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Merged
merged 16 commits into from
Jan 9, 2023
Merged

Rename duplicate column names in read_json(orient='split') #50370

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482a87f
Rename duplicate column names in read_json(orient='split')
datapythonista Dec 21, 2022
c89cba5
Add issue number to TODO
datapythonista Dec 21, 2022
f701a92
Merge remote-tracking branch 'upstream/main' into json_no_dupes
datapythonista Dec 26, 2022
fbc093d
Finish renaming of _dedup_names and check keys before column renaming
datapythonista Dec 26, 2022
6d30477
Black
datapythonista Dec 26, 2022
4252e2f
Support multiindex and fix linters
datapythonista Dec 28, 2022
2c59ef8
Restoring commented code
datapythonista Dec 28, 2022
a6ea294
isort
datapythonista Dec 28, 2022
5572574
Merge remote-tracking branch 'upstream/main' into json_no_dupes
datapythonista Dec 28, 2022
10a9036
Fix doctest
datapythonista Dec 28, 2022
792ec14
Merge remote-tracking branch 'upstream/main' into json_no_dupes
datapythonista Dec 28, 2022
5a0cd30
Being more specific with xfail
datapythonista Dec 28, 2022
83a94e2
Merge remote-tracking branch 'upstream/main' into json_no_dupes
datapythonista Dec 28, 2022
8bad27c
Update whatsnew issue number
datapythonista Dec 28, 2022
0b865fe
Merge main
datapythonista Dec 28, 2022
3d49073
Merge main
datapythonista Jan 6, 2023
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2 changes: 1 addition & 1 deletion doc/source/whatsnew/v2.0.0.rst
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Expand Up @@ -489,7 +489,7 @@ Other API changes
new DataFrame (shallow copy) instead of the original DataFrame, consistent with other
methods to get a full slice (for example ``df.loc[:]`` or ``df[:]``) (:issue:`49469`)
- Disallow computing ``cumprod`` for :class:`Timedelta` object; previously this returned incorrect values (:issue:`50246`)
-
- Loading a JSON file with duplicate columns using ``read_json(orient='split')`` renames columns to avoid duplicates, as :func:`read_csv` and the other readers do (:issue:`XXX`)
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PR number here? other than that looks good

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Added, thanks!


.. ---------------------------------------------------------------------------
.. _whatsnew_200.deprecations:
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70 changes: 70 additions & 0 deletions pandas/io/common.py
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Expand Up @@ -6,6 +6,7 @@
abstractmethod,
)
import codecs
from collections import defaultdict
import dataclasses
import functools
import gzip
Expand All @@ -26,7 +27,9 @@
IO,
Any,
AnyStr,
DefaultDict,
Generic,
Hashable,
Literal,
Mapping,
Sequence,
Expand Down Expand Up @@ -67,6 +70,7 @@
is_list_like,
)

from pandas.core.indexes.api import MultiIndex
from pandas.core.shared_docs import _shared_docs

_VALID_URLS = set(uses_relative + uses_netloc + uses_params)
Expand Down Expand Up @@ -1181,3 +1185,69 @@ def _get_binary_io_classes() -> tuple[type, ...]:
binary_classes += (type(reader),)

return binary_classes


def is_potential_multi_index(
columns: Sequence[Hashable] | MultiIndex,
index_col: bool | Sequence[int] | None = None,
) -> bool:
"""
Check whether or not the `columns` parameter
could be converted into a MultiIndex.

Parameters
----------
columns : array-like
Object which may or may not be convertible into a MultiIndex
index_col : None, bool or list, optional
Column or columns to use as the (possibly hierarchical) index

Returns
-------
bool : Whether or not columns could become a MultiIndex
"""
if index_col is None or isinstance(index_col, bool):
index_col = []

return bool(
len(columns)
and not isinstance(columns, MultiIndex)
and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
)


def dedup_names(
names: Sequence[Hashable], is_potential_multi_index: bool
) -> Sequence[Hashable]:
"""
Rename column names if duplicates exist.

Currently the renaming is done by appending a period and an autonumeric,
but a custom pattern may be supported in the future.

