Extração_Sidra_PAM_e_TerraClimate_para_municipios_e_culturas [COLAB].ipynb

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        "<a href=\"https://colab.research.google.com/github/andrebelem/pythonverse/blob/main/Extra%C3%A7%C3%A3o_Sidra_PAM_e_TerraClimate_para_municipios_e_culturas%20%5BCOLAB%5D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Script \"Combo\" para extração de dados climáticos de um município\n",
        "\n",
        "Autor: [A.L. Belem](https://github.com/andrebelem)<br>\n",
        "UFF @ 2023<br><br>\n",
        "\n",
        "O objetivo deste \"combo\" é extrair dados climáticos e de produção agrícola de uma ou mais regiões/culturas. Ele é derivado de vários exercícios e notebooks em Python desenvolvidos para a disciplina de Modelagem e Otimização de Sistemas Agrícolas e Ambientais, mas apresentado de forma mais *organizada* (e sim ! ele é quase um \"presente\" para quem está procurando juntar dados climáticos e de produção de uma vez só.<br>\n",
        "Lembrando....<br>\n",
        "(1) obter dados para a cultura ([veja aqui](http://api.sidra.ibge.gov.br/desctabapi.aspx?c=5457)) diretamente do site do IBGE. <br>\n",
        "(2) obter o código da cidade (é possivel conseguir isso direto do IBGE). <br>\n",
        "(3) usar o código para baixar os dados de produção na cidade. Note que na API, o nível de cidade é N6 ([veja aqui](http://api.sidra.ibge.gov.br/desctabapi.aspx?c=5457)).\n",
        "\n",
        "Essas informações são necessarias \"antes\" de você iniciar o processo.\n",
        "\n"
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        "# Primeiro, é necessário instalar algumas bibliotecas\n",
        "%%capture\n",
        "!pip install salem\n",
        "!pip install rasterio"
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        "# Será necessário ter a malha territorial brasileira do IBGE. Você pode pega-la do site do IBGE ou\n",
        "# do meu github (cortesia), como estou fazendo abaixo\n",
        "%%capture\n",
        "!wget https://github.com/andrebelem/pythonverse/raw/main/datasets/BR_Municipios_2022.zip\n",
        "# note que irá BEM mais rápido na sua adaptação se você baixar diretamente os shape do estado que está trabalhando.\n",
        "\n",
        "# você também irá usar o sidrapam que criei (disponível no meu github)\n",
        "!wget https://raw.githubusercontent.com/andrebelem/pythonverse/main/sidrapam.py"
      ],
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        "## Passo 1 - selecionando municípios e suas culturas de interesse\n",
        "\n",
        "Por conta dos problemas que o SIDRA/IBGE tem apresentado de tempos em tempos com relação à certificados de acesso, eu criei uma alternativa que vocês podem usar para extrair dados de cultura de cidades, da tabela 5457.\n",
        "\n",
        "Agora que o `sidrapam` está aqui instalado na sua máquina virtual, vou usar a função `get_pam` para extrair dados do SIDRA com base no array de municipios, código de municípios, produtos e código de produtos. Para este exemplo, estou obtendo dados da produção de Café (em grão) Total, nos municípios de 'Altinópolis','Batatais','Cajuru'. Veja abaixo:"
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        "import sidrapam\n",
        "\n",
        "# note que você tem que passar esses 4 arrays (que contém os nomes, e os códigos, tanto de municípios quanto de produtos)\n",
        "\n",
        "municipios = ['Altinópolis','Batatais','Cajuru'] # use quantos quiser, mas pode demorar bastante\n",
        "codigos_mun = ['3501004','3505906','3509403']\n",
        "\n",
        "produtos = ['Café (em grão) Total'] # pode ser mais de um\n",
        "codigos_prod = ['40139'] # pode ser mais de um\n",
        "\n",
        "df_pam = sidrapam.get_pam(municipios,codigos_mun,produtos,codigos_prod)"
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      "source": [
        "# note que temos agora um dataframe com o que precisamos\n",
        "df_pam\n",
        "# Obs: Área Plantada e Área Colhida são em Ha, QtdeColhida \"geralmente\" é em kilos, e Rendimento é em kg/Ha"
      ],
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              "      Ano  A.Plantada  A.Colhida  QtdeColhida  Rendimento  \\\n",
              "0    1974         NaN     2494.0       6235.0      2500.0   \n",
              "1    1975         NaN     4746.0       7062.0      1488.0   \n",
              "2    1976         NaN     3394.0       3540.0      1043.0   \n",
              "3    1977         NaN     5424.0      14104.0      2600.0   \n",
              "4    1978         NaN     7545.0      15668.0      2077.0   \n",
              "..    ...         ...        ...          ...         ...   \n",
              "142  2018      1780.0     1780.0       3204.0      1800.0   \n",
              "143  2019      1780.0     1780.0       2456.0      1380.0   \n",
              "144  2020      2000.0     2000.0       2760.0      1380.0   \n",
              "145  2021      2000.0     2000.0       3360.0      1680.0   \n",
              "146  2022      2000.0     2000.0       3120.0      1560.0   \n",
              "\n",
              "                  Produto Cod.Produto    Municipio Cod.Municipio  \n",
              "0    Café (em grão) Total       40139  Altinópolis       3501004  \n",
              "1    Café (em grão) Total       40139  Altinópolis       3501004  \n",
              "2    Café (em grão) Total       40139  Altinópolis       3501004  \n",
              "3    Café (em grão) Total       40139  Altinópolis       3501004  \n",
              "4    Café (em grão) Total       40139  Altinópolis       3501004  \n",
              "..                    ...         ...          ...           ...  \n",
              "142  Café (em grão) Total       40139       Cajuru       3509403  \n",
              "143  Café (em grão) Total       40139       Cajuru       3509403  \n",
              "144  Café (em grão) Total       40139       Cajuru       3509403  \n",
              "145  Café (em grão) Total       40139       Cajuru       3509403  \n",
              "146  Café (em grão) Total       40139       Cajuru       3509403  \n",
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              "      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-0fddf05f-f6e7-4c97-993e-1805913e6846 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_pam",
              "summary": "{\n  \"name\": \"df_pam\",\n  \"rows\": 147,\n  \"fields\": [\n    {\n      \"column\": \"Ano\",\n      \"properties\": {\n        \"dtype\": \"int16\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          1987,\n          2019,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A.Plantada\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3194.9983328916983,\n        \"min\": 750.0,\n        \"max\": 11923.0,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          8287.0,\n          3500.0,\n          750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"A.Colhida\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2898.70540645239,\n        \"min\": 750.0,\n        \"max\": 11923.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          7500.0,\n          2396.0,\n          8027.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"QtdeColhida\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6892.297898470182,\n        \"min\": 541.0,\n        \"max\": 48600.0,\n        \"num_unique_values\": 125,\n        \"samples\": [\n          18000.0,\n          17000.0,\n          3600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rendimento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 687.0558795161455,\n        \"min\": 167.0,\n        \"max\": 4860.0,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          1452.0,\n          1740.0,\n          1499.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Produto\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Caf\\u00e9 (em gr\\u00e3o) Total\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cod.Produto\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"40139\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Municipio\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Altin\\u00f3polis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cod.Municipio\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"3501004\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora que minha variável `df_pam` tem tudo que eu quero, vou gravar e guardar em excel. Obviamente você deve renomear para o arquivo que você quiser (para saber do que se trata)."
      ],
      "metadata": {
        "id": "gMZ_RHKAgnZZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# renomeando para ser um dataframe do pandas\n",
        "my_data = df_pam\n",
        "\n",
        "# que tal gravar no seu disco ?\n",
        "my_data.to_excel(r'my_data.xlsx', index = False)\n",
        "from google.colab import files\n",
        "files.download('my_data.xlsx')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "f1ObvJ5Eglun",
        "outputId": "16e6afe7-a762-4c60-fa32-22af4e7275c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_a061c2e1-cf69-4173-aea2-76820e8ec12d\", \"my_data.xlsx\", 11147)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Passo 2 - Com base na área do município, extrai todos os dados climáticos\n",
        "\n",
        "Aqui vamos usar o mesmo código de cada município, para extrair os dados climáticos do TerraClimate (https://www.climatologylab.org/terraclimate.html).\n",
        "O TerraClimate é um conjunto de dados climáticos global de alta resolução espacial e temporal. Ele integra observações climáticas de estações meteorológicas terrestres, satélites e reanálises climáticas em um único conjunto de dados coeso. O TerraClimate fornece uma ampla gama de variáveis climáticas, incluindo temperatura, precipitação, umidade e radiação solar, entre outras, em uma grade espacial de resolução de 4 km para todo o mundo. Esses dados são úteis para uma variedade de aplicações, incluindo estudos climáticos, modelagem hidrológica, monitoramento agrícola e muito mais.\n",
        "\n",
        "A sequência é ler os shapefiles dos municípios, e com base nos arquivos do TerraClimate, extrair as informações que eu necessito."
