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MDExOlB1bGxSZXF1ZXN0MzY3MTYxNjcz
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REF: tighten what we accept in TimedeltaIndex._simple_new
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MDExOlB1bGxSZXF1ZXN0MzY3MTY1MDg4
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CLN: internals.managers
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| 2020-01-25T23:27:06Z
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MEMBER
| null |
Mostly annotations, use fastpath for SingleBlockManager constructor where possible.
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MDExOlB1bGxSZXF1ZXN0MzY3MTY4NjQ1
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Move DataFrame.info() to live with similar functions
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| 2
| 2020-01-26T00:23:12Z
| 2020-01-29T04:39:19Z
| 2020-01-26T01:15:23Z
|
CONTRIBUTOR
| null |
#31233
- [x ] closes #31233
(Not sure these are relevant for a doc change?)
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0MzY3MTcyNzA0
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|
REF: make PeriodIndex.get_value wrap PeriodIndex.get_loc
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| 6
| 2020-01-26T01:34:53Z
| 2020-03-12T13:31:07Z
| 2020-01-28T02:22:56Z
|
MEMBER
| null |
AFAICT the only behavior this changes is in how datetime objects are treated. After this they are more consistent.
```
dti = pd.date_range("2016-01-01", periods=3, freq="MS")
pi = dti.to_period("H")
ser = pd.Series(range(7, 10), index=pi)
key = dti[0]
```
master
```
>>> pi.get_loc(key)
0
>>> pi.get_value(ser, key)
KeyError: Timestamp('2016-01-01 00:00:00', freq='MS')
>>> ser.loc[key]
7
>>> ser[key]
KeyError: Timestamp('2016-01-01 00:00:00', freq='MS')
```
Under this PR, the get_value and `__getitem__` calls both return 7.
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BUG Series rolling count ignores min periods
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This PR targets 1.0.x branch and is a manual backport for #30923
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Backport PR: Series rolling count ignores min_periods
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CLN: dont return anything from _validate_indexer; annotations
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MDExOlB1bGxSZXF1ZXN0MzY3MTc5OTgw
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Update testing.py
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CI: Fix jedi upgrades causes deprecation warning
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| 555,204,560
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MDU6SXNzdWU1NTUyMDQ1NjA=
| 31,324
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CI: seems jedi caused ci to fail in certain versions
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| 0
| 2020-01-26T09:47:19Z
| 2020-01-27T12:28:10Z
| 2020-01-27T12:28:10Z
|
MEMBER
| null |
```
self = <pandas.tests.indexes.test_base.TestIndex object at 0x7f666b28c990>
ip = <IPython.core.interactiveshell.InteractiveShell object at 0x7f666b28ccd0>
@async_mark()
async def test_tab_complete_warning(self, ip):
# ***/issues/16409
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; idx = pd.Index([1, 2])"
await ip.run_code(code)
with tm.assert_produces_warning(None):
with provisionalcompleter("ignore"):
> list(ip.Completer.completions("idx.", 4))
pandas/tests/indexes/test_base.py:2418:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7f666b2a0a50>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('DeprecationWarning', DeprecationWarning('Deprecated since version 0.16.0. Use get_signatures()[...].params'), '/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/jedi/cache.py', 111)]
```
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BUG: MultiIndex intersection with sort=False does not preserve order
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| 0
| 2020-01-26T10:05:17Z
| 2020-02-12T16:04:23Z
| 2020-02-12T16:04:23Z
|
CONTRIBUTOR
| null |
```python
import pandas as pd
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
idx = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
left = idx[2::-1]
print(left)
#> MultiIndex([('baz', 'one'),
#> ('bar', 'two'),
#> ('bar', 'one')],
#> names=['first', 'second'])
right = idx[:5]
print(right)
#> MultiIndex([('bar', 'one'),
#> ('bar', 'two'),
#> ('baz', 'one'),
#> ('baz', 'two'),
#> ('foo', 'one')],
#> names=['first', 'second'])
# expected same order as left
intersect = left.intersection(right, sort=False)
print(intersect)
#> MultiIndex([('bar', 'two'),
#> ('baz', 'one'),
#> ('bar', 'one')],
#> names=['first', 'second'])
```
<sup>Created on 2020-01-25 by the [reprexpy package](https://github.com/crew102/reprexpy)</sup>
#### Problem description
The intersection of 2 `MultiIndex` with `sort=False` does not preserve the order, whereas other logical operators on Index and MultiIndex preserve order when `sort=False`.
#### Expected Output
The intersection of 2 `MultiIndex` with `sort=False` should return the common elements in the order of the left side.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : ca3bfcc54676b04ac5150c913328d26920bb2591
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0+212.gca3bfcc54
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.1
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 5.3.0
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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BUG: inconsistent behaviors for Index.union() and Index.intersection() with duplicates
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| 5
| 2020-01-26T16:09:21Z
| 2021-03-04T02:01:10Z
| 2021-03-04T02:01:10Z
|
CONTRIBUTOR
| null |
While working on #31312, I noticed that the behavior of `Index.union()` and `Index.intersection()` is inconsistent when there are duplicates in one of the Index.
```python
import pandas as pd
import traceback
a = pd.Index([1, 2, 2, 3])
b = pd.Index([3, 3, 4])
def test_setops(left, right):
for op in ["intersection", "union"]:
for sort in [None, False]:
result = getattr(left, op)(right, sort=sort)
print(f"sort = {sort}, {op}: {result} -> has duplicates: {result.has_duplicates}")
test_setops(a, b)
#> sort = None, intersection: Int64Index([3, 3], dtype='int64') -> has duplicates: True
#> sort = False, intersection: Int64Index([3, 3], dtype='int64') -> has duplicates: True
#> sort = None, union: Int64Index([1, 2, 2, 3, 3, 4], dtype='int64') -> has duplicates: True
#> sort = False, union: Int64Index([1, 2, 2, 3, 4], dtype='int64') -> has duplicates: True
arrays = [['a', 'b', 'b', 'c'],
['1', '2', '2', '1']]
a_mi = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
arrays = [['c', 'c', 'd'],
['1', '1', '2']]
b_mi = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
test_setops(a_mi, b_mi)
#> sort = None, intersection: MultiIndex([('c', '1')],
#> names=['first', 'second']) -> has duplicates: False
#> sort = False, intersection: MultiIndex([('c', '1')],
#> names=['first', 'second']) -> has duplicates: False
#> sort = None, union: MultiIndex([('a', '1'),
#> ('b', '2'),
#> ('c', '1'),
#> ('d', '2')],
#> names=['first', 'second']) -> has duplicates: False
#> sort = False, union: MultiIndex([('a', '1'),
#> ('b', '2'),
#> ('c', '1'),
#> ('d', '2')],
#> names=['first', 'second']) -> has duplicates: False
```
<sup>Created on 2020-01-26 by the [reprexpy package](https://github.com/crew102/reprexpy)</sup>
#### Problem description
1. The behavior of `intersection()` and `union()` when duplicates are present is not consistent between `Index` and `MultiIndex`. Those operations return duplicates with `Index` but not with `MultiIndex`. The documentation doesn't clearly state what to expect.
2. When duplicates are present, the size of the result of `Index.union()` depends on sort is None or False.
3. If duplicates are present on only one side, `Index.intersection()` always return duplicates.
Here are more succinct examples for 2. and 3.
```python
import pandas as pd
a = pd.Index([1, 2, 2, 3])
b = pd.Index([3, 3, 4])
# expected [1, 2, 2, 3, 3, 3, 4]
a.union(b, sort=None)
#> Int64Index([1, 2, 2, 3, 3, 4], dtype='int64')
a.union(b, sort=False)
#> Int64Index([1, 2, 2, 3, 4], dtype='int64')
# expected [3]
a.intersection(b, sort=None)
#> Int64Index([3, 3], dtype='int64')
a.intersection(b, sort=False)
#> Int64Index([3, 3], dtype='int64')
# expected [3, 3]
b.intersection(a, sort=None)
#> Int64Index([3, 3], dtype='int64')
b.intersection(a, sort=False)
#> Int64Index([3, 3], dtype='int64')
```
<sup>Created on 2020-01-26 by the [reprexpy package](https://github.com/crew102/reprexpy)</sup>
#### Expected Output
For consistency and clarity, I think it would be better to enforce unicity in the index returned by logical operations. `Index.union()` and `Index.interesection()` are the only ones allowing duplicates.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : ca3bfcc54676b04ac5150c913328d26920bb2591
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0+212.gca3bfcc54
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.1
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 5.3.0
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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MDU6SXNzdWU1NTUyNTE4MDE=
| 31,327
|
Consider moving tests/ outside the pandas module
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| 2020-01-26T16:23:22Z
| 2020-01-26T19:03:04Z
| 2020-01-26T18:13:55Z
|
NONE
| null |
#### Problem description
Hi.
After a `pip install pandas` the `lib/site-packages/pands/tests/` includes a lot of testing code which is definitely not relevant for me and many other end users of pandas.
This bloats the installation and makes installation slower.
I'm working on packaging a python environment to distribute with a preinstalled set of modules and application and there are too many popular 3rd-party modules which include unneeded test code, like `numpy`, `IPython` `jupyterlab`, etc, which needs to be striped to keep the package size down. I'll be reporting issues to these projects as well.
#### Suggestion
Therefore, my suggestion is to keep the `pandas` module streamlined, and move the tests out. Perhaps create a `pandas-unittests` module if people are interested in it, or just expect users to checkout the code. Another possibility would be to skip packaging the `tests` folder and `conftest.py` when creating packages to upload to `pypi.org`.
Since you are now almost releasing 1.0 it might be a bit short notice to include this is such a big release. But for the next major release, it could work.
Thank you very much for your attention.
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MDExOlB1bGxSZXF1ZXN0MzY3MjM1OTk0
| 31,328
|
Ensure conversion to "native" types for integer EA
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| 2020-01-26T16:31:34Z
| 2020-10-24T03:01:56Z
| 2020-10-24T03:01:56Z
|
CONTRIBUTOR
| null |
- [x] closes #29738
- [x] tests added / passed
- [x] passes `black pandas`
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BUG: ser.at match ser.loc with Float64Index
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| 1
| 2020-01-26T18:39:44Z
| 2020-01-31T04:16:09Z
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MEMBER
| null |
xref #31163.
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Rows order when using slice(None) on MultiIndex Dataframe.loc
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| 0
| 2020-01-26T19:03:28Z
| 2020-11-25T20:51:29Z
| 2020-11-25T20:51:29Z
|
CONTRIBUTOR
| null |
#### Code Sample
```python
df = pd.DataFrame(
np.arange(12).reshape((4, 3)),
index=[["a", "a", "b", "b"], [1, 2, 1, 2]],
columns=[["Ohio", "Ohio", "Colorado"], ["Green", "Red", "Green"]],
)
df.loc[(slice(None), [2,1]), :]
# actual output, same as df, order of slice(None) level take absolut precedence
Ohio Colorado
Green Red Green
a 1 0 1 2
2 3 4 5
b 1 6 7 8
2 9 10 11
```
#### Problem description
When working on issue #22797, I came across this situation about ordering the result of loc. Should the level not explicitely requested be used when ordering the result, and how.
This may be mostly cosmetic, are one should not rely on how the resulted row are ordered, and may be a non issue. If so, feel free to close it.
#### Expected Output
```
# Second level is prioritary on first level
Ohio Colorado
Green Red Green
a 2 3 4 5
b 2 9 10 11
a 1 0 1 2
b 1 6 7 8
# ---- or ----
# Keep order of first level, then second level
Ohio Colorado
Green Red Green
a 2 3 4 5
1 0 1 2
b 2 9 10 11
1 6 7 8
```
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MDExOlB1bGxSZXF1ZXN0MzY3MjYxMDUx
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BUG: Increased support for subclassed types.
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| 2
| 2020-01-26T21:03:41Z
| 2020-05-10T15:02:08Z
| 2020-05-10T15:02:05Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDU6SXNzdWU1NTUzMDMxMDc=
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Adding keep parameter to merge method
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[] | null | 6
| 2020-01-26T23:17:32Z
| 2021-07-27T05:25:45Z
| 2021-07-27T05:25:45Z
|
NONE
| null |
Consider the following example:
import pandas as pd
x = pd.DataFrame({'A': [1, 2, 3], 'X': [11, 12, 13]})
y = pd.DataFrame({'A': [1, 1, 2, 2, 3, 3], 'Y': [111, 111, 222, 222, 333, 333]})
We want to extend x with column 'Y' from y, based on 'A'.
x1 = pd.merge(x, y, how='left', on='A')
When we check the result, we see new rows coming from y when a key in 'A' occurs multiple times in y.
It would be great to do is this way:
x1 = pd.merge(x, y, how='left', on='A', keep='first')
However, unfortunately, merge does not have this keep parameter.
So, we have to figure out a workaround:
Before we could merge x and y, duplicates based on 'A' from y must be removed.
x1 = pd.merge(x, y.drop_duplicates(subset='A', keep='first'), on='A')
This issue generates lots of suffering for many people, who are confused because merging results in increasing number of rows.
https://stackoverflow.com/questions/37095161/number-of-rows-changes-even-after-pandas-merge-with-left-option
By implementing a feature of adding 'keep' parameter to merge, we can easily avoid increasing number of rows in our initial dataframe.
Additional notes:
As drop_duplicates has this keep parameter, it is easy to make it backward compatible by adding 'keep' (True by default) parameter to merge. In this case it should have the same result as earlier,
pd.merge(x, y, how='left', on='A', keep=True) == pd.merge(x, y, how='left', on='A')
Based on my ideas, keep='first' (example: pd.merge(x, y, how='left', on='A', keep='first')) would result the same as pd.merge(x, y.drop_duplicates(subset='A', keep='first'), on='A') currently, (where subset parameter of drop_duplicates is the same as on parameter of merge.
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MDExOlB1bGxSZXF1ZXN0MzY3MzA2MDQz
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DEPR: indexing Series with single-entry list
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| 2
| 2020-01-27T04:20:55Z
| 2020-02-10T20:55:40Z
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|
MEMBER
| null |
- [x] closes #31299
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Could use a better warning message, suggestions welcome.
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DEPR: row-based indexing in DataFrame.__getitem__
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| 2020-01-27T04:24:57Z
| 2021-11-20T23:23:15Z
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|
MEMBER
| null |
Discussed recently in #9595. The two tests this edits are the only two that go down this path AFAICT.
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REF: share code for set-like ops in DTI/TDI/PI
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| 2
| 2020-01-27T04:41:04Z
| 2020-02-09T17:30:46Z
| 2020-02-09T16:51:59Z
|
MEMBER
| null |
The DatetimeIndexOpsMixin._concat_same_type this has can probably become the ExtensionIndex._concat_same_type default. I expect IntervalIndex can be updated to look the same pretty easily, not so sure about CategoricalIndex based on a quick look.
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|
MDU6SXNzdWU1NTUzNTMzMDM=
| 31,336
|
json normalize max level not working
|
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| 2020-01-27T04:44:32Z
| 2020-06-11T23:07:38Z
| 2020-01-27T05:23:49Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pandas.io.json import json_normalize
data = [{'id': 1,
'name': "Cole Volk",
'fitness': {'height': 130, 'weight': 60}},
{'name': "Mose Reg",
'fitness': {'height': 130, 'weight': 60}},
{'id': 2, 'name': 'Faye Raker',
'fitness': {'height': 130, 'weight': 60}}]
json_normalize(data, max_level=0)
```
#### Problem description
The code above is from the pandas docs. I'm running python 3.6.4 and pandas==0.22.0
**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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MDExOlB1bGxSZXF1ZXN0MzY3MzY3MzM0
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|
CLN: some minor cleanups
|
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| 0
| 2020-01-27T08:24:18Z
| 2020-01-27T21:05:08Z
| 2020-01-27T15:40:29Z
|
CONTRIBUTOR
| null |
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MDU6SXNzdWU1NTU0OTMxNzg=
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BUG: Timestamp UTC offset incorrect for dateutil tz in ambiguous DST time
|
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| 12
| 2020-01-27T10:47:32Z
| 2020-02-26T12:35:04Z
| 2020-02-26T12:35:04Z
|
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
If we make a Timestamp in an ambiguous DST period while specifying via the offset (or by supplying `Timestamp.value` directly) that the time is before DST switch, the representation then shows that this is after DST switch. This is backed up by calling `Timestamp.tz.utcoffset(Timestamp)`.
```python
IN:
t1 = pd.Timestamp(1382837400000000000, tz='dateutil/Europe/London')
t1
OUT:
Timestamp('2013-10-27 01:30:00+0100', tz='dateutil/GB-Eire')
IN:
t2 = pd.Timestamp(1382837400000000000, tz='Europe/London')
t2
OUT:
Timestamp('2013-10-27 01:30:00+0000', tz='Europe/London')
```
#### Problem description
The reason for this bug looks to be buried deep in the interaction of `pandas` and `dateutil`.
So this is what I've been able to dig up. When we try to determine whether we are in DST or not, we rely on `timezone.utcoffset` of the underlying timezone package. What gets executed in `dateutil` is this:
```python
def utcoffset(self, dt):
...
return self._find_ttinfo(dt).delta
def _find_ttinfo(self, dt):
idx = self._resolve_ambiguous_time(dt)
...
def _resolve_ambiguous_time(self, dt):
idx = self._find_last_transition(dt)
# If we have no transitions, return the index
_fold = self._fold(dt)
if idx is None or idx == 0:
return idx
# If it's ambiguous and we're in a fold, shift to a different index.
idx_offset = int(not _fold and self.is_ambiguous(dt, idx))
return idx - idx_offset
```
`dateutil` is expecting an ordinary `datetime.timedelta` object here, so this is what it does:
1. Use `_find_last_transition` to get the index of the last DST transition before `dt`. This is done by computing `timedelta.total_seconds` since epoch time. Our `pandas.Timedelta.total_seconds` is smart, and returns different `total_seconds` for before and after `DST`, since we basically return `Timedelta.value` which is the same as `Timestamp.value` when counting since epoch time (because of how `_Timestamp.__sub__` in `c_timestamp.pyx` is implemented).
This is what we do (doesn't care about `dt.replace(tzinfo=None)`):
```python
def total_seconds(self):
"""
Total duration of timedelta in seconds (to microsecond precision).
"""
# GH 31043
# Microseconds precision to avoid confusing tzinfo.utcoffset
return (self.value - self.value % 1000) / 1e9
```
This is what `datetime.timedelta` does (loses DST awareness after `dt.replace(tzinfo=None)`):
```python
def total_seconds(self):
"""Total seconds in the duration."""
return ((self.days * 86400 + self.seconds) * 10**6 +
self.microseconds) / 10**6
```
2. The remainder of `_resolve_ambiguous_time` corrects for ambiguous times, since `datetime.timedelta.total_seconds` after `dt.replace(tzinfo=None)` isn't DST-aware. It checks if we are in an ambiguous period and if this is the first time this time has occured: this is what `self._fold` is for. [fold](https://www.python.org/dev/peps/pep-0495/#the-fold-attribute) is 0 for the first time, and 1 for the second time. If it's the first time, `dateutil` shifts the relevant transition index back by 1, since it thinks that `total_seconds` always returns the number of seconds calculated using the second time.
I'd like to discuss how we are going to approach this. From what I see, there isn't much we can do on our end. ~Making `total_seconds` non-DST-aware by default is bad, because that would be making our implementation less precise unless the user passes a parameter.~ Scratch that. The problem isn't so much the `total_seconds` implementation as it is the `Timestamp.__sub__` implementation which preserves `value` when we subtract epoch time.
Another approach is to go to `dateutil` with this and implement a check there to avoid running the correction if they are dealing with a `pandas.Timedelta`. Might be tricky to do without introducing a dependency on pandas, though.
First came across this while solving #24329 in #30995
#### Expected Output
```python
IN:
t1
OUT:
Timestamp('2013-10-27 01:30:00+0000', tz='dateutil/GB-Eire')
```
#### 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 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : ru_RU.UTF-8
LOCALE : None.None
pandas : 0.26.0.dev0+1947.gca3bfcc54.dirty
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 5.2.0
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
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MDU6SXNzdWU1NTU0OTk1MTI=
| 31,339
|
Sometimes one day of when reading date fields
|
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| 2020-01-27T11:00:16Z
| 2020-01-27T11:51:38Z
| 2020-01-27T11:51:37Z
|
NONE
| null |
#### Code Sample
```python
import datetime
import pandas
df = pandas.read_excel('28422_LZ.xlsx', sheet_name='Laufzettel')
stamp = df[df.columns[3]][16].timestamp()
print('first date =', datetime.datetime.utcfromtimestamp(stamp).strftime('%Y-%m-%d'), '(one day of)')
df = pandas.read_excel('test.xlsx', sheet_name='Tabelle1')
stamp = df[df.columns[0]][0
[28422_LZ.xlsx](https://github.com/pandas-dev/pandas/files/4116748/28422_LZ.xlsx)
[test.xlsx](https://github.com/pandas-dev/pandas/files/4116749/test.xlsx)
].timestamp()
print('seconde date =', datetime.datetime.utcfromtimestamp(stamp).strftime('%Y-%m-%d'), '(correct)')
```
#### Problem description
I read two date fields from two excel sheets. First date is one day off, second date is okay. I cannot see any reason for the difference in behavior. First date ('recalculated time') should be 2015-09-18.
#### Expected Output
```
first date = 2015-09-18 (!!!)
seconde date = 1978-03-16 (correct)
```
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.0-6-amd64
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 18.1
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
None
[28422_LZ.xlsx](https://github.com/pandas-dev/pandas/files/4116717/28422_LZ.xlsx)
[test.xlsx](https://github.com/pandas-dev/pandas/files/4116718/test.xlsx)
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MDU6SXNzdWU1NTU1MTM2OTc=
| 31,340
|
Bug: loc assignment results in wrong dtype
|
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| 5
| 2020-01-27T11:28:01Z
| 2020-02-01T23:20:37Z
| 2020-02-01T23:20:37Z
|
NONE
| null |
Let's see output of this code snippet
```python
df = pd.DataFrame({'id': ['A'], 'a': [1.2], 'b': [0.0], 'c': [-2.5]})
cols = ['a', 'b', 'c']
df.loc[:, cols] = df.loc[:, cols].astype('float32')
print(df.dtypes)
```
#### Problem description
In this code we expect columns `'a', 'b', 'c'` will have types `float32`. But in fact the result is:
```
>>> print(df.dtypes)
id object
a float32
b int64
c float32
dtype: object
```
Bug occurs when the value in column is close to integer. In other words, for `df = pd.DataFrame({'id': ['A'], 'a': [1.2], 'b': [1.0], 'c': [-2.5]})` behavior will be the same. However, if df has 2 rows or more the result is right:
```
>>> df = pd.DataFrame({'id': ['A', 'B'], 'a': [1.2, 0.55], 'b': [0.0, 0.0], 'c': [-2.5, 12.56]})
>>> df.loc[:, cols] = df.loc[:, cols].astype('float32')
>>> df.dtypes
id object
a float32
b float32
c float32
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.6-200.fc28.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.4
dateutil : 2.7.5
pip : 19.2.3
setuptools : 41.6.0
Cython : 0.28.2
pytest : 3.5.1
hypothesis : None
sphinx : 1.7.4
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.2.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.10
IPython : 6.4.0
pandas_datareader: None
bs4 : 4.6.3
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 3.0.1
numexpr : 2.6.5
odfpy : None
openpyxl : 2.5.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.14
tables : 3.4.3
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.4
</details>
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Backport PR #31323 on branch 1.0.x (CI: Fix jedi upgrades causes deprecation warning)
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| 2020-01-27T14:22:05Z
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NONE
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Backport PR #31323: CI: Fix jedi upgrades causes deprecation warning
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CI Failing on IPython tab complete test
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| 2020-01-27T14:09:39Z
| 2020-01-27T14:14:49Z
| 2020-01-27T14:14:49Z
|
CONTRIBUTOR
| null |
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=26868&view=logs&j=a3a13ea8-7cf0-5bdb-71bb-6ac8830ae35c&t=add65f64-6c25-5783-8fd6-d9aa1b63d9d4
```
##[error]2 test(s) failed, 61943 test(s) collected.
Skipping uploading of coverage data.
=================================== FAILURES ===================================
______________ TestCategoricalWarnings.test_tab_complete_warning _______________
[gw1] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <contextlib._GeneratorContextManager object at 0x7fec44a81210>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('DeprecationWarning', DeprecationWarning('Deprecated since version 0.16.0. Use get_signatures()[...].params'), '/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/jedi/cache.py', 111)]
../../../miniconda3/envs/pandas-dev/lib/python3.7/contextlib.py:119: AssertionError
_____________________ TestIndex.test_tab_complete_warning ______________________
[gw0] linux -- Python 3.7.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.indexes.test_base.TestIndex object at 0x7f6d1ff31850>
ip = <IPython.core.interactiveshell.InteractiveShell object at 0x7f6d1ff31b90>
@async_mark()
async def test_tab_complete_warning(self, ip):
# ***/issues/16409
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; idx = pd.Index([1, 2])"
await ip.run_code(code)
with tm.assert_produces_warning(None):
with provisionalcompleter("ignore"):
> list(ip.Completer.completions("idx.", 4))
pandas/tests/indexes/test_base.py:2418:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <contextlib._GeneratorContextManager object at 0x7f6d1ebc7a50>
type = None, value = None, traceback = None
def __exit__(self, type, value, traceback):
if type is None:
try:
> next(self.gen)
E AssertionError: Caused unexpected warning(s): [('DeprecationWarning', DeprecationWarning('Deprecated since version 0.16.0. Use get_signatures()[...].params'), '/home/vsts/miniconda3/envs/pandas-dev/lib/python3.7/site-packages/jedi/cache.py', 111)]
../../../miniconda3/envs/pandas-dev/lib/python3.7/contextlib.py:119: AssertionError
```
taking a look now.
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MDExOlB1bGxSZXF1ZXN0MzY3NTM5MTQ4
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TST/CLN: remove setup_method from test_dtypes
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MDU6SXNzdWU1NTU2NDc4MzY=
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Why [] operation on float index doesn't work the same way as int index?
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| 2020-01-27T15:24:28Z
| 2021-07-27T05:27:35Z
| 2021-07-27T05:27:35Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> s1 = pd.Series(list('abcde'),index=[5,7,9,11,13])
>>> s2 = pd.Series(list('abcde'),index=[5.5,7.5,9.5,11.5,13.5])
>>> print(s1[2:])
>>> print(s2[2:])
9 c
11 d
13 e
dtype: object
5.5 a
7.5 b
9.5 c
11.5 d
13.5 e
dtype: object
```
#### Problem description
I'm a little confused with the [] operation on numerical index.
It seems that when [] applied to the int index, it is compared with the exact position, whereas the [] operation on float is compared with the index's value.
Thanks!
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MDU6SXNzdWU1NTU2NTA1MTk=
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|
ENH: `Styler.highlight_null` to accept `subset` argument
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| 2020-01-27T15:28:22Z
| 2020-03-10T20:30:08Z
| 2020-03-10T20:30:08Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({"A": [0, None], "B": [0, None]})
df.style.highlight_null(null_color="red", subset=["A"])
```
out:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-c4119d081571> in <module>
----> 1 df.style.highlight_null(null_color="red", subset=["A"])
TypeError: highlight_null() got an unexpected keyword argument 'subset'
```
#### Problem description
Most of the `Styler` built-in methods support `subset` argument, except `highlight_null`.
I think it would be nice if we make it consistent with `highlight_min` and `highlight_max` etc.
