n  \"rows\": 27,\n  \"fields\": [\n    {\n      \"column\": \"estado\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"PA\",\n          \"PB\",\n          \"SC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2208,\n        \"min\": 155,\n        \"max\": 11006,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          2024,\n          823,\n          11006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "contagem_de_estados.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "8yY3p2TbOjRz",
        "outputId": "97165eba-bb25-446c-b762-deee1db00feb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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gXr16admyZZo1a5buuecePf300+F+S2FHTw4AAJFnD/cBP/74Y11yySW66KKLJEnHH3+8XnrpJS1dulRSWy/OI488ojvvvFOXXHKJJOn5559Xdna23njjDY0ePVrr1q3Te++9p08//VRDhw6VJD322GP6yU9+oj//+c/Ky8vTCy+8IJ/Pp2eeeUYOh0Mnn3yyli9frocffjgkDMUienIAAIi8sPfknH322Vq4cKE2bNggSVqxYoX+/e9/68ILL5Qkbd68WZWVlSouLg4+x+12a9iwYSotLZUklZaWKi0tLRhwJKm4uFhWq1VlZWXBNueff74cDkewTUlJicrLy7V3795wv62woicHAIDIC3tPzh133CGv16t+/frJZrPJ7/frgQce0JgxYyRJlZWVkqTs7OyQ52VnZwf3VVZWKisrK7RQu13p6ekhbQoLCw86Rvu+7t27H1Rbc3Ozmpv3Bwuv13ssb/WoBZd2oCcHAICICXtPzquvvqoXXnhBL774oj7//HM999xz+vOf/6znnnsu3C91xGbMmCG32x28FRQUmFIHPTkAAERe2EPO7373O91xxx0aPXq0Bg0apKuuukq33nqrZsyYIUnKycmRJFVVVYU8r6qqKrgvJydH1dXVIftbW1u1Z8+ekDaHOsaBr/Ft06ZNk8fjCd4qKiqO8d0enYx9EwIyTw4AAJET9pDT0NAgqzX0sDabTYFAQJJUWFionJwcLVy4MLjf6/WqrKxMRUVFkqSioiLV1NRo2bJlwTaLFi1SIBDQsGHDgm2WLFmilpaWYJsFCxaob9++h/ypSpKcTqdcLlfIzQztPTl76pvlZ2kHAAAiIuwh5+KLL9YDDzyg+fPn6+uvv9bcuXP18MMP69JLL5UkWSwWTZ48Wffff7/eeustrVq1SldffbXy8vI0cuRISVL//v01YsQIjRs3TkuXLtVHH32kSZMmafTo0crLy5MkXXnllXI4HBo7dqzWrFmjV155RY8++qimTJkS7rcUdunJDlksUsCQ9tTTmwMAQCSEfeDxY489prvuuks33XSTqqurlZeXpxtuuEHTp08Ptrn99ttVX1+v8ePHq6amRueee67ee+89JSYmBtu88MILmjRpkoYPHy6r1arLL79cs2fPDu53u93617/+pYkTJ2rIkCHKyMjQ9OnTY/7ycUmy26zqkeLQrjqfdtY2B3t2AABA+FiMA6ci7mK8Xq/cbrc8Hk/Uf7oa8cgSra+s1fO/PlPnn5QZ1dcGACCedfT7m7WrTMIVVgAARBYhxyTMegwAQGQRckzS3pNDyAEAIDIIOSZpnyuHn6sAAIgMQo5J9vfkcAk5AACRQMgxSXD9KnpyAACICEKOSRiTAwBAZBFyTNLek7OnwadWf8DkagAA6HwIOSbpnuyQzWqRwdIOAABEBCHHJDarRekpbVdYVTMuBwCAsCPkmCiTCQEBAIgYQo6JMljaAQCAiCHkmGh/Tw5jcgAACDdCjokyUpn1GACASCHkmIgxOQAARA4hx0SZjMkBACBiCDkmoicHAIDIIeSYKHh1FSEHAICwI+SYqL0np6ahRb5WlnYAACCcCDkmciclyG61SJJ219ObAwBAOBFyTGS1WoILde6qZa4cAADCiZBjsuBcOXVNJlcCAEDnQsgxWSY9OQAARAQhx2TtP1dxhRUAAOFFyDEZEwICABAZhByT0ZMDAEBkEHJM1t6Ts4ueHAAAwoqQYzJ6cgAAiAxCjsky911CTk8OAADhRcgxWWa3REmSt6lVTS1+k6sBAKDzIOSYzJVkl8PW9j/D7nrmygEAIFwIOSazWCzK6LZv1mN+sgIAIGwIOTEggyusAAAIO0JODMjkCisAAMKOkBMD9q9ETsgBACBcCDkxILi0Az05AACEDSEnBrQPPN5FyAEAIGwIOTEgM7VtrpxdtVxCDgBAuBByYkDwEnJ6cgAACBtCTgxgkU4AAMKPkBMD2ufJqW1maQcAAMKFkBMDUp12Oe1t/1Mw6zEAAOFByIkBbUs7cBk5AADhRMiJEYzLAQAgvAg5MYKeHAAAwouQEyP29+QwVw4AAOFAyIkRmcG5cppMrgQAgM6BkBMj6MkBACC8CDkxgjE5AACEFyEnRgR7cgg5AACEBSEnRgR7criEHACAsIhIyNm2bZt+9atfqUePHkpKStKgQYP02WefBfcbhqHp06crNzdXSUlJKi4u1pdffhlyjD179mjMmDFyuVxKS0vT2LFjVVdXF9Jm5cqVOu+885SYmKiCggLNnDkzEm8nKtp7chp8ftU3t5pcDQAA8S/sIWfv3r0655xzlJCQoHfffVdr167VQw89pO7duwfbzJw5U7Nnz9acOXNUVlamlJQUlZSUqKlp/5VFY8aM0Zo1a7RgwQLNmzdPS5Ys0fjx44P7vV6vLrjgAvXq1UvLli3TrFmzdM899+jpp58O91uKihSnXUkJNkn8ZAUAQFgYYTZ16lTj3HPPPez+QCBg5OTkGLNmzQpuq6mpMZxOp/HSSy8ZhmEYa9euNSQZn376abDNu+++a1gsFmPbtm2GYRjGk08+aXTv3t1obm4Oee2+fft2uFaPx2NIMjweT4efE0nn/WmR0WvqPOOzr3ebXQoAADGro9/fYe/JeeuttzR06FD94he/UFZWlk477TT913/9V3D/5s2bVVlZqeLi4uA2t9utYcOGqbS0VJJUWlqqtLQ0DR06NNimuLhYVqtVZWVlwTbnn3++HA5HsE1JSYnKy8u1d+/eQ9bW3Nwsr9cbcoslGe1z5TAuBwCAYxb2kPPVV1/pqaeeUp8+ffT+++9rwoQJ+s1vfqPnnntOklRZWSlJys7ODnlednZ2cF9lZaWysrJC9tvtdqWnp4e0OdQxDnyNb5sxY4bcbnfwVlBQcIzvNrzax+XsrGOuHAAAjlXYQ04gENDpp5+uBx98UKeddprGjx+vcePGac6cOeF+qSM2bdo0eTye4K2iosLskkJwhRUAAOET9pCTm5urAQMGhGzr37+/tmzZIknKycmRJFVVVYW0qaqqCu7LyclRdXV1yP7W1lbt2bMnpM2hjnHga3yb0+mUy+UKucUS5soBACB8wh5yzjnnHJWXl4ds27Bhg3r16iVJKiwsVE5OjhYuXBjc7/V6VVZWpqKiIklSUVGRampqtGzZsmCbRYsWKRAIaNiwYcE2S5YsUUtLS7DNggUL1Ldv35ArueIJPTkAAIRP2EPOrbfeqk8++UQPPvigNm7cqBdffFFPP/20Jk6cKEmyWCyaPHmy7r//fr311ltatWqVrr76auXl5WnkyJGS2np+RowYoXHjxmnp0qX66KOPNGnSJI0ePVp5eXmSpCuvvFIOh0Njx47VmjVr9Morr+jRRx/VlClTwv2WooaeHAAAwsce7gOeccYZmjt3rqZNm6b77rtPhYWFeuSRRzRmzJhgm9tvv1319fUaP368ampqdO655+q9995TYmJisM0LL7ygSZMmafjw4bJarbr88ss1e/bs4H63261//etfmjhxooYMGaKMjAxNnz49ZC6deENPDgAA4WMxDMMwuwizeL1eud1ueTyemBifU7GnQefN/ECJCVatu2+ELBaL2SUBABBzOvr9zdpVMaS9J6epJaA6lnYAAOCYEHJiSJLDpm7Otl8QdzFXDgAAx4SQE2OY9RgAgPAg5MQYrrACACA8CDkxhiusAAAID0JOjKEnBwCA8CDkxBh6cgAACA9CToyhJwcAgPAg5MQYenIAAAgPQk6M2d+Twzw5AAAcC0JOjAnOk1PXrC684gYAAMeMkBNj2n+u8rUG5G1iaQcAAI4WISfGJCbYlJrYvrQD43IAADhahJwYlMngYwAAjhkhJwZlcBk5AADHjJATg+jJAQDg2BFyYhATAgIAcOwIOTEoeBk5PTkAABw1Qk4MYkJAAACOHSEnBrG0AwAAx46QE4MYkwMAwLEj5MSg9p6cXSztAADAUSPkxKAe+wYet/gNeRpbTK4GAID4RMiJQU67Te6kBEmMywEA4GgRcmJU+7icnYzLAQDgqBByYhRz5QAAcGwIOTEqMzVREnPlAABwtAg5MSovrS3k/HPZVjX6/CZXAwBA/CHkxKiri45XjxSH1u7w6rZ/rOBScgAAjhAhJ0Ydl5akOVcNUYLNovkrd+ixRRvNLgkAgLhCyIlhZxyfrv+8ZKAk6eEFG/Te6kqTKwIAIH4QcmLc6DN76tqzj5ckTXl1udbt8JpbEAAAcYKQEwfuvKi/zj0xQw0+v65/7jPtZu4cAAC+FyEnDthtVj1+5Wk6vkeyttU0asL/fi5fa8DssgAAiGmEnDiRluzQ364ZqlSnXUu/3qO731rNFVcAAHwHQk4cOTErVbOvOE0Wi/TS0go9X/qN2SUBABCzCDlx5kf9sjTtwn6SpPvmrdVHG3eZXBEAALGJkBOHxp13gi477Tj5A4ZueuFzfb2r3uySAACIOYScOGSxWPTgZYN0akGaPI0tuv75z+RtajG7LAAAYgohJ04lJtj09FVDlONK1MbqOk1+ebn8AQYiAwDQjpATx7JciXr66iFy2q1atL5aM99fb3ZJAADEDEJOnDslP02zfjFYkvTXxV9p7hdbTa4IAIDYQMjpBH42OE8Tf9RbkjT1n6v0xZa9JlcEAID5CDmdxG9/3Fc/HpAtX2tAN/zPMlV6mswuCQAAUxFyOgmr1aK/jDpVfbNTVV3brPH/85maWvxmlwUAgGkIOZ1IN6ddf7tmqLonJ2jlVo9u/8dKln4AAHRZhJxOpiA9WU+OGSK71aK3VmzXU4s3mV0SAACmIOR0QkW9e+ien50sSZr1frkWrK0yuSIAAKKPkNNJ/eqsXvrVWT1lGNKtryzXxupas0sCACCqCDmd2N0Xn6xhhemqa27VuOeXydPI0g8AgK6DkNOJJdisemLM6cpzJ2rzrnpNfvkLln4AAHQZEQ85f/zjH2WxWDR58uTgtqamJk2cOFE9evRQt27ddPnll6uqKnTcyJYtW3TRRRcpOTlZWVlZ+t3vfqfW1taQNh9++KFOP/10OZ1OnXjiiXr22Wcj/XbiTkY3p/561VA57VZ9UL5Tf1mwweySAACIioiGnE8//VR//etfdcopp4Rsv/XWW/X222/rtdde0+LFi7V9+3Zddtllwf1+v18XXXSRfD6fPv74Yz333HN69tlnNX369GCbzZs366KLLtKPfvQjLV++XJMnT9b111+v999/P5JvKS4Nynfrj5cPkiQ9/sFGvbtqh8kVAQAQBUaE1NbWGn369DEWLFhg/OAHPzBuueUWwzAMo6amxkhISDBee+21YNt169YZkozS0lLDMAzjnXfeMaxWq1FZWRls89RTTxkul8tobm42DMMwbr/9duPkk08Oec1Ro0YZJSUlHa7R4/EYkgyPx3O0bzOu/Ofba4xeU+cZ/e9611i/w2t2OQAAHJWOfn9HrCdn4sSJuuiii1RcXByyfdmyZWppaQnZ3q9fP/Xs2VOlpaWSpNLSUg0aNEjZ2dnBNiUlJfJ6vVqzZk2wzbePXVJSEjzGoTQ3N8vr9YbcupI7Luync07soQafX+P/5zN5GhiIDADovCIScl5++WV9/vnnmjFjxkH7Kisr5XA4lJaWFrI9OztblZWVwTYHBpz2/e37vquN1+tVY2PjIeuaMWOG3G538FZQUHBU7y9e2W1WPXbF6crvnqRvdjfoZgYiAwA6sbCHnIqKCt1yyy164YUXlJiYGO7DH5Np06bJ4/EEbxUVFWaXFHXpKQ49fdVQJSZYtWTDTs16v9zskgAAiIiwh5xly5apurpap59+uux2u+x2uxYvXqzZs2fLbrcrOztbPp9PNTU1Ic+rqqpSTk6OJCknJ+egq63aH39fG5fLpaSkpEPW5nQ65XK5Qm5d0YA8l2b+fLAkac7iTXp7xXaTKwIAIPzCHnKGDx+uVatWafny5cHb0KFDNWbMmOD9hIQELVy4MPic8vJybdmyRUVFRZKkoqIirVq1StXV1cE2CxYskMvl0oABA4JtDjxGe5v2Y+C7/Wxwnm74wQmSpN/9Y4XWbu9a45MAAJ2fPdwHTE1N1cCBA0O2paSkqEePHsHtY8eO1ZQpU5Seni6Xy6Wbb75ZRUVFOuussyRJF1xwgQYMGKCrrrpKM2fOVGVlpe68805NnDhRTqdTknTjjTfq8ccf1+23365f//rXWrRokV599VXNnz8/3G+p07q9pJ/Wbvfq/77cpfH/85nennSuuqc4zC4LAICwMGXG47/85S/66U9/qssvv1znn3++cnJy9Prrrwf322w2zZs3TzabTUVFRfrVr36lq6++Wvfdd1+wTWFhoebPn68FCxZo8ODBeuihh/S3v/1NJSUlZryluGSzWvTYFaepZ3qytu5t1KSXPlerP2B2WQAAhIXFMIwue3mN1+uV2+2Wx+PpsuNzJGl9pVeXPfmxGnx+XX9uoe786QCzSwIA4LA6+v3N2lVQvxyXHvpF20Dkv/17s974YpvJFQEAcOwIOZAkXTgoVxN/1FuSNPWfK7V6m8fkigAAODaEHARN+XFf/ahvpppbAxr//GfaVddsdkkAABw1Qg6CbFaLHhl9mgozUrTd06SJL3yuFgYiAwDiFCEHIdxJCfqvq4eom9Ouss179MD8dWaXBADAUSHk4CAnZqXq4V+2DUR+9uOv9dpnXW/5CwBA/CPk4JAuODlHtwzvI0n6wxurtaKixtyCAAA4QoQcHNYtw/uouH+2fK0B3fA/y7Sn3md2SQAAdBghB4dltVr0l1GDdUJmiiq9TbrrjdXqwnNHAgDiDCEH3yk1MUGPjjpNdqtF81ft0Nsrd5hdEgAAHULIwfcalO/WpP84UZJ01xurVe1tMrkiAAC+HyEHHTLxRydq4HEueRpbdMfrq/jZCgAQ8wg56JAEm1UP//JUOWxWLVpfrVe5rBwAEOMIOeiwk7JT9dsLTpIk3ff2WlXsaTC5IgAADo+QgyNy/XknaGiv7qr3+XX7P1YqEOBnKwBAbCLk4IjYrBb9+ReDlZRgU+lXu/V86ddmlwQAwCERcnDEjs9I0e9/0k+S9Mf31uurnXUmVwQAwMEIOTgqY4b10rknZqipJaDfvrZCraxWDgCIMYQcHBWr1aKZPz9FqU67vthSo6f/7yuzSwIAIAQhB0ctLy1Jd//sZEnSXxZs0PpKr8kVAQCwHyEHx+Ty049Tcf9stfgNTXllhXyt/GwFAIgNhBwcE4vFogcvG6juyQlau8OrxxZ9aXZJAABIIuQgDLJSE3X/yEGSpCc/3KTlFTXmFgQAgAg5CJOLTsnVzwbnyR8w9NtXl6upxW92SQCALo6Qg7C575KTlZnq1Kad9frz++VmlwMA6OIIOQibtGSH/nR5289W//3RZpV9tdvkigAAXRkhB2H1H/2yNWpogQxDuu0fK1TX3Gp2SQCALoqQg7C786f9dVxakir2NOrBd9aZXQ4AoIsi5CDsUhMTNOsXp0iSXizbosUbdppcEQCgKyLkICLO7p2ha88+XpI09R8r5WloMbcgAECXQ8hBxEwd0U+FGSmq9Dbp3rfXmF0OAKCLIeQgYpIcNj30y8GyWqTXv9im91ZXml0SAKALsZtdADq303t2140/6K0nP9ykP8xdpd31zbJaLLJaJIsskkWyWiyySLK037e0Pbf9/oH7LRaLTuuZpqzURDPfFgAgDhByEHG3FPfRovXVWl9Zqz/MXX3Mx0t12vXwqFP14wHZYagOANBZWQzDMMwuwixer1dut1sej0cul8vscjq1TTvr9Mj/+1JNLX4ZhiHDkAxJgQPu799uKBBo+2sYCm4zDGlXXbO+3t0gSfrN8D6aPLyPrFaLqe8NABBdHf3+JuQQcuKKrzWgB+av1XOl30iSftQ3U4+MOk3u5ASTKwMAREtHv78ZeIy44rBbde8lA/XQLwbLabfqg/Kd+tkT/9b6Sq/ZpQEAYgwhB3Hp8iH5+ueEs5XfPUnf7G7QpU98rLdWbDe7LABADCHkIG4NPM6ttyedq/P6ZKixxa/fvPSFHpi/Vq3+gNmlAQBiACEHca17ikPPXnemJvywtyTpv/5vs37132XaVddscmUAALMRchD3bFaLpo7op6fGnK4Uh02ffLVHFz/2by2vqDG7NACAiQg56DQuHJSrNyaeoxMyUrTD06RfzinVK59uMbssAIBJCDnoVPpkp+qNSeeouH+2fP6Apv5zlX4/d5WaW/1mlwYAiDJCDjodV2KCnr5qiH7745NksUgvlm3RqL9+oh2eRrNLAwBEESEHnZLVatHNw/vomWvPkCvRruUVNbr4sX+r7KvdZpcGAIgSQg46tR/1zdLbN5+rfjmp2lXn05i/lenvH21WF57oGwC6DEIOOr1ePVL0+k1n62eD89QaMHTv22s14X8/15vLt2nzrnoCDwB0UqxdxdpVXYZhGHrmo6/14Dvr5A/s/9i7Eu06JT9Ng/LdGpzv1qD8NOW5E2WxsPAnAMQiFujsAEJO1/TFlr2a+8U2rdzq0dodXvlaD54hOaOboy34HOfW4AK3Bh2XpsxUpwnVAgC+jZDTAYQc+FoD2lBVq5VbPVq1rUYrKjwqr6oN6elpl+dO1KB8t07JT9PgfQGI1c8BIPoIOR1AyMGhNLX4tXaHV6u2erRia41WbvVo0846Heq/lON7JOuU/DSdku/W4II0nZznUrLDHv2iAaALMS3kzJgxQ6+//rrWr1+vpKQknX322frTn/6kvn37Bts0NTXpt7/9rV5++WU1NzerpKRETz75pLKzs4NttmzZogkTJuiDDz5Qt27ddM0112jGjBmy2/d/gXz44YeaMmWK1qxZo4KCAt1555269tprO1wrIQcdVdfcqtXbPMHgs2qbR9/sbjiondUinZSdqlMO6PHpm5Mqh50x/gAQLqaFnBEjRmj06NE644wz1Nraqt///vdavXq11q5dq5SUFEnShAkTNH/+fD377LNyu92aNGmSrFarPvroI0mS3+/XqaeeqpycHM2aNUs7duzQ1VdfrXHjxunBBx+UJG3evFkDBw7UjTfeqOuvv14LFy7U5MmTNX/+fJWUlHSoVkIOjkVNg08rt3q0cmuNVuz7W+U9eGFQh92q/rkuDQ4GH7d6Z3aT1crAZgA4GjHzc9XOnTuVlZWlxYsX6/zzz5fH41FmZqZefPFF/fznP5ckrV+/Xv3791dpaanOOussvfvuu/rpT3+q7du3B3t35syZo6lTp2rnzp1yOByaOnWq5s+fr9WrVwdfa/To0aqpqdF7773XodoIOQi3Km+TVlS0/cTV/lOXp7HloHYpDpsGHufWqQVp+o9+WRp2Qg8TqgWA+NTR7++IDx7weDySpPT0dEnSsmXL1NLSouLi4mCbfv36qWfPnsGQU1paqkGDBoX8fFVSUqIJEyZozZo1Ou2001RaWhpyjPY2kydPPmwtzc3Nam7e/y9tr9cbjrcIBGW7EnXByTm64OQcSW2XrX+zuyEYeFZurdHqbV7V+/wq27xHZZv36K9LvtJZJ6Tr1uKTCDsAEEYRDTmBQECTJ0/WOeeco4EDB0qSKisr5XA4lJaWFtI2OztblZWVwTYHBpz2/e37vquN1+tVY2OjkpKSDqpnxowZuvfee8Py3oCOsFgsOj4jRcdnpOiSU4+TJLX6A9q4s04rKzxa+vUevbV8uz75ao9GPf2Jzu7dQ7f++CSdcXy6yZUDQPyL6GjIiRMnavXq1Xr55Zcj+TIdNm3aNHk8nuCtoqLC7JLQBdltVvXLcemXZxToz78YrA9/90ONGdZTCTaLPt60W7+YU6qr/rtMy77Za3apABDXIhZyJk2apHnz5umDDz5Qfn5+cHtOTo58Pp9qampC2ldVVSknJyfYpqqq6qD97fu+q43L5TpkL44kOZ1OuVyukBtgtry0JD1w6SB9cNsPdcWZPWW3WvR/X+7S5U99rKufWaovthB2AOBohD3kGIahSZMmae7cuVq0aJEKCwtD9g8ZMkQJCQlauHBhcFt5ebm2bNmioqIiSVJRUZFWrVql6urqYJsFCxbI5XJpwIABwTYHHqO9TfsxgHiT3z1ZMy5rCzujhhbIZrVoyYaduvTJj3Xd35dq5dYas0sEgLgS9qurbrrpJr344ot68803Q+bGcbvdwR6WCRMm6J133tGzzz4rl8ulm2++WZL08ccfS9p/CXleXp5mzpypyspKXXXVVbr++usPuoR84sSJ+vWvf61FixbpN7/5DZeQo9P4Zne9Hlu0UXO/2Bacgbm4f5YmF5+kgce5Ta4OAMxj2iXkh1vU8O9//3twor72yQBfeumlkMkA23+KkqRvvvlGEyZM0IcffqiUlBRdc801+uMf/3jQZIC33nqr1q5dq/z8fN11111MBohO5+td9Zq96Eu98cU2ta828eMB2Zpc3Ecn5xF2AHQ9MTNPTiwj5CCebNpZp8cWfqk3V2wPLjEx4uQcTf5xH/XL4fMLoOsg5HQAIQfxaGN1rR5duFHzVu4PO+f1yVC2K1EpDpuSnfa2vw67Upz7/yYlhD5OdrS1s9tYcgJAfCHkdAAhB/FsQ1WtHv1/X2r+qh3HdByH3aoUh00pTrty3Ykq6J6s/O5Jyk9PVkH3ZBWkJynXnSQby1AAiBGEnA4g5KAz2FBVq0++2q36Zr8afK37//r8amhuVb2vVQ0+v+qbW9Xo86t+3/3WQMf/07dbLcpLS1JBetK+4NMWhAr2BaGMbo7DjscDgHCLmWUdAETWSdmpOik79Yif52sNhIQhb1OrttU0auveBlXsaf/boG01jWrxG9qyp0Fb9jRI2n3QsRITrMrvnqyC7kn62al5uvS0/INfEACijJADdFEOu1UOu0Npyfu3DenV/aB2/oChKm+TKvY0qGJv476/Ddq6p1EVextU6W1SU0tAG6vrtLG6Th+U75TNatXPBudF8d0AwMEIOQC+k23fT1V5aUkadoj9za1+7ahpUsXeBs1bsUOvfFah2/+xQoU9UjQon0vcAZiHyyoAHBOn3abjM1J0Xp9MPXjZIP2wb6aaWgIa/z+fqbq2yezyAHRhhBwAYWOzWjT7itN0QmaKdniaNOF/P1dzq9/ssgB0UYQcAGHlSkzQ364eqtREu5Z9s1d3zl2tLnwRJwATEXIAhN0Jmd30+JWny2qRXlu2VX//6GuzSwLQBRFyAETED07K1O9/0l+SdP/8tfq/L3eaXBGAroaQAyBixp5bqMtPz1fAkCa9+IW+3lVvdkkAuhBCDoCIsVgseuDSgTq1IE2exhZd//xnqm1qMbssAF0EIQdARCUm2PT0VUOU7XJqY3Wdbnl5ufxHsKQEABwtQg6AiMtyJerpq4bKabdq0fpq/flf5WaXBKALIOQAiIrBBWma+fNTJElPfbhJby7fZnJFADo7Qg6AqLnk1ON04w96S5Ju/8dKrdxaY25BADo1Qg6AqPpdSV/9R78sNbcGNP75Zar2svQDgMgg5ACIKpvVokdGn6remSmq9Dbphv9dxtIPACKCkAMg6lyJCfrbNWfIlWjXF1tq9AeWfgAQAYQcAKYozEjRE2Paln74x7KteoalHwCEGSEHgGnO65OpP1w0QJL0wPy1WrKBpR8AhA8hB4Cpfn3O8frFkPalHz7XZpZ+ABAmhBwAprJYLLr/0oE6vWeavE2tuv65T+Vl6QcAYWAxuvBoP6/XK7fbLY/HI5fLZXY5QJdWXdukSx7/SDs8TTrrhHQN75ctm9Uim9Uiq9Uim8Uim1WyWa2yWSWrpW2frf1vSLu2W1KCTSlOm5IcdiUn2JTstMlhs8pisZj9dgEcg45+fxNyCDlAzFi11aOfz/lYza2BiL2GzWpRssO272Y/6H6Sw6aU4Ha7slxO9c7spt6ZKUpPcRCQgBjQ0e9vexRrAoDvNCjfrf+9fphe/bRCLf6A/IYUCBjyBwy1BgwFjLb77X+DN8Noa2cY8gckfyCg1oCh5paA6n2tavD55dsXnPwBQ7VNraptapXUfET1pSUnqHdmN52Y2U29s1L2hZ9uKkhPls1K+AFiDT059OQAXUKrP6CGFr8afX7VN7cFn7bb/vuNvlbVt29vbru/w9OojdV12lbTqMP9v6XDZlVhRkpI8Dkxq5sKM1KU4uTfkkC40ZMDAAew26xy2axyJSYc1fMbfX5t3lWvjTvrtKm6Tpt21mnTznp9tbNOza0BlVfVqryq9qDn5bkTVZCerMQEm5x2qxz7bk77/sdOu1UO2wH37d9u29Y+NdGutOQEpSU7lOKw8dMZ8D0IOQDQAUkOmwbkuTQgL/Rfjf6Aoe01jaHhp7pem3bWaXe9T9s9TdruCf/6XHarRe6kBLmTE5SW1BZ80vY9dift33bgfndSghITrLLo2MKRzWqRw87FuYh9hBwAOAY2q0UF6ckqSE/Wj/pmhezbW+/Tpp112u5pUnOLXz5/QL7WgJpb2/623W8bL+Tzt20/cF9wvz+gppaAaptatLehRb7WtjFHu+t92l3vM+V9Z6Y6ld89Sfndk/f93X//uLQkJSbYTKkLOBAhBwAipHuKQ0NT0sN+3KYWv2oaWlTT6JOnoUU1jS37/vr2bW977Gncv83T0KLa5taw1bCztlk7a5v1xZaaQ+4nBCEWEHIAIM4kJtiU47Ypx514RM9r8QfU4j/2y/MbfX5tr2nS1r0N2rq38YC/bffrff4OhaAeKQ6lJSeoe3LbX3dS++P999v3t/3