      ],
      "metadata": {
        "id": "FWbmif_4QFdX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Iniciando o programa - lendo os municípios...')\n",
        "import geopandas as gpd\n",
        "\n",
        "municipios_BR = gpd.read_file('BR_Municipios_2022.zip') # leitura do shapefile\n",
        "# **Atenção** essa operação pode demorar um pouco já que são mais de 5000 municípios no arquivo completo do Brasil\n",
        "\n",
        "shape_mun = municipios_BR[municipios_BR['CD_MUN'].isin(codigos_mun)]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5maenbgdc9ag",
        "outputId": "7571c118-2e11-4db4-ed67-942e6f3b1a3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iniciando o programa - lendo os municípios...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# se você quiser ver como ficou ... Note que os 3 municípios são contíguos, mas não precisaria ser assim.\n",
        "shape_mun.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "ljeI_11Cg7ou",
        "outputId": "c2e9e963-7516-4c73-eb48-c8e7e9971926"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora, vamos pegar os dados do TerraClimate dentro de cada município. Especificamente neste exemplo, estou focando apenas nos parâmetros: **tmax** Tempertura Máxima, **tmin** Temperatura Mínima, **ppt** Precipitação, **srad** Radiação Solar e **aet** Evapotranspiração. Note que todos esses dados são médias mensais, em um grid regular de 4 km (acesse o [TerraClimate](https://www.climatologylab.org/terraclimate.html) para entender o formato dos dados)."
      ],
      "metadata": {
        "id": "7DSReFIDiJPW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# inicializa todos os arquivos de parâmetros climáticos\n",
        "# note que aqui tem apenas tmax, tmin, ppt, srad e aet\n",
        "\n",
        "filesnc = ['http://thredds.northwestknowledge.net:8080/thredds/dodsC/agg_terraclimate_tmax_1958_CurrentYear_GLOBE.nc',\n",
        "           'http://thredds.northwestknowledge.net:8080/thredds/dodsC/agg_terraclimate_tmin_1958_CurrentYear_GLOBE.nc',\n",
        "           'http://thredds.northwestknowledge.net:8080/thredds/dodsC/agg_terraclimate_ppt_1958_CurrentYear_GLOBE.nc',\n",
        "           'http://thredds.northwestknowledge.net:8080/thredds/dodsC/agg_terraclimate_srad_1958_CurrentYear_GLOBE.nc',\n",
        "           'http://thredds.northwestknowledge.net:8080/thredds/dodsC/agg_terraclimate_aet_1958_CurrentYear_GLOBE.nc']"
      ],
      "metadata": {
        "id": "y7hyuSCH-yr3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import xarray as xr\n",
        "import salem\n",
        "\n",
        "# note que esse passo pega TODOS os dados do TerraClimate e baixa para o seu municipio,\n",
        "# mas só irá fazer isso se o arquivo <municipio>_climate.nc não existir\n",
        "# note que pode demorar um pouco porque são vários conjuntos de dados.\n",
        "\n",
        "fnames = [] # inicia um array para os nomes de arquivo\n",
        "\n",
        "for i,name_mun in enumerate(municipios):\n",
        "# aqui grava em um netcdf com o nome do municipio. Verifique se o nome está de acordo\n",
        "    fname = name_mun + '_climate.nc'\n",
        "    if \"/\" in fname:\n",
        "        fname = fname.replace(\"/\",\"-\")\n",
        "    if \" \" in fname:\n",
        "        fname = fname.replace(\" \",\"_\")\n",
        "    print(fname)\n",
        "    fnames.append(fname)\n",
        "\n",
        "    if not os.path.exists(fname):\n",
        "        dclimate = xr.Dataset() # inicializa os dados climáticos\n",
        "        roi = gpd.GeoDataFrame(shape_mun.iloc[[i]]) # separa a region-of-interest\n",
        "        print(f'netcdf aberto e pronto.', end=\"\")\n",
        "        # agora vou baixar os dados climáticos para a mesoregião toda com o salem\n",
        "        # aqui simplesmente \"apontamos para cada arquivo\" de dados climáticos\n",
        "        print(f'Iniciando laço para dados climáticos...', end=\"\")\n",
        "        for fnc in filesnc:\n",
        "            dset = xr.open_dataset(fnc)\n",
        "            varname = list(dset.keys()) # aqui extrai o nome da variável\n",
        "            print(f'varname:{varname[0]}.', end=\"\")\n",
        "            myvar = dset.salem.subset(shape=roi) # faz um subset (primeiro! não sei porque mas fazendo roi direto ele dá erro)\n",
        "            myvar = myvar.salem.roi(shape=roi)  # apaga o que está fora do roi\n",
        "            myvar_mean = myvar[varname[0]].mean(dim=('lat','lon')).rename(varname[0]+'_mean') # extrai a média regional (por tempo)\n",
        "            myvar_median = myvar[varname[0]].median(dim=('lat','lon')).rename(varname[0]+'_median') # mediana\n",
        "            myvar_std = myvar[varname[0]].std(dim=('lat','lon')).rename(varname[0]+'_std') # e desvio padrão\n",
        "            dclimate = xr.merge([dclimate,myvar_mean,myvar_median,myvar_std],compat='no_conflicts') # merge com o dataset aberto\n",
        "\n",
        "\n",
        "        dclimate.to_netcdf(fname) # grava um nectdf no disco com o nome da mesorregião\n",
        "        print(f'Dados climáticos gravados.')\n",
        "        dclimate.close()\n",
        "    else:\n",
        "        print(f'Arquivo de dados climáticos já existe para roi')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2g97X0E5ilbR",
        "outputId": "939f36c7-fa48-4ecd-f849-5315a922a1f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading salem-sample-data...\n",
            "Altinópolis_climate.nc\n",
            "netcdf aberto e pronto.Iniciando laço para dados climáticos...varname:tmax.varname:tmin.varname:ppt.varname:srad.varname:aet.Dados climáticos gravados.\n",
            "Batatais_climate.nc\n",
            "netcdf aberto e pronto.Iniciando laço para dados climáticos...varname:tmax.varname:tmin.varname:ppt.varname:srad.varname:aet.Dados climáticos gravados.\n",
            "Cajuru_climate.nc\n",
            "netcdf aberto e pronto.Iniciando laço para dados climáticos...varname:tmax.varname:tmin.varname:ppt.varname:srad.varname:aet.Dados climáticos gravados.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Veja que o bloco anterior vai criar uma série de arquivos `nc`, um para cada cidade, e com todas as variáveis climáticas selecionadas."
      ],
      "metadata": {
        "id": "H9R_1Ny3uUUK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# se você quiser ainda gravar esses arquivos no seu computador, basta executar o código abaixo\n",
        "from google.colab import files\n",
        "\n",
        "for file in fnames:\n",
        "    files.download(file)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "msuY1xW3yhKB",
        "outputId": "95cce03f-68b1-402c-e88e-8a0c06a2d747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_12a65000-f5a4-483f-9ccd-6490ed3f64c8\", \"Altin\\u00f3polis_climate.nc\", 77814)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_ad8779ce-d8a6-4947-9ce9-a763681ba3d3\", \"Batatais_climate.nc\", 77814)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f16aa478-3173-4bf1-88cc-5bf2beb32836\", \"Cajuru_climate.nc\", 77814)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Convertendo em um dataframe\n",
        "\n",
        "Note que criamos arquivos netcdf com o clima de cada cidade. Você pode transformar cada um destes conjuntos de dados em um dataframe (que basicamente é uma tabela), separando os meses de seu interesse. Note que este conhecimento é algo que você deve ter previamente, por exemplo, *quais os meses de plantio de uma determinada cultura*, ou *quais os meses de colheita de uma determinada cultura*. <br>\n",
        "Neste exemplo, vamos trabalhar com o arquivo da **primeira cidade** ..."
      ],
      "metadata": {
        "id": "dkZH3y2kSsV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Os arquivos são:')\n",
        "for file in fnames:\n",
        "    print(f'     {file}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOIKb85oycS-",
        "outputId": "eadfe03a-4fca-41b7-fd2e-93b2b055f6f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Os arquivos são:\n",
            "     Altinópolis_climate.nc\n",
            "     Batatais_climate.nc\n",
            "     Cajuru_climate.nc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imagine agora que, com base no calendário do zoneamento agrícola da CONAB para obter essas estimativas, meus períodos de **plantio**,**crescimento** e **colheita** são definidos dessa forma:\n",
        "\n",
        "- plantio: **Outubro à Dezembro** (mes 10 à 12) (**do ano anterior à estatística!**)\n",
        "- crescimento: **Janeiro à Março** (mes 1 à 3) (no próprio ano da estatística)\n",
        "- colheita: **Março à Abril** (mes 3 à 4) (no próprio ano da estatística)\n",
        "\n",
        "Basta definir esses períodos abaixo."
