INSTRUCTION
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Parameters
----------
keyarr : list-like
Indexer to convert.
Returns
-------
indexer : numpy.ndarray or None
Return an ndarray or None if cannot convert.
keyarr : numpy.ndarray
Return tuple-safe keys. | def _convert_listlike_indexer(self, keyarr, kind=None):
"""
Parameters
----------
keyarr : list-like
Indexer to convert.
Returns
-------
indexer : numpy.ndarray or None
Return an ndarray or None if cannot convert.
keyarr : numpy.ndarray
Return tuple-safe keys.
"""
if isinstance(keyarr, Index):
keyarr = self._convert_index_indexer(keyarr)
else:
keyarr = self._convert_arr_indexer(keyarr)
indexer = self._convert_list_indexer(keyarr, kind=kind)
return indexer, keyarr |
Consistent invalid indexer message. | def _invalid_indexer(self, form, key):
"""
Consistent invalid indexer message.
"""
raise TypeError("cannot do {form} indexing on {klass} with these "
"indexers [{key}] of {kind}".format(
form=form, klass=type(self), key=key,
kind=type(key))) |
Create index with target's values (move/add/delete values
as necessary).
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in original index. | def reindex(self, target, method=None, level=None, limit=None,
tolerance=None):
"""
Create index with target's values (move/add/delete values
as necessary).
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in original index.
"""
# GH6552: preserve names when reindexing to non-named target
# (i.e. neither Index nor Series).
preserve_names = not hasattr(target, 'name')
# GH7774: preserve dtype/tz if target is empty and not an Index.
target = _ensure_has_len(target) # target may be an iterator
if not isinstance(target, Index) and len(target) == 0:
attrs = self._get_attributes_dict()
attrs.pop('freq', None) # don't preserve freq
values = self._data[:0] # appropriately-dtyped empty array
target = self._simple_new(values, dtype=self.dtype, **attrs)
else:
target = ensure_index(target)
if level is not None:
if method is not None:
raise TypeError('Fill method not supported if level passed')
_, indexer, _ = self._join_level(target, level, how='right',
return_indexers=True)
else:
if self.equals(target):
indexer = None
else:
if self.is_unique:
indexer = self.get_indexer(target, method=method,
limit=limit,
tolerance=tolerance)
else:
if method is not None or limit is not None:
raise ValueError("cannot reindex a non-unique index "
"with a method or limit")
indexer, missing = self.get_indexer_non_unique(target)
if preserve_names and target.nlevels == 1 and target.name != self.name:
target = target.copy()
target.name = self.name
return target, indexer |
Create a new index with target's values (move/add/delete values as
necessary) use with non-unique Index and a possibly non-unique target.
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in original index. | def _reindex_non_unique(self, target):
"""
Create a new index with target's values (move/add/delete values as
necessary) use with non-unique Index and a possibly non-unique target.
Parameters
----------
target : an iterable
Returns
-------
new_index : pd.Index
Resulting index.
indexer : np.ndarray or None
Indices of output values in original index.
"""
target = ensure_index(target)
indexer, missing = self.get_indexer_non_unique(target)
check = indexer != -1
new_labels = self.take(indexer[check])
new_indexer = None
if len(missing):
length = np.arange(len(indexer))
missing = ensure_platform_int(missing)
missing_labels = target.take(missing)
missing_indexer = ensure_int64(length[~check])
cur_labels = self.take(indexer[check]).values
cur_indexer = ensure_int64(length[check])
new_labels = np.empty(tuple([len(indexer)]), dtype=object)
new_labels[cur_indexer] = cur_labels
new_labels[missing_indexer] = missing_labels
# a unique indexer
if target.is_unique:
# see GH5553, make sure we use the right indexer
new_indexer = np.arange(len(indexer))
new_indexer[cur_indexer] = np.arange(len(cur_labels))
new_indexer[missing_indexer] = -1
# we have a non_unique selector, need to use the original
# indexer here
else:
# need to retake to have the same size as the indexer
indexer[~check] = -1
# reset the new indexer to account for the new size
new_indexer = np.arange(len(self.take(indexer)))
new_indexer[~check] = -1
new_index = self._shallow_copy_with_infer(new_labels, freq=None)
return new_index, indexer, new_indexer |
The join method *only* affects the level of the resulting
MultiIndex. Otherwise it just exactly aligns the Index data to the
labels of the level in the MultiIndex.
If ```keep_order == True```, the order of the data indexed by the
MultiIndex will not be changed; otherwise, it will tie out
with `other`. | def _join_level(self, other, level, how='left', return_indexers=False,
keep_order=True):
"""
The join method *only* affects the level of the resulting
MultiIndex. Otherwise it just exactly aligns the Index data to the
labels of the level in the MultiIndex.
If ```keep_order == True```, the order of the data indexed by the
MultiIndex will not be changed; otherwise, it will tie out
with `other`.
"""
from .multi import MultiIndex
def _get_leaf_sorter(labels):
"""
Returns sorter for the inner most level while preserving the
order of higher levels.
"""
if labels[0].size == 0:
return np.empty(0, dtype='int64')
if len(labels) == 1:
lab = ensure_int64(labels[0])
sorter, _ = libalgos.groupsort_indexer(lab, 1 + lab.max())
return sorter
# find indexers of beginning of each set of
# same-key labels w.r.t all but last level
tic = labels[0][:-1] != labels[0][1:]
for lab in labels[1:-1]:
tic |= lab[:-1] != lab[1:]
starts = np.hstack(([True], tic, [True])).nonzero()[0]
lab = ensure_int64(labels[-1])
return lib.get_level_sorter(lab, ensure_int64(starts))
if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
raise TypeError('Join on level between two MultiIndex objects '
'is ambiguous')
left, right = self, other
flip_order = not isinstance(self, MultiIndex)
if flip_order:
left, right = right, left
how = {'right': 'left', 'left': 'right'}.get(how, how)
level = left._get_level_number(level)
old_level = left.levels[level]
if not right.is_unique:
raise NotImplementedError('Index._join_level on non-unique index '
'is not implemented')
new_level, left_lev_indexer, right_lev_indexer = \
old_level.join(right, how=how, return_indexers=True)
if left_lev_indexer is None:
if keep_order or len(left) == 0:
left_indexer = None
join_index = left
else: # sort the leaves
left_indexer = _get_leaf_sorter(left.codes[:level + 1])
join_index = left[left_indexer]
else:
left_lev_indexer = ensure_int64(left_lev_indexer)
rev_indexer = lib.get_reverse_indexer(left_lev_indexer,
len(old_level))
new_lev_codes = algos.take_nd(rev_indexer, left.codes[level],
allow_fill=False)
new_codes = list(left.codes)
new_codes[level] = new_lev_codes
new_levels = list(left.levels)
new_levels[level] = new_level
if keep_order: # just drop missing values. o.w. keep order
left_indexer = np.arange(len(left), dtype=np.intp)
mask = new_lev_codes != -1
if not mask.all():
new_codes = [lab[mask] for lab in new_codes]
left_indexer = left_indexer[mask]
else: # tie out the order with other
if level == 0: # outer most level, take the fast route
ngroups = 1 + new_lev_codes.max()
left_indexer, counts = libalgos.groupsort_indexer(
new_lev_codes, ngroups)
# missing values are placed first; drop them!
left_indexer = left_indexer[counts[0]:]
new_codes = [lab[left_indexer] for lab in new_codes]
else: # sort the leaves
mask = new_lev_codes != -1
mask_all = mask.all()
if not mask_all:
new_codes = [lab[mask] for lab in new_codes]
left_indexer = _get_leaf_sorter(new_codes[:level + 1])
new_codes = [lab[left_indexer] for lab in new_codes]
# left_indexers are w.r.t masked frame.
# reverse to original frame!
if not mask_all:
left_indexer = mask.nonzero()[0][left_indexer]
join_index = MultiIndex(levels=new_levels, codes=new_codes,
names=left.names, verify_integrity=False)
if right_lev_indexer is not None:
right_indexer = algos.take_nd(right_lev_indexer,
join_index.codes[level],
allow_fill=False)
else:
right_indexer = join_index.codes[level]
if flip_order:
left_indexer, right_indexer = right_indexer, left_indexer
if return_indexers:
left_indexer = (None if left_indexer is None
else ensure_platform_int(left_indexer))
right_indexer = (None if right_indexer is None
else ensure_platform_int(right_indexer))
return join_index, left_indexer, right_indexer
else:
return join_index |
Attempt to convert an array of data into an integer index.
Parameters
----------
data : The data to convert.
copy : Whether to copy the data or not.
name : The name of the index returned.
Returns
-------
int_index : data converted to either an Int64Index or a
UInt64Index
Raises
------
ValueError if the conversion was not successful. | def _try_convert_to_int_index(cls, data, copy, name, dtype):
"""
Attempt to convert an array of data into an integer index.
Parameters
----------
data : The data to convert.
copy : Whether to copy the data or not.
name : The name of the index returned.
Returns
-------
int_index : data converted to either an Int64Index or a
UInt64Index
Raises
------
ValueError if the conversion was not successful.
"""
from .numeric import Int64Index, UInt64Index
if not is_unsigned_integer_dtype(dtype):
# skip int64 conversion attempt if uint-like dtype is passed, as
# this could return Int64Index when UInt64Index is what's desrired
try:
res = data.astype('i8', copy=False)
if (res == data).all():
return Int64Index(res, copy=copy, name=name)
except (OverflowError, TypeError, ValueError):
pass
# Conversion to int64 failed (possibly due to overflow) or was skipped,
# so let's try now with uint64.
try:
res = data.astype('u8', copy=False)
if (res == data).all():
return UInt64Index(res, copy=copy, name=name)
except (OverflowError, TypeError, ValueError):
pass
raise ValueError |
Coerces data to ndarray.
Converts other iterables to list first and then to array.
Does not touch ndarrays.
Raises
------
TypeError
When the data passed in is a scalar. | def _coerce_to_ndarray(cls, data):
"""
Coerces data to ndarray.
Converts other iterables to list first and then to array.
Does not touch ndarrays.
Raises
------
TypeError
When the data passed in is a scalar.
"""
if not isinstance(data, (np.ndarray, Index)):
if data is None or is_scalar(data):
cls._scalar_data_error(data)
# other iterable of some kind
if not isinstance(data, (ABCSeries, list, tuple)):
data = list(data)
data = np.asarray(data)
return data |
We need to coerce a scalar to a compat for our index type.
Parameters
----------
item : scalar item to coerce | def _coerce_scalar_to_index(self, item):
"""
We need to coerce a scalar to a compat for our index type.
Parameters
----------
item : scalar item to coerce
"""
dtype = self.dtype
if self._is_numeric_dtype and isna(item):
# We can't coerce to the numeric dtype of "self" (unless
# it's float) if there are NaN values in our output.
dtype = None
return Index([item], dtype=dtype, **self._get_attributes_dict()) |
Check value is valid for scalar op. | def _assert_can_do_op(self, value):
"""
Check value is valid for scalar op.
"""
if not is_scalar(value):
msg = "'value' must be a scalar, passed: {0}"
raise TypeError(msg.format(type(value).__name__)) |
Faster check for ``name in self`` when we know `name` is a Python
identifier (e.g. in NDFrame.__getattr__, which hits this to support
. key lookup). For indexes that can't hold identifiers (everything
but object & categorical) we just return False.
https://github.com/pandas-dev/pandas/issues/19764 | def _can_hold_identifiers_and_holds_name(self, name):
"""
Faster check for ``name in self`` when we know `name` is a Python
identifier (e.g. in NDFrame.__getattr__, which hits this to support
. key lookup). For indexes that can't hold identifiers (everything
but object & categorical) we just return False.
https://github.com/pandas-dev/pandas/issues/19764
"""
if self.is_object() or self.is_categorical():
return name in self
return False |
Append a collection of Index options together.
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index | def append(self, other):
"""
Append a collection of Index options together.
Parameters
----------
other : Index or list/tuple of indices
Returns
-------
appended : Index
"""
to_concat = [self]
if isinstance(other, (list, tuple)):
to_concat = to_concat + list(other)
else:
to_concat.append(other)
for obj in to_concat:
if not isinstance(obj, Index):
raise TypeError('all inputs must be Index')
names = {obj.name for obj in to_concat}
name = None if len(names) > 1 else self.name
return self._concat(to_concat, name) |
Return a new Index of the values set with the mask.
See Also
--------
numpy.ndarray.putmask | def putmask(self, mask, value):
"""
Return a new Index of the values set with the mask.
See Also
--------
numpy.ndarray.putmask
"""
values = self.values.copy()
try:
np.putmask(values, mask, self._convert_for_op(value))
return self._shallow_copy(values)
except (ValueError, TypeError) as err:
if is_object_dtype(self):
raise err
# coerces to object
return self.astype(object).putmask(mask, value) |
Determine if two Index objects contain the same elements. | def equals(self, other):
"""
Determine if two Index objects contain the same elements.
"""
if self.is_(other):
return True
if not isinstance(other, Index):
return False
if is_object_dtype(self) and not is_object_dtype(other):
# if other is not object, use other's logic for coercion
return other.equals(self)
try:
return array_equivalent(com.values_from_object(self),
com.values_from_object(other))
except Exception:
return False |
Similar to equals, but check that other comparable attributes are
also equal. | def identical(self, other):
"""
Similar to equals, but check that other comparable attributes are
also equal.
"""
return (self.equals(other) and
all((getattr(self, c, None) == getattr(other, c, None)
for c in self._comparables)) and
type(self) == type(other)) |
Return the label from the index, or, if not present, the previous one.
Assuming that the index is sorted, return the passed index label if it
is in the index, or return the previous index label if the passed one
is not in the index.
Parameters
----------
label : object
The label up to which the method returns the latest index label.
Returns
-------
object
The passed label if it is in the index. The previous label if the
passed label is not in the sorted index or `NaN` if there is no
such label.
See Also
--------
Series.asof : Return the latest value in a Series up to the
passed index.
merge_asof : Perform an asof merge (similar to left join but it
matches on nearest key rather than equal key).
Index.get_loc : An `asof` is a thin wrapper around `get_loc`
with method='pad'.
Examples
--------
`Index.asof` returns the latest index label up to the passed label.
>>> idx = pd.Index(['2013-12-31', '2014-01-02', '2014-01-03'])
>>> idx.asof('2014-01-01')
'2013-12-31'
If the label is in the index, the method returns the passed label.
>>> idx.asof('2014-01-02')
'2014-01-02'
If all of the labels in the index are later than the passed label,
NaN is returned.
>>> idx.asof('1999-01-02')
nan
If the index is not sorted, an error is raised.
>>> idx_not_sorted = pd.Index(['2013-12-31', '2015-01-02',
... '2014-01-03'])
>>> idx_not_sorted.asof('2013-12-31')
Traceback (most recent call last):
ValueError: index must be monotonic increasing or decreasing | def asof(self, label):
"""
Return the label from the index, or, if not present, the previous one.
Assuming that the index is sorted, return the passed index label if it
is in the index, or return the previous index label if the passed one
is not in the index.
Parameters
----------
label : object
The label up to which the method returns the latest index label.
Returns
-------
object
The passed label if it is in the index. The previous label if the
passed label is not in the sorted index or `NaN` if there is no
such label.
See Also
--------
Series.asof : Return the latest value in a Series up to the
passed index.
merge_asof : Perform an asof merge (similar to left join but it
matches on nearest key rather than equal key).
Index.get_loc : An `asof` is a thin wrapper around `get_loc`
with method='pad'.
Examples
--------
`Index.asof` returns the latest index label up to the passed label.
>>> idx = pd.Index(['2013-12-31', '2014-01-02', '2014-01-03'])
>>> idx.asof('2014-01-01')
'2013-12-31'
If the label is in the index, the method returns the passed label.
>>> idx.asof('2014-01-02')
'2014-01-02'
If all of the labels in the index are later than the passed label,
NaN is returned.
>>> idx.asof('1999-01-02')
nan
If the index is not sorted, an error is raised.
>>> idx_not_sorted = pd.Index(['2013-12-31', '2015-01-02',
... '2014-01-03'])
>>> idx_not_sorted.asof('2013-12-31')
Traceback (most recent call last):
ValueError: index must be monotonic increasing or decreasing
"""
try:
loc = self.get_loc(label, method='pad')
except KeyError:
return self._na_value
else:
if isinstance(loc, slice):
loc = loc.indices(len(self))[-1]
return self[loc] |
Find the locations (indices) of the labels from the index for
every entry in the `where` argument.
As in the `asof` function, if the label (a particular entry in
`where`) is not in the index, the latest index label upto the
passed label is chosen and its index returned.
If all of the labels in the index are later than a label in `where`,
-1 is returned.
`mask` is used to ignore NA values in the index during calculation.
Parameters
----------
where : Index
An Index consisting of an array of timestamps.
mask : array-like
Array of booleans denoting where values in the original
data are not NA.
Returns
-------
numpy.ndarray
An array of locations (indices) of the labels from the Index
which correspond to the return values of the `asof` function
for every element in `where`. | def asof_locs(self, where, mask):
"""
Find the locations (indices) of the labels from the index for
every entry in the `where` argument.
As in the `asof` function, if the label (a particular entry in
`where`) is not in the index, the latest index label upto the
passed label is chosen and its index returned.
If all of the labels in the index are later than a label in `where`,
-1 is returned.
`mask` is used to ignore NA values in the index during calculation.
Parameters
----------
where : Index
An Index consisting of an array of timestamps.
mask : array-like
Array of booleans denoting where values in the original
data are not NA.
Returns
-------
numpy.ndarray
An array of locations (indices) of the labels from the Index
which correspond to the return values of the `asof` function
for every element in `where`.
"""
locs = self.values[mask].searchsorted(where.values, side='right')
locs = np.where(locs > 0, locs - 1, 0)
result = np.arange(len(self))[mask].take(locs)
first = mask.argmax()
result[(locs == 0) & (where.values < self.values[first])] = -1
return result |
Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
that sorted the index itself.
Parameters
----------
return_indexer : bool, default False
Should the indices that would sort the index be returned.
ascending : bool, default True
Should the index values be sorted in an ascending order.
Returns
-------
sorted_index : pandas.Index
Sorted copy of the index.
indexer : numpy.ndarray, optional
The indices that the index itself was sorted by.
