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https://api.github.com/repos/pandas-dev/pandas/issues/26713 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26713/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26713/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26713/events | https://github.com/pandas-dev/pandas/issues/26713 | 453,663,317 | MDU6SXNzdWU0NTM2NjMzMTc= | 26,713 | Performance for TimedeltaArray.__iter__ and PeriodArray.__iter__ | {
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} | 1 | 2019-06-07T19:34:28Z | 2020-10-14T18:19:25Z | 2020-10-14T18:19:25Z | CONTRIBUTOR | null | Right now `DatetimeArray.__iter__` does it's boxing in chunks. We could do something similar for TimedeltaArray and possibly PeriodArray. | {
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```
=================================== FAILURES ===================================
_______________________ TestDataFramePlots.test_subplots _______________________
[gw1] linux -- Python 3.6.8 /home/travis/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.plotting.test_frame.TestDataFramePlots object at 0x7f507ed05128>
@pytest.mark.slow
def test_subplots(self):
df = DataFrame(np.random.rand(10, 3),
index=list(string.ascii_letters[:10]))
for kind in ['bar', 'barh', 'line', 'area']:
axes = df.plot(kind=kind, subplots=True, sharex=True, legend=True)
self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
assert axes.shape == (3, )
for ax, column in zip(axes, df.columns):
self._check_legend_labels(ax,
labels=[pprint_thing(column)])
for ax in axes[:-2]:
self._check_visible(ax.xaxis) # xaxis must be visible for grid
self._check_visible(ax.get_xticklabels(), visible=False)
self._check_visible(
> ax.get_xticklabels(minor=True), visible=False)
pandas/tests/plotting/test_frame.py:387:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <pandas.tests.plotting.test_frame.TestDataFramePlots object at 0x7f507ed05128>
collections = <a list of 2 Text xticklabel objects>, visible = False
def _check_visible(self, collections, visible=True):
"""
Check each artist is visible or not
Parameters
----------
collections : matplotlib Artist or its list-like
target Artist or its list or collection
visible : bool
expected visibility
"""
from matplotlib.collections import Collection
if not isinstance(collections,
Collection) and not is_list_like(collections):
collections = [collections]
for patch in collections:
> assert patch.get_visible() == visible
E AssertionError
pandas/tests/plotting/common.py:137: AssertionError
```
I'm looking into it now, but will have to go in 30 minutes. | {
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} | 6 | 2019-06-07T20:36:34Z | 2019-06-08T04:22:48Z | 2019-06-08T04:22:44Z | CONTRIBUTOR | null | Closes #26714
xref https://github.com/pandas-dev/pandas/issues/26716 | {
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} | 3 | 2019-06-07T20:40:10Z | 2019-12-04T13:10:24Z | 2019-12-04T13:10:24Z | CONTRIBUTOR | null | Present in the last two builds on master
- https://travis-ci.org/pandas-dev/pandas/jobs/542915088
- https://travis-ci.org/pandas-dev/pandas/jobs/542931830
```
gw0 I
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/hypothesis/_settings.py:193: HypothesisDeprecationWarning: The timeout setting can safely be removed with no effect.
verbosity=self.verbosity,
[gw0] node down: Not properly terminated
Replacing crashed worker gw0
```
Something is crashing our pytest-xdist worker. We used pytest-xdist 1.28.0 in the last green build https://travis-ci.org/pandas-dev/pandas/jobs/542868383, so it's likely not pytest-xdist itself.
Edit: Seems to be an issue with feather / pyarrow. We segfault when collecting tests: https://travis-ci.org/pandas-dev/pandas/jobs/542977519#L3052. https://github.com/pandas-dev/pandas/pull/26715 is skipping, use this issue to restore.
```
collecting 34877 items / 1 skipped / 34876 selected Fatal Python error: Segmentation fault
Current thread 0x00007fb9015dd740 (most recent call first):
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 922 in create_module
File "<frozen importlib._bootstrap>", line 571 in module_from_spec
File "<frozen importlib._bootstrap>", line 658 in _load_unlocked
File "<frozen importlib._bootstrap>", line 955 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 971 in _find_and_load
File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/pyarrow/__init__.py", line 47 in <module>
File "<frozen importlib._bootstrap>", line 219 in _call_with_frames_removed
File "<frozen importlib._bootstrap_external>", line 678 in exec_module
File "<frozen importlib._bootstrap>", line 665 in _load_unlocked
File "<frozen importlib._bootstrap>", line 955 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 971 in _find_and_load
File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/_pytest/outcomes.py", line 168 in importorskip
File "/home/travis/build/pandas-dev/pandas/pandas/tests/io/test_feather.py", line 13 in <module>
File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/_pytest/assertion/rewrite.py", line 304 in load_module
File "<frozen importlib._bootstrap>", line 626 in _load_backward_compatible
File "<frozen importlib._bootstrap>", line 656 in _load_unlocked
File "<frozen importlib._bootstrap>", line 955 in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 971 in _find_and_load
File "/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/py/_path/local.py", line 701 in pyimport
``` | {
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] | closed | false | null | [] | null | 11 | 2019-06-07T20:45:19Z | 2019-09-17T02:11:42Z | 2019-08-28T16:14:39Z | NONE | null | - [ ] closes #22529
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 2 | 2019-06-07T21:44:50Z | 2019-08-21T16:32:26Z | 2019-08-21T16:32:25Z | NONE | null | #### Code Sample
```python
df['Quarter'] = pd.PeriodIndex(df['date'], freq='Q')
fig, ax = plt.subplots(figsize=(10,5))
df.plot.bar(x='Quarter', y ='ape', ax=ax, xlim=('2015Q1', '2019Q1'), color="tab:green").grid(alpha=.4)
```
#### Problem description
for bar plot, xlim option fails and always gives the same range; line plot works though.
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```python
import pandas as pd
divider_one = '=' * 75
divider_two = '+' * 75
divider_three = '#' * 75
####################################################################################################
# There are three declarations for the `base_df_*` DataFrame:
# one initializes it with two elements then adds one, two, and three more columns; and
# one initializes it with three elements then adds one, two, and three more columns; and
# one initializes it with four elements then adds one, two, and three more columns.
#
# It appears that the `SettingWithcopyWarning` is triggered when a DataFrame with four or more columns is
# used to derive another DataFrame by slicing and then an attempt is made to add a new column to
# the derived DataFrame. There is no such warning when the original DataFrame has fewer
# columns.
#
# To test this, uncomment the appropriate function to test.
#
####################################################################################################
def test_base_df_with_two_initial_elements():
base_df_one = pd.DataFrame({
'col1': ['col1 value one', 'col1 value two', 'col1 value three'],
'col2': ['col2 value one', 'col2 value two', 'col2 value three'],
})
print(divider_one)# This is with a base df w/ 2
print('Setting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be no error.')
base_df_one_sub['newcol'] = 'values'
print(divider_one)
print(divider_two)# This is with a base df w/ 3
print('Adding col3 to base.')
base_df_one['col3'] = 'col3 values'
print('Resetting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be no error.')
base_df_one_sub['newcol'] = 'values'
print(divider_two)
print(divider_three)# This is with a base df w/ 4
print('Adding col4 to base.')
base_df_one['col4'] = 'col4 values'
print('Resetting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be an error.')
base_df_one_sub['newcol'] = 'values'
print(divider_three)
def test_base_df_with_three_initial_elements():
base_df_one = pd.DataFrame({
'col1': ['col1 value one', 'col1 value two', 'col1 value three'],
'col2': ['col2 value one', 'col2 value two', 'col2 value three'],
'col3': ['col3 value one', 'col3 value two', 'col3 value three'],
})
print(divider_one)# This is with a base df w/ 3
print('Setting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be no error.')
base_df_one_sub['newcol'] = 'values'
print(divider_one)
print(divider_two)# This is with a base df w/ 4
print('Adding col4 to base.')
base_df_one['col4'] = 'col3 values'
print('Resetting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be an error.')
base_df_one_sub['newcol'] = 'values'
print(divider_two)
print(divider_three)# This is with a base df w/ 5
print('Adding col5 to base.')
base_df_one['col5'] = 'col4 values'
print('Resetting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be an error.')
base_df_one_sub['newcol'] = 'values'
print(divider_three)
def test_base_df_with_four_initial_elements():
base_df_one = pd.DataFrame({
'col1': ['col1 value one', 'col1 value two', 'col1 value three'],
'col2': ['col2 value one', 'col2 value two', 'col2 value three'],
'col3': ['col3 value one', 'col3 value two', 'col3 value three'],
'col4': ['col4 value one', 'col4 value two', 'col4 value three'],
})
print(divider_one)# This is with a base df w/ 4
print('Setting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be an error.')
base_df_one_sub['newcol'] = 'values'
print(divider_one)
print(divider_two)# This is with a base df w/ 5
print('Adding col5 to base.')
base_df_one['col5'] = 'col3 values'
print('Resetting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be an error.')
base_df_one_sub['newcol'] = 'values'
print(divider_two)
print(divider_three)# This is with a base df w/ 6
print('Adding col6 to base.')
base_df_one['col6'] = 'col4 values'
print('Resetting the df sub from slice.')
base_df_one_sub = base_df_one[['col1', 'col2']]
print('Adding newcol to sub.')
print('Should be an error.')
base_df_one_sub['newcol'] = 'values'
print(divider_three)
# Choose which function to test to confirm and uncomment.
# test_base_df_with_two_initial_elements()
# test_base_df_with_three_initial_elements()
# test_base_df_with_four_initial_elements()
```
#### Problem description
It appears that the `SettingWithcopyWarning` is triggered when a DataFrame with four or more columns is used to derive another DataFrame by slicing and then an attempt is made to add a new column to the derived DataFrame. There is no such warning when the original DataFrame has fewer columns.
#### Expected Output
I'm not precisely sure what to expect, but I would imagine that the number of columns in a source DataFrame shouldn't drive a warning against misuse of views of DataFrames. The documentation directs users to use `.loc[]` but only when the warning is issued. If there is potential wonkiness due to setting values from views or copies generally, I'd expect it'd be more helpful when the warning is triggered consistently.
#### 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 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.0
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-06-07T22:15:10Z | 2019-06-08T21:22:31Z | 2019-06-08T21:04:28Z | CONTRIBUTOR | null | - [x] closes #22044
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
```python
df = pd.DataFrame({
'a': np.arange(1_000_000, dtype=np.int32),
'b': np.arange(1_000_000, dtype=np.int64),
'c': np.arange(1_000_000, dtype=float),
}).astype({'a': 'category', 'b': 'category'})
%timeit df.set_index(['a', 'b'])
```
On my machine, this takes ~20ms. The current implem takes 140ms.
Any suggestion for tests ? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26722 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26722/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26722/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26722/events | https://github.com/pandas-dev/pandas/pull/26722 | 453,715,337 | MDExOlB1bGxSZXF1ZXN0Mjg2MzUwMzg2 | 26,722 | REF: Refactor signature of RangeIndex._simple_new | {
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} | 11 | 2019-06-07T22:25:36Z | 2019-06-14T15:54:47Z | 2019-06-14T12:42:00Z | CONTRIBUTOR | null | This PR refactors ``RangeIndex._simple_new``, so its signature is the same as ``Index._simple_new``. This will help typing later on, as currently mypy complains about the different signatures. In short a ``_simple_new`` now expects a ``range`` as its input. As a ``range`` is immutable, the code is easy to reason about.
Additionally, the signature of ``RangeIndex.from_range`` has dropped the ``**kwargs`` part of its signature. This makes the class method a bit easier to reason about. Other classes with a ``from_*`` class method don't have a ``**kwargs`` part.
After this PR, ``RangeIndex`` will be ready for adding type hints.
