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} | 1 | 2020-02-24T09:57:12Z | 2020-02-24T18:45:21Z | 2020-02-24T18:44:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/32218 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32218/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32218/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32218/events | https://github.com/pandas-dev/pandas/issues/32218 | 569,821,312 | MDU6SXNzdWU1Njk4MjEzMTI= | 32,218 | Inconsistent behavior when DataFrame with strings and None is created from lists or dictionary. | {
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"gis... | null | 6 | 2020-02-24T12:20:47Z | 2021-07-29T04:32:05Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
pd.__version__
# df created from list, None is casted to str
df = pd.DataFrame(["1", "2", None], columns=["a"], dtype="str")
type(df.loc[2].values[0])
# Equivalent df created from dict, None remains NoneType
df = pd.DataFrame({"a": ["1", "2", None]}, dtype="str")
type(df.loc[2].values[0]))
```
#### Problem description
None is not casted consistently for DataFrames with None values and dtype set to str.
If DataFrame is created from list, then None is casted to str -> None -> "None".
If DataFrame is created from dict, then None remains NoneType -> None -> None.
IMO, the latter is the preferred behavior. I hope you consider this when continuing your work on string types and na types in future versions.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 1 | 2020-02-24T15:54:58Z | 2020-03-12T02:28:59Z | 2020-03-12T02:28:59Z | CONTRIBUTOR | null | ```python
import pandas as pd
df = pd.DataFrame({
'a' : [0,0,1,1,2,2,3,3],
'b' : [1,2,3,4,5,6,7,8]
},
dtype='Int64')
df.groupby('a').mean()
```
#### Problem description
Using the new nullable integer data type, calling `mean` after grouping results in a `TypeError`. Using `int64` dtype it works:
```python
import pandas as pd
df = pd.DataFrame({
'a' : [0,0,1,1,2,2,3,3],
'b' : [1,2,3,4,5,6,7,8]
},
dtype='int64')
print(df.groupby('a').mean())
```
as does keeping `Int64` dtype but taking a single column to give a `SeriesGroupBy`:
```python
import pandas as pd
df = pd.DataFrame({
'a' : [0,0,1,1,2,2,3,3],
'b' : [1,2,3,4,5,6,7,8]
},
dtype='Int64')
print(df.groupby('a')['b'].mean())
```
The error does not occur when calling `min`, `max` or `first`, but does also occur with `median` and `std`.
#### Expected Output
```
b
a
0 1.5
1 3.5
2 5.5
3 7.5
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
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Sample failed pipeline: https://github.com/pandas-dev/pandas/runs/464853016 | {
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Hopefully, this fixes the flaky test
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Right formula can be found here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html | {
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} | 5 | 2020-02-24T20:55:12Z | 2020-03-12T13:19:45Z | 2020-03-12T02:29:00Z | MEMBER | null | - [x] closes #32219
- [x] tests added / passed
- [x] passes `black pandas`
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It looks like this was due to the `TypeError` not being caught | {
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} | 3 | 2020-02-24T21:58:32Z | 2020-02-26T02:18:52Z | 2020-02-26T02:07:34Z | MEMBER | null | The benchmark I'm using for this (and upcoming related PRs) is based on the asv that is most affected by removing `fast_apply` (see #32086).
```
import numpy as np
from pandas import *
%load_ext line_profiler
def get_df():
N = 10 ** 4
labels = np.random.randint(0, 2000, size=N)
labels2 = np.random.randint(0, 3, size=N)
df = DataFrame(
{
"key": labels,
"key2": labels2,
"value1": np.random.randn(N),
"value2": ["foo", "bar", "baz", "qux"] * (N // 4),
}
)
return df
df = get_df()
gb = df.groupby("key")
%prun -s cumulative gb.apply(lambda x: 1)
```
If we disable `fast_apply` on master, this gives:
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.278 0.278 groupby.py:701(apply)
1 0.000 0.000 0.278 0.278 groupby.py:750(_python_apply_general)
1 0.009 0.009 0.275 0.275 ops.py:151(apply)
1987 0.003 0.000 0.257 0.000 ops.py:858(__iter__)
1986 0.003 0.000 0.251 0.000 ops.py:889(_chop)
1986 0.003 0.000 0.247 0.000 indexing.py:814(__getitem__)
1986 0.001 0.000 0.243 0.000 indexing.py:1462(_getitem_axis)
1986 0.003 0.000 0.242 0.000 indexing.py:1488(_get_slice_axis)
1986 0.007 0.000 0.230 0.000 generic.py:3470(_slice)
1986 0.008 0.000 0.203 0.000 managers.py:713(get_slice)
1987 0.005 0.000 0.129 0.000 managers.py:125(__init__)
1987 0.003 0.000 0.060 0.000 managers.py:634(_consolidate_check)
1987 0.026 0.000 0.059 0.000 managers.py:215(_rebuild_blknos_and_blklocs)
1987 0.003 0.000 0.056 0.000 managers.py:635(<listcomp>)
5961 0.015 0.000 0.053 0.000 blocks.py:335(ftype)
```
If we disable `fast_apply` on this PR:
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.198 0.198 groupby.py:701(apply)
1 0.000 0.000 0.198 0.198 groupby.py:750(_python_apply_general)
1 0.008 0.008 0.195 0.195 ops.py:151(apply)
1979 0.002 0.000 0.176 0.000 ops.py:903(__iter__)
1978 0.002 0.000 0.172 0.000 ops.py:934(_chop)
1978 0.003 0.000 0.169 0.000 indexing.py:814(__getitem__)
1978 0.001 0.000 0.165 0.000 indexing.py:1462(_getitem_axis)
1978 0.003 0.000 0.164 0.000 indexing.py:1488(_get_slice_axis)
1978 0.006 0.000 0.153 0.000 generic.py:3470(_slice)
1978 0.007 0.000 0.129 0.000 managers.py:713(get_slice)
1980 0.004 0.000 0.061 0.000 managers.py:125(__init__)
1980 0.021 0.000 0.052 0.000 managers.py:215(_rebuild_blknos_and_blklocs)
1978 0.002 0.000 0.048 0.000 managers.py:723(<listcomp>)
5934 0.010 0.000 0.045 0.000 blocks.py:310(getitem_block)
5942 0.003 0.000 0.031 0.000 blocks.py:275(make_block_same_class)
```
We save almost 30% by lazifying the consolidation check and consolidate on _slice. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32225 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32225/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32225/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32225/events | https://github.com/pandas-dev/pandas/issues/32225 | 570,207,214 | MDU6SXNzdWU1NzAyMDcyMTQ= | 32,225 | Treatment of Pre-Epoch datetimes in windows | {
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} | [] | closed | false | null | [] | null | 1 | 2020-02-24T23:19:07Z | 2020-02-25T18:49:01Z | 2020-02-25T18:49:01Z | NONE | null | #### Code Sample
```python
import datetime
datetime.datetime(1953,1,23,0).astimezone(datetime.timezone(datetime.timedelta(days=-1, seconds=57600), 'Pacific Standard Time'))
```
#### Problem description
```pandas._libs.tslibs.tzconversion._tz_convert_tzlocal_utc``` calls ```dt.astimezone()```, which fails on Windows for pre-Epoch datetimes (see, e.g., code sample above) with ```OSError: [Errno 22] Invalid argument.``` This appears to be related to this issue https://github.com/dateutil/dateutil/issues/197, where time.localtime() can't take negative values on Windows.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
# Edit
The error appears to be in the datetutil module, not directly in datetime. See the stacktrace below:
<details>
Exception ignored in: 'pandas._libs.tslibs.tzconversion._tz_convert_tzlocal_utc'
Traceback (most recent call last):
File "C:\Users\jbishop\AppData\Roaming\Python\Python37\site-packages\dateutil\tz\_common.py", line 144, in fromutc
return f(self, dt)
File "C:\Users\jbishop\AppData\Roaming\Python\Python37\site-packages\dateutil\tz\_common.py", line 258, in fromutc
dt_wall = self._fromutc(dt)
File "C:\Users\jbishop\AppData\Roaming\Python\Python37\site-packages\dateutil\tz\_common.py", line 222, in _fromutc
dtoff = dt.utcoffset()
File "C:\Users\jbishop\AppData\Roaming\Python\Python37\site-packages\dateutil\tz\tz.py", line 222, in utcoffset
if self._isdst(dt):
File "C:\Users\jbishop\AppData\Roaming\Python\Python37\site-packages\dateutil\tz\tz.py", line 291, in _isdst
dstval = self._naive_is_dst(dt)
File "C:\Users\jbishop\AppData\Roaming\Python\Python37\site-packages\dateutil\tz\tz.py", line 260, in _naive_is_dst
return time.localtime(timestamp + time.timezone).tm_isdst
OSError: [Errno 22] Invalid argument
</details>
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} | 4 | 2020-02-24T23:50:30Z | 2020-02-25T23:23:59Z | 2020-02-25T21:49:52Z | MEMBER | null | cc @WillAyd @simonjayhawkins got the [go ahead](https://github.com/pandas-dev/pandas/pull/32110#issuecomment-589974610) the other day to take these off Jeff's plate | {
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} | 2 | 2020-02-25T01:43:33Z | 2020-02-25T18:55:56Z | 2020-02-25T18:55:56Z | MEMBER | null | Haven't had time to debug but seeing this in the logs when setting up the 32 bit linux environment:
```sh
Traceback (most recent call last):
File "/home/vsts/miniconda3/bin/conda", line 7, in <module>
from conda.cli import main
ImportError: No module named conda.cli
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} | 1 | 2020-02-25T02:32:51Z | 2020-02-25T20:51:01Z | 2020-02-25T20:40:23Z | MEMBER | null | xref #32226. | {
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} | [] | closed | false | null | [] | null | 2 | 2020-02-25T03:30:52Z | 2020-02-25T04:24:04Z | 2020-02-25T04:24:04Z | CONTRIBUTOR | null | - [ ] closes #31647
Though, I don't know what @MarcoGorelli meant in the 5'th issue.
