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} | 1 | 2019-12-27T18:17:32Z | 2019-12-27T19:28:32Z | 2019-12-27T19:28:19Z | MEMBER | null | - [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
As noted in https://github.com/pandas-dev/pandas/issues/6581, sounds like we are okay breaking compatibility with `np.compress` | {
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} | 10 | 2019-12-27T18:38:13Z | 2020-05-03T16:49:49Z | 2020-05-03T16:09:15Z | CONTRIBUTOR | null | - [x] closes #11278
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Description:
In single object indexer case,
datetime string converted to datetime-like object. Because indexing use ".xs" which is ".get_loc" that has type casting labels code.
But in list indexer,
there is no type check and casting code. So i add them.
this bug happen not only series but also dataframe And "datetimeindex" as well as "periodIndex"
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} | 8 | 2019-12-27T19:17:05Z | 2020-01-03T05:23:39Z | 2020-01-03T01:17:46Z | MEMBER | null | - [x] closes #24716
- [x] tests added / passed
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... | null | 13 | 2019-12-27T20:27:15Z | 2021-08-29T23:11:51Z | null | CONTRIBUTOR | null | xref #19602
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.25.3'
In [3]: df = pd.DataFrame({'mixed' : [1, 2, 'abc', 'def'], 'ints': [100, 200, 3
...: 00, 400]})
In [4]: df
Out[4]:
mixed ints
0 1 100
1 2 200
2 abc 300
3 def 400
In [5]: df.dtypes
Out[5]:
mixed object
ints int64
dtype: object
In [6]: df.query('ints < 300').set_index('mixed').index
Out[6]: Int64Index([1, 2], dtype='int64', name='mixed')
In [7]: df.set_index('mixed').query('ints < 300').index
Out[7]: Index([1, 2], dtype='object', name='mixed')
```
#### Problem description
In the above, I start with a `DataFrame` with a column `mixed` that has both integer and string values.
In statement [6], I do a query on a different column and then set the index to be the column `mixed`. The resulting index now has an `int64` dtype as opposed to having the dtype preserved from the original column.
But in statement [7], I first set the index, and then do the query, and now the index has the `object` dtype.
This becomes an issue if one does some computation on the queried DataFrame and then create the index `mixed`, and then you want to merge it back to the original `DataFrame`. Now the original one will have `mixed` as dtype `'O'` and the new one has `mixed` as dtype '`int`'
#### Expected Output
From statement [6], I would have expected:
```
Index([1, 2], dtype='object', name='mixed')
```
#### 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 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.54.2
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
</details>
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Sorry to intrude, I think there might be a trouble with parsing strings containing timezone data as 'GMT+1'.
Basically, I have a list of timestamps stored as strings:
'''
0 Sat Mar 30 2019 21:00:00 GMT+0100
1 Sat Mar 30 2019 22:00:00 GMT+0100
2 Sat Mar 30 2019 23:00:00 GMT+0100
3 Sun Mar 31 2019 00:00:00 GMT+0100
'''
I add them in a dataframe as datetime object.
'''
GC['date'] = pd.to_datetime(my_timestamps)
'''
As a result, I get:
'''
0 2019-03-30 21:00:00-01:00
1 2019-03-30 22:00:00-01:00
2 2019-03-30 23:00:00-01:00
'''
As far as I googled the topic, the offset should not be reversed. It should stay +1.
See for instance this page discussing utcoffset and giving an example with Singapore timezone, that produces following output:
https://pythontic.com/datetime/datetime/utcoffset
'''
Singapore Time instance:2017-02-14 12:15:01.000099+08:00
UTC Offset for Singapore Time:8:00:00
'''
Offset is +8 hours, and if you check time difference between Singapore and UTC:
if Singapore is 5 AM the 2nd of January, UTC is 21 PM the 1str of January: the +8 offset has to be removed to get UTC timezone.
So in short, GMT+1 should be translated into +01:00 and not -01:00 as currently the case.
Coming back to my own topic, as I have DST in my timestamp list, and that I further use these timestamps as indexes in a panda dataframe, the verify_integrity check fails because it detects timestamps as being the same:
'''
14 Sun Oct 27 2019 02:00:00 GMT+0200
15 Sun Oct 27 2019 02:00:00 GMT+0100
16 Sun Oct 27 2019 03:00:00 GMT+0100
'''
becomes
'''
14 2019-10-27 02:00:00-02:00
15 2019-10-27 02:00:00-01:00
16 2019-10-27 03:00:00-01:00
'''
and if finally converted with
'''
GC['date2'] = pd.to_datetime(my_timestamps, utc=True)
'''
are written according utc time:
'''
14 2019-10-27 04:00:00+00:00
15 2019-10-27 03:00:00+00:00
16 2019-10-27 04:00:00+00:00
'''
Lines 14 & 16 have the same timestamp, which is not true.
Please, is this a bug?
Or should I do this in other way?
I thank you in advance for your attention.
Have a good day,
Bests,
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} | 3 | 2019-12-27T22:46:28Z | 2020-01-03T11:58:55Z | 2020-01-03T11:55:36Z | CONTRIBUTOR | null | Hi, this is a quick fix for #15914.
After reading the issue I'm not sure if this is meant to be supported though, please let me know!
I ran into this issue when trying to pass to `read_excel` a binary file downloaded with `requests` so I think it would make sense to support this natively without the user having to figure out the need to call `io.BytesIO` first. If so, do we need to update other docstrings?
Thank you!
- [x] closes #15914
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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] | closed | false | null | [] | null | 10 | 2019-12-27T23:24:01Z | 2020-01-24T04:38:29Z | 2020-01-24T04:38:29Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# The existing implementation is:
def maybe_box_datetimelike(value):
# turn a datetime like into a Timestamp/timedelta as needed
if isinstance(value, (np.datetime64, datetime)):
value = tslibs.Timestamp(value)
elif isinstance(value, (np.timedelta64, timedelta)):
value = tslibs.Timedelta(value)
return value
# Proposed improvement:
def maybe_box_datetimelike(value):
# turn a datetime like into a Timestamp/timedelta as needed
if isinstance(value, (np.datetime64, datetime)) and not isinstance(value, tslibs.Timestamp):
value = tslibs.Timestamp(value)
elif isinstance(value, (np.timedelta64, timedelta)):
value = tslibs.Timedelta(value)
return value
```
#### Problem description
This function determines whether `value` is of type `(np.datetime64, datetime)` and if so, converts it into `tslibs.Timestamp`. However, the class `tslibs.Timestamp` is already a subclass of `datetime`. Therefore, even if the object `value` is already of type `tslibs.Timestamp`, it will be needlessly converted one more time. This issue has large performance, when working with large dataframes, which contain datet time objects. This issue could be fixed by changing the condition
`if isinstance(value, (np.datetime64, datetime)):`
to:
`if isinstance(value, (np.datetime64, datetime)) and not isinstance(value, tslibs.Timestamp):` | {
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} | 4 | 2019-12-28T00:07:25Z | 2019-12-30T16:25:05Z | 2019-12-30T13:18:55Z | MEMBER | null | Motivation: we have a non-trivial number of places where we do `pytest.xfail` within a test (as opposed to via decorator). This has the downside of always being non-strict, so incorrectly-xfailed tests will never show up as xpassing.
