CLN: replace %s syntax with .format in core.dtypes and core.sparse (#…

…17270)

pandas/core/dtypes/cast.py

@@ -516,7 +516,8 @@ def maybe_cast_item(obj, item, dtype):
         if dtype in (np.object_, np.bool_):
             obj[item] = chunk.astype(np.object_)
         elif not issubclass(dtype, (np.integer, np.bool_)):  # pragma: no cover
-            raise ValueError("Unexpected dtype encountered: %s" % dtype)
+            raise ValueError("Unexpected dtype encountered: {dtype}"
+                             .format(dtype=dtype))
 
 
 def invalidate_string_dtypes(dtype_set):
@@ -620,8 +621,9 @@ def astype_nansafe(arr, dtype, copy=True):
         elif dtype == np.int64:
             return arr.view(dtype)
         elif dtype != _NS_DTYPE:
-            raise TypeError("cannot astype a datetimelike from [%s] to [%s]" %
-                            (arr.dtype, dtype))
+            raise TypeError("cannot astype a datetimelike from [{from_dtype}] "
+                            "to [{to_dtype}]".format(from_dtype=arr.dtype,
+                                                     to_dtype=dtype))
         return arr.astype(_NS_DTYPE)
     elif is_timedelta64_dtype(arr):
         if dtype == np.int64:
@@ -640,8 +642,9 @@ def astype_nansafe(arr, dtype, copy=True):
                 result[mask] = np.nan
                 return result
 
-            raise TypeError("cannot astype a timedelta from [%s] to [%s]" %
-                            (arr.dtype, dtype))
+            raise TypeError("cannot astype a timedelta from [{from_dtype}] "
+                            "to [{to_dtype}]".format(from_dtype=arr.dtype,
+                                                     to_dtype=dtype))
 
         return arr.astype(_TD_DTYPE)
     elif (np.issubdtype(arr.dtype, np.floating) and
@@ -926,7 +929,7 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
                     dtype = _NS_DTYPE
                 else:
                     raise TypeError("cannot convert datetimelike to "
-                                    "dtype [%s]" % dtype)
+                                    "dtype [{dtype}]".format(dtype=dtype))
             elif is_datetime64tz:
 
                 # our NaT doesn't support tz's
@@ -943,7 +946,7 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
                     dtype = _TD_DTYPE
                 else:
                     raise TypeError("cannot convert timedeltalike to "
-                                    "dtype [%s]" % dtype)
+                                    "dtype [{dtype}]".format(dtype=dtype))
 
             if is_scalar(value):
                 if value == iNaT or isna(value):
@@ -982,7 +985,8 @@ def maybe_cast_to_datetime(value, dtype, errors='raise'):
                 return tslib.ints_to_pydatetime(ints)
 
             # we have a non-castable dtype that was passed
-            raise TypeError('Cannot cast datetime64 to %s' % dtype)
+            raise TypeError('Cannot cast datetime64 to {dtype}'
+                            .format(dtype=dtype))
 
     else:
 

pandas/core/dtypes/common.py

@@ -1854,10 +1854,10 @@ def _validate_date_like_dtype(dtype):
     try:
         typ = np.datetime_data(dtype)[0]
     except ValueError as e:
-        raise TypeError('%s' % e)
+        raise TypeError('{error}'.format(error=e))
     if typ != 'generic' and typ != 'ns':
-        raise ValueError('%r is too specific of a frequency, try passing %r' %
-                         (dtype.name, dtype.type.__name__))
+        msg = '{name!r} is too specific of a frequency, try passing {type!r}'
+        raise ValueError(msg.format(name=dtype.name, type=dtype.type.__name__))
 
 
 _string_dtypes = frozenset(map(_get_dtype_from_object, (binary_type,
@@ -1924,6 +1924,6 @@ def pandas_dtype(dtype):
     if dtype in [object, np.object_, 'object', 'O']:
         return npdtype
     elif npdtype.kind == 'O':
-        raise TypeError('dtype {0} not understood'.format(dtype))
+        raise TypeError('dtype {dtype} not understood'.format(dtype=dtype))
 
     return npdtype

pandas/core/sparse/array.py

@@ -52,8 +52,8 @@ def _arith_method(op, name, str_rep=None, default_axis=None, fill_zeros=None,
     def wrapper(self, other):
         if isinstance(other, np.ndarray):
             if len(self) != len(other):
-                raise AssertionError("length mismatch: %d vs. %d" %
-                                     (len(self), len(other)))
+                raise AssertionError("length mismatch: {self} vs. {other}"
+                                     .format(self=len(self), other=len(other)))
             if not isinstance(other, ABCSparseArray):
                 dtype = getattr(other, 'dtype', None)
                 other = SparseArray(other, fill_value=self.fill_value,
@@ -66,7 +66,8 @@ def wrapper(self, other):
 
             return _wrap_result(name, result, self.sp_index, fill)
         else:  # pragma: no cover
-            raise TypeError('operation with %s not supported' % type(other))
+            raise TypeError('operation with {other} not supported'
+                            .format(other=type(other)))
 
     if name.startswith("__"):
         name = name[2:-2]
@@ -218,9 +219,9 @@ def __new__(cls, data, sparse_index=None, index=None, kind='integer',
             else:
                 values = _sanitize_values(data)
                 if len(values) != sparse_index.npoints:
-                    raise AssertionError("Non array-like type {0} must have"
-                                         " the same length as the"
-                                         " index".format(type(values)))
+                    raise AssertionError("Non array-like type {type} must "
+                                         "have the same length as the index"
+                                         .format(type=type(values)))
         # Create array, do *not* copy data by default
         if copy:
             subarr = np.array(values, dtype=dtype, copy=True)
@@ -330,9 +331,10 @@ def __len__(self):
             return 0
 
