def _isfinite(values): if is_datetime_or_timedelta_dtype(values): return isnull(values) if (is_complex_dtype(values) or is_float_dtype(values) or is_integer_dtype(values) or is_bool_dtype(values)): return ~np.isfinite(values) return ~np.isfinite(values.astype('float64'))
def _value_counts_arraylike(values, dropna=True): is_datetimetz = com.is_datetimetz(values) is_period = (isinstance(values, gt.ABCPeriodIndex) or com.is_period_arraylike(values)) orig = values from pandas.core.series import Series values = Series(values).values dtype = values.dtype if com.is_datetime_or_timedelta_dtype(dtype) or is_period: from pandas.tseries.index import DatetimeIndex from pandas.tseries.period import PeriodIndex if is_period: values = PeriodIndex(values) freq = values.freq values = values.view(np.int64) keys, counts = htable.value_count_scalar64(values, dropna) if dropna: msk = keys != iNaT keys, counts = keys[msk], counts[msk] # convert the keys back to the dtype we came in keys = keys.astype(dtype) # dtype handling if is_datetimetz: if isinstance(orig, gt.ABCDatetimeIndex): tz = orig.tz else: tz = orig.dt.tz keys = DatetimeIndex._simple_new(keys, tz=tz) if is_period: keys = PeriodIndex._simple_new(keys, freq=freq) elif com.is_integer_dtype(dtype): values = com._ensure_int64(values) keys, counts = htable.value_count_scalar64(values, dropna) elif com.is_float_dtype(dtype): values = com._ensure_float64(values) keys, counts = htable.value_count_scalar64(values, dropna) else: values = com._ensure_object(values) mask = com.isnull(values) keys, counts = htable.value_count_object(values, mask) if not dropna and mask.any(): keys = np.insert(keys, 0, np.NaN) counts = np.insert(counts, 0, mask.sum()) return keys, counts
def _bn_ok_dtype(dt, name): # Bottleneck chokes on datetime64 if (not is_object_dtype(dt) and not is_datetime_or_timedelta_dtype(dt)): # bottleneck does not properly upcast during the sum # so can overflow if name == 'nansum': if dt.itemsize < 8: return False return True return False
def value_counts(values, sort=True, ascending=False, normalize=False, bins=None, dropna=True): """ Compute a histogram of the counts of non-null values. Parameters ---------- values : ndarray (1-d) sort : boolean, default True Sort by values ascending : boolean, default False Sort in ascending order normalize: boolean, default False If True then compute a relative histogram bins : integer, optional Rather than count values, group them into half-open bins, convenience for pd.cut, only works with numeric data dropna : boolean, default True Don't include counts of NaN Returns ------- value_counts : Series """ from pandas.core.series import Series from pandas.tools.tile import cut from pandas import Index, PeriodIndex, DatetimeIndex name = getattr(values, 'name', None) values = Series(values).values if bins is not None: try: cat, bins = cut(values, bins, retbins=True) except TypeError: raise TypeError("bins argument only works with numeric data.") values = cat.codes if com.is_categorical_dtype(values.dtype): result = values.value_counts(dropna) else: dtype = values.dtype is_period = com.is_period_arraylike(values) is_datetimetz = com.is_datetimetz(values) if com.is_datetime_or_timedelta_dtype( dtype) or is_period or is_datetimetz: if is_period: values = PeriodIndex(values) elif is_datetimetz: tz = getattr(values, 'tz', None) values = DatetimeIndex(values).tz_localize(None) values = values.view(np.int64) keys, counts = htable.value_count_scalar64(values, dropna) if dropna: from pandas.tslib import iNaT msk = keys != iNaT keys, counts = keys[msk], counts[msk] # localize to the original tz if necessary if is_datetimetz: keys = DatetimeIndex(keys).tz_localize(tz) # convert the keys back to the dtype we came in else: keys = keys.astype(dtype) elif com.is_integer_dtype(dtype): values = com._ensure_int64(values) keys, counts = htable.value_count_scalar64(values, dropna) elif com.is_float_dtype(dtype): values = com._ensure_float64(values) keys, counts = htable.value_count_scalar64(values, dropna) else: values = com._ensure_object(values) mask = com.isnull(values) keys, counts = htable.value_count_object(values, mask) if not dropna and mask.any(): keys = np.insert(keys, 0, np.NaN) counts = np.insert(counts, 0, mask.sum()) if not isinstance(keys, Index): keys = Index(keys) result = Series(counts, index=keys, name=name) if bins is not None: # TODO: This next line should be more efficient result = result.reindex(np.arange(len(cat.categories)), fill_value=0) result.index = bins[:-1] if sort: result = result.sort_values(ascending=ascending) if normalize: result = result / float(values.size) return result
