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https://api.github.com/repos/pandas-dev/pandas/issues/40719 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40719/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40719/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40719/events | https://github.com/pandas-dev/pandas/issues/40719 | 847,813,580 | MDU6SXNzdWU4NDc4MTM1ODA= | 40,719 | BUG: tm.assert_frame_equal fails to raise near int64 implementation bounds | {
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] | open | false | null | [] | null | 4 | "2021-04-01T03:23:04Z" | "2021-05-21T17:28:43Z" | null | MEMBER | null | passing check_exact=True makes it raise as expected, but I was surprised to find it necessary. This likely hides some bugs.
Motivating example adapted from tests.groupby.test_function.test_cummin.
```
dtype = np.dtype(np.int64)
min_val = np.iinfo(dtype).min
base_df = DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]})
expected_mins = [3, 3, 3, 2, 2, 2, 2, 1]
expected = DataFrame({"B": expected_mins}).astype(dtype)
df = base_df.astype(dtype)
# Test w/ min value for dtype
df.loc[[2, 6], "B"] = min_val
df.loc[[1, 5], "B"] = min_val + 1
result = df.groupby("A").cummin()
expected.loc[[1, 5], "B"] = min_val + 1
expected.loc[[2, 3, 6, 7], "B"] = min_val
tm.assert_frame_equal(result, expected) # <-- passes
tm.assert_frame_equal(result, expected, check_exact=True) # <-- raises
>>> result
B
0 3
1 -9223372036854775808
2 -9223372036854775808
3 -9223372036854775808
4 2
5 -9223372036854775808
6 -9223372036854775808
7 -9223372036854775808
>>> expected
B
0 3
1 -9223372036854775807
2 -9223372036854775808
3 -9223372036854775808
4 2
5 -9223372036854775807
6 -9223372036854775808
7 -9223372036854775808
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40720 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40720/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40720/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40720/events | https://github.com/pandas-dev/pandas/issues/40720 | 848,182,160 | MDU6SXNzdWU4NDgxODIxNjA= | 40,720 | BUG: GroupBy.apply doesn't add group keys to index whether setting group_keys=True or not. | {
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] | open | false | null | [] | null | 1 | "2021-04-01T08:07:38Z" | "2021-08-19T03:40:23Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
code
```python
import pandas as pd
print(pd.__version__)
data = {
'groupby_col': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B', ],
'agg_col': [1, 1, 0, 1, 0, 0, 0, 0, 1, 0],
}
df = pd.DataFrame(data)
print(df.groupby('groupby_col', group_keys=True).apply(lambda x: x.rolling(4).mean()))
print(df.groupby('groupby_col', group_keys=False).apply(lambda x: x.rolling(4).mean()))
```
output
```
1.2.3
agg_col
0 NaN
1 NaN
2 NaN
3 0.75
4 0.50
5 NaN
6 NaN
7 NaN
8 0.25
9 0.25
agg_col
0 NaN
1 NaN
2 NaN
3 0.75
4 0.50
5 NaN
6 NaN
7 NaN
8 0.25
9 0.25
```
The group keys should be add to index when setting group_keys=True(the default option). And when I use `pandas==1.1.5`, the output is as expected:
```
1.1.5
agg_col
groupby_col
A 0 NaN
1 NaN
2 NaN
3 0.75
4 0.50
B 5 NaN
6 NaN
7 NaN
8 0.25
9 0.25
agg_col
0 NaN
1 NaN
2 NaN
3 0.75
4 0.50
5 NaN
6 NaN
7 NaN
8 0.25
9 0.25
```
---
But I find that examples in https://github.com/pandas-dev/pandas/issues/38787#issuecomment-759660512 does work as expected even in 1.2.3:
code
```python
df = pd.DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
print(df.groupby("key", group_keys=True).apply(lambda x: x[:2]))
print(df.groupby("key", group_keys=False).apply(lambda x: x[:2]))
```
output
```
key value
key
1 0 1 0
1 1 1
2 3 2 3
4 2 4
3 6 3 6
7 3 7
key value
0 1 0
1 1 1
3 2 3
4 2 4
6 3 6
7 3 7
```
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] | closed | false | null | [] | null | 3 | "2021-04-01T08:11:16Z" | "2021-08-19T03:46:02Z" | "2021-08-19T03:46:02Z" | NONE | null | - [x ] I have checked that this issue has not already been reported.
- [x ] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
Found an interesting issue regarding the performance of to_sql() in combination with SQL Server.
OS: Centos/Redhat
DB Driver: ODBC Driver 17 for SQL Server
DB Server (remote): SQLServer 12
Performance:
34484 rows x 10 columns => >10k records per second
34484 rows x 12 columns => ~500 records per second
I tested it on two different Servers and DB's and the type of data doesn't seem to matter (all dtypes are object), the drop happens always if the df is havin more than 10 Columns
```python
engine = sqlalchemy.create_engine("mssql+pyodbc:///?odbc_connect=%s?charset=utf8" % params, fast_executemany=True)
sqlalchemy_connection = engine.connect()
....
df.to_sql(name='TEST', con=con, if_exists='append', index=False)
```
(Tested it now with postgresql, the "bug" does not appear there, get around 14k/sec)
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} | 2 | "2021-04-01T08:38:21Z" | "2021-04-01T18:17:35Z" | "2021-04-01T18:17:35Z" | MEMBER | null | I noticed the following behaviour:
```
In [43]: np.array([1, 2, 3]) == pd.Timedelta("1 days")
Out[43]: array([False, False, False])
In [44]: np.array([1, 2, 3]) == pd.Timedelta("nat")
Out[44]: False
```
@jbrockmendel do you know if this is something we control? (it's similar as the numeric/string comparison with numpy, but here the scalar is one defined by pandas) | {
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} | 1 | "2021-04-01T09:14:42Z" | "2021-04-01T18:17:40Z" | "2021-04-01T18:17:35Z" | MEMBER | null | Closes #40722
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} | 2 | "2021-04-01T10:38:22Z" | "2021-04-01T15:57:30Z" | "2021-04-01T15:56:24Z" | NONE | null | Fix for [#40714](https://github.com/pandas-dev/pandas/issues/40714)
The is_first_col function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Used ax.get_subplotspec().is_first_col() instead.
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} | 8 | "2021-04-01T10:39:32Z" | "2021-04-09T08:59:41Z" | "2021-04-09T01:41:44Z" | MEMBER | null | PR is draft. follow-up to #40679, https://github.com/pandas-dev/pandas/pull/40679#issuecomment-809642594 which is not yet merged and need to make window to run benchmarks https://github.com/pandas-dev/pandas/pull/40679#pullrequestreview-623553887
on master
```
>>> pd.__version__
'1.3.0.dev0+1180.gdd697e1cca'
>>>
>>> from pandas.core.arrays.string_arrow import ArrowStringDtype
>>>
>>> arr = ArrowStringDtype.construct_array_type()._from_sequence(["B", pd.NA, "A"])
>>>
>>> arr
<ArrowStringArray>
['B', <NA>, 'A']
Length: 3, dtype: arrow_string
>>>
>>> pd._libs.lib.infer_dtype(arr)
'unknown-array'
>>>
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} | 12 | "2021-04-01T12:11:41Z" | "2021-04-06T19:25:50Z" | "2021-04-06T19:18:52Z" | MEMBER | null | - [x] closes #20432
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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} | 5 | "2021-04-01T12:15:39Z" | "2021-04-02T06:29:24Z" | "2021-04-01T22:28:08Z" | MEMBER | null | See overview of supported operators: https://numexpr.readthedocs.io/projects/NumExpr3/en/latest/user_guide.html#supported-operators. By trying to use it, we were falling back to the (slower) `_masked_arith_op`
Discovered by investigating the arithmetic benchmarks at https://github.com/pandas-dev/pandas/issues/39146#issuecomment-799482699
For example using the `arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function floordiv>)` benchmark:
```python
import operator
dtype = np.float64
arr = np.random.randn(20000, 100)
df = pd.DataFrame(arr.astype(dtype))
scalar = 3.0
op = operator.floordiv
```
```
In [2]: %timeit op(df, scalar)
56.4 ms ± 723 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- master
24.3 ms ± 236 µs per loop (mean ± std. dev. of 7 runs, 10 loops each) <-- PR
```
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} | 6 | "2021-04-01T12:49:22Z" | "2021-04-02T15:12:16Z" | "2021-04-02T15:09:05Z" | MEMBER | null | While investigating https://github.com/pandas-dev/pandas/pull/40727, I noticed that we are often trying `_masked_arith_op`, while it will end up giving the exact same error (for example when using ints, which don't have NaNs).
From what I understand, I *think* that, in theory, we only need to fallback to a masked version of the op when having object dtype (I might be wrong here, though). So this PR tries that.
For numeric dtypes, all operations should simply work with NaNs?
For object dtype, we can have NaN or None, which doesn't necessarily work out of the box, so here we need to fallback to a masked operation.
ExtensionArrays don't end up here, and datetimelike dtypes get wrapped into EAs before getting here.
