本文整理匯總了Python中pandas._libs.lib.map_infer_mask方法的典型用法代碼示例。如果您正苦於以下問題：Python lib.map_infer_mask方法的具體用法？Python lib.map_infer_mask怎麽用？Python lib.map_infer_mask使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pandas._libs.lib的用法示例。
在下文中一共展示了lib.map_infer_mask方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _map
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import map_infer_mask [as 別名]
def _map(f, arr, na_mask=False, na_value=np.nan, dtype=object):
if not len(arr):
return np.ndarray(0, dtype=dtype)
if isinstance(arr, ABCSeries):
arr = arr.values
if not isinstance(arr, np.ndarray):
arr = np.asarray(arr, dtype=object)
if na_mask:
mask = isna(arr)
try:
convert = not all(mask)
result = lib.map_infer_mask(arr, f, mask.view(np.uint8), convert)
except (TypeError, AttributeError) as e:
# Reraise the exception if callable `f` got wrong number of args.
# The user may want to be warned by this, instead of getting NaN
if compat.PY2:
p_err = r'takes (no|(exactly|at (least|most)) ?\d+) arguments?'
else:
p_err = (r'((takes)|(missing)) (?(2)from \d+ to )?\d+ '
r'(?(3)required )positional arguments?')
if len(e.args) >= 1 and re.search(p_err, e.args[0]):
raise e
def g(x):
try:
return f(x)
except (TypeError, AttributeError):
return na_value
return _map(g, arr, dtype=dtype)
if na_value is not np.nan:
np.putmask(result, mask, na_value)
if result.dtype == object:
result = lib.maybe_convert_objects(result)
return result
else:
return lib.map_infer(arr, f) 示例2: _convert_to_ndarrays
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import map_infer_mask [as 別名]
def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False,
converters=None, dtypes=None):
result = {}
for c, values in compat.iteritems(dct):
conv_f = None if converters is None else converters.get(c, None)
if isinstance(dtypes, dict):
cast_type = dtypes.get(c, None)
else:
# single dtype or None
cast_type = dtypes
if self.na_filter:
col_na_values, col_na_fvalues = _get_na_values(
c, na_values, na_fvalues, self.keep_default_na)
else:
col_na_values, col_na_fvalues = set(), set()
if conv_f is not None:
# conv_f applied to data before inference
if cast_type is not None:
warnings.warn(("Both a converter and dtype were specified "
"for column {0} - only the converter will "
"be used").format(c), ParserWarning,
stacklevel=7)
try:
values = lib.map_infer(values, conv_f)
except ValueError:
mask = algorithms.isin(
values, list(na_values)).view(np.uint8)
values = lib.map_infer_mask(values, conv_f, mask)
cvals, na_count = self._infer_types(
values, set(col_na_values) | col_na_fvalues,
try_num_bool=False)
else:
# skip inference if specified dtype is object
try_num_bool = not (cast_type and is_string_dtype(cast_type))
# general type inference and conversion
cvals, na_count = self._infer_types(
values, set(col_na_values) | col_na_fvalues,
try_num_bool)
# type specified in dtype param
if cast_type and not is_dtype_equal(cvals, cast_type):
cvals = self._cast_types(cvals, cast_type, c)
result[c] = cvals
if verbose and na_count:
print('Filled %d NA values in column %s' % (na_count, str(c)))
return result 示例3: _convert_to_ndarrays
# 需要導入模塊: from pandas._libs import lib [as 別名]
# 或者: from pandas._libs.lib import map_infer_mask [as 別名]
def _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose=False,
converters=None, dtypes=None):
result = {}
for c, values in compat.iteritems(dct):
conv_f = None if converters is None else converters.get(c, None)
if isinstance(dtypes, dict):
cast_type = dtypes.get(c, None)
else:
# single dtype or None
cast_type = dtypes
if self.na_filter:
col_na_values, col_na_fvalues = _get_na_values(
c, na_values, na_fvalues)
else:
col_na_values, col_na_fvalues = set(), set()
if conv_f is not None:
# conv_f applied to data before inference
if cast_type is not None:
warnings.warn(("Both a converter and dtype were specified "
"for column {0} - only the converter will "
"be used").format(c), ParserWarning,
stacklevel=7)
try:
values = lib.map_infer(values, conv_f)
except ValueError:
mask = algorithms.isin(
values, list(na_values)).view(np.uint8)
values = lib.map_infer_mask(values, conv_f, mask)
cvals, na_count = self._infer_types(
values, set(col_na_values) | col_na_fvalues,
try_num_bool=False)
else:
# skip inference if specified dtype is object
try_num_bool = not (cast_type and is_string_dtype(cast_type))
# general type inference and conversion
cvals, na_count = self._infer_types(
values, set(col_na_values) | col_na_fvalues,
try_num_bool)
# type specificed in dtype param
if cast_type and not is_dtype_equal(cvals, cast_type):
cvals = self._cast_types(cvals, cast_type, c)
if issubclass(cvals.dtype.type, np.integer) and self.compact_ints:
cvals = lib.downcast_int64(
cvals, parsers.na_values,
self.use_unsigned)
result[c] = cvals
if verbose and na_count:
print('Filled %d NA values in column %s' % (na_count, str(c)))
return result