data_query.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SU0PNpUARnxu"
      },
      "source": [
        "https://odre.opendatasoft.com/explore/dataset/consommation-quotidienne-brute-regionale/table/?disjunctive.code_insee_region&disjunctive.region&sort=-consommation_brute_gaz_grtgaz\n",
        "\n",
        "https://api.meteo-concept.com/documentation\n",
        "\n",
        "https://www.meteomatics.com/en/api/getting-started/?msclkid=df3cf88b68ba1259531f9bd0826350c4&utm_source=bing&utm_medium=cpc&utm_campaign=API%20M%C3%A9t%C3%A9o%20(F,CH)&utm_term=m%C3%A9t%C3%A9o%20api&utm_content=Api%20Meteo\n",
        "\n",
        "https://github.com/Master2-IAFA/65307369"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "l6rGwayq1cV9"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import requests as rq\n",
        "from bs4 import BeautifulSoup as bs\n",
        "import pandas as pd\n",
        "from pandasql import sqldf\n",
        "from termcolor import colored"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "xSvyY7AOLH3x"
      },
      "outputs": [],
      "source": [
        "from contextlib import closing\n",
        "from urllib.request import urlopen\n",
        "import json\n",
        "import dateutil.parser"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0SOBMyrcb8Wk"
      },
      "source": [
        "### ***energie_odre_nb kh***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YQEzb_nI0hpb"
      },
      "outputs": [],
      "source": [
        "def get_data_query_energie():\n",
        "    url = 'https://odre.opendatasoft.com/api/records/1.0/search/?dataset=consommation-quotidienne-brute-regionale&q=&sort=-consommation_brute_gaz_grtgaz&facet=date_heure&facet=code_insee_region&facet=region'\n",
        "    lista = ['code_insee_region','date_heure', 'date', 'heure',  'region', 'consommation_brute_electricite_rte', 'consommation_brute_totale', 'consommation_brute_gaz_totale']\n",
        "    response = rq.get(url)\n",
        "    Reponse = response.content\n",
        "    parse_json = json.loads(Reponse)\n",
        "    n = parse_json['records'].__len__()\n",
        "    list_data = [x for x in parse_json['records']]\n",
        "    list_dic, df = {},{}\n",
        "\n",
        "    for i in range(n):\n",
        "        list_dic[i] = {key: list_data[i][\"fields\"][key] for key in lista}\n",
        "\n",
        "    data = pd.DataFrame.from_dict(list_dic[0], orient='index').T\n",
        "\n",
        "    for i in range(1,n):\n",
        "        df[i] = pd.DataFrame.from_dict(list_dic[i], orient='index').T\n",
        "        data = pd.concat([data,df[i]], axis=0)\n",
        "\n",
        "#     print(data)\n",
        "#     print(f'select columns from {lista}')\n",
        "    cols = str(input('\\n SELECT (columns):'))\n",
        "    where_condition = str(input(\"\\n FROM : (Data) \\n\\n WHERE (column = 'condition') : \"))\n",
        "    if cols and where_condition:\n",
        "        query = f\"\"\" \n",
        "            SELECT {cols} \n",
        "            FROM data\n",
        "            WHERE {where_condition}\n",
        "            \"\"\"\n",
        "    elif cols:\n",
        "        query = f\"\"\" \n",
        "            SELECT {cols} \n",
        "            FROM data\n",
        "            \"\"\"\n",
        "    else:\n",
        "        print('you did not select any column to query !!')\n",
        "#     TO USE : TRY .....\n",
        "    return sqldf(query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "Z2Gu7NG618x7",
        "outputId": "47d5e203-1308-4b93-e0c4-188a27561c4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Examples of queries to the Energy API:\n",
            "Q1:\n",
            "    select : *\n",
            "Q2:\n",
            "    select : *\n",
            "    where  : region='Normandie'\n",
            "Q3:\n",
            "    select : region,date,consommation_brute_electricite_rte\n",
            "    where  : region='Nouvelle-Aquitaine' and date='2022-03-27'\n",
            "\n",
            "\n",
            "\n",
            "Possibles columns: [code_insee_region, date_heure, date, heure, region, consommation_brute_electricite_rte,\n",
            "                    consommation_brute_totale,consommation_brute_gaz_totale]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>code_insee_region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  code_insee_region\n",
              "0                28"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(colored(\"\"\"\n",
        "Examples of queries to the Energy API:\n",
        "Q1:\n",
        "    select : *\n",
        "Q2:\n",
        "    select : *\n",
        "    where  : region='Normandie'\n",
        "Q3:\n",
        "    select : region,date,consommation_brute_electricite_rte\n",
        "    where  : region='Nouvelle-Aquitaine' and date='2022-03-27'\n",
        "\\n\"\"\", 'green'))\n",
        "\n",
        "print(colored(\"\"\"\n",
        "Possibles columns: [code_insee_region, date_heure, date, heure, region, consommation_brute_electricite_rte,\n",
