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First, we need to create a data source to attach to the Kaggle Notebook. This data source will be used to retrieve data from Kaggle and then a Pandas dataframe is created. The data is then copied to the notebook. # Create Data Source Define Data Source From Kaggle @datasource_kaggle(api_key = None, dataset_name = None, dataset_id = None, use_http = True, host = None, root_url = None, username = None, password = None, **kwargs) def kaggle_datasource(): """ Create Kaggle dataset. """ if dataset_name is None: dataset_name = 'Kaggle' dataset_id = '4cd053cef49d3' dataset_name_conflict = 'Could not create Kaggle dataset: Name conflict with another dataset.' dataset_id_conflict = 'Could not create Kaggle dataset: Id conflict with another dataset.' data_source = {'name': dataset_name, 'id': dataset_id, 'description': 'Created from Kaggle'} if dataset_name_conflict!= 'no': data_source['description'] = 'Conflict detected. Name conflict with another dataset.' data_source['conflict'] = {'name': dataset_name_conflict, 'id': dataset_id_conflict} return data_source Let’s review the function signature. The first argument is the arguments for the data source. These are the default arguments for any data source. In this example, we don’t use them. The second argument is the name of the dataset. You can use any string here. The dataset id. The dataset description. If there is a conflict, and the name of the dataset already exists, this function will return a description message. The third argument is to specify the credentials. In this example, we don’t use any credentials, but if we did use them, we would pass them into the function as default arguments. Now, we need to create the notebook cell to retrieve data from Kaggle. We’ll use this cell to load Kaggle data. Notebook cell to get data from Kaggle cell = ''' from kaggle import kaggle_datasource if __name

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