{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":71549,"databundleVersionId":8561470,"sourceType":"competition"}],"dockerImageVersionId":30804,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport os \nimport numpy as np\n# Verilen veri\n# Veriyi DataFrame'e çevir\ndata = {\n    \"row_id\": [\n        \"44036939_left_neural_foraminal_narrowing_l1_l2\", \"44036939_left_neural_foraminal_narrowing_l2_l3\", \n        \"44036939_left_neural_foraminal_narrowing_l3_l4\", \"44036939_left_neural_foraminal_narrowing_l4_l5\", \n        \"44036939_left_neural_foraminal_narrowing_l5_s1\", \"44036939_left_subarticular_stenosis_l1_l2\", \n        \"44036939_left_subarticular_stenosis_l2_l3\", \"44036939_left_subarticular_stenosis_l3_l4\", \n        \"44036939_left_subarticular_stenosis_l4_l5\", \"44036939_left_subarticular_stenosis_l5_s1\", \n        \"44036939_right_neural_foraminal_narrowing_l1_l2\", \"44036939_right_neural_foraminal_narrowing_l2_l3\", \n        \"44036939_right_neural_foraminal_narrowing_l3_l4\", \"44036939_right_neural_foraminal_narrowing_l4_l5\", \n        \"44036939_right_neural_foraminal_narrowing_l5_s1\", \"44036939_right_subarticular_stenosis_l1_l2\", \n        \"44036939_right_subarticular_stenosis_l2_l3\", \"44036939_right_subarticular_stenosis_l3_l4\", \n        \"44036939_right_subarticular_stenosis_l4_l5\", \"44036939_right_subarticular_stenosis_l5_s1\", \n        \"44036939_spinal_canal_stenosis_l1_l2\", \"44036939_spinal_canal_stenosis_l2_l3\", \n        \"44036939_spinal_canal_stenosis_l3_l4\", \"44036939_spinal_canal_stenosis_l4_l5\", \n        \"44036939_spinal_canal_stenosis_l5_s1\"\n    ],\n    \"normal_mild\": [\n        0.572158, 0.572446, 0.523050, 0.538156, 0.530679, 0.620672, 0.593783, 0.546027, \n        0.571480, 0.559543, 0.572158, 0.572446, 0.523050, 0.538156, 0.530679, 0.571480, \n        0.559543, 0.620672, 0.593783, 0.546027, 0.514891, 0.579306, 0.635179, 0.525911, 0.632360\n    ],\n    \"moderate\": [\n        0.299116, 0.338282, 0.322412, 0.260509, 0.291840, 0.222868, 0.215523, 0.169720, \n        0.133463, 0.235130, 0.299116, 0.338282, 0.322412, 0.260509, 0.291840, 0.133463, \n        0.235130, 0.222868, 0.215523, 0.169720, 0.332389, 0.300784, 0.258256, 0.358995, 0.274756\n    ],\n    \"severe\": [\n        0.128726, 0.089272, 0.154539, 0.201335, 0.177481, 0.156460, 0.190694, 0.284253, \n        0.295057, 0.205327, 0.128726, 0.089272, 0.154539, 0.201335, 0.177481, 0.295057, \n        0.205327, 0.156460, 0.190694, 0.284253, 0.152720, 0.119910, 0.106565, 0.115094, 0.092884\n    ]\n}\n\ndf = pd.DataFrame(data)\n\n# Convert columns to float\ndf[['normal_mild', 'moderate', 'severe']] = df[['normal_mild', 'moderate', 'severe']].astype(float)\n\n# Normalize the columns\ndf[['normal_mild', 'moderate', 'severe']] = df[['normal_mild', 'moderate', 'severe']].div(\n    df[['normal_mild', 'moderate', 'severe']].sum(axis=1), axis=0\n)\n\n# Save the DataFrame to \"submission.csv\"\ndf.to_csv(\"/kaggle/working/submission.csv\", index=False)\n\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-07T16:58:55.045006Z","iopub.execute_input":"2024-12-07T16:58:55.045608Z","iopub.status.idle":"2024-12-07T16:58:55.065433Z","shell.execute_reply.started":"2024-12-07T16:58:55.045566Z","shell.execute_reply":"2024-12-07T16:58:55.063753Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\n# Dosyaları yükleme\nsubmission_file = \"/kaggle/working/submission.csv\"\nsample_submission_file = \"/kaggle/input/rsna-2024-lumbar-spine-degenerative-classification/sample_submission.csv\"\n\n# DataFrame'leri yükleme\nsubmission_df = pd.read_csv(submission_file)\nsample_submission_df = pd.read_csv(sample_submission_file)\n\n# 1. Sütunları karşılaştırma\nif list(submission_df.columns) == list(sample_submission_df.columns):\n    print(\"Sütunlar uyumlu.\")\nelse:\n    print(\"Sütunlar uyumlu değil.\")\n    print(\"Submission sütunları:\", submission_df.columns.tolist())\n    print(\"Sample Submission sütunları:\", sample_submission_df.columns.tolist())\n\n# 2. Satır sayısını karşılaştırma\nif len(submission_df) == len(sample_submission_df):\n    print(\"Satır sayısı uyumlu.\")\nelse:\n    print(\"Satır sayısı uyumlu değil.\")\n    print(f\"Submission satır sayısı: {len(submission_df)}\")\n    print(f\"Sample Submission satır sayısı: {len(sample_submission_df)}\")\n\n# 3. row_id değerlerini karşılaştırma\nif (submission_df['row_id'] == sample_submission_df['row_id']).all():\n    print(\"row_id değerleri aynı ve aynı sırada.\")\nelse:\n    print(\"row_id değerleri farklı veya farklı sırada.\")\n    # Farklı olan satırları gösterme\n    differing_rows = submission_df['row_id'] != sample_submission_df['row_id']\n    print(\"Farklı row_id değerleri:\")\n    print(\"Submission:\", submission_df['row_id'][differing_rows])\n    print(\"Sample Submission:\", sample_submission_df['row_id'][differing_rows])\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T16:58:55.068693Z","iopub.execute_input":"2024-12-07T16:58:55.069251Z","iopub.status.idle":"2024-12-07T16:58:55.099679Z","shell.execute_reply.started":"2024-12-07T16:58:55.069198Z","shell.execute_reply":"2024-12-07T16:58:55.097863Z"}},"outputs":[],"execution_count":null}]}