This uses a workaround to dynamically add the pytest.mark.xfail marker within the test rather than raising pytest.xfail directly.
Thoughts? | {
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} | [] | closed | false | null | [] | null | 2 | 2019-12-28T00:19:15Z | 2019-12-28T01:42:57Z | 2019-12-28T01:42:57Z | MEMBER | null | ATM we coerce to object, but it would be more performant (and to me at least, more intuitive) to return the underlying ndarray without the mask. I guess this sacrifices consistency. | {
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} | 2 | 2019-12-28T16:30:52Z | 2019-12-30T13:24:08Z | 2019-12-30T13:16:01Z | CONTRIBUTOR | null | (DataFrame|Series).fillna have the same parameters as NDFrame.fillna, so the **kwargs is not needed. Also make the return type more specific. | {
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] | closed | false | null | [] | null | 1 | 2019-12-29T07:15:53Z | 2020-01-03T02:12:53Z | 2020-01-03T02:12:47Z | MEMBER | null | ### I'm not really sure if this is an "issue" or the intended behavior, but it just seems unnatural to me to get this result.
```python
import pandas as pd
df = pd.DataFrame({
'a': [1, 1, 2, 2],
'b': [3, 3, 4, 5],
})
df
a b
0 1 3
1 1 3
2 2 4
3 2 5
```
```python
df1 = df.groupby('a').apply(lambda x: x)
df1
a b
0 1 3
1 1 3
2 2 4
3 2 5
```
```python
df2 = df.groupby('a').apply(lambda x: x.copy())
df2
a b
a
1 0 1 3
1 1 3
2 2 2 4
3 2 5
```
Notice that the two produces different results.
A simple code trace tells me this difference comes from `BaseGrouper.apply` which return `mutated=False` for `.apply(lambda x: x)` and `mutate=True` for `.apply(lambda x: x.copy())`
This could cause unwanted result when we try to concatenate them.
```python
pd.concat([df1, df2])
a b
0 1 3
1 1 3
2 2 4
3 2 5
(1, 0) 1 3
(1, 1) 1 3
(2, 2) 2 4
(2, 3) 2 5
```
Good thing is, the `apply` method is able to handle this concatenation automatically.
```python
def func(data):
if 1 in data['a'].values:
return data
else:
return data.copy()
df.groupby('a').apply(func)
a b
0 1 3
1 1 3
2 2 4
3 2 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 : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.44.2
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : 3.6.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
</details>
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} | 2 | 2019-12-29T09:50:26Z | 2019-12-29T14:27:28Z | 2019-12-29T14:17:45Z | MEMBER | null | - [ ] closes #30527
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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#### Code Sample, a copy-pastable example if possible
Reproduction :
```python
import numpy as np
import pandas as pd
print(pd.__version__) # 0.25.3
animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
'height': [9.1, 6.0, 9.5, 34.0],
'weight': [7.9, 7.5, 9.9, 198.0]})
aggegators = {
'perc80' : ('height', lambda x:np.percentile(x, q=.80)),
'perc90' : ('height', lambda x:np.percentile(x, q=.90)),
}
animals.groupby('kind').agg(**aggegators)
```
#### Problem description
Applying multiple unnamed aggregation functions to the same column throws the exception below, but may not, I think.
`
"None of [MultiIndex([('height', '<lambda>'),\n ('height', '<lambda>')],\n )] are in the [columns]"
`
#### Expected Output
#### Code without unnamed functions works fine
```python
import numpy as np
import pandas as pd
print(pd.__version__) # 0.25.3
animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
'height': [9.1, 6.0, 9.5, 34.0],
'weight': [7.9, 7.5, 9.9, 198.0]})
def perc80(x):
return np.percentile(x,q=.80)
def perc90(x):
return np.percentile(x,q=.90)
aggegators2 = {
'perc80' : ('height', perc80),
'perc90' : ('height', perc90),
}
animals.groupby('kind').agg(**aggegators2)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
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 : 7.10.2
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 : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 0 | 2019-12-29T14:51:16Z | 2019-12-30T13:09:59Z | 2019-12-30T13:09:59Z | CONTRIBUTOR | null | we are using 3.5 on all travis co for the base environment: https://github.com/pandas-dev/pandas/blob/master/.travis.yml
this doesn’t ultimately matter as we create appropriate conda environments, but should move this forward | {
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} | 1 | 2019-12-29T15:08:25Z | 2021-03-14T16:59:50Z | null | MEMBER | null | This flag disallows usage of generic types that do not specify explicit type parameters. Moreover, built-in collections (such as list and dict) become disallowed as you should use their aliases from the typing module (such as List[int] and Dict[str, str]).
Enabling this would address common pull request review comments. @WillAyd | {
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} | 1 | 2019-12-29T15:29:42Z | 2019-12-31T11:06:07Z | 2019-12-30T13:09:59Z | MEMBER | null | - [x] closes #30538
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-12-29T18:45:17Z | 2020-01-26T01:03:37Z | 2020-01-26T01:03:37Z | MEMBER | null | The `Timestamp` constructor's performance could be improved when an existing `Timestamp` object is passed to it via an `isinstance`-like check:
```python
In [1]: import pandas as pd; pd.__version__
Out[1]: '0.26.0.dev0+1469.ge817ffff3'
In [2]: ts = pd.Timestamp('2020')
In [3]: def timestamp_isinstance_shortcircuit(ts):
...: if isinstance(ts, pd.Timestamp):
...: return ts
...: return pd.Timestamp(ts)
...:
In [4]: %timeit pd.Timestamp(ts)
849 ns ± 13.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [5]: %timeit timestamp_isinstance_shortcircuit(ts)
121 ns ± 0.279 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
```
Some care is needed in the constructor to check if other arguments have been passed, e.g. `tz`, where we wouldn't be able to directly return the `Timestamp` object.