      ],
      "metadata": {
        "id": "6gRypLNezY3q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# selecionando por tempo\n",
        "dset = xr.open_dataset(fnames[0])\n",
        "\n",
        "df_plantio = dset.sel(time=dset.time.dt.month.isin([10,11,12])).to_pandas() # <----- plantio\n",
        "df_cresc = dset.sel(time=dset.time.dt.month.isin([1,2,3])).to_pandas() # <---- crescimento\n",
        "df_colheita = dset.sel(time=dset.time.dt.month.isin([3,4])).to_pandas() # <----- colheita\n",
        "# aqui é importante notar que você NÃO DEVE transpor anos aqui. Por exemplo [11,12,1] ! Isso não funciona"
      ],
      "metadata": {
        "id": "sQ9wLRoxS9VX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# você pode dar uma olhada como ficou\n",
        "df_plantio.info() # por exemplo"
      ],
      "metadata": {
        "id": "RJwyqOZjTT4r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f92fbbc-20cd-4a95-f115-3208b41b449d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 198 entries, 1958-10-01 to 2023-12-01\n",
            "Data columns (total 15 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   tmax_mean    198 non-null    float32\n",
            " 1   tmax_median  198 non-null    float32\n",
            " 2   tmax_std     198 non-null    float32\n",
            " 3   tmin_mean    198 non-null    float32\n",
            " 4   tmin_median  198 non-null    float32\n",
            " 5   tmin_std     198 non-null    float32\n",
            " 6   ppt_mean     198 non-null    float64\n",
            " 7   ppt_median   198 non-null    float64\n",
            " 8   ppt_std      198 non-null    float64\n",
            " 9   srad_mean    198 non-null    float32\n",
            " 10  srad_median  198 non-null    float32\n",
            " 11  srad_std     198 non-null    float32\n",
            " 12  aet_mean     198 non-null    float32\n",
            " 13  aet_median   198 non-null    float32\n",
            " 14  aet_std      198 non-null    float32\n",
            "dtypes: float32(12), float64(3)\n",
            "memory usage: 15.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Os dados de cada cultura tem um índice definido pelo tempo e estão agrupados pelos meses\n",
        "# que definimos em cada período (plantio, cresc e colheita)\n",
        "# aqui, o segredo é fazer um \"laço\" entre os anos, recuperando os dados corretos dos meses\n",
        "# que correspondem ao ano \"estatístico\". Por exemplo: nos dados de 1990, o período de\n",
        "# plantio corresponde à outubro e novembro de 1989 (ou seja, um ano antes)\n",
        "\n",
        "ano_min = 1990 # por que eu decidi usar 1990 ? é apenas um detalhe\n",
        "ano_max = 2023 # <- note que não pode ser maior do que eu tenho gravado\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# cria um dataframe vazio\n",
        "plantio = pd.DataFrame(columns=list(df_plantio)) # pega o nome das colunas de qualquer um dos dataframes criados acima\n",
        "cresc = pd.DataFrame(columns=list(df_cresc))\n",
        "colheita = pd.DataFrame(columns=list(df_colheita))\n",
        "\n",
        "data_plantio = [] # cria matrizes vazias para cada bloco de dados\n",
        "data_cresc = []\n",
        "data_colheita = []\n",
        "ano_estatistico = []\n",
        "\n",
        "for ano in np.arange(ano_min,ano_max+1): #lembrando que o python sempre precisa desse +1 para fazer o laço corretamente\n",
        "  #print(f'Construindo os dados para o ano {ano}')\n",
        "  data_plantio.append(dict(df_plantio.loc[str(ano-1)].agg('mean')))\n",
        "  data_cresc.append(dict(df_cresc.loc[str(ano)].agg('mean'))) # <--- note que aqui é o ano estatístico\n",
        "  data_colheita.append(dict(df_colheita.loc[str(ano)].agg('mean')))\n",
        "  ano_estatistico.append(ano)\n",
        "\n",
        "# Correção sobre o erro que acontece quando `append`é usado. note que aqui foi trocado para pd.concat.\n",
        "#plantio = plantio.append(data_plantio,True) # agora junta tudo nos respectivos dataframes\n",
        "#cresc = cresc.append(data_cresc,True)\n",
        "#colheita = colheita.append(data_colheita,True)\n",
        "# antigo <------\n",
        "\n",
        "# novo !!! <------\n",
        "plantio = pd.DataFrame()\n",
        "cresc = pd.DataFrame()\n",
        "colheita = pd.DataFrame()\n",
        "\n",
        "plantio = pd.concat([plantio,pd.DataFrame(data_plantio)],ignore_index=True)\n",
        "cresc = pd.concat([cresc,pd.DataFrame(data_cresc)],ignore_index=True)\n",
        "colheita = pd.concat([colheita,pd.DataFrame(data_colheita)],ignore_index=True)\n",
        "# <-------\n",
        "\n",
        "plantio.columns = ['plantio_' + str(col) for col in plantio.columns] # adiciona o prefixo nas colunas\n",
        "cresc.columns = ['cresc_' + str(col) for col in cresc.columns]\n",
        "colheita.columns = ['colheita_' + str(col) for col in colheita.columns]"
      ],
      "metadata": {
        "id": "LNlz3Oy2ThcX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# agora concatenamos lateralmente (por coluna) tudo o que fizemos\n",
        "df_total = pd.DataFrame()\n",
        "df_total['ano'] = ano_estatistico\n",
        "df_total = pd.concat([df_total,plantio,cresc,colheita],axis=1)\n"
      ],
      "metadata": {
        "id": "QJj0zofLUHHI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# veja que agora você tem todos os valores em um dataframe\n",
        "df_total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_fR2PFPp1ILD",
        "outputId": "4fbb720d-f8d6-4ada-ce1f-ffed730d66f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     ano  plantio_tmax_mean  plantio_tmax_median  plantio_tmax_std  \\\n",
              "0   1990          26.509359            26.286665          0.860622   \n",
              "1   1991          28.288012            28.056665          0.864989   \n",
              "2   1992          28.044962            27.809996          0.866891   \n",
              "3   1993          27.872339            27.633331          0.869767   \n",
              "4   1994          28.854536            28.609999          0.877341   \n",
              "5   1995          28.450068            28.223330          0.866075   \n",
              "6   1996          27.515814            27.303331          0.852220   \n",
              "7   1997          27.513399            27.273329          0.870307   \n",
              "8   1998          28.719995            28.506666          0.855343   \n",
              "9   1999          27.472620            27.239996          0.863666   \n",
              "10  2000          27.649359            27.386665          0.887515   \n",
              "11  2001          28.468790            28.209993          0.880084   \n",
              "12  2002          27.375811            27.149996          0.859161   \n",
              "13  2003          29.496664            29.246666          0.888186   \n",
              "14  2004          27.833403            27.596663          0.870025   \n",
              "15  2005          27.101984            26.909996          0.839878   \n",
              "16  2006          27.578791            27.353331          0.863723   \n",
              "17  2007          27.666451            27.389997          0.906114   \n",
              "18  2008          28.459785            28.219999          0.880109   \n",
              "19  2009          27.202835            26.959997          0.870596   \n",
              "20  2010          27.869570            27.633329          0.870505   \n",
              "21  2011          27.566238            27.333330          0.867423   \n",
              "22  2012          27.332693            27.076662          0.886972   \n",
              "23  2013          28.967371            28.709997          0.883160   \n",
              "24  2014          27.673899            27.429998          0.876124   \n",
              "25  2015          28.668295            28.433329          0.873734   \n",
              "26  2016          28.878649            28.683329          0.837883   \n",
              "27  2017          27.985815            27.799997          0.845480   \n",
              "28  2018          29.544680            29.333330          0.869461   \n",
              "29  2019          27.935457            27.733330          0.847181   \n",
              "30  2020          28.818436            28.566664          0.882265   \n",
              "31  2021          28.463118            28.266663          0.877760   \n",
              "32  2022          27.642548            27.433329          0.845041   \n",
              "33  2023          27.077301            26.866663          0.884817   \n",
              "\n",
              "    plantio_tmin_mean  plantio_tmin_median  plantio_tmin_std  \\\n",
              "0           16.752905            16.759996          0.358170   \n",
              "1           18.231415            18.239998          0.356098   \n",
              "2           17.412054            17.419998          0.354859   \n",
              "3           16.415741            16.413330          0.359321   \n",
              "4           16.720139            16.723330          0.363522   \n",
              "5           17.949358            17.946665          0.359623   \n",
              "6           17.114820            17.133329          0.352770   \n",
              "7           17.856379            17.849997          0.359334   \n",
              "8           18.225035            18.229996          0.351233   \n",
              "9           16.976664            16.989996          0.357540   \n",
              "10          17.153685            17.133329          0.373582   \n",
              "11          17.973259            17.979998          0.365475   \n",
              "12          16.880068            16.886665          0.353272   \n",
              "13          19.000849            18.983332          0.371196   \n",
