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CLN: replace %s syntax with .format in missing.py, nanops.py, ops.py #17322

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merged 1 commit into from
Aug 24, 2017
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CLN: replace %s syntax with .format in missing.py, nanops.py, ops.py #17322

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41 changes: 26 additions & 15 deletions pandas/core/missing.py
View file
Original file line number Diff line number Diff line change
@@ -88,8 +88,8 @@ def clean_fill_method(method, allow_nearest=False):
valid_methods.append('nearest')
expecting = 'pad (ffill), backfill (bfill) or nearest'
if method not in valid_methods:
msg = ('Invalid fill method. Expecting %s. Got %s' %
(expecting, method))
msg = ('Invalid fill method. Expecting {expecting}. Got {method}'
.format(expecting=expecting, method=method))
raise ValueError(msg)
return method

@@ -104,8 +104,8 @@ def clean_interp_method(method, **kwargs):
raise ValueError("You must specify the order of the spline or "
"polynomial.")
if method not in valid:
raise ValueError("method must be one of {0}."
"Got '{1}' instead.".format(valid, method))
raise ValueError("method must be one of {valid}. Got '{method}' "
"instead.".format(valid=valid, method=method))

return method

@@ -146,8 +146,10 @@ def interpolate_1d(xvalues, yvalues, method='linear', limit=None,
valid_limit_directions = ['forward', 'backward', 'both']
limit_direction = limit_direction.lower()
if limit_direction not in valid_limit_directions:
raise ValueError('Invalid limit_direction: expecting one of %r, got '
'%r.' % (valid_limit_directions, limit_direction))
msg = ('Invalid limit_direction: expecting one of {valid!r}, '
'got {invalid!r}.')
raise ValueError(msg.format(valid=valid_limit_directions,
invalid=limit_direction))

from pandas import Series
ys = Series(yvalues)
@@ -248,7 +250,8 @@ def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None,
# TODO: Why is DatetimeIndex being imported here?
from pandas import DatetimeIndex # noqa
except ImportError:
raise ImportError('{0} interpolation requires Scipy'.format(method))
raise ImportError('{method} interpolation requires SciPy'
.format(method=method))

new_x = np.asarray(new_x)

@@ -466,7 +469,8 @@ def pad_1d(values, limit=None, mask=None, dtype=None):
dtype = values.dtype
_method = None
if is_float_dtype(values):
_method = getattr(algos, 'pad_inplace_%s' % dtype.name, None)
name = 'pad_inplace_{name}'.format(name=dtype.name)
_method = getattr(algos, name, None)
elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
_method = _pad_1d_datetime
elif is_integer_dtype(values):
@@ -476,7 +480,8 @@ def pad_1d(values, limit=None, mask=None, dtype=None):
_method = algos.pad_inplace_object

if _method is None:
raise ValueError('Invalid dtype for pad_1d [%s]' % dtype.name)
raise ValueError('Invalid dtype for pad_1d [{name}]'
.format(name=dtype.name))

if mask is None:
mask = isna(values)
@@ -490,7 +495,8 @@ def backfill_1d(values, limit=None, mask=None, dtype=None):
dtype = values.dtype
_method = None
if is_float_dtype(values):
_method = getattr(algos, 'backfill_inplace_%s' % dtype.name, None)
name = 'backfill_inplace_{name}'.format(name=dtype.name)
_method = getattr(algos, name, None)
elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
_method = _backfill_1d_datetime
elif is_integer_dtype(values):
@@ -500,7 +506,8 @@ def backfill_1d(values, limit=None, mask=None, dtype=None):
_method = algos.backfill_inplace_object

if _method is None:
raise ValueError('Invalid dtype for backfill_1d [%s]' % dtype.name)
raise ValueError('Invalid dtype for backfill_1d [{name}]'
.format(name=dtype.name))

if mask is None:
mask = isna(values)
@@ -515,7 +522,8 @@ def pad_2d(values, limit=None, mask=None, dtype=None):
dtype = values.dtype
_method = None
if is_float_dtype(values):
_method = getattr(algos, 'pad_2d_inplace_%s' % dtype.name, None)
name = 'pad_2d_inplace_{name}'.format(name=dtype.name)
_method = getattr(algos, name, None)
elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
_method = _pad_2d_datetime
elif is_integer_dtype(values):
@@ -525,7 +533,8 @@ def pad_2d(values, limit=None, mask=None, dtype=None):
_method = algos.pad_2d_inplace_object

if _method is None:
raise ValueError('Invalid dtype for pad_2d [%s]' % dtype.name)
raise ValueError('Invalid dtype for pad_2d [{name}]'
.format(name=dtype.name))

if mask is None:
mask = isna(values)
@@ -544,7 +553,8 @@ def backfill_2d(values, limit=None, mask=None, dtype=None):
dtype = values.dtype
_method = None
if is_float_dtype(values):
_method = getattr(algos, 'backfill_2d_inplace_%s' % dtype.name, None)
name = 'backfill_2d_inplace_{name}'.format(name=dtype.name)
_method = getattr(algos, name, None)
elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):
_method = _backfill_2d_datetime
elif is_integer_dtype(values):
@@ -554,7 +564,8 @@ def backfill_2d(values, limit=None, mask=None, dtype=None):
_method = algos.backfill_2d_inplace_object