Didn't know how to expand my previous Pull Request: #32212, as I have already deleted the files on my machine.
Here are the screenshots, @WillAyd:
In the first image a controversial change might be: log(0.5) -> -ln(2).
The Notes have been expanded with a simple example from the provided link. | {
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} | 1 | 2020-02-25T03:49:29Z | 2020-02-26T02:35:19Z | 2020-02-26T02:31:37Z | MEMBER | null | DatetimelikeIndex._concat_same_dtpye is identical to ExtensionIndex version, so re-use that. | {
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```python
# For ignoring case, I have to explicitly change the regex
replace_map = {
'[Q|q]uick[P|p]ay with [Z|z]elle payment to ': 'Payment to',
'[Q|q]uick[P|p]ay with [Z|z]elle payment from ': 'Payment from'
}
df.replace({'title': replace_map}, regex=True, inplace=True)
```
#### Problem description
According to [pandas docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) for `DataFrame.replace` the replace has ability to take regex input, but doesn't have the ability to take in regex flags such as `re.I` or `re.IGNORECASE` for case insensitive search. Can we have a flags argument to take in regex module flags as input.
#### Expected Output
```python
replace_map = {
'QuickPay with Zelle payment to ': 'Payment to',
'QuickPay with Zelle payment from ': 'Payment from'
}
# Can't we include flags as argument?
df.replace({'title': replace_map}, regex=True, inplace=True, flags=re.I)
```
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"description": "String extension data ty... | open | false | null | [] | null | 3 | 2020-02-25T08:01:03Z | 2021-07-29T04:39:01Z | null | CONTRIBUTOR | null | Hello,
I calculate entropies of real valued random variables thanks to `pandas` and `scipy.stats`. For some practical reason, I bin my df's values with `pd.cut` and the `labels` I assign to these binned valued has to be a string. I wanted to use the new "string" dtype introduced in 1.0.0, but there seems to be an issue, here is a code sample :
```
import pandas as pd
import numpy as np
from scipy.stats import entropy
df = pd.DataFrame({'val': np.random.rand(1000)})
bins = np.linspace(0, 1, 11)
labels = [str(int(10*i)) for i in bins[:-1]]
df['bin_val'] = pd.cut(df['val'], bins=bins, labels=labels)
# This works
print(entropy(df['bin_val'].value_counts()))
df['bin_val'] = df['bin_val'].astype("string")
# This raises an error
print(entropy(df['bin_val'].value_counts()))
```
The error raised is `TypeError: ufunc 'entr' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''` | {
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- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] added message to pytest.raises for test_constructor_dict
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```python
df = pd.read_excel()
```
As it been said in the new docs that read_excel can now read xlsb, I tried to run my xlsb file and still got and error.
```
Supports xls, xlsx, xlsm, xlsb, and odf file extensions read from a local filesystem or URL. Supports an option to read a single sheet or a list of sheets.
```
#### Expected Output
A working df.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.137+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.17.5
pytz : 2018.9
dateutil : 2.6.1
pip : 19.3.1
setuptools : 45.1.0
Cython : 0.29.15
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 5.5.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.6
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 2.5.9
pandas_gbq : 0.11.0
pyarrow : 0.14.1
pytables : None
pytest : 3.6.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.4.4
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : None
numba : 0.47.0
</details>
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} | [] | closed | false | null | [] | null | 2 | 2020-02-25T14:31:58Z | 2020-02-25T15:56:37Z | 2020-02-25T15:27:59Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
for lag in [2, 3, 7, 15, 21]:
df_tmp.set_index("date")
.groupby("group")
.resample("D")
.sum()
.groupby("group")
.shift()
.reset_index()
.fillna(0)
.groupby("group")
.rolling(f"{lag}D", on="date")
.agg(self.funcs)[self.cols]
```
I want to add sum_click_last2days, mean_click_last2days, sum_click_last4days, mean_click_last4days and so on depending on columns, functions and lag windows passed.
Here last n days mean rolling sum till last day. Hence, if we are running on 10th, we would want sum for 8th and 9th.
This is working fine for smaller dataset but is not efficient for a large dataset( 19M and there are 700K groups)
#### Problem description
This is working fine for smaller dataset but is not efficient for a large dataset( 19M and there are 700K groups)
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} | 0 | 2020-02-25T15:56:42Z | 2020-07-15T22:19:13Z | 2020-07-15T22:19:13Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
file_name = "https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv"
df = pd.read_csv(file_name)
df[['species','petal_width']].groupby('species', as_index=False).agg(min_width=pd.NamedAgg(column='petal_width', aggfunc='min'))
```
#### Problem description
When I try to use `as_index=False`with named aggregations it seems to be dropping values and replacing them with index. This is the output of the above code.
