CLN/BUG/TST: Fix and test multiple places using undefined names.

pandas/computation/align.py

@@ -10,6 +10,7 @@
 import pandas as pd
 from pandas import compat
 import pandas.core.common as com
+import pandas.computation.ops as ops
 
 
 def _align_core_single_unary_op(term):
@@ -170,10 +171,10 @@ def _align_core(terms):
 
     return typ, _zip_axes_from_type(typ, axes)
 
-
+# TODO: Add tests that cover this function!
 def _filter_terms(flat):
     # numeric literals
-    literals = frozenset(filter(lambda x: isinstance(x, Constant), flat))
+    literals = frozenset(filter(lambda x: isinstance(x, ops.Constant), flat))
 
     # these are strings which are variable names
     names = frozenset(flat) - literals

pandas/computation/eval.py

@@ -2,12 +2,7 @@
 
 """Top level ``eval`` module.
 """
-
-import numbers
-import numpy as np
-
 from pandas.core import common as com
-from pandas.compat import string_types
 from pandas.computation.expr import Expr, _parsers, _ensure_scope
 from pandas.computation.engines import _engines
 

pandas/core/algorithms.py

@@ -57,15 +57,6 @@ def unique(values):
     return _hashtable_algo(f, values.dtype)
 
 
-# def count(values, uniques=None):
-#     f = lambda htype, caster: _count_generic(values, htype, caster)
-
-#     if uniques is not None:
-#         raise NotImplementedError
-#     else:
-#         return _hashtable_algo(f, values.dtype)
-
-
 def _hashtable_algo(f, dtype):
     """
     f(HashTable, type_caster) -> result
@@ -78,16 +69,6 @@ def _hashtable_algo(f, dtype):
         return f(htable.PyObjectHashTable, com._ensure_object)
 
 
-def _count_generic(values, table_type, type_caster):
-    from pandas.core.series import Series
-
-    values = type_caster(values)
-    table = table_type(min(len(values), 1000000))
-    uniques, labels = table.factorize(values)
-
-    return Series(counts, index=uniques)
-
-
 def _match_generic(values, index, table_type, type_caster):
     values = type_caster(values)
     index = type_caster(index)

pandas/core/groupby.py

@@ -1,3 +1,4 @@
+import sys
 import types
 from functools import wraps
 import numpy as np
@@ -2123,8 +2124,6 @@ def filter(self, func, dropna=True, *args, **kwargs):
         >>> grouped = df.groupby(lambda x: mapping[x])
         >>> grouped.filter(lambda x: x['A'].sum() + x['B'].sum() > 0)
         """
-        from pandas.tools.merge import concat
-
         indexers = []
 
         obj = self._obj_with_exclusions

pandas/core/index.py

@@ -501,10 +501,11 @@ def is_int(v):
         # if we are mixed and have integers
         try:
             if is_positional and self.is_mixed():
+                # check that start and stop are valid
                 if start is not None:
-                    i = self.get_loc(start)
+                    self.get_loc(start)
                 if stop is not None:
-                    j = self.get_loc(stop)
+                    self.get_loc(stop)
                 is_positional = False
         except KeyError:
             if self.inferred_type == 'mixed-integer-float':

pandas/core/indexing.py

@@ -540,8 +540,6 @@ def _align_frame(self, indexer, df):
         raise ValueError('Incompatible indexer with DataFrame')
 
     def _align_panel(self, indexer, df):
-        is_frame = self.obj.ndim == 2
-        is_panel = self.obj.ndim >= 3
         raise NotImplementedError("cannot set using an indexer with a Panel yet!")
 
     def _getitem_tuple(self, tup):
@@ -581,11 +579,9 @@ def _multi_take_opportunity(self, tup):
             return False
 
