MAINT Use list and dict comprehension (#23894)

Author: rth

pandas/core/computation/align.py

@@ -30,9 +30,8 @@ def _align_core_single_unary_op(term):
 
 
 def _zip_axes_from_type(typ, new_axes):
-    axes = {}
-    for ax_ind, ax_name in compat.iteritems(typ._AXIS_NAMES):
-        axes[ax_name] = new_axes[ax_ind]
+    axes = {ax_name: new_axes[ax_ind]
+            for ax_ind, ax_name in compat.iteritems(typ._AXIS_NAMES)}
     return axes
 
 

pandas/core/config.py

@@ -395,11 +395,8 @@ def __init__(self, *args):
         self.ops = list(zip(args[::2], args[1::2]))
 
     def __enter__(self):
-        undo = []
-        for pat, val in self.ops:
-            undo.append((pat, _get_option(pat, silent=True)))
-
-        self.undo = undo
+        self.undo = [(pat, _get_option(pat, silent=True))
+                     for pat, val in self.ops]
 
         for pat, val in self.ops:
             _set_option(pat, val, silent=True)

pandas/core/dtypes/concat.py

@@ -373,10 +373,8 @@ def _maybe_unwrap(x):
         if sort_categories:
             categories = categories.sort_values()
 
-        new_codes = []
-        for c in to_union:
-            new_codes.append(_recode_for_categories(c.codes, c.categories,
-                                                    categories))
+        new_codes = [_recode_for_categories(c.codes, c.categories, categories)
+                     for c in to_union]
         new_codes = np.concatenate(new_codes)
     else:
         # ordered - to show a proper error message

pandas/core/frame.py

@@ -3569,11 +3569,8 @@ def reindexer(value):
 
     @property
     def _series(self):
-        result = {}
-        for idx, item in enumerate(self.columns):
-            result[item] = Series(self._data.iget(idx), index=self.index,
-                                  name=item)
-        return result
+        return {item: Series(self._data.iget(idx), index=self.index, name=item)
+                for idx, item in enumerate(self.columns)}
 
     def lookup(self, row_labels, col_labels):
         """
@@ -3593,9 +3590,8 @@ def lookup(self, row_labels, col_labels):
         -----
         Akin to::
 
-            result = []
-            for row, col in zip(row_labels, col_labels):
-                result.append(df.get_value(row, col))
+            result = [df.get_value(row, col)
+                      for row, col in zip(row_labels, col_labels)]
 
         Examples
         --------
@@ -4600,10 +4596,8 @@ def sort_values(self, by, axis=0, ascending=True, inplace=False,
         if len(by) > 1:
             from pandas.core.sorting import lexsort_indexer
 
-            keys = []
-            for x in by:
-                k = self._get_label_or_level_values(x, axis=axis)
-                keys.append(k)
+            keys = [self._get_label_or_level_values(x, axis=axis)
+                    for x in by]
             indexer = lexsort_indexer(keys, orders=ascending,
                                       na_position=na_position)
             indexer = ensure_platform_int(indexer)

pandas/core/groupby/ops.py

@@ -678,10 +678,8 @@ def groups(self):
 
         # this is mainly for compat
         # GH 3881
-        result = {}
-        for key, value in zip(self.binlabels, self.bins):
-            if key is not NaT:
-                result[key] = value
+        result = {key: value for key, value in zip(self.binlabels, self.bins)
+                  if key is not NaT}
         return result
 
     @property

pandas/core/internals/blocks.py

@@ -2295,10 +2295,7 @@ def convert(self, *args, **kwargs):
                          'convert_timedeltas']
         fn_inputs += ['copy']
 
-        fn_kwargs = {}
-        for key in fn_inputs:
-            if key in kwargs:
-                fn_kwargs[key] = kwargs[key]
+        fn_kwargs = {key: kwargs[key] for key in fn_inputs if key in kwargs}
 
         # operate column-by-column
         def f(m, v, i):

pandas/core/panel.py

@@ -336,9 +336,8 @@ def _compare_constructor(self, other, func):
             raise Exception('Can only compare identically-labeled '
                             'same type objects')
 
-        new_data = {}
-        for col in self._info_axis:
-            new_data[col] = func(self[col], other[col])
+        new_data = {col: func(self[col], other[col])
+                    for col in self._info_axis}
 
         d = self._construct_axes_dict(copy=False)
         return self._constructor(data=new_data, **d)
@@ -949,9 +948,8 @@ def to_frame(self, filter_observations=True):
             # size = N * K
             selector = slice(None, None)
 
-        data = {}
-        for item in self.items:
-            data[item] = self[item].values.ravel()[selector]
+        data = {item: self[item].values.ravel()[selector]
+                for item in self.items}
 
         def construct_multi_parts(idx, n_repeat, n_shuffle=1):
             # Replicates and shuffles MultiIndex, returns individual attributes

pandas/core/reshape/melt.py

@@ -448,9 +448,8 @@ def melt_stub(df, stub, i, j, value_vars, sep):
     value_vars_flattened = [e for sublist in value_vars for e in sublist]
     id_vars = list(set(df.columns.tolist()).difference(value_vars_flattened))
 
-    melted = []
-    for s, v in zip(stubnames, value_vars):
-        melted.append(melt_stub(df, s, i, j, v, sep))
+    melted = [melt_stub(df, s, i, j, v, sep)
+              for s, v in zip(stubnames, value_vars)]
     melted = melted[0].join(melted[1:], how='outer')
 
     if len(i) == 1:

pandas/core/sparse/frame.py

@@ -339,9 +339,8 @@ def to_dense(self):
     def _apply_columns(self, func):
         """ get new SparseDataFrame applying func to each columns """
 
-        new_data = {}
-        for col, series in compat.iteritems(self):
-            new_data[col] = func(series)
+        new_data = {col: func(series)
+                    for col, series in compat.iteritems(self)}
 
         return self._constructor(
             data=new_data, index=self.index, columns=self.columns,

pandas/core/window.py

@@ -2479,9 +2479,8 @@ def dataframe_from_int_dict(data, frame_template):
             else:
                 raise ValueError("'pairwise' is not True/False")
         else:
-            results = {}
-            for i, col in enumerate(arg1.columns):
-                results[i] = f(*_prep_binary(arg1.iloc[:, i], arg2))
+            results = {i: f(*_prep_binary(arg1.iloc[:, i], arg2))
+                       for i, col in enumerate(arg1.columns)}
             return dataframe_from_int_dict(results, arg1)
 
     else: