def test_get_loc_raises_bad_label(self, method):
index = Index([0, 1, 2])
if method:
msg = "not supported between"
else:
msg = "invalid key"
with pytest.raises(TypeError, match=msg):
index.get_loc([1, 2], method=method)
def test_get_loc(self, method):
index = Index([0, 1, 2])
warn = None if method is None else FutureWarning
with tm.assert_produces_warning(warn, match="deprecated"):
assert index.get_loc(1, method=method) == 1
if method:
with tm.assert_produces_warning(warn, match="deprecated"):
assert index.get_loc(1, method=method, tolerance=0) == 1
def test_get_loc_overflows(self):
# unique but non-monotonic goes through IndexEngine.mapping.get_item
idx = Index([0, 2, 1])
val = np.iinfo(np.int64).max + 1
with pytest.raises(KeyError, match=str(val)):
idx.get_loc(val)
with pytest.raises(KeyError, match=str(val)):
idx._engine.get_loc(val)
def test_get_loc_duplicates():
index = Index([2, 2, 2, 2])
result = index.get_loc(2)
expected = slice(0, 4)
assert result == expected
# pytest.raises(Exception, index.get_loc, 2)
index = Index(["c", "a", "a", "b", "b"])
rs = index.get_loc("c")
xp = 0
assert rs == xp
def test_get_loc_duplicates():
index = Index([2, 2, 2, 2])
result = index.get_loc(2)
expected = slice(0, 4)
assert result == expected
# pytest.raises(Exception, index.get_loc, 2)
index = Index(['c', 'a', 'a', 'b', 'b'])
rs = index.get_loc('c')
xp = 0
assert rs == xp
def test_get_loc_duplicates():
index = Index([2, 2, 2, 2])
result = index.get_loc(2)
expected = slice(0, 4)
assert result == expected
# pytest.raises(Exception, index.get_loc, 2)
index = Index(['c', 'a', 'a', 'b', 'b'])
rs = index.get_loc('c')
xp = 0
assert rs == xp
def test_get_loc_raises_bad_label(self, method):
index = Index([0, 1, 2])
if method:
msg = "not supported between"
err = TypeError
else:
msg = r"\[1, 2\]"
err = InvalidIndexError
with pytest.raises(err, match=msg):
index.get_loc([1, 2], method=method)
def test_get_loc_duplicates(self):
index = Index([2, 2, 2, 2])
result = index.get_loc(2)
expected = slice(0, 4)
assert result == expected
# FIXME: dont leave commented-out
# pytest.raises(Exception, index.get_loc, 2)
index = Index(["c", "a", "a", "b", "b"])
rs = index.get_loc("c")
xp = 0
assert rs == xp
def test_get_loc_duplicates(self):
index = Index([2, 2, 2, 2])
result = index.get_loc(2)
expected = slice(0, 4)
assert result == expected
index = Index(["c", "a", "a", "b", "b"])
rs = index.get_loc("c")
xp = 0
assert rs == xp
with pytest.raises(KeyError):
index.get_loc(2)
def _get_end(y_index: pd.Index, fh: ForecastingHorizon) -> int:
"""Compute the end of the last training window for a forecasting horizon.
For a time series index `y_index`, `y_index[end]` will give
the index of the training window.
Correspondingly, for a time series `y` with index `y_index`,
`y.iloc[end]` or `y.loc[y_index[end]]`
will provide the last index of the training window.
Parameters
----------
y_index : pd.Index
Index of time series
fh : int, timedelta, list or np.ndarray of ints or timedeltas
Returns
-------
end : int
0-indexed integer end of the training window
"""
# `fh` is assumed to be ordered and checked by `_check_fh` and `window_length` by
# `check_window_length`.
n_timepoints = y_index.shape[0]
assert isinstance(y_index, pd.Index)
