Python pandas._libs.lib.map_infer() Examples
The following are 26
code examples of pandas._libs.lib.map_infer().
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pandas._libs.lib
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Example #1
| Source File: blocks.py From recruit with Apache License 2.0 | 7 votes |
|
def get_values(self, dtype=None):
"""
return object dtype as boxed values, such as Timestamps/Timedelta
"""
if is_object_dtype(dtype):
values = self.values
if self.ndim > 1:
values = values.ravel()
values = lib.map_infer(values, self._box_func)
if self.ndim > 1:
values = values.reshape(self.values.shape)
return values
return self.values
Example #2
| Source File: blocks.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def get_values(self, dtype=None):
"""
return object dtype as boxed values, such as Timestamps/Timedelta
"""
if is_object_dtype(dtype):
values = self.values
if self.ndim > 1:
values = values.ravel()
values = lib.map_infer(values, self._box_func)
if self.ndim > 1:
values = values.reshape(self.values.shape)
return values
return self.values
Example #3
| Source File: datetimelike.py From recruit with Apache License 2.0 | 5 votes |
|
def _box_values(self, values):
"""
apply box func to passed values
"""
return lib.map_infer(values, self._box_func)
Example #4
| Source File: strings.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
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)
Example #5
| Source File: internals.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def get_values(self, dtype=None):
# return object dtype as Timestamps with the zones
if is_object_dtype(dtype):
f = lambda x: lib.Timestamp(x, tz=self.values.tz)
return lib.map_infer(
self.values.ravel(), f).reshape(self.values.shape)
return self.values
Example #6
| Source File: internals.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def get_values(self, dtype=None):
"""
return object dtype as boxed values, such as Timestamps/Timedelta
"""
if is_object_dtype(dtype):
return lib.map_infer(self.values.ravel(),
self._box_func).reshape(self.values.shape)
return self.values
Example #7
| Source File: strings.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
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)
Example #8
| Source File: internals.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def get_values(self, dtype=None):
# return object dtype as Timestamps with the zones
if is_object_dtype(dtype):
f = lambda x: lib.Timestamp(x, tz=self.values.tz)
return lib.map_infer(
self.values.ravel(), f).reshape(self.values.shape)
return self.values
Example #9
| Source File: internals.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def get_values(self, dtype=None):
"""
return object dtype as boxed values, such as Timestamps/Timedelta
"""
if is_object_dtype(dtype):
return lib.map_infer(self.values.ravel(),
self._box_func).reshape(self.values.shape)
return self.values
Example #10
| Source File: strings.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
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)
Example #11
| Source File: datetimelike.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def _box_values(self, values):
"""
apply box func to passed values
"""
return lib.map_infer(values, self._box_func)
Example #12
| Source File: strings.py From vnpy_crypto with MIT License | 5 votes |
|
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)
Example #13
| Source File: internals.py From vnpy_crypto with MIT License | 5 votes |
|
def get_values(self, dtype=None):
# return object dtype as Timestamps with the zones
if is_object_dtype(dtype):
return lib.map_infer(
self.values.ravel(), self._box_func).reshape(self.values.shape)
return self.values
Example #14
| Source File: internals.py From vnpy_crypto with MIT License | 5 votes |
|
def get_values(self, dtype=None):
"""
return object dtype as boxed values, such as Timestamps/Timedelta
"""
if is_object_dtype(dtype):
return lib.map_infer(self.values.ravel(),
self._box_func).reshape(self.values.shape)
return self.values
Example #15
| Source File: datetimelike.py From vnpy_crypto with MIT License | 5 votes |
|
def _box_values(self, values):
"""
apply box func to passed values
"""
return lib.map_infer(values, self._box_func)
Example #16
| Source File: strings.py From recruit with Apache License 2.0 | 5 votes |
|
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)
Example #17
| Source File: parsers.py From vnpy_crypto with MIT License | 4 votes |
|
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
Example #18
| Source File: strings.py From vnpy_crypto with MIT License | 4 votes |
|
def str_get_dummies(arr, sep='|'):
"""
Split each string in the Series by sep and return a frame of
dummy/indicator variables.
Parameters
----------
sep : string, default "|"
String to split on.
Returns
-------
dummies : DataFrame
Examples
--------
>>> Series(['a|b', 'a', 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 1 0 0
2 1 0 1
>>> Series(['a|b', np.nan, 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 0 0 0
2 1 0 1
See Also
--------
pandas.get_dummies
"""
arr = arr.fillna('')
try:
arr = sep + arr + sep
except TypeError:
arr = sep + arr.astype(str) + sep
tags = set()
for ts in arr.str.split(sep):
tags.update(ts)
tags = sorted(tags - set([""]))
dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
for i, t in enumerate(tags):
pat = sep + t + sep
dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
return dummies, tags
Example #19
| Source File: strings.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def str_get_dummies(arr, sep='|'):
"""
Split each string in the Series by sep and return a frame of
dummy/indicator variables.
