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MDU6SXNzdWU1NTY4MTczODA=
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Bug in AbstractHolidayCalendar.holidays
|
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| 1
| 2020-01-29T12:01:14Z
| 2020-02-09T20:54:38Z
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|
CONTRIBUTOR
| null |
...when there are no rules.
```python
from pandas.tseries.holiday import AbstractHolidayCalendar
class ExampleCalendar(AbstractHolidayCalendar):
pass
cal = ExampleCalendar()
```
```python
In [58]: cal.holidays(pd.Timestamp('01-Jan-2020'), pd.Timestamp('01-Jan-2021'))
Traceback (most recent call last):
File "<ipython-input-58-022244d4e794>", line 1, in <module>
cal.holidays(pd.Timestamp('01-Jan-2020'), pd.Timestamp('01-Jan-2021'))
File "C:\Users\dhirschf\envs\dev\lib\site-packages\pandas\tseries\holiday.py", line 422, in holidays
self._cache = (start, end, holidays.sort_index())
AttributeError: 'NoneType' object has no attribute 'sort_index'
In [59]: pd.__version__
Out[59]: '0.25.3'
```
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MDExOlB1bGxSZXF1ZXN0MzY4NTI2Njg1
| 31,416
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Fix bug in calculation of holidays
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| 9
| 2020-01-29T12:19:32Z
| 2020-02-09T23:25:28Z
| 2020-02-09T20:54:38Z
|
CONTRIBUTOR
| null |
Closes #31415
- [x] closes #xxxx
- [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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MDU6SXNzdWU1NTY4NTk5NDY=
| 31,417
|
helper method to sanitize column names of a pandas DataFrame
|
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| 2020-01-29T13:24:41Z
| 2021-07-27T05:34:42Z
| 2021-07-27T05:34:41Z
|
NONE
| null |
I will add a method to sanitize column names of a pandas DataFrame and make a pull request
Below is the test code
```python
from pandas import DataFrame
import pandas._testing as tm
def test_sanitize_column_names():
df = DataFrame(
{
"A ": [1],
"b": [1],
" 485 5(468a)44 44 4 ? $@e3 * C cc c D ": [1],
}
)
df.sanitize_column_names()
expected_df = DataFrame({"a": [1], "b": [1], "485_5468a44_44_4_e3_c_cc_c_d": [1]})
tm.assert_frame_equal(df, expected_df)
```
This issue is about an enhancement
|
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MDExOlB1bGxSZXF1ZXN0MzY4NTU2MjI2
| 31,418
|
Sanitize column names
|
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| 2020-01-29T13:27:32Z
| 2020-01-30T13:43:27Z
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|
NONE
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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MDExOlB1bGxSZXF1ZXN0MzY4NTk1Mjc0
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Backport PR #31150: API: generalized check_array_indexer for validating array-like getitem indexers
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| 2020-01-29T14:44:14Z
| 2020-01-29T16:30:33Z
| 2020-01-29T15:13:08Z
|
MEMBER
| null |
Backport https://github.com/pandas-dev/pandas/pull/31150
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|
Can't pre-define dtype when reading data
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| 2020-01-29T15:11:26Z
| 2020-01-29T17:29:26Z
| 2020-01-29T17:29:26Z
|
NONE
| null |
I am reading a pipe delimited file without headings into Pandas and I am using Pandas version 0.24.2. And this is public data so no worries around confidentiality.
The data looks like:
999778247820|R|JPMORGAN CHASE BANK, NATIONAL ASSOCIATION|7.375|113000|360|02/2001|04/2001|95|95|1|52|665|Y|P|SF|1|P|IL|601|30|FRM||1|N
999783196683|R|OTHER|7.25|59000|360|01/2001|04/2001|97|97|2|43|682|Y|P|PU|1|P|HI|967|30|FRM|676|1|N
999783470376|C|BANK OF AMERICA, N.A.|7.875|110000|360|12/2000|02/2001|74|74|2|26|700|N|P|SF|1|P|NY|125||FRM|698||N
999786911479|C|BANK OF AMERICA, N.A.|7.5|57000|360|12/2000|02/2001|90|90|1|28|699|N|P|SF|1|P|TX|781|25|FRM||1|N
999786913710|R|JPMORGAN CHASE BANK, NA|7.125|114000|360|01/2001|04/2001|73|73|2|16|745|N|C|SF|1|P|WA|992||FRM|||N
999788833695|B|OTHER|9|50000|360|10/2000|12/2000|90|90|2|40|674|N|P|SF|2|I|WI|535|25|FRM|737|1|N
This is the code I am using:
```python
orig_files_fnma = glob.glob("/...1/Acquisition*.txt")
col_names = ["loan_id", "origination_channel","seller_name","original_interest_rate","original_upb","original_loan_term","origination_date","first_payment_date","original_ltv","original_cltv","number_of_borrowers","original_dti",
"borrower_fico_at_origination","first_time_home_buyer_indicator", "loan_purpose","property_type","number_of_units","occupancy_type","property_state","zip_code_short","primary_mortgage_insurance_percent",
"product_type","coborrower_fico_at_origination","mortgage_insurance_type","relocation_mortgage_indicator"]
col_type = {"loan_id": "object","origination_channel": "object","seller_name": "object","original_interest_rate": "float","original_upb": "float","original_loan_term": "int","origination_date": "object",
"first_payment_date": "object","original_ltv": "object","original_cltv": "object","number_of_borrowers": "int","original_dti": "float","borrower_fico_at_origination": "int",
"first_time_home_buyer_indicator": "object", "loan_purpose": "object","property_type": "object","number_of_units": "int","occupancy_type": "object","property_state": "object",
"zip_code_short": "object","primary_mortgage_insurance_percent": "float",
"product_type": "object","coborrower_fico_at_origination": "int","mortgage_insurance_type": "object","relocation_mortgage_indicator": "object"}
dfs = []
temp_df = []
for orig_files_fnma in orig_files_fnma:
temp_df = pd.read_csv(orig_files_fnma, sep = '|', header = None, names = col_names, dtype = col_type, index_col = None, parse_dates=True, verbose = True, engine='python')
dfs.append(temp_df)
```
Always getting the following errors:
```python
Filled 1 NA values in column original_ltv
Filled 52 NA values in column original_cltv
ValueError: Unable to convert column number_of_borrowers to type int
```
I do find out if I don't pre-define the dtype and .astype to change the data type after loading. But ask if possible that I can pre-define the data type first like the code above.
Also, I want to define the length of object for 20 length. What is the right code to do so?
Thanks a lot!
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MDExOlB1bGxSZXF1ZXN0MzY4NjEwNzYz
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Backport PR #31411 on branch 1.0.x (COMPAT: dont pass dtype to datetime64)
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NONE
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Backport PR #31411: COMPAT: dont pass dtype to datetime64
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Inconsistent behavior when groupby pandas Categorical variables
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| 12
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| 2020-08-07T15:21:12Z
| 2020-08-07T15:21:12Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
test = pd.DataFrame(
{
'col1': pd.Series(pd.Categorical(["a", "b", "c", "a"],categories=["a", "b", "c", "d"])),
'col2': np.random.randint(0,100, size=4),
'col3': [12,32,23,22]
}
)
test['col2'] = pd.cut(test['col2'], [0,50,100])
# Group by two variables
print(test.groupby(['col1', 'col2']).sum())
# Group by a single variable
print(test.groupby(['col1']).sum())
```
This gives the result:
```
col1 col2
a (0, 50] 22.0
(50, 100] 12.0
b (0, 50] NaN
(50, 100] 32.0
c (0, 50] 23.0
(50, 100] NaN
d (0, 50] NaN
(50, 100] NaN
col3
col1
a 34
b 32
c 23
d 0
```
#### Problem description
Clearly there is no record for category 'd'. When you group by one column, category 'd' result in sum of 0. When you do group by two columns, those non-existed row result in NaN. Those two are inconsistent.
#### Expected Output
Both results for `d` rows should be the same. I think NaN makes more sense.
#### 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 : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 44.0.0.post20200102
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : 3.6.1
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
Cross ref to dask/dask#5838
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PERF: postpone imports in Index constructor
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| 2020-01-29T18:38:46Z
| 2020-01-29T17:25:29Z
|
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/30790, it's for improving this one mentioned in that issue:
```
arr_str = np.array(["foo", "bar", "baz"], dtype=object)
%timeit pd.Index(arr_str)
```
Only importing the index classes when we actually need them gives a 15-20% speedup.
Last week when profiling the Index constructor, I noticed that those imports actually take quite a bit of time (and it's a change that was introduced after 0.25, https://github.com/pandas-dev/pandas/pull/28141). I didn't get any further with profiling (this alone doesn't explain the full slowdown), but thought it's an easy one to already do anyway.
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Backport PR #31423 on branch 1.0.x (PERF: postpone imports in Index constructor)
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| 1
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NONE
| null |
Backport PR #31423: PERF: postpone imports in Index constructor
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REF: do all convert_tolerance casting inside Index.get_loc
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| 6
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MEMBER
| null |
As a bonus we get better exception messages in the affected tests.
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|
MEMBER
| null |
like Dtype, but doesnt include strings.
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REF: use inherit_names for TimedeltaIndex
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| 2020-01-31T16:04:55Z
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|
MEMBER
| null |
ATM we have two separate ways of doing inheritance/pass-through for our EA-backed Indexes. This moves TDI over to all-inherit_names, Will do the same for other EA-Indexes in their own PRs.
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BUG: regression when applying groupby aggregation on categorical colu…
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CONTRIBUTOR
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Backport of https://github.com/pandas-dev/pandas/pull/31359
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MEMBER
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Just a thought I had, to put the experimental features (pd.NA, string, boolean dtypes) in a separate section, better highlighting that those are experimental.
Since @TomAugspurger basically wants to release now, this is certainly not a blocker :)
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| 2020-01-29T21:13:04Z
| 2020-01-30T09:54:02Z
| 2020-01-29T21:42:21Z
|
CONTRIBUTOR
| null |
…31429)
Backport https://github.com/pandas-dev/pandas/pull/3142
|
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MDExOlB1bGxSZXF1ZXN0MzY4NzgwMTUx
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Backport PR #31430 on branch 1.0.x (release date)
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NONE
| null |
Backport PR #31430: release date
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REF: use inherit_names for PeriodIndex
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| 0
| 2020-01-29T22:42:03Z
| 2020-01-31T04:19:53Z
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|
MEMBER
| null |
analogous to #31427.
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MDU6SXNzdWU1NTcxNzIyNDg=
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|
pandas web and docs structure
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| 2020-01-29T23:10:23Z
| 2020-01-30T14:22:11Z
| 2020-01-30T14:22:11Z
|
CONTRIBUTOR
| null |
The site served by `web` assumes that the docs are served from a `/docs/` directory relative to the root. The content of that directory should be equivalent to what we have at https://pandas.pydata.org/pandas-docs/version/1.0.0/, so `/version/1.0.0/` or `/version/stable/`.
Should I symlink `/docs/` to `/version/stable/`? Does that mess with canonical urls?
cc @datapythonista.
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MDExOlB1bGxSZXF1ZXN0MzY4ODIxNjc0
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ENH: Added index to testing assert message when series values differ
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| 2020-01-29T23:44:41Z
| 2020-03-22T20:42:42Z
| 2020-03-22T20:42:35Z
|
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This implements the suggestion made in #31190
In short, where the values in 2 series differ, the assert exception message includes the index as well as the values. This helps when using assert_frame_equal with check_like=True, since the index may be reordered, making the output hard to understand.