URjDCkSHkAEAXkWCzKsF27GNpkh129ejm1KB890H7DMNQTUPLt8LP/hBUsbdBDQeEoCORmGBV2gHhJy3JoRx3onpnpgSvaMtMdTIgG0GEHABA2FgsFnVPcah7iuM7Q9C2mkbtqfft+2nNp70NLSE/we1t8B3wM1yL/AFDTS0BVbY0qdJ7+IHcqU67TsjqFhJ8emd2U68eyWEJeIgvhBwAQNQcGII6yjAM1Ta3tgWeA8ceNfi0dW9j8HL+b3bXq7a5VSsqarSioibkGHarRT17JIcEn96ZKeqd1e2opxVA7GMyQCYDBIBOobnVr292N4TMY7Rx3/0Gn/+wz8txJeqMwnSddUK6ik7oocKMFH7yinGsXdUBhBwA6PwMw1Clt0mbquu1sbpWm3bW7wtBdaryHjwuKNvl1Fkn9NBZJ/RQ0Qk91KtHMqEnxhByOoCQAwBdW21Ti1Zv86ps826VbtqtL7bUyPetK9ByXIkq6t1jX09PhgrSkwg9JiPkdAAhBwBwoKYWvz7fsleffLVHn2zarS8q9qrFH/o1medO1Fm99/f0FKQnm1Rt10XI6QBCDgDguzT6/Ppiy16VfrVbn3y1W8srag4KPcelJamodw8NznerX65LfXNSGcwcYYScDiDkAACORIOvVZ9/U6PSr3bpk6/2aEVFjVoDB3+NHpeWpH45qeqbk6p+uS71y0lVYUYKl7GHCSGnAwg5AIBj0eBr1Wdf79XSzXu0bodX6ytrta2m8ZBtHTarTszqpn45qeqXm6p+OW3hhwkMjxwhpwMIOQCAcPM0tmhDVa3W7/BqXWXb3/LKWtUf5jL29BRHsNfn5Dy3Ti1w64SMbrJaCT6HQ8jpAEIOACAaAgFD22oag7095ZW1Wlfp1de76nWIX7uU6rTrlAK3BuenaXBBmk4rSFOW68jWKuvMCDkdQMgBAJipqcWvL6vqtK7Sq3U7vFqzzauV22rU1HLwQqq57kSdWtAWegbnp+mUfLdSnF1z4QJCTgcQcgAAsabVH9CGqjot37c8xYqtNdpQVXtQj4/VIvXJSg0Gn1ML0nRSdjfZu8DgZkJOBxByAADxoK65Vau3efYHn4oabfccvFBpYoJVfbNTleSwyW61yma1yG61tP21WWSzWmXft63tseXgdlaL7Dar0lMcyktLVI4rSXlpiXInJcTMAOmOfn93zX4uAADiSDenPbjURLtqb1Nb6Nlao+UVNVpZ4WlboHSrJyI1JCZYletOUq47UTnuROW6E0Me57mTlJYcO0FIoieHnhwAQKcQCBj6alfboqStgYD8AUOtfkOtgYBaA0bwsT9g7Hu8f3uL/9uPA9pZ61Olt1E7apq0u97XoRqcdutB4WfsuYXq0c0Z1vdKTw4AAF2I1WrRiVnddGJWt7Afu6nFr2pvs7Z7GlXpaQr+3eFp0o5993fV+dTcGtDXuxv09e6G4HOvPfv4sNfTUXEfcp544gnNmjVLlZWVGjx4sB577DGdeeaZZpcFAECnkZhgU88eyerZ4/DrdDW3+lXladYOT+O+8NOkSk+jMsLci3Mk4jrkvPLKK5oyZYrmzJmjYcOG6ZFHHlFJSYnKy8uVlZVldnkAAHQZTvv3B6Foi+vrzB5++GGNGzdO1113nQYMGKA5c+YoOTlZzzzzjNmlAQAAk8VtyPH5fFq2bJmKi4uD26xWq4qLi1VaWnrI5zQ3N8vr9YbcAABA5xS3IWfXrl3y+/3Kzs4O2Z6dna3KyspDPmfGjBlyu93BW0FBQTRKBQAAJojbkHM0pk2bJo/HE7xVVFSYXRIAAIiQuB14nJGRIZvNpqqqqpDtVVVVysnJOeRznE6nnE7zRnkDAIDoidueHIfDoSFDhmjhwoXBbYFAQAsXLlRRUZGJlQEAgFgQtz05kjRlyhRdc801Gjp0qM4880w98sgjqq+v13XXXWd2aQAAwGRxHXJGjRqlnTt3avr06aqsrNSpp56q995776DByAAAoOth7SrWrgIAIK509Ps7bsfkAAAAfBdCDgAA6JQIOQAAoFMi5AAAgE4prq+uOlbtY65ZwwoAgPjR/r39fddOdemQU1tbK0msYQUAQByqra2V2+0+7P4ufQl5IBDQ9u3blZqaKovFErbjer1eFRQUqKKigkvTw4xzGzmc28jgvEYO5zZyYv3cGoah2tpa5eXlyWo9/MibLt2TY7ValZ+fH7Hju1yumPxwdAac28jh3EYG5zVyOLeRE8vn9rt6cNox8BgAAHRKhBwAANApEXIiwOl06u6775bT6TS7lE6Hcxs5nNvI4LxGDuc2cjrLue3SA48BAEDnRU8OAADolAg5AACgUyLkAACATomQAwAAOiVCTgQ88cQTOv7445WYmKhhw4Zp6dKlZpcU9+655x5ZLJaQW79+/cwuK+4sWbJEF198sfLy8mSxWPTGG2+E7DcMQ9OnT1dubq6SkpJUXFysL7/80pxi48z3ndtrr732oM/wiBEjzCk2jsyYMUNnnHGGUlNTlZWVpZEjR6q8vDykTVNTkyZOnKgePXqoW7duuvzyy1VVVWVSxfGjI+f2hz/84UGf2xtvvNGkio8cISfMXnnlFU2ZMkV33323Pv/8cw0ePFglJSWqrq42u7S4d/LJJ2vHjh3B27///W+zS4o79fX1Gjx4sJ544olD7p85c6Zmz56tOXPmqKysTCkpKSopKVFTU1OUK40/33duJWnEiBEhn+GXXnopihXGp8WLF2vixIn65JNPtGDBArW0tOiCCy5QfX19sM2tt96qt99+W6+99poWL16s7du367LLLjOx6vjQkXMrSePGjQv53M6cOdOkio+CgbA688wzjYkTJwYf+/1+Iy8vz5gxY4aJVcW/u+++2xg8eLDZZXQqkoy5c+cGHwcCASMnJ8eYNWtWcFtNTY3hdDqNl156yYQK49e3z61hGMY111xjXHLJJabU05lUV1cbkozFixcbhtH2GU1ISDBee+21YJt169YZkozS0lKzyoxL3z63hmEYP/jBD4xbbrnFvKKOET05YeTz+bRs2TIVFxcHt1mtVhUXF6u0tNTEyjqHL7/8Unl5eTrhhBM0ZswYbdmyxeySOpXNmzersrIy5PPrdrs1bNgwPr9h8uGHHyorK0t9+/bVhAkTtHv3brNLijsej0eSlJ6eLklatmyZWlpaQj63/fr1U8+ePfncHqFvn9t2L7zwgjIyMjRw4EBNmzZNDQ0NZpR3VLr0Ap3htmvXLvn9fmVnZ4dsz87O1vr1602qqnMYNmyYnn32WfXt21c7duzQvffeq/POO0+rV69Wamqq2eV1CpWVlZJ0yM9v+z4cvREjRuiyyy5TYWGhNm3apN///ve68MILVVpaKpvNZnZ5cSEQCGjy5Mk655xzNHDgQEltn1uHw6G0tLSQtnxuj8yhzq0kXXnllerVq5fy8vK0cuVKTZ06VeXl5Xr99ddNrLbjCDmICxdeeGHw/imnnKJhw4apV69eevXVVzV27FgTKwM6ZvTo0cH7gwYN0imnnKLevXvrww8/1PDhw02sLH5MnDhRq1evZjxeBBzu3I4fPz54f9CgQcrNzdXw4cO1adMm9e7dO9plHjF+rgqjjIwM2Wy2g0b1V1VVKScnx6SqOqe0tDSddNJJ2rhxo9mldBrtn1E+v9FxwgknKCMjg89wB02aNEnz5s3TBx98oPz8/OD2nJwc+Xw+1dTUhLTnc9txhzu3hzJs2DBJipvPLSEnjBwOh4YMGaKFCxcGtwUCAS1cuFBFRUUmVtb51NXVadOmTcrNzTW7lE6jsLBQOTk5IZ9fr9ersrIyPr8RsHXrVu3evZvP8PcwDEOTJk3S3LlztWjRIhUWFobsHzJkiBISEkI+t+Xl5dqyZQuf2+/xfef2UJYvXy5JcfO55eeqMJsyZYquueYaDR06VGeeeaYeeeQR1dfX67rrrjO7tLh222236eKLL1avXr20fft23X333bLZbLriiivMLi2u1NXVhfwLbPPmzVq+fLnS09PVs2dPTZ48Wffff7/69OmjwsJC3XXXXcrLy9PIkSPNKzpOfNe5TU9P17333qvLL79cOTk52rRpk26//XadeOKJKikpMbHq2Ddx4kS9+OKLevPNN5WamhocZ+N2u5WUlCS3262xY8dqypQpSk9Pl8vl0s0336yioiKdddZZJlcf277v3G7atEkvvviifvKTn6hHjx5auXKlbr31Vp1//vk65ZRTTK6+g8y+vKszeuyxx4yePXsaDofDOPPMM41PPvnE7JLi3qhRo4zc3FzD4XAYxx13nDFq1Chj48aNZpcVdz744AND0kG3a665xjCMtsvI77rrLiM7O9twOp3G8OHDjfLycnOLjhPfdW4bGhqMCy64wMjMzDQSEhKMXr16GePGjTMqKyvNLjvmHeqcSjL+/ve/B9s0NjYaN910k9G9e3cjOTnZuPTSS40dO3aYV3Sc+L5zu2XLFuP888830tPTDafTaZx44onG7373O8Pj8Zhb+BGwGIZhRDNUAQAARANjcgAAQKdEyAEAAJ0SIQcAAHRKhBwAANApEXIAAECnRMgBAACdEiEHAAB0SoQcAADQKRFyAABAp0TIAQAAnRIhBwAAdEqEHAAA0Cn9f3orXeu2x+ZnAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=contagem_de_estados, x='estado', y='total')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "zW1ly_23OtYe",
        "outputId": "6cf9981c-3619-41fa-9ba7-f980c296d02a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='estado', ylabel='total'>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=campanha, x='estado')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "8YhqJOiIPE4O",
        "outputId": "a5179517-ee1b-4145-f5da-b9bd357b150b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='estado', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "contagem_de_estados.plot(kind='pie', y='total')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "XmdxYb5LPjF3",
        "outputId": "17f16d5a-46d4-41f8-98a7-d2358d3e9a52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='total'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "contagem_de_estados.plot(kind='pie', y='total', labels= contagem_de_estados['estado'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "fAgUfHSoRYM8",
        "outputId": "0387a6ed-eac3-486d-997f-d8289da93d9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='total'>"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_por_estado = campanha['estado'].value_counts()\n",
        "total_geral = total_por_estado.sum()\n",
        "total_em_SP = total_por_estado.loc['SP']\n",
        "total_do_restante = total_geral - total_em_SP\n",
        "print(total_geral, total_em_SP, total_do_restante)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gz2xEZ3CSzJ1",
        "outputId": "288e8f5c-2dac-41a9-889a-eef6794cdba4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "50000 11006 38994\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados = {\n",
        "    'estado' : ['SP', 'outros'],\n",
        "    'total': [total_em_SP, total_do_restante]\n",
        "}\n",
        "dados = pd.DataFrame(dados)\n",
        "dados.head()"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   estado  total\n",
              "0      SP  11006\n",