def _view_if_needed(values): if is_datetime_or_timedelta_dtype(values): return values.view(np.int64) return values
def value_counts(values, sort=True, ascending=False, normalize=False, bins=None, dropna=True): """ Compute a histogram of the counts of non-null values. Parameters ---------- values : ndarray (1-d) sort : boolean, default True Sort by values ascending : boolean, default False Sort in ascending order normalize: boolean, default False If True then compute a relative histogram bins : integer, optional Rather than count values, group them into half-open bins, convenience for pd.cut, only works with numeric data dropna : boolean, default True Don't include counts of NaN Returns ------- value_counts : Series """ from pandas.core.series import Series from pandas.tools.tile import cut from pandas import Index, PeriodIndex, DatetimeIndex name = getattr(values, 'name', None) values = Series(values).values if bins is not None: try: cat, bins = cut(values, bins, retbins=True) except TypeError: raise TypeError("bins argument only works with numeric data.") values = cat.codes if com.is_categorical_dtype(values.dtype): result = values.value_counts(dropna) else: dtype = values.dtype is_period = com.is_period_arraylike(values) is_datetimetz = com.is_datetimetz(values) if com.is_datetime_or_timedelta_dtype(dtype) or is_period or \ is_datetimetz: if is_period: values = PeriodIndex(values) elif is_datetimetz: tz = getattr(values, 'tz', None) values = DatetimeIndex(values).tz_localize(None) values = values.view(np.int64) keys, counts = htable.value_count_scalar64(values, dropna) if dropna: msk = keys != iNaT keys, counts = keys[msk], counts[msk] # localize to the original tz if necessary if is_datetimetz: keys = DatetimeIndex(keys).tz_localize(tz) # convert the keys back to the dtype we came in else: keys = keys.astype(dtype) elif com.is_integer_dtype(dtype): values = com._ensure_int64(values) keys, counts = htable.value_count_scalar64(values, dropna) elif com.is_float_dtype(dtype): values = com._ensure_float64(values) keys, counts = htable.value_count_scalar64(values, dropna) else: values = com._ensure_object(values) mask = com.isnull(values) keys, counts = htable.value_count_object(values, mask) if not dropna and mask.any(): keys = np.insert(keys, 0, np.NaN) counts = np.insert(counts, 0, mask.sum()) if not isinstance(keys, Index): keys = Index(keys) result = Series(counts, index=keys, name=name) if bins is not None: # TODO: This next line should be more efficient result = result.reindex(np.arange(len(cat.categories)), fill_value=0) result.index = bins[:-1] if sort: result = result.sort_values(ascending=ascending) if normalize: result = result / float(values.size) return result
def to_numeric(arg, errors='raise'): """ Convert argument to a numeric type. Parameters ---------- arg : list, tuple, 1-d array, or Series errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaN - If 'ignore', then invalid parsing will return the input Returns ------- ret : numeric if parsing succeeded. Return type depends on input. Series if Series, otherwise ndarray Examples -------- Take separate series and convert to numeric, coercing when told to >>> import pandas as pd >>> s = pd.Series(['1.0', '2', -3]) >>> pd.to_numeric(s) >>> s = pd.Series(['apple', '1.0', '2', -3]) >>> pd.to_numeric(s, errors='ignore') >>> pd.to_numeric(s, errors='coerce') """ is_series = False is_index = False is_scalar = False if isinstance(arg, pd.Series): is_series = True values = arg.values elif isinstance(arg, pd.Index): is_index = True values = arg.asi8 if values is None: values = arg.values elif isinstance(arg, (list, tuple)): values = np.array(arg, dtype='O') elif np.isscalar(arg): if com.is_number(arg): return arg is_scalar = True values = np.array([arg], dtype='O') elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a list, tuple, 1-d array, or Series') else: values = arg if com.is_numeric_dtype(values): pass elif com.is_datetime_or_timedelta_dtype(values): values = values.astype(np.int64) else: values = com._ensure_object(values) coerce_numeric = False if errors in ('ignore', 'raise') else True try: values = lib.maybe_convert_numeric(values, set(), coerce_numeric=coerce_numeric) except: if errors == 'raise': raise if is_series: return pd.Series(values, index=arg.index, name=arg.name) elif is_index: # because we want to coerce to numeric if possible, # do not use _shallow_copy_with_infer return Index(values, name=arg.name) elif is_scalar: return values[0] else: return values