              "14          17.338438            17.329996          0.361266   \n",
              "15          16.606592            16.619997          0.351855   \n",
              "16          17.083616            17.089996          0.360192   \n",
              "17          17.171417            17.146666          0.378829   \n",
              "18          17.964394            17.953329          0.367027   \n",
              "19          16.707373            16.699997          0.362460   \n",
              "20          17.373756            17.366663          0.356262   \n",
              "21          17.070778            17.076662          0.358116   \n",
              "22          16.837446            16.819998          0.368637   \n",
              "23          18.471769            18.469995          0.369220   \n",
              "24          17.178579            17.176664          0.360499   \n",
              "25          18.172693            18.173330          0.359862   \n",
              "26          18.382479            18.403330          0.344945   \n",
              "27          17.324110            17.333328          0.354586   \n",
              "28          18.334040            18.333330          0.366269   \n",
              "29          17.960279            17.966661          0.356701   \n",
              "30          18.327658            18.333330          0.368332   \n",
              "31          18.048933            18.066664          0.372015   \n",
              "32          17.228365            17.266665          0.351337   \n",
              "33          16.517729            16.499998          0.379132   \n",
              "\n",
              "    plantio_ppt_mean  plantio_ppt_median  plantio_ppt_std  ...  \\\n",
              "0         150.458156          150.833333         4.201109  ...   \n",
              "1         119.423404          119.700000         2.498791  ...   \n",
              "2         172.858156          172.900000         4.586206  ...   \n",
              "3         234.026950          234.233333         3.831186  ...   \n",
              "4         178.825532          178.133333         3.689193  ...   \n",
              "5         209.341844          209.000000         6.008184  ...   \n",
              "6         227.795745          227.800000         3.757122  ...   \n",
              "7         242.060284          241.633333         5.882732  ...   \n",
              "8         215.834043          215.933333         6.081910  ...   \n",
              "9         191.885106          191.866667         5.770734  ...   \n",
              "10        153.077305          153.933333         2.794726  ...   \n",
              "11        207.663121          207.966667         2.462431  ...   \n",
              "12        249.612057          251.200000         6.240733  ...   \n",
              "13        231.690071          231.966667         2.784278  ...   \n",
              "14        151.004255          151.066667         3.017947  ...   \n",
              "15        216.804965          217.433333         4.627392  ...   \n",
              "16        206.641135          207.733333         4.794634  ...   \n",
              "17        256.448936          257.133333         7.383832  ...   \n",
              "18        206.824823          207.100000         7.105855  ...   \n",
              "19        224.962411          225.966667         4.729627  ...   \n",
              "20        306.848227          309.300000         6.850544  ...   \n",
              "21        236.746809          238.033333         9.183155  ...   \n",
              "22        228.637589          228.966667         6.469150  ...   \n",
              "23        218.938298          219.233333         7.215191  ...   \n",
              "24        163.536879          163.633333         4.084974  ...   \n",
              "25        141.385106          141.033333         5.528858  ...   \n",
              "26        234.899291          234.066667        10.522306  ...   \n",
              "27        188.929787          188.633333         2.962123  ...   \n",
              "28        196.237589          196.133333         2.694042  ...   \n",
              "29        189.907092          190.366667         4.265390  ...   \n",
              "30        139.284397          139.500000         4.282712  ...   \n",
              "31        169.924113          171.866667         8.578590  ...   \n",
              "32        200.499291          201.266667         6.834109  ...   \n",
              "33        170.809220          172.866667         8.937807  ...   \n",
              "\n",
              "    colheita_tmin_std  colheita_ppt_mean  colheita_ppt_median  \\\n",
              "0            0.316294          93.954255                93.60   \n",
              "1            0.319836         163.951064               166.60   \n",
              "2            0.312381         144.686170               144.55   \n",
              "3            0.319684         106.267021               106.55   \n",
              "4            0.322384         176.170213               178.50   \n",
              "5            0.318441         200.854255               204.10   \n",
              "6            0.321347         208.627660               211.55   \n",
              "7            0.326271          71.874468                72.30   \n",
              "8            0.316769         148.643617               149.70   \n",
              "9            0.323655         124.367021               126.75   \n",
              "10           0.325308          68.770213                68.75   \n",
              "11           0.331441          89.108511                89.05   \n",
              "12           0.342435         150.800000               152.80   \n",
              "13           0.324839          94.480851                96.35   \n",
              "14           0.333332         136.582979               139.05   \n",
              "15           0.329914         189.870213               193.10   \n",
              "16           0.315344          97.886170                99.10   \n",
              "17           0.325093         127.588298               128.30   \n",
              "18           0.314626         159.141489               159.80   \n",
              "19           0.318106         108.322340               109.75   \n",
              "20           0.325480         159.015957               160.85   \n",
              "21           0.330100         167.031915               169.20   \n",
              "22           0.330706         163.376596               165.45   \n",
              "23           0.322928         104.525532               104.30   \n",
              "24           0.327614         155.930851               157.65   \n",
              "25           0.327821         199.708511               203.35   \n",
              "26           0.339596         124.379787               125.25   \n",
              "27           0.329826         130.481915               132.40   \n",
              "28           0.330199         133.695745               135.65   \n",
              "29           0.328986         161.820213               165.70   \n",
              "30           0.312108          50.610638                52.60   \n",
              "31           0.370980          47.057447                47.05   \n",
              "32           0.365367          80.511702                81.75   \n",
              "33           0.328453         112.548936               111.85   \n",
              "\n",
              "    colheita_ppt_std  colheita_srad_mean  colheita_srad_median  \\\n",
              "0           2.750414          198.588287            198.050003   \n",
              "1           9.314017          159.002136            158.399994   \n",
              "2           3.305178          183.824463            183.500000   \n",
              "3           4.382551          192.158508            191.700012   \n",
              "4           7.602587          171.560638            171.150009   \n",
              "5           8.680850          183.279800            183.250000   \n",
              "6           6.659519          182.511703            181.899994   \n",
              "7           2.996805          177.345734            176.899994   \n",
              "8           4.739117          192.814911            192.250000   \n",
              "9           7.331259          193.209595            192.850006   \n",
              "10          2.352662          199.544678            199.200012   \n",
              "11          3.665095          201.713821            201.050003   \n",
              "12          7.865149          205.584045            205.000000   \n",
              "13          6.580410          177.167038            176.600006   \n",
              "14          8.578588          179.841492            179.450012   \n",
              "15          7.924930          176.760635            176.149994   \n",
              "16          6.829381          178.585114            178.350006   \n",
              "17          5.462298          200.654266            200.250000   \n",
              "18          6.296831          168.838287            168.200012   \n",
              "19          6.624263          183.975555            184.000000   \n",
              "20          6.641732          184.447876            183.899994   \n",
              "21         10.609611          152.038300            151.750000   \n",
              "22          7.900020          191.850006            191.200012   \n",
              "23          2.128275          172.517029            172.050003   \n",
              "24          5.547079          190.135101            190.000000   \n",
              "25          9.725597          166.308502            166.200012   \n",
              "26          3.891296          195.203201            194.550003   \n",
