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https://api.github.com/repos/pandas-dev/pandas/issues/30715 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30715/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30715/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30715/events | https://github.com/pandas-dev/pandas/pull/30715 | 545,471,645 | MDExOlB1bGxSZXF1ZXN0MzU5MzM5OTU3 | 30,715 | IntervalArray equality follow-ups | {
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} | 4 | 2020-01-05T21:50:47Z | 2020-01-08T15:55:24Z | 2020-01-06T00:22:12Z | MEMBER | null | Follow-ups to #30640 based on @jbrockmendel's comments.
Haven't addressed all the comments yet but pushing this up now so there's a record of it.
Changes thus far:
- Created `tests/arithmetic/test_interval.py ` and moved the tests there
- Used `make_wrapped_comparison_op` to add `__eq__` and `__ne__` to `IntervalArray`. | {
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Using `pandas==0.25.1` with `Python 3.7.1` on Debian, loading the following JSON lines file fails using `pandas.read_json()` but succeeds when read manually.
After looking into this a bit, I think it might be related to `NaN` in the JSON file which is not supported by the spec but accepted by `json.loads()`. If that turns out to be the case, it would be good to have an option to ignore those entries or at least provide a detailed error message.
Data file: https://gist.github.com/danijar/37ba75a6991d61de9e77755329bb5ef4
## Manual
Reading the file manually using `json.loads()` and passing it to a `pd.DataFrame` works fine:
```python
import json
import pandas as pd
with open(filename) as f:
df = pd.DataFrame([json.loads(l) for l in f.readlines()])
print(df) # Shows data frame as expected
```
<details><summary>Terminal output</summary>
```text
step train/return train/length episodes ... value_loss action_loss action_ent fps
0 1000 1.0 500.0 1.0 ... NaN NaN NaN NaN
1 2000 0.0 500.0 2.0 ... NaN NaN NaN NaN
2 3000 163.0 500.0 3.0 ... NaN NaN NaN NaN
3 4000 0.0 500.0 4.0 ... NaN NaN NaN NaN
4 5000 0.0 500.0 5.0 ... NaN NaN NaN NaN
.. ... ... ... ... ... ... ... ... ...
798 383000 0.0 500.0 383.0 ... NaN NaN NaN NaN
799 383000 NaN NaN NaN ... NaN NaN NaN 19.500059
800 384000 0.0 500.0 384.0 ... NaN NaN NaN NaN
801 384000 NaN NaN NaN ... NaN NaN NaN 19.608651
802 385000 1000.0 500.0 385.0 ... NaN NaN NaN NaN
[803 rows x 19 columns]
```
</details>
## Pandas
But reading the same file with `pandas.read_json()` fails with an Pandas internal error:
```python
import pandas as pd
df = pd.read_json(filename, lines=True) # ValueError: Expected object or value
```
<details><summary>Terminal output</summary>
```text
<path-to-python3.7>/site-packages/pandas/io/json/_json.py in read_json(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, numpy, precise_float, date_unit, encoding, lines, chunksize, compression)
590 return json_reader
591
--> 592 result = json_reader.read()
593 if should_close:
594 try:
<path-to-python3.7>/site-packages/pandas/io/json/_json.py in read(self)
713 elif self.lines:
714 data = ensure_str(self.data)
--> 715 obj = self._get_object_parser(self._combine_lines(data.split("\n")))
716 else:
717 obj = self._get_object_parser(self.data)
<path-to-python3.7>/site-packages/pandas/io/json/_json.py in _get_object_parser(self, json)
737 obj = None
738 if typ == "frame":
--> 739 obj = FrameParser(json, **kwargs).parse()
740
741 if typ == "series" or obj is None:
<path-to-python3.7>/site-packages/pandas/io/json/_json.py in parse(self)
847
848 else:
--> 849 self._parse_no_numpy()
850
851 if self.obj is None:
<path-to-python3.7>/site-packages/pandas/io/json/_json.py in _parse_no_numpy(self)
1091 if orient == "columns":
1092 self.obj = DataFrame(
-> 1093 loads(json, precise_float=self.precise_float), dtype=None
1094 )
1095 elif orient == "split":
ValueError: Expected object or value
```
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} | 1 | 2020-01-05T23:29:10Z | 2020-01-06T19:03:46Z | 2020-01-06T18:58:50Z | CONTRIBUTOR | null | Type up methods with a single return value type. | {
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"color": "207d... | closed | false | null | [] | null | 2 | 2020-01-06T00:47:09Z | 2020-01-13T23:16:54Z | 2020-01-13T23:16:53Z | MEMBER | null | This will take a few passes to do comprehensively, but trying to clean up error handling in the extension module. Right now failure points are allowed, which can lead to segfaults or surprising behaviour when debugging.
Trying to push closers towards the CPython error handling conventions:
https://docs.python.org/3/c-api/exceptions.html
So basically:
- Explicitly check for NULL from most C API functions except integer returning functions (where they return -1 and set PyErr) AND
- Don't set error messages from failing functions, as they should have already set it | {
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} | 1 | 2020-01-06T01:04:23Z | 2020-01-06T15:27:19Z | 2020-01-06T13:25:32Z | MEMBER | null | There are 2 remaining non-cosmetic differences between these methods remaining, which will be the subjects of upcoming PRs. Once those are addressed, we'll be able to de-duplicate the methods completely. | {
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} | 1 | 2020-01-06T01:14:01Z | 2020-01-06T17:47:48Z | 2020-01-06T17:36:32Z | MEMBER | null | One of the two non-cosmetic things mentioned in #30720.
There are a bunch of places where DTA or DTI do a compatibility check that for tz_awareness_compat, but not requiring the same tz. This check is analogous to `PeriodArray._check_compatible_with` and `TimedeltaArray._check_compatible_with`, so this adds a kwarg to _check_compatible_with so that we can use _check_compatible_with in all the relevant places and subsequently de-duplicate a bunch of code.
In addition to the comparisons, this is going to be relevant for searchsorted and insert, where we have slightly different behavior in a bunch of EA/Index subclasses. | {
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} | 3 | 2020-01-06T01:20:48Z | 2020-01-06T17:53:25Z | 2020-01-06T17:48:37Z | MEMBER | null | The second of two non-cosmetic changes mentioned in #30720. | {
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} | 6 | 2020-01-06T09:43:32Z | 2020-05-26T09:32:54Z | 2020-04-28T14:48:41Z | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
import skimage
from scipy import signal
for orient in [0, 1]:
th = int(input_img.shape[orient] / 100)
peaks, info = signal.find_peaks(1 - bw_img.mean(orient), prominence=.35, width=2)
for pk, w in zip(peaks, info['widths']):
w *= 2
if orient == 0:
sign = bw_img[:, pk]
else:
sign = bw_img[pk, :]
sign = pd.Series(sign).rolling(th).max()
```
#### Problem description
The above snippet is part of a function called in my main script. Running this results in either a `malloc: Incorrect checksum for freed object 0x7fbf626f1f30: probably modified after being freed.` error or a segmentation fault.
The culprit appears to be the `rolling().max()` line, since commenting out the line fixes the issue, as does replacing `.max()` with `.mean()`.
I can't seem to recreate the error running the above snippet alone, and I cannot figure out why. The input (`bw_img`) is just a 2D array (black and white image).
It might be related to this issue https://github.com/pandas-dev/pandas/issues/25893 expect my memory doesn't seem to be leaking. The two variants I keep seeing seem to be a checksum failed after changing deallocated memory, or that an attempted change of deallocated memory is caught.
python version: 3.6.5 (also tested on 3.7.0)
pandas version 0.25.3 (also tested 0.24 and 0.23)
Below the stacktrace:
```
Process: python3.6 [61410]
Path: /Users/USER/*/python3.6
Identifier: python3.6
Version: ???
Code Type: X86-64 (Native)
Parent Process: zsh [41537]
Responsible: python3.6 [61410]
User ID: 305159407
Date/Time: 2020-01-06 09:43:30.365 +0100
OS Version: Mac OS X 10.14.3 (18D109)
Report Version: 12
Bridge OS Version: 3.0 (14Y674)
Anonymous UUID: 842CB73B-82E5-7A43-1D47-0BCD9BFB56A9
Time Awake Since Boot: 5500 seconds
System Integrity Protection: enabled
Crashed Thread: 0 Dispatch queue: com.apple.main-thread
Exception Type: EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Exception Note: EXC_CORPSE_NOTIFY
Application Specific Information:
abort() called
python(61410,0x1134fe5c0) malloc: Incorrect checksum for freed object 0x7f8c83801610: probably modified after being freed.
Corrupt value: 0x28
Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 libsystem_kernel.dylib 0x00007fff5b5fb23e __pthread_kill + 10
1 libsystem_pthread.dylib 0x00007fff5b6b1c1c pthread_kill + 285
2 libsystem_c.dylib 0x00007fff5b5641c9 abort + 127
3 libsystem_malloc.dylib 0x00007fff5b6736e2 malloc_vreport + 545
4 libsystem_malloc.dylib 0x00007fff5b68786c malloc_zone_error + 184
5 libsystem_malloc.dylib 0x00007fff5b670103 tiny_free_list_remove_ptr + 544
6 libsystem_malloc.dylib 0x00007fff5b66daee tiny_free_no_lock + 933
7 libsystem_malloc.dylib 0x00007fff5b66d631 free_tiny + 483
8 _multiarray_umath.cpython-36m-darwin.so 0x0000000109b3357d _buffer_clear_info + 109
9 _multiarray_umath.cpython-36m-darwin.so 0x0000000109b334ff _dealloc_cached_buffer_info + 79
10 _multiarray_umath.cpython-36m-darwin.so 0x0000000109ae5152 array_dealloc + 18
11 window.cpython-36m-darwin.so 0x000000012c56a8a6 __pyx_fuse_9__pyx_f_6pandas_5_libs_6window__roll_min_max(tagPyArrayObject_fields*, long, long, _object*, _object*, int) + 3206
12 window.cpython-36m-darwin.so 0x000000012c569619 __pyx_fuse_9__pyx_pw_6pandas_5_libs_6window_59roll_max(_object*, _object*, _object*) + 425
13 algos.cpython-36m-darwin.so 0x000000012aa1a42c __pyx_FusedFunction_call + 812
14 python 0x0000000109410ae5 PyObject_Call + 101
15 python 0x00000001094eea1b _PyEval_EvalFrameDefault + 25787
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Thread 2:
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1 ??? 0x0000000054485244 0 + 1414025796
Thread 3:
0 libsystem_pthread.dylib 0x00007fff5b6ae3f8 start_wqthread + 0
1 ??? 0x0000000054485244 0 + 1414025796
Thread 4:
0 libsystem_pthread.dylib 0x00007fff5b6ae3f8 start_wqthread + 0
1 ??? 0x0000000054485244 0 + 1414025796
Thread 5:
0 libsystem_pthread.dylib 0x00007fff5b6ae3f8 start_wqthread + 0
1 ??? 0x0000000054485244 0 + 1414025796
Thread 6:
0 libsystem_pthread.dylib 0x00007fff5b6ae3f8 start_wqthread + 0
1 ??? 0x0000000054485244 0 + 1414025796
Thread 7:
0 libsystem_pthread.dylib 0x00007fff5b6ae3f8 start_wqthread + 0
1 ??? 0x0000000054485244 0 + 1414025796
Thread 8:
0 libsystem_pthread.dylib 0x00007fff5b6ae3f8 start_wqthread + 0
1 ??? 0x0000000054485244 0 + 1414025796
Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000000000000 rbx: 0x00000001134fe5c0 rcx: 0x00007ffee67f8168 rdx: 0x0000000000000000
rdi: 0x0000000000000307 rsi: 0x0000000000000006 rbp: 0x00007ffee67f81a0 rsp: 0x00007ffee67f8168
r8: 0x0000000000000000 r9: 0x00007ffee67f80c0 r10: 0x0000000000000000 r11: 0x0000000000000206
r12: 0x0000000000000307 r13: 0x0000000111e4d000 r14: 0x0000000000000006 r15: 0x000000000000002d
rip: 0x00007fff5b5fb23e rfl: 0x0000000000000206 cr2: 0x00007fff8e27a188
Logical CPU: 0
Error Code: 0x02000148
Trap Number: 133
VM Region Summary:
ReadOnly portion of Libraries: Total=708.7M resident=0K(0%) swapped_out_or_unallocated=708.7M(100%)
Writable regions: Total=483.8M written=0K(0%) resident=0K(0%) swapped_out=0K(0%) unallocated=483.8M(100%)
VIRTUAL REGION
REGION TYPE SIZE COUNT (non-coalesced)
=========== ======= =======
Activity Tracing 256K 2
Dispatch continuations 16.0M 2
Kernel Alloc Once 8K 2
MALLOC 170.5M 33
MALLOC guard page 16K 5
MALLOC_LARGE (reserved) 256K 3 reserved VM address space (unallocated)
STACK GUARD 36K 10
Stack 24.6M 10
VM_ALLOCATE 102.3M 174
VM_ALLOCATE (reserved) 160.0M 4 reserved VM address space (unallocated)
__DATA 42.7M 669
__FONT_DATA 4K 2
__LINKEDIT 253.2M 312
__TEXT 455.5M 557
__UNICODE 564K 2
shared memory 12K 4
=========== ======= =======
TOTAL 1.2G 1775
TOTAL, minus reserved VM space 1.0G 1775
```
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"de... | open | false | null | [] | null | 0 | 2020-01-06T09:58:28Z | 2021-07-25T04:38:45Z | null | NONE | null | The changes associated with #27991 (merged as part of v0.25.1) changed the behavior of `DataFrame._repr_html_()`
We use monkey patching to allow the automatic display (within Jupyter notebooks) of chemical structures stored in DataFrames as part of the RDKit project. Given how useful it is to be able to automatically render specialized data types in DataFrames, I would assume we're not the only ones doing this. For those who are interested, here's an example from the RDKit community demonstrating what this looks like - https://www.blopig.com/blog/2017/02/using-rdkit-to-load-ligand-sdfs-into-pandas-dataframes/
Previous to v0.25.1 we could simply monkey patch `DataFrame.to_html()` since it was called by `DataFrame._repr_html_()`, but now it looks like we need to patch both methods. Doing this kind of patching is always a bit fraught and having to do it twice really seems to be begging for support problems down the road. Note: A single patch to `DataFrameFormatter.to_html()` would also be possible, but it looks like that would make it impossible to for us to disable the specialized rendering on a DataFrame by DataFrame basis.
I'm happy to submit a PR with a fix if the maintainers agree that re-unifying these two is desirable.
I think this should probably be classified as a Cleanup item, but I didn't want to presume.
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] | closed | false | null | [] | null | 5 | 2020-01-06T10:49:08Z | 2020-05-08T16:44:35Z | 2020-05-08T16:44:34Z | NONE | null | - [X] closes #21892
- [ ] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] Add support for "category" dtype in read_json
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"id... | open | false | null | [] | null | 4 | 2020-01-06T11:37:51Z | 2020-04-26T21:12:04Z | null | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
pd.show_versions()
today = pd.to_datetime("today")
df = pd.DataFrame({'date': [today]})
assert isinstance(today, pd.Timestamp)
assert str(df.date.dtype) == 'datetime64[ns]'
delta = df.date - today # works fine
assert str(delta.dtype) == 'timedelta64[ns]'
df.eval("date - @today") # fails
```
Live demo: https://repl.it/repls/SelfassuredFrighteningNumber
#### Problem description
Subtraction works 'normally' but not when used inside DataFrame.eval or .query. AFAIK the two methods should be equivalent. eval fails with:
```python
Traceback (most recent call last):
File "main.py", line 13, in <module>
df.eval("date - @today") # fails
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/frame.py", line 3315, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/eval.py", line 322, in eval
parsed_expr = Expr(expr, engine=engine, parser=parser, env=env, truediv=truediv)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 830, in __init__
self.terms = self.parse()
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 847, in parse
return self._visitor.visit(self.expr)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 441, in visit
return visitor(node, **kwargs)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 447, in visit_Module
return self.visit(expr, **kwargs)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 441, in visit
return visitor(node, **kwargs)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 450, in visit_Expr
return self.visit(node.value, **kwargs)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 441, in visit
return visitor(node, **kwargs)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 565, in visit_BinOp
return self._maybe_evaluate_binop(op, op_class, left, right)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/computation/expr.py", line 536, in _maybe_evaluate_binop
" '{lhs}' and '{rhs}'".format(op=res.op, lhs=lhs.type, rhs=rhs.type)
TypeError: unsupported operand type(s) for -: 'datetime64[ns]' and '<class 'pandas._libs.tslibs.timestamps.Timestamp'>'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-1036-gcp
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1pip : 19.0.3
setuptools : 40.8.0
Cython : Nonepytest : None
hypothesis : None
sphinx : Noneblosc : None
feather : None
xlsxwriter : Nonelxml.etree : None
html5lib : None
pymysql : Nonepsycopg2 : None
jinja2 : None
IPython : Nonepandas_datareader: None
bs4 : None
bottleneck : Nonefastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 8 | 2020-01-06T11:44:08Z | 2020-01-07T12:06:51Z | 2020-01-07T12:06:51Z | NONE | null | Until 2019-11-02, https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com/ contains nightly builds of pandas for different platforms. Is there any chance that this will be continued? I am asking because nightly testing is very helpful for some downstream artifacts to discover unintentional breaking chances or regressions before a new pandas version gets released. Building pandas from source on CI takes very long though, up to a duration where it gets kinda unpractical for downstream projects. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30731/events | https://github.com/pandas-dev/pandas/issues/30731 | 545,692,458 | MDU6SXNzdWU1NDU2OTI0NTg= | 30,731 | KeyError: 0 error on groupby apply | {
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} | [] | closed | false | null | [] | null | 4 | 2020-01-06T12:02:25Z | 2020-06-27T03:54:55Z | 2020-02-17T15:19:42Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
def aggfunc(df):
# operation that rely on df having the grouping column present.
# Goes in again here without the grouping key and if my operation would rely on this, it would fail.
return pd.Series([0.2,0.2], index=[12,13])
mydf=pd.DataFrame({"a":[datetime.datetime.today(),datetime.datetime.today()],"b":[1,2],"c":[5,6]})
mydf.groupby("a").apply(aggfunc)
```
Looks like groupby.apply crashes when using datetime aggregation and returning non-datetime data.
The problem is here: `pandas.core.groupby.generic._recast_datetimelike_result`
`/pandas/core/groupby/generic.py:1857`
```
obj_cols = [
idx for idx in range(len(result.columns)) if is_object_dtype(result.dtypes[idx])
]
```
E.g. My result columns are 12,13 and this is trying to iterate through the 0,1 which is the range.
The code in `/pandas/core/groupby/generic.py:1857` will fail with the above and an exception will be caught here: `pandas/core/groupby/groupby.py:726`. because of gh-20949 it is trying again without the grouping key. It should have worked from the beggining and this exception is not there to catch this kind of error.
The work around for this is to return a Series or DataFrame with the index reset, however this should not be a requirement.
The right way is to not use range in the `_recast_datetimelike_result` function.
Thank you
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.1.1
setuptools : 42.0.2.post20191201
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.6.9
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30732 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30732/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30732/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30732/events | https://github.com/pandas-dev/pandas/issues/30732 | 545,694,356 | MDU6SXNzdWU1NDU2OTQzNTY= | 30,732 | to_csv swallows exception when writing to S3 | {
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"following_url": "https://api.github.com/users/alimcmaster1/following{/other_use... | null | 3 | 2020-01-06T12:07:37Z | 2020-09-05T00:01:23Z | 2020-09-05T00:01:23Z | CONTRIBUTOR | null | I'm not sure if this issue belongs to `pandas` or `s3fs`.