Similar story for the `Timedelta` constructor (should be done in a separate PR):
```python
In [6]: td = pd.Timedelta('1 day')
In [7]: def timedelta_isinstance_shortcircuit(td):
...: if isinstance(td, pd.Timedelta):
...: return td
...: return pd.Timedelta(td)
...:
In [8]: %timeit pd.Timedelta(td)
800 ns ± 1.35 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
In [9]: %timeit timedelta_isinstance_shortcircuit(td)
120 ns ± 0.269 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
```
xref #30520 | {
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- [x] closes #23566
- [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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} | 1 | 2019-12-29T20:47:00Z | 2019-12-30T08:10:07Z | 2019-12-30T08:09:56Z | CONTRIBUTOR | null | Reviewed and updated to f-string for:
pandas/compat/pickle_compat.py
pandas/_config/config.py
pandas/core/arrays/boolean.py
- [x] Contributes to #29547
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Marking pickle_compat.py as done but I left the existing `.format` as is, as I believe this is a good use case for `.format` | {
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} | 2 | 2019-12-29T20:55:00Z | 2019-12-30T13:54:33Z | 2019-12-30T13:08:30Z | MEMBER | null | ```
pandas\core\arrays\integer.py:549: error: Argument "copy" to "astype_nansafe" has incompatible type "None"; expected "bool"
pandas\core\arrays\boolean.py:527: error: Argument "copy" to "astype_nansafe" has incompatible type "None"; expected "bool"
``` | {
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} | 0 | 2019-12-29T21:38:07Z | 2019-12-31T14:02:44Z | 2019-12-31T12:41:02Z | MEMBER | null | pandas\core\computation\align.py:22: error: Incompatible types in assignment (expression has type "Type[Any]", variable has type "partial[Any]")
pandas\core\computation\align.py:26: error: Incompatible types in assignment (expression has type "Tuple[partial[Any], None]", variable has type "Tuple[partial[Any]]")
pandas\core\computation\align.py:28: error: Incompatible types in assignment (expression has type "Tuple[partial[Any], Any]", variable has type "Tuple[partial[Any]]")
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} | 2 | 2019-12-29T22:08:29Z | 2019-12-30T13:55:41Z | 2019-12-30T13:28:32Z | MEMBER | null | pandas\core\computation\eval.py:334: error: Cannot instantiate abstract class 'AbstractEngine' with abstract attribute '_evaluate' | {
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"description": "Memory or execu... | open | false | null | [] | null | 26 | 2019-12-29T22:59:51Z | 2021-10-14T07:57:50Z | null | MEMBER | null | Suppose we have a Categorical with many unused categories:
```
cat = pd.Categorical(range(24), categories=range(10**5))
df = pd.DataFrame({"A": cat, "B": range(24), "C": range(24), "D": 1})
gb = df.groupby(["A", "B", "C"])
>>> gb.size() # memory balloons to 9+ GB before i kill it
```
There are only 24 rows in this DataFrame, so we shouldn't be creating millions of groups.
Without the Categorical, but just a large cross-product that implies many empty groups, this works fine:
```
df = pd.DataFrame({n: range(12) for n in range(8)})
gb = df.groupby(list(range(7)))
gb.size() # <-- works fine
```
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} | 4 | 2019-12-30T03:23:00Z | 2020-01-18T17:24:11Z | 2020-01-18T17:20:04Z | MEMBER | null | A `raise` is indented one indent further than it should be. Fixing this surfaces a failing test that is fixed by decoding bytes before passing it to bs4. | {
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} | 6 | 2019-12-30T07:00:09Z | 2020-01-01T01:43:01Z | 2019-12-31T15:04:45Z | MEMBER | null | - [x] closes #5764
- [x] closes #7883
- [x] closes #7820
- [x] closes #8720
- [x] closes #6783
- [x] closes #7072
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"... | open | false | null | [] | null | 4 | 2019-12-30T08:18:05Z | 2021-07-25T04:31:24Z | null | NONE | null | #### Code Sample
```python
# code 1
def get_pid():
info = pd.read_csv('s3://vision.algo.data/cyz/tmp/test/11.csv', header=None, names=['pid'])
print(f'pid num : {len(info)}')
# code 2
def get_pid():
info = pd.read_csv('s3://vision.algo.data/cyz/tmp/test/11.csv', header=None, names=['pid'], memory_map=False)
print(f'pid num : {len(info)}')
while True:
get_pid()
time.sleep(4)
```
#### Problem description
when I use `code1`, if s3 file changed, `info` not change.
`code2` can find the change.
The difference between these two code is parameter `memory_map`, actually, default `memory_map` is False, so I was confused.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-1098-aws
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.10.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : 0.4.0
scipy : 1.3.3
sqlalchemy : 1.3.11
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-12-30T10:55:14Z | 2019-12-30T22:13:49Z | 2019-12-30T22:13:40Z | CONTRIBUTOR | null | - [x] xref #30192
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Output of `python3 scripts/validate_docstrings.py pandas.DataFrame.head`
```bash
################################################################################
###################### Docstring (pandas.DataFrame.head) ######################
################################################################################
Return the first `n` rows.
This function returns the first `n` rows for the object based
on position. It is useful for quickly testing if your object
has the right type of data in it.
For negative values of `n`, this function returns all rows except
the last `n` rows, equivalent to ``df[:-n]``.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
same type as caller
The first `n` rows of the caller object.
See Also
--------
DataFrame.tail: Returns the last `n` rows.
Examples
--------
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the first 5 lines
>>> df.head()
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
Viewing the first `n` lines (three in this case)
>>> df.head(3)
animal
0 alligator
1 bee
2 falcon
For negative values of `n`
>>> df.head(-3)
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.DataFrame.head" correct. :)
```
Output of `python3 scripts/validate_docstrings.py pandas.DataFrame.tail`
```bash
################################################################################
###################### Docstring (pandas.DataFrame.tail) ######################
################################################################################
Return the last `n` rows.
This function returns last `n` rows from the object based on
position. It is useful for quickly verifying data, for example,
after sorting or appending rows.
For negative values of `n`, this function returns all rows except
the first `n` rows, equivalent to ``df[n:]``.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
type of caller
The last `n` rows of the caller object.
See Also
--------
DataFrame.head : The first `n` rows of the caller object.
Examples
--------
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last 5 lines
>>> df.tail()
animal
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last `n` lines (three in this case)
>>> df.tail(3)
animal
6 shark
7 whale
8 zebra
For negative values of `n`
>>> df.tail(-3)
animal
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.DataFrame.tail" correct. :)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/30557 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30557/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30557/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30557/events | https://github.com/pandas-dev/pandas/issues/30557 | 543,853,196 | MDU6SXNzdWU1NDM4NTMxOTY= | 30,557 | Should I be able to initialise a Series with dtype PeriodDtype? | {
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} | [] | closed | false | null | [] | null | 3 | 2019-12-30T11:49:25Z | 2019-12-30T12:12:07Z | 2019-12-30T12:12:06Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.Series([pd.PeriodIndex(['2000-03'], freq='M')]) # works
pd.Series([pd.PeriodIndex(['2000-03'], freq='M')], dtype=pd.PeriodDtype('M')) # fails
```
#### Problem description
The second command fails with
```
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
This confused me, as I am able to initialise Series with other dtypes:
```
pd.Series([pd.Interval(left=0, right=5)], dtype=pd.IntervalDtype()) # works
pd.Series([1], dtype=pd.Int64Dtype()) # works
pd.Series([1], dtype=pd.CategoricalDtype()) # works
```
#### Expected Output
Probably something like this
```
0 PeriodIndex(['2000-03'], dtype='period[M]', fr...
dtype: 'period[M]'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 2ec41bdc9b6dd59f48249262aa46e384698aa648
python : 3.7.3.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_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0+1471.g2ec41bdc9.dirty
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191201
Cython : 0.29.14
pytest : 5.3.2
hypothesis : 4.55.4
sphinx : 2.3.0
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.9.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
s3fs : 0.4.0
scipy : 1.4.0
sqlalchemy : 1.3.11
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.6
numba : 0.46.0
</details>
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} | 0 | 2019-12-30T13:11:54Z | 2019-12-30T17:54:27Z | 2019-12-30T17:49:41Z | MEMBER | null | - [x] closes #30445
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 4 | 2019-12-30T14:53:57Z | 2020-10-05T01:11:35Z | null | NONE | null | It appears that rolling aggregations on groupby objects do not behave as expected. It looks like the `win_type` parameter is ignored. Here's a minimal example:
#### Code Sample
In this example, a rolling mean is calculated with "uniform" weights and also with "blackman" weights. In each case, the mean is calculated on the ungrouped data and the grouped data, which _should_ give the same result as there is only one group.
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({'groups': ['g']*10,
'data': np.sin(np.arange(10))})
groups = df[['data', 'groups']].groupby('groups')
# Rolling mean with uniform weights
df['uniform'] = df['data'].rolling(4, win_type=None).mean()
df['grp_uniform'] = groups.rolling(4, win_type=None).mean().values
# Rolling mean with a e.g. blackman weights
df['blackman'] = df['data'].rolling(4, win_type='blackman').mean()
df['grp_blackman'] = groups.rolling(4, win_type='blackman').mean().values
# The results should be equal, whether on grouped or ungrouped data
assert df['uniform'].equals(df['grp_uniform']) # This passes
assert df['blackman'].equals(df['grp_blackman']) # This fails
print(df)
```
#### Problem description
The last assert statement fails: the rolling aggregation on the groupby object does not work as expected.
In that last calculation, it looks like the win_type='blackman' parameter was simply ignored, and the calculation was done with uniform weights.
#### Expected Output
Here is the resulting dataframe from the calculations above. The last two columns ought to be identical (but are not):
<pre>
groups data uniform grp_uniform blackman grp_blackman
0 g 0.000000 NaN NaN NaN NaN
1 g 0.841471 NaN NaN NaN NaN
2 g 0.909297 NaN NaN NaN NaN
3 g 0.141120 0.472972 0.472972 0.875384 0.472972
4 g -0.756802 0.283771 0.283771 0.525209 0.283771
5 g -0.958924 -0.166327 -0.166327 -0.307841 -0.166327
6 g -0.279415 -0.463506 -0.463506 -0.857863 -0.463506
7 g 0.656987 -0.334539 -0.334539 -0.619170 -0.334539
8 g 0.989358 0.102001 0.102001 0.188786 0.102001
9 g 0.412118 0.444762 0.444762 0.823172 0.444762
</pre>
This is possibly related to similar issues #26597 and #26462.
Here's my best effort at diagnosing what is going on under the hood... it looks like the `pandas.core.window.RollingGroupby` class inherits the `mean()` method from the `Rolling` class, and hence completely ignores the win_type parameter.
#### 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 158 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.1
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
</details>
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} | 1 | 2019-12-30T17:28:58Z | 2020-01-01T03:32:42Z | 2020-01-01T02:45:56Z | MEMBER | null | - [X] closes #27169
- [X] closes #30365
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
xref #30366
cc @WillAyd
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Ref work done here: https://github.com/pandas-dev/pandas/pull/29712 | {
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} | 27 | 2019-12-30T19:23:21Z | 2020-06-24T22:25:21Z | 2020-06-24T22:25:21Z | CONTRIBUTOR | null | closes #9457
Greetings! This is my first pull-request on an open source project, so I hope I did not miss anything at least too obvious... Thank you, in advance :smile:
I do have a few questions:
* Should I add the `versionadded` directive in all docstrings that were changed?
* Which "whatsnew" release notes should I add this change to? v1.0.0?
* Should #9457 also be closed, as from my understanding this was also the underlying issue?
* Should I be the one ticking the checkboxes below?
This fixes the issue where:
# Fails because it's doing (1 - .1 / .1001)
assert_almost_equal(0.1, 0.1001, check_less_precise=True)
# Works as intuitively expected
assert_almost_equal(
0.1, 0.1001,
atol=0.01,
)
Commit message below:
-------
This commit makes `assert_almost_equal` accept both relative and
absolute precision when comparing numbers, through two new keyword
arguments: `rtol`, and `atol`, respectively.
Under the hood, `_libs.testing.assert_almost_equal` is now calling
`math.isclose`, instead of an adaptaion of
[numpy.testing.assert_almost_equal](https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.testing.assert_almost_equal.html).
- [x] closes #13357
- [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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} | 1 | 2019-12-30T20:29:44Z | 2019-12-30T21:48:21Z | 2019-12-30T21:48:21Z | CONTRIBUTOR | null | Enforces the deprecation on tz-aware series / index
converting to UTC and returning a datetime64[ns] ndarray.
Closes https://github.com/pandas-dev/pandas/issues/6581 (this is the last one). | {
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] | closed | false | null | [] | null | 2 | 2019-12-30T20:42:33Z | 2019-12-31T16:12:02Z | 2019-12-31T16:12:02Z | CONTRIBUTOR | null | This is captured in the `SeriesConstructor.time_constructor` asv.
```python
In [8]: import pandas as pd
...: import numpy as np
...:
...: data = np.arange(1000)
...: index = pd.date_range('2000', periods=len(data))
...: data = dict(zip(index, data))
...: s = pd.Series(data, index=index)
```
On master
```python
848 ms ± 10.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
On 0.25.x
```python
...: %timeit Series(data, index=index)
82.5 ms ± 2.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
```
Looking into a bit now. We're spending a lot more time in `ensure_index` -> `is_period_dtype` / `is_dtype` / `construct_from_string`. | {
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} | 0 | 2019-12-30T20:49:30Z | 2019-12-31T12:37:14Z | 2019-12-31T09:08:04Z | CONTRIBUTOR | null | Adds return type hints to some top-level funcs. I will take the rest over 1-2 more PRs.
