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def | Cope.experimental.data._cast2dataframe (func) |
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def | Cope.experimental.data.installLibs (libs=['pandas', 'numpy', 'imblearn', 'ipywidgets', 'seaborn', 'scipy', 'matplotlib']) |
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def | Cope.experimental.data.addVerbose (func) |
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def | Cope.experimental.data._cleaning_func (**decorator_kwargs) |
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def | Cope.experimental.data.insertSample (df, sample, index=-1) |
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def | Cope.experimental.data.ensureIterable (obj, useList=False) |
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def | Cope.experimental.data.ensureNotIterable (obj, emptyBecomes=None) |
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def | Cope.experimental.data.getOutliers (data, zscore=None) |
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def | Cope.experimental.data.normalizePercentage (p, error='Percentage is of the wrong type(int or float expected)') |
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def | Cope.experimental.data.isiterable (obj, includeStr=False) |
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def | Cope.experimental.data.sort_dict_by_value_length (d) |
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pd.DataFrame | Cope.experimental.data.timeFeatures (df) |
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pd.DataFrame | Cope.experimental.data.catagorical (df, time=False) |
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pd.DataFrame | Cope.experimental.data.quantitative (df, time=True) |
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def | Cope.experimental.data.isTimeFeature (pd.Series s) |
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def | Cope.experimental.data.isCatagorical (pd.Series s, time=False) |
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def | Cope.experimental.data.isQuantatative (pd.Series s, time=True) |
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def | Cope.experimental.data.missingSummary (df, thresh=.6) |
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def | Cope.experimental.data.significantCorrelations (df, thresh=.5) |
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def | Cope.experimental.data.getNiceTypesTable (df, types=None) |
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def | Cope.experimental.data.percentCountPlot (data, feature, target=None, ax=None, title='Percentage of values used in {}') |
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def | Cope.experimental.data.column_entropy (pd.Series column, base=e) |
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def | Cope.experimental.data.pretty_2_column_array (a, limit=30, paren=None) |
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def | Cope.experimental.data.pretty_counts (pd.Series s, paren=False) |
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def | Cope.experimental.data.meanConfInterval (data, confidence=0.95, mean=False) |
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def | Cope.experimental.data.showOutliers (data, column, zscore, **snsArgs) |
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def | Cope.experimental.data.interactWithOutliers (df, feature=None, step=.2) |
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def | Cope.experimental.data.handle_outliers (col, Union['remove', 'constrain'] method='remove', zscore=3, log=...) |
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def | Cope.experimental.data.handle_missing (col, Union[pd.Series, 'remove', 'mean', 'median', 'mode', 'random', 'balanced_random', Any] method, missing_value=np.nan, log=...) |
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def | Cope.experimental.data.query (pd.DataFrame df, str column, str query, Union[pd.Series, 'remove', 'new', 'mean', 'median', 'mode', 'random', 'balanced_random', Any] method, true=1, false=0, verbose=False) |
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def | Cope.experimental.data.remove (col, val, log=...) |
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def | Cope.experimental.data.bin (col, Union['frequency', 'width', Tuple, List] method, amt=5, log=...) |
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def | Cope.experimental.data.rescale (df, return_scaler=False, log=...) |
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def | Cope.experimental.data.convert_time (df_or_col, str col=None, Union['timestamp'] method='timestamp', verbose=False) |
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def | Cope.experimental.data.convert_numeric (df, str col=None, Union['assign', 'one_hot_encode'] method='one_hot_encode', returnAssignments=False, skip=[], verbose=False) |
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def | Cope.experimental.data.split (*data, amt=.2, Union['random', 'chunk', 'head', 'tail'] method='random', target=[], splitTargets=False, seed=42) |
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def | Cope.experimental.data.explore (data, target=None, stats=None, additionalStats=[], missing=True, corr=.55, entropy=None, start='Description', startFeature=None, startx=None, starty=None, startHue=None, alpha=None) |
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def | Cope.experimental.data.suggestedCleaning (df, target) |
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def | Cope.experimental.data._cleanColumn (df, args, column, verbose, ignoreWarnings=False) |
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pd.DataFrame | Cope.experimental.data.clean (pd.DataFrame df, Dict[str, Dict[str, Any]] config, bool verbose=False, str split=None) |
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def | Cope.experimental.data.resample (X, y, Union['oversample', 'undersample', 'mixed'] method='oversample', seed=None) |
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def | Cope.experimental.data.evaluateQuantitative (test, testPredictions, train=None, trainPredictions=None, accuracy=3, explain=False, compact=False, line=False, log=...) |
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def | Cope.experimental.data.evaluateCatagorical (test, testPredictions, train=None, trainPredictions=None, accuracy=3, curve=False, confusion=False, explain=False, compact=False) |
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def | Cope.experimental.data.evaluate (catagorical, test, testPredictions, train=None, trainPredictions=None, accuracy=3, curve=False, confusion=False, explain=False, compact=False, line=False) |
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def | Cope.experimental.data.importances (tree, names=None, rtn=False, graph=True, best=.01) |
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def | Cope.experimental.data.saveStats (file, name, model, testY, predY, trainY=None, trainPredY=None, notes='', new=False, show=True, save=True) |
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def | Cope.experimental.data.plot_history (history) |
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