              "1  outros  38994"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dados",
              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"estado\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"outros\",\n          \"SP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19790,\n        \"min\": 11006,\n        \"max\": 38994,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          38994,\n          11006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.plot(kind='pie', y='total', labels=dados['estado'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
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        "outputId": "9d07597a-e946-4544-b091-fb6ea6f8c1f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='total'>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_de_outros_estados = campanha.query('estado != \"SP\"')['estado'].value_counts()\n",
        "total_de_outros_estados.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "m00-aDITT0bs",
        "outputId": "daaf8049-3bce-41eb-c407-6adaab4a8d54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "estado\n",
              "MG    4902\n",
              "RJ    4221\n",
              "BA    3524\n",
              "RS    2701\n",
              "PR    2698\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estado</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MG</th>\n",
              "      <td>4902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RJ</th>\n",
              "      <td>4221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BA</th>\n",
              "      <td>3524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RS</th>\n",
              "      <td>2701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PR</th>\n",
              "      <td>2698</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(data=campanha.query('estado != \"SP\"'),\n",
        "              order=total_de_outros_estados.index,\n",
        "              palette = 'mako',\n",
        "              hue='estado',\n",
        "              stat = 'percent',\n",
        "              x='estado')\n",
        "plt.title('Distribuição das campanhas nos estados exceto em SP')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "kdu8dC-rVwjY",
        "outputId": "2abca164-5ebd-4aeb-f5a3-678eb832a0d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha['roi'] = (campanha['receita'] - campanha['custo']) / campanha['custo']\n",
        "campanha.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2ZokEGv7WF00",
        "outputId": "cad8b82f-53ee-4e17-fb4e-79a198fb9df2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id_campanha  impressoes  cliques       canal  custo  receita estado  \\\n",
              "0            1      152431     9462       Email  13258    32309     MG   \n",
              "1            1       39688     3218         PPC  16391    16568     BA   \n",
              "2            1      104506     4756  Google Ads   6189    19029     AL   \n",
              "3            1       80768     3566  Google Ads   5215        0     PI   \n",
              "4            1       54932     3645  Google Ads  16767     8825     RJ   \n",
              "\n",
              "     pais       ctr       roi  \n",
              "0  Brasil  6.207399  1.436944  \n",
              "1  Brasil  8.108244  0.010799  \n",
              "2  Brasil  4.550935  2.074649  \n",
              "3  Brasil  4.415115 -1.000000  \n",
              "4  Brasil  6.635477 -0.473669  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e73527b9-e314-43fc-94cd-c997e9ad1d58\" 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>Id_campanha</th>\n",
              "      <th>impressoes</th>\n",
              "      <th>cliques</th>\n",
              "      <th>canal</th>\n",
              "      <th>custo</th>\n",
              "      <th>receita</th>\n",
              "      <th>estado</th>\n",
              "      <th>pais</th>\n",
              "      <th>ctr</th>\n",
              "      <th>roi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>152431</td>\n",
              "      <td>9462</td>\n",
              "      <td>Email</td>\n",
              "      <td>13258</td>\n",
              "      <td>32309</td>\n",
              "      <td>MG</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.207399</td>\n",
              "      <td>1.436944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>39688</td>\n",
              "      <td>3218</td>\n",
              "      <td>PPC</td>\n",
              "      <td>16391</td>\n",
              "      <td>16568</td>\n",
              "      <td>BA</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>8.108244</td>\n",
              "      <td>0.010799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>104506</td>\n",
              "      <td>4756</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>6189</td>\n",
              "      <td>19029</td>\n",
              "      <td>AL</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.550935</td>\n",
              "      <td>2.074649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>80768</td>\n",
              "      <td>3566</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>5215</td>\n",
              "      <td>0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>4.415115</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>54932</td>\n",
              "      <td>3645</td>\n",
              "      <td>Google Ads</td>\n",
              "      <td>16767</td>\n",
              "      <td>8825</td>\n",
              "      <td>RJ</td>\n",
              "      <td>Brasil</td>\n",
              "      <td>6.635477</td>\n",
              "      <td>-0.473669</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-e73527b9-e314-43fc-94cd-c997e9ad1d58')\"\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-e73527b9-e314-43fc-94cd-c997e9ad1d58 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-e73527b9-e314-43fc-94cd-c997e9ad1d58');\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-b244e9f9-d64c-43ce-90fe-ebdb1490030a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b244e9f9-d64c-43ce-90fe-ebdb1490030a')\"\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-b244e9f9-d64c-43ce-90fe-ebdb1490030a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "campanha",
