CLN;: remove compat.lzip

Author: topper-123

pandas/compat/__init__.py

@@ -5,7 +5,7 @@
 Cross-compatible functions for different versions of Python.
 
 Key items to import for compatible code:
-* lists: lrange(), lmap(), lzip()
+* lists: lrange(), lmap()
 
 Other items:
 * platform checker
@@ -24,10 +24,6 @@ def lrange(*args, **kwargs):
     return list(range(*args, **kwargs))
 
 
-def lzip(*args, **kwargs):
-    return list(zip(*args, **kwargs))
-
-
 def lmap(*args, **kwargs):
     return list(map(*args, **kwargs))
 

pandas/core/arrays/categorical.py

@@ -7,7 +7,6 @@
 from pandas._config import get_option
 
 from pandas._libs import algos as libalgos, lib
-from pandas.compat import lzip
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import (
     Appender, Substitution, cache_readonly, deprecate_kwarg)
@@ -2692,4 +2691,4 @@ def _factorize_from_iterables(iterables):
     if len(iterables) == 0:
         # For consistency, it should return a list of 2 lists.
         return [[], []]
-    return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
+    return map(list, zip(*[_factorize_from_iterable(it) for it in iterables]))

pandas/core/frame.py

@@ -31,7 +31,7 @@
 from pandas.util._validators import (validate_bool_kwarg,
                                      validate_axis_style_args)
 
-from pandas.compat import PY36, lmap, lzip, raise_with_traceback
+from pandas.compat import PY36, lmap, raise_with_traceback
 from pandas.compat.numpy import function as nv
 from pandas.core.dtypes.cast import (
     maybe_upcast,
@@ -1754,7 +1754,7 @@ def from_items(cls, items, columns=None, orient='columns'):
                       "preserve the key order.",
                       FutureWarning, stacklevel=2)
 
-        keys, values = lzip(*items)
+        keys, values = zip(*items)
 
         if orient == 'columns':
             if columns is not None:
@@ -4447,7 +4447,7 @@ def _maybe_casted_values(index, labels=None):
             if isinstance(self.index, MultiIndex):
                 names = [n if n is not None else ('level_%d' % i)
                          for (i, n) in enumerate(self.index.names)]
-                to_insert = lzip(self.index.levels, self.index.codes)
+                to_insert = zip(self.index.levels, self.index.codes)
             else:
                 default = 'index' if 'index' not in self else 'level_0'
                 names = ([default] if self.index.name is None

pandas/core/generic.py

@@ -15,7 +15,7 @@
 from pandas._config import config
 
 from pandas._libs import Timestamp, iNaT, properties
-from pandas.compat import lzip, set_function_name, to_str
+from pandas.compat import set_function_name, to_str
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import (
@@ -6505,7 +6505,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
                 regex = True
 
             items = list(to_replace.items())
-            keys, values = lzip(*items) or ([], [])
+            keys, values = zip(*items) if len(items) else ([], [])
 
             are_mappings = [is_dict_like(v) for v in values]
 
@@ -6519,7 +6519,7 @@ def replace(self, to_replace=None, value=None, inplace=False, limit=None,
                 value_dict = {}
 
                 for k, v in items:
-                    keys, values = lzip(*v.items()) or ([], [])
+                    keys, values = list(zip(*v.items())) or ([], [])
                     if set(keys) & set(values):
                         raise ValueError("Replacement not allowed with "
                                          "overlapping keys and values")

pandas/core/groupby/generic.py

@@ -15,7 +15,6 @@
 import numpy as np
 
 from pandas._libs import Timestamp, lib
-from pandas.compat import lzip
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import Appender, Substitution
 
@@ -781,13 +780,13 @@ def _aggregate_multiple_funcs(self, arg, _level):
                     FutureWarning, stacklevel=3)
 
             columns = list(arg.keys())
-            arg = list(arg.items())
+            arg = arg.items()
         elif any(isinstance(x, (tuple, list)) for x in arg):
             arg = [(x, x) if not isinstance(x, (tuple, list)) else x
                    for x in arg]
 
             # indicated column order
-            columns = lzip(*arg)[0]
+            columns = next(zip(*arg))
         else:
             # list of functions / function names
             columns = []
@@ -797,7 +796,7 @@ def _aggregate_multiple_funcs(self, arg, _level):
                 else:
                     # protect against callables without names
                     columns.append(com.get_callable_name(f))
-            arg = lzip(columns, arg)
+            arg = zip(columns, arg)
 
         results = OrderedDict()
         for name, func in arg:

pandas/core/groupby/ops.py

@@ -13,7 +13,6 @@
 from pandas._libs import NaT, iNaT, lib
 import pandas._libs.groupby as libgroupby
 import pandas._libs.reduction as reduction
-from pandas.compat import lzip
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import cache_readonly
 
