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DOC: Partial fix SA04 errors in docstrings #28792
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- [x] xref #28792
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MDExOlB1bGxSZXF1ZXN0Mzk1MjU1NzIw
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BUG: conversion of empty DataFrame to SparseDtype (#33113)
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| null |
- [x] closes #33113
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzk1MjU2NTYw
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|
Fix ambiguous reference to "previous" section
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|
CONTRIBUTOR
| null |
This section originally came just after "10 minutes to pandas",
but now it's after the "Getting started tutorials".
Also, the examples have diverged a bit, so let's not pretend
that we are creating the same objects.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1ODk4MDIxMzc=
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|
.gt() add support for single column dataframes
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| 2020-03-29T14:16:45Z
| 2020-08-17T22:03:33Z
| 2020-08-17T22:03:33Z
|
NONE
| null |
It would be great if when comparing df to series like in pandas .gt() or .lt() one could compare also to single column dataframes instead of series.
Maybe also extend it to other functions?
Thanks
This works:
```
df1 = pd.DataFrame(np.array([[2,2],[1,1], [0,0]]), columns = ['a1','a2'])
df2 = pd.Series([2,2,2], name = 'b')
df1.lt(df2, axis=0)
```
This doesnt
```
df1 = pd.DataFrame(np.array([[2,2],[1,1], [0,0]]), columns = ['a1','a2'])
df2 = pd.DataFrame([2,2,2], columns = ['b'])
df1.lt(df2, axis=0)
```
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DOC: Updating capitalization of doc/source/development
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| 6
| 2020-03-29T15:50:25Z
| 2020-04-17T11:08:20Z
| 2020-04-03T00:12:37Z
|
CONTRIBUTOR
| null |
Regarding issue #32550. Changes to documentation folder doc/source/development to capitalise title strings and keep keyword exceptions as is
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CLN: Remove unused cdef variables
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| 2
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| 2020-03-29T18:06:32Z
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MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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MDExOlB1bGxSZXF1ZXN0Mzk1MjkyNTE0
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REF: remove placement kwarg from Block.concat_same_type
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MEMBER
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On the path to getting concat logic out of internals
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REF: DataFrame.isna internals access
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MDExOlB1bGxSZXF1ZXN0Mzk1MzAwNzM5
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REF: reshape.concat operate on arrays, not SingleBlockManagers
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| 3
| 2020-03-29T18:57:11Z
| 2020-04-10T17:49:18Z
| 2020-04-10T16:10:01Z
|
MEMBER
| null |
cc @TomAugspurger is there a better way to handle the assert_series_equal patch for the PandasArray tests?
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CLN: Added static types
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| 3
| 2020-03-29T19:33:37Z
| 2020-04-06T08:41:06Z
| 2020-04-03T19:41:19Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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|
DataFrame / Series reductions return NaN rather than NA when below min_count
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| 2020-03-29T19:38:47Z
| 2020-03-30T12:33:24Z
| 2020-03-30T12:33:24Z
|
MEMBER
| null |
1.0.3 or master:
```python
import numpy as np
import pandas as pd
df = pd.DataFrame({"a": [1.0, np.nan]})
print(df.sum(min_count=2))
# a NaN
# dtype: float64
print(df["a"].sum(min_count=2))
# nan
```
but the documentation says we should get `NA` when we're below `min_count`. This kind of gets to the question of whether this is a "missing value" or an "undefined operation," if this actually matters, as well as the actual meaning of `NA`.
In the new masked reductions under `/core/array_algos/masked_reductions.py` we're (correctly?) outputting `NA`.
cc @jorisvandenbossche
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|
Explain how and when to use indirect fixtures
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CONTRIBUTOR
| null |
As mentioned in https://github.com/pandas-dev/pandas/pull/32963#issuecomment-605675606, it would be worthwhile to document this. I'm now sure if we already have consensus on that though. Would be good to make a proposal in a PR and then have a discussion on it.
|
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Feature request: anchoring suffix for hours
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| 2020-03-29T20:33:53Z
| 2020-05-16T16:50:08Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# It is possible to use anchor suffix for weeks
off1 = pd.tseries.frequencies.to_offset('1W-SUN')
# The anchor does effect when using period_range() or creating a period
ts_start = pd.Timestamp('2020-03-25 10:00') # ts_start is a Wednesday
p1 = pd.Period(ts_start, freq=off1)
# Anchor is working: p1 with weekly frequency starts a Monday
p1.start_time
>>> Out: Timestamp('2020-03-23 00:00:00')
# But it doesn't work with '4H' frequency
off2 = pd.tseries.frequencies.to_offset('4H')
# No anchor (expecting one with reference being midnight)
off2.is_anchored()
>>> False
p2 = pd.Period(ts_start, freq=off2)
# Expecting '2020-03-25 08:00:00'
p2.start_time
>>> Timestamp('2020-03-25 10:00:00')
# Trying syntax similar to weekly anchoring
off3 = pd.tseries.frequencies.to_offset('4H-0')
>>> ValueError: Invalid frequency: 4H-0
```
#### Problem description
Please, can anchoring suffix be made available for hours, using midnight as the reference point?
Thanks for your feebdack,
Have a good day,
Bests,
|
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build_table_schema references non-existent _as_json_table_type
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| 2020-03-29T20:39:53Z
| 2020-04-29T22:49:08Z
| 2020-04-29T22:49:08Z
|
NONE
| null |
#### Problem description
Documentation issue: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.json.build_table_schema.html
(and master branch contains this as well)
Description contains:
```
See _as_json_table_type for conversion types.
```
Which I cannot find anywhere else in the documentation
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BUG: Copying PeriodIndex levels on MultiIndex loses weakrefs
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| 2020-03-29T22:19:14Z
| 2020-03-31T17:10:07Z
| 2020-03-31T17:10:07Z
|
CONTRIBUTOR
| null |
As per [comment](https://github.com/pandas-dev/pandas/pull/32669#issuecomment-605525216) by @jacobaustin123:
```python
import pandas as pd
idx = pd.MultiIndex.from_arrays([pd.PeriodIndex([pd.Period("2019Q1"), pd.Period("2019Q2")], name='b')])
idx2 = pd.MultiIndex.from_arrays([idx._get_level_values(level) for level in range(idx.nlevels)])
all(x.is_monotonic for x in idx2.levels) # raises an error
```
#### Problem description
The weakly referenced PeriodIndex er dropped before intended, so the ``PeriodEngine`` gets a ``None`` instead of the PeriodIndex.
#### Expected Output
The above should return True.
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MDU6SXNzdWU1ODk4OTkwNjE=
| 33,132
|
BUG: wrong df.groupby().groups when grouping with [Grouper(freq=), ...]
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[] | null | 5
| 2020-03-29T22:19:19Z
| 2020-11-09T16:42:02Z
| null |
NONE
| null |
#### Code
```python
import pandas as pd
from datetime import datetime
mi = pd.MultiIndex.from_product([pd.date_range(datetime.today(), periods=2),
["C", "D"]], names=["alpha", "beta"])
df = pd.DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi)
result = df.groupby([pd.Grouper(level="alpha", freq='D'), "beta"])
print(len(result), result.ngroups)
# 2 4
print(result.groups)
# {(Timestamp('2020-04-05 00:00:00'), 'C'): MultiIndex([('2020-04-05 15:04:51.580573', 'C')], names=['alpha', 'beta']),
# (Timestamp('2020-04-06 00:00:00'), 'D'): MultiIndex([('2020-04-05 15:04:51.580573', 'D')], names=['alpha', 'beta'])}
```
#### Problem description
This issue is an extension of the bug reported in #26326. The PR #26374 resolved the bug for the case of when we have a nested `BaseGrouper`. Nonetheless, having a nested `BinGrouper` still results in wrong behavior, as can be checked by the above code.
Note that `len(result)` is based on `len(result.groups)`, and that `result.groups` should return the following:
```python
# {(Timestamp('2020-04-05 00:00:00'), 'C'): MultiIndex([('2020-04-05 15:04:51.580573', 'C')], names=['alpha', 'beta']),
# (Timestamp('2020-04-05 00:00:00'), 'D'): MultiIndex([('2020-04-05 15:04:51.580573', 'D')], names=['alpha', 'beta']),
# (Timestamp('2020-04-06 00:00:00'), 'C'): MultiIndex([('2020-04-05 15:04:51.580573', 'C')], names=['alpha', 'beta']),
# (Timestamp('2020-04-06 00:00:00'), 'D'): MultiIndex([('2020-04-05 15:04:51.580573', 'D')], names=['alpha', 'beta'])}
```
<details>
```
INSTALLED VERSIONS
------------------
commit : 7673357191709036faad361cbb5f31a802703249
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.8-1-MANJARO
Version : #1 SMP PREEMPT Thu Mar 5 20:29:51 UTC 2020
machine : x86_64
processor :
byteorder : little
LC_ALL : C.UTF-8
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1027.g767335719.dirty
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.6.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.15.0
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0
```
</details>
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BUG: to_datetime with infer_datetime_format dropped timezone names
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| 2
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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CONTRIBUTOR
| null |
- [x] closes #33131
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Closes #33131, a weakref was released too early.