When writing to non-existing bucket or bucket without proper permissions no exception is raised. E.g. the following code will be executed normally:
```python
df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
df.to_csv('s3://very.weird.and.certainly.nonexistent.bucket/data.csv') # No exception
```
In contrast, when writing to a local file without proper permissions results in an exception as it should be:
```python
dff.to_csv('/data.csv') # PermissionError: [Errno 13] Permission denied: '/data.csv'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.1.post20191125
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.0.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.12.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.4.4
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30733 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30733/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30733/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30733/events | https://github.com/pandas-dev/pandas/issues/30733 | 545,697,515 | MDU6SXNzdWU1NDU2OTc1MTU= | 30,733 | Fix SS03 issues in docstrings | {
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Current errors found:
```
$ ./scripts/validate_docstrings.py --errors=SS03
None:None:SS03:pandas.tseries.offsets.DateOffset.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BusinessDay.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BusinessHour.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.CustomBusinessDay.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.CustomBusinessHour.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.MonthOffset.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.MonthEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.MonthBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BusinessMonthEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BusinessMonthBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.CustomBusinessMonthEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.CustomBusinessMonthBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.SemiMonthOffset.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.SemiMonthEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.SemiMonthBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Week.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.WeekOfMonth.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.LastWeekOfMonth.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.QuarterOffset.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BQuarterEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BQuarterBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.QuarterEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.QuarterBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.YearOffset.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BYearEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BYearBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.YearEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.YearBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.FY5253.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.FY5253Quarter.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Easter.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Tick.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Day.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Hour.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Minute.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Second.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Milli.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Micro.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.Nano.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BDay.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BMonthEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.BMonthBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.CBMonthEnd.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.CBMonthBegin.normalize:Summary does not end with a period
None:None:SS03:pandas.tseries.offsets.CDay.normalize:Summary does not end with a period
None:None:SS03:pandas.Timestamp.isoweekday:Summary does not end with a period
None:None:SS03:pandas.Timestamp.weekday:Summary does not end with a period
None:None:SS03:pandas.DatetimeIndex.freqstr:Summary does not end with a period
None:None:SS03:pandas.PeriodIndex.freqstr:Summary does not end with a period
pandas/pandas/core/window/indexers.py:34:SS03:pandas.api.indexers.BaseIndexer:Summary does not end with a period
None:None:SS03:pandas.io.formats.style.Styler.loader:Summary does not end with a period
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} | 1 | 2020-01-06T13:10:35Z | 2020-01-16T23:41:26Z | 2020-01-16T20:30:54Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2020-01-06T13:11:30Z | 2020-01-07T00:01:14Z | 2020-01-07T00:01:14Z | MEMBER | null | ```
In [1]: from pandas.util.testing import assert_frame_equal
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-1-79d99d902fdd> in <module>
----> 1 from pandas.util.testing import assert_frame_equal
ImportError: cannot import name 'assert_frame_equal' from 'pandas.util.testing' (/home/joris/scipy/pandas/pandas/util/testing/__init__.py)
```
It works when accessing from top-level import:
```
In [3]: pd.util.testing.assert_frame_equal
/home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning: pandas._testing.assert_frame_equal is deprecated. Please use pandas.testing.assert_frame_equal instead.
#!/home/joris/miniconda3/envs/dev/bin/python
Out[3]: <function pandas._testing.assert_frame_equal(left, right, check_dtype=True, check_index_type='equiv', check_column_type='equiv', check_frame_type=True, check_less_precise=False, check_names=True, by_blocks=False, check_exact=False, check_datetimelike_compat=False, check_categorical=True, check_like=False, obj='DataFrame')>
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30736 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30736/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30736/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30736/events | https://github.com/pandas-dev/pandas/issues/30736 | 545,722,610 | MDU6SXNzdWU1NDU3MjI2MTA= | 30,736 | Inconsistency/bug when selecting from a data-frame using an unsorted DatetimeIndex | {
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"color": "00... | closed | false | null | [] | null | 2 | 2020-01-06T13:19:52Z | 2021-07-25T04:40:42Z | 2021-07-25T04:40:36Z | NONE | null | #### The following is a MWE of the error:
```python
# Create an index
from_, to_ = pd.to_datetime(['2016-01-01', '2016-06-01'])
index = pd.date_range(from_, to_, freq='1min')
hour = pd.Timedelta('1h')
# Disorder the index:
index = pd.to_datetime(np.random.choice(index, len(index), replace=False))
# Create a DF using that index
df = pd.DataFrame(np.arange(2*len(index)).reshape(-1, 2), index = index)
# Now, select date-range:
df[from_:to_] # ----> Fine! (Unordered)
df[df.index[2]:to_] # ----> Fine! (Unordered)
df[from_:to_ + hour] # ----> KeyError: Timestamp('2016-06-01 01:00:00')
df[str(from_):str(to_ + hour)] # ----> Fine (Unordered)
df.sort_index()[from_:to_ + hour] # ----> Fine (Ordered)
```
#### Problem description
There are quite a few problems with this behavior:
1. It's counter-intuitive that the query will succeed when such slight changes cause differences in behavior.
1. The error-message on the 3nd case is very uninformative, considering what has to be done to fix this problem (sort/convert to string).
1. How come this query works when using items that are already within the index as ranges, but not when using external datetimes?
1. How come using the internal datatype `Timestamp` yields worse results compared to using the external type (`str`)?
####
<Details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 43.0.0
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</Details``>
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] | closed | false | null | [] | null | 2 | 2020-01-06T13:32:31Z | 2020-01-08T20:31:01Z | 2020-01-06T21:37:40Z | MEMBER | null | - [x] closes #23922
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2020-01-06T13:47:43Z | 2020-01-29T12:04:57Z | 2020-01-29T12:04:57Z | MEMBER | null | Currently, the following does not work (but probably should):
```
In [12]: arr1 = pd.array([1, 2, 3])
In [13]: arr2 = pd.array([0, 2])
In [14]: arr1[arr2]
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-14-1646c66d4d26> in <module>
----> 1 arr1[arr2]
~/scipy/pandas/pandas/core/arrays/integer.py in __getitem__(self, item)
375 item = check_bool_array_indexer(self, item)
376
--> 377 return type(self)(self._data[item], self._mask[item])
378
379 def _coerce_to_ndarray(self, dtype=None, na_value=lib._no_default):
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
So that raises the following questions:
- This should probably simply work for the above case? (converting the IntegerArray to a numpy integer array, instead of object array, so numpy's indexing works) We might want to combine this with the boolean array checking?
- What if there are missing values? This should probably simply raise an error for now (which is what pandas 0.25 also does), although we could consider propagating an NA value as well, I think.
For Series, this seems to work partly. For `iloc` it works for the case without missing values. For `__getitem__` you get the same error as above.
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-01-06T14:59:27Z | 2020-01-09T19:19:07Z | 2020-01-09T19:19:07Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> df = (
... pd.DataFrame(
... {
... "name": ["Alice", "Bob"],
... "score": [9.5, 8],
... "employed": [False, True],
... "kids": [0, 0],
... "gender": ["female", "male"],
... }
... )
... .set_index(["name", "employed", "kids", "gender"])
... .unstack(["gender"], fill_value=0)
... )
>>> df.unstack(["employed", "kids"], fill_value=0)
score
gender female male
employed False True False True
kids 0 0 0 0
name
Alice 9.5 NaN 0.0 NaN
Bob NaN 0.0 NaN 8.0
```
#### Problem description
when unstacking with a list of levels on a DataFrame that already has a columns MultiIndex, fill_value is ignored.
#### Expected Output
```python
>>> df.unstack("employed", fill_value=0).unstack("kids", fill_value=0)
score
gender female male
employed False True False True
kids 0 0 0 0
name
Alice 9.5 0.0 0.0 0.0
Bob 0.0 0.0 0.0 8.0
>>>
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 4206fd42cc5cd20204c0c5f192f7e59f204ad48d
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+1622.g4206fd42c
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.6.0.post20191030
Cython : 0.29.13
pytest : 5.2.2
hypothesis : 4.36.2
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : 1.2.2
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.2.2
s3fs : 0.3.4
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : 3.5.1
tabulate : None
xarray : 0.13.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.2
numba : 0.46.0
</details>
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"d... | open | false | null | [] | null | 23 | 2020-01-06T15:25:40Z | 2021-04-09T17:27:56Z | null | CONTRIBUTOR | null | Would it make sense to remove `tests` folder from the pandas distribution? It takes roughly 33% of the whole package weight.
It is especially important when using pandas inside the AWS Lambdas, where the deployment package size is limited to 50 MB zipped and 5 MB might really make a difference.
```
# Uncompressed
du -h -s pandas*
46.5M pandas
30.9M pandas_no_tests
# Compressed
du -h -s pandas*
14.7M pandas.zip
10.1M pandas_no_tests.zip
``` | {
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} | 19 | 2020-01-06T15:30:03Z | 2020-11-25T21:39:25Z | null | MEMBER | null | Master:
```
In [14]: idx = pd.interval_range(0, 1000, 1000)
In [15]: %timeit getattr(idx, '_ndarray_values', idx)
1.29 ms ± 30.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [16]: %timeit idx.closed
321 ns ± 2.66 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
while on 0.25.3:
```
In [13]: idx = pd.interval_range(0, 1000, 1000)
In [14]: %timeit getattr(idx, '_ndarray_values', idx)
90.5 ns ± 2.09 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
In [15]: %timeit idx.closed
105 ns ± 1.61 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
```
(just checked a few attributes, didn't check if it is related to those specific ones or getattr in general)
I think this is a cause / one of the causes of several regressions that can currently be seen at https://pandas.pydata.org/speed/pandas/ (eg https://pandas.pydata.org/speed/pandas/#reshape.Cut.time_cut_timedelta?p-bins=1000&commits=6efc2379-b9de33e3) | {
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} | 6 | 2020-01-06T16:17:46Z | 2020-04-18T18:00:34Z | 2020-02-11T23:01:02Z | CONTRIBUTOR | null | Currently we do not support multi row inserts into sqlite databases
when `to_sql` is passed `method="multi"` - despite the documentation
suggesting that this is supported.
Adding support for this is straightforward - it only needs us
to implement a single method on the SQLiteTable class and so
this PR does just that.
- [x] closes #29921
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 4 | 2020-01-06T16:28:56Z | 2020-01-06T19:28:11Z | 2020-01-06T19:28:11Z | MEMBER | null | Recent regression in the `categoricals.CategoricalSlicing.time_getitem_list` benchmark: https://pandas.pydata.org/speed/pandas/#categoricals.CategoricalSlicing.time_getitem_list?commits=6efc2379-b9de33e3
Reproducible example for this benchmark:
```
N = 10 ** 6
categories = ["a", "b", "c"]
values = [0] * N + [1] * N + [2] * N
data = pd.Categorical.from_codes(values, categories=categories)
list_ = list(range(10000))
%timeit data[list_]
```
Now, this slowdown is due to the changes in https://github.com/pandas-dev/pandas/pull/30308. Categorical `__getitem__` now checks if the key is a boolean indexer: https://github.com/pandas-dev/pandas/pull/30308/files#diff-f3b2ea15ba728b55cab4a1acd97d996d
So this slowdown is of course expected, and also only for Categorical itself (eg pd.Series indexing already handles this boolean checking). So in that light, we can certainly ignore this regression.
But, this led me think: maybe the ExtensionArrays are a good place to start not supporting object dtype as boolean indexer? (and so not add support for it now, which also avoids this performance regression) | {
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} | 5 | 2020-01-06T16:50:02Z | 2020-01-07T00:01:18Z | 2020-01-07T00:01:14Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/30735
This avoids using _DeprecatedModule, which doesn't work for
direct imports from a module. Sorry for the importlib magic, but
I think this is the correct way to do things.
cc @jorisvandenbossche. | {
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- [x] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
We moved our script to numpydoc, and it already had some improvements there. The script does like 80% of our validation, so what I'm doing here is to call numpydoc validation, and then call our custom validation (things that for different reasons weren't moved to numpydoc). | {
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} | 5 | 2020-01-06T17:42:48Z | 2020-01-06T19:29:39Z | 2020-01-06T19:28:11Z | CONTRIBUTOR | null | Convert to an array earlier on.
Closes https://github.com/pandas-dev/pandas/issues/30744 | {
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} | [] | closed | false | null | [] | null | 2 | 2020-01-06T18:13:38Z | 2020-01-06T20:57:37Z | 2020-01-06T20:57:37Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({"A": [[1, 2], [3, 4]], "B": ["x", "y"]}, index=[0, 0])
df.explode("A")
```
Output:
```
A B
0 1 x
0 2 x
0 3 x
0 4 x
0 1 y
0 2 y
0 3 y
0 4 y
```
#### Problem description
We are getting rows with e.g. `A=3` and `B=x`, which never appears in the original data. The `.explode()` method appears to be effectively combining rows with the same index value before splitting them, which is surprising at least to me.
#### Expected Output
```
A B
0 1 x
0 2 x
0 3 y
0 4 y
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.77-70.82.amzn1.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : C.UTF-8
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.2.0
Cython : 0.28.4
pytest : 5.2.0
hypothesis : 4.38.1
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/30749 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30749/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30749/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30749/events | https://github.com/pandas-dev/pandas/pull/30749 | 545,868,404 | MDExOlB1bGxSZXF1ZXN0MzU5NjU1NDM4 | 30,749 | Fix PR08 errors | {
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} | 1 | 2020-01-06T18:22:38Z | 2020-01-06T18:59:49Z | 2020-01-06T18:59:43Z | CONTRIBUTOR | null | Fixes PR08 errors. When I ran the script, a lot of them seem to be false positives, these are the ones I'm pretty sure should be fixed:
```
pandas.infer_freq: Parameter "index" description should start with a capital letter
pandas.MultiIndex.get_loc_level: Parameter "drop_level" description should start with a capital letter
```
Related to #27977 cc @datapythonista
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/30750 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30750/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30750/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30750/events | https://github.com/pandas-dev/pandas/issues/30750 | 545,871,615 | MDU6SXNzdWU1NDU4NzE2MTU= | 30,750 | NA is not included in MultiIndex.levels if we construct MI with nan | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 9 | 2020-01-06T18:30:30Z | 2021-07-25T04:44:22Z | null | MEMBER | null | If we construct MI with `nan`, and check the `levels`, output does not contain `nan`,
```python
>>> tuples = [["A", "B"], ["A", np.nan], ["B", "A"]]
>>> mi = pd.MultiIndex.from_tuples(tuples, names=list("ab"))
>>> mi.levels
FrozenList([['A', 'B'], ['A', 'B']])
```
Tracking it down, this is due to `pd.Categorical` does not include NA in `categories`:
```python
>>> pd.Categorical(['a', 'b', None])
[a, b, NaN]
Categories (2, object): [a, b]
```
While `inferred_type` does indicate it is a mixed type, so `np.nan` should be accepted.
```python
>>> tuples = [["A", "B"], ["A", np.nan], ["B", "A"]]
>>> mi = pd.MultiIndex.from_tuples(tuples, names=list("ab"))
>>> mi.inferred_type
'mixed'
```
However, if the `nan` is gotten by operations, the `nan` is included in levels, e.g.
```python
>>> l = [["a", np.NaN, 12, 12], [None, "a", 12.3, 33.], ["b", np.nan, 12.3, 123], ["a", "a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c", "d"])
>>> grouped = df.groupby(by=["a", "b"], dropna=False).sum()
>>> grouped.index.levels
FrozenList([['a', 'b', nan], ['a', nan]])
```
This is quite inconsistent though, is it an intended behaviour? | {
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} | 6 | 2020-01-06T20:27:00Z | 2020-01-06T23:36:19Z | 2020-01-06T23:36:18Z | NONE | null | Getting a warning emitted from this line of code. Here is the fix I used to get rid of the warning, and here is the code that generated it on my setup (Anaconda3, Windows 10):
```
import pandas as pd
ts_columns = []
for col in df.columns:
if isinstance(df[col].dtype, Timestamp):
ts_columns.append(col)
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-01-06T21:19:39Z | 2020-01-07T01:41:30Z | 2020-01-06T23:58:36Z | MEMBER | null | Also TDI.insert trying to parse strings to Timedelta, which neither DTI nor PI do. | {
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} | 27 | 2020-01-06T21:20:51Z | 2020-03-23T12:06:19Z | 2020-03-23T10:31:11Z | MEMBER | null | - [ ] closes #xxxx
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Unify test cases of #30467 #30708 #30737 | {
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} | 1 | 2020-01-06T21:21:37Z | 2020-01-06T22:26:26Z | 2020-01-06T22:26:25Z | CONTRIBUTOR | null | This was failing the wheel build.
https://travis-ci.org/MacPython/pandas-wheels/jobs/633451994.
I tried briefly to write a code check for this, but didn't succeed. | {
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} | 7 | 2020-01-06T21:26:35Z | 2020-01-09T16:26:05Z | 2020-01-09T13:18:27Z | MEMBER | null | This started out as a cosmetic-only branch and ended up finding a broken corner case. The relevant change is in timedeltas L416 where `if self.freq is not None:` is now `if self.size and self.freq is not None:`
Using _check_compatible_with causes us to raise TypeError instead of ValueError in a couple of the DatetimeIndex.insert cases. | {
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} | 0 | 2020-01-06T21:39:42Z | 2020-01-06T23:52:02Z | 2020-01-06T23:52:02Z | CONTRIBUTOR | null | I don't think we want this
```python
In [1]: import pandas as pd
In [2]: df = pd.DataFrame({"sparse": [1, 2], "b": pd.SparseArray([1, 2])})
/Users/taugspurger/.virtualenvs/pandas-dev/bin/ipython:1: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
#!/Users/taugspurger/Envs/pandas-dev/bin/python
In [3]: df.sparse
Out[3]:
0 1
1 2
Name: sparse, dtype: int64
```
That should instead return the accessor. | {
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} | 1 | 2020-01-06T21:44:39Z | 2020-01-06T23:52:05Z | 2020-01-06T23:52:02Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/30758 | {
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} | 5 | 2020-01-06T22:38:46Z | 2020-01-12T14:55:37Z | 2020-01-12T14:32:16Z | CONTRIBUTOR | null | More typing. | {
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} | 0 | 2020-01-06T22:40:35Z | 2020-01-06T23:39:57Z | 2020-01-06T23:39:57Z | CONTRIBUTOR | null | After moving StringArray to use pd.NA `.astype(object)` had
NA instead of NaN, so the output was object rather than float. | {
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} | 2 | 2020-01-06T23:29:22Z | 2020-01-08T18:17:11Z | 2020-01-08T14:02:13Z | MEMBER | null | also a bug in `DataFrame.asof` with a PeriodIndex returning an incorrectly-named Series. | {
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} | 1 | 2020-01-06T23:31:50Z | 2020-01-07T01:40:36Z | 2020-01-07T00:39:39Z | MEMBER | null | Following this we should be able to use shallow_copy in indexes.extension more, which will help with perf (xref #30717) | {
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} | 5 | 2020-01-06T23:34:09Z | 2020-01-10T17:02:42Z | 2020-01-08T18:41:50Z | CONTRIBUTOR | null | - [x] closes https://github.com/pandas-dev/pandas/pull/30656#discussion_r363060184
- [x] tests added / passed
- modified most tests that use `pd.arrays.SparseArray` to just import `SparseArray`
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
- N/A
For @jreback to review based on his comment previous PR #30656
In a few files (see below), left it as is because usage was pretty local (and allows `pd.arrays.SparseArray` reference to be tested in code)
```bash
$ grep -c -r arrays.SparseArray . | grep -v ":0"
./dtypes/test_generic.py:1
./frame/methods/test_quantile.py:2
./series/test_missing.py:2
```
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} | 25 | 2020-01-06T23:35:31Z | 2020-04-08T17:27:44Z | 2020-04-08T17:22:44Z | CONTRIBUTOR | null | - [x] closes https://github.com/pandas-dev/pandas/issues/29896
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Addresses an issue which appears to have existed since 0.23.0 where bugs in the `get_indexer()` method for the `MultiIndex` class cause incorrect reindexing behavior for multi-indexed DataFrames.
motivating, example, from (the issue)
```python
>>>
>>> df = pd.DataFrame({
... 'a': [0, 0, 0, 0],
... 'b': [0, 2, 3, 4],
... 'c': ['A', 'B', 'C', 'D']
... }).set_index(['a', 'b'])
>>>
>>> df
c
a b
0 0 A
2 B
3 C
4 D
>>> df.index
MultiIndex([(0, 0),
(0, 2),
(0, 3),
(0, 4)],
names=['a', 'b'])
>>> mi_2 = pd.MultiIndex.from_product([[0], [-1, 0, 1, 3, 4, 5]])
>>> mi_2
MultiIndex([(0, -1),
(0, 0),
(0, 1),
(0, 3),
(0, 4),
(0, 5)],
)
```
as expected, without a `method` value:
```python
>>> df.reindex(mi_2)
c
0 -1 NaN
0 A
1 NaN
3 C
4 D
5 NaN
```
using `method="backfill"`, it is:
```python
>>>
>>> df.reindex(mi_2, method="backfill")
c
0 -1 A
0 A
1 D
3 A
4 A
5 C
>>>
```
but should (IMHO) be:
```python
>>> df.reindex(mi_2, method="backfill")
c
0 -1 A
0 A
1 B
3 C
4 D
5 NaN
>>>
```
similarly, using `method="pad"`, it is:
```python
>>> df.reindex(mi_2, method="pad")
c
0 -1 NaN
0 NaN
1 D
3 NaN
4 A
5 C
```
but should (IMHO) be:
```python
>>> df.reindex(mi_2, method="pad")
c
0 -1 NaN
0 A
1 A
3 C
4 D
5 D
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} | 1 | 2020-01-07T00:00:28Z | 2020-01-07T13:33:32Z | 2020-01-07T01:48:04Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-01-07T05:18:48Z | 2020-01-07T16:11:42Z | 2020-01-07T12:16:03Z | MEMBER | null | Small simplification: modify the `breaks` metadata before creating an `IntervalIndex` then create and an `IntervalIndex` from the modified `breaks`. The existing approach creates an `IntervalIndex`, modifies the first `Interval`, then creates a new `IntervalIndex` with the updated first `Interval`.