              "27          6.294371          189.067017            188.650009   \n",
              "28          7.001925          188.042572            187.600006   \n",
              "29         12.792565          173.900024            173.300003   \n",
              "30          4.804216          202.136169            201.950012   \n",
              "31          1.975466          185.659576            185.100006   \n",
              "32          6.079808          199.123413            198.850006   \n",
              "33          5.619266          188.700012            188.300003   \n",
              "\n",
              "    colheita_srad_std  colheita_aet_mean  colheita_aet_median  \\\n",
              "0            1.863540          98.210648            98.050003   \n",
              "1            1.905950          91.225540            91.150002   \n",
              "2            1.733370          93.356384            93.199997   \n",
              "3            1.746540          99.892563           100.000000   \n",
              "4            1.882605          98.782974            98.449997   \n",
              "5            1.536736         100.327667           100.250000   \n",
              "6            2.027107          93.628723            93.400002   \n",
              "7            2.045221          85.906387            85.750000   \n",
              "8            1.842212         103.927658           103.500000   \n",
              "9            1.797109          92.253189            91.650002   \n",
              "10           1.749547          86.571274            86.850006   \n",
              "11           1.916160          92.746811            92.500000   \n",
              "12           1.896602          98.400002            98.199997   \n",
              "13           1.985546          88.301064            87.800003   \n",
              "14           1.845548          98.022346            97.750000   \n",
              "15           1.948658          96.173401            96.000000   \n",
              "16           1.828502          88.832977            88.550003   \n",
              "17           2.179941         106.974472           106.900002   \n",
              "18           1.989673          93.261703            93.150002   \n",
              "19           1.585841          85.629791            85.150002   \n",
              "20           2.003211         102.868088           102.300003   \n",
              "21           2.321365          86.964897            86.949997   \n",
              "22           1.978200         104.648933           104.500000   \n",
              "23           1.840387          91.727654            91.300003   \n",
              "24           1.583284         106.362762           105.900002   \n",
              "25           1.541103          91.311707            91.000000   \n",
              "26           1.942652          93.258514            93.450005   \n",
              "27           1.737041         104.773407           104.500000   \n",
              "28           1.799391          94.294678            94.199997   \n",
              "29           1.902456          95.171280            94.849998   \n",
              "30           1.719381          73.186172            75.050003   \n",
              "31           1.819881          70.118088            70.550003   \n",
              "32           2.013078          95.252129            96.050003   \n",
              "33           1.659246          97.680855            97.450005   \n",
              "\n",
              "    colheita_aet_std  \n",
              "0           1.322055  \n",
              "1           1.435378  \n",
              "2           1.346261  \n",
              "3           1.007849  \n",
              "4           1.456836  \n",
              "5           1.567611  \n",
              "6           1.305333  \n",
              "7           0.878819  \n",
              "8           2.039986  \n",
              "9           1.536734  \n",
              "10          2.644563  \n",
              "11          1.427186  \n",
              "12          1.227939  \n",
              "13          1.781836  \n",
              "14          1.789732  \n",
              "15          1.623518  \n",
              "16          1.212209  \n",
              "17          1.230130  \n",
              "18          1.557178  \n",
              "19          1.385144  \n",
              "20          2.035839  \n",
              "21          1.420069  \n",
              "22          1.745194  \n",
              "23          1.501375  \n",
              "24          1.919772  \n",
              "25          1.398989  \n",
              "26          2.130808  \n",
              "27          2.441470  \n",
              "28          1.264870  \n",
              "29          1.646939  \n",
              "30          5.060564  \n",
              "31          3.115820  \n",
              "32          2.304680  \n",
              "33          1.619957  \n",
              "\n",
              "[34 rows x 46 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ff9db8f-2bbe-4fbc-a912-7b0527689b81\" 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>ano</th>\n",
              "      <th>plantio_tmax_mean</th>\n",
              "      <th>plantio_tmax_median</th>\n",
              "      <th>plantio_tmax_std</th>\n",
              "      <th>plantio_tmin_mean</th>\n",
              "      <th>plantio_tmin_median</th>\n",
              "      <th>plantio_tmin_std</th>\n",
              "      <th>plantio_ppt_mean</th>\n",
              "      <th>plantio_ppt_median</th>\n",
              "      <th>plantio_ppt_std</th>\n",
              "      <th>...</th>\n",
              "      <th>colheita_tmin_std</th>\n",
              "      <th>colheita_ppt_mean</th>\n",
              "      <th>colheita_ppt_median</th>\n",
              "      <th>colheita_ppt_std</th>\n",
              "      <th>colheita_srad_mean</th>\n",
              "      <th>colheita_srad_median</th>\n",
              "      <th>colheita_srad_std</th>\n",
              "      <th>colheita_aet_mean</th>\n",
              "      <th>colheita_aet_median</th>\n",
              "      <th>colheita_aet_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1990</td>\n",
              "      <td>26.509359</td>\n",
              "      <td>26.286665</td>\n",
              "      <td>0.860622</td>\n",
              "      <td>16.752905</td>\n",
              "      <td>16.759996</td>\n",
              "      <td>0.358170</td>\n",
              "      <td>150.458156</td>\n",
              "      <td>150.833333</td>\n",
              "      <td>4.201109</td>\n",
              "      <td>...</td>\n",
              "      <td>0.316294</td>\n",
              "      <td>93.954255</td>\n",
              "      <td>93.60</td>\n",
              "      <td>2.750414</td>\n",
              "      <td>198.588287</td>\n",
              "      <td>198.050003</td>\n",
              "      <td>1.863540</td>\n",
              "      <td>98.210648</td>\n",
              "      <td>98.050003</td>\n",
              "      <td>1.322055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1991</td>\n",
              "      <td>28.288012</td>\n",
              "      <td>28.056665</td>\n",
              "      <td>0.864989</td>\n",
              "      <td>18.231415</td>\n",
              "      <td>18.239998</td>\n",
              "      <td>0.356098</td>\n",
              "      <td>119.423404</td>\n",
              "      <td>119.700000</td>\n",
              "      <td>2.498791</td>\n",
              "      <td>...</td>\n",
              "      <td>0.319836</td>\n",
              "      <td>163.951064</td>\n",
              "      <td>166.60</td>\n",
              "      <td>9.314017</td>\n",
              "      <td>159.002136</td>\n",
              "      <td>158.399994</td>\n",
              "      <td>1.905950</td>\n",
              "      <td>91.225540</td>\n",
              "      <td>91.150002</td>\n",
              "      <td>1.435378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1992</td>\n",
              "      <td>28.044962</td>\n",
              "      <td>27.809996</td>\n",
              "      <td>0.866891</td>\n",
              "      <td>17.412054</td>\n",
              "      <td>17.419998</td>\n",
              "      <td>0.354859</td>\n",
              "      <td>172.858156</td>\n",
              "      <td>172.900000</td>\n",
              "      <td>4.586206</td>\n",
              "      <td>...</td>\n",
              "      <td>0.312381</td>\n",
              "      <td>144.686170</td>\n",
              "      <td>144.55</td>\n",
              "      <td>3.305178</td>\n",
              "      <td>183.824463</td>\n",
              "      <td>183.500000</td>\n",
              "      <td>1.733370</td>\n",
              "      <td>93.356384</td>\n",
              "      <td>93.199997</td>\n",
              "      <td>1.346261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1993</td>\n",
              "      <td>27.872339</td>\n",
              "      <td>27.633331</td>\n",
              "      <td>0.869767</td>\n",
              "      <td>16.415741</td>\n",
              "      <td>16.413330</td>\n",
              "      <td>0.359321</td>\n",
              "      <td>234.026950</td>\n",
              "      <td>234.233333</td>\n",
              "      <td>3.831186</td>\n",
              "      <td>...</td>\n",
              "      <td>0.319684</td>\n",
              "      <td>106.267021</td>\n",
              "      <td>106.55</td>\n",
              "      <td>4.382551</td>\n",
              "      <td>192.158508</td>\n",
              "      <td>191.700012</td>\n",
              "      <td>1.746540</td>\n",
              "      <td>99.892563</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.007849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1994</td>\n",
              "      <td>28.854536</td>\n",
