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Merged
merged 2 commits into from
Dec 7, 2019
+30 −51
Merged

CLN: f-string in pandas/core/arrays/sparse/* #30121

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e83a1c2
clean pandas/core/arrays/sparse/*
ganevgv Dec 7, 2019
59e2c2b
passes black
ganevgv Dec 7, 2019
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10 changes: 2 additions & 8 deletions pandas/core/arrays/sparse/accessor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -323,13 +323,7 @@ def _prep_index(data, index, columns):
columns = ibase.default_index(K)

if len(columns) != K:
raise ValueError(
"Column length mismatch: {columns} vs. {K}".format(
columns=len(columns), K=K
)
)
raise ValueError(f"Column length mismatch: {len(columns)} vs. {K}")
if len(index) != N:
raise ValueError(
"Index length mismatch: {index} vs. {N}".format(index=len(index), N=N)
)
raise ValueError(f"Index length mismatch: {len(index)} vs. {N}")
return index, columns
55 changes: 21 additions & 34 deletions pandas/core/arrays/sparse/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,13 +143,13 @@ def _sparse_array_op(
name = name[1:]

if name in ("and", "or") and dtype == "bool":
opname = "sparse_{name}_uint8".format(name=name)
opname = f"sparse_{name}_uint8"
# to make template simple, cast here
left_sp_values = left.sp_values.view(np.uint8)
right_sp_values = right.sp_values.view(np.uint8)
result_dtype = np.bool
else:
opname = "sparse_{name}_{dtype}".format(name=name, dtype=dtype)
opname = f"sparse_{name}_{dtype}"
left_sp_values = left.sp_values
right_sp_values = right.sp_values

Expand Down Expand Up @@ -364,16 +364,16 @@ def __init__(
sparse_values = np.asarray(data, dtype=dtype)
if len(sparse_values) != sparse_index.npoints:
raise AssertionError(
"Non array-like type {type} must "
"have the same length as the index".format(type=type(sparse_values))
f"Non array-like type {type(sparse_values)} must "
"have the same length as the index"
)
self._sparse_index = sparse_index
self._sparse_values = sparse_values
self._dtype = SparseDtype(sparse_values.dtype, fill_value)

@classmethod
def _simple_new(
cls, sparse_array: np.ndarray, sparse_index: SparseIndex, dtype: SparseDtype,
cls, sparse_array: np.ndarray, sparse_index: SparseIndex, dtype: SparseDtype
) -> "SparseArray":
new = cls([])
new._sparse_index = sparse_index
Expand Down Expand Up @@ -412,7 +412,7 @@ def from_spmatrix(cls, data):
length, ncol = data.shape

if ncol != 1:
raise ValueError("'data' must have a single column, not '{}'".format(ncol))
raise ValueError(f"'data' must have a single column, not '{ncol}'")

# our sparse index classes require that the positions be strictly
# increasing. So we need to sort loc, and arr accordingly.
Expand Down Expand Up @@ -771,7 +771,7 @@ def __getitem__(self, key):
elif hasattr(key, "__len__"):
return self.take(key)
else:
raise ValueError("Cannot slice with '{}'".format(key))
raise ValueError(f"Cannot slice with '{key}'")

return type(self)(data_slice, kind=self.kind)

Expand All @@ -791,9 +791,7 @@ def _get_val_at(self, loc):

def take(self, indices, allow_fill=False, fill_value=None):
if is_scalar(indices):
raise ValueError(
"'indices' must be an array, not a scalar '{}'.".format(indices)
)
raise ValueError(f"'indices' must be an array, not a scalar '{indices}'.")
indices = np.asarray(indices, dtype=np.int32)

if indices.size == 0:
Expand Down Expand Up @@ -932,8 +930,8 @@ def _concat_same_type(cls, to_concat):
if not (len(set(fill_values)) == 1 or isna(fill_values).all()):
warnings.warn(
"Concatenating sparse arrays with multiple fill "
"values: '{}'. Picking the first and "
"converting the rest.".format(fill_values),
f"values: '{fill_values}'. Picking the first and "
"converting the rest.",
PerformanceWarning,
stacklevel=6,
)
Expand Down Expand Up @@ -1153,11 +1151,7 @@ def _reduce(self, name, skipna=True, **kwargs):
method = getattr(self, name, None)

if method is None:
raise TypeError(
"cannot perform {name} with type {dtype}".format(
name=name, dtype=self.dtype
)
)
raise TypeError(f"cannot perform {name} with type {self.dtype}")

if skipna:
arr = self
Expand Down Expand Up @@ -1253,7 +1247,7 @@ def cumsum(self, axis=0, *args, **kwargs):
nv.validate_cumsum(args, kwargs)

if axis is not None and axis >= self.ndim: # Mimic ndarray behaviour.
raise ValueError("axis(={axis}) out of bounds".format(axis=axis))
raise ValueError(f"axis(={axis}) out of bounds")

if not self._null_fill_value:
return SparseArray(self.to_dense()).cumsum()
Expand Down Expand Up @@ -1367,7 +1361,7 @@ def sparse_unary_method(self) -> "SparseArray":
dtype = SparseDtype(values.dtype, fill_value)
return cls._simple_new(values, self.sp_index, dtype)

name = "__{name}__".format(name=op.__name__)
name = f"__{op.__name__}__"
return compat.set_function_name(sparse_unary_method, name, cls)

