MAINT: Remove non-standard and inconsistently-used imports (#17085)

Author: jbrockmendel

pandas/core/frame.py

@@ -20,7 +20,6 @@
 import warnings
 from textwrap import dedent
 
-from numpy import nan as NA
 import numpy as np
 import numpy.ma as ma
 
@@ -436,7 +435,7 @@ def _init_dict(self, data, index, columns, dtype=None):
                     else:
                         v = np.empty(len(index), dtype=dtype)
 
-                    v.fill(NA)
+                    v.fill(np.nan)
                 else:
                     v = data[k]
                 data_names.append(k)
@@ -1437,8 +1436,8 @@ def to_csv(self, path_or_buf=None, sep=",", na_rep='', float_format=None,
         columns : sequence, optional
             Columns to write
         header : boolean or list of string, default True
-            Write out column names. If a list of string is given it is assumed
-            to be aliases for the column names
+            Write out the column names. If a list of strings is given it is
+            assumed to be aliases for the column names
         index : boolean, default True
             Write row names (index)
         index_label : string or sequence, or False, default None
@@ -1622,8 +1621,9 @@ def to_parquet(self, fname, engine='auto', compression='snappy',
         to_parquet(self, fname, engine,
                    compression=compression, **kwargs)
 
-    @Substitution(header='Write out column names. If a list of string is given, \
-it is assumed to be aliases for the column names')
+    @Substitution(header='Write out the column names. If a list of strings '
+                         'is given, it is assumed to be aliases for the '
+                         'column names')
     @Appender(fmt.docstring_to_string, indents=1)
     def to_string(self, buf=None, columns=None, col_space=None, header=True,
                   index=True, na_rep='NaN', formatters=None, float_format=None,
@@ -2805,7 +2805,7 @@ def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
 
         return frame
 
-    def _reindex_index(self, new_index, method, copy, level, fill_value=NA,
+    def _reindex_index(self, new_index, method, copy, level, fill_value=np.nan,
                        limit=None, tolerance=None):
         new_index, indexer = self.index.reindex(new_index, method=method,
                                                 level=level, limit=limit,
@@ -2814,8 +2814,8 @@ def _reindex_index(self, new_index, method, copy, level, fill_value=NA,
                                            copy=copy, fill_value=fill_value,
                                            allow_dups=False)
 
-    def _reindex_columns(self, new_columns, method, copy, level, fill_value=NA,
-                         limit=None, tolerance=None):
+    def _reindex_columns(self, new_columns, method, copy, level,
+                         fill_value=np.nan, limit=None, tolerance=None):
         new_columns, indexer = self.columns.reindex(new_columns, method=method,
                                                     level=level, limit=limit,
                                                     tolerance=tolerance)
@@ -3794,7 +3794,7 @@ def _combine_series(self, other, func, fill_value=None, axis=None,
     def _combine_series_infer(self, other, func, level=None,
                               fill_value=None, try_cast=True):
         if len(other) == 0:
-            return self * NA
+            return self * np.nan
 
         if len(self) == 0:
             # Ambiguous case, use _series so works with DataFrame
@@ -3948,7 +3948,7 @@ def combine(self, other, func, fill_value=None, overwrite=True):
 
             if do_fill:
                 arr = _ensure_float(arr)
-                arr[this_mask & other_mask] = NA
+                arr[this_mask & other_mask] = np.nan
 
             # try to downcast back to the original dtype
             if needs_i8_conversion_i:
@@ -4567,7 +4567,7 @@ def _apply_empty_result(self, func, axis, reduce, *args, **kwds):
                 pass
 
         if reduce:
-            return Series(NA, index=self._get_agg_axis(axis))
+            return Series(np.nan, index=self._get_agg_axis(axis))
         else:
             return self.copy()
 
@@ -5185,7 +5185,7 @@ def corr(self, method='pearson', min_periods=1):
 
                     valid = mask[i] & mask[j]
                     if valid.sum() < min_periods:
-                        c = NA
+                        c = np.nan
                     elif i == j:
                         c = 1.
                     elif not valid.all():
@@ -5509,7 +5509,7 @@ def idxmin(self, axis=0, skipna=True):
         axis = self._get_axis_number(axis)
         indices = nanops.nanargmin(self.values, axis=axis, skipna=skipna)
         index = self._get_axis(axis)
-        result = [index[i] if i >= 0 else NA for i in indices]
+        result = [index[i] if i >= 0 else np.nan for i in indices]
         return Series(result, index=self._get_agg_axis(axis))
 