EDIT; Performance improvements:
```python
>>> rng = pd.RangeIndex(1_000_000)
>>> sl = slice(0, 900_000)
>>> %timeit rng[sl] # slicing
7.65 µs ± 8.11 ns per loop # master
1.85 µs ± 8.11 ns per loop # this PR
```
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https://api.github.com/repos/pandas-dev/pandas/issues/26723 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26723/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26723/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26723/events | https://github.com/pandas-dev/pandas/issues/26723 | 453,725,033 | MDU6SXNzdWU0NTM3MjUwMzM= | 26,723 | DOC/CI: Doc build is failing with sphinx 2.1 | {
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```
Recursion error:
maximum recursion depth exceeded while calling a Python object
This can happen with very large or deeply nested source files. You can carefully increase the default Python recursion limit of 1000 in conf.py with e.g.:
import sys; sys.setrecursionlimit(1500)
```
I tried increasing the limit from 1,000 to 20,000 but still the same error, so I guess the problem is something else.
I think this is the first job that failed: https://travis-ci.org/pandas-dev/pandas/jobs/542714631 (PR #26697) | {
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This PR adds the ability to _prepend_ indices to existing ones using `.set_index()`, in the same vein as the `append` argument.
This avoid having to do the awkward alternatives:
- `.reset_index().set_index(...)`
- `.set_index(append=True).swaplevel(...)` or `.set_index(append=True).reorder_levels(...)`
`prepend` is mutually exclusive with `append`. The code will test for this and raise ValueError if not.
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": ... | closed | false | null | [] | null | 0 | 2019-06-08T00:41:16Z | 2019-06-08T02:05:35Z | 2019-06-08T02:05:35Z | MEMBER | null | Quite a few CI failures lately so apologies if this was missed but I don't think we have an issue for this one. It looks like we are getting some failures with error message checking on NumPy dev. I think this is a result of this change upstream:
https://github.com/numpy/numpy/pull/13721/files
Here's a sample build failure:
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=12453&view=logs&jobId=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&taskId=264dbb61-dd41-5ada-cfaa-25751f4c2dca&lineStart=70&lineEnd=71&colStart=1&colEnd=1
Need to update and maybe simplify the error message checking on our end.
cc @simonjayhawkins if you have thoughts | {
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} | 5 | 2019-06-08T00:46:21Z | 2019-06-08T02:13:13Z | 2019-06-08T02:05:35Z | MEMBER | null | - [X] closes #26725
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | [] | closed | false | null | [] | null | 1 | 2019-06-08T03:33:45Z | 2019-06-08T04:24:56Z | 2019-06-08T04:24:53Z | CONTRIBUTOR | null | xref https://github.com/pandas-dev/pandas/issues/26723 (leaving open, to unpin). | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26728 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26728/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26728/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26728/events | https://github.com/pandas-dev/pandas/issues/26728 | 453,760,630 | MDU6SXNzdWU0NTM3NjA2MzA= | 26,728 | Import pandas is stopping python service | {
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} | [] | closed | false | null | [] | null | 2 | 2019-06-08T06:00:46Z | 2019-06-08T11:32:38Z | 2019-06-08T11:32:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
```
#### Problem description
I just imported the pandas library which is restarting the shell and throwing an alert pop up stating PYTHON HAS STOPPED WORKING and this is in win 10[64bit]
Version:
C:\>python --version
Python 3.7.0a2
NOTE: I tried with IDLE,IDE and Command prompt all is resulting the same, So kindly help me on this.
Kindly refer the snapshot below,
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https://api.github.com/repos/pandas-dev/pandas/issues/26729 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26729/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26729/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26729/events | https://github.com/pandas-dev/pandas/pull/26729 | 453,764,367 | MDExOlB1bGxSZXF1ZXN0Mjg2Mzg1MDA3 | 26,729 | TST: troubleshot numexpr test | {
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} | [] | closed | false | null | [] | null | 2 | 2019-06-08T06:56:51Z | 2019-08-24T12:01:35Z | 2019-06-08T08:35:22Z | MEMBER | null | seemingly unrelated test failure to changes in PR #26662
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=12414
```
_______ TestAlignment.test_basic_series_frame_alignment[numexpr-python] ________
[gw0] linux -- Python 3.6.8 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.computation.test_eval.TestAlignment object at 0x7f5f831aa5c0>
engine = 'numexpr', parser = 'python'
def test_basic_series_frame_alignment(self, engine, parser):
def testit(r_idx_type, c_idx_type, index_name):
df = mkdf(10, 7, data_gen_f=f, r_idx_type=r_idx_type,
c_idx_type=c_idx_type)
index = getattr(df, index_name)
s = Series(np.random.randn(5), index[:5])
if should_warn(s.index, df.index):
with tm.assert_produces_warning(RuntimeWarning):
res = pd.eval('s + df', engine=engine, parser=parser)
else:
res = pd.eval('s + df', engine=engine, parser=parser)
if r_idx_type == 'dt' or c_idx_type == 'dt':
expected = df.add(s) if engine == 'numexpr' else s + df
else:
expected = s + df
assert_frame_equal(res, expected)
# only test dt with dt, otherwise weird joins result
args = product(['i', 'u', 's'], ['i', 'u', 's'], ('index', 'columns'))
with warnings.catch_warnings(record=True):
# avoid warning about comparing strings and ints
warnings.simplefilter("ignore", RuntimeWarning)
for r_idx_type, c_idx_type, index_name in args:
> testit(r_idx_type, c_idx_type, index_name)
pandas/tests/computation/test_eval.py:934:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
pandas/tests/computation/test_eval.py:925: in testit
assert_frame_equal(res, expected)
pandas/_libs/testing.pyx:65: in pandas._libs.testing.assert_almost_equal
cpdef assert_almost_equal(a, b,
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> raise_assert_detail(obj, msg, lobj, robj)
E AssertionError: DataFrame.columns are different
E
E DataFrame.columns values are different (100.0 %)
E [left]: Index([ 0, 1, 2, 3, 4,
E '0בקבנ899זק', '28ףן4כנךטש', '63צגהק5שץן', '7סף4נלדקסב', 'אגןנםצככד0',
E 'וכ2ףזאמאןא', 'ל8לצאןחצךר'],
E dtype='object')
E [right]: Index(['0בקבנ899זק', '28ףן4כנךטש', '63צגהק5שץן', '7סף4נלדקסב', 'אגןנםצככד0',
E 'וכ2ףזאמאןא', 'ל8לצאןחצךר', 0, 1, 2,
E 3, 4],
E dtype='object')
pandas/_libs/testing.pyx:178: AssertionError
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26730 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26730/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26730/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26730/events | https://github.com/pandas-dev/pandas/issues/26730 | 453,768,545 | MDU6SXNzdWU0NTM3Njg1NDU= | 26,730 | Series.round behavior with ExtensionArrays | {
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] | closed | false | null | [] | null | 16 | 2019-06-08T07:55:45Z | 2019-08-02T14:58:51Z | 2019-07-08T01:52:50Z | NONE | null | @TomAugspurger, in your blog post [Moral Philosophy for pandas or: What is .values?](https://tomaugspurger.github.io/pandas-moral-philosophy.html) you wrote
> In pandas 0.24, we'll (hopefully) have a good answer for what series.values is: a NumPy array or an ExtensionArray. For regular data types represented by NumPy, you'll get an ndarray. For extension types (implemented in pandas or elsewhere) you'll get an ExtensionArray. If you're using Series.values, you can rely on the set of methods common to each.
I just ran into an issue because this doesn't happen in some places.
[pint](https://github.com/hgrecco/pint) is a popular package for working with units, useful for engineering/sci calculations. Recently they introduced an EA called [pint-pandas](https://github.com/hgrecco/pint-pandas) for better interaction with pandas,
it's still a little rough, but very promising.
Here's an failure example:
```
In [2]: from pandas import Series
...: import pint
...: from chemtools import Ch, UREG as ureg
...: from pint import UnitRegistry
...:
...: ureg=UnitRegistry()
...: grams=ureg.g
...:
...: s=Series((1.5*grams,2.5*grams),dtype='pint[g]')
...: s.round()
<...>
AttributeError: 'numpy.ndarray' object has no attribute 'rint'
```
The problem is that `Series.round()` calls `com.values_from_object(self)` instead of the EA-aware `self.values`.
https://github.com/pandas-dev/pandas/blob/cf25c5cd3ab1f647f780167df622c74b737fa8f5/pandas/core/series.py#L2054-L2085
`com.values_from_object(self)` percolates down to ExtensionBlock.to_dense():
https://github.com/pandas-dev/pandas/blob/cf25c5cd3ab1f647f780167df622c74b737fa8f5/pandas/core/internals/blocks.py#L1729-L1730
Which calls `np.asarray(self.values)`, this convert the underlying `PintArray` in to a numpy array with object dtype, which is not numeric, and so numpy complains that `rint` isn't implemented. If `Series.round()` would invoked `round()` on `self.values` directly, the underlying EA implementation would get a chance to implement a suitable method, or delegate to numpy in an appropriate way.
You may want to make life easier for EA developers, by providing a default implementation for things like round(), I suppose it may work in some cases, but then there still needs to be a way for an EA to provide its own implementation of functions like `round`. Many `Series` methods use `com.values_from_object(self)`, and its the the same problem everywhere.
cc @hgrecco (pint developer) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26731 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26731/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26731/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26731/events | https://github.com/pandas-dev/pandas/issues/26731 | 453,781,100 | MDU6SXNzdWU0NTM3ODExMDA= | 26,731 | .astype() and Decimal | {
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"descript... | closed | false | null | [] | null | 8 | 2019-06-08T10:46:31Z | 2021-11-08T20:13:44Z | 2019-07-18T20:02:30Z | NONE | null | Begin from 0.24.0 next code raise exception
```python
import decimal
import pandas as pd
ser = pd.Series([1, 2])
ser.astype(decimal.Decimal)
```
Traceback
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/generic.py", line 5691, in astype
**kwargs)
File "/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/managers.py", line 531, in astype
return self.apply('astype', dtype=dtype, **kwargs)
File "/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/managers.py", line 395, in apply
applied = getattr(b, f)(**kwargs)
File "/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/blocks.py", line 534, in astype
**kwargs)
File "/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/internals/blocks.py", line 595, in _astype
dtype = pandas_dtype(dtype)
File "/home/shadchin/t_d/venv/local/lib/python2.7/site-packages/pandas/core/dtypes/common.py", line 2029, in pandas_dtype
raise TypeError("dtype '{}' not understood".format(dtype))
TypeError: dtype '<class 'decimal.Decimal'>' not understood
```
#### Problem description
This begin from #21160
After was https://github.com/pandas-dev/pandas/blob/0409521665bd436a10aea7e06336066bf07ff057/pandas/core/dtypes/common.py#L2013
Now https://github.com/pandas-dev/pandas/blob/cb00deb94500205fcb27a33cc1d0df79a9727f8b/pandas/core/internals/blocks.py#L595
`pandas_dtype()` does not understand `decimal.Decimal`
Is it expected behavior or regress?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.101-26
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26732 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26732/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26732/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26732/events | https://github.com/pandas-dev/pandas/pull/26732 | 453,783,799 | MDExOlB1bGxSZXF1ZXN0Mjg2Mzk3NTA5 | 26,732 | Remove isort dep | {
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- [x] closes #26661
- [x] tests added / passed
- [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff
cc @datapythonista | {
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```
__________________ TestDataFramePlots.test_df_grid_settings ___________________
[gw1] win32 -- Python 3.7.3 C:\Users\simon\Anaconda3\envs\pandas-dev\python.exe
self = <pandas.tests.plotting.test_frame.TestDataFramePlots object at 0x0000016EE807BD30>
@pytest.mark.slow
def test_df_grid_settings(self):
# Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
self._check_grid_settings(
DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4]}),
> plotting._core._dataframe_kinds, kws={'x': 'a', 'y': 'b'})
pandas\tests\plotting\test_frame.py:2938:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <pandas.tests.plotting.test_frame.TestDataFramePlots object at 0x0000016EE807BD30>
obj = a b
0 1 2
1 2 3
2 3 4, kinds = ['scatter', 'hexbin']
kws = {'x': 'a', 'y': 'b'}
def _check_grid_settings(self, obj, kinds, kws={}):
# Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
import matplotlib as mpl
def is_grid_on():
xticks = self.plt.gca().xaxis.get_major_ticks()
yticks = self.plt.gca().yaxis.get_major_ticks()