| | min_width |
|---:|:------------|
| 0 | setosa |
| 1 | versicolor |
| 2 | virginica |
#### Expected Output
| | species | min_width |
|---:|:-----------|--------------:|
| 0 | setosa | 0.1 |
| 1 | versicolor | 1 |
| 2 | virginica | 1.4 |
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.138+
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
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
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.4
tables : None
tabulate : None
xarray : 0.12.2
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
</details>
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} | 5 | 2020-02-25T16:57:42Z | 2021-11-20T23:21:51Z | 2020-02-25T18:47:35Z | MEMBER | null | closes #32229 | {
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} | 5 | 2020-02-25T17:42:41Z | 2020-03-09T19:17:42Z | 2020-03-09T15:42:17Z | CONTRIBUTOR | null | - [x] closes #32146
- [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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```python
# Your code here
df.style.hide_index()
```
#### Problem description
How do I convert a Style object back to a dataframe to be outputted by nbconvert as a latex file or pdf
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} | [] | closed | false | null | [] | null | 2 | 2020-02-25T21:34:11Z | 2020-02-26T08:37:12Z | 2020-02-25T22:21:55Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pyarrow as pa
df = pd.DataFrame([{'Minutes5UTC': '2020-02-25T21:15:00+00:00', 'Minutes5DK': '2020-02-25T22:15:00'}])
df['Minutes5DK'] = pd.to_datetime(df.Minutes5DK)
df['Minutes5UTC'] = pd.to_datetime(df.Minutes5UTC)
context = pa.default_serialization_context()
pa.deserialize(pa.serialize(df).to_buffer().to_pybytes())
```
#### Problem description
When following the [procedure outlined here](https://stackoverflow.com/a/57986261/5085211) to use `pyarrow` to serialize/deserialize data frames, the above example fails with the following traceback:
```
--------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-9-6f75cc47c6d5> in <module>
----> 1 pa.deserialize(pa.serialize(df).to_buffer().to_pybytes())
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/serialization.pxi in pyarrow.lib.deserialize()
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/serialization.pxi in pyarrow.lib.deserialize_from()
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/serialization.pxi in pyarrow.lib.SerializedPyObject.deserialize()
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/serialization.pxi in pyarrow.lib.SerializationContext._deserialize_callback()
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/serialization.py in _deserialize_pandas_dataframe(data)
167
168 def _deserialize_pandas_dataframe(data):
--> 169 return pdcompat.serialized_dict_to_dataframe(data)
170
171 def _serialize_pandas_series(obj):
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/pandas_compat.py in serialized_dict_to_dataframe(data)
661 def serialized_dict_to_dataframe(data):
662 import pandas.core.internals as _int
--> 663 reconstructed_blocks = [_reconstruct_block(block)
664 for block in data['blocks']]
665
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/pandas_compat.py in <listcomp>(.0)
661 def serialized_dict_to_dataframe(data):
662 import pandas.core.internals as _int
--> 663 reconstructed_blocks = [_reconstruct_block(block)
664 for block in data['blocks']]
665
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/pandas_compat.py in _reconstruct_block(item, columns, extension_columns)
707 klass=_int.CategoricalBlock)
708 elif 'timezone' in item:
--> 709 dtype = make_datetimetz(item['timezone'])
710 block = _int.make_block(block_arr, placement=placement,
711 klass=_int.DatetimeTZBlock,
~/miniconda3/envs/emission/lib/python3.8/site-packages/pyarrow/pandas_compat.py in make_datetimetz(tz)
734 def make_datetimetz(tz):
735 tz = pa.lib.string_to_tzinfo(tz)
--> 736 return _pandas_api.datetimetz_type('ns', tz=tz)
737
738
TypeError: 'NoneType' object is not callable
```
Perhaps interestingly, if I comment out the two `pd.to_datetime` lines, the thing works (perhaps unsurprisingly), but if I then include them again, the original reproducing example all of a sudden works. That is, this works:
```python
import pandas as pd
import pyarrow as pa
df = pd.DataFrame([{'Minutes5UTC': '2020-02-25T21:15:00+00:00', 'Minutes5DK': '2020-02-25T22:15:00'}])
context = pa.default_serialization_context()
pa.deserialize(pa.serialize(df).to_buffer().to_pybytes())
df = pd.DataFrame([{'Minutes5UTC': '2020-02-25T21:15:00+00:00', 'Minutes5DK': '2020-02-25T22:15:00'}])
df['Minutes5DK'] = pd.to_datetime(df.Minutes5DK)
df['Minutes5UTC'] = pd.to_datetime(df.Minutes5UTC)
context = pa.default_serialization_context()
pa.deserialize(pa.serialize(df).to_buffer().to_pybytes())
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.9.0-11-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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] | closed | false | null | [] | null | 3 | 2020-02-25T21:52:23Z | 2020-03-23T01:11:45Z | 2020-03-23T01:11:45Z | CONTRIBUTOR | null | #### Code Sample
Hi there, I'm trying to do something like this:
```python
BUSDAY_OFFSET = pd.offsets.CustomBusinessDay(calendar=MY_HOLIDAY_CALENDAR)
my_rollback = pd.date_range('2010-05-10', '2020-02-25') + BUSDAY_OFFSET - BUSDAY_OFFSET
```
and I am getting the following warning:
```
C:\Users\my_username\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\arrays\datetimes.py:743: PerformanceWarning: Non-vectorized DateOffset being applied to Series or DatetimeIndex
"or DatetimeIndex", PerformanceWarning)
```
#### Problem description
The custom business day non-vectorized addition/subtraction works, but when performed on the order of millions of dates (ridiculous because I could take out duplicate dates, I know, but it was my original use case), it takes a non-trivial amount of time, whereas `` pd.offsets.Day()`` can be added and subtracted in trivial time.
I checked _datetimes.py_ and CustomBusinessDay's apply_index() method is not implemented, and it is not clear from any indication whether that's intended (as in, the user should override it?) or not. [The doc page is here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.tseries.offsets.CustomBusinessDay.apply_index.html#pandas.tseries.offsets.CustomBusinessDay.apply_index).
Can anyone familiar with CustomBusinessDay's structure comment on whether there can be a simple fix?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 5.0.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.12
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | [] | closed | false | null | [] | null | 2 | 2020-02-25T22:01:03Z | 2020-10-10T14:14:57Z | 2020-04-02T00:06:25Z | MEMBER | null | (currently not right)
- [ ] closes #25408
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2020-02-26T02:00:45Z | 2020-02-27T14:55:30Z | 2020-02-27T12:52:36Z | MEMBER | null | We lose a little bit of ground on the range and slice (not really sure why), pick up a bigger amount of ground on list or ndarray.
```
In [3]: ser = pd.Series(range(10**5))
In [4]: key = range(100, 200)
In [5]: key2 = list(key)
In [6]: key3 = slice(100, 200)
In [7]: key4 = np.array(key2)
In [16]: %timeit ser.iloc[key] = 1
56.3 µs ± 1.19 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
62.9 µs ± 1.77 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
In [17]: %timeit ser.iloc[key2] = 1
95.6 µs ± 2.69 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
55.6 µs ± 922 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
In [20]: %timeit ser.iloc[key3] = 1
49 µs ± 756 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
50.6 µs ± 1.01 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
In [21]: %timeit ser.iloc[key4] = 1
71.6 µs ± 1.98 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- master
45.7 µs ± 427 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) # <-- PR
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} | 4 | 2020-02-26T02:13:12Z | 2020-03-03T01:59:38Z | 2020-03-03T01:56:51Z | MEMBER | null | troubleshooting an apparently-unrelated Travis failure there | {
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} | 8 | 2020-02-26T02:58:14Z | 2021-01-04T00:15:57Z | 2021-01-04T00:15:57Z | NONE | null | ```python
d = pd.to_datetime(['2020-11-02', '2019-01-02', '2020-01-02', '2020-02-04', '2020-11-03', '2019-11-03', '2019-11-13', '2019-11-13'])
a = np.arange(len(d))
b = np.random.rand(len(d))
df = pd.DataFrame({'d': d, 'a': a, 'b': b})
t = df.groupby(['d', 'a'], sort=False).mean()
```
The index of `t` is certainly not sorted, but `t.index.is_lexsorted()` returns True.
Another more subtle example is
```python
d = [3,4,10,0,1,2,5,3]
a = np.arange(len(d))
b = np.random.rand(len(d))
df = pd.DataFrame({'d': d, 'a': a, 'b': b})
t = df.groupby(['d', 'a'], sort=False).mean()
```
This time the lexsort flag is correct. However, calling sortlevel will not sort the new MultiIndex correctly, that is, `t.index.sortlevel(['d', 'a'])[0]` returns
```
MultiIndex([( 3, 0),
( 3, 7),
( 4, 1),
(10, 2),
( 0, 3),
( 1, 4),
( 2, 5),
( 5, 6)],
names=['d', 'a'])
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: 0.8.1
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.45.1
</details>
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} | 1 | 2020-02-26T03:42:23Z | 2020-02-26T16:08:13Z | 2020-02-26T12:27:25Z | MEMBER | null | DatetimeIndex remains an outlier, upcoming PR | {
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} | 2 | 2020-02-26T04:58:26Z | 2020-03-04T15:19:36Z | 2020-03-04T14:28:53Z | MEMBER | null | The benchmark I'm using for this is the same as for #32224, based on the asv that is most affected by removing `fast_apply` (see #32086)
```
import numpy as np
from pandas import *
%load_ext line_profiler
def get_df():
N = 10 ** 4
labels = np.random.randint(0, 2000, size=N)
labels2 = np.random.randint(0, 3, size=N)
df = DataFrame(
{
"key": labels,
"key2": labels2,
"value1": np.random.randn(N),
"value2": ["foo", "bar", "baz", "qux"] * (N // 4),
}
)
return df
df = get_df()
gb = df.groupby("key")
%prun -s cumulative gb.apply(lambda x: 1)
```
If we disable `fast_apply` on master, this gives:
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.207 0.207 groupby.py:701(apply)
1 0.000 0.000 0.207 0.207 groupby.py:750(_python_apply_general)
1 0.008 0.008 0.204 0.204 ops.py:151(apply)
1994 0.003 0.000 0.191 0.000 ops.py:858(__iter__)
1993 0.003 0.000 0.187 0.000 ops.py:889(_chop)
1993 0.003 0.000 0.184 0.000 indexing.py:814(__getitem__)
1993 0.001 0.000 0.180 0.000 indexing.py:1462(_getitem_axis)
1993 0.003 0.000 0.179 0.000 indexing.py:1488(_get_slice_axis)
1993 0.007 0.000 0.167 0.000 generic.py:3474(_slice)
1993 0.007 0.000 0.140 0.000 managers.py:713(get_slice)
1994 0.004 0.000 0.068 0.000 managers.py:125(__init__)
1994 0.027 0.000 0.059 0.000 managers.py:215(_rebuild_blknos_and_blklocs)
1993 0.002 0.000 0.050 0.000 managers.py:723(<listcomp>)
5979 0.010 0.000 0.048 0.000 blocks.py:310(getitem_block)
5983 0.003 0.000 0.033 0.000 blocks.py:275(make_block_same_class)
```
If we disable `fast_apply` on this PR:
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.136 0.136 groupby.py:701(apply)
1 0.000 0.000 0.136 0.136 groupby.py:750(_python_apply_general)
1 0.007 0.007 0.134 0.134 ops.py:151(apply)
1984 0.002 0.000 0.120 0.000 ops.py:903(__iter__)
1983 0.002 0.000 0.117 0.000 ops.py:934(_chop)
1983 0.003 0.000 0.114 0.000 indexing.py:814(__getitem__)
1983 0.001 0.000 0.110 0.000 indexing.py:1462(_getitem_axis)
1983 0.003 0.000 0.109 0.000 indexing.py:1488(_get_slice_axis)
1983 0.006 0.000 0.098 0.000 generic.py:3474(_slice)
1983 0.006 0.000 0.076 0.000 managers.py:742(get_slice)
1983 0.002 0.000 0.048 0.000 managers.py:752(<listcomp>)
5949 0.010 0.000 0.046 0.000 blocks.py:310(getitem_block)
5957 0.003 0.000 0.032 0.000 blocks.py:275(make_block_same_class)
5960 0.006 0.000 0.029 0.000 blocks.py:3023(make_block)
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32262 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32262/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32262/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32262/events | https://github.com/pandas-dev/pandas/issues/32262 | 571,154,671 | MDU6SXNzdWU1NzExNTQ2NzE= | 32,262 | Rolling groupby should not maintain the by column in the resulting DataFrame | {
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} | 6 | 2020-02-26T08:35:11Z | 2021-03-29T13:27:25Z | 2021-03-29T13:27:25Z | NONE | null | When performing a `rolling` operation on a `groupby` object, the index level will be incorrectly maintained in the dataframe.