This also improves the api completeness for missing values styling and formatting. [(as discussed here)](https://github.com/pandas-dev/pandas/pull/29118#discussion_r340733887)
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-862.14.4.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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TYP: core.arrays.boolean
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| 3
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|
CONTRIBUTOR
| null |
As proposed in #31345 and as a follow-up PR for [#29118](https://github.com/pandas-dev/pandas/pull/29118#discussion_r340733887)
- [x] closes #31345
- [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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read_sql routes sql query as table name snowflake-sqlalchemy
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| 2020-01-27T15:51:27Z
| 2020-05-16T04:11:56Z
| null |
NONE
| null |
#### Code Sample
with [snowflake-sqlalchemy ](https://github.com/snowflakedb/snowflake-sqlalchemy) installed:
```python
from sqlalchemy import create_engine
import pandas as pd
CONN_STRING='snowflake://user:password@account/database'
engine=create_engine(CONN_STRING)
conn=engine.connect()
sql= 'SELECT COUNT(*) FROM "DATABASE_NAME"."SCHEMA_NAME"."TABLE_NAME"'
##this errors (stack below)
r=pd.read_sql(sql)
## this does not
r=pd.read_sql_query(sql)
```
stack:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/pandas/io/sql.py", line 420, in read_sql
pandas_sql.meta.reflect(only=[sql])
File "/usr/local/lib/python3.7/site-packages/sqlalchemy/sql/schema.py", line 4261, in reflect
"in %r%s: (%s)" % (bind.engine, s, ", ".join(missing))
sqlalchemy.exc.InvalidRequestError: Could not reflect: requested table(s) not available in Engine(snowflake://ethan:***@yxxxxxx.us-east-1/REDACTED_DB_NAME): (SELECT COUNT(*) FROM "REDACTED_DB_NAME"."REDACTED_DB_SCHEMA"."REDACTED_DB_TABLE")
```
#### Problem description
`read_sql` attempts to read sql queries as tables when running against snowflake. Calling `read_sql_query` is a workaround.
looks like [this line](https://github.com/pandas-dev/pandas/blob/5c37a0b9caaed81ff3999d05b16be1303d184d77/pandas/io/sql.py#L415):
```
try:
_is_table_name = pandas_sql.has_table(sql)
except Exception:
# using generic exception to catch errors from sql drivers (GH24988)
_is_table_name = False
```
is incorrectly determining statements to be table names.
#### Expected Output
pd dataframe result of query.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.76-linuxkit
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 44.0.0
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : 2.3.1
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 : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
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CLN: remove construct_from_string from various
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BUG: Timedelta components rounded by float imprecision
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| 2020-01-27T16:19:06Z
| 2020-02-02T21:08:57Z
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|
CONTRIBUTOR
| null |
#### Problem description
It appears that there is some premature rounding happening in the `Timedelta` constructor that makes it so the unit-adjusted sums of the `days`, `seconds`, `microseconds` and `nanoseconds` attributes do not sum to the total number of nanoseconds. A fundamental assumption of the `datetime.timedelta` type (and breaking this assumption breaks Liskov substitutability) is that the total time difference at the precision of microseconds can be represented by summing up the unit-adjusted `days`, `seconds` and `microseconds` attributes, and it's how `datetime.total_seconds()` works.
I believe that this is the root cause of issue #31043, which was "fixed" with what is essentially a workaround in PR #31155, as I mentioned in [this comment](https://github.com/pandas-dev/pandas/pull/31155#discussion_r370721655).
At the moment the most obvious effect is that bug #31043 only is fixed for recent versions of `dateutil`, but presumably it will show up in other places where standard `datetime` arithmetic is being used on `pandas` timestamps.
#### Code Sample, a copy-pastable example if possible
```python
def to_ns(td):
ns = td.days * 86400
ns += td.seconds
ns *= 1000000
ns += td.microseconds
ns *= 1000
ns += td.nanoseconds
return ns
td = timedelta(1552211999999999872, unit="ns")
print(td.value) # 1552211999999999872
print(to_ns(td)) # 1552212000000000872
```
#### Actual output:
```
1552211999999999872
1552212000000000872
```
#### Expected output
```
1552211999999999872
1552211999999999872
```
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| 2020-09-13T20:22:37Z
| 2020-09-13T20:22:37Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
from pandas.core.reshape.util import cartesian_product
dims = [np.arange(0, 22, dtype=np.int16) for i in range(12)] + [(np.arange(15128, dtype=np.int16)), ]
cartesian_product(dims)
# ValueError: negative dimensions are not allowed
```
#### Problem description
This issue is similar to #9096, when performing a `groupby` over many columns with a large key space and invoking a reduction over one column, the solution is computed correctly but an error occurs when constructing the result's index with `MultiIndex.from_product`. The error occurs in `cartesian_product`, where very large signed integer arithmetic overflows.
https://github.com/pandas-dev/pandas/blob/0c50950f2a7e32887eff6be5979f09772091e1de/pandas/core/reshape/util.py#L42-L54
The overflow at this point can be averted by using `np.uintp` instead of `np.intp` as the dtype for `fromiter`.
Current code `cumprodX`:
```
array([ 22, 484, 10648,
234256, 5153632, 113379904,
2494357888, 54875873536, 1207269217792,
26559922791424, 584318301411328, 12855002631049216,
-8443705008292528128], dtype=int64)
```
With `uintp` `cumprodX`:
```
array([ 22, 484, 10648,
234256, 5153632, 113379904,
2494357888, 54875873536, 1207269217792,
26559922791424, 584318301411328, 12855002631049216,
10003039065417023488], dtype=uint64)
```
Unfortunately, the calculation of `b` is converted to a `float64` automatically due to the real division at line 49, though this can be corrected by using floor division.
Current code `b`:
```
array([4.54683594e+17, 2.06674361e+16, 9.39428913e+14, 4.27013142e+13,
1.94096883e+12, 8.82258558e+10, 4.01026617e+09, 1.82284826e+08,
8.28567391e+06, 3.76621542e+05, 1.71191610e+04, 7.78143681e+02,
1.00000000e+00])
```
With floordiv `b`:
```
array([454683593882591976, 20667436085572362, 939428912980561,
42701314226389, 1940968828472, 88225855839,
4010266174, 182284826, 8285673,
376621, 17119, 778,
1], dtype=uint64)
```
Even worse, while the value of `b[i]` in `np.repeat` is a very large unsigned integer, passing it into `np.repeat` seems to still fail due to a sign error:
```python
np.repeat(np.array(range(22), dtype=np.int16), 454683593882591976)
ValueError Traceback (most recent call last)
<ipython-input-83-c5f3bb52f270> in <module>
----> 1 np.repeat(np.array(range(22), dtype=np.int16), 454683593882591976)
<__array_function__ internals> in repeat(*args, **kwargs)
...\lib\site-packages\numpy\core\fromnumeric.py in repeat(a, repeats, axis)
479
480 """
--> 481 return _wrapfunc(a, 'repeat', repeats, axis=axis)
482
483
...\lib\site-packages\numpy\core\fromnumeric.py in _wrapfunc(obj, method, *args, **kwds)
59
60 try:
---> 61 return bound(*args, **kwds)
62 except TypeError:
63 # A TypeError occurs if the object does have such a method in its
ValueError: negative dimensions are not allowed
```
Curiously enough, if you vary the array argument to `np.repeat`, while holding `b[i]` constant, the error changes:
```python
np.repeat(np.array(range(22), dtype=np.int16), 454683593882591976)
# ValueError: negative dimensions are not allowed
np.repeat(np.array(range(20), dtype=np.int16), 454683593882591976)
# ValueError: array is too big; `arr.size * arr.dtype.itemsize` is larger than the maximum possible size.
np.repeat(np.array(range(2), dtype=np.int16), 454683593882591976)
# MemoryError: Unable to allocate array with shape (909367187765183952,) and data type int16
```
The issue is of course, I'm asking to create an array more than two times larger than the maximum value of `Py_ssize_t`, which would be impossible to index, even if I had the memory to allocate.
There does not appear to be an ultimate solution within `pandas`, but the code leading into `numpy.repeat` still misbehaves.
#### Expected Output
The expected output would be the exceptionally large cross-product of all of the dimensions described by the raw keys above, or to receive an error indicating the key space is too large.
Since creating an array larger than is physically possible is out of the question, some sort of error checking or warning in the documentation would be helpful.
Would something like this be safe though?
```python
lenX = np.fromiter((len(x) for x in X), dtype=np.intp)
cumprodX = np.cumproduct(lenX)
a = np.roll(cumprodX, 1)
if np.any(cumprodX < 0):
raise ValueError("Product space too large to allocate arrays!")
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.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 : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.0.0.post20200113
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 : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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| 31,356
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AssertionError creating array with StringDtype instead of StringDtype(), can we have a friendlier error message?