        "                    consommation_brute_totale,consommation_brute_gaz_totale]\"\"\"\n",
        "    , 'blue'))\n",
        "get_data_query_energie()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYjn8W8zcI0E"
      },
      "source": [
        "### ***API meteo***\n",
        "idea : \n",
        "\n",
        "\n",
        "1.   faire 3 fonction chacune pour un API\n",
        "2.   faire un lien entre les 3 fonction; de facon a verifier au niveau des requete la database / colomns interroges\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "d52CdrcWFH4o"
      },
      "outputs": [],
      "source": [
        "    # prévision météo à un endroit grâce au code insee\n",
        "url1 = 'https://api.meteo-concept.com/api/forecast/daily?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&insee=35238' \n",
        "    # prévision météo dans qlq (3) jours\n",
        "url2 = 'https://api.meteo-concept.com/api/forecast/daily/3/period/2?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&amp;insee=31555'\n",
        "# info sur ephemeride\n",
        "url3 = 'https://api.meteo-concept.com/api/ephemeride/1?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&insee=35238'\n",
        "    # Localisation des villes\n",
        "url4 = 'https://api.meteo-concept.com/api/location/city?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&insee=31555'\n",
        "\n",
        "WEATHER = {\n",
        "        0: \"Soleil\",\n",
        "        1: \"Peu nuageux\",\n",
        "        2: \"Ciel voilé\",\n",
        "        3: \"Nuageux\",\n",
        "        4: \"Très nuageux\",\n",
        "        5: \"Couvert\",\n",
        "        6: \"Brouillard\",\n",
        "        7: \"Brouillard givrant\",\n",
        "        10: \"Pluie faible\",\n",
        "        11: \"Pluie modérée\",\n",
        "        12: \"Pluie forte\",\n",
        "        13: \"Pluie faible verglaçante\",\n",
        "        14: \"Pluie modérée verglaçante\",\n",
        "        15: \"Pluie forte verglaçante\",\n",
        "        16: \"Bruine\",\n",
        "        20: \"Neige faible\",\n",
        "        21: \"Neige modérée\",\n",
        "        22: \"Neige forte\",\n",
        "        30: \"Pluie et neige mêlées faibles\",\n",
        "        31: \"Pluie et neige mêlées modérées\",\n",
        "        32: \"Pluie et neige mêlées fortes\",\n",
        "        40: \"Averses de pluie locales et faibles\",\n",
        "        41: \"Averses de pluie locales\",\n",
        "        42: \"Averses locales et fortes\",\n",
        "        43: \"Averses de pluie faibles\",\n",
        "        44: \"Averses de pluie\",\n",
        "        45: \"Averses de pluie fortes\",\n",
        "        46: \"Averses de pluie faibles et fréquentes\",\n",
        "        47: \"Averses de pluie fréquentes\",\n",
        "        48: \"Averses de pluie fortes et fréquentes\",\n",
        "        60: \"Averses de neige localisées et faibles\",\n",
        "        61: \"Averses de neige localisées\",\n",
        "        62: \"Averses de neige localisées et fortes\",\n",
        "        63: \"Averses de neige faibles\",\n",
        "        64: \"Averses de neige\",\n",
        "        65: \"Averses de neige fortes\",\n",
        "        66: \"Averses de neige faibles et fréquentes\",\n",
        "        67: \"Averses de neige fréquentes\",\n",
        "        68: \"Averses de neige fortes et fréquentes\",\n",
        "        70: \"Averses de pluie et neige mêlées localisées et faibles\",\n",
        "        71: \"Averses de pluie et neige mêlées localisées\",\n",
        "        72: \"Averses de pluie et neige mêlées localisées et fortes\",\n",
        "        73: \"Averses de pluie et neige mêlées faibles\",\n",
        "        74: \"Averses de pluie et neige mêlées\",\n",
        "        75: \"Averses de pluie et neige mêlées fortes\",\n",
        "        76: \"Averses de pluie et neige mêlées faibles et nombreuses\",\n",
        "        77: \"Averses de pluie et neige mêlées fréquentes\",\n",
        "        78: \"Averses de pluie et neige mêlées fortes et fréquentes\",\n",
        "        100: \"Orages faibles et locaux\",\n",
        "        101: \"Orages locaux\",\n",
        "        102: \"Orages fort et locaux\",\n",
        "        103: \"Orages faibles\",\n",
        "        104: \"Orages\",\n",
        "        105: \"Orages forts\",\n",
        "        106: \"Orages faibles et fréquents\",\n",
        "        107: \"Orages fréquents\",\n",
        "        108: \"Orages forts et fréquents\",\n",
        "        120: \"Orages faibles et locaux de neige ou grésil\",\n",
        "        121: \"Orages locaux de neige ou grésil\",\n",
        "        122: \"Orages locaux de neige ou grésil\",\n",
        "        123: \"Orages faibles de neige ou grésil\",\n",
        "        124: \"Orages de neige ou grésil\",\n",
        "        125: \"Orages de neige ou grésil\",\n",
        "        126: \"Orages faibles et fréquents de neige ou grésil\",\n",
