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https://api.github.com/repos/pandas-dev/pandas/issues/25813 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25813/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25813/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25813/events | https://github.com/pandas-dev/pandas/pull/25813 | 423,541,026 | MDExOlB1bGxSZXF1ZXN0MjYzMDkyNjE4 | 25,813 | BUG: Fix bug when using df.query with "str.contains()" | {
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"color": "37... | closed | false | null | [] | null | 6 | 2019-03-21T01:36:14Z | 2019-05-19T18:28:30Z | 2019-05-19T18:28:30Z | NONE | null | - [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixes bug in on line 588 of `pandas/core/computation/expr.py`: `AttributeError: 'dict' object has no attribute 'append'`
Also addresses the specific example in #22172
Example:
```python
import pandas as pd
df = pd.DataFrame([["I", "XYZ"], ["IJ", None]], columns=['A', 'B'])
df.query("B.str.contains('Z', na=False)", engine="python")
```
```
Traceback (most recent call last):
File "./test_query.py", line 12, in <module>
result = df.query("B.str.contains('Z', na=False)", engine="python")
File "./lib/python3.6/site-packages/pandas/core/frame.py", line 2847, in query
res = self.eval(expr, **kwargs)
File "./lib/python3.6/site-packages/pandas/core/frame.py", line 2962, in eval
return _eval(expr, inplace=inplace, **kwargs)
File "./lib/python3.6/site-packages/pandas/core/computation/eval.py", line 291, in eval
truediv=truediv)
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 739, in __init__
self.terms = self.parse()
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 756, in parse
return self._visitor.visit(self.expr)
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 321, in visit
return visitor(node, **kwargs)
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 327, in visit_Module
return self.visit(expr, **kwargs)
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 321, in visit
return visitor(node, **kwargs)
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 330, in visit_Expr
return self.visit(node.value, **kwargs)
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 321, in visit
return visitor(node, **kwargs)
File "./lib/python3.6/site-packages/pandas/core/computation/expr.py", line 588, in visit_Call_35
kwargs.append(ast.keyword(
AttributeError: 'dict' object has no attribute 'append'
```
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https://api.github.com/repos/pandas-dev/pandas/issues/25814 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25814/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25814/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25814/events | https://github.com/pandas-dev/pandas/issues/25814 | 423,565,289 | MDU6SXNzdWU0MjM1NjUyODk= | 25,814 | Cannot use categorical IntervalIndex as index when creating pivot_table | {
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} | 1 | 2019-03-21T04:07:16Z | 2019-06-13T20:41:44Z | 2019-06-13T20:41:44Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({"A":[1, 1, 2, 2], "B":[1,2,3,4]})
df['C'] = pd.cut(df['A'],[0,2])
df.pivot_table(index='C', values='B', aggfunc=np.sum)
```
#### Problem description
Cannot use IntervalIndex as index when creating pivot_table. It works fine on 0.23.4.
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\interval.py in _get_prev_label(label)
75 else:
76 raise TypeError('cannot determine next label for type {typ!r}'
---> 77 .format(typ=type(label)))
78
79
TypeError: cannot determine next label for type <class 'str'>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: zh_CN.GBK@cjknarrow
LOCALE: None.None
pandas: 0.24.2
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: Nonetput of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25815 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25815/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25815/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25815/events | https://github.com/pandas-dev/pandas/issues/25815 | 423,584,278 | MDU6SXNzdWU0MjM1ODQyNzg= | 25,815 | wrong aggregated results when pivot_table index is ordered categorical data | {
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} | 8 | 2019-03-21T06:12:15Z | 2019-11-25T15:17:19Z | 2019-07-09T21:29:23Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
_c = np.arange(5,0,-1)
df = pd.DataFrame(np.stack([_c]*3).T, columns=['A', 'B', 'C'])
df["D"] = pd.Series(["A","B","B","A", "A"])
df["E"] = pd.cut(x=df["A"],bins=[0,3,6,10])
print(pd.pivot_table(df, index='E', columns=['D'], values="B", aggfunc="sum", margins=True))
```
``` ipython
Output is
D A B All
E
(0, 3] 3 3 9
(3, 6] 5 4 6
All 8 7 15
```
#### Problem description
the data is as follows:
``` Ipython
A B C D E
0 5 5 5 A (3, 6]
1 4 4 4 B (3, 6]
2 3 3 3 B (0, 3]
3 2 2 2 A (0, 3]
4 1 1 1 A (0, 3]
```
Margin calculation results are in wrong order
(0, 3] should have a sum value of 6,
(3, 6] should have a sum value of 9
If I dont use column argument, results are correct.
``` Ipython
B
E
(0, 3] 6
(3, 6] 9
(6, 10] 0
All 15
```
I've tried both .23.4 and .24.2 . They have the same results.
#### Expected Output
``` Ipython
D A B All
E
(0, 3] 3 3 6
(3, 6] 5 4 9
All 8 7 15
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: zh_CN.GBK@cjknarrow
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25816 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25816/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25816/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25816/events | https://github.com/pandas-dev/pandas/issues/25816 | 423,584,531 | MDU6SXNzdWU0MjM1ODQ1MzE= | 25,816 | Series.replace throws TypeError only on large dataset | {
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"id... | open | false | null | [] | null | 4 | 2019-03-21T06:13:36Z | 2019-11-02T02:13:56Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
d = 1000000
df = pd.DataFrame({'A': np.random.randint(1,100,d)})
mapvalue = {i: i+1 for i in range(d)}
res = df['A'].replace(mapvalue)
```
#### Problem description
This throws "_TypeError: Cannot compare types 'ndarray(dtype=int32)' and 'int'_".
What's weird is that, the code works fine when d = 100000, 10000, 1000 or lower. The error occurs only when d = 1000000
I found that if I changed line 3 to `mapvalue = {i: i+1 for i in range(100)}`, the problem goes away. However, the above behavior is still weird.
pandas version: 0.24.2
numpy version: 1.16.2 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25817 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25817/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25817/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25817/events | https://github.com/pandas-dev/pandas/pull/25817 | 423,590,505 | MDExOlB1bGxSZXF1ZXN0MjYzMTMwMjk3 | 25,817 | TST: Remove compat.PY2 and compat.PY3 flags from tests | {
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} | 4 | 2019-03-21T06:46:57Z | 2019-03-21T14:58:04Z | 2019-03-21T11:23:46Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
These flags become trivial after dropping Python 2. There are still some Python 2 specific things that can be removed from the tests but this should eliminate a lot of the low hanging fruit.
Can split this up into separate PRs if need be; there are lots of changes but they're of low complexity and disjoint from each other, so shouldn't be too bad to review. Can wait on the other PRs mentioned in the master tracker issue if we want those to go in first. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25818 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25818/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25818/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25818/events | https://github.com/pandas-dev/pandas/issues/25818 | 423,601,855 | MDU6SXNzdWU0MjM2MDE4NTU= | 25,818 | sort_index for MultiIndex DataFrame silently fill NaN for index column | {
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"id": 71268330,... | open | false | null | [] | null | 0 | 2019-03-21T07:36:55Z | 2021-06-27T20:35:39Z | null | NONE | null | #### Code Sample
```python
mi = pd.MultiIndex.from_tuples([['A0', 'B0'],['A0', 'B1'],
['A1', 'B0'],['A1', 'B1'],['A3', np.nan] ], names=['ia','ib'])
df = pd.DataFrame(np.arange(10).reshape(5,2), mi,columns=['bar', 'foo'])
df2=df.copy()
df2.index.set_levels(['B1','B0'],level=1,inplace=True)
df2
bar foo
ia ib
A0 B1 0 1
B0 2 3
A1 B1 4 5
B0 6 7
A3 NaN 8 9
# now sort_index to df2 will fill B0 for (A3,NaN) row, but sort_index of df wouldn't
df2.sort_index()
bar foo
ia ib
A0 B0 2 3
B1 0 1
A1 B0 6 7
B1 4 5
A3 B0 8 9
```
#### Problem description
It is suspect that the unsort level in MultiIndex DataFrame lead to NaN auto fill.
**Note**: I come with "unsort level in MultiIndex" DataFrame after some broadcast operation.
Here is the code
```python
def mklbl(prefix, n):
return ["%s%s" % (prefix, i) for i in range(n)]
miindex = pd.MultiIndex.from_product([mklbl('A', 3),
mklbl('B', 2),
mklbl('C', 2),
mklbl('D', 2)],names=['ia','ib','ic','id'])
micolumns = ['foo','bah']
dfmi = pd.DataFrame(np.arange(len(miindex) * len(micolumns))
.reshape((len(miindex), len(micolumns))),
index=miindex,
columns=micolumns).sort_index().sort_index(axis=1)
dfmi=dfmi.drop('A2')
# now dfmi first level index contain list of value more than it actually hold
# and it may lead to the index level change from ['A0', 'A1', 'A2'] to ['A1','A0']
bs=dfmi.loc[('A0','B0')].copy().rename({'D1':'D2'})
# this will lead to NaN in some row due to broadcast.
(dfmi+bs).sort_index()
bah foo
ic id ia ib
C0 D0 A0 B0 2.0 0.0
B1 10.0 8.0
A1 B0 18.0 16.0
B1 26.0 24.0
D1 A0 B0 NaN NaN
B1 NaN NaN
A1 B0 NaN NaN
B1 NaN NaN
D2 A0 NaN NaN NaN
C1 D0 A0 B0 10.0 8.0
B1 18.0 16.0
A1 B0 26.0 24.0
B1 34.0 32.0
D1 A0 B0 NaN NaN
B1 NaN NaN
A1 B0 NaN NaN
B1 NaN NaN
D2 A0 NaN NaN NaN # again the (D2,NaN,NaN) get change
```
#### Expected Output
sort_index should not change the contain of DataFrame.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 4.1.0
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25819 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25819/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25819/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25819/events | https://github.com/pandas-dev/pandas/issues/25819 | 423,661,882 | MDU6SXNzdWU0MjM2NjE4ODI= | 25,819 | Slow read_parquet in version 0.24.1 vs 0.23.3 | {
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} | [] | closed | false | null | [] | null | 5 | 2019-03-21T10:32:27Z | 2019-03-22T09:54:44Z | 2019-03-22T09:54:43Z | NONE | null | Below is my code:
import pandas as pd
df = pd.read_parquet("/data/train.parquet"
The time taken to read this file (size 8GB) is about 3 min with pandas version 0.24.1. This took less than 1 min with pandas version 0.23.3.
Any clue as to why this may happen?
Thanks!
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https://api.github.com/repos/pandas-dev/pandas/issues/25820 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25820/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25820/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25820/events | https://github.com/pandas-dev/pandas/pull/25820 | 423,693,483 | MDExOlB1bGxSZXF1ZXN0MjYzMjA3MDE5 | 25,820 | [PERF] Get rid of MultiIndex conversion in IntervalIndex.is_monotonic methods | {
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} | 8 | 2019-03-21T11:56:57Z | 2019-04-22T02:41:40Z | 2019-04-21T16:02:26Z | CONTRIBUTOR | null | - xref #24813
- passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
```
All benchmarks:
before after ratio
[85572de5] [445fbe56]
<master> <GH24813-inc-dec>
- 261±6ns 134±3ns 0.51 index_object.IntervalIndexMethod.time_monotonic_inc
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] | closed | false | null | [] | null | 5 | 2019-03-21T13:02:27Z | 2019-12-11T08:51:13Z | 2019-03-26T05:37:52Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import pymysql
import time
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
connect = pymysql.Connect(
host=host,
port=port,
user=user,
passwd=passwd,
db=db,
charset='utf8'
)
cursor = connect.cursor()
sql = """SELECT * FROM table WHERE id = 1;"""
start_time = time.time()
cursor.execute(sql)
for row in cursor.fetchall():
print(row)
end_time = time.time()
print("sql execute time:%s" % (end_time - start_time))
start_time = time.time()
df_mysql = pd.read_sql(sql, con=connect)
end_time = time.time()
print("sql execute time:%s" % (end_time - start_time))
con_url = "mysql+pymysql://{0}:{1}@{2}:{3}/{4}?charset={5}"
engine = create_engine(con_url.format(user, passwd, host, port, db, charset))
DBSession = sessionmaker(bind=engine)
session = DBSession()
start_time = time.time()
raw_data_df = pd.read_sql(sql, session.bind)
end_time = time.time()
print("sql execute time:%s" % (end_time - start_time))
```
#### Problem description
pandas read_sql con use sqlalchemy engine slow much than pymysql connection
#### Expected Output
sql execute time:0.02592611312866211
sql execute time:0.028751611709594727
sql execute time:0.5061156749725342
#### Output of ``pd.show_versions()``
pandas ==0.23.0
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https://api.github.com/repos/pandas-dev/pandas/issues/25822 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25822/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25822/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25822/events | https://github.com/pandas-dev/pandas/pull/25822 | 423,732,815 | MDExOlB1bGxSZXF1ZXN0MjYzMjM3OTY4 | 25,822 | Implement --with-debugging-symbols switch in setup.py | {
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} | 4 | 2019-03-21T13:36:56Z | 2019-03-28T14:30:52Z | 2019-03-21T15:12:09Z | CONTRIBUTOR | null | - [x] closes N/A
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This implements a switch that enables building Pandas with debugging symbols - useful for debugging or profiling native parts of the code. | {
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"de... | open | false | null | [] | null | 6 | 2019-03-21T14:54:32Z | 2021-06-27T20:38:34Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
import pandas as pd
event = pd.Series({"a": "hello"})
pd.eval("event.str.match('hello').a") # returns numpy.bool_(True)
pd.eval("event.str.match('hello').a and event.str.match('hello').a") # raises TypeError
```
#### Problem description
When running the last line of the code above, the following error is produced:
```
TypeError Traceback (most recent call last)
<ipython-input-1-4a1286720430> in <module>
5 pd.eval("event.str.match('hello').a") # returns np.bool_(True)
6
----> 7 pd.eval("event.str.match('hello').a and event.str.match('hello').a") # raises TypeError
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/eval.py in eval(expr, parser, engine, truediv, local_dict, global_dict, resolvers, level, target, inplace)
292
293 parsed_expr = Expr(expr, engine=engine, parser=parser, env=env,
--> 294 truediv=truediv)
295
296 # construct the engine and evaluate the parsed expression
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in __init__(self, expr, engine, parser, env, truediv, level)
747 self.env.scope['truediv'] = truediv
748 self._visitor = _parsers[parser](self.env, self.engine, self.parser)
--> 749 self.terms = self.parse()
750
751 @property
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in parse(self)
764 def parse(self):
765 """Parse an expression"""
--> 766 return self._visitor.visit(self.expr)
767
768 @property
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
329 method = 'visit_' + node.__class__.__name__
330 visitor = getattr(self, method)
--> 331 return visitor(node, **kwargs)
332
333 def visit_Module(self, node, **kwargs):
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in visit_Module(self, node, **kwargs)
335 raise SyntaxError('only a single expression is allowed')
336 expr = node.body[0]
--> 337 return self.visit(expr, **kwargs)
338
339 def visit_Expr(self, node, **kwargs):
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
329 method = 'visit_' + node.__class__.__name__
330 visitor = getattr(self, method)
--> 331 return visitor(node, **kwargs)
332
333 def visit_Module(self, node, **kwargs):
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in visit_Expr(self, node, **kwargs)
338
339 def visit_Expr(self, node, **kwargs):
--> 340 return self.visit(node.value, **kwargs)
341
342 def _rewrite_membership_op(self, node, left, right):
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in visit(self, node, **kwargs)
329 method = 'visit_' + node.__class__.__name__
330 visitor = getattr(self, method)
--> 331 return visitor(node, **kwargs)
332
333 def visit_Module(self, node, **kwargs):
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in visit_BoolOp(self, node, **kwargs)
691
692 operands = node.values
--> 693 return reduce(visitor, operands)
694
695
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in visitor(x, y)
688 op, op_class, lhs, rhs = self._maybe_transform_eq_ne(
689 node, lhs, rhs)
--> 690 return self._maybe_evaluate_binop(op, node.op, lhs, rhs)
691
692 operands = node.values
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/expr.py in _maybe_evaluate_binop(self, op, op_class, lhs, rhs, eval_in_python, maybe_eval_in_python)
404 maybe_eval_in_python=('==', '!=', '<', '>',
405 '<=', '>=')):
--> 406 res = op(lhs, rhs)
407
408 if res.has_invalid_return_type:
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/ops.py in __init__(self, op, lhs, rhs, **kwargs)
335 self.rhs = rhs
336
--> 337 self._disallow_scalar_only_bool_ops()
338
339 self.convert_values()
~/anaconda3/lib/python3.7/site-packages/pandas/core/computation/ops.py in _disallow_scalar_only_bool_ops(self)
440 if ((self.lhs.is_scalar or self.rhs.is_scalar) and
441 self.op in _bool_ops_dict and
--> 442 (not (issubclass(self.rhs.return_type, (bool, np.bool_)) and
443 issubclass(self.lhs.return_type, (bool, np.bool_))))):
444 raise NotImplementedError("cannot evaluate scalar only bool ops")
TypeError: issubclass() arg 1 must be a class
```
The expected output of this operation is a boolean indicating `numpy.bool_(True)` or `numpy.bool_(False)`. This is the output when running the following code.
```python
event.str.match("hello").a and event.str.match("hello").a # outputs numpy.bool_(True)
```
Also, when using Python's builtin booleans with `pd.eval`, the behavior is as expected.
```python
pd.eval("True and True") # returns True
```
I think that this behavior should be used when running boolean operations on `numpy.bool_` types.
#### Expected Output
Expected output of `pd.eval("event.str.match('hello').a and event.str.match('hello').a")`:
`True`
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.5.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.7.3
Cython: 0.29.4
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.0
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.14
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.17
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25824 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25824/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25824/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25824/events | https://github.com/pandas-dev/pandas/issues/25824 | 423,892,361 | MDU6SXNzdWU0MjM4OTIzNjE= | 25,824 | Core dump when converting to category with duplicate column names | {
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} | 3 | 2019-03-21T18:59:07Z | 2019-03-22T15:19:54Z | 2019-03-21T21:10:49Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
X = np.random.normal(size=(100, 2))
X_df = pd.DataFrame(X, columns=[0, 0])
X_df[0] = X_df[0].astype('category')
```
#### Problem description
Stack overflow in the Python interpreter
#### Expected Output
No core dump.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 3.6.2
pip: 10.0.1
setuptools: 39.2.0
Cython: 0.29.2
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.8.2
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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] | closed | false | null | [] | null | 4 | 2019-03-21T20:15:51Z | 2019-03-22T19:11:59Z | 2019-03-22T19:11:59Z | NONE | null | I am graphing the results of the measurements of a humidity sensor over time.
I'm using Python 3.7.1 and Pandas 0.24.2.
I have a list called dateTimeList with date and time strings:
```
dateTimeList = ['15.3.2019 11:44:27', '15.3.2019 12:44:33', '15.3.2019 13:44:39']
```
I wrote this code where `index` is a DatetimeIndex object and `humList` is a list of floats.
```python
index = pd.to_datetime(dateTimeList, format='%d.%m.%Y %H:%M:%S')
ts = pd.Series(humList, index)
plt.figure(figsize=(12.80, 7.20))
ts.plot(title='Gráfico de Humedad en el Tiempo', style='g', marker='o')
plt.xlabel('Tiempo [días]')
plt.ylabel('Humedad [V]')
plt.grid()
plt.savefig('Hum_General'+'.png', bbox_inches='tight')
plt.show()
```
And I have this two results, one with data from February[1] and the other one with data from March[2].
The problem is that in March instead of leaving the year 2019, sequences of 00 12 00 12 appear on the x axis. I think it is important to note that this only happens on the data of March, since February is ok, and the data of both months have the same structure. Day and Month are shown correctly on both plots.
[1]: https://www.dropbox.com/s/ckv6zeqb0psh6ih/Hum_General_Febrero.png?dl=0
[2]: https://www.dropbox.com/s/nzvbwnuf5fkuim1/Hum_General_Marzo.png?dl=0
I also tried with:
```
index = [ pd.to_datetime(date, format='%d.%m.%Y %H:%M:%S') for date in dateTimeList]
```
Now `index` is a list of Timestamps objects. Same Results.
Full Code, Data and Outputs:
[CodeandData.zip](https://github.com/pandas-dev/pandas/files/2997619/CodeandData.zip)
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} | 5 | 2019-03-21T21:34:11Z | 2019-03-22T19:48:49Z | 2019-03-22T19:46:59Z | CONTRIBUTOR | null | **Background:** In NumPy, I've been working to expand the operations supported for `np.timedelta64` (`m8`) operands, adding support for modulus, floordiv and divmod operations. Historically, we've allowed casting from integer and bool types to `m8`, which may seem innocent enough, but is arguably sloppy from a units perspective.
Furthermore, with the introduction of new `m8` operation support, signed and unsigned integers are now able to interconvert in [undesirable ways](https://github.com/numpy/numpy/issues/12927) by sneaking through casts to `m8`. So, core devs have suggested [prohibiting casts from int / bool types to `m8`](https://github.com/numpy/numpy/pull/13061). A prime concern here for me is the effect on downstream libraries that are known to heavily use datetime operations, like `pandas`.
**Feedback**: It would be great to get some feedback here from the `pandas` perspective. To get the discussion started, I've opened this PR with some perhaps crude changes that allow the `pandas` master branch test suite to pass for me locally (via `./test_fash.sh` anyway) with the NumPy feature branch linked above.
How disruptive is this for you? Will we need to issue warnings / deprecate? Should we just not do this at all? | {
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} | 3 | 2019-03-21T23:58:41Z | 2019-03-22T11:22:37Z | 2019-03-22T11:22:34Z | CONTRIBUTOR | null | When running `ci/code_checks.sh` on a local repository that has also been used to run `asv` benchmarks, a couple of the checks recurse into the `asv_bench/env` directory created for running benchmark environments. This massively increases the runtime and leads to linting errors on non-pandas packages, which is not informative.
This change simply adds `env` to the `isort` exclude list and the tailing whitespace `grep` call, which resolves the issue.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 1 | 2019-03-22T03:09:20Z | 2019-03-26T19:59:07Z | 2019-03-26T17:09:37Z | MEMBER | null | This is a follow up to https://github.com/pandas-dev/pandas/pull/21093/files#r188805397 which wasn't working with Py27. Now that that is a thing of the past we should be able to use the more idiomatic Substitution approach to generating this docstring
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} | 4 | 2019-03-22T03:50:01Z | 2019-03-31T14:46:08Z | 2019-03-30T19:21:17Z | CONTRIBUTOR | null | - [x] addresses the other half of #25790
- [x] pandas/tests/arrays/ tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25830 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25830/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25830/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25830/events | https://github.com/pandas-dev/pandas/issues/25830 | 424,038,351 | MDU6SXNzdWU0MjQwMzgzNTE= | 25,830 | bug!!when `json_normalize` string has `null` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25831 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25831/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25831/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25831/events | https://github.com/pandas-dev/pandas/issues/25831 | 424,073,964 | MDU6SXNzdWU0MjQwNzM5NjQ= | 25,831 | Can't sort DataFrame when level=[] and sort_remaining=False | {
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} | 3 | 2019-03-22T07:08:00Z | 2021-06-27T20:39:05Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
# Create a DataFrame with a MultiIndex.
df = pd.DataFrame({
"a": [0, 2, 1],
"b": [3, 0, 2],
"c": [0, 0, 1]
}).set_index(["a", "b"])
# This works.
df.sort_index(level=[], sort_remaining=True)
# This throws an IndexError: list index out of range.
df.sort_index(level=[], sort_remaining=False)
```
#### Problem description
Pandas is unable to sort the index of a DataFrame when level=[] and sort_remaining=False. It works when sort_remaining=True, though. This is inconsistent behavior; it doesn't make sense for one to work but not the other.
The above code worked as intended in 0.22, but started throwing an error in 0.23. I believe that the commit that broke it was [this one](https://github.com/pandas-dev/pandas/commit/bc37ea2e05019a89adaa48159b220483598d1898#diff-1e79abbbdd150d4771b91ea60a4e1cc7). By changing
```
if level:
```
to
```
if level is not None:
```
the conditional is now entered when `level=[]`, whereas it wasn't previously (since an empty list is treated as `False`).
#### Expected Output
The offending line of code should be equivalent to `df`.
(You might wonder how the above situation would even arise; no one would ever deliberately write the above line of code, since it is a no-op. But it can happen when the `level=` argument is being programmatically generated.)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 16.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 3.3.2
pip: 18.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.15.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.9
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.2.0
xlsxwriter: 1.0.2
lxml.etree: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25832 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25832/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25832/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25832/events | https://github.com/pandas-dev/pandas/issues/25832 | 424,129,329 | MDU6SXNzdWU0MjQxMjkzMjk= | 25,832 | python3.7 pip install pandas==0.22.0 error | {
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] | closed | false | null | [] | null | 1 | 2019-03-22T10:00:48Z | 2019-03-22T11:21:51Z | 2019-03-22T11:21:44Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
pip install pandas==0.22.0
```
#### Problem description
----------------------------------------
Command "c:\myprogram\anaconda37\python.exe c:\myprogram\anaconda37\lib\site-packages\pip install --ignore-installed --no-user --prefix C:\Users\qiaj\AppData\Local\Temp\pip-build-env-ls0v0cdg\overlay --no-warn-script-location --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- wheel setuptools Cython "numpy==1.9.3; python_version=='3.5'" "numpy==1.12.1; python_version=='3.6'" "numpy==1.13.1; python_version>='3.7'"" failed with error code 1 in None
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25833 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25833/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25833/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25833/events | https://github.com/pandas-dev/pandas/issues/25833 | 424,140,800 | MDU6SXNzdWU0MjQxNDA4MDA= | 25,833 | RecursionError with self referencing column in str(df) on DataFrame | {
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"description": "__repr__ of pandas objec... | open | false | null | [] | null | 1 | 2019-03-22T10:28:59Z | 2020-09-22T04:05:45Z | null | NONE | null | ```python
# Your code here
b = a = pd.Series(
[2, 1, 3],
index=[2, 1, 3],
)
# display(b)
a['Hi'] = b
print(str(a))
```
This raises
```python
RecursionError: maximum recursion depth exceeded in __instancecheck__
```
It is annoying when this happens in interactive mode.
#### Expected Output
```
2 2
1 1
3 3
Hi 2 2
1 1
3 3
dtype: int64
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.4
numpy: 1.16.1
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25834 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25834/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25834/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25834/events | https://github.com/pandas-dev/pandas/issues/25834 | 424,210,763 | MDU6SXNzdWU0MjQyMTA3NjM= | 25,834 | Writing empty DataFrame to HDF file | {
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... | closed | false | null | [] | null | 1 | 2019-03-22T13:32:11Z | 2019-03-22T15:39:49Z | 2019-03-22T15:39:48Z | NONE | null | I am not able to save empy DataFrames to HDF files.
#### Code Sample #1
```python
import pandas as pd
df = pd.DataFrame(columns=['x','y'])
df.to_hdf('temp.h5', 'xxx')
df2 = pd.read_hdf('temp.h5', 'xxx')
```
#### Output #1
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 389, in read_hdf
return store.select(key, auto_close=auto_close, **kwargs)
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 740, in select
return it.get_result()
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 1518, in get_result
results = self.func(self.start, self.stop, where)
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 733, in func
columns=columns)
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 2986, in read
idx=i), start=_start, stop=_stop)
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 2575, in read_index
_, index = self.read_index_node(getattr(self.group, key), **kwargs)
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 2676, in read_index_node
data = node[start:stop]
File ".../Python-3.6.3/lib/python3.6/site-packages/tables/vlarray.py", line 675, in __getitem__
return self.read(start, stop, step)
File ".../Python-3.6.3/lib/python3.6/site-packages/tables/vlarray.py", line 811, in read
listarr = self._read_array(start, stop, step)
File "tables/hdf5extension.pyx", line 2106, in tables.hdf5extension.VLArray._read_array (tables/hdf5extension.c:24649)
ValueError: cannot set WRITEABLE flag to True of this array
```
#### Code Sample #2 (with `format='table'`):
```python
import pandas as pd
df = pd.DataFrame(columns=['x','y'])
df.to_hdf('temp.h5', 'xxx', format='table')
df2 = pd.read_hdf('temp.h5', 'xxx')
```
#### Output #2
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 389, in read_hdf
return store.select(key, auto_close=auto_close, **kwargs)
File ".../Python-3.6.3/lib/python3.6/site-packages/pandas/io/pytables.py", line 722, in select
raise KeyError('No object named {key} in the file'.format(key=key))
KeyError: 'No object named xxx in the file'
```
#### Problem description
It is not possible to save (and read back) an empty DataFrame to an HDF file.
#### Expected Output
No error messages. Empty DataFrame can be saved and read back to/from an HDF file.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.11.6.el7.x86_64
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: 0.24.2
pytest: 3.3.2
pip: 19.0.3
setuptools: 39.0.1
Cython: 0.27.3
numpy: 1.16.1
scipy: 1.1.0
pyarrow: 0.11.1
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-03-22T14:03:49Z | 2019-03-25T19:36:53Z | 2019-03-22T16:31:05Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas
pandas.Series(dtype='O').sum()
```
#### Problem description
Performing the above code segment with pandas 0.24.2 gives me
- (numpy.bool_) `False` on a Mac with homebrew python 3.6.4
- (int) `0` on a Windows 10 with Anaconda python 3.6.7
While I would prefer `0` for my own purposes, I suppose it is not easy to decide on an expected output here. But I would argue that it should at least be consistent.
#### Output of ``pd.show_versions()``
- MAC
<details>
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: 0.24.2
pytest: 3.6.2
pip: 19.0.3
setuptools: 39.0.1
Cython: None
numpy: 1.14.2
scipy: 1.0.1
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.7.2
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
23846nemo/linearization.py000418:1
LinterAtom Ide DiagnosticsActivate Power Mode
Python 3 | idle
CRLFUTF-8PythonGitHubGit (0)4 updates
</details>
- WIN
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
47327experiments\pyomo_nl.py50:1
Python 3 | idle
CRLFenUS,deDEUTF-8Python
developmentFetchGitHubGit (45)
</details>
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} | 3 | 2019-03-22T14:33:42Z | 2019-03-25T11:05:54Z | 2019-03-22T16:18:38Z | CONTRIBUTOR | null | - [x] fixes a warning introduced in #23981
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Using unparenthesized macro parameters is dangerous, so wrap `c` in braces.
Example warning fixed by adding `tolower_ascii()`:
```
In file included from pandas/_libs/lib.c:643:0:
pandas/_libs/src/parse_helper.h: In function ‘lowercase’:
pandas/_libs/src/parse_helper.h:141:26: warning: implicit declaration of function ‘tolower_ascii’; did you mean ‘toupper_ascii’? [-Wimplicit-function-declaration]
for (; *p; ++p) *p = tolower_ascii(*p);
^~~~~~~~~~~~~
toupper_ascii
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25837 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25837/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25837/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25837/events | https://github.com/pandas-dev/pandas/issues/25837 | 424,267,989 | MDU6SXNzdWU0MjQyNjc5ODk= | 25,837 | to_msgpack converts tuples of length 1 to str iff no object(str) columns are present. | {
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```python
import pandas as pd
from pandas.testing import assert_frame_equal
for name, df in [
("mixed len tuples", pd.DataFrame({"x": [("one",), ("two", "three")]})),
("len(1) tuples", pd.DataFrame({"x": [("one",), ("two",)]})),
(
"len(1) tuples+str_column",
pd.DataFrame({"x": [("one",), ("two",)], "y": ["a", "b"]}),
),
(
"len(1) tuples+categorical",
pd.DataFrame({"x": [("one",), ("two",)], "y": pd.Categorical(["a", "b"])}),
),
]:
df.to_msgpack("example")
df2 = pd.read_msgpack("example")
if df.equals(df2):
print("%s -> ok" % name)
else:
print("%s -> different" % name)
print(df2.x)
```
#### Problem description
DataFrame.to_msgpack() handles tuples - except when they are of length 1 and no string column is present.
