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} | 3 | 2020-02-03T13:34:48Z | 2020-02-08T08:11:59Z | 2020-02-08T08:11:34Z | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
pd.DataFrame([[np.nan]], columns=["NA"]).to_json(orient="records")
# '[{"NA":null}]'
pd.DataFrame([[None]], columns=["NA"]).to_json(orient="records")
# '[{"NA":null}]'
pd.DataFrame([[pd.NA]], columns=["NA"]).to_json(orient="records")
# '[{"NA":{}}]'
```
#### Problem Description
`pd.NA` should behave as `np.nan` and `None` when converting a DataFrame to JSON.
#### Expected Output
In the resulting JSON the `pd.NA` instances should be converted to JSON's `null`.
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} | 11 | 2020-02-03T14:13:46Z | 2020-02-05T14:56:00Z | 2020-02-05T14:55:26Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/31522
cc @jbrockmendel. Just raising a `TypeError` when that assert failed didn't work. The `finally` still runs, which raised an assertion error.
It seemed easier to try to just support this case. IIUC, it only occurs when an `(P, n_rows)` input block gets split into `P` result blocks. I believe that
1. The result blocks should all have the same dtype
2. The input block must not have been an extension block, since it's 2d
So it *should* be safe to just cast the result values into an ndarray. Hopefully...
Are there any edge cases I'm not considering? Some kind of `agg` that returns a result that can't be put in a 2D block? Even something like `.agg(lambda x: pd.Period())`
won't hit this, since it has to be a Cython function. | {
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} | 1 | 2020-02-03T14:41:32Z | 2020-02-03T15:39:45Z | 2020-02-03T15:25:54Z | MEMBER | null | backport https://github.com/pandas-dev/pandas/pull/31521 | {
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} | 3 | 2020-02-03T15:02:57Z | 2020-06-02T21:04:15Z | 2020-06-02T21:04:15Z | MEMBER | null | In #28854, the note
> In the current implementation applymap calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row.
was removed from the `DataFrame.apply` docstring.
However, it is still in the [docstring](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.applymap.html?highlight=applymap#pandas.DataFrame.applymap) of `DataFrame.applymap`.
Does it need to be removed there too?
EDIT
----
As far as I can tell, this behaviour (calling the function twice to decide which path to take) still happens for `groupby.apply` and is what a user was asking about in #31111. Does it need to be documented in Groupby.Apply? Do end-users need to know about such internals? | {
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} | 1 | 2020-02-03T15:03:58Z | 2020-02-05T06:28:31Z | 2020-02-05T01:26:33Z | MEMBER | null | @jorisvandenbossche you wanted to put more tests in for this issue. I put the user reported code example into a test. Did you have anything else in mind? I didn't merge it with the above one since the above acted on an empty Dataframe.
- [x] closes #31605
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2020-02-03T15:07:27Z | 2020-02-03T19:39:29Z | 2020-02-03T19:39:23Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/31514 | {
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} | 8 | 2020-02-03T15:10:53Z | 2020-02-19T00:26:28Z | 2020-02-19T00:26:28Z | NONE | null | ```python
import pandas as pd
df = pd.DataFrame(
{
"foo": [pd.Timestamp("2019"), pd.Timestamp("2020")],
"bar": [pd.Timestamp("2018"), pd.Timestamp("2021")],
}
)
df2 = df[["foo"]]
print(df - df2)
```
#### Problem description
The above snippet raises the following exception:
```
Traceback (most recent call last):
File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 149, in na_arithmetic_op
result = expressions.evaluate(op, str_rep, left, right)
File ".v
env/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 208, in evaluate
return _evaluate(op, op_str, a, b)
File ".venv/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 70, in _evaluate_standard
return op(a, b)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/common.py", line 64, in new_method
return method(self, other)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 500, in wrapper
result = arithmetic_op(lvalues, rvalues, op, str_rep)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 192, in arithmetic_op
res_values = dispatch_to_extension_op(op, lvalues, rvalues)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/dispatch.py", line 125, in dispatch_to_extension_op
res_values = op(left, right)
File ".venv/lib/python3.6/site-packages/pandas/core/arrays/datetimelike.py", line 1390, in __rsub__
f"cannot subtract {type(self).__name__} from {type(other).__name__}"
TypeError: cannot subtract DatetimeArray from ndarray
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "pandas_bug.py", line 36, in <module>
print(df2 - df)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 703, in f
new_data = left._combine_frame(right, pass_op, fill_value)
File ".venv/lib/python3.6/site-packages/pandas/core/frame.py", line 5297, in _combine_frame
new_data = ops.dispatch_to_series(self, other, _arith_op)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 416, in dispatch_to_series
new_data = expressions.evaluate(column_op, str_rep, left, right)
File ".venv/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 208, in evaluate
return _evaluate(op, op_str, a, b)
File ".venv/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 70, in _evaluate_standard
return op(a, b)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 385, in column_op
return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 385, in <dictcomp>
return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 121, in na_op
return na_arithmetic_op(x, y, op, str_rep)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 151, in na_arithmetic_op
result = masked_arith_op(left, right, op)
File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 75, in masked_arith_op
assert isinstance(x, np.ndarray), type(x)
```
This is a 1.0.0 regression; in 0.25.3, the operation succeeds and the unmatched `bar` column is filled with `NaN` in the output.
The same error occurs with:
* Any combination of incompatible columns (strict subset, strict superset, overlapping, disjoint)
* Calling the `subtract` method instead of using the subtraction operator
* Timezone-aware `Timestamp`s as well as timezone-naive
It does *not* seem to occur with:
* Mismatches on the row index; transposing the dataframes in the above example prevents the errors occuring.
* `pd.Series` objects with mismatched indexes (e.g. calling the above on the first row of each dataframe works fine)
* Other dtypes; `bool`, `float`, and `int` seem to work fine. Similarly, if the dataframes are explicitly cast to dtype `object`, the operation succeeds.
#### Expected Output
```
bar foo
0 NaN 0 days
1 NaN 0 days
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 5.3.5
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
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31624 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31624/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31624/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31624/events | https://github.com/pandas-dev/pandas/pull/31624 | 559,200,338 | MDExOlB1bGxSZXF1ZXN0MzcwNDEzMzUx | 31,624 | DOC: link on nullables in indexing | {
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closes #31537 | {
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} | 3 | 2020-02-03T17:07:27Z | 2020-02-06T23:45:59Z | 2020-02-06T23:43:04Z | MEMBER | null | The only 2-liners here are _convert_scalar_indexer and _convert_slice_indexer, which are effectively `axis = min(axis, self.ndim-1)`, but that is unnecessary since all places where these are called have axis < self.ndim. | {
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- [x] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2020-02-03T20:37:44Z | 2020-02-03T23:26:28Z | 2020-02-03T23:20:27Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/31449
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} | 2 | 2020-02-03T20:57:09Z | 2020-03-10T14:08:01Z | 2020-03-10T14:08:01Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```pytb
In [1]: import pandas as pd
In [2]: s = pd.Series(['a', None], dtype="string")
In [3]: s.str.repeat([1, 2])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~/sandbox/pandas/pandas/core/strings.py in rep(x, r)
781 try:
--> 782 return bytes.__mul__(x, r)
783 except TypeError:
TypeError: descriptor '__mul__' requires a 'bytes' object but received a 'NAType'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-3-a01827562f7a> in <module>
----> 1 s.str.repeat([1, 2])
~/sandbox/pandas/pandas/core/strings.py in wrapper(self, *args, **kwargs)
1950 )
1951 raise TypeError(msg)
-> 1952 return func(self, *args, **kwargs)
1953
1954 wrapper.__name__ = func_name
~/sandbox/pandas/pandas/core/strings.py in repeat(self, repeats)
2780 @forbid_nonstring_types(["bytes"])
2781 def repeat(self, repeats):
-> 2782 result = str_repeat(self._parent, repeats)
2783 return self._wrap_result(result)
2784
~/sandbox/pandas/pandas/core/strings.py in str_repeat(arr, repeats)
785
786 repeats = np.asarray(repeats, dtype=object)
--> 787 result = libops.vec_binop(com.values_from_object(arr), repeats, rep)
788 return result
789
~/sandbox/pandas/pandas/_libs/ops.pyx in pandas._libs.ops.vec_binop()
239 result[i] = y
240 else:
--> 241 raise
242
243 return maybe_convert_bool(result.base) # `.base` to access np.ndarray
~/sandbox/pandas/pandas/_libs/ops.pyx in pandas._libs.ops.vec_binop()
232 y = right[i]
233 try:
--> 234 result[i] = op(x, y)
235 except TypeError:
236 if x is None or is_nan(x):
~/sandbox/pandas/pandas/core/strings.py in rep(x, r)
782 return bytes.__mul__(x, r)
783 except TypeError:
--> 784 return str.__mul__(x, r)
785
786 repeats = np.asarray(repeats, dtype=object)
TypeError: descriptor '__mul__' requires a 'str' object but received a 'NAType'
```
#### Problem description
The `str_repeat` method correctly handles NA values when `repeats` is a scalar, but fails when its a sequence.
#### Expected Output
```python
0 a
1 <NA>
dtype: string
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... | open | false | null | [] | null | 4 | 2020-02-03T22:03:19Z | 2021-07-27T05:47:02Z | null | NONE | null | #### Code Sample
```python
Series([0, 1, 2, True]).replace({0: 'a', True: 'x', False: 'y'})
```
Output:
| | 0 |
|---:|:----|
| 0 | y |
| 1 | x |
| 2 | 2 |
| 3 | x |
#### Problem description
I expected zero to be replaced with 'a', and 1 to remain untouched.
- documentation does not clarify whether `replace()` checks for equality or for identity.
- both inexperienced and experienced users may fall in the trap of the code as shown above; if the current behaviour is desired, would it be reasonable to add a warning when the user's attempts can lead to certainly unintended results (as above)?
#### Expected Output
| | 0 |
|---:|:----|
| 0 | a |
| 1 | 1 |
| 2 | 2 |
| 3 | x |
#### Due research
Stack Overflow:
- [How to map True and False to 'Yes' and 'No' in a pandas data frame for columns of dtype bool only?](https://stackoverflow.com/questions/45196626/how-to-map-true-and-false-to-yes-and-no-in-a-pandas-data-frame-for-columns-o) (6k views, 6/8 upvotes) works around this problem by only using booleans in columns with `dtype == bool`
- [I wonder when replace 0 on a Series(or dataframe), will False also be replaced?](https://stackoverflow.com/questions/52128402/i-wonder-when-replace-0-on-a-seriesor-dataframe-will-false-also-be-replaced/52128984#52128984) demonstrates that replacing zeros will not touch booleans if the `dtype == bool`.
- I verified that my problem exists with `dtype in {int, float}` (i.e. after removing `True` from Series)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.2.0
Cython : None
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
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
pytest : 5.2.1
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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... | closed | false | null | [] | null | 8 | 2020-02-03T22:33:52Z | 2021-06-08T16:48:40Z | 2021-06-08T16:48:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
!pip install pandas==1.0.0
import datetime
import pandas as pd
pd.Series([datetime.datetime.now(), datetime.datetime.now(), None]).astype('int64')
```
=>
```
ValueError: Cannot convert NaT values to integer
```
#### Problem description
In 0.25.*, the above ran without an exception, while in 1.0.0, it now throws an exception. While the new behavior may be OK, it would be better as first a deprecation warning with a suggested migration.
(We had the same code for at least 1-2 years w/out issue. Still unclear on the proper fix for getting the int64 or int32 epoch out in a way that is friendly for both pre/post 1.0.0.)
#### Expected Output
Same behavior as 0.25.*:
```
0 1580769049934654000
1 1580769049934659000
2 -9223372036854775808
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
/usr/local/lib/python3.6/dist-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
/usr/local/lib/python3.6/dist-packages/pandas_datareader/compat/__init__.py:18: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
from pandas.util.testing import assert_frame_equal
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.17.5
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 45.1.0
Cython : 0.29.14
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 5.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.47.0
</details>
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} | 1 | 2020-02-04T01:58:07Z | 2020-02-05T01:18:18Z | 2020-02-05T01:13:11Z | MEMBER | null | This will actually allow us to remove e.g. CategoricalIndex.get_loc by moving the 2 relevant lines into CategoricalIndex._maybe_cast_indexer. Large parts of DTI/TDI/PI.get_loc will also be simplifiable.
We've got both Index._maybe_cast_indexer and Index._convert_scalar_indexer which hopefully we'll only need one of. | {
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} | 1 | 2020-02-04T02:02:09Z | 2020-02-05T01:12:15Z | 2020-02-05T01:12:09Z | CONTRIBUTOR | null | Updated `pandas/tests/frame/test_repr_info.py` to use f-strings
ref: https://github.com/pandas-dev/pandas/issues/29547
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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@TomAugspurger can you confirm that the change made here to the `__finalize__` call is correct? If so, suggest a test?
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} | 11 | 2020-02-04T02:45:15Z | 2020-05-15T08:47:48Z | 2020-02-09T17:19:45Z | CONTRIBUTOR | null | - closes #29079
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https://api.github.com/repos/pandas-dev/pandas/issues/31643 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31643/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31643/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31643/events | https://github.com/pandas-dev/pandas/issues/31643 | 559,488,902 | MDU6SXNzdWU1NTk0ODg5MDI= | 31,643 | BUG: pandas.cut does not give the right answer with nullable integer Series input | {
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} | [] | closed | false | null | [] | null | 3 | 2020-02-04T05:00:20Z | 2020-02-05T02:56:09Z | 2020-02-05T02:56:08Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy
import pandas
input_array = numpy.array([1, 2, numpy.nan, 4, 5])
print('Input array: %s\n' % input_array)
bins = numpy.array([0.5, 2.5, 4.5, 6.5])
print ('Using bins: %s\n' % bins)
test_series = pandas.Series(input_array).astype('Int64')
print('Test series:\n\n%s\n' % test_series)
print('Results of cut:\n\n%s\n'% pandas.cut(test_series, bins))
```
Console output:
````
Input array: [ 1. 2. nan 4. 5.]
Using bins: [0.5 2.5 4.5 6.5]
Test series:
0 1
1 2
2 NaN
3 4
4 5
dtype: Int64
Results of cut:
0 (0.5, 2.5]
1 (0.5, 2.5]
2 NaN
3 (0.5, 2.5]
4 (4.5, 6.5]
dtype: category
Categories (3, interval[float64]): [(0.5, 2.5] < (2.5, 4.5] < (4.5, 6.5]]
````
#### Problem description
`output[3]` should be `(2.5, 4.5]` and not `(0.5, 2.5]` as shown.
Tested in pandas 1.0.0 and 0.25.3.
#### Expected Output
````
Input array: [ 1. 2. nan 4. 5.]
Using bins: [0.5 2.5 4.5 6.5]
Test series:
0 1
1 2
2 NaN
3 4
4 5
dtype: Int64
Results of cut:
0 (0.5, 2.5]
1 (0.5, 2.5]
2 NaN
3 (2.5, 4.5]
4 (4.5, 6.5]
dtype: category
Categories (3, interval[float64]): [(0.5, 2.5] < (2.5, 4.5] < (4.5, 6.5]]
````
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-173-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.6.1
pip : 20.0.2
setuptools : 40.6.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.11.1
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
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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"following_url": "https://api.github.com/users/charlesdong1991/following{/o... | null | 17 | 2020-02-04T08:02:13Z | 2021-07-27T14:56:29Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>>pd.Series(['A','B']).replace(r'.','C',regex=True)
0 C
1 C
dtype: object
pd.Series(['A','B']).astype('string').replace(r'.','C',regex=True)
0 A
1 B
dtype: string
```
#### Problem description
It seems that replace doesn't work with the string type Series.
Why these two codes return different results? | {
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this is the first result gotten by googling extending pandas, however, seems not rendered correctly, help is welcome! | {
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"description": "Arithmetic, Comparison, ... | open | false | null | [] | null | 3 | 2020-02-04T09:30:09Z | 2021-07-27T05:49:08Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.__version__
'1.1.0.dev0+288.gac61a8a62'
>>> pd.core.arrays.IntervalArray.from_breaks([1, 2, 3, 4]) == (i for i in range(10))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\arrays\interval.py", line 562, in __eq__
if len(self) != len(other):
TypeError: object of type 'generator' has no len()
>>>
```
#### Problem description
Raises TypeError
mypy gives `pandas\core\arrays\interval.py:563: error: Argument 1 to "len" has incompatible type "object"; expected "Sized"`
similar problem with IntegerArray
```python
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\pandas\pandas\core\ops\common.py", line 64, in new_method
return method(self, other)
File "C:\Users\simon\pandas\pandas\core\arrays\integer.py", line 531, in cmp_method
if len(self) != len(other):
TypeError: len() of unsized object
>>>
```
and other Extension Arrays.
#### Expected Output
Not to raise TypeError
I think returning
```
<BooleanArray>
[False, False, False]
Length: 3, dtype: boolean
```
would be consistent with numpy array
```python
>>> import numpy as np
>>> np.array([1, 2, 3]) == (i for i in range(10))
array([False, False, False])
```
and Series
```python
>>> pd.Series([1, 2, 3]) == (i for i in range(10))
0 False
1 False
2 False
dtype: bool
>>>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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"following_url": "https://api.github.com/users/mehersaipreetam/following{/... | null | 8 | 2020-02-04T11:08:50Z | 2020-03-12T10:22:03Z | 2020-03-12T10:22:03Z | MEMBER | null | A few issues I've noticed:
1) It says
> When adjust is False, weighted averages are calculated recursively as:
>
> weighted_average[0] = arg[0]; weighted_average[i] = (1-alpha)*weighted_average[i-1] + alpha*arg[i].
>
but `arg` isn't defined
2) The phrase "When adjust is False, weighted averages are calculated recursively as:" appears as bold, but I don't think this was the intention
3)
```
\text{for} halflife > 0
```
should be
```
\text{for } halflife > 0
```
4) In the "notes" section, ```alpha``` is used, while in the description of the parameters, it's
```
:math:`\alpha`
```
5) The respective doc for Series could be updated, as it says
```
axis{0 or ‘index’, 1 or ‘columns’}, default 0
``` | {
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} | 7 | 2020-02-04T14:15:01Z | 2020-02-04T19:26:00Z | 2020-02-04T19:26:00Z | NONE | null | Indexing a multi-index seemingly went from O(1) to O(N):
I did a bisect, and found this was caused by the `_shallow_copy` here: https://github.com/pandas-dev/pandas/commit/b0f33b3f40d5742531e31aef90573b9592489592#diff-4ffd1c69d47e0ac9f2de4f9e3e4a118cR643.
#### Code Sample
```python
from time import perf_counter as time
import pandas as pd
for N in [1000, 2000, 4000, 8000, 16000, 32000]:
values = list(range(N))
df = pd.DataFrame({ 'a': values })
df['b'] = 1
df.set_index(['a', 'b'], inplace = True)
t = time()
df.loc[values]
t = time() - t
print(N, t)
``` | {
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} | 3 | 2020-02-04T14:22:56Z | 2020-02-05T00:22:03Z | 2020-02-05T00:22:03Z | NONE | null | #### Code Sample, a copy-pastable example if possible
This code:
```python
>>> df = pd.DataFrame({'date': ['Aug2020', 'November 2020']})
>>> df['parsed'] = df['date'].apply(pd.to_datetime)
>>> end = df.loc[df['parsed'].idxmax()]
>>> end['parsed'].replace(day=2)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'numpy.datetime64' object has no attribute 'replace'
```
worked in Pandas 0.25.3, but raises since 1.0.0.
I think there might be an issue with unboxing values when there are mixed types in the dataframe:
```python
>>> df = pd.DataFrame({'date': ['Aug2020', 'November 2020']})
>>> new = (
... df
... .assign(
... parsed=lambda x: x['date'].apply(pd.to_datetime),
... parsed2 = lambda x: x['date'].apply(pd.to_datetime)
... )
... )
>>> new['parsed'].iloc[0]
Timestamp('2020-08-01 00:00:00')
>>> new.iloc[0]['parsed']
numpy.datetime64('2020-08-01T00:00:00.000000000') # unboxed type
>>> new2 = new.drop(columns=['date'])
>>> new2['parsed'].iloc[0]
Timestamp('2020-08-01 00:00:00')
>>> new2.iloc[0]['parsed']
Timestamp('2020-08-01 00:00:00') # boxed type now that we've dropped the string column
```
#### Problem description
to_datetime can "sometimes" result in a np.datetime64 return type.
np.datetime64 is not a valid return type for to_datetime (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html), it should always be a datetimelike.
#### Expected Output
As in previous versions of Pandas:
```python
>>> df = pd.DataFrame({'date': ['Aug2020', 'November 2020']})
>>> df['parsed'] = df['date'].apply(pd.to_datetime)
>>> end = df.loc[df['parsed'].idxmax()]
>>> end['parsed'].replace(day=2)
Timestamp('2020-11-02 00:00:00')
```
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.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 : en_GB.UTF-8
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.2.0
Cython : 0.29.14
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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... | null | 2 | 2020-02-04T14:27:26Z | 2021-07-27T05:49:30Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
from pd.plotting import autocorrelation_plot
# read a subset of sales data from rossman sales data into a DataFrame
# set Date column to the index
autocorrelation_plot(df_sales['Sales'], figsize=(10, 8))
```
#### Problem description
Plotting works fine without the `figsize` argument, but errors with it. Stack trace below.
Other pandas.plotting charts accept a `figsize` argument.
#### Expected Output
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-df3d57963679> in <module>
----> 1 auto_plot(df_sales['Sales'], figsize=(10, 8))
~/miniconda3/envs/main/lib/python3.7/site-packages/pandas/plotting/_misc.py in autocorrelation_plot(series, ax, **kwargs)
418 """
419 plot_backend = _get_plot_backend("matplotlib")
--> 420 return plot_backend.autocorrelation_plot(series=series, ax=ax, **kwargs)
421
422
~/miniconda3/envs/main/lib/python3.7/site-packages/pandas/plotting/_matplotlib/misc.py in autocorrelation_plot(series, ax, **kwds)
425 ax.set_xlabel("Lag")
426 ax.set_ylabel("Autocorrelation")
--> 427 ax.plot(x, y, **kwds)
428 if "label" in kwds:
429 ax.legend()
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/__init__.py in inner(ax, *args, **kwargs)
1865 "the Matplotlib list!)" % (label_namer, func.__name__),
1866 RuntimeWarning, stacklevel=2)
-> 1867 return func(ax, *args, **kwargs)
1868
1869 inner.__doc__ = _add_data_doc(inner.__doc__,
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/axes/_axes.py in plot(self, *args, **kwargs)
1526 kwargs = cbook.normalize_kwargs(kwargs, _alias_map)
1527
-> 1528 for line in self._get_lines(*args, **kwargs):
1529 self.add_line(line)
1530 lines.append(line)
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/axes/_base.py in _grab_next_args(self, *args, **kwargs)
404 this += args[0],
405 args = args[1:]
--> 406 for seg in self._plot_args(this, kwargs):
407 yield seg
408
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/axes/_base.py in _plot_args(self, tup, kwargs)
394 "with non-matching shapes is deprecated.")
395 for j in xrange(max(ncx, ncy)):
--> 396 seg = func(x[:, j % ncx], y[:, j % ncy], kw, kwargs)
397 ret.append(seg)
398 return ret
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/axes/_base.py in _makeline(self, x, y, kw, kwargs)
298 default_dict = self._getdefaults(None, kw)
299 self._setdefaults(default_dict, kw)
--> 300 seg = mlines.Line2D(x, y, **kw)
301 return seg
302
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/lines.py in __init__(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)
419 # update kwargs before updating data to give the caller a
420 # chance to init axes (and hence unit support)
--> 421 self.update(kwargs)
422 self.pickradius = pickradius
423 self.ind_offset = 0
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/artist.py in update(self, props)
886 try:
887 ret = [_update_property(self, k, v)
--> 888 for k, v in props.items()]
889 finally:
890 self.eventson = store
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/artist.py in <listcomp>(.0)
886 try:
887 ret = [_update_property(self, k, v)
--> 888 for k, v in props.items()]
889 finally:
890 self.eventson = store
~/miniconda3/envs/main/lib/python3.7/site-packages/matplotlib/artist.py in _update_property(self, k, v)
879 func = getattr(self, 'set_' + k, None)
880 if not callable(func):
--> 881 raise AttributeError('Unknown property %s' % k)
882 return func(v)
883
AttributeError: Unknown property figsize
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.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 : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 2.2.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 3 | 2020-02-04T15:00:02Z | 2020-02-04T20:08:40Z | 2020-02-04T19:26:00Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/31648.
This should have been caught by the benchmark MultiIdnex.time_index_slice. Confirming that now.. | {
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} | 4 | 2020-02-04T15:09:16Z | 2020-10-03T05:07:49Z | 2020-02-05T01:09:00Z | MEMBER | null | Looking into the usage question of https://github.com/pandas-dev/pandas/issues/31572, I noticed that specifying the version to allow writing nanoseconds to parquet worked in plain pyarrow code, but not with pandas' `to_parquet`.
This is because we hardcode `coerce_timestamps="ms"` while the default is `None`, which has version-dependent behaviour (eg if version="2.0", actually write the nanosecond data) | {
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} | 4 | 2020-02-04T15:33:34Z | 2020-05-29T10:48:00Z | 2020-02-26T02:19:00Z | MEMBER | null | Timestamp constructor now raises explanatory error message when year, month or day is missing
- [x] closes #31200
- [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 | 2020-02-04T16:30:51Z | 2020-02-05T02:47:04Z | 2020-02-05T02:44:44Z | MEMBER | null | This simplifies a try/except block and moves us towards being able to tighten what we accept in _convert_scalar_indexer among others. I think we'll soon be able to get rid of the kludge on L866/868. | {
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- [x] tests added / passed
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See discussion https://github.com/pandas-dev/pandas/pull/16386#discussion_r374807689
cc @jorisvandenbossche @jschendel | {
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} | 4 | 2020-02-04T18:39:33Z | 2020-02-05T20:22:31Z | 2020-02-05T01:00:29Z | CONTRIBUTOR | null | xref #29547
for
- [ ] pandas/util/_print_versions.py
- [ ] pandas/tests/tseries/frequencies/test_to_offset.py
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} | 6 | 2020-02-04T19:00:19Z | 2020-02-05T03:08:08Z | null | CONTRIBUTOR | null | ERROR: type should be string, got "\r\nhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.pipe.html#pandas.core.groupby.GroupBy.pipe\r\n\r\nhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.pipe.html#pandas.core.groupby.GroupBy.apply\r\n\r\n#### Problem description\r\n\r\nGroupBy.apply() and GroupBy.pipe() have missing links in their See also section. Might be other missing links in other functions of GroupBy.\r\n\r\nSpecifically, the links that are not working are Series.apply, DataFrame.apply, Series.pipe, DataFrame.pipe\r\n\r\n#### Expected Output\r\n\r\nLinks should take you to named functions\r\n" | {
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and https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.rdivmod.html#pandas.Series.rdivmod
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Included in https://github.com/pandas-dev/pandas/pull/31664. So this won't need to be backported. | {
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} | 3 | 2020-02-04T22:19:19Z | 2020-02-05T13:23:23Z | 2020-02-05T00:31:26Z | CONTRIBUTOR | null | Fixed by falling back to the wrapped Python version when the cython
version raises NotImplementedError.
Closes https://github.com/pandas-dev/pandas/issues/31450 | {
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} | 5 | 2020-02-04T22:53:05Z | 2020-02-05T10:17:06Z | 2020-02-05T10:17:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame(
[[123, "a", 1.0], [123, "b", 2.0]],
columns=["id", "category", "value"]
)
df = df.set_index(["id", "category"])
df[df.value < 0].groupby("id").sum()
```
#### Problem description
When groupby is over a `Int64Index` in a `MultiIndex` for an empty DataFrame, the groupby fails with error: `ValueError: Unable to fill values because Int64Index cannot contain NA`
#### Expected Output
The groupby should not raise an error, instead the code above should output an empty DataFrame as would happen for `df[df.value < 0].groupby("category").sum()`
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.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 : 1.0.0
numpy : 1.18.1
pytz : 2019.2
dateutil : 2.8.0
pip : 18.1
setuptools : 41.6.0
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.1.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.6
tables : None
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
```
</details> | {
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} | 3 | 2020-02-04T23:01:56Z | 2020-02-05T02:50:39Z | 2020-02-05T02:45:09Z | MEMBER | null | There's nothing about this that particularly benefits from being in cython (I think until recently this was used within index.pyx) and its clearer in python. Plus we get a slightly smaller/faster build. | {
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} | 2 | 2020-02-05T00:06:23Z | 2020-02-06T23:41:13Z | 2020-02-06T23:41:13Z | NONE | null | #### Example code:
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),columns=['col_a', 'col_b', 'col_c'])
excelwritename = '/tmp/Test%s.xlsx' %pd.datetime.now(pytz.timezone('US/Pacific')).strftime('%b%d_%I_%M %p')
with pd.ExcelWriter(excelwritename, engine='xlsxwriter',datetime_format='mmm dd yyyy hh:mm AM/PM') as writer:
df.to_excel(writer,columns=['col_a','col_b'],sheet_name = 'xyz')
#### Problem description:
specifying column names to output to excel is no longer working in pandas 1.0.0 and it outputs all columns in the df.
Checked different versions of xlsxwriter, but pandas 0.25.3 works fine, while 1.0.0 does not.
#### Expected Output
It should only output columns that are specified, but it outputs all columns of df
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 4.54.2
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.0
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.0
numba : 0.48.0
</details>
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} | 16 | 2020-02-05T00:55:52Z | 2020-02-19T10:00:44Z | 2020-02-19T00:26:28Z | MEMBER | null | - [x] closes #31623
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
cc @TomAugspurger this is pretty ugly, and I'm not sure how well it will behave if either frame has MultiIndex colums.
On the plus side, it could improve perf in the many-columns-but-small-intersection case.
The ugliness might be improved by moving this check to before the _align_method_FRAME call | {
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} | 1 | 2020-02-05T03:29:43Z | 2020-02-07T00:06:45Z | 2020-02-07T00:04:21Z | MEMBER | null | There was a usage in MultiIndex.get_value that was just removed, leaving just the one in sparse.array that this changes to handle in python-space. | {
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} | 7 | 2020-02-05T04:02:58Z | 2020-08-30T05:04:56Z | 2020-02-15T09:02:04Z | NONE | null | Does anyone could help? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31683 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31683/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31683/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31683/events | https://github.com/pandas-dev/pandas/issues/31683 | 560,129,377 | MDU6SXNzdWU1NjAxMjkzNzc= | 31,683 | Why does CategoricalIndex.get_value call _convert_scalar_indexer? | {
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} | [] | closed | false | null | [] | null | 5 | 2020-02-05T04:14:54Z | 2020-02-07T00:12:53Z | 2020-02-07T00:12:52Z | MEMBER | null | cc @jreback @jorisvandenbossche @TomAugspurger the meat of CategoricalIndex.get_value reads:
```
def get_value(self, series, key):
k = key
try:
k = self._convert_scalar_indexer(k, kind="getitem")
indexer = self.get_loc(k)
return series.take([indexer])[0]
except (KeyError, TypeError):
pass
# we might be a positional inexer
return Index.get_value(self, series, key)
```
The thing is, if we actually track down that _convert_scalar_indexer call, with `kind="getitem"` it always passes through to the base class method, which ends up checking:
```
if kind == "getitem" and is_float(key):
if not self.is_floating():
self._invalid_indexer("label", key)
```
But AFAICT `CategoricalIndex.is_floating()` is always False, so it looks like the call in `get_value` is a no-op, which I expect was not the original intention.
Also possibly undesired:
```
idx = pd.Index(range(4)).astype("f8") / 2
cidx = pd.CategoricalIndex(idx)
ser1 = pd.Series(range(4), index=idx)
ser2 = pd.Series(range(4), index=cidx)
>>> ser1[1.0]
2
>>> ser2[1.0]
[...] TypeError [...]
>>> ser1[2]
[...] KeyError [...]
>>> ser2[2]
2
```
Can anyone confirm if the no-op call and/or different `__getitem__` behavior is intentional? | {
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} | 10 | 2020-02-05T06:41:15Z | 2020-03-10T14:09:34Z | 2020-03-10T14:08:01Z | CONTRIBUTOR | null | - [x] closes #31632
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"gists_url"... | null | 13 | 2020-02-05T08:43:50Z | 2021-07-28T04:38:36Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame([[1.0, 2e25],[np.nan, 0.1]])
# Works when applied to individual columns
print(df[0].where(pd.notnull(df[0]), None))
print(df[1].where(pd.notnull(df[1]), None))
# Breaks for whole dataframe
print(df.where(pd.notnull(df), None))
```
#### Problem description
The above code does not work with 1.0.0, but used to work with at least 0.25.0.
Replacing large floats with pd.where breaks
Running pd.where on a dataframe that contains large float values and the replacement value is of a different dtype throws `OverflowError: int too big to convert`:
```
applied = getattr(b, f)(**kwargs)
File "***\venv\lib\site-packages\pandas\core\internals\blocks.py", line 1426, in where
return self._maybe_downcast(blocks, "infer")
File "***\venv\lib\site-packages\pandas\core\internals\blocks.py", line 514, in _maybe_downcast
return _extend_blocks([b.downcast(downcast) for b in blocks])
File "***\venv\lib\site-packages\pandas\core\internals\blocks.py", line 514, in <listcomp>
return _extend_blocks([b.downcast(downcast) for b in blocks])
File "***\venv\lib\site-packages\pandas\core\internals\blocks.py", line 552, in downcast
return self.split_and_operate(None, f, False)
File "***\venv\lib\site-packages\pandas\core\internals\blocks.py", line 496, in split_and_operate
nv = f(m, v, i)
File "***\venv\lib\site-packages\pandas\core\internals\blocks.py", line 549, in f
val = maybe_downcast_to_dtype(val, dtype="infer")
File "***\venv\lib\site-packages\pandas\core\dtypes\cast.py", line 135, in maybe_downcast_to_dtype
converted = maybe_downcast_numeric(result, dtype, do_round)
File "***\venv\lib\site-packages\pandas\core\dtypes\cast.py", line 222, in maybe_downcast_numeric
new_result = trans(result).astype(dtype)
OverflowError: int too big to convert
```
Replacing data one column at a time works.
#### Expected Output
```
Name: 1, dtype: float64
0 1
0 1 2e+25
1 None 0.1
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
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 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 10.0.1
setuptools : 39.1.0
Cython : None
pytest : 4.4.0
hypothesis : None
sphinx : 2.0.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.4.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.3
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 0 | 2020-02-05T09:00:05Z | 2020-02-05T10:17:11Z | 2020-02-05T10:17:06Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/31670 (the actual fix was already in #29243) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31691 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31691/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31691/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31691/events | https://github.com/pandas-dev/pandas/issues/31691 | 560,267,731 | MDU6SXNzdWU1NjAyNjc3MzE= | 31,691 | Inconsistent kwargs argument 'color' passed to upstream matplotlib plot functions | {
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"id": 241332... | closed | false | null | [] | null | 14 | 2020-02-05T10:11:50Z | 2020-12-14T17:27:38Z | 2020-12-14T17:27:38Z | NONE | null | In matplotlib.pyplot.bar , there is a keyword argument called 'color' which can control the color of all bars as well as each bar, e.g.,
<img width="639" alt="image" src="https://user-images.githubusercontent.com/10172392/73830682-08257200-4840-11ea-9aa0-64bb6bb2b9ab.png">
However, in Pandas DataFrame.plot.bar, by passing a list into 'color', the color of all bars is controlled only by the 1st element in the list, i.e.,
<img width="630" alt="image" src="https://user-images.githubusercontent.com/10172392/73831000-900b7c00-4840-11ea-8121-fe139365fdfc.png">
Ironically, if we pass in a list of list into 'color', we can control the color of each bar, i.e.,
<img width="651" alt="image" src="https://user-images.githubusercontent.com/10172392/73832150-9995e380-4842-11ea-9e35-6345364168b5.png">
So my question is why the behavior of the 'color' argument different from that in matplotlib? Is this intended inconsistency? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31692 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31692/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31692/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31692/events | https://github.com/pandas-dev/pandas/pull/31692 | 560,270,921 | MDExOlB1bGxSZXF1ZXN0MzcxMjg5Mzg5 | 31,692 | Backport PR #31690 on branch 1.0.x (TST: add test for regression in groupby with empty MultiIndex level) | {
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} | 0 | 2020-02-05T10:17:17Z | 2020-02-05T11:09:51Z | 2020-02-05T11:09:51Z | NONE | null | Backport PR #31690: TST: add test for regression in groupby with empty MultiIndex level | {
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} | [] | closed | false | null | [] | null | 2 | 2020-02-05T10:45:52Z | 2020-02-05T12:10:34Z | 2020-02-05T12:10:34Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'TIME': [pd.to_datetime('2020-01-01 12:00')], 'STRING': ['ABC']})
print(type(df.iloc[0]['TIME']))
```
#### Problem description
In 0.25.3 the code above results in `<class 'pandas._libs.tslibs.timestamps.Timestamp'>` whereas in 1.0.0 it is `numpy.datetime64`. I could not find anything about this specifically in the release notes. Is it intended? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31694 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31694/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31694/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31694/events | https://github.com/pandas-dev/pandas/issues/31694 | 560,304,014 | MDU6SXNzdWU1NjAzMDQwMTQ= | 31,694 | ENH: Support dateutil.tz.tzrange objects | {
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"description": "Timezone... | open | false | null | [] | null | 1 | 2020-02-05T11:16:21Z | 2020-02-05T17:57:47Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
mytz = dateutil.tz.tzrange("EST", -18000, "EDT", -14400,
dateutil.relativedelta.relativedelta(hours=+2, month=4,
weekday=dateutil.relativedelta.SU(+1)),
dateutil.relativedelta.relativedelta(hours=+1, month=10,
weekday=dateutil.relativedelta.SU(-1)))
pd.to_datetime('20190204', utc=True).tz_convert(mytz)
```
> ---------------------------------------------------------------------------
> AttributeError Traceback (most recent call last)
> pandas\_libs\tslibs\timezones.pyx in pandas._libs.tslibs.timezones.get_dst_info()
>