         # just too complicated
-        for indexer, ax in zip(tup,self.obj._data.axes):
+        for ax in self.obj._data.axes:
             if isinstance(ax, MultiIndex):
                 return False
-            elif com._is_bool_indexer(indexer):
-                return False
 
         return True
 
@@ -637,6 +633,7 @@ def _getitem_lowerdim(self, tup):
                 if not ax0.is_lexsorted_for_tuple(tup):
                     raise e1
                 try:
+                    # Check for valid axis
                     loc = ax0.get_loc(tup[0])
                 except KeyError:
                     raise e1
@@ -933,6 +930,7 @@ class _IXIndexer(_NDFrameIndexer):
     """ A primarily location based indexer, with integer fallback """
 
     def _has_valid_type(self, key, axis):
+        # check for valid axis (raises if invalid)
         ax = self.obj._get_axis(axis)
 
         if isinstance(key, slice):
@@ -945,7 +943,7 @@ def _has_valid_type(self, key, axis):
             return True
 
         else:
-
+            # check for valid key/axis combo (raises if invalid)
             self._convert_scalar_indexer(key, axis)
 
         return True

pandas/core/internals.py

@@ -1,7 +1,6 @@
 import itertools
 import re
 from datetime import datetime, timedelta
-import copy
 from collections import defaultdict
 
 import numpy as np
@@ -589,9 +588,9 @@ def setitem(self, indexer, value):
             values = self._try_coerce_result(values)
             values = self._try_cast_result(values, dtype)
             return [make_block(transf(values), self.items, self.ref_items, ndim=self.ndim, fastpath=True)]
-        except (ValueError, TypeError) as detail:
+        except (ValueError, TypeError):
             raise
-        except (Exception) as detail:
+        except Exception:
             pass
 
         return [ self ]
@@ -3681,6 +3680,7 @@ def _lcd_dtype(l):
     have_complex = len(counts[ComplexBlock]) > 0
     have_dt64 = len(counts[DatetimeBlock]) > 0
     have_td64 = len(counts[TimeDeltaBlock]) > 0
+    # TODO: Use this.
     have_sparse = len(counts[SparseBlock]) > 0
     have_numeric = have_float or have_complex or have_int
 

pandas/core/panel.py

@@ -3,13 +3,10 @@
 """
 # pylint: disable=E1103,W0231,W0212,W0621
 
-from pandas.compat import map, zip, range, lrange, lmap, u, OrderedDict, OrderedDefaultdict
-from pandas import compat
 import sys
 import numpy as np
-from pandas.core.common import (PandasError,
-                                _try_sort, _default_index, _infer_dtype_from_scalar,
-                                notnull)
+from pandas.core.common import (PandasError, _try_sort, _default_index,
+                                _infer_dtype_from_scalar)
 from pandas.core.categorical import Categorical
 from pandas.core.index import (Index, MultiIndex, _ensure_index,
                                _get_combined_index)
@@ -20,10 +17,10 @@
 from pandas.core.frame import DataFrame
 from pandas.core.generic import NDFrame, _shared_docs
 from pandas import compat
+from pandas.compat import zip, range, lrange, u, OrderedDict, OrderedDefaultdict
 from pandas.util.decorators import deprecate, Appender, Substitution
 import pandas.core.common as com
 import pandas.core.ops as ops
-import pandas.core.nanops as nanops
 import pandas.computation.expressions as expressions
 
 

pandas/io/ga.py

@@ -394,7 +394,6 @@ def _get_match(obj_store, name, id, **kwargs):
     id_ok = lambda item: id is not None and item.get('id') == id
     key_ok = lambda item: key is not None and item.get(key) == val
 
-    match = None
     if obj_store.get('items'):
         # TODO look up gapi for faster lookup
         for item in obj_store.get('items'):

pandas/io/parsers.py

@@ -720,6 +720,9 @@ def _extract_multi_indexer_columns(self, header, index_names, col_names, passed_
             ic = [ ic ]
         sic = set(ic)
 
