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wassname
30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
import sklearn
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from sklearn.preprocessing import StandardScaler, OrdinalEncoder
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from sklearn_pandas import DataFrameMapper
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def normalize_encode_dataframe(df, encoder=OrdinalEncoder):
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"""Normalise numeric data, encode categorical data."""
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columns_input_numeric = list(df._get_numeric_data().columns)
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columns_categorical = list(set(df.columns)-set(columns_input_numeric))
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transformers= [([n], StandardScaler()) for n in columns_input_numeric] + \
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[([n], encoder()) for n in columns_categorical]
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scaler = DataFrameMapper(transformers, df_out=True)
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df_norm = scaler.fit_transform(df)
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return df_norm, scaler
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def timeseries_split(df, test_fraction=0.2, dropna=None):
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"""Split timeseries data with test in the future"""
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# If there are lots of nan's we can ignore them when splitting into portions
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if isinstance(dropna, list):
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index = df.dropna(subset=dropna).index
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elif dropna is True:
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index = df.dropna().index
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else:
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index = df.index
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i = int(len(index)*(1- test_fraction))
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dt = index.values[i]
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return df.loc[:dt], df.loc[dt:]
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