This yields a slight performance improvement but doesn't seem dramatic enough to warrant a whatsnew entry, though I can add one if desired.
On this branch:
```python
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+1668.ga6c08fc02'
In [2]: a = np.arange(10**5)
In [3]: %timeit pd.qcut(a, 10**4)
273 ms ± 914 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
On `master`:
```python
In [1]: import numpy as np; import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+1667.g40bff2fed'
In [2]: a = np.arange(10**5)
In [3]: %timeit pd.qcut(a, 10**4)
317 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
``` | {
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} | 1 | 2020-01-07T05:50:58Z | 2020-01-07T23:45:46Z | 2020-01-07T23:45:41Z | MEMBER | null | - [x] closes #13230
- [x] closes #13820
- [x] closes #13758
- [x] closes #13228
- [x] closes #13208
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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] | closed | false | null | [] | null | 4 | 2020-01-07T05:59:47Z | 2021-07-25T04:46:56Z | 2021-07-25T04:46:56Z | NONE | null | ## Overview
I found that the number of lines increased when I read a csv file by `pd.read_csv` from S3.
I also confirmed that several rows are duplicated with the transformation of types (their values are transformed into object-type).
This might be related to [pandas.read_csv duplicate issue](https://stackoverflow.com/questions/51790913/pandas-read-csv-duplicate-issue) , but I cannot solve the problem by removing the `.pyc` extension file.
Do you have any ideas about the reason for this phenomenon?
And how should I fix the problem?
## Details
The number of lines of the original S3 file is 34715 (one line is a header).
```
***$ wc -l data/foo.csv
34715 data/foo.csv
```
I build and run the following docker image.
Dockerfile:
```
FROM python:3.6.8-stretch
WORKDIR .
RUN set -x \
apt-get -y install postgresql
COPY ./foo/requirements.txt ./
RUN pip3 install --upgrade pip
RUN pip3 install --no-cache-dir -r requirements.txt
COPY . .
```
pipfile:
```
antiorm==1.2.1
boto3==1.9.218
botocore==1.12.218
certifi==2019.6.16
chardet==3.0.4
connection==2019.4.13
db==0.1.1
docutils==0.15.2
execute==1.2
fsspec==0.4.3
idna==2.8
Jinja2==2.10.1
jinjasql==0.1.7
jmespath==0.9.4
luigi==2.8.8
MarkupSafe==1.1.1
numpy==1.17.0
pandas==0.25.2
psycopg2==2.8.3
public==2019.4.13
python-dateutil==2.8.0
pytz==2019.1
PyYAML==5.1.1
requests==2.22.0
s3fs==0.3.3
s3transfer==0.2.1
six==1.12.0
urllib3==1.25.3
yml==0.0.1
```
docker commands:
```
docker build -t pd-test -f ./Dockerfile .
docker run -it pd-test /bin/bash
```
The number of rows (`df.shape[0]`) increases: 34714 -> 34851
```
$ python
>>> import pandas as pd
>>> df = pd.read_csv("s3://*/.*csv", converters={"foo": str})
sys:1: DtypeWarning: Columns (2,4,6,11,14,17,19,22,25,28,31,34,37,40,46,50,54,55,57,58,59,62,67,68,69,70,74,76,77) have mixed types. Specify dtype option on import or set low_memory=False.
>>> df.shape
(34851, 78)
>>> pd.__version__
'0.25.2'
>>> s3fs.__version__
'0.3.3'
```
Column names are included in the dataframe.
```
>>> df.iloc[34714].values == df.columns
array([ True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True, True, True, True,
True, True, True, True, True, True])
```
I also found that `df.iloc[0]` and `df.iloc[34715]` were the same except that the type of `df.iloc[34715]` was `object`.
On the other hand, the number of lines did not increase without the docker environment.
```
In [2]: df = pd.read_csv("s3://*/*.csv", converters={"foo": str})
*/.pyenv/versions/3.6.1/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2728: DtypeWarning: Columns (46) have mixed types. Specify dtype option on import or set low_memory=False.
interactivity=interactivity, compiler=compiler, result=result)
In [3]: df.shape
Out[3]: (34714, 78)
In [9]: pd.__version__
Out[9]: '0.25.2'
In [11]: s3fs.__version__
Out[11]: '0.3.3'
``` | {
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} | 1 | 2020-01-07T06:20:00Z | 2020-01-07T20:46:58Z | 2020-01-07T20:46:54Z | MEMBER | null | And by `utf-16`, we mean the string `"utf-16"`
Closes https://github.com/pandas-dev/pandas/issues/24130 | {
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} | 3 | 2020-01-07T06:59:05Z | 2020-01-13T08:50:29Z | 2020-01-07T11:50:26Z | NONE | null | For DataFrameGroupBy, if any group contains duplicate index, boxplot will crash. See code below for illustration, setting crash=True will give rise to duplicate index in Group 1 (2nd group), causing boxplot to crash.
```
import pandas as pd
import numpy.random as rnd
crash=True
if crash:
index = pd.date_range(start='1/1/2018', end='1/6/2018').append(pd.date_range(start='1/6/2018', end='1/15/2018'))
else:
index = pd.date_range(start='1/1/2018', end='1/16/2018')
df = pd.DataFrame(data={'value':rnd.randn(16), 'group':[i for i in range(4) for j in range(4)]}, index=index)
dfg = df.groupby('group')
dfg.boxplot(subplots=False)
```
The error stack trace looks like the following:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-51-a9576feccdf0> in <module>
11 df = pd.DataFrame(data={'value':rnd.randn(16), 'group':[i for i in range(4) for j in range(4)]}, index=index)
12 dfg = df.groupby('group')
---> 13 dfg.boxplot(subplots=False)
14 dfg
~/anaconda3/lib/python3.7/site-packages/pandas/plotting/_core.py in boxplot_frame_groupby(grouped, subplots, column, fontsize, rot, grid, ax, figsize, layout, sharex, sharey, **kwds)
498 sharex=sharex,
499 sharey=sharey,
--> 500 **kwds
501 )
502
~/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/boxplot.py in boxplot_frame_groupby(grouped, subplots, column, fontsize, rot, grid, ax, figsize, layout, sharex, sharey, **kwds)
398 keys, frames = zip(*grouped)
399 if grouped.axis == 0:
--> 400 df = pd.concat(frames, keys=keys, axis=1)
401 else:
402 if len(frames) > 1:
~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
256 )
257
--> 258 return op.get_result()
259
260
~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in get_result(self)
471
472 new_data = concatenate_block_managers(
--> 473 mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy
474 )
475 if not self.copy:
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
2057 blocks.append(b)
2058
-> 2059 return BlockManager(blocks, axes)
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in __init__(self, blocks, axes, do_integrity_check)
141
142 if do_integrity_check:
--> 143 self._verify_integrity()
144
145 self._consolidate_check()
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in _verify_integrity(self)
343 for block in self.blocks:
344 if block._verify_integrity and block.shape[1:] != mgr_shape[1:]:
--> 345 construction_error(tot_items, block.shape[1:], self.axes)
346 if len(self.items) != tot_items:
347 raise AssertionError(
~/anaconda3/lib/python3.7/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)
1717 raise ValueError("Empty data passed with indices specified.")
1718 raise ValueError(
-> 1719 "Shape of passed values is {0}, indices imply {1}".format(passed, implied)
1720 )
1721
ValueError: Shape of passed values is (18, 8), indices imply (16, 8)
```
From practical point of view, when people use boxplot, it is not necessary to ensure no duplicate index, therefore, boxplot should work regardless of whether there exist duplicate index or not, it is irrelevant. Interestingly, DataFrame.boxplot does not crash when there exist duplicate index. | {
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} | 26 | 2020-01-07T07:22:50Z | 2020-01-08T22:35:14Z | 2020-01-08T22:34:58Z | MEMBER | null | A script is easier to execute and manage as we manage our style-checking tools.
Started with `flake8`, `black`, and `isort`, as those are the main ones for Python-related changes (for reference, we didn't even have `isort` in the checklist beforehand).
If we're happy with the structure, we can always add more OR modify checks if we want going forward while keeping it easy for folks to check their PR's. | {
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} | 0 | 2020-01-07T11:00:27Z | 2020-01-07T12:09:44Z | 2020-01-07T12:09:44Z | MEMBER | null | Partially addresses:
https://github.com/pandas-dev/pandas/issues/19159 | {
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"description": "Concat, ... | open | false | null | [] | null | 3 | 2020-01-07T11:49:26Z | 2021-07-25T04:48:11Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> df1 = pd.DataFrame()
>>> df2 = pd.DataFrame()
>>> df3 = pd.DataFrame()
>>> df1['1'] = range(0,10)
>>> df2['2'] = range(0,20,2)
>>> df3['2'] = range(0,30,3)
>>> df = pd.concat([df1, df2, df3], axis=1)
>>> df
1 2 2
0 0 0 0
1 1 2 3
2 2 4 6
3 3 6 9
4 4 8 12
5 5 10 15
6 6 12 18
7 7 14 21
8 8 16 24
9 9 18 27
>>> df['2']
2 2
0 0 0
1 2 3
2 4 6
3 6 9
4 8 12
5 10 15
6 12 18
7 14 21
8 16 24
9 18 27
>>> df['2'] = range(0,50,5)
>>> df
1 2 2
0 0 0 0
1 1 5 5
2 2 10 10
3 3 15 15
4 4 20 20
5 5 25 25
6 6 30 30
7 7 35 35
8 8 40 40
9 9 45 45
>>>
```
#### Problem description
**why**
Concat on dataframes containing same column name leads to multiple entries with same column name.(it should append the columns with column_name_1 and column_name_2, similar to merge). On performing actions on the column(as shown in above example) it leads to action replicated to both the columns.
**Version**
3.6.8 (default, Apr 25 2019, 21:02:35) \n[GCC 4.8.5 20150623 (Red Hat 4.8.5-36)]
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-957.12.2.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.0
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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```python
import pandas as pd
pd.__version__
df_1 = pd.DataFrame({'c_bool': [None, None, True, True, True,False]})
print(df_1['c_bool'].dtypes)
df_1['c_bool'] = df_1['c_bool'].astype(bool)
print(df_1['c_bool'].dtypes)
df_1
# '0.24.2'
# object
# bool
# c_bool
# 0 False
# 1 False
# 2 True
# 3 True
# 4 True
# 5 False
```
I have a column that has some None/True/False values. Initially, the datatype of that column is 'object'. After I convert it to bool as datatype, Null values are converting to False. Which is not expected behavior. | {
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2020-01-07T12:44:08Z | 2020-01-08T07:50:07Z | 2020-01-07T21:27:06Z | MEMBER | null | cc @TomAugspurger I propose to keep the old deprecated imports (as long as they are not removed), so the benchmarks can still be run when eg doing a comparison of 0.25 with current master.
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"id"... | closed | false | null | [] | null | 2 | 2020-01-07T13:18:45Z | 2020-01-08T01:24:20Z | 2020-01-08T01:24:20Z | MEMBER | null | I see couple of `FutureWarning` in the benchmarks that can be fixed by simply updating the code to the proposed version.
```
·· /home/runner/work/pandas/pandas/asv_bench/benchmarks/algorithms.py:8: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
from pandas.util import testing as tm
/home/runner/work/pandas/pandas/asv_bench/benchmarks/sparse.py:5: FutureWarning: The pandas.SparseArray class is deprecated and will be removed from pandas in a future version. Use pandas.arrays.SparseArray instead.
from pandas import MultiIndex, Series, SparseArray, date_range
```
See for example: https://github.com/pandas-dev/pandas/pull/30746/checks#step:13:21 | {
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} | 1 | 2020-01-07T14:03:33Z | 2020-01-10T20:42:06Z | 2020-01-09T15:58:02Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [ ] tests added / passed
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- [ ] whatsnew entry
I think this is a bit outdated, since we are using black and it's doing the work for us, right?
according to git blame, this is 2 years old. | {
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```python
df = pd.read_stata('xx.dta')
```
#### Problem description
I was trying to use the above command to load Stata 16 data, but got an error saying
```python
Version of given Stata file is not 104, 105, 108, 111 (Stata 7SE), 113 (Stata 8/9), 114 (Stata 10/11), 115 (Stata 12), 117 (Stata 13), or 118 (Stata 14)
```
I updated pandas to version 0.25.1, the issue persists. How could I load Stata 16 data without degrading the dataset? Thanks.
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} | 7 | 2020-01-07T15:44:56Z | 2020-01-28T04:00:51Z | 2020-01-27T12:29:36Z | MEMBER | null | Closes #27357
This adds an internal version of `take` with the behaviour of setting `_is_copy` for DataFrames that the public `take` did before, so this version can be used in the indexing code (where we want to keep track of parent dataframe with `_is_copy` for SettingWithCopyWarnings).
I named it `_take_with_is_copy` which is literally what it is doing, but happy to hear alternatives.
This then updates the deprecation to fully ignore the keyword and indicate in the deprecation message the keyword has no effect anymore.
I checked https://github.com/pandas-dev/pandas/pull/27349 and https://github.com/pandas-dev/pandas/pull/30615 to ensure that where previously the internal version was used or `is_copy` was specified, now the appropriate function is used. I suppose that in some of the cases where I now use the internal `_take_with_is_copy` this is not actually needed, but it's the safest thing anyway (it will do the same as it did before). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30785 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30785/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30785/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30785/events | https://github.com/pandas-dev/pandas/issues/30785 | 546,379,663 | MDU6SXNzdWU1NDYzNzk2NjM= | 30,785 | Rename api.extensions._no_default to extension.no_default | {
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} | 0 | 2020-01-07T16:27:15Z | 2020-01-07T19:03:02Z | 2020-01-07T19:03:02Z | MEMBER | null | Follow-up on https://github.com/pandas-dev/pandas/pull/30322, which exposed `lib._no_default` in `pandas.api.extensions` | {
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} | 1 | 2020-01-07T16:31:38Z | 2020-01-07T21:00:36Z | 2020-01-07T20:45:35Z | MEMBER | null | Broken off from #30717. | {
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} | 4 | 2020-01-07T16:45:16Z | 2020-04-28T06:00:47Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
series = pd.Series([0, 1, 2, 3, 4, pd.np.nan, 6, 7], dtype='Int64')
breaks = [0, 2, 4, 6, 8]
breaks_cut = pd.cut(series, breaks)
breaks_cut
```
```
0 NaN
1 (0.0, 2.0]
2 (0.0, 2.0]
3 (2.0, 4.0]
4 (2.0, 4.0]
5 NaN
6 (0.0, 2.0]
7 (6.0, 8.0]
dtype: category
Categories (4, interval[int64]): [(0, 2] < (2, 4] < (4, 6] < (6, 8]]
```
#### Problem Description
When using the `pd.Int64` nullable integer data type, `pd.cut()` unexpectedly bins the first non-`np.nan` value after an `np.nan` into the lowest interval. In the above example, the number `6` is binned into `(0.0, 2.0]`.
#### Expected Output
```
0 NaN
1 (0.0, 2.0]
2 (0.0, 2.0]
3 (2.0, 4.0]
4 (2.0, 4.0]
5 NaN
6 (4.0, 6.0]
7 (6.0, 8.0]
dtype: category
Categories (4, interval[int64]): [(0, 2] < (2, 4] < (4, 6] < (6, 8]]
```
Note that using an `IntervalIndex` produces the expected output.
```python
import pandas as pd
series = pd.Series([0, 1, 2, 3, 4, pd.np.nan, 6, 7], dtype='Int64')
breaks = [0, 2, 4, 6, 8]
intervals = [pd.Interval(x, y) for x, y in zip(breaks[:-1], breaks[1:])]
interval_index = pd.IntervalIndex(intervals)
interval_cut = pd.cut(series, interval_index)
interval_cut
```
#### Output of `pd.show_versions()`
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-37-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200102
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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} | 2 | 2020-01-07T17:05:11Z | 2020-01-07T19:03:06Z | 2020-01-07T19:03:02Z | CONTRIBUTOR | null | Changes lib._no_default to lib.no_default, uses it in more places.
Closes #30785 | {
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} | 8 | 2020-01-07T17:25:23Z | 2020-01-09T08:26:16Z | 2020-01-09T02:57:55Z | MEMBER | null | Todo item of https://github.com/pandas-dev/pandas/issues/29556, consolidating common code for IntegerArray and BooleanArray.
This is only a start, there is more to share. | {
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} | 27 | 2020-01-07T18:06:36Z | 2020-11-25T21:43:34Z | 2020-11-25T21:43:34Z | MEMBER | null | I ran a full benchmark on a separate machine locally, comparing current master against 0.25.3.