# for mpl 2.2.2, gridOn and gridline.get_visible disagree.
# for new MPL, they are the same.
if self.mpl_ge_3_0_0:
xoff = all(not g.gridline.get_visible() for g in xticks)
yoff = all(not g.gridline.get_visible() for g in yticks)
else:
xoff = all(not g.gridOn for g in xticks)
yoff = all(not g.gridOn for g in yticks)
return not (xoff and yoff)
spndx = 1
for kind in kinds:
self.plt.subplot(1, 4 * len(kinds), spndx)
spndx += 1
mpl.rc('axes', grid=False)
obj.plot(kind=kind, **kws)
> assert not is_grid_on()
E AssertionError
```
cc @TomAugspurger
<details>
INSTALLED VERSIONS
------------------
commit: 8fe9f69d213a58daa45fbaef2d248a546ef2b2b1
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.25.0.dev0+686.g8fe9f69d2
pytest: 4.5.0
pip: 19.1.1
setuptools: 40.6.3
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.12.1
IPython: 7.5.0
sphinx: 2.0.1
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.3
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: 0.2.1
fastparquet: 0.3.0
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-06-08T14:01:56Z | 2019-06-09T17:48:35Z | 2019-06-08T19:51:09Z | MEMBER | null | `skip_if_no` returns a decorator that applies a pytest mark.
pytest marks are decorators and can be applied directly to test functions.
This PR changes `skip_if_no` to return just the pytest mark.
`skip_if_no` can then be used in (pytest.param(... , marks=) situations. eg. pandas/tests/io/test_html.py
could also be used in pandas/tests/io/test_excel.py but not done here to avoid overlapping PRs.
`safe_import` could potentially be renamed `_safe_import` since would not be needed directly in tests.
cc @WillAyd | {
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cc. @WillAyd
Note I only decided to raise an error when the engine specified to `read_excel` was different to that of the ExcelFile - otherwise around 15 test cases would need to be changed - let me know what you think
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removed small amount of code related to self.ndim > 2
also a few changes to make the NotImplemented errors for self.ndim>2 more consistent and added test for these. (might be worth having a decorator for these) | {
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} | 6 | 2019-06-08T19:56:05Z | 2019-06-09T01:06:17Z | 2019-06-08T21:25:20Z | MEMBER | null | Because read_excel and ExcelWriter have different parametrization requirements it makes things clearer for now to separate these out.
Maybe longer term they can be more intelligently combined but for now I think this still helps clean up requirements for adding new engines
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} | 4 | 2019-06-08T20:43:36Z | 2019-12-25T20:32:50Z | 2019-06-08T21:44:11Z | CONTRIBUTOR | null | - Follow up from #26732 ( i missed a file )
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"description": "Requires... | open | false | null | [] | null | 13 | 2019-06-08T21:21:48Z | 2021-07-10T02:33:59Z | null | NONE | null | I'd like to be able to _prepend_ indices to existing ones using `.set_index()`, in the same vein as the `append` argument.
This avoid having to do the awkward alternatives:
- `.reset_index().set_index(...)`
- `.set_index(append=True).swaplevel(...)` or `.set_index(append=True).reorder_levels(...)`
My first suggestion is to add a `prepend` keyword, mutually exclusive with `append`, but discussion is welcomed.
Associated PR: https://github.com/pandas-dev/pandas/pull/26724 | {
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Related to https://github.com/pandas-dev/pandas/issues/22509#issuecomment-500103115 where `groupby.transform('rank')` applies the wrong code path to 'rank'
because it is treated as an aggregator (array->scalar) instead of a shape-preserving operation.
```python
In [82]: df=pd.DataFrame(dict(price=[1,2,2,3,3,3],cost=(1,2,1,3,2,1)))
...: df
Out[82]:
price cost
0 1 1
1 2 2
2 2 1
3 3 3
4 3 2
5 3 1
In [83]: g=df.groupby('price')
...: g.agg('rank').astype(int)
Out[83]:
cost
0 1
1 2
2 1
3 3
4 2
5 1```
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} | 5 | 2019-06-08T22:21:44Z | 2019-06-11T00:19:03Z | 2019-06-11T00:18:58Z | CONTRIBUTOR | null | Deprecation of `Series.ftype`, `Series.ftypes` and `DataFrame.ftypes`
closes #26705
- [x] Updated the docstrings for the sphinx documentation
- [x] Updated the unit tests for testing deprecation warnings | {
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"d... | closed | false | null | [] | null | 3 | 2019-06-08T23:39:44Z | 2020-09-23T18:04:06Z | 2020-09-23T18:04:06Z | MEMBER | null | #26414 splitted the pandas plotting code into two different modules:
- `pandas.plotting`: Generic plotting framework that defined an API (with docs) and is able to plot with a selectable backend (will be selectable with a pandas option)
- `pandas.plotting._matplotlib`: Matplotlib backend
Our tests in `pandas/tests/plotting` are currently testing both together (calling the pandas plotting framework, which will call the matplotlib backend). But ideally we'd like to have two different groups of tests:
- One with the current code but calling the matplotlib backend directly
- A test set of the generic pandas plotting framework, using a mock backend (needs to be implemented)
@jreback let me know if this makes sense to you | {
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} | 9 | 2019-06-09T00:05:52Z | 2019-06-11T12:20:37Z | 2019-06-11T00:14:59Z | CONTRIBUTOR | null | - [x] closes #26610
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"description": nul... | open | false | null | [] | null | 44 | 2019-06-09T00:23:15Z | 2021-07-10T02:35:53Z | null | MEMBER | null | In #26414 we splitted the pandas plotting module into a general plotting framework able to call different backends and the current matplotlib backends. The idea is that other backends can be implemented in a simpler way, and be used with a common API by pandas users.
The API defined by the current matplotlib backend includes the objects listed next, but this API can probably be simplified. Here is the list with questions/proposals:
Non-controversial methods to keep in the API (They provide the `Series.plot(kind='line')`... functionality):
- LinePlot
- BarPlot
- BarhPlot
- HistPlot
- BoxPlot
- KdePlot
- AreaPlot
- PiePlot
- ScatterPlot
- HexBinPlot
Plotting functions provided in pandas (e.g. `pandas.plotting.andrews_curves(df)`)
- andrews_curves
- autocorrelation_plot
- bootstrap_plot
- lag_plot
- parallel_coordinates
- radviz
- scatter_matrix
- table
Should those be part of the API and other backends should also implement them? Would it make sense to convert to the format `.plot` (e.g. `DataFrame.plot(kind='autocorrelation')`...)? Does it make sense to keep out of the API, or move to a third-party module?
Redundant methods that can possibly be removed:
- hist_series
- hist_frame
- boxplot
- boxplot_frame
- boxplot_frame_groupby
In the case of `boxplot`, we currently have several ways of generating a plot (calling mainly the same code):
1. `DataFrame.plot.boxplot()`
2. `DataFrame.plot(kind='box')`
3. `DataFrame.boxplot()`
4. `pandas.plotting.boxplot(df)`
Personally, I'd deprecate number 4, and for number 3, deprecate or at least not require a separate `boxplot_frame` method in the backend, but try to reuse `BoxPlot` (for number 3 comments, same applies to `hist`).
For `boxplot_frame_groupby`, didn't check in detail, but not sure if `BoxPlot` could be reused for this?
Functions to register converters:
- register
- deregister
Do those make sense for other backends?
Deprecated in pandas 0.23, to be removed:
- tsplot
To see what each of these functions do in practise, it may be useful this notebook by @liirusuk: https://github.com/python-sprints/pandas_plotting_library/blob/master/AllPlottingExamples.ipynb
CC: @pandas-dev/pandas-core @tacaswell, @jakevdp, @philippjfr, @PatrikHlobil | {
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} | 0 | 2019-06-09T00:32:44Z | 2019-06-11T18:46:38Z | 2019-06-11T18:46:38Z | MEMBER | null | After #26745 we should be able to move imports in `pandas/plotting/_matplotlib/*` to the top of the file, as they shouldn't create cycles.
See https://github.com/pandas-dev/pandas/pull/26414#discussion_r291821639 | {
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} | 5 | 2019-06-09T00:33:27Z | 2019-12-25T20:35:28Z | 2019-06-28T15:37:40Z | CONTRIBUTOR | null | - [x] related to #23334
- [x] passes git diff upstream/master -u -- "*.py" | flake8 --diff | {
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} | 9 | 2019-06-09T01:09:33Z | 2019-06-11T18:46:45Z | 2019-06-11T18:46:38Z | CONTRIBUTOR | null | - [x] closes #26748
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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closes #26705
- [x] Updated the docstrings for the sphinx documentation
- [x] Updated the unit tests for testing deprecation warnings
- [x] Added Git Issue Number to unit tests | {
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} | 5 | 2019-06-09T10:00:01Z | 2019-06-09T21:51:58Z | 2019-06-09T21:51:39Z | MEMBER | null | When the doc build fails, we still try to create the redirected pages, which fails, and adds to the log a misleading error (it looks in the logs like the problem is not the original error but that something is failing in the redirects).
See for example https://travis-ci.org/pandas-dev/pandas/jobs/542714631:
```
Recursion error:
maximum recursion depth exceeded while calling a Python object
This can happen with very large or deeply nested source files. You can carefully increase the default Python recursion limit of 1000 in conf.py with e.g.:
import sys; sys.setrecursionlimit(1500)
Traceback (most recent call last):
File "./make.py", line 341, in <module>
sys.exit(main())
File "./make.py", line 337, in main
return getattr(builder, args.command)()
File "./make.py", line 226, in html
self._add_redirects()
File "./make.py", line 209, in _add_redirects
with open(path, 'w') as moved_page_fd:
FileNotFoundError: [Errno 2] No such file or directory: '/home/travis/build/pandas-dev/pandas/doc/build/html/generated/pandas.api.extensions.ExtensionArray.argsort.html'
```
Here I make the redirects be created only if the doc build succeeded. Also opening the browser if we're building a single page. | {
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} | 5 | 2019-06-09T11:33:34Z | 2019-06-21T09:37:18Z | 2019-06-21T09:37:18Z | MEMBER | null | - [x] closes #14130
- [x] tets added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adding option to specify the plotting backend. This does not change the plotting API, so for now backends are expected to implement the existing methods in the matplotlib API. This API is expected to change as a result of the discussions in #26747.
