def repeat_index(index, n):
"""Repeat each element in `index` `n` times."""
if not isinstance(index, pd.Index):
index = pd.Index(index)
if checks.is_default_index(index): # ignore simple ranges without name
return pd.RangeIndex(start=0, stop=len(index) * n, step=1)
return np.repeat(index, n)
def tile_index(index, n):
"""Tile the whole `index` `n` times."""
if not isinstance(index, pd.Index):
index = pd.Index(index)
if checks.is_default_index(index): # ignore simple ranges without name
return pd.RangeIndex(start=0, stop=len(index) * n, step=1)
if isinstance(index, pd.MultiIndex):
return pd.MultiIndex.from_tuples(np.tile(index, n), names=index.names)
return pd.Index(np.tile(index, n), name=index.name)
def repeat_index(index, n, ignore_default=None):
"""Repeat each element in `index` `n` times."""
from vectorbt import settings
if ignore_default is None:
ignore_default = settings.broadcasting['ignore_default']
if not isinstance(index, pd.Index):
index = pd.Index(index)
if checks.is_default_index(index) and ignore_default: # ignore simple ranges without name
return pd.RangeIndex(start=0, stop=len(index) * n, step=1)
return np.repeat(index, n)
def tile_index(index, n, ignore_default=None):
"""Tile the whole `index` `n` times."""
from vectorbt import settings
if ignore_default is None:
ignore_default = settings.broadcasting['ignore_default']
if not isinstance(index, pd.Index):
index = pd.Index(index)
if checks.is_default_index(index) and ignore_default: # ignore simple ranges without name
return pd.RangeIndex(start=0, stop=len(index) * n, step=1)
if isinstance(index, pd.MultiIndex):
return pd.MultiIndex.from_tuples(np.tile(index, n), names=index.names)
return pd.Index(np.tile(index, n), name=index.name)
def drop_redundant_levels(index):
"""Drop levels in `index` that either have a single unnamed value or a range from 0 to n."""
if not isinstance(index, pd.MultiIndex):
return index
levels_to_drop = []
for i in range(index.nlevels):
if len(index) > 1 and len(index.levels[i]) == 1 and index.levels[i].name is None:
levels_to_drop.append(i)
elif checks.is_default_index(index.get_level_values(i)):
levels_to_drop.append(i)
# Remove redundant levels only if there are some non-redundant levels left
if len(levels_to_drop) < index.nlevels:
return index.droplevel(levels_to_drop)
return index
def repeat_index(index: tp.IndexLike,
n: int,
ignore_default: tp.Optional[bool] = None) -> tp.Index:
"""Repeat each element in `index` `n` times.
Set `ignore_default` to None to use the default."""
from vectorbt import settings
if ignore_default is None:
ignore_default = settings.broadcasting['ignore_default']
index = to_any_index(index)
if checks.is_default_index(
index) and ignore_default: # ignore simple ranges without name
return pd.RangeIndex(start=0, stop=len(index) * n, step=1)
return index.repeat(n)
def tile_index(index: tp.IndexLike,
n: int,
ignore_default: tp.Optional[bool] = None) -> tp.Index:
"""Tile the whole `index` `n` times.
Set `ignore_default` to None to use the default."""
from vectorbt import settings
if ignore_default is None:
ignore_default = settings.broadcasting['ignore_default']
index = to_any_index(index)
if checks.is_default_index(
index) and ignore_default: # ignore simple ranges without name
return pd.RangeIndex(start=0, stop=len(index) * n, step=1)
if isinstance(index, pd.MultiIndex):
return pd.MultiIndex.from_tuples(np.tile(index, n), names=index.names)
return pd.Index(np.tile(index, n), name=index.name)
def broadcast_index(args,
to_shape,
index_from=None,
axis=0,
ignore_sr_names=None,
**kwargs):
"""Produce a broadcast index/columns.
Args:
*args (array_like): Array-like objects.
to_shape (tuple): Target shape.
index_from (None, int, str or array_like): Broadcasting rule for this index/these columns.