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[] | null | 1
| 2020-01-27T17:13:59Z
| 2021-07-27T05:28:59Z
| null |
NONE
| null |
The following fails with an `AssertionError` on pandas 1.0.0rc0:
```python
pd.Series(["a", "b"], dtype=pd.StringDtype)
```
I wrote this by mistake — this works:
```python
pd.Series(["a", "b"], dtype=pd.StringDtype())
```
When running the former, I get a traceback with an AssertionError:
```
Traceback (most recent call last):
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/construction.py", line 534, in _try_cast
if is_integer_dtype(dtype):
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/dtypes/common.py", line 827, in is_integer_dtype
return _is_dtype_type(arr_or_dtype, classes_and_not_datetimelike(np.integer))
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/dtypes/common.py", line 1732, in _is_dtype_type
return condition(np.dtype(arr_or_dtype).type)
TypeError: data type not understood
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "mwe18.py", line 2, in <module>
pd.Series(["a", "b"], dtype=pd.StringDtype)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/series.py", line 304, in __init__
data = sanitize_array(data, index, dtype, copy, raise_cast_failure=True)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/construction.py", line 438, in sanitize_array
subarr = _try_cast(data, dtype, copy, raise_cast_failure)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/construction.py", line 562, in _try_cast
subarr = array_type(arr, dtype=dtype, copy=copy)
File "/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/pandas/core/arrays/string_.py", line 180, in _from_sequence
assert dtype == "string"
AssertionError
```
Coming from numpy, I find it unconventional that the type ending in `dtype` is not itself a valid dtype, but rather a constructor for such a dtype. Therefore, I would consider that a friendlier error message could be in order (or maybe it should even work).
I'm running pandas 1.0.0rc0 on Python 3.7.6.
The full environment:
<details>
# packages in environment at /media/nas/x21324/miniconda3/envs/py37e:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 0_gnu conda-forge
affine 2.3.0 py_0 conda-forge
aggdraw 1.3.11 py37hc56bac0_0 conda-forge
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bleach 3.1.0 py_0 conda-forge
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bokeh 1.4.0 py37_0 conda-forge
boost-cpp 1.70.0 h8e57a91_2 conda-forge
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botocore 1.14.8 py_0 conda-forge
brotli 1.0.7 he1b5a44_1000 conda-forge
bzip2 1.0.8 h516909a_2 conda-forge
c-ares 1.15.0 h516909a_1001 conda-forge
ca-certificates 2019.11.28 hecc5488_0 conda-forge
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chardet 3.0.4 py37_1003 conda-forge
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```python
# Your code here
```
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MDU6SXNzdWU1NTU3Mjk0MjM=
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Using matplotlib scatter legends?
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| 2020-01-27T17:41:20Z
| 2021-01-27T15:47:43Z
| 2021-01-27T15:47:43Z
|
NONE
| null |
Matplotlib now allows doing legends for categorical data in scatter:
https://matplotlib.org/gallery/lines_bars_and_markers/scatter_with_legend.html
Is that something that pandas could/should use?
I feel like there's a tricky issue in deciding whether the color variable is categorical but basically matplotlib allows us now to treat the case and I'm not sure if treating all cases as continuous makes the most sense.
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BUG/REG: multiindex nested tuples with duplicates
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| 2020-01-27T18:21:58Z
| 2020-01-30T09:58:54Z
| 2020-01-28T01:59:32Z
|
MEMBER
| null |
- [x] closes #30892
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
there is an underlying problem with MultiIndex.get_loc that needs to get worked out, but that's going to take a while. This fixes the reported regression.
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| 35
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| 2020-03-27T13:32:46Z
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MEMBER
| null |
- [ ] closes #31256
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Backport PR #31148: DOC: replace long table of contents on home page with main links
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#31148
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Avoid Index DeprecationWarning in Series getitem
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| 6
| 2020-01-27T20:04:03Z
| 2020-01-28T20:55:45Z
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|
CONTRIBUTOR
| null |
xref https://github.com/pandas-dev/pandas/issues/30867
cc @jorisvandenbossche. The alternative is to use a warnings filter inside SingleBlockManger.get_slice to filter the warning, but I think avoiding the warning in the first place is a bit nicer.
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Backport PR #30784: API: DataFrame.take always returns a copy
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MEMBER
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Backport #30784
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to_numeric with errors = "coerce" is adding digits at the end
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#### Code Sample, a copy-pastable example if possible
```python
# Your code here
#problem: pandas to_numeric might give some errors when using coerce
# it is adding digits at the end.
import pandas as pd
#minimal example
data = [{'value': '.'}, {'value': '.'}, {'value': '.'}, {'value': '.'}, {'value': '243.164'}, {'value': '245.968'}, {'value': '249.585'}, {'value': '259.745'}, {'value': '265.742'}, {'value': '272.567'}]
df = pd.DataFrame(data,columns=['value'])
df.value = pd.to_numeric(df.value,errors='coerce')
#looks as if all is good:
df.value
#but
df.value[4]
#this can be random:
data2 = [{'value': '.'}, {'value': '.'}, {'value': '.'}, {'value': '.'}, {'value': '243.164'}, {'value': '245.968'}, {'value': '249.585'}, {'value': '259.745'}, {'value': '265.742'}, {'value': '272.567'}, {'value': '279.196'}, {'value': '280.366'}, {'value': '275.034'}, {'value': '271.351'}, {'value': '272.889'}, {'value': '270.627'}, {'value': '280.828'}, {'value': '290.383'}, {'value': '308.153'}, {'value': '319.945'}, {'value': '336.0'}, {'value': '344.09'}, {'value': '351.385'}, {'value': '356.178'}, {'value': '359.82'}, {'value': '361.03'}, {'value': '367.701'}, {'value': '380.812'}, {'value': '387.98'}, {'value': '391.749'}, {'value': '391.171'}, {'value': '385.97'}, {'value': '385.345'}, {'value': '386.121'}, {'value': '390.996'}, {'value': '399.734'}, {'value': '413.073'}, {'value': '421.532'}, {'value':
'430.221'}, {'value': '437.092'}, {'value': '439.746'}, {'value': '446.01'}, {'value': '451.191'}, {'value': '460.463'}, {'value': '469.779'}, {'value': '472.025'}, {'value': '479.49'}, {'value': '474.864'}, {'value': '467.54'}, {'value': '471.978'}]
#now 4, 36, and 47 are wrong, with different endings.
df2 = pd.DataFrame(data2,columns=['value'])
df2.value = pd.to_numeric(df2.value,errors='coerce')
df2.value[[4,36,47]].to_list()
```
#### Problem description
Current behavior: pandas to_numeric, when using errors='coerce' seem to randomly add
decimals at the end of the number.
#### Expected Output
no decimals should be added at the end.
#### 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 85 Stepping 4, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.12
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: 0.7.4
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.1.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.5
tables : 3.5.2
xarray : 0.12.3
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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| 2020-01-31T09:25:59Z
| 2020-01-31T04:18:02Z
|
MEMBER
| null |
pandas\core\reshape\reshape.py:378: error: "Index" has no attribute "levels"; maybe "nlevels"?
pandas\core\reshape\reshape.py:381: error: "Index" has no attribute "codes"
pandas\core\reshape\reshape.py:383: error: "Index" has no attribute "codes"
pandas\core\reshape\reshape.py:436: error: Incompatible types in assignment (expression has type "_Unstacker", variable has type "partial[_Unstacker]")
pandas\core\reshape\reshape.py:444: error: "partial[_Unstacker]" has no attribute "get_result"
pandas\core\reshape\reshape.py:1000: error: Incompatible types in assignment (expression has type "None", variable has type "Index")
pandas\core\reshape\reshape.py:1009: error: Incompatible types in assignment (expression has type "float", variable has type "int")
pandas\core\reshape\reshape.py:1013: error: Need type annotation for 'sp_indices'
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MDU6SXNzdWU1NTU4Mzc0NDM=
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|
Index name assignment resolution logic changes depending on whether DataFrame has any rows
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| 2
| 2020-01-27T21:15:57Z
| 2020-09-08T13:45:09Z
| 2020-09-08T13:45:09Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
def get_name_of_assigned_index(df_size, series_size):
df = pd.DataFrame({}, index=pd.RangeIndex(df_size, name='df_index'))
series = pd.Series(1.23, index=pd.RangeIndex(series_size, name='series_index'))
df['series'] = series
return df.index.name
```
#### Problem description
`get_name_of_assigned_index(n, m)` returns `'df_index'` *unless* `n = 0`, in which case it returns `'series_index'`.
I don't know which of these is necessarily the better behavior, but the resulting index name ought to be consistent regardless of how many - or few - rows are in the DataFrame. Subsequent operations may rely on expectations about that name, so this behavior, wherein the name changes as an unintuitive consequence of a rowless frame, could cause bugs.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.5-smp-821.23.0.0
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: None
setuptools: unknown
Cython: None
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 2.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.6.10dev0
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: 0+unknown
pandas_datareader: None
gcsfs: None
</details>
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MDU6SXNzdWU1NTU4NDM2OTU=
| 31,369
|
Center align the text and icons in documentation index
|
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| 2020-01-27T21:27:49Z
| 2020-01-28T16:18:19Z
| 2020-01-28T16:18:19Z
|
CONTRIBUTOR
| null |
At https://dev.pandas.io/docs/index.html, the alignment is slightly off. Especially notable for User Guide and API reference
cc @jorisvandenbossche
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MDU6SXNzdWU1NTU4NTEzMDM=
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|
REF: IntervalIndex dispatch more methods to IntervalArray
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| 2020-01-27T21:43:20Z
| 2020-09-22T00:57:19Z
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|
MEMBER
| null |
I think we can de-duplicate some more code by a) noticing that IntervalArray._check_closed_matches is performing the same task as DatetimeLikeArrayMixin._check_compatible_with and b) dispatching to IntervalArray methods more.