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TST: make sure fixture params are sorted list
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| 1
| 2020-01-30T01:14:52Z
| 2020-01-30T19:21:33Z
| 2020-01-30T19:15:24Z
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MEMBER
| null |
ATM running tests with --n 4 breaks locally because the tests that get collected dont match across processes.
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MDExOlB1bGxSZXF1ZXN0MzY4ODU0NDU2
| 31,437
|
BUG: PeriodIndex.is_monotonic with leading NaT
|
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| 3
| 2020-01-30T02:09:48Z
| 2020-01-31T15:53:04Z
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|
MEMBER
| null |
Before long I expect to just have PeriodEngine subclass DatetimeEngine.
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MDExOlB1bGxSZXF1ZXN0MzY4ODU3NjA1
| 31,438
|
DOC: Fix 1.0.0 contributors
|
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| 0
| 2020-01-30T02:24:22Z
| 2020-01-31T22:50:56Z
| 2020-01-30T08:29:56Z
|
CONTRIBUTOR
| null |
I've also applied this locally in the doc build env.
|
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MDU6SXNzdWU1NTcyMzc3NzI=
| 31,439
|
"Tuple of Lists" method doesn't applicable for DataFrame
|
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| 2020-01-30T02:59:08Z
| 2020-01-30T12:31:24Z
| 2020-01-30T12:31:24Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>>df = pd.DataFrame({'col':np.ones(9)},index=pd.MultiIndex.\
from_product([['A','B','C'],['a','b','c']])).sort_index()
>>>df['col'].loc[(['A','B'],['b','c'])]
A b 1.0
c 1.0
B b 1.0
c 1.0
Name: col, dtype: float64
>>>df.loc[(['A','B'],['b','c'])]
KeyError: "None of [Index(['b', 'c'], dtype='object')] are in the [columns]"
```
#### Problem description
It seems that the "tuple of lists" method doesn't applicable for dataframe.
Originally, I thought these two operations will return the same numerical result.
|
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| 557,260,636
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MDExOlB1bGxSZXF1ZXN0MzY4ODgyMzgw
| 31,440
|
BUG: Fix qcut for nullable integers
|
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| 4
| 2020-01-30T04:34:24Z
| 2020-02-02T22:22:40Z
| 2020-02-02T22:19:06Z
|
MEMBER
| null |
- [x] tests added / passed
- [x] passes `black pandas`
- [x] whatsnew entry
Related to #31389 but doesn't close any issues that I'm aware of. Seems the problems were indexing into a numpy array with a BooleanArray and using searchsorted in the presence of pd.NA.
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TypeError: copy() takes no keyword arguments
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| 8
| 2020-01-30T07:53:02Z
| 2020-02-01T15:14:52Z
| 2020-02-01T15:14:52Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(['A', 'A', 'B', 'B'], columns=['grps'])
df.groupby('grps').apply(lambda x: x.index.to_list())
```
```python
Traceback (most recent call last):
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 735, in apply
result = self._python_apply_general(f)
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 751, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 171, in apply
result_values, mutated = splitter.fast_apply(f, group_keys)
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 925, in fast_apply
return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
File "pandas/_libs/reduction.pyx", line 505, in pandas._libs.reduction.apply_frame_axis0
TypeError: copy() takes no keyword arguments
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3319, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-a3e576516eed>", line 4, in <module>
df.groupby('grps').apply(lambda x: x.index.to_list())
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 746, in apply
return self._python_apply_general(f)
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 751, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 171, in apply
result_values, mutated = splitter.fast_apply(f, group_keys)
File "/home/bay2rng/miniconda3/envs/p356_rtp_production_scheduling/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 925, in fast_apply
return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
File "pandas/_libs/reduction.pyx", line 505, in pandas._libs.reduction.apply_frame_axis0
TypeError: copy() takes no keyword arguments
```
#### Problem description
This snippet is working well on version 0.25.3. It breaks in version 1.0.0.
#### Expected Output
```python
A [0, 1]
B [2, 3]
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-72-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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Backport PR #31438 on branch 1.0.x (DOC: Fix 1.0.0 contributors)
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NONE
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Backport PR #31438: DOC: Fix 1.0.0 contributors
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NONE
| null |
Backport PR #30907: DOC: add 1.0.1 whatsnew file
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MDU6SXNzdWU1NTczNjEwOTA=
| 31,444
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Enhancement: code sugar for index.get_level_values
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[] | null | 1
| 2020-01-30T09:18:23Z
| 2020-04-25T13:35:28Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
data.index.get_level_values('foobar') == 42
# would like to have data.index['foobar'] == 42
```
#### Problem description
Filtering data frame row by one column of multi index is quite common.
Unless I've missed something, the current implementation need `get_index_value`. It would be nice to have a more direct way to select one MultiIndex column, like we can do with the data frame columns.
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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Feature request: code sugar for index.get_level_values
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| 2020-01-30T09:21:09Z
| 2020-01-30T11:20:25Z
| 2020-01-30T09:26:27Z
|
NONE
| null |
(Sorry, I have closed issue 31444 by mistake, hence re-creating it)
#### Code Sample
```python
data.index.get_level_values('foobar') == 42
# would like to have data.index['foobar'] == 42
```
#### Problem description
Filtering data frame row by one column of multi index is quite common.
Unless I've missed something, the current implementation need `get_index_value`. It would be nice to have a more direct way to select one MultiIndex column, like we can do with the data frame columns.
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
```
</details>
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MDU6SXNzdWU1NTc0MTQ1Mzg=
| 31,446
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REGR: Array.__setitem__ failing with nullable boolean mask
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| 3
| 2020-01-30T10:53:50Z
| 2020-02-01T14:37:26Z
| 2020-02-01T14:37:26Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series([1, 2], dtype='Int64')
s.where(s > 1)
```
#### Problem description
The code above raises
`TypeError: bad operand type for unary ~: 'BooleanArray'`
#### Expected Output
```
0 <NA>
1 2
dtype: Int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.0
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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REGR: to_csv(na_rep) truncates values
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| 4
| 2020-01-30T11:28:04Z
| 2020-02-01T15:21:12Z
| 2020-02-01T15:21:12Z
|
CONTRIBUTOR
| null |
#### Code Sample
```python
>>> import pandas as pd
>>> pd.__version__
'1.0.0rc0'
>>> pd.Series(range(8, 12)).to_csv(na_rep='-')
',0\r\n0,8\r\n1,9\r\n2,1\r\n3,1\r\n'
```
#### Problem description
Despite (or due to?) #29975 and #30146, using `na_rep` still makes `to_csv()` truncate values. In this case integers.
#### Expected Output
',0\r\n0,8\r\n1,9\r\n2,10\r\n3,11\r\n'
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : Dutch_Netherlands.1252
pandas : 1.0.0rc0
numpy : 1.18.1
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 : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 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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MDU6SXNzdWU1NTc0NjYzOTc=
| 31,448
|
DOC: to_csv is not properly linked from read_csv docstring
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| 3
| 2020-01-30T12:39:00Z
| 2020-01-31T12:08:37Z
| 2020-01-31T12:08:37Z
|
NONE
| null | ERROR: type should be string, got "\r\nhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html#pandas.read_csv\r\n\r\n"
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MDU6SXNzdWU1NTc0NzUxNzc=
| 31,449
|
REGR: Assignment to MultiIndex fails for pandas 1.0.0.
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| 5
| 2020-01-30T12:54:39Z
| 2020-02-03T23:20:27Z
| 2020-02-03T23:20:27Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
This problem occurred with ``pandas 1.0.0 py37he350917_0 conda-forge``, but did not before or at least not since 0.24.
```python
import pandas as pd
import numpy as np
index = pd.MultiIndex.from_tuples([("a", "c"), ("b", "x"), ("a", "d")], names=["l1", "l2"])
df = pd.DataFrame(index=index, data=np.arange(3), columns=["e"])
df.loc["a", "e"] = np.arange(99, 101)
```
#### Problem description
The error message is the following
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-13-b62e661ba0e4> in <module>
----> 1 df.loc["a", "e"] = np.arange(99, 101)
C:\tools\miniconda3\envs\respy\lib\site-packages\pandas\core\indexing.py in __setitem__(self, key, value)
668 key = com.apply_if_callable(key, self.obj)
669 indexer = self._get_setitem_indexer(key)
--> 670 self._setitem_with_indexer(indexer, value)
671
672 def _validate_key(self, key, axis: int):
C:\tools\miniconda3\envs\respy\lib\site-packages\pandas\core\indexing.py in _setitem_with_indexer(self, indexer, value)
927 # we can directly set the series here
928 # as we select a slice indexer on the mi
--> 929 idx = index._convert_slice_indexer(idx)
930 obj._consolidate_inplace()
931 obj = obj.copy()
C:\tools\miniconda3\envs\respy\lib\site-packages\pandas\core\indexes\base.py in _convert_slice_indexer(self, key, kind)
2917
2918 # potentially cast the bounds to integers
-> 2919 start, stop, step = key.start, key.stop, key.step
2920
2921 # figure out if this is a positional indexer
AttributeError: 'numpy.ndarray' object has no attribute 'start'
```
If the MultiIndex would have been sorted, ``key`` in the last part of the traceback or ``indexer`` in the first part would have been a slice and everything is fine. Since the MultiIndex is unsorted ``key`` or ``indexer`` are a boolean array which has no start stop attributes.
I am sorry for not digging further, but I have not inspected pandas internals before.
#### Expected Output
```
e
l1 l2
a c 99
b x 1
a d 100
```
#### 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 69 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.46.0
</details>
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MDU6SXNzdWU1NTc0ODEzNDg=
| 31,450
|
REGR: groupby().agg fails on categorical column in pandas 1.0.0
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| 3
| 2020-01-30T13:05:54Z
| 2020-02-05T00:31:25Z
| 2020-02-05T00:31:25Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
tbl = pd.DataFrame({"col_num": [1, 1, 2, 3]})
tbl["col_cat"] = tbl["col_num"].astype("category")
# The following line works in 0.25.3 but throws exception in 1.0.0
df = tbl.groupby("col_num").agg({"col_cat": "first"})
```
#### Problem description
agg() on categorical column works without any warnings in 0.25.3 but throws exception in pandas 1.0.0:
<details>
NotImplementedError Traceback (most recent call last)
<ipython-input-2-663038a86e20> in <module>
----> 1 df = tbl.groupby("col_num").agg({"col_cat": "first"})
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
938 func = _maybe_mangle_lambdas(func)
939
--> 940 result, how = self._aggregate(func, *args, **kwargs)
941 if how is None:
942 return result
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
426
427 try:
--> 428 result = _agg(arg, _agg_1dim)
429 except SpecificationError:
430
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\base.py in _agg(arg, func)
393 result = {}
394 for fname, agg_how in arg.items():
--> 395 result[fname] = func(fname, agg_how)
396 return result
397
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\base.py in _agg_1dim(name, how, subset)
377 "nested dictionary is ambiguous in aggregation"
378 )
--> 379 return colg.aggregate(how)
380
381 def _agg_2dim(name, how):
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\generic.py in aggregate(self, func, *args, **kwargs)
245
246 if isinstance(func, str):
--> 247 return getattr(self, func)(*args, **kwargs)
248
249 elif isinstance(func, abc.Iterable):
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\groupby.py in f(self, **kwargs)
1376 # try a cython aggregation if we can
1377 try:
-> 1378 return self._cython_agg_general(alias, alt=npfunc, **kwargs)
1379 except DataError:
1380 pass
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
888
889 result, agg_names = self.grouper.aggregate(
--> 890 obj._values, how, min_count=min_count
891 )
892
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\ops.py in aggregate(self, values, how, axis, min_count)
579 ) -> Tuple[np.ndarray, Optional[List[str]]]:
580 return self._cython_operation(
--> 581 "aggregate", values, how, axis, min_count=min_count
582 )
583
~\AppData\Local\Continuum\miniconda3\envs\dev37\lib\site-packages\pandas\core\groupby\ops.py in _cython_operation(self, kind, values, how, axis, min_count, **kwargs)
453 # are not setup for dim transforming
454 if is_categorical_dtype(values) or is_sparse(values):
--> 455 raise NotImplementedError(f"{values.dtype} dtype not supported")
456 elif is_datetime64_any_dtype(values):
457 if how in ["add", "prod", "cumsum", "cumprod"]:
NotImplementedError: category dtype not supported
</details>
#### Expected Output
no error, just groupped dataframe.
#### 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 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.0
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.7
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5MDc3Njky
| 31,451
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DOC: Fixed example section in pandas/core/dtypes/*.py
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| 2020-03-19T23:29:34Z
| 2020-02-10T09:13:13Z
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| 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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Series .attrs is not correctly maintained/propagated in to_frame
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| 3
| 2020-01-30T14:13:58Z
| 2020-04-06T16:19:02Z
| 2020-04-06T16:19:01Z
|
NONE
| null |
Hi, it seems that the `.attrs` dict (for storing metadata) is not always propagated correctly. E.g.
#### Code Sample
```python
s = pd.Series([0,1,2], name="x")
s.attrs["mydata"] = "test"
s.attrs
# >> {'mydata': 'test'}
df = s.to_frame()
df.x.attrs
# >> {}
```
#### Problem description
As @jorisvandenbossche commented [here](https://github.com/pandas-dev/pandas/pull/29062#issuecomment-580245961), it seems that this is because there are a few places in pandas where calls to `finalize` are missing.
I ran into it mainly in the "expanddim" direction (from Series to DF). Copying, subsetting and manipulating a Dataframe column that has `attrs` explicitly set, in contrast, seems to work without losing the metadata. I haven't tried more complicated cases, like aggregations (I don't know much about pandas internals, but from previous attempts to implement metadata in my own subclass I seem to remember that these have special code paths in the BlockManager parts, where I suspect the same or related problem may occur...).
#### Expected Output
```python
df.x.attrs
# >> {'mydata': 'test'}
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.0.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 : 1.0.0
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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pandas._testing.assert_produces_warning docstring unclear?
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| 2020-01-30T14:24:33Z
| 2021-07-27T05:36:18Z
| null |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas._testing as tm
import warnings
def func():
warnings.warn("foo", FutureWarning)
# The following block will raise: AssertionError: Did not see expected warning of class 'FutureWarning'
with tm.assert_produces_warning(FutureWarning, filter_level="ignore", check_stacklevel=False):
func()
# This will run just fine
with tm.assert_produces_warning(FutureWarning, filter_level="always", check_stacklevel=False):
func()
```
#### Problem description
The docstring reads
```
filter_level : str or None, default "always"
Specifies whether warnings are ignored, displayed, or turned
into errors.
Valid values are:
* "error" - turns matching warnings into exceptions
* "ignore" - discard the warning
* "always" - always emit a warning
* "default" - print the warning the first time it is generated
from each location
* "module" - print the warning the first time it is generated
from each module
* "once" - print the warning the first time it is generated
```
so I was expecting that, with 'ignore', no error would be raised, regardless of whether the error was produced or not.
I'm also not convinced the description for 'always' is correct - should it be "always emit a warning", or "always emit an error"?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 8f9e726128e486b354c485ec8e8a5c3cde2b94fe
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-76-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0+1982.g8f9e72612.dirty
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 5.3.0
sphinx : 2.3.1
blosc : 1.8.3
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.7
numba : 0.47.0
</details>
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TYP: Type annotaion for test decorators
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| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I am not 100% sure this is something we want, or at least with this implementation.
cc @simonjayhawkins
|
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jobs failling with error raise RuntimeError("Cannot cythonize without Cython installed.")\n RuntimeError: Cannot cythonize without Cython installed.\n
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[] | null | 3
| 2020-01-30T15:01:56Z
| 2020-02-04T21:36:03Z
| 2020-02-04T21:36:03Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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| 557,556,066
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MDExOlB1bGxSZXF1ZXN0MzY5MTI1OTM1
| 31,456
|
BUG: Groupby.apply wasn't allowing for functions which return lists
|
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| 3
| 2020-01-30T15:07:16Z
| 2020-02-25T17:12:23Z
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|
MEMBER
| null |
- [ ] closes #31441
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
|
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Timedelta multiplication crashes for large arrays
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NONE
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#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series(range(10001))
delta_t = pd.Timedelta("30T")
print(s * delta_t)
```
`Traceback:`
<details>
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-14-a39fa18ca12d> in <module>
4 delta_t = pd.Timedelta("30T")
5
----> 6 print(s * delta_t)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\ops\common.py in new_method(self, other)
62 other = item_from_zerodim(other)
63
---> 64 return method(self, other)
65
66 return new_method
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\ops\__init__.py in wrapper(left, right)
498 lvalues = extract_array(left, extract_numpy=True)
499 rvalues = extract_array(right, extract_numpy=True)
--> 500 result = arithmetic_op(lvalues, rvalues, op, str_rep)
501
502 return _construct_result(left, result, index=left.index, name=res_name)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in arithmetic_op(left, right, op, str_rep)
194 else:
195 with np.errstate(all="ignore"):
--> 196 res_values = na_arithmetic_op(lvalues, rvalues, op, str_rep)
197
198 return res_values
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\ops\array_ops.py in na_arithmetic_op(left, right, op, str_rep)
147
148 try:
--> 149 result = expressions.evaluate(op, str_rep, left, right)
150 except TypeError:
151 result = masked_arith_op(left, right, op)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, op_str, a, b, use_numexpr)
206 use_numexpr = use_numexpr and _bool_arith_check(op_str, a, b)
207 if use_numexpr:
--> 208 return _evaluate(op, op_str, a, b)
209 return _evaluate_standard(op, op_str, a, b)
210
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_numexpr(op, op_str, a, b)
112 f"a_value {op_str} b_value",
113 local_dict={"a_value": a_value, "b_value": b_value},
--> 114 casting="safe",
115 )
116
~\AppData\Local\Continuum\anaconda3\lib\site-packages\numexpr\necompiler.py in evaluate(ex, local_dict, global_dict, out, order, casting, **kwargs)
820 # Create a signature
821 signature = [(name, getType(arg)) for (name, arg) in
--> 822 zip(names, arguments)]
823
824 # Look up numexpr if possible.
~\AppData\Local\Continuum\anaconda3\lib\site-packages\numexpr\necompiler.py in <listcomp>(.0)
819
820 # Create a signature
--> 821 signature = [(name, getType(arg)) for (name, arg) in
822 zip(names, arguments)]
823
~\AppData\Local\Continuum\anaconda3\lib\site-packages\numexpr\necompiler.py in getType(a)
701 if kind == 'S':
702 return bytes
--> 703 raise ValueError("unknown type %s" % a.dtype.name)
704
705
ValueError: unknown type object
```
</details>
#### Problem description
This works if the series is initialised with "large arrays" (len > 10000). It seems it crashes just with timedeltas, but not with float/int values instead of `delta_t`.
I managed to get around this error by calling:
```python
t = pd.Series([delta_t] * len(s)) # create series of timedeltas
print(s * t)
```
It worked fine until pandas 0.25.3
#### Expected Output
0 0 days 00:00:00
1 0 days 00:30:00
2 0 days 01:00:00
3 0 days 01:30:00
4 0 days 02:00:00
...
9996 208 days 06:00:00
9997 208 days 06:30:00
9998 208 days 07:00:00
9999 208 days 07:30:00
10000 208 days 08:00:00
Length: 10001, dtype: timedelta64[ns]
#### 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 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 4.54.2
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.2
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 : 0.47.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5MTM5OTIz
| 31,458
|
Fix to_csv and to_excel links on read_csv, read_table and read_excel See Also docs
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CONTRIBUTOR
| null |
- [x] closes #31448
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
|
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MDU6SXNzdWU1NTc1OTEwMDc=
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ENH: pd.cut should be able to return a Series of IntervalDtype
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| 2020-01-30T16:01:59Z
| 2021-07-27T05:36:59Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: import random
In [3]: df=pd.DataFrame({'x': [random.randint(0,10) for i in range(100)]})
In [4]: pd.cut(df.x, [2*i for i in range(6)])
Out[4]:
0 (6.0, 8.0]
1 NaN
2 (0.0, 2.0]
3 (2.0, 4.0]
4 (0.0, 2.0]
...
95 (8.0, 10.0]
96 (2.0, 4.0]
97 (8.0, 10.0]
98 (0.0, 2.0]
99 (8.0, 10.0]
Name: x, Length: 100, dtype: category
Categories (5, interval[int64]): [(0, 2] < (2, 4] < (4, 6] < (6, 8] < (8, 10]]
```
#### Problem description
By default, the return type of `pd.cut` is categories, but we know we are creating intervals, so shouldn't it be `IntervalDtype` ?
#### Expected Output
```python
In [5]: pd.cut(df.x, [2*i for i in range(6)]).astype('Interval')
Out[5]:
0 (6.0, 8.0]
1 NaN
2 (0.0, 2.0]
3 (2.0, 4.0]
4 (0.0, 2.0]
...
95 (8.0, 10.0]
96 (2.0, 4.0]
97 (8.0, 10.0]
98 (0.0, 2.0]
99 (8.0, 10.0]
Name: x, Length: 100, dtype: interval
```
IMHO, I shouldn't need to do the 'astype('Interval')`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 42.0.2.post20191203
Cython : None
pytest : 5.3.2
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.2.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.2
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.6
numba : None
</details>
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MDU6SXNzdWU1NTc1OTM2NDU=
| 31,460
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DOC: Example of DataFrame.to_csv() is not showing properly
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| 0
| 2020-01-30T16:05:51Z
| 2020-01-31T07:19:57Z
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|
CONTRIBUTOR
| null |
#### Problem description
Example of DataFrame.to_csv() is not showing properly. See the screenshoot below:
#### Expected Output
I'll propose a PR fixing the docstring
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DOC: Fix DataFrame.to_csv example
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CONTRIBUTOR
| null |
- [x] closes #31460
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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Some code cleanups
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| 1
| 2020-01-30T16:30:24Z
| 2020-01-31T16:26:35Z
| 2020-01-31T03:12:02Z
|
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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MDU6SXNzdWU1NTc2MjYzOTg=
| 31,463
|
Regression: objToJSON "nonvoid function does not return a value" error is back
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| 8
| 2020-01-30T16:57:31Z
| 2020-02-02T17:09:19Z
| 2020-02-02T17:09:19Z
|
NONE
| null |
There was an issue in 2013, #5326, where `initObjToJSON` in `pandas/_libs/src/ujson/python/objToJSON.c` was causing a build error due to it lacking a return value. Specifically, the issue was:
```
pandas/_libs/src/ujson/python/objToJSON.c: In function ‘initObjToJSON’:
pandas/_libs/src/ujson/python/objToJSON.c:181:1: error: control reaches end of non-void function [-Werror=return-type]
```
This was fixed at the time in #5334. However, a recent commit, #30710, removed the return value again. This has caused the warning/error to reappear, causing our builds of pandas 1.0.0 to fail. The warning is appearing in your CI builds as well. The difference is that we have it configured as an error.