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} | 1 | "2021-04-01T13:57:25Z" | "2021-08-19T03:46:38Z" | null | NONE | null | #### Code Sample, a copy-pastable example
```python
import pandas as pd
series = pd.Series(['1', '2', '3', '4'], dtype='object')
series.astype(pd.Float64Dtype())
```
the above produces a `TypeError: object cannot be converted to a FloatingDtype`:
<details>
```python-traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-20-a9dee24fff9e> in <module>
15 assert series.astype('float64').dtype == 'float64'
16 # TypeError: object cannot be converted to a FloatingDtype - same for DataFrames
---> 17 series.astype(pd.Float64Dtype())
18
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/generic.py in astype(self, dtype, copy, errors)
5875 else:
5876 # else, only a single dtype is given
-> 5877 new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
5878 return self._constructor(new_data).__finalize__(self, method="astype")
5879
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/internals/managers.py in astype(self, dtype, copy, errors)
629 self, dtype, copy: bool = False, errors: str = "raise"
630 ) -> "BlockManager":
--> 631 return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
632
633 def convert(
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/internals/managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
425 applied = b.apply(f, **kwargs)
426 else:
--> 427 applied = getattr(b, f)(**kwargs)
428 except (TypeError, NotImplementedError):
429 if not ignore_failures:
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/internals/blocks.py in astype(self, dtype, copy, errors)
671 vals1d = values.ravel()
672 try:
--> 673 values = astype_nansafe(vals1d, dtype, copy=True)
674 except (ValueError, TypeError):
675 # e.g. astype_nansafe can fail on object-dtype of strings
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/dtypes/cast.py in astype_nansafe(arr, dtype, copy, skipna)
1017 # dispatch on extension dtype if needed
1018 if is_extension_array_dtype(dtype):
-> 1019 return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)
1020
1021 if not isinstance(dtype, np.dtype):
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/arrays/floating.py in _from_sequence(cls, scalars, dtype, copy)
278 cls, scalars, *, dtype=None, copy: bool = False
279 ) -> "FloatingArray":
--> 280 values, mask = coerce_to_array(scalars, dtype=dtype, copy=copy)
281 return FloatingArray(values, mask)
282
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/arrays/floating.py in coerce_to_array(values, dtype, mask, copy)
160 "mixed-integer-float",
161 ]:
--> 162 raise TypeError(f"{values.dtype} cannot be converted to a FloatingDtype")
163
164 elif is_bool_dtype(values) and is_float_dtype(dtype):
TypeError: object cannot be converted to a FloatingDtype
```
</details>
#### Problem description
With `float64`, this conversion works, and as the series' dtype would get inferred as `object` even if no dtype was specified, it is unexpected to me that we would not be able to convert to the `Float64Dtype` from that.
#### Expected Output
```python
pd.Series([1.0, 2.0, 3.0, 4.0], dtype=Float64Dtype)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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} | 2 | "2021-04-01T14:07:16Z" | "2021-04-28T14:38:51Z" | "2021-04-28T14:38:51Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
series = pd.Series([1.0, 2.0, 3.0, 4.4], dtype=pd.Float64Dtype())
pd.qcut(series, 2)
```
#### Problem description
`pd.qcut` currently accepts the nullable `Int64Dtype` as well as `'float64'`, so I would expect it to work with the `Float64Dtype` as well. Instead the following error is produced:
```python-traceback
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-29-1db98f70db38> in <module>
1 series = pd.Series([1.0,2.0,3.0,4.0], dtype=pd.Float64Dtype())
----> 2 pd.qcut(series, 2)
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/reshape/tile.py in qcut(x, q, labels, retbins, precision, duplicates)
356 quantiles = q
357 bins = algos.quantile(x, quantiles)
--> 358 fac, bins = _bins_to_cuts(
359 x,
360 bins,
~/.pyenv/versions/3.8.2/envs/woodwork/lib/python3.8/site-packages/pandas/core/reshape/tile.py in _bins_to_cuts(x, bins, right, labels, precision, include_lowest, dtype, duplicates, ordered)
408
409 if include_lowest:
--> 410 ids[x == bins[0]] = 1
411
412 na_mask = isna(x) | (ids == len(bins)) | (ids == 0)
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
#### Expected Output
Should match that of `float64`
```
0 (0.999, 2.5]
1 (0.999, 2.5]
2 (2.5, 4.0]
3 (2.5, 4.0]
dtype: category
Categories (2, interval[float64]): [(0.999, 2.5] < (2.5, 4.0]]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 5 | "2021-04-01T14:39:30Z" | "2021-06-16T13:25:12Z" | "2021-06-16T00:20:10Z" | CONTRIBUTOR | null | This is an extension PR to #40422 (most of this code is included there), and must be committed after it.
This PR only adds the `_parse_latex_css_conversion` function and associated `convert_css` argument.
Below is the relevant section added to the user guide:
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} | 3 | "2021-04-01T14:44:35Z" | "2021-08-19T03:47:15Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype())
ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype())
assert floats.replace({1.0: 9}).dtype == 'int64' # results in [9, 2, 3, 4]
assert floats.replace({1.0: 9.0}).dtype == 'float64'
assert ints.replace({1: 9}).dtype == 'int64'
assert ints.replace({1: 9.0}).dtype == 'float64'
```
#### Problem description
The dtype of the resulting series is being changed when `replace` is being called with `Int64Dtype` and `Float64Dtype` series'.
There are two problems here, from what I can tell:
- We lose the nullable dtypes in favor of `int64` and `float64`.
- When doing `replace` on a float series with an integer value, the resulting series gets converted to ints, truncating the decimals
#### Expected Output
```python
assert floats.replace({1.0: 9}).dtype == pd.Float64Dtype() # Matching how an int passed to replace gets converted to a float with float64
assert floats.replace({1.0: 9.0}).dtype == pd.Float64Dtype()
assert ints.replace({1: 9}).dtype == pd.Int64Dtype()
assert ints.replace({1: 9.0}).dtype == pd.Float64Dtype() # Matching how int -> float if a float is passed to replace
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Sun Jul 5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 41.2.0
Cython : None
pytest : 6.0.1
hypothesis : None
sphinx : 3.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.18.1
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 2 | "2021-04-01T16:43:21Z" | "2021-04-02T18:24:33Z" | "2021-04-02T01:43:12Z" | MEMBER | null | I think this is much closer to "the correct" abstraction here (i find the layered abstractions with similar names in core.groupby hard to follow). Contains \approx 0 groupby logic (takes ngroups as an argument in some methods) and is only about how to wrap/call/unwrap the libgroupby functions.
There are a few more parts of _cython_operation I'd like to move, but I'm holding off on that pending some non-refactor work that touches those pieces.
No specific plans, but I'm hopeful that something like this can be re-used to de-duplicate logic in nanops and maybe algorithms.
This uses classmethods/staticmethods (i like the explicit statelessness) and passes how+kind as arguments, but an alternative would be to instantiate with how+kind passed to `__init__`.
The only potentially-logical change is that when defining `out_dtype`, this returns "float64" instead of "float". I haven't checked if that makes a difference on 32bit builds, but after weeks of dealing with np.intp much prefer the explicitness.
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] | open | false | null | [] | null | 6 | "2021-04-01T18:47:58Z" | "2021-08-19T03:48:32Z" | null | MEMBER | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> df = pd.DataFrame({'a': [1], 'b': ['2'], 'c': [3], 'd': [3]}).astype({'a': 'category', 'c': 'category', 'd': 'category'})
>>> df
a b c d
0 1 2 3 3
>>> df.dtypes.value_counts()
category 2
category 1
object 1
dtype: int64
```
#### Problem description
`category` appears twice with different counts
#### Expected Output
Either
```python
category 3
object 1
dtype: int64
```
or
```python
CategoricalDtype(categories=[3], ordered=False) 2
CategoricalDtype(categories=[1], ordered=False) 1
object 1
dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 8064973159c46d3c4608e3aaf637196e224eba91
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-70-generic
Version : #78-Ubuntu SMP Fri Mar 19 13:29:52 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.3.0.dev0+1211.g8064973159
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 20.3.3
setuptools : 49.6.0.post20201009
Cython : 0.29.22
pytest : 6.2.2
hypothesis : 6.8.1
sphinx : 3.5.2
blosc : None
feather : None
xlsxwriter : 1.3.7
lxml.etree : 4.6.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : 1.3.2
fsspec : 0.8.7
fastparquet : 0.5.0
gcsfs : 0.7.2
matplotlib : 3.3.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : 2.0.0
pyxlsb : None
s3fs : 0.5.2
scipy : 1.6.1
sqlalchemy : 1.4.2
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.17.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.52.0
</details>
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} | 1 | "2021-04-01T18:53:48Z" | "2021-04-02T06:58:52Z" | "2021-04-01T21:24:52Z" | CONTRIBUTOR | null | minor changes here to decouple the render dict `d` into sections for code readability.
this is also first part of other changes to improve readability of this method.