Having return types typed up will be good for the user experience (better piping etc.). | {
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} | 2 | 2019-12-30T20:51:29Z | 2020-01-01T02:36:27Z | 2020-01-01T02:36:20Z | MEMBER | null | - xref #30486 #30542
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 2 | 2019-12-30T21:02:16Z | 2020-01-02T13:59:22Z | 2020-01-02T13:59:22Z | NONE | null | I am curious if the following is the intended behavior:
```python
x = pd.Series([None] * 10)
y = [False] * 3 + [True] * 5 + [False] * 2
x[y] = range(5)
x
# 0 None
# 1 None
# 2 None
# 3 3
# 4 4
# 5 0
# 6 1
# 7 2
# 8 None
# 9 None
```
The expectation was that it would work as with `x.loc[y] = range(5)`:
```python
# 0 None
# 1 None
# 2 None
# 3 0
# 4 1
# 5 2
# 6 3
# 7 4
# 8 None
# 9 None
```
If so, where can one read about the logic behind?
<details>
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.4
dateutil : 2.5.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 3.8.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.8
IPython : 6.5.0
pandas_datareader: None
xlsxwriter : None
pandas_gbq : None
pyarrow : 0.15.1
bs4 : 4.7.1
numexpr : None
xarray : None
fastparquet : None
tables : None
s3fs : None
odfpy : None
pytables : None
xlrd : None
gcsfs : None
xlwt : None
sqlalchemy : 1.3.4
scipy : 1.3.1
openpyxl : None
lxml.etree : 3.8.0
matplotlib : 3.0.3
bottleneck : None
</details>
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This is causing some tests to fail in #28399 and is something that should fixed regardless. Some care is needed in order to ensure that `is_string_dtype` doesn't return `True` once this update has been made.
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This is partially blocking #28399 as tests are failing there due to `kind` being `None`.
cc @TomAugspurger
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} | 3 | 2019-12-30T21:30:14Z | 2019-12-31T16:12:10Z | 2019-12-31T16:12:03Z | CONTRIBUTOR | null | Closes https://github.com/pandas-dev/pandas/issues/30564
PR
```
[ 50.00%] ··· series_methods.SeriesConstructor.time_constructor ok
[ 50.00%] ··· ====== ==========
data
------ ----------
None 670±0μs
dict 84.4±0ms
====== ==========
```
master
```
[ 50.00%] ··· series_methods.SeriesConstructor.time_constructor ok
[ 50.00%] ··· ====== =========
data
------ ---------
None 756±0μs
dict 725±0ms
====== =========
```
No whatsnew, since the regression was only on master. | {
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} | 2 | 2019-12-30T22:07:17Z | 2019-12-31T14:03:19Z | 2019-12-31T12:27:26Z | MEMBER | null | pandas\core\dtypes\generic.py:7: error: 'classmethod' used with a non-method
pandas\core\config_init.py:310: error: Name 'get_ipython' is not defined
pandas\core\indexes\category.py:588: error: Need type annotation for 'missing' (hint: "missing: List[<type>] = ...") | {
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pandas\io\json\_normalize.py:299: error: Argument 1 to "zip" has incompatible type "Union[str, List[Any], None]"; expected "Iterable[Any]" | {
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This is extremely ugly, but gets the job done. It's probably worth doing to avoid silently failing, but not sure.
cc @topper-123. | {
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"de... | open | false | null | [] | null | 0 | 2019-12-31T04:30:05Z | 2020-09-22T01:35:38Z | null | CONTRIBUTOR | null | came across this blog / repo which shows nice issue metrics for matplotlib - should be very easy to create one for pandas
https://github.com/LaurenceMolloy/RepoDash
cc @pandas-dev/pandas-core
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} | 1 | 2019-12-31T08:38:02Z | 2020-01-01T02:17:37Z | 2020-01-01T02:15:33Z | MEMBER | null | - [ ] xref #30486 #30542
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-12-31T10:46:18Z | 2020-02-13T00:54:28Z | 2020-01-03T13:10:49Z | MEMBER | null | - [x] closes #30114
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
@jreback
I just looked back at #30114 and found out that i overlooked to add `ignore_index` to `sort_index`, and this might be the reason this issue is still left open. I added this to `sort_index` to close this issue. | {
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} | 2 | 2019-12-31T12:55:01Z | 2020-01-03T11:41:09Z | 2020-01-02T13:59:22Z | MEMBER | null | Series.__setitem__ gives wrong result with bool indexer and when length of new data matches the number of Trues and new data is neither a Series nor a numpy array
- [x] closes #30567
- [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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} | 8 | 2019-12-31T13:15:49Z | 2020-01-02T01:16:18Z | 2020-01-02T01:16:17Z | NONE | null | ```python
import pandas as pd
import numpy as np
import time
# print(pd.show_versions())
# create the columns and rows of the df
assets = np.array(range(3400))
dates = np.array(range(5000))
df_bool = pd.DataFrame(False, index=dates, columns=assets)
# create the array from the df
a = df_bool.to_numpy()
valid_since = {asset: np.random.choice(dates, size=1)[0] for asset in assets}
h = time.time()
for asset in assets:
start = valid_since[asset]
df_bool.loc[start].loc[asset] = True
print(time.time() - h)
h = time.time()
for asset in assets:
start = valid_since[asset]
df_bool.iloc[start].iloc[asset] = True
print(time.time() - h)
h = time.time()
for asset in assets:
start = valid_since[asset]
df_bool.loc[start, asset] = True
print(time.time() - h)
h = time.time()
for asset in assets:
start = valid_since[asset]
df_bool.iloc[start, asset] = True
print(time.time() - h)
h = time.time()
for asset in assets:
start = valid_since[asset]
a[start, asset] = True
print(time.time() - h)
```
#### Problem description
Hi, happy new year xD. I have a problem with the performance using the loc and iloc when it has the comma, for instance the df.iloc [1000, 1000] takes more time in the code of example that df.iloc [1000] .iloc [1000] and I don't understand why this is happen, and Why the access of df.loc [1000, 1000] is so slow, the code take a lot of time when use this part.
Note: All in the code use direct accessing is not doing any kind of slicing or taking multiple assets or dates at the same time it only needs to update one cells at time.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 40.8.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: 0.8.1
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
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 : 1.2.0
xlwt : None
xlsxwriter : 1.1.8
None
</details>
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} | 16 | 2019-12-31T13:32:48Z | 2020-01-05T16:27:52Z | 2020-01-05T16:21:07Z | CONTRIBUTOR | null | Next step to #30565.
Next up will be the pd.read_* functions. | {
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So far just testing how this looks like in the CI. Will open small PRs and get this implementing iteratively if this looks good. | {
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} | 21 | 2019-12-31T16:07:32Z | 2020-05-28T18:48:46Z | 2020-05-09T20:10:36Z | MEMBER | null | - [x] closes #3729
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Note that this PR will *NOT* fix the issue for `pivot_table` for now, the reason is that there is already an argument called `dropna` in `pivot_table` and it has slightly different meaning, currently it means: `Do not include columns whose entries are all NaN`.
I would propose a change in the follow-up PR for this since this is an api change: change the name of current `dropna` to `drop_all_na` maybe? and then add `dropna` to it and it is aligned with the `dropna` in groupby.
Summary:
After this PR, it will optional to inlcude NaN in group keys, e.g. below, and i also add example in docstring as well:
```python
a = [['a', 'b', 12, 12, 12], ['a', None, 12.3, 233., 12], ['b', 'a', 123.23, 123, 1], ['a', 'b', 1, 1, 1.]]
df = pd.DataFrame(a, columns=['a', 'b', 'c', 'd', 'e'])
df.groupby(by=['a', 'b']).sum()
```
will get
with `dropna=False`,
```python
df.groupby(by=['a', 'b'], dropna=False).sum()
```
For Series, it is the same:
```python
s = pd.Series([1, 2, 3, 3], index=["a", 'a', 'b', np.nan])
s.groupby(level=0).sum()
s.groupby(level=0, dropna=False).sum()
```
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} | 4 | 2019-12-31T17:09:11Z | 2020-01-03T21:41:05Z | 2020-01-01T01:34:36Z | CONTRIBUTOR | null | - [x ] closes #30356
- [ ] tests added / passed
- N/A
- [ ] passes `black pandas`
- Didn't run - changes too many files
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Added test in `setup.py` to check if nthreads is positive and on Windows.
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} | 1 | 2019-12-31T17:29:20Z | 2020-01-01T03:09:51Z | 2020-01-01T02:18:25Z | MEMBER | null | first of two PRs to separate array casting/inference out of `Index.__new__`. Once both are in place, we'll be able to do all inference/casting up-front and simplify the constructor quite a bit. We'll also be able to look into sharing code between Index/Series/array, and address a handful of outstanding issues with the Index constructor. | {
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} | 34 | 2019-12-31T20:31:38Z | 2020-01-27T19:14:57Z | 2020-01-09T21:17:06Z | MEMBER | null | - [x] closes #13601
- [x] closes #27125
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This changes the behavior of `idx[:, None]` to return an ndarray instead of an invalid Index, needed to keep matplotlib tests working.
See also #20285 which this does not entirely close. That becomes pretty easy to address though once a decision is made on whether to treat `[[0, 1], [2, 3]]` like `[(0, 1), (2, 3)]` (the latter becomes a MultiIndex, the former currently becomes an invalid Index) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/30589 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/30589/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/30589/comments | https://api.github.com/repos/pandas-dev/pandas/issues/30589/events | https://github.com/pandas-dev/pandas/issues/30589 | 544,268,553 | MDU6SXNzdWU1NDQyNjg1NTM= | 30,589 | How can I force Pandas `read_html` function to read digit field as string not integer | {
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} | [] | closed | false | null | [] | null | 1 | 2019-12-31T20:49:23Z | 2020-01-02T17:57:43Z | 2020-01-02T17:57:43Z | NONE | null | Is there a possible way to convert the field from `int` to `str`?
> I have explored the issues like https://github.com/pandas-dev/pandas/issues/10534, https://github.com/pandas-dev/pandas/issues/21379, https://github.com/gte620v/pandas/blob/5cb8243f2dd31cc2155627f29cfc89bbf6d4b84b/pandas/io/tests/test_html.py#L715
>
> I do not think `converters` arg fit for our usage since the table is updated everyday and it may add a new column, then we need manually add a new key to the parameter
Here is the entire stacktrace when I used the function
```
PS C:\Users\Zhenye.na\Desktop> python3 .\dash-prod.py
.\dash-prod.py:4: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
from collections import Mapping, Iterable
Traceback (most recent call last):
File ".\dash-prod.py", line 59, in <module>
df = pd.read_html(response.text, skiprows=1)
File "C:\Users\Zhenye.na\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\pandas\io\html.py", line 1105, in read_html
displayed_only=displayed_only,
File "C:\Users\Zhenye.na\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\pandas\io\html.py", line 915, in _parse
for table in tables:
File "C:\Users\Zhenye.na\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\pandas\io\html.py", line 213, in <genexpr>
return (self._parse_thead_tbody_tfoot(table) for table in tables)
File "C:\Users\Zhenye.na\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\pandas\io\html.py", line 411, in _parse_thead_tbody_tfoot
header = self._expand_colspan_rowspan(header_rows)
File "C:\Users\Zhenye.na\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.7_qbz5n2kfra8p0\LocalCache\local-packages\Python37\site-packages\pandas\io\html.py", line 459, in _expand_colspan_rowspan
colspan = int(self._attr_getter(td, "colspan") or 1)
ValueError: invalid literal for int() with base 10: '\\"1\\"'
```
The core usage of `read_html` function code is as follows:
```python
response = requests.get(url, headers=hdrs)
df = pd.read_html(response.text, skiprows=1)[0]
print(df)
```
I would love to use the `read_html` function to extract the table in the response returned from the REST API. I have test the function in a small scale table, which contains only digits and it works. But for the data returned from REST API contains characters and digits.
Here is a demo of what the table looks like: (Assume `DC1` and `Location 1` has one `'\n'` symbol separated)
| Date | DC 1 Location 1 | DC 2 Location 2 | DC 3 Location 3 |
|:-----:|:----------------:|:-----------------:|:-----------------:|
| 03/04 | 1.23.4 | 1.23.4 | 1.23.4 |
| 04/05 | 1.23.4 | 1.23.4 | 1.23.4 |
I assume the error message may because of the `'.'` symbol in field like `1.23.4` but I am not sure how to fix it.
Any ideas or thoughts are appreciated!
Thanks!
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} | 10 | 2019-12-31T22:44:06Z | 2020-01-03T13:18:34Z | 2020-01-03T13:18:34Z | CONTRIBUTOR | null | #### Problem description
I've been studying the new `string`, `boolean` and `Intxx` dtypes and think it would be worthwhile to add something about the strings that you are allowed to use with extension arrays in specifying the dtypes. It could be an additional column in the dtypes table here:
https://dev.pandas.io/docs/getting_started/basics.html#dtypes
I think the following table is correct:
Data Type | Array | Possible Strings
------------|--------------|---------------------------------------|
`DatetimeTZDtype` | `DatetimeArray` | `'datetime64[ns, <tz>]'`
`CategoricalDtype` | `Categorical` | `'category'`
`PeriodDtype` | `PeriodArray` | `'period[<freq>]' or 'Period[<freq>]'`
`SparseDtype` | `SparseArray` | `'Sparse'`, `'Sparse[int]'`, `'Sparse[int32, 0]'`, `'Sparse[int64, 0]'`, `'Sparse[float64, nan]'`, `'Sparse[float32, nan]'`
`IntervalDtype` | `IntervalArray` | `'interval'`, `'Interval'`, `'Interval[<np.numeric>]'`, `'Interval[datetime64[ns, <tz>]]'`, `'Interval[timedelta64[<freq>]]'`
`Int64Dtype` (and others) | `IntegerArray` | `'Int8'`, `'Int16'`, `'Int32'`, `'Int64'`, `'UInt8'`, `'UInt16'`, `'UInt32'`, `'UInt64'`
`StringDtype` | `StringArray` | `'string'`
`BooleanDtype` | `BooleanArray` | `'boolean'`
I also think we may want to make it clear that if you specify a string not in that table, it needs to be a string acceptable as a `numpy` dtype.