Would it be possible to set a valid return value for the function? Thank you.
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read_json with typ="series" of json list of bools results in timestamps/Exception
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| 1
| 2020-01-30T16:59:16Z
| 2020-04-08T17:13:08Z
| 2020-04-08T17:13:08Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.read_json('[true, true, false]', typ="series")
```
results in the following Pandas Series object in older Pandas versions:
```
0 1970-01-01 00:00:01
1 1970-01-01 00:00:01
2 1970-01-01 00:00:00
dtype: datetime64[ns]
```
Since 1.0.0 it raises TypeError: <class 'bool'> is not convertible to datetime
#### Problem description
The expected output would be a Pandas Series of bools. Note that
* with typ="frame" it works and the result is a dataframe with one column with bool values
* with convert_dates set to False correctly outputs a Series of boolean values
This is a problem because
* users would expect a Series of bools (and neither an exception nor a series of timestamps)
* it is inconsistent with the "frame" case
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.13-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : de_DE.UTF-8
LOCALE : de_DE.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : 0.29.14
pytest : 5.2.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.2.4
pyxlsb : None
s3fs : None
scipy : 1.3.2
sqlalchemy : 1.3.11
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
</details>
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Issue pyodbc and to_sql HIVE
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[] | null | 3
| 2020-01-30T17:14:21Z
| 2020-05-16T04:12:33Z
| null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
engine_clus= pyodbc.connect(connection_string, autocommit=True)
df.to_sql("table", con=engine_clus, if_exists='append', index = False, schema = 'schema')
#### Problem description
We need to load in HIVE a dataframe which contains information from a source cluster.
Using pyodbc we can create, drop and select but we can't do inserts because of the following error:
DatabaseError: Execution failed on sql 'SELECT name FROM sqlite_master WHERE type='table' AND name=?;': ('HY000', '[HY000] [Microsoft][Hardy] (79) Failed to reconnect to server. (79) (SQLPrepare)')
#### Expected Output
We expected to be able to insert information from a dataframe.
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.5.final.0
python-bits : 64
OS : Windows
OS-release : 10
byteorder : little
LC_ALL : None
LANG : es
LOCALE : None.None
pandas : 0.25.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 10.0.1
setuptools : 45.1.0
Cython : 0.28.2
pytest : 3.5.1
hypothesis : None
sphinx : 1.7.4
blosc : None
feather : None
xlsxwriter : 1.0.4
lxml.etree : 4.2.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 6.4.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.1
matplotlib : 2.2.2
numexpr : 2.6.5
odfpy : None
openpyxl : 2.5.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.7
tables : 3.4.3
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.4
</details>
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MDU6SXNzdWU1NTc2NDM5NjE=
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pandas.apply() converts all columns to object datatype
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| 7
| 2020-01-30T17:28:18Z
| 2020-07-01T16:53:35Z
| 2020-07-01T16:53:35Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame([['a', 1], ['b', 2]], columns=['col1', 'col2'])
df.dtypes
df.apply(lambda x: x.dtype)
```
#### Problem description
Column datatypes are converted to object if one columns is dtype=object
#### Expected Output
Columns should maintain datatypes when passed to apply
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-1029-gcp
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.5
pytest : 4.3.0
hypothesis : None
sphinx : 1.8.4
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.3.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.3.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : 0.3.0
lxml.etree : 4.3.0
matplotlib : 3.0.2
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.0
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.3.0
pyxlsb : None
s3fs : 0.2.0
scipy : 1.2.1
sqlalchemy : 1.2.18
tables : 3.4.4
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
numba : 0.42.0
</details>
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MDU6SXNzdWU1NTc2NTY3Mjk=
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pd.ExcelFile closes stream on destruction in pandas 1.0.0
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| 7
| 2020-01-30T17:54:53Z
| 2020-03-12T12:18:21Z
| 2020-03-12T12:18:21Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python3
import pandas as pd
print(pd.__version__)
a = open('some_file.xlsx', 'rb')
x = pd.ExcelFile(a)
del x
print(a.read())
```
#### Problem description
Above script behaves in different way in pandas 0.25.3 and 1.0.0:
```bash
$ python3 t1.py
0.25.3
b''
$ sudo pip3 install pandas --upgrade --quiet
$ python3 t1.py
1.0.0
Traceback (most recent call last):
File "t1.py", line 6, in <module>
print(a.read())
ValueError: read of closed file
```
It seems that stream is closed when ExcelFile is destroyed - and I don't see why.
#### Expected Output
I'd expect either notice in release notes, or the same output in 0.25.3 and 1.0.0.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-1028-gcp
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 9.0.1
setuptools : 39.0.1
Cython : None
pytest : 4.3.0
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 0.999999999
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : None
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : 2.5.14
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 4.3.0
pyxlsb : None
s3fs : None
scipy : 1.2.0
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
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REF: use inherit_names for DTI
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| 1
| 2020-01-30T18:13:38Z
| 2020-01-31T04:19:20Z
| 2020-01-31T03:09:34Z
|
MEMBER
| null |
xref #31427, #31433. With all three of these in, we can then remove indexes.datetimelike.DatetimelikeDelegateMixin
Using inherit_names, we also have the option of making tz, tzinfo, dtype cache_readonly if that turns out to make a difference.
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| 557,665,572
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MDU6SXNzdWU1NTc2NjU1NzI=
| 31,469
|
REGR: __setitem__ with integer slices on Int/RangeIndex is broken (label instead of positional)
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| 15
| 2020-01-30T18:13:38Z
| 2020-02-01T14:22:06Z
| 2020-02-01T14:22:05Z
|
NONE
| null |
There's an backward incompatible change in pandas 1.0 that I didn't find in the changelog. I might have just overlooked it though.
```python
import numpy as np
X = pd.DataFrame(np.zeros((100, 1)))
X[-4:] = 1
X
```
In pandas 0.25.3 or lower, this results in the last four entries of X to be 1 and all the others zero. In pandas 1.0, it results in all entries of X being 1.
I assume it's a change of indexing axis 0 or axis 1?
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MDExOlB1bGxSZXF1ZXN0MzY5MjQyNTEx
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BUG: groupby().agg fails on categorical column
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| 14
| 2020-01-30T19:20:30Z
| 2020-02-05T08:24:35Z
| 2020-02-05T00:32:33Z
|
MEMBER
| null |
- [ ] closes #31450
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 557,699,732
|
MDU6SXNzdWU1NTc2OTk3MzI=
| 31,471
|
TypeError when calculating min/max of period column using groupby
|
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| 1
| 2020-01-30T19:23:28Z
| 2020-02-04T22:52:27Z
| 2020-02-04T22:52:27Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
periods = pd.period_range(start="2019-01", periods=4, freq="M")
groups = [1, 1, 2, 2]
df = pd.DataFrame({"periods": periods, "groups": groups})
result = df.groupby("groups")["periods"].min()
```
#### Problem description
The last line of the example throws a _TypeError: data type not understood_.
<details>
Traceback (most recent call last):
File "test.py", line 6, in <module>
result = df.groupby("groups")["periods"].min()
File "...\site-packages\pandas\core\groupby\groupby.py", line 1378, in f
return self._cython_agg_general(alias, alt=npfunc, **kwargs)
File "...\site-packages\pandas\core\groupby\groupby.py", line 889, in _cython_agg_general
result, agg_names = self.grouper.aggregate(
File "...\site-packages\pandas\core\groupby\ops.py", line 580, in aggregate
return self._cython_operation(
File "...\site-packages\pandas\core\groupby\ops.py", line 573, in _cython_operation
result = result.astype(orig_values.dtype)
TypeError: data type not understood
</details>
#### Expected Output
The `result` DataFrame should contain the earliest period (in this case 2019-01) for each grouping slice.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : English_United Kingdom.1252
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5Mjc1Mzc4
| 31,472
|
DOC: Fix examples in documentation
|
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| 8
| 2020-01-30T20:36:31Z
| 2020-03-14T14:05:25Z
| 2020-03-07T20:10:21Z
|
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| 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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MDExOlB1bGxSZXF1ZXN0MzY5MzIyNDg2
| 31,473
|
CLN: clean signature in Groupby.add_numeric_operations
|
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| 0
| 2020-01-30T22:33:41Z
| 2020-02-12T22:13:34Z
| 2020-01-31T03:51:26Z
|
CONTRIBUTOR
| null |
Avoid **kwars, replace with named parameters.
|
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|
Not supported for DataFrames Exception triggered by a simple inspect.getmembers() call on a DataFrame
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| 8
| 2020-01-31T00:19:16Z
| 2020-04-21T12:53:51Z
| 2020-04-21T12:53:51Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import inspect
import pandas as pd
df = pd.DataFrame()
try:
members = inspect.getmembers(df)
except Exception as e:
print(e)
```
#### Problem description
A "**Not supported for DataFrames Exception**" is triggered by a simple inspect.getmembers() call on a DataFrame instance. There are some useful libraries like the Python 'fire' library which are disabled by this bug, which was not present in Pandas Release 0.24.2 but emerged in 0.25.0
#### Expected Output
A list of DataFrame members (without crashing)
#### Output of ``pd.show_versions()``
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-26-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5MzUyNzUz
| 31,475
|
BUG: Period[us] start_time off by 1 nanosecond
|
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| 2020-02-04T00:13:45Z
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MEMBER
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MDU6SXNzdWU1NTc4NTY4ODM=
| 31,476
|
DEPR: DataFrame.__getitem__[str] sometimes slices on index
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[] | null | 1
| 2020-01-31T01:27:32Z
| 2020-09-24T19:57:39Z
| 2020-09-24T19:57:39Z
|
MEMBER
| null |
xref #31334, #9595
- This is a tiny corner case, at least as measured by tests cases (2 tests reach this)
- With this removed, the `__getitem__` API becomes much simpler to describe: "DataFrame.__getitem__` is always column-based"
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MDExOlB1bGxSZXF1ZXN0MzY5Mzc1OTU5
| 31,477
|
REGR: Fix TypeError in groupby min / max of period column
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| 2020-02-04T22:54:20Z
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| null |
- [x] closes #31471
- [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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MDU6SXNzdWU1NTc4ODkzOTc=
| 31,478
|
round method throws error if DataFrame includes Int64Dtype
|
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| 2021-04-05T01:27:00Z
| 2021-04-05T01:27:00Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> s = pd.Series([1, 2, None], dtype="Int64")
>>> s.round(1)
AttributeError: 'float' object has no attribute 'rint'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.8/site-packages/pandas/core/series.py", line 2146, in round
result = com.values_from_object(self).round(decimals)
TypeError: loop of ufunc does not support argument 0 of type float which has no callable rint method
```
#### Problem description
round method throws above error.
Expected behavior is same to int64 dtype and ignore NaN.