        "        127: \"Orages fréquents de neige ou grésil\",\n",
        "        128: \"Orages fréquents de neige ou grésil\",\n",
        "        130: \"Orages faibles et locaux de pluie et neige mêlées ou grésil\",\n",
        "        131: \"Orages locaux de pluie et neige mêlées ou grésil\",\n",
        "        132: \"Orages fort et locaux de pluie et neige mêlées ou grésil\",\n",
        "        133: \"Orages faibles de pluie et neige mêlées ou grésil\",\n",
        "        134: \"Orages de pluie et neige mêlées ou grésil\",\n",
        "        135: \"Orages forts de pluie et neige mêlées ou grésil\",\n",
        "        136: \"Orages faibles et fréquents de pluie et neige mêlées ou grésil\",\n",
        "        137: \"Orages fréquents de pluie et neige mêlées ou grésil\",\n",
        "        138: \"Orages forts et fréquents de pluie et neige mêlées ou grésil\",\n",
        "        140: \"Pluies orageuses\",\n",
        "        141: \"Pluie et neige mêlées à caractère orageux\",\n",
        "        142: \"Neige à caractère orageux\",\n",
        "        210: \"Pluie faible intermittente\",\n",
        "        211: \"Pluie modérée intermittente\",\n",
        "        212: \"Pluie forte intermittente\",\n",
        "        220: \"Neige faible intermittente\",\n",
        "        221: \"Neige modérée intermittente\",\n",
        "        222: \"Neige forte intermittente\",\n",
        "        230: \"Pluie et neige mêlées\",\n",
        "        231: \"Pluie et neige mêlées\",\n",
        "        232: \"Pluie et neige mêlées\",\n",
        "        235: \"Averses de grêle\",\n",
        "    }\n",
        "\n",
        "list1 = [\"insee\",\"cp\", \"latitude\", \"longitude\",\"day\", \"datetime\", \"wind10m\", \"gust10m\", \"dirwind10m\", \"rr10\", \"rr1\", \"probarain\",\n",
        "            \"weather\", \"tmin\", \"tmax\", \"sun_hours\", \"etp\", \"probafrost\", \"probafog\", \"probawind70\", \"probawind100\", \"gustx\"]\n",
        "list2 = []\n",
        "list3 = []\n",
        "list4 = [\"insee\", \"cp\", \"latitude\", \"longitude\", \"altitude\", \"name\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "FaEMbJdqgcbD"
      },
      "outputs": [],
      "source": [
        "def df_query(columns, data):\n",
        "  cols = str(input('\\n SELECT (columns):'))\n",
        "  where_condition = str(input(\"\\n FROM : (data) \\n\\n WHERE (column = 'condition') : \"))\n",
        "  if cols and where_condition:\n",
        "        query = f\"\"\" \n",
        "            SELECT {cols} \n",
        "            FROM data\n",
        "            WHERE {where_condition}\n",
        "            \"\"\"\n",
        "  elif cols:\n",
        "        query = f\"\"\" \n",
        "            SELECT {cols} \n",
        "            FROM data\n",
        "            \"\"\"\n",
        "  else:\n",
        "        print('you did not select any column to query !!')\n",
        "#     TO USE : TRY .....\n",
        "  return sqldf(query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "E79YkubqXuXR"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "https://api.meteo-concept.com/api/location/city?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&=31553\n",
            "\n",
            "La ville de Rennes (35000) a pour coordonnées 48.112,-1.6819.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>insee</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [insee]\n",
              "Index: []"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Localisation des villes \n",
        "def localiser_ville(code):\n",
        "  # code = 31555; toulouse\n",
        "  url4 = 'https://api.meteo-concept.com/api/location/city?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&='+str(code)\n",
        "  print(url4)\n",
        "  with closing(urlopen(url4)) as f:\n",
        "\t  city = json.loads(f.read())['city']\n",
        "\t  print(u'\\nLa ville de {} ({}) a pour coordonnées {},{}.'.format(city['name'], city['cp'], city['latitude'], city['longitude']))\n",
        "  df_4 = pd.DataFrame.from_dict(pd.json_normalize(city), orient = 'columns')\n",
        "  return df_4\n",
        "\n",
        "# print(df_4)\n",
        "code = 31553\n",
        "df = localiser_ville(code)\n",
        "df_query(df.columns, df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "kZmT3qV0XOrX",
        "outputId": "2553f155-93b7-40e3-943a-03b54b8e63e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "14\n",
            "Le temps  à  31209  est : Averses de neige localisées et faibles\n"
          ]
        },
        {
          "ename": "PandaSQLException",