              "      <td>28.609999</td>\n",
              "      <td>0.877341</td>\n",
              "      <td>16.720139</td>\n",
              "      <td>16.723330</td>\n",
              "      <td>0.363522</td>\n",
              "      <td>178.825532</td>\n",
              "      <td>178.133333</td>\n",
              "      <td>3.689193</td>\n",
              "      <td>...</td>\n",
              "      <td>0.322384</td>\n",
              "      <td>176.170213</td>\n",
              "      <td>178.50</td>\n",
              "      <td>7.602587</td>\n",
              "      <td>171.560638</td>\n",
              "      <td>171.150009</td>\n",
              "      <td>1.882605</td>\n",
              "      <td>98.782974</td>\n",
              "      <td>98.449997</td>\n",
              "      <td>1.456836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1995</td>\n",
              "      <td>28.450068</td>\n",
              "      <td>28.223330</td>\n",
              "      <td>0.866075</td>\n",
              "      <td>17.949358</td>\n",
              "      <td>17.946665</td>\n",
              "      <td>0.359623</td>\n",
              "      <td>209.341844</td>\n",
              "      <td>209.000000</td>\n",
              "      <td>6.008184</td>\n",
              "      <td>...</td>\n",
              "      <td>0.318441</td>\n",
              "      <td>200.854255</td>\n",
              "      <td>204.10</td>\n",
              "      <td>8.680850</td>\n",
              "      <td>183.279800</td>\n",
              "      <td>183.250000</td>\n",
              "      <td>1.536736</td>\n",
              "      <td>100.327667</td>\n",
              "      <td>100.250000</td>\n",
              "      <td>1.567611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1996</td>\n",
              "      <td>27.515814</td>\n",
              "      <td>27.303331</td>\n",
              "      <td>0.852220</td>\n",
              "      <td>17.114820</td>\n",
              "      <td>17.133329</td>\n",
              "      <td>0.352770</td>\n",
              "      <td>227.795745</td>\n",
              "      <td>227.800000</td>\n",
              "      <td>3.757122</td>\n",
              "      <td>...</td>\n",
              "      <td>0.321347</td>\n",
              "      <td>208.627660</td>\n",
              "      <td>211.55</td>\n",
              "      <td>6.659519</td>\n",
              "      <td>182.511703</td>\n",
              "      <td>181.899994</td>\n",
              "      <td>2.027107</td>\n",
              "      <td>93.628723</td>\n",
              "      <td>93.400002</td>\n",
              "      <td>1.305333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1997</td>\n",
              "      <td>27.513399</td>\n",
              "      <td>27.273329</td>\n",
              "      <td>0.870307</td>\n",
              "      <td>17.856379</td>\n",
              "      <td>17.849997</td>\n",
              "      <td>0.359334</td>\n",
              "      <td>242.060284</td>\n",
              "      <td>241.633333</td>\n",
              "      <td>5.882732</td>\n",
              "      <td>...</td>\n",
              "      <td>0.326271</td>\n",
              "      <td>71.874468</td>\n",
              "      <td>72.30</td>\n",
              "      <td>2.996805</td>\n",
              "      <td>177.345734</td>\n",
              "      <td>176.899994</td>\n",
              "      <td>2.045221</td>\n",
              "      <td>85.906387</td>\n",
              "      <td>85.750000</td>\n",
              "      <td>0.878819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1998</td>\n",
              "      <td>28.719995</td>\n",
              "      <td>28.506666</td>\n",
              "      <td>0.855343</td>\n",
              "      <td>18.225035</td>\n",
              "      <td>18.229996</td>\n",
              "      <td>0.351233</td>\n",
              "      <td>215.834043</td>\n",
              "      <td>215.933333</td>\n",
              "      <td>6.081910</td>\n",
              "      <td>...</td>\n",
              "      <td>0.316769</td>\n",
              "      <td>148.643617</td>\n",
              "      <td>149.70</td>\n",
              "      <td>4.739117</td>\n",
              "      <td>192.814911</td>\n",
              "      <td>192.250000</td>\n",
              "      <td>1.842212</td>\n",
              "      <td>103.927658</td>\n",
              "      <td>103.500000</td>\n",
              "      <td>2.039986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1999</td>\n",
              "      <td>27.472620</td>\n",
              "      <td>27.239996</td>\n",
              "      <td>0.863666</td>\n",
              "      <td>16.976664</td>\n",
              "      <td>16.989996</td>\n",
              "      <td>0.357540</td>\n",
              "      <td>191.885106</td>\n",
              "      <td>191.866667</td>\n",
              "      <td>5.770734</td>\n",
              "      <td>...</td>\n",
              "      <td>0.323655</td>\n",
              "      <td>124.367021</td>\n",
              "      <td>126.75</td>\n",
              "      <td>7.331259</td>\n",
              "      <td>193.209595</td>\n",
              "      <td>192.850006</td>\n",
              "      <td>1.797109</td>\n",
              "      <td>92.253189</td>\n",
              "      <td>91.650002</td>\n",
              "      <td>1.536734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2000</td>\n",
              "      <td>27.649359</td>\n",
              "      <td>27.386665</td>\n",
              "      <td>0.887515</td>\n",
              "      <td>17.153685</td>\n",
              "      <td>17.133329</td>\n",
              "      <td>0.373582</td>\n",
              "      <td>153.077305</td>\n",
              "      <td>153.933333</td>\n",
              "      <td>2.794726</td>\n",
              "      <td>...</td>\n",
              "      <td>0.325308</td>\n",
              "      <td>68.770213</td>\n",
              "      <td>68.75</td>\n",
              "      <td>2.352662</td>\n",
              "      <td>199.544678</td>\n",
              "      <td>199.200012</td>\n",
              "      <td>1.749547</td>\n",
              "      <td>86.571274</td>\n",
              "      <td>86.850006</td>\n",
              "      <td>2.644563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2001</td>\n",
              "      <td>28.468790</td>\n",
              "      <td>28.209993</td>\n",
              "      <td>0.880084</td>\n",
              "      <td>17.973259</td>\n",
              "      <td>17.979998</td>\n",
              "      <td>0.365475</td>\n",
              "      <td>207.663121</td>\n",
              "      <td>207.966667</td>\n",
              "      <td>2.462431</td>\n",
              "      <td>...</td>\n",
              "      <td>0.331441</td>\n",
              "      <td>89.108511</td>\n",
              "      <td>89.05</td>\n",
              "      <td>3.665095</td>\n",
              "      <td>201.713821</td>\n",
              "      <td>201.050003</td>\n",
              "      <td>1.916160</td>\n",
              "      <td>92.746811</td>\n",
              "      <td>92.500000</td>\n",
              "      <td>1.427186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2002</td>\n",
              "      <td>27.375811</td>\n",
              "      <td>27.149996</td>\n",
              "      <td>0.859161</td>\n",
              "      <td>16.880068</td>\n",
              "      <td>16.886665</td>\n",
              "      <td>0.353272</td>\n",
              "      <td>249.612057</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>6.240733</td>\n",
              "      <td>...</td>\n",
              "      <td>0.342435</td>\n",
              "      <td>150.800000</td>\n",
              "      <td>152.80</td>\n",
              "      <td>7.865149</td>\n",
              "      <td>205.584045</td>\n",
              "      <td>205.000000</td>\n",
              "      <td>1.896602</td>\n",
              "      <td>98.400002</td>\n",
              "      <td>98.199997</td>\n",
              "      <td>1.227939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2003</td>\n",
              "      <td>29.496664</td>\n",
              "      <td>29.246666</td>\n",
              "      <td>0.888186</td>\n",
              "      <td>19.000849</td>\n",
              "      <td>18.983332</td>\n",
              "      <td>0.371196</td>\n",
              "      <td>231.690071</td>\n",
              "      <td>231.966667</td>\n",
              "      <td>2.784278</td>\n",
              "      <td>...</td>\n",
              "      <td>0.324839</td>\n",
              "      <td>94.480851</td>\n",
              "      <td>96.35</td>\n",
              "      <td>6.580410</td>\n",
              "      <td>177.167038</td>\n",
              "      <td>176.600006</td>\n",
              "      <td>1.985546</td>\n",
              "      <td>88.301064</td>\n",
              "      <td>87.800003</td>\n",
              "      <td>1.781836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2004</td>\n",
              "      <td>27.833403</td>\n",
              "      <td>27.596663</td>\n",
              "      <td>0.870025</td>\n",
              "      <td>17.338438</td>\n",
              "      <td>17.329996</td>\n",
              "      <td>0.361266</td>\n",
              "      <td>151.004255</td>\n",
              "      <td>151.066667</td>\n",
              "      <td>3.017947</td>\n",
              "      <td>...</td>\n",
              "      <td>0.333332</td>\n",
              "      <td>136.582979</td>\n",
              "      <td>139.05</td>\n",
              "      <td>8.578588</td>\n",
              "      <td>179.841492</td>\n",
              "      <td>179.450012</td>\n",
              "      <td>1.845548</td>\n",
              "      <td>98.022346</td>\n",
              "      <td>97.750000</td>\n",