Some identified cases:
- [x] Indexing slowdown due to `extract_array`, reproducer below at https://github.com/pandas-dev/pandas/issues/30790#issuecomment-572928516
- [ ] `Index.__new__`, reproducer below at https://github.com/pandas-dev/pandas/issues/30790#issuecomment-571959377
- [ ] IntervalIndex (or all ExtensionIndex?) attribute access: https://github.com/pandas-dev/pandas/issues/30742
- [x] `asof` due to additional copy / take: https://github.com/pandas-dev/pandas/pull/30615/#issuecomment-571531394
Full results:
<details>
```
before after ratio
[62a87bf4] [526b2f36]
<v0.25.3^0> <benchmarks-run>
+ 31.4±0.4ms 127±1ms 4.03 eval.Eval.time_chained_cmp('python', 'all')
+ 41.7±0.4ms 129±0.9ms 3.08 eval.Eval.time_chained_cmp('python', 1)
+ 12.5±0.04μs 37.1±0.2μs 2.98 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string', 'non_monotonic')
+ 14.8±0.07μs 38.3±0.6μs 2.59 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string', 'unique_monotonic_inc')
+ 87.8±8μs 220±20μs 2.50 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+ 58.4±0.2μs 142±0.3μs 2.43 ctors.SeriesDtypesConstructors.time_index_from_array_string
+ 208±2μs 502±3μs 2.41 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'transformation')
+ 208±1μs 497±2μs 2.39 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct')
+ 210±0.7μs 499±7μs 2.38 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation')
+ 209±2μs 496±3μs 2.37 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct')
+ 210±2μs 497±2μs 2.37 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation')
+ 213±2μs 504±6μs 2.37 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'transformation')
+ 20.8±0.3μs 49.2±0.6μs 2.36 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string', 'nonunique_monotonic_inc')
+ 210±1μs 497±4μs 2.36 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct')
+ 213±2μs 503±2μs 2.36 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct')
+ 214±3μs 504±2μs 2.36 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'transformation')
+ 213±2μs 500±3μs 2.35 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'direct')
+ 218±2μs 513±2μs 2.35 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct')
+ 215±1μs 505±2μs 2.35 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct')
+ 213±0.9μs 499±3μs 2.34 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation')
+ 219±2μs 514±2μs 2.34 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'transformation')
+ 215±0.9μs 504±3μs 2.34 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'transformation')
+ 214±1μs 501±3μs 2.34 groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'transformation')
+ 219±1μs 512±2μs 2.33 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'transformation')
+ 219±2μs 511±1μs 2.33 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'transformation')
+ 213±2μs 497±3μs 2.33 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'direct')
+ 220±2μs 514±2μs 2.33 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct')
+ 220±2μs 513±3μs 2.33 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct')
+ 221±2μs 512±4μs 2.32 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'transformation')
+ 216±2μs 500±2μs 2.32 groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'direct')
+ 220±2μs 509±1μs 2.32 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct')
+ 217±1μs 498±2μs 2.30 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'direct')
+ 218±2μs 498±2μs 2.29 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'transformation')
+ 219±0.5μs 497±1μs 2.27 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'direct')
+ 218±0.6μs 496±0.9μs 2.27 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'transformation')
+ 583±3ms 1.32±0s 2.27 groupby.Apply.time_copy_overhead_single_col
+ 223±0.7μs 495±0.8μs 2.22 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'direct')
+ 1.47±0.01s 3.26±0.01s 2.21 groupby.Apply.time_copy_function_multi_col
+ 223±1μs 494±0.6μs 2.21 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'transformation')
+ 28.2±2ms 62.2±0.4ms 2.21 frame_methods.Apply.time_apply_ref_by_name
+ 7.63±0.2ms 16.8±0.1ms 2.20 timeseries.AsOf.time_asof('DataFrame')
+ 228±0.9μs 494±1μs 2.17 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct')
+ 229±0.4μs 493±1μs 2.15 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'transformation')
+ 237±1μs 509±0.2μs 2.14 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'direct')
+ 238±0.4μs 509±0.6μs 2.14 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'transformation')
+ 231±0.9μs 493±0.8μs 2.14 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'transformation')
+ 234±5μs 500±1μs 2.14 groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'direct')
+ 231±0.9μs 491±0.5μs 2.13 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'direct')
+ 236±3μs 499±2μs 2.12 groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'transformation')
+ 250±0.4μs 511±1μs 2.05 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'direct')
+ 253±0.5μs 517±1μs 2.04 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'transformation')
+ 250±0.6μs 509±0.5μs 2.03 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'transformation')
+ 255±2μs 517±2μs 2.03 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'direct')
+ 2.16±0ms 4.32±0.3ms 2.00 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'sum')
+ 285±3μs 566±7μs 1.99 groupby.GroupByMethods.time_dtype_as_field('datetime', 'last', 'transformation')
+ 2.17±0.02ms 4.30±0.7ms 1.99 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'sum')
+ 285±3μs 564±2μs 1.97 groupby.GroupByMethods.time_dtype_as_field('datetime', 'last', 'direct')
+ 296±0.9μs 577±3μs 1.94 groupby.GroupByMethods.time_dtype_as_field('datetime', 'first', 'transformation')
+ 25.3±0.2μs 49.2±0.4μs 1.94 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+ 2.29±0.01ms 4.46±0.3ms 1.94 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'mean')
+ 298±2μs 577±1μs 1.93 groupby.GroupByMethods.time_dtype_as_field('datetime', 'first', 'direct')
+ 2.30±0.04ms 4.43±0.6ms 1.93 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'mean')
+ 285±1μs 545±0.4μs 1.91 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'direct')
+ 285±1μs 544±2μs 1.91 groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'direct')
+ 285±1μs 544±1μs 1.91 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'transformation')
+ 286±0.5μs 545±0.8μs 1.91 groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'transformation')
+ 290±1μs 552±2μs 1.90 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'transformation')
+ 289±2μs 551±1μs 1.90 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'transformation')
+ 289±3μs 548±0.8μs 1.90 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'direct')
+ 290±0.6μs 549±0.6μs 1.90 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'direct')
+ 2.96±0.2ms 5.50±0.4ms 1.86 rolling.ExpandingMethods.time_expanding('Series', 'int', 'sum')
+ 312±3μs 572±1μs 1.83 groupby.GroupByMethods.time_dtype_as_field('datetime', 'max', 'transformation')
+ 311±2μs 569±3μs 1.83 groupby.GroupByMethods.time_dtype_as_field('datetime', 'max', 'direct')
+ 327±3μs 599±10μs 1.83 groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'direct')
+ 330±2μs 596±2μs 1.81 groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'transformation')
+ 327±1μs 583±0.9μs 1.78 groupby.GroupByMethods.time_dtype_as_field('datetime', 'min', 'direct')
+ 326±1μs 582±2μs 1.78 groupby.GroupByMethods.time_dtype_as_field('datetime', 'min', 'transformation')
+ 3.24±0.2ms 5.72±0.4ms 1.76 rolling.ExpandingMethods.time_expanding('Series', 'int', 'mean')
+ 345±3μs 607±3μs 1.76 groupby.GroupByMethods.time_dtype_as_field('float', 'first', 'transformation')
+ 347±2μs 610±3μs 1.76 groupby.GroupByMethods.time_dtype_as_field('float', 'first', 'direct')
+ 3.32±0.04ms 5.77±0.4ms 1.74 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'kurt')
+ 2.19±0.01ms 3.80±0.07ms 1.73 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest')
+ 1.98±0.02ms 3.44±0.4ms 1.73 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'sum')
+ 2.19±0.01ms 3.79±0.02ms 1.73 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'lower')
+ 2.20±0.01ms 3.79±0.02ms 1.73 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear')
+ 2.19±0.02ms 3.79±0.1ms 1.73 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'higher')
+ 2.20±0.01ms 3.79±0ms 1.73 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'lower')
+ 2.20±0.01ms 3.79±0.02ms 1.72 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher')
+ 2.19±0.01ms 3.77±0.01ms 1.72 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'midpoint')
+ 3.37±0.07ms 5.79±0.5ms 1.72 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'kurt')
+ 2.20±0.02ms 3.78±0.08ms 1.72 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'linear')
+ 2.20±0.01ms 3.78±0.01ms 1.72 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint')
+ 2.20±0.01ms 3.77±0.01ms 1.72 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'nearest')
+ 2.18±0ms 3.74±0.07ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest')
+ 355±2μs 608±4μs 1.71 groupby.GroupByMethods.time_dtype_as_field('float', 'sum', 'transformation')
+ 2.17±0ms 3.72±0.07ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'midpoint')
+ 2.18±0.01ms 3.73±0.1ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'higher')
+ 2.18±0.01ms 3.73±0.09ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'midpoint')
+ 354±2μs 606±1μs 1.71 groupby.GroupByMethods.time_dtype_as_field('float', 'sum', 'direct')
+ 3.24±0.2ms 5.54±0.4ms 1.71 rolling.Methods.time_rolling('Series', 1000, 'int', 'sum')
+ 390±0.8μs 666±1μs 1.71 groupby.GroupByMethods.time_dtype_as_field('float', 'ffill', 'transformation')
+ 2.18±0.01ms 3.72±0.1ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear')
+ 2.18±0.01ms 3.71±0.08ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'linear')
+ 2.18±0.01ms 3.73±0.08ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'lower')
+ 2.18±0ms 3.72±0.07ms 1.71 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'higher')
+ 355±2μs 606±4μs 1.70 groupby.GroupByMethods.time_dtype_as_field('float', 'mean', 'direct')
+ 2.18±0.01ms 3.71±0.07ms 1.70 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest')
+ 356±0.6μs 606±2μs 1.70 groupby.GroupByMethods.time_dtype_as_field('float', 'mean', 'transformation')
+ 2.18±0.01ms 3.71±0.08ms 1.70 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'lower')
+ 389±0.8μs 662±1μs 1.70 groupby.GroupByMethods.time_dtype_as_field('float', 'bfill', 'transformation')
+ 357±1μs 607±2μs 1.70 groupby.GroupByMethods.time_dtype_as_field('float', 'prod', 'direct')
+ 390±0.9μs 662±2μs 1.70 groupby.GroupByMethods.time_dtype_as_field('float', 'ffill', 'direct')
+ 390±0.6μs 661±2μs 1.70 groupby.GroupByMethods.time_dtype_as_field('float', 'bfill', 'direct')
+ 3.28±0.06ms 5.55±0.5ms 1.69 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'skew')
+ 3.36±0.2ms 5.68±0.4ms 1.69 rolling.Methods.time_rolling('Series', 1000, 'int', 'mean')
+ 352±0.5μs 595±0.4μs 1.69 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'transformation')
+ 359±2μs 605±3μs 1.69 groupby.GroupByMethods.time_dtype_as_field('float', 'prod', 'transformation')
+ 352±0.9μs 594±2μs 1.69 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'direct')
+ 368±2μs 619±2μs 1.68 groupby.GroupByMethods.time_dtype_as_field('float', 'var', 'transformation')
+ 366±2μs 616±4μs 1.68 groupby.GroupByMethods.time_dtype_as_field('float', 'min', 'transformation')
+ 369±2μs 620±0.8μs 1.68 groupby.GroupByMethods.time_dtype_as_field('float', 'var', 'direct')
+ 366±1μs 612±1μs 1.67 groupby.GroupByMethods.time_dtype_as_field('float', 'min', 'direct')
+ 364±0.7μs 608±2μs 1.67 groupby.GroupByMethods.time_dtype_as_field('float', 'max', 'transformation')
+ 364±1μs 607±2μs 1.67 groupby.GroupByMethods.time_dtype_as_field('float', 'max', 'direct')
+ 395±0.5μs 659±0.8μs 1.67 groupby.GroupByMethods.time_dtype_as_field('datetime', 'bfill', 'direct')
+ 395±0.7μs 659±2μs 1.67 groupby.GroupByMethods.time_dtype_as_field('datetime', 'ffill', 'direct')
+ 394±0.6μs 657±2μs 1.67 groupby.GroupByMethods.time_dtype_as_field('datetime', 'bfill', 'transformation')
+ 395±0.1μs 655±1μs 1.66 groupby.GroupByMethods.time_dtype_as_field('datetime', 'ffill', 'transformation')
+ 392±1μs 649±1μs 1.65 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'transformation')
+ 393±2μs 650±1μs 1.65 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct')
+ 392±3μs 646±3μs 1.65 groupby.GroupByMethods.time_dtype_as_field('float', 'median', 'transformation')
+ 391±2μs 644±2μs 1.65 groupby.GroupByMethods.time_dtype_as_field('float', 'median', 'direct')
+ 3.86±0.1ms 6.34±0.03ms 1.64 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'count')
+ 397±0.6μs 652±2μs 1.64 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'transformation')
+ 410±0.5μs 671±2μs 1.64 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'transformation')
+ 410±2μs 670±1μs 1.63 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'direct')
+ 398±1μs 650±1μs 1.63 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'direct')
+ 421±1μs 688±0.8μs 1.63 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'transformation')
+ 2.87±0.2ms 4.68±0.3ms 1.63 rolling.ExpandingMethods.time_expanding('Series', 'float', 'sum')
+ 422±2μs 688±1μs 1.63 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'direct')
+ 419±2μs 684±3μs 1.63 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'direct')
+ 404±2μs 658±2μs 1.63 groupby.GroupByMethods.time_dtype_as_field('int', 'last', 'direct')
+ 418±1μs 682±3μs 1.63 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'transformation')
+ 405±2μs 659±2μs 1.63 groupby.GroupByMethods.time_dtype_as_field('int', 'last', 'transformation')
+ 421±2μs 684±2μs 1.62 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'direct')
+ 422±0.8μs 681±2μs 1.61 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'transformation')
+ 7.11±0.09ms 11.4±0.3ms 1.61 timeseries.AsOf.time_asof_nan('DataFrame')
+ 429±2μs 686±1μs 1.60 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'direct')
+ 420±1μs 671±0.8μs 1.60 groupby.GroupByMethods.time_dtype_as_field('int', 'first', 'transformation')
+ 421±0.9μs 672±1μs 1.60 groupby.GroupByMethods.time_dtype_as_field('int', 'first', 'direct')
+ 427±0.7μs 681±1μs 1.60 groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'transformation')
+ 427±1μs 681±2μs 1.59 groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'direct')
+ 430±3μs 687±0.7μs 1.59 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'transformation')
+ 448±1μs 708±2μs 1.58 groupby.GroupByMethods.time_dtype_as_group('float', 'ffill', 'transformation')
+ 449±0.7μs 708±2μs 1.58 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'direct')
+ 448±1μs 706±1μs 1.58 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'transformation')
+ 449±1μs 706±2μs 1.57 groupby.GroupByMethods.time_dtype_as_group('float', 'ffill', 'direct')
+ 2.02±0.02ms 3.17±0.6ms 1.57 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'sum')
+ 453±2μs 710±3μs 1.57 groupby.GroupByMethods.time_dtype_as_group('int', 'bfill', 'direct')
+ 452±2μs 707±1μs 1.57 groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'direct')
+ 3.85±0.06ms 6.02±0.02ms 1.56 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'count')
+ 450±1μs 703±0.9μs 1.56 groupby.GroupByMethods.time_dtype_as_field('int', 'bfill', 'transformation')
+ 449±1μs 700±1μs 1.56 groupby.GroupByMethods.time_dtype_as_field('int', 'ffill', 'transformation')
+ 3.91±0.07ms 6.09±0.06ms 1.56 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'count')
+ 452±2μs 705±2μs 1.56 groupby.GroupByMethods.time_dtype_as_group('int', 'bfill', 'transformation')
+ 453±2μs 706±2μs 1.56 groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'transformation')
+ 450±1μs 700±1μs 1.56 groupby.GroupByMethods.time_dtype_as_field('int', 'bfill', 'direct')
+ 4.12±0.1ms 6.41±0.04ms 1.55 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'count')
+ 450±1μs 700±0.9μs 1.55 groupby.GroupByMethods.time_dtype_as_field('int', 'ffill', 'direct')
+ 3.16±0.02ms 4.91±0.09ms 1.55 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'min')
+ 4.04±0.06ms 6.27±0.1ms 1.55 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'count')
+ 3.11±0.01ms 4.81±0.08ms 1.55 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'higher')
+ 447±1μs 691±1μs 1.55 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'direct')
+ 3.11±0.01ms 4.81±0.08ms 1.55 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'linear')
+ 446±0.9μs 689±0.9μs 1.55 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'transformation')
+ 521±1μs 805±2μs 1.54 groupby.GroupByMethods.time_dtype_as_field('datetime', 'quantile', 'transformation')
+ 446±2μs 688±2μs 1.54 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'transformation')
+ 3.17±0.01ms 4.89±0.08ms 1.54 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'max')
+ 448±0.8μs 690±0.4μs 1.54 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'transformation')
+ 448±1μs 691±1μs 1.54 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'direct')
+ 1.83±0.01ms 2.83±0.3ms 1.54 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'sum')
+ 3.13±0.02ms 4.82±0.06ms 1.54 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'lower')
+ 442±0.5μs 680±0.4μs 1.54 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'transformation')
+ 3.10±0.02ms 4.76±0.08ms 1.54 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'max')
+ 444±0.7μs 682±1μs 1.53 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'transformation')
+ 13.7±0.9μs 21.1±0.2μs 1.53 algorithms.MaybeConvertObjects.time_maybe_convert_objects
+ 441±0.8μs 676±0.8μs 1.53 groupby.GroupByMethods.time_dtype_as_field('int', 'max', 'direct')
+ 442±0.8μs 679±0.7μs 1.53 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'direct')
+ 448±2μs 687±2μs 1.53 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'direct')
+ 3.13±0.01ms 4.80±0.07ms 1.53 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'midpoint')
+ 4.08±0.06ms 6.26±0.1ms 1.53 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'count')
+ 524±2μs 803±2μs 1.53 groupby.GroupByMethods.time_dtype_as_field('datetime', 'quantile', 'direct')