See Also
--------
Series.sort_values : Sort values of a Series.
DataFrame.sort_values : Sort values in a DataFrame.
Examples
--------
>>> idx = pd.Index([10, 100, 1, 1000])
>>> idx
Int64Index([10, 100, 1, 1000], dtype='int64')
Sort values in ascending order (default behavior).
>>> idx.sort_values()
Int64Index([1, 10, 100, 1000], dtype='int64')
Sort values in descending order, and also get the indices `idx` was
sorted by.
>>> idx.sort_values(ascending=False, return_indexer=True)
(Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2])) | def sort_values(self, return_indexer=False, ascending=True):
"""
Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
that sorted the index itself.
Parameters
----------
return_indexer : bool, default False
Should the indices that would sort the index be returned.
ascending : bool, default True
Should the index values be sorted in an ascending order.
Returns
-------
sorted_index : pandas.Index
Sorted copy of the index.
indexer : numpy.ndarray, optional
The indices that the index itself was sorted by.
See Also
--------
Series.sort_values : Sort values of a Series.
DataFrame.sort_values : Sort values in a DataFrame.
Examples
--------
>>> idx = pd.Index([10, 100, 1, 1000])
>>> idx
Int64Index([10, 100, 1, 1000], dtype='int64')
Sort values in ascending order (default behavior).
>>> idx.sort_values()
Int64Index([1, 10, 100, 1000], dtype='int64')
Sort values in descending order, and also get the indices `idx` was
sorted by.
>>> idx.sort_values(ascending=False, return_indexer=True)
(Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))
"""
_as = self.argsort()
if not ascending:
_as = _as[::-1]
sorted_index = self.take(_as)
if return_indexer:
return sorted_index, _as
else:
return sorted_index |
Return the integer indices that would sort the index.
Parameters
----------
*args
Passed to `numpy.ndarray.argsort`.
**kwargs
Passed to `numpy.ndarray.argsort`.
Returns
-------
numpy.ndarray
Integer indices that would sort the index if used as
an indexer.
See Also
--------
numpy.argsort : Similar method for NumPy arrays.
Index.sort_values : Return sorted copy of Index.
Examples
--------
>>> idx = pd.Index(['b', 'a', 'd', 'c'])
>>> idx
Index(['b', 'a', 'd', 'c'], dtype='object')
>>> order = idx.argsort()
>>> order
array([1, 0, 3, 2])
>>> idx[order]
Index(['a', 'b', 'c', 'd'], dtype='object') | def argsort(self, *args, **kwargs):
"""
Return the integer indices that would sort the index.
Parameters
----------
*args
Passed to `numpy.ndarray.argsort`.
**kwargs
Passed to `numpy.ndarray.argsort`.
Returns
-------
numpy.ndarray
Integer indices that would sort the index if used as
an indexer.
See Also
--------
numpy.argsort : Similar method for NumPy arrays.
Index.sort_values : Return sorted copy of Index.
Examples
--------
>>> idx = pd.Index(['b', 'a', 'd', 'c'])
>>> idx
Index(['b', 'a', 'd', 'c'], dtype='object')
>>> order = idx.argsort()
>>> order
array([1, 0, 3, 2])
>>> idx[order]
Index(['a', 'b', 'c', 'd'], dtype='object')
"""
result = self.asi8
if result is None:
result = np.array(self)
return result.argsort(*args, **kwargs) |
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing. | def get_value(self, series, key):
"""
Fast lookup of value from 1-dimensional ndarray. Only use this if you
know what you're doing.
"""
# if we have something that is Index-like, then
# use this, e.g. DatetimeIndex
# Things like `Series._get_value` (via .at) pass the EA directly here.
s = getattr(series, '_values', series)
if isinstance(s, (ExtensionArray, Index)) and is_scalar(key):
# GH 20882, 21257
# Unify Index and ExtensionArray treatment
# First try to convert the key to a location
# If that fails, raise a KeyError if an integer
# index, otherwise, see if key is an integer, and
# try that
try:
iloc = self.get_loc(key)
return s[iloc]
except KeyError:
if (len(self) > 0 and
(self.holds_integer() or self.is_boolean())):
raise
elif is_integer(key):
return s[key]
s = com.values_from_object(series)
k = com.values_from_object(key)
k = self._convert_scalar_indexer(k, kind='getitem')
try:
return self._engine.get_value(s, k,
tz=getattr(series.dtype, 'tz', None))
except KeyError as e1:
if len(self) > 0 and (self.holds_integer() or self.is_boolean()):
raise
try:
return libindex.get_value_box(s, key)
except IndexError:
raise
except TypeError:
# generator/iterator-like
if is_iterator(key):
raise InvalidIndexError(key)
else:
raise e1
except Exception: # pragma: no cover
raise e1
except TypeError:
# python 3
if is_scalar(key): # pragma: no cover
raise IndexError(key)
raise InvalidIndexError(key) |
Fast lookup of value from 1-dimensional ndarray.
Notes
-----
Only use this if you know what you're doing. | def set_value(self, arr, key, value):
"""
Fast lookup of value from 1-dimensional ndarray.
Notes
-----
Only use this if you know what you're doing.
"""
self._engine.set_value(com.values_from_object(arr),
com.values_from_object(key), value) |
Guaranteed return of an indexer even when non-unique.
This dispatches to get_indexer or get_indexer_nonunique
as appropriate. | def get_indexer_for(self, target, **kwargs):
"""
Guaranteed return of an indexer even when non-unique.
This dispatches to get_indexer or get_indexer_nonunique
as appropriate.
"""
if self.is_unique:
return self.get_indexer(target, **kwargs)
indexer, _ = self.get_indexer_non_unique(target, **kwargs)
return indexer |
Group the index labels by a given array of values.
Parameters
----------
values : array
Values used to determine the groups.
Returns
-------
groups : dict
{group name -> group labels} | def groupby(self, values):
"""
Group the index labels by a given array of values.
Parameters
----------
values : array
Values used to determine the groups.
Returns
-------
groups : dict
{group name -> group labels}
"""
# TODO: if we are a MultiIndex, we can do better
# that converting to tuples
if isinstance(values, ABCMultiIndex):
values = values.values
values = ensure_categorical(values)
result = values._reverse_indexer()
# map to the label
result = {k: self.take(v) for k, v in result.items()}
return result |
Map values using input correspondence (a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping correspondence.
Returns
-------
applied : Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned. | def map(self, mapper, na_action=None):
"""
Map values using input correspondence (a dict, Series, or function).
Parameters
----------
mapper : function, dict, or Series
Mapping correspondence.
na_action : {None, 'ignore'}
If 'ignore', propagate NA values, without passing them to the
mapping correspondence.
Returns
-------
applied : Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index.
If the function returns a tuple with more than one element
a MultiIndex will be returned.
"""
from .multi import MultiIndex
new_values = super()._map_values(mapper, na_action=na_action)
attributes = self._get_attributes_dict()
# we can return a MultiIndex
if new_values.size and isinstance(new_values[0], tuple):
if isinstance(self, MultiIndex):
names = self.names
elif attributes.get('name'):
names = [attributes.get('name')] * len(new_values[0])
else:
names = None
return MultiIndex.from_tuples(new_values,
names=names)
attributes['copy'] = False
if not new_values.size:
# empty
attributes['dtype'] = self.dtype
return Index(new_values, **attributes) |
Return a boolean array where the index values are in `values`.
Compute boolean array of whether each index value is found in the
passed set of values. The length of the returned boolean array matches
the length of the index.
Parameters
----------
values : set or list-like
Sought values.
.. versionadded:: 0.18.1
Support for values as a set.
level : str or int, optional
Name or position of the index level to use (if the index is a
`MultiIndex`).
Returns
-------
is_contained : ndarray
NumPy array of boolean values.
See Also
--------
Series.isin : Same for Series.
DataFrame.isin : Same method for DataFrames.
Notes
-----
In the case of `MultiIndex` you must either specify `values` as a
list-like object containing tuples that are the same length as the
number of levels, or specify `level`. Otherwise it will raise a
``ValueError``.
If `level` is specified:
- if it is the name of one *and only one* index level, use that level;
- otherwise it should be a number indicating level position.
Examples
--------
>>> idx = pd.Index([1,2,3])
>>> idx
Int64Index([1, 2, 3], dtype='int64')
Check whether each index value in a list of values.
>>> idx.isin([1, 4])
array([ True, False, False])
>>> midx = pd.MultiIndex.from_arrays([[1,2,3],
... ['red', 'blue', 'green']],
... names=('number', 'color'))
>>> midx
MultiIndex(levels=[[1, 2, 3], ['blue', 'green', 'red']],
codes=[[0, 1, 2], [2, 0, 1]],
names=['number', 'color'])
Check whether the strings in the 'color' level of the MultiIndex
are in a list of colors.
>>> midx.isin(['red', 'orange', 'yellow'], level='color')
array([ True, False, False])
To check across the levels of a MultiIndex, pass a list of tuples:
>>> midx.isin([(1, 'red'), (3, 'red')])
array([ True, False, False])
For a DatetimeIndex, string values in `values` are converted to
Timestamps.
>>> dates = ['2000-03-11', '2000-03-12', '2000-03-13']
>>> dti = pd.to_datetime(dates)
>>> dti
DatetimeIndex(['2000-03-11', '2000-03-12', '2000-03-13'],
dtype='datetime64[ns]', freq=None)
>>> dti.isin(['2000-03-11'])
array([ True, False, False]) | def isin(self, values, level=None):
"""
Return a boolean array where the index values are in `values`.
Compute boolean array of whether each index value is found in the
passed set of values. The length of the returned boolean array matches
the length of the index.
Parameters
----------
values : set or list-like
Sought values.
.. versionadded:: 0.18.1
Support for values as a set.
level : str or int, optional
Name or position of the index level to use (if the index is a
`MultiIndex`).
Returns
-------
is_contained : ndarray
NumPy array of boolean values.
See Also
--------
Series.isin : Same for Series.
DataFrame.isin : Same method for DataFrames.
Notes
-----
In the case of `MultiIndex` you must either specify `values` as a
list-like object containing tuples that are the same length as the
number of levels, or specify `level`. Otherwise it will raise a
``ValueError``.
If `level` is specified:
- if it is the name of one *and only one* index level, use that level;
- otherwise it should be a number indicating level position.
Examples
--------
>>> idx = pd.Index([1,2,3])
>>> idx
Int64Index([1, 2, 3], dtype='int64')
Check whether each index value in a list of values.
>>> idx.isin([1, 4])
array([ True, False, False])
>>> midx = pd.MultiIndex.from_arrays([[1,2,3],
... ['red', 'blue', 'green']],
... names=('number', 'color'))
>>> midx
MultiIndex(levels=[[1, 2, 3], ['blue', 'green', 'red']],
codes=[[0, 1, 2], [2, 0, 1]],
names=['number', 'color'])
Check whether the strings in the 'color' level of the MultiIndex
are in a list of colors.
>>> midx.isin(['red', 'orange', 'yellow'], level='color')
array([ True, False, False])
To check across the levels of a MultiIndex, pass a list of tuples:
>>> midx.isin([(1, 'red'), (3, 'red')])
array([ True, False, False])
For a DatetimeIndex, string values in `values` are converted to
Timestamps.
>>> dates = ['2000-03-11', '2000-03-12', '2000-03-13']
>>> dti = pd.to_datetime(dates)
>>> dti
DatetimeIndex(['2000-03-11', '2000-03-12', '2000-03-13'],
dtype='datetime64[ns]', freq=None)
>>> dti.isin(['2000-03-11'])
array([ True, False, False])
"""
if level is not None:
self._validate_index_level(level)
return algos.isin(self, values) |
For an ordered or unique index, compute the slice indexer for input
labels and step.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, default None
kind : string, default None
Returns
-------
indexer : slice
Raises
------
KeyError : If key does not exist, or key is not unique and index is
not ordered.
Notes
-----
This function assumes that the data is sorted, so use at your own peril
Examples
---------
This is a method on all index types. For example you can do:
>>> idx = pd.Index(list('abcd'))
>>> idx.slice_indexer(start='b', end='c')
slice(1, 3)
>>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')])
>>> idx.slice_indexer(start='b', end=('c', 'g'))
slice(1, 3) | def slice_indexer(self, start=None, end=None, step=None, kind=None):
"""
For an ordered or unique index, compute the slice indexer for input
labels and step.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, default None
kind : string, default None
Returns
-------
indexer : slice
Raises
------
KeyError : If key does not exist, or key is not unique and index is
not ordered.
Notes
-----
This function assumes that the data is sorted, so use at your own peril
Examples
---------
This is a method on all index types. For example you can do:
>>> idx = pd.Index(list('abcd'))
>>> idx.slice_indexer(start='b', end='c')
slice(1, 3)
>>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')])
>>> idx.slice_indexer(start='b', end=('c', 'g'))
slice(1, 3)
"""
start_slice, end_slice = self.slice_locs(start, end, step=step,
kind=kind)
# return a slice
if not is_scalar(start_slice):
raise AssertionError("Start slice bound is non-scalar")
if not is_scalar(end_slice):
raise AssertionError("End slice bound is non-scalar")
return slice(start_slice, end_slice, step) |
If we have a float key and are not a floating index, then try to cast
to an int if equivalent. | def _maybe_cast_indexer(self, key):
"""
If we have a float key and are not a floating index, then try to cast
to an int if equivalent.
"""
if is_float(key) and not self.is_floating():
try:
ckey = int(key)
if ckey == key:
key = ckey
except (OverflowError, ValueError, TypeError):
pass
return key |
If we are positional indexer, validate that we have appropriate
typed bounds must be an integer. | def _validate_indexer(self, form, key, kind):
"""
If we are positional indexer, validate that we have appropriate
typed bounds must be an integer.
"""
assert kind in ['ix', 'loc', 'getitem', 'iloc']
if key is None:
pass
elif is_integer(key):
pass
elif kind in ['iloc', 'getitem']:
self._invalid_indexer(form, key)
return key |
Calculate slice bound that corresponds to given label.
Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
of given label.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'ix', 'loc', 'getitem'} | def get_slice_bound(self, label, side, kind):
"""
Calculate slice bound that corresponds to given label.
Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
of given label.
Parameters
----------
label : object
side : {'left', 'right'}
kind : {'ix', 'loc', 'getitem'}
"""
assert kind in ['ix', 'loc', 'getitem', None]
if side not in ('left', 'right'):
raise ValueError("Invalid value for side kwarg,"
" must be either 'left' or 'right': %s" %
(side, ))
original_label = label
# For datetime indices label may be a string that has to be converted
# to datetime boundary according to its resolution.
label = self._maybe_cast_slice_bound(label, side, kind)
# we need to look up the label
try:
slc = self._get_loc_only_exact_matches(label)
except KeyError as err:
try:
return self._searchsorted_monotonic(label, side)
except ValueError:
# raise the original KeyError
raise err
if isinstance(slc, np.ndarray):
# get_loc may return a boolean array or an array of indices, which
# is OK as long as they are representable by a slice.
if is_bool_dtype(slc):
slc = lib.maybe_booleans_to_slice(slc.view('u1'))
else:
slc = lib.maybe_indices_to_slice(slc.astype('i8'), len(self))
if isinstance(slc, np.ndarray):
raise KeyError("Cannot get %s slice bound for non-unique "
"label: %r" % (side, original_label))
if isinstance(slc, slice):
if side == 'left':
return slc.start
else:
return slc.stop
else:
if side == 'right':
return slc + 1
else:
return slc |
Compute slice locations for input labels.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, defaults None
If None, defaults to 1
kind : {'ix', 'loc', 'getitem'} or None
Returns
-------
start, end : int
See Also
--------
Index.get_loc : Get location for a single label.
Notes
-----
This method only works if the index is monotonic or unique.
Examples
---------
>>> idx = pd.Index(list('abcd'))
>>> idx.slice_locs(start='b', end='c')
(1, 3) | def slice_locs(self, start=None, end=None, step=None, kind=None):
"""
Compute slice locations for input labels.
Parameters
----------
start : label, default None
If None, defaults to the beginning
end : label, default None
If None, defaults to the end
step : int, defaults None
If None, defaults to 1
kind : {'ix', 'loc', 'getitem'} or None
Returns
-------
start, end : int
See Also
--------
Index.get_loc : Get location for a single label.
Notes
-----
This method only works if the index is monotonic or unique.
Examples
---------
>>> idx = pd.Index(list('abcd'))
>>> idx.slice_locs(start='b', end='c')
(1, 3)
"""
inc = (step is None or step >= 0)
if not inc:
# If it's a reverse slice, temporarily swap bounds.
start, end = end, start
# GH 16785: If start and end happen to be date strings with UTC offsets
# attempt to parse and check that the offsets are the same
if (isinstance(start, (str, datetime))
and isinstance(end, (str, datetime))):
try:
ts_start = Timestamp(start)
ts_end = Timestamp(end)
except (ValueError, TypeError):
pass
else:
if not tz_compare(ts_start.tzinfo, ts_end.tzinfo):
raise ValueError("Both dates must have the "
"same UTC offset")
start_slice = None
if start is not None:
start_slice = self.get_slice_bound(start, 'left', kind)
if start_slice is None:
start_slice = 0
end_slice = None
if end is not None:
end_slice = self.get_slice_bound(end, 'right', kind)
if end_slice is None:
end_slice = len(self)
if not inc:
# Bounds at this moment are swapped, swap them back and shift by 1.
#
# slice_locs('B', 'A', step=-1): s='B', e='A'
#
# s='A' e='B'
# AFTER SWAP: | |
# v ------------------> V
# -----------------------------------
# | | |A|A|A|A| | | | | |B|B| | | | |
# -----------------------------------
# ^ <------------------ ^
# SHOULD BE: | |
# end=s-1 start=e-1
#
end_slice, start_slice = start_slice - 1, end_slice - 1
# i == -1 triggers ``len(self) + i`` selection that points to the
# last element, not before-the-first one, subtracting len(self)
# compensates that.
if end_slice == -1:
end_slice -= len(self)
if start_slice == -1:
start_slice -= len(self)
return start_slice, end_slice |
Make new Index with passed location(-s) deleted.
Returns
-------
new_index : Index | def delete(self, loc):
"""
Make new Index with passed location(-s) deleted.
Returns
-------
new_index : Index
"""
return self._shallow_copy(np.delete(self._data, loc)) |
Make new Index inserting new item at location.