              "      <td>1.789732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2005</td>\n",
              "      <td>27.101984</td>\n",
              "      <td>26.909996</td>\n",
              "      <td>0.839878</td>\n",
              "      <td>16.606592</td>\n",
              "      <td>16.619997</td>\n",
              "      <td>0.351855</td>\n",
              "      <td>216.804965</td>\n",
              "      <td>217.433333</td>\n",
              "      <td>4.627392</td>\n",
              "      <td>...</td>\n",
              "      <td>0.329914</td>\n",
              "      <td>189.870213</td>\n",
              "      <td>193.10</td>\n",
              "      <td>7.924930</td>\n",
              "      <td>176.760635</td>\n",
              "      <td>176.149994</td>\n",
              "      <td>1.948658</td>\n",
              "      <td>96.173401</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>1.623518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2006</td>\n",
              "      <td>27.578791</td>\n",
              "      <td>27.353331</td>\n",
              "      <td>0.863723</td>\n",
              "      <td>17.083616</td>\n",
              "      <td>17.089996</td>\n",
              "      <td>0.360192</td>\n",
              "      <td>206.641135</td>\n",
              "      <td>207.733333</td>\n",
              "      <td>4.794634</td>\n",
              "      <td>...</td>\n",
              "      <td>0.315344</td>\n",
              "      <td>97.886170</td>\n",
              "      <td>99.10</td>\n",
              "      <td>6.829381</td>\n",
              "      <td>178.585114</td>\n",
              "      <td>178.350006</td>\n",
              "      <td>1.828502</td>\n",
              "      <td>88.832977</td>\n",
              "      <td>88.550003</td>\n",
              "      <td>1.212209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2007</td>\n",
              "      <td>27.666451</td>\n",
              "      <td>27.389997</td>\n",
              "      <td>0.906114</td>\n",
              "      <td>17.171417</td>\n",
              "      <td>17.146666</td>\n",
              "      <td>0.378829</td>\n",
              "      <td>256.448936</td>\n",
              "      <td>257.133333</td>\n",
              "      <td>7.383832</td>\n",
              "      <td>...</td>\n",
              "      <td>0.325093</td>\n",
              "      <td>127.588298</td>\n",
              "      <td>128.30</td>\n",
              "      <td>5.462298</td>\n",
              "      <td>200.654266</td>\n",
              "      <td>200.250000</td>\n",
              "      <td>2.179941</td>\n",
              "      <td>106.974472</td>\n",
              "      <td>106.900002</td>\n",
              "      <td>1.230130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2008</td>\n",
              "      <td>28.459785</td>\n",
              "      <td>28.219999</td>\n",
              "      <td>0.880109</td>\n",
              "      <td>17.964394</td>\n",
              "      <td>17.953329</td>\n",
              "      <td>0.367027</td>\n",
              "      <td>206.824823</td>\n",
              "      <td>207.100000</td>\n",
              "      <td>7.105855</td>\n",
              "      <td>...</td>\n",
              "      <td>0.314626</td>\n",
              "      <td>159.141489</td>\n",
              "      <td>159.80</td>\n",
              "      <td>6.296831</td>\n",
              "      <td>168.838287</td>\n",
              "      <td>168.200012</td>\n",
              "      <td>1.989673</td>\n",
              "      <td>93.261703</td>\n",
              "      <td>93.150002</td>\n",
              "      <td>1.557178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2009</td>\n",
              "      <td>27.202835</td>\n",
              "      <td>26.959997</td>\n",
              "      <td>0.870596</td>\n",
              "      <td>16.707373</td>\n",
              "      <td>16.699997</td>\n",
              "      <td>0.362460</td>\n",
              "      <td>224.962411</td>\n",
              "      <td>225.966667</td>\n",
              "      <td>4.729627</td>\n",
              "      <td>...</td>\n",
              "      <td>0.318106</td>\n",
              "      <td>108.322340</td>\n",
              "      <td>109.75</td>\n",
              "      <td>6.624263</td>\n",
              "      <td>183.975555</td>\n",
              "      <td>184.000000</td>\n",
              "      <td>1.585841</td>\n",
              "      <td>85.629791</td>\n",
              "      <td>85.150002</td>\n",
              "      <td>1.385144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2010</td>\n",
              "      <td>27.869570</td>\n",
              "      <td>27.633329</td>\n",
              "      <td>0.870505</td>\n",
              "      <td>17.373756</td>\n",
              "      <td>17.366663</td>\n",
              "      <td>0.356262</td>\n",
              "      <td>306.848227</td>\n",
              "      <td>309.300000</td>\n",
              "      <td>6.850544</td>\n",
              "      <td>...</td>\n",
              "      <td>0.325480</td>\n",
              "      <td>159.015957</td>\n",
              "      <td>160.85</td>\n",
              "      <td>6.641732</td>\n",
              "      <td>184.447876</td>\n",
              "      <td>183.899994</td>\n",
              "      <td>2.003211</td>\n",
              "      <td>102.868088</td>\n",
              "      <td>102.300003</td>\n",
              "      <td>2.035839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2011</td>\n",
              "      <td>27.566238</td>\n",
              "      <td>27.333330</td>\n",
              "      <td>0.867423</td>\n",
              "      <td>17.070778</td>\n",
              "      <td>17.076662</td>\n",
              "      <td>0.358116</td>\n",
              "      <td>236.746809</td>\n",
              "      <td>238.033333</td>\n",
              "      <td>9.183155</td>\n",
              "      <td>...</td>\n",
              "      <td>0.330100</td>\n",
              "      <td>167.031915</td>\n",
              "      <td>169.20</td>\n",
              "      <td>10.609611</td>\n",
              "      <td>152.038300</td>\n",
              "      <td>151.750000</td>\n",
              "      <td>2.321365</td>\n",
              "      <td>86.964897</td>\n",
              "      <td>86.949997</td>\n",
              "      <td>1.420069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2012</td>\n",
              "      <td>27.332693</td>\n",
              "      <td>27.076662</td>\n",
              "      <td>0.886972</td>\n",
              "      <td>16.837446</td>\n",
              "      <td>16.819998</td>\n",
              "      <td>0.368637</td>\n",
              "      <td>228.637589</td>\n",
              "      <td>228.966667</td>\n",
              "      <td>6.469150</td>\n",
              "      <td>...</td>\n",
              "      <td>0.330706</td>\n",
              "      <td>163.376596</td>\n",
              "      <td>165.45</td>\n",
              "      <td>7.900020</td>\n",
              "      <td>191.850006</td>\n",
              "      <td>191.200012</td>\n",
              "      <td>1.978200</td>\n",
              "      <td>104.648933</td>\n",
              "      <td>104.500000</td>\n",
              "      <td>1.745194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2013</td>\n",
              "      <td>28.967371</td>\n",
              "      <td>28.709997</td>\n",
              "      <td>0.883160</td>\n",
              "      <td>18.471769</td>\n",
              "      <td>18.469995</td>\n",
              "      <td>0.369220</td>\n",
              "      <td>218.938298</td>\n",
              "      <td>219.233333</td>\n",
              "      <td>7.215191</td>\n",
              "      <td>...</td>\n",
              "      <td>0.322928</td>\n",
              "      <td>104.525532</td>\n",
              "      <td>104.30</td>\n",
              "      <td>2.128275</td>\n",
              "      <td>172.517029</td>\n",
              "      <td>172.050003</td>\n",
              "      <td>1.840387</td>\n",
              "      <td>91.727654</td>\n",
              "      <td>91.300003</td>\n",
              "      <td>1.501375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2014</td>\n",
              "      <td>27.673899</td>\n",
              "      <td>27.429998</td>\n",
              "      <td>0.876124</td>\n",
              "      <td>17.178579</td>\n",
              "      <td>17.176664</td>\n",
              "      <td>0.360499</td>\n",
              "      <td>163.536879</td>\n",
              "      <td>163.633333</td>\n",
              "      <td>4.084974</td>\n",
              "      <td>...</td>\n",
              "      <td>0.327614</td>\n",
              "      <td>155.930851</td>\n",
              "      <td>157.65</td>\n",
              "      <td>5.547079</td>\n",
              "      <td>190.135101</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>1.583284</td>\n",
              "      <td>106.362762</td>\n",
              "      <td>105.900002</td>\n",
              "      <td>1.919772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2015</td>\n",
              "      <td>28.668295</td>\n",
              "      <td>28.433329</td>\n",
              "      <td>0.873734</td>\n",
              "      <td>18.172693</td>\n",
              "      <td>18.173330</td>\n",
              "      <td>0.359862</td>\n",
              "      <td>141.385106</td>\n",
              "      <td>141.033333</td>\n",
              "      <td>5.528858</td>\n",
              "      <td>...</td>\n",
              "      <td>0.327821</td>\n",
              "      <td>199.708511</td>\n",
              "      <td>203.35</td>\n",
              "      <td>9.725597</td>\n",
              "      <td>166.308502</td>\n",
              "      <td>166.200012</td>\n",
              "      <td>1.541103</td>\n",
              "      <td>91.311707</td>\n",