```
>>> s = pd.Series([1, 2], dtype="int64")
>>> s.round(1)
0 1
1 2
dtype: int64
```
#### Expected Output
```
>>> s = pd.Series([1, 2, None], dtype="Int64")
>>> s.round(1)
>>> s
0 1
1 2
2 <NA>
dtype: Int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.13-arch1-1
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NTc4OTA4NDY=
| 31,479
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multi-dimensional-indexing deprecation warning has inconsistent stack level.
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| 3
| 2020-01-31T03:31:08Z
| 2021-06-15T23:48:02Z
| 2021-06-15T23:48:02Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
import warnings
import pandas as pd
warnings.simplefilter("always")
x = pd.Series([], dtype="float64")
df = pd.DataFrame(
{
"year": [2018, 2018, 2018],
"month": [1, 1, 1],
"day": [1, 2, 3],
"value": [1, 2, 3],
}
)
df["date"] = pd.to_datetime(df[["year", "month", "day"]])
monthly = df[["date", "value"]].groupby(["date"]).sum()
dates = monthly.index
dates[:, None]
x.index[:, None]
```
#### Problem description
Running the above script gives:
```
sharon@22:14 ➤ python /tmp/pd_test.py
/tmp/pd_test.py:21: DeprecationWarning: Support for multi-dimensional indexing (e.g. `index[:, None]`) on an Index is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
dates[:, None]
/home/tcaswell/.virtualenvs/sys37/lib/python3.7/site-packages/pandas/core/indexes/range.py:708: DeprecationWarning: Support for multi-dimensional indexing (e.g. `index[:, None]`) on an Index is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.
return super().__getitem__(key)
```
I think the issue is that https://github.com/pandas-dev/pandas/blob/7d616b951c08bbd90e36bee2c184569556d57093/pandas/core/indexers.py#L263-L270 is pushing the warning up a fixed number of stack layers, but due to implementation details it maybe deeper / less deep than expected.
#### Expected Output
pandas should be the module reported in the warning about slicing in both cases or neither case. You may want to use something like https://github.com/matplotlib/matplotlib/blob/e4dbf4e7b5241e7d74f79adc854f84a954b45e8f/lib/matplotlib/cbook/__init__.py#L2013-L2032 to find the first stack not in pandas to put the warning at.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-38-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0rc0
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 3.10.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.12.0.dev
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.0rc2.post1219+g3d8a6cb83
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 3.10.1
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.14.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : None
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| 1
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| 2020-01-31T15:48:02Z
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MEMBER
| null |
Made possibly by #31433.
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Backport PR #31461 on branch 1.0.x (DOC: Fix DataFrame.to_csv example)
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NONE
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Backport PR #31461: DOC: Fix DataFrame.to_csv example
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BUG: objToJson.c - fix return value
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| 2020-01-31T08:08:46Z
| 2020-02-02T17:10:20Z
| 2020-02-02T17:09:19Z
|
CONTRIBUTOR
| null |
- [x] closes #31463
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=27217&view=logs&j=3a03f79d-0b41-5610-1aa4-b4a014d0bc70&t=fe74a338-551b-5fbb-553d-25f48b1836e8&l=687
Seems like this warning is somehow causing an error in the users builds of pandas
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MDExOlB1bGxSZXF1ZXN0MzY5NDczNjM4
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TST: preserve dtypes on assignment
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| 2
| 2020-01-31T08:49:46Z
| 2020-02-01T23:20:49Z
| 2020-02-01T23:20:37Z
|
CONTRIBUTOR
| null |
- [x] closes #31340
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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| 3
| 2020-01-31T09:14:04Z
| 2020-02-06T09:08:02Z
| 2020-02-01T14:37:26Z
|
MEMBER
| null |
- [x] closes #31446
- [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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MDExOlB1bGxSZXF1ZXN0MzY5NDkzNTkx
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CLN: named parameters for GroupBy.(mean|median|var|std)
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| 7
| 2020-01-31T09:41:52Z
| 2020-02-04T16:25:32Z
| 2020-02-02T22:46:32Z
|
CONTRIBUTOR
| null |
Drops *args & **kwargs, replace with named parameters for groupby methods mean, median, var & std. Similar to #31473.
This PR has the side effect that the raised error when a parameter is not allowed, is now ``TypeError`` instead of ``UnsupportedFunctionCall``, so technically a API change...
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DOC: Parameter doc strings for Groupby.(sum|prod|min|max|first|last)
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| 1
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| 2020-02-12T22:12:38Z
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CONTRIBUTOR
| null |
Follow-up to #31473.
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Maybe wrong default axis with operators (add, sub, mul, div) between datetime-indexed df and series 1.0.0
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| 3
| 2020-01-31T10:20:31Z
| 2020-10-02T22:39:17Z
| 2020-10-02T22:39:17Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
index = pd.date_range(start='2020', periods=5)
df = pd.DataFrame([[1, 2, 3]] * 5, columns=['a', 'b', 'c'], index=index)
series = pd.Series([10, 20, 30, 40, 50], index=index)
print(df + series)
```
| | 2020-01-01 00:00:00 | 2020-01 02 00:00:00 | 2020-01-03 00:00:00 | 2020-01-04 00:00:00 | 2020-01-05 00:00:00 | a | b | c |
|---|---|---|---|---|---|---|---|---|
| 2020-01-01 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-01-02 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-01-03 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-01-04 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2020-01-05 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
#### Problem description
According to the docs (https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#data-alignment-and-arithmetic):
> When doing an operation between DataFrame and Series, the default behavior is to align the Series index on the DataFrame columns, thus broadcasting row-wise
> In the special case of working with time series data, if the DataFrame index contains dates, the broadcasting will be column-wise
It seems to me that in both cases now the broadcasting is row-wise.
Is this an expected change for pandas 1.0.0 (I hope not - I never saw any FutureWarnings about it)? If so, the docs (and the examples) must be updated.
The same happens for the operators `-`, `/`, `*`, `%`
#### Expected Output
Not sure if this is the expected output anymore, but it used to be equivalent to:
```python
df.add(series, axis=0)
```
| | a | b | c |
|---|-----|---|---|
| 2020-01-01 | 11 | 12 | 13 |
| 2020-01-02 | 21 | 22 | 23 |
| 2020-01-03 | 31 | 32 | 33 |
| 2020-01-04 | 41 | 42 | 43 |
| 2020-01-05 | 51 | 52 | 53 |
Although I can't replicate it, I'm pretty sure this was the behaviour until pandas 0.25.3
#### 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 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : 5.3.4
hypothesis : 4.54.2
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : 1.2.7
lxml.etree : 4.4.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.2
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.4
pyxlsb : None
s3fs : 0.4.0
scipy : 1.3.2
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 : 0.47.0
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MDU6SXNzdWU1NTgwNDM4MjM=
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Unclosed file on EmptyDataError
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| 1
| 2020-01-31T10:25:27Z
| 2020-03-15T00:37:42Z
| 2020-03-15T00:37:42Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas
import psutil
proc = psutil.Process()
try:
data = pandas.read_csv('kek.csv', engine='python') # kek.csv is an empty file
except pandas.errors.EmptyDataError:
pass
print(proc.open_files())
```
#### Problem description
Hi, there might be an unwanted behavior on attempt to read an empty `.csv` file, providing a
filename instead of handler, so it cannot be closed anymore by user on exception.
#### Expected Output
`[]`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.13-1.el7.elrepo.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.2.2
setuptools : 45.1.0
Cython : 0.29.14
pytest : 5.0.1
hypothesis : 4.32.2
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : 0.9.2
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.1
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
pytest : 5.0.1
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
tabulate : 0.8.3
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
numba : 0.45.1
</details>
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MDU6SXNzdWU1NTgwNTM0MTk=
| 31,489
|
~ operator on Series with BooleanDtype casts to object and Series with object dtype gives TypeError comparing to Series with BooleanDtype
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| 4
| 2020-01-31T10:42:26Z
| 2020-01-31T16:23:46Z
| 2020-01-31T16:23:46Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Using pandas 1.0.0:
```python
~pandas.Series([True, False], dtype=pandas.BooleanDtype())
```
Or:
```python
s1 = pandas.Series([True, False], dtype=pandas.BooleanDtype())
s2 = pandas.Series([False, True], dtype=pandas.BooleanDtype())
(~s1).equals(s2) and (~s2).equals(s1)
```
#### Problem description
Using the `pandas.BooleanDtype()` dtype, the `~` operator unexpectedly:
* results in a `Series` with dtype `object` instead of `pandas.BooleanDtype()`
* operates on the boolean values as if they were integers (resulting in `-2` for `~True` and `-1` for `~False`), rather than booleans (resulting in `False` for `~True` and `True` for `~False`).
This is unexpected and undesirable, because:
* On numpy arrays or `Series` with dtype `np.bool_`, the same operation behaves differently:
- The dtype remains `np.bool_`
- `~True` results in `False` and `~False` results in `True`
* When having a Series with a boolean dtype, the `~` operator is a convenient way to negate the value
As a workaround, I'm using `numpy.logical_not(s1)`, which does preserve the dtype and have the expected result.
The second example results in an exception `TypeError: data type not understood`, because (~s1) with its `object` dtype doesn't understand the comparison against `s2` with its `pandas.BooleanDtype()` dtype.
#### Expected Output
I would expect that:
* the first example results in `pandas.Series([False, True], dtype=pandas.BooleanDtype())`
* the second example results in `True`
#### Output of ``pd.show_versions()``
<details>
In [35]: pandas.show_versions()
/media/nas/x21324/miniconda3/envs/py37e/lib/python3.7/site-packages/fastparquet/dataframe.py:5: FutureWarning: pandas.core.index is deprecated and will be removed in a future version. The public classes are available in the top-level namespace.
from pandas.core.index import CategoricalIndex, RangeIndex, Index, MultiIndex
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 4.12.14-lp150.12.82-default
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.0
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : 0.29.14
pytest : 5.3.5
hypothesis : None
sphinx : 2.3.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.0
IPython : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : 3.6.1
tabulate : None
xarray : 0.14.1
xlrd : None
xlwt : None
xlsxwriter : None
numba : 0.48.0
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MDExOlB1bGxSZXF1ZXN0MzY5NTMxMjc2
| 31,490
|
DOC: Fix the description of the 'day' field accessor in DatetimeArray
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| 1
| 2020-01-31T11:11:56Z
| 2020-01-31T11:51:18Z
| 2020-01-31T11:51:10Z
|
CONTRIBUTOR
| null |
Fix a wrong description in the field accessor `day` of the DatetimeArray class
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Error in pd.to_datetime, with argument np.datetime64 array of 51 elements
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| 2020-02-01T14:29:37Z
| 2020-02-01T14:29:37Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
d = np.datetime64("2000-01-01", "s")
pd.to_datetime(np.full(50, d))
# Works
pd.to_datetime(np.full(51, d))
# ERROR
# pandas._libs.tslibs.np_datetime.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 29999249045-07-29 00:00:00
pd.to_datetime(pd.Series(np.full(51, d)))
```
#### Problem description
With pandas 1.0, we get a new issue when using pd.to_datetime to convert an existing np.datetime64 array with 51 elements.
This works with 50 elements, or when converting to a Series beforehand, so I believe the problems comes from inside implicit conversion.
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MDU6SXNzdWU1NTgwODYwMjQ=
| 31,492
|
Large dataframes aren't truncated in LaTeX output
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| 2020-01-31T13:03:04Z
| 2020-01-31T13:03:03Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
pd.DataFrame(np.random.rand(300,4),columns=['a','b','c','d']).to_latex()
```
#### Problem description
I'm using Jupyter notebook to write a long document using pandas, which I eventually will nbconvert to a PDF. In the notebook with HTML output, long dataframes are truncated as expected and I can control the number of rows with `pd.set_option('max_rows', 5)`. When displaying as LaTeX, however, long dataframes are not truncated, so the above code will generate a 300 row LaTeX table. My real dataframes are much longer. This breaks PDF export.