```
import numpy as np
import pandas as pd
df = pd.DataFrame({'idx_L1': ['Level_1'] * 10 + ['Level_2'] * 10 + ['Level_3'] * 10,
'idx_L2': ([1] * 5 + [2] * 5 + [3] * 5) * 2,
'vals': np.arange(30)})
df.groupby(['idx_L1', 'idx_L2']).rolling(3).sum()
```
This issue has been discussed in #14013.
It has been closed eventhough the bug still exists in v1.01. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32263 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32263/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32263/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32263/events | https://github.com/pandas-dev/pandas/issues/32263 | 571,183,062 | MDU6SXNzdWU1NzExODMwNjI= | 32,263 | bug: inconsistent Series indexing crash, depending on DataFrame index type?? | {
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} | [] | closed | false | null | [] | null | 1 | 2020-02-26T09:17:40Z | 2020-02-28T11:52:07Z | 2020-02-28T11:52:07Z | NONE | null | Recently, I encountered a very strange bug when indexing pd.Series in Pandas groupby object.
```
if False:
df = pd.DataFrame({'app':['a', 'b', 'c', 'd', 'e'], 'sessionId':['1', '1', '2', '3', '3']},
index=['a1', 'a2', 'a3', 'a4', 'a5']) # this will not cause the last line to crash
else:
df = pd.DataFrame({'app': ['a', 'b', 'c', 'd', 'e'], 'sessionId': ['1', '1', '2', '3', '3']},
index=[1,2,3,4,5]) # this will cause the last line to crash
df1 = [v.app[0] for k, v in df.groupby('sessionId')] # the crashing line
```
As shown in the above code, if df.index elements are string, pd.Timestamp, etc., the last line will not crash; however, if df.index elements are int, float, etc., the last line will crash. Ridiculously, in both cases `type(v.app)` is pd.Series, yet one is index-able, while the other one is not. It is worth noting that the crashing line has not even touched df.index, so why does the value type of df.index elements matters?
So do we have another general inconsistency due to incompetent programming? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32264 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32264/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32264/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32264/events | https://github.com/pandas-dev/pandas/issues/32264 | 571,202,703 | MDU6SXNzdWU1NzEyMDI3MDM= | 32,264 | [to_datetime] TypeError: Argument 'date_string' has incorrect type (expected str, got numpy.str_) | {
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... | open | false | null | [] | null | 4 | 2020-02-26T09:49:36Z | 2021-09-29T21:06:15Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
import numpy as np
value = np.str_('2019-02-04 10:18:46.297000+0000')
arr = [value]
s = pd.Series(arr)
pd.to_datetime(value) # good
pd.to_datetime(s.iloc[0]) # good
pd.to_datetime(arr[0]) # good
```
```python
pd.to_datetime(arr) # error
```
```python
pd.to_datetime(s) # error
```
Traceback:
<details>
```python
TypeError Traceback (most recent call last)
pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
TypeError: Expected unicode, got numpy.str_
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-72-bab25c808085> in <module>
----> 1 pd.to_datetime(s) # error
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
726 result = arg.map(cache_array)
727 else:
--> 728 values = convert_listlike(arg._values, format)
729 result = arg._constructor(values, index=arg.index, name=arg.name)
730 elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/tools/datetimes.py in _convert_listlike_datetimes(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
445 errors=errors,
446 require_iso8601=require_iso8601,
--> 447 allow_object=True,
448 )
449
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1850 dayfirst=dayfirst,
1851 yearfirst=yearfirst,
-> 1852 require_iso8601=require_iso8601,
1853 )
1854 except ValueError as e:
pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime()
pandas/_libs/tslib.pyx in pandas._libs.tslib.array_to_datetime_object()
TypeError: Argument 'date_string' has incorrect type (expected str, got numpy.str_)
```
</details>
#### Problem description
`to_datetime` raises a `TypeError: Argument 'date_string' has incorrect type (expected str, got numpy.str_)` when the arg is a list-like of numpy.str_ objects, but not when the arg is a single numpy.str_ object.
#### Expected Output
I expect that it works with numpy.str_ like it does with str.
`assert (pd.to_datetime(s) == pd.to_datetime(s.astype(str))).all()`
Or at least to be consistent between `pd.to_datetime(s)` and `pd.to_datetime(s.iloc[0])`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-40-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.2
dateutil : 2.8.0
pip : 20.0.2
setuptools : 45.0.0
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.2.17
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
numba : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/32265 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32265/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32265/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32265/events | https://github.com/pandas-dev/pandas/issues/32265 | 571,219,870 | MDU6SXNzdWU1NzEyMTk4NzA= | 32,265 | API: distinguish NA vs NaN in floating dtypes | {
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"description": "np.nan, ... | open | false | null | [] | null | 56 | 2020-02-26T10:15:24Z | 2021-07-29T04:41:21Z | null | MEMBER | null | **Context**: in the original `pd.NA` proposal (https://github.com/pandas-dev/pandas/issues/28095) the topic about `pd.NA` vs `np.nan` was raised several times. And also in the recent pandas-dev mailing list discussion on pandas 2.0 it came up (both in context of `np.nan` for float as `pd.NaT` for datetime-like).
With the introduction of `pd.NA`, and if we want consistent "NA behaviour" across dtypes at some point in the future, I think there are two options for float dtypes:
- Keep using `np.nan` as we do now, but change its behaviour (e.g. in comparison ops) to match `pd.NA`
- Start using `pd.NA` in float dtypes
Personally, I think the first one is not really an option. Keeping it as np.nan, but deviating from numpy's behaviour feels like a non-starter to me. And it would also give a discrepancy between the vectorized behaviour in pandas containers vs the scalar behaviour of `np.nan`.
For the second option, there are still multiple ways this could be implemented (a single array that still uses np.nan as the missing value sentinel but we convert this to pd.NA towards the user, versus a masked approach like we do for the nullable integers). But in this issue, I would like to focus on the user-facing behaviour we want: Do we want to have both np.nan and pd.NA, or only allow pd.NA? Should np.nan still be considered as "missing" or should that be optional? What to do on conversion from/to numpy? (And the answer to some of those questions will also determine which of the two possible implementations is preferrable)
---
**Actual discussion items:** assume we are going to add floating dtypes that use `pd.NA` as missing value indicator. Then the following question comes up:
> If I have a Series[float64] could it contain both np.nan and pd.NA, and these signify different things?
So yes, it is *technically possible* to have both np.nan and pd.NA with different behaviour (`np.nan` as "normal", unmasked value in the actual data, `pd.NA` tracked in the mask). But we also need to decide if we *want* this.