Parameters
----------
sep : string, default "|"
String to split on.
Returns
-------
dummies : DataFrame
See Also
--------
get_dummies
Examples
--------
>>> pd.Series(['a|b', 'a', 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 1 0 0
2 1 0 1
>>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 0 0 0
2 1 0 1
"""
arr = arr.fillna('')
try:
arr = sep + arr + sep
except TypeError:
arr = sep + arr.astype(str) + sep
tags = set()
for ts in arr.str.split(sep):
tags.update(ts)
tags = sorted(tags - {""})
dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
for i, t in enumerate(tags):
pat = sep + t + sep
dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
return dummies, tags
Example #20
| Source File: format.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def _format_strings(self):
if self.float_format is None:
float_format = get_option("display.float_format")
if float_format is None:
fmt_str = ('{{x: .{prec:d}g}}'
.format(prec=get_option("display.precision")))
float_format = lambda x: fmt_str.format(x=x)
else:
float_format = self.float_format
formatter = (
self.formatter if self.formatter is not None else
(lambda x: pprint_thing(x, escape_chars=('\t', '\r', '\n'))))
def _format(x):
if self.na_rep is not None and is_scalar(x) and isna(x):
if x is None:
return 'None'
elif x is NaT:
return 'NaT'
return self.na_rep
elif isinstance(x, PandasObject):
return u'{x}'.format(x=x)
else:
# object dtype
return u'{x}'.format(x=formatter(x))
vals = self.values
if isinstance(vals, Index):
vals = vals._values
elif isinstance(vals, ABCSparseArray):
vals = vals.values
is_float_type = lib.map_infer(vals, is_float) & notna(vals)
leading_space = self.leading_space
if leading_space is None:
leading_space = is_float_type.any()
fmt_values = []
for i, v in enumerate(vals):
if not is_float_type[i] and leading_space:
fmt_values.append(u' {v}'.format(v=_format(v)))
elif is_float_type[i]:
fmt_values.append(float_format(v))
else:
if leading_space is False:
# False specifically, so that the default is
# to include a space if we get here.
tpl = u'{v}'
else:
tpl = u' {v}'
fmt_values.append(tpl.format(v=_format(v)))
return fmt_values
Example #21
| Source File: strings.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def str_get_dummies(arr, sep='|'):
"""
Split each string in the Series by sep and return a frame of
dummy/indicator variables.
Parameters
----------
sep : string, default "|"
String to split on.
Returns
-------
dummies : DataFrame
Examples
--------
>>> Series(['a|b', 'a', 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 1 0 0
2 1 0 1
>>> Series(['a|b', np.nan, 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 0 0 0
2 1 0 1
See Also
--------
pandas.get_dummies
"""
arr = arr.fillna('')
try:
arr = sep + arr + sep
except TypeError:
arr = sep + arr.astype(str) + sep
tags = set()
for ts in arr.str.split(sep):
tags.update(ts)
tags = sorted(tags - set([""]))
dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
for i, t in enumerate(tags):
pat = sep + t + sep
dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
return dummies, tags
Example #22
| Source File: format.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def _format_strings(self):
if self.float_format is None:
float_format = get_option("display.float_format")
if float_format is None:
fmt_str = ('{{x: .{prec:d}g}}'
.format(prec=get_option("display.precision")))
float_format = lambda x: fmt_str.format(x=x)
else:
float_format = self.float_format
formatter = (
self.formatter if self.formatter is not None else
(lambda x: pprint_thing(x, escape_chars=('\t', '\r', '\n'))))
def _format(x):
if self.na_rep is not None and lib.checknull(x):
if x is None:
return 'None'
elif x is pd.NaT:
return 'NaT'
return self.na_rep
elif isinstance(x, PandasObject):
return u'{x}'.format(x=x)
else:
# object dtype
return u'{x}'.format(x=formatter(x))
vals = self.values
if isinstance(vals, Index):
vals = vals._values
elif isinstance(vals, ABCSparseArray):
vals = vals.values
is_float_type = lib.map_infer(vals, is_float) & notna(vals)
leading_space = is_float_type.any()
fmt_values = []
for i, v in enumerate(vals):
if not is_float_type[i] and leading_space:
fmt_values.append(u' {v}'.format(v=_format(v)))
elif is_float_type[i]:
fmt_values.append(float_format(v))
else:
fmt_values.append(u' {v}'.format(v=_format(v)))
return fmt_values
Example #23
| Source File: format.py From recruit with Apache License 2.0 | 4 votes |
|
def _format_strings(self):