If people like @TomAugspurger and @jorisvandenbossche think this is useful, I'll add a column to that table in the docs (or maybe have to use a separate table because of the length of the last column above).
Also, should we consider allowing `'Boolean'` and `'String'` and `'Category'`, i.e. type names with a leading capital letter? We're inconsistent in terms of what case is allowed in different places for the strings representing dtypes (see `period/Period` and `interval/Interval`)
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} | 3 | 2020-01-01T02:01:00Z | 2020-01-05T21:39:10Z | 2020-01-05T21:39:10Z | NONE | null | This code works fine
````
pd.date_range(start='2020-11-25 10:00',periods=14,
freq=pd.offsets.CustomBusinessHour(start='10:00'))
````
but if I add holidays then it produces more than 14 periods
````
pd.date_range(start='2020-11-25 10:00',periods=14,
freq=pd.offsets.CustomBusinessHour(start='10:00',holidays=['2020-11-26']))
````
Output:
````
DatetimeIndex(['2020-11-25 10:00:00', '2020-11-25 11:00:00',
'2020-11-25 12:00:00', '2020-11-25 13:00:00',
'2020-11-25 14:00:00', '2020-11-25 15:00:00',
'2020-11-25 16:00:00', '2020-11-27 10:00:00',
'2020-11-27 11:00:00', '2020-11-27 12:00:00',
'2020-11-27 13:00:00', '2020-11-27 14:00:00',
'2020-11-27 15:00:00', '2020-11-27 16:00:00',
'2020-11-30 10:00:00', '2020-11-30 11:00:00',
'2020-11-30 12:00:00', '2020-11-30 13:00:00',
'2020-11-30 14:00:00', '2020-11-30 15:00:00',
'2020-11-30 16:00:00', '2020-12-01 10:00:00',
'2020-12-01 11:00:00', '2020-12-01 12:00:00',
'2020-12-01 13:00:00', '2020-12-01 14:00:00',
'2020-12-01 15:00:00', '2020-12-01 16:00:00',
'2020-12-02 10:00:00', '2020-12-02 11:00:00',
'2020-12-02 12:00:00'],
dtype='datetime64[ns]', freq='CBH')
````
If I replace `periods` with the corresponding `end` then everything works fine.
````
pd.date_range(start='2020-11-25 10:00',end='2020-11-27 16:00:00',
freq=pd.offsets.CustomBusinessHour(start='10:00',holidays=['2020-11-26']))
````
I am not sure why date_range has this weird behavior. Appreciate any help. | {
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} | 2 | 2020-01-01T02:59:51Z | 2020-01-01T04:37:15Z | 2020-01-01T04:28:06Z | MEMBER | null | The only actual changed behavior is in the new `_is_convertible_to_index_for_join` method where it first checks:
```
if isinstance(other, cls):
return False
```
In master, TimedeltaIndex uses _is_convertible_to_index which returns True here. The DTI method behavior is unchanged. | {
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} | 0 | 2020-01-01T03:38:59Z | 2020-01-03T02:48:19Z | 2020-01-01T05:26:01Z | MEMBER | null | The new function operates only on arrays, not Indexes. This gets us close to being able to just use `pd.array` here. | {
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} | 3 | 2020-01-01T03:40:02Z | 2020-01-01T16:02:32Z | 2020-01-01T14:44:57Z | NONE | null | <img width="488" alt="df" src="https://user-images.githubusercontent.com/55054825/71637854-1991c200-2c75-11ea-8090-b21ccb13ba93.PNG">
I have to calculate business hours between two time stamp.
Challenge is Monday and Saturday have different business hours than rest of the weekdays.
Monday starts at 2:30 AM and ends at 11:59 PM. Saturday starts at 00:00 AM and ends at 6:30 AM.
Rest of the weekdays starts at 00:00 AM and ends at 11:59 PM.
* All times are in IST.
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} | 1 | 2020-01-01T03:43:53Z | 2020-01-01T21:14:22Z | 2020-01-01T18:19:08Z | MEMBER | null | There isn't a practical way to actually get an `IntervalTree` with `int32`/`float32` dtypes since `IntervalIndex` is backed by two pandas indexes and we don't have a `Int32Index` or `Float32Index`. These indexes are used to create the underlying `IntervalTree` that backs an `IntervalIndex`, so we're guaranteed to have `int64`/`float64` dtype data when initializing.
The only way that comes to mind that a user could create an `IntervalTree` with `int32`/`float32` dtype would be by explicitly initializing a standalone `IntervalTree` with `int32`/`float32` arrays, which doesn't seem particularly likely.
This should also help with build times as it results in 8 less node classes being generated.
Didn't add a whatsnew note since I don't think `IntervalTree` is user facing (could be wrong?) but can add one if desired since this is technically a breaking change. | {
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} | 1 | 2020-01-01T05:12:17Z | 2020-01-01T16:32:50Z | 2020-01-01T16:03:07Z | MEMBER | null | this is the last of the trivial ones AFAICT. To get the others we're going to have to smooth out small differences in behavior, which should happen in non-refactoring PRs. | {
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} | 1 | 2020-01-01T11:53:53Z | 2020-01-06T09:15:27Z | 2020-01-02T00:58:16Z | CONTRIBUTOR | null | Currently DataFrame.itertuples() has an off by one error
when it inspects whether or not it should return namedtuples
or plain tuples in it's response.
This PR addresses that bug by correcting the condition
that is used when making the check.
Closes: #28282
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- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2020-01-01T14:25:47Z | 2020-01-01T16:07:15Z | 2020-01-01T16:07:12Z | CONTRIBUTOR | null | - [x] Contributes to #29547
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} | 2 | 2020-01-01T19:32:52Z | 2020-01-08T20:30:05Z | 2020-01-07T23:17:53Z | MEMBER | null | - [x] ref https://github.com/pandas-dev/pandas/pull/30461#discussion_r362323260
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} | 1 | 2020-01-02T00:11:05Z | 2020-01-02T02:40:33Z | 2020-01-02T02:36:50Z | MEMBER | null | the tm.assert_produces_warning `clear` kwarg is no longer used, we could consider removing it | {
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] | closed | false | null | [] | null | 13 | 2020-01-02T00:16:52Z | 2020-04-02T18:38:17Z | 2020-04-01T21:51:42Z | MEMBER | null | May differ on other platforms, but currently I see about 19 warnings from C extensions still left. I think we should aim to clean these up and set the `Werror` flag in CI as can help be proactive in identifying bugs from extensions
Here is the list of remaining warnings from a parallel build, so order may not be relevant
```sh
warning: pandas/_libs/groupby.pyx:1088:26: Unreachable code
warning: pandas/_libs/window/aggregations.pyx:59:47: Buffer unpacking not optimized away.
