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https://api.github.com/repos/pandas-dev/pandas/issues/29614 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29614/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29614/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29614/events | https://github.com/pandas-dev/pandas/issues/29614 | 522,661,931 | MDU6SXNzdWU1MjI2NjE5MzE= | 29,614 | Cannot load CSV file into SQL using to_sql where data contains literal "NA" | {
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] | closed | false | null | [] | null | 2 | 2019-11-14T06:51:40Z | 2019-11-17T19:49:28Z | 2019-11-17T19:49:23Z | NONE | null | Error
```
runfile('/Users/********/Documents/GitHub/********/Python/temp.py', wdir='/Users/********/Documents/GitHub/********/Python')
1 records processed.
2 records processed.
3 records processed.
Traceback (most recent call last):
File "<ipython-input-14-3bcf066bc8aa>", line 1, in <module>
runfile('/Users/********/Documents/GitHub/********/Python/temp.py', wdir='/Users/********/Documents/GitHub/********/Python')
File "/Users/********/Library/Python/3.6/lib/python/site-packages/spyder_kernels/customize/spydercustomize.py", line 827, in runfile
execfile(filename, namespace)
File "/Users/********/Library/Python/3.6/lib/python/site-packages/spyder_kernels/customize/spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "/Users/********/Documents/GitHub/********/Python/temp.py", line 37, in <module>
chunk.to_sql('MyTable',sqldata,index=False,if_exists='append',chunksize=chunksize)
File "/Users/********/Library/Python/3.6/lib/python/site-packages/pandas/core/generic.py", line 2712, in to_sql
method=method,
File "/Users/********/Library/Python/3.6/lib/python/site-packages/pandas/io/sql.py", line 518, in to_sql
method=method,
File "/Users/********/Library/Python/3.6/lib/python/site-packages/pandas/io/sql.py", line 1750, in to_sql
table.insert(chunksize, method)
File "/Users/********/Library/Python/3.6/lib/python/site-packages/pandas/io/sql.py", line 756, in insert
exec_insert(conn, keys, chunk_iter)
File "/Users/********/Library/Python/3.6/lib/python/site-packages/pandas/io/sql.py", line 1468, in _execute_insert
conn.executemany(self.insert_statement(), data_list)
IntegrityError: NOT NULL constraint failed: MyTable.FileName
```
Code Snippet
```python
"""
SQL load fail example
CSV in Files.csv:
--8<--
"FileName","CreatedBy"
"File1","Jonathan"
"File2","Jonathan"
"File3","Derek"
"NA","Derek"
--8<--
"""
import pandas as pd
import sqlite3 as sql
sqldata = sql.connect('/Users/********/Downloads/myDB.db',isolation_level=None)
sqldata.execute('pragma journal_mode=wal;')
sqldata.execute('pragma cache_size=-16384;')
sqldata.execute('pragma synchronous=0;')
sqldata.execute('pragma temp_store=3;')
csr = sqldata.cursor()
create_table_stmt = f'''
CREATE TABLE MyTable (
FileName TEXT COLLATE NOCASE NOT NULL,
CreatedBy TEXT COLLATE NOCASE DEFAULT NULL
);
'''
columns={}
columns['FileName']=''
columns['CreatedBy']=''
csr.execute(f'DROP TABLE IF EXISTS MyTable;')
csr.execute(create_table_stmt)
with open('/Users/********/Downloads/Files.csv','r',encoding='utf-8') as evdfile:
chunksize = 1
chunktotal = 0
for chunk in pd.read_csv(evdfile,delimiter=',',names=columns.keys(),skiprows=1,skip_blank_lines=True,engine='python',index_col=False,encoding='utf-8',chunksize=chunksize):
chunk.to_sql('MyTable',sqldata,index=False,if_exists='append',chunksize=chunksize)
chunktotal += len(chunk)
print(f'{chunktotal} records processed.')
sqldata.commit()
sqldata.close()
```
I can import this csv file to sqlite using the .import command and .mode csv without either error occurring and the data is intact so it appears that sqlite is not the issue per se.
```
sqlite> select * from Files2
...> ;
File1,Jonathan
File2,Jonathan
File3,Derek
NA,Derek
sqlite>
```
#### Expected Output
As above for the sqlite import.
If I run the same code but don't drop/create the table beforehand it loads without error.
```
n [17]: runfile('/Users/********/Documents/GitHub/********/Python/temp.py', wdir='/Users/********/Documents/GitHub/********/Python')
1 records processed.
2 records processed.
3 records processed.
4 records processed.
```
BUT! The NA is missing from the filename in table.
```
sqlite> select * from myTable;
File1,Jonathan
File2,Jonathan
File3,Derek
,Derek
sqlite>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | [] | closed | false | null | [] | null | 1 | 2019-11-14T12:42:04Z | 2019-11-14T16:35:14Z | 2019-11-14T16:35:01Z | MEMBER | null | When I was trying to horizontally concatenate several DataFrames which contain some overlapping column names, and those overlapping columns would remain unchanged. However, it might be quite useful to have a `suffixes` argument as e.g `DataFrame.merge` has in practice, so that could easily add meaningful suffix to distinguish those overlapped columns. And this argument will be used only when `axis=1`.
While a walkaround for this is: `pd.merge(df1, df2, left_index=True, right_index=True)`, but this cannot be applied in more dfs.
Just found out that this might be similar to #21791 and if this is the arg we want to add, I would be happy to work on this.
Minimum reproducible code:
```python
>>> df1 = pd.DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]})
>>> df2 = pd.DataFrame({"a": [4, 5, 6], "b": [7, 8, 9]})
>>> pd.concat([df1, df2], axis=1)
a | b | a | b
-- | -- | -- | --
1 | 2 | 4 | 7
2 | 3 | 5 | 8
3 | 4 | 6 | 9
```
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} | 6 | 2019-11-14T14:09:50Z | 2019-11-17T15:38:15Z | 2019-11-17T14:25:05Z | CONTRIBUTOR | null | Discussed on the dev call yesterday.
cc @pandas-dev/pandas-core. Any sections I'm missing? Any statements you disagree with?
xref https://github.com/pandas-dev/pandas-governance/issues/11 (writing down a policy for triaging once we have one). | {
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} | 4 | 2019-11-14T15:01:34Z | 2020-08-03T23:19:27Z | 2020-08-03T23:19:27Z | CONTRIBUTOR | null | This is a very strange error with a long traceback. I found #15680 and #11324 mentioning similar things, but neither seem to cover the behavior here
#### Code Sample, a copy-pastable example:
```python
import datetime
import pandas as pd
recs = [{'LIVE': 1,
'ITEM': '001',
'DATE': datetime.date(2019, 10, 1)},
{'LIVE': 2,
'ITEM': '002',
'DATE': datetime.date(2019, 10, 2)},
{'LIVE': 3,
'ITEM': '003',
'DATE': datetime.date(2019, 10, 1)}]
pd.DataFrame(recs).groupby(['ITEM', 'DATE']).apply(lambda df: df.head(1))
```
on 0.25.3 this raises this convoluted KeyError:
<details>
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2896 try:
-> 2897 return self._engine.get_loc(key)
2898 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: Timestamp('2019-10-01 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _make_concat_multiindex(indexes, keys, levels, names)
631 try:
--> 632 i = level.get_loc(key)
633 except KeyError:
~/.local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2898 except KeyError:
-> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key))
2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: Timestamp('2019-10-01 00:00:00')
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
724 try:
--> 725 result = self._python_apply_general(f)
726 except Exception:
~/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
744 return self._wrap_applied_output(
--> 745 keys, values, not_indexed_same=mutated or self.mutated
746 )
~/.local/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _wrap_applied_output(self, keys, values, not_indexed_same)
371 elif isinstance(v, DataFrame):
--> 372 return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
373 elif self.grouper.groupings is not None:
~/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _concat_objects(self, keys, values, not_indexed_same)
972 names=group_names,
--> 973 sort=False,
974 )
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
254 copy=copy,
--> 255 sort=sort,
256 )
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)
427
--> 428 self.new_axes = self._get_new_axes()
429
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _get_new_axes(self)
521
--> 522 new_axes[self.axis] = self._get_concat_axis()
523 return new_axes
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _get_concat_axis(self)
577 concat_axis = _make_concat_multiindex(
--> 578 indexes, self.keys, self.levels, self.names
579 )
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _make_concat_multiindex(indexes, keys, levels, names)
635 "Key {key!s} not in level {level!s}".format(
--> 636 key=key, level=level
637 )
ValueError: Key 2019-10-01 00:00:00 not in level Index([2019-10-01, 2019-10-02], dtype='object', name='DATE')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2896 try:
-> 2897 return self._engine.get_loc(key)
2898 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: Timestamp('2019-10-01 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _make_concat_multiindex(indexes, keys, levels, names)
631 try:
--> 632 i = level.get_loc(key)
633 except KeyError:
~/.local/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2898 except KeyError:
-> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key))
2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: Timestamp('2019-10-01 00:00:00')
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-32-cf7f934e7c7f> in <module>
12 'DATE': datetime.date(2019, 10, 1)}]
13
---> 14 pd.DataFrame(recs).groupby(['ITEM', 'DATE']).apply(lambda df: df.head(1))
~/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs)
735
736 with _group_selection_context(self):
--> 737 return self._python_apply_general(f)
738
739 return result
~/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f)
743
744 return self._wrap_applied_output(
--> 745 keys, values, not_indexed_same=mutated or self.mutated
746 )
747
~/.local/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _wrap_applied_output(self, keys, values, not_indexed_same)
370 return DataFrame()
371 elif isinstance(v, DataFrame):
--> 372 return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
373 elif self.grouper.groupings is not None:
374 if len(self.grouper.groupings) > 1:
~/.local/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _concat_objects(self, keys, values, not_indexed_same)
971 levels=group_levels,
972 names=group_names,
--> 973 sort=False,
974 )
975 else:
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
253 verify_integrity=verify_integrity,
254 copy=copy,
--> 255 sort=sort,
256 )
257
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)
426 self.copy = copy
427
--> 428 self.new_axes = self._get_new_axes()
429
430 def get_result(self):
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _get_new_axes(self)
520 new_axes[i] = ax
521
--> 522 new_axes[self.axis] = self._get_concat_axis()
523 return new_axes
524
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _get_concat_axis(self)
576 else:
577 concat_axis = _make_concat_multiindex(
--> 578 indexes, self.keys, self.levels, self.names
579 )
580
~/.local/lib/python3.7/site-packages/pandas/core/reshape/concat.py in _make_concat_multiindex(indexes, keys, levels, names)
634 raise ValueError(
635 "Key {key!s} not in level {level!s}".format(
--> 636 key=key, level=level
637 )
638 )
ValueError: Key 2019-10-01 00:00:00 not in level Index([2019-10-01, 2019-10-02], dtype='object', name='DATE')
```
</details>
oddly, both of these work
```python
pd.DataFrame(recs).groupby(['ITEM']).apply(lambda df: df.head(1))
pd.DataFrame(recs).groupby(['DATE']).apply(lambda df: df.head(1))
```
also, the behavior is the same if I modify recs so that only 1 distinct date is present
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.1-1.el7.elrepo.x86_64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.10
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29618 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29618/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29618/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29618/events | https://github.com/pandas-dev/pandas/issues/29618 | 522,946,244 | MDU6SXNzdWU1MjI5NDYyNDQ= | 29,618 | Int64 can't be used in pandas.eval() | {
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{
"color": "4E9A06",
"default": false,
"description": null,
"id": 76812,
"name": "Enhancement",
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},
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"default": false,
"description": "Extendin... | open | false | null | [] | null | 5 | 2019-11-14T15:51:32Z | 2020-11-10T13:43:17Z | null | NONE | null | This code works fine
```python
import pandas as pd
d = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'], dtype="Float64")
d.eval('c = b - a', inplace=True)
```
### This code fails with the following stacktrace below
```python
d = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'], dtype="Int64")
d.eval('c = b - a', inplace=True)
```
```text
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-43-42645933ba81> in <module>
----> 1 d.eval('c = b - a', inplace=True)
2 d
~/.local/lib/python3.6/site-packages/pandas/core/frame.py in eval(self, expr, inplace, **kwargs)
3313 kwargs["target"] = self
3314 kwargs["resolvers"] = kwargs.get("resolvers", ()) + tuple(resolvers)
-> 3315 return _eval(expr, inplace=inplace, **kwargs)
3316
3317 def select_dtypes(self, include=None, exclude=None):
~/.local/lib/python3.6/site-packages/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
320 )
321
--> 322 parsed_expr = Expr(expr, engine=engine, parser=parser, env=env, truediv=truediv)
323
324 # construct the engine and evaluate the parsed expression
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in __init__(self, expr, engine, parser, env, truediv, level)
828 self.env.scope["truediv"] = truediv
829 self._visitor = _parsers[parser](self.env, self.engine, self.parser)
--> 830 self.terms = self.parse()
831
832 @property
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in parse(self)
845 def parse(self):
846 """Parse an expression"""
--> 847 return self._visitor.visit(self.expr)
848
849 @property
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in visit_Module(self, node, **kwargs)
445 raise SyntaxError("only a single expression is allowed")
446 expr = node.body[0]
--> 447 return self.visit(expr, **kwargs)
448
449 def visit_Expr(self, node, **kwargs):
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in visit_Assign(self, node, **kwargs)
663 )
664
--> 665 return self.visit(node.value, **kwargs)
666
667 def visit_Attribute(self, node, **kwargs):
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
439 method = "visit_" + node.__class__.__name__
440 visitor = getattr(self, method)
--> 441 return visitor(node, **kwargs)
442
443 def visit_Module(self, node, **kwargs):
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in visit_BinOp(self, node, **kwargs)
563 op, op_class, left, right = self._maybe_transform_eq_ne(node)
564 left, right = self._maybe_downcast_constants(left, right)
--> 565 return self._maybe_evaluate_binop(op, op_class, left, right)
566
567 def visit_Div(self, node, **kwargs):
~/.local/lib/python3.6/site-packages/pandas/core/computation/expr.py in _maybe_evaluate_binop(self, op, op_class, lhs, rhs, eval_in_python, maybe_eval_in_python)
531 res = op(lhs, rhs)
532
--> 533 if res.has_invalid_return_type:
534 raise TypeError(
535 "unsupported operand type(s) for {op}:"
~/.local/lib/python3.6/site-packages/pandas/core/computation/ops.py in has_invalid_return_type(self)
223 types = self.operand_types
224 obj_dtype_set = frozenset([np.dtype("object")])
--> 225 return self.return_type == object and types - obj_dtype_set
226
227 @property
~/.local/lib/python3.6/site-packages/pandas/core/computation/ops.py in return_type(self)
217 if self.op in (_cmp_ops_syms + _bool_ops_syms):
218 return np.bool_
--> 219 return _result_type_many(*(term.type for term in com.flatten(self)))
220
221 @property
~/.local/lib/python3.6/site-packages/pandas/core/computation/common.py in _result_type_many(*arrays_and_dtypes)
20 argument limit """
21 try:
---> 22 return np.result_type(*arrays_and_dtypes)
23 except ValueError:
24 # we have > NPY_MAXARGS terms in our expression
<__array_function__ internals> in result_type(*args, **kwargs)
TypeError: data type not understood
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-35-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 41.6.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.0
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : 0.3.5
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29619 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29619/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29619/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29619/events | https://github.com/pandas-dev/pandas/pull/29619 | 522,951,955 | MDExOlB1bGxSZXF1ZXN0MzQxMDUxMDUz | 29,619 | Update nullable integer docs with None instead of np.nan | {
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} | 3 | 2019-11-14T16:00:41Z | 2019-11-14T18:26:02Z | 2019-11-14T18:07:11Z | CONTRIBUTOR | null | Update nullable integer docs with None instead of np.nan
- [X] closes #29599
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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So I decided to reinstall all of the packages.
And while running the command ```python -m pip install -r requirements-dev.txt``` I got this output:
```
Building wheels for collected packages: bottleneck, pyarrow
Building wheel for bottleneck (PEP 517) ... done
Created wheel for bottleneck: filename=Bottleneck-1.3.0-cp38-cp38-linux_x86_64.whl size=402563 sha256=113ba04f2528195389b7c40b22edd36aef331a2b28728754c3f14c3f4db54146
Stored in directory: /home/bummy/.cache/pip/wheels/10/90/86/d59b7357e08bbc4c1018dffede068dc2ecb170b16bf20024bc
Building wheel for pyarrow (PEP 517) ... error
ERROR: Command errored out with exit status 1:
command: /home/bummy/Documents/Github/Community/Python/Venvs/venv-pandas/bin/python /home/bummy/Documents/Github/Community/Python/Venvs/venv-pandas/lib/python3.8/site-packages/pip/_vendor/pep517/_in_process.py build_wheel /tmp/tmpunb7hoky
cwd: /tmp/pip-install-4yj1nxdu/pyarrow
Complete output (427 lines):
running bdist_wheel
running build
running build_py
creating build
creating build/lib.linux-x86_64-3.8
creating build/lib.linux-x86_64-3.8/pyarrow
copying pyarrow/util.py -> build/lib.linux-x86_64-3.8/pyarrow
copying pyarrow/types.py -> build/lib.linux-x86_64-3.8/pyarrow
.....................................................
copying pyarrow/tests/data/parquet/v0.7.1.column-metadata-handling.parquet -> build/lib.linux-x86_64-3.8/pyarrow/tests/data/parquet
copying pyarrow/tests/data/parquet/v0.7.1.parquet -> build/lib.linux-x86_64-3.8/pyarrow/tests/data/parquet
copying pyarrow/tests/data/parquet/v0.7.1.some-named-index.parquet -> build/lib.linux-x86_64-3.8/pyarrow/tests/data/parquet
running build_ext
creating build/temp.linux-x86_64-3.8
-- Running cmake for pyarrow
cmake -DPYTHON_EXECUTABLE=/home/bummy/Documents/Github/Community/Python/Venvs/venv-pandas/bin/python -DPYARROW_BOOST_USE_SHARED=on -DCMAKE_BUILD_TYPE=release /tmp/pip-install-4yj1nxdu/pyarrow
error: command 'cmake' failed with exit status 1
----------------------------------------
ERROR: Failed building wheel for pyarrow
Running setup.py clean for pyarrow
Successfully built bottleneck
Failed to build pyarrow
ERROR: Could not build wheels for pyarrow which use PEP 517 and cannot be installed directly
```
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} | 13 | 2019-11-14T20:58:53Z | 2020-04-11T01:32:21Z | 2020-04-11T01:32:20Z | CONTRIBUTOR | null | `test_thousands_macau_index_col` in `test_html` fails using beautifulsoup4 version 4.8.1 and passes using version 4.7.1.
Example failures:
https://travis-ci.org/pandas-dev/pandas/jobs/611742966
As pointed out by @jreback in this PR - https://github.com/pandas-dev/pandas/pull/29603
Example failing build pre my changes in #29513
https://travis-ci.org/pandas-dev/pandas/jobs/611347428
Attempting to debug issue and will report to bs4. Should we pin in the meantime to version <= 4.7.1?
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} | 4 | 2019-11-14T22:17:40Z | 2020-12-19T02:14:08Z | 2020-12-19T02:14:08Z | CONTRIBUTOR | null | #### Code Sample
```python
x = pd.Series(1, index=pd.bdate_range('2010-03-31', periods=100))
print(x.first('1M')) # Returns March and April
print(x.first('2M')) # Returns March, April and May
# etc...
```
#### Problem description
In case if the first index of the series falls on the last day of the month, `Series.first('1M')` returns two first months (this one day + the next month).
If the first value is not on the last day of the month - result is correct:
```python
x = pd.Series(1, index=pd.bdate_range('2010-03-30', periods=100))
print(x.first('1M')) # Returns only March
print(x.first('2M')) # Returns March and April
# etc...
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : 0.1.6
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.0.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.12.1
pytables : None
s3fs : 0.3.4
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.4.4
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-11-14T22:38:46Z | 2019-11-17T23:06:35Z | 2019-11-17T23:06:35Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [7]: pd.Series([], dtype=object).str.isalnum()
Out[7]: Series([], dtype: object)
```
#### Problem description
On empty series, we return an object-dtype result, rather than bool. I might expect
this to be a boolean dtype to match the non-empty case.
#### Expected Output
```python
In [14]: pd.Series([], dtype=object).str.isalnum()
Out[14]: Series([], dtype: bool)
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} | 1 | 2019-11-14T22:56:59Z | 2019-11-17T10:23:50Z | 2019-11-17T10:23:44Z | CONTRIBUTOR | null | In #29624 I made change so random kwargs weren't passed to ``Index._simple_new``. I'v found out after merging that the selected solution dropped non-empty kwargs, which isn't great either.
This version ensures that passing non-accepted kwargs raises, so ``pd.Index(['a'], foo="foo")`` raises a TypeError. Instead of raising a TypeError, should we deprecate adding non-accepted kwargs?
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} | 2 | 2019-11-15T00:22:48Z | 2019-11-18T01:57:20Z | 2019-11-18T01:57:11Z | MEMBER | null | splitting this off of branches that do non-CLN stuff | {
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} | 1 | 2019-11-15T01:23:24Z | 2019-11-20T15:35:29Z | 2019-11-20T13:02:06Z | MEMBER | null | I think some of this can be made easier to reason about (like with making FrameApply less stateful), as a preliminary this is just some mild annotations etc | {
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} | 2 | 2019-11-15T09:07:00Z | 2019-11-18T07:55:25Z | 2019-11-18T07:55:22Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/29587
cc @jbrockmendel | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29633 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29633/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29633/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29633/events | https://github.com/pandas-dev/pandas/issues/29633 | 523,420,546 | MDU6SXNzdWU1MjM0MjA1NDY= | 29,633 | Series.str.rsplit not working with regex patterns (v 0.24.2 and 0.25.2) | {
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"description": "String extension data ty... | open | false | null | [] | null | 3 | 2019-11-15T11:36:55Z | 2020-05-03T20:34:31Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
S = pd.Series(['1+1=2'])
S.str.rsplit(r'\+|=')
# returns:
0 [1+1=2]
S.str.rsplit(r'\+|=', expand=True)
# returns:
0
0 1+1=2
```
#### Problem description
The `str.rsplit` method is not recognizing regex patterns. This example above is from the documentation.
#### Expected Output
```python
S.str.rsplit(r'\+|=')
0 [1, 2, 3]
S.str.rsplit(r'\+|=', expand=True)
0 1 2
0 1 1 2
```
#### Output of ``pd.show_versions()``
Version 0.24.2:
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 5.0.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.12
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
Version 0.25.2:
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | [] | closed | false | null | [] | null | 1 | 2019-11-15T12:22:46Z | 2019-11-28T05:58:08Z | 2019-11-28T05:58:08Z | NONE | null | ```python
import pandas as pd
pd.__version__
>> '0.25.0'
# Your code here
def test_in_test(val):
return
def test(row):
print(1)
print(2)
print('val', row[0])
return pd.Series([1,2,3,4,5,6,7])
test_df = pd.DataFrame({'a':range(2), 'b':range(2,2)})
test_df
>>
a | b
0 | 2
1 | 3
test_df.apply(test, axis=1)
>> output
1
2
val 0
1
2
val 0
1
2
val 1
>> expected output
1
2
val 0
1
2
val 1
```
#### Problem description
Why does pandas `apply` do function on the first row one more time?
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https://api.github.com/repos/pandas-dev/pandas/issues/29635 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29635/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29635/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29635/events | https://github.com/pandas-dev/pandas/issues/29635 | 523,441,969 | MDU6SXNzdWU1MjM0NDE5Njk= | 29,635 | Gropuby using pd.Series.mode throws error when `by` column contains values with same starting value | {
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"default": false,
"description": null,
"id": 233160,
"name": "Groupby",
"node_id": "MDU6TGFiZWwyMzMxNjA=",
"url": "https://api.github.com/repos/pandas-dev/pandas/labels/Groupby"
},
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"id": 7... | open | false | null | [] | null | 3 | 2019-11-15T12:25:29Z | 2021-08-12T21:40:42Z | null | NONE | null | #### Groupby with pd.Series.mode throws error, if `by` values starts with a value from previous row.
```python
df2 = pd.DataFrame({
"Name" : ['Thomas', 'Thomas', 'Thomas John'],
"Credit" : [1200, 1300, 900],
"Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
'Mood':pd.Series.mode,
'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)
```
#### Problem description
When I execute the above code I got error
```
Exception: Must produce aggregated value
.....
....
and a lot of stack traces..
```
If I change the third name to John instead of Thomas John, it works as expected. ie The following code works.
```python
df2 = pd.DataFrame({
"Name" : ['Thomas', 'Thomas', 'John'],
"Credit" : [1200, 1300, 900],
"Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
'Mood':pd.Series.mode,
'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)
```
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
Groupby should work, even if first letters of column values are same
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.16.final.0
python-bits: 64
OS: Darwin
OS-release: 19.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.23.3
pytest: None
pip: 10.0.1
setuptools: 40.0.0
Cython: None
numpy: 1.14.5
scipy: 0.13.0b1
pyarrow: None
xarray: None
IPython: 5.7.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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"description": "to_sql, ... | open | false | null | [] | null | 77 | 2019-11-15T14:43:09Z | 2021-11-18T02:24:18Z | null | NONE | null | This PR adds SQL `upsert` functionality to the `to_sql` method. As outlined in the issue [discussion](https://github.com/pandas-dev/pandas/issues/14553#issuecomment-536743090), there will be 2 types of upsert:
1. `upsert_delete` - prioritizes new data from incoming dataframe. Deletes clashing records in the database and then inserts new dataframe.
2. `upsert_ignore` - prioritizes what is already in the database, over what is coming in from the new dataframe. Deletes records which already exist, from existing dataframe before inserting the rest into the db.
Both upserts currently only check for **primary key** duplicates.
Please note 1 important change over the design proposal made in #14553 - `upsert_delete` and `upsert_ignore` have now been made options for the `if_exists` arg, rather than the `method` argument. This was revised, because on second consideration, `upsert` was deemed mutually exclusive to `append` and `replace`, but not to the `method` argument, so making it part of `if_exists` seemed the more sound design.
- [ ] closes #14553
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry | {
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"de... | closed | false | null | [] | null | 6 | 2019-11-15T14:53:32Z | 2019-11-16T22:33:22Z | 2019-11-16T22:33:01Z | CONTRIBUTOR | null | edit: probably closing this: See https://github.com/pandas-dev/pandas/pull/29640 for a simpler alternative.
In preparation for https://github.com/pandas-dev/pandas/pull/29597, this changes the string methods implemetation to
* Use masks to apply the function just to valid values.
* Disables all unnecessary inference on the result.
This works quite well for the new StringDtype, since we know the values are always string. The object-dtype behavior is a bit more complex. I'll note that inline below.
Additionally, it fixes https://github.com/pandas-dev/pandas/issues/29624
edit: Oh, forgot another change. String methods returning numeric values will return a nullable integer type. So `Series[string].str.count('a')` returns an Int64Dtype, rather than a maybe int or maybe float (depending on the presence of NAs). Will document that. | {
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] | closed | false | null | [] | null | 3 | 2019-11-15T16:21:41Z | 2020-03-02T13:55:36Z | 2020-03-02T13:55:35Z | NONE | null | I have a data set of multiple work lists for my job and I am trying to sort values before dropping duplicates, but the code below give me this error:
import pandas as pd
Disputes = Disputes.sort_values(axis=1, by=['Invoice Number', 'Report Date'], inplace = True).drop_duplicates(subset=['Invoice Number', 'Report Date'], keep=['last'])
TypeError: sort_values() got an unexpected keyword argument 'by'
I have tried to use multiple versions of Pandas to fix the issue as has been suggested by multiple posts of Stack Overflow and on Github. However, I still get the same error.
Essentially I would like to use the sort values function without getting this error so that I can focus on the rest of my projects.
Here is the output to show my version of Pandas:
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
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} | 19 | 2019-11-15T16:45:16Z | 2021-10-05T23:19:12Z | 2021-01-09T22:50:11Z | NONE | null | Fixes #29484
- [ ] closes #29484
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 12 | 2019-11-15T17:06:59Z | 2019-11-19T16:22:42Z | 2019-11-19T16:22:38Z | CONTRIBUTOR | null | Intended as an alternative to https://github.com/pandas-dev/pandas/pull/29637.
This is a much smaller change. It only changes the codepath for StringDtype.
I think this is OK since someday (well down the road) we'll want to deprecate
the `.str` accessor on object-dtype Series. When we enforce that, we can just
delete the entire old implementation.
The API change is limited to always returning Int64Dtype for numeric outputs, rather than int if there's no NAs and float if there are any.
When BoolArray is done, we'll change that for the boolean-returning ones.
As a side benefit, we get a nice perf boost, since we have deterministic output dtypes we
can skip an object-dtype allocation.
```python
In [2]: s = pd.Series(['a'] * 100_000 + [None], dtype="string")
In [3]: t = s.astype(object)
In [4]: %timeit s.str.count("a")
50.7 ms ± 2.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [5]: %timeit t.str.count("a")
62.7 ms ± 742 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
``` | {
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} | 6 | 2019-11-15T17:58:00Z | 2019-11-18T15:59:36Z | 2019-11-18T00:27:07Z | MEMBER | null | After some investigation it turns out that the DTA casting in `_try_cast` is made necessary because we incorrectly pass datetime64tz to `_aggregate_series_fast`, which calls the cython `libreduction.SeriesGrouper`, which casts the input to `ndarray`, losing the timezone. By not going through the cython path for datetime64tz, we avoid the need to re-cast.
That in turn surfaces a new problem, which is that _aggregate_series_pure_python checks the first group's result slightly differently than the cython version does (see libreduction._extract_result). This changes the way the pure-python version does it to more closely match the cython version. I intend to make these match more precisely in an upcoming pass.
If merged, makes #29589 unnecessary.
cc @jreback @jorisvandenbossche @WillAyd | {
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} | 1 | 2019-11-15T18:04:59Z | 2019-11-16T22:19:11Z | 2019-11-16T20:58:21Z | MEMBER | null | Orthogonal to #29641.
_try_cast is a no-op if passed a scalar, so is unnecessary for some reduction/aggregation ops. After #29641 i think we can get rid of more of these calls. | {
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```
installing dbs
psql: could not connect to server: No such file or directory
Is the server running locally and accepting
connections on Unix domain socket "/tmp/.s.PGSQL.5432"?
The command "ci/setup_env.sh" failed and exited with 2 during .
Your build has been stopped.
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```
flake8 --version
3.7.9 (flake8-comprehensions: 3.1.0, mccabe: 0.6.1, pycodestyle: 2.5.0, pyflakes: 2.1.1) CPython 3.7.3 on Linux
Linting .py code
##[error]./pandas/io/pytables.py(1344,21): error C414: Unnecessary list call within sorted().
##[error]./pandas/io/parsers.py(1608,18): error C413: Unnecessary reversed call around sorted() - use sorted(..., reverse=True).
##[error]./pandas/io/parsers.py(1640,18): error C413: Unnecessary reversed call around sorted() - use sorted(..., reverse=True).
##[error]./pandas/core/frame.py(4284,22): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/core/frame.py(7271,25): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/core/tools/datetimes.py(860,11): error C414: Unnecessary list call within sorted().
##[error]./pandas/core/tools/datetimes.py(869,14): error C414: Unnecessary list call within sorted().
##[error]./pandas/core/internals/managers.py(263,23): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/core/indexes/multi.py(371,52): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/core/indexes/multi.py(1995,21): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/core/indexes/multi.py(1996,20): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/core/arrays/categorical.py(1113,23): error C414: Unnecessary list call within set().
##[error]./pandas/core/arrays/sparse/scipy_sparse.py(54,26): error C414: Unnecessary list call within sorted().
##[error]./pandas/util/testing.py(1955,18): error C413: Unnecessary list call around sorted().
##[error]./pandas/tests/test_multilevel.py(260,19): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/test_base.py(656,20): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/test_parquet.py(408,22): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2809,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2813,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2818,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2838,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2860,17): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2919,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2925,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2931,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2939,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2961,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2968,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2977,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(2995,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(3017,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(3024,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(3035,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/pytables/test_store.py(3055,24): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/sas/test_xport.py(107,21): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/io/formats/test_to_excel.py(265,10): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/frame/test_alter_axes.py(384,23): error C414: Unnecessary list call within sorted().
##[error]./pandas/tests/frame/test_alter_axes.py(748,20): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/frame/test_reshape.py(702,25): error C414: Unnecessary list call within sorted().
##[error]./pandas/tests/frame/test_analytics.py(1247,21): error C414: Unnecessary list call within sorted().
##[error]./pandas/tests/api/test_api.py(16,22): error C414: Unnecessary list call within sorted().
##[error]./pandas/tests/series/test_datetime_values.py(211,20): error C413: Unnecessary list call around sorted().
##[error]./pandas/tests/series/test_datetime_values.py(216,22): error C413: Unnecessary list call around sorted().
##[error]./pandas/tests/series/test_datetime_values.py(224,22): error C413: Unnecessary list call around sorted().
##[error]./pandas/tests/series/test_datetime_values.py(233,22): error C413: Unnecessary list call around sorted().
##[error]./pandas/tests/series/test_api.py(264,20): error C413: Unnecessary list call around sorted().
##[error]./pandas/tests/series/test_api.py(268,41): error C413: Unnecessary list call around sorted().
##[error]./pandas/tests/series/test_duplicates.py(88,31): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/series/test_alter_axes.py(236,21): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/series/test_timeseries.py(607,28): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/tseries/offsets/test_offsets.py(4256,33): error C414: Unnecessary list call within sorted().
##[error]./pandas/tests/tseries/offsets/test_offsets.py(4265,33): error C414: Unnecessary list call within sorted().
##[error]./pandas/tests/tseries/offsets/test_offsets.py(4272,33): error C414: Unnecessary list call within sorted().
##[error]./pandas/tests/indexes/timedeltas/test_setops.py(42,37): error C414: Unnecessary list call within set().
##[error]./pandas/tests/indexes/timedeltas/test_setops.py(42,56): error C414: Unnecessary list call within set().
##[error]./pandas/tests/indexes/timedeltas/test_setops.py(53,37): error C414: Unnecessary list call within set().
##[error]./pandas/tests/indexes/timedeltas/test_setops.py(53,55): error C414: Unnecessary list call within set().
##[error]./pandas/tests/indexes/timedeltas/test_setops.py(62,37): error C414: Unnecessary list call within set().
##[error]./pandas/tests/indexes/timedeltas/test_setops.py(62,55): error C414: Unnecessary list call within set().
##[error]./pandas/tests/reshape/merge/test_merge.py(863,25): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/reshape/merge/test_join.py(627,25): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/indexing/test_categorical.py(475,27): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/indexing/multiindex/test_xs.py(164,10): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/indexing/multiindex/test_loc.py(227,14): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/indexing/multiindex/test_loc.py(235,14): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/groupby/test_function.py(1303,14): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/groupby/test_function.py(1304,14): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/plotting/test_misc.py(269,26): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/plotting/test_misc.py(282,14): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./pandas/tests/plotting/test_misc.py(283,14): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./asv_bench/benchmarks/categoricals.py(167,16): error C416: Unnecessary list comprehension - rewrite using list().
##[error]./asv_bench/benchmarks/frame_ctor.py(102,23): error C416: Unnecessary list comprehension - rewrite using list().
```
most likely a new flake8 version. Can pin for now and fix later, or just update them now. | {
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} | 1 | 2019-11-15T20:17:14Z | 2021-09-06T21:50:54Z | 2021-09-06T21:50:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
> s1 = pd.Series(['x', 'y', None], index=[0, 0, 0])
> s1.isna()
0 False
0 False
0 True
dtype: bool
> s2 = pd.Series(['x', 'y', np.nan], index=[0, 0, 0])
> s2.isna()
0 False
0 False
0 True
dtype: bool
# But groupby doesn't!
> s1.groupby(level=0).last()
0 None
dtype: object
> s2.groupby(level=0).last()
0 y
dtype: object
# Expectation: these should output the same thing.
```
#### Problem description
The rest of pandas seems to treat None as the same as np.nan, so it is inconsistent and head-scratching why groupby's functions wouldn't. The expected behavior would be for first/last/nth to treat None & `np.nan` like every other pandas function does.
#### Expected Output
```python
> s1.groupby(level=0).last()
0 y
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
...
pandas: 0.24.1
...
</details>
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} | 3 | 2019-11-15T20:17:55Z | 2019-11-16T01:24:17Z | 2019-11-16T01:24:05Z | MEMBER | null | - [x] closes #29644
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 3 | 2019-11-15T20:26:36Z | 2019-11-16T20:26:37Z | 2019-11-16T20:26:37Z | CONTRIBUTOR | null | ```python
df = pd.DataFrame({'A': [0.1, 0.2, 0.3], 'B': [1.0, 2.0, 3.0]}, dtype='Sparse[float64, nan]')
df1 = df.iloc[0]
df1._is_view
```
#### Problem description
When calling `.iloc` on a sparse DataFrame, it seems to return a copy rather than a view. Is this expected behavior?
#### Expected Output
Getting a view back would be preferred.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-66-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 0.999999999
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.2.1
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 2 | 2019-11-15T21:17:57Z | 2019-11-17T14:07:03Z | 2019-11-17T14:06:56Z | MEMBER | null | Currently, only maintainers and triagers can assign issues.
This implements a hack, so when any contributor writes the keyword `take` in an issue, GitHub actions will automatically assign the issue to the commenter.
I think this should be very helpful once people start to assign issues to themselves, since adding the `Assigned to nobody` filter should let people find issues that nobody is working on. May be in the future we want to implement another action to unassign issues after a period of inactivity, but so far this should be much better than just having people adding comments.
@pandas-dev/pandas-core what do you think?
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"description": "Concat, ... | open | false | null | [] | null | 0 | 2019-11-15T21:58:56Z | 2021-07-23T04:35:29Z | null | CONTRIBUTOR | null | #### Validate the relationship of two DataFrames being joined in terms of set theory
Take this example:
```python
df1 = DataFrame({"col1": [1, 2, 3], "col_left": ["a", "b", "c"]})
df2 = DataFrame({"col1": [2, 3, 4], "col_right": ["d", "e", "f"]})
test = merge(
df1,
df2,
on="col1",
how="outer",
indicator=True
)
```
I want to validate that resulting set `set(test['_merge']` will equal `{'left_only', 'right_only', 'both'}`. In other words validate that the two datasets have the expected relationship in terms of set theory when they are joined.
When the validate parameter was added to pandas I found it very useful. It reminds me a of a similar parameter seen in Stata. You can see this in the [Stata Merge Documentation.](https://www.stata.com/manuals13/dmerge.pdf) documentation.
There is another feature in the same Stata documentation that I wish to port over: `assert(results) specify required match results`. That is the intention of this Feature Request.
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```python
from io import StringIO
import pandas
input = StringIO("some\tcontent\n")
pandas.read_fwf(input)
```
#### Problem description
If you run the above code with ``-bb``, it crashes with ``BytesWarning``:
```
％ python -bb repro.py
Traceback (most recent call last):
File "repro.py", line 6, in <module>
pandas.read_fwf(input)
File "/Users/fsouza/.venvs/pandas-debug/lib/python3.7/site-packages/pandas/io/parsers.py", line 791, in read_fwf
return _read(filepath_or_buffer, kwds)
File "/Users/fsouza/.venvs/pandas-debug/lib/python3.7/site-packages/pandas/io/parsers.py", line 457, in _read
parser = TextFileReader(fp_or_buf, **kwds)
File "/Users/fsouza/.venvs/pandas-debug/lib/python3.7/site-packages/pandas/io/parsers.py", line 895, in __init__
self._make_engine(self.engine)
File "/Users/fsouza/.venvs/pandas-debug/lib/python3.7/site-packages/pandas/io/parsers.py", line 1147, in _make_engine
self._engine = klass(self.f, **self.options)
File "/Users/fsouza/.venvs/pandas-debug/lib/python3.7/site-packages/pandas/io/parsers.py", line 3709, in __init__
PythonParser.__init__(self, f, **kwds)
File "/Users/fsouza/.venvs/pandas-debug/lib/python3.7/site-packages/pandas/io/parsers.py", line 2348, in __init__
r"[^-^0-9^{decimal}]+".format(decimal=self.decimal)
BytesWarning: str() on a bytes instance
```
It doesn't crash without that flag, but we do end-up with the incorrect regexp:
```
>>> decimal = b'.'
>>> r"[^-^0-9^{decimal}]+".format(decimal=decimal)
[^-^0-9^b'.']+
```
Notice how you end-up with an unexpected regexp.
#### Expected Output
No crash.
#### Output of ``pd.show_versions()``
<details>
```
>>> pandas.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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between left and right dataframes. Updated docs and tests for new
feature.
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-11-16T02:59:26Z | 2019-12-25T20:27:02Z | 2019-12-06T01:10:29Z | CONTRIBUTOR | null | - [x] closes #27179
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Current behaviour on master of isort formatting:
Finishing up stale PR @https://github.com/pandas-dev/pandas/pull/27334
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Not sure if this makes sense, but let's try if this is the problem. | {
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} | 3 | 2019-11-16T08:47:17Z | 2019-11-19T16:44:36Z | 2019-11-19T13:31:14Z | CONTRIBUTOR | null | The function was returning True in case shown in added test. The cause
of the problem was sorting Blocks of DataFrame by type, and then
mgr_locs before comparison. It resulted in arranging the identical blocks
in the same way, which resulted in having the same two lists of blocks.
Changing sorting order to (mgr_locs, type) resolves the problem, while not
interrupting the other aspects of comparison.
- [x] closes #28839
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 2 | 2019-11-16T12:29:49Z | 2019-11-16T18:02:50Z | 2019-11-16T18:02:36Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3], })
print("m" + df["a"].astype(str))
```
#### Problem description
```
Traceback (most recent call last):
File "D:/test/plot1.py", line 4, in <module>
print("m" + df["a"].astype(str))
File "C:\Python37\lib\site-packages\pandas\core\ops\__init__.py", line 1016, in wrapper
assert np.isnat(right)
TypeError: ufunc 'isnat' is only defined for datetime and timedelta.
```
#### Expected Output
m1
m2
m3
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 6 | 2019-11-17T03:04:04Z | 2019-11-21T13:06:07Z | 2019-11-21T13:06:07Z | MEMBER | null | May be I'm missing something, but is there a reason to use different pytest versions in our CI builds? I see we're using those in the dependencies files:
- pytest
- pytest>=4.0.2
- pytest>=4.0.2,<5.0
- pytest=4.5.0
- pytest>=5.0.0
- pytest>=5.0.1
Since pytest is not a pandas dependency, but just a tool we use, feels like we should just use always the same version (the latest). All those (all versions since pytest 4) support Python >3.6, so I don't think there is any limitation that prevents this.
Am I missing something? Can we always use the same version?
I'm thinking on having a separate file for the tools needed for all builds (pytest, pytest-xdist...), and install both dependency files, the one of the build, and the common one. So we don't have to duplicate those in every file (it's faster to maintain and make changes, and also easier to see what dependencies we have and what we're testing in every build).
@jreback @TomAugspurger does this make sense? | {
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} | 2 | 2019-11-17T03:28:29Z | 2019-11-17T15:38:11Z | 2019-11-17T13:34:26Z | MEMBER | null | Some annotations. I'm finding that annotations in this directory are really fragile; adding a type to one thing will cause a mypy complaint in somewhere surprising. | {
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} | 2 | 2019-11-17T03:46:26Z | 2019-11-18T17:18:44Z | 2019-11-18T13:37:18Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"color": "ea91a4... | closed | false | null | [] | null | 4 | 2019-11-17T04:15:11Z | 2019-11-21T19:59:09Z | 2019-11-18T00:32:28Z | MEMBER | null | mypy does _not_ play nicely with DeepChainMap. @WillAyd @simonjayhawkins any ideas about the 4 places where i had to put `# type: ignore`?
Made `_update` private just to make it easier to reason about this thing | {
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... | open | false | null | [] | null | 0 | 2019-11-17T08:58:17Z | 2020-05-08T00:41:39Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
naValues = ''
testdf = pd.read_excel('read_excel_bug.xlsx',
sheet_name=0,header=0, names=None, index_col=None,
usecols=None, squeeze=False, dtype=None, engine=None,
converters=None, true_values=None, false_values=None,
skiprows=None, nrows=None,
na_values=naValues, keep_default_na=False, verbose=False,
parse_dates=False, date_parser=None, thousands=None, comment=None,
skip_footer=0, skipfooter=0, convert_float=True, mangle_dupe_cols=True)
testdf
```
#### Problem description
I set the na_values param to None, and make keep_default_na=False, according to the doc, after execution cells of the “数据” column should be #DIV/0!, but in fact they are all NaN.
index |机构 |全称 |社会信用代码 |账号| 数据
-----|-----|---------|----------------|--------|------
0 |机构1| 机构1的全称| 9.144030e+17 |alading |NaN
1 |NaN |NaN |NaN |NaN |NaN
#### Expected Output
index |机构 |全称 |社会信用代码 |账号| 数据
-----|-----|---------|----------------|--------|------
0 |机构1| 机构1的全称| 9.144030e+17 |alading |#DIV/0!
1 |NaN |NaN |NaN |NaN |#DIV/0!
[read_excel_bug.xlsx](https://github.com/pandas-dev/pandas/files/3855400/read_excel_bug.xlsx)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.2
numpy : 1.14.2
pytz : 2017.3
dateutil : 2.6.1
pip : 19.0.3
setuptools : 38.4.0
Cython : 0.27.3
pytest : 3.3.2
hypothesis : None
sphinx : 1.6.6
blosc : None
feather : None
xlsxwriter : 1.0.2
lxml.etree : 4.1.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.2.1
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.1.1
matplotlib : 2.1.2
numexpr : 2.6.4
odfpy : None
openpyxl : 2.4.10
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.0.0
sqlalchemy : 1.2.1
tables : 3.4.2
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.2
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | closed | false | null | [] | null | 13 | 2019-11-17T12:51:05Z | 2020-02-12T00:34:23Z | 2020-02-12T00:34:22Z | MEMBER | null | - [x] closes #24980 , xref #28538
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I still see people posting issue for the change of behaviour in `to_string(index=False)` on pandas such as #28538 , so I think it might be worth it to reopen my previous stalled PR #25000 and see if could get it solved this time.
All the changes have been made based on the reviews in PR #25000 and for detailed summary and explanation, please check https://github.com/pandas-dev/pandas/pull/25000#issuecomment-458714951
Feel free to take a look and any review and comments are very welcomed! | {
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"d... | open | false | null | [] | null | 17 | 2019-11-17T15:02:55Z | 2021-07-23T04:38:30Z | null | MEMBER | null | In #28531 we started to parallelize tests in the CI by file. Meaning that each file in `pandas/tests/` will be assigned to a core, and will run fully there. Before, each individual test was assigned to a core. This makes things much simpler, since tests that cannot run in parallel just need to live in the same file. But it implies, that if a test file is big and contains many tests, it will slow down the running of all tests.
As an example, imagine we have 1,000 tests that take one second each, and we have 100 cores. 10 tests will be assigned to each core, and the test suite will run in 10 seconds using the 100 cores. But with the new set up, if we have a file with 100 tests, all them will run in a single core, and the test suite will take the 100 seconds that the core will need to run the 100 tests.
In practice, after #28531 most of the builds in the CI didn't increases much the time to run. Only the [coverage build](https://github.com/pandas-dev/pandas/blob/master/.travis.yml#L49) is in some cases taking significantly more (from around 30 minutes to around 45 minutes).
The solution to this is:
- See how long each test file takes
- Split the files taking longer in a meaningful way
Note that tests can be skipped if dependencies are not found. For example, if matplotlib is not installed in the environment, the plotting tests will simply be skipped. So, it's needed to create an environment with the dependencies of the build `ci/deps/travis-36-cov.yaml`.
To call the exact tests as the build, see the call in [run_tests.sh](https://github.com/pandas-dev/pandas/blob/master/ci/run_tests.sh#L28), and replace the parameters by the ones defined in the [build](https://github.com/pandas-dev/pandas/blob/master/.travis.yml#L49). | {
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} | 1 | 2019-11-17T16:40:28Z | 2019-11-19T15:20:55Z | 2019-11-19T13:30:18Z | MEMBER | null | Implement SeriesGroupBy._transform_general to match DataFrameGroupBy._transform_general, re-arrange the checks within the two `transform` methods to be in the same order and be more linear. Make the two _transform_fast methods have closer-to-matching signatures | {
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} | 2 | 2019-11-17T16:59:06Z | 2019-12-25T20:24:59Z | 2019-11-17T22:48:18Z | CONTRIBUTOR | null | This will pin black version in ci and for local dev.
(This will avoid code checks failing when a new black version is released)
As per comment from @jreback here https://github.com/pandas-dev/pandas/pull/29508#discussion_r346316578 | {
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} | 1 | 2019-11-17T17:59:27Z | 2019-12-25T20:35:11Z | 2019-11-19T04:06:58Z | CONTRIBUTOR | null | - Towards #26344
- This makes `windows.yml` and `posix.yml` steps pretty similar and they could potentially be merged/reduce duplication? (Will leave this for a separate PR)
- We also now use `ci/run_tests.sh` for windows test stage.
Reference https://github.com/pandas-dev/pandas/pull/27195 where I began this. | {
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} | 0 | 2019-11-17T18:10:47Z | 2019-11-18T06:48:39Z | 2019-11-17T20:55:54Z | CONTRIBUTOR | null | Add a type hint in class ``_Groupby`` for ``BaseGrouper`` to aid navigating the code base. | {
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} | 3 | 2019-11-17T18:12:01Z | 2020-01-15T13:40:35Z | 2020-01-15T13:40:35Z | CONTRIBUTOR | null | ```
[gw1] linux -- Python 3.6.6 /home/travis/miniconda3/envs/pandas-dev/bin/python
data = '👍...'
@pytest.mark.single
@pytest.mark.clipboard
@pytest.mark.skipif(not _DEPS_INSTALLED, reason="clipboard primitives not installed")
@pytest.mark.parametrize("data", ["\U0001f44d...", "Ωœ∑´...", "abcd..."])
def test_raw_roundtrip(data):
# PR #25040 wide unicode wasn't copied correctly on PY3 on windows
clipboard_set(data)
> assert data == clipboard_get()
E AssertionError: assert '👍...' == ''
E - 👍...
pandas/tests/io/test_clipboard.py:264: AssertionError
```
Examples:
https://travis-ci.org/pandas-dev/pandas/jobs/613141276?utm_medium=notification&utm_source=github_status
https://travis-ci.org/pandas-dev/pandas/builds/613153224?utm_source=github_status&utm_medium=notification
Potentially related change:
https://github.com/pandas-dev/pandas/pull/28531 | {
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} | 1 | 2019-11-17T19:49:22Z | 2019-11-18T00:46:42Z | 2019-11-18T00:20:37Z | MEMBER | null | Breaks off easier parts | {
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} | 10 | 2019-11-17T20:19:47Z | 2019-11-21T13:07:04Z | 2019-11-21T13:06:07Z | MEMBER | null | - [X] closes #29664
In our CI builds we're using different versions of pytest, pytest-xdist and other tools. Looks like Python 3.5 was forcing part of this, since some packages were not available.
In this PR I standardize the tools and versions we use in all packages, with only two exceptions:
- In 32 bits we install `Cython` from pip instead of conda, since conda doesn't have the version we want
- `pytest-azurepipelines` is installed only in builds currently running in pipelines
I also reorganize a bit the builds so dependencies are easier to find. First is always Python, then the block with the tools, which is always the same with the exceptions mentioned above, and finally the dependencies. | {
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} | 1 | 2019-11-17T21:10:33Z | 2019-11-17T22:48:55Z | 2019-11-17T22:47:52Z | MEMBER | null | Follow up to #29607 where `exclude` was added to `pyproject.toml`, so it no longer needs to be explicitly specified. Missed this reference; checked for additional missed references but didn't find any. | {
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} | 1 | 2019-11-17T21:32:41Z | 2019-11-18T04:55:50Z | 2019-11-17T23:06:35Z | CONTRIBUTOR | null | …dtype, not bool
Added dtype=bool argument to make _noarg_wrapper() return a bool Series
- [x] closes #29624
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 2 | 2019-11-17T22:08:29Z | 2019-11-17T23:15:28Z | 2019-11-17T23:01:50Z | MEMBER | null | By avoiding that `IndexError`, we can get rid of an `except Exception` in `io.pytables` | {
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} | 1 | 2019-11-17T23:42:21Z | 2019-11-18T01:28:47Z | 2019-11-18T00:34:45Z | MEMBER | null | This file is pretty tough, so for now this just goes through and adds `str` annotations in places where it is unambiguous (e.g. the variable is passed to `getattr`) and places that can be directly reasoned about from there. | {
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} | 3 | 2019-11-18T02:50:06Z | 2019-11-18T04:41:16Z | 2019-11-18T04:41:16Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/29546#issuecomment-554807243
Looks like the code in one of the checks in `ci/code_checks.sh` uses another encoding for spaces and other characters. I guess it was generated from Windows, Azure-pipelines is not sensitive to the different encoding, but GitHub actions is.
Probably worth doing some more research and adding a check that makes sure this new encoding is not introduced anymore.
CC: @gfyoung @jreback | {
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} | 3 | 2019-11-18T12:44:53Z | 2020-01-14T12:52:26Z | 2020-01-14T12:52:26Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
ipdb> value = np.array([np.datetime64(1, 'ms')], dtype="<M8[ms]")
ipdb> pd.Series(value)
0 1970-01-01 00:00:00.001
dtype: datetime64[ns]
ipdb> value = np.array([np.datetime64(1, 'ms')], dtype=">M8[ms]")
ipdb> pd.Series(value)
*** pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2285384-04-02 23:52:07
```
#### Problem description
Seems like something weird is going on when converting the big-endian timestamp, which does not happen for the little-endian one.
#### Expected Output
The same as for the little-endian data type.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
ipdb> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.5.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191101
Cython : None
pytest : 5.0.1
hypothesis : 4.44.2
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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"d... | open | false | null | [] | null | 6 | 2019-11-18T13:04:48Z | 2019-12-31T16:25:11Z | null | MEMBER | null | Not sure if anyone know exactly what we are testing, but after removing Python 3.5 and some other old stuff, I think we've got lots of builds testing almost the same things, and the lack of a clear structure is preventing us from finding inconsistencies easily.
Some things that seem wrong:
- I don't think we're running tests with `network` marker in any build (we run `not slow and not network` and `slow`)
- We just run the `slow` in allowed failures (not sure if that's intentional)
- `py36_locale_slow` is not actually running slow tests: https://github.com/pandas-dev/pandas/blob/master/ci/azure/posix.yml#L32
- We only test in Mac with 2 year old versions of numpy and dateutil (never with recent ones)
- In windows we also test only with old numpy versions 1.14 and 1.15, never recent ones
To fix those, what I think it'd make more sense is:
- Remove from the dependencies files the CI system (travis, azure) and the OS (windows...), I don't think those add value
- Get a dependencies file with all the dependencies without specifying versions (conda should give the latest) and test that on Linux, Windows and Mac
- Get a dependencies file with the minimum supported versions (similar to `azure-36-minimum_versions.yaml`, but including all dependencies, the current just got a small set). Test it in Linux (possibly in Windows and Mac if important, but I don't think so)
- Use the same dependencies files to test the slow and network tests
- For the locales, use couple of the previous builds
- For everything that we need to test that is not covered in the previous builds, create a dependencies file explaining exactly what we want to test with that (e.g. 32 bits, numpydev)
Not sure I'm missing something, but I think this should make things much simpler, test more things, and we can even save couple of builds (we have 12 now)
CC: @jreback @TomAugspurger
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} | 11 | 2019-11-18T14:23:11Z | 2019-12-12T13:24:24Z | 2019-12-12T13:24:24Z | MEMBER | null | In the new missing values support, and especially while implementing the BooleanArray (https://github.com/pandas-dev/pandas/pull/29555/), the question comes up: what should `any` and `all` do in presence of missing values?
*edit from Tom: Here's a proposed table of behavior*
| case | input | output |
| ----- | -------------------------------- | -------|
| 1. | `all([True, NA], skipna=False)` | NA |
| 2. | `all([False, NA], skipna=False)` | False |
| 3. | `all([NA], skipna=False)` | NA |
| 4. | `all([], skipna=False)` | True |
| 5. | `any([True, NA], skipna=False)` | True |
| 6. | `any([False, NA], skipna=False)` | NA |
| 7. | `any([NA], skipna=False)` | NA |
| 8. | `any([], skipna=False)` | False |
| case | input | output |
| ----- | ------------------------------- | -------|
| 9. | `all([True, NA], skipna=True)` | True |
| 10. | `all([False, NA], skipna=True)` | False |
| 11. | `all([NA], skipna=True)` | True |
| 12. | `all([], skipna=True)` | True |
| 13. | `any([True, NA], skipna=True)` | True |
| 14. | `any([False, NA], skipna=True)` | False |
| 15. | `any([NA], skipna=True)` | False |
| 16. | `any([], skipna=True)` | False |
Some context:
Currently, if having bools with NaNs, you end up with a object dtype, and the behaviour of `any`/`all` with object dtype has all kinds of corner cases. @xhochy recently opened https://github.com/pandas-dev/pandas/issues/27709 for this (but opening a new issue since want to focus here the behaviour in boolean dtype, the behaviour in object dtype might still deviate)
The documentation of `any` says (https://dev.pandas.io/docs/reference/api/pandas.Series.any.html)
> Return whether any element is True, potentially over an axis.
>
>Returns False unless there at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).
>
> ...
>
> skipna : bool, default True
> Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero.
and similar for `all` (https://dev.pandas.io/docs/reference/api/pandas.Series.all.html).
### Default behaviour with `skipna=True`
in case of some NA's and some True/False values, I think the behaviour is clear: `any`/`all` are reductions, and in pandas we use `skipna=True` for reductions.
So you get something like this:
(I am still using `np.nan` here as missing value, since the pd.NA PR is not yet merged / combined with the BooleanArray PR; but let's focus on return value)
```
In [2]: pd.Series([True, False, np.nan]).any()
Out[2]: True
In [3]: pd.Series([True, False, np.nan]).all()
Out[3]: False
In [4]: pd.Series([True, True, np.nan]).all()
Out[4]: True
```
(although when interpreting NA as "unknown", it might look a bit strange to return True in the last case since the NA might still be True or False)
### Behaviour for all-NA in case of `skipna=True`
This is a case that is described in the current docs: "If the entire row/column is NA and skipna is True, then the result will be False, as for an empty row/column", and is indeed consistent with skipping all NAs -> any/all of empty set.
And then, we follow numpy's behaviour (False for `any`, True for `all`):
```
In [8]: np.array([], dtype=bool).any()
Out[8]: False
In [9]: np.array([], dtype=bool).all()
Out[9]: True
```
(although I don't find this necessarily very intuitive, this seems more a consequence of the algorithm starting with a base "identity" value of False/True for any/all)
### Behaviour with `skipna=False`
Here comes the more tricky part. Currently, with object dtype, we have some buggy behaviour (see https://github.com/pandas-dev/pandas/issues/27709), and it depends on the order of the values and which missing value (np.nan or None) is used.
With BooleanArray we won't have this problem (there is only a single NA + we don't need to rely on numpy's buggy object dtype behaviour). But I am not sure we should follow what is currently in the docs:
> If skipna is False, then NA are treated as True, because these are not equal to zero.
This follows from numpy's behaviour with floats:
```
In [10]: np.array([0, np.nan]).any()
Out[10]: True
```
and while this might make sense in float context, I am not sure we should follow this behaviour and our docs and do:
```
>>> pd.Series([False, pd.NA], dtype="boolean").any()
True
```
I think this should rather give False or NA instead of True.
While for object dtype it might make sense to align the behaviour with float (as argued in https://github.com/pandas-dev/pandas/issues/27709#issuecomment-517703540), for a boolean dtype we can probably use the behaviour we defined for NA in logical operations (eg `False | NA = NA`, so in that case, the above should give NA).
But are we ok with `any`/`all` not returning a boolean in this case? (note, you only have this if someone specifically set `skipna=False`)
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} | 7 | 2019-11-18T14:46:49Z | 2021-05-31T14:27:04Z | 2021-05-31T14:27:04Z | NONE | null | ```
In [12]: pd.set_option('mode.use_inf_as_na',False)
In [13]: df
Out[13]:
B
2018-09-25 14:47:31.500 0.0
2018-04-03 22:55:13.000 1.0
2018-09-04 09:10:07.500 2.0
2018-11-30 10:32:08.000 NaN
2018-03-27 14:21:12.500 4.0
2018-09-28 14:30:36.500 6.0
2018-07-16 21:56:48.000 7.0
2018-12-05 23:53:59.500 8.0
2018-09-04 09:46:58.500 9.0
2018-11-09 09:14:08.500 10.0
In [14]: df.sort_index()
Out[14]:
B
2018-03-27 14:21:12.500 4.0
2018-04-03 22:55:13.000 1.0
2018-07-16 21:56:48.000 7.0
2018-09-04 09:10:07.500 2.0
2018-09-04 09:46:58.500 9.0
2018-09-25 14:47:31.500 0.0
2018-09-28 14:30:36.500 6.0
2018-11-09 09:14:08.500 10.0
2018-11-30 10:32:08.000 NaN
2018-12-05 23:53:59.500 8.0
In [15]: pd.set_option('mode.use_inf_as_na',True)
In [16]: df.sort_index()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-16-02a473b4132d> in <module>
----> 1 df.sort_index()
g:\py372\lib\site-packages\pandas\core\frame.py in sort_index(self, axis, lev
ascending, inplace, kind, na_position, sort_remaining, by)
5088
5089 indexer = nargsort(
-> 5090 labels, kind=kind, ascending=ascending, na_position=n
osition
5091 )
5092
g:\py372\lib\site-packages\pandas\core\sorting.py in nargsort(items, kind, as
ding, na_position)
244
245 items = extract_array(items)
--> 246 mask = np.asarray(isna(items))
247
248 if is_extension_array_dtype(items):
g:\py372\lib\site-packages\pandas\core\dtypes\missing.py in isna(obj)
120 Name: 1, dtype: bool
121 """
--> 122 return _isna(obj)
123
124
g:\py372\lib\site-packages\pandas\core\dtypes\missing.py in _isna_old(obj)
177 return _isna_ndarraylike_old(obj)
178 elif isinstance(obj, ABCGeneric):
--> 179 return obj._constructor(obj._data.isna(func=_isna_old))
180 elif isinstance(obj, list):
181 return _isna_ndarraylike_old(np.asarray(obj, dtype=object))
AttributeError: 'DatetimeArray' object has no attribute '_constructor'
``` | {
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... | open | false | null | [] | null | 2 | 2019-11-18T14:47:25Z | 2021-07-23T04:41:15Z | null | NONE | null | `df.plot(subplots=True)` will create one subplot per column. Is there a way to group multiple columns on the same subplot (and leave the rest of the column separated)?
I'd be happy to submit a PR if that's something you'd consider? In terms of API `subplot` could accept a list of tuples where each tuple indicates which columns should be grouped together. | {
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... | open | false | null | [] | null | 2 | 2019-11-18T16:01:13Z | 2021-07-23T04:42:06Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
df = pd.read_parquet(
'https://github.com/WillKoehrsen/Data-Analysis/blob/master/plotly/data/medium_data_2019_01_06?raw=true'
)
```
#### Problem description
Sorry I am newbie in pandas, and before today newer knows about *parquet*.
I was just reading an article about *plotly* and try to execute some code (look above).
I’ve received the error message. But when I’ve read the [DOCs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html?highlight=read_parquet#pandas.read_parquet) there was no any clear word about necessary previously installed library or modules like ‘pyarrow’, ‘fastparquet’.