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BUG: fix ngroups and len(groups) inconsistency when using [Grouper(freq=)] (GH33132)
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SeriesGroupBy.quantile doesn't work for nullable integers
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| 2020-04-07T23:10:11Z
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MEMBER
| null |
```python
import pandas as pd
df = pd.DataFrame(
{"a": ["x", "x", "y", "y"], "b": pd.array([1, 2, 3, 4], dtype="Int64")}
)
df.groupby("a")["b"].quantile(0.5)
```
raises
```python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-1-ef27f953b945> in <module>
4 {"a": ["x", "x", "y", "y"], "b": pd.array([1, 2, 3, 4], dtype="Int64")}
5 )
----> 6 df.groupby("a")["b"].quantile(0.5)
~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1911 post_processing=post_processor,
1912 q=q,
-> 1913 interpolation=interpolation,
1914 )
1915 else:
~/opt/miniconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, cython_dtype, aggregate, needs_values, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
2289 func = partial(func, ngroups)
2290
-> 2291 func(**kwargs) # Call func to modify indexer values in place
2292
2293 if result_is_index:
pandas/_libs/groupby.pyx in pandas._libs.groupby.__pyx_fused_cpdef()
TypeError: No matching signature found
```
cc @ghuname xref https://github.com/pandas-dev/pandas/issues/33071
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BUG: DataFrame.resample raised AmbiguousTimeError at a midnight DST transition
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BUG: Fix SeriesGroupBy.quantile for nullable integers
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REF: de-curry unstack functions
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| 1
| 2020-03-30T01:39:12Z
| 2020-03-30T21:56:02Z
| 2020-03-30T21:09:52Z
|
MEMBER
| null |
I find this much easier to follow without the double-partial. We also avoid re-doing some work, though AFAICT its pretty tiny perf-wise.
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MDExOlB1bGxSZXF1ZXN0Mzk1MzY4NjY1
| 33,140
|
BUG: to_sql no longer raises an AttributeError when saving an OBB date
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MEMBER
| null |
- [x] closes #26761
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWU1OTAyODM2MTc=
| 33,141
|
Pandas cut method gives an error if labels are non-unique
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| 8
| 2020-03-30T13:19:13Z
| 2020-05-01T00:31:48Z
| 2020-05-01T00:31:48Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
data = pd.DataFrame({'morf':[8142, 8153, 8161]})
bins = [8140, 8150, 8160, 8163]
labels = ['Adenocarcinomas', 'Other specific carcinomas', 'Adenocarcinomas']
data['group'] = pd.cut(data['morf'], bins, labels=labels)
```
#### Problem description
I want to use pandas cut to fill in labels for ranges that are scattered. E.g. I want to map the following table onto my data, where the definitions span ranges that are scattered across the morphology continuum:
The above code gives the error:
ValueError: Categorical categories must be unique
#### Expected Output
I expect the example above to run and give back the labeled dataframe without an error.
My workaround is to add the index to the labels and then remove that again after the cut.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200209
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk1NzA3MTI5
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|
CLN: unused import _libs/reduction + remove "noqa" comment in _libs/__init__.py
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MEMBER
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- [ ] closes #xxxx
- [ ] tests added / passed
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STY: Using __all__; removed "noqa" comment
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- [ ] closes #xxxx
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MDExOlB1bGxSZXF1ZXN0Mzk1NzQwMzA0
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add match message for pytest.raises()
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| null |
- [x] ref #30999
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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CLN: Use doc decorator for case using __doc__
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CONTRIBUTOR
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- [ ] works for #31942
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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MDU6SXNzdWU1OTA0MTgyNzk=
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Error in slicing by datetime January 1, 2020
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| 2021-01-05T02:27:21Z
| 2021-01-05T02:27:21Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
dt = {'No': {pd.Timestamp('2020-01-01 00:00:00'): 123,
pd.Timestamp('1900-01-01 00:00:00'): 345,
pd.Timestamp('2017-04-18 00:00:00'): 946,
pd.Timestamp('2020-01-02 00:00:00'): 940 },
'CB': {pd.Timestamp('2020-01-01 00:00:00'): 'Sc',
pd.Timestamp('1900-01-01 00:00:00'): 'Dr',
pd.Timestamp('2017-04-18 00:00:00'): 'St',
pd.Timestamp('2020-01-02 00:00:00'): 'Sc'}}
df = pd.DataFrame(dt)
# **only January 1 does not work properly**
print(df['2019-12-31':])
print(df['2020-01-01':])
print(df['2020-01-02':])
# **All work properly**
print(df[df.index >='2019-12-31'])
print(df[df.index >='2020-01-01'])
print(df[df.index >='2020-01-02'])
```
#### Problem description
Slicing by datetime index on January 1, 2020 returns the whole DataFrame
**print(df['2020-01-01':])**
Shows the whole DataFrame
#### Expected Output
# only January 1 does not work properly
**print(df['2020-01-01':])**
incorrectly shows the whole DataFrame
No CB
2020-01-01 123 Sc
1900-01-01 345 Dr
2017-04-18 946 St
2020-01-02 946 Sc
**print(df['2020-01-02':])**
No CB
2020-01-02 946 Sc
# All work properly
**print(df[df.index >='2019-12-31'])**
No CB
2020-01-01 123 Sc
2020-01-02 946 Sc
**print(df[df.index >='2020-01-01'])**
#works properly
No CB
2020-01-01 123 Sc
2020-01-02 946 Sc
**print(df[df.index >='2020-01-02'])**
No CB
2020-01-01 123 Sc
2020-01-02 946 Sc
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.2.final.0
python-bits : 32
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.2
pytz : 2018.9
dateutil : 2.8.0
pip : 20.0.2
setuptools : 40.6.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 3.0.2
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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DOC: Fix capitalization among headings in documentation files (#32550)
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CONTRIBUTOR
| null |
Headers updated for the following files:
```
- [x] doc/source/user_guide/categorical.rst
- [x] doc/source/user_guide/computation.rst
- [x] doc/source/user_guide/cookbook.rst
- [x] doc/source/user_guide/gotchas.rst
- [x] doc/source/user_guide/groupby.rst
```
Other files updated in #32944, #32978 , #32991 and #33149
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More Json parametrize
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MEMBER
| null |
follow on to #31191 - still a few more passes to go
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DOC: Fix capitalization among headings in documentation files (#32550)
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CONTRIBUTOR
| null |
Headers updated for the following files:
```
- [x] doc/source/user_guide/options.rst
- [x] doc/source/user_guide/reshaping.rst
- [x] doc/source/user_guide/text.rst
- [x] doc/source/user_guide/timeseries.rst
- [x] doc/source/user_guide/visualization.rst
```
Other files updated in #32944, #32978, #32991 and #33147
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REF: misplaced DataFrame arithmetic tests
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API: require scalar result from the scalar .at indexer
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[] | null | 21
| 2020-03-30T19:21:42Z
| 2021-07-31T01:53:25Z
| null |
MEMBER
| null |
The `.at` indexer is documented as the fast, restricted version of `loc` to "access a scalar value" (https://pandas.pydata.org/docs/user_guide/indexing.html#fast-scalar-value-getting-and-setting).
However, currently, there is not always a guarantee that the result will actually be a scalar. For example, with a Series with duplicate index values:
```
In [3]: s = pd.Series([1, 2, 3], index=['a', 'b', 'a'])
In [4]: s.at['a']
Out[4]:
a 1
a 3
dtype: int64
```
There are some other possible cases that could give a non-scalar value as well:
- MultiIndex if not all levels are indexed (#26989)
- DataFrame with duplicate index labels in index and/or columns (#33041)
However, those cases currently all fail (error or produce wrong result, see below for examples).
So a question that can be posed: **should we consider those cases as bugs, or should we rather actually require a scalar result** and thus see the series behaviour above as too liberal?
Since those linked issues have open PRs to "fix" them, I think we should first decide on what guarantees on the `.at` API we want to provide. And personally, I think taking the documented intention of accessing a scalar would be a plus (it gives a certainty about the type of the result, i.e. always a scalar, and not sometimes a series or dataframe depening on the value of the label passed to `at`).