          "evalue": "(sqlite3.OperationalError) no such column: code\n[SQL:  \n            SELECT insee \n            FROM data\n            WHERE code = 31553\n            ]\n(Background on this error at: https://sqlalche.me/e/14/e3q8)",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1818\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1819\u001b[1;33m                     self.dialect.do_execute(\n\u001b[0m\u001b[0;32m   1820\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[1;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[0;32m    731\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m         \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    733\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mOperationalError\u001b[0m: no such column: code",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[1;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandasql\\sqldf.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, query, env)\u001b[0m\n\u001b[0;32m     60\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mread_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     62\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mDatabaseError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mread_sql\u001b[1;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize)\u001b[0m\n\u001b[0;32m    592\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 593\u001b[1;33m         return pandas_sql.read_query(\n\u001b[0m\u001b[0;32m    594\u001b[0m             \u001b[0msql\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mread_query\u001b[1;34m(self, sql, index_col, coerce_float, parse_dates, params, chunksize, dtype)\u001b[0m\n\u001b[0;32m   1559\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1560\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1561\u001b[0m         \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1404\u001b[0m         \u001b[1;34m\"\"\"Simple passthrough to SQLAlchemy connectable\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1405\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnectable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecution_options\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1406\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, statement, *multiparams, **params)\u001b[0m\n\u001b[0;32m   1290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1291\u001b[1;33m             return self._exec_driver_sql(\n\u001b[0m\u001b[0;32m   1292\u001b[0m                 \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_exec_driver_sql\u001b[1;34m(self, statement, multiparams, params, execution_options, future)\u001b[0m\n\u001b[0;32m   1594\u001b[0m         \u001b[0mdialect\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1595\u001b[1;33m         ret = self._execute_context(\n\u001b[0m\u001b[0;32m   1596\u001b[0m             \u001b[0mdialect\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1861\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1862\u001b[1;33m             self._handle_dbapi_exception(\n\u001b[0m\u001b[0;32m   1863\u001b[0m                 \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[1;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[0;32m   2042\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mshould_wrap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2043\u001b[1;33m                 util.raise_(\n\u001b[0m\u001b[0;32m   2044\u001b[0m                     \u001b[0msqlalchemy_exception\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwith_traceback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrom_\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\util\\compat.py\u001b[0m in \u001b[0;36mraise_\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m   1818\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1819\u001b[1;33m                     self.dialect.do_execute(\n\u001b[0m\u001b[0;32m   1820\u001b[0m                         \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32mc:\\ProgramData\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[1;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[0;32m    731\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 732\u001b[1;33m         \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    733\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mOperationalError\u001b[0m: (sqlite3.OperationalError) no such column: code\n[SQL:  \n            SELECT insee \n            FROM data\n            WHERE code = 31553\n            ]\n(Background on this error at: https://sqlalche.me/e/14/e3q8)",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[1;31mPandaSQLException\u001b[0m                         Traceback (most recent call last)",
            "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_4412\\3431283381.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mcode\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m31553\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprevision_ville\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mdf_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_4412\\716006437.py\u001b[0m in \u001b[0;36mdf_query\u001b[1;34m(columns, data)\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'you did not select any column to query !!'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;31m#     TO USE : TRY .....