`_concat_same_dtype` could become (this could even go as the default in ExtensionIndex)
```
def _concat_same_dtype(self, to_concat, name):
arr = type(self._data)._concat_same_type(to_concat)
return type(self)._simple_new(arr, name=name)
```
`insert` could look like (also could go into ExtensionIndex):
```
def insert(self, loc, item):
new_arr = type(self._data)._from_sequence([item])
to_concat = [self[:loc]._data, new_arr, self.loc[loc:]._data]
res_values = type(self._data)._concat_same_type(to_concat)
return type(self)._simple_new(res_values, name=self.name)
```
Tried this and it chokes on item=NaT.
The integer-loc case for `delete` could look like:
```
def delete(self, loc):
if is_integer(loc):
to_concat = [self[:loc]._data, self[loc+1:]._data]
arr = type(self._data)._concat_same_type(to_concat)
return type(self)._simple_new(arr, name=self.name)
```
Probably others. Thoughts, cc @jschendel
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CLN: tighten what we pass to Index._shallow_copy
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MDU6SXNzdWU1NTU4ODU5MDU=
| 31,372
|
Allow non-inplace option for `ndframe.update()`
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| 2020-01-27T23:03:27Z
| 2020-01-28T09:41:30Z
| 2020-01-28T09:40:46Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
t = pd.Series([10, 11, 12], index=range(3, 6))
print(s.update(t).reindex([1, 2, 3, 4, 5]))
```
#### Problem description
Obviously the previous doesn't work. `s.update(t)` is executed inplace, and doesn't return anything. I see no reasons why one shouldn't be able to call `update` and get a new object (for example to allow method chaining).
The easiest way of implementing this is by allowing a parameter that makes the function return the updated object:
```python
print(s.update(t, inplace=False).reindex([1, 2, 3, 4, 5]))
```
**I know there is already a plan about deprecating `inplace`, but I'm not sure it involves `update`. I guess that `update` was designed to be similar to the same method of a dict object.**
**Update**
Closed as it was solved in https://github.com/pandas-dev/pandas/issues/10730
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Backport PR #31290: BUG: Handle IntegerArray in pd.cut
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| 2020-01-28T02:15:36Z
| 2020-01-28T19:32:43Z
| 2020-01-28T18:51:25Z
|
MEMBER
| null |
xref #31358
|
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MDExOlB1bGxSZXF1ZXN0MzY3Nzk4NTY4
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DEPR: Deprecate core
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| 2020-01-31T03:08:52Z
| 2020-01-31T03:08:52Z
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| null |
- [x] closes #27522
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I haven't actually performed the refactor(pandas.core -> pandas._core) yet so tests are failing because of the warnings.
|
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Backport PR #31238: REGR: Prevent indexes that aren't directly backedby numpy from entering libreduction code paths
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| 1
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| 2020-02-24T18:13:51Z
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MEMBER
| null |
Backport #31238
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MDU6SXNzdWU1NTYwMjQ2NTI=
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|
Pandas: cannot set a Timestamp with a non-timestamp
|
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| 2020-01-28T07:11:51Z
| 2020-01-29T05:11:35Z
| 2020-01-29T05:11:35Z
|
NONE
| null |
I have a data-frame of the following order: https://imgur.com/a/fp2hKZe on which i ran the following code
```python
for i in r.index[1:]:
if (r.at[i, 'lbl'] == r.at[i-1, 'lbl']) & (r.at[i, 'lbl'] == 'f'):
r.at[i, 'date'] = r.at[i, 'date'] - r.at[i-1, 'date']
elif r.at[i, 'lbl'] == 'f':
r.at[i, 'date'] = r.at[i, 'date'] - r.at[i-1, 'date']
```
#### Problem description
When i run the same code for a much smaller data-set, it gives proper output
```python
data = pd.DataFrame(data=[[101,'f',datetime.date(2017,12,28)],
[101, 'f', datetime.date(2019,8,27)],
[101,'m',datetime.date(2013,4,12)],
[101,'m',datetime.date(2015,6,25)],
[101, 'f', datetime.date(2016,8,16)],
[102,'m', datetime.date(2019,8,15)],
[102,'m',datetime.date(2019,6,25)]],
columns=["TC", "lbl", "date"])
```
```python
for i in data.index[1:]:
if (data.at[i, 'lbl'] == data.at[i-1, 'lbl']) & (data.at[i, 'lbl'] == 'f'):
data.at[i, 'date'] = data.at[i, 'date'] - data.at[i-1, 'date']
elif data.at[i, 'lbl'] == 'f':
data.at[i, 'date'] = data.at[i, 'date'] - data.at[i-1, 'date']
```
But when i change the data-set, i get The **ValueError**
#### Expected Output
| TC | lbl | date
-- | -- | -- | --
0 | 101 | f | 2017-12-28
1 | 101 | f | 607 days, 0:00:00
2 | 101 | m | 2013-04-12
3 | 101 | m | 2015-06-25
4 | 101 | f | 418 days, 0:00:00
5 | 102 | m | 2019-08-15
6 | 102 | m | 2019-06-25
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-32-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_IN
LOCALE : en_IN.ISO8859-1
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
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MDExOlB1bGxSZXF1ZXN0MzY3ODYxOTU3
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BUG: Timedelta components no longer rounded with high precision integers
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| 0
| 2020-01-28T07:16:54Z
| 2020-02-03T14:06:59Z
| 2020-02-02T21:08:57Z
|
MEMBER
| null |
- [x] closes #31354
- [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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| 556,034,646
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MDU6SXNzdWU1NTYwMzQ2NDY=
| 31,381
|
loc on Series returning scalar when result size is 1
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| 3
| 2020-01-28T07:38:23Z
| 2020-01-28T11:56:49Z
| 2020-01-28T11:56:23Z
|
NONE
| null |
#### Code Sample
```python
# Create a DataFrame with index and one Series
df = pd.DataFrame({"id":[1,2,3,3],"val":["abcde","bbcde","cbcde","dbcde"]})
df.set_index("id",inplace=True)
```
Calling `.loc` when the result length is 2 returns a Series (as expected):
```
type(df['val'].loc[3])
```
Output:
```
pandas.core.series.Series
```
Calling `.loc` when the result length is 1 returns a `str`:
```python
# use .loc when the result length is 1:
type(df['val'].loc[1])
```
Output:
```
str
```
#### Problem description
The fact that the same code returns different types causes inconsistencies. If I run `len` on the result, I would get 2 for `id=3` and 5 for `id=1` or `id=2`, since the string length is 5:
```python
len(df['val'].loc[3])
```
Output:
```
2
```
When calling `loc[1]`:
```python
len(df['val'].loc[1])
```
Output:
```
5
```
#### Expected Output
`.loc` should return a Series regardless of the size. Otherwise this causes bugs when trying to evaluate the length of the output.
```python
# Expected output:
type(df['val'].loc[1])
```
Output:
```
pandas.core.series.Series
```
```python
len(df['val'].loc[1])
```
Output:
```
1
```
#### 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 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.10.2
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 : 1.3.2
sqlalchemy : 1.3.12
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzY3ODkyMTMw
| 31,382
|
DOC: reduce long error tracebacks in 1.0 whatsnew + some clean-up
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| 0
| 2020-01-28T08:49:35Z
| 2020-01-28T13:07:49Z
| 2020-01-28T12:48:07Z
|
MEMBER
| null |
Made some of the ipython blocks into literal code blocks to avoid showing long error tracebacks (that only distract from the core text). Plus did a few other small formatting clean-ups, and combined 2 pyarrow related entries.
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Raise error in read_csv when arguments header and prefix both are not None
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| 11
| 2020-01-28T09:40:27Z
| 2020-02-03T16:13:05Z
| 2020-02-03T15:52:28Z
|
CONTRIBUTOR
| null |
- [x] closes #27394
- [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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MDU6SXNzdWU1NTYxMDQ2NDY=
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pd.read_sas with chunksize option raises IndexError
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| 2020-01-28T10:09:38Z
| 2020-02-03T13:21:52Z
| 2020-02-03T13:21:52Z
|
NONE
| null |
Good morning,
Using Python 3.6.
My problem seems to be close to issue [#14734](https://github.com/pandas-dev/pandas/issues/14734) but with a different error type though. However, please forgive my lack of competence, but I am not able to understand 1. if my issue is really similar and 2. if the issue that seemed to surround some sas files has been solved or if there might still be some probleùs with some sas files (that i cannot provide for reasons detailed just below).
I have read the rules about posting but i cannot attach a sample of my data or reproduce the entire error message as the data i am working on is located on a server without access to internet. I apologize for this inconvenience. I’ll try to reproduce most of what is requested however below.
I am working with very big sas files (data on each job, hence millions of lines) and got memory error when i was trying to simple read them (they open fine in R or stata strangely). Therefore i searched and find the pandas.read_sas option to work with chunks of the data. My code is now the following:
```python
import pandas as pd
df_chunk = pd.read_sas(r'file.sas7bdat', chunksize=500)
for chunk in df_chunk:
chunk_list.append(chunk)
```
At this point i get the following error (I am reproducing it here manually as i cannot copy paste):
```python
line 660, in _chunk_to_dataframe
if self.column_formats[j] in const.sas_date_formats:
IndexError: list index out of range
```
I am aware the exposition of my issue is truncated and probably incomplete but many thanks for any help you could provide,
Axelle
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CLN: more consistent error message for construct_from_string wrong type
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| 2020-02-25T17:12:40Z
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MEMBER
| null |
Resurrection of #24853. Have resolved conflicts and updated according to the last review it received. The original PR fixed some unrelated minor typos, so I've kept them in (and added a fix for another absolutely minor typo I'd noticed)
- [x] closes #1135
- [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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MDU6SXNzdWU1NTYxODI4ODM=
| 31,389
|
Handle ExtensionArrays in cut
|
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| 3
| 2020-01-28T12:40:10Z
| 2020-05-13T02:53:32Z
| null |
CONTRIBUTOR
| null |
Followup to https://github.com/pandas-dev/pandas/pull/31290. Currently `pd.cut` doesn't play nicely with all extension arrays. To support them, I think we'll need one addition to the interface.