If we prefer the more strict scalar result requirement, we can deprecate the Series case (but only if accessing a duplicated label) and provide better error messages for the error cases.
---
Examples of the failing behaviours (on pandas 1.0.1):
```python
# duplicated row label -> you get a numpy array (which I would say is always wrong)
>>> df = pd.DataFrame(np.random.randn(3, 2), index=['a', 'b', 'a'], columns=['A', 'B'])
>>> df.at['a', 'A']
array([-0.02914828, 0.2856617 ])
# duplicated column label -> error
>>> df = pd.DataFrame(np.random.randn(3, 2), index=['a', 'b', 'c'], columns=['A', 'A'])
>>> df.at['a', 'A']
...
AttributeError: 'BlockManager' object has no attribute 'T'
# Selecting one level of a MultiIndex -> error
>>> s = pd.Series(np.random.randn(4), index=pd.MultiIndex.from_product([['A', 'B'], [1, 2]]))
>>> s.at['A']
...
KeyError: 'A'
During handling of the above exception, another exception occurred:
...
AttributeError: 'numpy.ndarray' object has no attribute '_values'
# selecting all MultiIndex levels -> this should actually work (#26989)
>>> s.at['A', 1]
...
TypeError: _get_value() got multiple values for argument 'takeable'
# DataFrame with all MultiIndex levels specified works OK
>>>s.to_frame('col').at[('A', 1), 'col']
1.341421652269149
```
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REF: PeriodIndex test_indexing tests
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| 1
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MEMBER
| null |
This should be it for the PeriodIndex tests for now.
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Feature request: first & last for rolling()
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| 2020-03-30T22:10:48Z
| 2020-04-01T23:46:35Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# This works:
df1 = df.resample(to_freq,
closed='left',
kind='period',
).agg(OrderedDict([('Open', 'first'),
('Close', 'last'),
]))
# This doesn't:
df2 = df.rolling(my_indexer).agg(
OrderedDict([('Open', 'first'),
('Close', 'last') ]))
>>> AttributeError: 'first' is not a valid function for 'Rolling' object
df3 = df.rolling(my_indexer).agg(
OrderedDict([
('Close', 'last') ]))
>>> AttributeError: 'last' is not a valid function for 'Rolling' object
```
Please, could 'first' and 'last' be made available for rolling window?
Thanks for your support!
Bests,
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Backport PR #33102 on branch 1.0.x (PERF: fix performance regression in memory_usage(deep=True) for object dtype)
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NONE
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Backport PR #33102: PERF: fix performance regression in memory_usage(deep=True) for object dtype
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BUG: isna_old with td64, dt64tz, period
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- [ ] closes #xxxx
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Started as a CLN branch and i left that in for now, can separate if desired. the bugfix is on L287.
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CLN: use ._data less in reshape
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MDU6SXNzdWU1OTA3MjY2MTQ=
| 33,161
|
Use empty_frame fixture
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| 3
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| 2020-05-22T03:45:14Z
| null |
MEMBER
| null |
#33148 introduced an empty_frame fixture in the top level conftest. At the same time, we have a lot of `... = DataFrame()` assignments in our tests. We should standardize to use the fixture where appropriate
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|
create rolling values from function that returns a numpy value
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| 2020-03-31T02:12:31Z
| 2020-03-31T03:13:39Z
| 2020-03-31T03:13:32Z
|
NONE
| null |
Hi all,
I am trying to create rolling values from a function that returns a numpy value, to get an idea of how stable to value is over time
see my below attempt
' ' 'python
array([-9.51263882e-03, -2.81717483e-02, 9.43949087e-05, ...,
-9.07504803e-03, -4.77400512e-03, 1.51740085e-03])
[def dist_range(x, y):
return (np.max(np.abs(x - y), axis=1) - np.min(np.abs(x - y), axis=1)) / (np.max(np.abs(x - y), axis=1) + np.min(np.abs(x - y), axis=1))
##### RangeEn-B (mSampEn)
def RangeEn_B(x, emb_dim=2, tolerance=.1, dist=dist_range):
n = np.shape(x)
n = np.max(n)
tVecs = np.zeros((n - emb_dim, emb_dim + 1))
for i in range(tVecs.shape[0]):
tVecs[i, :] = x[i:i + tVecs.shape[1]]
counts = []
for m in [emb_dim, emb_dim + 1]:
counts.append(0)
# get the matrix that we need for the current m
tVecsM = tVecs[:n - m + 1, :m]
# successively calculate distances between each pair of template vectors
for i in range(len(tVecsM)):
dsts = dist(tVecsM, tVecsM[i])
# delete self-matching
dsts = np.delete(dsts, i, axis=0)
# delete undefined distances coming from zero segments
# dsts = [x for i, x in enumerate(dsts) if not np.isnan(x) and not np.isinf(x)]
# count how many 'defined' distances are smaller than the tolerance
# if (dsts):
counts[-1] += np.sum(dsts < tolerance)/(n - m - 1)
if counts[1] == 0:
# log would be infinite => cannot determine RangeEn_B
RangeEn_B = np.nan
else:
# compute log of summed probabilities
RangeEn_B = -np.log(1.0 * counts[1] / counts[0])
return RangeEn_B](url)
#Assume matrix is a series
t = pd.Series(rs_num)
t=t.rolling(window=5, min_periods=2).apply(RangeEn_B).dropna()
' ' '
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Json parametrize more2
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REF: collect .get tests
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CLN: Replace DataFrame() with empty_frame() in tests
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NONE
| null |
- [ ] closes #33161
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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DOC: document support for in-memory HDFStore
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| 2020-03-31T05:39:30Z
| 2020-06-20T20:08:40Z
| 2020-06-20T20:08:40Z
|
CONTRIBUTOR
| null |
It seems to be currently imposible to have an in-memory HDFStore.
Any of my attempts at using `io.BytesIO` failed.
This can be useful for applications where the bytes of the resulting HDF file need to be sent over the network.
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CLN: Replace DataFrame() with empty_frame in tests
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NONE
| null |
- [ ] closes #33161
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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Quantile function fails when performing groupby on Time Zone Aware Timestamps
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[] | null | 5
| 2020-03-31T07:16:35Z
| 2021-07-31T01:56:00Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Maybe **not a high priority bug**, but I have the feeling it can easily fixed. I just have not enough understanding on how it should be fixed. Please find below the MCVE to reproduce it:
```
import numpy as np
import pandas as pd
# Sample Dataset:
n = 200
c = np.random.choice([0,1,2], size=(n,))
d = np.random.randn(n)
t = pd.date_range(start='2020-04-19 00:00:00', freq='1T', periods=n, tz='UTC')
df = pd.DataFrame([r for r in zip(c, t, d)], columns=['category', 'timestamp', 'value'])
df['rtime'] = df['timestamp'].dt.floor('1H')
# Failing operation:
df.groupby('rtime').quantile([0.1, 0.5, 0.9])
```
#### Problem description
The traceback of the error is a bit laconic and I have not enough experience in Pandas source code to cover all details of this error:
```bash
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-35-aff2c9a206a6> in <module>
----> 1 df.groupby('rtime').quantile([0.1,0.2])
/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py in quantile(self, q, interpolation)
1926 interpolation=interpolation,
1927 )
-> 1928 for qi in q
1929 ]
1930 result = concat(results, axis=0, keys=q)
/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py in <listcomp>(.0)
1926 interpolation=interpolation,
1927 )
-> 1928 for qi in q
1929 ]
1930 result = concat(results, axis=0, keys=q)
/usr/local/lib/python3.6/dist-packages/pandas/core/groupby/groupby.py in _get_cythonized_result(self, how, cython_dtype, aggregate, needs_values, needs_mask, needs_ngroups, result_is_index, pre_processing, post_processing, **kwargs)
2289 func = partial(func, ngroups)
2290
-> 2291 func(**kwargs) # Call func to modify indexer values in place
2292
2293 if result_is_index:
pandas/_libs/groupby.pyx in pandas._libs.groupby.__pyx_fused_cpdef()
TypeError: No matching signature found
```
I have found similar issues on GitHub with the same exception, but I think it is too generic to be the same related problem. Additionally, I may have found a simple corner case issue with TZ aware timestamp.
I had some hard time to reproduce the error when building the MCVE, finally I found out that it is related to the existence of an extra columns holding Time Zone aware timestamps.