Follows Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index | def insert(self, loc, item):
"""
Make new Index inserting new item at location.
Follows Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
"""
_self = np.asarray(self)
item = self._coerce_scalar_to_index(item)._ndarray_values
idx = np.concatenate((_self[:loc], item, _self[loc:]))
return self._shallow_copy_with_infer(idx) |
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
Returns
-------
dropped : Index
Raises
------
KeyError
If not all of the labels are found in the selected axis | def drop(self, labels, errors='raise'):
"""
Make new Index with passed list of labels deleted.
Parameters
----------
labels : array-like
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
Returns
-------
dropped : Index
Raises
------
KeyError
If not all of the labels are found in the selected axis
"""
arr_dtype = 'object' if self.dtype == 'object' else None
labels = com.index_labels_to_array(labels, dtype=arr_dtype)
indexer = self.get_indexer(labels)
mask = indexer == -1
if mask.any():
if errors != 'ignore':
raise KeyError(
'{} not found in axis'.format(labels[mask]))
indexer = indexer[~mask]
return self.delete(indexer) |
Add in comparison methods. | def _add_comparison_methods(cls):
"""
Add in comparison methods.
"""
cls.__eq__ = _make_comparison_op(operator.eq, cls)
cls.__ne__ = _make_comparison_op(operator.ne, cls)
cls.__lt__ = _make_comparison_op(operator.lt, cls)
cls.__gt__ = _make_comparison_op(operator.gt, cls)
cls.__le__ = _make_comparison_op(operator.le, cls)
cls.__ge__ = _make_comparison_op(operator.ge, cls) |
Add in the numeric add/sub methods to disable. | def _add_numeric_methods_add_sub_disabled(cls):
"""
Add in the numeric add/sub methods to disable.
"""
cls.__add__ = make_invalid_op('__add__')
cls.__radd__ = make_invalid_op('__radd__')
cls.__iadd__ = make_invalid_op('__iadd__')
cls.__sub__ = make_invalid_op('__sub__')
cls.__rsub__ = make_invalid_op('__rsub__')
cls.__isub__ = make_invalid_op('__isub__') |
Add in numeric methods to disable other than add/sub. | def _add_numeric_methods_disabled(cls):
"""
Add in numeric methods to disable other than add/sub.
"""
cls.__pow__ = make_invalid_op('__pow__')
cls.__rpow__ = make_invalid_op('__rpow__')
cls.__mul__ = make_invalid_op('__mul__')
cls.__rmul__ = make_invalid_op('__rmul__')
cls.__floordiv__ = make_invalid_op('__floordiv__')
cls.__rfloordiv__ = make_invalid_op('__rfloordiv__')
cls.__truediv__ = make_invalid_op('__truediv__')
cls.__rtruediv__ = make_invalid_op('__rtruediv__')
cls.__mod__ = make_invalid_op('__mod__')
cls.__divmod__ = make_invalid_op('__divmod__')
cls.__neg__ = make_invalid_op('__neg__')
cls.__pos__ = make_invalid_op('__pos__')
cls.__abs__ = make_invalid_op('__abs__')
cls.__inv__ = make_invalid_op('__inv__') |
Validate if we can perform a numeric unary operation. | def _validate_for_numeric_unaryop(self, op, opstr):
"""
Validate if we can perform a numeric unary operation.
"""
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
"{opstr} for type: {typ}"
.format(opstr=opstr, typ=type(self).__name__)) |
Return valid other; evaluate or raise TypeError if we are not of
the appropriate type.
Notes
-----
This is an internal method called by ops. | def _validate_for_numeric_binop(self, other, op):
"""
Return valid other; evaluate or raise TypeError if we are not of
the appropriate type.
Notes
-----
This is an internal method called by ops.
"""
opstr = '__{opname}__'.format(opname=op.__name__)
# if we are an inheritor of numeric,
# but not actually numeric (e.g. DatetimeIndex/PeriodIndex)
if not self._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op {opstr} "
"for type: {typ}"
.format(opstr=opstr, typ=type(self).__name__))
if isinstance(other, Index):
if not other._is_numeric_dtype:
raise TypeError("cannot evaluate a numeric op "
"{opstr} with type: {typ}"
.format(opstr=opstr, typ=type(other)))
elif isinstance(other, np.ndarray) and not other.ndim:
other = other.item()
if isinstance(other, (Index, ABCSeries, np.ndarray)):
if len(self) != len(other):
raise ValueError("cannot evaluate a numeric op with "
"unequal lengths")
other = com.values_from_object(other)
if other.dtype.kind not in ['f', 'i', 'u']:
raise TypeError("cannot evaluate a numeric op "
"with a non-numeric dtype")
elif isinstance(other, (ABCDateOffset, np.timedelta64, timedelta)):
# higher up to handle
pass
elif isinstance(other, (datetime, np.datetime64)):
# higher up to handle
pass
else:
if not (is_float(other) or is_integer(other)):
raise TypeError("can only perform ops with scalar values")
return other |
Add in numeric methods. | def _add_numeric_methods_binary(cls):
"""
Add in numeric methods.
"""
cls.__add__ = _make_arithmetic_op(operator.add, cls)
cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
cls.__rsub__ = _make_arithmetic_op(ops.rsub, cls)
cls.__rpow__ = _make_arithmetic_op(ops.rpow, cls)
cls.__pow__ = _make_arithmetic_op(operator.pow, cls)
cls.__truediv__ = _make_arithmetic_op(operator.truediv, cls)
cls.__rtruediv__ = _make_arithmetic_op(ops.rtruediv, cls)
# TODO: rmod? rdivmod?
cls.__mod__ = _make_arithmetic_op(operator.mod, cls)
cls.__floordiv__ = _make_arithmetic_op(operator.floordiv, cls)
cls.__rfloordiv__ = _make_arithmetic_op(ops.rfloordiv, cls)
cls.__divmod__ = _make_arithmetic_op(divmod, cls)
cls.__mul__ = _make_arithmetic_op(operator.mul, cls)
cls.__rmul__ = _make_arithmetic_op(ops.rmul, cls) |
Add in numeric unary methods. | def _add_numeric_methods_unary(cls):
"""
Add in numeric unary methods.
"""
def _make_evaluate_unary(op, opstr):
def _evaluate_numeric_unary(self):
self._validate_for_numeric_unaryop(op, opstr)
attrs = self._get_attributes_dict()
attrs = self._maybe_update_attributes(attrs)
return Index(op(self.values), **attrs)
_evaluate_numeric_unary.__name__ = opstr
return _evaluate_numeric_unary
cls.__neg__ = _make_evaluate_unary(operator.neg, '__neg__')
cls.__pos__ = _make_evaluate_unary(operator.pos, '__pos__')
cls.__abs__ = _make_evaluate_unary(np.abs, '__abs__')
cls.__inv__ = _make_evaluate_unary(lambda x: -x, '__inv__') |
Add in logical methods. | def _add_logical_methods(cls):
"""
Add in logical methods.
"""
_doc = """
%(desc)s
Parameters
----------
*args
These parameters will be passed to numpy.%(outname)s.
**kwargs
These parameters will be passed to numpy.%(outname)s.
Returns
-------
%(outname)s : bool or array_like (if axis is specified)
A single element array_like may be converted to bool."""
_index_shared_docs['index_all'] = dedent("""
See Also
--------
Index.any : Return whether any element in an Index is True.
Series.any : Return whether any element in a Series is True.
Series.all : Return whether all elements in a Series are True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to True because these are not equal to zero.
Examples
--------
**all**
True, because nonzero integers are considered True.
>>> pd.Index([1, 2, 3]).all()
True
False, because ``0`` is considered False.
>>> pd.Index([0, 1, 2]).all()
False
**any**
True, because ``1`` is considered True.
>>> pd.Index([0, 0, 1]).any()
True
False, because ``0`` is considered False.
>>> pd.Index([0, 0, 0]).any()
False
""")
_index_shared_docs['index_any'] = dedent("""
See Also
--------
Index.all : Return whether all elements are True.
Series.all : Return whether all elements are True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to True because these are not equal to zero.
Examples
--------
>>> index = pd.Index([0, 1, 2])
>>> index.any()
True
>>> index = pd.Index([0, 0, 0])
>>> index.any()
False
""")
def _make_logical_function(name, desc, f):
@Substitution(outname=name, desc=desc)
@Appender(_index_shared_docs['index_' + name])
@Appender(_doc)
def logical_func(self, *args, **kwargs):
result = f(self.values)
if (isinstance(result, (np.ndarray, ABCSeries, Index)) and
result.ndim == 0):
# return NumPy type
return result.dtype.type(result.item())
else: # pragma: no cover
return result
logical_func.__name__ = name
return logical_func
cls.all = _make_logical_function('all', 'Return whether all elements '
'are True.',
np.all)
cls.any = _make_logical_function('any',
'Return whether any element is True.',
np.any) |
create and return a BaseGrouper, which is an internal
mapping of how to create the grouper indexers.
This may be composed of multiple Grouping objects, indicating
multiple groupers
Groupers are ultimately index mappings. They can originate as:
index mappings, keys to columns, functions, or Groupers
Groupers enable local references to axis,level,sort, while
the passed in axis, level, and sort are 'global'.
This routine tries to figure out what the passing in references
are and then creates a Grouping for each one, combined into
a BaseGrouper.
If observed & we have a categorical grouper, only show the observed
values
If validate, then check for key/level overlaps | def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
observed=False, mutated=False, validate=True):
"""
create and return a BaseGrouper, which is an internal
mapping of how to create the grouper indexers.
This may be composed of multiple Grouping objects, indicating
multiple groupers
Groupers are ultimately index mappings. They can originate as:
index mappings, keys to columns, functions, or Groupers
Groupers enable local references to axis,level,sort, while
the passed in axis, level, and sort are 'global'.
This routine tries to figure out what the passing in references
are and then creates a Grouping for each one, combined into
a BaseGrouper.
If observed & we have a categorical grouper, only show the observed
values
If validate, then check for key/level overlaps
"""
group_axis = obj._get_axis(axis)
# validate that the passed single level is compatible with the passed
# axis of the object
if level is not None:
# TODO: These if-block and else-block are almost same.
# MultiIndex instance check is removable, but it seems that there are
# some processes only for non-MultiIndex in else-block,
# eg. `obj.index.name != level`. We have to consider carefully whether
# these are applicable for MultiIndex. Even if these are applicable,
# we need to check if it makes no side effect to subsequent processes
# on the outside of this condition.
# (GH 17621)
if isinstance(group_axis, MultiIndex):
if is_list_like(level) and len(level) == 1:
level = level[0]
if key is None and is_scalar(level):
# Get the level values from group_axis
key = group_axis.get_level_values(level)
level = None
else:
# allow level to be a length-one list-like object
# (e.g., level=[0])
# GH 13901
if is_list_like(level):
nlevels = len(level)
if nlevels == 1:
level = level[0]
elif nlevels == 0:
raise ValueError('No group keys passed!')
else:
raise ValueError('multiple levels only valid with '
'MultiIndex')
if isinstance(level, str):
if obj.index.name != level:
raise ValueError('level name {} is not the name of the '
'index'.format(level))
elif level > 0 or level < -1:
raise ValueError(
'level > 0 or level < -1 only valid with MultiIndex')
# NOTE: `group_axis` and `group_axis.get_level_values(level)`
# are same in this section.
level = None
key = group_axis
# a passed-in Grouper, directly convert
if isinstance(key, Grouper):
binner, grouper, obj = key._get_grouper(obj, validate=False)
if key.key is None:
return grouper, [], obj
else:
return grouper, {key.key}, obj
# already have a BaseGrouper, just return it
elif isinstance(key, BaseGrouper):
return key, [], obj
# In the future, a tuple key will always mean an actual key,
# not an iterable of keys. In the meantime, we attempt to provide
# a warning. We can assume that the user wanted a list of keys when
# the key is not in the index. We just have to be careful with
# unhashble elements of `key`. Any unhashable elements implies that
# they wanted a list of keys.
# https://github.com/pandas-dev/pandas/issues/18314
is_tuple = isinstance(key, tuple)
all_hashable = is_tuple and is_hashable(key)
if is_tuple:
if ((all_hashable and key not in obj and set(key).issubset(obj))
or not all_hashable):
# column names ('a', 'b') -> ['a', 'b']
# arrays like (a, b) -> [a, b]
msg = ("Interpreting tuple 'by' as a list of keys, rather than "
"a single key. Use 'by=[...]' instead of 'by=(...)'. In "
"the future, a tuple will always mean a single key.")
warnings.warn(msg, FutureWarning, stacklevel=5)
key = list(key)
if not isinstance(key, list):
keys = [key]
match_axis_length = False
else:
keys = key
match_axis_length = len(keys) == len(group_axis)
# what are we after, exactly?
any_callable = any(callable(g) or isinstance(g, dict) for g in keys)
any_groupers = any(isinstance(g, Grouper) for g in keys)
any_arraylike = any(isinstance(g, (list, tuple, Series, Index, np.ndarray))
for g in keys)
# is this an index replacement?
if (not any_callable and not any_arraylike and not any_groupers and
match_axis_length and level is None):
if isinstance(obj, DataFrame):
all_in_columns_index = all(g in obj.columns or g in
obj.index.names for g in keys)
elif isinstance(obj, Series):
all_in_columns_index = all(g in obj.index.names for g in keys)
if not all_in_columns_index:
keys = [com.asarray_tuplesafe(keys)]
if isinstance(level, (tuple, list)):
if key is None:
keys = [None] * len(level)
levels = level
else:
levels = [level] * len(keys)
groupings = []
exclusions = []
# if the actual grouper should be obj[key]
def is_in_axis(key):
if not _is_label_like(key):
try:
obj._data.items.get_loc(key)
except Exception:
return False
return True
# if the grouper is obj[name]
def is_in_obj(gpr):
try:
return id(gpr) == id(obj[gpr.name])
except Exception:
return False
for i, (gpr, level) in enumerate(zip(keys, levels)):
if is_in_obj(gpr): # df.groupby(df['name'])
in_axis, name = True, gpr.name
exclusions.append(name)
elif is_in_axis(gpr): # df.groupby('name')
if gpr in obj:
if validate:
obj._check_label_or_level_ambiguity(gpr)
in_axis, name, gpr = True, gpr, obj[gpr]
exclusions.append(name)
elif obj._is_level_reference(gpr):
in_axis, name, level, gpr = False, None, gpr, None
else:
raise KeyError(gpr)
elif isinstance(gpr, Grouper) and gpr.key is not None:
# Add key to exclusions
exclusions.append(gpr.key)
in_axis, name = False, None
else:
in_axis, name = False, None
if is_categorical_dtype(gpr) and len(gpr) != obj.shape[axis]:
raise ValueError(
("Length of grouper ({len_gpr}) and axis ({len_axis})"
" must be same length"
.format(len_gpr=len(gpr), len_axis=obj.shape[axis])))
# create the Grouping
# allow us to passing the actual Grouping as the gpr
ping = (Grouping(group_axis,
gpr,
obj=obj,
name=name,
level=level,
sort=sort,
observed=observed,
in_axis=in_axis)
if not isinstance(gpr, Grouping) else gpr)
groupings.append(ping)
if len(groupings) == 0:
raise ValueError('No group keys passed!')
# create the internals grouper
grouper = BaseGrouper(group_axis, groupings, sort=sort, mutated=mutated)
return grouper, exclusions, obj |
Parameters
----------
obj : the subject object
validate : boolean, default True
if True, validate the grouper
Returns
-------
a tuple of binner, grouper, obj (possibly sorted) | def _get_grouper(self, obj, validate=True):
"""
Parameters
----------
obj : the subject object
validate : boolean, default True
if True, validate the grouper
Returns
-------
a tuple of binner, grouper, obj (possibly sorted)
"""
self._set_grouper(obj)
self.grouper, exclusions, self.obj = _get_grouper(self.obj, [self.key],
axis=self.axis,
level=self.level,
sort=self.sort,
validate=validate)
return self.binner, self.grouper, self.obj |
given an object and the specifications, setup the internal grouper
for this particular specification
Parameters
----------
obj : the subject object
sort : bool, default False
whether the resulting grouper should be sorted | def _set_grouper(self, obj, sort=False):
"""
given an object and the specifications, setup the internal grouper
for this particular specification
Parameters
----------
obj : the subject object
sort : bool, default False
whether the resulting grouper should be sorted
"""
if self.key is not None and self.level is not None:
raise ValueError(
"The Grouper cannot specify both a key and a level!")
# Keep self.grouper value before overriding
if self._grouper is None:
self._grouper = self.grouper
# the key must be a valid info item
if self.key is not None:
key = self.key
# The 'on' is already defined
if (getattr(self.grouper, 'name', None) == key and
isinstance(obj, ABCSeries)):
ax = self._grouper.take(obj.index)
else:
if key not in obj._info_axis:
raise KeyError(
"The grouper name {0} is not found".format(key))
ax = Index(obj[key], name=key)
else:
ax = obj._get_axis(self.axis)
if self.level is not None:
level = self.level
# if a level is given it must be a mi level or
# equivalent to the axis name
if isinstance(ax, MultiIndex):
level = ax._get_level_number(level)
ax = Index(ax._get_level_values(level),
name=ax.names[level])
else:
if level not in (0, ax.name):
raise ValueError(
"The level {0} is not valid".format(level))
# possibly sort
if (self.sort or sort) and not ax.is_monotonic:
# use stable sort to support first, last, nth
indexer = self.indexer = ax.argsort(kind='mergesort')
ax = ax.take(indexer)
obj = obj._take(indexer, axis=self.axis, is_copy=False)
self.obj = obj
self.grouper = ax
return self.grouper |
Load pickled pandas object (or any object) from file.
.. warning::
Loading pickled data received from untrusted sources can be
unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__.
Parameters
----------
path : str
File path where the pickled object will be loaded.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer', then use
gzip, bz2, xz or zip if path ends in '.gz', '.bz2', '.xz',
or '.zip' respectively, and no decompression otherwise.
Set to None for no decompression.
.. versionadded:: 0.20.0
Returns
-------
unpickled : same type as object stored in file
See Also
--------
DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
Series.to_pickle : Pickle (serialize) Series object to file.
read_hdf : Read HDF5 file into a DataFrame.
read_sql : Read SQL query or database table into a DataFrame.
read_parquet : Load a parquet object, returning a DataFrame.