# For purely in-sample forecasting horizons, the last split point is the end of the
# training data.
# Otherwise, the last point must ensure that the last horizon is within the data.
null = 0 if array_is_int(fh) else pd.Timedelta(0)
fh_offset = null if fh.is_all_in_sample() else fh[-1]
if array_is_int(fh):
return n_timepoints - fh_offset - 1
else:
return y_index.get_loc(y_index[-1] - fh_offset)
def StudentDetailInfo(self):
if self.file != '' and self.SeatNum.get().upper() in self.SeatData:
data = self.data
idx = Index(self.SeatData)
locData = idx.get_loc(self.SeatNum.get().upper())
Marks = array(data['Total [ 20 ]'])[locData + 2]
NameValue = array(data['Name'])[locData]
Grade = array(data['Grade'])[locData + 2]
Remark = array(data['Grade'])[locData + 1].replace('..', 'l')
RemarkColor = ['green' if Remark == 'Successful' else 'red']
CGPA = array(data['Grade'])[locData]
StudentFrame = Toplevel()
StudentFrame.geometry('700x350')
SeatLabel = ttk.Label(master=StudentFrame,
text='Seat No. : {}'.format(
self.SeatNum.get().upper()))
SeatLabel.grid(padx=20, pady=20, sticky='W')
Name = ttk.Label(master=StudentFrame,
text='Name : {}'.format(NameValue))
Name.grid(row=1, column=0, padx=20, sticky='W')
MarksLabel = ttk.Label(master=StudentFrame,
text='Marks : {}'.format(Marks))
MarksLabel.grid(row=2, column=0, padx=20, pady=20, sticky='W')
GradeLabel = ttk.Label(master=StudentFrame,
text='Grade : {}'.format(Grade))
GradeLabel.grid(row=3, column=0, padx=20, sticky='W')
CGPALabel = ttk.Label(master=StudentFrame,
text='CGPA : {}'.format(CGPA))
CGPALabel.grid(row=4, column=0, padx=20, pady=20, sticky='W')
RemarkLabel = Label(master=StudentFrame,
text='Remark : {}'.format(Remark),
fg=RemarkColor)
RemarkLabel.grid(row=5, column=0, padx=20, sticky='W')
else:
messagebox.showwarning('Error 404', 'File not found')
def test_get_loc_nan_object_dtype_nonmonotonic_nonunique(self):
# case that goes through _maybe_get_bool_indexer
idx = Index(["foo", np.nan, None, "foo", 1.0, None], dtype=object)
# we dont raise KeyError on nan
res = idx.get_loc(np.nan)
assert res == 1
# we only match on None, not on np.nan
res = idx.get_loc(None)
expected = np.array([False, False, True, False, False, True])
tm.assert_numpy_array_equal(res, expected)
# we don't match at all on mismatched NA
with pytest.raises(KeyError, match="NaT"):
idx.get_loc(NaT)
def _normalize_index(
indexer: Union[slice, np.integer, int, str,
Sequence[Union[int, np.integer]], np.ndarray, pd.Index, ],
index: pd.Index,
) -> Union[slice, int, np.ndarray]: # ndarray of int
if not isinstance(index, pd.RangeIndex):
assert (
index.dtype != float and index.dtype != int
), "Don’t call _normalize_index with non-categorical/string names"
# the following is insanely slow for sequences,
# we replaced it using pandas below
def name_idx(i):
if isinstance(i, str):
i = index.get_loc(i)
return i
if isinstance(indexer, slice):
start = name_idx(indexer.start)
stop = name_idx(indexer.stop)
# string slices can only be inclusive, so +1 in that case
if isinstance(indexer.stop, str):
stop = None if stop is None else stop + 1
step = indexer.step
return slice(start, stop, step)
elif isinstance(indexer, (np.integer, int)):
return indexer
elif isinstance(indexer, str):
return index.get_loc(indexer) # int
elif isinstance(indexer,
(Sequence, np.ndarray, pd.Index, spmatrix, np.matrix)):
if hasattr(indexer,
"shape") and ((indexer.shape == (index.shape[0], 1)) or
(indexer.shape == (1, index.shape[0]))):
if isinstance(indexer, spmatrix):
indexer = indexer.toarray()
indexer = np.ravel(indexer)
if not isinstance(indexer, (np.ndarray, pd.Index)):
indexer = np.array(indexer)
if issubclass(indexer.dtype.type, (np.integer, np.floating)):
return indexer # Might not work for range indexes
elif issubclass(indexer.dtype.type, np.bool_):
if indexer.shape != index.shape:
raise IndexError(
f"Boolean index does not match AnnData’s shape along this "
f"dimension. Boolean index has shape {indexer.shape} while "
f"AnnData index has shape {index.shape}.")
positions = np.where(indexer)[0]
return positions # np.ndarray[int]
else: # indexer should be string array
positions = index.get_indexer(indexer)
if np.any(positions < 0):
not_found = indexer[positions < 0]
raise KeyError(
f"Values {list(not_found)}, from {list(indexer)}, "
"are not valid obs/ var names or indices.")