     def __unicode__(self):
-        return '%s\nFill: %s\n%s' % (printing.pprint_thing(self),
-                                     printing.pprint_thing(self.fill_value),
-                                     printing.pprint_thing(self.sp_index))
+        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))
 
     def disable(self, other):
         raise NotImplementedError('inplace binary ops not supported')
@@ -377,8 +379,8 @@ def fill_value(self, value):
         if is_dtype_equal(self.dtype, new_dtype):
             self._fill_value = fill_value
         else:
-            msg = 'unable to set fill_value {0} to {1} dtype'
-            raise ValueError(msg.format(value, self.dtype))
+            msg = 'unable to set fill_value {fill} to {dtype} dtype'
+            raise ValueError(msg.format(fill=value, dtype=self.dtype))
 
     def get_values(self, fill=None):
         """ return a dense representation """
@@ -466,7 +468,8 @@ def take(self, indices, axis=0, allow_fill=True,
         nv.validate_take(tuple(), kwargs)
 
         if axis:
-            raise ValueError("axis must be 0, input was {0}".format(axis))
+            raise ValueError("axis must be 0, input was {axis}"
+                             .format(axis=axis))
 
         if is_integer(indices):
             # return scalar
@@ -482,12 +485,12 @@ def take(self, indices, axis=0, allow_fill=True,
                        'all indices must be >= -1')
                 raise ValueError(msg)
             elif (n <= indices).any():
-                msg = 'index is out of bounds for size {0}'
-                raise IndexError(msg.format(n))
+                msg = 'index is out of bounds for size {size}'.format(size=n)
+                raise IndexError(msg)
         else:
             if ((indices < -n) | (n <= indices)).any():
-                msg = 'index is out of bounds for size {0}'
-                raise IndexError(msg.format(n))
+                msg = 'index is out of bounds for size {size}'.format(size=n)
+                raise IndexError(msg)
 
         indices = indices.astype(np.int32)
         if not (allow_fill and fill_value is not None):
@@ -543,8 +546,8 @@ def astype(self, dtype=None, copy=True):
             else:
                 fill_value = dtype.type(self.fill_value)
         except ValueError:
-            msg = 'unable to coerce current fill_value {0} to {1} dtype'
-            raise ValueError(msg.format(self.fill_value, dtype))
+            msg = 'unable to coerce current fill_value {fill} to {dtype} dtype'
+            raise ValueError(msg.format(fill=self.fill_value, dtype=dtype))
         return self._simple_new(sp_values, self.sp_index,
                                 fill_value=fill_value)
 

pandas/core/sparse/frame.py

@@ -214,11 +214,11 @@ def _prep_index(self, data, index, columns):
             columns = _default_index(K)
 
         if len(columns) != K:
-            raise ValueError('Column length mismatch: %d vs. %d' %
-                             (len(columns), K))
+            raise ValueError('Column length mismatch: {columns} vs. {K}'
+                             .format(columns=len(columns), K=K))
         if len(index) != N:
-            raise ValueError('Index length mismatch: %d vs. %d' %
-                             (len(index), N))
+            raise ValueError('Index length mismatch: {index} vs. {N}'
+                             .format(index=len(index), N=N))
         return index, columns
 
     def to_coo(self):
@@ -725,17 +725,17 @@ def _maybe_rename_join(self, other, lsuffix, rsuffix):
         to_rename = self.columns.intersection(other.columns)
         if len(to_rename) > 0:
             if not lsuffix and not rsuffix:
-                raise ValueError('columns overlap but no suffix specified: %s'
-                                 % to_rename)
+                raise ValueError('columns overlap but no suffix specified: '
+                                 '{to_rename}'.format(to_rename=to_rename))
 
             def lrenamer(x):
                 if x in to_rename:
-                    return '%s%s' % (x, lsuffix)
+                    return '{x}{lsuffix}'.format(x=x, lsuffix=lsuffix)
                 return x
 
             def rrenamer(x):
                 if x in to_rename:
-                    return '%s%s' % (x, rsuffix)
+                    return '{x}{rsuffix}'.format(x=x, rsuffix=rsuffix)
                 return x
 
             this = self.rename(columns=lrenamer)

pandas/core/sparse/list.py

@@ -35,15 +35,16 @@ def __init__(self, data=None, fill_value=np.nan):
 
     def __unicode__(self):
         contents = '\n'.join(repr(c) for c in self._chunks)
-        return '%s\n%s' % (object.__repr__(self), pprint_thing(contents))
+        return '{self}\n{contents}'.format(self=object.__repr__(self),
+                                           contents=pprint_thing(contents))
 
     def __len__(self):
         return sum(len(c) for c in self._chunks)
 
     def __getitem__(self, i):
         if i < 0:
             if i + len(self) < 0:  # pragma: no cover
-                raise ValueError('%d out of range' % i)
+                raise ValueError('{index} out of range'.format(index=i))
             i += len(self)
 
         passed = 0

pandas/core/sparse/series.py

@@ -65,7 +65,8 @@ def wrapper(self, other):
                                      index=self.index,
                                      name=self.name)
         else:  # pragma: no cover
-            raise TypeError('operation with %s not supported' % type(other))
+            raise TypeError('operation with {other} not supported'
+                            .format(other=type(other)))
 
     wrapper.__name__ = name
     if name.startswith("__"):
@@ -295,7 +296,8 @@ def shape(self):
     def __unicode__(self):
         # currently, unicode is same as repr...fixes infinite loop
         series_rep = Series.__unicode__(self)
-        rep = '%s\n%s' % (series_rep, repr(self.sp_index))
+        rep = '{series}\n{index!r}'.format(series=series_rep,
+                                           index=self.sp_index)
         return rep
 
     def __array_wrap__(self, result, context=None):