\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0msqldf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandasql\\sqldf.py\u001b[0m in \u001b[0;36msqldf\u001b[1;34m(query, env, db_uri)\u001b[0m\n\u001b[0;32m    154\u001b[0m     \u001b[1;33m>>\u001b[0m\u001b[1;33m>\u001b[0m \u001b[0msqldf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"select avg(x) from df;\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m     \"\"\"\n\u001b[1;32m--> 156\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mPandaSQL\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdb_uri\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\pandasql\\sqldf.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, query, env)\u001b[0m\n\u001b[0;32m     61\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mread_sql\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mDatabaseError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mPandaSQLException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mResourceClosedError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m                 \u001b[1;31m# query returns nothing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mPandaSQLException\u001b[0m: (sqlite3.OperationalError) no such column: code\n[SQL:  \n            SELECT insee \n            FROM data\n            WHERE code = 31553\n            ]\n(Background on this error at: https://sqlalche.me/e/14/e3q8)"
          ]
        }
      ],
      "source": [
        "# prévision météo à un endroit grâce au code insee\n",
        "def prevision_ville(code):\n",
        "  # code = 35238\n",
        "  url1 = 'https://api.meteo-concept.com/api/forecast/daily?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&insee='+str(code)\n",
        "  with closing(urlopen(url1)) as f:\n",
        "\t  forecast = json.loads(f.read())['forecast']\n",
        "\t  print(len(forecast))\n",
        "\t  c = 0\n",
        "\t  for x in forecast:\n",
        "\t\t  if c != x['insee']:\n",
        "\t\t\t  print(\"Le temps  à \" , x['insee'], \" est :\", WEATHER[x['weather']])\n",
        "\t\t  c = x['insee']\n",
        "\n",
        "  df_1 = pd.DataFrame.from_dict(pd.json_normalize(forecast), orient = 'columns')\n",
        "  return df_1\n",
        "  \n",
        "code = 31553\n",
        "df = prevision_ville(code)\n",
        "df_query(df.columns, df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "TTsEXpDdWvb8",
        "outputId": "4a0589f5-f8b5-4920-fafb-07c355d61b4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Le temps prévu pour l'après-midi dans 2 jours est : \"Averses de pluie faibles\"\n",
            "\n",
            " SELECT (columns):*\n",
            "\n",
            " FROM : (data) \n",
            "\n",
            " WHERE (column = 'condition') : \n"
          ]
        },
        {
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>insee</th>\n",
              "      <th>cp</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>day</th>\n",
              "      <th>period</th>\n",
              "      <th>datetime</th>\n",
              "      <th>temp2m</th>\n",
              "      <th>wind10m</th>\n",
              "      <th>gust10m</th>\n",
              "      <th>dirwind10m</th>\n",
              "      <th>rr10</th>\n",
              "      <th>rr1</th>\n",
              "      <th>probarain</th>\n",
              "      <th>weather</th>\n",
              "      <th>probafrost</th>\n",
              "      <th>probafog</th>\n",
              "      <th>probawind70</th>\n",
              "      <th>probawind100</th>\n",
              "      <th>gustx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35238</td>\n",
              "      <td>35000</td>\n",
              "      <td>48.112</td>\n",
              "      <td>-1.6819</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2022-11-27T13:00:00+0100</td>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>37</td>\n",
              "      <td>195</td>\n",
              "      <td>2</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   insee     cp  latitude  longitude  day  period                  datetime  \\\n",
              "0  35238  35000    48.112    -1.6819    2       2  2022-11-27T13:00:00+0100   \n",
              "\n",
              "   temp2m  wind10m  gust10m  dirwind10m  rr10  rr1  probarain  weather  \\\n",
              "0      13       25       37         195     2    3         70       43   \n",
              "\n",
              "   probafrost  probafog  probawind70  probawind100  gustx  \n",
              "0           0         0            0             0     47  "
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# prévision météo dans qlq jours \n",
        "def prev_qlq_jours(jours, code):\n",
        "  url2 = 'https://api.meteo-concept.com/api/forecast/daily/'+str(jours)+'/period/2?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&amp;insee='+str(code)\n",
        "  with closing(urlopen(url2)) as f:\n",
        "    forecast = json.loads(f.read())['forecast']\n",
        "    print(u\"\\nLe temps prévu pour l'après-midi dans \"+str(jours)+\" jours est : \\\"{}\\\"\".format(WEATHER[forecast['weather']]))\n",
        "\n",
