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MAINT: refactor from_items() using from_dict() #22094

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MAINT: refactor from_items() using from_dict() #22094

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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.24.0.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -391,6 +391,7 @@ Removal of prior version deprecations/changes

- The ``LongPanel`` and ``WidePanel`` classes have been removed (:issue:`10892`)
- Several private functions were removed from the (non-public) module ``pandas.core.common`` (:issue:`22001`)
- :meth: `~pandas.DataFrame.from_items` has been refactored and the deprecation warning from v0.23.0 has been removed (i.e. it is no longer deprecated)
-
-

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61 changes: 11 additions & 50 deletions pandas/core/frame.py
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Expand Up @@ -54,7 +54,6 @@
ensure_int64,
ensure_platform_int,
is_list_like,
is_nested_list_like,
is_iterator,
is_sequence,
is_named_tuple)
Expand Down Expand Up @@ -1445,17 +1444,13 @@ def to_records(self, index=True, convert_datetime64=None):
def from_items(cls, items, columns=None, orient='columns'):
"""Construct a dataframe from a list of tuples

.. deprecated:: 0.23.0
`from_items` is deprecated and will be removed in a future version.
Use :meth:`DataFrame.from_dict(dict(items)) <DataFrame.from_dict>`
instead.
:meth:`DataFrame.from_dict(OrderedDict(items)) <DataFrame.from_dict>`
may be used to preserve the key order.

Convert (key, value) pairs to DataFrame. The keys will be the axis
index (usually the columns, but depends on the specified
orientation). The values should be arrays or Series.

`from_items(items)` is equivalent to
:meth:`DataFrame.from_dict(OrderedDict(items)) <DataFrame.from_dict>`.

Parameters
----------
items : sequence of (key, value) pairs
Expand All @@ -1473,57 +1468,23 @@ def from_items(cls, items, columns=None, orient='columns'):
frame : DataFrame
"""

warnings.warn("from_items is deprecated. Please use "
"DataFrame.from_dict(dict(items), ...) instead. "
"DataFrame.from_dict(OrderedDict(items)) may be used to "
"preserve the key order.",
FutureWarning, stacklevel=2)

keys, values = lzip(*items)
odict = collections.OrderedDict(items)

if orient == 'columns':
if columns is not None:
columns = ensure_index(columns)

idict = dict(items)
if len(idict) < len(items):
if not columns.equals(ensure_index(keys)):
raise ValueError('With non-unique item names, passed '
'columns must be identical')
arrays = values
else:
arrays = [idict[k] for k in columns if k in idict]
return cls.from_dict(odict).reindex(columns=columns)
else:
columns = ensure_index(keys)
arrays = values

# GH 17312
# Provide more informative error msg when scalar values passed
try:
return cls._from_arrays(arrays, columns, None)

except ValueError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
return cls.from_dict(odict, orient)

elif orient == 'index':
if columns is None:
# we can produce a DataFrame even in this case,
# but raise for consistency with previous versions
raise TypeError("Must pass columns with orient='index'")

keys = ensure_index(keys)

# GH 17312
# Provide more informative error msg when scalar values passed
try:
arr = np.array(values, dtype=object).T
data = [lib.maybe_convert_objects(v) for v in arr]
return cls._from_arrays(data, columns, keys)

except TypeError:
if not is_nested_list_like(values):
raise ValueError('The value in each (key, value) pair '
'must be an array, Series, or dict')
# reindex will not be needed once GH 8425 is fixed
idx = odict.keys()
return cls.from_dict(odict, orient, columns=columns).reindex(idx)

else: # pragma: no cover
raise ValueError("'orient' must be either 'columns' or 'index'")
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65 changes: 17 additions & 48 deletions pandas/tests/frame/test_constructors.py
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Expand Up @@ -1283,87 +1283,56 @@ def test_constructor_manager_resize(self):

def test_constructor_from_items(self):
items = [(c, self.frame[c]) for c in self.frame.columns]
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
recons = DataFrame.from_items(items)
recons = DataFrame.from_items(items)
tm.assert_frame_equal(recons, self.frame)

# pass some columns
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
recons = DataFrame.from_items(items, columns=['C', 'B', 'A'])
recons = DataFrame.from_items(items, columns=['C', 'B', 'A'])
tm.assert_frame_equal(recons, self.frame.loc[:, ['C', 'B', 'A']])

# orient='index'

row_items = [(idx, self.mixed_frame.xs(idx))
for idx in self.mixed_frame.index]
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
recons = DataFrame.from_items(row_items,
columns=self.mixed_frame.columns,
orient='index')
recons = DataFrame.from_items(row_items,
columns=self.mixed_frame.columns,
orient='index')
tm.assert_frame_equal(recons, self.mixed_frame)
assert recons['A'].dtype == np.float64

with tm.assert_raises_regex(TypeError,
"Must pass columns with "
"orient='index'"):
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
DataFrame.from_items(row_items, orient='index')
DataFrame.from_items(row_items, orient='index')

# orient='index', but thar be tuples
arr = construct_1d_object_array_from_listlike(
[('bar', 'baz')] * len(self.mixed_frame))
self.mixed_frame['foo'] = arr
row_items = [(idx, list(self.mixed_frame.xs(idx)))
for idx in self.mixed_frame.index]
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
recons = DataFrame.from_items(row_items,
columns=self.mixed_frame.columns,
orient='index')
recons = DataFrame.from_items(row_items,
columns=self.mixed_frame.columns,
orient='index')
tm.assert_frame_equal(recons, self.mixed_frame)
assert isinstance(recons['foo'][0], tuple)

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
rs = DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
orient='index',
columns=['one', 'two', 'three'])
rs = DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
orient='index',
columns=['one', 'two', 'three'])
xp = DataFrame([[1, 2, 3], [4, 5, 6]], index=['A', 'B'],
columns=['one', 'two', 'three'])
tm.assert_frame_equal(rs, xp)

def test_constructor_from_items_scalars(self):
# GH 17312
with tm.assert_raises_regex(ValueError,
r'The value in each \(key, value\) '
'pair must be an array, Series, or dict'):
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
DataFrame.from_items([('A', 1), ('B', 4)])

with tm.assert_raises_regex(ValueError,
r'The value in each \(key, value\) '
'pair must be an array, Series, or dict'):
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
DataFrame.from_items([('A', 1), ('B', 2)], columns=['col1'],
orient='index')

def test_from_items_deprecation(self):
# GH 17320
with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
r'If using all scalar values, '
'you must pass an index'):
DataFrame.from_items([('A', 1), ('B', 4)])

with tm.assert_produces_warning(FutureWarning,
check_stacklevel=False):
DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
columns=['col1', 'col2', 'col3'],
orient='index')
DataFrame.from_items([('A', 1), ('B', 2)], columns=['col1'],
orient='index')

def test_constructor_mix_series_nonseries(self):
df = DataFrame({'A': self.frame['A'],
Expand Down