**Maybe the fix it is just about updating function signature to add TZ aware timestamps.**
The problem can be circonvolved using one of the following writing:
```python
df.groupby('rtime')['value'].quantile([0.1,0.2])
```
Or:
```python
df['timestamp'] = df['timestamp'].dt.tz_convert(None)
df.groupby('rtime').quantile([0.1,0.2])
```
Or:
```python
df.pop('timestamp')
df.groupby('rtime').quantile([0.1,0.2])
```
Which strongly suggests it is the existence of the TZ Aware extra column `timestamp` that makes the function `quantile` fail.
#### Expected Output
Expected output might be no distinction in flow when performing `groupby` operations on dataframe holding TimeZone aware timestamp as it does with TZ naive timestamp.
Note: Thank you for building such a great tool, `pandas` is a first class middleware. Your efforts are strongly appreciated. Let me know how I can help, I would be happy to understand how this can be corrected.
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 46.1.3
Cython : 0.29.14
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.4
html5lib : 0.999999999
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.4
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : None
</details>
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CLN: Added static types
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| 3
| 2020-03-31T08:46:57Z
| 2020-04-06T08:43:09Z
| 2020-04-03T19:42:16Z
|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
I'll revert any change related to styling, I applied "black" formatting in some places, just so I could think straight.
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MDU6SXNzdWU1OTA5MjUwODE=
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Feature request: convert period to timedelta
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| 2020-03-31T20:32:12Z
| 2020-03-31T17:20:49Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
mPI = pd.period_range(start='2020-02-01 10:00', end='2020-05-20 10:00', freq='1M')
mPI[0]
>>> Period('2020-02', 'M')
td = pd.Timedelta(mPI[0])
>>> ValueError: Value must be Timedelta, string, integer, float, timedelta or convertible, not Period
td = pd.to_timedelta(mPI[0])
>>> ValueError: Value must be Timedelta, string, integer, float, timedelta or convertible, not Period
mPI[0].to_timedelta()
>>> AttributeError: 'Period' object has no attribute 'to_timedelta'
# ok... write some more code then..
td = mPI[1].start_time - mPI[0].start_time
>>> Timedelta('29 days')
```
#### Problem description
Hi,
I would like to retrieve the duration of a period as a timedelta.
The 1st three examples are code I thought could be handled by pandas, but they are not.
The last one is an example of result I would like to retrieve.
Here, it is fine, I have the next period, so I can do the difference between the two successive periods.
If you don't have it, you are forced to create it.
Please, can pd.Period.to_timedelta() be defined in pandas?
Thanks for your help and support!
Bests,
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Resampling consistency issue/question
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| 2020-03-31T09:21:16Z
| 2021-07-31T01:57:50Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
periods = 7
start = '20100131'
dates = pd.date_range(start=start, freq='M', periods=periods)
df = pd.DataFrame(range(periods), index=dates, columns=['a'])
print('Original: freq="M"')
print(df)
print('\n-> "b" -> "BM"')
print(df.resample('b').last().resample('BM').last())
print('\n-> "BM"')
print(df.resample('BM').last())
print('\n"M"-date_range:')
print(pd.date_range(start=start, freq='M', periods=periods))
print('\n"b"-date_range:')
print(pd.date_range(start=start, freq='b', end=dates[-1]))
print('\n"BM"-date_range:')
print(pd.date_range(start=start, freq='BM', end=dates[-1]))
```
Original: freq="M"
a
2010-01-31 0
2010-02-28 1
2010-03-31 2
2010-04-30 3
2010-05-31 4
2010-06-30 5
2010-07-31 6
-> "b" -> "BM"
a
2010-01-29 0.0
2010-02-26 1.0
2010-03-31 2.0
2010-04-30 3.0
2010-05-31 4.0
2010-06-30 5.0
2010-07-30 6.0
-> "BM"
a
2010-02-26 0.0
2010-03-31 2.0
2010-04-30 3.0
2010-05-31 4.0
2010-06-30 5.0
2010-07-30 NaN
2010-08-31 6.0
"M"-date_range:
DatetimeIndex(['2010-01-31', '2010-02-28', '2010-03-31', '2010-04-30',
'2010-05-31', '2010-06-30', '2010-07-31'],
dtype='datetime64[ns]', freq='M')
"b"-date_range:
DatetimeIndex(['2010-02-01', '2010-02-02', '2010-02-03', '2010-02-04',
'2010-02-05', '2010-02-08', '2010-02-09', '2010-02-10',
'2010-02-11', '2010-02-12',
...
'2010-07-19', '2010-07-20', '2010-07-21', '2010-07-22',
'2010-07-23', '2010-07-26', '2010-07-27', '2010-07-28',
'2010-07-29', '2010-07-30'],
dtype='datetime64[ns]', length=130, freq='B')
"BM"-date_range:
DatetimeIndex(['2010-02-26', '2010-03-31', '2010-04-30', '2010-05-31',
'2010-06-30', '2010-07-30'],
dtype='datetime64[ns]', freq='BM')
#### Problem description
I am not sure if this behaviour is desired.
I would assume that the conversion from M->b would not include a date pre-dateing the min of M-dates. But if it does, I would assume that the conversion from M->BM would also include '2010-01-29'. The same argument would apply for the '2010-7-30'. It feels a bit inconsistent, but it could also be that I am off and I miss to understand the full logic.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.5.4
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
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value_counts not working correctly on (some?) ExtensionArrays
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| 2020-03-31T09:29:46Z
| 2020-05-01T00:34:43Z
| 2020-05-01T00:34:43Z
|
NONE
| null |
#### Code Sample
```python
pd.Series(list("abcde"), dtype="string").value_counts(normalize=True)
```
```bash
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-40-718821f804b4> in <module>
1 # lang.value_counts(normalize=True)
----> 2 pd.Series(list("abcde"), dtype="string").value_counts(normalize=True)
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/base.py in value_counts(self, normalize, sort, ascending, bins, dropna)
1233 normalize=normalize,
1234 bins=bins,
-> 1235 dropna=dropna,
1236 )
1237 return result
~/anaconda/envs/grapy/lib/python3.7/site-packages/pandas/core/algorithms.py in value_counts(values, sort, ascending, normalize, bins, dropna)
729
730 if normalize:
--> 731 result = result / float(counts.sum())
732
733 return result
AttributeError: 'IntegerArray' object has no attribute 'sum'
```
#### Problem description
The problem seems to be that `value_counts()` on a string extension dtype returns an Int64 dtype, and `sum` is not implemented for IntegerArrays , although it is for Series with ExtensionArrays:
#### Expected Output
```python
vc = pd.Series(list("abcde"), dtype="string").value_counts(normalize=False)
print(vc)
print(vc / vc.sum())
```
```bash
d 1
e 1
b 1
c 1
a 1
dtype: Int64
d 0.2
e 0.2
b 0.2
c 0.2
a 0.2
dtype: float64
```
May be related to #22843?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.0
pip : 20.0.2
setuptools : 46.1.1.post20200322
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
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Setting histogram weights for multiple columns fails
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| 3
| 2020-03-31T10:07:25Z
| 2020-04-10T16:59:56Z
| 2020-04-10T16:59:56Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame(dict(zip(['A', 'B'], np.random.randn(2, 100))))
df.plot.hist(weights=0.1*np.ones(shape=(100, 2))) # fail
```
#### Problem description
Trying to plot a histogram of a multi-column data frame with weights fails with `ValueError: weights should have the same shape as a.` There is no error for a single column. It doesn't work with:
```python
df.plot.hist(weights=0.1*np.ones(shape=(100,))) # fail
```
either.
#### Expected Output
A `matplotlib` figure.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.1.post20200323
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.3
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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CLN: using C-API of datetime
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CI: add doctest check for done modules
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MDExOlB1bGxSZXF1ZXN0Mzk2Mjc2OTI0
| 33,177
|
Issue #33161 - Use empty_frame fixture
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| 2020-04-01T12:50:18Z
| 2020-03-31T15:21:56Z
|
NONE
| null |
Replaced Empty data frames with fixture empty_frame()
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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WEB: Moving pandas blog to the website
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| 2020-03-31T12:35:55Z
| 2020-04-07T20:04:50Z
| 2020-04-07T20:04:50Z
|
MEMBER
| null |
Moving the pandas blog posts (now in a separate repo/project) to the `web/community/blog/` in our website. The static site generator we're using for the web, renders them automatically using our layout:
The posts list doesn't have much visual change, just links to the new path:
The main change here, besides adding the posts markdown files (and images) is that the static site generator is now able to not only fetch from rss, but also from the file system.
CC: @TomAugspurger
Btw @TomAugspurger , I increased the number of posts we show in the list, and see that there are some about dask-ml and other things unrelated to pandas being fetched from your blog. Would be great if you can add a pandas tag to your blog, so we only fetch the relevant content.