if _method is None:
raise ValueError('Invalid dtype for backfill_2d [%s]' % dtype.name)
raise ValueError('Invalid dtype for backfill_2d [{name}]'
.format(name=dtype.name))

if mask is None:
mask = isna(values)
8 changes: 4 additions & 4 deletions pandas/core/nanops.py
View file
Original file line number Diff line number Diff line change
@@ -70,9 +70,8 @@ def __call__(self, f):
def _f(*args, **kwargs):
obj_iter = itertools.chain(args, compat.itervalues(kwargs))
if any(self.check(obj) for obj in obj_iter):
raise TypeError('reduction operation {0!r} not allowed for '
'this dtype'.format(
f.__name__.replace('nan', '')))
msg = 'reduction operation {name!r} not allowed for this dtype'
raise TypeError(msg.format(name=f.__name__.replace('nan', '')))
try:
with np.errstate(invalid='ignore'):
return f(*args, **kwargs)
@@ -786,7 +785,8 @@ def _ensure_numeric(x):
try:
x = complex(x)
except Exception:
raise TypeError('Could not convert %s to numeric' % str(x))
raise TypeError('Could not convert {value!s} to numeric'
.format(value=x))
return x

# NA-friendly array comparisons
84 changes: 46 additions & 38 deletions pandas/core/ops.py
View file
Original file line number Diff line number Diff line change
@@ -63,9 +63,9 @@ def _create_methods(arith_method, comp_method, bool_method,

def names(x):
if x[-1] == "_":
return "__%s_" % x
return "__{name}_".format(name=x)
else:
return "__%s__" % x
return "__{name}__".format(name=x)
else:
names = lambda x: x

@@ -388,8 +388,8 @@ def _validate(self, lvalues, rvalues, name):
if name not in ('__div__', '__truediv__', '__mul__', '__rmul__'):
raise TypeError("can only operate on a timedelta and an "
"integer or a float for division and "
"multiplication, but the operator [%s] was"
"passed" % name)
"multiplication, but the operator [{name}] "
"was passed".format(name=name))

# 2 timedeltas
elif ((self.is_timedelta_lhs and
@@ -400,9 +400,9 @@ def _validate(self, lvalues, rvalues, name):
if name not in ('__div__', '__rdiv__', '__truediv__',
'__rtruediv__', '__add__', '__radd__', '__sub__',
'__rsub__'):
raise TypeError("can only operate on a timedeltas for "
"addition, subtraction, and division, but the"
" operator [%s] was passed" % name)
raise TypeError("can only operate on a timedeltas for addition"
", subtraction, and division, but the operator"
" [{name}] was passed".format(name=name))

# datetime and timedelta/DateOffset
elif (self.is_datetime_lhs and
@@ -411,23 +411,24 @@ def _validate(self, lvalues, rvalues, name):
if name not in ('__add__', '__radd__', '__sub__'):
raise TypeError("can only operate on a datetime with a rhs of "
"a timedelta/DateOffset for addition and "
"subtraction, but the operator [%s] was "
"passed" % name)
"subtraction, but the operator [{name}] was "
"passed".format(name=name))

elif (self.is_datetime_rhs and
(self.is_timedelta_lhs or self.is_offset_lhs)):
if name not in ('__add__', '__radd__', '__rsub__'):
raise TypeError("can only operate on a timedelta/DateOffset "
"with a rhs of a datetime for addition, "
"but the operator [%s] was passed" % name)
"but the operator [{name}] was passed"
.format(name=name))

# 2 datetimes
elif self.is_datetime_lhs and self.is_datetime_rhs:

if name not in ('__sub__', '__rsub__'):
raise TypeError("can only operate on a datetimes for"
" subtraction, but the operator [%s] was"
" passed" % name)
" subtraction, but the operator [{name}] was"
" passed".format(name=name))

# if tz's must be equal (same or None)
if getattr(lvalues, 'tz', None) != getattr(rvalues, 'tz', None):
@@ -439,8 +440,8 @@ def _validate(self, lvalues, rvalues, name):

if name not in ('__add__', '__radd__'):
raise TypeError("can only operate on a timedelta/DateOffset "
"and a datetime for addition, but the "
"operator [%s] was passed" % name)
"and a datetime for addition, but the operator"
" [{name}] was passed".format(name=name))
else:
raise TypeError('cannot operate on a series without a rhs '
'of a series/ndarray of type datetime64[ns] '
@@ -498,7 +499,7 @@ def _convert_to_array(self, values, name=None, other=None):
values = values.to_timestamp().to_series()
elif name not in ('__truediv__', '__div__', '__mul__', '__rmul__'):
raise TypeError("incompatible type for a datetime/timedelta "
"operation [{0}]".format(name))
"operation [{name}]".format(name=name))
elif inferred_type == 'floating':
if (isna(values).all() and
name in ('__add__', '__radd__', '__sub__', '__rsub__')):
@@ -508,8 +509,9 @@ def _convert_to_array(self, values, name=None, other=None):
elif self._is_offset(values):
return values
else:
raise TypeError("incompatible type [{0}] for a datetime/timedelta"
" operation".format(np.array(values).dtype))
raise TypeError("incompatible type [{dtype}] for a "
"datetime/timedelta operation"
.format(dtype=np.array(values).dtype))