I propose
1) mark with some symbol [here](https://pandas.pydata.org/pandas-docs/stable/reference/io.html#pickling) the functions with needs in previous installed modules or drivers. And some text about it.
Yeah, I know about preinstalled json, pickle, csv. But what about excell? hdf5? html? who knows?
2) Do not use default value for source, really have to be previously installed.
pandas.read_parquet(path, ***engine='auto'***, columns=None, **kwargs)
#### Expected Output
here it's much better https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_sql_table.html#pandas.read_sql_table :
pandas.read_sql_table(table_name, ***con***, schema=None, index_col=None, coerce_float=True, parse_dates=None, columns=None, chunksize=None)
<details>
sorry for my English
</details> | {
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} | 3 | 2019-11-18T16:02:26Z | 2019-11-20T12:46:24Z | 2019-11-20T12:46:19Z | CONTRIBUTOR | null | - [x] closes #17605
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This is a simple, low-impact fix for #17605. | {
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} | 3 | 2019-11-18T16:11:51Z | 2021-05-27T19:07:24Z | 2019-11-18T17:08:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'ind':[np.nan,1],'col':[0,1]}).groupby('ind').sum()
df
```
results in
```
col
ind
1.0 1
```
#### Problem description
Groupby ignores groups that contain a np.nan in any group-defining column.
I'd understand (well, not really, but it would be slightly more sane) if groups were ignored that had np.nans in grouped columns, but it doesn't make any sense to me to ignore groups that have np.nans in group-defining columns.
Until this is fixed properly, does anyone have a quick and dirty fix for me?
#### Expected Output
```
col
ind
NaN 0
1.0 1
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor:
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 41.0.1
Cython: 0.28.4
numpy: 1.15.4
scipy: 1.2.1
pyarrow: 0.14.0
xarray: None
IPython: 7.5.0
sphinx: 1.7.5
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.3.2
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 1 | 2019-11-18T16:12:06Z | 2019-11-20T17:47:34Z | 2019-11-20T17:15:59Z | MEMBER | null | Once this is assured, there are a lot of other things (including in core.computation!) that we can infer in follow-ups. | {
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} | 2 | 2019-11-18T17:41:11Z | 2020-01-16T00:35:17Z | 2019-11-19T13:26:59Z | MEMBER | null | ref #29666 and comments from @jbrockmendel and @gfyoung looks like the compat header can now be removed, now that we are on 3.6.1 as a minimum | {
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} | 4 | 2019-11-18T17:45:11Z | 2019-11-19T15:07:06Z | 2019-11-19T15:07:02Z | CONTRIBUTOR | null | - [x] closes #29544
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Split test cases in pandas/tests/frame/test_indexing into a sub directory (like pandas/tests/indexing). Still main file is pretty huge. Will update this PR with after suggestions and will fix style in a separate commit. | {
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} | 3 | 2019-11-18T19:01:52Z | 2019-11-19T14:48:51Z | 2019-11-19T13:19:16Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/29001 | {
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} | 4 | 2019-11-18T21:55:51Z | 2020-04-26T20:24:17Z | 2020-04-26T20:24:17Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
On `master` the following raises for `how='outer'` and `how='right'` with duplicate `suffixes`:
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+958.g545d17529'
In [2]: df1 = pd.DataFrame({'A': list('ab'), 'B': [0, 1]})
In [3]: df2 = pd.DataFrame({'A':list('ac'), 'B': [100, 200]})
In [4]: pd.merge(df1, df2, on="A", how="outer", suffixes=("_x", "_x"))
---------------------------------------------------------------------------
ValueError: Buffer has wrong number of dimensions (expected 1, got 0)
In [5]: pd.merge(df1, df2, on="A", how="right", suffixes=("_x", "_x"))
---------------------------------------------------------------------------
ValueError: Buffer has wrong number of dimensions (expected 1, got 0)
```
Note that above works with `how='inner'` and `how='left'`:
```python
In [6]: pd.merge(df1, df2, on="A", how="inner", suffixes=("_x", "_x"))
Out[6]:
A B_x B_x
0 a 0 100
In [7]: pd.merge(df1, df2, on="A", how="left", suffixes=("_x", "_x"))
Out[7]:
A B_x B_x
0 a 0 100.0
1 b 1 NaN
```
Likewise, if unique `suffixes` are specified then `how='outer'` and `how='right'` work fine:
```python
In [8]: pd.merge(df1, df2, on="A", how="outer", suffixes=("_x", "_y"))
Out[8]:
A B_x B_y
0 a 0.0 100.0
1 b 1.0 NaN
2 c NaN 200.0
In [9]: pd.merge(df1, df2, on="A", how="right", suffixes=("_x", "_y"))
Out[9]:
A B_x B_y
0 a 0.0 100
1 c NaN 200
```
#### Problem description
`pandas.merge` raises for `how='outer'` and `how='right'` with duplicate `suffixes`.
#### Expected Output
I'd expect `In [4]` and `In [5]` not to raise and produce output similar to `Out[8]` and `Out[9]` but with the duplicate suffix names.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 545d1752987f8e325f5ad3d94f0143c453de28cc
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.26.0.dev0+958.g545d17529
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 8 | 2019-11-18T23:05:21Z | 2019-11-21T16:02:06Z | 2019-11-21T13:07:58Z | MEMBER | null | Also add a whatsnew note for #29641 and a comment in core.apply that ive been meaning to get in | {
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} | 3 | 2019-11-18T23:30:48Z | 2021-05-31T14:27:04Z | 2021-05-31T14:27:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.show_versions()
# Create Multi-Index
df = pd.DataFrame({'col1':[1,2,3], 'col2':[11,12,13]})
df = pd.concat([df], keys=['multi'], names=['level1'], axis=1)
for i in range(100):
df[i, 'colA'] = 10
a = pd.DataFrame({'col1':[1,2,3], 'col2':[11,12,13]})
a = pd.concat([a], keys=['multi'], names=['level1'], axis=1)
df = df.append(a, ignore_index=True)
print(df['multi'])
```
#### Problem description
Raises an exception "KeyError: 'multi' ", although this should perfectly work, as the key "Multi" exists, e.g when printing df.columns.
The most strange thing is:
- The step in which this exception is raised depends on the Name of the key "Multi".. Sometimes 2nd loop, sometimes 6th,...
- During the first loop it works perfectly and prints the df
However it has to do something with using "integer" and "string" column-names for level1 --- if i use an integer, e.g. -1 instead of "Multi", this seems to work.
#### Expected Output
It prints 100 times the dataframe
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 39.0.1
Cython : None
pytest : 5.2.2
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 9 | 2019-11-18T23:54:09Z | 2019-11-25T23:49:23Z | 2019-11-25T23:45:28Z | MEMBER | null | - [x] closes #29189
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @WillAyd | {
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} | 6 | 2019-11-18T23:57:08Z | 2019-11-21T13:04:17Z | 2019-11-21T13:04:13Z | CONTRIBUTOR | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
ref #29547 | {
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} | 1 | 2019-11-19T01:33:24Z | 2019-11-19T15:19:50Z | 2019-11-19T13:20:14Z | MEMBER | null | Working towards ironing out the small differences between several places that do roughly the same extraction. Getting the pure-python versions will be appreciably harder, kept separate.
This avoids an unnecessary `np.isscalar` call. | {
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} | 1 | 2019-11-19T02:12:24Z | 2019-11-20T15:16:57Z | 2019-11-20T13:12:12Z | MEMBER | null | should be orthogonal to #29692. | {
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} | 4 | 2019-11-19T06:16:01Z | 2020-01-16T00:35:18Z | 2019-11-20T12:58:14Z | MEMBER | null | Just giving these a look seem to be a lot of unused definitions / functions | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29705 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29705/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29705/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29705/events | https://github.com/pandas-dev/pandas/issues/29705 | 524,830,092 | MDU6SXNzdWU1MjQ4MzAwOTI= | 29,705 | Misaligned X axis when plotting datetime indexed series with regular and irregular time index | {
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... | open | false | null | [] | null | 4 | 2019-11-19T07:39:01Z | 2021-07-23T04:42:43Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Create sample data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
p1d = {'2016-08-10 10:00:00': 2.290438,
'2016-10-12 08:20:00': 1.314112,
'2016-11-15 12:45:00': 0.213702,
'2017-04-27 18:30:00': 0.256794,
'2017-05-30 11:10:00': 4.112614,
'2017-07-19 09:18:00': 10.600000}
p1 = pd.Series(p1d)
p1.index=pd.to_datetime(p1.index)
p2d = {'2016-08-09 09:15:00': 1.57970,
'2016-10-11 13:15:00': 0.73000,
'2017-04-27 12:30:00': 0.15900,
'2017-05-31 16:10:00': 1.65440,
'2018-05-24 12:00:00': 0.79260,
'2018-10-25 11:20:00': 0.34500}
p2 = pd.Series(p2d)
p2.index=pd.to_datetime(p2.index)
p3d = {'2016-11-15 09:00:00': 0.094900,
'2017-04-28 11:10:00': 0.055600,
'2017-05-30 16:00:00': 0.659600,
'2017-06-09 17:15:00': 0.300200,
'2018-05-24 16:45:00': 0.329800,
'2018-09-18 15:40:00': 0.200452}
p3 = pd.Series(p3d)
p3.index = pd.to_datetime(p3.index)
ts_index = pd.date_range('2016-01-01','2018-12-31',freq='H')
ts1 = pd.Series(index=ts_index, data=np.random.uniform(low=0.2,high=10,
size=ts_index.size))
ts2 = pd.Series(index=ts_index, data=np.random.uniform(low=0.1,high=3,
size=ts_index.size))
ts3 = pd.Series(index=ts_index, data=np.random.uniform(low=0.05,high=1,
size=ts_index.size))
# plot
fig_ts, axs_ts = plt.subplots(3,1,sharex=False)
ts1.plot(ax=axs_ts[0])
p1.plot(ax=axs_ts[0],style='o')
ts2.plot(ax=axs_ts[1])
p2.plot(ax=axs_ts[1],style='o')
ts3.plot(ax=axs_ts[2])
p3.plot(ax=axs_ts[2],style='o')
fig_ts.tight_layout()
```
#### Problem description
Data on the second axis is not plotted correctly on the X axis. The irregular point data is shifted relative to the other data. It seems this is caused by the uncommon timestamp between the first entry of the irregular timeseries and the regular timeseries.
The following changes causes the data to be plotted correctly, I assume due to having matching timestamps of the first entries:
- Changing the first datetime p2 to full hour (e.g. '2016-08-09 09:15:00' to '2016-08-09 09:00:00'
- Changing frequency of ts2 to '15T', 15 minutes
It does not matter if later data points in the irregular series have timestamps that are not common with the regular series
Changing the plot order for the second axis also causes the data to be plotted on the correct place along the X axis, so plotting the irregular timeseries before the regular:
`p2.plot(ax=axs_ts[1],style='o', zorder=10)`
`ts2.plot(ax=axs_ts[1], zorder=1)`
This does however cause other issues such as different X axis labels, and using this method it will also fail if using sharex=True.