@@ -259,7 +258,7 @@ def groups(self):
         if len(self.groupings) == 1:
             return self.groupings[0].groups
         else:
-            to_groupby = lzip(*(ping.grouper for ping in self.groupings))
+            to_groupby = zip(*(ping.grouper for ping in self.groupings))
             to_groupby = Index(to_groupby)
             return self.axis.groupby(to_groupby)
 

pandas/core/indexes/multi.py

@@ -9,7 +9,6 @@
 
 from pandas._libs import (
     Timestamp, algos as libalgos, index as libindex, lib, tslibs)
-from pandas.compat import lzip
 from pandas.compat.numpy import function as nv
 from pandas.errors import PerformanceWarning, UnsortedIndexError
 from pandas.util._decorators import Appender, cache_readonly, deprecate_kwarg
@@ -402,7 +401,7 @@ def from_tuples(cls, tuples, sortorder=None, names=None):
         elif isinstance(tuples, list):
             arrays = list(lib.to_object_array_tuples(tuples).T)
         else:
-            arrays = lzip(*tuples)
+            arrays = zip(*tuples)
 
         return MultiIndex.from_arrays(arrays, sortorder=sortorder, names=names)
 
@@ -511,7 +510,7 @@ def from_frame(cls, df, sortorder=None, names=None):
         if not isinstance(df, ABCDataFrame):
             raise TypeError("Input must be a DataFrame")
 
-        column_names, columns = lzip(*df.iteritems())
+        column_names, columns = zip(*df.iteritems())
         names = column_names if names is None else names
         return cls.from_arrays(columns, sortorder=sortorder, names=names)
 
@@ -2933,7 +2932,7 @@ def union(self, other, sort=None):
                                                 other._ndarray_values],
                                                sort=sort)
 
-        return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
+        return MultiIndex.from_arrays(zip(*uniq_tuples), sortorder=0,
                                       names=result_names)
 
     def intersection(self, other, sort=False):
@@ -2976,7 +2975,7 @@ def intersection(self, other, sort=False):
                               codes=[[]] * self.nlevels,
                               names=result_names, verify_integrity=False)
         else:
-            return MultiIndex.from_arrays(lzip(*uniq_tuples), sortorder=0,
+            return MultiIndex.from_arrays(zip(*uniq_tuples), sortorder=0,
                                           names=result_names)
 
     def difference(self, other, sort=None):
@@ -3144,7 +3143,7 @@ def isin(self, values, level=None):
 
 
 def _sparsify(label_list, start=0, sentinel=''):
-    pivoted = lzip(*label_list)
+    pivoted = list(zip(*label_list))
     k = len(label_list)
 
     result = pivoted[:start + 1]
@@ -3168,7 +3167,7 @@ def _sparsify(label_list, start=0, sentinel=''):
 
         prev = cur
 
-    return lzip(*result)
+    return list(zip(*result))
 
 
 def _get_na_rep(dtype):

pandas/core/reshape/concat.py

@@ -6,7 +6,7 @@
 
 import pandas.core.dtypes.concat as _concat
 
-from pandas import DataFrame, Index, MultiIndex, Series, compat
+from pandas import DataFrame, Index, MultiIndex, Series
 from pandas.core import common as com
 from pandas.core.arrays.categorical import (
     _factorize_from_iterable, _factorize_from_iterables)
@@ -536,7 +536,7 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None):
 
     if ((levels is None and isinstance(keys[0], tuple)) or
             (levels is not None and len(levels) > 1)):
-        zipped = compat.lzip(*keys)
+        zipped = list(zip(*keys))
         if names is None:
             names = [None] * len(zipped)
 

pandas/core/reshape/merge.py

@@ -9,7 +9,6 @@
 import numpy as np
 
 from pandas._libs import hashtable as libhashtable, join as libjoin, lib
-from pandas.compat import lzip
 from pandas.errors import MergeError
 from pandas.util._decorators import Appender, Substitution
 
@@ -1512,7 +1511,7 @@ def flip(xs):
             labels = list(string.ascii_lowercase[:len(xs)])
             dtypes = [x.dtype for x in xs]
             labeled_dtypes = list(zip(labels, dtypes))
-            return np.array(lzip(*xs), labeled_dtypes)
+            return np.array(list(zip(*xs)), labeled_dtypes)
 
         # values to compare
         left_values = (self.left.index.values if self.left_index else

pandas/io/formats/format.py

@@ -15,7 +15,6 @@
 from pandas._libs import lib
 from pandas._libs.tslib import format_array_from_datetime
 from pandas._libs.tslibs import NaT, Timedelta, Timestamp, iNaT
-from pandas.compat import lzip
 
 from pandas.core.dtypes.common import (
     is_categorical_dtype, is_complex_dtype, is_datetime64_dtype,
@@ -753,7 +752,7 @@ def _get_formatted_column_labels(self, frame):
 
         if isinstance(columns, ABCMultiIndex):
             fmt_columns = columns.format(sparsify=False, adjoin=False)
-            fmt_columns = lzip(*fmt_columns)
+            fmt_columns = list(zip(*fmt_columns))
             dtypes = self.frame.dtypes._values
 
             # if we have a Float level, they don't use leading space at all