return positions # np.ndarray[int]
else:
raise IndexError(
f"Unknown indexer {indexer!r} of type {type(indexer)}")
def all_sem_performance(self):
if self.file != '' and self.SeatNum.get().upper() in self.SeatData:
data = self.data
col = data.columns
Subjects = [''.join(list(c)[:-6]) for c in col][3:10]
SubjectData = []
Student = []
for i in range(0, len(self.SeatData), 6):
idx = Index(self.SeatData)
locData = idx.get_loc(self.SeatData[i])
StudentData = data.loc[locData + 2, :]
OneData = array(StudentData[3:10]).astype(int)
if self.SeatData[i] == self.SeatNum.get().upper():
Student = array(StudentData[3:10]).astype(int)
SubjectData.append(OneData)
SubjectData = transpose(SubjectData)
MaxOfAll = [max(i) for i in SubjectData]
AvgOfAll = [average(i) for i in SubjectData]
fig = figure()
ax = subplot(111)
title('Your Performance vs Class')
xlabel('Subjects')
ylabel('Marks Range')
ax.bar(arange(7) + 0.00, MaxOfAll, color='#004c6d', width=0.25)
ax.bar(arange(7) + 0.25, AvgOfAll, color='#286d8a', width=0.25)
ax.bar(arange(7) + 0.50, Student, color='#008cc9', width=0.25)
xticks(arange(7), Subjects)
ax.legend(labels=['Max', 'Average', self.SeatNum.get().upper()])
if self.cb[1].get():
fig.savefig('Saved Images\Your Performance VS Class.pdf')
fig.show()
else:
messagebox.showwarning('Error 404', 'File not found')
def test_get_loc(self):
# GH 12531
cidx1 = CategoricalIndex(list("abcde"), categories=list("edabc"))
idx1 = Index(list("abcde"))
assert cidx1.get_loc("a") == idx1.get_loc("a")
assert cidx1.get_loc("e") == idx1.get_loc("e")
for i in [cidx1, idx1]:
with pytest.raises(KeyError, match="'NOT-EXIST'"):
i.get_loc("NOT-EXIST")
# non-unique
cidx2 = CategoricalIndex(list("aacded"), categories=list("edabc"))
idx2 = Index(list("aacded"))
# results in bool array
res = cidx2.get_loc("d")
tm.assert_numpy_array_equal(res, idx2.get_loc("d"))
tm.assert_numpy_array_equal(
res, np.array([False, False, False, True, False, True])
)
# unique element results in scalar
res = cidx2.get_loc("e")
assert res == idx2.get_loc("e")
assert res == 4
for i in [cidx2, idx2]:
with pytest.raises(KeyError, match="'NOT-EXIST'"):
i.get_loc("NOT-EXIST")
# non-unique, sliceable
cidx3 = CategoricalIndex(list("aabbb"), categories=list("abc"))
idx3 = Index(list("aabbb"))
# results in slice
res = cidx3.get_loc("a")
assert res == idx3.get_loc("a")
assert res == slice(0, 2, None)
res = cidx3.get_loc("b")
assert res == idx3.get_loc("b")
assert res == slice(2, 5, None)
for i in [cidx3, idx3]:
with pytest.raises(KeyError, match="'c'"):
i.get_loc("c")
def _insert_dict_val(key: tuple, choice_index: pd.Index, val, new_array):
max_levels = choice_index.nlevels
if max_levels == 1:
# Dotted dict keys are not supported for multinomial models
assert len(key) == 1
loc = choice_index.get_loc(key[0])
new_array[0, loc] = val
else:
'''
Dotted dict literals require care to ensure explicit reindexing with nested logit models. For example,
specifying the key "A.B" in a 3-level model (where "A.B.C" exists and is meaningful) there is need to
disambiguate between the node with the unique ID of ('A', 'B', '.') (i.e. the parent node of "A.B.C"),
versus applying a value to ALL children of that parent node. Pandas during reindexing will apply value
to ALL nodes with the "A.B" pattern e.g. the parent and all of its children.
To avoid this, some special naming conventions are enforced here (assuming max_level = 3):
"A.B" refers to the parent node ONLY. The output new key is ('A', 'B', '.')
"A.B._" refers to ALL children of parent node A.B. The output new key is ('A', 'B')
The edge case "A._.B" (where an underscore occurs in the middle of a name) is technically meaningless,
so the code below assumes that "A._" was meant instead
'''
new_key = []
partial_key = False
for sub_key in key:
if sub_key == '_':
partial_key = True
break
new_key.append(str(sub_key))
delta = ['.'] * (max_levels - len(new_key))
if partial_key:
locs = choice_index.get_loc(tuple(new_key))
parent_loc = choice_index.get_loc(tuple(new_key + delta))
new_array[0, locs] = val
new_array[0, parent_loc] = 0
else:
loc = choice_index.get_loc(tuple(new_key + delta))
new_array[0, loc] = val
def test_parse_combinations(self):
ability_names = Series(["Ability 1", "Ability 2", "Ability 3"])
ability_index = Index(ability_names)
print(ability_index.get_loc("Ability 1"))
print(ability_index)
index = SpreadsheetReader.parse_combination_string(ability_index, "Ability 2 > Ability 1")
self.assertTrue(numpy.array_equal(index, [1, 0]))
pass
def _splitter(index: pd.Index, *args, **kwargs):
if isinstance(index, pd.MultiIndex):
group_indexes = [
splitter(index[index.get_loc(group)], *args, **kwargs)
for group in unique_level_rows(index)
]
return concat_indices([gi[0]
for gi in group_indexes]), concat_indices(
[gi[1] for gi in group_indexes])
else:
splitter(index, *args, **kwargs)
def _get_train_start(
start, window_length: ACCEPTED_WINDOW_LENGTH_TYPES, y: pd.Index
) -> int:
if is_timedelta_or_date_offset(x=window_length):
train_start = y.get_loc(
max(y[min(start, len(y) - 1)] - window_length, min(y))
)
if start >= len(y):
train_start += 1
else:
train_start = start - window_length
return train_start
def _split_for_initial_window(self, y: pd.Index) -> SPLIT_ARRAY_TYPE:
"""Get train/test splits for non-empty initial window.