if self.float_format is None:
float_format = get_option("display.float_format")
if float_format is None:
fmt_str = ('{{x: .{prec:d}g}}'
.format(prec=get_option("display.precision")))
float_format = lambda x: fmt_str.format(x=x)
else:
float_format = self.float_format
formatter = (
self.formatter if self.formatter is not None else
(lambda x: pprint_thing(x, escape_chars=('\t', '\r', '\n'))))
def _format(x):
if self.na_rep is not None and is_scalar(x) and isna(x):
if x is None:
return 'None'
elif x is NaT:
return 'NaT'
return self.na_rep
elif isinstance(x, PandasObject):
return u'{x}'.format(x=x)
else:
# object dtype
return u'{x}'.format(x=formatter(x))
vals = self.values
if isinstance(vals, Index):
vals = vals._values
elif isinstance(vals, ABCSparseArray):
vals = vals.values
is_float_type = lib.map_infer(vals, is_float) & notna(vals)
leading_space = self.leading_space
if leading_space is None:
leading_space = is_float_type.any()
fmt_values = []
for i, v in enumerate(vals):
if not is_float_type[i] and leading_space:
fmt_values.append(u' {v}'.format(v=_format(v)))
elif is_float_type[i]:
fmt_values.append(float_format(v))
else:
if leading_space is False:
# False specifically, so that the default is
# to include a space if we get here.
tpl = u'{v}'
else:
tpl = u' {v}'
fmt_values.append(tpl.format(v=_format(v)))
return fmt_values
Example #24
| Source File: strings.py From recruit with Apache License 2.0 | 4 votes |
|
def str_get_dummies(arr, sep='|'):
"""
Split each string in the Series by sep and return a frame of
dummy/indicator variables.
Parameters
----------
sep : string, default "|"
String to split on.
Returns
-------
dummies : DataFrame
See Also
--------
get_dummies
Examples
--------
>>> pd.Series(['a|b', 'a', 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 1 0 0
2 1 0 1
>>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 0 0 0
2 1 0 1
"""
arr = arr.fillna('')
try:
arr = sep + arr + sep
except TypeError:
arr = sep + arr.astype(str) + sep
tags = set()
for ts in arr.str.split(sep):
tags.update(ts)
tags = sorted(tags - {""})
dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
for i, t in enumerate(tags):
pat = sep + t + sep
dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
return dummies, tags
Example #25
| Source File: strings.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def str_get_dummies(arr, sep='|'):
"""
Split each string in the Series by sep and return a frame of
dummy/indicator variables.
Parameters
----------
sep : string, default "|"
String to split on.
Returns
-------
dummies : DataFrame
Examples
--------
>>> Series(['a|b', 'a', 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 1 0 0
2 1 0 1
>>> Series(['a|b', np.nan, 'a|c']).str.get_dummies()
a b c
0 1 1 0
1 0 0 0
2 1 0 1
See Also
--------
pandas.get_dummies
"""
arr = arr.fillna('')
try:
arr = sep + arr + sep
except TypeError:
arr = sep + arr.astype(str) + sep
tags = set()
for ts in arr.str.split(sep):
tags.update(ts)
tags = sorted(tags - set([""]))
dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
for i, t in enumerate(tags):
pat = sep + t + sep
dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
return dummies, tags
Example #26
| Source File: format.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def _format_strings(self):
if self.float_format is None:
float_format = get_option("display.float_format")
if float_format is None:
fmt_str = ('{{x: .{prec:d}g}}'
.format(prec=get_option("display.precision")))
float_format = lambda x: fmt_str.format(x=x)
else:
float_format = self.float_format
formatter = (
self.formatter if self.formatter is not None else
(lambda x: pprint_thing(x, escape_chars=('\t', '\r', '\n'))))
def _format(x):
if self.na_rep is not None and lib.checknull(x):
if x is None:
return 'None'
elif x is pd.NaT:
return 'NaT'
return self.na_rep
elif isinstance(x, PandasObject):
return u'{x}'.format(x=x)
else:
# object dtype
return u'{x}'.format(x=formatter(x))
vals = self.values
if isinstance(vals, Index):
vals = vals._values
elif isinstance(vals, ABCSparseArray):
vals = vals.values
is_float_type = lib.map_infer(vals, is_float) & notna(vals)
leading_space = is_float_type.any()
fmt_values = []
for i, v in enumerate(vals):
if not is_float_type[i] and leading_space:
fmt_values.append(u' {v}'.format(v=_format(v)))
elif is_float_type[i]:
fmt_values.append(float_format(v))
else:
fmt_values.append(u' {v}'.format(v=_format(v)))
return fmt_values