        "  df_2 = pd.DataFrame.from_dict(pd.json_normalize(forecast), orient = 'columns')\n",
        "  return df_2\n",
        "\n",
        "code = 31553\n",
        "df = prev_qlq_jours(2, code)\n",
        "df_query(df.columns, df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "O_hVbH0qYBjR",
        "outputId": "e92505aa-5f44-4dc9-878f-c69823ff2d38"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Demain, à Thil, le soleil se lèvera à 08:01 et se couchera à 17:23.\n",
            "On comptera 2 minutes de soleil de moins qu'aujourd'hui.\n",
            "\n",
            " SELECT (columns):*\n",
            "\n",
            " FROM : (data) \n",
            "\n",
            " WHERE (column = 'condition') : \n"
          ]
        },
        {
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              "      <th></th>\n",
              "      <th>city.insee</th>\n",
              "      <th>city.cp</th>\n",
              "      <th>city.name</th>\n",
              "      <th>city.latitude</th>\n",
              "      <th>city.longitude</th>\n",
              "      <th>city.altitude</th>\n",
              "      <th>ephemeride.latitude</th>\n",
              "      <th>ephemeride.longitude</th>\n",
              "      <th>ephemeride.insee</th>\n",
              "      <th>ephemeride.day</th>\n",
              "      <th>ephemeride.datetime</th>\n",
              "      <th>ephemeride.sunrise</th>\n",
              "      <th>ephemeride.sunset</th>\n",
              "      <th>ephemeride.duration_day</th>\n",
              "      <th>ephemeride.diff_duration_day</th>\n",
              "      <th>ephemeride.moon_age</th>\n",
              "      <th>ephemeride.moon_phase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b1f8c4b8-f099-45af-9718-af42b1ae8cb7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  city.insee city.cp city.name  city.latitude  city.longitude  city.altitude  \\\n",
              "0      31553   31530      Thil        43.7102           1.153            212   \n",
              "\n",
              "   ephemeride.latitude  ephemeride.longitude ephemeride.insee  ephemeride.day  \\\n",
              "0              43.7102                 1.153            31553               1   \n",
              "\n",
              "        ephemeride.datetime ephemeride.sunrise ephemeride.sunset  \\\n",
              "0  2022-11-26T00:00:00+0100              08:01             17:23   \n",
              "\n",
              "  ephemeride.duration_day  ephemeride.diff_duration_day  ephemeride.moon_age  \\\n",
              "0                   09:22                            -2                  2.3   \n",
              "\n",
              "  ephemeride.moon_phase  \n",
              "0     Premier croissant  "
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# infos sur éphéméride\n",
        "\n",
        "def info_ephem(code):\n",
        "  # code = 35238\n",
        "  url3 = 'https://api.meteo-concept.com/api/ephemeride/1?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&insee='+str(code)\n",
        "\n",
        "  with closing(urlopen(url3)) as f:\n",
        "    cityEph = json.loads(f.read())\n",
        "    print(u'Demain, à {}, le soleil se lèvera à {} et se couchera à {}.'.format(cityEph['city']['name'], cityEph['ephemeride']['sunrise'], cityEph['ephemeride']['sunset']))\n",
        "    print(u'On comptera {} minutes de soleil de {} qu\\'aujourd\\'hui.'.format(abs(cityEph['ephemeride']['diff_duration_day']), 'moins' if cityEph['ephemeride']['diff_duration_day'] <= 0 else 'plus'))\n",
        "\n",
        "  df_eph = pd.DataFrame.from_dict(pd.json_normalize(cityEph), orient = 'columns')\n",
        "  return df_eph\n",
        "\n",
        "code = 31553\n",
        "df = info_ephem(code)\n",
        "df_query(df.columns, df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "1cPicV8daKrt",
        "outputId": "df9fb6a2-cba6-4ac9-d59f-e02a6cb59ab1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Le week-end prochain est dans 1 jours ! Les températures mini/maxi à Thil seront :\n",
            "\tSamedi   : 6°C/11°C\n",
            "\tDimanche : 6°C/12°C\n",
            "\n",
            " SELECT (columns):*\n",
            "\n",
            " FROM : (data) \n",
            "\n",
            " WHERE (column = 'condition') : \n"
          ]
        },
        {
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>insee</th>\n",
              "      <th>cp</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>day</th>\n",
              "      <th>datetime</th>\n",
              "      <th>wind10m</th>\n",
              "      <th>gust10m</th>\n",
              "      <th>dirwind10m</th>\n",
              "      <th>rr10</th>\n",
              "      <th>...</th>\n",
              "      <th>weather</th>\n",
              "      <th>tmin</th>\n",
              "      <th>tmax</th>\n",
              "      <th>sun_hours</th>\n",
              "      <th>etp</th>\n",
              "      <th>probafrost</th>\n",
              "      <th>probafog</th>\n",
              "      <th>probawind70</th>\n",