Parameters
----------
y : pd.Index
Index of the time series to split
Returns
-------
(np.ndarray, np.ndarray)
Integer indices of the train/test windows
"""
fh = _check_fh(self.fh)
if not self.start_with_window:
raise ValueError(
"`start_with_window` must be True if `initial_window` is given"
)
if self.initial_window <= self.window_length:
raise ValueError("`initial_window` must greater than `window_length`")
if is_timedelta_or_date_offset(x=self.initial_window):
initial_window_threshold = y.get_loc(y[0] + self.initial_window)
else:
initial_window_threshold = self.initial_window
# For in-sample forecasting horizons, the first split must ensure that
# in-sample test set is still within the data.
if not fh.is_all_out_of_sample() and abs(fh[0]) >= initial_window_threshold:
initial_start = abs(fh[0]) - self.initial_window + 1
else:
initial_start = 0
if is_timedelta_or_date_offset(x=self.initial_window):
initial_end = y.get_loc(y[initial_start] + self.initial_window)
else:
initial_end = initial_start + self.initial_window
train = self._get_train_window(
y=y, train_start=initial_start, split_point=initial_end
)
test = initial_end + fh.to_numpy() - 1
return train, test
def parse_combination_string(ability_names: Index,
combination: str) -> List[int]:
"""
Parses combination string
:param ability_names:
:param combination:
:return:
"""
return [
ability_names.get_loc(entry.strip())
for entry in combination.split(">")
]
pass
def _split(self, y: pd.Index) -> SPLIT_GENERATOR_TYPE:
n_timepoints = y.shape[0]
cutoffs = check_cutoffs(cutoffs=self.cutoffs)
fh = _check_fh(fh=self.fh)
window_length = check_window_length(
window_length=self.window_length, n_timepoints=n_timepoints
)
_check_cutoffs_and_y(cutoffs=cutoffs, y=y)
_check_cutoffs_fh_y(cutoffs=cutoffs, fh=fh, y=y)
max_fh = fh.max()
max_cutoff = np.max(cutoffs)
for cutoff in cutoffs:
if is_int(x=window_length) and is_int(x=cutoff):
train_start = cutoff - window_length
elif is_timedelta_or_date_offset(x=window_length) and is_datetime(x=cutoff):
train_start = y.get_loc(max(y[0], cutoff - window_length))
else:
raise TypeError(
f"Unsupported combination of types: "
f"`window_length`: {type(window_length)}, "
f"`cutoff`: {type(cutoff)}"
)
split_point = cutoff if is_int(x=cutoff) else y.get_loc(y[y <= cutoff][-1])
training_window = self._get_train_window(
y=y, train_start=train_start + 1, split_point=split_point + 1
)
test_window = cutoff + fh.to_numpy()
if is_datetime(x=max_cutoff) and is_timedelta(x=max_fh):
test_window = test_window[test_window >= y.min()]
test_window = np.array(
[y.get_loc(timestamp) for timestamp in test_window]
)
yield training_window, test_window
def _get_start(self, y: pd.Index, fh: ForecastingHorizon) -> int:
"""Get the first split point."""
# By default, the first split point is the index zero, the first
# observation in
# the data.
start = 0
# If we start with a full window, the first split point depends on the window
# length.
if hasattr(self, "start_with_window") and self.start_with_window:
if hasattr(self, "initial_window") and self.initial_window is not None:
if hasattr(self, "step_length"):
step_length = self._get_step_length(x=self.step_length)
else:
step_length = 1
if is_timedelta_or_date_offset(x=self.initial_window):
start = y.get_loc(y[start] + self.initial_window) + step_length
else:
start += self.initial_window + step_length
else:
if is_timedelta_or_date_offset(x=self.window_length):
start = y.get_loc(y[start] + self.window_length)
else:
start += self.window_length
# For in-sample forecasting horizons, the first split must ensure that
# in-sample test set is still within the data.
if not fh.is_all_out_of_sample():
fh_min = abs(fh[0])
if fh_min >= start:
start = fh_min + 1
return start
class Graph:
def __init__(self, size: int, names: List[str]):
"""
Constructs a graph object (weighted, undirected)
:param size: Number of nodes in the graph
:param names: Names of the nodes
"""
self.adjacency = numpy.zeros((size, size)) # Weights between nodes
self.size = size
self.names = names
self.name_index = Index(names)
def minimum_distance(self, nodes_from: Union[int, List[int]],
nodes_to: Union[int, List[int]]) -> float:
"""
Gets the minimum distance
:param nodes_from: Index of node or nodes from which to compute the distance
:param nodes_to: Index of node or nodes to which to compute the distance
:return: Minimum distance between nodes
"""
if isinstance(nodes_from, Iterable):
return numpy.min(
[numpy.min(self.adjacency[i, nodes_to]) for i in nodes_from])
return numpy.min(self.adjacency[nodes_from, nodes_to])
def path_length(self, nodes: List[int]) -> float:
"""
:param nodes:
:return: Length of path
"""
if len(nodes) is 1:
return 0
length = 0
for index in range(len(nodes) - 1):
length = length + self.adjacency[nodes[index], nodes[index + 1]]
return length
def get_node_index(self, name: str) -> int:
"""
Gets index of node
:param name: Name of node
:return: index of node
"""
return self.name_index.get_loc(name)
def get_node_indices(self, names: List[str]) -> List[int]:
return [self.get_node_index(name) for name in names]
def ClassGrowthInfo(self):
if self.file != '' and self.SeatNum.get().upper() in self.SeatData:
semData = [
mean(self.data.iloc[:, i])
for i in range(1,
len(self.data.columns) - 2)
]
idx = Index(self.SeatData)
locData = idx.get_loc(self.SeatNum.get().upper())
StudentData = array(self.data.loc[locData, :])[1:-2]
plot(semData)
plot(StudentData)
ylim(5, 10)
legend(labels=['Average', self.SeatNum.get().upper()])
xticks(arange(6), ['I', 'II', 'III', 'IV', 'V', 'VI'])
show()
else:
messagebox.showwarning('Error 404', 'File not found.')
def IndStud(self):
if self.file != '' and self.SeatNum.get().upper() in self.SeatData:
data = self.data
col = data.columns
idx = Index(self.SeatData)
locData = idx.get_loc(self.SeatNum.get().upper())
StudentData = data.loc[locData + 2, :]
SubjectData = array(StudentData[3:10]).astype(int)
Subjects = [''.join(list(c)[:-6]) for c in col][3:10]
fig = figure()
ax = subplot(111)
title('Marks of ' + self.SeatNum.get().upper())
xlabel('Subjects')
ylabel('Marks Range')
ylim(0, 75)
ax.bar(Subjects, SubjectData)
fig.show()
else:
messagebox.showwarning('Error 404', 'File not found')
def _split(self, y: pd.Index) -> SPLIT_GENERATOR_TYPE:
n_timepoints = y.shape[0]
window_length = check_window_length(self.window_length, n_timepoints)
fh = _check_fh(self.fh)
end = _get_end(y_index=y, fh=fh)
if window_length is None:
start = 0
elif is_int(window_length):
start = end - window_length + 1
else:
start = np.argwhere(y > y[end] - window_length).flatten()[0]
train = self._get_train_window(y=y, train_start=start, split_point=end + 1)
if array_is_int(fh):
test = end + fh.to_numpy()
else:
test = np.array([y.get_loc(y[end] + x) for x in fh.to_pandas()])
yield train, test
class Grouping(object):
def __init__(self, index, names=None):
"""
index : index-like
Can be pandas MultiIndex or Index or array-like. If array-like
and is a MultipleIndex (more than one grouping variable),
groups are expected to be in each row. E.g., [('red', 1),
('red', 2), ('green', 1), ('green', 2)]
names : list or str, optional
The names to use for the groups. Should be a str if only
one grouping variable is used.
Notes
-----
If index is already a pandas Index then there is no copy.
"""
if isinstance(index, (Index, MultiIndex)):
if names is not None:
if hasattr(index, 'set_names'): # newer pandas
index.set_names(names, inplace=True)
else:
index.names = names
self.index = index
else: # array_like
if _is_hierarchical(index):
self.index = _make_hierarchical_index(index, names)
else:
self.index = Index(index, name=names)
if names is None:
names = _make_generic_names(self.index)
if hasattr(self.index, 'set_names'):
self.index.set_names(names, inplace=True)
else:
self.index.names = names
self.nobs = len(self.index)
self.nlevels = len(self.index.names)
self.slices = None
@property
def index_shape(self):
if hasattr(self.index, 'levshape'):
return self.index.levshape
else:
return self.index.shape
@property
def levels(self):
if hasattr(self.index, 'levels'):
return self.index.levels
else:
return pd.Categorical(self.index).levels
@property
def labels(self):
# this was index_int, but that's not a very good name...
codes = getattr(self.index, 'codes', None)
if codes is None:
if hasattr(self.index, 'labels'):
codes = self.index.labels
else:
codes = pd.Categorical(self.index).codes[None]
return codes
@property
def group_names(self):
return self.index.names
def reindex(self, index=None, names=None):
"""
Resets the index in-place.
"""
# NOTE: this is not of much use if the rest of the data does not change
# This needs to reset cache
if names is None:
names = self.group_names
self = Grouping(index, names)
def get_slices(self, level=0):
"""
Sets the slices attribute to be a list of indices of the sorted
groups for the first index level. I.e., self.slices[0] is the
index where each observation is in the first (sorted) group.
"""
# TODO: refactor this
groups = self.index.get_level_values(level).unique()
groups = np.array(groups)
groups.sort()
if isinstance(self.index, MultiIndex):
self.slices = [
self.index.get_loc_level(x, level=level)[0] for x in groups
]
else:
self.slices = [self.index.get_loc(x) for x in groups]
def count_categories(self, level=0):
"""
Sets the attribute counts to equal the bincount of the (integer-valued)
labels.