Accepts the following values:
* 'default' - take the value from `vectorbt.settings.broadcasting`
* 'strict' - ensure that all pandas objects have the same index/columns
* 'stack' - stack different indexes/columns using `vectorbt.base.index_fns.stack_indexes`
* 'ignore' - ignore any index/columns
* integer - use the index/columns of the i-nth object in `args`
* None - use the original index/columns of the objects in `args`
* everything else will be converted to `pd.Index`
axis (int): Set to 0 for index and 1 for columns.
ignore_sr_names (bool): Whether to ignore Series names if they are in conflict.
Conflicting Series names are those that are different but not None.
**kwargs: Keyword arguments passed to `vectorbt.base.index_fns.stack_indexes`.
For defaults, see `vectorbt.settings.broadcasting`.
!!! note
Series names are treated as columns with a single element but without a name.
If a column level without a name loses its meaning, better to convert Series to DataFrames
with one column prior to broadcasting. If the name of a Series is not that important,
better to drop it altogether by setting it to None.
"""
from vectorbt import settings
if ignore_sr_names is None:
ignore_sr_names = settings.broadcasting['ignore_sr_names']
index_str = 'columns' if axis == 1 else 'index'
to_shape_2d = (to_shape[0], 1) if len(to_shape) == 1 else to_shape
# maxlen stores the length of the longest index
maxlen = to_shape_2d[1] if axis == 1 else to_shape_2d[0]
new_index = None
if index_from is not None:
if isinstance(index_from, int):
# Take index/columns of the object indexed by index_from
if not checks.is_pandas(args[index_from]):
raise TypeError(
f"Argument under index {index_from} must be a pandas object"
)
new_index = index_fns.get_index(args[index_from], axis)
elif isinstance(index_from, str):
if index_from == 'ignore':
# Ignore index/columns
new_index = pd.RangeIndex(start=0, stop=maxlen, step=1)
elif index_from in ('stack', 'strict'):
# Check whether all indexes/columns are equal
last_index = None # of type pd.Index
index_conflict = False
for arg in args:
if checks.is_pandas(arg):
index = index_fns.get_index(arg, axis)
if last_index is not None:
if not pd.Index.equals(index, last_index):
index_conflict = True
last_index = index
continue
if not index_conflict:
new_index = last_index
else:
# If pandas objects have different index/columns, stack them together
for arg in args:
if checks.is_pandas(arg):
index = index_fns.get_index(arg, axis)
if axis == 1 and checks.is_series(
arg) and ignore_sr_names:
# ignore Series name
continue
if checks.is_default_index(index):
# ignore simple ranges without name
continue
if new_index is None:
new_index = index
else:
if index_from == 'strict':
# If pandas objects have different index/columns, raise an exception
if not pd.Index.equals(index, new_index):
raise ValueError(
f"Broadcasting {index_str} is not allowed when {index_str}_from=strict"
)
# Broadcasting index must follow the rules of a regular broadcasting operation
# https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html#general-broadcasting-rules
# 1. rule: if indexes are of the same length, they are simply stacked
# 2. rule: if index has one element, it gets repeated and then stacked
if pd.Index.equals(index, new_index):
continue
if len(index) != len(new_index):
if len(index) > 1 and len(new_index) > 1:
raise ValueError(
"Indexes could not be broadcast together"
)
if len(index) > len(new_index):
new_index = index_fns.repeat_index(
new_index, len(index))
elif len(index) < len(new_index):
index = index_fns.repeat_index(
index, len(new_index))
new_index = index_fns.stack_indexes(
new_index, index, **kwargs)
else:
raise ValueError(
f"Invalid value {index_from} for {'columns' if axis == 1 else 'index'}_from"
)
else:
new_index = index_from
if new_index is not None:
if maxlen > len(new_index):
if index_from == 'strict':
raise ValueError(
f"Broadcasting {index_str} is not allowed when {index_str}_from=strict"
)
# This happens only when some numpy object is longer than the new pandas index
# In this case, new pandas index (one element) should be repeated to match this length.
if maxlen > 1 and len(new_index) > 1:
raise ValueError("Indexes could not be broadcast together")
new_index = index_fns.repeat_index(new_index, maxlen)
elif index_from is not None:
# new_index=None can mean two things: 1) take original metadata or 2) reset index/columns
# In case when index_from is not None, we choose 2)
new_index = pd.RangeIndex(start=0, stop=maxlen, step=1)
return new_index
def broadcast_index(args, to_shape, index_from=None, axis=0, **kwargs):
"""Produce a broadcasted index/columns.