              "      <th>probawind100</th>\n",
              "      <th>gustx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31209</td>\n",
              "      <td>31480</td>\n",
              "      <td>43.6893</td>\n",
              "      <td>1.0917</td>\n",
              "      <td>5</td>\n",
              "      <td>2022-11-30T01:00:00+0100</td>\n",
              "      <td>10</td>\n",
              "      <td>21</td>\n",
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              "      <td>0.2</td>\n",
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              "      <td>10</td>\n",
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              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-783378a4-51b9-48cb-a7c0-42c298caa925')\"\n",
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              "\n",
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              "          const element = document.querySelector('#df-783378a4-51b9-48cb-a7c0-42c298caa925');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   insee     cp  latitude  longitude  day                  datetime  wind10m  \\\n",
              "0  31209  31480   43.6893     1.0917    5  2022-11-30T01:00:00+0100       10   \n",
              "\n",
              "   gust10m  dirwind10m  rr10  ...  weather  tmin  tmax  sun_hours  etp  \\\n",
              "0       21         135   0.2  ...        3     4     9          3    0   \n",
              "\n",
              "   probafrost  probafog  probawind70  probawind100  gustx  \n",
              "0          30        10            0             0     21  \n",
              "\n",
              "[1 rows x 22 columns]"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Prévisions météo par jour \n",
        "def prev_weekend(day, code):\n",
        "  # 31555\n",
        "  url = 'https://api.meteo-concept.com/api/forecast/daily?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&insee&insee='+str(code)\n",
        "  with closing(urlopen(url)) as f:\n",
        "    decoded = json.loads(f.read())\n",
        "    (city,forecast) = (decoded[k] for k in ('city','forecast'))\n",
        "\n",
        "    saturday = None\n",
        "    for i,f in enumerate(forecast):\n",
        "        day = dateutil.parser.parse(f['datetime']).weekday() # Lundi : 0, Mardi : 1, etc.\n",
        "        if day == 5:\n",
        "            saturday = i\n",
        "            break\n",
        "\n",
        "    print(u\"Le week-end prochain est dans {} jours ! Les températures mini/maxi à {} seront :\".format(saturday, city['name']))\n",
        "    print(u\"\\tSamedi   : {}°C/{}°C\".format(forecast[saturday]['tmin'],forecast[saturday]['tmax']))\n",
        "    print(u\"\\tDimanche : {}°C/{}°C\".format(forecast[saturday+1]['tmin'],forecast[saturday+1]['tmax']))\n",
        "\n",
        "  # day = saturday\n",
        "  df_j = pd.DataFrame.from_dict(pd.json_normalize(forecast[day]), orient = 'columns')\n",
        "  return df_j\n",
        "\n",
        "code = 31553\n",
        "day = saturday\n",
        "\n",
        "df = prev_weekend(day, code)\n",
        "df_query(df.columns, df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "id": "VKmZr6fGbIcs",
        "outputId": "dab64c2d-5b75-4262-936b-ef15699d62ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Humidité relative de l'air (%)\n",
            "   ∧\n",
            "100│                             \n",
            " 90│                             \n",
            " 80│          █      █      █    \n",
            " 70│          █      █      █    \n",
            " 60│   █      █      █      █    \n",
            " 50│   █      █      █      █    \n",
            " 40│   █      █      █      █    \n",
            " 30│   █      █      █      █    \n",
            " 20│   █      █      █      █    \n",
            " 10│   █      █      █      █    \n",
            "   └─────────────────────────────>\n",
            "     14:00  17:00  20:00  23:00  \n",
            "\n",
            " SELECT (columns):*\n",
            "\n",
            " FROM : (data) \n",
            "\n",
            " WHERE (column = 'condition') : \n"
          ]
        },
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              "      <th>gust10m</th>\n",
              "      <th>dirwind10m</th>\n",
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              "      <th>probarain</th>\n",
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              "\n",
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              "          document.querySelector('#df-d7119ba5-c4e1-4a90-99a2-ad225bf8a7b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d7119ba5-c4e1-4a90-99a2-ad225bf8a7b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   insee     cp  latitude  longitude                  datetime  temp2m  rh2m  \\\n",
              "0  35238  35000    48.112    -1.6819  2022-11-25T14:00:00+0100      13    68   \n",
              "1  35238  35000    48.112    -1.6819  2022-11-25T17:00:00+0100      12    80   \n",