This was touchec upon a bit in the original issue, but not really further discussed. Quoting a few things from the original thread in https://github.com/pandas-dev/pandas/issues/28095:
> [@Dr-Irv in [comment](https://github.com/pandas-dev/pandas/issues/28095#issuecomment-543162838)] I think it is important to distinguish between NA meaning "data missing" versus NaN meaning "not a number" / "bad computational result".
vs
> [@datapythonista in [comment](https://github.com/pandas-dev/pandas/issues/28095#issuecomment-539021471)] I think NaN and NaT, when present, should be copied to the mask, and then we can forget about them (for what I understand values with True in the NA mask won't be ever used).
So I think those two describe nicely the two options we have on the question **do we want both `pd.NA` and `np.nan` in a float dtype and have them signify different things?** -> 1) Yes, we can have both, versus 2) No, towards the user, we only have pd.NA and "disallow" NaN (or interpret / convert any NaN on input to NA).
A reason to have both is that they can signify different things (another reason is that most other data tools do this as well, I will put some comparisons in a separate post).
That reasoning was given by @Dr-Irv in https://github.com/pandas-dev/pandas/issues/28095#issuecomment-538786581: there are times when I get NaN as a result of a computation, which indicates that I did something numerically wrong, versus NaN meaning "missing data". So should there be separate markers - one to mean "missing value" and the other to mean "bad computational result" (typically `0/0`) ?
A dummy example showing how both can occur:
```python
>>> pd.Series([0, 1, 2]) / pd.Series([0, 1, pd.NA])
0 NaN
1 1.0
2 <NA>
dtype: float64
```
The NaN is introduced by the computation, the NA is propagated from the input data (although note that in an arithmetic operation like this, NaN would also propagate).
So, yes, it is possible and potentially desirable to allow both `pd.NA` and `np.nan` in floating dtypes. But, it also brings up several questions / complexities. Foremost, **should NaN still be considered as missing**? Meaning, should it be seen as missing in functions like `isna`/`notna`/`dropna`/`fillna` ? Or should that be an option? Should NaN still be considered as missing (and thus skipped) in reducing operations (that have a `skipna` keyword, like sum, mean, etc)?
Personally, I think we will need to keep NaN as missing, or at least initially. But, that will also introduce inconsistencies: although NaN would be seen as missing in the methods mentioned above, in arithmeric / comparison / scalar ops, it would behave as NaN and not as NA (so eg comparison gives False instead of propagating). It also means that in the missing-related methods, we will need to check for both NaN in the values as the mask (which can also have performance implications).
---
Some other various considerations:
* Having both pd.NA and NaN (np.nan) might actually be more confusing for users.
* If we want a consistent indicator and behavior for missing values across dtypes, I think we need a separate concept from NaN for float dtypes (i.e. pd.NA). Changing the behavior of NaN when inside a pandas container seems like a non-starter (the behavior of NaN is well defined in IEEE 754, and it would also deviate from the underlying numpy array)
* How do we handle compatibility with numpy?
The solution that we have come up (for now) for the other nullable dtypes is to convert to object dtype by default, and have a `to_numpy(.., na_value=np.nan)` explicit conversion.
But given how np.nan is in practice used in the whole pydata ecosystem as a missing value indicator, this might be annoying.
For conversion to numpy, see also some relevant discussion in https://github.com/pandas-dev/pandas/issues/30038
* What with conversion / inference on input?
Eg creating a Series from a float numpy array with NaNs (`pdSeries(np.array([0.1, np.nan]))`) Do we convert NaNs to NA automatically by default?
cc @pandas-dev/pandas-core @Dr-Irv @dsaxton
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https://api.github.com/repos/pandas-dev/pandas/issues/32266 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32266/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32266/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32266/events | https://github.com/pandas-dev/pandas/issues/32266 | 571,226,261 | MDU6SXNzdWU1NzEyMjYyNjE= | 32,266 | Pandas 1.0.1 - .rolling().min() and .rolling().max() create memory leak at <__array_function__ internals>:6 | {
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} | 7 | 2020-02-26T10:24:20Z | 2020-07-13T13:08:58Z | 2020-07-10T13:56:46Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import tracemalloc, linecache
import sys, os
import pandas as pd
def display_top_mem(snapshot, key_type='lineno', limit=10):
"""function for displaying lines of code taking most memory"""
snapshot = snapshot.filter_traces((
tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
tracemalloc.Filter(False, "<unknown>"),
))
top_stats = snapshot.statistics(key_type)
print("Top %s lines" % limit)
for index, stat in enumerate(top_stats[:limit], 1):
frame = stat.traceback[0]
# replace "/path/to/module/file.py" with "module/file.py"
filename = os.sep.join(frame.filename.split(os.sep)[-2:])
print("#%s: %s:%s: %.1f KiB"
% (index, filename, frame.lineno, stat.size / 1024))
line = linecache.getline(frame.filename, frame.lineno).strip()
if line:
print(' %s' % line)
other = top_stats[limit:]
if other:
size = sum(stat.size for stat in other)
print("%s other: %.1f KiB" % (len(other), size / 1024))
total = sum(stat.size for stat in top_stats)
print("Total allocated size: %.1f KiB" % (total / 1024))
def main():
tracemalloc.start()
periods = 745
df_init = pd.read_csv('./mem_debug_data.csv', index_col=0)
for i in range(100):
df = df_init.copy()
df['l:c:B'] = df['c:B'].rolling(periods).min()
df['h:c:B'] = df['c:B'].rolling(periods).max()
#df['l:c:B'] = df['c:B'].rolling(periods).mean()
#df['h:c:B'] = df['c:B'].rolling(periods).median()
snapshot = tracemalloc.take_snapshot()
display_top_mem(snapshot, limit=3)
print(f'df size {sys.getsizeof(df)/1024} KiB')
print(f'{i} ##################')
if __name__ == '__main__':
main()
```
#### Problem description
Pandas `rolling().min()` and `rolling().max()` functions create memory leaks. I've run a tracemalloc line based memory profiling and `<__array_function__ internals>:6` seems to always grow in size for every loop iteration in the script above with both of these functions present. For 1000 itereations it will consume around 650MB or RAM, whereas for example if `rolling().min()` and `rolling().max()` is changed to `rolling().mean()`and `rolling().median()` an run for 1000 iterations, RAM consumption will stay constant at around 4MB. Therefore `rolling().min()` and `rolling().max()` seem to be the problem.
The output of this script running for 100 iterations with `<__array_function__ internals>:6` constantly increasing in size can be found here: https://pastebin.com/nvGKgmPq
CSV file `mem_debug_data.csv` used in the script can be found here: http://www.sharecsv.com/s/ad8485d8a0a24a5e12c62957de9b13bd/mem_debug_data.csv
#### Expected Output
Running `rolling().min()` and `rolling().max()` constantly over time should not grow RAM consumption.
#### Output of ``pd.show_versions()``
<details>
> INSTALLED VERSIONS
> ------------------
> commit : None
> python : 3.7.6.final.0
> python-bits : 64
> OS : Linux
> OS-release : 4.15.0-88-generic
> machine : x86_64
> processor : x86_64
> byteorder : little
> LC_ALL : None
> LANG : en_GB.UTF-8
> LOCALE : en_GB.UTF-8
>
> pandas : 1.0.1
> numpy : 1.18.1
> pytz : 2019.3
> dateutil : 2.8.1
> pip : 19.2.3
> setuptools : 41.2.0
> Cython : None
> pytest : None
> hypothesis : None
> sphinx : None
> blosc : None
> feather : None
> xlsxwriter : None
> lxml.etree : None
> html5lib : None
> pymysql : None
> psycopg2 : None
> jinja2 : 2.11.1
> IPython : 7.12.0
> pandas_datareader: None
> bs4 : None
> bottleneck : None
> fastparquet : None
> gcsfs : None
> lxml.etree : None
> matplotlib : 3.1.3
> numexpr : None
> odfpy : None
> openpyxl : None
> pandas_gbq : None
> pyarrow : None
> pytables : None
> pytest : None
> pyxlsb : None
> s3fs : None
> scipy : 1.4.1
> sqlalchemy : None
> tables : None
> tabulate : None
> xarray : None
> xlrd : None
> xlwt : None
> xlsxwriter : None
> numba : None
</details>
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} | 0 | 2020-02-26T12:40:34Z | 2020-02-26T15:33:16Z | 2020-02-26T15:33:16Z | NONE | null | Backport PR #32214: BUG: Cast pd.NA to pd.NaT in to_datetime | {
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] | closed | false | null | [] | null | 1 | 2020-02-26T12:54:54Z | 2020-11-09T18:06:03Z | 2020-11-09T18:06:03Z | NONE | null | #### Code Sample
```python
import pandas as pd
df1 = pd.DataFrame({
'dt': pd.to_datetime(['2017-07-17 00:00:00', '2018-08-08 00:00:00']),
'dt_tz': pd.to_datetime(['2017-07-17 00:00:00', '2018-08-08 00:00:00'], utc=True),
'char': ['a', 'b'],
'num': [1, 2]
})
df2 = pd.DataFrame({
'dt': pd.to_datetime(['2019-09-19 00:00:00', '2020-02-20 00:00:00']),
'dt_tz': pd.to_datetime(['2019-09-19 00:00:00', '2020-02-20 00:00:00'], utc=True),
'char': ['c', 'd'],
'num': [3, 4],
})
mask = pd.Series([False, False])
# the following lines do nothing (as expected) because mask is all False
df1.loc[mask, "dt"] = df2["dt"]
df1.loc[mask, "char"] = df2["char"]
df1.loc[mask, "num"] = df2["num"]
```
```python
# this line should do nothing, too. But instead it raises a ValueError
df1.loc[mask, "dt_tz"] = df2["dt_tz"]
```
Traceback:
<details>
```python
ValueError Traceback (most recent call last)
<ipython-input-3-17df28957215> in <module>
----> 1 df1.loc[mask, "dt_tz"] = df2["dt_tz"]
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)
668 key = com.apply_if_callable(key, self.obj)
669 indexer = self._get_setitem_indexer(key)
--> 670 self._setitem_with_indexer(indexer, value)
671
672 def _validate_key(self, key, axis: int):
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)
1009 labels, value, plane_indexer, lplane_indexer, self.obj
1010 ):
-> 1011 setter(labels[0], value)
1012
1013 # per label values
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/indexing.py in setter(item, v)
959 s._consolidate_inplace()
960 s = s.copy()
--> 961 s._data = s._data.setitem(indexer=pi, value=v)
962 s._maybe_update_cacher(clear=True)
963
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/internals/managers.py in setitem(self, **kwargs)
559
560 def setitem(self, **kwargs):
--> 561 return self.apply("setitem", **kwargs)
562
563 def putmask(self, **kwargs):
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/internals/managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/internals/blocks.py in setitem(self, indexer, value)
2439 isinstance(indexer, np.ndarray) and indexer.size == 0
2440 ):
-> 2441 return super().setitem(indexer, value)
2442
2443 obj_vals = self.values.astype(object)
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/internals/blocks.py in setitem(self, indexer, value)
1794 indexer = indexer[0]
1795
-> 1796 check_setitem_lengths(indexer, value, self.values)
1797 self.values[indexer] = value
1798 return self
~/.virtualenvs/lr/lib/python3.7/site-packages/pandas/core/indexers.py in check_setitem_lengths(indexer, value, values)
112 ):
113 raise ValueError(
--> 114 "cannot set using a list-like indexer "
115 "with a different length than the value"
116 )
ValueError: cannot set using a list-like indexer with a different length than the value
```
</details>
#### Problem description
When mask is all False and the column is a localized datetime, it raises the error: `ValueError: cannot set using a list-like indexer with a different length than the value`.
This doesn't happens when the column is another type like naive datetime, str or int.
#### Expected Output
`df1.loc[mask, "dt_tz"] = df2["dt_tz"]` shouldn't raise an error, just do nothing.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-40-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.0.0
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
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sqlalchemy : 1.2.17
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xlwt : 1.3.0
xlsxwriter : None
numba : None
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} | 1 | 2020-02-26T17:34:05Z | 2020-02-29T02:56:04Z | 2020-02-29T02:55:52Z | NONE | null | This may be related to [https://github.com/pandas-dev/pandas/pull/31484](url).
Unary application of the `~` operator seems to operate bitwise on an underlying integer array rather than the expected negation of a boolean:
```python
>>> s = Series([True, False], dtype="boolean")
>>> ~s
0 -2
1 -1
dtype: object
>>> s = Series([True, False], dtype="bool")
>>> ~s
0 False
1 True
dtype: bool
```
This behavior prevents the correct handling of boolean indexing:
```python
>>> a = pd.Series(list("abcd"))
>>> s = pd.Series([True, False, True, False], dtype="boolean")
>>> a[s]
0 a
2 c
dtype: object
>>> a[~s]
...
KeyError: "None of [Int64Index([-2, -1, -2, -1], dtype='int64')] are in the [index]"
```
The problem goes away if both of your series are `ExtensionArray` types:
```python
>>> a = pd.Series(list('abcd'), dtype="string")
>>> s = pd.Series([True, False, True, False], dtype="boolean")
>>> a[s]
0 a
2 c
dtype: object
>>> a[~s]
1 b
3 d
dtype: string
```
## Edit
I updated to version `1.0.1` and the initial issue goes away (i.e. the unary `~` gives the expected result). However, the `dtype` reverts to `object` after assignment:
```python
>>> s = pd.Series([True, False, pd.NA], dtype="boolean") # same behavior whether or not you include an `NA`
>>> s
0 True
1 False
2 <NA>
dtype: boolean
>>> ~s
0 False
1 True
2 <NA>
dtype: boolean
>>> s[3] = True
>>> s
0 True
1 False
2 <NA>
3 True
dtype: object
>>> ~s
0 -2
1 -1
2 <NA>
3 -2
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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} | 2 | 2020-02-26T18:09:49Z | 2020-02-27T16:21:28Z | 2020-02-27T16:19:14Z | MEMBER | null | cc @MomIsBestFriend there are three things about these tests that I think would make for good (separate) follow-ups if you're interested:
1) They are just begging to be parametrized
2) We don't have a systematic way of naming/organizing tests that are for two specific methods rather than one specific method, which is the idea behind tests.(frame|series).methods. (see also: head/tail, first_valid_index/last_valid_index)
3) These tests could/should also be shared/parametrized over Series vs DataFrame, but we dont have a systematic home or naming convention for these. | {
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} | 1 | 2020-02-26T20:03:33Z | 2020-03-03T03:28:43Z | 2020-03-03T03:28:43Z | NONE | null | #### Code Sample
### Series as category
df = pd.Series(['a','a','b','c']).astype('category')
print(df.shift(1))
print(df)
print(df.shift(1) != df)
OUTPUT:
0 NaN
1 a
2 a
3 b
dtype: category
Categories (3, object): [a, b, c]
0 a
1 a
2 b
3 c
dtype: category
Categories (3, object): [a, b, c]
0 False
1 False
2 True
3 True
dtype: bool
### Series as str
df = pd.Series(['a','a','b','c']).astype('str')
print(df.shift(1))
print(df)
print(df.shift(1) != df)
OUTPUT:
0 NaN
1 a
2 a
3 b
dtype: object
0 a
1 a
2 b
3 c
dtype: object
0 True
1 False
2 True
3 True
dtype: bool
```
#### Problem description
The behaviour of NaN in comparison operators is different for type category and str. See example code - the first element is NaN in both instances, but the second instance equates to false, and the first equates to true for a != operation. For a == operation for a category, the behavior is as expected.