Obviously I can get round this by just calling `head()`. However, given that this a tutorial, it would be nice to be able to display a table in the PDF that will match the HTML output, i.e. with the rows of dots in the middle to represent data not shown. Could `to_latex()` honour the `max_rows` option? Is there any workaround here?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.3
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.3.4
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.3.3
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.8.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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Backport PR #31183: BUG: Series/Frame invert dtypes'
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MEMBER
| null |
We forgot to backport this: https://github.com/pandas-dev/pandas/pull/31183
Closes #31489
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DOC: move whatnew entry for invert from 1.0.0 t 1.0.1
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MEMBER
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see https://github.com/pandas-dev/pandas/pull/31493, forgot to backport, so the fix will only be included in 1.0.1
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Series shift method breaks for series of pandas Intervals in Pandas 1.0 (works in 0.25.3)
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| 6
| 2020-01-31T14:01:52Z
| 2020-02-04T16:56:08Z
| 2020-02-04T16:56:08Z
|
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Problem code example 1
import pandas as pd
test = pd.Series(index=[1, 2], data=[pd.Interval(pd.Timestamp("2020-09-04 10:00:00"), pd.Timestamp("2020-11-30 14:00:00")), pd.Interval(pd.Timestamp("2020-08-14 10:00:00"), pd.Timestamp("2020-09-21 14:00:00"))])
test.shift(1)
# Problem code example 2
import pandas as pd
test = pd.Series(index=[1, 2], data=[pd.Interval(1, 2), pd.Interval(3, 4)])
test.shift(1)
```
#### Problem description
Calling the shift method on an integer indexed Pandas series of Pandas intervals throws opaque exceptions. The same code works as expected in Pandas 0.25.3. This is definitely a breaking change - I'm unsure if it is intentional. I'm assuming it should still work the same as 0.25.3 for now. I tried searching for any documented changes to the shift method behaviour but didn't find any.
Exception traceback for example 1:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-7a1f088b5a14> in <module>
4 test = pd.Series(index=[1, 2], data=[pd.Interval(pd.Timestamp("2020-09-04 10:00:00"), pd.Timestamp("2020-11-30 14:00:00")),
5 pd.Interval(pd.Timestamp("2020-08-14 10:00:00"), pd.Timestamp("2020-09-21 14:00:00"))])
----> 6 test.shift(1)
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\series.py in shift(self, periods, freq, axis, fill_value)
4183 def shift(self, periods=1, freq=None, axis=0, fill_value=None):
4184 return super().shift(
-> 4185 periods=periods, freq=freq, axis=axis, fill_value=fill_value
4186 )
4187
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\generic.py in shift(self, periods, freq, axis, fill_value)
9043 if freq is None:
9044 new_data = self._data.shift(
-> 9045 periods=periods, axis=block_axis, fill_value=fill_value
9046 )
9047 else:
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\internals\managers.py in shift(self, **kwargs)
571
572 def shift(self, **kwargs):
--> 573 return self.apply("shift", **kwargs)
574
575 def fillna(self, **kwargs):
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\internals\blocks.py in shift(self, periods, axis, fill_value)
1908 return [
1909 self.make_block_same_class(
-> 1910 self.values.shift(periods=periods, fill_value=fill_value),
1911 placement=self.mgr_locs,
1912 ndim=self.ndim,
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\base.py in shift(self, periods, fill_value)
623
624 empty = self._from_sequence(
--> 625 [fill_value] * min(abs(periods), len(self)), dtype=self.dtype
626 )
627 if periods > 0:
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\interval.py in _from_sequence(cls, scalars, dtype, copy)
243 @classmethod
244 def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 245 return cls(scalars, dtype=dtype, copy=copy)
246
247 @classmethod
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\interval.py in __new__(cls, data, closed, dtype, copy, verify_integrity)
182 copy=copy,
183 dtype=dtype,
--> 184 verify_integrity=verify_integrity,
185 )
186
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\interval.py in _simple_new(cls, left, right, closed, copy, dtype, verify_integrity)
202 raise TypeError(msg)
203 elif dtype.subtype is not None:
--> 204 left = left.astype(dtype.subtype)
205 right = right.astype(dtype.subtype)
206
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\indexes\numeric.py in astype(self, dtype, copy)
393 if needs_i8_conversion(dtype):
394 raise TypeError(
--> 395 f"Cannot convert Float64Index to dtype {dtype}; integer "
396 "values are required for conversion"
397 )
TypeError: Cannot convert Float64Index to dtype datetime64[ns]; integer values are required for conversion
```
Exception traceback for example 2
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-6804e066ee18> in <module>
4 test = pd.Series(index=[1, 2], data=[pd.Interval(1, 2),
5 pd.Interval(3, 4)])
----> 6 test.shift(1)
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\series.py in shift(self, periods, freq, axis, fill_value)
4183 def shift(self, periods=1, freq=None, axis=0, fill_value=None):
4184 return super().shift(
-> 4185 periods=periods, freq=freq, axis=axis, fill_value=fill_value
4186 )
4187
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\generic.py in shift(self, periods, freq, axis, fill_value)
9043 if freq is None:
9044 new_data = self._data.shift(
-> 9045 periods=periods, axis=block_axis, fill_value=fill_value
9046 )
9047 else:
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\internals\managers.py in shift(self, **kwargs)
571
572 def shift(self, **kwargs):
--> 573 return self.apply("shift", **kwargs)
574
575 def fillna(self, **kwargs):
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, filter, **kwargs)
440 applied = b.apply(f, **kwargs)
441 else:
--> 442 applied = getattr(b, f)(**kwargs)
443 result_blocks = _extend_blocks(applied, result_blocks)
444
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\internals\blocks.py in shift(self, periods, axis, fill_value)
1908 return [
1909 self.make_block_same_class(
-> 1910 self.values.shift(periods=periods, fill_value=fill_value),
1911 placement=self.mgr_locs,
1912 ndim=self.ndim,
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\base.py in shift(self, periods, fill_value)
623
624 empty = self._from_sequence(
--> 625 [fill_value] * min(abs(periods), len(self)), dtype=self.dtype
626 )
627 if periods > 0:
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\interval.py in _from_sequence(cls, scalars, dtype, copy)
243 @classmethod
244 def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 245 return cls(scalars, dtype=dtype, copy=copy)
246
247 @classmethod
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\interval.py in __new__(cls, data, closed, dtype, copy, verify_integrity)
182 copy=copy,
183 dtype=dtype,
--> 184 verify_integrity=verify_integrity,
185 )
186
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\arrays\interval.py in _simple_new(cls, left, right, closed, copy, dtype, verify_integrity)
202 raise TypeError(msg)
203 elif dtype.subtype is not None:
--> 204 left = left.astype(dtype.subtype)
205 right = right.astype(dtype.subtype)
206
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\indexes\numeric.py in astype(self, dtype, copy)
399 # TODO(jreback); this can change once we have an EA Index type
400 # GH 13149
--> 401 arr = astype_nansafe(self.values, dtype=dtype)
402 return Int64Index(arr)
403 return super().astype(dtype, copy=copy)
~\Miniconda3\envs\jupyter\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
866
867 if not np.isfinite(arr).all():
--> 868 raise ValueError("Cannot convert non-finite values (NA or inf) to integer")
869
870 elif is_object_dtype(arr):
ValueError: Cannot convert non-finite values (NA or inf) to integer
```
#### Expected Output
This is the actual output I got executing with pandas 0.25.3
1 NaN
2 (2020-09-04 10:00:00, 2020-11-30 14:00:00]
dtype: object
1 NaN
2 (1, 2]
dtype: object
#### Output of ``pd.show_versions()``
<details>
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : 5.3.5
hypothesis : None
sphinx : None
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 : 7.11.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.2
numexpr : None
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.3.5
pyxlsb : None
s3fs : 0.2.2
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.6
xarray : 0.14.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.2.7
numba : 0.48.0
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5NTk3NDA1
| 31,496
|
Backport PR #31494 on branch 1.0.x (DOC: move whatnew entry for invert from 1.0.0 t 1.0.1)
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| 0
| 2020-01-31T14:04:52Z
| 2020-01-31T16:24:17Z
| 2020-01-31T16:24:17Z
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NONE
| null |
Backport PR #31494: DOC: move whatnew entry for invert from 1.0.0 t 1.0.1
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StringDtype not preserved when writing + reading parquet or feather
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| 2020-02-02T21:29:23Z
| 2020-01-31T14:28:14Z
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NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import tempfile
import pandas
df1 = pandas.DataFrame(
["a", "b", "c"], dtype=pandas.StringDtype(), columns=["test"])
with tempfile.NamedTemporaryFile() as fn:
df1.to_parquet(fn)
df2 = pandas.read_parquet(fn)
print(df1.equals(df2))
print(df1.dtypes)
print(df2.dtypes)
```
#### Problem description
When writing a DataFrame to parquet or feather, then reading the same file, columns with dtype `StringDtype()` are not preserved but read as `object` dtype.
The code above gives:
```
False
test string
dtype: object
test object
dtype: object
```
I have tested with with pandas 1.0.0 and the latest pandas git master. When using parquet or feather, the dtype is lost. When using pickle, it is preserved. HDF5 is not supported on writing (see #31199).
I'm not sure how this should be solved. At least, there should be a warning when attempting to write something that won't be preserved upon reading. Perhaps this should be an error (exception) unless an overwrite-flag is passed.
#### Expected Output
Ideally, I would expect to get the same as for pickle:
```
True
test string
dtype: object
test string
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
False
test string
dtype: object
test object
dtype: object
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.12.14-lp150.12.82-default
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.1.0.dev0+241.g761bceb77
numpy : 1.17.5
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200119
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.0
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
None
</details>
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MDU6SXNzdWU1NTgyMjc1NzQ=
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Add method to handle mixed string and non-string types in object column.
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[] | null | 8
| 2020-01-31T16:18:07Z
| 2021-07-27T05:40:51Z
| 2021-07-27T05:40:51Z
|
NONE
| null |
#### Problem description
Since the version 1.0.0 and the creation of the StringDtype the use of an object column to handle string would decrease. Nevertheless, and [as in the new documentation is stated](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v1.0.0.html)
```
There isn’t a clear way to select just text while excluding non-text, but still object-dtype columns.
```
The way how I had handle this issue in the past have changed throught the time and depending on my needs. I think this is a great opportunity to define a way to proceed in this cases. I dont know the feelings of the devs on this issue. In any case, here we go my two cents to have something to start a discussion about this:
- Series.select_dtypes() : Select subset of rows based on the type. maybe with an option to convert non matching rows to NA instead of dropping it.