> AttributeError: 'NoneType' object has no attribute 'total_seconds'
> Exception ignored in: 'pandas._libs.tslibs.conversion.localize_tso'
> Traceback (most recent call last):
> File "pandas\_libs\tslibs\timezones.pyx", line 220, in pandas._libs.tslibs.timezones.get_dst_info
> AttributeError: 'NoneType' object has no attribute 'total_seconds'
> Out[243]: Timestamp('2019-02-04 00:00:00')
#### Problem description
`tz_convert` fails on custom timezone with DST provided. Here is the example with `dateutil` but it would fail with any custom timezone with DST offset
#### Expected Output
`Timestamp('2019-02-03 19:00:00-0500', tz='mytz')`
#### 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 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : 0.14.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31695 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31695/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31695/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31695/events | https://github.com/pandas-dev/pandas/pull/31695 | 560,315,969 | MDExOlB1bGxSZXF1ZXN0MzcxMzI2NzQ2 | 31,695 | DOC: Removed Notes from DataFrame.applymap | {
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Documentation screenshot:
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https://api.github.com/repos/pandas-dev/pandas/issues/31696 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31696/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31696/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31696/events | https://github.com/pandas-dev/pandas/issues/31696 | 560,360,457 | MDU6SXNzdWU1NjAzNjA0NTc= | 31,696 | Where did check_bool_array_indexer move to? | {
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} | [] | closed | false | null | [] | null | 2 | 2020-02-05T13:07:27Z | 2020-02-05T13:39:07Z | 2020-02-05T13:36:13Z | NONE | null | I was leveraging the following:
```
pd.api.indexers.check_bool_array_indexer
```
And at v1.0.0, it is gone, what is the comparable operation to this?
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https://api.github.com/repos/pandas-dev/pandas/issues/31697 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31697/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31697/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31697/events | https://github.com/pandas-dev/pandas/issues/31697 | 560,361,772 | MDU6SXNzdWU1NjAzNjE3NzI= | 31,697 | `DataFrame.resample()` breaks when rule parameter is of type `DateOffset` | {
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"description": "DateOffsets",
"id": ... | open | false | null | [] | null | 5 | 2020-02-05T13:09:50Z | 2021-07-28T04:40:05Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# Some exemplary data
df = pd.DataFrame({'Timestamp': pd.date_range('2020-01', '2020-03', periods=5),
'Amount': [5, 10, 15, 20, 25]}, columns=['Timestamp', 'Amount'])
working = df.resample('MS', on='Timestamp').sum() # This one works
failing = df.resample(pd.DateOffset(months=1), on='Timestamp').sum() # This one fails
```
#### Problem description
According to the documentation, the `rule=` parameter of `DataFrame.resample()` can be of type `DateOffset`, `Timedelta` or `str`. Apparently, it actually expects some of the `DateOffset` subclasses (like `MonthEnd`, `BusinessDay`, etc.), because `rule_code` attribute is not defined in the parent class. Hence, the error:
```
File "~/.conda/envs/py37-simple/lib/python3.7/site-packages/pandas/core/generic.py", line 8114, in resample
level=level,
File "~/.conda/envs/py37-simple/lib/python3.7/site-packages/pandas/core/resample.py", line 1269, in resample
tg = TimeGrouper(**kwds)
File "~/.conda/envs/py37-simple/lib/python3.7/site-packages/pandas/core/resample.py", line 1341, in __init__
rule = freq.rule_code
File "~/.conda/envs/py37-simple/lib/python3.7/site-packages/pandas/tseries/offsets.py", line 460, in rule_code
return self._prefix
File "~/.conda/envs/py37-simple/lib/python3.7/site-packages/pandas/tseries/offsets.py", line 456, in _prefix
raise NotImplementedError("Prefix not defined")
NotImplementedError: Prefix not defined
```
#### Expected Output
The `resample()` method is expected to work with plain `DateOffset` instances passed for `rule=` attribute.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0.post20200106
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
-------------------------------------------
I didn't apply the fixtures to the rest of `test_ops.py` yet since @jreback voiced some [concerns in this comment](https://github.com/pandas-dev/pandas/pull/30147#discussion_r356587642). | {
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... | open | false | null | [] | null | 2 | 2020-02-05T15:35:28Z | 2021-07-28T04:40:49Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"date": pd.date_range("2020", freq="D", periods=10),
"value": np.random.rand(10),
}
)
shifted_dates = df.date.dt.shift(1)
```
#### Problem description
I always thought that the `dt` accessor should replicate more or less the functionality of a DatetimeIndex to a series or a column of a dataframe. If I can do it with a DatetimeIndex, I should be able to do it with a collection of datetimes too, right?
#### Expected Output
This is my workaround to obtain the expected output. It's not very nice.
```python
index = df.set_index("date").index
index.freq = index.inferred_freq
shifted_dates = df.date.copy()
shifted_dates[:] = index.shift(1)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.5
hypothesis : 5.4.1
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.2
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/31707 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31707/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31707/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31707/events | https://github.com/pandas-dev/pandas/pull/31707 | 560,479,684 | MDExOlB1bGxSZXF1ZXN0MzcxNDYyNTMz | 31,707 | Manul backport of pr 31666 on 1.0.x | {
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https://api.github.com/repos/pandas-dev/pandas/issues/31708 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/31708/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/31708/comments | https://api.github.com/repos/pandas-dev/pandas/issues/31708/events | https://github.com/pandas-dev/pandas/issues/31708 | 560,492,729 | MDU6SXNzdWU1NjA0OTI3Mjk= | 31,708 | Series.astype(str, skipna=True) vanished in the 1.0 release | {
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"description": "Functionality that used ... | open | false | null | [] | null | 14 | 2020-02-05T16:39:32Z | 2021-04-06T16:01:03Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.Series([None, 42]).astype(str, skipna=True)
```
results in `TypeError: astype() got an unexpected keyword argument 'skipna'`
#### Problem description
The provided snippet used to work before the 1.0 release. With the `skipna` argument, the `None` was preserved as a real `np.nan` type, instead of being converted to the string `nan`. The `skipna` argument seems to have gone in the 1.0 release and I can't find any way to restore this behavior without a custom function being passed to `apply`.