+ 479±1μs 735±0.2μs 1.53 groupby.GroupByMethods.time_dtype_as_field('int', 'var', 'transformation')
+ 3.13±0.01ms 4.80±0.07ms 1.53 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'linear')
+ 3.10±0.01ms 4.76±0.07ms 1.53 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'lower')
+ 447±0.4μs 685±2μs 1.53 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'transformation')
+ 3.13±0.01ms 4.79±0.08ms 1.53 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'nearest')
+ 443±0.7μs 678±1μs 1.53 groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'transformation')
+ 445±0.7μs 681±0.9μs 1.53 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'direct')
+ 3.13±0.01ms 4.79±0.06ms 1.53 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'higher')
+ 443±0.5μs 677±1μs 1.53 groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'direct')
+ 3.11±0.01ms 4.76±0.09ms 1.53 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'midpoint')
+ 447±2μs 683±1μs 1.53 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'direct')
+ 3.14±0.03ms 4.79±0.09ms 1.53 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'min')
+ 3.12±0.01ms 4.75±0.08ms 1.52 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'nearest')
+ 3.28±0.02ms 4.99±0.09ms 1.52 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'max')
+ 3.29±0.05ms 5.01±0.1ms 1.52 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'min')
+ 4.40±0.3ms 6.68±0.4ms 1.52 rolling.ExpandingMethods.time_expanding('Series', 'int', 'kurt')
+ 18.8±0.2μs 28.6±0.3μs 1.52 categoricals.CategoricalSlicing.time_getitem_list_like('monotonic_incr')
+ 444±1μs 674±2μs 1.52 groupby.GroupByMethods.time_dtype_as_field('int', 'max', 'transformation')
+ 481±3μs 731±2μs 1.52 groupby.GroupByMethods.time_dtype_as_field('int', 'var', 'direct')
+ 497±3μs 753±2μs 1.52 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'transformation')
+ 19.0±0.4μs 28.8±0.3μs 1.51 categoricals.CategoricalSlicing.time_getitem_list_like('monotonic_decr')
+ 497±3μs 753±2μs 1.51 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'direct')
+ 2.17±0.02ms 3.28±0.6ms 1.51 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'mean')
+ 3.26±0.01ms 4.90±0.01ms 1.51 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'min')
+ 3.24±0.01ms 4.87±0.02ms 1.50 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'max')
+ 19.1±0.5μs 28.6±0.2μs 1.50 categoricals.CategoricalSlicing.time_getitem_list_like('non_monotonic')
+ 2.02±0.04ms 3.03±0.4ms 1.50 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'sum')
+ 3.28±0.01ms 4.91±0.01ms 1.50 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'max')
+ 106±0.5μs 158±1μs 1.50 timeseries.SortIndex.time_sort_index(True)
+ 3.33±0.05ms 4.96±0.01ms 1.49 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'min')
+ 3.16±0.01ms 4.70±0.01ms 1.49 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'max')
+ 3.16±0.02ms 4.70±0.02ms 1.49 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'midpoint')
+ 3.17±0.01ms 4.70±0.03ms 1.48 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'lower')
+ 3.17±0.01ms 4.70±0.03ms 1.48 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'linear')
+ 3.16±0.01ms 4.69±0.02ms 1.48 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'higher')
+ 4.55±0.3ms 6.73±0.4ms 1.48 rolling.Methods.time_rolling('Series', 1000, 'int', 'kurt')
+ 3.14±0.2ms 4.65±0.3ms 1.48 rolling.Methods.time_rolling('Series', 10, 'float', 'sum')
+ 2.18±0.03ms 3.23±0.2ms 1.48 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'mean')
+ 3.21±0.01ms 4.74±0.01ms 1.48 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'min')
+ 3.17±0.02ms 4.69±0.02ms 1.48 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'nearest')
+ 3.22±0.02ms 4.75±0.02ms 1.47 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'midpoint')
+ 3.14±0.2ms 4.62±0.3ms 1.47 rolling.Methods.time_rolling('Series', 1000, 'float', 'sum')
+ 3.22±0.02ms 4.75±0.03ms 1.47 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'nearest')
+ 3.22±0.02ms 4.73±0.02ms 1.47 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'linear')
+ 585±2μs 859±3μs 1.47 multiindex_object.Values.time_datetime_level_values_sliced
+ 3.23±0.01ms 4.74±0.02ms 1.47 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'higher')
+ 3.22±0.02ms 4.72±0.01ms 1.46 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'lower')
+ 4.56±0.3ms 6.67±0.4ms 1.46 rolling.ExpandingMethods.time_expanding('Series', 'int', 'skew')
+ 2.16±0.04ms 3.16±0.7ms 1.46 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'mean')
+ 3.27±0.2ms 4.78±0.3ms 1.46 rolling.Methods.time_rolling('Series', 10, 'float', 'mean')
+ 538±3μs 786±3μs 1.46 groupby.GroupByMethods.time_dtype_as_field('float', 'std', 'direct')
+ 2.03±0.01ms 2.96±0.4ms 1.46 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'mean')
+ 541±2μs 787±4μs 1.46 groupby.GroupByMethods.time_dtype_as_field('float', 'std', 'transformation')
+ 3.31±0.05ms 4.82±0.8ms 1.45 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'skew')
+ 4.51±0.3ms 6.55±0.4ms 1.45 rolling.Methods.time_rolling('Series', 1000, 'int', 'skew')
+ 643±2μs 934±2μs 1.45 groupby.GroupByMethods.time_dtype_as_group('float', 'quantile', 'direct')
+ 645±2μs 936±2μs 1.45 groupby.GroupByMethods.time_dtype_as_group('float', 'quantile', 'transformation')
+ 3.23±0.2ms 4.69±0.4ms 1.45 rolling.Methods.time_rolling('Series', 10, 'int', 'sum')
+ 3.28±0.2ms 4.76±0.3ms 1.45 rolling.Methods.time_rolling('Series', 1000, 'float', 'mean')
+ 627±1μs 909±3μs 1.45 groupby.GroupByMethods.time_dtype_as_field('int', 'quantile', 'direct')
+ 628±0.7μs 911±2μs 1.45 groupby.GroupByMethods.time_dtype_as_field('int', 'quantile', 'transformation')
+ 638±1μs 924±3μs 1.45 groupby.GroupByMethods.time_dtype_as_group('int', 'quantile', 'direct')
+ 641±0.9μs 928±2μs 1.45 groupby.GroupByMethods.time_dtype_as_group('datetime', 'quantile', 'direct')
+ 641±1μs 926±1μs 1.45 groupby.GroupByMethods.time_dtype_as_group('datetime', 'quantile', 'transformation')
+ 638±2μs 921±3μs 1.44 groupby.GroupByMethods.time_dtype_as_group('int', 'quantile', 'transformation')
+ 633±1μs 914±5μs 1.44 groupby.GroupByMethods.time_dtype_as_field('float', 'quantile', 'direct')
+ 633±2μs 911±1μs 1.44 groupby.GroupByMethods.time_dtype_as_field('float', 'quantile', 'transformation')
+ 3.16±0.2ms 4.54±0.4ms 1.44 rolling.ExpandingMethods.time_expanding('Series', 'float', 'mean')
+ 3.36±0.2ms 4.83±0.4ms 1.44 rolling.Methods.time_rolling('Series', 10, 'int', 'mean')
+ 599±1μs 858±2μs 1.43 groupby.GroupByMethods.time_dtype_as_field('object', 'ffill', 'direct')
+ 598±1μs 856±3μs 1.43 groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'direct')
+ 599±1μs 856±2μs 1.43 groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'transformation')
+ 601±1μs 859±2μs 1.43 groupby.GroupByMethods.time_dtype_as_field('object', 'ffill', 'transformation')
+ 8.43±0.7ms 11.9±0.2ms 1.41 series_methods.NanOps.time_func('std', 1000000, 'float64')
+ 3.14±0.02ms 4.40±0.4ms 1.40 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'kurt')
+ 7.77±0.2ms 10.8±0.02ms 1.39 timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc')
+ 651±3μs 900±2μs 1.38 groupby.GroupByMethods.time_dtype_as_field('int', 'std', 'direct')
+ 653±3μs 898±2μs 1.38 groupby.GroupByMethods.time_dtype_as_field('int', 'std', 'transformation')
+ 3.22±0.02ms 4.41±0.3ms 1.37 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'kurt')
+ 5.65±0.06ms 7.75±0.09ms 1.37 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string', 'nonunique_monotonic_inc')
+ 679±2μs 930±3μs 1.37 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'transformation')
+ 681±3μs 930±3μs 1.37 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'direct')
+ 3.19±0.02ms 4.35±0.8ms 1.37 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'kurt')
+ 753±10μs 1.03±0.01ms 1.36 groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'direct')
+ 752±10μs 1.02±0.01ms 1.36 groupby.GroupByMethods.time_dtype_as_field('object', 'all', 'transformation')
+ 756±10μs 1.03±0.01ms 1.36 groupby.GroupByMethods.time_dtype_as_field('object', 'any', 'direct')
+ 3.26±0.2ms 4.41±0.1ms 1.35 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'higher')
+ 3.26±0.2ms 4.40±0.09ms 1.35 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'higher')
+ 761±7μs 1.03±0.01ms 1.35 groupby.GroupByMethods.time_dtype_as_field('object', 'any', 'transformation')
+ 3.27±0.2ms 4.40±0.1ms 1.35 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'midpoint')
+ 3.26±0.2ms 4.39±0.1ms 1.35 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'lower')
+ 3.26±0.2ms 4.39±0.1ms 1.35 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'nearest')
+ 6.49±0.09ms 8.73±0.2ms 1.35 timeseries.ResampleSeries.time_resample('period', '1D', 'ohlc')
+ 3.26±0.2ms 4.38±0.1ms 1.34 rolling.Quantile.time_quantile('Series', 10, 'int', 1, 'linear')
+ 3.32±0.04ms 4.46±0.4ms 1.34 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'skew')
+ 3.81±0.03ms 5.11±0.1ms 1.34 rolling.ExpandingMethods.time_expanding('Series', 'int', 'std')
+ 3.26±0.2ms 4.38±0.1ms 1.34 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'linear')
+ 3.00±0.01ms 4.03±0.2ms 1.34 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'kurt')
+ 3.29±0.2ms 4.40±0.1ms 1.34 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'nearest')
+ 3.24±0.2ms 4.33±0.05ms 1.34 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'linear')
+ 16.5±0.06μs 22.0±0.3μs 1.34 categoricals.CategoricalSlicing.time_getitem_slice('monotonic_decr')
+ 3.29±0.2ms 4.40±0.1ms 1.34 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'midpoint')
+ 3.25±0.2ms 4.33±0.05ms 1.34 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'lower')
+ 4.45±0.3ms 5.94±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'nearest')
+ 4.45±0.3ms 5.93±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'linear')
+ 4.45±0.3ms 5.91±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'higher')
+ 1.04±0.01ms 1.39±0.01ms 1.33 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'float')
+ 3.29±0.2ms 4.37±0.06ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'nearest')
+ 3.25±0.2ms 4.32±0.05ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'midpoint')
+ 1.09±0.01ms 1.45±0.01ms 1.33 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'float')
+ 4.50±0.3ms 5.97±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'linear')
+ 3.31±0.2ms 4.39±0.1ms 1.33 rolling.Quantile.time_quantile('Series', 10, 'int', 0, 'lower')
+ 4.45±0.3ms 5.91±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'lower')
+ 3.25±0.3ms 4.32±0.06ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'midpoint')
+ 3.25±0.2ms 4.31±0.05ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'linear')
+ 3.25±0.2ms 4.32±0.05ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'higher')
+ 4.49±0.3ms 5.97±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'higher')
+ 3.26±0.2ms 4.32±0.05ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'nearest')
+ 25.2±0.4ms 33.4±0.4ms 1.33 io.hdf.HDF.time_read_hdf('fixed')
+ 4.45±0.3ms 5.90±0.4ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 1, 'midpoint')
+ 4.51±0.3ms 5.98±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'nearest')
+ 4.51±0.3ms 5.98±0.3ms 1.33 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'midpoint')
+ 5.98±0.01ms 7.92±0.1ms 1.32 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string', 'non_monotonic')
+ 3.25±0.2ms 4.31±0.05ms 1.32 rolling.Quantile.time_quantile('Series', 1000, 'int', 1, 'lower')
+ 3.15±0.03ms 4.17±0.3ms 1.32 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'skew')
+ 3.14±0.02ms 4.15±0.8ms 1.32 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'skew')
+ 4.52±0.3ms 5.97±0.3ms 1.32 rolling.Quantile.time_quantile('Series', 1000, 'float', 0, 'lower')
+ 5.94±0.06ms 7.85±0.1ms 1.32 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string', 'unique_monotonic_inc')
+ 4.56±0.3ms 6.01±0.4ms 1.32 rolling.Methods.time_rolling('Series', 10, 'int', 'kurt')
+ 3.28±0.2ms 4.32±0.05ms 1.32 rolling.Quantile.time_quantile('Series', 1000, 'int', 0, 'higher')
+ 17.0±0.2μs 22.4±0.2μs 1.32 categoricals.CategoricalSlicing.time_getitem_slice('non_monotonic')
+ 16.8±0.09μs 22.0±0.4μs 1.31 categoricals.CategoricalSlicing.time_getitem_slice('monotonic_incr')
+ 540±5μs 708±9μs 1.31 period.Indexing.time_intersection
+ 4.29±0.3ms 5.62±0.4ms 1.31 rolling.ExpandingMethods.time_expanding('Series', 'float', 'kurt')
+ 4.39±0.3ms 5.71±0.1ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'nearest')
+ 4.40±0.3ms 5.71±0.2ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'lower')
+ 976±6μs 1.27±0.01ms 1.30 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'nonunique_monotonic_inc')
+ 4.37±0.3ms 5.68±0.1ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'nearest')
+ 962±10μs 1.25±0.02ms 1.30 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'non_monotonic')
+ 4.37±0.3ms 5.68±0.3ms 1.30 rolling.Methods.time_rolling('Series', 10, 'float', 'max')
+ 4.38±0.3ms 5.68±0.1ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'higher')
+ 4.40±0.3ms 5.71±0.1ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'linear')
+ 4.39±0.3ms 5.70±0.1ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'midpoint')
+ 684±3μs 887±2μs 1.30 groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'direct')
+ 4.38±0.3ms 5.68±0.09ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'linear')
+ 4.38±0.3ms 5.67±0.1ms 1.30 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'midpoint')
+ 4.38±0.3ms 5.67±0.1ms 1.29 rolling.Quantile.time_quantile('Series', 10, 'float', 1, 'lower')
+ 4.41±0.3ms 5.70±0.1ms 1.29 rolling.Quantile.time_quantile('Series', 10, 'float', 0, 'higher')
+ 6.20±0.07μs 7.99±0.1μs 1.29 categoricals.Indexing.time_get_loc
+ 1.24±0ms 1.59±0.01ms 1.29 frame_methods.Quantile.time_frame_quantile(1)
+ 1.89±0ms 2.43±0.01ms 1.29 groupby.GroupByMethods.time_dtype_as_field('float', 'pct_change', 'direct')
+ 685±5μs 882±2μs 1.29 groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'transformation')
+ 37.5±0.3ms 48.3±0.6ms 1.29 frame_ctor.FromDicts.time_nested_dict_index
+ 679±3μs 873±3μs 1.29 groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'direct')
+ 1.89±0ms 2.43±0.01ms 1.29 groupby.GroupByMethods.time_dtype_as_field('float', 'pct_change', 'transformation')
+ 405±2μs 521±3μs 1.29 index_object.IntervalIndexMethod.time_intersection(1000)
+ 291±3μs 373±2μs 1.28 join_merge.Concat.time_concat_empty_left(1)
+ 679±4μs 872±3μs 1.28 groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'transformation')
+ 4.39±0.3ms 5.63±0.2ms 1.28 rolling.Methods.time_rolling('Series', 10, 'float', 'min')
+ 422±3μs 539±1μs 1.28 index_object.IntervalIndexMethod.time_intersection_one_duplicate(1000)
+ 292±3μs 373±2μs 1.28 join_merge.Concat.time_concat_empty_right(1)
+ 980±9μs 1.25±0.01ms 1.28 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'unique_monotonic_inc')
+ 904±6μs 1.15±0.01ms 1.27 stat_ops.SeriesOps.time_op('std', 'float')
+ 463±0.2ns 589±9ns 1.27 indexing.MethodLookup.time_lookup_iloc
+ 3.17±0.01ms 4.04±0.2ms 1.27 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'skew')
+ 689±2μs 874±1μs 1.27 groupby.GroupByMethods.time_dtype_as_field('int', 'prod', 'direct')
+ 4.51±0.3ms 5.72±0.4ms 1.27 rolling.Methods.time_rolling('Series', 10, 'int', 'skew')
+ 691±4μs 875±3μs 1.27 groupby.GroupByMethods.time_dtype_as_field('int', 'mean', 'direct')
+ 37.8±0.1ms 47.8±0.7ms 1.27 frame_ctor.FromDicts.time_nested_dict_index_columns
+ 831±6μs 1.05±0ms 1.27 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'non_monotonic')
+ 4.44±0.3ms 5.61±0.07ms 1.26 rolling.Methods.time_rolling('Series', 1000, 'float', 'max')
+ 689±3μs 871±2μs 1.26 groupby.GroupByMethods.time_dtype_as_field('int', 'prod', 'transformation')
+ 703±1μs 888±2μs 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'transformation')
+ 691±2μs 873±2μs 1.26 groupby.GroupByMethods.time_dtype_as_field('int', 'mean', 'transformation')
+ 739±2μs 933±4μs 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'direct')
+ 829±5μs 1.05±0ms 1.26 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'unique_monotonic_inc')
+ 4.52±0.2ms 5.71±0.06ms 1.26 rolling.ExpandingMethods.time_expanding('Series', 'int', 'min')
+ 703±2μs 887±2μs 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'direct')
+ 633±5μs 798±2μs 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'direct')
+ 4.49±0.3ms 5.66±0.08ms 1.26 rolling.Methods.time_rolling('Series', 1000, 'float', 'min')
+ 737±1μs 928±1μs 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'transformation')
+ 631±3μs 794±1μs 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'transformation')
+ 1.98±0.01ms 2.49±0ms 1.26 groupby.GroupByMethods.time_dtype_as_field('int', 'pct_change', 'direct')
+ 2.81±0.05ms 3.52±0.01ms 1.26 rolling.Methods.time_rolling('DataFrame', 10, 'float', 'std')
+ 731±3μs 918±20μs 1.26 groupby.GroupByMethods.time_dtype_as_field('int', 'median', 'transformation')
+ 731±3μs 918±2μs 1.26 groupby.GroupByMethods.time_dtype_as_field('int', 'median', 'direct')
+ 4.48±0.3ms 5.62±0.2ms 1.25 rolling.Methods.time_rolling('Series', 10, 'int', 'min')
+ 4.52±0.2ms 5.67±0.07ms 1.25 rolling.ExpandingMethods.time_expanding('Series', 'int', 'max')
+ 1.98±0ms 2.48±0.01ms 1.25 groupby.GroupByMethods.time_dtype_as_field('int', 'pct_change', 'transformation')
+ 104±3μs 130±0.6μs 1.25 indexing.NumericSeriesIndexing.time_getitem_scalar(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+ 2.07±0ms 2.58±0.01ms 1.25 groupby.GroupByMethods.time_dtype_as_group('int', 'pct_change', 'direct')
+ 4.47±0.3ms 5.59±0.4ms 1.25 rolling.Methods.time_rolling('Series', 10, 'float', 'kurt')
+ 4.46±0.2ms 5.58±0.2ms 1.25 rolling.ExpandingMethods.time_expanding('Series', 'float', 'max')
+ 725±1μs 906±1μs 1.25 timeseries.ResetIndex.time_reest_datetimeindex('US/Eastern')
+ 111±2ms 138±1ms 1.25 reshape.Cut.time_qcut_timedelta(1000)
+ 1.08±0.01ms 1.35±0.01ms 1.25 indexing.NonNumericSeriesIndexing.time_getitem_list_like('period', 'non_monotonic')
+ 2.06±0ms 2.57±0.01ms 1.25 groupby.GroupByMethods.time_dtype_as_group('int', 'pct_change', 'transformation')
+ 37.6±0.4ms 46.8±0.6ms 1.25 frame_ctor.FromDicts.time_list_of_dict
+ 1.18±0ms 1.47±0ms 1.24 groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'direct')
+ 53.3±0.3μs 66.3±0.6μs 1.24 indexing.CategoricalIndexIndexing.time_getitem_list_like('monotonic_decr')