Examples
--------
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> pd.to_pickle(original_df, "./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl") | def read_pickle(path, compression='infer'):
"""
Load pickled pandas object (or any object) from file.
.. warning::
Loading pickled data received from untrusted sources can be
unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__.
Parameters
----------
path : str
File path where the pickled object will be loaded.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
For on-the-fly decompression of on-disk data. If 'infer', then use
gzip, bz2, xz or zip if path ends in '.gz', '.bz2', '.xz',
or '.zip' respectively, and no decompression otherwise.
Set to None for no decompression.
.. versionadded:: 0.20.0
Returns
-------
unpickled : same type as object stored in file
See Also
--------
DataFrame.to_pickle : Pickle (serialize) DataFrame object to file.
Series.to_pickle : Pickle (serialize) Series object to file.
read_hdf : Read HDF5 file into a DataFrame.
read_sql : Read SQL query or database table into a DataFrame.
read_parquet : Load a parquet object, returning a DataFrame.
Examples
--------
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> pd.to_pickle(original_df, "./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl")
"""
path = _stringify_path(path)
f, fh = _get_handle(path, 'rb', compression=compression, is_text=False)
# 1) try standard libary Pickle
# 2) try pickle_compat (older pandas version) to handle subclass changes
# 3) try pickle_compat with latin1 encoding
try:
with warnings.catch_warnings(record=True):
# We want to silence any warnings about, e.g. moved modules.
warnings.simplefilter("ignore", Warning)
return pickle.load(f)
except Exception: # noqa: E722
try:
return pc.load(f, encoding=None)
except Exception: # noqa: E722
return pc.load(f, encoding='latin1')
finally:
f.close()
for _f in fh:
_f.close() |
Pickle (serialize) object to file.
Parameters
----------
obj : any object
Any python object.
path : str
File path where the pickled object will be stored.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
A string representing the compression to use in the output file. By
default, infers from the file extension in specified path.
.. versionadded:: 0.20.0
protocol : int
Int which indicates which protocol should be used by the pickler,
default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
values for this parameter depend on the version of Python. For Python
2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value.
For Python >= 3.4, 4 is a valid value. A negative value for the
protocol parameter is equivalent to setting its value to
HIGHEST_PROTOCOL.
.. [1] https://docs.python.org/3/library/pickle.html
.. versionadded:: 0.21.0
See Also
--------
read_pickle : Load pickled pandas object (or any object) from file.
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_sql : Write DataFrame to a SQL database.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Examples
--------
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> pd.to_pickle(original_df, "./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl") | def to_pickle(obj, path, compression='infer',
protocol=pickle.HIGHEST_PROTOCOL):
"""
Pickle (serialize) object to file.
Parameters
----------
obj : any object
Any python object.
path : str
File path where the pickled object will be stored.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
A string representing the compression to use in the output file. By
default, infers from the file extension in specified path.
.. versionadded:: 0.20.0
protocol : int
Int which indicates which protocol should be used by the pickler,
default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible
values for this parameter depend on the version of Python. For Python
2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value.
For Python >= 3.4, 4 is a valid value. A negative value for the
protocol parameter is equivalent to setting its value to
HIGHEST_PROTOCOL.
.. [1] https://docs.python.org/3/library/pickle.html
.. versionadded:: 0.21.0
See Also
--------
read_pickle : Load pickled pandas object (or any object) from file.
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_sql : Write DataFrame to a SQL database.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Examples
--------
>>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> pd.to_pickle(original_df, "./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl")
"""
path = _stringify_path(path)
f, fh = _get_handle(path, 'wb',
compression=compression,
is_text=False)
if protocol < 0:
protocol = pickle.HIGHEST_PROTOCOL
try:
f.write(pickle.dumps(obj, protocol=protocol))
finally:
f.close()
for _f in fh:
_f.close() |
Return a masking array of same size/shape as arr
with entries equaling any member of values_to_mask set to True | def mask_missing(arr, values_to_mask):
"""
Return a masking array of same size/shape as arr
with entries equaling any member of values_to_mask set to True
"""
dtype, values_to_mask = infer_dtype_from_array(values_to_mask)
try:
values_to_mask = np.array(values_to_mask, dtype=dtype)
except Exception:
values_to_mask = np.array(values_to_mask, dtype=object)
na_mask = isna(values_to_mask)
nonna = values_to_mask[~na_mask]
mask = None
for x in nonna:
if mask is None:
# numpy elementwise comparison warning
if is_numeric_v_string_like(arr, x):
mask = False
else:
mask = arr == x
# if x is a string and arr is not, then we get False and we must
# expand the mask to size arr.shape
if is_scalar(mask):
mask = np.zeros(arr.shape, dtype=bool)
else:
# numpy elementwise comparison warning
if is_numeric_v_string_like(arr, x):
mask |= False
else:
mask |= arr == x
if na_mask.any():
if mask is None:
mask = isna(arr)
else:
mask |= isna(arr)
# GH 21977
if mask is None:
mask = np.zeros(arr.shape, dtype=bool)
return mask |
Logic for the 1-d interpolation. The result should be 1-d, inputs
xvalues and yvalues will each be 1-d arrays of the same length.
Bounds_error is currently hardcoded to False since non-scipy ones don't
take it as an argument. | def interpolate_1d(xvalues, yvalues, method='linear', limit=None,
limit_direction='forward', limit_area=None, fill_value=None,
bounds_error=False, order=None, **kwargs):
"""
Logic for the 1-d interpolation. The result should be 1-d, inputs
xvalues and yvalues will each be 1-d arrays of the same length.
Bounds_error is currently hardcoded to False since non-scipy ones don't
take it as an argument.
"""
# Treat the original, non-scipy methods first.
invalid = isna(yvalues)
valid = ~invalid
if not valid.any():
# have to call np.asarray(xvalues) since xvalues could be an Index
# which can't be mutated
result = np.empty_like(np.asarray(xvalues), dtype=np.float64)
result.fill(np.nan)
return result
if valid.all():
return yvalues
if method == 'time':
if not getattr(xvalues, 'is_all_dates', None):
# if not issubclass(xvalues.dtype.type, np.datetime64):
raise ValueError('time-weighted interpolation only works '
'on Series or DataFrames with a '
'DatetimeIndex')
method = 'values'
valid_limit_directions = ['forward', 'backward', 'both']
limit_direction = limit_direction.lower()
if limit_direction not in valid_limit_directions:
msg = ('Invalid limit_direction: expecting one of {valid!r}, '
'got {invalid!r}.')
raise ValueError(msg.format(valid=valid_limit_directions,
invalid=limit_direction))
if limit_area is not None:
valid_limit_areas = ['inside', 'outside']
limit_area = limit_area.lower()
if limit_area not in valid_limit_areas:
raise ValueError('Invalid limit_area: expecting one of {}, got '
'{}.'.format(valid_limit_areas, limit_area))
# default limit is unlimited GH #16282
if limit is None:
# limit = len(xvalues)
pass
elif not is_integer(limit):
raise ValueError('Limit must be an integer')
elif limit < 1:
raise ValueError('Limit must be greater than 0')
from pandas import Series
ys = Series(yvalues)
# These are sets of index pointers to invalid values... i.e. {0, 1, etc...
all_nans = set(np.flatnonzero(invalid))
start_nans = set(range(ys.first_valid_index()))
end_nans = set(range(1 + ys.last_valid_index(), len(valid)))
mid_nans = all_nans - start_nans - end_nans
# Like the sets above, preserve_nans contains indices of invalid values,
# but in this case, it is the final set of indices that need to be
# preserved as NaN after the interpolation.
# For example if limit_direction='forward' then preserve_nans will
# contain indices of NaNs at the beginning of the series, and NaNs that
# are more than'limit' away from the prior non-NaN.
# set preserve_nans based on direction using _interp_limit
if limit_direction == 'forward':
preserve_nans = start_nans | set(_interp_limit(invalid, limit, 0))
elif limit_direction == 'backward':
preserve_nans = end_nans | set(_interp_limit(invalid, 0, limit))
else:
# both directions... just use _interp_limit
preserve_nans = set(_interp_limit(invalid, limit, limit))
# if limit_area is set, add either mid or outside indices
# to preserve_nans GH #16284
if limit_area == 'inside':
# preserve NaNs on the outside
preserve_nans |= start_nans | end_nans
elif limit_area == 'outside':
# preserve NaNs on the inside
preserve_nans |= mid_nans
# sort preserve_nans and covert to list
preserve_nans = sorted(preserve_nans)
xvalues = getattr(xvalues, 'values', xvalues)
yvalues = getattr(yvalues, 'values', yvalues)
result = yvalues.copy()
if method in ['linear', 'time', 'index', 'values']:
if method in ('values', 'index'):
inds = np.asarray(xvalues)
# hack for DatetimeIndex, #1646
if needs_i8_conversion(inds.dtype.type):
inds = inds.view(np.int64)
if inds.dtype == np.object_:
inds = lib.maybe_convert_objects(inds)
else:
inds = xvalues
result[invalid] = np.interp(inds[invalid], inds[valid], yvalues[valid])
result[preserve_nans] = np.nan
return result
sp_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'barycentric', 'krogh', 'spline', 'polynomial',
'from_derivatives', 'piecewise_polynomial', 'pchip', 'akima']
if method in sp_methods:
inds = np.asarray(xvalues)
# hack for DatetimeIndex, #1646
if issubclass(inds.dtype.type, np.datetime64):
inds = inds.view(np.int64)
result[invalid] = _interpolate_scipy_wrapper(inds[valid],
yvalues[valid],
inds[invalid],
method=method,
fill_value=fill_value,
bounds_error=bounds_error,
order=order, **kwargs)
result[preserve_nans] = np.nan
return result |
Passed off to scipy.interpolate.interp1d. method is scipy's kind.
Returns an array interpolated at new_x. Add any new methods to
the list in _clean_interp_method. | def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
bounds_error=False, order=None, **kwargs):
"""
Passed off to scipy.interpolate.interp1d. method is scipy's kind.
Returns an array interpolated at new_x. Add any new methods to
the list in _clean_interp_method.
"""
try:
from scipy import interpolate
# TODO: Why is DatetimeIndex being imported here?
from pandas import DatetimeIndex # noqa
except ImportError:
raise ImportError('{method} interpolation requires SciPy'
.format(method=method))
new_x = np.asarray(new_x)
# ignores some kwargs that could be passed along.
alt_methods = {
'barycentric': interpolate.barycentric_interpolate,
'krogh': interpolate.krogh_interpolate,
'from_derivatives': _from_derivatives,
'piecewise_polynomial': _from_derivatives,
}
if getattr(x, 'is_all_dates', False):
# GH 5975, scipy.interp1d can't hande datetime64s
x, new_x = x._values.astype('i8'), new_x.astype('i8')
if method == 'pchip':
try:
alt_methods['pchip'] = interpolate.pchip_interpolate
except AttributeError:
raise ImportError("Your version of Scipy does not support "
"PCHIP interpolation.")
elif method == 'akima':
try:
from scipy.interpolate import Akima1DInterpolator # noqa
alt_methods['akima'] = _akima_interpolate
except ImportError:
raise ImportError("Your version of Scipy does not support "
"Akima interpolation.")
interp1d_methods = ['nearest', 'zero', 'slinear', 'quadratic', 'cubic',
'polynomial']
if method in interp1d_methods:
if method == 'polynomial':
method = order
terp = interpolate.interp1d(x, y, kind=method, fill_value=fill_value,
bounds_error=bounds_error)
new_y = terp(new_x)
elif method == 'spline':
# GH #10633, #24014
if isna(order) or (order <= 0):
raise ValueError("order needs to be specified and greater than 0; "
"got order: {}".format(order))
terp = interpolate.UnivariateSpline(x, y, k=order, **kwargs)
new_y = terp(new_x)
else:
# GH 7295: need to be able to write for some reason
# in some circumstances: check all three
if not x.flags.writeable:
x = x.copy()
if not y.flags.writeable:
y = y.copy()
if not new_x.flags.writeable:
new_x = new_x.copy()
method = alt_methods[method]
new_y = method(x, y, new_x, **kwargs)
return new_y |
Convenience function for interpolate.BPoly.from_derivatives.
Construct a piecewise polynomial in the Bernstein basis, compatible
with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of array-likes
yi[i][j] is the j-th derivative known at xi[i]
order: None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
der : int or list
How many derivatives to extract; None for all potentially nonzero
derivatives (that is a number equal to the number of points), or a
list of derivatives to extract. This numberincludes the function
value as 0th derivative.
extrapolate : bool, optional
Whether to extrapolate to ouf-of-bounds points based on first and last
intervals, or to return NaNs. Default: True.
See Also
--------
scipy.interpolate.BPoly.from_derivatives
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R. | def _from_derivatives(xi, yi, x, order=None, der=0, extrapolate=False):
"""
Convenience function for interpolate.BPoly.from_derivatives.
Construct a piecewise polynomial in the Bernstein basis, compatible
with the specified values and derivatives at breakpoints.
Parameters
----------
xi : array_like
sorted 1D array of x-coordinates
yi : array_like or list of array-likes
yi[i][j] is the j-th derivative known at xi[i]
order: None or int or array_like of ints. Default: None.
Specifies the degree of local polynomials. If not None, some
derivatives are ignored.
der : int or list
How many derivatives to extract; None for all potentially nonzero
derivatives (that is a number equal to the number of points), or a
list of derivatives to extract. This numberincludes the function
value as 0th derivative.
extrapolate : bool, optional
Whether to extrapolate to ouf-of-bounds points based on first and last
intervals, or to return NaNs. Default: True.
See Also
--------
scipy.interpolate.BPoly.from_derivatives
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R.
"""
from scipy import interpolate
# return the method for compat with scipy version & backwards compat
method = interpolate.BPoly.from_derivatives
m = method(xi, yi.reshape(-1, 1),
orders=order, extrapolate=extrapolate)
return m(x) |
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
Parameters
----------
xi : array_like
A sorted list of x-coordinates, of length N.
yi : array_like
A 1-D array of real values. `yi`'s length along the interpolation
axis must be equal to the length of `xi`. If N-D array, use axis
parameter to select correct axis.
x : scalar or array_like
Of length M.
der : int or list, optional
How many derivatives to extract; None for all potentially
nonzero derivatives (that is a number equal to the number
of points), or a list of derivatives to extract. This number
includes the function value as 0th derivative.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
See Also
--------
scipy.interpolate.Akima1DInterpolator
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R, | def _akima_interpolate(xi, yi, x, der=0, axis=0):
"""
Convenience function for akima interpolation.
xi and yi are arrays of values used to approximate some function f,
with ``yi = f(xi)``.
See `Akima1DInterpolator` for details.
Parameters
----------
xi : array_like
A sorted list of x-coordinates, of length N.
yi : array_like
A 1-D array of real values. `yi`'s length along the interpolation
axis must be equal to the length of `xi`. If N-D array, use axis
parameter to select correct axis.
x : scalar or array_like
Of length M.
der : int or list, optional
How many derivatives to extract; None for all potentially
nonzero derivatives (that is a number equal to the number
of points), or a list of derivatives to extract. This number
includes the function value as 0th derivative.
axis : int, optional
Axis in the yi array corresponding to the x-coordinate values.
See Also
--------
scipy.interpolate.Akima1DInterpolator
Returns
-------
y : scalar or array_like
The result, of length R or length M or M by R,
"""
from scipy import interpolate
try:
P = interpolate.Akima1DInterpolator(xi, yi, axis=axis)
except TypeError:
# Scipy earlier than 0.17.0 missing axis
P = interpolate.Akima1DInterpolator(xi, yi)
if der == 0:
return P(x)
elif interpolate._isscalar(der):
return P(x, der=der)
else:
return [P(x, nu) for nu in der] |
Perform an actual interpolation of values, values will be make 2-d if
needed fills inplace, returns the result. | def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None,
dtype=None):
"""
Perform an actual interpolation of values, values will be make 2-d if
needed fills inplace, returns the result.
"""
transf = (lambda x: x) if axis == 0 else (lambda x: x.T)
# reshape a 1 dim if needed
ndim = values.ndim
if values.ndim == 1:
if axis != 0: # pragma: no cover
raise AssertionError("cannot interpolate on a ndim == 1 with "
"axis != 0")
values = values.reshape(tuple((1,) + values.shape))
if fill_value is None:
mask = None
else: # todo create faster fill func without masking
mask = mask_missing(transf(values), fill_value)
method = clean_fill_method(method)
if method == 'pad':
values = transf(pad_2d(
transf(values), limit=limit, mask=mask, dtype=dtype))
else:
values = transf(backfill_2d(
transf(values), limit=limit, mask=mask, dtype=dtype))
# reshape back
if ndim == 1:
values = values[0]
return values |
Cast values to a dtype that algos.pad and algos.backfill can handle. | def _cast_values_for_fillna(values, dtype):
"""
Cast values to a dtype that algos.pad and algos.backfill can handle.
"""
# TODO: for int-dtypes we make a copy, but for everything else this
# alters the values in-place. Is this intentional?
if (is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype) or
is_timedelta64_dtype(dtype)):
values = values.view(np.int64)
elif is_integer_dtype(values):
# NB: this check needs to come after the datetime64 check above
values = ensure_float64(values)
return values |
If this is a reversed op, then flip x,y
If we have an integer value (or array in y)
and we have 0's, fill them with the fill,
return the result.
Mask the nan's from x. | def fill_zeros(result, x, y, name, fill):
"""
If this is a reversed op, then flip x,y
If we have an integer value (or array in y)
and we have 0's, fill them with the fill,
return the result.
Mask the nan's from x.
"""
if fill is None or is_float_dtype(result):
return result
if name.startswith(('r', '__r')):
x, y = y, x
is_variable_type = (hasattr(y, 'dtype') or hasattr(y, 'type'))
is_scalar_type = is_scalar(y)
if not is_variable_type and not is_scalar_type:
return result
if is_scalar_type:
y = np.array(y)
if is_integer_dtype(y):
if (y == 0).any():
# GH 7325, mask and nans must be broadcastable (also: PR 9308)
# Raveling and then reshaping makes np.putmask faster
mask = ((y == 0) & ~np.isnan(result)).ravel()
shape = result.shape
result = result.astype('float64', copy=False).ravel()
np.putmask(result, mask, fill)
# if we have a fill of inf, then sign it correctly
# (GH 6178 and PR 9308)
if np.isinf(fill):
signs = y if name.startswith(('r', '__r')) else x
signs = np.sign(signs.astype('float', copy=False))
negative_inf_mask = (signs.ravel() < 0) & mask
np.putmask(result, negative_inf_mask, -fill)
if "floordiv" in name: # (PR 9308)
nan_mask = ((y == 0) & (x == 0)).ravel()
np.putmask(result, nan_mask, np.nan)
result = result.reshape(shape)
return result |
Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes
of the numerator or the denominator.