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DOC: Fixed examples in pandas/core/arrays/
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MEMBER
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- [ ] closes #xxxx
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BUG: support min/max functions for rolling windows with custom BaseIndexer
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| 2020-03-31T13:48:19Z
| 2020-04-09T06:35:39Z
| 2020-04-03T19:58:23Z
|
MEMBER
| null |
- [X] xref #32865
- [X] 1 tests added / 1 passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
## The problem
We currently don't support several rolling window functions when building a rolling window object using a custom class descended from `pandas.api.indexers.Baseindexer`. The implementations were written with backward-looking windows in mind, and this led to these functions breaking.
Currently, using these functions returns a `NotImplemented` error thanks to #33057, but ideally we want to update the implementations, so that they will work without a performance hit. This is what I aim to do over a series of PRs.
## Scope of PR
This PR proposes an alternative implementation for the `min` and `max` rolling window functions implemented in the `_roll_min_max_variable` function in the `aggregations.pyx` file.
## Perf note
This implementation is slightly faster than the one on master branch (tested with a `Series` with 10e6 randomly generated values and `timeit`). If necessary, I can add a benchmark to the benchmark suite, although it must be noted that we need to benchmark with large arrays of data to minimize the effect of overhead.
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MDU6SXNzdWU1OTExMzczNzg=
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Timestamp' object has no attribute 'split' problem
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| 2020-03-31T17:14:24Z
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NONE
| null |
import pandas as pd
import numpy as np
from sklearn.svm import SVR
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
df1 = pd.read_excel("npl90.xlsx",parse_dates=True)
dates = []
npl = []
df1.shape
df=df1.iloc[::-1]
df.tail(1)
df=df.head(len(df)-1)
df
df_dates=df.loc[:, "Date"]
df_npl=df.loc[:, 'NPL90+']
#Create independent data set x
for date in df_dates:
dates.append([int(date.split('-')[1])])
def predict_npl(dates, npl, x):
svr_lin= SVR(kernel="linear", C=1e3)
svr_poly= SVR(kernel="poly", C=1e3, degree=2)
svr_rbf=SVR(kernel="rbf", C=1e3, gamma=0.1)
#Training
svr_poly.fit(df_dates, df_npl)
svr_lin.fit(df_dates, df_npl)
svr_rbf.fit(df_dates, df_npl)
#Create the linear regression
lin_reg=LinearRegression()
#Train the Linear regression model
lin_reg.fit(df_dates, df_npl)
#Plot models
plt.scatter(df_dates, df_npl, color="black", label="original data")
plt.plot(df_dates, svr_rbf.predict(df_dates), color='red', label= 'SVR RBF')
plt.plot(df_dates, svr_poly.predict(df_dates), color='blue', label= 'SVR POLY')
plt.plot(df_dates, svr_lin.predict(df_dates), color='green', label= 'SVR LIN')
plt.plot(df_dates, lin_reg.predict(df_dates), color='grey', label= 'LIN Reg')
plt.xlabel('Date')
plt.ylabel('NPL')
plt.title('Regression')
plt.legend()
plt.show()
return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0], lin_reg.predict(x)[0]
#Predict price on
predicted_npl=predict_npl(dates, npl,"2020-03-01")
print(predicted_npl)
[npl90.xlsx](https://github.com/pandas-dev/pandas/files/4409483/npl90.xlsx)
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MDU6SXNzdWU1OTExODEyODk=
| 33,182
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Support pydata/sparse arrays in DataFrame
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| 2020-03-31T14:59:58Z
| 2020-05-26T19:51:17Z
| null |
CONTRIBUTOR
| null |
This is a discussion topic for adding the ability to store [pydata/sparse](https://github.com/pydata/sparse) ndarrays inside a DataFrame. It's not proposing that we actually do this at this point.
### Background
`sparse` implements a sparse ndarray that implements (much of) the NumPy API. This differs from `scipy.sparse` matricies which are strictly 2D and have their own API. It also differs from pandas' SparseArray, which is strictly 1D and implements the ExtensionArray interface.
### Motivation
In some workflows (especially machine learning) it's common to convert a dense 1D array to a sparse 2D array. The sparse 2D array is often very wide, and so isn't well-suited to storage in a DataFrame with SparseArray values. Each column of the 2D table needs to be stored independently, which at a minimum requires one Python object per column, and makes it difficult (but not impossible) to have these 1D arrays be views on some 2D object.
Since `sparse` implements the ndarray interface, we can in theory just store the `sparse.COO` array where we normally store a 2D ndarray, inside or Block. Indeed, with a minor change, this works
```diff
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 8deeb415c1..105c1c1a64 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -427,6 +427,7 @@ class DataFrame(NDFrame):
dtype: Optional[Dtype] = None,
copy: bool = False,
):
+ import sparse
if data is None:
data = {}
if dtype is not None:
@@ -459,7 +460,7 @@ class DataFrame(NDFrame):
data = data.copy()
mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
- elif isinstance(data, (np.ndarray, Series, Index)):
+ elif isinstance(data, (np.ndarray, Series, Index, sparse.COO)):
if data.dtype.names:
data_columns = list(data.dtype.names)
data = {k: data[k] for k in data_columns}
```
Which lets us *store* the 2D array
```python
In [1]: import sparse
In [2]: import pandas as pd
In [3]: df = pd.DataFrame(sparse.zeros((10, 4)))
In [4]: df._data.blocks[0]
Out[4]: FloatBlock: slice(0, 4, 1), 4 x 10, dtype: float64
In [5]: df._data.blocks[0].values
Out[5]: <COO: shape=(4, 10), dtype=float64, nnz=0, fill_value=0.0>
```
However, many things don't work. Notably
1. Anything calling `asarray(arr)` will raise. Sparse doesn't allow implicit conversion from sparse to dense. This includes things like the DataFrame repr
2. Anything touching Cython (groupby, join, factorize, etc.) will likely raise.
So this would primarily be useful for storing data, at least initially.
### Arguments Against
The biggest argument against allowing this is that pandas is potentially moving to a column store in the future. In this future, we don't have 2D blocks, so the value of a 2D sparse array diminishes. We may be able to do similar tricks as we'll do with numpy.ndarray, where 1D columns are views on a 2D object.
The second argument against is that we could potentially make the EA interface 2D, and implement an EA compatible wrapper around a pydata/sparse array (similar to `PandasArray`).
Finally, we can't really hope to offer "full" support for sparse-backed columns. Things like joining not working on sparse columns will cause user confusion, that may be hard to document and explain.
cc @adrinjalali and @hameerabbasi, just for awareness. Most of the discussion will likely be on the pandas side of things though.
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REF: fillna tests
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| 2020-03-31T16:06:42Z
| 2020-03-31T17:34:41Z
| 2020-03-31T16:38:07Z
|
MEMBER
| null |
This gets them all for tests.indexes, a fraction for tests.series, and none for DataFrame. This will take multiple passes
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REF/CLN: test_get_dummies
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TST: cover search_sorted scalar mixed timezones case
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| 1
| 2020-03-31T17:17:12Z
| 2020-03-31T18:24:29Z
| 2020-03-31T18:24:23Z
|
CONTRIBUTOR
| null |
- [X] closes #30086
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Raises a `ValueError` on version 0.25.1 (reporter's version), as expected:
```
>>> import pandas as pd
>>> pd.__version__
'0.25.1'
>>> ser = pd.Series(date_range("20120101", periods=10, freq="2D", tz="UTC"))
>>> val = pd.Timestamp("20120102", tz="America/New_York")
>>> res = ser.searchsorted(val)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/jamescobonkerr/venv/lib/python3.7/site-packages/pandas/core/series.py", line 2694, in searchsorted
return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)
File "/Users/jamescobonkerr/venv/lib/python3.7/site-packages/pandas/core/algorithms.py", line 1887, in searchsorted
result = arr.searchsorted(value, side=side, sorter=sorter)
File "/Users/jamescobonkerr/venv/lib/python3.7/site-packages/pandas/core/arrays/datetimelike.py", line 666, in searchsorted
self._check_compatible_with(value)
File "/Users/jamescobonkerr/venv/lib/python3.7/site-packages/pandas/core/arrays/datetimes.py", line 591, in _check_compatible_with
own=self.tz, other=other.tz
ValueError: Timezones don't match. 'UTC != America/New_York'
```
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MDU6SXNzdWU1OTEyODg3Nzg=
| 33,186
|
pandas >= 1.0.0 fails to read an old HDF5 file
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open
| false
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[] | null | 4
| 2020-03-31T17:31:53Z
| 2020-09-10T00:19:07Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.read_hdf("beads1.h5", "features")
```
#### Problem description
While working on pandas 0.25, this fails starting with pandas 1.0.0. The HDF file was created in November 2015 or even earlier and is inside the [zip file](https://github.com/pandas-dev/pandas/files/4410582/beads1.zip). Other files created at the same time seem to work.