+ 2.82±0.04ms 3.51±0.01ms 1.24 rolling.Methods.time_rolling('DataFrame', 1000, 'float', 'std')
+ 468±3ns 582±3ns 1.24 indexing.MethodLookup.time_lookup_loc
+ 53.7±0.5μs 66.7±0.4μs 1.24 indexing.CategoricalIndexIndexing.time_getitem_list_like('non_monotonic')
+ 4.46±0.3ms 5.54±0.4ms 1.24 rolling.Methods.time_rolling('Series', 1000, 'float', 'kurt')
+ 4.47±0.2ms 5.55±0.2ms 1.24 rolling.Methods.time_rolling('Series', 10, 'int', 'max')
+ 1.18±0ms 1.47±0.01ms 1.24 groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'transformation')
+ 2.21±0.01ms 2.75±0.01ms 1.24 groupby.GroupByMethods.time_dtype_as_group('float', 'pct_change', 'direct')
+ 2.22±0.01ms 2.75±0ms 1.24 groupby.GroupByMethods.time_dtype_as_group('float', 'pct_change', 'transformation')
+ 46.5±0.2ms 57.8±0.4ms 1.24 frame_ctor.FromDicts.time_nested_dict
+ 53.6±0.6μs 66.5±0.6μs 1.24 indexing.CategoricalIndexIndexing.time_getitem_list_like('monotonic_incr')
+ 4.48±0.3ms 5.55±0.2ms 1.24 rolling.ExpandingMethods.time_expanding('Series', 'float', 'min')
+ 739±1μs 916±2μs 1.24 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'direct')
+ 36.5±0.8μs 45.2±0.2μs 1.24 indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_decr')
+ 774±2μs 958±3μs 1.24 groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'direct')
+ 765±1μs 946±3μs 1.24 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'transformation')
+ 12.6±0.3ms 15.5±0.08ms 1.24 io.hdf.HDFStoreDataFrame.time_query_store_table_wide
+ 4.47±0.3ms 5.52±0.4ms 1.24 rolling.ExpandingMethods.time_expanding('Series', 'float', 'skew')
+ 727±2μs 897±2μs 1.23 groupby.GroupByMethods.time_dtype_as_field('int', 'sum', 'transformation')
+ 75.4±0.8ms 93.0±0.2ms 1.23 reshape.Cut.time_qcut_datetime(1000)
+ 741±3μs 913±3μs 1.23 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'direct')
+ 766±3μs 943±2μs 1.23 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation')
+ 743±2μs 914±1μs 1.23 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'transformation')
+ 768±2μs 945±2μs 1.23 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'direct')
+ 777±3μs 957±0.7μs 1.23 groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'transformation')
+ 743±2μs 913±1μs 1.23 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'transformation')
+ 3.31±0.03μs 4.06±0.03μs 1.23 categoricals.Contains.time_categorical_index_contains
+ 4.42±0.3ms 5.43±0.4ms 1.23 rolling.Methods.time_rolling('Series', 1000, 'float', 'skew')
+ 730±3μs 896±2μs 1.23 groupby.GroupByMethods.time_dtype_as_field('int', 'sum', 'direct')
+ 771±5μs 946±2μs 1.23 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct')
+ 2.98±0.02ms 3.65±0.01ms 1.23 rolling.Methods.time_rolling('DataFrame', 10, 'int', 'std')
+ 770±3μs 943±0.7μs 1.23 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'transformation')
+ 156±1ms 191±0.3ms 1.22 inference.ToNumericDowncast.time_downcast('string-float', 'unsigned')
+ 1.29±0ms 1.58±0.01ms 1.22 groupby.GroupByMethods.time_dtype_as_field('int', 'sem', 'direct')
+ 4.43±0.3ms 5.42±0.4ms 1.22 rolling.Methods.time_rolling('Series', 10, 'float', 'skew')
+ 1.29±0ms 1.57±0.01ms 1.22 groupby.GroupByMethods.time_dtype_as_field('int', 'sem', 'transformation')
+ 770±9μs 941±2μs 1.22 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct')
+ 4.51±0.3ms 5.51±0.1ms 1.22 rolling.Methods.time_rolling('Series', 1000, 'int', 'max')
+ 156±0.9ms 190±0.8ms 1.22 inference.ToNumericDowncast.time_downcast('string-float', 'integer')
+ 147±3μs 180±1μs 1.22 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'unique_monotonic_inc')
+ 148±0.9μs 181±0.4μs 1.22 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'unique_monotonic_inc')
+ 1.34±0ms 1.63±0.01ms 1.22 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'direct')
+ 47.3±0.1ms 57.7±0.7ms 1.22 frame_ctor.FromDicts.time_nested_dict_columns
+ 4.57±0.3ms 5.57±0.1ms 1.22 rolling.Methods.time_rolling('Series', 1000, 'int', 'min')
+ 156±2ms 190±0.4ms 1.22 inference.ToNumericDowncast.time_downcast('string-float', 'signed')
+ 674±0.8μs 821±2μs 1.22 groupby.GroupByMethods.time_dtype_as_field('object', 'nunique', 'direct')
+ 1.34±0.01ms 1.63±0.01ms 1.22 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'transformation')
+ 725±9μs 882±3μs 1.22 timeseries.ResetIndex.time_reest_datetimeindex(None)
+ 8.02±0.05μs 9.75±0.04μs 1.22 categoricals.Indexing.time_shallow_copy
+ 147±0.8μs 179±0.2μs 1.22 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.Int64Index'>, 'nonunique_monotonic_inc')
+ 148±0.3μs 180±0.3μs 1.22 indexing.NumericSeriesIndexing.time_getitem_slice(<class 'pandas.core.indexes.numeric.UInt64Index'>, 'nonunique_monotonic_inc')
+ 1.17±0.02ms 1.41±0.01ms 1.21 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'nonunique_monotonic_inc')
+ 26.3±0.07ms 31.8±0.08ms 1.21 strings.Contains.time_contains(False)
+ 3.00±0.01ms 3.62±0.01ms 1.21 rolling.Methods.time_rolling('DataFrame', 1000, 'int', 'std')
+ 3.25±0.01ms 3.92±0.02ms 1.21 io.csv.ReadCSVFloatPrecision.time_read_csv(';', '.', 'round_trip')
+ 2.99±0.03ms 3.61±0.01ms 1.21 rolling.ExpandingMethods.time_expanding('DataFrame', 'float', 'std')
+ 458±0.4μs 553±3μs 1.21 categoricals.Constructor.time_from_codes_all_int8
+ 326±4μs 394±7μs 1.21 reindex.Fillna.time_float_32('backfill')
+ 3.23±0.01ms 3.90±0.7ms 1.21 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'sum')
+ 3.21±0.02ms 3.88±0.7ms 1.21 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'sum')
+ 66.8±0.6μs 80.5±1μs 1.20 categoricals.IsMonotonic.time_categorical_series_is_monotonic_increasing
+ 11.5±0.2μs 13.8±0.7μs 1.20 indexing.CategoricalIndexIndexing.time_get_loc_scalar('monotonic_incr')
+ 65.3±1ms 78.4±1ms 1.20 reshape.Cut.time_cut_timedelta(1000)
+ 3.21±0.02ms 3.86±0.7ms 1.20 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'sum')
+ 3.26±0.01ms 3.90±0.01ms 1.20 io.csv.ReadCSVFloatPrecision.time_read_csv(',', '.', 'round_trip')
+ 67.0±0.6μs 80.0±1μs 1.20 categoricals.IsMonotonic.time_categorical_series_is_monotonic_decreasing
+ 1.17±0.04μs 1.40±0.06μs 1.19 index_cached_properties.IndexCache.time_is_monotonic('RangeIndex')
+ 942±2μs 1.13±0ms 1.19 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'transformation')
+ 1.05±0.01ms 1.26±0.02ms 1.19 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'non_monotonic')
+ 943±2μs 1.12±0ms 1.19 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'direct')
+ 38.3±2μs 45.6±0.4μs 1.19 indexing.CategoricalIndexIndexing.time_getitem_slice('monotonic_incr')
+ 3.13±0.01ms 3.72±0.01ms 1.19 rolling.ExpandingMethods.time_expanding('DataFrame', 'int', 'std')
+ 3.84±0.1ms 4.56±0.4ms 1.19 stat_ops.SeriesMultiIndexOps.time_op(0, 'mean')
+ 473±0.8μs 562±2μs 1.19 timeseries.ToDatetimeCacheSmallCount.time_unique_date_strings(True, 50)
+ 5.89±0.02ms 7.00±0.01ms 1.19 timeseries.ResampleSeries.time_resample('period', '5min', 'mean')
+ 810±4μs 962±3μs 1.19 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'direct')
+ 472±0.9μs 561±2μs 1.19 timeseries.ToDatetimeCacheSmallCount.time_unique_date_strings(False, 50)
+ 811±2μs 962±2μs 1.19 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'transformation')
+ 8.41±0.03μs 9.98±0.1μs 1.19 indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime', 'non_monotonic')
+ 8.18±0.1ms 9.69±0.04ms 1.18 groupby.Apply.time_scalar_function_single_col
+ 40.2±0.1μs 47.6±2μs 1.18 ctors.SeriesDtypesConstructors.time_index_from_array_floats
+ 175±0.5μs 207±1μs 1.18 series_methods.NanOps.time_func('std', 1000, 'float64')
+ 3.47±0.01ms 4.10±0.7ms 1.18 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'mean')
+ 3.52±0.05ms 4.16±0.8ms 1.18 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'mean')
+ 34.0±0.07ms 40.1±0.2ms 1.18 strings.Methods.time_len
+ 43.3±0.3ms 51.0±0.08ms 1.18 reshape.Cut.time_cut_datetime(1000)
+ 40.8±0.2ms 48.1±1ms 1.18 stat_ops.FrameMultiIndexOps.time_op(0, 'kurt')
+ 3.50±0.01ms 4.13±0.7ms 1.18 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'mean')
+ 3.63±0.02ms 4.28±0.04ms 1.18 rolling.Methods.time_rolling('Series', 1000, 'int', 'std')
+ 303±4μs 357±5μs 1.18 join_merge.JoinNonUnique.time_join_non_unique_equal
+ 38.5±1μs 45.2±0.4μs 1.18 indexing.CategoricalIndexIndexing.time_getitem_slice('non_monotonic')
+ 5.97±0.02ms 7.02±0.04ms 1.17 indexing.MultiIndexing.time_index_slice
+ 8.42±0.2ms 9.87±0.7ms 1.17 rolling.Methods.time_rolling('Series', 1000, 'int', 'count')
+ 3.64±0.05ms 4.27±0.01ms 1.17 rolling.Methods.time_rolling('Series', 10, 'int', 'std')
+ 8.45±0.2ms 9.89±0.7ms 1.17 rolling.Methods.time_rolling('Series', 10, 'int', 'count')
+ 8.51±0.2ms 9.96±0.7ms 1.17 rolling.Methods.time_rolling('Series', 10, 'float', 'count')
+ 1.50±0.04μs 1.76±0.09μs 1.17 index_cached_properties.IndexCache.time_is_monotonic_decreasing('Int64Index')
+ 9.20±0.02ms 10.8±0.07ms 1.17 rolling.Pairwise.time_pairwise(None, 'corr', False)
+ 8.51±0.2ms 9.95±0.7ms 1.17 rolling.Methods.time_rolling('Series', 1000, 'float', 'count')
+ 1.28±0ms 1.49±0ms 1.16 series_methods.Map.time_map('dict', 'category')
+ 561±7μs 651±3μs 1.16 timeseries.ToDatetimeCacheSmallCount.time_unique_date_strings(False, 500)
+ 705±8ms 819±5ms 1.16 stat_ops.Correlation.time_corr_wide_nans('spearman')
+ 4.68±0.02ms 5.43±0.7ms 1.16 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'kurt')
+ 802±3μs 931±30μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc')
+ 190±5ms 221±4ms 1.16 io.json.ToJSONISO.time_iso_format('records')
+ 478±3μs 552±3μs 1.16 strings.Encode.time_encode_decode
+ 4.33±0.02ms 5.00±0.7ms 1.15 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'skew')
+ 225±0.5ms 260±2ms 1.15 io.json.ToJSONISO.time_iso_format('columns')
+ 974±5μs 1.12±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'transformation')
+ 1.20±0ms 1.39±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'direct')
+ 7.71±0.01μs 8.89±0.02μs 1.15 dtypes.Dtypes.time_pandas_dtype('Int8')
+ 6.32±0.01ms 7.28±0.02ms 1.15 rolling.Pairwise.time_pairwise(None, 'cov', False)
+ 220±1μs 254±0.4μs 1.15 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'non_monotonic')
+ 21.2±0.1ms 24.4±0.1ms 1.15 reshape.Cut.time_qcut_datetime(10)
+ 143±0.4μs 164±0.4μs 1.15 series_methods.NanOps.time_func('std', 1000, 'int64')
+ 982±3μs 1.13±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'direct')
+ 1.06±0ms 1.22±0.01ms 1.15 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation')
+ 35.8±0.3ms 41.1±0.7ms 1.15 io.hdf.HDFStoreDataFrame.time_read_store_mixed
+ 987±5μs 1.13±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'direct')
+ 194±4μs 223±3μs 1.15 timeseries.SortIndex.time_get_slice(False)
+ 1.20±0ms 1.38±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'transformation')
+ 981±3μs 1.12±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'direct')
+ 8.08±0.03ms 9.26±0.08ms 1.15 reshape.Cut.time_cut_datetime(4)
+ 19.8±0.2ms 22.7±0.07ms 1.15 reshape.Cut.time_qcut_datetime(4)
+ 990±1μs 1.13±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'direct')
+ 1.24±0.04μs 1.43±0.04μs 1.15 index_cached_properties.IndexCache.time_is_monotonic('Int64Index')
+ 989±2μs 1.13±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'transformation')
+ 550±2ms 630±3ms 1.15 groupby.Groups.time_series_groups('int64_large')
+ 1.07±0ms 1.23±0ms 1.15 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'transformation')
+ 989±3μs 1.13±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'transformation')
+ 1.05±0ms 1.21±0.01ms 1.15 groupby.GroupByMethods.time_dtype_as_field('int', 'cummax', 'transformation')
+ 990±5μs 1.13±0ms 1.15 groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'transformation')
+ 4.62±0.02ms 5.30±0.7ms 1.15 rolling.VariableWindowMethods.time_rolling('DataFrame', '1d', 'int', 'kurt')
+ 128±3ms 146±3ms 1.14 io.json.ToJSON.time_to_json('records', 'df_int_floats')
+ 1.07±0ms 1.23±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'direct')
+ 1.07±0ms 1.23±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'direct')
+ 196±0.5μs 224±1μs 1.14 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'nonunique_monotonic_inc')
+ 1.07±0ms 1.23±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'transformation')
+ 1.06±0ms 1.22±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct')
+ 4.67±0.02ms 5.34±0.7ms 1.14 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'int', 'kurt')
+ 2.61±0.01ms 2.98±0.03ms 1.14 groupby.RankWithTies.time_rank_ties('int64', 'max')
+ 2.80±0.01ms 3.21±0.01ms 1.14 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'int')
+ 4.34±0.05ms 4.96±0.8ms 1.14 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'int', 'skew')
+ 143±0.2μs 163±0.2μs 1.14 series_methods.NanOps.time_func('std', 1000, 'int32')
+ 2.58±0.01ms 2.94±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('float32', 'average')
+ 144±0.7μs 164±0.3μs 1.14 series_methods.NanOps.time_func('std', 1000, 'int8')
+ 1.08±0ms 1.23±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'direct')
+ 2.55±0.01ms 2.92±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('float64', 'average')
+ 2.55±0.01ms 2.92±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('float64', 'max')
+ 128±0.7ms 146±0.5ms 1.14 io.json.ReadJSON.time_read_json('split', 'int')
+ 1.05±0ms 1.20±0ms 1.14 groupby.GroupByMethods.time_dtype_as_field('int', 'cummax', 'direct')
+ 37.6±0.1ms 42.9±0.1ms 1.14 strings.Methods.time_endswith
+ 2.57±0.01ms 2.94±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('float32', 'first')
+ 1.08±0ms 1.23±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'transformation')
+ 2.59±0.02ms 2.95±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('float32', 'dense')
+ 2.57±0ms 2.93±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('float32', 'max')
+ 196±0.5μs 224±0.3μs 1.14 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'non_monotonic')
+ 8.07±0.02ms 9.20±0.06ms 1.14 sparse.Arithmetic.time_intersect(0.1, nan)
+ 1.04±0ms 1.19±0ms 1.14 groupby.GroupByMethods.time_dtype_as_field('int', 'cumsum', 'transformation')
+ 1.05±0ms 1.19±0ms 1.14 groupby.GroupByMethods.time_dtype_as_field('int', 'cumsum', 'direct')
+ 2.61±0.02ms 2.97±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('int64', 'min')
+ 2.55±0.01ms 2.91±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('float64', 'min')
+ 189±1ms 215±1ms 1.14 io.json.ToJSONLines.time_float_int_lines
+ 1.05±0ms 1.20±0ms 1.14 groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'direct')
+ 2.77±0.01ms 3.15±0.01ms 1.14 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'int')
+ 3.78±0.03ms 4.30±0.04ms 1.14 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f6536433620>, False, 'float')
+ 238±2μs 271±0.4μs 1.14 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'nonunique_monotonic_inc')
+ 9.28±0.03ms 10.6±0.02ms 1.14 rolling.Pairwise.time_pairwise(1000, 'corr', False)
+ 2.58±0.01ms 2.93±0.02ms 1.14 groupby.RankWithTies.time_rank_ties('float32', 'min')
+ 1.06±0ms 1.20±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'transformation')
+ 2.61±0.01ms 2.96±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('int64', 'average')
+ 1.17±0ms 1.33±0ms 1.14 series_methods.Map.time_map('dict', 'int')
+ 1.46±0ms 1.65±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'transformation')
+ 1.06±0ms 1.21±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'transformation')
+ 1.06±0ms 1.20±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct')
+ 6.37±0.02ms 7.23±0.01ms 1.14 rolling.Pairwise.time_pairwise(1000, 'cov', False)
+ 2.59±0.01ms 2.94±0.01ms 1.14 groupby.RankWithTies.time_rank_ties('datetime64', 'min')
+ 1.06±0ms 1.21±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'transformation')
+ 1.06±0ms 1.21±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'direct')
+ 133±0.8ms 151±0.7ms 1.14 io.json.ReadJSON.time_read_json('split', 'datetime')
+ 1.06±0ms 1.21±0ms 1.13 groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'direct')
+ 4.00±0.03ms 4.54±0.04ms 1.13 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f6536433620>, True, 'float')
+ 31.8±0.01ms 36.1±0.07ms 1.13 frame_methods.Equals.time_frame_nonunique_unequal
+ 2.59±0.01ms 2.93±0ms 1.13 groupby.RankWithTies.time_rank_ties('datetime64', 'max')
+ 9.24±0.02ms 10.5±0.02ms 1.13 rolling.Pairwise.time_pairwise(10, 'corr', False)
+ 2.59±0.01ms 2.93±0.01ms 1.13 groupby.RankWithTies.time_rank_ties('datetime64', 'dense')
+ 227±0.8μs 257±1μs 1.13 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'unique_monotonic_inc')
+ 2.33±0.01ms 2.64±0.01ms 1.13 io.csv.ReadCSVFloatPrecision.time_read_csv(';', '.', 'high')
+ 2.58±0.01ms 2.92±0.01ms 1.13 groupby.RankWithTies.time_rank_ties('float64', 'dense')
+ 1.36±0ms 1.54±0ms 1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'direct')
+ 31.8±0.06ms 36.0±0.03ms 1.13 frame_methods.Equals.time_frame_nonunique_equal
+ 226±7μs 256±1μs 1.13 timeseries.SortIndex.time_get_slice(True)
+ 2.59±0.01ms 2.93±0.02ms 1.13 groupby.RankWithTies.time_rank_ties('datetime64', 'average')
+ 1.06±0ms 1.20±0ms 1.13 groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'transformation')
+ 1.40±0ms 1.58±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'direct')
+ 2.62±0.01ms 2.96±0.01ms 1.13 groupby.RankWithTies.time_rank_ties('int64', 'dense')
+ 9.41±0.03ms 10.6±0.1ms 1.13 reshape.Cut.time_cut_datetime(10)
+ 2.62±0.01ms 2.96±0.01ms 1.13 groupby.RankWithTies.time_rank_ties('int64', 'first')
+ 1.40±0ms 1.58±0ms 1.13 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'transformation')
+ 1.36±0ms 1.54±0ms 1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'transformation')
+ 6.93±0.1μs 7.82±0.2μs 1.13 index_cached_properties.IndexCache.time_engine('DatetimeIndex')
+ 2.33±0.01ms 2.63±0.01ms 1.13 io.csv.ReadCSVFloatPrecision.time_read_csv(',', '.', 'high')
+ 1.41±0ms 1.59±0ms 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'transformation')
+ 1.40±0ms 1.58±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'transformation')
+ 1.40±0ms 1.58±0ms 1.13 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'direct')
+ 2.42±0.02ms 2.73±0.01ms 1.13 io.csv.ReadCSVFloatPrecision.time_read_csv(';', '.', None)
+ 1.46±0.01ms 1.64±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'direct')
+ 159±4ms 179±4ms 1.13 io.json.ToJSON.time_to_json('index', 'df_int_floats')
+ 183±0.5ms 207±2ms 1.13 io.json.ToJSONISO.time_iso_format('values')
+ 294±2μs 332±3μs 1.13 inference.NumericInferOps.time_multiply(<class 'numpy.int8'>)
