CLN: fix all flake8 warnings in pandas/tools

Author: Wes McKinney <[email protected]>

Closes #12082 from wesm/style/tools and squashes the following commits:

53a297c [Wes McKinney] DOC: Clean up pivot_table docstrings a bit to be more numpydoc-like
6d59488 [Wes McKinney] Fix flake8 warnings in pandas/tools/tests
0fce522 [Wes McKinney] CLN: flake8 warnings in pandas/tools/*.py
85134cd [Wes McKinney] STY: autopep8

Author: wesm

pandas/tools/merge.py

@@ -181,8 +181,8 @@ def __init__(self, left, right, how='inner', on=None,
         elif isinstance(self.indicator, bool):
             self.indicator_name = '_merge' if self.indicator else None
         else:
-            raise ValueError('indicator option can only accept boolean or string arguments')
-
+            raise ValueError(
+                'indicator option can only accept boolean or string arguments')
 
         # note this function has side effects
         (self.left_join_keys,
@@ -191,7 +191,8 @@ def __init__(self, left, right, how='inner', on=None,
 
     def get_result(self):
         if self.indicator:
-            self.left, self.right = self._indicator_pre_merge(self.left, self.right)
+            self.left, self.right = self._indicator_pre_merge(
+                self.left, self.right)
 
         join_index, left_indexer, right_indexer = self._get_join_info()
 
@@ -225,9 +226,11 @@ def _indicator_pre_merge(self, left, right):
 
         for i in ['_left_indicator', '_right_indicator']:
             if i in columns:
-                raise ValueError("Cannot use `indicator=True` option when data contains a column named {}".format(i))
+                raise ValueError("Cannot use `indicator=True` option when "
+                                 "data contains a column named {}".format(i))
         if self.indicator_name in columns:
-            raise ValueError("Cannot use name of an existing column for indicator column")
+            raise ValueError(
+                "Cannot use name of an existing column for indicator column")
 
         left = left.copy()
         right = right.copy()
@@ -245,11 +248,15 @@ def _indicator_post_merge(self, result):
         result['_left_indicator'] = result['_left_indicator'].fillna(0)
         result['_right_indicator'] = result['_right_indicator'].fillna(0)
 
-        result[self.indicator_name] = Categorical((result['_left_indicator'] + result['_right_indicator']), categories=[1,2,3])
-        result[self.indicator_name] = result[self.indicator_name].cat.rename_categories(['left_only', 'right_only', 'both'])
-
-        result = result.drop(labels=['_left_indicator', '_right_indicator'], axis=1)
+        result[self.indicator_name] = Categorical((result['_left_indicator'] +
+                                                   result['_right_indicator']),
+                                                  categories=[1, 2, 3])
+        result[self.indicator_name] = (
+            result[self.indicator_name]
+            .cat.rename_categories(['left_only', 'right_only', 'both']))
 
+        result = result.drop(labels=['_left_indicator', '_right_indicator'],
+                             axis=1)
         return result
 
     def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
@@ -274,8 +281,9 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
                             continue
 
                         right_na_indexer = right_indexer.take(na_indexer)
-                        result.iloc[na_indexer,key_indexer] = com.take_1d(self.right_join_keys[i],
-                                                                          right_na_indexer)
+                        result.iloc[na_indexer, key_indexer] = (
+                            com.take_1d(self.right_join_keys[i],
+                                        right_na_indexer))
                     elif name in self.right:
                         if len(self.right) == 0:
                             continue
@@ -285,8 +293,9 @@ def _maybe_add_join_keys(self, result, left_indexer, right_indexer):
                             continue
 
                         left_na_indexer = left_indexer.take(na_indexer)
-                        result.iloc[na_indexer,key_indexer] = com.take_1d(self.left_join_keys[i],
-                                                                          left_na_indexer)
+                        result.iloc[na_indexer, key_indexer] = (
+                            com.take_1d(self.left_join_keys[i],
+                                        left_na_indexer))
             elif left_indexer is not None \
                     and isinstance(self.left_join_keys[i], np.ndarray):
 
