@@ -75,17 +75,18 @@ def cut(x, bins, right=True, labels=None, retbins=False, precision=3,
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Examples
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--------
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>>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]), 3, retbins=True)
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- ([(0.19, 3.367], (0.19, 3.367], (0.19, 3.367], (3.367, 6.533], (6.533, 9.7], (0.19, 3.367]]
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- Categories (3, interval[float64]): [(0.19, 3.367] < (3.367, 6.533] < (6.533, 9.7]], array([ 0.1905 , 3.36666667, 6.53333333, 9.7 ]))
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+ ... # doctest: +ELLIPSIS
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+ ([(0.19, 3.367], (0.19, 3.367], (0.19, 3.367], (3.367, 6.533], ...
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+ Categories (3, interval[float64]): [(0.19, 3.367] < (3.367, 6.533] ...
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>>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]),
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- ... 3, labels=["good","medium","bad"])
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+ ... 3, labels=["good", "medium", "bad"])
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... # doctest: +SKIP
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[good, good, good, medium, bad, good]
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Categories (3, object): [good < medium < bad]
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>>> pd.cut(np.ones(5), 4, labels=False)
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- array([1, 1, 1, 1, 1], dtype=int64 )
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+ array([1, 1, 1, 1, 1])
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"""
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# NOTE: this binning code is changed a bit from histogram for var(x) == 0
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@@ -181,15 +182,17 @@ def qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):
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Examples
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--------
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>>> pd.qcut(range(5), 4)
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+ ... # doctest: +ELLIPSIS
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[(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]
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- Categories (4, interval[float64]): [(-0.001, 1.0] < (1.0, 2.0] < (2.0, 3.0] < (3.0, 4.0]]
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+ Categories (4, interval[float64]): [(-0.001, 1.0] < (1.0, 2.0] ...
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- >>> pd.qcut(range(5), 3, labels=["good","medium","bad"])
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+ >>> pd.qcut(range(5), 3, labels=["good", "medium", "bad"])
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+ ... # doctest: +SKIP
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[good, good, medium, bad, bad]
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Categories (3, object): [good < medium < bad]
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>>> pd.qcut(range(5), 4, labels=False)
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- array([0, 0, 1, 2, 3], dtype=int64 )
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+ array([0, 0, 1, 2, 3])
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"""
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x_is_series , series_index , name , x = _preprocess_for_cut (x )
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