"""
# TODO: refactor this not to set an attribute. Why would we do this?
self.counts = np.bincount(self.labels[level])
def check_index(self, is_sorted=True, unique=True, index=None):
"""Sanity checks"""
if not index:
index = self.index
if is_sorted:
test = pd.DataFrame(lrange(len(index)), index=index)
test_sorted = test.sort()
if not test.index.equals(test_sorted.index):
raise Exception('Data is not be sorted')
if unique:
if len(index) != len(index.unique()):
raise Exception('Duplicate index entries')
def sort(self, data, index=None):
"""Applies a (potentially hierarchical) sort operation on a numpy array
or pandas series/dataframe based on the grouping index or a
user-supplied index. Returns an object of the same type as the
original data as well as the matching (sorted) Pandas index.
"""
if index is None:
index = self.index
if data_util._is_using_ndarray_type(data, None):
if data.ndim == 1:
out = pd.Series(data, index=index, copy=True)
out = out.sort_index()
else:
out = pd.DataFrame(data, index=index)
out = out.sort_index(inplace=False) # copies
return np.array(out), out.index
elif data_util._is_using_pandas(data, None):
out = data
out = out.reindex(index) # copies?
out = out.sort_index()
return out, out.index
else:
msg = 'data must be a Numpy array or a Pandas Series/DataFrame'
raise ValueError(msg)
def transform_dataframe(self, dataframe, function, level=0, **kwargs):
"""Apply function to each column, by group
Assumes that the dataframe already has a proper index"""
if dataframe.shape[0] != self.nobs:
raise Exception('dataframe does not have the same shape as index')
out = dataframe.groupby(level=level).apply(function, **kwargs)
if 1 in out.shape:
return np.ravel(out)
else:
return np.array(out)
def transform_array(self, array, function, level=0, **kwargs):
"""Apply function to each column, by group
"""
if array.shape[0] != self.nobs:
raise Exception('array does not have the same shape as index')
dataframe = pd.DataFrame(array, index=self.index)
return self.transform_dataframe(dataframe,
function,
level=level,
**kwargs)
def transform_slices(self, array, function, level=0, **kwargs):
"""Apply function to each group. Similar to transform_array but does
not coerce array to a DataFrame and back and only works on a 1D or 2D
numpy array. function is called function(group, group_idx, **kwargs).
"""
array = np.asarray(array)
if array.shape[0] != self.nobs:
raise Exception('array does not have the same shape as index')
# always reset because level is given. need to refactor this.
self.get_slices(level=level)
processed = []
for s in self.slices:
if array.ndim == 2:
subset = array[s, :]
elif array.ndim == 1:
subset = array[s]
processed.append(function(subset, s, **kwargs))
processed = np.array(processed)
return processed.reshape(-1, processed.shape[-1])
# TODO: this is not general needs to be a PanelGrouping object
def dummies_time(self):
self.dummy_sparse(level=1)
return self._dummies
def dummies_groups(self, level=0):
self.dummy_sparse(level=level)
return self._dummies
def dummy_sparse(self, level=0):
"""create a sparse indicator from a group array with integer labels
Parameters
----------
groups : ndarray, int, 1d (nobs,)
An array of group indicators for each observation. Group levels
are assumed to be defined as consecutive integers, i.e.
range(n_groups) where n_groups is the number of group levels.
A group level with no observations for it will still produce a
column of zeros.
Returns
-------
indi : ndarray, int8, 2d (nobs, n_groups)
an indicator array with one row per observation, that has 1 in the
column of the group level for that observation
Examples
--------
>>> g = np.array([0, 0, 2, 1, 1, 2, 0])
>>> indi = dummy_sparse(g)
>>> indi
<7x3 sparse matrix of type '<type 'numpy.int8'>'
with 7 stored elements in Compressed Sparse Row format>
>>> indi.todense()
matrix([[1, 0, 0],
[1, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 0],
[0, 0, 1],
[1, 0, 0]], dtype=int8)
current behavior with missing groups
>>> g = np.array([0, 0, 2, 0, 2, 0])
>>> indi = dummy_sparse(g)
>>> indi.todense()
matrix([[1, 0, 0],
[1, 0, 0],
[0, 0, 1],
[1, 0, 0],
[0, 0, 1],
[1, 0, 0]], dtype=int8)
"""
indi = dummy_sparse(self.labels[level])
self._dummies = indi
def test_get_loc_raises_missized_tolerance(self):
index = Index([0, 1, 2])
with pytest.raises(ValueError, match="tolerance size must match"):
with tm.assert_produces_warning(FutureWarning, match="deprecated"):
index.get_loc(1.1, "nearest", tolerance=[1, 1])
def test_get_loc_tolerance_no_method_raises(self):
index = Index([0, 1, 2])
with pytest.raises(ValueError, match="tolerance .* valid if"):
index.get_loc(1.1, tolerance=1)
def _parse_one_index(base_idx: pd.Index,
index_1d: Union[INDEX1D, None],
index_name: str,
view_index: CINDEX = None) -> CINDEX:
""" index_name: 'row' or 'column' """
if view_index is not None:
base_idx = base_idx[view_index]
indexer = None
if isinstance(index_1d, slice):
step = 1 if index_1d.step is None else index_1d.step
start = index_1d.start
if isinstance(start, str):
if start not in base_idx:
raise ValueError(
f"Cannot locate slice.start '{start}' in {index_name} index!"