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] | null | 11 | "2021-04-01T18:55:55Z" | "2021-04-15T21:21:56Z" | "2021-04-15T21:21:56Z" | MEMBER | null | `fillna` has a different return type according to whether `inplace` is `True` or `False`. We could overload it to provide a better typing experience
See https://github.com/pandas-dev/pandas/pull/40197 for a very similar issue
Task here is:
1. read through #40197, make sure you understand it
2. try doing the same for `fillna`
3. check it works. See the above PR for an example, but it should be something like the following (but for `fillna` instead of `reset_index` - some of the other arguments probably need changing too)
```python
# t.py
import pandas as pd
inplace: bool
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0))
reveal_type(pd.DataFrame([1,2,3]).reset_index(inplace=False))
reveal_type(pd.DataFrame([1,2,3]).reset_index(drop=True, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, drop=False, inplace=True))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, inplace=True, col_fill='a'))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, col_fill='a'))
reveal_type(pd.DataFrame([1,2,3]).reset_index(inplace=True))
reveal_type(pd.DataFrame([1,2,3]).reset_index(inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).reset_index(drop=True, inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, drop=False, inplace=inplace))
reveal_type(pd.DataFrame([1,2,3]).reset_index(level=0, inplace=inplace, col_fill='a'))
reveal_type(pd.DataFrame([1,2,3]).reset_index(inplace=inplace))
```
output:
```python
$ mypy t.py
t.py:5: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:6: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:7: note: Revealed type is 'None'
t.py:8: note: Revealed type is 'None'
t.py:9: note: Revealed type is 'None'
t.py:10: note: Revealed type is 'None'
t.py:11: note: Revealed type is 'pandas.core.frame.DataFrame'
t.py:12: note: Revealed type is 'None'
t.py:13: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:14: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:15: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:16: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:17: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
t.py:18: note: Revealed type is 'Union[pandas.core.frame.DataFrame, None]'
```
---
I've marked this as 'good first issue' as it doesn't require any knowledge of the pandas core codebase, though it's probably not the easiest if you're not at least somewhat familiar with mypy / typing | {
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https://api.github.com/repos/pandas-dev/pandas/issues/40738 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40738/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40738/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40738/events | https://github.com/pandas-dev/pandas/pull/40738 | 848,722,532 | MDExOlB1bGxSZXF1ZXN0NjA3NjE1MjEz | 40,738 | BUG: Fix nanvar for large float16 arrays | {
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] | closed | false | null | [] | null | 6 | "2021-04-01T19:15:53Z" | "2021-05-02T23:48:50Z" | "2021-05-02T23:48:38Z" | NONE | null | Summary of bug:
Above a certain length, calling `.std()` and `.var()` on a Series or DataFrame fails for float types. The is easily shown with float16 in the following example:
```python
>>> import pandas as pd
>>> import numpy as np
>>> zeros = 32760
>>> ones = 32759
>>> pd.Series([0] * zeros + [1] * ones).astype("float16").var()
0.25
>>> zeros = 32760
>>> ones = 32760
>>> pd.Series([0] * zeros + [1] * ones).astype("float16").var()
0.0
```
What is happening here is that both `count` and `d` overflow to `inf` as 65519 is the largest integer float16 can represent. Example:
```python
>>> import numpy as np
>>> np.float16(65519)
65500.0
>>> np.float16(65520)
inf
```
This makes the variance go to 0.0. Since the `avg` calculation on line 927 of this file casts to float64 anyway, it seems like there is no downside to not casting `count` and `d`. It will also improve accuracy in cases where the overflow does not happen.
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.2.3
numpy : 1.20.1
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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#### Is your feature request related to a problem?
I was trying to perform a method chained logical mask without an intermediate assignment and found the method of passing the logical mask or callable to `.loc` to be verbose. I am trying to reduce that verbosity.
Examples
```python
(df_xtra_metadata.loc[(df_xtra_metadata['column'] == val) & (df_xtra_metadata['column2'] >= val2)]
.reset_index()
)
# or with a callable, shorter but still long
(df_xtra_metadata.loc[lambda x: (x['column'] == val) & (x['column2'] >= val2)]
.reset_index()
)
# not as long but requires assigning and deleting the mask in the scope
mask = (df_xtra_metadata['column'] == val) & (df_xtra_metadata['column2'] >= val2)
df.loc[mask].reset_index()
```
#### Describe the solution you'd like
I would like an object similar to `pd.IndexSlice` but instead is initialized with some metadata and then when called (which takes in the dataframe) computes the logical mask.
#### API breaking implications
This feature would add to the existing API but in a non obtrusive way and would not conflict with any of the current API.
#### Describe alternatives you've considered
I haven't considered any alternatives, I wrote this for myself and thought maybe it would be useful to others.
#### Additional context
So below is the code I wrote toying around with this idea.
```python
from __future__ import annotations
from operator import and_, eq, ge, getitem, gt, invert, le, lt, ne, or_, xor
from typing import Any, Callable, Literal, Union
import pandas as pd
class LogicalSlice:
"""Create objects to more easily calculate logical masks inplace for slicing DataFrames."""
ops = {
">": gt,
"<": lt,
">=": ge,
"<=": le,
"==": eq,
"!=": ne,
"in": pd.Series.isin,
}
__slots__ = ("_repr", "_closure", "_body")
def __init__(
self, column: Any, op: Literal[">", "<", ">=", "<=", "==", "!=", "in"], val: Any
) -> None:
operation: Union[gt, lt, ge, le, eq, ne, pd.Series.isin] = self.ops[op]
def closure(df: pd.DataFrame) -> pd.Series:
return operation(getitem(df, column), val)
self._closure: Callable[pd.DataFrame, pd.Series] = closure
if isinstance(column, str):
col: str = f"'{column}'"
else:
col: str = str(column)
if op == "in":
op_rhs: str = f".isin({val})"
else:
op_rhs: str = f" {op} {val}"
body: str = f"df[{col}]{op_rhs}"
self._set_body(body)
def __call__(self, df: pd.DataFrame) -> pd.Series:
return self._closure(df)
def __repr__(self) -> str:
return self._repr
def __invert__(self) -> LogicalSlice:
inverted: LogicalSlice = self._compose(invert, self)
body: str = f"~({self._body})"
inverted._set_body(body)
return inverted
def __and__(self, other: LogicalSlice) -> LogicalSlice:
conjunction: LogicalSlice = self._compose(and_, self, other)
body: str = f"({self._body}) & ({other._body})"
conjunction._set_body(body)
return conjunction
def __or__(self, other: LogicalSlice) -> LogicalSlice:
disjunction: LogicalSlice = self._compose(or_, self, other)
body: str = f"({self._body}) | ({other._body})"
disjunction._set_body(body)
return disjunction
def __xor__(self, other: LogicalSlice):
symetric_difference: LogicalSlice = self._compose(xor, self, other)
body: str = f"({self._body}) ^ ({other._body})"
symetric_difference._set_body(body)
return symetric_difference
def _set_body(self, body: str) -> None:
self._body = body
self._repr = f"lambda df: {body}"
@classmethod
def _compose(
cls, operation: Union[invert, xor, and_, or_], *operands: LogicalSlice
) -> LogicalSlice:
new_logical_slice = cls.__new__(cls)
def closure(df: pd.DataFrame) -> pd.Series:
return operation(*(operand(df) for operand in operands))
new_logical_slice._closure = closure
return new_logical_slice
```
Here is some example code of the API. I suppose it should
```python
In [7]: import pandas as pd
In [8]: import numpy as np
In [9]: mask = pd.LogicalSlice
In [10]: # create dummy dataframe for example
In [11]: df = pd.DataFrame(np.random.randn(8, 5), index=list(range(8)), columns=[1,2,3,4,5])
In [12]: df
Out[12]:
1 2 3 4 5
0 2.146687 1.111394 0.782805 -0.664520 -0.775198
1 0.266829 1.021803 0.112757 1.912192 -0.629621
2 0.796544 -0.584199 -0.958174 0.817412 -0.148881
3 0.436404 0.528461 0.911095 0.854496 2.047070
4 -0.144312 -2.274132 0.129994 -2.413109 1.248950
5 -0.233113 -2.028095 -0.164058 0.912749 2.717956
6 -1.537385 1.881949 -1.567367 0.489918 -1.142897
7 -0.411876 -1.044270 -0.869536 0.878700 0.077144
In [13]: # get the rows where column 1 is greater than 0 using pd.LogicalSlice
In [16]: df.loc[mask(1, ">", 0)]
Out[16]:
1 2 3 4 5
0 2.146687 1.111394 0.782805 -0.664520 -0.775198
1 0.266829 1.021803 0.112757 1.912192 -0.629621
2 0.796544 -0.584199 -0.958174 0.817412 -0.148881
3 0.436404 0.528461 0.911095 0.854496 2.047070
In [17]: # bind the mask to a variable for later user
In [18]: non_neg = mask(1, ">", 0)
In [19]: # repr displays a lambda but its a closure under the hood
In [20]: non_neg
Out[20]: lambda df: df[1] > 0
In [21]: df.loc[non_neg]
Out[21]:
1 2 3 4 5
0 2.146687 1.111394 0.782805 -0.664520 -0.775198
1 0.266829 1.021803 0.112757 1.912192 -0.629621
2 0.796544 -0.584199 -0.958174 0.817412 -0.148881
3 0.436404 0.528461 0.911095 0.854496 2.047070
In [22]: # multiple LogicalSlices can be combined using bitwise operators
In [23]: non_neg & mask(2, ">", 0)
Out[23]: lambda df: (df[1] > 0) & (df[2] > 0)
In [24]: df.loc[non_neg & mask(2, ">", 0)]
Out[24]:
1 2 3 4 5
0 2.146687 1.111394 0.782805 -0.664520 -0.775198
1 0.266829 1.021803 0.112757 1.912192 -0.629621
3 0.436404 0.528461 0.911095 0.854496 2.047070
In [25]: # the traditional method
In [26]: df.loc[(df[1] > 0) & (df[2] > 0)]
Out[26]:
1 2 3 4 5
0 2.146687 1.111394 0.782805 -0.664520 -0.775198
1 0.266829 1.021803 0.112757 1.912192 -0.629621
3 0.436404 0.528461 0.911095 0.854496 2.047070
In [27]: # combining multiple masks
In [28]: ~(non_neg & mask(2, ">", 0)) | mask(4, "<", 0)
Out[28]: lambda df: (~((df[1] > 0) & (df[2] > 0))) | (df[4] < 0)
In [29]: big_mask = ~(non_neg & mask(2, ">", 0)) | mask(4, "<", 0)
In [30]: big_mask
Out[30]: lambda df: (~((df[1] > 0) & (df[2] > 0))) | (df[4] < 0)
In [31]: df.loc[big_mask]
Out[31]:
1 2 3 4 5
0 2.146687 1.111394 0.782805 -0.664520 -0.775198
2 0.796544 -0.584199 -0.958174 0.817412 -0.148881
4 -0.144312 -2.274132 0.129994 -2.413109 1.248950
5 -0.233113 -2.028095 -0.164058 0.912749 2.717956
6 -1.537385 1.881949 -1.567367 0.489918 -1.142897
7 -0.411876 -1.044270 -0.869536 0.878700 0.077144
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} | 0 | "2021-04-01T21:07:39Z" | "2021-04-02T02:24:12Z" | "2021-04-02T01:47:09Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
xref #40386 (does not close) | {
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} | 0 | "2021-04-01T22:47:14Z" | "2021-04-01T22:47:26Z" | "2021-04-01T22:47:26Z" | CONTRIBUTOR | null | This reverts commit e6f7e7b20ebb1512d6debee5e8592b0b5351ac49.
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} | 6 | "2021-04-02T03:27:00Z" | "2021-04-13T17:19:40Z" | "2021-04-13T17:16:26Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/40745 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/40745/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/40745/comments | https://api.github.com/repos/pandas-dev/pandas/issues/40745/events | https://github.com/pandas-dev/pandas/pull/40745 | 849,145,718 | MDExOlB1bGxSZXF1ZXN0NjA3OTY4OTg3 | 40,745 | CLN: create core Styler rendering functions and subclass conditional styling applications | {
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} | 3 | "2021-04-02T10:45:13Z" | "2021-04-12T15:00:22Z" | "2021-04-12T14:49:33Z" | CONTRIBUTOR | null | Here is a first draft at splitting up `style.py`, ahead of adding new LaTeX functionality
### Key Points
- All rendering and formatting methods moved to `style_render.py`:
```
class StylerRenderer:
jinja2_loader
def render()
def format()
def _translate()
def _compute()
+ their module level helper functions
```
- `style.py` maintains all the conditional application methods and the utility methods and is subclass:
```
class Styler(StylerRenderer):
...
```
All methods copied, no changes made, other than deprivatizing shared method names.
Technically this now works:
```
df = pd.DataFrame([['a b', 'c v']])
from pandas.io.formats.style_render import StylerRenderer
StylerRenderer(df).format(str.upper).render()
``` | {
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] | closed | false | null | [] | null | 1 | "2021-04-02T11:36:23Z" | "2021-05-01T01:10:49Z" | "2021-04-30T06:26:33Z" | NONE | null | - [x] closes #30275
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
Enhancement for Xlsxwriter to enable the usage of `set_column` for datetime columns for setting the format , just like how it can be done for numbers.
Exception includes the datetime columns which already have a formatting, for eg - `DatetimeIndex`
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we may want to combine this with #39908, and merge #40708 before #39908
#39908 makes changes to the user facing api that affects the existing StringDtype so that may need an separate section in the release notes.
other sections of the documentation may also need updating. e.g. doc/source/user_guide/text.rst, doc/source/development/roadmap.rst and doc/source/user_guide/basics.rst | {
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] | open | false | null | [] | null | 0 | "2021-04-02T12:55:29Z" | "2021-04-06T01:53:02Z" | null | CONTRIBUTOR | null | The loop variable `engine` should probably be used here instead of `self.engine`:
https://github.com/pandas-dev/pandas/blob/1367cacd7c90694a5d0563d46dd10a1e8a3ea8b3/pandas/tests/computation/test_eval.py#L466
Making this change leads to a few failed tests:
```
======================================================================== short test summary info =========================================================================
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-0-in] - AssertionError: Input are different
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-0-not in] - AssertionError: Input are different
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-1-in] - AssertionError: Input are different
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-1-not in] - AssertionError: Input are different
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-2-in] - AssertionError: Input are different
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-2-not in] - AssertionError: Input are different
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-3-in] - AssertionError: Input are different
FAILED pandas/tests/computation/test_eval.py::TestEvalNumexprPandas::test_compound_invert_op[4-3-not in] - AssertionError: Input are different
``` | {
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} | 2 | "2021-04-02T13:01:32Z" | "2021-04-15T10:35:47Z" | "2021-04-15T09:31:34Z" | MEMBER | null | only a few tests updated in this PR to ensure this approach is accepted.
if we try to add directly to the parametrization of the existing tests we get `ImportError` on environments without pyarrow installed. https://github.com/pandas-dev/pandas/pull/40679#discussion_r603345758 | {
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} | 4 | "2021-04-02T13:45:43Z" | "2021-04-08T15:22:24Z" | "2021-04-06T11:31:49Z" | NONE | null | # Inconsistencies of pandas merge
I tried to do an outer merge two dataframes and I observed that `left_only` values are missing but if I take a subset of those data `left_only` values appear
unfortunately I am not allowed to share those data and I have not yet found a minimal test case
**From full dataset**
```
(Pdb) outer_full = pd.merge(left_df, right_df,how='outer',on=['start', 'stop', 'reference', 'alternative'], indicator=True)
(Pdb) outer_full[outer_full['sequence_id_y']=='something']
start stop reference alternative sequence_id_x sequence_id_y _merge
886 23604 23604 C A sample_of_interest something both
63840 8512 8512 A G sample_of_interest something both
65224 28270 28271 TA T sample_of_interest something both
124138 28111 28111 A G sample_of_interest something both
182893 27972 27972 C T sample_of_interest something both
241900 913 913 C T sample_of_interest something both
299398 18171 18171 C T sample_of_interest something both
300613 21990 21993 TTTA T sample_of_interest something both
358445 9190 9190 G T sample_of_interest something both
359600 6954 6954 T C sample_of_interest something both
418297 28282 28282 T A sample_of_interest something both
476408 23063 23063 A T sample_of_interest something both
541127 241 241 C T sample_of_interest something both
815175 28977 28977 C T sample_of_interest something both
874649 23709 23709 C T sample_of_interest something both
934071 11287 11296 GTCTGGTTTT G sample_of_interest something both
995638 24914 24914 G C sample_of_interest something both
1054348 28280 28280 G C sample_of_interest something both
1112331 5986 5986 C T sample_of_interest something both
1171810 28281 28281 A T sample_of_interest something both
1229819 14676 14676 C T sample_of_interest something both
1478469 21764 21770 ATACATG A NaN something right_only
```
**subset**
```
(Pdb) outer = pd.merge(left_df, right_df[right_df['sequence_id']=='something'],how='outer',on=['start', 'stop', 'reference', 'alternative'], indicator=True)
outer
start stop reference alternative sequence_id_x sequence_id_y _merge
0 23604 23604 C A sample_of_interest something both
1 8512 8512 A G sample_of_interest something both
2 28270 28271 TA T sample_of_interest something both
3 705 705 A G sample_of_interest NaN left_only
4 28111 28111 A G sample_of_interest something both
5 29442 29442 A G sample_of_interest NaN left_only
6 27972 27972 C T sample_of_interest something both
7 913 913 C T sample_of_interest something both
8 18171 18171 C T sample_of_interest something both
9 21990 21993 TTTA T sample_of_interest something both
10 9190 9190 G T sample_of_interest something both
11 6954 6954 T C sample_of_interest something both
12 5264 5264 A G sample_of_interest NaN left_only
13 28282 28282 T A sample_of_interest something both
14 23063 23063 A T sample_of_interest something both
15 241 241 C T sample_of_interest something both
16 28977 28977 C T sample_of_interest something both
17 23709 23709 C T sample_of_interest something both
18 11287 11296 GTCTGGTTTT G sample_of_interest something both
19 24914 24914 G C sample_of_interest something both
20 28280 28280 G C sample_of_interest something both
21 5986 5986 C T sample_of_interest something both
22 1438 1438 T C sample_of_interest NaN left_only
23 28281 28281 A T sample_of_interest something both
24 14676 14676 C T sample_of_interest something both
25 21764 21770 ATACATG A NaN something right_only
``` | {
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] | closed | false | null | [] | null | 2 | "2021-04-02T13:59:53Z" | "2021-08-19T03:55:38Z" | "2021-08-19T03:55:37Z" | NONE | null | #### Is your feature request related to a problem?
I wish I could keep rows in a dataframe where any data point is missing while using pivot_table method.
#### Describe the solution you'd like
`pivot_table` should get a new parameter `keep_na` that enables keeping the rows of the dataframe containing NANs. Alternatively the current behaviour of `drop_na` could be enhanced to handle not only the all NaN rows, but any NaN rows as well.
#### API breaking implications
`keep_na` must be False (or None) by default to make it backward compatible.
#### Describe alternatives you've considered
The current workaround is to fill the index columns manually with some dummy shit, appy pivot_table, then revert dummy values to NaNs.
#### Additional context
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': ['a', np.nan, 'a', 'a'], 'B': ['b', 'b', np.nan, 'b'], 'X': [5, 10, 15, 20]})
A B X
0 a b 5
1 NaN b 10
2 a NaN 15
3 a b 20
output:
pd.pivot_table(df, index=['A', 'B'], values=['X'], aggfunc=np.sum)
A B X
0 a b 25
expected output:
pd.pivot_table(df, index=['A', 'B'], values=['X'], aggfunc=np.sum, keep_na=True)
A B X
0 a b 25
1 NaN b 10
2 a NaN 15
```
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} | 1 | "2021-04-02T14:05:44Z" | "2021-06-05T20:50:12Z" | "2021-04-02T22:41:20Z" | CONTRIBUTOR | null | part of #40570
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] | closed | false | null | [] | null | 3 | "2021-04-02T15:33:21Z" | "2021-04-03T23:38:19Z" | "2021-04-03T23:38:19Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
#### Code Sample
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({"int": [1,2,3], "float": [1.,2.,3.]})
# Below shows the unexpected differences
df.select_dtypes(np.number)
>>> int float
>>> 0 1 1.0
>>> 1 2 2.0
>>> 2 3 3.0
df[df.columns[df.dtypes.eq(np.number)]]
>>> float
>>> 0 1.0
>>> 1 2.0
>>> 2 3.0
# More explicitly
df.dtypes.eq(np.number) #<- returns False for int64 type
>>> int False
>>> float True
>>> dtype: bool
```
#### Problem description
I realize this may be as intended or just an edge case that has not been handled, but I thought it was worth documenting as an issue. There is, what I would consider, unexpected behavior when trying to filter a dataframe to retrieve numeric columns. As shown in the above examples, when using the `df.select_dtypes(np.number)`, I get back what I would expected. However, when using `df.dtypes.eq(np.number)`, I get back a `pd.Series` noting that the column container an `int64` type is not equivalent to `np.number`.