Args:
*args (array_like): Array-like objects.
to_shape (tuple): Target shape.
index_from (None, int, str or array_like): Broadcasting rule for this index/these columns.
Accepts the following values:
* `'default'` - take the value from `vectorbt.defaults.broadcasting`
* `None` - use the original index/columns of the objects in `args`
* `int` - use the index/columns of the i-nth object in `args`
* `'strict'` - ensure that all pandas objects have the same index/columns
* `'stack'` - stack different indexes/columns using `vectorbt.base.index_fns.stack_indexes`
* everything else will be converted to `pd.Index`
axis (int): Set to 0 for index and 1 for columns.
**kwargs: Keyword arguments passed to `vectorbt.base.index_fns.stack_indexes`.
For defaults, see `vectorbt.defaults.broadcasting`.
"""
index_str = 'columns' if axis == 1 else 'index'
new_index = None
if axis == 1 and len(to_shape) == 1:
to_shape = (to_shape[0], 1)
maxlen = to_shape[1] if axis == 1 else to_shape[0]
if index_from is not None:
if isinstance(index_from, int):
# Take index/columns of the object indexed by index_from
if not checks.is_pandas(args[index_from]):
raise TypeError(
f"Argument under index {index_from} must be a pandas object"
)
new_index = index_fns.get_index(args[index_from], axis)
elif isinstance(index_from, str):
if index_from in ('stack', 'strict'):
# If pandas objects have different index/columns, stack them together
# maxlen stores the length of the longest index
for arg in args:
if checks.is_pandas(arg):
index = index_fns.get_index(arg, axis)
if checks.is_default_index(index):
# ignore simple ranges without name
continue
if new_index is None:
new_index = index
else:
if index_from == 'strict':
# If pandas objects have different index/columns, raise an exception
if not pd.Index.equals(index, new_index):
raise ValueError(
f"Broadcasting {index_str} is not allowed for {index_str}_from=strict"
)
# Broadcasting index must follow the rules of a regular broadcasting operation
# https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html#general-broadcasting-rules
# 1. rule: if indexes are of the same length, they are simply stacked
# 2. rule: if index has one element, it gets repeated and then stacked
if pd.Index.equals(index, new_index):
continue
if len(index) != len(new_index):
if len(index) > 1 and len(new_index) > 1:
raise ValueError(
"Indexes could not be broadcast together"
)
if len(index) > len(new_index):
new_index = index_fns.repeat_index(
new_index, len(index))
elif len(index) < len(new_index):
index = index_fns.repeat_index(
index, len(new_index))
new_index = index_fns.stack_indexes(
new_index, index, **kwargs)
else:
raise ValueError(
f"Invalid value {index_from} for {'columns' if axis == 1 else 'index'}_from"
)
else:
new_index = index_from
if new_index is not None:
if maxlen > len(new_index):
if index_from == 'strict':
raise ValueError(
f"Broadcasting {index_str} is not allowed for {index_str}_from=strict"
)
# This happens only when some numpy object is longer than the new pandas index
# In this case, new pandas index (one element) should be repeated to match this length.
if maxlen > 1 and len(new_index) > 1:
raise ValueError("Indexes could not be broadcast together")
new_index = index_fns.repeat_index(new_index, maxlen)
return new_index
def test_is_default_index(self):
assert checks.is_default_index(pd.DataFrame([[1, 2, 3]]).columns)
assert checks.is_default_index(pd.Series([1, 2, 3]).to_frame().columns)
assert checks.is_default_index(pd.Index([0, 1, 2]))
assert not checks.is_default_index(pd.Index([0, 1, 2], name='name'))