We need an array of integers to pass to searchsorted in https://github.com/pandas-dev/pandas/blob/4edcc5541ff3f6470f5e3c083cb83136119e6f0c/pandas/core/reshape/tile.py#L394. I think the only requirement is that the integer-encoded values need to have the same ordering as the original values. (I forget the math term for this type of mapping).
It doesn't matter what value is used for missing values, as long as it's distinct.
We can't quite use `factorize(arr)[0]` since it doesn't have the ordering requirement.
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MDExOlB1bGxSZXF1ZXN0MzY3OTk1MTg0
| 31,390
|
Backport PR #31382 on branch 1.0.x (DOC: reduce long error tracebacks in 1.0 whatsnew + some clean-up)
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| 0
| 2020-01-28T12:49:01Z
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NONE
| null |
Backport PR #31382: DOC: reduce long error tracebacks in 1.0 whatsnew + some clean-up
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MDU6SXNzdWU1NTYyMTE1NDE=
| 31,391
|
SAS class comment mentions inexistant path
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[] | null | 2
| 2020-01-28T13:31:44Z
| 2020-01-29T08:15:00Z
| 2020-01-29T08:15:00Z
|
MEMBER
| null |
`pandas/asv_bench/benchmarks/io/sas.py` :
> class SAS:
>
> params = ["sas7bdat", "xport"]
> param_names = ["format"]
>
> def setup(self, format):
> # Read files that are located in 'pandas/io/tests/sas/data'
However, the files are in 'pandas/tests/io/sas/data'.
EDIT:
unrelated typo: there's lots of places in the codebase that use "rather then" instead of "rather than". As this is also a minor typo that doesn't affect any code, I think it'd be OK to address both in the same pull request
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MDExOlB1bGxSZXF1ZXN0MzY4MDY0Njg3
| 31,392
|
DOC: Fix image alignment
|
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| 0
| 2020-01-28T15:06:31Z
| 2020-01-28T16:18:20Z
| 2020-01-28T16:18:20Z
|
CONTRIBUTOR
| null |
Closes #31369
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Backport PR #29393 on branch 1.0.x (BUG: GH25495 incorrect dtype when using .loc to set Categorical value for column in 1-row DataFrame)
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| 2020-01-28T16:00:40Z
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NONE
| null |
Backport PR #29393: BUG: GH25495 incorrect dtype when using .loc to set Categorical value for column in 1-row DataFrame
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MDExOlB1bGxSZXF1ZXN0MzY4MTA0Mzgx
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Backport PR #31392 on branch 1.0.x (DOC: Fix image alignment)
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NONE
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Backport PR #31392: DOC: Fix image alignment
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GH31391 - Fixed typos and fixed inexistant path in a SAS class comment
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| 2020-01-29T01:48:35Z
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CONTRIBUTOR
| null |
Simple change: just fixed some typos and incorrect path as reported in #31391
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TypeError when using 'comment=...' in read_csv from a file
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| 2020-03-03T03:02:23Z
| 2020-03-03T03:02:23Z
|
NONE
| null |
#### Code Sample
Given a data file `data.csv` with a line that is commented out:
```csv
+1.280000e+002,-4.078996e+001
+2.560000e+002,-5.155923e+001
# +3.840000e+002,-7.221378e+001
+5.120000e+002,-7.918677e+001
+6.400000e+002,-7.919656e+001
```
```python
import pandas as pd
pd.read_csv('data.csv', sep=None, index_col=0, header=None, engine="python", comment='#')
```
#### Problem description
It raises a `TypeError` when using the `comment` parameter:
<details>
<summary>TypeError</summary>
```python
TypeError Traceback (most recent call last)
<ipython-input-17-c89b3c3e691f> in <module>
----> 1 pd.read_csv('data.csv', sep=None, comment='#')
~/.local/share/virtualenvs/openqlab/lib/python3.8/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names,
index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfoote
r, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirs
t, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encodin
g, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
683 )
684
--> 685 return _read(filepath_or_buffer, kwds)
686
687 parser_f.__name__ = name
~/.local/share/virtualenvs/openqlab/lib/python3.8/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
455
456 # Create the parser.
--> 457 parser = TextFileReader(fp_or_buf, **kwds)
458
459 if chunksize or iterator:
~/.local/share/virtualenvs/openqlab/lib/python3.8/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
893 self.options["has_index_names"] = kwds["has_index_names"]
894
--> 895 self._make_engine(self.engine)
896
897 def close(self):
~/.local/share/virtualenvs/openqlab/lib/python3.8/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
1145 ' "python-fwf")'.format(engine=engine)
1146 )
-> 1147 self._engine = klass(self.f, **self.options)
1148
1149 def _failover_to_python(self):
~/.local/share/virtualenvs/openqlab/lib/python3.8/site-packages/pandas/io/parsers.py in __init__(self, f, **kwds)
2297 # Set self.data to something that can read lines.
2298 if hasattr(f, "readline"):
-> 2299 self._make_reader(f)
2300 else:
2301 self.data = f
~/.local/share/virtualenvs/openqlab/lib/python3.8/site-packages/pandas/io/parsers.py in _make_reader(self, f)
2427 self.pos += 1
2428 self.line_pos += 1
-> 2429 sniffed = csv.Sniffer().sniff(line)
2430 dia.delimiter = sniffed.delimiter
2431 if self.encoding is not None:
/usr/lib64/python3.8/csv.py in sniff(self, sample, delimiters)
179
180 quotechar, doublequote, delimiter, skipinitialspace = \
--> 181 self._guess_quote_and_delimiter(sample, delimiters)
182 if not delimiter:
183 delimiter, skipinitialspace = self._guess_delimiter(sample,
/usr/lib64/python3.8/csv.py in _guess_quote_and_delimiter(self, data, delimiters)
220 r'(?:^|\n)(?P<quote>["\']).*?(?P=quote)(?:$|\n)'): # ".*?" (no delim, no space)
221 regexp = re.compile(restr, re.DOTALL | re.MULTILINE)
--> 222 matches = regexp.findall(data)
223 if matches:
224 break
TypeError: expected string or bytes-like object
```
</details>
Without the comment in the data file and without the parameter `comment='#'` everything works as expected.
**It seems that `sep=None` ist the problem here.**
When using `sep=','` it works. But in our case, the import is part of a general importer that should accept a variety of different files. Thus, we must use `sep=None`.
#### Expected Output
I would expect the following output:
```python
Out[18]:
1
0
128.0 -40.78996
256.0 -51.55923
512.0 -79.18677
640.0 -79.19656
[4 rows x 1 columns]
```
#### Output of ``pd.show_versions()``
<details>
<summary>Details</summary>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.13-201.fc31.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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TYP: Annotate
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| 2020-01-31T04:10:15Z
| 2020-01-31T03:34:57Z
|
MEMBER
| null |
AFAICT having the more specific annotations for the Index subclasses _data attribute is a blocker for a bunch of other annotations.
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MDU6SXNzdWU1NTYzODUyNjA=
| 31,398
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NTile function
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| 2020-01-28T18:21:05Z
| 2020-03-06T23:10:47Z
| 2020-03-06T23:09:22Z
|
NONE
| null |
#### Problem description
I've investigated on pandas a `ntile` operation alternative. I just found `qcut` but it seems there is a difference between them (at least I could have the same results I expected).
for more details about `NTile`[3] :
> NTILE() function allows you to divide ordered rows in the partition into a specified number of ranked groups as equal size as possible. These ranked groups are called buckets.
> The NTILE() function assigns each group a bucket number starting from 1. For each row in a group, the NTILE() function assigns a bucket number representing the group to which the row belongs.
I have locally a python version of this function, is this something I could open a PR to pandas? if yes, I would be very happy to work on this PR.
any feedback and/or guidance would be very appreciated.
some references about ntile:
[1] https://docs.oracle.com/cd/B19306_01/server.102/b14200/functions101.htm
[2] https://www.sqlservertutorial.net/sql-server-window-functions/sql-server-ntile-function/
[3]https://www.postgresqltutorial.com/postgresql-ntile-function/
thanks!
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MDExOlB1bGxSZXF1ZXN0MzY4MTg3MDky
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|
PERF: avoid is_bool_indexer check where possible
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| 8
| 2020-01-28T19:27:47Z
| 2020-02-04T16:16:12Z
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|
MEMBER
| null |
xref #30349, not a lot of ground to pick up here, but we can avoid a few calls
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MDU6SXNzdWU1NTY0MTk1MzY=
| 31,400
|
read_csv error for a long entry
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| 2020-01-28T19:30:31Z
| 2020-01-28T21:10:47Z
| 2020-01-28T21:10:47Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.read_csv(fileName, delimiter='\t', comment='#', skip_blank_lines=True)
```
#### Problem description
After reading a csv file, all the entries after a particular column in only one row shows as 'na', not the real value. This is due to the character length (469 including spaces) in that particular column entry as removing some characters do load the data properly. Is there any way to load the file properly? I don't mind truncating each entry not to cause this issue.