def to_numeric(arg, errors='raise', downcast=None): """ Convert argument to a numeric type. Parameters ---------- arg : list, tuple, 1-d array, or Series errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaN - If 'ignore', then invalid parsing will return the input downcast : {'integer', 'signed', 'unsigned', 'float'} , default None If not None, and if the data has been successfully cast to a numerical dtype (or if the data was numeric to begin with), downcast that resulting data to the smallest numerical dtype possible according to the following rules: - 'integer' or 'signed': smallest signed int dtype (min.: np.int8) - 'unsigned': smallest unsigned int dtype (min.: np.uint8) - 'float': smallest float dtype (min.: np.float32) As this behaviour is separate from the core conversion to numeric values, any errors raised during the downcasting will be surfaced regardless of the value of the 'errors' input. In addition, downcasting will only occur if the size of the resulting data's dtype is strictly larger than the dtype it is to be cast to, so if none of the dtypes checked satisfy that specification, no downcasting will be performed on the data. .. versionadded:: 0.19.0 Returns ------- ret : numeric if parsing succeeded. Return type depends on input. Series if Series, otherwise ndarray Examples -------- Take separate series and convert to numeric, coercing when told to >>> import pandas as pd >>> s = pd.Series(['1.0', '2', -3]) >>> pd.to_numeric(s) 0 1.0 1 2.0 2 -3.0 dtype: float64 >>> pd.to_numeric(s, downcast='float') 0 1.0 1 2.0 2 -3.0 dtype: float32 >>> pd.to_numeric(s, downcast='signed') 0 1 1 2 2 -3 dtype: int8 >>> s = pd.Series(['apple', '1.0', '2', -3]) >>> pd.to_numeric(s, errors='ignore') 0 apple 1 1.0 2 2 3 -3 dtype: object >>> pd.to_numeric(s, errors='coerce') 0 NaN 1 1.0 2 2.0 3 -3.0 dtype: float64 """ if downcast not in (None, 'integer', 'signed', 'unsigned', 'float'): raise ValueError('invalid downcasting method provided') is_series = False is_index = False is_scalar = False if isinstance(arg, pd.Series): is_series = True values = arg.values elif isinstance(arg, pd.Index): is_index = True values = arg.asi8 if values is None: values = arg.values elif isinstance(arg, (list, tuple)): values = np.array(arg, dtype='O') elif np.isscalar(arg): if com.is_number(arg): return arg is_scalar = True values = np.array([arg], dtype='O') elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a list, tuple, 1-d array, or Series') else: values = arg try: if com.is_numeric_dtype(values): pass elif com.is_datetime_or_timedelta_dtype(values): values = values.astype(np.int64) else: values = com._ensure_object(values) coerce_numeric = False if errors in ('ignore', 'raise') else True values = lib.maybe_convert_numeric(values, set(), coerce_numeric=coerce_numeric) except Exception: if errors == 'raise': raise # attempt downcast only if the data has been successfully converted # to a numerical dtype and if a downcast method has been specified if downcast is not None and com.is_numeric_dtype(values): typecodes = None if downcast in ('integer', 'signed'): typecodes = np.typecodes['Integer'] elif downcast == 'unsigned' and np.min(values) > 0: typecodes = np.typecodes['UnsignedInteger'] elif downcast == 'float': typecodes = np.typecodes['Float'] # pandas support goes only to np.float32, # as float dtypes smaller than that are # extremely rare and not well supported float_32_char = np.dtype(np.float32).char float_32_ind = typecodes.index(float_32_char) typecodes = typecodes[float_32_ind:] if typecodes is not None: # from smallest to largest for dtype in typecodes: if np.dtype(dtype).itemsize < values.dtype.itemsize: values = com._possibly_downcast_to_dtype(values, dtype) # successful conversion if values.dtype == dtype: break if is_series: return pd.Series(values, index=arg.index, name=arg.name) elif is_index: # because we want to coerce to numeric if possible, # do not use _shallow_copy_with_infer return Index(values, name=arg.name) elif is_scalar: return values[0] else: return values
def to_numeric(arg, errors='raise', downcast=None): """ Convert argument to a numeric type. Parameters ---------- arg : list, tuple, 1-d array, or Series errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaN - If 'ignore', then invalid parsing will return the input downcast : {'integer', 'signed', 'unsigned', 'float'} , default None If not None, and if the data has been successfully cast to a numerical dtype (or if the data was numeric to begin with), downcast that resulting data to the smallest numerical dtype possible according to the following rules: - 'integer' or 'signed': smallest signed int dtype (min.: np.int8) - 'unsigned': smallest unsigned int dtype (min.: np.uint8) - 'float': smallest float dtype (min.: np.float32) As this behaviour is separate from the core conversion to numeric values, any errors raised during the downcasting will be surfaced regardless of the value of the 'errors' input. In addition, downcasting will only occur if the size of the resulting data's dtype is strictly larger than the dtype it is to be cast to, so if none of the dtypes checked satisfy that specification, no downcasting will be performed on the data. .. versionadded:: 0.19.0 Returns ------- ret : numeric if parsing succeeded. Return type depends on input. Series if Series, otherwise ndarray Examples -------- Take separate series and convert to numeric, coercing when told to >>> import pandas as pd >>> s = pd.Series(['1.0', '2', -3]) >>> pd.to_numeric(s) 0 1.0 1 2.0 2 -3.0 dtype: float64 >>> pd.to_numeric(s, downcast='float') 0 1.0 1 2.0 2 -3.0 dtype: float32 >>> pd.to_numeric(s, downcast='signed') 0 1 1 2 2 -3 dtype: int8 >>> s = pd.Series(['apple', '1.0', '2', -3]) >>> pd.to_numeric(s, errors='ignore') 0 apple 1 1.0 2 2 3 -3 dtype: object >>> pd.to_numeric(s, errors='coerce') 0 NaN 1 1.0 2 2.0 3 -3.0 dtype: float64 """ if downcast not in (None, 'integer', 'signed', 'unsigned', 'float'): raise ValueError('invalid downcasting method provided') is_series = False is_index = False is_scalar = False if isinstance(arg, pd.Series): is_series = True values = arg.values elif isinstance(arg, pd.Index): is_index = True values = arg.asi8 if values is None: values = arg.values elif isinstance(arg, (list, tuple)): values = np.array(arg, dtype='O') elif np.isscalar(arg): if com.is_number(arg): return arg is_scalar = True values = np.array([arg], dtype='O') elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a list, tuple, 1-d array, or Series') else: values = arg try: if com.is_numeric_dtype(values): pass elif com.is_datetime_or_timedelta_dtype(values): values = values.astype(np.int64) else: values = com._ensure_object(values) coerce_numeric = False if errors in ('ignore', 'raise') else True values = lib.maybe_convert_numeric(values, set(), coerce_numeric=coerce_numeric) except Exception: if errors == 'raise': raise # attempt downcast only if the data has been successfully converted # to a numerical dtype and if a downcast method has been specified if downcast is not None and com.is_numeric_dtype(values): typecodes = None if downcast in ('integer', 'signed'): typecodes = np.typecodes['Integer'] elif downcast == 'unsigned' and np.min(values) > 0: typecodes = np.typecodes['UnsignedInteger'] elif downcast == 'float': typecodes = np.typecodes['Float'] # pandas support goes only to np.float32, # as float dtypes smaller than that are # extremely rare and not well supported float_32_char = np.dtype(np.float32).char float_32_ind = typecodes.index(float_32_char) typecodes = typecodes[float_32_ind:] if typecodes is not None: # from smallest to largest for dtype in typecodes: if np.dtype(dtype).itemsize < values.dtype.itemsize: values = com._possibly_downcast_to_dtype( values, dtype) # successful conversion if values.dtype == dtype: break if is_series: return pd.Series(values, index=arg.index, name=arg.name) elif is_index: # because we want to coerce to numeric if possible, # do not use _shallow_copy_with_infer return Index(values, name=arg.name) elif is_scalar: return values[0] else: return values