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} | 3 | 2019-06-09T14:03:32Z | 2019-06-12T18:52:34Z | 2019-06-12T18:52:30Z | CONTRIBUTOR | null | - [x] closes #11123
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2019-06-09T15:18:50Z | 2019-06-10T02:27:44Z | 2019-06-09T21:51:12Z | MEMBER | null | - [X] closes #24472
@simonjayhawkins
Just moved everything as is. Some of the class structures are unnecessary now but leaving for follow ups | {
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} | 9 | 2019-06-10T01:58:04Z | 2019-06-12T16:52:48Z | 2019-06-12T15:44:56Z | MEMBER | null | xref #25995, #17936
AFAICT these warnings are caused by cython casting `len(some_ndarray)` to `int` or `Py_ssize_t` but casting `len(some_memoryview)` to `size_t`. Easiest fix is to declare the affected len variables as `size_t` since we know they'll never be negative. | {
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} | 2 | 2019-06-10T03:20:47Z | 2019-06-10T13:56:20Z | 2019-06-10T12:30:42Z | MEMBER | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 6 | 2019-06-10T03:30:26Z | 2019-06-22T18:07:48Z | 2019-06-22T17:19:03Z | MEMBER | null | I'm much more confident about the other two BLD PRs (#26757, #26758) than this, largely because:
- [ ] in a couple of comments here I wrote that a certain term should always be non-negative. I haven't actually confirmed that this is always the case
- [ ] This changed things from int64_t to uint64_t, but could also reasonably have changed them to size_t. I don't have a compelling reason for the correctness of the uint64_t choice. | {
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} | 0 | 2019-06-10T04:14:03Z | 2019-06-21T02:06:35Z | 2019-06-21T02:06:35Z | CONTRIBUTOR | null | This shouldn't warn, since it's a Series.plot.
The warning should only show up for `Axes.plot(datetimelike, ndarray)`.
```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: ts = pd.Series(np.random.randn(1000),
...: index=pd.date_range('1/1/2000', periods=1000))
...: ts = ts.cumsum()
In [4]: ts.plot()
/Users/taugspurger/sandbox/pandas/pandas/plotting/_matplotlib/converter.py:96: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register
matplotlib converters.
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26761 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26761/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26761/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26761/events | https://github.com/pandas-dev/pandas/issues/26761 | 453,998,844 | MDU6SXNzdWU0NTM5OTg4NDQ= | 26,761 | to_sql Out of bounds nanosecond timestamp | {
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"description": "to_sql, r... | closed | false | null | [] | null | 13 | 2019-06-10T05:03:03Z | 2020-03-30T16:24:41Z | 2020-03-30T16:24:41Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
df.to_sql(TableName,index=False,if_exists="replace")
```
#### Problem description
I upgraded my pandas to 0.24.2 recently and encountered below error, which will not pop before.
When I am using python to transfer some data to sql server. Some date is set to 9999-12-31 and it will cause error: OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 9999-12-31 00:00:00
#### Expected Output
We should be able to transfer those date. Thanks
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 3.8.0
pip: 19.0.2
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.16.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.9
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26762 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26762/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26762/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26762/events | https://github.com/pandas-dev/pandas/issues/26762 | 454,026,580 | MDU6SXNzdWU0NTQwMjY1ODA= | 26,762 | duplicated() (and drop_duplicates()) incorrectly identifying unique/different rows as duplicates | {
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} | [] | closed | false | null | [] | null | 2 | 2019-06-10T07:02:13Z | 2019-06-10T16:37:05Z | 2019-06-10T16:37:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series.from_csv('duplicated_int64-bug.csv')
d = {}
non_dupe_python = set()
for k, val in s.iteritems():
if k not in d:
d[k] = val
else:
if val != d[k]:
non_dupe_python.add(k)
dup = s.duplicated(keep=False)
has_2_entries = dup.groupby(level=0).count()
has_2_entries = has_2_entries[has_2_entries>1].index
non_dupe_pd = set(dup[dup==0].index & has_2_entries)
print "Non-dupes (Python, %d): %r" % (len(non_dupe_python), non_dupe_python)
print "Non-dupes (Series.duplicated, %d): %r" % (len(non_dupe_pd), non_dupe_pd)
print "False duplicates: %r" % (non_dupe_python-non_dupe_pd)
print "\nTwo false duplicates (problem here)"
print s.loc[1793]
print dup.loc[1793]
print s.loc[1795]
print dup.loc[1795]
print "\nA correctly-identified non-duplicate"
print s.loc[6080]
print dup.loc[6080]
print "\nA correctly-identified duplicate"
print s.loc[1]
print dup.loc[1]
```
[duplicated_int64-bug.csv.zip](https://github.com/pandas-dev/pandas/files/3270864/duplicated_int64-bug.csv.zip)
My output:
```
Non-dupes (Python, 9): set([6080, 1793, 1795, 6084, 1797, 1798, 5968, 5975, 6086])
Non-dupes (Series.duplicated, 7): set([6080, 6084, 1797, 1798, 5968, 5975, 6086])
False duplicates: set([1793, 1795])
Two false duplicates (problem here)
1793 1334122803464982
1793 1334122794298518
dtype: int64
1793 True
1793 True
dtype: bool
1795 1334122803464982
1795 1334122794298518
dtype: int64
1795 True
1795 True
dtype: bool
A correctly-identified non-duplicate
6080 91475737356800
6080 91475737482944
dtype: int64
6080 False
6080 False
dtype: bool
A correctly-identified duplicate
1 93251501583580
1 93251501583580
dtype: int64
1 True
1 True
dtype: bool
```
#### Problem description
Using 0.24.2 I was comparing two ~6,000-row DataFrames before and after some modifications and looking for modified rows by using concat and then drop_duplicates (keep=False, although IIRC the issue also happens with other arguments to keep) and found that it was reporting false duplicates (i.e. missing rows that were in fact modified.)
Stepping into ```drop_duplicates()```, then ```duplicated()```, then at the output of ```get_group_index()``` (a series of key->int64 pairs which looks like a total ordering of the DataFrame that I basically dumped into the attached csv) shows a problem most likely in ```duplicated_int64``` which looks like a Cython or native module (which unfortunately I don't have the cycles or toolchain to look into but maybe there's a false hash collision?)
The repro and csv above includes ~12000 int64->int64 pairs with a variety of duplicated and non-duplicated pairs and some keys that only appear once.
I couldn't find a similar report although this appears to be similar in principle to https://github.com/pandas-dev/pandas/issues/11864 (problem in ```duplicated_int64```) and wasn't fixed by that. Repro above should be able to reproduce & isolate the problem.
#### Expected Output
As shown above the two actual values for index 1793 are different and should not be reported as duplicate rows (i.e. ```dup.loc[1793]``` should return ```False```)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 18.0
setuptools: 38.4.0
Cython: None
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2013.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 1.3.1
openpyxl: 2.5.2
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: None
lxml.etree: None
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.3.3
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 7 | 2019-06-10T10:15:53Z | 2019-06-11T14:49:29Z | 2019-06-10T17:32:01Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/26755#pullrequestreview-247416090
the larger files `pandas/tests/io/excel/test_readers.py` and `pandas/tests/io/excel/test_writers.py` have been left for separate PRs
any idiom changes other than required to declass are also omitted to make the diff easier to check. | {
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} | 3 | 2019-06-10T10:55:26Z | 2019-06-14T02:06:04Z | 2019-06-13T20:41:45Z | CONTRIBUTOR | null | Close (#25814). When use IntervalIndex for pivot table, raise
TypeError: cannot determine next label for type <class 'str'>
- [x] closes #25814
- [x] tets added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 10 | 2019-06-10T13:35:02Z | 2021-07-10T02:36:51Z | null | MEMBER | null | We currently expose all of the following items from pandas.core.api via the API:
```python
from pandas.core.api import (
# dtype
Int8Dtype, Int16Dtype, Int32Dtype, Int64Dtype, UInt8Dtype,
UInt16Dtype, UInt32Dtype, UInt64Dtype, CategoricalDtype,
PeriodDtype, IntervalDtype, DatetimeTZDtype,
# missing
isna, isnull, notna, notnull,
# indexes
Index, CategoricalIndex, Int64Index, UInt64Index, RangeIndex,
Float64Index, MultiIndex, IntervalIndex, TimedeltaIndex,
DatetimeIndex, PeriodIndex, IndexSlice,
# tseries
NaT, Period, period_range, Timedelta, timedelta_range,
Timestamp, date_range, bdate_range, Interval, interval_range,
DateOffset,
# conversion
to_numeric, to_datetime, to_timedelta,
# misc
np, Grouper, factorize, unique, value_counts, NamedAgg,
array, Categorical, set_eng_float_format, Series, DataFrame,
Panel)
```
A pseudo-prioritized list of annotations I think we need out of this would be the below. Open to suggestions on how to prioritize and obviously community PRs are very welcome!
- [ ] DataFrame
- [ ] Series
- [ ] Index
- [ ] MultiIndex
- [ ] Categorical
- [ ] CategoricalIndex
- [ ] Datetimelike indices
- [ ] Numeric indices
...
These don't necessarily need to be completed in order. Will continue to expand checklist as we tackle more items so if you see something you'd like to tackle feel free to call it out | {
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} | [] | closed | false | null | [] | null | 1 | 2019-06-10T14:02:04Z | 2019-06-10T14:13:09Z | 2019-06-10T14:13:09Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In[89]: df = pd.DataFrame({'a':[11111,22222,3333]})
In[90]: df
Out[90]:
a
0 11111
1 22222
2 3333
In[91]: df.astype('float16')
Out[91]:
a
0 11112.0
1 22224.0
2 3332.0
In[92]: df.astype('float32')
Out[92]:
a
0 11111.0
1 22222.0
2 3333.0
In[93]: df['a'].astype('float16').astype('int16')
Out[93]:
0 11112
1 22224
2 3332
Name: a, dtype: int16
In[106]: df['a'].astype('float16').describe()
Out[106]:
count 3.000000e+00
mean 1.222400e+04
std inf
min 3.332000e+03
25% 7.222000e+03
50% 1.111200e+04
75% 1.666800e+04
max 2.222400e+04
```
#### Problem description
I'm trying to improve performance by changing the dtypes to more efficient ones in my code. I came across this use case which seems very odd to me. I expected `astype('float16')` to fail or turn to `inf` if it didnt manage to change the type, not alter the data.
I also saw, that in the .descibe() method, if a dtype is 'float16' some fields come back as `inf` when I expected it to round it up and not just have `inf` - if the column is `float32` then describe will work, but I dont want to double up on memory just for the describe method to function.
#### Expected Output
In the conversion to dtypes - fail if its impossible to convert or put `inf` in the place of the data that cant be converted.