Parameters
----------
x : ndarray
y : ndarray
result : ndarray
copy : bool (default False)
Whether to always create a new array or try to fill in the existing
array if possible.
Returns
-------
filled_result : ndarray
Examples
--------
>>> x = np.array([1, 0, -1], dtype=np.int64)
>>> y = 0 # int 0; numpy behavior is different with float
>>> result = x / y
>>> result # raw numpy result does not fill division by zero
array([0, 0, 0])
>>> mask_zero_div_zero(x, y, result)
array([ inf, nan, -inf]) | def mask_zero_div_zero(x, y, result, copy=False):
"""
Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes
of the numerator or the denominator.
Parameters
----------
x : ndarray
y : ndarray
result : ndarray
copy : bool (default False)
Whether to always create a new array or try to fill in the existing
array if possible.
Returns
-------
filled_result : ndarray
Examples
--------
>>> x = np.array([1, 0, -1], dtype=np.int64)
>>> y = 0 # int 0; numpy behavior is different with float
>>> result = x / y
>>> result # raw numpy result does not fill division by zero
array([0, 0, 0])
>>> mask_zero_div_zero(x, y, result)
array([ inf, nan, -inf])
"""
if is_scalar(y):
y = np.array(y)
zmask = y == 0
if zmask.any():
shape = result.shape
nan_mask = (zmask & (x == 0)).ravel()
neginf_mask = (zmask & (x < 0)).ravel()
posinf_mask = (zmask & (x > 0)).ravel()
if nan_mask.any() or neginf_mask.any() or posinf_mask.any():
# Fill negative/0 with -inf, positive/0 with +inf, 0/0 with NaN
result = result.astype('float64', copy=copy).ravel()
np.putmask(result, nan_mask, np.nan)
np.putmask(result, posinf_mask, np.inf)
np.putmask(result, neginf_mask, -np.inf)
result = result.reshape(shape)
return result |
Fill nulls caused by division by zero, casting to a diffferent dtype
if necessary.
Parameters
----------
op : function (operator.add, operator.div, ...)
left : object (Index for non-reversed ops)
right : object (Index fof reversed ops)
result : ndarray
Returns
-------
result : ndarray | def dispatch_missing(op, left, right, result):
"""
Fill nulls caused by division by zero, casting to a diffferent dtype
if necessary.
Parameters
----------
op : function (operator.add, operator.div, ...)
left : object (Index for non-reversed ops)
right : object (Index fof reversed ops)
result : ndarray
Returns
-------
result : ndarray
"""
opstr = '__{opname}__'.format(opname=op.__name__).replace('____', '__')
if op in [operator.truediv, operator.floordiv,
getattr(operator, 'div', None)]:
result = mask_zero_div_zero(left, right, result)
elif op is operator.mod:
result = fill_zeros(result, left, right, opstr, np.nan)
elif op is divmod:
res0 = mask_zero_div_zero(left, right, result[0])
res1 = fill_zeros(result[1], left, right, opstr, np.nan)
result = (res0, res1)
return result |
Get indexers of values that won't be filled
because they exceed the limits.
Parameters
----------
invalid : boolean ndarray
fw_limit : int or None
forward limit to index
bw_limit : int or None
backward limit to index
Returns
-------
set of indexers
Notes
-----
This is equivalent to the more readable, but slower
.. code-block:: python
def _interp_limit(invalid, fw_limit, bw_limit):
for x in np.where(invalid)[0]:
if invalid[max(0, x - fw_limit):x + bw_limit + 1].all():
yield x | def _interp_limit(invalid, fw_limit, bw_limit):
"""
Get indexers of values that won't be filled
because they exceed the limits.
Parameters
----------
invalid : boolean ndarray
fw_limit : int or None
forward limit to index
bw_limit : int or None
backward limit to index
Returns
-------
set of indexers
Notes
-----
This is equivalent to the more readable, but slower
.. code-block:: python
def _interp_limit(invalid, fw_limit, bw_limit):
for x in np.where(invalid)[0]:
if invalid[max(0, x - fw_limit):x + bw_limit + 1].all():
yield x
"""
# handle forward first; the backward direction is the same except
# 1. operate on the reversed array
# 2. subtract the returned indices from N - 1
N = len(invalid)
f_idx = set()
b_idx = set()
def inner(invalid, limit):
limit = min(limit, N)
windowed = _rolling_window(invalid, limit + 1).all(1)
idx = (set(np.where(windowed)[0] + limit) |
set(np.where((~invalid[:limit + 1]).cumsum() == 0)[0]))
return idx
if fw_limit is not None:
if fw_limit == 0:
f_idx = set(np.where(invalid)[0])
else:
f_idx = inner(invalid, fw_limit)
if bw_limit is not None:
if bw_limit == 0:
# then we don't even need to care about backwards
# just use forwards
return f_idx
else:
b_idx = list(inner(invalid[::-1], bw_limit))
b_idx = set(N - 1 - np.asarray(b_idx))
if fw_limit == 0:
return b_idx
return f_idx & b_idx |
[True, True, False, True, False], 2 ->
[
[True, True],
[True, False],
[False, True],
[True, False],
] | def _rolling_window(a, window):
"""
[True, True, False, True, False], 2 ->
[
[True, True],
[True, False],
[False, True],
[True, False],
]
"""
# https://stackoverflow.com/a/6811241
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides) |
Return console size as tuple = (width, height).
Returns (None,None) in non-interactive session. | def get_console_size():
"""Return console size as tuple = (width, height).
Returns (None,None) in non-interactive session.
"""
from pandas import get_option
display_width = get_option('display.width')
# deprecated.
display_height = get_option('display.max_rows')
# Consider
# interactive shell terminal, can detect term size
# interactive non-shell terminal (ipnb/ipqtconsole), cannot detect term
# size non-interactive script, should disregard term size
# in addition
# width,height have default values, but setting to 'None' signals
# should use Auto-Detection, But only in interactive shell-terminal.
# Simple. yeah.
if in_interactive_session():
if in_ipython_frontend():
# sane defaults for interactive non-shell terminal
# match default for width,height in config_init
from pandas._config.config import get_default_val
terminal_width = get_default_val('display.width')
terminal_height = get_default_val('display.max_rows')
else:
# pure terminal
terminal_width, terminal_height = get_terminal_size()
else:
terminal_width, terminal_height = None, None
# Note if the User sets width/Height to None (auto-detection)
# and we're in a script (non-inter), this will return (None,None)
# caller needs to deal.
return (display_width or terminal_width, display_height or terminal_height) |
check if we're running in an interactive shell
returns True if running under python/ipython interactive shell | def in_interactive_session():
""" check if we're running in an interactive shell
returns True if running under python/ipython interactive shell
"""
from pandas import get_option
def check_main():
try:
import __main__ as main
except ModuleNotFoundError:
return get_option('mode.sim_interactive')
return (not hasattr(main, '__file__') or
get_option('mode.sim_interactive'))
try:
return __IPYTHON__ or check_main() # noqa
except NameError:
return check_main() |
Code the categories to ensure we can groupby for categoricals.
If observed=True, we return a new Categorical with the observed
categories only.
If sort=False, return a copy of self, coded with categories as
returned by .unique(), followed by any categories not appearing in
the data. If sort=True, return self.
This method is needed solely to ensure the categorical index of the
GroupBy result has categories in the order of appearance in the data
(GH-8868).
Parameters
----------
c : Categorical
sort : boolean
The value of the sort parameter groupby was called with.
observed : boolean
Account only for the observed values
Returns
-------
New Categorical
If sort=False, the new categories are set to the order of
appearance in codes (unless ordered=True, in which case the
original order is preserved), followed by any unrepresented
categories in the original order.
Categorical or None
If we are observed, return the original categorical, otherwise None | def recode_for_groupby(c, sort, observed):
"""
Code the categories to ensure we can groupby for categoricals.
If observed=True, we return a new Categorical with the observed
categories only.
If sort=False, return a copy of self, coded with categories as
returned by .unique(), followed by any categories not appearing in
the data. If sort=True, return self.
This method is needed solely to ensure the categorical index of the
GroupBy result has categories in the order of appearance in the data
(GH-8868).
Parameters
----------
c : Categorical
sort : boolean
The value of the sort parameter groupby was called with.
observed : boolean
Account only for the observed values
Returns
-------
New Categorical
If sort=False, the new categories are set to the order of
appearance in codes (unless ordered=True, in which case the
original order is preserved), followed by any unrepresented
categories in the original order.
Categorical or None
If we are observed, return the original categorical, otherwise None
"""
# we only care about observed values
if observed:
unique_codes = unique1d(c.codes)
take_codes = unique_codes[unique_codes != -1]
if c.ordered:
take_codes = np.sort(take_codes)
# we recode according to the uniques
categories = c.categories.take(take_codes)
codes = _recode_for_categories(c.codes,
c.categories,
categories)
# return a new categorical that maps our new codes
# and categories
dtype = CategoricalDtype(categories, ordered=c.ordered)
return Categorical(codes, dtype=dtype, fastpath=True), c
# Already sorted according to c.categories; all is fine
if sort:
return c, None
# sort=False should order groups in as-encountered order (GH-8868)
cat = c.unique()
# But for groupby to work, all categories should be present,
# including those missing from the data (GH-13179), which .unique()
# above dropped
cat = cat.add_categories(
c.categories[~c.categories.isin(cat.categories)])
return c.reorder_categories(cat.categories), None |
Reverse the codes_to_groupby to account for sort / observed.
Parameters
----------
c : Categorical
sort : boolean
The value of the sort parameter groupby was called with.
ci : CategoricalIndex
The codes / categories to recode
Returns
-------
CategoricalIndex | def recode_from_groupby(c, sort, ci):
"""
Reverse the codes_to_groupby to account for sort / observed.
Parameters
----------
c : Categorical
sort : boolean
The value of the sort parameter groupby was called with.
ci : CategoricalIndex
The codes / categories to recode
Returns
-------
CategoricalIndex
"""
# we re-order to the original category orderings
if sort:
return ci.set_categories(c.categories)
# we are not sorting, so add unobserved to the end
return ci.add_categories(
c.categories[~c.categories.isin(ci.categories)]) |
return our implementation | def get_engine(engine):
""" return our implementation """
if engine == 'auto':
engine = get_option('io.parquet.engine')
if engine == 'auto':
# try engines in this order
try:
return PyArrowImpl()
except ImportError:
pass
try:
return FastParquetImpl()
except ImportError:
pass
raise ImportError("Unable to find a usable engine; "
"tried using: 'pyarrow', 'fastparquet'.\n"
"pyarrow or fastparquet is required for parquet "
"support")
if engine not in ['pyarrow', 'fastparquet']:
raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")
if engine == 'pyarrow':
return PyArrowImpl()
elif engine == 'fastparquet':
return FastParquetImpl() |
Write a DataFrame to the parquet format.
Parameters
----------
path : str
File path or Root Directory path. Will be used as Root Directory path
while writing a partitioned dataset.
.. versionchanged:: 0.24.0
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output. If
``False``, they will not be written to the file. If ``None``, the
engine's default behavior will be used.
.. versionadded 0.24.0
partition_cols : list, optional, default None
Column names by which to partition the dataset
Columns are partitioned in the order they are given
.. versionadded:: 0.24.0
kwargs
Additional keyword arguments passed to the engine | def to_parquet(df, path, engine='auto', compression='snappy', index=None,
partition_cols=None, **kwargs):
"""
Write a DataFrame to the parquet format.
Parameters
----------
path : str
File path or Root Directory path. Will be used as Root Directory path
while writing a partitioned dataset.
.. versionchanged:: 0.24.0
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
compression : {'snappy', 'gzip', 'brotli', None}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output. If
``False``, they will not be written to the file. If ``None``, the
engine's default behavior will be used.
.. versionadded 0.24.0
partition_cols : list, optional, default None
Column names by which to partition the dataset
Columns are partitioned in the order they are given
.. versionadded:: 0.24.0
kwargs
Additional keyword arguments passed to the engine
"""
impl = get_engine(engine)
return impl.write(df, path, compression=compression, index=index,
partition_cols=partition_cols, **kwargs) |
Load a parquet object from the file path, returning a DataFrame.
.. versionadded 0.21.0
Parameters
----------
path : string
File path
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
columns : list, default=None
If not None, only these columns will be read from the file.
.. versionadded 0.21.1
**kwargs
Any additional kwargs are passed to the engine.
Returns
-------
DataFrame | def read_parquet(path, engine='auto', columns=None, **kwargs):
"""
Load a parquet object from the file path, returning a DataFrame.
.. versionadded 0.21.0
Parameters
----------
path : string
File path
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
columns : list, default=None
If not None, only these columns will be read from the file.
.. versionadded 0.21.1
**kwargs
Any additional kwargs are passed to the engine.
Returns
-------
DataFrame
"""
impl = get_engine(engine)
return impl.read(path, columns=columns, **kwargs) |
Generate bin edge offsets and bin labels for one array using another array
which has bin edge values. Both arrays must be sorted.
Parameters
----------
values : array of values
binner : a comparable array of values representing bins into which to bin
the first array. Note, 'values' end-points must fall within 'binner'
end-points.
closed : which end of bin is closed; left (default), right
Returns
-------
bins : array of offsets (into 'values' argument) of bins.
Zero and last edge are excluded in result, so for instance the first
bin is values[0:bin[0]] and the last is values[bin[-1]:] | def generate_bins_generic(values, binner, closed):
"""
Generate bin edge offsets and bin labels for one array using another array
which has bin edge values. Both arrays must be sorted.
Parameters
----------
values : array of values
binner : a comparable array of values representing bins into which to bin
the first array. Note, 'values' end-points must fall within 'binner'
end-points.
closed : which end of bin is closed; left (default), right
Returns
-------
bins : array of offsets (into 'values' argument) of bins.
Zero and last edge are excluded in result, so for instance the first
bin is values[0:bin[0]] and the last is values[bin[-1]:]
"""
lenidx = len(values)
lenbin = len(binner)
if lenidx <= 0 or lenbin <= 0:
raise ValueError("Invalid length for values or for binner")
# check binner fits data
if values[0] < binner[0]:
raise ValueError("Values falls before first bin")
if values[lenidx - 1] > binner[lenbin - 1]:
raise ValueError("Values falls after last bin")
bins = np.empty(lenbin - 1, dtype=np.int64)
j = 0 # index into values
bc = 0 # bin count
# linear scan, presume nothing about values/binner except that it fits ok
for i in range(0, lenbin - 1):
r_bin = binner[i + 1]
# count values in current bin, advance to next bin
while j < lenidx and (values[j] < r_bin or
(closed == 'right' and values[j] == r_bin)):
j += 1
bins[bc] = j
bc += 1
return bins |
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group | def get_iterator(self, data, axis=0):
"""
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group
"""
splitter = self._get_splitter(data, axis=axis)
keys = self._get_group_keys()
for key, (i, group) in zip(keys, splitter):
yield key, group |
dict {group name -> group indices} | def indices(self):
""" dict {group name -> group indices} """
if len(self.groupings) == 1:
return self.groupings[0].indices
else:
label_list = [ping.labels for ping in self.groupings]
keys = [com.values_from_object(ping.group_index)
for ping in self.groupings]
return get_indexer_dict(label_list, keys) |
Compute group sizes | def size(self):
"""
Compute group sizes
"""
ids, _, ngroup = self.group_info
ids = ensure_platform_int(ids)
if ngroup:
out = np.bincount(ids[ids != -1], minlength=ngroup)
else:
out = []
return Series(out,
index=self.result_index,
dtype='int64') |
dict {group name -> group labels} | def groups(self):
""" dict {group name -> group labels} """
if len(self.groupings) == 1:
return self.groupings[0].groups
else:
to_groupby = lzip(*(ping.grouper for ping in self.groupings))
to_groupby = Index(to_groupby)
return self.axis.groupby(to_groupby) |
dict {group name -> group labels} | def groups(self):
""" dict {group name -> group labels} """
# this is mainly for compat
# GH 3881
result = {key: value for key, value in zip(self.binlabels, self.bins)
if key is not NaT}
return result |
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group | def get_iterator(self, data, axis=0):
"""
Groupby iterator
Returns
-------
Generator yielding sequence of (name, subsetted object)
for each group
"""
if isinstance(data, NDFrame):
slicer = lambda start, edge: data._slice(
slice(start, edge), axis=axis)
length = len(data.axes[axis])
else:
slicer = lambda start, edge: data[slice(start, edge)]
length = len(data)
start = 0
for edge, label in zip(self.bins, self.binlabels):
if label is not NaT:
yield label, slicer(start, edge)
start = edge
if start < length:
yield self.binlabels[-1], slicer(start, None) |
Normalize semi-structured JSON data into a flat table.
Parameters
----------
data : dict or list of dicts
Unserialized JSON objects
record_path : string or list of strings, default None
Path in each object to list of records. If not passed, data will be
assumed to be an array of records
meta : list of paths (string or list of strings), default None
Fields to use as metadata for each record in resulting table
meta_prefix : string, default None
record_prefix : string, default None
If True, prefix records with dotted (?) path, e.g. foo.bar.field if
path to records is ['foo', 'bar']
errors : {'raise', 'ignore'}, default 'raise'
* 'ignore' : will ignore KeyError if keys listed in meta are not
always present
* 'raise' : will raise KeyError if keys listed in meta are not
always present
.. versionadded:: 0.20.0
sep : string, default '.'