This is unexpected, the round tripping shoudl neither depend on the tuples being longer nor on the dtypes of other columns.
#### Expected Output
mixed len tuples -> ok
len(1) tuples -> ok
len(1) tuples+str_column -> ok
len(1) tuples+categorical -> ok
len(mixed) tuples+int -> ok
###actual output
mixed len tuples -> ok
len(1) tuples -> different
0 one
1 two
Name: x, dtype: object
len(1) tuples+str_column -> ok
len(1) tuples+categorical -> different
0 one
1 two
Name: x, dtype: object
#### Output of ``pd.show_versions()``
This is on todays master.
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+301.gf2bcb9f00
pytest: 3.10.1
pip: 19.0.1
setuptools: 40.8.0
Cython: 0.29.4
numpy: 1.16.1
scipy: 1.2.0
pyarrow: 0.12.0
xarray: None
IPython: 7.3.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: 0.4.0
matplotlib: 3.0.2
openpyxl: None
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
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"description": "duplicat... | closed | false | null | [] | null | 3 | 2019-03-22T15:58:22Z | 2021-03-02T09:35:15Z | 2020-04-03T03:17:53Z | NONE | null | #### Enhancement description
The present implementation of `df.duplicated()` (and hence `df.drop_duplicates()`) only has two options for users that wish to keep exactly one from each set of duplicates (`'first'` and `'last'`). In some use cases, if the data is already ordered in some way, these options can potentially introduce bias. It would be useful to have an option that allows for the duplicate selected to be randomly (but also deterministically) chosen.
#### Details
In our use case, we are able to produce the desired result by other means: we wish to remove all-but-one events with a duplicate in the 'eventNumber' column, so we introduce an additional 'random number' column using `pd.utils.hash_pandas_object(df, index=False)`. We then sort the dataframe by this column, apply `df.drop_duplicates()` using either keep='first' or keep='last', and then sort by index again (thanks to @chrisburr for this solution).
By using a hash instead of a standard RNG, the numbers used in the sorting are deterministic & repeatable. It also remains stable when entries are added/removed but the rest are not modified, which is desirable not not necessarily required. But this method requires the hash having knowledge of another subset of the columns in which there are no duplicates, and so would require the underlying functions (the `duplicated_{{dtype}}` functions in `pandas/_libs/hashtable_func_helper.pxi.in`) to receive an additional argument. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25839 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25839/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25839/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25839/events | https://github.com/pandas-dev/pandas/issues/25839 | 424,403,835 | MDU6SXNzdWU0MjQ0MDM4MzU= | 25,839 | df.to_hdf5() in table append mode very slow with data_columns=True, append=True, index=False | {
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"default"... | open | false | null | [] | null | 2 | 2019-03-22T21:19:16Z | 2020-04-29T02:02:37Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(123,456, size=(300,225)), columns=['COL'+str(i) for i in range(225)])
df.loc[:,:'COL10'] = df.loc[:,:'COL10'].astype(str)
df.loc[:,'COL11':'COL20'] = df.loc[:,'COL11':'COL20'].astype(float)
df.to_hdf(r'test.h5', 'df', mode='w', data_columns=True, append=True, index=False)
# <-- Quite Fast on first run creation of the table.
df.to_hdf(r'test.h5', 'df', mode='a', data_columns=True, append=True, index=False)
# <-- 3.6 Minutes for 2nd run trying to append to the table. Note index=False
```
#### Problem description
``df.to_hdf()`` In Append Table mode used to work very quickly when you set each column as a data column and want _no_ index created. ``data_columns=True, append=True, index=False``. On the new version of Pandas and PyTables it seems to be taking a very long to append additional rows.
#### Expected Output
Appending HDF5 Table rows with ''data_columns=True, append=True, index=False'' should create all columns as data columns, but not create an index and should be quite fast.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 4.0.0
pip: 19.0.3
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: 0.12.0
xarray: 0.11.0
IPython: 7.1.1
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: 1.6.2
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.1.6
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-03-22T21:34:27Z | 2019-03-23T21:37:04Z | 2019-03-23T20:08:42Z | MEMBER | null | Also avoid having a dependency in `compat` on `core`. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25841 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25841/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25841/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25841/events | https://github.com/pandas-dev/pandas/pull/25841 | 424,410,922 | MDExOlB1bGxSZXF1ZXN0MjYzNzcxMzEz | 25,841 | Remove compat code in tseries | {
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- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 1 | 2019-03-22T21:59:04Z | 2019-03-23T02:04:13Z | 2019-03-23T02:04:07Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
df = pd.DataFrame.from_records(data=data, coerce_float=False, index=['date'])
# date field a datetime.datetime values
account_id amount
date
2018-01-01 1 100.0
2018-01-01 1 50.0
2018-06-01 1 200.0
2018-07-01 2 100.0
2018-10-01 2 200.0
```
#### Problem description
How can I "pad" my dataframe with leading and trailing "empty dates". I have tried to reindex on a date_range and period_range, I have tried to merge another index. I have tried all sorts of things all day, and I have read alot of the docs.
I have a simple dataframe with columns `transaction_date`, `transaction_amount`, and `transaction_account`. I want to group this dataframe so that it is grouped by account at the first level, and then by year, and then by month. Then I want a column for each month, with the `sum` of that month's transaction amount value.
This seems like it should be something that is easy to do.
#### Expected Output
This is the closest I have gotten:
```
df = pd.DataFrame.from_records(data=data, coerce_float=False, index=['date'])
df = df.groupby(['account_id', df.index.year, df.index.month])
df = df.resample('M').sum().fillna(0)
print(df)
account_id amount
account_id date date date
1 2018 1 2018-01-31 2 150.0
6 2018-06-30 1 200.0
2 2018 7 2018-07-31 2 100.0
10 2018-10-31 2 200.0
```
**And this is what I want to achieve (basically reindex the data by `date_range(start='2018-01-01', period=12, freq='M')`**
*(Ideally I would want the month to be transposed by year across the top as columns)*
```
amount
account_id Year Month
1 2018 1 150.0
2 NaN
3 NaN
4 NaN
5 NaN
6 200.0
....
12 200.0
2 2018 1 NaN
....
7 100.0
....
10 200.0
....
12 NaN
```
If you want some stack overflow credit -> https://stackoverflow.com/questions/55308342/leading-and-trailing-padding-dates-in-pandas-dataframe | {
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} | 4 | 2019-03-22T23:53:09Z | 2019-04-21T16:37:05Z | 2019-04-21T16:37:05Z | CONTRIBUTOR | null | I can reproduces this in pandas (development version, 0.24.0+, but not 0.23.4) with this minimal example:
```
import datetime
import pandas as pd
import pytz
dates = [
datetime.datetime(2019, 1, 1, 12, tzinfo=pytz.utc),
datetime.datetime(2018, 4, 1, 17, 13, tzinfo=pytz.utc),
]
df = pd.DataFrame({"dates": dates})
print(df.dtypes)
df2 = pd.DataFrame({"dates": dates}, dtype="datetime64[ns]")
print(df2.dtypes)
```
It prints:
```
# df
dates datetime64[ns, UTC] <-- I expect this.
dtype: object
# df2
dates datetime64[ns, UTC] <-- I didn't expect this.
dtype: object
```
There do appear to be a lot of changes to `datetime64` behavior in the changelog for 0.24.0 http://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.24.0.html so maybe this is intended behavior? Maybe the distinction between `datetime64[ns, UTC]` and `datetime64[ns]` when you pass in an explicit `dtype` shouldn't actually be a meaningful difference? | {
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} | 15 | 2019-03-23T02:44:14Z | 2020-01-16T00:34:37Z | 2019-03-26T15:45:07Z | MEMBER | null | - [X] closes #14468
I think this is the minimal amount of work required to get MyPy configured for CI. Note that the list of files is the only [documented](https://mypy.readthedocs.io/en/latest/running_mypy.html#reading-a-list-of-files-from-a-file) way to whitelist things that I could find (see also https://github.com/python/mypy/issues/1476)
I think we want to add this to the Checks_and_doc job though I couldn't see where that was actually configured (maybe an access thing on Azure?) so can do as a quick follow up
@jreback @gwrome | {
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} | 3 | 2019-03-23T04:36:02Z | 2019-03-23T20:14:38Z | 2019-03-23T20:13:30Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Some more low hanging fruit in the Python 2 removal process. This should remove all references to the following functions in `pandas.compat` which are redundantly defined for Python 3:
- `filter`
- `map`
- `range`
- `zip`
- `next`
- `FileNotFoundError`
- `ResourceWarning` | {
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] | closed | false | null | [] | null | 4 | 2019-03-23T07:29:15Z | 2020-01-21T04:23:19Z | 2020-01-21T04:23:18Z | NONE | null | Hi All,
I have a pandas dataframe which I constructed after doing a crosstab. When I try to `reset_index()` it gives me an error. Saying `TypeError: cannot insert an item into a CategoricalIndex that is not already an existing category` this happens sometimes and does not happen sometimes. The pandas dataframe was constructed using dask `.compute()` method.
Would anyone be able to tell me what is the issue and how to rectify it. I have `pandas 0.24.2 ` running.
Thanks
Michael | {
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```python
import numpy as np
import pandas as pd
df=pd.DataFrame()
df['day'] =np.arange(1,32)
df['day']=df['day'].apply(lambda x: "{:0>2d}".format(x) )
df['month']='01'
df['year']='2018'
df['date']=df['day']+'/'+df['month']+'/'+df['year']
df.to_csv('mydates.csv', index=False)
#same results whether you use parse_dates or not
imp = pd.read_csv('mydates.csv',parse_dates=['date'])
imp['day extracted']=pd.DatetimeIndex(imp['date']).day
print(imp)
```
#### Problem description
**If you import a csv containing dates with read_csv(), pandas may assign different date formats within the same column, i.e. one row may be dd-mm-yyyy and another mm-dd-yyyy** . This is shown in the example above by extracting the day. This happens also when the date column is not ambiguous, i.e. when it contains days from 1st to the 31st. With a column containing only 1-1-2000 or 3-4-2010 you can't infer which is the day and which the month, but with columns containing days till the 31st there cannot be any ambiguity.