In the descrbe() function - have the option to round the numbers so you will not get `inf`
#### Bigger picture
this thing came out of me trying to make some optimisation to our memory usage doing something like this:
```
@staticmethod
def optimize_float(series):
low_consumption = series.astype('float16')
if any(low_consumption.isin([np.inf])):
medium_consumption = series.astype('float32')
if any(medium_consumption.isin([np.inf])):
return series.astype('float64')
return medium_consumption
return low_consumption
```
If this is a bad idea, then its my bad - but changing the data still seems weird to me
#### Output of ``pd.show_versions()``
<details>
In[110]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.26.1
numpy: 1.15.4
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml: 4.1.1
bs4: 4.4.1
html5lib: 0.9999999
sqlalchemy: 1.2.12
pymysql: 0.9.2
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: 0.2.1
pandas_gbq: 0.6.1
pandas_datareader: None
</details>
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} | 3 | 2019-06-10T14:33:19Z | 2019-06-11T05:35:28Z | 2019-06-11T00:16:58Z | CONTRIBUTOR | null | - [x] closes #26660
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I am not sure whether I had put my issue in whatsnew entry in appropriate place. | {
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} | 12 | 2019-06-10T17:02:38Z | 2019-06-27T22:05:10Z | 2019-06-27T21:59:49Z | MEMBER | null | Not at all sure this is the optimal way to do this, cc @WillAyd for suggestions | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26770 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26770/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26770/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26770/events | https://github.com/pandas-dev/pandas/pull/26770 | 454,272,825 | MDExOlB1bGxSZXF1ZXN0Mjg2NzU4NjE0 | 26,770 | Fix matplotlib converter registering warning | {
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} | 15 | 2019-06-10T17:11:09Z | 2019-06-21T02:06:57Z | 2019-06-21T02:06:35Z | CONTRIBUTOR | null | Closes #26760
Tests were being skipped, because the module name changed in https://github.com/pandas-dev/pandas/pull/18307/files. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26771 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26771/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26771/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26771/events | https://github.com/pandas-dev/pandas/issues/26771 | 454,290,522 | MDU6SXNzdWU0NTQyOTA1MjI= | 26,771 | HDFStore performance issue of .select_as_multiple | {
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"default"... | open | false | null | [] | null | 0 | 2019-06-10T17:55:03Z | 2020-05-16T20:38:12Z | null | CONTRIBUTOR | null | ```python
def init_and_store(sh_0, sh_1):
'Init DataFrame and store it using HDFStore into 2 parts'
df = pd.DataFrame(np.random.rand(sh_0, sh_1))
cols1 = list(df.columns[:sh_1//2])
cols2 = list(df.columns[sh_1//2:])
with pd.HDFStore('test.h5') as store:
store.put('df1', df[cols1], 't')
store.put('df2', df[cols2], 't')
print ('DataFrame shape is', df.shape)
def get_select():
'Retrieve stored df with .select method'
start = pd.Timestamp.now()
with pd.HDFStore('test.h5') as store:
df1 = store.select('df1')
df2 = store.select('df2')
out = pd.concat([df1, df2], axis=1)
print ('SLCT: Select done in %5.2f seconds. Out shape is'%
(pd.Timestamp.now()-start).total_seconds(), out.shape)
def get_select_as_multiple():
'Retrieve stored df with .select_as_multiple method'
start = pd.Timestamp.now()
with pd.HDFStore('test.h5') as store:
out = store.select_as_multiple(['df1', 'df2'])
print ('MULT: Select done in %5.2f seconds. Out shape is'%
(pd.Timestamp.now()-start).total_seconds(), out.shape)
def testing(sh_0, sh_1):
'Compare time of .select and .select_as_multiple by sh_0, sh_1'
init_and_store(sh_0, sh_1)
get_select()
get_select_as_multiple()
print()
```
Now some tests:
```python
for sh_1 in [4, 8, 24]:
testing(10**6, sh_1)
```
```python
DataFrame shape is (1000000, 4)
SLCT: Select done in 0.07 seconds. Out shape is (1000000, 4)
MULT: Select done in 25.20 seconds. Out shape is (1000000, 4)
DataFrame shape is (1000000, 8)
SLCT: Select done in 0.10 seconds. Out shape is (1000000, 8)
MULT: Select done in 14.81 seconds. Out shape is (1000000, 8)
DataFrame shape is (1000000, 24)
SLCT: Select done in 0.37 seconds. Out shape is (1000000, 24)
MULT: Select done in 6.45 seconds. Out shape is (1000000, 24)
```
So problems:
1. `.select_as_multiple` is _much_ slower comparatively to plain `.select`.
- Expected - at least nearly same performance (because in opposite case one may use only `.select` with some `.concat` overhead).
2. Time of `.select_as_multiple` _decreasing_ in case of increasing number of columns (but still far slower than `.select`).
- Expected is also increasing time of execution, because we increase our data size (up to 6 times).
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 5.0.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.7
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: 0.12.1
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26772 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26772/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26772/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26772/events | https://github.com/pandas-dev/pandas/pull/26772 | 454,320,851 | MDExOlB1bGxSZXF1ZXN0Mjg2Nzk1NzI3 | 26,772 | BENCH: fix noisy asv benchmarks that were running on exhausted generators | {
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} | 3 | 2019-06-10T19:01:43Z | 2019-08-14T07:00:54Z | 2019-06-21T02:03:03Z | CONTRIBUTOR | null | Generators get consumed on first use, yielding abnormally fast benchmark times on the `n>1` iterations. Fortunately we can instruct `asv` to call `setup()` prior to every sample by setting `number = 1` and `repeat` appropriately. My local runs suggest the typical number of samples is `~150-250`, so an upper limit of `250` appears to be a good fit.
Here is current `master` with old benchmark version:
```
[ 75.00%] ··· Running (ctors.SeriesConstructors.time_series_constructor--).
[100.00%] ··· ctors.SeriesConstructors.time_series_constructor 4/40 failed
[100.00%] ··· ===================================== =============== ============= ============== =============
-- with_index / dtype
------------------------------------- ----------------------------------------------------------
data_fmt False / float False / int True / float True / int
===================================== =============== ============= ============== =============
<function gen_of_str> 130±0.9μs 116±6μs failed failed
<function gen_of_tuples> 113±3μs 112±20μs failed failed
===================================== =============== ============= ============== =============
```
And with the fixed benchmarks, we see the existing ones falsely report a `40x` speedup:
```
[ 50.00%] ··· ===================================== =============== ============= ============== ============
-- with_index / dtype
------------------------------------- ---------------------------------------------------------
data_fmt False / float False / int True / float True / int
===================================== =============== ============= ============== ============
<function gen_of_str> 4.61±0.1ms 4.21±0.07ms n/a n/a
<function gen_of_tuples> 3.39±0.1ms 3.43±0.07ms n/a n/a
===================================== =============== ============= ============== ============
```
Additionally, this PR resolves a few failing `asv` tests that I introduced in the first iteration.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2019-06-10T19:11:37Z | 2019-07-17T11:49:32Z | 2019-07-17T11:49:29Z | CONTRIBUTOR | null | The `.T` operator can be quite slow on mixed-type `DataFrame`s due to the creation of `object` dtype columns. In comparison to direct construction with `DataFrame.from_dict()` can generally be much more efficient.
Making that swap inside `pd.read_json()` yields a `~5-6x` speedup for the `orient='index'` case:
```
before after ratio
[d47fc0cb] [b0fd99ec]
<read_json_speedup~1> <read_json_speedup>
- 5.37±0.03s 907±5ms 0.17 io.json.ReadJSON.time_read_json('index', 'int')
- 5.27±0.01s 804±3ms 0.15 io.json.ReadJSON.time_read_json('index', 'datetime')
```
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 3 | 2019-06-10T19:55:36Z | 2019-06-10T19:59:05Z | 2019-06-10T19:57:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
`import pandas as pd
import pandas.io.formats.excel
mypath = <Filepath>
#print(mypath)
from os import listdir
from os.path import isfile, join
#print (listdir(mypath))
textfiles = [ join(mypath,f) for f in listdir(mypath) if '.TXT' in f]
print(textfiles)
for textfile in textfiles:
df = pd.read_csv(textfile,sep='|', dtype=str)
#df.dtypes
#df.ROW = df.ROW.astype(str)
df.io.formats.excel.header_style = None
filename=textfile.split('.')[0]
df.to_excel(filename+'.xlsx', 'Sheet1', index=False) # if your file is tab delimited
`
```
#### Problem description
'df.io.formats.excel.header_style = None' doesn't work any more.
#### Expected Output
How can I clean header format after convert to excel.
#### Output of ``pd.show_versions()``
Currently header is bold and has a border.
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} | 3 | 2019-06-10T22:39:04Z | 2019-06-10T23:32:45Z | 2019-06-10T23:32:39Z | CONTRIBUTOR | null | - [N/A] closes #xxxx
- [none] tests added / passed
- [no (online Github minor change)] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [probably not necessary either?] whatsnew entry
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} | 0 | 2019-06-11T07:56:18Z | 2019-09-18T14:02:12Z | 2019-09-18T14:02:12Z | MEMBER | null | cc @TomAugspurger another one ...
Passing a sparse matrix to the SparseDataFrame constructor can generate a flood of warning message (I think because each column of the sparse matrix is converted to a SparseSeries, triggering the warning each time):
```
In [2]: import scipy.sparse
In [3]: sparse = scipy.sparse.rand(10, 10, 0.5)
In [4]: pd.SparseDataFrame(sparse)
/home/joris/miniconda3/envs/dev37/bin/ipython:1: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
#!/home/joris/miniconda3/envs/dev37/bin/python
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
/home/joris/scipy/pandas/pandas/core/sparse/frame.py:230: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.
>>> series = pd.Series(pd.SparseArray(...))
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
sparse_index=BlockIndex(N, blocs, blens))
Out[4]:
0 1 2 3 4 5 6 7 8 9
0 0.856466 0.482052 0.289636 NaN 0.369485 NaN 0.907167 NaN NaN 0.531253
1 NaN NaN 0.305235 NaN NaN NaN 0.744132 0.001257 NaN 0.406300
2 NaN NaN 0.638244 NaN NaN NaN 0.436574 NaN NaN 0.225573
3 0.881299 NaN 0.645047 0.203569 NaN 0.341134 NaN 0.553526 0.539011 0.682595
4 0.481258 NaN NaN 0.919469 NaN 0.101564 NaN NaN NaN NaN
5 0.244346 0.282246 NaN NaN NaN NaN NaN NaN 0.412830 NaN
6 0.051837 NaN 0.101553 0.512826 0.985031 0.679645 0.248445 NaN 0.081652 NaN
7 0.048519 NaN 0.621497 0.483698 NaN NaN NaN 0.548918 NaN 0.716907
8 NaN 0.801704 NaN NaN NaN 0.611459 0.669670 NaN 0.835271 0.336365
9 0.302106 NaN 0.672587 0.609117 0.570041 0.662911 0.684364 NaN NaN 0.795059
``` | {
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} | 13 | 2019-06-11T08:39:16Z | 2019-06-27T17:24:53Z | 2019-06-27T17:24:53Z | MEMBER | null | Recent change in master:
```
In [11]: index = pd.RangeIndex(0, 10)
In [12]: index.union(pd.Index([0.5, 1.5]))
Out[12]: Index([0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], dtype='object')
```
the dtype should be float.
cc @topper-123 | {
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} | 8 | 2019-06-11T08:56:54Z | 2021-07-28T01:02:24Z | 2021-07-28T01:02:24Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy
In [34]: df = pd.DataFrame(
...: data={
...: 'channel': [1, 2, 3],
...: 'A': ['String 1', numpy.NaN, 'String 2'],
...: 'B': [pd.Timestamp('2019-06-11 11:00:00'), numpy.NaN, pd.Timestamp('2019-06-11 12:00:00')]
...: }
...: )
In [35]: df
Out[35]:
channel A B
0 1 String 1 2019-06-11 11:00:00
1 2 NaN NaT
2 3 String 2 2019-06-11 12:00:00
In [36]: df2 = pd.DataFrame(
...: data={'A': ['String 3'], 'B': [pd.Timestamp('2019-06-11 12:00:00')]}
...: )
In [37]: df2
Out[37]:
A B
0 String 3 2019-06-11 12:00:00
In [38]: df.loc[df['A'].isna() , ['A', 'B']] = df2.values
In [39]: df
Out[39]:
channel A B
0 1 String 1 1560250800000000000
1 2 String 3 2019-06-11 12:00:00
2 3 String 2 1560254400000000000
```
#### Problem description
Hello,
Assigning new values to rows using`.loc` and `.values` change the type of the column (look how column `B`, which was a `datetime64`, is now a mix of integer and datetime)
Note: the same exact code produces the expected output when using pandas 0.22.0, 0.23.4 but broked in pandas 0.24.0 ; also, if I remove the `A` column, the output is correct (no unwanted datetime to integer conversion)
Thanks for your help!