              "summary": "{\n  \"name\": \"campanha\",\n  \"rows\": 50000,\n  \"fields\": [\n    {\n      \"column\": \"Id_campanha\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 578,\n        \"min\": 1,\n        \"max\": 2000,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          1861,\n          354,\n          1334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"impressoes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38217,\n        \"min\": 100,\n        \"max\": 200000,\n        \"num_unique_values\": 42072,\n        \"samples\": [\n          144427,\n          79136,\n          76659\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cliques\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2612,\n        \"min\": 1,\n        \"max\": 19484,\n        \"num_unique_values\": 10960,\n        \"samples\": [\n          1585,\n          7059,\n          12201\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"canal\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"PPC\",\n          \"Social Media\",\n          \"Google Ads\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"custo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6072,\n        \"min\": 2723,\n        \"max\": 80000,\n        \"num_unique_values\": 19420,\n        \"samples\": [\n          21610,\n          4432,\n          13584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"receita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10365,\n        \"min\": 0,\n        \"max\": 134842,\n        \"num_unique_values\": 23135,\n        \"samples\": [\n          20524,\n          14915,\n          20474\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estado\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 27,\n        \"samples\": [\n          \"MS\",\n          \"PA\",\n          \"DF\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pais\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Brasil\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ctr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6041190438542303,\n        \"min\": 0.9926629261976695,\n        \"max\": 10.0,\n        \"num_unique_values\": 49874,\n        \"samples\": [\n          4.860034069817701\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"roi\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0256978637652134,\n        \"min\": -1.0,\n        \"max\": 18.981646945182323,\n        \"num_unique_values\": 48726,\n        \"samples\": [\n          -0.2859885920822274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(campanha['roi'])\n",
        "plt.title('Distribuição do roi das campanhas')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "id": "eOoP0xcveMwm",
        "outputId": "c127d83f-e33a-4c0e-9ae6-481a1ba5b065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metricas_google_ads = campanha.query('canal == \"Google Ads\"')\n",
        "metricas_email = campanha.query('canal == \"Email\"')\n",
        "\n",
        "media_metricas_google_ads = metricas_google_ads['roi'].mean()\n",
        "media_metricas_email = metricas_email['roi'].mean()\n",
        "\n",
        "print(media_metricas_google_ads, media_metricas_email)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_NjYdXnqeYNL",
        "outputId": "99ba1dd3-8e8f-4e96-e5fa-bce9e76e8a8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.13674573394615885 0.16903111529695838\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "[\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    20000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000,\n",
        "    30000\n",
        "    ]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJLk_Dq4ezS_",
        "outputId": "6d32341a-b1a5-4228-b067-4a5e6d4ed833"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 20000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000,\n",
              " 30000]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "google_ads = [20000] * 10 + [30000] * 10\n",
        "mean(google_ads)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "id": "j5N7pV_Uf8Or",
        "outputId": "e8837860-5a56-4858-faa6-e2dfdf5cb2bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'mean' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-77-da7007b0a6a0>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mgoogle_ads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m20000\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m30000\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgoogle_ads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'mean' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "google_ads = [20000] * 10 + [30000] * 10\n",
        "np.mean(google_ads)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inJD9p1bgJJt",
        "outputId": "f32b3d2a-4ed4-4451-e899-f0318a437e33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000.0"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "meta_ads = [40000] * 10 + [10000] * 10\n",
        "np.mean(meta_ads)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "is1ourw9gX87",
        "outputId": "e4f7922f-5ce5-4f87-bb6d-2450cb757ec2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000.0"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: Imprima a média e a mediana do google_ads e do meta_ads\n",
        "\n",
        "import numpy as np\n",
        "print(f\"Média do google_ads: {np.mean(google_ads)}\")\n",
        "print(f\"Mediana do google_ads: {np.median(google_ads)}\")\n",
        "print(f\"Média do meta_ads: {np.mean(meta_ads)}\")\n",
        "print(f\"Mediana do meta_ads: {np.median(meta_ads)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59St0e7Tgi6f",
        "outputId": "4f5b2182-c438-41af-b798-500072474846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Média do google_ads: 25000.0\n",
            "Mediana do google_ads: 25000.0\n",
            "Média do meta_ads: 25000.0\n",
            "Mediana do meta_ads: 25000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "campanha0 = [25000] * 20\n",
        "np.mean(campanha0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzE9qcOHhVID",
        "outputId": "9cae30a4-f16a-458c-8f16-f7148f29bb8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25000.0"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.std(campanha0), np.std(google_ads), np.std(meta_ads)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCBdjHd4hdJP",
        "outputId": "289af592-dee4-46e1-9feb-fc55c370734f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 5000.0, 15000.0)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot([metricas_google_ads['roi'], metricas_email['roi']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "YOviv0OqhkKD",
        "outputId": "81019a41-e11d-4092-cbdc-bd56e061cbdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'whiskers': [<matplotlib.lines.Line2D at 0x7c1aa3d1b580>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa3d185b0>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa399a920>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa3999660>],\n",
