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lidanyang 2023-12-15 10:06:46 +08:00
parent 48d542d383
commit 2fe9f2b9cf
1 changed files with 0 additions and 50 deletions
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50
metagpt/tools/functions/libs/data_preprocess.py
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@ -151,53 +151,3 @@ def get_column_info(df: pd.DataFrame) -> dict:
columns=["Column_name", "Data_type", "NaN_Frequency(%)", "N_unique"],
)
return samples.to_dict(orient='list')
#
#
# if __name__ == '__main__':
# def run():
# V = {
# 'a': [-1, 2, 3, 6, 5, 4],
# 'b': [1.1, 2.2, 3.3, 6.6, 5.5, 4.4],
# 'c': ['aa', 'bb', 'cc', 'dd', 'ee', 'ff'],
# 'd': [1, None, 3, None, 5, 4],
# 'e': [1.1, np.NAN, 3.3, None, 5.5, 4.4],
# 'f': ['aa', np.NAN, 'cc', None, '', 'ff'],
#
# }
#
# df = pd.DataFrame(V)
# print(df.dtypes)
#
# numeric_features = ['a', 'b', 'd', 'e']
# numeric_features_wo_miss = ['a', 'b', ]
# categorial_features = ['c', 'f']
#
# df_ = fill_missing_value(df.copy(), numeric_features)
# print(df_)
# df_ = fill_missing_value(df.copy(), categorial_features, strategy='constant', fill_value='hehe')
# print(df_)
#
# df_ = fill_missing_value(df.copy(), numeric_features, strategy='constant', fill_value=999)
# print(df_)
#
# # df_ = label_encode(df.copy(), numeric_features + categorial_features, )
# # print(df_)
#
# df_ = split_bins(df.copy(), numeric_features_wo_miss, strategy='quantile')
# print(df_)
#
# df_ = min_max_scale(df.copy(), numeric_features, )
# print(df_)
#
# df_ = standard_scale(df.copy(), numeric_features, )
# print(df_)
#
# df_ = log_transform(df.copy(), numeric_features, )
# print(df_)
#
# df_ = max_abs_scale(df.copy(), numeric_features, )
# print(df_)
#
# df_ = robust_scale(df.copy(), numeric_features, )
# print(df_)
# run()