#### Expected Output
```python
df.dtypes.eq(np.number)
>>> int True
>>> float True
>>> dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
Version : Darwin Kernel Version 18.7.0: Tue Aug 20 16:57:14 PDT 2019; root:xnu-4903.271.2~2/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.4
pytz : 2020.4
dateutil : 2.8.1
pip : 21.0.1
setuptools : 50.3.1.post20201107
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.6.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.4
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.17.0
xlrd : None
xlwt : None
numba : None
</details>
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} | 3 | "2021-04-02T15:52:21Z" | "2021-06-23T13:14:52Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
>>> ser = pd.to_datetime(pd.Series(["2021-03-27"]), format="%Y-%m-%d")
>>> ser
0 2021-03-27
dtype: datetime64[ns]
>>> ser.to_csv()
',0\n0,2021-03-27\n'
>>> ser.astype("category")
0 2021-03-27
dtype: category
Categories (1, datetime64[ns]): [2021-03-27]
>>> ser.astype("category").to_csv()
',0\n0,1616803200000000000\n'
>>> ser.astype("category").to_csv(date_format="%Y-%m-%dT%H:%M:%S")
',0\n0,1616803200000000000\n'
```
#### Problem description
When a categorical datetime is written as CSV, it disregards date_format (even if given explicitly) and writes the timestamp in integer nanoseconds.
#### Expected Output
```python
>>> ser.astype("category").to_csv()
',0\n0,2021-03-27\n'
>>> ser.astype("category").to_csv(date_format="%Y-%m-%dT%H:%M:%S")
',0\n0,2021-03-27T00:00:00\n'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.8-arch1-1
Version : #1 SMP PREEMPT Sun, 21 Mar 2021 01:55:51 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL :
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.5
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.20.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-04-02T17:20:06Z" | "2021-04-08T14:31:38Z" | "2021-04-08T12:46:05Z" | MEMBER | null | on its own this is perf-neutral. It is in conjunction with implementing BlockManager.get_slice in cython that i see real gains | {
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} | 0 | "2021-04-02T17:47:42Z" | "2021-04-05T15:52:27Z" | "2021-04-05T14:56:59Z" | MEMBER | null | In some cases these are pre-emptive for things that pop up when not-yet-pushed .pyi files are implemented | {
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Task is:
- remove `keep-runtime-typing` from:
https://github.com/pandas-dev/pandas/blob/cc84a2383c641998d4817cfaeb33c64f017b7454/.pre-commit-config.yaml#L57
- run `pre-commit run pyupgrade --all-files`
- run `pre-commit run flake8 --all-files`, fixup any errors (probably some about extra imports)
Finally, run `pre-commit run --all-files` (this may take a few minutes), fixup any hooks which don't pass, and when everything passes, open a pull request :rocket: | {
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} | 1 | "2021-04-02T18:38:31Z" | "2021-04-08T14:26:44Z" | "2021-04-08T12:53:37Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 5 | "2021-04-02T19:29:52Z" | "2021-04-13T12:26:52Z" | "2021-04-13T12:24:53Z" | MEMBER | null | The latest version of the theme added the capability to collapse/expand subsections in the sidebar (without directly navigating to the page), see eg the new dropdown arrows on https://pandas.pydata.org/docs/dev/getting_started/index.html
But, an unintended consequence is that the build takes*much* longer (this the reason that the Web / Doc CI build is now always slow on the current open PRs), and this is especially due to our API reference page, which has more than 1000 subpages, and all those pages now have the full navigation structure to all other 1000 pages.
In this PR I am trying to override the base theme template for the sidebar navigation, to not do this for the reference section of our docs.
xref https://github.com/pydata/pydata-sphinx-theme/issues/364 | {
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- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandas as pd
df = pd.DataFrame(
{'kind' : ['a', 'b', 'a'],
'value' : [1, 2, 3]})
res: pd.Series = df['kind'] == 'a'
print(type(res))
```
#### Problem description
`mypy` returns the following error:
```
$ mypy test.py
test.py:7: error: Incompatible types in assignment (expression has type "bool", variable has type
"Series")
res: pd.Series = df['kind'] == 'a'
^
Found 1 error in 1 file (checked 1 source file)
$ mypy --version
mypy 0.812
```
But actual type of the result at runtime is `Series`:
```
$ python test.py
<class 'pandas.core.series.Series'>
```
#### Expected Output
No compile errors.
#### Output of ``pd.show_versions()``
<details>
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.0-70-generic
Version : #78-Ubuntu SMP Fri Mar 19 13:29:52 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
```
</details>
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} | 1 | "2021-04-03T03:51:47Z" | "2021-04-05T15:53:09Z" | "2021-04-05T14:56:17Z" | MEMBER | null | mercifully getting towards the end of these | {
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} | 3 | "2021-04-03T04:26:55Z" | "2021-04-14T14:27:38Z" | "2021-04-14T14:25:47Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
@simonjayhawkins mypy is unhappy about `Timedelta.__new__` potentially returning `NaT`. Thoughts on how to handle this? (it'll come up again in Timestamp and Period)
```
pandas/_libs/tslibs/timedeltas.pyi:51: error: "__new__" must return a class instance (got "Union[Timedelta, NaTType]") [misc]
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} | 6 | "2021-04-03T04:33:24Z" | "2021-04-14T14:14:30Z" | "2021-04-14T12:56:24Z" | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
xref #40719
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} | 5 | "2021-04-03T06:57:48Z" | "2021-04-06T16:03:01Z" | "2021-04-03T09:49:28Z" | MEMBER | null | The doc builds are failing since yesterday (in enhancingperf.rst, where using the cython ipython magic). By comparing the env from the last working / first failing build on master, one of the differences is a version bump of the `decorator` package, which is a dependency of IPython.
So checking if pinning this package fixes the issue. | {
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} | 2 | "2021-04-03T13:26:16Z" | "2021-05-10T09:34:28Z" | "2021-05-10T09:34:28Z" | NONE | null | The min & max markers (the short lines at the ends of the whiskers) aren't visible with the 'dark_background' theme
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import matplotlib.pyplot as plt
# Data is from the pandas plotting demo
# https://pandas.pydata.org/docs/getting_started/intro_tutorials/04_plotting.html
air_quality = pd.read_csv("air_quality_no2.csv", index_col=0, parse_dates=True)
plt.style.use('default')
air_quality.plot.box()
plt.style.use('dark_background')
air_quality.plot.box()
```
#### Problem description
The min & max markers (the short lines at the ends of the whiskers) aren't visible with the 'dark_background' theme
I initially reported this as matplotlib bug here: https://github.com/matplotlib/matplotlib/issues/19778
They concluded it was an issue with pandas.
diegopetrola said: "I also briefly investigated pandas and it seems they have a function called `_get_standard_colors()` in line 83 of the script called `boxplot.py`. I am fairly confident that it is this function that overrides the theme and causes the issue."
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.72-microsoft-standard-WSL2
Version : #1 SMP Wed Oct 28 23:40:43 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.1
pytz : 2020.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0
Cython : 0.29.21
pytest : None
hypothesis : None
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.5.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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- [ ] tests added / passed
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- [ ] whatsnew entry
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] tests added / passed
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] | closed | false | null | [] | null | 5 | "2021-04-03T23:36:01Z" | "2021-04-04T22:38:38Z" | "2021-04-04T15:20:57Z" | NONE | null | _Problem description:_
I'm reading CSV historical prices data with `pandas.read_csv(...),` but the problem is with reading the dates column - I want the values to be in `pandas.Timestamp` type, but the method returns them in `datetime` instead.
_Question:_
**What's wrong here?**
Why does pandas return `<class 'datetime.datetime'>` instead of `<class 'pandas._libs.tslibs.timestamps.Timestamp'>`, and how could I read in the Timestamp?
_Data example:_
```
Local time | Open | High | Low | Close | Volume
28.02.2014 02:00:00.000 GMT+0200 | 1.37067 | 1.3825 | 1.36943 | 1.38042 | 176839
```
_Reading attempts:_
1.
```
data = pd.read_csv(fp, parse_dates = ["Local time"], dtype={'Local time': pd.Timestamp})
#data = pd.read_csv(fp, dtype={'Local time': pd.Timestamp})
```
Result: `TypeError: dtype '<class 'pandas._libs.tslibs.timestamps.Timestamp'>' not understood`.
2.
```
data = pd.read_csv(fp, parse_dates = ["Local time"])
print(type(data['Local time'][0]))
```
Result: `<class 'datetime.datetime'>`.
3.
```
data = pd.read_csv(fp, parse_dates = ["Local time"])
print(type(pd.Timestamp(data['Local time'][0])))
```
Result: `<class 'pandas._libs.tslibs.timestamps.Timestamp'>`. | {
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} | 1 | "2021-04-04T01:47:31Z" | "2021-04-05T16:28:57Z" | "2021-04-05T15:24:48Z" | CONTRIBUTOR | null | `factorize_from_iterable` didn't return an `CategoricalIndex`, if its input was a categorical-like. Instead it returned a plain `Categorical`. This PR fixes that + adds return types.
Also makes the return value for `factorize_from_iterables` clearer and easier to read.
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} | 0 | "2021-04-04T02:32:38Z" | "2021-04-08T14:40:11Z" | "2021-04-08T12:55:38Z" | MEMBER | null | This makes it easier to identify BlockManager methods which are 1D/2D-specific, which lets us push further on #40625 | {
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} | 5 | "2021-04-04T13:59:26Z" | "2021-04-09T07:36:47Z" | "2021-04-09T00:52:25Z" | MEMBER | null | - [x] closes #40044
- [x] xref #39522
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
I spent several hours on this, and I don't think there's a simple solution at the moment (though I'd love to be proved wrong!)
Background:
in Nov 2017, #18222 was opened, in which it was noted that marker info isn't preserved in legends. This is because we were getting legend handles using `leg.legendHandles`:
https://github.com/pandas-dev/pandas/blob/b623a9df3cf3d69fbdddab0f02a03ad69281c8b0/pandas/plotting/_matplotlib/core.py#L574
Due to [an open issue in matplotlib](https://github.com/matplotlib/matplotlib/issues/11357), `leg.legendHandles` doesn't preserve marker info. So, in #27808, the code was updated to use `ax.get_legend_handles_labels`:
https://github.com/pandas-dev/pandas/blob/5d3b492743e199e82e3018bb943545181ed0018f/pandas/plotting/_matplotlib/core.py#L604
This preserves marker info _but_ has the problem that it may contain additional objects which aren't visible in the legend. The result of this is the permutations observed in #39522 .
I think that having wrong colours in the legend, as in #39522 and #40044, is _far_ worse than the legend not preserving marker info. Solving everything together doesn't currently seem doable, at least not until https://github.com/matplotlib/matplotlib/issues/11357 is solved in matplotlib.
So my suggestion is to revert some of the changes in #27808, so that at least the colors are right, and when the matplotlib issue is addressed, then the marker info can be preserved.
----
#39522 on master:
on this branch:
---
#40044 on master:
on this branch:
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} | 2 | "2021-04-04T16:50:54Z" | "2021-04-09T03:37:42Z" | "2021-04-09T03:33:04Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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Behavior:
Wanted:
there is df.style.set_properties(**{'white-space': 'nowrap'}), but I could not find a way to set it as default for pandas.
#### Solution I'd like
Implementation of `pd.options.display.wrap_cells`
#### Alternatives considered
##### Per DataFrame:
```
df.style.set_properties(**{'white-space': 'nowrap'})
```
##### Per Notebook
(from https://stackoverflow.com/a/65371335)
In the first cell of your notebook put
```
%%html
<style>
/* Any CSS style can go in here. */
.dataframe th {
font-size: 18px;
}
.dataframe td {
font-size: 16px;
}
</style>
```
#### Additional context
Tried all of those:
```
pd.options.display.width
pd.options.display.max_colwidth
pd.options.display.expand_frame_repr
```
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} | 5 | "2021-04-05T09:16:48Z" | "2021-10-10T17:42:31Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [x] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
df = pd.read_csv(myfile) #contains 2 columns, Time in ms and Voltage in mV
xlabel = "Time in ms"
ylabel = "Volate in mV"
xlim = [0,120]
ylim = [0,8]
df.plot(x = "Time", xlabel= xlabel, ylabel=ylabel, xlim = xlim, ylim = ylim, kind="line")
plt.show()
```
#### Problem description
I am plotting a large dataset using `dataframe.plot()` in pandas. Dataset contains data in csv format. As per documentation, I specify `xlim `and `ylim` as arg in df.plot. Now the axes take the xlim and ylim values accordingly but the figure does not scale to these values and plot shows some part of of graph but not the area defined by 'xlim' and 'ylim'.
without xlim :
with xlim set :
sample files here: [samples](https://github.com/regmibijay/opencarp-analyzer/tree/main/samples)
project: [project](https://github.com/regmibijay/opencarp-analyzer)
#### Expected Output
I want to plot only the specified area. In my example image it would be the spike.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-19041-Microsoft
Version : #488-Microsoft Mon Sep 01 13:43:00 PST 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.3
IPython : 7.22.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.4.1
numexpr : None
odfpy : None
openpyxl : 3.0.7
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.2
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-05T10:43:49Z" | "2021-04-05T13:21:52Z" | "2021-04-05T13:08:04Z" | MEMBER | null | These checks can be consolidated using multi-line expressions and verbose regex
I think this should make it easier to extend them, so it should be straightforward to port over the remaining ones in code_checks.sh without having to keep increasing the number of hooks
It also reduces the total number of distinct checks (26 now, down from 31 on master), leading to less visual clutter every time we make a commit
The output will still be clear, as it'll just show the line in which an offending pattern was found. E.g. inserting `import conftest` into `pandas/tests/io/formats/test_info.py` results in:
```console
$ pre-commit run unwanted-patterns-in-tests -a
Unwanted patterns in tests...............................................Failed
- hook id: unwanted-patterns-in-tests
- exit code: 1
pandas/tests/io/formats/test_info.py:2:import conftest
``` | {
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} | 2 | "2021-04-05T13:59:54Z" | "2021-04-09T19:51:08Z" | "2021-04-09T19:46:59Z" | MEMBER | null | There's been a couple of recent CI failures due to differences in flake8 versions: #40481, #40457
CI failures due to style checks are really annoying, so I think it's worth adding a check to ensure they're in-sync. There's already a hook with `pyyaml` as additional dependency, so that environment will be reused for this check. | {
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} | 1 | "2021-04-05T14:06:59Z" | "2021-04-09T01:40:08Z" | "2021-04-09T01:40:08Z" | MEMBER | null | According to flake8's creator, [the `Application` class from flake8 isn't meant to be used a library](https://github.com/s-weigand/flake8-nb/issues/111#issuecomment-804997221). As their sole maintainer isn't the original author, the library's internals could well change without warning, and this would break `scripts/validate_docstrings`.
This part probably wants changing:
https://github.com/pandas-dev/pandas/blob/77ea98e9b76f872dd0f2a1f3c8aa7871ae23d8bd/scripts/validate_docstrings.py#L186-L199
We're already writing the docstring to a temporary file, so we could just run `flake8` on that via `subprocess.run` | {
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} | 4 | "2021-04-05T16:26:07Z" | "2021-09-30T15:27:38Z" | "2021-04-13T12:13:36Z" | MEMBER | null | - [x] closes #40533
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them | {
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} | 4 | "2021-04-05T16:33:56Z" | "2021-04-06T11:30:21Z" | "2021-04-06T11:30:15Z" | NONE | null |
#### Problem description
Today I get a error from AutoML in Azuer is `Error: The specified time column, 'SolarDate', contains rows with duplicate timestamps. If your data contains multiple time series, review the time series identifier column setting to define the time series identifiers for your data. If the dataset needs to be aggregated, please provide freq and target_aggregation_function parameters and run AutoML again.`
So I check for duplicates in 'SolarDate' - a datetime column, the result is this column is duplicated at a certain time of day
#### Output of ``pd.show_versions()``
`df[['Solar_Date']]`
```
df['Solar_Date'] = pd.to_datetime(df['Solar_Date'],format="%Y/%m/%d %H:%M:%S")
df[df.duplicated(subset=['Solar_Date'])][['Solar_Date']]
```
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- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 4 | "2021-04-05T18:15:32Z" | "2021-04-05T19:24:18Z" | "2021-04-05T18:37:52Z" | CONTRIBUTOR | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
[notebook](https://colab.research.google.com/drive/1NYuEzyU3QDERoY7KOvD_-HxfGBvG517F?usp=sharing)
```python
In [1]: import pandas as pd
...: df = pd.DataFrame({
...: 'int': [1,2,3],
...: 'flt': [3.14,1.0,2.0],
...: 'str': ['z', 'a', 'b'],
...: 'bool': [True, False, True],
...: }, index=['a','a','b'])
...: df
Out[1]:
int flt str bool
a 1 3.14 z True
a 2 1.00 a False
b 3 2.00 b True
In [2]: df.dtypes
Out[2]:
int int64
flt float64
str object
bool bool
dtype: object
In [3]: df.sum(numeric_only=True) # WAI without level= arg
Out[3]:
int 6.00
flt 6.14
bool 2.00
dtype: float64
In [4]: df.sum()
Out[4]:
int 6
flt 6.14
str zab
bool 2
dtype: object
In [5]: df.sum(level=0, numeric_only=None) # behaves as if numeric_only is True no matter the input
Out[5]:
int flt bool
a 3 4.14 1
b 3 2.00 1
In [6]: df.sum(level=0, numeric_only=True)
Out[6]:
int flt bool
a 3 4.14 1
b 3 2.00 1
In [8]: df.max(level=0, numeric_only=None) # behaves as if numeric_only is None no matter the input
Out[8]:
int flt str bool
a 2 3.14 z True
b 3 2.00 b True
In [9]: df.max(level=0, numeric_only=True)
Out[9]:
int flt str bool
a 2 3.14 z True
b 3 2.00 b True
```
#### Problem description
The documentation for numeric aggregation methods on DataFrame (mean, median, sum, max, ..) indicates that `numeric_only` will drop all but float, int, and boolean columns. This works as expected in the traditional mode, but the argument seems to be ignored when doing a grouped aggregation via the `level=` arg. Some methods (e.g. sum) behave as if numeric_only is always True, some (e.g. max) behave as if its always None.