#### Expected Output
A series with `[np.nan, '42']`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.1-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.utf8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3
setuptools : 44.0.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: 0.8.1
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 2 | 2020-02-05T16:43:15Z | 2020-02-07T01:54:35Z | 2020-02-07T01:46:52Z | MEMBER | null | `_convert_scalar_indexer` is called with kind="iloc" from only one place, and in that case
1) the base class method is equivalent to just the 1-liner `self._validate_indexer("positional", key, "iloc")`
2) all subclasses just call the base class method
So by inlining that 1-liner, we can take the "iloc" case out of `_convert_scalar_indexer` altogether.
kind=None is never passed, so we can rip that right out.
Ultimately I want to disentable/de-duplicate/disambiguate `_convert_scalar_indexer` vs `_maybe_cast_indexer`
Partial overlap with #31625. | {
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} | 9 | 2020-02-05T16:47:31Z | 2020-10-16T21:35:56Z | 2020-03-17T01:34:15Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> index = pd.date_range('2020', 'now', freq='1h')
>>> arr = np.zeros_like(index)
>>> arr.setflags(write=False)
>>> pd.Series(arr, index=index).resample('1d').agg('last')
-------------------------------------------------------------------------
~/pandas/pandas/_libs/groupby.pyx in pandas._libs.groupby.group_last()
~/pandas/pandas/_libs/groupby.cpython-37m-x86_64-linux-gnu.so in View.MemoryView.memoryview_cwrapper()
~/pandas/pandas/_libs/groupby.cpython-37m-x86_64-linux-gnu.so in View.MemoryView.memoryview.__cinit__()
ValueError: buffer source array is read-only
```
#### Problem description
Groupby fails on some read-only buffers (I couldn't quickly reproduce it with `.groupby()` itself, sorry).
The prime solution would be to add `const` specifier to the input `values` here (and related entries):
https://github.com/pandas-dev/pandas/blob/0b6debf825db39f8f63d300cb8007a7996d002fc/pandas/_libs/groupby.pyx#L853
if it were not for Cython's non-support of const fused types (https://github.com/cython/cython/issues/1772), resolved in (https://github.com/cython/cython/pull/3118), but despite [miniscule change](https://github.com/cython/cython/pull/3118/files?file-filters%5B%5D=.py) only scheduled for release in Cython 3.0. I guess wait until then.
#### Expected Output
Resampling/groupby works with read-only arrays.
#### Output of ``pd.show_versions()``
pandas 1.1.0.dev0+361.gf0b00f887
cython 0.29.14
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] | closed | false | null | [] | null | 5 | 2020-02-05T16:50:12Z | 2020-02-06T11:54:38Z | 2020-02-06T00:51:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
import numpy as np
df = pd.read_csv("./Artikelstatus_Augsburg.csv", sep=';', parse_dates=['VON_DTM','BIS_DTM'], infer_datetime_format=True)
print(df.info())
print(df.head())
```
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14472 entries, 0 to 14471
Data columns (total 4 columns):
Artikel 14472 non-null int64
ATTRIBUT 14472 non-null int64
VON_DTM 14472 non-null datetime64[ns]
BIS_DTM 14472 non-null object
dtypes: datetime64[ns](1), int64(2), object(1)
memory usage: 452.3+ KB
None
Artikel ATTRIBUT VON_DTM BIS_DTM
0 17 0 2018-10-01 00:00:00 2018-11-21 16:25:18
1 17 3 2018-11-21 16:25:19 2999-12-31 23:59:59
2 35 0 2018-10-01 00:00:00 2018-11-30 11:38:21
3 35 2 2018-11-30 11:38:22 2018-12-04 17:09:05
4 35 0 2018-12-04 17:09:06 2018-12-05 11:09:24
```
#### Problem description
`read_csv` with parameteter `parse_dates` uses `datetime64[ns]` as default. That datatype, however, only covers dates between the years 1678 an 2262. Many systems use hardcoded special dates for `-Inf` or `Inf`, including `0001-01-01 00:00:00`, `2999-12-31 23:59:59` or `9999-12-31 23:59:59`.
The current behavior of `read_csv` is that an `object` column instead of a `datetime64[us]` is returned. Using the `date_parser` argument leads to a huge performance drop and even solutions like `na_values = ['2999-12-31 23:59:59']` do not work.
Instead of an `object` column, a `datetime64[us]` column should be returned, when an out-of-bounds date is found. Possible approaches to solve the issue include autodectection by `csv.Sniffer`, a separate parameter `datetime_unit` or backtracking.
#### Expected Output
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14472 entries, 0 to 14471
Data columns (total 4 columns):
Artikel 14472 non-null int64
ATTRIBUT 14472 non-null int64
VON_DTM 14472 non-null datetime64[ns]
BIS_DTM 14472 non-null datetime64[us]
dtypes: datetime64[ns](1), datetime64[us](1), int64(2)
memory usage: 452.3+ KB
None
Artikel ATTRIBUT VON_DTM BIS_DTM
0 17 0 2018-10-01 00:00:00 2018-11-21 16:25:18
1 17 3 2018-11-21 16:25:19 2999-12-31 23:59:59
2 35 0 2018-10-01 00:00:00 2018-11-30 11:38:21
3 35 2 2018-11-30 11:38:22 2018-12-04 17:09:05
4 35 0 2018-12-04 17:09:06 2018-12-05 11:09:24
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-1052-gcp
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.2.2
setuptools: 41.0.1
Cython: 0.29.14
numpy: 1.16.4
scipy: 1.3.1
pyarrow: 0.15.1
xarray: None
IPython: 7.8.0
sphinx: 2.2.0
patsy: 0.5.0
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.10
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 2 | 2020-02-05T17:24:05Z | 2020-02-05T21:06:32Z | 2020-02-05T21:06:25Z | CONTRIBUTOR | null | Adds newline before versionadded in `dataframe.melt` so that sphinx/rst can pick it up and render it correctly.
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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} | 1 | 2020-02-05T17:41:31Z | 2020-02-06T23:58:07Z | 2020-02-06T23:57:36Z | MEMBER | null | This makes Index.get_value match ExtensionIndex.get_value, so we can remove the latter.
Along with implementing _should_fallback_to_positional, this allows us to rip out Float64Index.get_value. | {
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} | 0 | 2020-02-05T18:22:20Z | 2020-02-05T19:08:12Z | 2020-02-05T19:08:08Z | CONTRIBUTOR | null | Need to figure out a better system for this :/ I've already tagged and pushed 1.0.1, so I'm applying this to my local doc build. | {
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