Python pandas.core.series.Series.sort_values() Examples
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Example #1
Source File: categorical.py From Splunking-Crime with GNU Affero General Public License v3.0 | 6 votes |
def argsort(self, ascending=True, kind='quicksort', *args, **kwargs): """ Returns the indices that would sort the Categorical instance if 'sort_values' was called. This function is implemented to provide compatibility with numpy ndarray objects. While an ordering is applied to the category values, arg-sorting in this context refers more to organizing and grouping together based on matching category values. Thus, this function can be called on an unordered Categorical instance unlike the functions 'Categorical.min' and 'Categorical.max'. Returns ------- argsorted : numpy array See also -------- numpy.ndarray.argsort """ ascending = nv.validate_argsort_with_ascending(ascending, args, kwargs) result = np.argsort(self._codes.copy(), kind=kind, **kwargs) if not ascending: result = result[::-1] return result
Example #2
Source File: categorical.py From elasticintel with GNU General Public License v3.0 | 6 votes |
def argsort(self, ascending=True, kind='quicksort', *args, **kwargs): """ Returns the indices that would sort the Categorical instance if 'sort_values' was called. This function is implemented to provide compatibility with numpy ndarray objects. While an ordering is applied to the category values, arg-sorting in this context refers more to organizing and grouping together based on matching category values. Thus, this function can be called on an unordered Categorical instance unlike the functions 'Categorical.min' and 'Categorical.max'. Returns ------- argsorted : numpy array See also -------- numpy.ndarray.argsort """ ascending = nv.validate_argsort_with_ascending(ascending, args, kwargs) result = np.argsort(self._codes.copy(), kind=kind, **kwargs) if not ascending: result = result[::-1] return result
Example #3
Source File: categorical.py From recruit with Apache License 2.0 | 4 votes |
def _from_inferred_categories(cls, inferred_categories, inferred_codes, dtype, true_values=None): """ Construct a Categorical from inferred values. For inferred categories (`dtype` is None) the categories are sorted. For explicit `dtype`, the `inferred_categories` are cast to the appropriate type. Parameters ---------- inferred_categories : Index inferred_codes : Index dtype : CategoricalDtype or 'category' true_values : list, optional If none are provided, the default ones are "True", "TRUE", and "true." Returns ------- Categorical """ from pandas import Index, to_numeric, to_datetime, to_timedelta cats = Index(inferred_categories) known_categories = (isinstance(dtype, CategoricalDtype) and dtype.categories is not None) if known_categories: # Convert to a specialized type with `dtype` if specified. if dtype.categories.is_numeric(): cats = to_numeric(inferred_categories, errors="coerce") elif is_datetime64_dtype(dtype.categories): cats = to_datetime(inferred_categories, errors="coerce") elif is_timedelta64_dtype(dtype.categories): cats = to_timedelta(inferred_categories, errors="coerce") elif dtype.categories.is_boolean(): if true_values is None: true_values = ["True", "TRUE", "true"] cats = cats.isin(true_values) if known_categories: # Recode from observation order to dtype.categories order. categories = dtype.categories codes = _recode_for_categories(inferred_codes, cats, categories) elif not cats.is_monotonic_increasing: # Sort categories and recode for unknown categories. unsorted = cats.copy() categories = cats.sort_values() codes = _recode_for_categories(inferred_codes, unsorted, categories) dtype = CategoricalDtype(categories, ordered=False) else: dtype = CategoricalDtype(cats, ordered=False) codes = inferred_codes return cls(codes, dtype=dtype, fastpath=True)
Example #4
Source File: categorical.py From vnpy_crypto with MIT License | 4 votes |
def _from_inferred_categories(cls, inferred_categories, inferred_codes, dtype): """Construct a Categorical from inferred values For inferred categories (`dtype` is None) the categories are sorted. For explicit `dtype`, the `inferred_categories` are cast to the appropriate type. Parameters ---------- inferred_categories : Index inferred_codes : Index dtype : CategoricalDtype or 'category' Returns ------- Categorical """ from pandas import Index, to_numeric, to_datetime, to_timedelta cats = Index(inferred_categories) known_categories = (isinstance(dtype, CategoricalDtype) and dtype.categories is not None) if known_categories: # Convert to a specialzed type with `dtype` if specified if dtype.categories.is_numeric(): cats = to_numeric(inferred_categories, errors='coerce') elif is_datetime64_dtype(dtype.categories): cats = to_datetime(inferred_categories, errors='coerce') elif is_timedelta64_dtype(dtype.categories): cats = to_timedelta(inferred_categories, errors='coerce') if known_categories: # recode from observation order to dtype.categories order categories = dtype.categories codes = _recode_for_categories(inferred_codes, cats, categories) elif not cats.is_monotonic_increasing: # sort categories and recode for unknown categories unsorted = cats.copy() categories = cats.sort_values() codes = _recode_for_categories(inferred_codes, unsorted, categories) dtype = CategoricalDtype(categories, ordered=False) else: dtype = CategoricalDtype(cats, ordered=False) codes = inferred_codes return cls(codes, dtype=dtype, fastpath=True)