#### Expected Output
I would expect both to have the same output.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.12.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.4.0
Cython : 0.29.15
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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"default"... | open | false | null | [] | null | 0 | 2020-02-26T21:39:18Z | 2021-07-29T04:42:50Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
import timeit
# data set up:
data_size = 1234
query_size = 10000
data_ndarray = np.random.randint(100000, size=data_size)
data_series = pd.Series(data_ndarray)
list_ = list(range(query_size))
range_index = pd.Index(range(query_size))
array_index = pd.Index(list_)
series = array_index.to_series()
ndarray = array_index.to_numpy()
print(f"{data_series.dtype=}, {range_index.dtype=}, {array_index.dtype=}")
N = 1000
def run(name, f):
return name, timeit.timeit(f, number=N) / N
df = pd.DataFrame(
[
run("list", lambda: data_series.isin(list_)),
run("range index", lambda: data_series.isin(range_index)),
run("array index", lambda: data_series.isin(array_index)),
run("series", lambda: data_series.isin(series)),
run("ndarray", lambda: data_series.isin(ndarray)),
# variations on using indices
run("array index.to_numpy()", lambda: data_series.isin(array_index.to_numpy())),
run("range index.__contains__", lambda: data_series.apply(range_index.__contains__)),
run("array index.__contains__", lambda: data_series.apply(array_index.__contains__)),
run("array index.get_indexer", lambda: array_index.get_indexer(data_series.values) != -1),
# poke into the internals
run("array index._engine.mapping.__contains__", lambda: data_series.apply(array_index._engine.mapping.__contains__)),
run("array index._engine.get_indexer", lambda: array_index._engine.get_indexer(data_series.values) != -1),
# numpy for comparison
run("np.isin", lambda: np.isin(data_ndarray, ndarray)),
],
columns=["name", "time"]
).set_index("name")
# double check that the get_indexer version works correctly
assert (data_series.isin(array_index) == (array_index._engine.get_indexer(data_series.values) != -1)).all()
print(df.sort_values("time"))
```
Output:
```
data_series.dtype=dtype('int64'), range_index.dtype=dtype('int64'), array_index.dtype=dtype('int64')
time
name
array index._engine.get_indexer 0.000016
array index.get_indexer 0.000048
array index.to_numpy() 0.000199
ndarray 0.000211
series 0.000224
np.isin 0.000227
array index._engine.mapping.__contains__ 0.000292
array index.__contains__ 0.000586
list 0.000624
range index.__contains__ 0.000755
range index 0.001444
array index 0.001457
```
#### Problem description
The exact ratios and numbers depend very much on the data , especially because the `get_indexer` forms are `O(data_size)` (with O(1) indexing into the index's hash table), while the other forms likely depend on `query_size`, at least to pay the cost of converting data.
#### Expected Output
I'd expect:
- using a pandas type to always be faster than a Python types like `list`,
- it to not be much slower than even a simple variant like `.isin(index.to_numpy())`,
- it to use the hash table if it exists, given how much faster that is
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
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} | 5 | 2020-02-26T21:56:55Z | 2020-03-02T15:34:15Z | 2020-02-27T12:39:49Z | CONTRIBUTOR | null | Edit `_from_nested_dict()` by using defaultdict for optimization in performance
- [ ] closes #32209
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2020-02-26T22:08:46Z | 2020-02-27T18:21:38Z | 2020-02-27T16:56:47Z | MEMBER | null | Looks like the ssh key we have in the settings is not working as expected in #32074.
While we fix it (I don't have access to the settings, so can't check much), this prevents master from failing for the publishing of the dev docs to GitHub pages. | {
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} | 1 | 2020-02-26T22:18:02Z | 2020-03-03T02:08:07Z | 2020-03-03T01:58:27Z | MEMBER | null | Adds a test for incorrect behavior in DTI.where and TDI.where that is fixed in master. | {
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} | 4 | 2020-02-26T22:19:03Z | 2020-03-04T17:16:02Z | 2020-03-04T13:52:31Z | CONTRIBUTOR | null | closes #32205, closes #32220
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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1. Generally more white space between pictures and texts/headers
(example image above): https://pandas.io/docs/getting_started/intro_tutorials/01_table_oriented.html#each-column-in-a-dataframe-is-a-series
2. The blue boxes that contain `To user guide` should be replaced by a question like `Want more information?`
(example blue box above): https://pandas.io/docs/getting_started/intro_tutorials/03_subset_data.html#how-do-i-filter-specific-rows-from-a-dataframe
3. h1 headings appear too similar in size to an h2 heading when they follow each other
(example) https://pandas.io/docs/getting_started/intro_tutorials/06_calculate_statistics.html#how-to-calculate-summary-statistics
4. Some of the data topics seems to domain specific
(example) https://pandas.io/docs/getting_started/intro_tutorials/09_timeseries.html
5. The different topics within the page (sections that start with a h1 header) could be separated more clearly
(example in the section separation here): https://pandas.io/docs/getting_started/intro_tutorials/07_reshape_table_layout.html#pivot-table
6. The pandas cheatsheet should be linked in the `Getting started` section: https://pandas.io/docs/getting_started/index.html#getting-started
7. Mention `tidyverse` in `Comparisons with R/ R libraries`: https://pandas.io/docs/getting_started/comparison/comparison_with_r.html
8. The pivot table diagram was not terribly intuitive. Recommended to use values instead of colors
(example) https://pandas.io/docs/getting_started/intro_tutorials/07_reshape_table_layout.html#pivot-table
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} | 25 | 2020-02-27T07:53:20Z | 2020-03-06T12:31:08Z | 2020-03-06T12:30:37Z | CONTRIBUTOR | null | - [x] closes https://github.com/pandanistas/pandanistas_sprint_jakarta2020/issues/19
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
output of `python scripts/validate_docstrings.py pandas.Series.argmin`:
```
################################################################################
################################## Validation ##################################
################################################################################
```
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} | 3 | 2020-02-27T08:22:44Z | 2020-03-11T01:48:34Z | 2020-03-11T01:48:34Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> df = pd.DataFrame(data=[["abc", 123, True]])
>>> print(df)
0 1 2
0 abc 123 True
>>> print(df.dtypes)
0 object
1 int64
2 bool
dtype: object
>>> df = df.convert_dtypes()
>>> print(df)
0 1 2
0 abc 123 True
>>> print(df.dtypes)
0 string
1 Int64
2 boolean
dtype: object
>>> df = df.convert_dtypes()
>>> print(df)
0 1 2
0 b'abc' 123 1
>>> print(df.dtypes)
0 object
1 Int64
2 Int64
dtype: object
```
#### Problem description
Applying convert_dtypes() to a column with dtype `string` converts it to a column dtype 'object' (and the individual values from `str` type to `bytes` type).
Applying convert_dtypes() to a column with dtype `boolean` converts it to a column dtype 'Int64' (and the individual values from `bool` type to `int` type).
#### Expected Output
convert_dtypes() should keep StringDtype columns as StringDtype and BooleanDtype columns as BooleanDtype.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Malaysia.1252
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
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```python
import pandas as pd
categories = ['B', 'A', 'C']
a = pd.DataFrame({'x':categories})
a['y'] = 1
a['value'] = 1
a['x'] = a['x'].astype('category')
# the sort_values is important here to trigger the bug
b = a.copy().sort_values('x')
a['x'] = a['x'].cat.set_categories(categories)
print(a['x'])
print(b['x'])
a = a.set_index(['x', 'y'], )['value']
b = b.set_index(['x', 'y'], )['value']
a.mul(b)
```
#### Problem description
In pandas 1.01+ the result of running the above code is wrong, where it returns the correct result in 0.25.3
The output of the above in 1.0.5 gives two NaN values with duplicate index entry:
It is triggered with the following conditions:
- having a multi-index
- a difference in the order of the categories in the index (using the set_categories)
- a difference in order of the index itself (using the sort_values)
#### Expected Output
The expected output is returned in 0.25.3:
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.5
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.1.3.post20200325
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.19.3
sphinx : 3.1.2
blosc : None
feather : None
xlsxwriter : 1.2.9
lxml.etree : 4.5.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.16.1
pandas_datareader: None
bs4 : 4.9.1
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.2
matplotlib : 3.2.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.4
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.18
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.9
numba : 0.50.1
</details>
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
EDIT
----
probably better/less invasive to make sure .groupby(, sort=False) returns an object where the index has codes which reflect the ordering | {
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- [x] tests added / passed
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... | open | false | null | [] | null | 2 | 2020-02-27T13:00:32Z | 2020-06-02T13:36:03Z | null | MEMBER | null | Sample data
```python
>>> import pandas as pd
>>> pd.__version__ # on current master
'1.1.0.dev0+613.g97c0ce962'
>>> df = pd.DataFrame({'key': list('aaabbb'), 'value': [1, 2, 3, 3, 2, 1]})
>>> df
key value
0 a 1
1 a 2
2 a 3
3 b 3
4 b 2
5 b 1
```
Issue
```python
>>> df.groupby('key')
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001C0D32FC400>
# DataFrameGroupBy.corrwith has no issues
>>> df.groupby('key').corrwith(pd.Series([1,2,3,1,2,3]))
value
key
a 1.0
b -1.0
>>> df.groupby('key')['value']
<pandas.core.groupby.generic.SeriesGroupBy object at 0x000001C0D279BF28>
# SeriesGroupBy.corrwith is not implemented
>>> df.groupby('key')['value'].corrwith(pd.Series([1, 2, 3, 1, 2, 3]))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\users\xxx\xxx\pandas\pandas\core\groupby\groupby.py", line 580, in __getattr__
f"'{type(self).__name__}' object has no attribute '{attr}'"
AttributeError: 'SeriesGroupBy' object has no attribute 'corrwith'
```
#### Problem description
As shown above, DataFrameGroupBy.corrwith has no issues and works as expected. However, the corresponding SeriesGroupBy.corrwith is not implemented and reports error.
