{"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":"markdown","source":"This notebook provides the absolute minimum code necessary for making a submission and is meant for copying into your own pipeline. If you're looking for a more detailed explanation of how the API works, take a look at [this notebook instead](https://www.kaggle.com/sohier/detailed-api-introduction).","metadata":{}},{"cell_type":"code","source":"import gresearch_crypto\nimport numpy as np\nenv = gresearch_crypto.make_env()\niter_test = env.iter_test()\nfor (test_df, sample_prediction_df) in iter_test:\n    sample_prediction_df['Target'] = np.random.randn(len(sample_prediction_df))*.01\n    env.predict(sample_prediction_df)","metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","papermill":{"duration":0.025192,"end_time":"2020-10-19T15:08:36.590409","exception":false,"start_time":"2020-10-19T15:08:36.565217","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2021-11-02T16:03:13.764101Z","iopub.execute_input":"2021-11-02T16:03:13.765095Z","iopub.status.idle":"2021-11-02T16:03:13.859696Z","shell.execute_reply.started":"2021-11-02T16:03:13.764958Z","shell.execute_reply":"2021-11-02T16:03:13.858958Z"},"trusted":true},"execution_count":null,"outputs":[]}]}