Meilleure IA pour coder en Python : comparatif gratuit 2026

pd.DataFrame:\n \"\"\"T\u00e9l\u00e9charge un CSV, nettoie les donn\u00e9es et trie par date.\n\n Args:\n url: URL du fichier CSV.\n date_column: Nom de la colonne date.\n encoding: Encodage du fichier.\n timeout: Timeout en secondes.\n\n Returns:\n DataFrame nettoy\u00e9 et tri\u00e9 par date.\n\n Raises:\n requests.HTTPError: Si le t\u00e9l\u00e9chargement \u00e9choue.\n KeyError: Si la colonne date n\u0027existe pas.\n \"\"\"\n response = requests.get(url, timeout=timeout)\n response.raise_for_status()\n\n df = pd.read_csv(\n StringIO(response.text),\n encoding=encoding,\n )\n\n df = df.dropna(how=\"all\").drop_duplicates().reset_index(drop=True)\n df[date_column] = pd.to_datetime(df[date_column])\n\n return df.sort_values(date_column).reset_index(drop=True)' }, chatgpt: { name: 'ChatGPT', color: 'green', score: 82, metrics: { proprete: 80, performance: 85, docstring: 75, erreurs: 88, idiomatique: 82 }, code: 'import pandas as pd\nimport requests\nfrom io import StringIO\n\ndef download_and_clean_csv(url, date_col=\"date\"):\n \"\"\"\n Downloads a CSV file from a URL,\n removes duplicates and empty rows,\n and returns a sorted DataFrame.\n \"\"\"\n # Download the file\n response = requests.get(url)\n response.raise_for_status()\n \n # Read into DataFrame\n csv_data = StringIO(response.text)\n df = pd.read_csv(csv_data)\n \n # Clean: remove empty rows\n df = df.dropna(how=\"all\")\n \n # Remove duplicates\n df = df.drop_duplicates()\n \n # Convert date column and sort\n df[date_col] = pd.to_datetime(df[date_col])\n df = df.sort_values(by=date_col)\n df = df.reset_index(drop=True)\n \n return df' }, copilot: { name: 'Copilot', color: 'blue', score: 76, metrics: { proprete: 75, performance: 78, docstring: 60, erreurs: 80, idiomatique: 85 }, code: 'import pandas as pd\nimport requests\nfrom io import StringIO\n\ndef fetch_csv(url, date_col=\"date\"):\n r = requests.get(url)\n df = pd.read_csv(StringIO(r.text))\n df = df.dropna(how=\"all\")\n df = df.drop_duplicates()\n df[date_col] = pd.to_datetime(df[date_col])\n return df.sort_values(date_col).reset_index(drop=True)' }, ollama: { name: 'Qwen2.5 (local)', color: 'orange', score: 71, metrics: { proprete: 70, performance: 75, docstring: 55, erreurs: 72, idiomatique: 82 }, code: 'import pandas as pd\nimport requests\nfrom io import StringIO\n\ndef get_clean_csv(url: str) -> pd.DataFrame:\n \"\"\"Download and clean CSV data.\"\"\"\n resp = requests.get(url)\n df = pd.read_csv(StringIO(resp.text))\n \n # remove empty rows and duplicates\n df.dropna(how=\"all\", inplace=True)\n df.drop_duplicates(inplace=True)\n \n # parse dates and sort\n df[\"date\"] = pd.to_datetime(df[\"date\"])\n df.sort_values(\"date\", inplace=True)\n df.reset_index(drop=True, inplace=True)\n \n return df' } }, getColorClass(color, type) { const map = { violet: { bg: 'bg-violet-400', text: 'text-violet-400', border: 'border-violet-400' }, green: { bg: 'bg-green-400', text: 'text-green-400', border: 'border-green-400' }, blue: { bg: 'bg-blue-400', text: 'text-blue-400', border: 'border-blue-400' }, orange: { bg: 'bg-orange-400', text: 'text-orange-400', border: 'border-orange-400' } }; return map[color][type]; } }" class="glass rounded-2xl p-6 mb-10">