#### Expected Output
```
Out[39]:
channel A B
0 1 String 1 2019-06-11 11:00:00
1 2 String 3 2019-06-11 12:00:00
2 3 String 2 2019-06-11 12:00:00
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 5.1.7-300.fc30.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.1.1
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26783 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26783/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26783/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26783/events | https://github.com/pandas-dev/pandas/issues/26783 | 454,709,989 | MDU6SXNzdWU0NTQ3MDk5ODk= | 26,783 | Explanation of assign by list to filtered DataFrame | {
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] | closed | false | null | [] | null | 1 | 2019-06-11T14:02:25Z | 2019-11-02T21:45:00Z | 2019-11-02T21:44:53Z | NONE | null | Based by [this question](https://stackoverflow.com/q/56544290/2901002):
Why if assign scalar and Series in list, error is raised:
wert = 2.5
df = pd.DataFrame([['Test', 12, None, None], ['Test2', 15, None, None]], columns=['A','B','C','D'])
mask = (df['A'] == 'Test2')
df.loc[mask, ['C', 'D']] = [1, df['B'] * wert]
>ValueError: cannot set using a list-like indexer with a different length than the value
But if filter in both side it working nice?
mask = (df['A'] == 'Test')
df.loc[mask, ['C', 'D']] = [1, df.loc[mask, 'B'] * wert]
print (df)
A B C D
0 Test 12 1 30
1 Test2 15 None None
Error is correct?
Thank you.
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} | 8 | 2019-06-11T14:27:58Z | 2020-12-12T11:27:28Z | 2020-12-12T11:27:27Z | MEMBER | null | master tracker for follow-ups to #26755 and related PRs
- [ ] use pytest.importorskip/not use the class structure https://github.com/pandas-dev/pandas/pull/26755#pullrequestreview-247416090
- [ ] location of fixtures https://github.com/pandas-dev/pandas/pull/26755#pullrequestreview-247404136
- [ ] imports and try/except https://github.com/pandas-dev/pandas/pull/26764#pullrequestreview-247587125
- [ ] xlwt dependency/create a write_ext fixture https://github.com/pandas-dev/pandas/pull/26764#pullrequestreview-247589748
- [x] ~~use of safe_import in pytest.param https://github.com/pandas-dev/pandas/pull/26735#issuecomment-500156276~~ #27139
- [ ] parametrisation of write tests https://github.com/pandas-dev/pandas/pull/26740#pullrequestreview-247380174
- [ ] consider patch filename extension https://github.com/pandas-dev/pandas/pull/26740#pullrequestreview-247382411
- [ ] align the engine / ext usage in the Writer classes https://github.com/pandas-dev/pandas/pull/26662#pullrequestreview-246112315
- [ ] get rid of the None engine tests and just have a separate test that ensures how that gets bound to a particular engine https://github.com/pandas-dev/pandas/pull/26662#discussion_r291258182 (#28749)
- [x] ~~engine and ext injected into tests https://github.com/pandas-dev/pandas/pull/26662#discussion_r291260131~~ #28753
- [ ] add some doc-strings https://github.com/pandas-dev/pandas/pull/26579#pullrequestreview-245987920
- [ ] test_bad_engine_raises shouldn't parametrised https://github.com/pandas-dev/pandas/pull/26543#issuecomment-497126487 (#28749)
- [x] ~~Eliminate _WriterBase class~~ #28753
cc @WillAyd | {
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} | 5 | 2019-06-11T14:38:21Z | 2019-06-11T19:23:30Z | 2019-06-11T19:23:24Z | MEMBER | null | Since numpydoc is released, we can do some cleanup of what was introduced in https://github.com/pandas-dev/pandas/pull/24098
cc @FHaase | {
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} | 5 | 2019-06-11T14:39:57Z | 2019-06-12T09:02:14Z | 2019-06-12T09:02:14Z | MEMBER | null | - [x] xref #24173
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fixing some docs warnings, still many more, but will open few PRs with fixes, try to get a clean build and start failing the CI for doc warnings. | {
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} | 4 | 2019-06-11T15:42:21Z | 2019-06-12T13:59:30Z | 2019-06-11T18:31:24Z | MEMBER | null | this PR removes 2 unnecessary fixtures from `pandas\tests\groupby\conftest.py`
and a few small changes to remove use of `tm.getSeriesData()` with a couple of exceptions:
- conftest files: `pandas\conftest.py` and `pandas\tests\frame\conftest.py` (needed for fixtures)
- `pandas\tests\frame\common.py` (this module will be removed on completion of #22471)
- `pandas\tests\io\json\test_pandas.py` (would benefit from fixturisation and access to other frame fixtures, separate PR and then maybe have a code-checks rule for `getSeriesData` in `test_*.py` files)
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https://api.github.com/repos/pandas-dev/pandas/issues/26788 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26788/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26788/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26788/events | https://github.com/pandas-dev/pandas/issues/26788 | 454,768,276 | MDU6SXNzdWU0NTQ3NjgyNzY= | 26,788 | DOC: Fix doc warning "Code input with no code" | {
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When building the documentation `./doc/make.py html`, among other warnings we find the next:
```
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/IPython/sphinxext/ipython_directive.py:1020: UserWarning: Code input with no code at /home/travis/build/pandas-dev/pandas/doc/source/user_guide/advanced.rst, line 724
warnings.warn(message)
/home/travis/miniconda3/envs/pandas-dev/lib/python3.6/site-packages/IPython/sphinxext/ipython_directive.py:1020: UserWarning: Code input with no code at /home/travis/build/pandas-dev/pandas/doc/source/user_guide/cookbook.rst, line 1300
warnings.warn(message)
```
All code blocks seem to have code, so it's not obvious why we get the issues. Also, the reported lines aren't probably correct. We should research why this is failing, and fix the problem. | {
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} | 1 | 2019-06-11T15:55:31Z | 2019-06-11T18:46:18Z | 2019-06-11T18:46:14Z | MEMBER | null | In the whatsnews page, the example for the new "MultiIndex constructed from levels and codes" is failing:
https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html#multiindex-constructed-from-levels-and-codes
The example was added in #26408, @jiangyue12392 can you have a look please? | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26790 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26790/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26790/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26790/events | https://github.com/pandas-dev/pandas/issues/26790 | 454,776,818 | MDU6SXNzdWU0NTQ3NzY4MTg= | 26,790 | DOC: IPython code blocks without In/Out prompts? | {
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"description": "Requires discussion f... | open | false | null | [] | null | 1 | 2019-06-11T16:02:57Z | 2021-07-07T16:12:43Z | null | MEMBER | null | We currently use the ipython sphinx directive to run the code examples in our rst docs. This generates output like:
```
In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8])
In [4]: s
Out[4]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
```
(first code block from http://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html)
This uses the `In []` and `Out []` that are characteristic for IPython. But, this also generates quite some "visual clutter" (certainly if the number gets bigger > 10 or > 100).
Assume this would be possible\*, what would people think about using plain python-style prompts?
Something like (for the above example)
```
>>> s = pd.Series([1, 3, 5, np.nan, 6, 8])
>>> s
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
```
This has less visual clutter IMO (although many people are probably using ipython, so are familiar to the In/Out prompts)
\* the IPython sphinx directives allows to customize the input and output prompts, but this currently expects that a number is inserted into it. So the above will not work without adapting the ipython_directive. | {
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} | 2 | 2019-06-11T16:26:28Z | 2019-06-14T08:22:40Z | 2019-06-14T08:22:40Z | MEMBER | null | xref #24173
Looks like we don't have `RangeIndex` in the documentation, and this is causing warnings:
```
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.DataFrame.sparse'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.T'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.array'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.base'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.data'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.dtype'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.dtype_str'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.flags'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.hasnans'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.inferred_type'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.is_all_dates'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.is_monotonic'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.is_unique'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.itemsize'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.nbytes'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.ndim'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.shape'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.size'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.start'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.step'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.stop'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.strides'
pandas\doc\source\reference\index.rst:: WARNING: toctree contains reference to nonexisting document 'reference/api/pandas.RangeIndex.values'
```
In https://github.com/pandas-dev/pandas/pull/26786/files I'm adding the class, but still will require the methods.
From similar classes I see that we just add to the autosummary the methods specific to the index classes, and not the ones inherited from the parent `Index`. I guess we want to do the same here, but we need to make sure there are no warnings related to `RangeIndex` in the docs build.
We also want to make sure the docstring of `RangeIndex` is correct, and has the appropriate attributes and methods. | {
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] | closed | false | null | [] | null | 5 | 2019-06-11T17:13:54Z | 2020-09-25T22:43:34Z | 2020-09-25T22:43:34Z | CONTRIBUTOR | null | Part of #26766
<details>
<summary>
- [x] Part 1 #26867
</summary>
- `_constructor`
- `_constructor_expanddim`
- `__init__`
- `axes`
- `shape`
- `_is_homogeneous_type`
</details>
<details>
<summary>
- [x] Part 2 (Rendering Methods) #28453
</summary>
- `_repr_fits_vertical_`
- `_repr_fits_horizontal_`
- `_info_repr`
- `__repr__`
- `_repr_html_`
- `to_string`
</details>
<details>
<summary>
- [ ] Part 3 #28575
</summary>
- `style`
- `items`
- `iteritems`
- `iterrows`
- `itertuples`
- `__len__`
- `dot`
- `__matmul__`
- `__rmatmul__`
</details>
<details>
<summary>
- [ ] Part 4 (IO methods)
</summary>
- `from_dict`
- `to_numpy`
- `to_dict`
- `to_gbq`
- `from_records`
- `to_records`
- `from_items`
- `_from_arrays`
- `to_stata`
- `to_feather`
- `to_parquet`
- `to_html`
</details>
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} | 0 | 2019-06-11T18:14:32Z | 2019-08-20T17:45:06Z | 2019-08-20T17:45:06Z | CONTRIBUTOR | null | Typically we silence these
```python
import pandas as pd
import numpy as np
a = pd.DataFrame({"A": [0, 2], "B": [0, 4]})
b = pd.DataFrame({"A": [0, 2], "C": [0, 4]}, index=[0, 2])
pdf1 = pd.DataFrame({'A': np.arange(10),
'B': [np.nan, 1, 2, 3, 4] * 2,
'C': [np.nan] * 10,
'D': np.arange(10)},
index=list('abcdefghij'), columns=list('ABCD'))
pdf2 = pd.DataFrame(np.random.randn(10, 4),
index=list('abcdefghjk'), columns=list('ABCX'))
pdf1.div(pdf2, fill_value=0)
```
ouput
```
/Users/taugspurger/sandbox/pandas/pandas/core/frame.py:5138: RuntimeWarning: divide by zero encountered in true_divide
return func(left, right)
/Users/taugspurger/sandbox/pandas/pandas/core/frame.py:5138: RuntimeWarning: invalid value encountered in true_divide
return func(left, right)
Out[46]:
A B C D X
a -0.000000 0.000000 -0.0 NaN 0.0
b -1.381770 -0.870061 0.0 inf -0.0
c -7.686931 -5.446084 0.0 inf 0.0
d -5.396100 1.323770 -0.0 inf 0.0
e 4.114989 -4.120187 -0.0 inf 0.0
f -15.748104 0.000000 0.0 inf 0.0
g -7.147032 0.990337 -0.0 inf 0.0
h -3.421108 -3.465087 0.0 inf 0.0
i inf inf NaN inf NaN
j 27.476268 40.169326 -0.0 inf -0.0
k -0.000000 0.000000 0.0 NaN -0.0
``` | {
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} | 3 | 2019-06-11T19:38:13Z | 2019-07-11T15:47:51Z | 2019-06-12T01:42:51Z | CONTRIBUTOR | null | - [x] closes #22268
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/26795 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26795/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26795/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26795/events | https://github.com/pandas-dev/pandas/pull/26795 | 454,891,859 | MDExOlB1bGxSZXF1ZXN0Mjg3MjUyNjMz | 26,795 | DOC: Document existing functionality of pandas.DataFrame.to_sql() #11886 | {
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} | 18 | 2019-06-11T20:36:35Z | 2019-08-31T19:15:42Z | 2019-08-30T17:09:04Z | CONTRIBUTOR | null | - [x] Adds documentation for #11886
- [ ] doc build fails (may be related to #26723)
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] **What's new?** Adds documentation to an existing functionality. In fact, the functionality is documented at `pandas.io.sql.to_sql` but it's not visible [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_sql.html).