+ 7.28±0.04ms 8.19±0.01ms 1.13 io.sas.SAS.time_read_sas('xport')
+ 2.42±0.01ms 2.73±0.01ms 1.13 io.csv.ReadCSVFloatPrecision.time_read_csv(',', '.', None)
+ 1.39±0ms 1.56±0ms 1.13 groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'transformation')
+ 1.42±0ms 1.59±0ms 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'direct')
+ 224±5ms 252±4ms 1.13 io.json.ToJSONISO.time_iso_format('split')
+ 692±10μs 779±20μs 1.13 inference.NumericInferOps.time_multiply(<class 'numpy.float32'>)
+ 2.13±0.01ms 2.39±0ms 1.12 series_methods.Map.time_map('dict', 'object')
+ 3.66±0.01ms 4.11±0.01ms 1.12 io.csv.ReadCSVParseDates.time_multiple_date
+ 1.39±0ms 1.56±0ms 1.12 groupby.GroupByMethods.time_dtype_as_field('int', 'rank', 'direct')
+ 86.7±1ms 97.3±2ms 1.12 frame_ctor.FromRecords.time_frame_from_records_generator(None)
+ 646±5ms 726±2ms 1.12 groupby.Groups.time_series_groups('object_large')
+ 126±3ms 141±3ms 1.12 io.json.ToJSON.time_to_json('records', 'df_int_float_str')
+ 3.01±0.01ms 3.38±0.02ms 1.12 io.csv.ReadCSVParseDates.time_baseline
+ 291±1μs 327±2μs 1.12 inference.NumericInferOps.time_add(<class 'numpy.int8'>)
+ 8.08±0.01ms 9.05±0.09ms 1.12 sparse.Arithmetic.time_intersect(0.01, nan)
+ 38.0±0.3ms 42.6±0.07ms 1.12 strings.Methods.time_startswith
+ 21.2±0.03ms 23.7±0.04ms 1.12 io.csv.ReadCSVConcatDatetimeBadDateValue.time_read_csv('nan')
+ 19.3±0.09ms 21.6±0.1ms 1.12 io.csv.ReadCSVConcatDatetimeBadDateValue.time_read_csv('')
+ 188±1ms 211±2ms 1.12 io.json.ToJSONLines.time_float_int_str_lines
+ 539±3μs 603±1μs 1.12 series_methods.Map.time_map('Series', 'category')
+ 288±1μs 323±2μs 1.12 inference.NumericInferOps.time_subtract(<class 'numpy.int8'>)
+ 291±0.8μs 325±2μs 1.12 inference.NumericInferOps.time_subtract(<class 'numpy.uint8'>)
+ 6.43±0.02ms 7.19±0.02ms 1.12 rolling.Pairwise.time_pairwise(10, 'cov', False)
+ 269±0.7μs 301±2μs 1.12 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'unique_monotonic_inc')
+ 292±1μs 326±1μs 1.12 inference.NumericInferOps.time_add(<class 'numpy.uint8'>)
+ 296±0.7μs 330±1μs 1.11 inference.NumericInferOps.time_multiply(<class 'numpy.uint8'>)
+ 8.85±0.3ms 9.86±0.5ms 1.11 timeseries.ResampleSeries.time_resample('datetime', '5min', 'ohlc')
+ 178±2ms 198±0.1ms 1.11 frame_ctor.FromDicts.time_nested_dict_int64
+ 13.5±0.04μs 15.1±0.4μs 1.11 indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime', 'nonunique_monotonic_inc')
+ 29.0±0.2ms 32.3±0.3ms 1.11 frame_ctor.FromLists.time_frame_from_lists
+ 3.09±0.05ms 3.43±0.03ms 1.11 rolling.VariableWindowMethods.time_rolling('DataFrame', '1h', 'float', 'sum')
+ 8.93±0.06μs 9.92±0.1μs 1.11 dtypes.Dtypes.time_pandas_dtype('Int16')
+ 398±3μs 442±3μs 1.11 inference.NumericInferOps.time_subtract(<class 'numpy.int16'>)
+ 244±6ms 270±5ms 1.11 io.json.ToJSONISO.time_iso_format('index')
+ 28.4±0.3ms 31.4±0.1ms 1.11 groupby.AggFunctions.time_different_python_functions_multicol
+ 5.50±0.03ms 6.08±0.04ms 1.11 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f6536433620>, False, 'int')
+ 11.2±0.03ms 12.4±0.05ms 1.11 io.hdf.HDFStoreDataFrame.time_query_store_table
+ 47.8±0.2μs 52.8±0.3μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_scalar('period', 'non_monotonic')
+ 227±0.7ms 251±1ms 1.10 strings.Slice.time_vector_slice
+ 2.78±0.02ms 3.08±0.03ms 1.10 io.csv.ReadCSVFloatPrecision.time_read_csv(';', '_', 'round_trip')
+ 399±3μs 441±9μs 1.10 inference.NumericInferOps.time_add(<class 'numpy.uint16'>)
+ 2.23±0s 2.46±0s 1.10 groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'transformation')
+ 192±2ms 212±2ms 1.10 io.stata.Stata.time_write_stata('tc')
+ 3.10±0.03ms 3.42±0.03ms 1.10 rolling.VariableWindowMethods.time_rolling('DataFrame', '50s', 'float', 'sum')
+ 5.74±0.03ms 6.32±0.04ms 1.10 ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f6536433620>, True, 'int')
+ 2.23±0s 2.46±0.01s 1.10 groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'direct')
+ 105M 115M 1.10 rolling.PeakMemFixed.peakmem_fixed
+ 2.79±0.01ms 3.07±0.02ms 1.10 io.csv.ReadCSVFloatPrecision.time_read_csv(',', '_', None)
+ 3.13±0s 3.44±0s 1.10 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct')
+ 6.05±0.02ms 6.66±0.02ms 1.10 reshape.SimpleReshape.time_stack
+ 405±2μs 446±7μs 1.10 inference.NumericInferOps.time_multiply(<class 'numpy.int16'>)
+ 2.12±0s 2.33±0s 1.10 groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'direct')
+ 251±3ms 276±5ms 1.10 io.stata.StataMissing.time_write_stata('tc')
+ 18.4±0.2ms 20.2±0.06ms 1.10 reshape.Cut.time_qcut_timedelta(4)
+ 2.77±0.01ms 3.05±0.01ms 1.10 io.csv.ReadCSVFloatPrecision.time_read_csv(';', '_', None)
+ 3.13±0s 3.44±0s 1.10 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'transformation')
- 1.10±0.01s 996±6ms 0.91 reshape.Unstack.time_without_last_row('category')
- 16.9±0.1ms 15.4±0.06ms 0.91 frame_methods.Apply.time_apply_lambda_mean
- 262±1μs 238±0.3μs 0.91 indexing.CategoricalIndexIndexing.time_getitem_bool_array('monotonic_decr')
- 3.97±0.01ms 3.59±0ms 0.91 offset.OffsetSeriesArithmetic.time_add_offset(<SemiMonthBegin: day_of_month=15>)
- 3.71±0.2μs 3.36±0.1μs 0.90 index_cached_properties.IndexCache.time_inferred_type('IntervalIndex')
- 16.7±0.9ms 15.0±0.07ms 0.90 stat_ops.Rank.time_average_old('DataFrame', False)
- 117±1ms 106±0.6ms 0.90 io.json.ToJSON.time_to_json('split', 'df_date_idx')
- 7.54±0.6μs 6.78±0.2μs 0.90 index_cached_properties.IndexCache.time_shape('TimedeltaIndex')
- 5.96±0.03ms 5.35±0.1ms 0.90 frame_methods.Interpolate.time_interpolate_some_good('infer')
- 4.43±0.05μs 3.98±0.01μs 0.90 series_methods.SeriesGetattr.time_series_datetimeindex_repr
- 186±6ms 167±1ms 0.90 categoricals.Rank.time_rank_string
- 78.7±0.3ms 70.3±0.3ms 0.89 rolling.Apply.time_rolling('Series', 3, 'int', <built-in function sum>, False)
- 84.0±0.6ms 74.9±0.5ms 0.89 binary_ops.Ops.time_frame_comparison(False, 1)
- 84.6±0.9ms 75.4±1ms 0.89 binary_ops.Ops.time_frame_comparison(False, 'default')
- 74.2±2ms 66.1±0.3ms 0.89 rolling.Apply.time_rolling('Series', 300, 'int', <built-in function sum>, False)
- 169±1μs 151±0.9μs 0.89 frame_methods.Dtypes.time_frame_dtypes
- 98.6±0.7μs 87.6±0.8μs 0.89 series_methods.NanOps.time_func('argmax', 1000, 'float64')
- 79.7±0.4ms 70.7±0.2ms 0.89 rolling.Apply.time_rolling('DataFrame', 3, 'int', <built-in function sum>, False)
- 79.5±0.4ms 70.5±0.3ms 0.89 rolling.Apply.time_rolling('DataFrame', 3, 'float', <built-in function sum>, False)
- 75.1±2ms 66.6±0.2ms 0.89 rolling.Apply.time_rolling('DataFrame', 300, 'float', <built-in function sum>, False)
- 12.5±0.5ms 11.1±0.3ms 0.88 categoricals.Rank.time_rank_string_cat
- 79.1±0.8ms 69.9±0.1ms 0.88 rolling.Apply.time_rolling('Series', 3, 'float', <built-in function sum>, False)
- 3.71±0.02μs 3.27±0.03μs 0.88 dtypes.DtypesInvalid.time_pandas_dtype_invalid('scalar-int')
- 74.5±2ms 65.6±0.2ms 0.88 rolling.Apply.time_rolling('Series', 300, 'float', <built-in function sum>, False)
- 52.6±0.2μs 46.3±0.1μs 0.88 timedelta.TimedeltaIndexing.time_series_loc
- 75.6±2ms 66.4±0.08ms 0.88 rolling.Apply.time_rolling('DataFrame', 300, 'int', <built-in function sum>, False)
- 3.50±0.02ms 3.06±0.01ms 0.88 offset.OffsetSeriesArithmetic.time_add_offset(<BusinessDay>)
- 5.17±0.02ms 4.50±0.03ms 0.87 timeseries.ResampleSeries.time_resample('datetime', '1D', 'mean')
- 1.77±0.01ms 1.54±0ms 0.87 timeseries.ToDatetimeCacheSmallCount.time_unique_date_strings(True, 5000)
- 3.93±0.1μs 3.40±0.09μs 0.87 index_cached_properties.IndexCache.time_shape('PeriodIndex')
- 6.49±0.2μs 5.61±0.1μs 0.87 index_object.Indexing.time_get_loc('Int')
- 26.4±0.7μs 22.8±0.2μs 0.86 series_methods.SearchSorted.time_searchsorted('int32')
- 2.91±0.01ms 2.51±0.01ms 0.86 sparse.FromCoo.time_sparse_series_from_coo
- 6.52±0.03μs 5.62±0.04μs 0.86 index_object.Indexing.time_get_loc_sorted('Int')
- 920±2μs 793±1μs 0.86 frame_methods.Iteration.time_itertuples_raw_start
- 621±2μs 535±2μs 0.86 groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'direct')
- 26.5±0.7μs 22.9±0.4μs 0.86 series_methods.SearchSorted.time_searchsorted('int64')
- 274±2ms 235±2ms 0.86 io.json.ToJSON.time_to_json_wide('index', 'df_td_int_ts')
- 927±1μs 797±2μs 0.86 frame_methods.Iteration.time_itertuples_raw_read_first
- 26.1±0.3μs 22.4±0.2μs 0.86 series_methods.SearchSorted.time_searchsorted('uint8')
- 772±2μs 662±1μs 0.86 timeseries.ToDatetimeCacheSmallCount.time_unique_date_strings(True, 500)
- 597±2μs 512±2μs 0.86 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'direct')
- 622±2μs 532±1μs 0.86 groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'transformation')
- 600±2μs 513±2μs 0.85 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'transformation')
- 26.4±0.6μs 22.5±0.3μs 0.85 series_methods.SearchSorted.time_searchsorted('int8')
- 9.52±0.5μs 8.12±0.1μs 0.85 algorithms.DuplicatedUniqueIndex.time_duplicated_unique('float')
- 270±1ms 230±1ms 0.85 io.csv.ToCSV.time_frame('long')
- 273±2ms 233±2ms 0.85 io.json.ToJSON.time_to_json_wide('records', 'df_td_int_ts')
- 180±3ms 153±2ms 0.85 io.json.ToJSON.time_to_json('index', 'df_td_int_ts')
- 26.1±0.3μs 22.1±0.4μs 0.85 series_methods.SearchSorted.time_searchsorted('uint16')
- 26.8±0.3μs 22.7±0.1μs 0.85 series_methods.SearchSorted.time_searchsorted('int16')
- 152±1ms 128±2ms 0.85 io.json.ToJSON.time_to_json('records', 'df_td_int_ts')
- 9.50±0.03μs 8.04±0.06μs 0.85 algorithms.DuplicatedUniqueIndex.time_duplicated_unique('int')
- 26.5±0.3μs 22.3±0.09μs 0.84 series_methods.SearchSorted.time_searchsorted('uint32')
- 29.5±0.6μs 24.8±0.3μs 0.84 series_methods.SearchSorted.time_searchsorted('uint64')
- 4.61±0.04ms 3.84±0.02ms 0.83 timeseries.ResampleDatetetime64.time_resample
- 9.74±0.9μs 8.11±0.1μs 0.83 algorithms.DuplicatedUniqueIndex.time_duplicated_unique('string')
- 9.90±0.03ms 8.15±0.01ms 0.82 inference.DateInferOps.time_add_timedeltas
- 286±0.7ms 235±0.9ms 0.82 frame_methods.Apply.time_apply_user_func
- 70.4±3ms 57.9±1ms 0.82 plotting.SeriesPlotting.time_series_plot('line')
- 526±2ms 432±0.7ms 0.82 frame_methods.Nunique.time_frame_nunique
- 294±0.6ms 242±3ms 0.82 frame_methods.Duplicated.time_frame_duplicated_wide
- 256±2ms 210±0.4ms 0.82 io.json.ToJSON.time_to_json_wide('split', 'df_td_int_ts')
- 3.46±0.02ms 2.84±0.03ms 0.82 frame_methods.Interpolate.time_interpolate_some_good(None)
- 4.10±0.04ms 3.35±0.01ms 0.82 groupby.Datelike.time_sum('date_range')
- 256±1ms 209±2ms 0.82 io.json.ToJSON.time_to_json_wide('values', 'df_td_int_ts')
- 199±20ms 161±0.4ms 0.81 algorithms.Factorize.time_factorize(True, 'string')
- 10.3±0.2ms 8.36±0.02ms 0.81 inference.DateInferOps.time_subtract_datetimes
- 216±0.8μs 175±0.7μs 0.81 period.Indexing.time_unique
- 47.4±0.1μs 38.2±0.3μs 0.81 series_methods.NanOps.time_func('argmax', 1000, 'int64')
- 246±1ms 198±0.8ms 0.80 frame_methods.Interpolate.time_interpolate('infer')
- 7.62±0.01ms 6.10±0.05ms 0.80 io.hdf.HDFStoreDataFrame.time_store_info
- 34.0±0.08ms 27.2±0.05ms 0.80 io.csv.ToCSV.time_frame('mixed')
- 47.2±0.2μs 37.5±0.1μs 0.80 series_methods.NanOps.time_func('argmax', 1000, 'int8')
- 47.4±0.2μs 37.6±0.2μs 0.79 series_methods.NanOps.time_func('argmax', 1000, 'int32')
- 10.5±0.05ms 8.28±0.01ms 0.79 reindex.DropDuplicates.time_frame_drop_dups(True)
- 160±4ms 126±4ms 0.79 io.json.ToJSON.time_to_json('columns', 'df_td_int_ts')
- 188±2ms 148±2ms 0.79 frame_methods.Interpolate.time_interpolate(None)
- 21.1±0.4ms 16.6±1ms 0.78 algorithms.FactorizeUnique.time_factorize(False, 'string')
- 12.0±0.04ms 9.33±0.02ms 0.78 reindex.DropDuplicates.time_frame_drop_dups_na(True)
- 316±1μs 242±2μs 0.76 index_object.SetOperations.time_operation('datetime', 'union')
- 2.34±0.02ms 1.78±0.01ms 0.76 categoricals.CategoricalSlicing.time_getitem_bool_array('monotonic_incr')
- 2.36±0.01ms 1.79±0.01ms 0.76 categoricals.CategoricalSlicing.time_getitem_bool_array('monotonic_decr')
- 14.2±0.2μs 10.7±0.08μs 0.76 timeseries.AsOf.time_asof_single_early('Series')
- 15.1±0.1ms 11.4±0.1ms 0.76 io.csv.ToCSVDatetime.time_frame_date_formatting
- 15.3±0.2μs 11.5±0.2μs 0.75 timeseries.DatetimeIndex.time_get('tz_aware')
- 621±2ms 466±0.8ms 0.75 package.TimeImport.time_import
- 243±0.9μs 181±0.7μs 0.74 timeseries.DatetimeIndex.time_unique('dst')
- 2.68±0.01ms 1.98±0.01ms 0.74 timeseries.ResampleDataFrame.time_method('mean')
- 60.7±2ms 44.5±0.1ms 0.73 io.hdf.HDFStoreDataFrame.time_write_store_table_wide
- 919±5ns 667±20ns 0.73 timedelta.TimedeltaIndexing.time_shape
- 1.29±0.01ms 931±1μs 0.72 offset.OffsetSeriesArithmetic.time_add_offset(<DateOffset: days=2, months=2>)
- 9.40±0.1μs 6.70±0.3μs 0.71 timedelta.TimedeltaIndexing.time_get_loc
- 935±10ns 658±6ns 0.70 period.Indexing.time_shape
- 497±3ms 349±2ms 0.70 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct')
- 502±5ms 351±3ms 0.70 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'transformation')
- 224±3ms 156±0.9ms 0.70 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'transformation')
- 218±2ms 151±1ms 0.69 groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'direct')
- 505±4ms 350±2ms 0.69 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct')
- 11.2±0.08μs 7.76±0.05μs 0.69 timedelta.TimedeltaIndexing.time_shallow_copy
- 219±2ms 151±0.6ms 0.69 groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'transformation')
- 506±2ms 350±2ms 0.69 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'transformation')
- 227±9ms 157±0.7ms 0.69 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct')
- 325±3ms 223±0.6ms 0.69 groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'direct')
- 139±0.8ms 95.2±0.6ms 0.68 io.json.ToJSON.time_to_json('values', 'df_td_int_ts')
- 30.9±1ms 21.1±0.6ms 0.68 algorithms.Duplicated.time_duplicated(False, 'string')
- 31.5±0.2ms 21.5±0.06ms 0.68 stat_ops.Correlation.time_corrwith_cols('pearson')
- 331±6ms 225±1ms 0.68 groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'transformation')
- 12.0±4μs 8.08±0.05μs 0.68 algorithms.DuplicatedUniqueIndex.time_duplicated_unique('uint')
- 1.06±0.01ms 709±3μs 0.67 offset.OffsetSeriesArithmetic.time_add_offset(<BusinessMonthEnd>)
- 1.07±0ms 709±5μs 0.67 offset.OffsetSeriesArithmetic.time_add_offset(<BusinessYearEnd: month=12>)
- 1.06±0ms 702±3μs 0.66 offset.OffsetSeriesArithmetic.time_add_offset(<BusinessQuarterEnd: startingMonth=3>)
- 45.9±4ms 30.4±0.1ms 0.66 algorithms.Factorize.time_factorize(False, 'string')
- 1.03±0.01ms 681±2μs 0.66 offset.OffsetSeriesArithmetic.time_add_offset(<BusinessQuarterBegin: startingMonth=3>)
- 1.04±0ms 687±2μs 0.66 offset.OffsetSeriesArithmetic.time_add_offset(<YearEnd: month=12>)
- 163±1ms 107±1ms 0.66 io.json.ToJSON.time_to_json('split', 'df_td_int_ts')
- 1.03±0ms 678±1μs 0.66 offset.OffsetSeriesArithmetic.time_add_offset(<BusinessYearBegin: month=1>)
- 1.03±0ms 676±3μs 0.65 offset.OffsetSeriesArithmetic.time_add_offset(<QuarterEnd: startingMonth=3>)
- 1.03±0ms 670±1μs 0.65 offset.OffsetSeriesArithmetic.time_add_offset(<BusinessMonthBegin>)
- 1.03±0ms 671±4μs 0.65 offset.OffsetSeriesArithmetic.time_add_offset(<MonthEnd>)
- 255±1ms 167±0.6ms 0.65 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'direct')
- 1.02±0ms 662±0.7μs 0.65 offset.OffsetSeriesArithmetic.time_add_offset(<QuarterBegin: startingMonth=3>)
- 1.01±0ms 659±5μs 0.65 offset.OffsetSeriesArithmetic.time_add_offset(<YearBegin: month=1>)
- 1.01±0.01ms 652±1μs 0.65 offset.OffsetSeriesArithmetic.time_add_offset(<MonthBegin>)
- 258±2ms 166±1ms 0.64 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'transformation')
- 252±2ms 163±1ms 0.64 eval.Eval.time_and('python', 'all')
- 206M 133M 0.64 reshape.Cut.peakmem_cut_interval(10)
- 206M 133M 0.64 reshape.Cut.peakmem_cut_interval(4)
- 207M 133M 0.64 reshape.Cut.peakmem_cut_interval(1000)
- 4.76±0.02ms 3.03±0.01ms 0.64 timeseries.ResampleDataFrame.time_method('min')
- 983±2ms 618±1ms 0.63 io.json.ReadJSON.time_read_json('index', 'datetime')
- 1.02±0ms 640±2μs 0.63 offset.OffsetSeriesArithmetic.time_add_offset(<Day>)
- 3.87±0ms 2.41±0.01ms 0.62 index_object.SetOperations.time_operation('datetime', 'intersection')
- 150±4ms 92.7±4ms 0.62 binary_ops.Ops.time_frame_multi_and(False, 'default')
- 151±4ms 92.9±3ms 0.62 binary_ops.Ops.time_frame_multi_and(False, 1)
- 263±2ms 162±1ms 0.62 eval.Eval.time_and('python', 1)
- 1.92±0.05ms 1.18±0.06ms 0.62 reindex.LevelAlign.time_align_level
- 8.72±0.08μs 5.35±0.03μs 0.61 timeseries.DatetimeIndex.time_get('repeated')
- 191±0.6μs 117±0.5μs 0.61 timedelta.TimedeltaIndexing.time_unique
- 22.7±0.2ms 13.8±0.06ms 0.61 algorithms.Duplicated.time_duplicated('last', 'string')
- 1.12±0.01s 679±1ms 0.60 io.json.ReadJSON.time_read_json('index', 'int')
- 8.52±0.07μs 5.14±0.02μs 0.60 timeseries.DatetimeIndex.time_get('tz_naive')
- 4.75±0.01ms 2.86±0ms 0.60 timeseries.ResampleDataFrame.time_method('max')
- 8.49±0.05μs 5.12±0.02μs 0.60 timeseries.DatetimeIndex.time_get('dst')
- 22.8±0.3ms 13.7±0.02ms 0.60 algorithms.Duplicated.time_duplicated('first', 'string')
- 149±4ms 87.2±3ms 0.59 binary_ops.Ops.time_frame_multi_and(True, 'default')
- 160±4ms 93.6±3ms 0.59 binary_ops.Ops.time_frame_multi_and(True, 1)
- 1.95±0ms 1.12±0ms 0.57 groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'direct')
- 1.95±0.01ms 1.11±0ms 0.57 groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'transformation')
- 2.10±0.06ms 1.20±0.06ms 0.57 reindex.LevelAlign.time_reindex_level
- 652±1ms 348±1ms 0.53 stat_ops.Correlation.time_corrwith_rows('pearson')
- 1.83±0.03ms 949±8μs 0.52 replace.FillNa.time_replace(True)
- 10.0±0.5ms 5.06±0.8ms 0.50 binary_ops.Timeseries.time_timestamp_ops_diff('US/Eastern')
- 184±10ms 89.7±1ms 0.49 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function pow>)
- 128±1ms 61.4±0.5ms 0.48 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function floordiv>)
- 197±20ms 93.4±4ms 0.47 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function pow>)
- 124±2ms 58.6±0.5ms 0.47 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function floordiv>)
- 9.59±0.4ms 4.51±0.1ms 0.47 rolling.EWMMethods.time_ewm('Series', 10, 'int', 'mean')
- 125±1ms 58.7±0.5ms 0.47 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function floordiv>)
- 126±2ms 59.0±0.5ms 0.47 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function floordiv>)
- 133±1ms 61.2±0.7ms 0.46 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function floordiv>)
- 202±10ms 92.7±4ms 0.46 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function pow>)