The only way I have found to fix this is to declare the date format, either explicitly or "semi-explicitly" with dayfirst. However, it is a BIG problem because it means I must explicitly declare every single date format, even when there shouldn't be any ambiguity.
This happens to me with pandas 0.23.4 and Python 3.7.1 on Windows 10. For reasons I don't understand, conda doesn't update pandas to 0.24.2
The same issue has been reported on stackoverflow: https://stackoverflow.com/questions/55309199/pandas-read-csv-can-apply-different-date-formats-within-the-same-column-is-it?noredirect=1#comment97348008_55309199 but I haven't found it mentioned in github.
#### Expected Output
The column 'day extracted' should have been identical to the column 'day' created with np.arange()
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 1 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
None
</details>
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} | 7 | 2019-03-23T08:19:03Z | 2019-05-14T17:13:01Z | 2019-05-14T17:13:01Z | NONE | null | #### Code Sample: create a small csv with dates, then imports it and times the import
```python
import numpy as np
import pandas as pd
import time
import timeit
start_time=time.time()
n=int(100e3)
out =pd.DataFrame()
out['day']=np.random.randint(1,12,n)
out['month']=np.random.randint(1,12,n)
out['year']=np.random.randint(2000,2010,n)
out['date1']= out.day.astype(str) + '-' + out.month.astype(str) + '-' + out.year.astype(str)
out['date2']= out['day'].map('{:0>2d}'.format ) + '-' + out['month'].map('{:0>2d}'.format ) + '-' + out.year.astype(str)
out.to_csv('test dates.csv')
print('Exported in ')
print(time.time()-start_time)
def noparse():
return pd.read_csv('test dates.csv', dtype={'date1':str,'date2':str} )
def parse1():
return pd.read_csv('test dates.csv', parse_dates=['date1','date2'], dayfirst=True )
def parse2():
df = pd.read_csv('test dates.csv', dtype={'date1':str,'date2':str} )
for d in ['date1','date2']:
df[d]= pd.to_datetime( df[d], dayfirst=True )
return df
def parse3():
df = pd.read_csv('test dates.csv', dtype={'date1':str,'date2':str} )
# formatting described at: http://php.net/manual/en/function.strftime.php
df['date1']=pd.to_datetime(df['date1'], format='%d-%m-%Y')
df['date2']=pd.to_datetime(df['date2'], format='%d-%m-%Y')
return df
results=pd.DataFrame()
results['no parse'] = timeit.Timer( "noparse()", globals=globals() ).repeat(repeat=3, number =1)
results['parse 1']=timeit.Timer( "parse1()", globals=globals() ).repeat(repeat=3, number =1)
results['parse 2']=timeit.Timer( "parse2()", globals=globals() ).repeat(repeat=3, number =1)
results['parse 3']=timeit.Timer( "parse3()", globals=globals() ).repeat(repeat=3, number =1)
print(results)
```
#### Problem description
I am trying to import a CSV with dates, where the days are always < 13 , i.e. there is no way to infer the proper format - it must be specified explicitly (how can you tell whether 1-2 is Jan 2nd or Feb 1st?).
What I noticed is that **setting the date format with dayfirst is 25 to 35x (I tried it on two PCs) slower than setting the format explicitly with format='%d-%m-%Y'**
How can this be? It's insane. I appreciate there may be a little bit of additional overhead, as dayfirst must guess the position of the year, but 25x to 35x slower leaves me speechless. **With dayfirst it took 16 seconds to import a csv with 2 date columns and only 100k rows.**
There must be something very wrong with how read_csv implements dayfirst - I'm hoping it shouldn't be too complicated to fix it?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 1 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.23.4
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
None
</details>
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] | closed | false | null | [] | null | 2 | 2019-03-23T17:44:17Z | 2019-03-24T00:56:05Z | 2019-03-24T00:56:04Z | NONE | null | #### Code Sample
I'm using /anaconda3/bin/python
3.6.8 |Anaconda, Inc.| (default, Dec 29 2018, 19:04:46)
pandas 0.24.2
```python
import pandas as pd
```
#### Problem description
when running "import pandas as pd" I get:
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-29-7dd3504c366f> in <module>()
----> 1 import pandas as pd
/anaconda3/lib/python3.6/site-packages/pandas/__init__.py in <module>()
40 import pandas.core.config_init
41
---> 42 from pandas.core.api import *
43 from pandas.core.sparse.api import *
44 from pandas.tseries.api import *
/anaconda3/lib/python3.6/site-packages/pandas/core/api.py in <module>()
5 import numpy as np
6
----> 7 from pandas.core.arrays.integer import (
8 Int8Dtype,
9 Int16Dtype,
/anaconda3/lib/python3.6/site-packages/pandas/core/arrays/integer.py in <module>()
14 is_bool_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype,
15 is_list_like, is_object_dtype, is_scalar)
---> 16 from pandas.core.dtypes.dtypes import register_extension_dtype
17 from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
18 from pandas.core.dtypes.missing import isna, notna
ImportError: cannot import name 'register_extension_dtype'
Any Advice? thank you
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https://api.github.com/repos/pandas-dev/pandas/issues/25850 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25850/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25850/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25850/events | https://github.com/pandas-dev/pandas/issues/25850 | 424,530,111 | MDU6SXNzdWU0MjQ1MzAxMTE= | 25,850 | Assignment to DataFrame not working but dtypes changed | {
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] | closed | false | null | [] | null | 1 | 2019-03-23T18:22:26Z | 2019-03-23T21:55:55Z | 2019-03-23T21:55:55Z | NONE | null | #### Code Sample
```python
# python version
# Python 3.7.2 (default, Feb 21 2019, 17:35:59) [MSC v.1915 64 bit (AMD64)] :: Anaconda, Inc. on win32
import pandas as pd
import numpy as np
dataset = [[[i for i in range(5)], ] for i in range(5)]
dataset = pd.DataFrame(dataset, columns=['test']) # generate a test dataset with shape (5, 5)
empty_numpy = np.arange(25).reshape(5, 5)
empty_numpy.fill(np.nan)
# Solution 1: change the below code into 'empty_frame = pd.DataFrame(empty_numpy)' then everything will be fine
empty_frame = pd.DataFrame(empty_numpy, columns=[str(i) for i in range(5)])
series = dataset['test']
target_frame = pd.DataFrame(list(series))
# Solution 2: run `empty_frame[:] = target_frame` twice, work fine to me.
# ==================================================================
# First try.
empty_frame[:] = target_frame
print("="*40)
print(f"Data types of empty_frame: {empty_frame.dtypes}")
print("="*40)
print("Result of first try: ")
print(empty_frame)
print("="*40)
# Second try.
empty_frame[:] = target_frame
print(f"Data types of empty_frame: {empty_frame.dtypes}")
print("="*40)
print("Result of second try: ")
print(empty_frame)
print("="*40)
# ====================================================================
```
#### Problem description
*Assignment to DataFrame not working but dtypes changed*
New to data-science, I wanna to asign the `target_frame` to the `empty_frame`, but it's not working until asign again. And during the asignments, the dtypes of `empty_frame` has changed from `int32` to `float64` and finally setup to `int64`.
There are two solutions for this problem but I don't know why:
* as I showed in my code, try the asignment twice in one run.
* remove the columns' name when creating `empty_frame`.
It cost me a whole night to figure out what happens, the code above has been extracted and simplified. Two things I want to figure out:
1. why `empty_frame`'s data types changed.
2. why the solutions showed in my code can solve this asignment problem.
Thanks.
#### Expected Output
```python
"""
========================================
Data types of empty_frame: 0 float64
1 float64
2 float64
3 float64
4 float64
dtype: object
========================================
Result of first try:
0 1 2 3 4
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
========================================
Data types of empty_frame: 0 int64
1 int64
2 int64
3 int64
4 int64
dtype: object
========================================
Result of second try:
0 1 2 3 4
0 0 1 2 3 4
1 0 1 2 3 4
2 0 1 2 3 4
3 0 1 2 3 4
4 0 1 2 3 4
========================================
"""
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.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: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-03-23T19:27:58Z | 2019-04-05T16:54:44Z | 2019-04-05T16:54:44Z | CONTRIBUTOR | null | This is a followup to the discussion in #25746.
PyDateTime_CheckExact is being used as a proxy for isinstance(obj, Timestamp) checks for performance reasons. However, if a subclass of the std datetime lib is being used, these checks are not sufficient to determine if an object is a Timestamp or not. Thus a performant replacement must be found.
I have a working fix, but need to do some more performance testing and add proper test cases. If that goes well, I will submit a PR for this. | {
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} | 6 | 2019-03-23T20:51:38Z | 2019-03-24T17:20:34Z | 2019-03-24T15:15:23Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Another incremental step in removing Python 2. Only non-standard changes were to `pandas/tests/test_strings.py` where I deleted a bunch of code, as removing `u` resulted in duplicate test scenarios. | {
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} | 15 | 2019-03-23T23:06:55Z | 2019-04-05T18:13:57Z | 2019-04-05T16:54:44Z | CONTRIBUTOR | null | - [x] closes #25851
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
PyDateTime_CheckExact is being used as a proxy for isinstance(obj, Timestamp) checks for performance reasons. However, if a subclass of the std datetime lib is being used, these checks are not sufficient to determine if an object is a Timestamp or not.
As discussed in #25746, any solution will be less performant than PyDateTime_CheckExact. The best solution found was to use isinstance(obj, _Timestamp), and that is what I implemented here.
Of course, a few additional changes were necessary to be able to use that check.
Since asv isn't very good for cython code, I've done performance testing on all the modified functions by running them 10,000 times in a function, then timing that function in `%timeit`. The results are below:
```
----------- PR --------- -------- Master -------
datetime timestamp datetime timestamp
array_to_datetime 34.3 ms 34.5 ms 34.2 ms 34.5 ms
localize_pydatetime 15 ms 67.7 ms 15.2 ms 57.5 ms
normalize_date 12.7 ms 91 ms 12.8 ms 78.3 ms
Timedelta addition 1.05 ms 23 ms 1.04 ms 22.9 ms
Timestamp constructor 11.1 ms 9.85 ms 10.9 ms 9.79 ms
tz_localize N/A 63.7 ms N/A 53.5 ms
normalize N/A 90.6 ms N/A 78.5 ms
```
As you can see, some of the functions used with a `Timestamp` are affected, with a few unaffected. Where there is an impact, it appears to be 15-20% less performant.
