def __setitem__(self, key, value):
"""Sets values based on `key`.
All the functionality of ``ndarray.__setitem__()`` is supported
(including fancy indexing), plus a special support for expressions.
Parameters
----------
key : string, int, tuple, slice
If string and it matches a column name, this will be set to
`value`. If string, but not a column name, it will be
interpreted as a boolean expression (computed via `ctable.eval`)
and the rows where these values are true will be set to `value`.
If int or slice, then the corresponding rows will be set to
`value`.
value : object
The values to be set.
See Also
--------
ctable.eval
"""
if isinstance(key, (bytes, str)):
# First, check if the key is a column name
if key in self.names:
# Yes, so overwrite it
self.cols[key] = value
return
# Else, convert value into a structured array
value = utils.to_ndarray(value, self.dtype)
# Check if key is a condition actually
if isinstance(key, (bytes, str)):
# Convert key into a boolean array
# key = self.eval(key)
# The method below is faster (specially for large ctables)
rowval = 0
for nrow in self.where(key, outcols=["nrow__"]):
nrow = nrow[0]
if len(value) == 1:
for name in self.names:
self.cols[name][nrow] = value[name]
else:
for name in self.names:
self.cols[name][nrow] = value[name][rowval]
rowval += 1
return
# key should int or slice, so modify the rows
for name in self.names:
self.cols[name][key] = value[name]
return
def __setitem__(self, key, value):
"""Sets values based on `key`.
All the functionality of ``ndarray.__setitem__()`` is supported
(including fancy indexing), plus a special support for expressions.
Parameters
----------
key : string, int, tuple, slice
If string and it matches a column name, this will be set to
`value`. If string, but not a column name, it will be
interpreted as a boolean expression (computed via `ctable.eval`)
and the rows where these values are true will be set to `value`.
If int or slice, then the corresponding rows will be set to
`value`.
value : object
The values to be set.
See Also
--------
ctable.eval
"""
if isinstance(key, (bytes, str)):
# First, check if the key is a column name
if key in self.names:
# Yes, so overwrite it
self.cols[key] = value
return
# Else, convert value into a structured array
value = utils.to_ndarray(value, self.dtype)
# Check if key is a condition actually
if isinstance(key, (bytes, str)):
# Convert key into a boolean array
# key = self.eval(key)
# The method below is faster (specially for large ctables)
rowval = 0
for nrow in self.where(key, outcols=["nrow__"]):
nrow = nrow[0]
if len(value) == 1:
for name in self.names:
self.cols[name][nrow] = value[name]
else:
for name in self.names:
self.cols[name][nrow] = value[name][rowval]
rowval += 1
return
# key should int or slice, so modify the rows
for name in self.names:
self.cols[name][key] = value[name]
return
def __setitem__(self, key, value):
"""
x.__setitem__(key, value) <==> x[key] = value
Sets values based on `key`. All the functionality of
``ndarray.__setitem__()`` is supported (including fancy
indexing), plus a special support for expressions:
Parameters
----------
key : string
The corresponding ctable column name will be set to `value`. If
not a column name, it will be interpret as a boolean expression
(computed via `ctable.eval`) and the rows where these values are
true will be set to `value`.
See Also
--------
ctable.eval
"""
# First, convert value into a structured array
value = utils.to_ndarray(value, self.dtype)
# Check if key is a condition actually
if type(key) is bytes:
# Convert key into a boolean array
# key = self.eval(key)
# The method below is faster (specially for large ctables)
rowval = 0
for nrow in self.where(key, outcols=["nrow__"]):
nrow = nrow[0]
if len(value) == 1:
for name in self.names:
self.cols[name][nrow] = value[name]
else:
for name in self.names:
self.cols[name][nrow] = value[name][rowval]
rowval += 1
return
# Then, modify the rows
for name in self.names:
self.cols[name][key] = value[name]
return
def __setitem__(self, key, value):
"""
x.__setitem__(key, value) <==> x[key] = value
Sets values based on `key`. All the functionality of
``ndarray.__setitem__()`` is supported (including fancy
indexing), plus a special support for expressions:
Parameters
----------
key : string
The corresponding ctable column name will be set to `value`. If
not a column name, it will be interpret as a boolean expression
(computed via `ctable.eval`) and the rows where these values are
true will be set to `value`.
See Also
--------
ctable.eval
"""
# First, convert value into a structured array
value = utils.to_ndarray(value, self.dtype)
# Check if key is a condition actually
if type(key) is bytes:
# Convert key into a boolean array
# key = self.eval(key)
# The method below is faster (specially for large ctables)
rowval = 0
for nrow in self.where(key, outcols=["nrow__"]):
nrow = nrow[0]
if len(value) == 1:
for name in self.names:
self.cols[name][nrow] = value[name]
else:
for name in self.names:
self.cols[name][nrow] = value[name][rowval]
rowval += 1
return
# Then, modify the rows
for name in self.names:
self.cols[name][key] = value[name]
return