warning: pandas/_libs/window/aggregations.pyx:59:47: Buffer unpacking not optimized away.
warning: pandas/_libs/window/aggregations.pyx:60:47: Buffer unpacking not optimized away.
warning: pandas/_libs/window/aggregations.pyx:60:47: Buffer unpacking not optimized away.
pandas/_libs/hashing.c:25387:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
static PyObject *__pyx_memview_get_object(const char *itemp) {
^
pandas/_libs/hashing.c:25392:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
static int __pyx_memview_set_object(const char *itemp, PyObject *obj) {
^
pandas/_libs/hashtable.c:63498:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
static PyObject *__pyx_memview_get_object(const char *itemp) {
^
pandas/_libs/hashtable.c:63503:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
static int __pyx_memview_set_object(const char *itemp, PyObject *obj) {
^
pandas/_libs/algos.c:81415:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:83050:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:83956:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:84862:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:85039:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:85945:3: warning: code will never be executed [-Wunreachable-code]
__Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_arr.rcbuffer->pybuffer);
^~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:89901:19: warning: assigning to '__pyx_t_5numpy_uint8_t *' (aka 'unsigned char *') from 'const __pyx_t_5numpy_uint8_t *' (aka 'const unsigned char *') discards qualifiers [-Wincompatible-pointer-types-discards-qualifiers]
__pyx_v_v = (&(*((__pyx_t_5numpy_uint8_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_values.data + __pyx_t_13 * __pyx_v_values.strides[0]) ) + __pyx_t_14 * __pyx_v_values.strides[1]) ))));
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:92699:19: warning: assigning to '__pyx_t_5numpy_int8_t *' (aka 'signed char *') from 'const __pyx_t_5numpy_int8_t *' (aka 'const signed char *') discards qualifiers [-Wincompatible-pointer-types-discards-qualifiers]
__pyx_v_v = (&(*((__pyx_t_5numpy_int8_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_values.data + __pyx_t_13 * __pyx_v_values.strides[0]) ) + __pyx_t_14 * __pyx_v_values.strides[1]) ))));
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:98041:19: warning: assigning to '__pyx_t_5numpy_int16_t *' (aka 'short *') from 'const __pyx_t_5numpy_int16_t *' (aka 'const short *') discards qualifiers [-Wincompatible-pointer-types-discards-qualifiers]
__pyx_v_v = (&(*((__pyx_t_5numpy_int16_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_values.data + __pyx_t_13 * __pyx_v_values.strides[0]) ) + __pyx_t_14 * __pyx_v_values.strides[1]) ))));
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:103383:19: warning: assigning to '__pyx_t_5numpy_int32_t *' (aka 'int *') from 'const __pyx_t_5numpy_int32_t *' (aka 'const int *') discards qualifiers [-Wincompatible-pointer-types-discards-qualifiers]
__pyx_v_v = (&(*((__pyx_t_5numpy_int32_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_values.data + __pyx_t_13 * __pyx_v_values.strides[0]) ) + __pyx_t_14 * __pyx_v_values.strides[1]) ))));
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:107434:19: warning: assigning to '__pyx_t_5numpy_int64_t *' (aka 'long *') from 'const __pyx_t_5numpy_int64_t *' (aka 'const long *') discards qualifiers [-Wincompatible-pointer-types-discards-qualifiers]
__pyx_v_v = (&(*((__pyx_t_5numpy_int64_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_values.data + __pyx_t_13 * __pyx_v_values.strides[0]) ) + __pyx_t_14 * __pyx_v_values.strides[1]) ))));
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:110194:19: warning: assigning to '__pyx_t_5numpy_float32_t *' (aka 'float *') from 'const __pyx_t_5numpy_float32_t *' (aka 'const float *') discards qualifiers [-Wincompatible-pointer-types-discards-qualifiers]
__pyx_v_v = (&(*((__pyx_t_5numpy_float32_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_values.data + __pyx_t_13 * __pyx_v_values.strides[0]) ) + __pyx_t_14 * __pyx_v_values.strides[1]) ))));
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas/_libs/algos.c:112955:19: warning: assigning to '__pyx_t_5numpy_float64_t *' (aka 'double *') from 'const __pyx_t_5numpy_float64_t *' (aka 'const double *') discards qualifiers [-Wincompatible-pointer-types-discards-qualifiers]
__pyx_v_v = (&(*((__pyx_t_5numpy_float64_t const *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_values.data + __pyx_t_13 * __pyx_v_values.strides[0]) ) + __pyx_t_14 * __pyx_v_values.strides[1]) ))));
^ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2 warnings generated.
2 warnings generated.
13 warnings generated.
pandas/_libs/tslibs/conversion.c:33343:20: warning: unused function '__pyx_memview_get_object' [-Wunused-function]
static PyObject *__pyx_memview_get_object(const char *itemp) {
^
pandas/_libs/tslibs/conversion.c:33348:12: warning: unused function '__pyx_memview_set_object' [-Wunused-function]
static int __pyx_memview_set_object(const char *itemp, PyObject *obj) {
^
2 warnings generated.
``` | {
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} | 2 | 2020-01-02T00:36:40Z | 2020-09-09T23:04:43Z | 2020-01-02T04:01:44Z | CONTRIBUTOR | null | A follow up issue from #30296 , where it was discussed that pd.datetime should also be deprecated to only allow imports directly from datetime
```
>>> import pandas as pd
>>> pd.datetime
<class 'datetime.datetime'>
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] | closed | false | null | [] | null | 3 | 2020-01-02T00:46:35Z | 2020-01-16T00:33:27Z | 2020-01-02T15:41:12Z | MEMBER | null | ...by leveraging a lot of the recent work to enable parallel CI
Overriding default values documented here:
https://asv.readthedocs.io/en/stable/asv.conf.json.html#build-command-install-command-uninstall-command
I've picked 4 as the value assuming most people running benchmarks will have 2-4 cores. YMMV | {
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} | 1 | 2020-01-02T02:00:41Z | 2020-01-03T00:36:54Z | 2020-01-03T00:36:40Z | CONTRIBUTOR | null | - [ ] xref #29547
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2020-01-02T03:08:10Z | 2020-03-02T22:35:16Z | 2020-01-03T01:05:17Z | CONTRIBUTOR | null | Adding TypeVars to NDFrame methods that don't return optional values. This ensures that e.g. ``(DataFrame|Series).astype`` and ``(DataFrame|Series).copy`` have a known return type, which is nice.
Also adds ``PandasObject._ensure_type``, which is a method used to solve the problem with optional return values, but where we actually know the return type. This is a helper method to help with the tediousness of optional return values experienced in #30565. | {
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