return values

@@ -866,8 +868,8 @@ def wrapper(self, other, axis=None):
with np.errstate(all='ignore'):
res = na_op(values, other)
if is_scalar(res):
raise TypeError('Could not compare %s type with Series' %
type(other))
raise TypeError('Could not compare {typ} type with Series'
.format(typ=type(other)))

# always return a full value series here
res = _values_from_object(res)
@@ -906,9 +908,10 @@ def na_op(x, y):
y = bool(y)
result = lib.scalar_binop(x, y, op)
except:
raise TypeError("cannot compare a dtyped [{0}] array with "
"a scalar of type [{1}]".format(
x.dtype, type(y).__name__))
msg = ("cannot compare a dtyped [{dtype}] array "
"with a scalar of type [{type}]"
).format(dtype=x.dtype, type=type(y).__name__)
raise TypeError(msg)

return result

@@ -1140,14 +1143,17 @@ def _align_method_FRAME(left, right, axis):
""" convert rhs to meet lhs dims if input is list, tuple or np.ndarray """

def to_series(right):
msg = 'Unable to coerce to Series, length must be {0}: given {1}'
msg = ('Unable to coerce to Series, length must be {req_len}: '
'given {given_len}')
if axis is not None and left._get_axis_name(axis) == 'index':
if len(left.index) != len(right):
raise ValueError(msg.format(len(left.index), len(right)))
raise ValueError(msg.format(req_len=len(left.index),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.index)
else:
if len(left.columns) != len(right):
raise ValueError(msg.format(len(left.columns), len(right)))
raise ValueError(msg.format(req_len=len(left.columns),
given_len=len(right)))
right = left._constructor_sliced(right, index=left.columns)
return right

@@ -1161,15 +1167,16 @@ def to_series(right):

elif right.ndim == 2:
if left.shape != right.shape:
msg = ("Unable to coerce to DataFrame, "
"shape must be {0}: given {1}")
raise ValueError(msg.format(left.shape, right.shape))
msg = ("Unable to coerce to DataFrame, shape "
"must be {req_shape}: given {given_shape}"
).format(req_shape=left.shape, given_shape=right.shape)
raise ValueError(msg)

right = left._constructor(right, index=left.index,
columns=left.columns)
else:
msg = 'Unable to coerce to Series/DataFrame, dim must be <= 2: {0}'
raise ValueError(msg.format(right.shape, ))
raise ValueError('Unable to coerce to Series/DataFrame, dim '
'must be <= 2: {dim}'.format(dim=right.shape))

return right

@@ -1278,7 +1285,8 @@ def na_op(x, y):

return result

@Appender('Wrapper for flexible comparison methods %s' % name)
@Appender('Wrapper for flexible comparison methods {name}'
.format(name=name))
def f(self, other, axis=default_axis, level=None):

other = _align_method_FRAME(self, other, axis)
@@ -1299,7 +1307,7 @@ def f(self, other, axis=default_axis, level=None):


def _comp_method_FRAME(func, name, str_rep, masker=False):
@Appender('Wrapper for comparison method %s' % name)
@Appender('Wrapper for comparison method {name}'.format(name=name))
def f(self, other):
if isinstance(other, pd.DataFrame): # Another DataFrame
return self._compare_frame(other, func, str_rep)
@@ -1349,9 +1357,9 @@ def na_op(x, y):
# work only for scalars
def f(self, other):
if not is_scalar(other):
raise ValueError('Simple arithmetic with %s can only be '
'done with scalar values' %
self._constructor.__name__)
raise ValueError('Simple arithmetic with {name} can only be '
'done with scalar values'
.format(name=self._constructor.__name__))

return self._combine(other, op)

@@ -1384,7 +1392,7 @@ def na_op(x, y):

return result

@Appender('Wrapper for comparison method %s' % name)
@Appender('Wrapper for comparison method {name}'.format(name=name))
def f(self, other, axis=None):
# Validate the axis parameter
if axis is not None:
@@ -1394,8 +1402,8 @@ def f(self, other, axis=None):
return self._compare_constructor(other, na_op, try_cast=False)
elif isinstance(other, (self._constructor_sliced, pd.DataFrame,
ABCSeries)):
raise Exception("input needs alignment for this object [%s]" %
self._constructor)
raise Exception("input needs alignment for this object [{object}]"
.format(object=self._constructor))
else:
return self._combine_const(other, na_op, try_cast=False)