The error message is:
```
TypeError Traceback (most recent call last)
<ipython-input-2-507ed8d14986> in <module>
----> 1 pd.read_hdf("Software/sdt-python/tests/data_chromatic/beads1.h5", "features")
~/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py in read_hdf(path_or_buf, key, mode, errors, where, start, stop, columns, iterator, chunksize, **kwargs)
426 iterator=iterator,
427 chunksize=chunksize,
--> 428 auto_close=auto_close,
429 )
430 except (ValueError, TypeError, KeyError):
~/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py in select(self, key, where, start, stop, columns, iterator, chunksize, auto_close)
812 )
813
--> 814 return it.get_result()
815
816 def select_as_coordinates(
~/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py in get_result(self, coordinates)
1827
1828 # directly return the result
-> 1829 results = self.func(self.start, self.stop, where)
1830 self.close()
1831 return results
~/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py in func(_start, _stop, _where)
796 # function to call on iteration
797 def func(_start, _stop, _where):
--> 798 return s.read(start=_start, stop=_stop, where=_where, columns=columns)
799
800 # create the iterator
~/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py in read(self, where, columns, start, stop)
3067 for i in range(self.nblocks):
3068
-> 3069 blk_items = self.read_index(f"block{i}_items")
3070 values = self.read_array(f"block{i}_values", start=_start, stop=_stop)
3071
~/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py in read_index(self, key, start, stop)
2757 elif variety == "regular":
2758 node = getattr(self.group, key)
-> 2759 index = self.read_index_node(node, start=start, stop=stop)
2760 return index
2761 else: # pragma: no cover
~/anaconda3/lib/python3.7/site-packages/pandas/io/pytables.py in read_index_node(self, node, start, stop)
2878 data, kind, encoding=self.encoding, errors=self.errors
2879 ),
-> 2880 **kwargs,
2881 )
2882
~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py in __new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs)
407 if new_dtype is not None:
408 return cls(
--> 409 new_data, dtype=new_dtype, copy=False, name=name, **kwargs
410 )
411
~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py in __new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs)
411
412 if kwargs:
--> 413 raise TypeError(f"Unexpected keyword arguments {repr(set(kwargs))}")
414 if subarr.ndim > 1:
415 # GH#13601, GH#20285, GH#27125
TypeError: Unexpected keyword arguments {'freq'}
```
#### Expected Output
```
x y z signal bg mass \
0 128.664558 2.966443 0.0 4189.596881 417.007853 27574.470714
1 355.112881 3.865741 0.0 1061.439002 266.074445 12902.725487
2 169.607263 6.025977 0.0 3103.641263 273.718417 22003.694114
3 396.070422 6.681506 0.0 799.983142 227.225677 9527.816676
4 127.806587 9.435608 0.0 4543.589962 332.126465 33712.910416
.. ... ... ... ... ... ...
61 284.447272 56.068638 0.0 502.772436 221.098239 7438.331854
62 381.438571 96.586069 0.0 287.953711 227.066782 1912.087223
63 217.752114 3.596804 0.0 150.887499 206.701723 1118.474095
64 258.435664 54.131181 0.0 125.772954 209.084140 2859.549002
65 328.954684 100.785512 0.0 506.879163 224.691759 5695.560215
size frame
0 1.023476 0
1 1.390924 0
2 1.062239 0
3 1.376786 0
4 1.086697 0
.. ... ...
61 1.534483 0
62 1.028022 0
63 1.086166 0
64 1.902240 0
65 1.337291 0
[66 rows x 8 columns]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-88-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : de_AT.UTF-8
LOCALE : de_AT.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0
</details>
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ADMIN: Create separate issue templates for different usecases
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| 2
| 2020-03-31T17:54:33Z
| 2020-04-06T08:24:06Z
| 2020-04-03T19:43:12Z
|
MEMBER
| null |
revival of #31551 originally opened by @jschendel
----
> I recall this being mentioned on one of the core dev calls a few months back, and took inspiration from [rapidsai/cudf](https://github.com/rapidsai/cudf) for the various templates.
>
> Added templates for the following use cases:
>
> * Bug Report
>
> * This is largely the same as the current issue template
>
> * Documentation Enhancement
>
> * Documentation Error
>
> * Feature Request
>
> * Submit Question
>
> * This attempts to direct users to StackOverflow for usage questions
>
>
> We could also add a Blank Template for opening an issue without any template provided, but opted not to do that for now, as I don't want to encourage people to bypass these templates. Could certainly add one if there's a consensus that we want this.
>
> I've created a [local repo](https://github.com/jschendel/pandas-templates/issues) where you can see these templates in action since I couldn't figure out another way to actually display these. Click the "New Issue" button to see what these changes would look like.
>
>
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pd.read_csv header inference
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open
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[] | null | 1
| 2020-03-31T18:12:28Z
| 2021-07-31T02:02:11Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
With [data.csv.txt](https://github.com/pandas-dev/pandas/files/4410722/data.csv.txt):
```python
>>> pd.read_csv('/tmp/data.csv.txt') # header='infer' default
1540 125205 PRODAJA 04-JUN-18 -3 -.625 -.726
0 1540 125205 PRODAJA 29-JUN-18 -1.00 -0.62 -0.75
1 1540 125205 PRODAJA 09-JUL-18 -3.00 -0.62 -0.77
2 1540 125205 PRODAJA 25-JUL-18 -2.00 -0.41 -0.51
3 1540 125205 PRODAJA 01-NOV-18 -3.00 -0.62 -0.74
4 1540 125205 PRODAJA 24-AUG-18 -1.00 -0.21 -0.26
```
#### Problem description
Given above-like data, a simple RangeIndex header would be preferred.
As a heuristic, floats are rarely header representatives. As are values that are equal to next row's values. When six sevenths of values are rare header representatives, those are unlikely.
One can override it all by passing `header=None`, but the inference does seem to be lacking.
#### Expected Output
```py
>>> pd.read_csv('/tmp/data.csv')
0 1 2 3 4 5 6
0 1540 125205 PRODAJA 04-JUN-18 -3.00 -0.62 -0.73
1 1540 125205 PRODAJA 29-JUN-18 -1.00 -0.62 -0.75
2 1540 125205 PRODAJA 09-JUL-18 -3.00 -0.62 -0.77
3 1540 125205 PRODAJA 25-JUL-18 -2.00 -0.41 -0.51
4 1540 125205 PRODAJA 01-NOV-18 -3.00 -0.62 -0.74
5 1540 125205 PRODAJA 24-AUG-18 -1.00 -0.21 -0.26
```
#### Output of ``pd.show_versions()``
<details>
1.1.0.dev0+786.gec7734169
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk2NTk3Nzk0
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REF: set_axis tests
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MEMBER
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Fully parametrized over Series/DataFrame
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CLN: Correct docstring to describe actual functionality.
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CONTRIBUTOR
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- [x] closes #20560
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REF: misplaced sort_index test
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MDExOlB1bGxSZXF1ZXN0Mzk2Njk3NTc4
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CLN: Rename ordered_fixture --> ordered
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MEMBER
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A pretty small and straightforward rename to make things more readable and consistent with our existing naming convention for fixtures.
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REF: test_mutate_columns -> test_setitem
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| 591,552,415
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MDU6SXNzdWU1OTE1NTI0MTU=
| 33,194
|
Pandas 1.03: pandas.tseries.offsets.QuarterBegin not showing correct date
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| 2020-04-01T01:35:06Z
| 2020-04-08T11:00:59Z
| 2020-04-08T11:00:59Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
from datetime import datetime
from pandas.tseries.offsets import QuarterBegin
now = datetime.datetime(2020, 1, 12)
now - QuarterBegin()
```
#### Problem description
The code shows the following results:
now - QuarterBegin()
Out[28]: Timestamp('2019-12-01 00:00:00')
which is not expected.