@classmethod
Expand Down Expand Up @@ -1401,11 +1395,7 @@ def sparse_arithmetic_method(self, other):
# TODO: look into _wrap_result
if len(self) != len(other):
raise AssertionError(
(
"length mismatch: {self} vs. {other}".format(
self=len(self), other=len(other)
)
)
(f"length mismatch: {len(self)} vs. {len(other)}")
)
if not isinstance(other, SparseArray):
dtype = getattr(other, "dtype", None)
Expand All @@ -1414,7 +1404,7 @@ def sparse_arithmetic_method(self, other):
)
return _sparse_array_op(self, other, op, op_name)

name = "__{name}__".format(name=op.__name__)
name = f"__{op.__name__}__"
return compat.set_function_name(sparse_arithmetic_method, name, cls)

@classmethod
Expand All @@ -1434,9 +1424,7 @@ def cmp_method(self, other):
# TODO: make this more flexible than just ndarray...
if len(self) != len(other):
raise AssertionError(
"length mismatch: {self} vs. {other}".format(
self=len(self), other=len(other)
)
f"length mismatch: {len(self)} vs. {len(other)}"
)
other = SparseArray(other, fill_value=self.fill_value)

Expand All @@ -1454,7 +1442,7 @@ def cmp_method(self, other):
dtype=np.bool_,
)

name = "__{name}__".format(name=op.__name__)
name = f"__{op.__name__}__"
return compat.set_function_name(cmp_method, name, cls)

@classmethod
Expand All @@ -1473,11 +1461,10 @@ def _add_comparison_ops(cls):
# Formatting
# -----------
def __repr__(self) -> str:
return "{self}\nFill: {fill}\n{index}".format(
self=printing.pprint_thing(self),
fill=printing.pprint_thing(self.fill_value),
index=printing.pprint_thing(self.sp_index),
)
pp_str = printing.pprint_thing(self)
pp_fill = printing.pprint_thing(self.fill_value)
pp_index = printing.pprint_thing(self.sp_index)
return f"{pp_str}\nFill: {pp_fill}\n{pp_index}"

def _formatter(self, boxed=False):
# Defer to the formatter from the GenericArrayFormatter calling us.
Expand Down
14 changes: 6 additions & 8 deletions pandas/core/arrays/sparse/dtype.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,9 +79,7 @@ def __init__(self, dtype: Dtype = np.float64, fill_value: Any = None) -> None:
fill_value = na_value_for_dtype(dtype)

if not is_scalar(fill_value):
raise ValueError(
"fill_value must be a scalar. Got {} instead".format(fill_value)
)
raise ValueError(f"fill_value must be a scalar. Got {fill_value} instead")
self._dtype = dtype
self._fill_value = fill_value

Expand Down Expand Up @@ -163,7 +161,7 @@ def subtype(self):

@property
def name(self):
return "Sparse[{}, {}]".format(self.subtype.name, self.fill_value)
return f"Sparse[{self.subtype.name}, {self.fill_value}]"

def __repr__(self) -> str:
return self.name
Expand Down Expand Up @@ -201,7 +199,7 @@ def construct_from_string(cls, string):
-------
SparseDtype
"""
msg = "Could not construct SparseDtype from '{}'".format(string)
msg = f"Could not construct SparseDtype from '{string}'"
if string.startswith("Sparse"):
try:
sub_type, has_fill_value = cls._parse_subtype(string)
Expand All @@ -210,14 +208,14 @@ def construct_from_string(cls, string):
else:
result = SparseDtype(sub_type)
msg = (
"Could not construct SparseDtype from '{}'.\n\nIt "
f"Could not construct SparseDtype from '{string}'.\n\nIt "
"looks like the fill_value in the string is not "
"the default for the dtype. Non-default fill_values "
"are not supported. Use the 'SparseDtype()' "
"constructor instead."
)
if has_fill_value and str(result) != string:
raise TypeError(msg.format(string))
raise TypeError(msg)
return result
else:
raise TypeError(msg)
Expand Down Expand Up @@ -253,7 +251,7 @@ def _parse_subtype(dtype: str) -> Tuple[str, bool]:
elif dtype == "Sparse":
subtype = "float64"
else:
raise ValueError("Cannot parse {}".format(dtype))
raise ValueError(f"Cannot parse {dtype}")
return subtype, has_fill_value

@classmethod
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/arrays/sparse/scipy_sparse.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ def _coo_to_sparse_series(A, dense_index: bool = False):
try:
s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
except AttributeError:
raise TypeError("Expected coo_matrix. Got {} instead.".format(type(A).__name__))
raise TypeError(f"Expected coo_matrix. Got {type(A).__name__} instead.")
s = s.sort_index()
s = s.astype(SparseDtype(s.dtype))
if dense_index:
Expand Down