Nested records will generate names separated by sep,
e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
.. versionadded:: 0.20.0
Returns
-------
frame : DataFrame
Examples
--------
>>> from pandas.io.json import json_normalize
>>> data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
... {'name': {'given': 'Mose', 'family': 'Regner'}},
... {'id': 2, 'name': 'Faye Raker'}]
>>> json_normalize(data)
id name name.family name.first name.given name.last
0 1.0 NaN NaN Coleen NaN Volk
1 NaN NaN Regner NaN Mose NaN
2 2.0 Faye Raker NaN NaN NaN NaN
>>> data = [{'state': 'Florida',
... 'shortname': 'FL',
... 'info': {
... 'governor': 'Rick Scott'
... },
... 'counties': [{'name': 'Dade', 'population': 12345},
... {'name': 'Broward', 'population': 40000},
... {'name': 'Palm Beach', 'population': 60000}]},
... {'state': 'Ohio',
... 'shortname': 'OH',
... 'info': {
... 'governor': 'John Kasich'
... },
... 'counties': [{'name': 'Summit', 'population': 1234},
... {'name': 'Cuyahoga', 'population': 1337}]}]
>>> result = json_normalize(data, 'counties', ['state', 'shortname',
... ['info', 'governor']])
>>> result
name population info.governor state shortname
0 Dade 12345 Rick Scott Florida FL
1 Broward 40000 Rick Scott Florida FL
2 Palm Beach 60000 Rick Scott Florida FL
3 Summit 1234 John Kasich Ohio OH
4 Cuyahoga 1337 John Kasich Ohio OH
>>> data = {'A': [1, 2]}
>>> json_normalize(data, 'A', record_prefix='Prefix.')
Prefix.0
0 1
1 2 | def json_normalize(data, record_path=None, meta=None,
meta_prefix=None,
record_prefix=None,
errors='raise',
sep='.'):
"""
Normalize semi-structured JSON data into a flat table.
Parameters
----------
data : dict or list of dicts
Unserialized JSON objects
record_path : string or list of strings, default None
Path in each object to list of records. If not passed, data will be
assumed to be an array of records
meta : list of paths (string or list of strings), default None
Fields to use as metadata for each record in resulting table
meta_prefix : string, default None
record_prefix : string, default None
If True, prefix records with dotted (?) path, e.g. foo.bar.field if
path to records is ['foo', 'bar']
errors : {'raise', 'ignore'}, default 'raise'
* 'ignore' : will ignore KeyError if keys listed in meta are not
always present
* 'raise' : will raise KeyError if keys listed in meta are not
always present
.. versionadded:: 0.20.0
sep : string, default '.'
Nested records will generate names separated by sep,
e.g., for sep='.', { 'foo' : { 'bar' : 0 } } -> foo.bar
.. versionadded:: 0.20.0
Returns
-------
frame : DataFrame
Examples
--------
>>> from pandas.io.json import json_normalize
>>> data = [{'id': 1, 'name': {'first': 'Coleen', 'last': 'Volk'}},
... {'name': {'given': 'Mose', 'family': 'Regner'}},
... {'id': 2, 'name': 'Faye Raker'}]
>>> json_normalize(data)
id name name.family name.first name.given name.last
0 1.0 NaN NaN Coleen NaN Volk
1 NaN NaN Regner NaN Mose NaN
2 2.0 Faye Raker NaN NaN NaN NaN
>>> data = [{'state': 'Florida',
... 'shortname': 'FL',
... 'info': {
... 'governor': 'Rick Scott'
... },
... 'counties': [{'name': 'Dade', 'population': 12345},
... {'name': 'Broward', 'population': 40000},
... {'name': 'Palm Beach', 'population': 60000}]},
... {'state': 'Ohio',
... 'shortname': 'OH',
... 'info': {
... 'governor': 'John Kasich'
... },
... 'counties': [{'name': 'Summit', 'population': 1234},
... {'name': 'Cuyahoga', 'population': 1337}]}]
>>> result = json_normalize(data, 'counties', ['state', 'shortname',
... ['info', 'governor']])
>>> result
name population info.governor state shortname
0 Dade 12345 Rick Scott Florida FL
1 Broward 40000 Rick Scott Florida FL
2 Palm Beach 60000 Rick Scott Florida FL
3 Summit 1234 John Kasich Ohio OH
4 Cuyahoga 1337 John Kasich Ohio OH
>>> data = {'A': [1, 2]}
>>> json_normalize(data, 'A', record_prefix='Prefix.')
Prefix.0
0 1
1 2
"""
def _pull_field(js, spec):
result = js
if isinstance(spec, list):
for field in spec:
result = result[field]
else:
result = result[spec]
return result
if isinstance(data, list) and not data:
return DataFrame()
# A bit of a hackjob
if isinstance(data, dict):
data = [data]
if record_path is None:
if any([isinstance(x, dict) for x in y.values()] for y in data):
# naive normalization, this is idempotent for flat records
# and potentially will inflate the data considerably for
# deeply nested structures:
# {VeryLong: { b: 1,c:2}} -> {VeryLong.b:1 ,VeryLong.c:@}
#
# TODO: handle record value which are lists, at least error
# reasonably
data = nested_to_record(data, sep=sep)
return DataFrame(data)
elif not isinstance(record_path, list):
record_path = [record_path]
if meta is None:
meta = []
elif not isinstance(meta, list):
meta = [meta]
meta = [m if isinstance(m, list) else [m] for m in meta]
# Disastrously inefficient for now
records = []
lengths = []
meta_vals = defaultdict(list)
if not isinstance(sep, str):
sep = str(sep)
meta_keys = [sep.join(val) for val in meta]
def _recursive_extract(data, path, seen_meta, level=0):
if isinstance(data, dict):
data = [data]
if len(path) > 1:
for obj in data:
for val, key in zip(meta, meta_keys):
if level + 1 == len(val):
seen_meta[key] = _pull_field(obj, val[-1])
_recursive_extract(obj[path[0]], path[1:],
seen_meta, level=level + 1)
else:
for obj in data:
recs = _pull_field(obj, path[0])
# For repeating the metadata later
lengths.append(len(recs))
for val, key in zip(meta, meta_keys):
if level + 1 > len(val):
meta_val = seen_meta[key]
else:
try:
meta_val = _pull_field(obj, val[level:])
except KeyError as e:
if errors == 'ignore':
meta_val = np.nan
else:
raise KeyError("Try running with "
"errors='ignore' as key "
"{err} is not always present"
.format(err=e))
meta_vals[key].append(meta_val)
records.extend(recs)
_recursive_extract(data, record_path, {}, level=0)
result = DataFrame(records)
if record_prefix is not None:
result = result.rename(
columns=lambda x: "{p}{c}".format(p=record_prefix, c=x))
# Data types, a problem
for k, v in meta_vals.items():
if meta_prefix is not None:
k = meta_prefix + k
if k in result:
raise ValueError('Conflicting metadata name {name}, '
'need distinguishing prefix '.format(name=k))
# forcing dtype to object to avoid the metadata being casted to string
result[k] = np.array(v, dtype=object).repeat(lengths)
return result |
Reshape long-format data to wide. Generalized inverse of DataFrame.pivot
Parameters
----------
data : DataFrame
groups : dict
{new_name : list_of_columns}
dropna : boolean, default True
Examples
--------
>>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
... 'team': ['Red Sox', 'Yankees'],
... 'year1': [2007, 2007], 'year2': [2008, 2008]})
>>> data
hr1 hr2 team year1 year2
0 514 545 Red Sox 2007 2008
1 573 526 Yankees 2007 2008
>>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
team year hr
0 Red Sox 2007 514
1 Yankees 2007 573
2 Red Sox 2008 545
3 Yankees 2008 526
Returns
-------
reshaped : DataFrame | def lreshape(data, groups, dropna=True, label=None):
"""
Reshape long-format data to wide. Generalized inverse of DataFrame.pivot
Parameters
----------
data : DataFrame
groups : dict
{new_name : list_of_columns}
dropna : boolean, default True
Examples
--------
>>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
... 'team': ['Red Sox', 'Yankees'],
... 'year1': [2007, 2007], 'year2': [2008, 2008]})
>>> data
hr1 hr2 team year1 year2
0 514 545 Red Sox 2007 2008
1 573 526 Yankees 2007 2008
>>> pd.lreshape(data, {'year': ['year1', 'year2'], 'hr': ['hr1', 'hr2']})
team year hr
0 Red Sox 2007 514
1 Yankees 2007 573
2 Red Sox 2008 545
3 Yankees 2008 526
Returns
-------
reshaped : DataFrame
"""
if isinstance(groups, dict):
keys = list(groups.keys())
values = list(groups.values())
else:
keys, values = zip(*groups)
all_cols = list(set.union(*[set(x) for x in values]))
id_cols = list(data.columns.difference(all_cols))
K = len(values[0])
for seq in values:
if len(seq) != K:
raise ValueError('All column lists must be same length')
mdata = {}
pivot_cols = []
for target, names in zip(keys, values):
to_concat = [data[col].values for col in names]
import pandas.core.dtypes.concat as _concat
mdata[target] = _concat._concat_compat(to_concat)
pivot_cols.append(target)
for col in id_cols:
mdata[col] = np.tile(data[col].values, K)
if dropna:
mask = np.ones(len(mdata[pivot_cols[0]]), dtype=bool)
for c in pivot_cols:
mask &= notna(mdata[c])
if not mask.all():
mdata = {k: v[mask] for k, v in mdata.items()}
return data._constructor(mdata, columns=id_cols + pivot_cols) |
r"""
Wide panel to long format. Less flexible but more user-friendly than melt.
With stubnames ['A', 'B'], this function expects to find one or more
group of columns with format
A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,...
You specify what you want to call this suffix in the resulting long format
with `j` (for example `j='year'`)
Each row of these wide variables are assumed to be uniquely identified by
`i` (can be a single column name or a list of column names)
All remaining variables in the data frame are left intact.
Parameters
----------
df : DataFrame
The wide-format DataFrame
stubnames : str or list-like
The stub name(s). The wide format variables are assumed to
start with the stub names.
i : str or list-like
Column(s) to use as id variable(s)
j : str
The name of the sub-observation variable. What you wish to name your
suffix in the long format.
sep : str, default ""
A character indicating the separation of the variable names
in the wide format, to be stripped from the names in the long format.
For example, if your column names are A-suffix1, A-suffix2, you
can strip the hyphen by specifying `sep='-'`
.. versionadded:: 0.20.0
suffix : str, default '\\d+'
A regular expression capturing the wanted suffixes. '\\d+' captures
numeric suffixes. Suffixes with no numbers could be specified with the
negated character class '\\D+'. You can also further disambiguate
suffixes, for example, if your wide variables are of the form
A-one, B-two,.., and you have an unrelated column A-rating, you can
ignore the last one by specifying `suffix='(!?one|two)'`
.. versionadded:: 0.20.0
.. versionchanged:: 0.23.0
When all suffixes are numeric, they are cast to int64/float64.
Returns
-------
DataFrame
A DataFrame that contains each stub name as a variable, with new index
(i, j).
Notes
-----
All extra variables are left untouched. This simply uses
`pandas.melt` under the hood, but is hard-coded to "do the right thing"
in a typical case.
Examples
--------
>>> np.random.seed(123)
>>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
... "A1980" : {0 : "d", 1 : "e", 2 : "f"},
... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
... "X" : dict(zip(range(3), np.random.randn(3)))
... })
>>> df["id"] = df.index
>>> df
A1970 A1980 B1970 B1980 X id
0 a d 2.5 3.2 -1.085631 0
1 b e 1.2 1.3 0.997345 1
2 c f 0.7 0.1 0.282978 2
>>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
... # doctest: +NORMALIZE_WHITESPACE
X A B
id year
0 1970 -1.085631 a 2.5
1 1970 0.997345 b 1.2
2 1970 0.282978 c 0.7
0 1980 -1.085631 d 3.2
1 1980 0.997345 e 1.3
2 1980 0.282978 f 0.1
With multiple id columns
>>> df = pd.DataFrame({
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
... 'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
... 'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
birth famid ht1 ht2
0 1 1 2.8 3.4
1 2 1 2.9 3.8
2 3 1 2.2 2.9
3 1 2 2.0 3.2
4 2 2 1.8 2.8
5 3 2 1.9 2.4
6 1 3 2.2 3.3
7 2 3 2.3 3.4
8 3 3 2.1 2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
ht
famid birth age
1 1 1 2.8
2 3.4
2 1 2.9
2 3.8
3 1 2.2
2 2.9
2 1 1 2.0
2 3.2
2 1 1.8
2 2.8
3 1 1.9
2 2.4
3 1 1 2.2
2 3.3
2 1 2.3
2 3.4
3 1 2.1
2 2.9
Going from long back to wide just takes some creative use of `unstack`
>>> w = l.unstack()
>>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
>>> w.reset_index()
famid birth ht1 ht2
0 1 1 2.8 3.4
1 1 2 2.9 3.8
2 1 3 2.2 2.9
3 2 1 2.0 3.2
4 2 2 1.8 2.8
5 2 3 1.9 2.4
6 3 1 2.2 3.3
7 3 2 2.3 3.4
8 3 3 2.1 2.9
Less wieldy column names are also handled
>>> np.random.seed(0)
>>> df = pd.DataFrame({'A(quarterly)-2010': np.random.rand(3),
... 'A(quarterly)-2011': np.random.rand(3),
... 'B(quarterly)-2010': np.random.rand(3),
... 'B(quarterly)-2011': np.random.rand(3),
... 'X' : np.random.randint(3, size=3)})
>>> df['id'] = df.index
>>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
A(quarterly)-2010 A(quarterly)-2011 B(quarterly)-2010 ...
0 0.548814 0.544883 0.437587 ...
1 0.715189 0.423655 0.891773 ...
2 0.602763 0.645894 0.963663 ...
X id
0 0 0
1 1 1
2 1 2
>>> pd.wide_to_long(df, ['A(quarterly)', 'B(quarterly)'], i='id',
... j='year', sep='-')
... # doctest: +NORMALIZE_WHITESPACE
X A(quarterly) B(quarterly)
id year
0 2010 0 0.548814 0.437587
1 2010 1 0.715189 0.891773
2 2010 1 0.602763 0.963663
0 2011 0 0.544883 0.383442
1 2011 1 0.423655 0.791725
2 2011 1 0.645894 0.528895
If we have many columns, we could also use a regex to find our
stubnames and pass that list on to wide_to_long
>>> stubnames = sorted(
... set([match[0] for match in df.columns.str.findall(
... r'[A-B]\(.*\)').values if match != [] ])
... )
>>> list(stubnames)
['A(quarterly)', 'B(quarterly)']
All of the above examples have integers as suffixes. It is possible to
have non-integers as suffixes.
>>> df = pd.DataFrame({
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
birth famid ht_one ht_two
0 1 1 2.8 3.4
1 2 1 2.9 3.8
2 3 1 2.2 2.9
3 1 2 2.0 3.2
4 2 2 1.8 2.8
5 3 2 1.9 2.4
6 1 3 2.2 3.3
7 2 3 2.3 3.4
8 3 3 2.1 2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
sep='_', suffix='\w')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
ht
famid birth age
1 1 one 2.8
two 3.4
2 one 2.9
two 3.8
3 one 2.2
two 2.9
2 1 one 2.0
two 3.2
2 one 1.8
two 2.8
3 one 1.9
two 2.4
3 1 one 2.2
two 3.3
2 one 2.3
two 3.4
3 one 2.1
two 2.9 | def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'):
r"""
Wide panel to long format. Less flexible but more user-friendly than melt.
With stubnames ['A', 'B'], this function expects to find one or more
group of columns with format
A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,...
You specify what you want to call this suffix in the resulting long format
with `j` (for example `j='year'`)
Each row of these wide variables are assumed to be uniquely identified by
`i` (can be a single column name or a list of column names)
All remaining variables in the data frame are left intact.
Parameters
----------
df : DataFrame
The wide-format DataFrame
stubnames : str or list-like
The stub name(s). The wide format variables are assumed to
start with the stub names.
i : str or list-like
Column(s) to use as id variable(s)
j : str
The name of the sub-observation variable. What you wish to name your
suffix in the long format.
sep : str, default ""
A character indicating the separation of the variable names
in the wide format, to be stripped from the names in the long format.
For example, if your column names are A-suffix1, A-suffix2, you
can strip the hyphen by specifying `sep='-'`
.. versionadded:: 0.20.0
suffix : str, default '\\d+'
A regular expression capturing the wanted suffixes. '\\d+' captures
numeric suffixes. Suffixes with no numbers could be specified with the
negated character class '\\D+'. You can also further disambiguate
suffixes, for example, if your wide variables are of the form
A-one, B-two,.., and you have an unrelated column A-rating, you can
ignore the last one by specifying `suffix='(!?one|two)'`
.. versionadded:: 0.20.0
.. versionchanged:: 0.23.0
When all suffixes are numeric, they are cast to int64/float64.
Returns
-------
DataFrame
A DataFrame that contains each stub name as a variable, with new index
(i, j).
Notes
-----
All extra variables are left untouched. This simply uses
`pandas.melt` under the hood, but is hard-coded to "do the right thing"
in a typical case.
Examples
--------
>>> np.random.seed(123)
>>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
... "A1980" : {0 : "d", 1 : "e", 2 : "f"},
... "B1970" : {0 : 2.5, 1 : 1.2, 2 : .7},
... "B1980" : {0 : 3.2, 1 : 1.3, 2 : .1},
... "X" : dict(zip(range(3), np.random.randn(3)))
... })
>>> df["id"] = df.index
>>> df
A1970 A1980 B1970 B1980 X id
0 a d 2.5 3.2 -1.085631 0
1 b e 1.2 1.3 0.997345 1
2 c f 0.7 0.1 0.282978 2
>>> pd.wide_to_long(df, ["A", "B"], i="id", j="year")
... # doctest: +NORMALIZE_WHITESPACE
X A B
id year
0 1970 -1.085631 a 2.5
1 1970 0.997345 b 1.2
2 1970 0.282978 c 0.7
0 1980 -1.085631 d 3.2
1 1980 0.997345 e 1.3
2 1980 0.282978 f 0.1
With multiple id columns
>>> df = pd.DataFrame({
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
... 'ht1': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
... 'ht2': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
birth famid ht1 ht2
0 1 1 2.8 3.4
1 2 1 2.9 3.8
2 3 1 2.2 2.9
3 1 2 2.0 3.2
4 2 2 1.8 2.8
5 3 2 1.9 2.4
6 1 3 2.2 3.3
7 2 3 2.3 3.4
8 3 3 2.1 2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
ht
famid birth age
1 1 1 2.8
2 3.4
2 1 2.9
2 3.8
3 1 2.2
2 2.9
2 1 1 2.0
2 3.2
2 1 1.8
2 2.8
3 1 1.9
2 2.4
3 1 1 2.2
2 3.3
2 1 2.3
2 3.4
3 1 2.1
2 2.9
Going from long back to wide just takes some creative use of `unstack`
>>> w = l.unstack()
>>> w.columns = w.columns.map('{0[0]}{0[1]}'.format)
>>> w.reset_index()
famid birth ht1 ht2
0 1 1 2.8 3.4
1 1 2 2.9 3.8
2 1 3 2.2 2.9
3 2 1 2.0 3.2
4 2 2 1.8 2.8
5 2 3 1.9 2.4
6 3 1 2.2 3.3
7 3 2 2.3 3.4
8 3 3 2.1 2.9
Less wieldy column names are also handled
>>> np.random.seed(0)
>>> df = pd.DataFrame({'A(quarterly)-2010': np.random.rand(3),
... 'A(quarterly)-2011': np.random.rand(3),
... 'B(quarterly)-2010': np.random.rand(3),
... 'B(quarterly)-2011': np.random.rand(3),
... 'X' : np.random.randint(3, size=3)})
>>> df['id'] = df.index
>>> df # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS
A(quarterly)-2010 A(quarterly)-2011 B(quarterly)-2010 ...
