DATA_HUB['kaggle_house_train'] = ( #@save DATA_URL + 'kaggle_house_pred_train.csv', '585e9cc93e70b39160e7921475f9bcd7d31219ce') DATA_HUB['kaggle_house_test'] = ( #@save DATA_URL + 'kaggle_house_pred_test.csv', 'fa19780a7b011d9b009e8bff8e99922a8ee2eb90') 当然,在下载后使...
(DATA_PATH/path/filename).unlink() defdownload_kaggle_dataset( dataset_details,username=None,key=None,competition=False ): api=get_authenticated_kaggle_api(username,key) ifapiisnotNone: ifcompetition: _download_competition_dataset(api,dataset_details) logger.info("Donwload completed. Unzipping..")...
Folderforupload, containing data files and a special dataset-metadata.json file (https://github.com/Kaggle/kaggle-api/wiki/Dataset-Metadata). Defaults to current working directory 1. 2. 3. 4. 5. 6. 使用实例: kaggle datasets init-p/path/to/dataset 1. 2.2.5 创建新数据集 如果要创建新的...
zip,tar}] [-d]required arguments:-m VERSION_NOTES, --message VERSION_NOTESMessage describing the new versionoptional arguments:-h, --help show this help message and exit-p FOLDER, --path FOLDERFolder for upload, containing data files and a special dataset-metadata.json file (https...
train_data=pd.read_csv(download('kaggle_house_train'))test_data=pd.read_csv(download('kaggle_...
那么可以省略步骤3和4,按照自己的方法在环境中导入数据集即可。...mkdir /content/data/dogsbreed/ PATH = "/content/data/dogsbreed/" from google.colab import files # load...kaggle competitions download -p /content/data/dogsbreed/ dog-breed-identification 从结果中可以看到,我们已经从kaggle...成功的...
kaggle datasets version -p /path/to/dataset -m "Updated data" Download metadata for an existing dataset usage: kaggle datasets metadata [-h] [-p PATH] [dataset] required arguments: dataset Dataset URL suffix in format <owner>/<dataset-name> (use "kaggle datasets list" to show options) op...
Titanic Data Science Solutions Notebook 1. Titanic Data Science Solutions 亮点 大量使用seaborn来plot数据,比matplotlib简单。FacetGride简单强大,可惜survived/none survived分列在两个图中,不像后面将两个图叠加在一起。 在5个notebook中对数据的解释最为细致清楚,特征处理的代码极为简洁 ...
datasets.MNIST(root="./minist/",train=False,download=True,transform=transform) dl_train = torch.utils.data.DataLoader(ds_train, batch_size=batch_size, shuffle=True, num_workers=2,drop_last=True) dl_val = torch.utils.data.DataLoader(ds_val, batch_size=batch_size, shuffle=False, num_...
Download the raw data set[2]: python download_dataset.py Unpack the zipped data and pre-process it to create a TensorFlow input pipeline and a config json file used bymain.pyfor a desired task usingdataloader.py(remember to specify the kind of model for which you intend to use the data...