#### Expected Output
```python
>>> df.groupby('key')['value'].corrwith(pd.Series([1, 2, 3, 1, 2, 3]))
value
key
a 1.0
b -1.0
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} | 1 | 2020-02-27T15:31:27Z | 2020-02-29T03:07:24Z | 2020-02-29T03:07:24Z | NONE | null | In the `pandas.to_numeric` [documentation page](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_numeric.html), in the **See also** section, `convert_dtypes` has no link attached to it.
I am not familiar with contributing to the docs but I believe this is because the docstring of `to_numeric` refers to `convert_dtypes` instead of `DataFrame.convert_dtypes`.
https://github.com/pandas-dev/pandas/blob/29d6b0232aab9576afa896ff5bab0b994760495a/pandas/core/tools/numeric.py#L67-L73 | {
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} | 3 | 2020-02-27T16:07:45Z | 2020-07-22T10:53:39Z | null | CONTRIBUTOR | null |
```python
import numpy as np
import pandas as pd
pd.DataFrame(np.random.random((4,3)), columns=['a','b','c'])
```
Generates
```
| | a | b | c |
|---+----------+-----------+----------|
| 0 | 0.87267 | 0.429153 | 0.545497 |
| 1 | 0.596757 | 0.811766 | 0.797137 |
| 2 | 0.175432 | 0.0254871 | 0.560906 |
| 3 | 0.426507 | 0.39993 | 0.418244 |
```
#### Problem description
Org-mode or Jupyter Notebooks often include Pandas table output. Pandas does try to intelligently limit printed output, but there are times when it is useful to have tables larger than can fit on screen. In this case, having column names repeated at the bottom (or even every `repeat` row) would improve readability and usability.
#### Expected Output
If set to print at bottom, it would be nice to have the option to generate this:
```
| | a | b | c |
|---+----------+-----------+----------|
| 0 | 0.87267 | 0.429153 | 0.545497 |
| 1 | 0.596757 | 0.811766 | 0.797137 |
| 2 | 0.175432 | 0.0254871 | 0.560906 |
| 3 | 0.426507 | 0.39993 | 0.418244 |
|---+----------+-----------+----------|
| | a | b | c |
```
If set to print headers every 2nd row, then it might look like this:
```
| | a | b | c |
|---+----------+-----------+----------|
| 0 | 0.87267 | 0.429153 | 0.545497 |
| 1 | 0.596757 | 0.811766 | 0.797137 |
|---+----------+-----------+----------|
| | a | b | c |
|---+----------+-----------+----------|
| 2 | 0.175432 | 0.0254871 | 0.560906 |
| 3 | 0.426507 | 0.39993 | 0.418244 |
|---+----------+-----------+----------|
| | a | b | c |
```
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} | 1 | 2020-02-27T16:31:16Z | 2020-02-28T16:16:32Z | 2020-02-28T11:49:24Z | MEMBER | null | Delay validation check in get_slice is also nice. | {
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} | 1 | 2020-02-27T16:41:15Z | 2020-02-27T17:53:49Z | 2020-02-27T17:53:35Z | CONTRIBUTOR | null | - [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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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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... | null | 6 | 2020-02-27T18:06:27Z | 2021-07-29T04:45:55Z | null | MEMBER | null | xref #32279
For what I see, when a build in the CI for a commit to master fails, only the user who triggered the action receives an alert (assuming the user has notifications enabled in [their settings](https://github.com/settings/notifications#github-actions-heading)).
I think for Travis and pipelines, some people are receiving notifications for all the failures. Ideally we would like to be able to do the same for actions. I had a quick look, but not sure how this can be enabled. I checked in the settings, but no option seems to exist. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/32302 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/32302/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/32302/comments | https://api.github.com/repos/pandas-dev/pandas/issues/32302/events | https://github.com/pandas-dev/pandas/pull/32302 | 572,264,142 | MDExOlB1bGxSZXF1ZXN0MzgwOTk4Mjc4 | 32,302 | TYP: Groupby sum|prod|min|max|first|last methods | {
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"description": "type annotation... | closed | false | null | [] | null | 11 | 2020-02-27T18:14:32Z | 2020-05-15T21:21:23Z | 2020-05-15T21:20:59Z | CONTRIBUTOR | null | This makes two things:
* makes mypy know the signatures of the agg methods (``sum``, ``prod``, etc.) on Groupby
* gives the agg methods on Groupby objects a return type
This ensues that mypy and IDE's always know that the return type of e.g. ``df.groupby('a').sum()`` is a DataFrame, which is nice when chaining. | {
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I guess it's not as simple as the key content being incorrect, I guess it can't be obtained from the secrets, and probably the used key is just empty.
The relevant code is in the [yaml of the GitHub action](https://github.com/pandas-dev/pandas/blob/master/.github/workflows/ci.yml#L180).
Besides making sure that the key is correct (that it's the private and not the public, it's complete...), I guess a good test to help identify the problem could be replace the content of the key by some random text, and echo it and see that we're ready getting the full content with no garbage in it.
I think just few people have access to it, @TomAugspurger do you have time for it? Or do you know who else has access to the settings? I think it was said in some thread, but I fail to remember. | {
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} | 1 | 2020-02-27T18:25:01Z | 2020-09-09T01:01:00Z | 2020-03-03T03:28:43Z | MEMBER | null | - [x] closes #32276
- [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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- [x] closes #32106
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry (`1.1.0`)
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} | 13 | 2020-02-27T18:46:41Z | 2021-11-12T02:15:12Z | null | MEMBER | null | ```python
import pandas as pd
left = pd.DataFrame({"a": [1, 2, pd.NA]}, dtype="Int64")
right = pd.DataFrame({"b": [1, 2, pd.NA]}, dtype="Int64")
pd.merge(left, right, how="inner", left_on="a", right_on="b")
# a b
# 0 1 1
# 1 2 2
# 2 <NA> <NA>
```
(Above is from 1.0.1 and master)
I think when joining on a nullable column we should not be matching `NA` with `NA` and should only be joining where we have unambiguous equality (as in SQL). Also worth noting that this is the same as what happens when we have `NaN` which also seems incorrect, so could be an opportunity to fix this behavior?
```python
pd.merge(left.astype(float), right.astype(float), how="inner", left_on="a", right_on="b")
# a b
# 0 1.0 1.0
# 1 2.0 2.0
# 2 NaN NaN
```
#### Expected Output
```
a b
0 1 1
1 2 2
```
cc @jorisvandenbossche | {
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"following_url": "https://api.github.com/users/SouravPal1995/following{/other_... | null | 9 | 2020-02-27T19:06:20Z | 2021-01-29T17:12:14Z | null | MEMBER | null | Another piece of feedback from the CZI EOSS kickoff meeting that @WillAyd and I attended:
The current https://pandas.io/ home page has 2 `Getting started` links:
<img width="1433" alt="Screen Shot 2020-02-27 at 10 56 32 AM" src="https://user-images.githubusercontent.com/10647082/75477029-7bf30000-5950-11ea-8fc5-d0280c69067f.png">
The upper right one in the top ribbon leads to https://pandas.io/getting_started.html
The bottom left one leads to https://pandas.io/docs/getting_started/index.html
People found it confusing that they linked to different pages and did not completely contain the same information. From a user experience perspective, it would be great to have these point to the same page, especially since both pages essentially have the same information. | {
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] | closed | false | null | [] | null | 3 | 2020-02-27T19:18:24Z | 2020-03-11T22:15:21Z | 2020-03-11T22:15:21Z | MEMBER | null | Another piece of feedback from the CZI EOSS kickoff meeting that @WillAyd and I attended:
In https://pandas.io/docs/getting_started/index.html, we link to the following pages in the left side:
<img width="1429" alt="Screen Shot 2020-02-27 at 11 09 33 AM" src="https://user-images.githubusercontent.com/10647082/75478083-2e779280-5952-11ea-9cf9-d49c308a69f1.png">
We got feedback to:
1. Have a dedicated link to the pandas cheat sheet: https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
2. Consolidate `Getting started tutorials`, `Tutorials`, and `10 minutes to pandas` (which is linked already in `Tutorials`) into a singular `Tutorials` page
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- Hyperlinks `commit logs`
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] | closed | false | null | [] | null | 3 | 2020-02-27T22:46:27Z | 2020-10-10T14:14:56Z | 2020-03-03T03:15:08Z | MEMBER | null | - [x] closes #31849
- [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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