@@ -384,8 +393,10 @@ def _get_merge_keys(self):
         left_drop = []
         left, right = self.left, self.right
 
-        is_lkey = lambda x: isinstance(x, (np.ndarray, ABCSeries)) and len(x) == len(left)
-        is_rkey = lambda x: isinstance(x, (np.ndarray, ABCSeries)) and len(x) == len(right)
+        is_lkey = lambda x: isinstance(
+            x, (np.ndarray, ABCSeries)) and len(x) == len(left)
+        is_rkey = lambda x: isinstance(
+            x, (np.ndarray, ABCSeries)) and len(x) == len(right)
 
         # ugh, spaghetti re #733
         if _any(self.left_on) and _any(self.right_on):
@@ -507,13 +518,13 @@ def _get_join_indexers(left_keys, right_keys, sort=False, how='inner'):
     from functools import partial
 
     assert len(left_keys) == len(right_keys), \
-            'left_key and right_keys must be the same length'
+        'left_key and right_keys must be the same length'
 
     # bind `sort` arg. of _factorize_keys
     fkeys = partial(_factorize_keys, sort=sort)
 
     # get left & right join labels and num. of levels at each location
-    llab, rlab, shape = map(list, zip( * map(fkeys, left_keys, right_keys)))
+    llab, rlab, shape = map(list, zip(* map(fkeys, left_keys, right_keys)))
 
     # get flat i8 keys from label lists
     lkey, rkey = _get_join_keys(llab, rlab, shape, sort)
@@ -524,7 +535,7 @@ def _get_join_indexers(left_keys, right_keys, sort=False, how='inner'):
     lkey, rkey, count = fkeys(lkey, rkey)
 
     # preserve left frame order if how == 'left' and sort == False
-    kwargs = {'sort':sort} if how == 'left' else {}
+    kwargs = {'sort': sort} if how == 'left' else {}
     join_func = _join_functions[how]
     return join_func(lkey, rkey, count, **kwargs)
 
@@ -563,8 +574,10 @@ def get_result(self):
             left_join_indexer = left_indexer
             right_join_indexer = right_indexer
 
-        lindexers = {1: left_join_indexer} if left_join_indexer is not None else {}
-        rindexers = {1: right_join_indexer} if right_join_indexer is not None else {}
+        lindexers = {
+            1: left_join_indexer} if left_join_indexer is not None else {}
+        rindexers = {
+            1: right_join_indexer} if right_join_indexer is not None else {}
 
         result_data = concatenate_block_managers(
             [(ldata, lindexers), (rdata, rindexers)],
@@ -586,7 +599,7 @@ def _get_multiindex_indexer(join_keys, index, sort):
     fkeys = partial(_factorize_keys, sort=sort)
 
     # left & right join labels and num. of levels at each location
-    rlab, llab, shape = map(list, zip( * map(fkeys, index.levels, join_keys)))
+    rlab, llab, shape = map(list, zip(* map(fkeys, index.levels, join_keys)))
     if sort:
         rlab = list(map(np.take, rlab, index.labels))
     else:
@@ -751,12 +764,13 @@ def _get_join_keys(llab, rlab, shape, sort):
 
     return _get_join_keys(llab, rlab, shape, sort)
 
-#----------------------------------------------------------------------
+# ---------------------------------------------------------------------
 # Concatenate DataFrame objects
 
 
 def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
-           keys=None, levels=None, names=None, verify_integrity=False, copy=True):
+           keys=None, levels=None, names=None, verify_integrity=False,
+           copy=True):
     """
     Concatenate pandas objects along a particular axis with optional set logic
     along the other axes. Can also add a layer of hierarchical indexing on the
@@ -885,10 +899,11 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None,
         else:
             # filter out the empties
             # if we have not multi-index possibiltes
-            df = DataFrame([ obj.shape for obj in objs ]).sum(1)
-            non_empties = df[df!=0]
-            if len(non_empties) and (keys is None and names is None and levels is None and join_axes is None):
-                objs = [ objs[i] for i in non_empties.index ]
+            df = DataFrame([obj.shape for obj in objs]).sum(1)
+            non_empties = df[df != 0]
+            if (len(non_empties) and (keys is None and names is None and
+                                      levels is None and join_axes is None)):
+                objs = [objs[i] for i in non_empties.index]
                 sample = objs[0]
 
         if sample is None:
@@ -917,12 +932,12 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None,
                 if ndim == max_ndim:
                     pass
 