#### Expected Output
I'd expect that `str` would be dropped iff `numeric_only=True`, all other examples would include the `str` column.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.17-1rodete5-amd64
Version : #1 SMP Debian 5.7.17-1rodete5 (2021-01-08)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.19.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.1
setuptools : 49.2.1
Cython : 0.29.13
pytest : 4.6.11
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.19.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 3.0.0
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.20
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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This PR stops casting when the func is a callable since we can't tell when casting will be more harmful than helpful. I think this results in a more expected/consistent behavior for users. | {
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} | 0 | "2021-04-06T03:41:08Z" | "2021-04-08T14:28:40Z" | "2021-04-08T12:47:18Z" | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 1 | "2021-04-06T04:23:55Z" | "2021-04-14T12:55:46Z" | "2021-04-14T12:55:40Z" | CONTRIBUTOR | null | - [x] closes #40346
- [x] tests added / passed
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- [ ] whatsnew entry - didn't think this was necessary, just added a test case to a solved problem.
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- [x] closes #35689
- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry(N.A.)
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} | 0 | "2021-04-06T07:41:50Z" | "2021-04-06T11:18:42Z" | "2021-04-06T11:18:39Z" | MEMBER | null | This reverts commit 64f08445ae76bc7d3318d1e46c24254087ed5186.
Reverts #40768, as it is now fixed upstream | {
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} | 1 | "2021-04-06T07:46:05Z" | "2021-04-07T16:40:00Z" | "2021-04-07T16:39:57Z" | MEMBER | null | If we have `t.py`:
```python
import pandas as pd
inplace: bool
reveal_type(pd.DataFrame([1,2,3]).reset_index(0, inplace=True, col_fill='a'))
```
then on `master`, the last line would be revelead as `Revealed type is 'Union[pandas.core.frame.DataFrame, None]'`. With the current changes, it will be revealed as `Revealed type is 'None'`
This function takes arguments `level, drop, inplace` - if `drop` isn't passed, then `inplace` needs to be passed by keyword, but `level` can be passed positionally | {
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} | 14 | "2021-04-06T11:35:24Z" | "2021-06-16T14:46:21Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
import io
# replicate the typical data I am reading with pd.rea_csv
dates=np.arange(0,600.1,0.1)
dateString="time, count\n"
for count, time in enumerate(dates):
dateString=dateString+"{:0.3f}, {}\n".format(time, count)
# create dataframe and convert 'time' as a datetime and put it to index.
df=pd.read_csv(io.BytesIO(dateString.encode()),sep=',')
df['date']=pd.to_datetime(df.time, unit='s')
df.set_index('date', inplace=True, drop=False)
df.shift(1, freq='infer') # <-- this is working with pandas 1.1.5 but not any more with 1.2.0 and above
```
```python-traceback
Traceback (most recent call last):
File "debugPandas.py", line 23, in <module>
df.shift(1, freq='infer')
File "\lib\site-packages\pandas\core\frame.py", line 4600, in shift
return super().shift(
File "\lib\site-packages\pandas\core\generic.py", line 9453, in shift
raise ValueError(msg)
ValueError: Freq was not set in the index hence cannot be inferred
```
#### Problem description
I am reading time series results from an other software with `pd.read_csv` and I put the time vector converted as datetime in index. At some stage in the data processing I need to use `dataframe.shift` using `freq='infer'`. This was working fine with pandas 1.1.5 but stopped working when upgrading to pandas 1.2.0 getting a `ValueError`. I could not find any reference of this in the change log so I guess it might be considered as a regression.
I worked it around forcing the proper frequency in the shift function instead of using `infer`
#### Investigations
<details>
Trying to go into more depth in the issue using the above dataframe `df`
```python
print(df.index.inferred_freq)
print(pd._libs.algos.unique_deltas(df.index.asi8))
```
returns for pandas 1.1.5
```
100L
[100000000]
```
and for pandas 1.2.x
```
None
[ 99999999 100000000 100000001]
```
There is some kind of rounding issue when converting the floats serie to datatime serie. This is not the case if the index is created directly from the `dates` ndarray so there might be something with the `read_csv` part.
This is working fine waterver version of pandas:
```python
dates=np.arange(0,600.1,0.1)
df=pd.DataFrame(data=dates, columns=['time'])
df['date']=pd.to_datetime(df.time, unit='s')
df.set_index('date', inplace=True, drop=False)
df.shift(1, freq='infer') # <-- works with pandas 1.2.x
print(df.index.inferred_freq) # --> 100L
print(pd._libs.algos.unique_deltas(df.index.asi8)) # --> [100000000]
```
</details>
#### Output of ``pd.show_versions()``
for pandas 1.2.3
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.14393
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
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 : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
for pandas 1.1.5
<details>
INSTALLED VERSIONS
------------------
commit : b5958ee1999e9aead1938c0bba2b674378807b3d
python : 3.9.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.14393
machine : AMD64
processor : Intel64 Family 6 Model 85 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United States.1252
pandas : 1.1.5
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 52.0.0.post20210125
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 : 7.21.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
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} | 2 | "2021-04-06T12:34:27Z" | "2021-04-07T21:10:19Z" | null | NONE | null | - [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
Note: This code sample requires pygeos, a python interface to the GEOS library. I don't know how to reproduce in a more minimal way without pygeos because the sample requires the numpy ufunc mechanism, which I can't easily exercise with sample code. Maybe someone with more C experience could make a smaller reproduction.
```python
>>> import pandas as pd
>>> import pygeos
>>> pd.array(["POINT (0 0)"])
<StringArray>
['POINT (0 0)']
Length: 1, dtype: string
>>> pygeos.from_wkt(pd.array(["POINT (0 0)"]))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/Caskroom/miniconda/base/envs/geopandas-dev/lib/python3.9/site-packages/pygeos/io.py", line 181, in from_wkt
return lib.from_wkt(geometry, **kwargs)
File "/usr/local/Caskroom/miniconda/base/envs/geopandas-dev/lib/python3.9/site-packages/pandas/core/arrays/numpy_.py", line 254, in __array_ufunc__
result = type(self)(result)
File "/usr/local/Caskroom/miniconda/base/envs/geopandas-dev/lib/python3.9/site-packages/pandas/core/arrays/string_.py", line 195, in __init__
self._validate()
File "/usr/local/Caskroom/miniconda/base/envs/geopandas-dev/lib/python3.9/site-packages/pandas/core/arrays/string_.py", line 200, in _validate
raise ValueError("StringArray requires a sequence of strings or pandas.NA")
ValueError: StringArray requires a sequence of strings or pandas.NA
```
#### Problem description
The `pygeos.from_wkt` function takes an array of strings and returns an array of geometry objects. But when this input array is a pandas `StringArray` we get an error. It seems that the `PandasArray.__array_ufunc__` implementation assumes the results of the ufunc will go into the same type of array as the input, in this case a `StringArray`, which produces an error when the results are not a string. (Credit to @jorisvandenbossche in a comment on pygeos/pygeos#338.)
#### Expected Output
If we pass a numpy array into `pygeos.from_wkt` it works fine and produces an output array of geometries.
```python
>>> import numpy as np
>>> np.array(["POINT (0 0)"])
array(['POINT (0 0)'], dtype='<U11')
>>> pygeos.from_wkt(np.array(["POINT (0 0)"]))
array([<pygeos.Geometry POINT (0 0)>], dtype=object)
```
I would expect the same output using a `StringArray`.
```python
>>> pygeos.from_wkt(pd.array(["POINT (0 0)"]))
array([<pygeos.Geometry POINT (0 0)>], dtype=object)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 20.3.0
Version : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 21.0.1
setuptools : 49.6.0.post20210108
Cython : None
pytest : 6.2.3
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.22.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 0 | "2021-04-06T14:11:13Z" | "2021-04-06T15:25:32Z" | "2021-04-06T15:25:32Z" | CONTRIBUTOR | null | To fix issue #40759 :
Changes here are from PEP585 (e.g. List[str] -> list[str]) and from PEP604 (Optional[str] -> str | None), both of which are only applied in the presence of from __future__ import annotations. Some imports became unused after this so those have been removed.
As there were changes in many files (97) this has been split up into 5 smaller PRs.
This is part 2 of 5.
Refer the discussion here : #40773
- [x] closes #40759
- [] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 1 | "2021-04-06T14:14:52Z" | "2021-04-06T16:18:44Z" | "2021-04-06T15:34:05Z" | CONTRIBUTOR | null | xref #40759 :
Changes here are from PEP585 (e.g. List[str] -> list[str]) and from PEP604 (Optional[str] -> str | None), both of which are only applied in the presence of from future import annotations. Some imports became unused after this so those have been removed.
As there were changes in many files (97) this has been split up into 5 smaller PRs.
This is PR 3 of 5.
Refer the discussion here : #40773
-
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 0 | "2021-04-06T14:16:05Z" | "2021-04-06T15:33:32Z" | "2021-04-06T15:33:32Z" | CONTRIBUTOR | null | xref #40759 :
Changes here are from PEP585 (e.g. List[str] -> list[str]) and from PEP604 (Optional[str] -> str | None), both of which are only applied in the presence of from future import annotations. Some imports became unused after this so those have been removed.
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Refer the discussion here : #40773
- [x] closes #xxxx
- [ ] tests added / passed
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- [ ] whatsnew entry
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} | 3 | "2021-04-06T14:16:50Z" | "2021-04-06T18:21:11Z" | "2021-04-06T18:08:31Z" | CONTRIBUTOR | null | To fix issue #40759 :
Changes here are from PEP585 (e.g. List[str] -> list[str]) and from PEP604 (Optional[str] -> str | None), both of which are only applied in the presence of from future import annotations. Some imports became unused after this so those have been removed.