Examples
--------
>>> dedup_names(["x", "y", "x", "x"])
['x', 'y', 'x.1', 'x.2']
"""
names = list(names) # so we can index
counts: DefaultDict[Hashable, int] = defaultdict(int)

for i, col in enumerate(names):
cur_count = counts[col]

while cur_count > 0:
counts[col] = cur_count + 1

if is_potential_multi_index:
# for mypy
assert isinstance(col, tuple)
col = col[:-1] + (f"{col[-1]}.{cur_count}",)
else:
col = f"{col}.{cur_count}"
cur_count = counts[col]

names[i] = col
counts[col] = cur_count + 1

return names
4 changes: 4 additions & 0 deletions pandas/io/json/_json.py
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Expand Up @@ -58,6 +58,7 @@
from pandas.io.common import (
IOHandles,
_extension_to_compression,
dedup_names,
file_exists,
get_handle,
is_fsspec_url,
Expand Down Expand Up @@ -1245,6 +1246,9 @@ def _parse(self) -> None:
str(k): v
for k, v in loads(json, precise_float=self.precise_float).items()
}
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need tests for this!

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The test I updated is already testing this. There was a test specific for duplicate columns that was parametrized for orient split, and check the column names. Am I missing something?

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@jreback do you mind having another look, and letting me know if the added test and my previous comment address your concern. This is ready to be merged pending your review. Thanks!

decoded["columns"] = dedup_names(
names=decoded["columns"], is_potential_multi_index=False
)
self.check_keys_split(decoded)
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decoded might not have 'columns', do you need to do the self.check_keys_split(decoded) call before the dedup_names one?

self.obj = DataFrame(dtype=None, **decoded)
elif orient == "index":
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59 changes: 3 additions & 56 deletions pandas/io/parsers/base_parser.py
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Expand Up @@ -10,7 +10,6 @@
TYPE_CHECKING,
Any,
Callable,
DefaultDict,
Hashable,
Iterable,
List,
Expand Down Expand Up @@ -89,6 +88,8 @@
from pandas.core.series import Series
from pandas.core.tools import datetimes as tools

from pandas.io.common import is_potential_multi_index

if TYPE_CHECKING:
from pandas import DataFrame

Expand Down Expand Up @@ -333,39 +334,14 @@ def extract(r):

return names, index_names, col_names, passed_names

@final
def _dedup_names(self, names: Sequence[Hashable]) -> Sequence[Hashable]:
names = list(names) # so we can index
counts: DefaultDict[Hashable, int] = defaultdict(int)
is_potential_mi = _is_potential_multi_index(names, self.index_col)

for i, col in enumerate(names):
cur_count = counts[col]

while cur_count > 0:
counts[col] = cur_count + 1

if is_potential_mi:
# for mypy
assert isinstance(col, tuple)
col = col[:-1] + (f"{col[-1]}.{cur_count}",)
else:
col = f"{col}.{cur_count}"
cur_count = counts[col]

names[i] = col
counts[col] = cur_count + 1

return names

@final
def _maybe_make_multi_index_columns(
self,
columns: Sequence[Hashable],
col_names: Sequence[Hashable] | None = None,
) -> Sequence[Hashable] | MultiIndex:
# possibly create a column mi here
if _is_potential_multi_index(columns):
if is_potential_multi_index(columns):
list_columns = cast(List[Tuple], columns)
return MultiIndex.from_tuples(list_columns, names=col_names)
return columns
Expand Down Expand Up @@ -1332,35 +1308,6 @@ def _get_na_values(col, na_values, na_fvalues, keep_default_na):
return na_values, na_fvalues


def _is_potential_multi_index(
columns: Sequence[Hashable] | MultiIndex,
index_col: bool | Sequence[int] | None = None,
) -> bool:
"""
Check whether or not the `columns` parameter
could be converted into a MultiIndex.

Parameters
----------
columns : array-like
Object which may or may not be convertible into a MultiIndex
index_col : None, bool or list, optional
Column or columns to use as the (possibly hierarchical) index

Returns
-------
bool : Whether or not columns could become a MultiIndex
"""
if index_col is None or isinstance(index_col, bool):
index_col = []

return bool(
len(columns)
and not isinstance(columns, MultiIndex)
and all(isinstance(c, tuple) for c in columns if c not in list(index_col))
)


def _validate_parse_dates_arg(parse_dates):
"""
Check whether or not the 'parse_dates' parameter
Expand Down
17 changes: 14 additions & 3 deletions pandas/io/parsers/c_parser_wrapper.py
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Expand Up @@ -30,6 +30,10 @@

from pandas.core.indexes.api import ensure_index_from_sequences

from pandas.io.common import (
dedup_names,
is_potential_multi_index,
)
from pandas.io.parsers.base_parser import (
ParserBase,
ParserError,
Expand Down Expand Up @@ -227,7 +231,10 @@ def read(
except StopIteration:
if self._first_chunk:
self._first_chunk = False
names = self._dedup_names(self.orig_names)
names = dedup_names(
self.orig_names,
is_potential_multi_index(self.orig_names, self.index_col),
)
index, columns, col_dict = self._get_empty_meta(
names,
self.index_col,
Expand Down Expand Up @@ -281,7 +288,9 @@ def read(
if self.usecols is not None:
names = self._filter_usecols(names)

names = self._dedup_names(names)
names = self._dedup_names(
names, is_potential_multi_index(names, self.index_col)
)