              "      <td>91.000000</td>\n",
              "      <td>1.398989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2016</td>\n",
              "      <td>28.878649</td>\n",
              "      <td>28.683329</td>\n",
              "      <td>0.837883</td>\n",
              "      <td>18.382479</td>\n",
              "      <td>18.403330</td>\n",
              "      <td>0.344945</td>\n",
              "      <td>234.899291</td>\n",
              "      <td>234.066667</td>\n",
              "      <td>10.522306</td>\n",
              "      <td>...</td>\n",
              "      <td>0.339596</td>\n",
              "      <td>124.379787</td>\n",
              "      <td>125.25</td>\n",
              "      <td>3.891296</td>\n",
              "      <td>195.203201</td>\n",
              "      <td>194.550003</td>\n",
              "      <td>1.942652</td>\n",
              "      <td>93.258514</td>\n",
              "      <td>93.450005</td>\n",
              "      <td>2.130808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2017</td>\n",
              "      <td>27.985815</td>\n",
              "      <td>27.799997</td>\n",
              "      <td>0.845480</td>\n",
              "      <td>17.324110</td>\n",
              "      <td>17.333328</td>\n",
              "      <td>0.354586</td>\n",
              "      <td>188.929787</td>\n",
              "      <td>188.633333</td>\n",
              "      <td>2.962123</td>\n",
              "      <td>...</td>\n",
              "      <td>0.329826</td>\n",
              "      <td>130.481915</td>\n",
              "      <td>132.40</td>\n",
              "      <td>6.294371</td>\n",
              "      <td>189.067017</td>\n",
              "      <td>188.650009</td>\n",
              "      <td>1.737041</td>\n",
              "      <td>104.773407</td>\n",
              "      <td>104.500000</td>\n",
              "      <td>2.441470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2018</td>\n",
              "      <td>29.544680</td>\n",
              "      <td>29.333330</td>\n",
              "      <td>0.869461</td>\n",
              "      <td>18.334040</td>\n",
              "      <td>18.333330</td>\n",
              "      <td>0.366269</td>\n",
              "      <td>196.237589</td>\n",
              "      <td>196.133333</td>\n",
              "      <td>2.694042</td>\n",
              "      <td>...</td>\n",
              "      <td>0.330199</td>\n",
              "      <td>133.695745</td>\n",
              "      <td>135.65</td>\n",
              "      <td>7.001925</td>\n",
              "      <td>188.042572</td>\n",
              "      <td>187.600006</td>\n",
              "      <td>1.799391</td>\n",
              "      <td>94.294678</td>\n",
              "      <td>94.199997</td>\n",
              "      <td>1.264870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2019</td>\n",
              "      <td>27.935457</td>\n",
              "      <td>27.733330</td>\n",
              "      <td>0.847181</td>\n",
              "      <td>17.960279</td>\n",
              "      <td>17.966661</td>\n",
              "      <td>0.356701</td>\n",
              "      <td>189.907092</td>\n",
              "      <td>190.366667</td>\n",
              "      <td>4.265390</td>\n",
              "      <td>...</td>\n",
              "      <td>0.328986</td>\n",
              "      <td>161.820213</td>\n",
              "      <td>165.70</td>\n",
              "      <td>12.792565</td>\n",
              "      <td>173.900024</td>\n",
              "      <td>173.300003</td>\n",
              "      <td>1.902456</td>\n",
              "      <td>95.171280</td>\n",
              "      <td>94.849998</td>\n",
              "      <td>1.646939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2020</td>\n",
              "      <td>28.818436</td>\n",
              "      <td>28.566664</td>\n",
              "      <td>0.882265</td>\n",
              "      <td>18.327658</td>\n",
              "      <td>18.333330</td>\n",
              "      <td>0.368332</td>\n",
              "      <td>139.284397</td>\n",
              "      <td>139.500000</td>\n",
              "      <td>4.282712</td>\n",
              "      <td>...</td>\n",
              "      <td>0.312108</td>\n",
              "      <td>50.610638</td>\n",
              "      <td>52.60</td>\n",
              "      <td>4.804216</td>\n",
              "      <td>202.136169</td>\n",
              "      <td>201.950012</td>\n",
              "      <td>1.719381</td>\n",
              "      <td>73.186172</td>\n",
              "      <td>75.050003</td>\n",
              "      <td>5.060564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2021</td>\n",
              "      <td>28.463118</td>\n",
              "      <td>28.266663</td>\n",
              "      <td>0.877760</td>\n",
              "      <td>18.048933</td>\n",
              "      <td>18.066664</td>\n",
              "      <td>0.372015</td>\n",
              "      <td>169.924113</td>\n",
              "      <td>171.866667</td>\n",
              "      <td>8.578590</td>\n",
              "      <td>...</td>\n",
              "      <td>0.370980</td>\n",
              "      <td>47.057447</td>\n",
              "      <td>47.05</td>\n",
              "      <td>1.975466</td>\n",
              "      <td>185.659576</td>\n",
              "      <td>185.100006</td>\n",
              "      <td>1.819881</td>\n",
              "      <td>70.118088</td>\n",
              "      <td>70.550003</td>\n",
              "      <td>3.115820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2022</td>\n",
              "      <td>27.642548</td>\n",
              "      <td>27.433329</td>\n",
              "      <td>0.845041</td>\n",
              "      <td>17.228365</td>\n",
              "      <td>17.266665</td>\n",
              "      <td>0.351337</td>\n",
              "      <td>200.499291</td>\n",
              "      <td>201.266667</td>\n",
              "      <td>6.834109</td>\n",
              "      <td>...</td>\n",
              "      <td>0.365367</td>\n",
              "      <td>80.511702</td>\n",
              "      <td>81.75</td>\n",
              "      <td>6.079808</td>\n",
              "      <td>199.123413</td>\n",
              "      <td>198.850006</td>\n",
              "      <td>2.013078</td>\n",
              "      <td>95.252129</td>\n",
              "      <td>96.050003</td>\n",
              "      <td>2.304680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2023</td>\n",
              "      <td>27.077301</td>\n",
              "      <td>26.866663</td>\n",
              "      <td>0.884817</td>\n",
              "      <td>16.517729</td>\n",
              "      <td>16.499998</td>\n",
              "      <td>0.379132</td>\n",
              "      <td>170.809220</td>\n",
              "      <td>172.866667</td>\n",
              "      <td>8.937807</td>\n",
              "      <td>...</td>\n",
              "      <td>0.328453</td>\n",
              "      <td>112.548936</td>\n",
              "      <td>111.85</td>\n",
              "      <td>5.619266</td>\n",
              "      <td>188.700012</td>\n",
              "      <td>188.300003</td>\n",
              "      <td>1.659246</td>\n",
              "      <td>97.680855</td>\n",
              "      <td>97.450005</td>\n",
              "      <td>1.619957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34 rows × 46 columns</p>\n",
              "</div>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        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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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_total"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos fazer um plot, por exemplo, da precipitação média\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define o estilo dos plots\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "# Cria um gráfico de linha com a média e sombreamento representando o desvio padrão\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.lineplot(x=df_total.ano, y=df_total['plantio_ppt_mean'], label='Média plantio')\n",
        "plt.fill_between(df_total.ano, df_total['plantio_ppt_mean'] - df_total['plantio_ppt_std'],\n",
        "                 df_total['plantio_ppt_mean'] + df_total['plantio_ppt_std'], alpha=0.2)\n",
        "\n",
        "sns.lineplot(x=df_total.ano, y=df_total['cresc_ppt_mean'], label='Média cresc')\n",
        "plt.fill_between(df_total.ano, df_total['cresc_ppt_mean'] - df_total['cresc_ppt_std'],\n",
        "                 df_total['cresc_ppt_mean'] + df_total['cresc_ppt_std'], alpha=0.2)\n",
        "\n",
        "sns.lineplot(x=df_total.ano, y=df_total['colheita_ppt_mean'], label='Média colheita')\n",
        "plt.fill_between(df_total.ano, df_total['colheita_ppt_mean'] - df_total['colheita_ppt_std'],\n",
        "                 df_total['colheita_ppt_mean'] + df_total['colheita_ppt_std'], alpha=0.2)\n",
        "\n",
        "# Adiciona rótulos e título\n",
        "plt.xlabel('Ano')\n",
        "plt.ylabel('Precipitação (Média ± Desvio Padrão) mm/mês')\n",
        "plt.title(f'Variabilidade da Precipitação em {municipios[0]} (Dados TerraClimate)')\n",
        "\n",
        "# Adiciona a legenda\n",
        "plt.legend()\n",
        "\n",
        "# Exibe o gráfico\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "ODctbgrA4mlB",
        "outputId": "eed5820b-a2cf-4a51-add7-ec0cba41ca53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Embora essa sequencia facilite bastante, ela só significa algo se você **conhece bem** os períodos de plantio, crescimento e colheita do seu município, para a cultura que você está estudando. Então, faça BEM as suas estatísticas.<br>\n",
        "\n",
        "Adapte o que for necessário. Boa programação."
      ],
      "metadata": {
        "id": "wscQBKmlhZu8"
      }
    }
  ]
}