Thanks,
Segun
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MDExOlB1bGxSZXF1ZXN0MzY4MTg4NDAw
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BUG: preserve object dtype for Index set ops
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MEMBER
| null |
I'd be fine with coercing if we did it consistently, but ATM we behave differently for monotonic-increasing vs otherwise.
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MDExOlB1bGxSZXF1ZXN0MzY4MjA0NTY1
| 31,402
|
CLN: remove maybe_box
|
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| 2020-01-28T20:06:28Z
| 2020-01-31T04:13:19Z
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|
MEMBER
| null |
Recently removed the last usaged from TDI/DTI.
|
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MDExOlB1bGxSZXF1ZXN0MzY4MjI1MDkz
| 31,403
|
Backport PR #31361 on branch 1.0.x (Avoid Index DeprecationWarning in Series getitem)
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| 2020-01-28T20:55:53Z
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NONE
| null |
Backport PR #31361: Avoid Index DeprecationWarning in Series getitem
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MDExOlB1bGxSZXF1ZXN0MzY4MjMwODc4
| 31,404
|
Add pandas blog to feed
|
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| 2020-01-28T21:10:39Z
| 2020-01-29T17:34:38Z
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|
CONTRIBUTOR
| null |
I don't think this needs to be backported.
|
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MDExOlB1bGxSZXF1ZXN0MzY4MjMyNjE5
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Backport PR #31136 on branch 1.0.x (COMPAT: Return NotImplemented for subclassing)
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| 2020-01-28T21:14:23Z
| 2020-01-28T22:17:03Z
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NONE
| null |
Backport PR #31136: COMPAT: Return NotImplemented for subclassing
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MDExOlB1bGxSZXF1ZXN0MzY4MjQ4NjYw
| 31,406
|
REF: consolidate DTI/TDI/PI get_value in ExtensionIndex, associated cleanups
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| 3
| 2020-01-28T21:50:47Z
| 2020-02-02T23:12:40Z
| 2020-02-02T22:52:06Z
|
MEMBER
| null |
@jschendel does the holds_integer change for IntervalIndex make sense?
@jreback is this a correct usage of self.holds_integer() as meaning "we only fall back to positional indexing if not self.holds_integer()"
|
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MDU6SXNzdWU1NTY1MjgzMTc=
| 31,407
|
CI: revert the warning change once IPython updates
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[] | null | 3
| 2020-01-28T22:49:21Z
| 2021-02-02T17:55:58Z
| null |
MEMBER
| null |
A couple days ago, there was a CI broken caused by the latest jedi release, and back then, made a patch to catch the warning. Ideally, after ipython updates and fixes the deprecated code, then the warning should be removed, and we should be able to revert the change in #31323
FYI, I created an issue on IPython: https://github.com/ipython/ipython/issues/12102
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MDExOlB1bGxSZXF1ZXN0MzY4MzA3NTQy
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|
REF: docstrings that dont need to go into shared_docs
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PERF/CLN: float-to-int casting
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| 6
| 2020-01-29T02:13:52Z
| 2020-02-02T23:16:45Z
| 2020-02-02T22:50:48Z
|
MEMBER
| null |
using float.is_integer is apparently much faster than casting and checking equality, especially for values between int32max and int64max (anecdotal based on small sample size)
```
cast1 = com.cast_scalar_indexer_master
cast2 = com.cast_scalar_indexer_PR
def cast3(key):
# Effectively what we have in _maybe_cast_indexer in master
if lib.is_float(key):
try:
ckey = int(key)
if ckey == key:
key = ckey
except (OverflowError, ValueError, TypeError):
pass
return key
key1 = np.float64(17179869184.0)
key2 = float(key1) * 2**30
key3 = np.float64(np.iinfo(np.int64).max)
In [87]: for func in [cast1, cast2, cast3]:
...: for key in [key1, key2, key3]:
...: %timeit func(key)
...:
647 ns ± 3.71 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
559 ns ± 27.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.98 µs ± 19.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
360 ns ± 4.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
353 ns ± 3.11 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
377 ns ± 6.54 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
568 ns ± 3.92 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
445 ns ± 15 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.91 µs ± 36.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
```
The middle three timeit results are the ones we get from this PR.
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Cross tabulations for categorical data doesn't work as the way expected in the guide
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| 2020-01-29T03:20:22Z
| 2020-04-27T05:33:25Z
| 2020-04-27T05:33:25Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [73]: foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
In [74]: bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
In [75]: pd.crosstab(foo, bar)
Out[75]:
col_0 d e
row_0
a 1 0
b 0 1
```
#### Problem description
In the latest [user guide](https://dev.pandas.io/docs/user_guide/reshaping.html#cross-tabulations), it says "Any input passed containing Categorical data will have all of its categories included in the cross-tabulation, even if the actual data does not contain any instances of a particular category.". But why the example doesn't work like this way? (lack of the row 'c' and column 'f')
Thanks !
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COMPAT: dont pass dtype to datetime64
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| 0
| 2020-01-29T03:34:46Z
| 2020-01-29T15:51:10Z
| 2020-01-29T15:12:06Z
|
MEMBER
| null |
Hopefully fixes npdev build
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MDExOlB1bGxSZXF1ZXN0MzY4MzcyNTc3
| 31,412
|
CLN: Replace '.format' with f-strings in some test files #29547
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| 4
| 2020-01-29T05:07:38Z
| 2020-02-02T03:06:03Z
| 2020-01-31T04:02:40Z
|
CONTRIBUTOR
| null |
Updates the below 2 test files to use f-strings (fast and preferred) instead of '.format' (old and slow)
1) pandas/tests/reshape/test_reshape.py
2) pandas/tests/scalar/period/test_period.py
For more details on the issue, refer: https://github.com/pandas-dev/pandas/issues/29547
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1NTY2NTc5MjI=
| 31,413
|
Series.append(DataFrame) should throw TypeError
|
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| 2
| 2020-01-29T06:06:21Z
| 2020-03-04T14:17:16Z
| 2020-03-04T14:17:16Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.__version__
'1.0.0rc0+233.gec0996c67'
>>> df = pd.DataFrame({'A':[1,2]})
>>> df.A.append(df)
0 A
0 1.0 NaN
1 2.0 NaN
0 NaN 1.0
1 NaN 2.0
```
#### Problem description
The behavior of append is the same as it is in 0.25.3 when a DataFrame is appended to Series. But it should actually throw a TypeError when passed a DataFrame.
We clearly document that the elements passed to append should be Series or list/tuple of Series.
Also according to [this](https://github.com/pandas-dev/pandas/issues/30975), a TypeError seems appropriate. The change can be implemented for 1.0.1 or 1.1 after [this](https://github.com/pandas-dev/pandas/pull/31036#issuecomment-577248825) discussion.
#### Expected Output
```python
>>> df = pd.DataFrame({'A':[1, 2]})
>>> df.A.append([df])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\Work\Git projects\pandas\pandas\core\series.py", line 2572, in append
raise TypeError(msg)
TypeError: to_append should be a Series or list/tuple of Series, got DataFrame
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : ec0996c6751326eed17a0bb456fe1c550689a618
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0rc0+233.gec0996c67.dirty
numpy : 1.17.0
pytz : 2018.9
dateutil : 2.8.0
pip : 19.3.1
setuptools : 44.0.0.post20200106
Cython : 0.29.14
pytest : 4.3.1
hypothesis : 5.1.5
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.6.3
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 : 0.15.1
pytables : None
pytest : 4.3.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.1
tables : 3.5.1
tabulate : 0.8.6
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
numba : 0.43.1
</details>
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MDU6SXNzdWU1NTY3Mzg1MTI=
| 31,414
|
Cannot replace all occurences of infs and nans to None with a single df.replace
|
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[] | null | 1
| 2020-01-29T09:29:26Z
| 2020-04-25T12:41:44Z
| null |
NONE
| null |
#### Example code
```python
>>> df = pd.DataFrame(
... {'foo':[
... np.nan,
... np.inf,
... 1.1,
... 2.2
... ],
... 'bar':[
... 1,
... -np.inf,
... 2.1,
... 3.2
... ]
... })
>>> df
foo bar
0 NaN 1.0
1 inf -inf
2 1.1 2.1
3 2.2 3.2
>>> df.replace({np.nan: None, -np.inf:None, np.inf:None})
foo bar
0 NaN 1.0
1 NaN NaN
2 1.1 2.1
3 2.2 3.2
>>>
```
#### Problem description
When trying to replace all occurrences of `NaN`s and `Inf`s to `None`s the resulting dataframe simply contains 'NaNs' for every occurrence, retaining the dtype of `float64` rather than the expected `object`. A work around is to simply issue the replace again with only `np.nan` to `None`.
This behavior was discovered when trying to convert the contents of a dataframe containing `Inf`s, and `NaN`s to `None`s in preparation for JSON compliant output via a dictionary for compatibility with third party tools rather than directly through panda's to_json() method (which admittedly would make this process unnecessary as it produces the compliant output already)
I strongly suspect this problem is related to inconsistencies outlined here: https://github.com/pandas-dev/pandas/issues/29024
#### Expected Output
I would expect a column type of `object` with `None`s in place as I see when replacing only NaNs.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.13-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0+233.gec0996c675
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3
setuptools : 44.0.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : 2.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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