)
start = base_idx.get_loc(start)
elif start is None:
start = 0
else:
if start < 0 or start >= base_idx.size:
raise ValueError(
f"slice.start '{start}' is out of the boundary [0, {base_idx.size}) for {index_name} index!"
)
stop = index_1d.stop
if isinstance(stop, str):
if stop not in base_idx:
raise ValueError(
f"Cannot locate slice.stop '{stop}' in {index_name} index!"
)
stop = base_idx.get_loc(stop) + np.sign(
step) # if str , use [] instead of [)
elif stop is None:
stop = base_idx.size
else:
if stop - step < 0 or stop - step >= base_idx.size:
raise ValueError(
f"slice.stop '{stop}' is out of the boundary [0, {base_idx.size}) for {index_name} index!"
)
indexer = slice(start, stop, step)
elif isinstance(index_1d, np.ndarray) and (index_1d.dtype.kind
in {'b', 'i', 'u'}):
assert index_1d.ndim == 1
if index_1d.dtype.kind == 'b':
if index_1d.size != base_idx.size:
raise ValueError(
f"{index_name} index size does not match: actual size {base_idx.size}, input size {index_1d.size}!"
)
indexer = np.where(index_1d)[0]
elif index_1d.dtype.kind == 'i' or index_1d.dtype.kind == 'u':
if np.any(index_1d < 0):
raise ValueError(
f"Detect negative values in {index_name} index!")
if np.any(index_1d >= base_idx.size):
raise ValueError(
f"Detect values exceeding the largest valid position {base_idx.size - 1} in {index_name} index!"
)
if np.unique(index_1d).size < index_1d.size:
raise ValueError(
f"{index_name} index values are not unique!")
indexer = index_1d
else:
if not isinstance(index_1d, pd.Index):
assert isinstance(index_1d, np.ndarray) and index_1d.ndim == 1
index_1d = pd.Index(index_1d)
indexer = _process_pd_index(base_idx, index_1d)
if view_index is not None:
if isinstance(view_index, slice):
if isinstance(indexer, slice):
indexer = slice(
view_index.start + (view_index.step * indexer.start),
view_index.start + (view_index.step * indexer.stop),
view_index.step * indexer.step)
else:
indexer = np.array(
range(view_index.start, view_index.stop,
view_index.step))[indexer]
else:
indexer = view_index[indexer]
return indexer
class Grouping(object):
def __init__(self, index, names=None):
"""
index : index-like
Can be pandas MultiIndex or Index or array-like. If array-like
and is a MultipleIndex (more than one grouping variable),
groups are expected to be in each row. E.g., [('red', 1),
('red', 2), ('green', 1), ('green', 2)]
names : list or str, optional
The names to use for the groups. Should be a str if only
one grouping variable is used.
Notes
-----
If index is already a pandas Index then there is no copy.
"""
if isinstance(index, (Index, MultiIndex)):
if names is not None:
if hasattr(index, 'set_names'): # newer pandas
index.set_names(names, inplace=True)
else:
index.names = names
self.index = index
else: # array-like
if _is_hierarchical(index):
self.index = _make_hierarchical_index(index, names)
else:
self.index = Index(index, name=names)
if names is None:
names = _make_generic_names(self.index)
if hasattr(self.index, 'set_names'):
self.index.set_names(names, inplace=True)
else:
self.index.names = names
self.nobs = len(self.index)
self.nlevels = len(self.index.names)
self.slices = None
@property
def index_shape(self):
if hasattr(self.index, 'levshape'):
return self.index.levshape
else:
return self.index.shape
@property
def levels(self):
if hasattr(self.index, 'levels'):
return self.index.levels
else:
return pd.Categorical(self.index).levels
@property
def labels(self):
# this was index_int, but that's not a very good name...
if hasattr(self.index, 'labels'):
return self.index.labels
else: # pandas version issue here
# Compat code for the labels -> codes change in pandas 0.15
# FIXME: use .codes directly when we don't want to support
# pandas < 0.15
tmp = pd.Categorical(self.index)
try:
labl = tmp.codes
except AttributeError:
labl = tmp.labels # Old pandsd
return labl[None]
@property
def group_names(self):
return self.index.names
def reindex(self, index=None, names=None):
"""
Resets the index in-place.
"""
# NOTE: this isn't of much use if the rest of the data doesn't change
# This needs to reset cache
if names is None:
names = self.group_names
self = Grouping(index, names)
def get_slices(self, level=0):
"""
Sets the slices attribute to be a list of indices of the sorted
groups for the first index level. I.e., self.slices[0] is the
index where each observation is in the first (sorted) group.
