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What's New in the? import sys if sys.version_info >= (3, 7): PIPE = 'PIPE' else: PIPE = 'PIPE[1]' # libs import numpy as np import pandas as pd # utils from... import _filter_param_whitelist __all__ = ["add_to_train_pool", "add_to_valid_pool", "update_cache"] def add_to_train_pool(data_dict, train_pool, test_pool=None, indices=None): """ Function to add the records of the specified dataset to train_pool Parameters ---------- data_dict : a Pandas DataFrame of lists of records train_pool : a pandas DataFrame of lists of records test_pool : a list of lists of records, the new data to keep when we execute the glm_rmse or probit model on the whole dataset. This is useful in many cases when you want to model the training data and the test data together. If None, then this will be None. indices : the indices of the records to be added, specified as a list of indices Returns ---------- The train_pool DataFrame containing the records added and the old train_pool DataFrame Notes ----- Because the train_pool is created from all the records in the dataset, there may be duplicates. If you want to use the train_ System Requirements: PC Minimum: OS: Windows XP/Vista/7/8/8.1/10 (32/64-bit) Processor: Intel Core 2 Duo E4600 2.66GHz or AMD Athlon II X2 Dual Core 4200+ with 2GB RAM Hard Disk: Minimum 8 GB of free disk space Video Card: Nvidia GeForce 8600/9600 or ATI Radeon HD 3870 or better SVGA Output: 1024x768 Screen resolution: 1024x768 Sound Card: DirectX 9
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