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https://api.github.com/repos/pandas-dev/pandas/issues/26796 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26796/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26796/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26796/events | https://github.com/pandas-dev/pandas/issues/26796 | 454,912,920 | MDU6SXNzdWU0NTQ5MTI5MjA= | 26,796 | limit_area and limit_direction do not have an effect when interpolation method is 'pad' | {
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} | 4 | 2019-06-11T21:30:21Z | 2020-12-01T22:05:06Z | 2020-12-01T22:05:06Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: s = pd.Series([
...: np.nan,
...: 1., np.nan,
...: 2., np.nan, np.nan,
...: 5., np.nan, np.nan, np.nan,
...: -1., np.nan, np.nan
...: ])
In [4]: s.interpolate(method='pad', limit_area='inside')
Out[4]:
0 NaN
1 1.0
2 1.0
3 2.0
4 2.0
5 2.0
6 5.0
7 5.0
8 5.0
9 5.0
10 -1.0
11 -1.0
12 -1.0
dtype: float64
# For method='linear' the `limit_area` kwarg works as expected
In [5]: s.interpolate(method='linear', limit_area='inside')
Out[5]:
0 NaN
1 1.0
2 1.5
3 2.0
4 3.0
5 4.0
6 5.0
7 3.5
8 2.0
9 0.5
10 -1.0
11 NaN
12 NaN
dtype: float64
```
#### Problem description
The kwargs `limit_area` and `limit_direction` for `interpolate()`, introduce in https://github.com/pandas-dev/pandas/pull/16513 do not seem to have an effect when using the method `pad`. They work with other interpolation methods, e.g. `linear`.
There are two different pathways for interpolate depending on the selected method.
https://github.com/pandas-dev/pandas/blob/b8ad9da4213be55c587db015c640d927aeb7feaf/pandas/core/internals/blocks.py#L1096-L1116
For pad, ffill and bfill `_interpolate_with_fill()` is used which calls `missing.interpolate_2d` which does not seem to recognize the keywords limit_direction and limit_area. They are silently ignored.
I might be able to fix that during https://github.com/pandas-dev/pandas/pull/25141, but maybe a fix needs more fundamental changes.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: eaacefd0957bfc6735c8782ac2ba571d7131d250
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: de_DE.UTF-8
pandas: 0.25.0.dev0+725.geaacefd09
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.11.1
xarray: 0.11.0
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.16
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/26797 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26797/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26797/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26797/events | https://github.com/pandas-dev/pandas/issues/26797 | 454,919,603 | MDU6SXNzdWU0NTQ5MTk2MDM= | 26,797 | How can i get the mean of a column and create another column with its results? | {
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] | closed | false | null | [] | null | 1 | 2019-06-11T21:50:04Z | 2019-06-12T01:45:11Z | 2019-06-12T01:45:11Z | NONE | null | So im working with a dataframe that contains info about the price at which several houses were sold in Melbourne during a certain time period.
It looks like this:
<img width="517" alt="5" src="https://user-images.githubusercontent.com/51389829/59308652-6de04080-8c56-11e9-88a2-6ac79ae9eaa5.png">
Now, here comes the tricky part. I have dropped all columns in my dataframe except for "Date" and "Price", and i have used the groupby function to group every row according its date.
I want to get the mean price (under the 'Price' column) of all the rows that have the same date value (under the 'Date' column). Then, i want to input those values in a new column (say, 'AvgPrice') so as to create a nice looking scatter plot with that new column and the dates.
So, in other words, i have many 'Date' values, with different 'Price' values, but i would like to get a single 'Date' value for each date in the dataframe, and every single 'Date' value has to have its own corresponding price (which will result from doing the mean).
I hope that was clear enough. I apologize beforehand for my english.
So far, i have tried this code:
df2['AvgP'] = df2.groupby('Date')['Price'].mean()
df2.head()
This is the result i get:
<img width="171" alt="graph" src="https://user-images.githubusercontent.com/51389829/59309123-9fa5d700-8c57-11e9-9b4d-a74ad0da9903.png">
The column is created, but there are only null values in it. Also, the dates dont seem to group correctly.
What am i doing wrong?
Any help will be greatly appreciated! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26798 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26798/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26798/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26798/events | https://github.com/pandas-dev/pandas/issues/26798 | 454,925,725 | MDU6SXNzdWU0NTQ5MjU3MjU= | 26,798 | Error importing name 'DataFrame' from 'pandas.core.api' | {
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] | closed | false | null | [] | null | 5 | 2019-06-11T22:09:47Z | 2019-06-12T12:41:07Z | 2019-06-12T12:41:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas
```
#### Problem description
Whenever I run a script containing pandas, I get the following error:
Traceback (most recent call last):
File "csv.py", line 1, in <module>
import pandas as pd
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\__init__.py", line 42, in <module>
from pandas.core.api import *
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\core\api.py", line 26, in <module>
from pandas.core.groupby import Grouper
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\core\groupby\__init__.py", line 1, in <module>
from pandas.core.groupby.groupby import GroupBy # noqa: F401
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\core\groupby\groupby.py", line 37, in <module>
from pandas.core.frame import DataFrame
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\core\frame.py", line 87, in <module>
from pandas.core.generic import NDFrame, _shared_docs
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\core\generic.py", line 49, in <module>
from pandas.io.formats.format import DataFrameFormatter, format_percentiles
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\io\formats\format.py", line 34, in <module>
from pandas.io.common import _expand_user, _stringify_path
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\io\common.py", line 5, in <module>
import csv
File "C:\Users\conne\Documents\Work\Career & New Jobs\2019\button\csv.py", line 2, in <module>
import pandasql as ps
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandasql\__init__.py", line 1, in <module>
from .sqldf import *
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandasql\sqldf.py", line 3, in <module>
from pandas.io.sql import to_sql, read_sql
File "C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\io\sql.py", line 26, in <module>
from pandas.core.api import DataFrame, Series
ImportError: cannot import name 'DataFrame' from 'pandas.core.api' (C:\Users\conne\Anaconda3\envs\asdf\lib\site-packages\pandas\core\api.py)
I am currently using the Anaconda Python distribution with the most recent version of pandas, but I've also used all combinations of the following:
- Python 3.7.3
- pandas 0.23
- pandas 0.24
Additionally, I've uninstalled and reinstalled pandas (using pip) and Python, and I just finished resetting Windows 10 on my machine, to no avail.
Interestingly, I am able to import pandas in a command line interface with no issue.
**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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} | 7 | 2019-06-12T00:29:02Z | 2019-06-25T18:00:19Z | 2019-06-25T16:11:51Z | MEMBER | null | Parametrization is straightforward.
Fixing xfailed test in test_properties is straightforward
In core.ops: ATM we have a nested closure that wraps all of na_op in a try/except, but there is really only one line we want to catch. This simplifies the closure and catches only the relevant line. | {
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} | 4 | 2019-06-12T02:27:29Z | 2019-06-12T14:08:25Z | 2019-06-12T11:13:53Z | MEMBER | null | @TomAugspurger it looks like _sparse_blockify is never hit. Is that no longer reachable? | {
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} | 2 | 2019-06-12T02:49:32Z | 2019-06-12T14:26:05Z | 2019-06-12T03:35:51Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
So in pandas 0.24.2, this gave you the identical response...
```python
ipomPython 3.7.3 (default, Mar 27 2019, 16:54:48)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.4.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas as pd
In [2]: x = pd.Series(['a', 'b', 'c'], name='foo')
In [3]: pd.get_dummies(x.astype('category'))
Out[3]:
a b c
0 1 0 0
1 0 1 0
2 0 0 1
In [4]: pd.get_dummies(pd.Categorical(x))
Out[4]:
a b c
0 1 0 0
1 0 1 0
2 0 0 1
In [5]: pd.__version__
Out[5]: '0.24.2'
```
In pandas 0.25.0 dev, I see some differences.
```python
Python 3.7.3 (default, Mar 27 2019, 16:54:48)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.5.0 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import pandas as pd
In [2]: x = pd.Series(['a', 'b', 'c'], name='foo')
In [3]: pd.get_dummies(x.astype('category'))
Out[3]:
foo a b c
0 1 0 0
1 0 1 0
2 0 0 1
In [4]: pd.get_dummies(pd.Categorical(x))
Out[4]:
a b c
0 1 0 0
1 0 1 0
2 0 0 1
In [5]: pd.__version__
Out[5]: '0.25.0.dev0+718.gdeffee680'
```
***Notice that the `astype` method now results in the index having a name. That didn't happen before.***
#### Problem description
So in 0.24.2, these 2 ways of converting a series of strings to categoricals to a dummies resulted in the same answer.
It now appears that they are different with one having a named index and the other not.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+718.gdeffee680
pytest: 4.5.0
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.8
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.5.0
sphinx: 2.1.0
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.3
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 11 | 2019-06-12T03:16:04Z | 2019-06-12T18:59:01Z | 2019-06-12T17:56:54Z | CONTRIBUTOR | null | Standardizes how we do our optional dependency checking.
1. Adds a new `import_optional_dependency` helper that handles optional importing, raising with a nice message, and warning / raising if the version is not correct.
2. Changes inline `try / except ImportError` to use `import_optional_dependency`
3. Documents all(?) optional dependencies with their minimum version.
4. Documents that this is how devs should do optional dependencies.
```pytb
>>> pd.read_gbq('')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/taugspurger/sandbox/pandas/pandas/io/gbq.py", line 140, in read_gbq
pandas_gbq = _try_import()
File "/Users/taugspurger/sandbox/pandas/pandas/io/gbq.py", line 14, in _try_import
extra=msg,
File "/Users/taugspurger/sandbox/pandas/pandas/compat/_optional.py", line 82, in import_optional_dependency
raise ImportError(message.format(name=name, extra=extra)) from None
ImportError: Missing optional dependency 'pandas_gbq'. pandas-gbq is required to load data from Google BigQuery. See the docs: https://pandas-gbq.readthedocs.io. Use pip or conda to install pandas_gbq.
``` | {
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} | 3 | 2019-06-12T03:21:48Z | 2019-06-12T03:41:05Z | 2019-06-12T03:25:04Z | NONE | null | ```
>>> import numpy as np
>>> import pandas as pd
>>> x = np.ma.masked_all( (5,2) )
>>> df = pd.DataFrame( x, columns=('a','b'))
>>> print( df.count() )
a 0
b 0
dtype: int64
>>> print( df.sum() )
a 0.0
b 0.0
dtype: float64
```
**when all missing data,
df.count() and df.sum() maybe better to return nan than 0.
just like df.mean.**
```
>>> df.mean()
a NaN
b NaN
dtype: float64
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"description": "Internal... | open | false | null | [] | null | 9 | 2019-06-12T08:56:22Z | 2021-07-10T02:39:11Z | null | MEMBER | null | (following the discussion in #26710)
Currently, most of the `io` functionality (csv, json, html, pickle, stata...) lives in `pandas/io`. But the `to_*` functions are implemented in `pandas/core/[generic|frame|series].py`. The tests live in `pandas/tests/io`. And their dependencies, are not explicit anywhere afaik, they are imported lazily so the needed libraries are reported once the function is used (some are listed in `environment.yml` but not all).
I propose to move every io type to a directory in `pandas/contrib/io` that will include:
- The `read_*` and `to_*` functions
- The docstrings with the documentation
- The tests
- A file with the dependencies
So, a example structure could be:
```
pandas/contrib/io/stata/__init__.py
pandas/contrib/io/stata/reader.py
pandas/contrib/io/stata/writer.py
pandas/contrib/io/stata/tests/*
pandas/contrib/io/stata/dependencies.yml
```
To call the functionality we could simply have something like this in `Series`, `DataFrame` (also something similar for `read_*`), but other ideas welcome:
```python
def __getattr__(self, name):
if name.startswith('to_'):
mod = importlib.import_module('pandas.contrib.io.{}'.format(name[3:]):
return mod.export_dataframe()
```
I see several advantages here:
- A clearer (more modular and more uniform) structure of the code (`generic.py` has more than 11k lines of code, a significant part are io related, same for `frame.py` with 8k...)