     def idxmax(self, axis=0, skipna=True):
@@ -5540,7 +5540,7 @@ def idxmax(self, axis=0, skipna=True):
         axis = self._get_axis_number(axis)
         indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
         index = self._get_axis(axis)
-        result = [index[i] if i >= 0 else NA for i in indices]
+        result = [index[i] if i >= 0 else np.nan for i in indices]
         return Series(result, index=self._get_agg_axis(axis))
 
     def _get_agg_axis(self, axis_num):
@@ -5778,9 +5778,8 @@ def isin(self, values):
         2   True   True
         """
         if isinstance(values, dict):
-            from collections import defaultdict
             from pandas.core.reshape.concat import concat
-            values = defaultdict(list, values)
+            values = collections.defaultdict(list, values)
             return concat((self.iloc[:, [i]].isin(values[col])
                            for i, col in enumerate(self.columns)), axis=1)
         elif isinstance(values, Series):
@@ -6143,7 +6142,7 @@ def _homogenize(data, index, dtype=None):
                     v = _dict_compat(v)
                 else:
                     v = dict(v)
-                v = lib.fast_multiget(v, oindex.values, default=NA)
+                v = lib.fast_multiget(v, oindex.values, default=np.nan)
             v = _sanitize_array(v, index, dtype=dtype, copy=False,
                                 raise_cast_failure=False)
 

pandas/core/generic.py

@@ -1207,7 +1207,7 @@ def _repr_latex_(self):
     columns : sequence, optional
         Columns to write
     header : boolean or list of string, default True
-        Write out column names. If a list of string is given it is
+        Write out the column names. If a list of strings is given it is
         assumed to be aliases for the column names
     index : boolean, default True
         Write row names (index)
@@ -1702,8 +1702,9 @@ def to_xarray(self):
             .. versionadded:: 0.20.0
             """
 
-    @Substitution(header='Write out column names. If a list of string is given, \
-it is assumed to be aliases for the column names.')
+    @Substitution(header='Write out the column names. If a list of strings '
+                         'is given, it is assumed to be aliases for the '
+                         'column names.')
     @Appender(_shared_docs['to_latex'] % _shared_doc_kwargs)
     def to_latex(self, buf=None, columns=None, col_space=None, header=True,
                  index=True, na_rep='NaN', formatters=None, float_format=None,

pandas/core/indexing.py

@@ -1,5 +1,5 @@
 # pylint: disable=W0223
-
+import textwrap
 import warnings
 import numpy as np
 from pandas.compat import range, zip
@@ -1288,13 +1288,13 @@ class _IXIndexer(_NDFrameIndexer):
 
     def __init__(self, obj, name):
 
-        _ix_deprecation_warning = """
-.ix is deprecated. Please use
-.loc for label based indexing or
-.iloc for positional indexing
+        _ix_deprecation_warning = textwrap.dedent("""
+            .ix is deprecated. Please use
+            .loc for label based indexing or
+            .iloc for positional indexing
 
-See the documentation here:
-http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated"""  # noqa
+            See the documentation here:
+            http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated""")  # noqa
 
         warnings.warn(_ix_deprecation_warning,
                       DeprecationWarning, stacklevel=3)

pandas/core/series.py

@@ -10,7 +10,6 @@
 import warnings
 from textwrap import dedent
 
-from numpy import nan, ndarray
 import numpy as np
 import numpy.ma as ma
 
@@ -210,13 +209,13 @@ def __init__(self, data=None, index=None, dtype=None, name=None,
                             data = np.nan
                     # GH #12169
                     elif isinstance(index, (PeriodIndex, TimedeltaIndex)):
-                        data = ([data.get(i, nan) for i in index]
+                        data = ([data.get(i, np.nan) for i in index]
                                 if data else np.nan)
                     else:
                         data = lib.fast_multiget(data, index.values,
                                                  default=np.nan)
                 except TypeError:
-                    data = ([data.get(i, nan) for i in index]
+                    data = ([data.get(i, np.nan) for i in index]
                             if data else np.nan)
 
             elif isinstance(data, SingleBlockManager):
@@ -1686,7 +1685,7 @@ def _binop(self, other, func, level=None, fill_value=None):
             result.name = None
         return result
 
-    def combine(self, other, func, fill_value=nan):
+    def combine(self, other, func, fill_value=np.nan):
         """
         Perform elementwise binary operation on two Series using given function
         with optional fill value when an index is missing from one Series or
@@ -2952,7 +2951,6 @@ def _dir_additions(self):
 Series._add_numeric_operations()
 Series._add_series_only_operations()
 Series._add_series_or_dataframe_operations()
-_INDEX_TYPES = ndarray, Index, list, tuple
 
 # -----------------------------------------------------------------------------
 # Supplementary functions