              " 'caps': [<matplotlib.lines.Line2D at 0x7c1aa3d18370>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa399ae30>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa399b430>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa39980d0>],\n",
              " 'boxes': [<matplotlib.lines.Line2D at 0x7c1aa3d1bcd0>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa3998850>],\n",
              " 'medians': [<matplotlib.lines.Line2D at 0x7c1aa39992d0>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa399aa40>],\n",
              " 'fliers': [<matplotlib.lines.Line2D at 0x7c1aa3999f00>,\n",
              "  <matplotlib.lines.Line2D at 0x7c1aa399b4c0>],\n",
              " 'means': []}"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=campanha.query('canal in [\"Google Ads\", \"Email\"]'),\n",
        "            x= 'canal',\n",
        "            y= 'roi')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "brdVlJe_irTy",
        "outputId": "5f195e42-e57f-4bd5-ce55-74c35f5cd470"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='canal', ylabel='roi'>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: mesmo gráfico mas colorido\n",
        "\n",
        "sns.boxplot(data=campanha.query('canal in [\"Google Ads\", \"Email\"]'),\n",
        "            x= 'canal',\n",
        "            y= 'roi', palette='Set2')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "sWQAkhzPi-lu",
        "outputId": "a53ebabb-96a5-42b3-ac9b-7c29eb3ccc0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-85-db93b5f3fc41>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.boxplot(data=campanha.query('canal in [\"Google Ads\", \"Email\"]'),\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='canal', ylabel='roi'>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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r1k1xcXGaPn26Dhw40O5zzp8/XxaLxePGqr8AYC6FhYWaPXu2li9frrffflvLly/X7NmzmQUaBq8GoCNHjmjYsGF69tlnWxw7evSotmzZokceeURbtmzRG2+8oZ07d2rSpEk/+LyDBg3SV199Zdw++OCDzmg+AMAHFRYWKjc3V/Hx8VqwYIHy8vK0YMECxcfHKzc3lxAESV4+BTZhwgRNmDCh1WNRUVFau3atx77FixcrLS1N5eXlOv/889t83uDgYMXGxnZoWwEAvs/pdCo/P192u11ZWVnGYOfExERlZWUpOztb+fn5LIUB/xoEXVtbK4vFoujo6Hbrdu3apbi4OCUkJCg9PV3l5eXt1jc0NMjhcHjcAAD+p7S0VNXV1Zo8eXKrM0FPmjRJ1dXVKi0t9VIL4Sv8JgAdO3ZMDz74oKZOnarIyMg260aMGKFly5ZpzZo1WrJkifbu3avLLrusxarAzS1cuFBRUVHGLT4+vjPeAgCgk9XU1EhSm3/H3fvddTAvvwhATU1NuvXWW+VyubRkyZJ2aydMmKBbbrlFQ4cO1bhx47R69WrV1NTotddea/Mxc+fOVW1trXGrqKjo6LcAADgL3GcI2vo77t7/Q2cSEPh8PgC5w88XX3yhtWvXttv705ro6GgNGDBAu3fvbrMmLCxMkZGRHjcAgP9JSkqSzWZTQUFBq0thrFq1SjabjauD4dsByB1+du3apXfeeUc9e/Y85eeor6/Xnj171KdPn05oIQDAl1itVqWnp6uoqEjZ2dkeS2FkZ2erqKhI6enpDICGd68Cq6+v9+iZ2bt3r4qLi3XOOeeoT58+uvnmm7Vlyxa99dZbOn78uCorKyVJ55xzjjGl+VVXXaUbbrhBGRkZkqQ5c+Zo4sSJ6tu3rw4cOKDHHntMQUFBmjp16tl/gwCAsy4tLU2ZmZnKz8/X/Pnzjf02m02ZmZnMBA1JXg5Amzdv1pgxY4ztrKwsSdKMGTM0f/58rVq1SpKUkpLi8bh3331XP/7xjyVJe/bs0aFDh4xj+/fv19SpU3X48GHZbDZdeuml2rRpk2w2W+e+GQCAz0hLS1NqaqpKS0tVU1Oj6OhoJSUl0fMDg8Xlcrm83Qhf43A4FBUVpdraWsYDBTin08kfSAAIEKfy/c2iKDCtwsJC5efnq7q62thns9mUnp5OFzkABDgCEEzJPVW+3W5XRkaG4uPjVVFRoYKCAuXm5jJOAAACHH39MJ3vT5WfmJio8PBwY6p8u92u/Pz8FpfQAgACBwEIpsNU+QAAAhBMh6nyAQAEIJgOU+UDAAhAMB2mygfMwel0qqSkRBs2bFBJSQnj+uCBq8BgOu6p8nNzc5Wdna1JkyYZV4GtWrVKRUVFyszMZD4gwI8xzQV+CBMhtoKJEM2BP5BAYCosLFROTo5CQ0PV2Nho7Hdvz5o1i894gGIiROAkMFU+EHicTqfy8vIkSYMGDdKUKVOMHt6VK1eqqKhIeXl5Sk1N5bNucvy/D1OzWq1KTk7WqFGjlJyczB9EwM+VlJTI4XDooosu0uzZsz3m+Zo9e7YGDBggh8OhkpISbzcVXsZfewBAwHAHm5tuuqnVeb5uuukmjzqYFwEIABBwLBaLt5sAH0cAAgAEjOTkZEnS//7v/7Y6zcVf//pXjzqYFwEIABAwkpOTFRkZqZ07d+qZZ55RWVmZvv32W5WVlRnbkZGRBCBwFRgAIHBYrVbdcccdysnJ0fbt21VUVGQcCw0NlSTdcccdXPAAeoAAAIElLS1Ns2bNajEPTGRkJHMAwUAPEAAg4KSlpWn48OF6++23dfDgQfXu3VvXXHONgoP52sMJ/EsAAASc1mZ6/+c//8lM7zAQgAAAAaWwsFC5ubmy2+3KyMgwZoIuKChQbm6uMjMzCUFgDBAAIHA4nU7l5+fLbrdr1qxZampq0pYtW9TU1KRZs2bJbrcrPz+fleFBDxAAIHCUlpaqurpaV155pWbPnt1iseMxY8Zoy5YtKi0t5VJ4kyMAAQACRk1NjSTptddea/UU2Ouvv+5RB/PiFBgAIGC4L30fMGCAsrKyPBZDdW83r4N5EYAAAIDpEIAAAAHD4XBIknbu3Kns7GyPpTDc283rYF6MAQIABIzo6GhJ0m233aZ//etfmj9/vnHMZrPp1ltv1WuvvWbUwbwIQACAgJGUlCSbzaZdu3YZi5/W1NQoOjpaAwYMUE5Ojmw2m5KSkrzdVHgZp8AAAAHDarUqPT1dRUVFysnJUXBwsOx2u4KDg5WTk6OioiKlp6ezGCpkcblcLm83wtc4HA5FRUWptraWKwUAwA+1thSGzWZjKYwAdyrf35wCAwAEnLS0NKWmpqq0tNQ4BZaUlETPDwxe/Zewfv16TZw4UXFxcbJYLFq5cqXHcZfLpUcffVR9+vRRly5dNHbsWO3atesHn/fZZ59Vv379FB4erhEjRqiwsLCT3gEAwFdZrVYlJydr1KhRSk5OJvzAg1f/NRw5ckTDhg3Ts88+2+rx3//+9/rDH/6gP/3pT/roo4/UrVs3jRs3TseOHWvzOV999VVlZWXpscce05YtWzRs2DCNGzdOBw8e7Ky3AQDwQU6nUyUlJdqwYYNKSkpY/wsefGYMkMVi0ZtvvqkpU6ZIOtH7ExcXp9mzZ2vOnDmSpNraWsXExGjZsmX6yU9+0urzjBgxQpdccokWL14s6cQHID4+Xr/85S/1m9/85qTawhggAPBvjAEyp1P5/vbZ/sC9e/eqsrJSY8eONfZFRUVpxIgR2rhxY6uPaWxs1CeffOLxGKvVqrFjx7b5GElqaGiQw+HwuAEA/FNhYaFyc3MVHx+vBQsWKC8vTwsWLFB8fLxyc3MZFgFJPhyAKisrJUkxMTEe+2NiYoxj33fo0CEdP378lB4jSQsXLlRUVJRxi4+PP8PWAwC8wel0Kj8/X3a7vdW1wOx2u/Lz8zkdBt8NQGfT3LlzVVtba9wqKiq83SQAwGkoLS1VdXW1Jk+eLEkeY4AkadKkSaqurlZpaak3mwkf4LOXwcfGxkqSqqqq1KdPH2N/VVWVUlJSWn1Mr169FBQUpKqqKo/9VVVVxvO1JiwsTGFhYWfeaACAV9XU1EiSDh48qMWLF7cYA3TLLbd41MG8fLYH6IILLlBsbKzWrVtn7HM4HProo480cuTIVh8TGhqqiy++2OMxTqdT69ata/MxAIDA4V7j67nnnmt1DNBzzz3nUQfz8moAqq+vV3FxsYqLiyWdGPhcXFys8vJyWSwWzZo1S0888YRWrVqlrVu3avr06YqLizOuFJOkq666yrjiS5KysrL04osv6uWXX9aOHTv0i1/8QkeOHNHMmTPP8rsDAJxtAwYMkNVqVWRkpH71q1+pqalJW7ZsUVNTk371q18pKipKVqtVAwYM8HZT4WVePQW2efNmjRkzxtjOysqSJM2YMUPLli3TAw88oCNHjuiee+5RTU2NLr30Uq1Zs0bh4eHGY/bs2aNDhw4Z27fddpuqq6v16KOPqrKyUikpKVqzZk2LgdEAgMBTVlYmp9Op2tpa3X333WpqajKOhYSEGNtlZWVKTk72VjPhA3xmHiBfwjxAAOCfNmzY4HFWoC0ZGRkaNWrUWWgRzibWAgMAmFLzLz273a5hw4YpNDRUjY2N+vTTT1VUVNSiDuZEAIKpOZ1OFksEAoh7fp9u3brp17/+tYKD/+9r7sorrzTGhTIPEAhAMC2mygcCj3t+nyNHjmjRokUaOnSowsLC1NDQoM8++0xHjhwx6oYOHerNpsLLCEAwJfdU+Xa7XRkZGYqPj1dFRYUKCgqUm5urzMxMQhDgx0aMGKGPP/7YOOUlnVgaKS0tjaUwIIkABBP6/lT57lNe7qnys7OzlZ+fr9TUVE6HAX4mOTlZK1eu1EcffaSUlBTFxMSoqalJISEhqqqqMsIPV4CBAATTcU+Vn5GR0SLgWK1WTZo0SfPnz1dpaSl/JAE/k5SUJIvFIpfLpa1btxrzzElSUFCQJMlisSgpKclLLYSvOOkAlJWVpccff1zdunUz5utpS3Z29hk3DOgs7inw21r01r2fqfIB/1NWVib37C7Hjx/3OObedrlczAOEkw9ARUVFxgRSzc+pfp/FYjnzVgGdyD0FfkVFhRITE1scdy+Gy1T5gP/5+uuvO7QOgeukA9C7777b6n3A3yQlJclms6mgoMBjDJB0YnzQqlWrZLPZ6CIH/FBtba1xPyUlRSEhITpy5Ii6deumpqYm45RY8zqY0xmPAdq/f78k6bzzzjvjxgBng9VqVXp6unJzc5Wdna1JkyYZV4GtWrVKRUVFyszMZAA04Ifq6uoknVj2ovn4H7fg4GB99913Rh3M67QCkNPp1BNPPKFnnnlG9fX1kqSIiAjNnj1b8+bN44sDPi8tLU2ZmZnKz8/X/Pnzjf02m41L4AE/dvjwYUlSU1OTgoODNWHCBI0ZM0bvvvuu/vGPf+i7777zqIN5nVYAmjdvnpYuXarf/e53Gj16tCTpgw8+0Pz583Xs2DE9+eSTHdpIoDOkpaUpNTWVmaCBANKjRw9JJ8ajRkdH629/+5v+9re/STrxA+fQoUNyuVxGHczrtALQyy+/rP/+7//WpEmTjH1Dhw7Vueeeq3vvvZcABL9htVq5EgQIIMeOHZN04kqv8847T9ddd50xE/Snn35qzPzuroN5nVYA+vrrr1sdIJqUlMTIevgV1gIDAkvzK5G3b9/uMQ4oJCSk1TqY02kFoGHDhmnx4sX6wx/+4LF/8eLFGjZsWIc0DOhsrAUGBJ6YmBjj/vdDTvPt5nUwJ4vLPWPUKVi/fr2uvfZanX/++Ro5cqQkaePGjaqoqNDq1at12WWXdXhDzyaHw6GoqCjV1tYqMjLS281BJ2i+FtjkyZM91gJzXwVGCAL8z3fffafbb7/duNqr+arvVqvV2L9s2TKPleIRGE7l+/uU+/qbmpq0YMECrV69WjfeeKNqampUU1OjG2+8UTt37vT78IPA9/21wBITExUeHm6sBWa325Wfn+/xhxOAfwgODpbdbldjY6MsFouSk5M1evRoJScny2KxqLGxUXa7nfCDUz8FFhISos8++0x9+vTRE0880RltAjpV87XAJKmkpMRjDBBrgQH+y+l0qry8XJGRkXI4HCopKfE4HhkZqfLycjmdTsb7mdxpReBp06YZl8ED/sa9xldVVZUWL17cYgzQLbfc4lEHwH+4f+BYLJZ2Z4LmBw5OKwB99913ysvL0zvvvKOLL75Y3bp18zjOYqjwZe41vp577jkNHz5cGRkZHmOAnnvuOY86AP7DfSVy3759VVFR4THhYc+ePdWvXz/t27ePK5ZxegFo27ZtGj58uKQTK+82x6WF8HUDBgyQ1WpVRESEZs2aZYwFSExM1KxZs/TLX/5SdXV1GjBggJdbCuBUORwOSdK+fftaHDt8+LARiNx1MK/TCkAshgp/VlZWJqfTKYfDoZycnBZrgbkXSSwrK6OLHPAzERERHvdvu+02DR8+XFu2bNGrr75qrAHWvA7mxDB4mI57bM+9996r1157rcVaYPfee6+ee+45xgABfqj5Ku8JCQnGuJ+mpiYlJCTo008/bVEHcyIAwXTcY3t69+6tRYsWtZgJevfu3R51APyH+9RX9+7dtXXrViPwSCfmAerevbvq6+tbPUUGc+EaQJhOUlKSbDabCgoKJEnJyckaNWqUcbpr1apVstlsrS73AsC3NTY2SpLq6+tbzOXldDpVX1/vUQfzIgDBdKxWq9LT01VUVKTs7GyVlZXp22+/VVlZmbKzs1VUVKT09HTmCAH8UP/+/Tu0DoGLU2AwpbS0NGVmZio/P7/FGCCWwQD81/nnn2/cHzx4sLp06WLMA/Ttt99q27ZtLepgTgQgmFZaWppSU1NZDR4IIB9++KFx3x122qpLSUk5Cy2Cr+IvPQAgYDQ0NHRoHQIXPUAwrcLCQuXn57dYCiM9PZ1TYICfuvDCC7V582ZJJ9aubGpqMo41377wwgu90j74DgIQTKmwsFC5ubmy2+0tlsLIzc1lHBDgp5qvRtA8/Hx/m1UL4POnwPr16yeLxdLidt9997Vav2zZsha14eHhZ7nV8GVOp1P5+fmy2+2aNWuWmpqatGXLFjU1NWnWrFmy2+3Kz89vcQktAN+3a9euDq1D4PL5HqCPP/5Yx48fN7a3bdumq6++2lixuzWRkZHauXOnsU3SR3Pu1aKvvPJKzZ49u8UpsDFjxmjLli2sFg34oaNHj3ZoHQKXzwcgm83msf273/1OF154oa644oo2H2OxWBQbG9vZTYOfci9x8dprr7V6Cuz111/3qAPgP5qv/t4RdQhcPn8KrLnGxkYtX75cd9xxR7u9OvX19erbt6/i4+M1efJkbd++vd3nbWhokMPh8LghcEVGRko6sSp8a6fAEhMTPeoA+I9jx451aB0Cl8/3ADW3cuVK1dTU6Pbbb2+z5qKLLlJeXp6GDh2q2tpaPf300xo1apS2b9+u8847r9XHLFy4UAsWLOikVsNX1dXVtXoKLDjYrz4WAJoJDw8/qR+xjA2FX/UALV26VBMmTFBcXFybNSNHjtT06dOVkpKiK664Qm+88YZsNpuef/75Nh8zd+5c1dbWGreKiorOaD58hPuP44EDB9TY2Kg777xTzz77rO688041Njbqq6++8qgD4D/OOeecDq1D4PKbn7pffPGF3nnnHb3xxhun9LiQkBDZ7XZjhe/WhIWFKSws7EybCD/hPrUVFxenxsZGLV26VEuXLpUk9erVS3FxcTpw4ACnwAA/dLJXb3KVJ/wmAL300kvq3bu3rrvuulN63PHjx7V161Zde+21ndQy+KuIiAjNmzdPZWVlxlIYAwYM0BNPPOHtpgE4Tb169VJZWdlJ1cHc/OIUmNPp1EsvvaQZM2a0GJ8xffp0zZ0719j+7W9/q7fffluff/65tmzZomnTpumLL77QXXfddbabDR/lPrW1c+dO5eTkKDg4WHa7XcHBwcrJyTH+eHIKDPA/ffv27dA6BC6/6AF65513VF5erjvuuKPFsfLyco/FK7/55hvdfffdqqysVI8ePXTxxRdrw4YNzOcCQ3R0tCTptttu07/+9a8Wq8Hfeuuteu2114w6AP7jZD+3fL7hFwHommuukcvlavXYe++957G9aNEiLVq06Cy0Cv4qKSlJNptNu3bt0jPPPNPiFFhOTo5sNpuSkpK83VQAp+hke27p4YVfnAIDOpLValV6erqKioq0aNEi7d+/X42Njdq/f78WLVqkoqIipaene/QsAvAPBCCcLL/oAQI6Wlpamq677jqtXr1aRUVFxn6r1arrrruOhVABP7V3717jvsVi8Th70Hy7eR3MiZ+4MKXCwkK99dZbCgoK8tgfFBSkt956S4WFhV5qGYAzERoaatz//tCJ5tvN62BO9ADBdJxOp/Ly8iRJgwcP1rBhwxQaGqrGxkZ9+umnKioqUl5enlJTUzkNBvgZBkHjZBGAYDolJSVyOByKi4tTRUWFxymw5hMhlpSUaPDgwV5sKYBT1a1btw6tQ+AiAMF0SkpKJJ1YCsNut+v6669v0QPkriMAAf6l+dp+HVGHwEUAgum4xwH06dNH+/fv9+gBstlsio2NVWVlZZtTLwDwXSc7uJlB0CAAwXS6d+8uSfrqq69kt9uVkZGh+Ph4VVRUaOXKlUYgctcB8B/fffedx3ZsbKy6deumI0eOqLKyss06mA8BCKYTERFh3He5XC1urdUB8A8hISEe281DT3t1MB8CEEynvr7euF9SUqLi4mJju/mlsc3rAPiHk71ykys8wb8AmE5kZKSkE4shuu+7RUVFGYskfv8YAN/ndDo7tA6Bix4gmM4555wjSfriiy9kt9t13XXXKSwsTA0NDfrss8+MMUDuOgD+o0+fPm2e9vp+HcyNAATTcS+GGhER0eo8QAkJCaqrq2MxVMAPXXzxxR6f6fbqYG4EIJiOezHU3NxcpaSktJgHqLi4WJmZmYwRAPxQTU1Nh9YhcBGAYEppaWnKzMxUfn5+i3mAMjMzWQwV8FPr168/6bobb7yxk1sDX0YAgmmlpaUpNTVVpaWlqqmpUXR0tJKSkuj5AfwYPUA4WQQgmJrValVycrK3mwGgg3AVGE4WAQim5nQ66QECAkhYWNhJzfIcFhZ2FloDX0YAgmkVFhZq+fLlOnTokLGvV69emjZtGmOAAD/lXvbiZOpgbvzUhSkVFhYqJydHDofDY7/D4VBOTo4KCwu91DIAZ+Lw4cMdWofARQ8QTMfpdCovL0+SNGjQIA0bNszjMviioiLl5eUpNTWV02GAnzl+/HiH1iFwEYBgOiUlJXI4HIqLi1N5ebnHZfA9e/ZUXFycDhw4oJKSEg0ePNiLLQUAdBZ+3sJ0SkpKJEkHDhxQXV2dx7G6ujodOHDAow6A/+jevXuH1iFw0QME03G5XMb9tk6Bfb8OgH/o1q2b6uvrT6oO5kYAgul07dpVkhQSEtLqKbCQkBA1NTUZdQD8R3h4eIfWIXARgGA6R48elSQ1NTW1uBKk+ba7DoD/CA0N7dA6BC7GAMHULBZLu9sA/Et5eXmH1iFwEYBgOu5z/yEhIerZs6fHMfcpsOZ1APxHQ0NDh9YhcHEKDKbjniW2qanJYxZoSR7bJzObLADAP9EDBNM52dNcnA4D/A+fb5wsAhBMZ8CAAcZ99+mu1rab1wHwDye7yCmLocKnA9D8+fNlsVg8bklJSe0+5vXXX1dSUpLCw8M1ZMgQrV69+iy1Fv5i//79xv1BgwZpxowZuueeezRjxgwNGjSo1ToA/uHYsWMdWofA5fNjgAYNGqR33nnH2A4ObrvJGzZs0NSpU7Vw4UJdf/31euWVVzRlyhRt2bKFJQ1gKCsrM+5v375dxcXFxnbzHqDmdQCAwOLTPUDSicATGxtr3Hr16tVmbW5ursaPH6/7779fAwcO1OOPP67hw4dr8eLFZ7HF8HXuru+ePXuqqanJ41hTU5NxZRhd5AAQuHw+AO3atUtxcXFKSEhQenp6u3M3bNy4UWPHjvXYN27cOG3cuLHd12hoaJDD4fC4IXBddtllkk5MehgUFKSRI0dq2rRpGjlypIKCgozJEN11AIDA49MBaMSIEVq2bJnWrFmjJUuWaO/evbrssstaLGDpVllZqZiYGI99MTExqqysbPd1Fi5cqKioKOMWHx/fYe8Bvqf54OauXbsqOTlZP/rRj5ScnOyx/AWDoAEgcPn0GKAJEyYY94cOHaoRI0aob9++eu2113TnnXd22OvMnTtXWVlZxrbD4SAEBbBXXnnFuF9XV6elS5e2WXfHHXecrWYBAM4in+4B+r7o6GgNGDBAu3fvbvV4bGysqqqqPPZVVVUpNja23ecNCwtTZGSkxw2By/1vpPkVX82593//3xIAIHD4VQCqr6/Xnj171KdPn1aPjxw5UuvWrfPYt3btWo0cOfJsNA9+wn2adPv27YqMjNS1116rmTNn6tprr1VkZKS2b9/uUQcACDw+HYDmzJmj999/X/v27dOGDRt0ww03KCgoSFOnTpUkTZ8+XXPnzjXqMzMztWbNGj3zzDMqLS3V/PnztXnzZmVkZHjrLcAH/eQnPzHuP/300xo+fLi6deum4cOH6+mnn261DgAQWHx6DND+/fs1depUHT58WDabTZdeeqk2bdokm80m6cRqvlbr/2W4UaNG6ZVXXtHDDz+shx56SImJiVq5ciVzAMHDe++9Z9y/55572q279tprz0KLAABnm08HoBUrVrR7vPkXmdstt9yiW265pZNahEBw8ODBDq0DAPgfnw5AQGf4/mSaMTExslgscrlcHgOf25t0EwDg3whAMJ24uDjjfo8ePTxCT48ePfTNN9+0qAMABBafHgQNdIY1a9YY991hp7Xt5nUAgMBCAILp1NfXd2gdAMD/EIBgOlFRUcZ9i8Xicaz5dvM6AEBgYQwQTCc8PNy4361bN11++eXq3bu3Dh48qPXr1xs9P83rAACBhQAE0/nyyy+N+/X19Vq9evUP1gEAAgunwAAAgOkQgGA65513nnE/JCTE41jz7eZ1AIDAQgCC6fTr18+439TU5HGs+XbzOgBAYCEAwXSio6M7tA4A4H8IQDCdmpqaDq0DAPgfAhBMZ9u2bZJazgHk5t7vrgMABB4CEEznyJEjkiSXyyVJSkhI0I033qiEhASP/e46AEDgYR4gmM7555+vvXv3SpK6d++uzz//XJ9//rmx7Z4I8fzzz/daGwEAnYseIJhO87E931/vq/k2Y4AAIHARgGA6X3/9dYfWAQD8DwEIpnPOOed0aB0AwP8QgGA6dXV1HVoHAPA/BCCYTnV1dYfWAQD8DwEIptOlS5cOrQMA+B8CEEynd+/eHtsRERHq1auXIiIi2q0DAAQO5gGC6XzxxRce28eOHVNTU5OOHz/ebh0AIHAQgGA635/huampqcWq8K3VAQACB6fAYDohISEdWgcA8D8EIJhOnz59OrQOAOB/CEAwnaNHj3ZoHQDA/xCAYDrffPNNh9YBAPwPAQim43Q6O7QOAOB/CEAwHQZBAwAIQAAAwHR8OgAtXLhQl1xyiSIiItS7d29NmTJFO3fubPcxy5Ytk8Vi8biFh4efpRbDH7hcrg6tAwD4H58OQO+//77uu+8+bdq0SWvXrlVTU5OuueaaH5ygLjIyUl999ZVxY0ZfNNfapIdnUgcA8D8+PRP0mjVrPLaXLVum3r1765NPPtHll1/e5uMsFotiY2M7u3nwU/QAAQB8ugfo+2prayVJ55xzTrt19fX16tu3r+Lj4zV58mRt37693fqGhgY5HA6PGwAACFx+E4CcTqdmzZql0aNHa/DgwW3WXXTRRcrLy1NBQYGWL18up9OpUaNGaf/+/W0+ZuHChYqKijJu8fHxnfEW4CPoAQIA+E0Auu+++7Rt2zatWLGi3bqRI0dq+vTpSklJ0RVXXKE33nhDNptNzz//fJuPmTt3rmpra41bRUVFRzcfAAD4EJ8eA+SWkZGht956S+vXr9d55513So8NCQmR3W7X7t2726wJCwtTWFjYmTYTAAD4CZ/uAXK5XMrIyNCbb76pf/3rX7rgggtO+TmOHz+urVu3srAlAAAw+HQP0H333adXXnlFBQUFioiIUGVlpSQpKipKXbp0kSRNnz5d5557rhYuXChJ+u1vf6sf/ehH6t+/v2pqavTUU0/piy++0F133eW19wEAAHyLTwegJUuWSJJ+/OMfe+x/6aWXdPvtt0uSysvLZbX+X0fWN998o7vvvluVlZXq0aOHLr74Ym3YsEHJyclnq9kAAMDH+XQAOpmrcN577z2P7UWLFmnRokWd1CIAABAIfHoMEAAAQGcgAAEAANMhAAEAANMhAAEAANPx6UHQ8G0NDQ06cOCAt5vRqfbu3evtJpyyuLg4JvYEgB9AAMJpO3DggObNm+ftZnQqf3x/Tz755GlNGgoAZkIAwmmLi4vTk08+6e1mnLKXXnqp3aVR3Pr376+ZM2eehRZ1rLi4OG83AQB8HgEIpy0sLMwvexoeeugh3XHHHSdVFx4efhZaBPgeTnH7Jk5xdxwCEEwnPDxcCQkJ+vzzz9usSUhIIPzA1DjF7Zs4xd1xLK6TmW7ZZBwOh6KiolRbW6vIyEhvNwed5OGHH241BCUkJOiJJ57wQosA3+GvPUCnEmr88RQ+PUDtO5XvbwJQKwhA5nHs2DE99dRT2rFjhwYOHKj777+fnh/Aj61Zs0b/8z//84N106dP1/jx489Ci3A2ncr3N/MAwdTCw8M1bdo0SdK0adMIP4CfO9lQQ/gBAQgAEFBeeeWVMzoOcyAAAQACziuvvKLp06d77Js+fTrhBwYCEAAgII0fP94Y6Pzkk09y2gseCEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0gr3dADM7dOiQ6urqvN0M0/vyyy89/hfeFRERoV69enm7GQACHAHISw4dOqTZc+aoqbHR203B//fcc895uwmQFBIaqmeeftrvQxA/cHwDP3B8iy/9wCEAeUldXZ2aGhsVPXqogqO6ebs5gE/4rvaIaj78THV1dT7zR/J0HDp0SHNmz1ZjU5O3m4L/jx84viE0JERPP/OMT3y+CUBeFhzVTSE9o7zdDAAdqK6uTo1NTZp20TmK6cqfWUCSqo5+p+U7v/aZHzh8MgGgk8R0DVZ891BvNwNAK/ziKrBnn31W/fr1U3h4uEaMGKHCwsJ2619//XUlJSUpPDxcQ4YM0erVq89SSwEAgD/w+QD06quvKisrS4899pi2bNmiYcOGady4cTp48GCr9Rs2bNDUqVN15513qqioSFOmTNGUKVO0bdu2s9xyAADgq3w+AGVnZ+vuu+/WzJkzlZycrD/96U/q2rWr8vLyWq3Pzc3V+PHjdf/992vgwIF6/PHHNXz4cC1evPgstxwAAPgqnw5AjY2N+uSTTzR27Fhjn9Vq1dixY7Vx48ZWH7Nx40aPekkaN25cm/WS1NDQIIfD4XEDAACBy6cD0KFDh3T8+HHFxMR47I+JiVFlZWWrj6msrDyleklauHChoqKijFt8fPyZNx4AAPgsnw5AZ8vcuXNVW1tr3CoqKrzdJAAA0Il8+jL4Xr16KSgoSFVVVR77q6qqFBsb2+pjYmNjT6leksLCwhQWFnbmDT4N39XWe+V1AV8UaJ+HqqNMhAi4+drnwacDUGhoqC6++GKtW7dOU6ZMkSQ5nU6tW7dOGRkZrT5m5MiRWrdunWbNmmXsW7t2rUaOHHkWWnzqaj7c6u0mAOgky3d+4+0mAGiDTwcgScrKytKMGTOUmpqqtLQ05eTk6MiRI5o5c6Ykafr06Tr33HO1cOFCSVJmZqauuOIKPfPMM7ruuuu0YsUKbd68WS+88II330abokcPUXBUd283A/AJ39XWB9SPgmkX9VBM1xBvNwPwCVVHm3zqR4HPB6DbbrtN1dXVevTRR1VZWamUlBStWbPGGOhcXl4uq/X/hjKNGjVKr7zyih5++GE99NBDSkxM1MqVKzV48GBvvYV2BUd1ZykMIEDFdA1hJmjAR/l8AJKkjIyMNk95vffeey323XLLLbrllls6uVUd47vaI95uAuAzAu3zUHX0O283AfAZvvZ58IsAFIgiIiIUEhqqmg8/83ZTAJ8SEhqqiIgIbzfjjERERCg0JETLd37t7aYAPiU0JMRnPt8Wl8vl8nYjfI3D4VBUVJRqa2sVGRnZaa9z6NAh1dXVddrz4+R8+eWXeu6553Tvvffq3HPP9XZzTC8iIsInVoo+U3y+fQOfb9/S2Z/vU/n+pgfIi3r16hUQf+gDxbnnnqsLLrjA281AgODz7Vv4fOP7mAgRAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDpfB47Q1NDTowIED3m7GGfvyyy89/tffxcXFKSwszNvNgJ/j8+2b+Hx3HCZCbMXZmgjR3+3du1fz5s3zdjPwPU8++STzneCM8fn2TXy+23cq398EoFYQgE5OoPxCDDT8QkRH4PPtm/h8t4+ZoHFWhIWF8UsECFB8vhHoGAQNAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMh9XgW+FyuSRJDofDyy0BAAAny/297f4ebw8BqBV1dXWSpPj4eC+3BAAAnKq6ujpFRUW1W2NxnUxMMhmn06kDBw4oIiJCFovF281BJ3M4HIqPj1dFRYUiIyO93RwAHYjPt7m4XC7V1dUpLi5OVmv7o3zoAWqF1WrVeeed5+1m4CyLjIzkDyQQoPh8m8cP9fy4MQgaAACYDgEIAACYDgEIphcWFqbHHntMYWFh3m4KgA7G5xttYRA0AAAwHXqAAACA6RCAAACA6RCAAACA6RCAgNPQr18/5eTkGNsWi0UrV670WnsAtLRv3z5ZLBYVFxeb6rVxcghA8Gu33367LBZLi9v48eM79XU//vhj3XPPPZ36GoA/qqysVGZmpvr376/w8HDFxMRo9OjRWrJkiY4ePert5p2RpKQkhYWFqbKy0ttNQQdgJmj4vfHjx+ull17y2NfZl7zabLZOfX7AH33++ecaPXq0oqOj9Z//+Z8aMmSIwsLCtHXrVr3wwgs699xzNWnSJG8387R88MEH+vbbb3XzzTfr5Zdf1oMPPujtJuEM0QMEvxcWFqbY2FiPW48ePSSdODX1/PPP6/rrr1fXrl01cOBAbdy4Ubt379aPf/xjdevWTaNGjdKePXuM59uzZ48mT56smJgYde/eXZdcconeeecdj9f8/ikwANK9996r4OBgbd68WbfeeqsGDhyohIQETZ48WX//+981ceJEo7a8vFyTJ09W9+7dFRkZqVtvvVVVVVUez7dkyRJdeOGFCg0N1UUXXaQ///nPHsdLS0t16aWXKjw8XMnJyXrnnXd+8HT0tm3bNGHCBHXv3l0xMTH62c9+pkOHDv3ge1u6dKl++tOf6mc/+5ny8vJaHC8sLJTdbld4eLhSU1NVVFTkcfybb75Renq6bDabunTposTExBY/3HB2EYAQ8B5//HFNnz5dxcXFSkpK0k9/+lP9/Oc/19y5c7V582a5XC5lZGQY9fX19br22mu1bt06FRUVafz48Zo4caLKy8u9+C4A33b48GG9/fbbuu+++9StW7dWa9yLSzudTk2ePFlff/213n//fa1du1aff/65brvtNqP2zTffVGZmpmbPnq1t27bp5z//uWbOnKl3331XknT8+HFNmTJFXbt21UcffaQXXnhB8+bNa7eNNTU1uvLKK2W327V582atWbNGVVVVuvXWW9t9XF1dnV5//XVNmzZNV199tWpra/Xvf//bOF5fX6/rr79eycnJ+uSTTzR//nzNmTPH4zkeeeQRlZSU6B//+Id27NihJUuWqFevXu2+LjqZC/BjM2bMcAUFBbm6devmcXvyySddLpfLJcn18MMPG/UbN250SXItXbrU2PeXv/zFFR4e3u7rDBo0yPXHP/7R2O7bt69r0aJFxrYk15tvvtkxbwrwQ5s2bXJJcr3xxhse+3v27Gl8Lh944AGXy+Vyvf32266goCBXeXm5Ubd9+3aXJFdhYaHL5XK5Ro0a5br77rs9nuuWW25xXXvttS6Xy+X6xz/+4QoODnZ99dVXxvG1a9d6fBb37t3rkuQqKipyuVwu1+OPP+665pprPJ6zoqLCJcm1c+fONt/bCy+84EpJSTG2MzMzXTNmzDC2n3/+eVfPnj1d3377rbFvyZIlHq89ceJE18yZM9t8DZx99ADB740ZM0bFxcUet//4j/8wjg8dOtS4HxMTI0kaMmSIx75jx47J4XBIOvFrbs6cORo4cKCio6PVvXt37dixgx4g4DQUFhaquLhYgwYNUkNDgyRpx44dio+PV3x8vFGXnJys6Oho7dixw6gZPXq0x3ONHj3aOL5z507Fx8crNjbWOJ6WltZuWz799FO9++676t69u3FLSkqSJI/T4N+Xl5enadOmGdvTpk3T66+/rrq6OqOtQ4cOVXh4uFEzcuRIj+f4xS9+oRUrViglJUUPPPCANmzY0G5b0fkYBA2/161bN/Xv37/N4yEhIcZ9dxd8a/ucTqckac6cOVq7dq2efvpp9e/fX126dNHNN9+sxsbGzmg+EBD69+8vi8WinTt3euxPSEiQJHXp0sUbzfJQX1+viRMn6r/+679aHOvTp0+rjykpKdGmTZtUWFjoMfD5+PHjWrFihe6+++6Teu0JEyboiy++0OrVq7V27VpdddVVuu+++/T000+f3pvBGaMHCPieDz/8ULfffrtuuOEGDRkyRLGxsdq3b5+3mwX4tJ49e+rqq6/W4sWLdeTIkXZrBw4cqIqKClVUVBj7SkpKVFNTo+TkZKPmww8/9Hjchx9+aBy/6KKLVFFR4TFw+uOPP273dYcPH67t27erX79+6t+/v8etrXFLS5cu1eWXX65PP/3Uo5c5KytLS5cuNdr62Wef6dixY8bjNm3a1OK5bDabZsyYoeXLlysnJ0cvvPBCu+1F5yIAwe81NDSosrLS43YyV3W0JTExUW+88YaKi4v16aef6qc//anROwSgbc8995y+++47paam6tVXX9WOHTu0c+dOLV++XKWlpQoKCpIkjR07VkOGDFF6erq2bNmiwsJCTZ8+XVdccYVSU1MlSffff7+WLVumJUuWaNeuXcrOztYbb7xhDC6++uqrdeGFF2rGjBn67LPP9OGHH+rhhx+W9H+9ut9333336euvv9bUqVP18ccfa8+ePfrnP/+pmTNn6vjx4y3qm5qa9Oc//1lTp07V4MGDPW533XWXPvroI23fvl0//elPZbFYdPfdd6ukpESrV69u0bPz6KOPqqCgQLt379b27dv11ltvaeDAgR323x6njgAEv7dmzRr16dPH43bppZee9vNlZ2erR48eGjVqlCZOnKhx48Zp+PDhHdhiIDBdeOGFKioq0tixYzV37lwNGzZMqamp+uMf/6g5c+bo8ccfl3QioBQUFKhHjx66/PLLNXbsWCUkJOjVV181nmvKlCnKzc3V008/rUGDBun555/XSy+9pB//+MeSpKCgIK1cuVL19fW65JJLdNdddxlXgTUfi9NcXFycPvzwQx0/flzXXHONhgwZolmzZik6OlpWa8uvw1WrVunw4cO64YYbWhwbOHCgBg4cqKVLl6p79+7629/+pq1bt8put2vevHktTrOFhoZq7ty5Gjp0qC6//HIFBQVpxYoVp/XfGR3D4nK5XN5uBAAAZ+rDDz/UpZdeqt27d+vCCy/0dnPg4whAAAC/9Oabb6p79+5KTEzU7t27lZmZqR49euiDDz7wdtPgB7gKDADgl+rq6vTggw+qvLxcvXr10tixY/XMM894u1nwE/QAAQAA02EQNAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEACcgn379slisai4uNjbTQFwBghAAADAdAhAAADAdAhAAPyK0+nU73//e/Xv319hYWE6//zz9eSTT0qSHnzwQQ0YMEBdu3ZVQkKCHnnkETU1NRmPnT9/vlJSUvTnP/9Z/fr1U1RUlH7yk5+orq7OqFmzZo0uvfRSRUdHq2fPnrr++uu1Z8+es/4+AXQuAhAAvzJ37lz97ne/0yOPPKKSkhK98soriomJkSRFRERo2bJlKikpUW5url588UUtWrTI4/F79uzRypUr9dZbb+mtt97S+++/r9/97nfG8SNHjigrK0ubN2/WunXrZLVadcMNN8jpdJ7V9wmgc7EUBgC/UVdXJ5vNpsWLF+uuu+76wfqnn35aK1as0ObNmyWd6AF66qmnVFlZqYiICEnSAw88oPXr12vTpk2tPsehQ4dks9m0detWDR48WPv27dMFF1ygoqIipaSkdNh7A3B20QMEwG/s2LFDDQ0Nuuqqq1o9/uqrr2r06NGKjY1V9+7d9fDDD6u8vNyjpl+/fkb4kaQ+ffro4MGDxvauXbs0depUJSQkKDIyUv369ZOkFs8DwL8RgAD4jS5durR5bOPGjUpPT9e1116rt956S0VFRZo3b54aGxs96kJCQjy2LRaLx+mtiRMn6uuvv9aLL76ojz76SB999JEktXgeAP4t2NsNAICTlZiYqC5dumjdunUtToFt2LBBffv21bx584x9X3zxxSk9/+HDh7Vz5069+OKLuuyyyyRJH3zwwZk3HIDPIQAB8Bvh4eF68MEH9cADDyg0NFSjR49WdXW1tm/frsTERJWXl2vFihW65JJL9Pe//11vvvnmKT1/jx491LNnT73wwgvq06ePysvL9Zvf/KaT3g0Ab+IUGAC/8sgjj2j27Nl69NFHNXDgQN122206ePCgJk2apF//+tfKyMhQSkqKNmzYoEceeeSUnttqtWrFihX65JNPNHjwYP3617/WU0891UnvBIA3cRUYAAAwHXqAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6fw/zENq2ojUa50AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}