0 0.548814 0.544883 0.437587 ...
1 0.715189 0.423655 0.891773 ...
2 0.602763 0.645894 0.963663 ...
X id
0 0 0
1 1 1
2 1 2
>>> pd.wide_to_long(df, ['A(quarterly)', 'B(quarterly)'], i='id',
... j='year', sep='-')
... # doctest: +NORMALIZE_WHITESPACE
X A(quarterly) B(quarterly)
id year
0 2010 0 0.548814 0.437587
1 2010 1 0.715189 0.891773
2 2010 1 0.602763 0.963663
0 2011 0 0.544883 0.383442
1 2011 1 0.423655 0.791725
2 2011 1 0.645894 0.528895
If we have many columns, we could also use a regex to find our
stubnames and pass that list on to wide_to_long
>>> stubnames = sorted(
... set([match[0] for match in df.columns.str.findall(
... r'[A-B]\(.*\)').values if match != [] ])
... )
>>> list(stubnames)
['A(quarterly)', 'B(quarterly)']
All of the above examples have integers as suffixes. It is possible to
have non-integers as suffixes.
>>> df = pd.DataFrame({
... 'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
... 'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
... 'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
... 'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
... })
>>> df
birth famid ht_one ht_two
0 1 1 2.8 3.4
1 2 1 2.9 3.8
2 3 1 2.2 2.9
3 1 2 2.0 3.2
4 2 2 1.8 2.8
5 3 2 1.9 2.4
6 1 3 2.2 3.3
7 2 3 2.3 3.4
8 3 3 2.1 2.9
>>> l = pd.wide_to_long(df, stubnames='ht', i=['famid', 'birth'], j='age',
sep='_', suffix='\w')
>>> l
... # doctest: +NORMALIZE_WHITESPACE
ht
famid birth age
1 1 one 2.8
two 3.4
2 one 2.9
two 3.8
3 one 2.2
two 2.9
2 1 one 2.0
two 3.2
2 one 1.8
two 2.8
3 one 1.9
two 2.4
3 1 one 2.2
two 3.3
2 one 2.3
two 3.4
3 one 2.1
two 2.9
"""
def get_var_names(df, stub, sep, suffix):
regex = r'^{stub}{sep}{suffix}$'.format(
stub=re.escape(stub), sep=re.escape(sep), suffix=suffix)
pattern = re.compile(regex)
return [col for col in df.columns if pattern.match(col)]
def melt_stub(df, stub, i, j, value_vars, sep):
newdf = melt(df, id_vars=i, value_vars=value_vars,
value_name=stub.rstrip(sep), var_name=j)
newdf[j] = Categorical(newdf[j])
newdf[j] = newdf[j].str.replace(re.escape(stub + sep), "")
# GH17627 Cast numerics suffixes to int/float
newdf[j] = to_numeric(newdf[j], errors='ignore')
return newdf.set_index(i + [j])
if not is_list_like(stubnames):
stubnames = [stubnames]
else:
stubnames = list(stubnames)
if any(col in stubnames for col in df.columns):
raise ValueError("stubname can't be identical to a column name")
if not is_list_like(i):
i = [i]
else:
i = list(i)
if df[i].duplicated().any():
raise ValueError("the id variables need to uniquely identify each row")
value_vars = [get_var_names(df, stub, sep, suffix) for stub in stubnames]
value_vars_flattened = [e for sublist in value_vars for e in sublist]
id_vars = list(set(df.columns.tolist()).difference(value_vars_flattened))
melted = [melt_stub(df, s, i, j, v, sep)
for s, v in zip(stubnames, value_vars)]
melted = melted[0].join(melted[1:], how='outer')
if len(i) == 1:
new = df[id_vars].set_index(i).join(melted)
return new
new = df[id_vars].merge(melted.reset_index(), on=i).set_index(i + [j])
return new |
Safe get multiple indices, translate keys for
datelike to underlying repr. | def _get_indices(self, names):
"""
Safe get multiple indices, translate keys for
datelike to underlying repr.
"""
def get_converter(s):
# possibly convert to the actual key types
# in the indices, could be a Timestamp or a np.datetime64
if isinstance(s, (Timestamp, datetime.datetime)):
return lambda key: Timestamp(key)
elif isinstance(s, np.datetime64):
return lambda key: Timestamp(key).asm8
else:
return lambda key: key
if len(names) == 0:
return []
if len(self.indices) > 0:
index_sample = next(iter(self.indices))
else:
index_sample = None # Dummy sample
name_sample = names[0]
if isinstance(index_sample, tuple):
if not isinstance(name_sample, tuple):
msg = ("must supply a tuple to get_group with multiple"
" grouping keys")
raise ValueError(msg)
if not len(name_sample) == len(index_sample):
try:
# If the original grouper was a tuple
return [self.indices[name] for name in names]
except KeyError:
# turns out it wasn't a tuple
msg = ("must supply a same-length tuple to get_group"
" with multiple grouping keys")
raise ValueError(msg)
converters = [get_converter(s) for s in index_sample]
names = (tuple(f(n) for f, n in zip(converters, name))
for name in names)
else:
converter = get_converter(index_sample)
names = (converter(name) for name in names)
return [self.indices.get(name, []) for name in names] |
Create group based selection.
Used when selection is not passed directly but instead via a grouper.
NOTE: this should be paired with a call to _reset_group_selection | def _set_group_selection(self):
"""
Create group based selection.
Used when selection is not passed directly but instead via a grouper.
NOTE: this should be paired with a call to _reset_group_selection
"""
grp = self.grouper
if not (self.as_index and
getattr(grp, 'groupings', None) is not None and
self.obj.ndim > 1 and
self._group_selection is None):
return
ax = self.obj._info_axis
groupers = [g.name for g in grp.groupings
if g.level is None and g.in_axis]
if len(groupers):
# GH12839 clear selected obj cache when group selection changes
self._group_selection = ax.difference(Index(groupers),
sort=False).tolist()
self._reset_cache('_selected_obj') |
Construct NDFrame from group with provided name.
Parameters
----------
name : object
the name of the group to get as a DataFrame
obj : NDFrame, default None
the NDFrame to take the DataFrame out of. If
it is None, the object groupby was called on will
be used
Returns
-------
group : same type as obj | def get_group(self, name, obj=None):
"""
Construct NDFrame from group with provided name.
Parameters
----------
name : object
the name of the group to get as a DataFrame
obj : NDFrame, default None
the NDFrame to take the DataFrame out of. If
it is None, the object groupby was called on will
be used
Returns
-------
group : same type as obj
"""
if obj is None:
obj = self._selected_obj
inds = self._get_index(name)
if not len(inds):
raise KeyError(name)
return obj._take(inds, axis=self.axis) |
Parameters
----------
ascending : bool, default True
If False, number in reverse, from length of group - 1 to 0.
Notes
-----
this is currently implementing sort=False
(though the default is sort=True) for groupby in general | def _cumcount_array(self, ascending=True):
"""
Parameters
----------
ascending : bool, default True
If False, number in reverse, from length of group - 1 to 0.
Notes
-----
this is currently implementing sort=False
(though the default is sort=True) for groupby in general
"""
ids, _, ngroups = self.grouper.group_info
sorter = get_group_index_sorter(ids, ngroups)
ids, count = ids[sorter], len(ids)
if count == 0:
return np.empty(0, dtype=np.int64)
run = np.r_[True, ids[:-1] != ids[1:]]
rep = np.diff(np.r_[np.nonzero(run)[0], count])
out = (~run).cumsum()
if ascending:
out -= np.repeat(out[run], rep)
else:
out = np.repeat(out[np.r_[run[1:], True]], rep) - out
rev = np.empty(count, dtype=np.intp)
rev[sorter] = np.arange(count, dtype=np.intp)
return out[rev].astype(np.int64, copy=False) |
Try to cast the result to our obj original type,
we may have roundtripped through object in the mean-time.
If numeric_only is True, then only try to cast numerics
and not datetimelikes. | def _try_cast(self, result, obj, numeric_only=False):
"""
Try to cast the result to our obj original type,
we may have roundtripped through object in the mean-time.
If numeric_only is True, then only try to cast numerics
and not datetimelikes.
"""
if obj.ndim > 1:
dtype = obj._values.dtype
else:
dtype = obj.dtype
if not is_scalar(result):
if is_datetime64tz_dtype(dtype):
# GH 23683
# Prior results _may_ have been generated in UTC.
# Ensure we localize to UTC first before converting
# to the target timezone
try:
result = obj._values._from_sequence(
result, dtype='datetime64[ns, UTC]'
)
result = result.astype(dtype)
except TypeError:
# _try_cast was called at a point where the result
# was already tz-aware
pass
elif is_extension_array_dtype(dtype):
# The function can return something of any type, so check
# if the type is compatible with the calling EA.
try:
result = obj._values._from_sequence(result, dtype=dtype)
except Exception:
# https://github.com/pandas-dev/pandas/issues/22850
# pandas has no control over what 3rd-party ExtensionArrays
# do in _values_from_sequence. We still want ops to work
# though, so we catch any regular Exception.
pass
elif numeric_only and is_numeric_dtype(dtype) or not numeric_only:
result = maybe_downcast_to_dtype(result, dtype)
return result |
Parameters:
-----------
func_nm: str
The name of the aggregation function being performed
Returns:
--------
bool
Whether transform should attempt to cast the result of aggregation | def _transform_should_cast(self, func_nm):
"""
Parameters:
-----------
func_nm: str
The name of the aggregation function being performed
Returns:
--------
bool
Whether transform should attempt to cast the result of aggregation
"""
return (self.size().fillna(0) > 0).any() and (
func_nm not in base.cython_cast_blacklist) |
Shared func to call any / all Cython GroupBy implementations. | def _bool_agg(self, val_test, skipna):
"""
Shared func to call any / all Cython GroupBy implementations.
"""
def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]:
if is_object_dtype(vals):
vals = np.array([bool(x) for x in vals])
else:
vals = vals.astype(np.bool)
return vals.view(np.uint8), np.bool
def result_to_bool(result: np.ndarray, inference: Type) -> np.ndarray:
return result.astype(inference, copy=False)
return self._get_cythonized_result('group_any_all', self.grouper,
aggregate=True,
cython_dtype=np.uint8,
needs_values=True,
needs_mask=True,
pre_processing=objs_to_bool,
post_processing=result_to_bool,
val_test=val_test, skipna=skipna) |
Compute mean of groups, excluding missing values.
Returns
-------
pandas.Series or pandas.DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5],
... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
Groupby one column and return the mean of the remaining columns in
each group.
>>> df.groupby('A').mean()
>>>
B C
A
1 3.0 1.333333
2 4.0 1.500000
Groupby two columns and return the mean of the remaining column.
>>> df.groupby(['A', 'B']).mean()
>>>
C
A B
1 2.0 2
4.0 1
2 3.0 1
5.0 2
Groupby one column and return the mean of only particular column in
the group.
>>> df.groupby('A')['B'].mean()
>>>
A
1 3.0
2 4.0
Name: B, dtype: float64 | def mean(self, *args, **kwargs):
"""
Compute mean of groups, excluding missing values.
Returns
-------
pandas.Series or pandas.DataFrame
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5],
... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])
Groupby one column and return the mean of the remaining columns in
each group.
>>> df.groupby('A').mean()
>>>
B C
A
1 3.0 1.333333
2 4.0 1.500000
Groupby two columns and return the mean of the remaining column.
>>> df.groupby(['A', 'B']).mean()
>>>
C
A B
1 2.0 2
4.0 1
2 3.0 1
5.0 2
Groupby one column and return the mean of only particular column in
the group.
>>> df.groupby('A')['B'].mean()
>>>
A
1 3.0
2 4.0
Name: B, dtype: float64
"""
nv.validate_groupby_func('mean', args, kwargs, ['numeric_only'])
try:
return self._cython_agg_general('mean', **kwargs)
except GroupByError:
raise
except Exception: # pragma: no cover
with _group_selection_context(self):
f = lambda x: x.mean(axis=self.axis, **kwargs)
return self._python_agg_general(f) |
Compute median of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex | def median(self, **kwargs):
"""
Compute median of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex
"""
try:
return self._cython_agg_general('median', **kwargs)
except GroupByError:
raise
except Exception: # pragma: no cover
def f(x):
if isinstance(x, np.ndarray):
x = Series(x)
return x.median(axis=self.axis, **kwargs)
with _group_selection_context(self):
return self._python_agg_general(f) |
Compute standard deviation of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom | def std(self, ddof=1, *args, **kwargs):
"""
Compute standard deviation of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
# TODO: implement at Cython level?
nv.validate_groupby_func('std', args, kwargs)
return np.sqrt(self.var(ddof=ddof, **kwargs)) |
Compute variance of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom | def var(self, ddof=1, *args, **kwargs):
"""
Compute variance of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
nv.validate_groupby_func('var', args, kwargs)
if ddof == 1:
try:
return self._cython_agg_general('var', **kwargs)
except Exception:
f = lambda x: x.var(ddof=ddof, **kwargs)
with _group_selection_context(self):
return self._python_agg_general(f)
else:
f = lambda x: x.var(ddof=ddof, **kwargs)
with _group_selection_context(self):
return self._python_agg_general(f) |
Compute standard error of the mean of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom | def sem(self, ddof=1):
"""
Compute standard error of the mean of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
Parameters
----------
ddof : integer, default 1
degrees of freedom
"""
return self.std(ddof=ddof) / np.sqrt(self.count()) |
Compute group sizes. | def size(self):
"""
Compute group sizes.
"""
result = self.grouper.size()
if isinstance(self.obj, Series):
result.name = getattr(self.obj, 'name', None)
return result |
Add numeric operations to the GroupBy generically. | def _add_numeric_operations(cls):
"""
Add numeric operations to the GroupBy generically.
"""
def groupby_function(name, alias, npfunc,
numeric_only=True, _convert=False,
min_count=-1):
_local_template = "Compute %(f)s of group values"
@Substitution(name='groupby', f=name)
@Appender(_common_see_also)
@Appender(_local_template)
def f(self, **kwargs):
if 'numeric_only' not in kwargs:
kwargs['numeric_only'] = numeric_only
if 'min_count' not in kwargs:
kwargs['min_count'] = min_count
self._set_group_selection()
try:
return self._cython_agg_general(
alias, alt=npfunc, **kwargs)
except AssertionError as e:
raise SpecificationError(str(e))
except Exception:
result = self.aggregate(
lambda x: npfunc(x, axis=self.axis))
if _convert:
result = result._convert(datetime=True)
return result
set_function_name(f, name, cls)
return f
def first_compat(x, axis=0):
def first(x):
x = x.to_numpy()
x = x[notna(x)]
if len(x) == 0:
return np.nan
return x[0]
if isinstance(x, DataFrame):
return x.apply(first, axis=axis)
else:
return first(x)
def last_compat(x, axis=0):
def last(x):
x = x.to_numpy()
x = x[notna(x)]
if len(x) == 0:
return np.nan
return x[-1]
if isinstance(x, DataFrame):
return x.apply(last, axis=axis)
else:
return last(x)
cls.sum = groupby_function('sum', 'add', np.sum, min_count=0)
cls.prod = groupby_function('prod', 'prod', np.prod, min_count=0)
cls.min = groupby_function('min', 'min', np.min, numeric_only=False)
cls.max = groupby_function('max', 'max', np.max, numeric_only=False)
cls.first = groupby_function('first', 'first', first_compat,
numeric_only=False)
cls.last = groupby_function('last', 'last', last_compat,
numeric_only=False) |
Provide resampling when using a TimeGrouper.
Given a grouper, the function resamples it according to a string
"string" -> "frequency".
See the :ref:`frequency aliases <timeseries.offset_aliases>`
documentation for more details.
Parameters
----------
rule : str or DateOffset
The offset string or object representing target grouper conversion.
*args, **kwargs
Possible arguments are `how`, `fill_method`, `limit`, `kind` and
`on`, and other arguments of `TimeGrouper`.
Returns
-------
Grouper
Return a new grouper with our resampler appended.
See Also
--------
Grouper : Specify a frequency to resample with when
grouping by a key.
DatetimeIndex.resample : Frequency conversion and resampling of
time series.
Examples
--------
>>> idx = pd.date_range('1/1/2000', periods=4, freq='T')
>>> df = pd.DataFrame(data=4 * [range(2)],
... index=idx,
... columns=['a', 'b'])
>>> df.iloc[2, 0] = 5
>>> df
a b
2000-01-01 00:00:00 0 1
2000-01-01 00:01:00 0 1
2000-01-01 00:02:00 5 1
2000-01-01 00:03:00 0 1
Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.
>>> df.groupby('a').resample('3T').sum()
a b
a
0 2000-01-01 00:00:00 0 2
2000-01-01 00:03:00 0 1
5 2000-01-01 00:00:00 5 1
Upsample the series into 30 second bins.