Example #5
Source File: categorical.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
def _from_inferred_categories(cls, inferred_categories, inferred_codes, dtype, true_values=None): """ Construct a Categorical from inferred values. For inferred categories (`dtype` is None) the categories are sorted. For explicit `dtype`, the `inferred_categories` are cast to the appropriate type. Parameters ---------- inferred_categories : Index inferred_codes : Index dtype : CategoricalDtype or 'category' true_values : list, optional If none are provided, the default ones are "True", "TRUE", and "true." Returns ------- Categorical """ from pandas import Index, to_numeric, to_datetime, to_timedelta cats = Index(inferred_categories) known_categories = (isinstance(dtype, CategoricalDtype) and dtype.categories is not None) if known_categories: # Convert to a specialized type with `dtype` if specified. if dtype.categories.is_numeric(): cats = to_numeric(inferred_categories, errors="coerce") elif is_datetime64_dtype(dtype.categories): cats = to_datetime(inferred_categories, errors="coerce") elif is_timedelta64_dtype(dtype.categories): cats = to_timedelta(inferred_categories, errors="coerce") elif dtype.categories.is_boolean(): if true_values is None: true_values = ["True", "TRUE", "true"] cats = cats.isin(true_values) if known_categories: # Recode from observation order to dtype.categories order. categories = dtype.categories codes = _recode_for_categories(inferred_codes, cats, categories) elif not cats.is_monotonic_increasing: # Sort categories and recode for unknown categories. unsorted = cats.copy() categories = cats.sort_values() codes = _recode_for_categories(inferred_codes, unsorted, categories) dtype = CategoricalDtype(categories, ordered=False) else: dtype = CategoricalDtype(cats, ordered=False) codes = inferred_codes return cls(codes, dtype=dtype, fastpath=True)
Example #6
Source File: categorical.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
def _from_inferred_categories(cls, inferred_categories, inferred_codes, dtype): """Construct a Categorical from inferred values For inferred categories (`dtype` is None) the categories are sorted. For explicit `dtype`, the `inferred_categories` are cast to the appropriate type. Parameters ---------- inferred_categories : Index inferred_codes : Index dtype : CategoricalDtype or 'category' Returns ------- Categorical """ from pandas import Index, to_numeric, to_datetime, to_timedelta cats = Index(inferred_categories) known_categories = (isinstance(dtype, CategoricalDtype) and dtype.categories is not None) if known_categories: # Convert to a specialzed type with `dtype` if specified if dtype.categories.is_numeric(): cats = to_numeric(inferred_categories, errors='coerce') elif is_datetime64_dtype(dtype.categories): cats = to_datetime(inferred_categories, errors='coerce') elif is_timedelta64_dtype(dtype.categories): cats = to_timedelta(inferred_categories, errors='coerce') if known_categories: # recode from observation oder to dtype.categories order categories = dtype.categories codes = _recode_for_categories(inferred_codes, cats, categories) elif not cats.is_monotonic_increasing: # sort categories and recode for unknown categories unsorted = cats.copy() categories = cats.sort_values() codes = _recode_for_categories(inferred_codes, unsorted, categories) dtype = CategoricalDtype(categories, ordered=False) else: dtype = CategoricalDtype(cats, ordered=False) codes = inferred_codes return cls(codes, dtype=dtype, fastpath=True)
Example #7
Source File: categorical.py From elasticintel with GNU General Public License v3.0 | 4 votes |
def _from_inferred_categories(cls, inferred_categories, inferred_codes, dtype): """Construct a Categorical from inferred values For inferred categories (`dtype` is None) the categories are sorted. For explicit `dtype`, the `inferred_categories` are cast to the appropriate type. Parameters ---------- inferred_categories : Index inferred_codes : Index dtype : CategoricalDtype or 'category' Returns ------- Categorical """ from pandas import Index, to_numeric, to_datetime, to_timedelta cats = Index(inferred_categories) known_categories = (isinstance(dtype, CategoricalDtype) and dtype.categories is not None) if known_categories: # Convert to a specialzed type with `dtype` if specified if dtype.categories.is_numeric(): cats = to_numeric(inferred_categories, errors='coerce') elif is_datetime64_dtype(dtype.categories): cats = to_datetime(inferred_categories, errors='coerce') elif is_timedelta64_dtype(dtype.categories): cats = to_timedelta(inferred_categories, errors='coerce') if known_categories: # recode from observation oder to dtype.categories order categories = dtype.categories codes = _recode_for_categories(inferred_codes, cats, categories) elif not cats.is_monotonic_increasing: # sort categories and recode for unknown categories unsorted = cats.copy() categories = cats.sort_values() codes = _recode_for_categories(inferred_codes, unsorted, categories) dtype = CategoricalDtype(categories, ordered=False) else: dtype = CategoricalDtype(cats, ordered=False) codes = inferred_codes return cls(codes, dtype=dtype, fastpath=True)