As there were changes in many files (97) this has been split up into 5 smaller PRs.
This is the final PR 5 of 5.
Checks in this one will pass only when other parts are merged as --keep-runtime-typing is removed the pyupgrade precommit hook.
Refer the discussion here : #40773
- [x] closes #40759
- [ ] tests added / passed
- [ ] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [ ] whatsnew entry
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} | 2 | "2021-04-06T16:39:15Z" | "2021-04-21T16:24:18Z" | "2021-04-21T16:24:18Z" | NONE | null | _Note_: This behaviour might be a bug. I'm assuming its an undocumented feature.
#### Location of the documentation
[pandas.to_datetime.html](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html)
#### Documentation problem
`to_datetime` outputs different types depending on if the timezones are mixed or not (assuming `utc=True` not passed in).
``` python
MIXED = ["2018-10-26 12:00 -0500", "2018-10-26 12:00 -0400"]
UNMIXED = ["2018-10-26 12:00 -0500", "2018-10-26 12:00 -0500"]
print(pandas.to_datetime(MIXED).dtype) # ==> object
print(pandas.to_datetime(UNMIXED).dtype) # ==> datetime64[ns, pytz.FixedOffset(-300)]
```
#### Suggested fix for documentation
Specify aforementioned behaviour in documentation (in the `returns` section). Or, if not intentional feature, update the API to be type-stable. | {
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} | 0 | "2021-04-06T17:33:13Z" | "2021-04-13T23:22:59Z" | "2021-04-13T23:22:59Z" | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
#### Code Sample, a copy-pastable example
```python
import pandas as pd
import numpy as np
X = pd.DataFrame(
{
"col1": [1, np.nan]
}
)
X.fillna({"col1": 2}, downcast={"col1": np.int64})
```
#### Problem description
[The docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html) state that supplying `downcast` as a dict of `item -> dtype` is acceptable here. I would have thought that this would fill NAs in `"col1"` with value `2` and then downcasted `"col1"` to dtype `int64`. Instead an error is raised.
```
AttributeError: 'dict' object has no attribute 'type'
```
#### Expected Output
```
col1
0 1
1 2
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 19.6.0
Version : Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.1
pytz : 2021.1
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.22
pytest : 6.2.2
hypothesis : None
sphinx : 3.5.3
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.6 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 7.21.0
pandas_datareader: None
bs4 : 4.9.3
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.3.4
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : 1.6.1
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
numba : 0.53.0
```
</details>
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] | open | false | null | [] | null | 1 | "2021-04-06T19:03:15Z" | "2021-10-24T18:09:09Z" | null | CONTRIBUTOR | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
#### Code Sample, a copy-pastable example
```python
import numpy
import pandas
class CustomDataFrame(pandas.DataFrame):
@property
def _constructor(self) -> type:
return CustomDataFrame
a = CustomDataFrame({'a': [1,2,3], 'b': ['A','B','C']})
print(type(a))
b = a.astype({'a': numpy.int64, 'b': object})
print(type(b))
```
#### Problem description
If I use `astype` on a DataFrame subclass, I would expect that the DataFrame class do not change, and only dtypes of columns change.
#### Expected Output
```
<class '__main__.CustomDataFrame'>
<class '__main__.CustomDataFrame'>
```
But I get:
```
<class '__main__.CustomDataFrame'>
<class 'pandas.core.frame.DataFrame'>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.8.0-48-generic
Version : #54~20.04.1-Ubuntu SMP Sat Mar 20 13:40:25 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
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
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-06T19:22:25Z" | "2021-04-07T19:51:36Z" | "2021-04-07T19:45:26Z" | MEMBER | null | Just a move, no change except defining `df` in `test_any` since fixture not in scope.
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} | 4 | "2021-04-06T19:33:40Z" | "2021-04-09T07:29:04Z" | "2021-04-09T01:40:08Z" | CONTRIBUTOR | null | - [x] closes #40784
- [ ] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
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} | 3 | "2021-04-06T21:12:03Z" | "2021-04-13T23:09:52Z" | "2021-04-13T23:09:01Z" | CONTRIBUTOR | null | #### Is your feature request related to a problem?
In order to address #38100 , I'd like to update `pandas.core.common.random_state` and anything that uses it to use the new and improved `numpy.random` API introduced in NumPy 1.17. That change is not the subject of this issue/discussion.
However, the easiest/cheapest/cleanest way to implement this would be to assume numpy 1.17, since `random_state` is only used by the `sample` method, rather than implement adapters or defensive programming. The [NEP 29](https://numpy.org/neps/nep-0029-deprecation_policy.html) deprecation policy gives the deprecation date for numpy 1.16 as Jan 13, 2021, so it would be in-line with this policy to deprecate.
I'd like to discuss this proposed change and the approach for how to implement it. In #38100, @jreback said he was "not averse" to this.
#### Describe the solution you'd like
I think that the only change initially would be to update install config files (e.g. setup.cfg) and docs to update the minimum numpy version, but no other code changes.
I think this change should be part of pandas 1.3.0 release. In line with https://github.com/pandas-dev/pandas/blob/master/pyproject.toml#L9, I suggest to make 1.17.3 the minimum numpy version.
Later pull requests would incrementally remove defensive logic to handle numpy 1.16 (e.g. `pandas.compat.numpy.np_version_under1p17`). Enhancements that depend on numpy 1.17 (e.g. using `numpy.random.Generator`) could be implemented in parallel with the work to tidy the compatibility code.
I'm happy if someone else wants to take this on, but otherwise I should be able to contribute some PRs myself.
#### API breaking implications
Mandatory dependency version would be incremented, but this is in line with the published deprecation policy.
pandas functionality should otherwise be unaffected since it already works with numpy 1.17+.
#### Describe alternatives you've considered
Doing nothing would be against the published deprecation policy and delay opportunities to implement enhancements that exploit numpy 1.17.
#### Additional context
N/A
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} | 1 | "2021-04-06T22:10:58Z" | "2021-04-07T13:44:58Z" | "2021-04-07T13:44:54Z" | CONTRIBUTOR | null | The line terminators are not escaped so we don't see the backslash here:
Source : https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html | {
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- [x] tests added / passed
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them
- [x] whatsnew entry
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] | open | false | null | [] | null | 3 | "2021-04-06T22:32:09Z" | "2021-11-01T19:25:02Z" | null | NONE | null | - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of pandas.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.
---
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import datetime
import pandas as pd
from dateutil.tz import tzfile
tz = tzfile("/usr/share/zoneinfo/America/Santiago")
dt_values = [pd.Timestamp('2021-04-03 23:00:00-03:00', tz=tz, freq='H'), pd.Timestamp('2021-04-03 23:00:00-04:00', tz=tz, freq='H'),]
dummy_values = ["first", "second"]
pd.DataFrame({"index": dt_values, "dummy_values": dummy_values}).set_index("index").reindex(["1", "2"])
```
#### Problem description
While the above code should replace the DatetimeIndex of the DataFrame with `["1", "2"]`, it instead causes a segmentation fault.
This seems to only happen during the Daylight Savings transition during this Chilean timezone, and it seems to only happen if the two values are before and after the timezone switch.
While debugging, it seems that sometime between when `self._engine.get_indexer` is called in `pandas/core/indexes/base.py:get_indexer` and when `utcoffset` is called in `dateutil/tz/tz.py` (the most granular I get get via stepping in pdb) - the value of the second index enters a broken state. Some values from within `utcoffset` are shown below.
```
(Pdb) type(dt)
<class 'pandas._libs.tslibs.timestamps.Timestamp'>
(Pdb) dt.to_pydatetime()
datetime.datetime(2021, 4, 3, 23, 0, tzinfo=tzfile('/usr/share/zoneinfo/America/Santiago'))
(Pdb) dt.tzinfo
tzfile('/usr/share/zoneinfo/America/Santiago')
(Pdb) dt.tz
tzfile('/usr/share/zoneinfo/America/Santiago')
# Notable incorrect results
(Pdb) dt.asm8
numpy.datetime64('1970-01-01T00:00:00.000000000')
(Pdb) dt.value
0
(Pdb) dt.to_datetime64()
numpy.datetime64('1970-01-01T00:00:00.000000000')
# Results in a segmentation fault
(Pdb) str(dt)
(Pdb) dt
```
In pandas version `1.1.4` I was able to solve this problem by setting `method="pad"` so that a different indexer would be used. However, in pandas `1.2.3` I get segmentation faults for all 3 values (`bfill`, `ffill`, None).
#### Expected Output
I would expect this to run normally - resetting the index of the DataFrame with the index given
```python
import datetime
import pandas as pd
from dateutil.tz import tzfile
tz = tzfile("/usr/share/zoneinfo/America/Santiago")
dt_values = [pd.Timestamp('2021-04-03 23:00:00-03:00', tz=tz, freq='H'), pd.Timestamp('2021-04-03 23:00:00-04:00', tz=tz, freq='H'),]
dummy_values = ["first", "second"]
pd.DataFrame({"index": dt_values, "dummy_values": dummy_values}).set_index("index").reindex(["1", "2"])
```
```
dummy_values
index
1 first
2 second
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : f2c8480af2f25efdbd803218b9d87980f416563e
python : 3.7.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.2.3
numpy : 1.20.2
pytz : 2021.1
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 : 2.10
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None
</details>
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} | 1 | "2021-04-06T22:41:56Z" | "2021-04-09T07:06:27Z" | "2021-04-07T19:44:38Z" | MEMBER | null | @jorisvandenbossche reports that #40510 was performance-hurting, so this rolls it back until we can sort out why that is. | {
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