#### Expected Output
Timestamp('2020-01-01 00:00:00')
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.2
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.2.0.post20200210
Cython : 0.29.15
pytest : 5.3.5
hypothesis : 5.5.4
sphinx : 2.4.0
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.12.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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Added test for #5091 (Missing Periods for some DateOffsets)
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| 2
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NONE
| null |
- [x] closes #5091
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to_csv / to_pickle optionally use fast gzip compressionlevel=1
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| 1
| 2020-04-01T01:57:23Z
| 2020-04-10T22:21:10Z
| 2020-04-10T22:21:10Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
Enhancement proposal
```python
>>> df.to_csv('data.csv.gz') # Awfully slow
>>> df.to_pickle('data.pkl.bz2') # Awfully slow
>>> df.to_csv('data.csv.gz', fast=True) # Uses fast compressionlevel=1
# or, better:
>>> pd.options.io.compressionlevel = 1
>>> df.to_pickle('data.pkl.bz2') # Uses fast compressionlevel=1
...
```
#### Problem description
Compression of large objects in pandas is slow.
Popular evidence comparing compression levels for average payloads shows [[1]](https://catchchallenger.first-world.info/wiki/Quick_Benchmark:_Gzip_vs_Bzip2_vs_LZMA_vs_XZ_vs_LZ4_vs_LZO) [[2]](https://tukaani.org/lzma/benchmarks.html) that compressed size is usually far less variable than compression time, which most often spans several folds.
`compressionlevel=1` is _orders of magnitude_ faster, whereas `compressionlevel=9` is only 10% smaller.
One optimizes for size, the other for speed, and much fewer people ever need something in between.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
1.1.0.dev0+786.gec7734169
</details>
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Requested ASV
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| 1
| 2020-04-01T03:19:34Z
| 2020-04-02T00:13:49Z
| 2020-04-02T00:06:13Z
|
MEMBER
| null |
I think this was requested a while ago and has been sitting in a local branch
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MDU6SXNzdWU1OTE2MDE4NzY=
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|
BUG: iloc setting columns not taking effect
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[] | null | 34
| 2020-04-01T03:56:38Z
| 2020-04-08T21:59:20Z
| null |
MEMBER
| null |
```
arr = np.random.randn(10 ** 6).reshape(500, 2000).astype(np.float64)
df = pd.DataFrame(arr)
df.iloc[:, 1000:] = df.iloc[:, 1000:].astype(np.float32)
>>> df.dtypes.value_counts()
float64 2000
dtype: int64
```
I would expect 1000 float64 columns and 1000 float32 columns. I get the expected behavior if I set a single column before trying to set all 1000:
```
df = pd.DataFrame(arr)
df[1000] = df[1000].astype(np.float32)
df.iloc[:, 1000:] = df.iloc[:, 1000:].astype(np.float32)
>>> df.dtypes.value_counts()
float32 1000
float64 1000
dtype: int64
```
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DOC: Fix Error in pandas.Series.last
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| 1
| 2020-04-01T06:13:23Z
| 2020-04-03T14:42:43Z
| 2020-04-03T14:42:31Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Related to #27977.
output of `python scripts/validate_docstrings.py pandas.Series.last`:
```
################################################################################
################################## Validation ##################################
################################################################################
```
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BUG: 'groupby()' results in shifted results for 'quantile()'
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| 4
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| 2020-05-25T21:58:07Z
| 2020-05-25T21:58:07Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Imports
import pandas as pd
import numpy as np
# Define x and bins
x = np.linspace(0, 1, 100)
bins = np.arange(0, 1, 0.1)
# Create Series object with the x-data
series = pd.Series(x, name='X')
# Group series by provided bins
data_cut = pd.cut(series, bins, include_lowest=True)
grp = series.groupby(by=data_cut)
# Calculate the 0.5 quantile of this group
perc = grp.quantile()
# For every group, print the 0.5 quantile as determined by that group and its values
for group, indices in grp.indices.items():
print()
print("Bin:", group)
print("Group quantile:", perc.loc[group])
grp_series = grp.get_group(group)
print("Group values quantile:", grp_series.quantile())
```
#### Problem description
NOTE: I see that there are several other issues already open that discuss problems with `groupby` and `quantile` in v1.0.3, but I figured that I post this anyway, as it is a very easy and simple reproducible example.
Executing the code above in pandas `v1.0.3` (or any `v1.0.x` version) results in the quantiles not agreeing with each other for every group, even though they should.
Instead, it seems that the quantile as calculated by a group is shifted by 1 group.
This will become 2 groups when using ``bins = np.arange(0, 0.9, 0.1)`` and goes away when using ``bins = np.arange(0, 1.1, 0.1)`` (or the ``bins = np.linspace(0, 1, 11)`` equivalent).
The problem above does not occur for pandas `v0.24.x`, but instead gives the proper output.
Additionally, replacing `x` and `bins` with
```python
# Define x and bins
x = np.linspace(1, 10, 100)
bins = 10**np.arange(0, 1, 0.1)
```
in order to use logarithmic values (that are not equally binned), will not result in a simple shift, but simply values that make no sense at all at first.
After some more testing, it seems that the `quantile` method of a `GroupBy` object, first goes through all values that are not in a group (their indices are NaN) and then goes through the remaining data normally.
Not sure if that is helpful.
#### Expected Output
The expected output is that the two different values that are printed are always the same for each group, regardless of the bins that are used.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-91-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : nl_NL.UTF-8
LOCALE : nl_NL.UTF-8
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200309
Cython : None
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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ENH: provide standard BaseIndexers in pandas.api.indexers
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| 1
| 2020-04-01T06:51:32Z
| 2020-04-08T17:20:50Z
| 2020-04-08T17:20:50Z
|
MEMBER
| null |
### Problem description
During my work on generalizing our rolling window functions, @mroeschke and @jreback have pointed out that it would be useful to expose a couple `BaseIndexer` subclasses as a part of our api (in pandas.api.indexers, to be specific). For instance, forward-looking windows are fairly common in signal analysis and the like, and it would benefit users if we had a default implementation of those.
This is the implementation I'm currently using for testing:
```python
class ForwardIndexer(BaseIndexer):
def get_window_bounds(self, num_values, min_periods, center, closed):
start = np.empty(num_values, dtype=np.int64)
end = np.empty(num_values, dtype=np.int64)
for i in range(num_values):
if i + min_periods <= num_values:
start[i] = i
end[i] = min(i + self.window_size, num_values)
else:
start[i] = i
end[i] = i + 1
return start, end
```
This should be rewritten to avoid the for loop, as it slows the function down a lot.
We could also add a backward indexer for completeness or skip it, because it can be done by supplying an integer when creating the rolling window object. Tell me what you think.
I'd be happy to submit a PR with a proposed implementation in a day or two.
#### Implementation details
I think we can put the implementation of the new class/classes in `pandas.core.window.indexers`.
#### Cross-references
xref #32865
xref #33180
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|
row sum and column mean works but row mean gives all NaN, on heterogenous data types
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| 2020-04-01T09:57:58Z
| 2020-04-02T02:40:40Z
| 2020-04-02T02:24:18Z
|
NONE
| null |
When different DataFrame rows are of different types, row sum works but row mean gives all NaN values.
```
df=pd.DataFrame({'a':[1,pd.to_timedelta('1D'),3.], 'b':[4,pd.to_timedelta('1.5D'),6.], 'c':[7,pd.to_timedelta('0.5D'),np.nan]})
# df=pd.DataFrame({'a':[1,2,3.], 'b':[4,5,6.], 'c':[7,8,np.nan]})
display(df.transpose())
display(df.transpose().sum(axis=0))
display(df.transpose().mean(axis=0))
display(df)
display(df.sum(axis=1))
display(df.mean(axis=1)) # bug
```
<img width="332" alt="image" src="https://user-images.githubusercontent.com/10172392/78124015-a4838380-7441-11ea-84fe-9bd45de54f1b.png">
As shown above, the last statement shows the bug by returning all NaN values which is incorrect. Interestingly, if the DataFrame entries are of the same type (uncomment the 2nd line), this bug does not occur.
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|
STY/TST: what standard to follow to import top-level objects in tests
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[] | null | 14
| 2020-04-01T10:59:39Z
| 2020-04-14T05:07:21Z
| null |
MEMBER
| null |
So this discussion regularly comes up (latest one being https://github.com/pandas-dev/pandas/pull/33184), so let's try to decide which one of the two we prefer.
In test code, do we do:
```
from pandas import Series
...
Series(...)
```
or
```
import pandas as pd
...
pd.Series(..)
```
Personally, I prefer the second, and from recent discussions I have the impression @WillAyd and @datapythonista do as well (is that correct?)
To be clear: consistency within a single file is most important (so not mixing Series and pd.Series in a single file).
But *when* someone does a PR to fix such inconsistencies in a file, it would be good if there is agreement on whether such a PR should replace `pd.Series` with `Series` or rather replace `Series` with `pd.Series`.