Note this fix also fixes (potentially) #25734, but if we want a separate test case for that issue I could modify my existing PR for that. | {
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] | closed | false | null | [] | null | 4 | 2019-03-24T00:18:27Z | 2019-03-24T05:56:25Z | 2019-03-24T05:49:10Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
In[11]: import pandas as pd
test = pd.Series(data=r'Tech \u0026 Innovation', index=range(2))
# can replace a string directly
In[11]: test[0].replace('\\u0026', '&')
Out[11]: 'Tech & Innovation'
# str.replace does not work with the same code:
In[12]: test.str.replace('\\u0026', '&')
Out[12]:
0 Tech \u0026 Innovation
1 Tech \u0026 Innovation
dtype: object
# it works when I use r''
In[13]: test.str.replace(r'\\u0026', '&')
Out[13]:
0 Tech & Innovation
1 Tech & Innovation
dtype: object
```
#### Problem description
I just found an inconsistent behavior of `Series.str.replace()` when I try to replace a unicode character. As shown in the example above, I can replace the character `\u0026` with `.replace('\\u0026', '&')` for a string, but `Series.str.replace()` only works when I add `r` in front of the string (`.str.replace(r'\\u0026', '&')`)
#### Expected Output
```python
In[11]: import pandas as pd
test = pd.Series(data=r'Tech \u0026 Innovation', index=range(2))
In[11]: test[0].replace('\\u0026', '&')
Out[11]: 'Tech & Innovation'
In[12]: test.str.replace('\\u0026', '&')
Out[12]:
0 Tech & Innovation
1 Tech & Innovation
dtype: object
```
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: AMD64 Family 23 Model 1 Stepping 1, AuthenticAMD
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: 3.2.1
pip: 19.0.3
setuptools: 38.4.0
Cython: 0.26.1
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: 0.9.6
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.1.0
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.0
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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} | 2 | 2019-03-24T00:34:00Z | 2019-03-27T15:33:23Z | 2019-03-26T04:04:41Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.read_sas("filename.sas7bdat")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.7/site-packages/pandas/io/sas/sasreader.py", line 61, in read_sas
chunksize=chunksize)
File "/usr/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py", line 98, in __init__
self._get_properties()
File "/usr/lib/python3.7/site-packages/pandas/io/sas/sas7bdat.py", line 114, in _get_properties
raise ValueError("magic number mismatch (not a SAS file?)")
ValueError: magic number mismatch (not a SAS file?)
```
#### Problem description
I have a SAS file I would like to read with `pandas`. The author of the dataset encrypted it with a password, which I have been given; however, I do not have SAS and as such want to read this file with pandas.
The command to load this file in SAS is
```
libname in "<file location>";
proc contents data=in.filename (read=my_password);
run;
```
There appears to be no equivalent argument in the pandas `read_sas` function to include this password.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.14.85-1-lts
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.23.4
pytest: 3.10.1
pip: 18.0
setuptools: 40.6.2
Cython: 0.29
numpy: 1.15.0
scipy: 1.2.1
pyarrow: 0.11.1
xarray: None
IPython: 6.5.0
sphinx: 1.8.4
patsy: 0.5.0
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.8
feather: 0.4.0
matplotlib: 3.0.2
openpyxl: 2.5.9
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: None
lxml: 4.2.5
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.7.0
```
</details>
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} | [] | closed | false | null | [] | null | 4 | 2019-03-24T01:31:28Z | 2019-03-24T15:35:51Z | 2019-03-24T15:35:45Z | CONTRIBUTOR | null | refs GH-21946
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [na] whatsnew entry
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} | 8 | 2019-03-24T02:50:56Z | 2020-10-16T01:16:23Z | 2020-10-16T01:16:23Z | NONE | null | ```python
idx=pd.date_range('1/1/2019', periods=5)
s = pd.Series([2,3, np.nan, 10,20], index=idx)
s.expanding(min_periods=1).count()
s.expanding(min_periods=100).count()
s.expanding(min_periods=10000).count()
```
#### Problem description
They should not be returning the same results because the Series has only 5 records.
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 1 | 2019-03-24T05:56:51Z | 2019-11-05T20:57:40Z | 2019-11-05T20:57:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
labels = np.array([[0, 0,1,1],
[0, 1, 0, 1]])
columns = pd.MultiIndex(
levels=[["a", "b"], ['%', '#']], labels=labels, names=['', ''])
df = pd.DataFrame(
[[1,-1,1,1],[-1,1,1,1]],
index=["hello", "world"],
columns=columns)
pct_subset = pd.IndexSlice[:, pd.IndexSlice[:, '%':'%']]
def color_negative_red(val):
color = 'red' if val < 0 else 'black'
return 'color: %s' % color
# Works on both 0.22 and 0.24.
df.loc[pct_subset]
# This works on 0.22, but `TypeError: unhashable type` on 0.24!
df.style.applymap(color_negative_red, subset=pct_subset)
```
#### Problem description
To quote from the docs on the `subset` keyword argument for `Styler.applymap` (https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html) :
> For row and column slicing, any valid indexer to .loc will work.
The code sample demonstrates an indexer for a dataframe with multindex columns that works with .loc, but doesn't work as the `subset` argument to `applymap`. Note that this indexer previously worked in pandas version 0.22, but a regression has been introduced in 0.24.2
#### Expected Output
Expected the indexer to apply the styling the "%" columns, and not throw an error.
#### Full Backtrace
<details>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-17-994feb9d52cf> in <module>()
1 df.style.format(
----> 2 '{:.1f}%', subset=pct_subset)
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/io/formats/style.pyc in format(self, formatter, subset)
399 subset = subset, self.data.columns
400
--> 401 sub_df = self.data.loc[subset]
402 row_locs = self.data.index.get_indexer_for(sub_df.index)
403 col_locs = self.data.columns.get_indexer_for(sub_df.columns)
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in __getitem__(self, key)
1492 except (KeyError, IndexError, AttributeError):
1493 pass
-> 1494 return self._getitem_tuple(key)
1495 else:
1496 # we by definition only have the 0th axis
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_tuple(self, tup)
866 def _getitem_tuple(self, tup):
867 try:
--> 868 return self._getitem_lowerdim(tup)
869 except IndexingError:
870 pass
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_lowerdim(self, tup)
967 # we may have a nested tuples indexer here
968 if self._is_nested_tuple_indexer(tup):
--> 969 return self._getitem_nested_tuple(tup)
970
971 # we maybe be using a tuple to represent multiple dimensions here
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_nested_tuple(self, tup)
1046
1047 current_ndim = obj.ndim
-> 1048 obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
1049 axis += 1
1050
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_axis(self, key, axis)
1900 raise ValueError('Cannot index with multidimensional key')
1901
-> 1902 return self._getitem_iterable(key, axis=axis)
1903
1904 # nested tuple slicing
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _getitem_iterable(self, key, axis)
1203 # A collection of keys
1204 keyarr, indexer = self._get_listlike_indexer(key, axis,
-> 1205 raise_missing=False)
1206 return self.obj._reindex_with_indexers({axis: [keyarr, indexer]},
1207 copy=True, allow_dups=True)
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_listlike_indexer(self, key, axis, raise_missing)
1152 if len(ax) or not len(key):
1153 key = self._convert_for_reindex(key, axis)
-> 1154 indexer = ax.get_indexer_for(key)
1155 keyarr = ax.reindex(keyarr)[0]
1156 else:
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexes/base.pyc in get_indexer_for(self, target, **kwargs)
4453 """
4454 if self.is_unique:
-> 4455 return self.get_indexer(target, **kwargs)
4456 indexer, _ = self.get_indexer_non_unique(target, **kwargs)
4457 return indexer
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexes/multi.pyc in get_indexer(self, target, method, limit, tolerance)
2157 method=method,
2158 limit=limit,
-> 2159 tolerance=tolerance)
2160
2161 if not self.is_unique:
/home/jeremysalwen/.local/lib/python2.7/site-packages/pandas/core/indexes/base.pyc in get_indexer(self, target, method, limit, tolerance)
2753 'backfill or nearest reindexing')
2754
-> 2755 indexer = self._engine.get_indexer(target._ndarray_values)
2756
2757 return ensure_platform_int(indexer)
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_indexer()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.lookup()
TypeError: unhashable type
```
</details>
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.14.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.20-1rodete1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 36.7.1
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.5
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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"descr... | closed | false | null | [] | null | 5 | 2019-03-24T10:49:02Z | 2019-03-24T20:57:10Z | 2019-03-24T20:56:45Z | CONTRIBUTOR | null | Following #25768, remove `TypeError` in `Interval.length`, as all endpoints should be comparable now.
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} | 1 | 2019-03-24T14:53:49Z | 2019-07-02T15:41:45Z | 2019-07-02T15:41:45Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [1]: import pandas as pd
In [2]: intv1 = pd.IntervalIndex.from_arrays([1, 3, 5], [2, 4, 6])
In [3]: intv1
Out[3]:
IntervalIndex([(1, 2], (3, 4], (5, 6]],
closed='right',
dtype='interval[int64]')
In [4]: intv1.get_loc(intv1[0])
Out[4]: 0
In [5]: intv2 = pd.IntervalIndex.from_arrays([4, 2, 1], [5, 3, 2])
In [6]: intv2
Out[6]:
IntervalIndex([(4, 5], (2, 3], (1, 2]],
closed='right',
dtype='interval[int64]')
In [7]: intv2.get_loc(intv2[0])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-7-00c58bb4fb1e> in <module>
----> 1 intv2.get_loc(intv2[0])
~/Code/pandas/pandas/core/indexes/interval.py in get_loc(self, key, method)
777 return slice(start, stop)
778 else:
--> 779 raise KeyError(original_key)
780
781 else:
KeyError: Interval(4, 5, closed='right')
```
#### Problem description
This `IntervalIndex` is non-overlapping_monotonic. `IntervalIndex.get_loc` runs in `KeyError`, even the key is its first element.
After `start, stop = self._find_non_overlapping_monotonic_bounds(key)`, `start` and `stop` get the same value. Thereafter, no conditional handles this case.
#### Expected Outcome
In [5]: intv2 = pd.IntervalIndex.from_arrays([4, 2, 1], [5, 3, 2])
In [6]: intv2
Out[6]:
IntervalIndex([(4, 5], (2, 3], (1, 2]],
closed='right',
dtype='interval[int64]')
In [7]: intv2.get_loc(intv2[0])
Out[7]: 0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 01988735f8abde6ddc108f244ef0bbaef35ed1dd
python: 3.7.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_HK.UTF-8
LOCALE: en_HK.UTF-8
pandas: 0.25.0.dev0+308.g01988735f
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.2.5
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 4 | 2019-03-24T20:25:55Z | 2019-03-26T00:22:25Z | 2019-03-25T23:24:33Z | MEMBER | null | There are a a couple locale-centric functions left in `tm` that have not yet been moved because they depend on `pd.options.display.encoding`. This PR implements a minimal `_config.display` that allows us to consolidate the locale functions. `_config.display` also defines `display.date_dayfirst` and `display.date_yearfirst`, since those are used by `tslibs.parsing`. So this completes the process of removing `tslib`'s dependency on `core`.