>>> df.groupby('a').resample('30S').sum()
a b
a
0 2000-01-01 00:00:00 0 1
2000-01-01 00:00:30 0 0
2000-01-01 00:01:00 0 1
2000-01-01 00:01:30 0 0
2000-01-01 00:02:00 0 0
2000-01-01 00:02:30 0 0
2000-01-01 00:03:00 0 1
5 2000-01-01 00:02:00 5 1
Resample by month. Values are assigned to the month of the period.
>>> df.groupby('a').resample('M').sum()
a b
a
0 2000-01-31 0 3
5 2000-01-31 5 1
Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.
>>> df.groupby('a').resample('3T', closed='right').sum()
a b
a
0 1999-12-31 23:57:00 0 1
2000-01-01 00:00:00 0 2
5 2000-01-01 00:00:00 5 1
Downsample the series into 3 minute bins and close the right side of
the bin interval, but label each bin using the right edge instead of
the left.
>>> df.groupby('a').resample('3T', closed='right', label='right').sum()
a b
a
0 2000-01-01 00:00:00 0 1
2000-01-01 00:03:00 0 2
5 2000-01-01 00:03:00 5 1
Add an offset of twenty seconds.
>>> df.groupby('a').resample('3T', loffset='20s').sum()
a b
a
0 2000-01-01 00:00:20 0 2
2000-01-01 00:03:20 0 1
5 2000-01-01 00:00:20 5 1 | def resample(self, rule, *args, **kwargs):
"""
Provide resampling when using a TimeGrouper.
Given a grouper, the function resamples it according to a string
"string" -> "frequency".
See the :ref:`frequency aliases <timeseries.offset_aliases>`
documentation for more details.
Parameters
----------
rule : str or DateOffset
The offset string or object representing target grouper conversion.
*args, **kwargs
Possible arguments are `how`, `fill_method`, `limit`, `kind` and
`on`, and other arguments of `TimeGrouper`.
Returns
-------
Grouper
Return a new grouper with our resampler appended.
See Also
--------
Grouper : Specify a frequency to resample with when
grouping by a key.
DatetimeIndex.resample : Frequency conversion and resampling of
time series.
Examples
--------
>>> idx = pd.date_range('1/1/2000', periods=4, freq='T')
>>> df = pd.DataFrame(data=4 * [range(2)],
... index=idx,
... columns=['a', 'b'])
>>> df.iloc[2, 0] = 5
>>> df
a b
2000-01-01 00:00:00 0 1
2000-01-01 00:01:00 0 1
2000-01-01 00:02:00 5 1
2000-01-01 00:03:00 0 1
Downsample the DataFrame into 3 minute bins and sum the values of
the timestamps falling into a bin.
>>> df.groupby('a').resample('3T').sum()
a b
a
0 2000-01-01 00:00:00 0 2
2000-01-01 00:03:00 0 1
5 2000-01-01 00:00:00 5 1
Upsample the series into 30 second bins.
>>> df.groupby('a').resample('30S').sum()
a b
a
0 2000-01-01 00:00:00 0 1
2000-01-01 00:00:30 0 0
2000-01-01 00:01:00 0 1
2000-01-01 00:01:30 0 0
2000-01-01 00:02:00 0 0
2000-01-01 00:02:30 0 0
2000-01-01 00:03:00 0 1
5 2000-01-01 00:02:00 5 1
Resample by month. Values are assigned to the month of the period.
>>> df.groupby('a').resample('M').sum()
a b
a
0 2000-01-31 0 3
5 2000-01-31 5 1
Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.
>>> df.groupby('a').resample('3T', closed='right').sum()
a b
a
0 1999-12-31 23:57:00 0 1
2000-01-01 00:00:00 0 2
5 2000-01-01 00:00:00 5 1
Downsample the series into 3 minute bins and close the right side of
the bin interval, but label each bin using the right edge instead of
the left.
>>> df.groupby('a').resample('3T', closed='right', label='right').sum()
a b
a
0 2000-01-01 00:00:00 0 1
2000-01-01 00:03:00 0 2
5 2000-01-01 00:03:00 5 1
Add an offset of twenty seconds.
>>> df.groupby('a').resample('3T', loffset='20s').sum()
a b
a
0 2000-01-01 00:00:20 0 2
2000-01-01 00:03:20 0 1
5 2000-01-01 00:00:20 5 1
"""
from pandas.core.resample import get_resampler_for_grouping
return get_resampler_for_grouping(self, rule, *args, **kwargs) |
Return a rolling grouper, providing rolling functionality per group. | def rolling(self, *args, **kwargs):
"""
Return a rolling grouper, providing rolling functionality per group.
"""
from pandas.core.window import RollingGroupby
return RollingGroupby(self, *args, **kwargs) |
Return an expanding grouper, providing expanding
functionality per group. | def expanding(self, *args, **kwargs):
"""
Return an expanding grouper, providing expanding
functionality per group.
"""
from pandas.core.window import ExpandingGroupby
return ExpandingGroupby(self, *args, **kwargs) |
Shared function for `pad` and `backfill` to call Cython method.
Parameters
----------
direction : {'ffill', 'bfill'}
Direction passed to underlying Cython function. `bfill` will cause
values to be filled backwards. `ffill` and any other values will
default to a forward fill
limit : int, default None
Maximum number of consecutive values to fill. If `None`, this
method will convert to -1 prior to passing to Cython
Returns
-------
`Series` or `DataFrame` with filled values
See Also
--------
pad
backfill | def _fill(self, direction, limit=None):
"""
Shared function for `pad` and `backfill` to call Cython method.
Parameters
----------
direction : {'ffill', 'bfill'}
Direction passed to underlying Cython function. `bfill` will cause
values to be filled backwards. `ffill` and any other values will
default to a forward fill
limit : int, default None
Maximum number of consecutive values to fill. If `None`, this
method will convert to -1 prior to passing to Cython
Returns
-------
`Series` or `DataFrame` with filled values
See Also
--------
pad
backfill
"""
# Need int value for Cython
if limit is None:
limit = -1
return self._get_cythonized_result('group_fillna_indexer',
self.grouper, needs_mask=True,
cython_dtype=np.int64,
result_is_index=True,
direction=direction, limit=limit) |
Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
Truthy (if a Series) or 'all', 'any' (if a DataFrame);
this is equivalent to calling dropna(how=dropna) before the
groupby.
Parameters
----------
n : int or list of ints
a single nth value for the row or a list of nth values
dropna : None or str, optional
apply the specified dropna operation before counting which row is
the nth row. Needs to be None, 'any' or 'all'
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
B
A
1 NaN
2 3.0
>>> g.nth(1)
B
A
1 2.0
2 5.0
>>> g.nth(-1)
B
A
1 4.0
2 5.0
>>> g.nth([0, 1])
B
A
1 NaN
1 2.0
2 3.0
2 5.0
Specifying `dropna` allows count ignoring ``NaN``
>>> g.nth(0, dropna='any')
B
A
1 2.0
2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any')
B
A
1 NaN
2 NaN
Specifying `as_index=False` in `groupby` keeps the original index.
>>> df.groupby('A', as_index=False).nth(1)
A B
1 1 2.0
4 2 5.0 | def nth(self, n, dropna=None):
"""
Take the nth row from each group if n is an int, or a subset of rows
if n is a list of ints.
If dropna, will take the nth non-null row, dropna is either
Truthy (if a Series) or 'all', 'any' (if a DataFrame);
this is equivalent to calling dropna(how=dropna) before the
groupby.
Parameters
----------
n : int or list of ints
a single nth value for the row or a list of nth values
dropna : None or str, optional
apply the specified dropna operation before counting which row is
the nth row. Needs to be None, 'any' or 'all'
%(see_also)s
Examples
--------
>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
B
A
1 NaN
2 3.0
>>> g.nth(1)
B
A
1 2.0
2 5.0
>>> g.nth(-1)
B
A
1 4.0
2 5.0
>>> g.nth([0, 1])
B
A
1 NaN
1 2.0
2 3.0
2 5.0
Specifying `dropna` allows count ignoring ``NaN``
>>> g.nth(0, dropna='any')
B
A
1 2.0
2 3.0
NaNs denote group exhausted when using dropna
>>> g.nth(3, dropna='any')
B
A
1 NaN
2 NaN
Specifying `as_index=False` in `groupby` keeps the original index.
>>> df.groupby('A', as_index=False).nth(1)
A B
1 1 2.0
4 2 5.0
"""
if isinstance(n, int):
nth_values = [n]
elif isinstance(n, (set, list, tuple)):
nth_values = list(set(n))
if dropna is not None:
raise ValueError(
"dropna option with a list of nth values is not supported")
else:
raise TypeError("n needs to be an int or a list/set/tuple of ints")
nth_values = np.array(nth_values, dtype=np.intp)
self._set_group_selection()
if not dropna:
mask_left = np.in1d(self._cumcount_array(), nth_values)
mask_right = np.in1d(self._cumcount_array(ascending=False) + 1,
-nth_values)
mask = mask_left | mask_right
out = self._selected_obj[mask]
if not self.as_index:
return out
ids, _, _ = self.grouper.group_info
out.index = self.grouper.result_index[ids[mask]]
return out.sort_index() if self.sort else out
if dropna not in ['any', 'all']:
if isinstance(self._selected_obj, Series) and dropna is True:
warnings.warn("the dropna={dropna} keyword is deprecated,"
"use dropna='all' instead. "
"For a Series groupby, dropna must be "
"either None, 'any' or 'all'.".format(
dropna=dropna),
FutureWarning,
stacklevel=2)
dropna = 'all'
else:
# Note: when agg-ing picker doesn't raise this,
# just returns NaN
raise ValueError("For a DataFrame groupby, dropna must be "
"either None, 'any' or 'all', "
"(was passed {dropna}).".format(
dropna=dropna))
# old behaviour, but with all and any support for DataFrames.
# modified in GH 7559 to have better perf
max_len = n if n >= 0 else - 1 - n
dropped = self.obj.dropna(how=dropna, axis=self.axis)
# get a new grouper for our dropped obj
if self.keys is None and self.level is None:
# we don't have the grouper info available
# (e.g. we have selected out
# a column that is not in the current object)
axis = self.grouper.axis
grouper = axis[axis.isin(dropped.index)]
else:
# create a grouper with the original parameters, but on the dropped
# object
from pandas.core.groupby.grouper import _get_grouper
grouper, _, _ = _get_grouper(dropped, key=self.keys,
axis=self.axis, level=self.level,
sort=self.sort,
mutated=self.mutated)
grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort)
sizes, result = grb.size(), grb.nth(n)
mask = (sizes < max_len).values
# set the results which don't meet the criteria
if len(result) and mask.any():
result.loc[mask] = np.nan
# reset/reindex to the original groups
if (len(self.obj) == len(dropped) or
len(result) == len(self.grouper.result_index)):
result.index = self.grouper.result_index
else:
result = result.reindex(self.grouper.result_index)
return result |
Return group values at the given quantile, a la numpy.percentile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value(s) between 0 and 1 providing the quantile(s) to compute.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
Method to use when the desired quantile falls between two points.
Returns
-------
Series or DataFrame
Return type determined by caller of GroupBy object.
See Also
--------
Series.quantile : Similar method for Series.
DataFrame.quantile : Similar method for DataFrame.
numpy.percentile : NumPy method to compute qth percentile.
Examples
--------
>>> df = pd.DataFrame([
... ['a', 1], ['a', 2], ['a', 3],
... ['b', 1], ['b', 3], ['b', 5]
... ], columns=['key', 'val'])
>>> df.groupby('key').quantile()
val
key
a 2.0
b 3.0 | def quantile(self, q=0.5, interpolation='linear'):
"""
Return group values at the given quantile, a la numpy.percentile.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value(s) between 0 and 1 providing the quantile(s) to compute.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
Method to use when the desired quantile falls between two points.
Returns
-------
Series or DataFrame
Return type determined by caller of GroupBy object.
See Also
--------
Series.quantile : Similar method for Series.
DataFrame.quantile : Similar method for DataFrame.
numpy.percentile : NumPy method to compute qth percentile.
Examples
--------
>>> df = pd.DataFrame([
... ['a', 1], ['a', 2], ['a', 3],
... ['b', 1], ['b', 3], ['b', 5]
... ], columns=['key', 'val'])
>>> df.groupby('key').quantile()
val
key
a 2.0
b 3.0
"""
def pre_processor(
vals: np.ndarray
) -> Tuple[np.ndarray, Optional[Type]]:
if is_object_dtype(vals):
raise TypeError("'quantile' cannot be performed against "
"'object' dtypes!")
inference = None
if is_integer_dtype(vals):
inference = np.int64
elif is_datetime64_dtype(vals):
inference = 'datetime64[ns]'
vals = vals.astype(np.float)
return vals, inference
def post_processor(
vals: np.ndarray,
inference: Optional[Type]
) -> np.ndarray:
if inference:
# Check for edge case
if not (is_integer_dtype(inference) and
interpolation in {'linear', 'midpoint'}):
vals = vals.astype(inference)
return vals
return self._get_cythonized_result('group_quantile', self.grouper,
aggregate=True,
needs_values=True,
needs_mask=True,
cython_dtype=np.float64,
pre_processing=pre_processor,
post_processing=post_processor,
q=q, interpolation=interpolation) |
Number each group from 0 to the number of groups - 1.
This is the enumerative complement of cumcount. Note that the
numbers given to the groups match the order in which the groups
would be seen when iterating over the groupby object, not the
order they are first observed.
.. versionadded:: 0.20.2
Parameters
----------
ascending : bool, default True
If False, number in reverse, from number of group - 1 to 0.
See Also
--------
.cumcount : Number the rows in each group.
Examples
--------
>>> df = pd.DataFrame({"A": list("aaabba")})
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').ngroup()
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
>>> df.groupby('A').ngroup(ascending=False)
0 1
1 1
2 1
3 0
4 0
5 1
dtype: int64
>>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
0 0
1 0
2 1
3 3
4 2
5 0
dtype: int64 | def ngroup(self, ascending=True):
"""
Number each group from 0 to the number of groups - 1.
This is the enumerative complement of cumcount. Note that the
numbers given to the groups match the order in which the groups
would be seen when iterating over the groupby object, not the
order they are first observed.
.. versionadded:: 0.20.2
Parameters
----------
ascending : bool, default True
If False, number in reverse, from number of group - 1 to 0.
See Also
--------
.cumcount : Number the rows in each group.
Examples
--------
>>> df = pd.DataFrame({"A": list("aaabba")})
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').ngroup()
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
>>> df.groupby('A').ngroup(ascending=False)
0 1
1 1
2 1
3 0
4 0
5 1
dtype: int64
>>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup()
0 0
1 0
2 1
3 3
4 2
5 0
dtype: int64
"""
with _group_selection_context(self):
index = self._selected_obj.index
result = Series(self.grouper.group_info[0], index)
if not ascending:
result = self.ngroups - 1 - result
return result |
Number each item in each group from 0 to the length of that group - 1.
Essentially this is equivalent to
>>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
----------
ascending : bool, default True
If False, number in reverse, from length of group - 1 to 0.
See Also
--------
.ngroup : Number the groups themselves.
Examples
--------
>>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
... columns=['A'])
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').cumcount()
0 0
1 1
2 2
3 0
4 1
5 3
dtype: int64
>>> df.groupby('A').cumcount(ascending=False)
0 3
1 2
2 1
3 1
4 0
5 0
dtype: int64 | def cumcount(self, ascending=True):
"""
Number each item in each group from 0 to the length of that group - 1.
Essentially this is equivalent to
>>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
----------
ascending : bool, default True
If False, number in reverse, from length of group - 1 to 0.
See Also
--------
.ngroup : Number the groups themselves.
Examples
--------
>>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']],
... columns=['A'])
>>> df
A
0 a
1 a
2 a
3 b
4 b
5 a
>>> df.groupby('A').cumcount()
0 0
1 1
2 2
3 0
4 1
5 3
dtype: int64
>>> df.groupby('A').cumcount(ascending=False)
0 3
1 2
2 1
3 1
4 0
5 0
dtype: int64
"""
with _group_selection_context(self):
index = self._selected_obj.index
cumcounts = self._cumcount_array(ascending=ascending)
return Series(cumcounts, index) |
Provide the rank of values within each group.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
na_option : {'keep', 'top', 'bottom'}, default 'keep'
* keep: leave NA values where they are
* top: smallest rank if ascending
* bottom: smallest rank if descending
pct : boolean, default False
Compute percentage rank of data within each group
axis : int, default 0
The axis of the object over which to compute the rank.
Returns
-------
DataFrame with ranking of values within each group | def rank(self, method='average', ascending=True, na_option='keep',
pct=False, axis=0):
"""
Provide the rank of values within each group.
Parameters
----------
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
* average: average rank of group
* min: lowest rank in group
* max: highest rank in group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups
ascending : boolean, default True
False for ranks by high (1) to low (N)
na_option : {'keep', 'top', 'bottom'}, default 'keep'
* keep: leave NA values where they are
* top: smallest rank if ascending
* bottom: smallest rank if descending
pct : boolean, default False
Compute percentage rank of data within each group
axis : int, default 0
The axis of the object over which to compute the rank.
Returns
-------
DataFrame with ranking of values within each group
"""
if na_option not in {'keep', 'top', 'bottom'}:
msg = "na_option must be one of 'keep', 'top', or 'bottom'"
raise ValueError(msg)
return self._cython_transform('rank', numeric_only=False,
ties_method=method, ascending=ascending,
na_option=na_option, pct=pct, axis=axis) |
Cumulative product for each group. | def cumprod(self, axis=0, *args, **kwargs):
"""
Cumulative product for each group.
"""
nv.validate_groupby_func('cumprod', args, kwargs,
['numeric_only', 'skipna'])
if axis != 0:
return self.apply(lambda x: x.cumprod(axis=axis, **kwargs))
return self._cython_transform('cumprod', **kwargs) |
Cumulative min for each group. | def cummin(self, axis=0, **kwargs):
"""
Cumulative min for each group.
"""
if axis != 0:
return self.apply(lambda x: np.minimum.accumulate(x, axis))
return self._cython_transform('cummin', numeric_only=False) |
Cumulative max for each group. | def cummax(self, axis=0, **kwargs):
"""
Cumulative max for each group.
"""
if axis != 0:
return self.apply(lambda x: np.maximum.accumulate(x, axis))
return self._cython_transform('cummax', numeric_only=False) |
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