{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import kaggle\nimport pandas as pd\nimport numpy as np\nfrom kaggle.api.kaggle_api_extended import KaggleApi\n# =============================================================================\n# functions\n# =============================================================================\ndef get_paths(dataset, i, is_train=True):\n    \"\"\"It creates a path to file on kaggle and path where to download\"\"\"\n    _id = dataset.iloc[i]['id']\n    target = dataset.iloc[i]['target']\n    folder = 'train' if is_train else 'test'\n    folder_0 = f'{_id[0]}'\n    folder_1 = f'{_id[1]}'\n    folder_2 = f'{_id[2]}'\n    path_folder = f\"{folder}/{folder_0}/{folder_1}/{folder_2}/\" #path where to download\n    path_from = f\"{folder}/{folder_0}/{folder_1}/{folder_2}/{_id}.npy\"\n    api.competition_download_file(competition, path_from , path=path_folder, force = True)\n    x = np.load(path_from)\n    return x, target\n# =============================================================================\n# download files from kaggle to your PC\n# =============================================================================\ncompetition = 'g2net-gravitational-wave-detection'\npath = 'dataset_main/' # your folder on PC\n\napi = KaggleApi()\napi.authenticate()\n\ntraining_labels = pd.read_csv(path + 'training_labels.csv') # You need to have this file's already downloaded\n\nrange_from = 0\nrange_to = 100 # You can choose how much files to download\n\nfor i in range(range_from, range_to+1):\n    x, target = get_paths(training_labels, i, is_train=True)\n\n\n#print(x, target)\n#print('len(x) = ', len(x))\n#print('len(x[0]) = ', len(x[0]))\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**If it was helpful for you don't forget to upvote, please**","metadata":{}}]}