def _bins_to_cuts(x,
bins,
right=True,
labels=None,
retbins=False,
precision=3,
name=None,
include_lowest=False):
if name is None and isinstance(x, Series):
name = x.name
x = np.asarray(x)
side = 'left' if right else 'right'
ids = bins.searchsorted(x, side=side)
if len(algos.unique(bins)) < len(bins):
raise Exception('Bin edges must be unique: %s' % repr(bins))
if include_lowest:
ids[x == bins[0]] = 1
na_mask = com.isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
increases = 0
while True:
try:
levels = _format_levels(bins,
precision,
right=right,
include_lowest=include_lowest)
except ValueError:
increases += 1
precision += 1
if increases >= 20:
raise
else:
break
else:
if len(labels) != len(bins) - 1:
raise ValueError('Bin labels must be one fewer than '
'the number of bin edges')
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, name=name)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
if not retbins:
return fac
return fac, bins
def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False, precision=3, name=None, include_lowest=False):
x_is_series = isinstance(x, Series)
series_index = None
if x_is_series:
series_index = x.index
if name is None:
name = x.name
x = np.asarray(x)
side = "left" if right else "right"
ids = bins.searchsorted(x, side=side)
if len(algos.unique(bins)) < len(bins):
raise ValueError("Bin edges must be unique: %s" % repr(bins))
if include_lowest:
ids[x == bins[0]] = 1
na_mask = isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
increases = 0
while True:
try:
levels = _format_levels(bins, precision, right=right, include_lowest=include_lowest)
except ValueError:
increases += 1
precision += 1
if increases >= 20:
raise
else:
break
else:
if len(labels) != len(bins) - 1:
raise ValueError("Bin labels must be one fewer than " "the number of bin edges")
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, ordered=True, fastpath=True)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
if x_is_series:
fac = Series(fac, index=series_index, name=name)
if not retbins:
return fac
return fac, bins
def _bins_to_cuts(x, bins, right=True, labels=None,
precision=3, include_lowest=False,
dtype=None, duplicates='raise'):
if duplicates not in ['raise', 'drop']:
raise ValueError("invalid value for 'duplicates' parameter, "
"valid options are: raise, drop")
unique_bins = algos.unique(bins)
if len(unique_bins) < len(bins):
if duplicates == 'raise':
raise ValueError("Bin edges must be unique: {}.\nYou "
"can drop duplicate edges by setting "
"the 'duplicates' kwarg".format(repr(bins)))
else:
bins = unique_bins
side = 'left' if right else 'right'
ids = bins.searchsorted(x, side=side)
if include_lowest:
ids[x == bins[0]] = 1
na_mask = isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
increases = 0
while True:
try:
levels = _format_levels(bins, precision, right=right,
include_lowest=include_lowest,
dtype=dtype)
except ValueError:
increases += 1
precision += 1
if increases >= 20:
raise
else:
break
else:
if len(labels) != len(bins) - 1:
raise ValueError('Bin labels must be one fewer than '
'the number of bin edges')
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, ordered=True, fastpath=True)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
return fac, bins
def _bins_to_cuts(x,
bins,
right=True,
labels=None,
retbins=False,
precision=3,
name=None,
include_lowest=False):
if name is None and isinstance(x, Series):
name = x.name
x = np.asarray(x)
side = 'left' if right else 'right'
ids = bins.searchsorted(x, side=side)
if include_lowest:
ids[x == bins[0]] = 1
na_mask = com.isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
fmt = lambda v: _format_label(v, precision=precision)
if right:
levels = [
'(%s, %s]' % (fmt(a), fmt(b))
for a, b in zip(bins, bins[1:])
]
if include_lowest:
levels[0] = '[' + levels[0][1:]
else:
levels = [
'[%s, %s)' % (fmt(a), fmt(b))
for a, b in zip(bins, bins[1:])
]
else:
if len(labels) != len(bins) - 1:
raise ValueError('Bin labels must be one fewer than '
'the number of bin edges')
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, name=name)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