The existing `get_locales` function has a `if PY3` check that is no longer necessary, so this removes the PY2 branch of that. | {
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} | 3 | 2019-03-24T20:54:16Z | 2019-03-25T11:37:28Z | 2019-03-24T21:48:59Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Another incremental step in removing Python 2. Doesn't look like `integer_types` was actually used anywhere. Otherwise mostly `long` --> `int`, or just removing `long` as the argument was already an `int` in Python 3.
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} | 3 | 2019-03-24T21:44:53Z | 2019-03-31T15:35:55Z | 2019-03-31T15:35:49Z | CONTRIBUTOR | null | - [x] closes #25766
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 6 | 2019-03-24T21:51:45Z | 2019-03-28T02:49:11Z | 2019-03-25T11:36:45Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
More Python 2 removal. Could use a second pair of eyes to make sure the regex I used to make these fixes wasn't overzealous anywhere.
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} | 4 | 2019-03-25T01:21:42Z | 2019-03-26T22:49:59Z | 2019-03-26T21:51:17Z | MEMBER | null | xref #24332
First commit adds the script code for checking. Remaining commits will fix any vestigial errors. | {
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} | 3 | 2019-03-25T11:38:32Z | 2019-03-26T11:38:21Z | 2019-03-26T11:38:21Z | CONTRIBUTOR | null | I think some PRs yesterday broke pandas-wheels: https://travis-ci.org/MacPython/pandas-wheels/jobs/510798584, likely all the same issues: https://travis-ci.org/MacPython/pandas-wheels/builds/510798582
if someone can you do a quick PR to update (not easy to test unfort).
```
______________________ TestFactorize.test_complex_sorting ______________________
[gw0] linux -- Python 3.6.6 /venv/bin/python
self = <pandas.tests.test_algos.TestFactorize object at 0x7f7cbf173710>
def test_complex_sorting(self):
# gh 12666 - check no segfault
x17 = np.array([complex(i) for i in range(17)], dtype=object)
msg = (r"unorderable types: {0} [<>] {0}".format(r"complex\(\)")
if _np_version_under1p14 else
r"'[<>]' not supported between instances of {0} and {0}".format(
"'complex'")
)
with pytest.raises(TypeError, match=msg):
> algos.factorize(x17[::-1], sort=True)
E AssertionError: Pattern 'unorderable types: complex\(\) [<>] complex\(\)' not found in ''>' not supported between instances of 'complex' and 'complex''
/venv/lib/python3.6/site-packages/pandas/tests/test_algos.py:237: AssertionError
_________________________ TestSafeSort.test_unsortable _________________________
[gw0] linux -- Python 3.6.6 /venv/bin/python
self = <pandas.tests.test_sorting.TestSafeSort object at 0x7f7cbecbce48>
def test_unsortable(self):
# GH 13714
arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
msg = (r"unorderable types: ({0} [<>] {1}|{1} [<>] {0})".format(
r"int\(\)", r"datetime\.datetime\(\)") # noqa: E126
if _np_version_under1p14 else
(r"'[<>]' not supported between instances of "
r"({0} and {1}|{1} and {0})").format(
"'int'", r"'datetime\.datetime'")
)
with pytest.raises(TypeError, match=msg):
> safe_sort(arr)
E AssertionError: Pattern 'unorderable types: (int\(\) [<>] datetime\.datetime\(\)|datetime\.datetime\(\) [<>] int\(\))' not found in ''>' not supported between instances of 'int' and 'datetime.datetime''
```
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https://api.github.com/repos/pandas-dev/pandas/issues/25868 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25868/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25868/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25868/events | https://github.com/pandas-dev/pandas/pull/25868 | 424,896,860 | MDExOlB1bGxSZXF1ZXN0MjY0MTE2MTE0 | 25,868 | DOC: use substitution decorator for business month classes (#25828) | {
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} | 6 | 2019-03-25T13:18:01Z | 2019-03-27T07:59:48Z | 2019-03-26T17:09:38Z | CONTRIBUTOR | null | - [X] closes #25828
- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Validation script
`CustomBusinessMonthEnd`
```
################################################################################
########## Docstring (pandas.tseries.offsets.CustomBusinessMonthEnd) ##########
################################################################################
DateOffset subclass representing one custom business month, incrementing
between end of month dates.
Parameters
----------
n : int, default 1
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
weekmask : str, Default 'Mon Tue Wed Thu Fri'
weekmask of valid business days, passed to ``numpy.busdaycalendar``
holidays : list
list/array of dates to exclude from the set of valid business days,
passed to ``numpy.busdaycalendar``
calendar : pd.HolidayCalendar or np.busdaycalendar
offset : timedelta, default timedelta(0)
################################################################################
################################## Validation ##################################
################################################################################
9 Errors found:
Summary should fit in a single line
Parameter "n" has no description
Parameter "normalize" description should finish with "."
Parameter "weekmask" description should start with a capital letter
Parameter "weekmask" description should finish with "."
Parameter "holidays" description should start with a capital letter
Parameter "holidays" description should finish with "."
Parameter "calendar" has no description
Parameter "offset" has no description
2 Warnings found:
See Also section not found
No examples section found
```
`CustomBusinessMonthBegin`
```
################################################################################
######### Docstring (pandas.tseries.offsets.CustomBusinessMonthBegin) #########
################################################################################
DateOffset subclass representing one custom business month, incrementing
between beginning of month dates.
Parameters
----------
n : int, default 1
normalize : bool, default False
Normalize start/end dates to midnight before generating date range
weekmask : str, Default 'Mon Tue Wed Thu Fri'
weekmask of valid business days, passed to ``numpy.busdaycalendar``
holidays : list
list/array of dates to exclude from the set of valid business days,
passed to ``numpy.busdaycalendar``
calendar : pd.HolidayCalendar or np.busdaycalendar
offset : timedelta, default timedelta(0)
################################################################################
################################## Validation ##################################
################################################################################
9 Errors found:
Summary should fit in a single line
Parameter "n" has no description
Parameter "normalize" description should finish with "."
Parameter "weekmask" description should start with a capital letter
Parameter "weekmask" description should finish with "."
Parameter "holidays" description should start with a capital letter
Parameter "holidays" description should finish with "."
Parameter "calendar" has no description
Parameter "offset" has no description
2 Warnings found:
See Also section not found
No examples section found
``` | {
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} | 1 | 2019-03-25T19:17:33Z | 2019-03-25T23:21:40Z | 2019-03-25T23:21:14Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame({'foo': ['2000-01-01T00:00:00.000Z+00:00']})
xf = df.copy()
xf.foo = df.foo.astype('datetime64[ns, UTC]') # Works
xf.foo = df.foo.astype('datetime64[us]') # Works
xf.foo = df.foo.astype('datetime64[us, UTC]') # Crashes
```
Traceback:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/dargueta/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/core/generic.py", line 5691, in astype
**kwargs)
File "/Users/dargueta/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 531, in astype
return self.apply('astype', dtype=dtype, **kwargs)
File "/Users/dargueta/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 395, in apply
applied = getattr(b, f)(**kwargs)
File "/Users/dargueta/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 534, in astype
**kwargs)
File "/Users/dargueta/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 595, in _astype
dtype = pandas_dtype(dtype)
File "/Users/dargueta/.pyenv/versions/3.7.2/lib/python3.7/site-packages/pandas/core/dtypes/common.py", line 2017, in pandas_dtype
dtype))
TypeError: data type 'datetime64[us, UTC]' not understood
data type 'datetime64[us, UTC]' not understood
```
#### Problem description
It appears that timestamps require the resolution to be in nanoseconds if you're going to have the Series be timezone-aware. This is confusing because I can cast a string to a timestamp of any valid resolution, but I can't use anything but nanoseconds if I want a tz-aware timestamp.
As for the relevance, I'm aware of #23990 but it doesn't seem to directly apply to `astype()`.
Context: This is a problem because there's downstream code that expects microsecond resolution *and* timezones and will choke otherwise (long story). I also need to use `astype` because the code needs to work with any dtype handed to it, and since the dtypes are being loaded from YAML files passing in an object is not an option.
#### Expected Output
A Series shouldn't require nanosecond resolution to be timezone-aware.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: None
pyarrow: 0.12.1
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: None
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
</details>
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} | 6 | 2019-03-25T20:48:47Z | 2019-03-25T22:18:51Z | 2019-03-25T20:55:14Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import traceback
import pandas as pd
import numpy as np
from multiprocessing.pool import ThreadPool
def f(arg):
s,idx = arg
try:
# s.loc[idx] # No problem
s.reindex(idx) # Fails
except Exception:
traceback.print_exc()
return None
def gen_args(n=10000):
a = np.arange(0, 3000000)
for i in xrange(n):
if i%1000 == 0:
# print "?",i
s = pd.Series(data=a, index=a)
f((s,a)) # <<< LOOK. IT WORKS HERE!!!
yield s, np.arange(0,1000)
# for arg in gen_args():
# f(arg) # Works just fine
t = ThreadPool(4)
for result in t.imap(f, gen_args(), chunksize=1):
pass
```
#### Problem description
pd.Series.reindex fails in a multi-threaded application.
This is a little surprising since I'm not asking for any writes.
The error also seems bogus: 'cannot reindex from a duplicate axis' ... the series does not have any duplicate axis and I was able to call ` s.reindex(idx)` in the main thread before the same failed in the pool's thread.