              "2  35238  35000    48.112    -1.6819  2022-11-25T20:00:00+0100      11    88   \n",
              "3  35238  35000    48.112    -1.6819  2022-11-25T23:00:00+0100      11    89   \n",
              "\n",
              "   wind10m  gust10m  dirwind10m  ...  probarain  weather  probafrost  \\\n",
              "0       14       36         266  ...         20        3           0   \n",
              "1        7       30         247  ...         20        3           0   \n",
              "2        7       25         235  ...         10        3           0   \n",
              "3        9       25         234  ...         10        3           0   \n",
              "\n",
              "   probafog  probawind70  probawind100  tsoil1  tsoil2  gustx  iso0  \n",
              "0         0            0             0      11      11     36   -99  \n",
              "1         0            0             0      11      11     30   -99  \n",
              "2         0            0             0       9      11     25   -99  \n",
              "3        10            0             0       8      11     25   -99  \n",
              "\n",
              "[4 rows x 22 columns]"
            ]
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Prévisions météo par ville et  par heure \n",
        "\n",
        "def prev_ville_heure(code,datetime ):\n",
        "  # 35238\n",
        "\n",
        "  url = 'https://api.meteo-concept.com/api/forecast/nextHours?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&insee='+str(code)\n",
        "  with closing(urlopen(url)) as f:\n",
        "    forecast = json.loads(f.read())['forecast']\n",
        "    print(u\"Humidité relative de l'air (%)\\n   ∧\")\n",
        "    for i in range(100, 0, -10):\n",
        "        print(u\"{: >3}│ \".format(i), end='')\n",
        "        for f in forecast:\n",
        "            if f['rh2m'] >= i:\n",
        "                print(u\"  █    \", end='')\n",
        "            else:\n",
        "                print(u\"       \", end='')\n",
        "        print(\"\")\n",
        "    print(\"   └─\", end='')\n",
        "    for f in forecast:\n",
        "        print(\"───────\", end='')\n",
        "    print(\">\\n     \", end='')\n",
        "    for f in forecast:\n",
        "        print(dateutil.parser.parse(f['datetime']).strftime('%H:%M  '), end='')\n",
        "    print(\"\")\n",
        "    \n",
        "  #datetime = 2022-11-25\n",
        "  df_vh = pd.DataFrame.from_dict(pd.json_normalize(forecast), orient = 'columns')\n",
        "  return df_vh\n",
        "\n",
        "datetime = 2022-11-25\n",
        "code = 35238\n",
        "df = prev_ville_heure(code, datetime )\n",
        "df_query(df.columns, df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "BGgGziBKxTG4"
      },
      "outputs": [],
      "source": [
        "# bilan ville\n",
        "# not complete yet\n",
        "def bilan_ville(ville):\n",
        "  # Rennes\n",
        "  with closing(urlopen('https://api.meteo-concept.com/api/location/cities?token=756f550d7a0f31da58721d31e518f252068b8c1e9a17c1d366d1e8e8edc405b2&search='+ville)) as f:\n",
        "     cities = json.loads(f.read())['cities']\n",
        "\n",
        "  df_v = pd.DataFrame.from_dict(pd.json_normalize(cities), orient = 'columns')\n",
        "  print(len(df_v))\n",
        "  print(df_v.columns)\n",
        "  #df_v  \n",
        "  \n",
        "\n",
        "\n",
        "\n",
        "  return df_v"
      ]
    },
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n",
            "Index(['insee', 'cp', 'latitude', 'longitude', 'altitude', 'name'], dtype='object')\n"
          ]
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              "      <th></th>\n",
              "      <th>insee</th>\n",
              "      <th>cp</th>\n",
              "      <th>latitude</th>\n",
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              "      <td>31555</td>\n",
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              "      <td>43.5964</td>\n",
              "      <td>1.4317</td>\n",
              "      <td>148</td>\n",
              "      <td>Toulouse</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39533</td>\n",
              "      <td>39230</td>\n",
              "      <td>46.8222</td>\n",
              "      <td>5.5831</td>\n",
              "      <td>258</td>\n",
              "      <td>Toulouse-le-Château</td>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "   insee     cp  latitude  longitude  altitude                 name\n",
              "0  31555  31000   43.5964     1.4317       148             Toulouse\n",
              "1  39533  39230   46.8222     5.5831       258  Toulouse-le-Château"
            ]
          },
          "execution_count": 112,
          "metadata": {},
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        }
      ],
      "source": [
        "ville = 'Toulouse'\n",
        "bilan_ville(ville)"
      ]
    }
  ],
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