"""
# TODO: refactor this
groups = self.index.get_level_values(level).unique()
groups = np.array(groups)
groups.sort()
if isinstance(self.index, MultiIndex):
self.slices = [self.index.get_loc_level(x, level=level)[0]
for x in groups]
else:
self.slices = [self.index.get_loc(x) for x in groups]
def count_categories(self, level=0):
"""
Sets the attribute counts to equal the bincount of the (integer-valued)
labels.
"""
# TODO: refactor this not to set an attribute. Why would we do this?
self.counts = np.bincount(self.labels[level])
def check_index(self, is_sorted=True, unique=True, index=None):
"""Sanity checks"""
if not index:
index = self.index
if is_sorted:
test = pd.DataFrame(lrange(len(index)), index=index)
test_sorted = test.sort()
if not test.index.equals(test_sorted.index):
raise Exception('Data is not be sorted')
if unique:
if len(index) != len(index.unique()):
raise Exception('Duplicate index entries')
def sort(self, data, index=None):
"""Applies a (potentially hierarchical) sort operation on a numpy array
or pandas series/dataframe based on the grouping index or a
user-supplied index. Returns an object of the same type as the
original data as well as the matching (sorted) Pandas index.
"""
if index is None:
index = self.index
if data_util._is_using_ndarray_type(data, None):
if data.ndim == 1:
out = pd.Series(data, index=index, copy=True)
out = out.sort_index()
else:
out = pd.DataFrame(data, index=index)
out = out.sort_index(inplace=False) # copies
return np.array(out), out.index
elif data_util._is_using_pandas(data, None):
out = data
out = out.reindex(index) # copies?
out = out.sort_index()
return out, out.index
else:
msg = 'data must be a Numpy array or a Pandas Series/DataFrame'
raise ValueError(msg)
def transform_dataframe(self, dataframe, function, level=0, **kwargs):
"""Apply function to each column, by group
Assumes that the dataframe already has a proper index"""
if dataframe.shape[0] != self.nobs:
raise Exception('dataframe does not have the same shape as index')
out = dataframe.groupby(level=level).apply(function, **kwargs)
if 1 in out.shape:
return np.ravel(out)
else:
return np.array(out)
def transform_array(self, array, function, level=0, **kwargs):
"""Apply function to each column, by group
"""
if array.shape[0] != self.nobs:
raise Exception('array does not have the same shape as index')
dataframe = pd.DataFrame(array, index=self.index)
return self.transform_dataframe(dataframe, function, level=level,
**kwargs)
def transform_slices(self, array, function, level=0, **kwargs):
"""Apply function to each group. Similar to transform_array but does
not coerce array to a DataFrame and back and only works on a 1D or 2D
numpy array. function is called function(group, group_idx, **kwargs).
"""
array = np.asarray(array)
if array.shape[0] != self.nobs:
raise Exception('array does not have the same shape as index')
# always reset because level is given. need to refactor this.
self.get_slices(level=level)
processed = []
for s in self.slices:
if array.ndim == 2:
subset = array[s, :]
elif array.ndim == 1:
subset = array[s]
processed.append(function(subset, s, **kwargs))
processed = np.array(processed)
return processed.reshape(-1, processed.shape[-1])
# TODO: this isn't general needs to be a PanelGrouping object
def dummies_time(self):
self.dummy_sparse(level=1)
return self._dummies
def dummies_groups(self, level=0):
self.dummy_sparse(level=level)
return self._dummies
def dummy_sparse(self, level=0):
"""create a sparse indicator from a group array with integer labels
Parameters
----------
groups: ndarray, int, 1d (nobs,) an array of group indicators for each
observation. Group levels are assumed to be defined as consecutive
integers, i.e. range(n_groups) where n_groups is the number of
group levels. A group level with no observations for it will still
produce a column of zeros.
Returns
-------
indi : ndarray, int8, 2d (nobs, n_groups)
an indicator array with one row per observation, that has 1 in the
column of the group level for that observation
Examples
--------
>>> g = np.array([0, 0, 2, 1, 1, 2, 0])
>>> indi = dummy_sparse(g)
>>> indi
<7x3 sparse matrix of type '<type 'numpy.int8'>'
with 7 stored elements in Compressed Sparse Row format>
>>> indi.todense()
matrix([[1, 0, 0],
[1, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 0],
[0, 0, 1],
[1, 0, 0]], dtype=int8)
current behavior with missing groups
>>> g = np.array([0, 0, 2, 0, 2, 0])
>>> indi = dummy_sparse(g)
>>> indi.todense()
matrix([[1, 0, 0],
[1, 0, 0],
[0, 0, 1],
[1, 0, 0],
[0, 0, 1],
[1, 0, 0]], dtype=int8)
"""
from scipy import sparse
groups = self.labels[level]
indptr = np.arange(len(groups)+1)
data = np.ones(len(groups), dtype=np.int8)
self._dummies = sparse.csr_matrix((data, groups, indptr))