# rename dict keys
data_tups = sorted(data.items())
Expand All @@ -303,7 +312,9 @@ def read(
# assert for mypy, orig_names is List or None, None would error in list(...)
assert self.orig_names is not None
names = list(self.orig_names)
names = self._dedup_names(names)
names = self._dedup_names(
names, is_potential_multi_index(names, self.index_col)
)

if self.usecols is not None:
names = self._filter_usecols(names)
Expand Down
14 changes: 12 additions & 2 deletions pandas/io/parsers/python_parser.py
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Expand Up @@ -37,6 +37,10 @@
from pandas.core.dtypes.common import is_integer
from pandas.core.dtypes.inference import is_dict_like

from pandas.io.common import (
dedup_names,
is_potential_multi_index,
)
from pandas.io.parsers.base_parser import (
ParserBase,
parser_defaults,
Expand Down Expand Up @@ -259,7 +263,10 @@ def read(
columns: Sequence[Hashable] = list(self.orig_names)
if not len(content): # pragma: no cover
# DataFrame with the right metadata, even though it's length 0
names = self._dedup_names(self.orig_names)
names = dedup_names(
self.orig_names,
is_potential_multi_index(self.orig_names, self.index_col),
)
# error: Cannot determine type of 'index_col'
index, columns, col_dict = self._get_empty_meta(
names,
Expand Down Expand Up @@ -293,7 +300,9 @@ def _exclude_implicit_index(
self,
alldata: list[np.ndarray],
) -> tuple[Mapping[Hashable, np.ndarray], Sequence[Hashable]]:
names = self._dedup_names(self.orig_names)
names = self._dedup_names(
self.orig_names, is_potential_multi_index(self.orig_names, self.index_col)
)

offset = 0
if self._implicit_index:
Expand Down Expand Up @@ -434,6 +443,7 @@ def _infer_columns(
if i not in this_unnamed_cols
] + this_unnamed_cols

# TODO: Use pandas.io.common.dedup_names instead (see #50371)
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@datapythonista This is one TODO you mentioned? Just to be sure and not doing rework? #50371

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Yes, correct.

for i in col_loop_order:
col = this_columns[i]
old_col = col
Expand Down
1 change: 1 addition & 0 deletions pandas/tests/io/json/test_pandas.py
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Expand Up @@ -117,6 +117,7 @@ def test_frame_non_unique_columns(self, orient, data):
expected.iloc[:, 0] = expected.iloc[:, 0].view(np.int64) // 1000000
elif orient == "split":
expected = df
expected.columns = ["x", "x.1"]
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should there also be a test for when the columns are a multiindex, the reader is read_json, and orient='split'?

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Good point. I'm not fully sure if it can be the case that read_json(orient='split') can generate a multiindex. What would the json look like?

I agree dedup_names can probably be better tested, and make sure that the multiindex cases are well tested and work as expected. In my opinion probably better to address it in #50371. For this PR multiindexing is irrelevant if I'm not missing something. #50371 should consolidate the two different versions of dedup_names and make sure they work for all use cases if the implementations are not exactly the same. I think ensuring proper testing makes more sense there, here the function is not modified, and I don't think any bug in the function can be introduced in this PR, as it's just been moved from a method to a function.

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here's an example which passes on 1.5.2, but which fails here:

data = [["a", "b"], ["c", "d"]]
df = DataFrame(data, index=[1, 2], columns=pd.MultiIndex.from_arrays([["x", "x"], ['a', 'x']]))
read_json(df.to_json(orient='split'), orient='split')

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Thanks @MarcoGorelli, this is very helpful. Looks like the json IO with orient='split' does try to support multiindexes, but the implementation in main seems buggy:

>>> df = DataFrame(data, index=[1, 2], columns=pd.MultiIndex.from_arrays([["2022", "2022"], ['JAN', 'FEB']]))
>>> df
  2022    
   JAN FEB
1    a   b
2    c   d
>>> print(df.to_json(orient='split'))
{"columns":[["2022","JAN"],["2022","FEB"]],"index":[1,2],"data":[["a","b"],["c","d"]]}
>>> read_json(df.to_json(orient='split'), orient='split')
  2022 JAN
  2022 FEB
1    a   b
2    c   d

I opened #50456 to address the problem, and fixed the use case with multiindex and added the test, but xfailed it for now, so the current bug can be fixed in an separate PR.


tm.assert_frame_equal(result, expected)

Expand Down