-                elif ndim != max_ndim-1:
+                elif ndim != max_ndim - 1:
                     raise ValueError("cannot concatenate unaligned mixed "
                                      "dimensional NDFrame objects")
 
                 else:
-                    name = getattr(obj,'name',None)
+                    name = getattr(obj, 'name', None)
                     if ignore_index or name is None:
                         name = current_column
                         current_column += 1
@@ -931,7 +946,7 @@ def __init__(self, objs, axis=0, join='outer', join_axes=None,
                     # to line up
                     if self._is_frame and axis == 1:
                         name = 0
-                    obj = sample._constructor({ name : obj })
+                    obj = sample._constructor({name: obj})
 
                 self.objs.append(obj)
 
@@ -957,17 +972,23 @@ def get_result(self):
             if self.axis == 0:
                 new_data = com._concat_compat([x._values for x in self.objs])
                 name = com._consensus_name_attr(self.objs)
-                return Series(new_data, index=self.new_axes[0], name=name).__finalize__(self, method='concat')
+                return (Series(new_data, index=self.new_axes[0], name=name)
+                        .__finalize__(self, method='concat'))
 
             # combine as columns in a frame
             else:
                 data = dict(zip(range(len(self.objs)), self.objs))
                 index, columns = self.new_axes
                 tmpdf = DataFrame(data, index=index)
-                # checks if the column variable already stores valid column names (because set via the 'key' argument
-                # in the 'concat' function call. If that's not the case, use the series names as column names
-                if columns.equals(Index(np.arange(len(self.objs)))) and not self.ignore_index:
-                    columns = np.array([ data[i].name for i in range(len(data)) ], dtype='object')
+                # checks if the column variable already stores valid column
+                # names (because set via the 'key' argument in the 'concat'
+                # function call. If that's not the case, use the series names
+                # as column names
+                if (columns.equals(Index(np.arange(len(self.objs)))) and
+                        not self.ignore_index):
+                    columns = np.array([data[i].name
+                                        for i in range(len(data))],
+                                       dtype='object')
                     indexer = isnull(columns)
                     if indexer.any():
                         columns[indexer] = np.arange(len(indexer[indexer]))
@@ -992,11 +1013,13 @@ def get_result(self):
                 mgrs_indexers.append((obj._data, indexers))
 
             new_data = concatenate_block_managers(
-                mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy)
+                mgrs_indexers, self.new_axes,
+                concat_axis=self.axis, copy=self.copy)
             if not self.copy:
                 new_data._consolidate_inplace()
 
-            return self.objs[0]._from_axes(new_data, self.new_axes).__finalize__(self, method='concat')
+            return (self.objs[0]._from_axes(new_data, self.new_axes)
+                    .__finalize__(self, method='concat'))
 
     def _get_result_dim(self):
         if self._is_series and self.axis == 1:
@@ -1091,7 +1114,7 @@ def _maybe_check_integrity(self, concat_index):
             if not concat_index.is_unique:
                 overlap = concat_index.get_duplicates()
                 raise ValueError('Indexes have overlapping values: %s'
-                                % str(overlap))
+                                 % str(overlap))
 
 
 def _concat_indexes(indexes):
@@ -1106,7 +1129,8 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None):
             names = [None] * len(zipped)
 
         if levels is None:
-            levels = [Categorical.from_array(zp, ordered=True).categories for zp in zipped]
+            levels = [Categorical.from_array(
+                zp, ordered=True).categories for zp in zipped]
         else:
             levels = [_ensure_index(x) for x in levels]
     else:
@@ -1152,7 +1176,7 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None):
             names = list(names)
         else:
             # make sure that all of the passed indices have the same nlevels
-            if not len(set([ i.nlevels for i in indexes ])) == 1:
+            if not len(set([i.nlevels for i in indexes])) == 1:
                 raise AssertionError("Cannot concat indices that do"
                                      " not have the same number of levels")
 