Possibly related issues:
#11574 - Misaligned x axis using shared axis (one series plotted per axis), not when plotted on the same axis as here.
#18571 - Misaligned x axis using twinx(), possibly same issue as here?
#### Expected Output
Plotting the data at the correct x axis coordinates.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.4
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29706 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29706/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29706/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29706/events | https://github.com/pandas-dev/pandas/issues/29706 | 524,864,263 | MDU6SXNzdWU1MjQ4NjQyNjM= | 29,706 | series.to_json + isoformat: bad serialization of naive dates (as utc stamps) | {
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} | 3 | 2019-11-19T08:57:09Z | 2021-07-23T04:44:16Z | null | NONE | null | #### Exhibited there
```python
from datetime import datetime
import pandas as pd
def test_json():
series = pd.Series(
[1., 2., 3.],
index=pd.date_range(datetime(2020, 1, 1), freq='H', periods=3)
)
jsonseries = series.to_json(date_format='iso')
assert jsonseries == (
'{"2020-01-01T00:00:00.000Z":1.0,'
'"2020-01-01T01:00:00.000Z":2.0,'
'"2020-01-01T02:00:00.000Z":3.0}'
)
series2 = pd.read_json(jsonseries, typ='series', dtype=False)
if pd.__version__.startswith('0.24'):
assert not getattr(series2.index.dtype, 'tz', False)
assert series.equals(series2)
elif pd.__version__.startswith('0.25'):
assert series2.index.dtype.tz.zone == 'UTC'
assert not series.equals(series2)
```
#### Problem description
In pandas 0.25, " Bug in read_json() where date strings with Z were not converted to a UTC timezone (GH26168) " made me realize an issue with series timestamp iso serialization.
Naive dates should not be converted within an UTC referential.
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https://api.github.com/repos/pandas-dev/pandas/issues/29707 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29707/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29707/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29707/events | https://github.com/pandas-dev/pandas/issues/29707 | 524,880,507 | MDU6SXNzdWU1MjQ4ODA1MDc= | 29,707 | .loc assignment with empty label list should not convert dtypes | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 3 | 2019-11-19T09:27:04Z | 2021-07-23T04:44:02Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'a':[2,3]})
print(df.a.dtype) # int64
df.loc[[]] = 0.1
print(df.a.dtype) # float64!
```
#### Problem description
The `.loc`-Assignment with empty label list does not change any dataframe row, so it should not convert the column datatype from integer to float.
Seen with the current pandas version 0.25.3.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-62-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_DK.utf8
LOCALE : en_DK.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29708/events | https://github.com/pandas-dev/pandas/issues/29708 | 524,983,319 | MDU6SXNzdWU1MjQ5ODMzMTk= | 29,708 | Indexing (__getitem__) of DataFrame/Series with ExtensionArray densifies the array | {
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} | 0 | 2019-11-19T12:37:31Z | 2019-12-04T14:15:14Z | 2019-12-04T14:15:14Z | MEMBER | null | I noticed in GeoPandas that filtering a series with a mask "densifies" the ExtensionArray (= converting the ExtesionArray to a materialized numpy array), which can potentially be very expensive (if the ExtensionArray doesn't store a numpy array of scalars under the hood).
This is quite problematic for such a basic operation (and problematic for the next version of GeoPandas, we will probably have to override `__getitem__` for this).
Example with pandas itself to see this. I used this small edit to check what call converts to a numpy array:
```patch
--- a/pandas/tests/extension/decimal/array.py
+++ b/pandas/tests/extension/decimal/array.py
@@ -81,6 +81,13 @@ class DecimalArray(ExtensionArray, ExtensionScalarOpsMixin):
def _from_factorized(cls, values, original):
return cls(values)
+ def __array__(self, dtype=None):
+ print("__array__ being called from:")
+ import inspect
+ frames = inspect.getouterframes(inspect.currentframe())
+ for frame in frames[:7]:
+ print(" {0} from {1}".format(frame.function, frame.filename))
+ return self._data
+
```
and then you see this:
```
In [1]: from pandas.tests.extension.decimal import DecimalArray, make_data
In [2]: a = DecimalArray(make_data())
In [3]: s = pd.Series(a)
In [4]: mask = s > 0.5
In [5]: subset = s[mask]
__array__ being called from:
__array__ from /home/joris/scipy/pandas/pandas/tests/extension/decimal/array.py
asarray from /home/joris/miniconda3/envs/dev/lib/python3.7/site-packages/numpy/core/_asarray.py
to_dense from /home/joris/scipy/pandas/pandas/core/internals/blocks.py
get_values from /home/joris/scipy/pandas/pandas/core/internals/managers.py
_internal_get_values from /home/joris/scipy/pandas/pandas/core/series.py
get_value from /home/joris/scipy/pandas/pandas/core/indexes/base.py
__getitem__ from /home/joris/scipy/pandas/pandas/core/series.py
```
(`s.loc[mask]` does not have the problem)
So this comes from the fact that we first try `index.get_value(..)` in `__getitem__` before doing anything else:
https://github.com/pandas-dev/pandas/blob/d134b476da04a9e0427cc2bea0694fefc3b054e4/pandas/core/series.py#L1075-L1079
And inside `Index.get_value`, this is calling `values_from_object`:
https://github.com/pandas-dev/pandas/blob/d134b476da04a9e0427cc2bea0694fefc3b054e4/pandas/core/indexes/base.py#L4620-L4621
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"description": "Related t... | open | false | null | [] | null | 1 | 2019-11-19T12:54:29Z | 2021-08-12T21:57:36Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
index = pd.date_range("2019-01-01", "2019-01-02", freq="D")
df = pd.DataFrame(index=index, columns=["A"]).fillna(1)
>>> df.at[pd.to_datetime('2019-01-01'), 'A'] = 0.98
>>> df
A
2019-01-01 0 # cast to column dtype
2019-01-02 1
>>> df['A'].dtype
dtype('int64')
>>> df.at['2019-01-01', 'A'] = 0.98
>>> df
A
2019-01-01 0.98 # column cast to value dtype
2019-01-02 1.00
>>> df['A'].dtype
dtype('float64')
```
#### Problem description
This issue arises when dealing with a Dataframe with a datetime index. When using `DataFrame.at[]` and indexing using a date string, the column dtype is promoted to the type of the provided value. However when using `DataFrame.at[]` and indexing using a datetime, the provided value is instead cast to the column's dtype. In the example, `0.98` is in this case indeed cast to 0.
#### Expected Output
That indexing `DataFrame.at[]` with either a date string or a datetime works the same.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 18.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.16.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 4.3.0
hypothesis : 4.7.11
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29710 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29710/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29710/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29710/events | https://github.com/pandas-dev/pandas/issues/29710 | 525,056,399 | MDU6SXNzdWU1MjUwNTYzOTk= | 29,710 | DEPR: Deprecate `.str` accessor on object-dtype. | {
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"defaul... | open | false | null | [] | null | 7 | 2019-11-19T14:48:19Z | 2021-07-23T04:51:29Z | null | CONTRIBUTOR | null | This issue discusses a future deprecation of the `.str` accessor on StringArray.
It's probably blocked by making `Series(['a', 'b', 'c'])` infer StringDtype, rather
than object dtype.
In https://github.com/pandas-dev/pandas/pull/29640, we split the implementation
of `.str` methods into two: one for the old `object`-dtype arrays and one for StringArray.
The StringArray implementation is much nicer since we know the result dtype statically
for most methods. We don't need to worry about the presence of NAs changing int to
floats or bool to object.
Given that it's nicer for the user (faster, more predictable) and it's nicer for us
(clean up old code), we should deprecate the `.str` accessor on object dtype.
There are a few complications. Certain `.str` methods aren't actually methods
called on scalar strings.
1. `.str.join` *can* work with Series where each row is a list of strings.
2. `.str.decode` works on *bytes*, not strings. It makes no sense on a StringArray (but
we can pretty easily implement a BytesArray)
3. `.str.get` is extremely flexible / complicated. It works on anything that
implements `__getitem__` or `.get`. So it's maybe useful for strings, but
also for nested Series storing lists / dicts.
For these, we may want separate accessors. Or we can keep the `.str` accessor
and deprecate every method except for those. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29711 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29711/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29711/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29711/events | https://github.com/pandas-dev/pandas/issues/29711 | 525,095,475 | MDU6SXNzdWU1MjUwOTU0NzU= | 29,711 | Dropna Subnet changes timestamp format in to_csv() | {
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... | open | false | null | [] | null | 4 | 2019-11-19T15:49:00Z | 2019-12-10T05:26:31Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import datetime
date_example_string = "1911180945"
ts = datetime.datetime.strptime(
date_example_string, "%y%m%d%H%M%S"
)
test_json = [
{
"created_at": "2019-11-18 16:28:42.932887",
"foo": "bar",
}
]
df = pd.DataFrame(test_json)
df["baz"] = ts
print ("=== before ===")
print (df.to_csv())
df["created_at"] = pd.to_datetime(
df["created_at"], infer_datetime_format=True, errors="coerce"
)
print("=== after ===")
print (df.to_csv())
print("=== dropna ===")
df = df.dropna(subset=["created_at"])
print(df.to_csv())
```
#### Problem description
When using pd.dropna, it changes the format of a datetime column to a different format when calling to_csv()
```
=== before ===
,created_at,foo,baz
0,2019-11-18 16:28:42.932887,bar,2019-11-18 09:04:05
=== after ===
,created_at,foo,baz
0,2019-11-18 16:28:42.932887,bar,2019-11-18 09:04:05
=== dropna ===
,created_at,foo,baz
0,2019-11-18 16:28:42.932887,bar,2019-11-18 09:04:05.000000
```
As you can see after calling dropna the format of the baz column is now 2019-11-18 09:04:05.000000
#### Expected Output
```
=== before ===
,created_at,foo,baz
0,2019-11-18 16:28:42.932887,bar,2019-11-18 09:04:05
=== after ===
,created_at,foo,baz
0,2019-11-18 16:28:42.932887,bar,2019-11-18 09:04:05
=== dropna ===
,created_at,foo,baz
0,2019-11-18 16:28:42.932887,bar,2019-11-18 09:04:05
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 20 | 2019-11-19T16:39:20Z | 2020-07-01T16:06:59Z | 2020-01-15T13:40:35Z | MEMBER | null | - [X] closes #29676
Fixes the clipboard problems in the CI. With this PR we're sure they are being run, and they work as expected. | {
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} | 2 | 2019-11-19T16:55:18Z | 2019-11-20T12:36:09Z | 2019-11-20T12:36:07Z | CONTRIBUTOR | null | This was leaking to stdout when the pytest `-s` flag was used. | {
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