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| 31,499
|
Pandas 1.0 no longer handles `numpy.str_`s as catgories
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| 5
| 2020-01-31T16:18:34Z
| 2020-02-04T16:23:59Z
| 2020-02-04T16:23:59Z
|
CONTRIBUTOR
| null |
#### Code Sample
```python
import pandas as pd
pd.Categorical(['1', '0', '1'], [np.str_('0'), np.str_('1')])
```
```pytb
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/angerer/Dev/Python/venvs/env-pandas-1/lib/python3.8/site-packages/pandas/core/arrays/categorical.py", line 385, in __init__
codes = _get_codes_for_values(values, dtype.categories)
File "/home/angerer/Dev/Python/venvs/env-pandas-1/lib/python3.8/site-packages/pandas/core/arrays/categorical.py", line 2576, in _get_codes_for_values
t.map_locations(cats)
File "pandas/_libs/hashtable_class_helper.pxi", line 1403, in pandas._libs.hashtable.StringHashTable.map_locations
TypeError: Expected unicode, got numpy.str_
```
#### Problem description
I know that having a list of `numpy.str_`s seems weird, but it easily happens when you use non-numpy algorithms on numpy arrays (e.g. `natsort.natsorted` in our case), or via comprehensions or so:
```py
>>> np.array(['1', '0'])[0].__class__
<class 'numpy.str_'>
>>> [type(s) for s in np.array(['1', '0'])]
[<class 'numpy.str_'>, <class 'numpy.str_'>]
```
#### Expected Output
A normal pd.Categorical
#### Pandas version
pandas 1.0
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MDExOlB1bGxSZXF1ZXN0MzY5NjYzNjcw
| 31,500
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DOC: Replaced "the the" with "the"
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| 2020-01-31T16:29:37Z
| 2020-02-01T10:51:13Z
| 2020-01-31T17:39:35Z
|
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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Can no longer slice DatetimeIndex with datetime.date values outside the index in 1.0.0
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| 2020-01-31T16:39:29Z
| 2020-02-01T14:56:13Z
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NONE
| null |
#### Code Sample
```python
import pandas as pd
import datetime as dt
s = pd.Series([0,1],pd.DatetimeIndex([dt.date(2019,1,1),dt.date(2019,1,2)]))
s[dt.date(2019,1,1):] # works
s[dt.date(2018,1,1):] # error
```
#### Problem description
As of 1.0.0 you can no longer slice a DatetimeIndex with dt.date values outside the index.
#### Error
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2645 try:
-> 2646 return self._engine.get_loc(key)
2647 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine._date_check_type()
KeyError: datetime.date(2018, 1, 1)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\datetimes.py in get_loc(self, key, method, tolerance)
714 try:
--> 715 return Index.get_loc(self, key, method, tolerance)
716 except (KeyError, ValueError, TypeError):
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2647 except KeyError:
-> 2648 return self._engine.get_loc(self._maybe_cast_indexer(key))
2649 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine._date_check_type()
KeyError: datetime.date(2018, 1, 1)
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1514764800000000000
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2645 try:
-> 2646 return self._engine.get_loc(key)
2647 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
KeyError: Timestamp('2018-01-01 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()
KeyError: 1514764800000000000
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\datetimes.py in get_loc(self, key, method, tolerance)
727 stamp = stamp.tz_localize(self.tz)
--> 728 return Index.get_loc(self, stamp, method, tolerance)
729 except KeyError:
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2647 except KeyError:
-> 2648 return self._engine.get_loc(self._maybe_cast_indexer(key))
2649 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.DatetimeEngine.get_loc()
KeyError: Timestamp('2018-01-01 00:00:00')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, label, side, kind)
4841 try:
-> 4842 slc = self.get_loc(label)
4843 except KeyError as err:
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\datetimes.py in get_loc(self, key, method, tolerance)
729 except KeyError:
--> 730 raise KeyError(key)
731 except ValueError as e:
KeyError: datetime.date(2018, 1, 1)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-47-a23c04333556> in <module>
3 s = pd.Series([0,1],pd.DatetimeIndex([dt.date(2019,1,1),dt.date(2019,1,2)]))
4 s[dt.date(2019,1,1):] # works
----> 5 s[dt.date(2018,1,1):] # error
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
908 key = check_bool_indexer(self.index, key)
909
--> 910 return self._get_with(key)
911
912 def _get_with(self, key):
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\series.py in _get_with(self, key)
913 # other: fancy integer or otherwise
914 if isinstance(key, slice):
--> 915 return self._slice(key)
916 elif isinstance(key, ABCDataFrame):
917 raise TypeError(
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\series.py in _slice(self, slobj, axis, kind)
863
864 def _slice(self, slobj: slice, axis: int = 0, kind=None):
--> 865 slobj = self.index._convert_slice_indexer(slobj, kind=kind or "getitem")
866 return self._get_values(slobj)
867
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in _convert_slice_indexer(self, key, kind)
2961 indexer = key
2962 else:
-> 2963 indexer = self.slice_indexer(start, stop, step, kind=kind)
2964
2965 return indexer
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\datetimes.py in slice_indexer(self, start, end, step, kind)
806
807 try:
--> 808 return Index.slice_indexer(self, start, end, step, kind=kind)
809 except KeyError:
810 # For historical reasons DatetimeIndex by default supports
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in slice_indexer(self, start, end, step, kind)
4711 slice(1, 3)
4712 """
-> 4713 start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)
4714
4715 # return a slice
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in slice_locs(self, start, end, step, kind)
4924 start_slice = None
4925 if start is not None:
-> 4926 start_slice = self.get_slice_bound(start, "left", kind)
4927 if start_slice is None:
4928 start_slice = 0
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in get_slice_bound(self, label, side, kind)
4843 except KeyError as err:
4844 try:
-> 4845 return self._searchsorted_monotonic(label, side)
4846 except ValueError:
4847 # raise the original KeyError
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\base.py in _searchsorted_monotonic(self, label, side)
4794 def _searchsorted_monotonic(self, label, side="left"):
4795 if self.is_monotonic_increasing:
-> 4796 return self.searchsorted(label, side=side)
4797 elif self.is_monotonic_decreasing:
4798 # np.searchsorted expects ascending sort order, have to reverse
C:\ProgramData\Miniconda3.7\lib\site-packages\pandas\core\indexes\datetimes.py in searchsorted(self, value, side, sorter)
851 elif not isinstance(value, DatetimeArray):
852 raise TypeError(
--> 853 "searchsorted requires compatible dtype or scalar, "
854 f"not {type(value).__name__}"
855 )
TypeError: searchsorted requires compatible dtype or scalar, not date
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Windows
OS-release : 7
machine : AMD64
processor : Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.0.0
numpy : 1.17.3
pytz : 2018.9
dateutil : 2.8.1
pip : 20.0.2
setuptools : 41.6.0.post20191030
Cython : 0.29.5
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : 1.7.0
feather : None
xlsxwriter : None
lxml.etree : 4.3.2
html5lib : None
pymysql : None
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.3
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
pytest : 5.1.2
pyxlsb : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.17
tables : None
tabulate : None
xarray : 0.12.1
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.45.1
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5NjcyNTU3
| 31,502
|
BUG: Fixed IntervalArray[int].shift
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| 5
| 2020-01-31T16:51:03Z
| 2020-02-04T16:56:08Z
| 2020-02-04T16:56:08Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/31495
In ExtensionArray.shift, we have the note
```
# Note: this implementation assumes that `self.dtype.na_value` can be
# stored in an instance of your ExtensionArray with `self.dtype`.
```
I wonder, should we have a method / property like
```
@property
def _can_hold_na_value(self):
return True
```
And for IntervalArray, we would do something like
```python
@property
def _can_hold_na_value(self):
return is_float_dtype(self.dtype.subtype)
```
That would let us deduplicate things, since the call to `_from_sequence` would know to not pass `dtype=self.dtype` and trigger a re-inference.
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API: query / boolean selection with nullable dtypes with NAs
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| 2020-01-31T17:09:33Z
| 2020-02-22T15:37:38Z
| 2020-02-22T15:37:38Z
|
CONTRIBUTOR
| null |
#### Code Sample
Create a dataframe with nullable integer, string, and float data types.
```python
>>> df = df = pd.DataFrame({'a': [1, 3, np.nan], 'b': ['rain', 'shine', None],
'a_float': [1.1, 3.3, np.nan]})
>>> df = df.convert_dtypes()
>>> df
```
| | a | b | a_float |
|---:|:-----|:------|----------:|
| 0 | 1 | rain | 1.1 |
| 1 | 3 | shine | 3.3 |
| 2 | `<NA>` | `<NA>` | nan |
Verify data types and attempt to use `query`
```python
>>> df.dtypes
a Int64
b string
a_float float64
>>> df.query('a > 2') # same as df[df['a'] > 2]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/frame.py in query(self, expr, inplace, **kwargs)
3227 try:
-> 3228 new_data = self.loc[res]
3229 except ValueError:
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/indexing.py in __getitem__(self, key)
1683 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1684 return self._getitem_axis(maybe_callable, axis=axis)
1685
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1798 return self._get_slice_axis(key, axis=axis)
-> 1799 elif com.is_bool_indexer(key):
1800 return self._getbool_axis(key, axis=axis)
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/common.py in is_bool_indexer(key)
133 if np.any(key.isna()):
--> 134 raise ValueError(na_msg)
135 return True
ValueError: cannot mask with array containing NA / NaN values
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-52-e5d239635d7b> in <module>
----> 1 df.query('a > 2')
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/frame.py in query(self, expr, inplace, **kwargs)
3230 # when res is multi-dimensional loc raises, but this is sometimes a
3231 # valid query
-> 3232 new_data = self[res]
3233
3234 if inplace:
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/frame.py in __getitem__(self, key)
2784
2785 # Do we have a (boolean) 1d indexer?
-> 2786 if com.is_bool_indexer(key):
2787 return self._getitem_bool_array(key)
2788
~/Documents/Code Practice/pandas-dev/pandas/pandas/core/common.py in is_bool_indexer(key)
132 if is_extension_array_dtype(key.dtype):
133 if np.any(key.isna()):
--> 134 raise ValueError(na_msg)
135 return True
136 elif isinstance(key, list):
ValueError: cannot mask with array containing NA / NaN values
>>> df.query('a_float > 2')
```
| | a | b | a_float |
|---:|----:|:------|----------:|
| 1 | 3 | shine | 3.3 |
Using `query` with strings works...
```python
>>> df.query('b == "rain"')
```
| | a | b | a_float |
|---:|----:|:-----|----------:|
| 0 | 1 | rain | 1.1 |
...but fails for boolean selection
```python
>>> df[df['b'] == 'rain']
ValueError: cannot mask with array containing NA / NaN values
```
strings also fail for inequalities
```
>>> df.query('b >= "rain"') # also df.query('b > "rain"')
ValueError: cannot mask with array containing NA / NaN values
```
#### Problem description
The `query` method behaves differently for nullable integers, strings, and floats. Here's my summary of how I think they work with the `query method` assuming there are missing values in the columns.
* nullable integers - fails
* strings - works with equality, fails with inequality
* float - works for all
I find it extremely difficult to use if the behavior for all of these types are different for query and boolean selection.
#### Expected Output
I think I would prefer to have both query and boolean selection working like they do with floats, where missing values evaluate as False in a condition. And even if there are missing values in the boolen mask itself, treat those as False. This would harmonize the behavior for all data types.
This would leave it up to the user to check for missing values. I believe SQL where clauses work in such a manner (missing values in conditions evaluate as False).