- We can better manage the dependencies (they would be explicit, and we don't need to have lazy dependencies if everything is imported in a lazy way). Around two thirds of our optional dependencies are for IO modules for what I've seen.
- In an easy way we can explicitly decide in every build which io modules we want to test, and avoid having all the `skip_if_no` that cause problems and tests stop being run without noticing
- Third party packages can be developed following a similar structure, so we can potentially add them to pandas if new formats become popular, or we can easily move to a third party project io packages that we consider they're not worth maintaining ourselves anymore.
Not part of this proposal, but I think in the future we could also move to `contrib` other parts that are not IO but could also benefit from being decoupled, like plotting or the extension arrays (that's why I think `contrib/io/` makes sense, so we can have `contrib/plotting`... in the future).
CC: @pandas-dev/pandas-core | {
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"description": "Duplicate issue... | closed | false | null | [] | null | 1 | 2019-06-12T09:09:48Z | 2019-06-12T18:23:21Z | 2019-06-12T12:40:35Z | NONE | null | According to the df.groupby docstring :
```
group_keys : bool, default True
When calling apply, add group keys to index to identify pieces.
```
But it seems to work for some cases and not others:
```
df = pd.DataFrame({'key': [1, 1, 1, 2, 2, 2, 3, 3, 3],
'value': range(9)})
df.groupby('key', group_keys=True).apply(lambda x: x.key) # index by groups
df.groupby('key', group_keys=True).apply(pd.np.sum) # index by groups
df.groupby('key', group_keys=True).apply(lambda x: x[:].key) # index by groups
df.groupby('key', group_keys=True).apply(lambda x:x-x.mean()) # does nothing
df.groupby('key', group_keys=True).apply(lambda x:x) # does nothing
```
For example, the following gives the same output regardles of the `group_keys` value
```
import pandas as pd
df=pd.DataFrame(dict(price=[10,10,20,20,30,30],color=[10,10,20,20,30,30],cost=(100,200,300,400,500,600)))
df.groupby(['price'],group_keys=False).apply(lambda x:x)
# result
price color cost
0 13 11 101
1 11 11 201
2 22 21 301
3 21 21 401
4 32 31 501
5 31 31 601
df.groupby(['price'],group_keys=True).apply(lambda x:x)
# same result
price color cost
0 13 11 101
1 11 11 201
2 22 21 301
3 21 21 401
4 32 31 501
5 31 31 601
```
xref #22545 for related groupby confusion.
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] | closed | false | null | [] | null | 2 | 2019-06-12T09:14:02Z | 2019-06-12T19:23:39Z | 2019-06-12T19:23:39Z | NONE | null | from docstring:
```
as_index : bool, default True
For aggregated output, return object with group labels as the
index. Only relevant for DataFrame input. as_index=False is
effectively "SQL-style" grouped output.
```
```python
In [26]: df=pd.DataFrame(dict(price=[10,10,20,20,30,30],color=[10,10,20,20,30,30],cost=(100,200,300,400,500,600)))
...: # makes a difference - result index is a multindex of groups
...: print(df.groupby(['price','color'],as_index=True).agg('sum'))
...: print(df.groupby(['price','color'],as_index=False).agg('sum'))
10 10 300
20 20 700
30 30 1100
price color cost
0 10 10 300
1 20 20 700
2 30 30 1100
In [30]:# does not makes a difference - result index is just a default RangeIndex
...: print(df.groupby(['price','color'],as_index=True).transform('sum'))
...: print(df.groupby(['price','color'],as_index=False).transform('sum'))
cost
0 300
1 300
2 700
3 700
4 1100
5 1100
cost
0 300
1 300
2 700
3 700
4 1100
5 1100
``` | {
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} | 7 | 2019-06-12T10:41:44Z | 2021-07-10T02:40:17Z | 2021-07-10T02:40:17Z | MEMBER | null | - [ ] pandas/tests/tseries/offsets/test_offsets.py #27085
- [ ] pandas/tests/test_resample.py #17806 (has now been split, some post split cleanup required)
- [ ] pandas/tests/io/test_pytables.py #18498
- [x] pandas/tests/test_window.py #19228
- [ ] pandas/tests/test_base.py #23877
- [x] pandas/tests/frame/test_indexing.py #25798
PRs welcome! | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26808 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26808/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26808/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26808/events | https://github.com/pandas-dev/pandas/issues/26808 | 455,150,612 | MDU6SXNzdWU0NTUxNTA2MTI= | 26,808 | Unable to set origin of inter-day aggregated intervals | {
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"description": "resample... | closed | false | null | [] | null | 4 | 2019-06-12T11:07:40Z | 2021-07-10T04:18:47Z | 2021-07-10T04:18:47Z | CONTRIBUTOR | null | Having a series like the one bellow, which I resample to `'2D'` frequency and the sum the resulting resampler:
```python
>>> dates = pandas.date_range('2019-01-01', '2019-01-10', freq='D')[[0, 4, 5, 8]]
>>> dates
DatetimeIndex(['2019-01-01', '2019-01-05', '2019-01-06', '2019-01-09'], dtype='datetime64[ns]', freq=None)
>>> pandas.Series(index=dates, data=[0, 1, 2, 3]).resample('2D').sum()
2019-01-01 0
2019-01-03 0
2019-01-05 3
2019-01-07 0
2019-01-09 3
Freq: 2D, dtype: int64
```
As a result, I got an index starting on the first date of the provided dates.
However, if I slightly change the dates so that the first one is `'2019-01-02'` instead of `'2019-01-01'`, I get completely different results:
```python
>>> dates2 = pandas.date_range('2019-01-01', '2019-01-10', freq='D')[[1, 4, 5, 8]]
>>> dates2
DatetimeIndex(['2019-01-02', '2019-01-05', '2019-01-06', '2019-01-09'], dtype='datetime64[ns]', freq=None)
>>> pandas.Series(index=dates2, data=[0, 1, 2, 3]).resample('2D').sum()
2019-01-02 0
2019-01-04 1
2019-01-06 2
2019-01-08 3
Freq: 2D, dtype: int64
```
There is no way to specify a base/origin date for the aggregated intervals like there is for intra-day resampling, right?
Use case
------------
Having multiple time series, not necessarily periodic, not necessarily starting/ending in the same dates, I would like to calculate the correlation between them. To do so, I first need to aggregate the results with a period between 1 day and 1 week (`'2D'`, `'3D'`, `'4D'`...). In order to be able to match the correlation I need indexes to be aligned with respect to a common start date.
Same problem would show up if I wanted to aggregate multiple weeks, months, years... (`'3W'`, `'5M'`, `'2Y'`...) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26809 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26809/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26809/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26809/events | https://github.com/pandas-dev/pandas/issues/26809 | 455,152,554 | MDU6SXNzdWU0NTUxNTI1NTQ= | 26,809 | Enhancement: Subset selection similar to R dplyr filter | {
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] | closed | false | null | [] | null | 5 | 2019-06-12T11:12:18Z | 2019-06-12T16:39:14Z | 2019-06-12T16:28:11Z | NONE | null | Edited: Fixed typo
This is a feature request. I start by showing how I usually work and then suggest a new method `NDFrame.subset` including an implementation sketch.
I have also written a longer explanation and some examples [here (personal blog)](http://rasmuse.github.io/pandas-subset.html) and [here (jupyter notebook)](https://github.com/rasmuse/pandas-subset/blob/ddd6e54bdb8b3bdbc820e8bc48bb792b474a3629/A%20better%20way%20to%20select%20subsets%20in%20pandas.ipynb).
Happy to discuss, and if there is interest in this I could probably provide a PR.
#### Code Sample
To filter row subsets of DataFrames or Series based on the values I usually write something like
```python
data_subset = (
data
.pipe(lambda d: d[d['some_column'] > 0])
.pipe(lambda d: d[complicated_predicate(d)])
# etc, chaining operations as necessary
)
```
#### Problem description
This works perfectly well but the syntax seems unneccesarily complicated given how often I do this operation. For comparison, using `filter()` in R's `dplyr` package you would write
```R
data.subset <- data %>%
filter(some.column > 0) %>%
filter(complicated.predicate(.)) %>%
#etc
```
To my eyes (although I don't use R much), the R code makes the intention more visible because
1. the R code has less brackets etc, and
2. the verb `filter` is much more specific than `pipe`.
#### Suggestion
I would suggest adding a method `subset` to `NDFrame`. A minimal implementation could be something like this:
```python
class NDFrame:
...
def subset(d, predicate, *args, **kwargs):
return d[predicate(d, *args, **kwargs)]
```
This could be used as follows:
```python
data_subset = (
data
.subset(lambda d: d['some_column'] > 0)
.subset(complicated_predicate)
# etc, chaining operations as necessary
)
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/26810 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/26810/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/26810/comments | https://api.github.com/repos/pandas-dev/pandas/issues/26810/events | https://github.com/pandas-dev/pandas/pull/26810 | 455,206,456 | MDExOlB1bGxSZXF1ZXN0Mjg3NTAxOTMy | 26,810 | DOC: Fixing some more warnings | {
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} | 15 | 2019-06-12T13:13:20Z | 2019-06-14T06:48:32Z | 2019-06-14T06:45:13Z | MEMBER | null | - [X] closes #26788, xref #24173
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Fixing some more warnings in the doc build, and small improvements to the interpolation doc.
CC: @jorisvandenbossche | {
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} | 2 | 2019-06-12T13:50:30Z | 2019-06-12T14:57:01Z | 2019-06-12T14:56:56Z | CONTRIBUTOR | null | ``compat.to_str`` is useful to have in cases where an object might be a ``bytes`` object, so it's moved to ``core.dtypes.common`` and renamed to ``ensure_str``.
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} | [] | closed | false | null | [] | null | 5 | 2019-06-12T15:51:29Z | 2019-06-12T16:22:27Z | 2019-06-12T16:22:26Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import numpy as np
...: import pandas as pd
...:
...: mat = np.random.random(size = (100,100))
...: arr = np.random.random(size=100)
...:
...: df = pd.DataFrame(mat)
...: ser = pd.Series(arr)
...:
...: print((mat @ arr)[:5])
...: print((mat @ ser)[:5])
...: print((df @ ser).values[:5])
...:
...: print(np.__version__)
...: print(pd.__version__)
[25.58954419 27.14049632 29.35058172 25.60100699 28.81915819]
[25.23415259 27.21156925 25.54180341 24.80747474 27.19878459] # !!!!!!!
[25.58954419 27.14049632 29.35058172 25.60100699 28.81915819]
1.16.4
0.23.4
```
#### Problem description
I'm not sure why this happens, but if you multiply an nd.array by a pd.Series, you get the wrong result returned in pandas `0.23.0 - 0.23.4`. It fails silently, which is *bad*. Even throwing a warning when this happens for `0.23` would be useful, because it takes some packages a while to get up to date with the major API changes in 0.24.
The problem no longer occurs with an upgrade to 0.24
I have tried this with numpy versions as far back at `1.15.0` and all fail in this manner. I have reproduced on Linux and MacOS machines.
Although this is fixed in an update, the silent return of a numerically incorrect result feels like it should be addressed.
#### Expected Output
The correct result.
#### Output of ``pd.show_versions()``
This is for the Macbook Pro, although error still occurs on Linux.
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.2
numpy: 1.15.0
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.8.5
patsy: None
dateutil: 2.6.0
pytz: 2018.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.4
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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