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|
Add test for #24615 (Accept PandasArray (with correct dtype) in DatetimeArray constructor)
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| 2020-04-01T13:32:47Z
| 2020-04-04T01:01:23Z
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NONE
| null |
issue #24615
I added a test for the issue 24615.
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| 5
| 2020-04-01T13:37:39Z
| 2021-10-04T19:13:41Z
| 2021-10-04T19:13:40Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```
dtypes = {
'created': 'int64',
'eventType' : 'category',
'severity' : 'category'
}
df = pd.read_json('dataset.json', lines=True, dtype=dtypes)
df.info()
```
Results into:
```
created int64
eventType object
severity object
```
Using .astype() instead converts types correctly:
```
df.astype(dtypes).info()
created int64
eventType category
severity category
```
#### Problem description
Should take take appropriate data type during DataFrame loading from disc.
#### Output of ``pd.show_versions()``
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
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| 591,933,185
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MDU6SXNzdWU1OTE5MzMxODU=
| 33,206
|
ENH: Add accessor for isocalendar returning iso year, week, and day
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| 1
| 2020-04-01T13:50:54Z
| 2020-04-12T21:34:32Z
| 2020-04-12T21:34:32Z
|
CONTRIBUTOR
| null |
This enhancement request is to add an accessor to DatetimeProperties for the ISO year, which is the year corresponding to the week calculated via ISO 8601.
Currently, we can access the ISO week via `week` and `weekofyear`, *and* `Timestamp` objects provide `isocalendar`, which returns the correct ISO year, week, and day.
### Examples
For example:
```python
df = pandas.DataFrame()
df["event_date"] = ["2020-01-01", "2019-12-31"]
df["event_date"] = df.event_date.astype("datetime64[D]")
df.event_date.dt.weekofyear.values, df.event_date.dt.year.values
```
outputs:
```python
(array([1, 1]), array([2020, 2019]))
```
and accessing the Timestamp objects
```python
df.event_date[0].isocalendar(), df.event_date[1].isocalendar()
```
yields
```python
((2020, 1, 3), (2020, 1, 2))
```
It would be great to have an accessor to get access this isocalendar year value for a series, e.g.:
```python
df.event_date.dt.year_for_weekofyear.values
```
```python
array([2020, 2020])
```
or *maybe even better* to have an isocalendar accessor where the same iso values are available like in `Timestamp.isocalendar`, e.g.
```python
df.event_date.dt.isocalendar.values
```
```python
array([[2020, 1, 3], [2020, 1, 2]]))
```
I am happy to try a PR here, but some input would be great as to:
- which direction to proceed
- naming, etc.
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|
Everything works fine
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| 2020-04-01T13:57:52Z
| 2020-04-01T14:24:24Z
| 2020-04-01T14:10:08Z
|
NONE
| null |
I made a stupid error) No bugs)
|
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MDExOlB1bGxSZXF1ZXN0Mzk3MDQ5MTI1
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DOC: Fixed examples in pandas/core/indexes/
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| 2
| 2020-04-01T14:56:35Z
| 2020-04-03T10:57:32Z
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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API/BUG: isna_old on EAs?
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| 2
| 2020-04-01T15:17:51Z
| 2020-05-10T18:50:58Z
| 2020-05-10T18:50:57Z
|
MEMBER
| null |
isna_new has a code path that handles EAs that isna_old doesnt, so the following will raise:
```
pd.set_option("mode.use_inf_as_na", True)
pd.isna(my_decimal_array)
```
Most of this can be fixed by adding to isna_ndarray_old:
```
if is_extension_array_dtype(values.dtype):
result = values.isna()
```
Then we can add into the interface tests:
```
def test_isna_old_extension_array(self, data_missing):
# TODO: do we need EA.isna to handle `inf`?
expected = data_missing.isna()
with cf.option_context("mode.use_inf_as_na", True):
result = pd.isna(data_missing)
tm.assert_equal(result, expected)
```
The two problems here are 1) as in the comment, this may be wrong/ambiguous if the EA has a concept of "inf" and 2) we still get a test failure on `StringArray([<NA>, 'A'])`
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| 591,997,838
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MDU6SXNzdWU1OTE5OTc4Mzg=
| 33,210
|
MacPython/pandas-wheels wheel builds failing
|
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| 6
| 2020-04-01T15:19:08Z
| 2020-04-28T22:54:21Z
| 2020-04-28T22:54:21Z
|
CONTRIBUTOR
| null |
This affects the linux builds, both py37 and py38, 32 or 64-bit.
https://dev.azure.com/pandas-dev/pandas-wheels/_build/results?buildId=30996&view=logs&j=517fe804-fa30-5dc2-1413-330699242c05&t=2e128ad5-2f7f-5333-3f34-c85b8fbc7250&l=1216
```
=================================== FAILURES ===================================
__________________________ test_no_permission[c_high] __________________________
[gw1] linux -- Python 3.7.0 /venv/bin/python
with pytest.raises(PermissionError, match=msg) as e:
> parser.read_csv(path)
/venv/lib/python3.7/site-packages/pandas/tests/io/parser/test_common.py:978:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/venv/lib/python3.7/site-packages/pandas/tests/io/parser/conftest.py:22: in read_csv
return read_csv(*args, **kwargs)
/venv/lib/python3.7/site-packages/pandas/io/parsers.py:676: in parser_f
return _read(filepath_or_buffer, kwds)
/venv/lib/python3.7/site-packages/pandas/io/parsers.py:449: in _read
parser = TextFileReader(fp_or_buf, **kwds)
/venv/lib/python3.7/site-packages/pandas/io/parsers.py:880: in __init__
self._make_engine(self.engine)
/venv/lib/python3.7/site-packages/pandas/io/parsers.py:1112: in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
/venv/lib/python3.7/site-packages/pandas/io/parsers.py:1935: in __init__
self._reader = parsers.TextReader(src, **kwds)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
> ???
E pandas.errors.EmptyDataError: No columns to parse from file
pandas/_libs/parsers.pyx:532: EmptyDataError
```
Looks to be from https://github.com/pandas-dev/pandas/pull/32839
cc @gfyoung @roberthdevries
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MDExOlB1bGxSZXF1ZXN0Mzk3MTIyOTAw
| 33,211
|
CLN: remove Block.is_categorical_astype
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MEMBER
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MDExOlB1bGxSZXF1ZXN0Mzk3MTM2MzMx
| 33,212
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CI: Make `isort` to check the "scripts" folder as well
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DOC: Added check for standard pandas reference
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| 2020-04-16T23:11:21Z
| 2020-04-16T23:11:21Z
|
CONTRIBUTOR
| null |
- [x] xref #32316
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As suggested by @datapythonista , I've added checks in `ci/code_checks.sh` to look if the pandas is being referenced in a standardized way i.e pandas, not *pandas* or Pandas
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MDExOlB1bGxSZXF1ZXN0Mzk3MTU3NDMw
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REF: .dot tests
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| 2020-04-01T18:21:13Z
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MEMBER
| null |
Fully parametrized over Series/DataFrame
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[PROPOSAL] Make Series.update() accept a dictionary
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| 5
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| 2020-04-26T19:49:37Z
| 2020-04-26T19:49:37Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
I propose to re-write the `Series.update() ` method to accept a dictionary object. This would be useful for example when you are iterating over a DataFrame using `DataFrame.iterrows()`, and you want to update the `row` object returned by `iterrows()` in order to build another DataFrame starting from the DataFrame you are iterating over
Here is an example that doesn't work:
```python
import pandas as pd
s = pd.Series({'city':'Rome'})
s.update({'country':'Italy'})
```
#### Problem description
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File "C:\InstalledPrograms\Anaconda3\lib\site-packages\pandas\core\series.py", line 2807, in update
> other = other.reindex_like(self)
> AttributeError: 'dict' object has no attribute 'reindex_like'
#### Simple use case
In this use case, an explicit conversion to dict() is needed
```
import random
import pandas as pd
def very_long_dictionary():
d = dict()
for c in range(2,10):
d[c] = random.randint(1,101)
return d
df = pd.DataFrame([[1,2],[3,4],[5,6]])
print(df)
outdl = list()
for index,row in df.iterrows():
#Explicit conversion to dict is needed
row = dict(row)
row.update(very_long_dictionary())
outdl.append(row)
newdf = pd.DataFrame(outdl)
print(newdf)
```
#### Expected Output
The series would update to the new value
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.5.4
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0Mzk3MTc3NTY5
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Added option to include/ignore file extensions in `scripts/validate_unwanted/patterns.py`
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| 1
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|
MEMBER
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
---
This is needed for the check in #32942 to ignore cython files.
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