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0+untagged.1.gce8af21.dirty
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
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MDU6SXNzdWU1NTgyNTYwMjE=
| 31,504
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IntervalArray[datetime64[ns]].shift() raises TypeError
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| 6
| 2020-01-31T17:11:08Z
| 2020-07-10T13:58:36Z
| null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
With pandas 0.25.3 or master,
```pytb
In [2]: arr = pd.arrays.IntervalArray.from_breaks(pd.date_range('2000', periods=4))
In [3]: arr.shift()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-e840978d5de6> in <module>
----> 1 arr.shift()
~/miniconda3/envs/pandas=0.25.1/lib/python3.7/site-packages/pandas/core/arrays/base.py in shift(self, periods, fill_value)
567
568 empty = self._from_sequence(
--> 569 [fill_value] * min(abs(periods), len(self)), dtype=self.dtype
570 )
571 if periods > 0:
~/miniconda3/envs/pandas=0.25.1/lib/python3.7/site-packages/pandas/core/arrays/interval.py in _from_sequence(cls, scalars, dtype, copy)
236 @classmethod
237 def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 238 return cls(scalars, dtype=dtype, copy=copy)
239
240 @classmethod
~/miniconda3/envs/pandas=0.25.1/lib/python3.7/site-packages/pandas/core/arrays/interval.py in __new__(cls, data, closed, dtype, copy, verify_integrity)
176 copy=copy,
177 dtype=dtype,
--> 178 verify_integrity=verify_integrity,
179 )
180
~/miniconda3/envs/pandas=0.25.1/lib/python3.7/site-packages/pandas/core/arrays/interval.py in _simple_new(cls, left, right, closed, copy, dtype, verify_integrity)
196 raise TypeError(msg.format(dtype=dtype))
197 elif dtype.subtype is not None:
--> 198 left = left.astype(dtype.subtype)
199 right = right.astype(dtype.subtype)
200
~/miniconda3/envs/pandas=0.25.1/lib/python3.7/site-packages/pandas/core/indexes/numeric.py in astype(self, dtype, copy)
367 "values are required for conversion"
368 ).format(dtype=dtype)
--> 369 raise TypeError(msg)
370 elif (
371 is_integer_dtype(dtype) and not is_extension_array_dtype(dtype)
TypeError: Cannot convert Float64Index to dtype datetime64[ns]; integer values are required for conversion
```
#### Problem description
We should be able to preserve the interval dtype, by placing NaT.
#### Expected Output
```python
IntervalArray([NaT, (2000-01-01, 2000-01-02], (2000-01-02, 2000-01-03]],
closed='right',
dtype='interval[datetime64[ns]]')
```
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| 31,505
|
REGR: `copy()` within `apply()` raises ValueError: cannot create a DatetimeTZBlock without a tz, as of 1.0.0
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| 0
| 2020-01-31T17:45:45Z
| 2020-02-05T08:15:33Z
| 2020-02-05T08:15:33Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
Minimal example:
```python
import pandas as pd
df = pd.DataFrame({"foo": [pd.Timestamp("2020", tz="UTC")]}, dtype="object")
df.apply(lambda col: col.copy()) # raises exception below
```
Real-life usage:
```python
def filter_dataframe_by_dict(df, filters):
"""
Filter the specified dataframe to only those rows which match the specified filters
Parameters
----------
df : pd.DataFrame
filters : Mapping
dict, keyed by a subset of `df.columns`
Returns
-------
pd.DataFrame
Same columns as `df`, including only those rows which match `filters` on all specified values.
"""
filters = pd.Series(filters, dtype="object")
mask = df[filters.index].apply(
# astype("object") calls `copy()` internally, and is necessary to ensure dtype-agnostic
# comparisons.
lambda row: row.astype("object").equals(filters), axis="columns"
)
return df[mask]
records = pd.DataFrame(columns = ["foo", "bar", "baz"])
records.loc[0] = {"foo": pd.Timestamp("2019", tz="UTC"), "bar": 1, "baz": 6.283}
records.loc[1] = {"foo": pd.Timestamp("2020", tz="UTC"), "bar": 2, "baz": 6.283}
filters = {"foo": pd.Timestamp("2020", tz="UTC")}
filter_dataframe_by_dict(records, filters) # raises below exception
```
Exception:
```
Traceback (most recent call last):
File "pandas_bug.py", line 29, in <module>
df.apply(lambda col: col.copy())
File ".venv/lib/python3.6/site-packages/pandas/core/frame.py", line 6875, in apply
return op.get_result()
File ".venv/lib/python3.6/site-packages/pandas/core/apply.py", line 186, in get_result
return self.apply_standard()
File ".venv/lib/python3.6/site-packages/pandas/core/apply.py", line 296, in apply_standard
values, self.f, axis=self.axis, dummy=dummy, labels=labels
File "pandas/_libs/reduction.pyx", line 617, in pandas._libs.reduction.compute_reduction
File "pandas/_libs/reduction.pyx", line 127, in pandas._libs.reduction.Reducer.get_result
File "pandas_bug.py", line 29, in <lambda>
df.apply(lambda row: row.copy(), axis="columns")
File ".venv/lib/python3.6/site-packages/pandas/core/generic.py", line 5810, in copy
data = self._data.copy(deep=deep)
File ".venv/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 794, in copy
res = self.apply("copy", deep=deep)
File ".venv/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 442, in apply
applied = getattr(b, f)(**kwargs)
File ".venv/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 696, in copy
return self.make_block_same_class(values, ndim=self.ndim)
File ".venv/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 281, in make_block_same_class
return make_block(values, placement=placement, ndim=ndim, klass=type(self))
File ".venv/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 3028, in make_block
return klass(values, ndim=ndim, placement=placement)
File ".venv/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 1723, in __init__
values = self._maybe_coerce_values(values)
File ".venv/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 2306, in _maybe_coerce_values
raise ValueError("cannot create a DatetimeTZBlock without a tz")
ValueError: cannot create a DatetimeTZBlock without a tz
```
#### Problem description
Essentially, the problem arises when `.copy()` is called from with an `.apply` call, on a row/column of a DataFrame which:
* has dtype `object`
* consists solely of tz-aware `Timestamp` objects
While this seems like a fairly artificial set of conditions, as the "real world example" above is intended to demonstrate, it can indeed occur "organically". Appending rows to an initially empty `DataFrame` results in the dtype defaulting to `object` for all columns, so if the `filters` passed to `filter_dataframe_by_dict` happen to only consist of timestamp-valued columns, the error conditions are met.
Note that the error *only* seems to arise in `.apply` calls; making the same calls on the rows return using `iterrrows()` or `iloc` works just fine.
This problem only occurs as of the latest pandas 1.0.0 release.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-74-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.0
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 19.3.1
setuptools : 41.6.0
Cython : None
pytest : 5.3.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : 5.3.0
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.11
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
```
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CLN: No need to use libindex.get_value_at
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MEMBER
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Made possible because Series._values returns DTA/TDA for datetime64/timedelta64.
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MDU6SXNzdWU1NTgyNzU2NTY=
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json_normalize in 1.0.0 with meta path specified - expects iterable
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| 13
| 2020-01-31T17:51:06Z
| 2021-10-20T16:35:23Z
| 2020-03-11T15:02:32Z
|
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import json
from pandas.io.json import json_normalize
the_json = """
[{"id": 99,
"data": [{"one": 1, "two": 2}]
}]
"""
print(json_normalize(json.loads(the_json),
record_path=['data'], meta=['id']))
```
#### Problem description
Through 0.25.3, this program generates a DataFrame with one row. In 1.0.0 it fails with an exception:
```
Traceback (most recent call last):
File "foo.py", line 11, in <module>
record_path=['data'], meta=['id']))
File "/home/dataczar/venvs/test/lib/python3.7/site-packages/pandas/util/_decorators.py", line 66, in wrapper
return alternative(*args, **kwargs)
File "/home/dataczar/venvs/test/lib/python3.7/site-packages/pandas/io/json/_normalize.py", line 327, in _json_normalize
_recursive_extract(data, record_path, {}, level=0)
File "/home/dataczar/venvs/test/lib/python3.7/site-packages/pandas/io/json/_normalize.py", line 314, in _recursive_extract
meta_val = _pull_field(obj, val[level:])
File "/home/dataczar/venvs/test/lib/python3.7/site-packages/pandas/io/json/_normalize.py", line 246, in _pull_field
f"{js} has non iterable value {result} for path {spec}. "
TypeError: {'id': 99, 'data': [{'one': 1, 'two': 2}]} has non iterable value 99 for path ['id']. Must be iterable or null.
```
I don't see any documentation changes that suggest a backwards-incompatible change. All my calls to `json_normalize` that don't use `meta` function as before.
#### Expected Output
Through 0.25.3, the output was:
```
one two id
0 1 2 99
```
#### Output of ``pd.show_versions()``
From my virtualenv with pandas 1.0.0:
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.2.0-042stab120.16
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 1.0.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None
</details>
From my virtualenv with 0.25.x:
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.2.0-042stab120.16
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.0
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 19.0.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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MDExOlB1bGxSZXF1ZXN0MzY5Njk2NTc1
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JSON Set Name Cleanup
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| 2020-01-31T17:53:15Z
| 2020-02-02T22:43:56Z
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MEMBER
| null |
Makes this consistent with List, Tuple
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Dead JSON Code Cleanup
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| 1
| 2020-01-31T18:11:52Z
| 2020-02-02T23:24:41Z
| 2020-02-02T22:42:11Z
|
MEMBER
| null |
made possible bc Series._values now returns DTA/TDA for datetime64/timedelta64
Small perf improvement
```
In [3]: dti = pd.date_range('2016-01-01', freq='D', periods=10**4)
In [4]: idx = list('abcdefghijklmnop')
In [5]: arr = np.random.random(len(idx)*len(dti)).reshape(len(dti), -1)
In [6]: df = pd.DataFrame(arr, index=dti, columns=idx)
In [7]: %timeit df2 = df._set_value("2043-05-14", "c", 4)
381 µs ± 14.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- master
330 µs ± 13 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) # <-- PR
```
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MDExOlB1bGxSZXF1ZXN0MzY5NzExMjk1
| 31,511
|
BUG: fix reindexing with a tz-aware index and method='nearest'
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| 5
| 2020-01-31T18:23:51Z
| 2020-02-03T03:48:40Z
| 2020-02-03T03:48:29Z
|
CONTRIBUTOR
| null |
- [x] closes #26683
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
First pandas PR, happy to make changes as needed. Is a whatsnew entry needed? And if so, I assume it should go in v1.0.1.rst?
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MDExOlB1bGxSZXF1ZXN0MzY5NzExMzE3
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CLN: inherit PeriodIndex._box_func
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| 1
| 2020-01-31T18:23:53Z
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MDExOlB1bGxSZXF1ZXN0MzY5NzE1ODg2
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|
REGR: Fixed truncation with na_rep
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| 1
| 2020-01-31T18:31:06Z
| 2020-02-01T15:21:16Z
| 2020-02-01T15:21:12Z
|
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/31447
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MDU6SXNzdWU1NTgzMTE4Njc=
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json_normalize Doc Broken
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| 2
| 2020-01-31T18:55:45Z
| 2020-02-03T19:39:23Z
| 2020-02-03T19:39:23Z
|
MEMBER
| null |
First google result for json_normalize will yield:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.io.json.json_normalize.html
This however is a dead link. I think the issue is we moved json_normalize to the top-level pd.namespace, so this works:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.json_normalize.html
The former should redirect to the latter
|
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"url": "https://api.github.com/repos/pandas-dev/pandas/issues/31514/reactions"
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|
https://api.github.com/repos/pandas-dev/pandas/issues/31514/timeline
| null | null | null |
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