@@ -1201,7 +1225,8 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None):
 
 
 def _should_fill(lname, rname):
-    if not isinstance(lname, compat.string_types) or not isinstance(rname, compat.string_types):
+    if (not isinstance(lname, compat.string_types) or
+            not isinstance(rname, compat.string_types)):
         return True
     return lname == rname
 

pandas/tools/pivot.py

@@ -24,13 +24,17 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
     ----------
     data : DataFrame
     values : column to aggregate, optional
-    index : a column, Grouper, array which has the same length as data, or list of them.
-        Keys to group by on the pivot table index.
-        If an array is passed, it is being used as the same manner as column values.
-    columns : a column, Grouper, array which has the same length as data, or list of them.
-        Keys to group by on the pivot table column.
-        If an array is passed, it is being used as the same manner as column values.
-    aggfunc : function, default numpy.mean, or list of functions
+    index : column, Grouper, array, or list of the previous
+        If an array is passed, it must be the same length as the data. The list
+        can contain any of the other types (except list).
+        Keys to group by on the pivot table index.  If an array is passed, it
+        is being used as the same manner as column values.
+    columns : column, Grouper, array, or list of the previous
+        If an array is passed, it must be the same length as the data. The list
+        can contain any of the other types (except list).
+        Keys to group by on the pivot table column.  If an array is passed, it
+        is being used as the same manner as column values.
+    aggfunc : function or list of functions, default numpy.mean
         If list of functions passed, the resulting pivot table will have
         hierarchical columns whose top level are the function names (inferred
         from the function objects themselves)
@@ -78,7 +82,8 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
         pieces = []
         keys = []
         for func in aggfunc:
-            table = pivot_table(data, values=values, index=index, columns=columns,
+            table = pivot_table(data, values=values, index=index,
+                                columns=columns,
                                 fill_value=fill_value, aggfunc=func,
                                 margins=margins)
             pieces.append(table)
@@ -124,7 +129,7 @@ def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
             m = MultiIndex.from_arrays(cartesian_product(table.index.levels))
             table = table.reindex_axis(m, axis=0)
         except AttributeError:
-            pass # it's a single level
+            pass  # it's a single level
 
         try:
             m = MultiIndex.from_arrays(cartesian_product(table.columns.levels))
@@ -197,7 +202,7 @@ def _add_margins(table, data, values, rows, cols, aggfunc,
         result, margin_keys, row_margin = marginal_result_set
     else:
         marginal_result_set = _generate_marginal_results_without_values(
-                table, data, rows, cols, aggfunc, margins_name)
+            table, data, rows, cols, aggfunc, margins_name)
         if not isinstance(marginal_result_set, tuple):
             return marginal_result_set
         result, margin_keys, row_margin = marginal_result_set
@@ -273,7 +278,8 @@ def _all_key(key):
                 except TypeError:
 
                     # we cannot reshape, so coerce the axis
-                    piece.set_axis(cat_axis, piece._get_axis(cat_axis)._to_safe_for_reshape())
+                    piece.set_axis(cat_axis, piece._get_axis(
+                        cat_axis)._to_safe_for_reshape())
                     piece[all_key] = margin[key]
 
                 table_pieces.append(piece)
@@ -349,13 +355,15 @@ def _all_key():
 def _convert_by(by):
     if by is None:
         by = []
-    elif (np.isscalar(by) or isinstance(by, (np.ndarray, Index, Series, Grouper))
+    elif (np.isscalar(by) or isinstance(by, (np.ndarray, Index,
+                                             Series, Grouper))
           or hasattr(by, '__call__')):
         by = [by]
     else:
         by = list(by)
     return by
 
+
 def crosstab(index, columns, values=None, rownames=None, colnames=None,
              aggfunc=None, margins=False, dropna=True):
     """