+        # TODO: Decide if this is necessary...
+        orig_header = list(header)
+
         # clean the index_names
         index_names = header.pop(-1)
         index_names, names, index_col = _clean_index_names(index_names,
@@ -2033,8 +2036,6 @@ def _stringify_na_values(na_values):
 def _get_na_values(col, na_values, na_fvalues):
     if isinstance(na_values, dict):
         if col in na_values:
-            values = na_values[col]
-            fvalues = na_fvalues[col]
             return na_values[col], na_fvalues[col]
         else:
             return _NA_VALUES, set()

pandas/io/pytables.py

@@ -29,10 +29,10 @@
 import pandas.core.common as com
 from pandas.tools.merge import concat
 from pandas import compat
-from pandas.compat import u_safe as u, PY3, range, lrange
+from pandas.compat import u_safe as u, PY3, range, lrange, lmap
 from pandas.io.common import PerformanceWarning
 from pandas.core.config import get_option
-from pandas.computation.pytables import Expr, maybe_expression
+from pandas.computation.pytables import Expr, maybe_expression, TermValue
 
 import pandas.lib as lib
 import pandas.algos as algos

pandas/stats/misc.py

@@ -5,6 +5,7 @@
 from pandas.core.api import Series, DataFrame, isnull, notnull
 from pandas.core.series import remove_na
 from pandas.compat import zip
+import pandas.core.common as com
 
 
 def zscore(series):
@@ -157,6 +158,7 @@ def bucketcat(series, cats):
     cats = np.asarray(cats)
 
     unique_labels = np.unique(cats)
+    # TODO: Add test case that reaches this code.
     unique_labels = unique_labels[com.notnull(unique_labels)]
 
     # group by
@@ -217,6 +219,7 @@ def _bucketpanel_by(series, xby, yby, xbins, ybins):
 
     labels = _uniquify(xlabels, ylabels, xbins, ybins)
 
+    # TODO: Add a test that reaches this part of the code.
     mask = com.isnull(labels)
     labels[mask] = -1
 
@@ -232,6 +235,7 @@ def relabel(key):
         xlab = xlabels[pos]
         ylab = ylabels[pos]
 
+        # TODO: Add a test that reaches this part of the code.
         return '%sx%s' % (int(xlab) if com.notnull(xlab) else 'NULL',
                           int(ylab) if com.notnull(ylab) else 'NULL')
 
@@ -251,6 +255,7 @@ def _bucketpanel_cat(series, xcat, ycat):
     sorted_ylabels = ylabels.take(sorter)
 
     unique_labels = np.unique(labels)
+    # TODO: Add a test that reaches this part of the code.
     unique_labels = unique_labels[com.notnull(unique_labels)]
 
     locs = sorted_labels.searchsorted(unique_labels)

pandas/tools/rplot.py

@@ -553,7 +553,6 @@ def work(self, fig=None, ax=None):
                 ax = fig.gca()
         x = self.data[self.aes['x']]
         y = self.data[self.aes['y']]
-        rvs = np.array([x, y])
         x_min = x.min()
         x_max = x.max()
         y_min = y.min()

pandas/tseries/converter.py

@@ -296,12 +296,14 @@ def __call__(self):
         try:
             start = dmin - delta
         except ValueError:
+            # TODO: Never used.
             start = _from_ordinal(1.0)
 
         try:
             stop = dmax + delta
         except ValueError:
             # The magic number!
+            # TODO: Never used.
             stop = _from_ordinal(3652059.9999999)
 
         nmax, nmin = dates.date2num((dmax, dmin))
@@ -357,12 +359,14 @@ def autoscale(self):
         try:
             start = dmin - delta
         except ValueError:
+            # TODO: Never used.
             start = _from_ordinal(1.0)
 
         try:
             stop = dmax + delta
         except ValueError:
             # The magic number!
+            # TODO: Never used.
             stop = _from_ordinal(3652059.9999999)
 
         dmin, dmax = self.datalim_to_dt()

pandas/tseries/plotting.py

@@ -75,7 +75,6 @@ def tsplot(series, plotf, **kwargs):
         args.append(style)
 
     lines = plotf(ax, *args, **kwargs)
-    label = kwargs.get('label', None)
 
     # set date formatter, locators and rescale limits
     format_dateaxis(ax, ax.freq)