if not retbins:
return fac
return fac, bins
def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False,
precision=3, name=None, include_lowest=False):
if name is None and isinstance(x, Series):
name = x.name
x = np.asarray(x)
side = 'left' if right else 'right'
ids = bins.searchsorted(x, side=side)
if include_lowest:
ids[x == bins[0]] = 1
na_mask = com.isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
increases = 0
while True:
try:
levels = _format_levels(bins, precision, right=right,
include_lowest=include_lowest)
except ValueError:
increases += 1
precision += 1
if increases >= 20:
raise
else:
break
else:
if len(labels) != len(bins) - 1:
raise ValueError('Bin labels must be one fewer than '
'the number of bin edges')
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, name=name)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
if not retbins:
return fac
return fac, bins
def _bins_to_cuts(x, bins, right=True, labels=None,
precision=3, include_lowest=False,
dtype=None):
side = 'left' if right else 'right'
ids = bins.searchsorted(x, side=side)
if len(algos.unique(bins)) < len(bins):
raise ValueError('Bin edges must be unique: %s' % repr(bins))
if include_lowest:
ids[x == bins[0]] = 1
na_mask = isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
increases = 0
while True:
try:
levels = _format_levels(bins, precision, right=right,
include_lowest=include_lowest,
dtype=dtype)
except ValueError:
increases += 1
precision += 1
if increases >= 20:
raise
else:
break
else:
if len(labels) != len(bins) - 1:
raise ValueError('Bin labels must be one fewer than '
'the number of bin edges')
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, ordered=True, fastpath=True)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
return fac, bins
def _bins_to_cuts(x, bins, right=True, labels=None, retbins=False,
precision=3, name=None, include_lowest=False):
if name is None and isinstance(x, Series):
name = x.name
x = np.asarray(x)
side = 'left' if right else 'right'
ids = bins.searchsorted(x, side=side)
if include_lowest:
ids[x == bins[0]] = 1
na_mask = com.isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
fmt = lambda v: _format_label(v, precision=precision)
if right:
levels = ['(%s, %s]' % (fmt(a), fmt(b))
for a, b in zip(bins, bins[1:])]
if include_lowest:
levels[0] = '[' + levels[0][1:]
else:
levels = ['[%s, %s)' % (fmt(a), fmt(b))
for a, b in zip(bins, bins[1:])]
else:
if len(labels) != len(bins) - 1:
raise ValueError('Bin labels must be one fewer than '
'the number of bin edges')
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, name=name)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
if not retbins:
return fac
return fac, bins
def _bins_to_cuts_new(
x,
bins,
right=True,
labels=None,
retbins=False,
precision=3,
name=None,
include_lowest=False,
):
x_is_series = isinstance(x, Series)
series_index = None
# Added this line to the original code
bins = np.array(sorted(list(set(bins))))
if x_is_series:
series_index = x.index
if name is None:
name = x.name
x = np.asarray(x)
side = "left" if right else "right"
ids = bins.searchsorted(x, side=side)
if len(algos.unique(bins)) < len(bins):
raise ValueError("Bin edges must be unique: %s" % repr(bins))
if include_lowest:
ids[x == bins[0]] = 1
na_mask = com.isnull(x) | (ids == len(bins)) | (ids == 0)
has_nas = na_mask.any()
if labels is not False:
if labels is None:
increases = 0
while True:
try:
levels = _format_levels(bins,
precision,
right=right,
include_lowest=include_lowest)
except ValueError:
increases += 1
precision += 1
if increases >= 20:
raise
else:
break
else:
if len(labels) != len(bins) - 1:
raise ValueError("Bin labels must be one fewer than "
"the number of bin edges")
levels = labels
levels = np.asarray(levels, dtype=object)
np.putmask(ids, na_mask, 0)
fac = Categorical(ids - 1, levels, ordered=True, fastpath=True)
else:
fac = ids - 1
if has_nas:
fac = fac.astype(np.float64)
np.putmask(fac, na_mask, np.nan)
if x_is_series:
fac = Series(fac, index=series_index, name=name)
if not retbins:
return fac
return fac, bins