- 132±1ms 60.3±0.5ms 0.46 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function floordiv>)
- 9.37±0.4ms 4.26±0.1ms 0.45 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'mean')
- 9.59±0.4ms 4.35±0.1ms 0.45 rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'mean')
- 9.43±0.3ms 4.25±0.1ms 0.45 rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'mean')
- 127±1ms 55.9±0.4ms 0.44 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function floordiv>)
- 128±1ms 56.1±0.5ms 0.44 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function floordiv>)
- 126±0.9μs 53.9±0.09μs 0.43 categoricals.CategoricalOps.time_categorical_op('__eq__')
- 127±0.6μs 54.3±0.2μs 0.43 categoricals.CategoricalOps.time_categorical_op('__gt__')
- 127±0.9μs 54.3±0.3μs 0.43 categoricals.CategoricalOps.time_categorical_op('__ge__')
- 75.5±0.5ms 27.1±0.6ms 0.36 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function mod>)
- 7.66±0.01ms 2.74±0.06ms 0.36 rolling.EWMMethods.time_ewm('DataFrame', 10, 'int', 'mean')
- 114±0.4ms 40.6±0.02ms 0.36 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function mod>)
- 5.33±0.07ms 1.87±0ms 0.35 series_methods.Dir.time_dir_strings
- 77.0±0.6ms 27.0±0.7ms 0.35 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function mod>)
- 7.67±0.03ms 2.69±0.02ms 0.35 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'int', 'mean')
- 114±1ms 39.7±0.03ms 0.35 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function mod>)
- 93.1±0.9ms 32.0±0.06ms 0.34 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function mod>)
- 86.7±0.7ms 29.5±0.03ms 0.34 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function mod>)
- 86.9±0.9ms 29.5±0.04ms 0.34 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function mod>)
- 7.52±0.02ms 2.55±0.01ms 0.34 rolling.EWMMethods.time_ewm('DataFrame', 10, 'float', 'mean')
- 7.53±0.02ms 2.54±0.01ms 0.34 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'float', 'mean')
- 88.8±0.7ms 29.7±0.01ms 0.33 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function mod>)
- 3.40±0.02s 1.08±0.01s 0.32 reshape.Cut.time_cut_interval(1000)
- 105±4ms 32.9±0.5ms 0.31 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function pow>)
- 333±4ms 102±0.2ms 0.31 binary_ops.Ops2.time_frame_float_div_by_zero
- 336±0.9ms 103±0.4ms 0.31 binary_ops.Ops2.time_frame_int_div_by_zero
- 97.8±1ms 29.5±0.4ms 0.30 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function pow>)
- 97.5±1ms 29.4±0.3ms 0.30 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function pow>)
- 2.91±0.01s 792±10ms 0.27 reshape.Cut.time_cut_interval(4)
- 2.98±0.03s 803±7ms 0.27 reshape.Cut.time_cut_interval(10)
- 96.2±0.8ms 25.3±0.7ms 0.26 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function pow>)
- 19.4±0.7ms 4.87±0.3ms 0.25 rolling.EWMMethods.time_ewm('Series', 10, 'int', 'std')
- 19.3±0.7ms 4.71±0.3ms 0.24 rolling.EWMMethods.time_ewm('Series', 1000, 'int', 'std')
- 2.34±0.02μs 557±10ns 0.24 multiindex_object.Integer.time_is_monotonic
- 19.5±0.3ms 4.61±0.2ms 0.24 rolling.EWMMethods.time_ewm('Series', 10, 'float', 'std')
- 19.5±0.3ms 4.61±0.2ms 0.24 rolling.EWMMethods.time_ewm('Series', 1000, 'float', 'std')
- 19.5±0.07ms 3.66±0.02ms 0.19 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'int', 'std')
- 19.6±0.07ms 3.66±0.02ms 0.19 rolling.EWMMethods.time_ewm('DataFrame', 10, 'int', 'std')
- 19.4±0.08ms 3.50±0.01ms 0.18 rolling.EWMMethods.time_ewm('DataFrame', 1000, 'float', 'std')
- 19.4±0.05ms 3.49±0.01ms 0.18 rolling.EWMMethods.time_ewm('DataFrame', 10, 'float', 'std')
- 18.7±0.2ms 2.98±0.01ms 0.16 stat_ops.Correlation.time_corr('spearman')
- 476±9ms 45.6±0.8ms 0.10 binary_ops.Ops2.time_frame_float_floor_by_zero
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- 64.1±1ms 3.57±0.3ms 0.06 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function sub>)
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- 64.5±0.3ms 3.57±0.3ms 0.06 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function sub>)
- 72.3±0.9ms 4.00±0.3ms 0.06 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function truediv>)
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- 71.2±1ms 3.85±0.2ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function truediv>)
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- 59.0±0.9ms 2.71±0.01ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function ge>)
- 74.2±3ms 3.41±0.3ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function truediv>)
- 59.3±0.9ms 2.72±0.02ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function lt>)
- 59.0±0.8ms 2.71±0ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function gt>)
- 58.8±0.5ms 2.69±0.02ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function eq>)
- 63.4±0.6ms 2.88±0.2ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function sub>)
- 64.1±0.9ms 2.91±0.2ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function add>)
- 69.1±1ms 3.13±0.2ms 0.05 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 3.0, <built-in function add>)
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- 63.7±1ms 2.85±0.09ms 0.04 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function mul>)
- 64.2±0.8ms 2.86±0.08ms 0.04 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function mul>)
- 64.1±0.8ms 2.83±0.1ms 0.04 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function sub>)
- 63.2±0.4ms 2.78±0.1ms 0.04 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function add>)
- 67.3±0.5ms 2.94±0.2ms 0.04 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function mul>)
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- 69.9±1ms 2.94±0.2ms 0.04 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 5.0, <built-in function sub>)
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- 56.5±0.4ms 1.46±0ms 0.03 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function ge>)
- 57.1±0.6ms 1.47±0ms 0.03 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function le>)
- 57.0±0.6ms 1.47±0ms 0.03 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function lt>)
- 56.8±0.8ms 1.46±0.01ms 0.03 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function gt>)
- 57.0±0.5ms 1.47±0ms 0.03 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function ge>)
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- 56.3±0.3ms 1.41±0.01ms 0.03 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function ge>)
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- 57.4±0.4ms 1.41±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function lt>)
- 57.3±0.4ms 1.41±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function ge>)
- 57.3±4ms 1.41±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function gt>)
- 56.9±0.5ms 1.40±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 5.0, <built-in function eq>)
- 57.8±0.5ms 1.42±0ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function ne>)
- 57.4±0.5ms 1.40±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function gt>)
- 57.9±5ms 1.42±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function ne>)
- 57.4±0.4ms 1.40±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 4, <built-in function eq>)
- 56.7±0.7ms 1.39±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function ne>)
- 56.9±0.7ms 1.38±0ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function ne>)
- 57.4±0.3ms 1.40±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 3.0, <built-in function eq>)
- 57.9±0.05ms 1.40±0ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.float64'>, 2, <built-in function eq>)
- 56.6±0.5ms 1.37±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 4, <built-in function eq>)
- 57.0±0.5ms 1.37±0.01ms 0.02 binary_ops.IntFrameWithScalar.time_frame_op_with_scalar(<class 'numpy.int64'>, 2, <built-in function eq>)
- 7.38±0.01s 115±1ms 0.02 index_object.IntervalIndexMethod.time_intersection_both_duplicate(100000)
- 276±2ms 2.27±0.1ms 0.01 frame_ctor.FromRange.time_frame_from_range
- 201±0.5ms 1.40±0ms 0.01 frame_methods.SelectDtypes.time_select_dtypes(1000)
- 27.1±0.04s 151±0.4ms 0.01 replace.ReplaceList.time_replace_list_one_match(False)
- 24.8±0.04s 93.4±0.4ms 0.00 replace.ReplaceList.time_replace_list(False)
- 25.3±0.02s 59.0±0.08ms 0.00 replace.ReplaceList.time_replace_list_one_match(True)
- 13.7±0.5ms 5.18±0.4μs 0.00 dtypes.InferDtypes.time_infer_skipna('np-int')
- 14.6±0.4ms 4.97±0.2μs 0.00 dtypes.InferDtypes.time_infer_skipna('np-null')
- 14.9±0.4ms 5.01±0.1μs 0.00 dtypes.InferDtypes.time_infer_skipna('np-floating')
- 331±1ms 6.55±0.03μs 0.00 index_object.IndexEquals.time_non_object_equals_multiindex
- 331±3ms 2.74±0.03μs 0.00 multiindex_object.Equals.time_equals_non_object_index
- 22.9±0.06s 115±1μs 0.00 replace.ReplaceList.time_replace_list(True)
SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
```
</details>
I already commented on a few PRs, for the rest would need to take a further look. Help is certainly welcome to check certain cases.
One recurrent theme seems to be a rather consistent slowdown of a bunch of groupby methods. This can also be seen on the benchmark machine (eg https://pandas.pydata.org/speed/pandas/index.html#groupby.GroupByMethods.time_dtype_as_group?p-dtype='int'&p-method='all'&p-method='any'&odfpy=)
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} | 1 | 2020-01-07T18:53:35Z | 2020-01-07T23:06:40Z | 2020-01-07T22:27:55Z | MEMBER | null | This bug was hidden by _ensure_datetimelike_to_i8, and the only other place where that is used is in _round. _round is clearer without using it, so ensure_datetimelike_to_i8 gets ripped out, and with it we can get rid of _ensure_localized. | {
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} | 1 | 2020-01-07T19:19:16Z | 2020-01-08T04:37:13Z | 2020-01-08T04:36:33Z | CONTRIBUTOR | null | This implements IntegerArray.to_numpy with similar semantics to BooleanArray.to_numpy. The implementation is now identical between BooleanArray & IntegerArray. #30789 will merge them.
1. `.to_numpy(dtype=float/bool/int)` will raise if there are missing values
2. `.astype(float)` will convert NA to NaN.
I've made a slight change from the BooleanArray implementation on master, which I'll annotate inline.
Closes https://github.com/pandas-dev/pandas/issues/30038 | {
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} | 1 | 2020-01-07T19:41:33Z | 2020-01-07T20:04:49Z | 2020-01-07T20:03:35Z | NONE | null | #### Code Sample
```python
import pandas
pandas.show_versions()
pandas.DatetimeIndex(['2013-01-01 09:58:29', '2013-01-01 09:58:30', '2013-01-01 09:58:30.001',
'2013-01-01 09:59:29', '2013-01-01 09:59:30', '2013-01-01 09:59:30.001'],
dtype='datetime64[ns]').round('min')
```
#### Problem description
The rounding direction changes in the 59th minute of the hour! For minutes 00-58 the method rounds down from 30 seconds or less. For the 59th minute of the hour it rounds up to the next hour.
There are a couple previous issues for rounding time in pandas, but I could not find one for this behavior.
#### Expected Output
Example output showing the problem using the above sample
```
Python 3.7.3 (default, Mar 27 2019, 09:23:15)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.9.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas
...: pandas.show_versions()
...: pandas.DatetimeIndex(['2013-01-01 09:58:29', '2013-01-01 09:58:30', '2013-01-01 09:58:30.001',
...: '2013-01-01 09:59:29', '2013-01-01 09:59:30', '2013-01-01 09:59:30.001'],
...: dtype='datetime64[ns]').round('min')
Out[1]:
DatetimeIndex(['2013-01-01 09:58:00', '2013-01-01 09:58:00',
'2013-01-01 09:59:00', '2013-01-01 09:59:00',
'2013-01-01 10:00:00', '2013-01-01 10:00:00'],
dtype='datetime64[ns]', freq=None)
```
The rounding direction should be the same for '2013-01-01 09:58:30' and '2013-01-01 09:59:30' but it is not. The first becomes '2013-01-01 09:58:00', while the later becomes '2013-01-01 10:00:00'.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.3
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2020-01-07T20:04:49Z | 2020-01-07T21:52:37Z | 2020-01-07T21:52:29Z | CONTRIBUTOR | null | In https://github.com/pandas-dev/pandas/pull/29212, we bumped the minimum Python to 3.6.1. @datapythonista mentioned an issue with 3.6.0 at https://github.com/pandas-dev/pandas/pull/29212#issuecomment-551370118
> Seems like Python 3.6 has something (I guess a bug) causing the error TypeError: only integer scalar arrays can be converted to a scalar index when converting strings to bytes in some of our cases.
I vaguely recall a few issues with 0.25.x requiring a point release of python 3.5. We should see if we can support 3.6.0 to save some headaches down the road. | {
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} | 1 | 2020-01-07T20:19:46Z | 2020-01-09T20:45:00Z | 2020-01-07T22:28:21Z | CONTRIBUTOR | null | Primarily reordering roughly in order of importance.
1. Some rewording for clarity
2. Fixed some links
3. Simplified the SemVer discussion | {
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} | 4 | 2020-01-07T22:10:47Z | 2020-04-03T11:15:44Z | 2020-01-08T14:09:40Z | CONTRIBUTOR | null | This aligns with xarray and h5py:
https://github.com/pandas-dev/pandas/pull/29062#issuecomment-545703586 | {
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} | 13 | 2020-01-07T23:14:28Z | 2020-01-10T17:02:42Z | 2020-01-09T12:29:05Z | CONTRIBUTOR | null | - [x] closes #30642
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
- N/A
Turns out that the existing tests were not testing deprecation correctly for both python 3.7 and python 3.6, so had to change some of the code in `test_api.py` to make that work right.
For `pd.SparseArray` in python 3.6, we can only issue a warning when the constructor `pd.SparseArray` is used. But this is consistent with `pd.datetime` and `pd.np` with python 3.6, which will issue warnings when things like `pd.datetime.now()` are called. However, for python 3.6, I could not figure out a way to issue a warning on `pd.datetime(2015, 10, 11, 0, 0)`, so we may just have to live with that.
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} | 1 | 2020-01-07T23:58:34Z | 2020-01-08T18:20:44Z | 2020-01-08T12:54:51Z | MEMBER | null | Broken off from #30717 | {
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} | 1 | 2020-01-08T00:02:52Z | 2020-01-08T18:07:47Z | 2020-01-08T03:21:17Z | MEMBER | null | It is only used by Index.insert, but PeriodIndex now overrides insert. | {
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This script creates `requirements-dev.txt` which contains the spelling mistake, so is also fixed (using the updated script). | {
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} | 1 | 2020-01-08T00:13:06Z | 2020-01-08T02:22:39Z | 2020-01-08T02:08:55Z | MEMBER | null | Let's us get rid of PeriodIndex._wrap_setop_result, soon we'll share code among the PeriodIndex set ops, so this will be less verbose | {
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} | 1 | 2020-01-08T00:41:42Z | 2020-01-08T01:17:56Z | 2020-01-08T01:17:39Z | NONE | null | Hey all, feature request here.
While I'm exploring data in the shell, I am _constantly_ forgetting the tilde prefix to a isnull() argument. There's something about the way I conceptualize the data and how I use pandas - "Okay, I have a dataframe, now I select a subset... this column, with variables that are not null.. Oh sh*t (backarrow, backarrow, backarrow)." This happens DOZENS of times a day. I've forked a copy of the code and am looking at implementing something for myself, but I thought I'd put this up and see if there's any interest in it. Yeah, it's redundant, but I think it would be an improved design and there are other redundant functions...
What do you think? | {
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} | 1 | 2020-01-08T00:53:34Z | 2020-01-08T16:17:01Z | 2020-01-08T12:55:47Z | MEMBER | null | Feature in Python 3.5 that should simplify instantiation of the JSON module and make it more "pythonic"
https://docs.python.org/3/c-api/module.html?highlight=multi%20phase#multi-phase-initialization
https://www.python.org/dev/peps/pep-0489/
Also removed a version string from within the extension, as I don't see where that is useful
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} | 3 | 2020-01-08T01:20:50Z | 2020-01-09T05:06:37Z | 2020-01-09T02:54:53Z | MEMBER | null | xref #30757 should go in before this because it contains the tests. After this, we'll be able to de-duplicate the two methods. | {
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} | 1 | 2020-01-08T01:26:01Z | 2020-01-08T18:01:46Z | 2020-01-08T12:49:25Z | MEMBER | null | They are not hit in tests, AFAICT they are subsumed by `__reduce__` methods | {
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} | 3 | 2020-01-08T01:28:01Z | 2020-01-13T13:05:17Z | 2020-01-13T13:05:17Z | MEMBER | null | The section about imports here: https://github.com/pandas-dev/pandas/wiki/Code-Style-and-Conventions#imports-aim-for-absolute
Can be moved to the code style guide in the documentation: https://dev.pandas.io/docs/development/code_style.html
This way we can remove the page from the wiki, that is mostly outdated. | {
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} | 9 | 2020-01-08T09:13:20Z | 2020-01-15T09:49:49Z | 2020-01-15T09:49:49Z | CONTRIBUTOR | null | Without supporting an incremental mode, the runtime overhead (reportably at 20s)
is unacceptable for efficient development. We can reactivate once a stable,
incremental mode works for `pandas`. | {
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} | 1 | 2020-01-08T12:59:36Z | 2020-01-09T09:34:17Z | 2020-01-09T09:34:12Z | MEMBER | null | xref discussion in https://github.com/pandas-dev/pandas/pull/28371
If in the future we want to always try to import pyarrow, having pyarrow 0.13 (instead of 0.12) as the minimum required version will make this easier. | {
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} | 1 | 2020-01-08T13:53:43Z | 2020-01-08T16:47:30Z | 2020-01-08T16:13:54Z | MEMBER | null | Admittedly this is not the first issue I address, but this one's been open for several months now and so I figured I'd take it
- [x] closes #26462
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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