```
File "<ipython-input-8-4121235a46fa>", line 6, in f
s.reindex(idx).values # Fails
File "/usr/local/lib/python2.7/dist-packages/pandas/core/series.py", line 2681, in reindex
return super(Series, self).reindex(index=index, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 3023, in reindex
fill_value, copy).__finalize__(self)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 3041, in _reindex_axes
copy=copy, allow_dups=False)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 3145, in _reindex_with_indexers
copy=copy)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/internals.py", line 4139, in reindex_indexer
self.axes[axis]._can_reindex(indexer)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/indexes/base.py", line 2944, in _can_reindex
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
```
#### Expected Output
Program should output nothing.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.candidate.1
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.22.0
pytest: None
pip: 18.1
setuptools: 40.6.2
Cython: 0.29.1
numpy: 1.16.1
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.5.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.8
feather: None
matplotlib: 2.1.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
```
</details>
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} | 7 | 2019-03-25T21:05:26Z | 2019-04-05T00:27:27Z | 2019-03-28T10:55:40Z | CONTRIBUTOR | null | #### Code Sample:
```python
import pandas as pd
# Create a DataFrame with an ordered categorical column, one category not present
df = pd.DataFrame(
dict(cat = pd.Series([3, 1, 2, 1, 3, 2],
dtype=pd.CategoricalDtype(
categories=[1, 2, 3, 4],
ordered=True)
),
val = pd.Series([1.5, 0.5, 1.0, 0.5, 1.5, 1.0])
)
)
```
**Including unobserved categories gives correct groups:**
```python
# Sum 'val' grouped by 'cat', including unobserved categories
df.groupby('cat', observed=False)['val'].agg('sum')
```
```
cat
1 1.0
2 2.0
3 3.0
4 0.0
Name: val, dtype: float64
```
**Excluding unobserved categories changes the order, groups are wrong:**
```python
# Sum 'val' grouped by 'cat', excluding unobserved categories
df.groupby('cat', observed=True)['val'].agg('sum')
```
```
cat
3 1.0
1 2.0
2 3.0
Name: val, dtype: float64
```
#### Problem description:
The sample code shows that grouping with an ordered factor does not respect the factor's order when ```'observed=True'``` is set. Instead, group labels are in order of first occurrence in the Categorical, _as if it were unordered_. The aggregation results, however, are in the Categorical's order.
Thus, the result is wrong.
**Related issues:** #25167
There, the Categorical was unordered, and the ```sort=True``` parameter did not work as expected in combination with ```observed=True```. In my case, ```sort``` makes no difference:
```python
df.groupby('cat', observed=True, sort=True)['val'].agg('sum')
df.groupby('cat', observed=True, sort=False)['val'].agg('sum')
```
both give the same, wrong result as shown above.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: 0.11.3
IPython: 7.1.1
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 1 | 2019-03-25T22:10:45Z | 2019-03-25T23:20:36Z | 2019-03-25T23:20:27Z | NONE | null | #### Code Sample,
```
# Create some example data
records = []
for i, x in enumerate(list('abcdefg')):
record = {
'id': i,
'letter': x
}
records.append(record)
df = pd.DataFrame({'data': records})
def _update_key_inplace(x):
# Modifies the dictionary object in-place
x['letter'] = 'foo'
before1 = copy.deepcopy(df) ## No better results for this
before2 = df.copy(deep=True)
df['data'].apply(_update_key)
```
#### Problem description
Running the code above will update the 'letter' key of the dictionaries for all instances (before1, before2 and df).
#### Expected Output
before1 and before2 are deep copies and should be unchanged.
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} | 5 | 2019-03-26T03:50:15Z | 2019-03-28T02:48:10Z | 2019-03-27T23:15:28Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Gets rid of the following from `pandas.compat`:
- `intern`
- `reduce`
- `unichr`
- `wraps`
- `get_range_parameters`
- `class_types`
- `reload`
- also removed `set_defaultencoding` as it used `reload`, was never used, and is PY2 specific
- `import_lzma`
- also removed `skip_if_no_lzma` since `lzma` is in the standard library for PY3
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} | 6 | 2019-03-26T04:31:37Z | 2019-03-26T14:45:25Z | 2019-03-26T11:38:22Z | MEMBER | null | - [X] maybe closes #25867
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https://api.github.com/repos/pandas-dev/pandas/issues/25875 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25875/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25875/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25875/events | https://github.com/pandas-dev/pandas/issues/25875 | 425,233,741 | MDU6SXNzdWU0MjUyMzM3NDE= | 25,875 | test_time failure in CI logs | {
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} | 7 | 2019-03-26T05:28:35Z | 2019-10-09T05:41:13Z | 2019-10-09T05:41:13Z | MEMBER | null | The following failure has shown up a couple times in CI today. Haven't looked in detail but assume it to be an unreliable test.
https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=9836&view=logs&jobId=a69e7846-138e-5465-0656-921e8964615b&taskId=56da51de-fd5a-5466-5244-b5f65d252624&lineStart=42&lineEnd=95&colStart=1&colEnd=64
```
=================================== FAILURES ===================================
_____________________________ TestTSPlot.test_time _____________________________
[gw0] linux -- Python 3.6.6 /home/vsts/miniconda3/envs/pandas-dev/bin/python
self = <pandas.tests.plotting.test_datetimelike.TestTSPlot object at 0x7f2889f69978>
@pytest.mark.slow
def test_time(self):
t = datetime(1, 1, 1, 3, 30, 0)
deltas = np.random.randint(1, 20, 3).cumsum()
ts = np.array([(t + timedelta(minutes=int(x))).time() for x in deltas])
df = DataFrame({'a': np.random.randn(len(ts)),
'b': np.random.randn(len(ts))},
index=ts)
fig, ax = self.plt.subplots()
df.plot(ax=ax)
# verify tick labels
fig.canvas.draw()
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
rs = l.get_text()
if len(rs) > 0:
if s != 0:
xp = time(h, m, s).strftime('%H:%M:%S')
else:
xp = time(h, m, s).strftime('%H:%M')
assert xp == rs
# change xlim
ax.set_xlim('1:30', '5:00')
# check tick labels again
fig.canvas.draw()
ticks = ax.get_xticks()
labels = ax.get_xticklabels()
for t, l in zip(ticks, labels):
m, s = divmod(int(t), 60)
h, m = divmod(m, 60)
rs = l.get_text()
if len(rs) > 0:
if s != 0:
xp = time(h, m, s).strftime('%H:%M:%S')
else:
xp = time(h, m, s).strftime('%H:%M')
> assert xp == rs
E AssertionError: assert '01:06:40' == '03:40'
E - 01:06:40
E + 03:40
``` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25876 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25876/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25876/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25876/events | https://github.com/pandas-dev/pandas/pull/25876 | 425,240,032 | MDExOlB1bGxSZXF1ZXN0MjY0MzgzNDQz | 25,876 | CLN: Remove PY2/3 checks in cython files | {
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} | 3 | 2019-03-26T05:57:23Z | 2019-03-26T15:22:54Z | 2019-03-26T11:39:51Z | MEMBER | null | - [x] xref #25725
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Remove PY2/3 checks in `pyx` files. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25877 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25877/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25877/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25877/events | https://github.com/pandas-dev/pandas/issues/25877 | 425,255,674 | MDU6SXNzdWU0MjUyNTU2NzQ= | 25,877 | Unexpected behaviour of groupby.apply with Datetime types | {
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... | closed | false | null | [] | null | 2 | 2019-03-26T06:59:26Z | 2019-10-31T14:41:52Z | 2019-10-31T14:41:52Z | NONE | null | Pandas Version: 0.24.1
#### Code Sample:
```python
import pandas as pd
import numpy as np
import datetime
def to_compact(df):
return pd.Series({'measurment_value': df['measurment_value'].values,
'measurement_time': df['measurement_time'].values})
data = [[0, 1.2, datetime.datetime(2019, 1, 1, 8, 1)],
[0, 1.3, datetime.datetime(2019, 1, 1, 8, 2)],
[0, 1.6, datetime.datetime(2019, 1, 1, 8, 3)],
[1, 1.2, datetime.datetime(2019, 1, 1, 8, 1)],
[1, 1.2, datetime.datetime(2019, 1, 1, 8, 2)],
[1, 1.2, datetime.datetime(2019, 1, 1, 8, 3)],
]
df = pd.DataFrame(data=data, columns=['feature', 'measurment_value', 'measurement_time'])
df.groupby('feature').apply(to_compact)
```
#### Problem description
Although the returned series of our `to_compact` function is perfectly fine, the return wrapper of `pandas.apply` tries to coerce the type of `measurement_time` to datetime, which obviously failes for arrays.
I argue that this is unexpected, as the dtype of for example floats, ints and other more standard types, changes just as expected in the resulting dataframe to object (Just like in the series).
Please note that this can simply be resolved by renaming the output fields in the `pd.Series`:
```python
def to_compact(df):
return pd.Series({'measurment_value': df['measurment_value'].values,
'measurement_time2': df['measurement_time'].values})
```
This clearly shouldn't have any effect on the output.
#### Actual Output
feature | measurment_value | measurement_time
-- | -- | --
0 | [1.2, 1.3, 1.6] | NaT
1 | [1.2, 1.2, 1.2] | NaT
#### Expected Output
feature | measurment_value | measurement_time
-- | -- | --
0 | [1.2, 1.3, 1.6] | [2019-01-01T08:01:00.000000000, 2019-01-01T08:...
1 | [1.2, 1.2, 1.2] | [2019-01-01T08:01:00.000000000, 2019-01-01T08:..
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-20-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.24.1
pytest: 4.2.0
pip: 19.0.1
setuptools: 40.7.3
Cython: 0.29.4
numpy: 1.16.2
scipy: 1.2.0
pyarrow: 0.12.1
xarray: None
IPython: 7.2.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.5.14
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.2
lxml.etree: 4.3.0
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.17
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.2.0
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25878 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25878/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25878/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25878/events | https://github.com/pandas-dev/pandas/issues/25878 | 425,262,745 | MDU6SXNzdWU0MjUyNjI3NDU= | 25,878 | pandas.DataFrame.applymap run not expected | {
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} | [] | closed | false | null | [] | null | 0 | 2019-03-26T07:24:19Z | 2019-03-26T07:30:12Z | 2019-03-26T07:30:12Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
0 1
0 1.000 2.120
1 3.356 4.567
>>> def test(x):
... print(x)
...
>>> df.applymap(lambda x: test(x))
1.0
3.356
1.0
3.356
2.12
4.567
```
#### Problem description
Hello, I run pandas.DataFrame.applymap, and it seems that **this function call twice on the first columns**. Is it a bug?
#### Expected Output
```python
1.0
3.356
2.12
4.567
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: None
pip: 10.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.15.4
scipy: 1.2.0
pyarrow: 0.11.1
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml: 4.2.5
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25879 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25879/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25879/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25879/events | https://github.com/pandas-dev/pandas/issues/25879 | 425,305,174 | MDU6SXNzdWU0MjUzMDUxNzQ= | 25,879 | read_csv / read_excel interpret str as datetime | {
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} | [] | closed | false | null | [] | null | 1 | 2019-03-26T09:22:33Z | 2019-03-26T12:05:02Z | 2019-03-26T12:05:02Z | NONE | null | #### Code Sample
```python
df2 = pd.read_excel('license_nvm_logins.xlsx', sheet_name='Common', dtype={'Total Usage':str})
df2.head(n=10)
```
```python
df2.dtypes()
User Name object
Feature Name object
Tag object
Machine Name object
Checkout Date datetime64[ns]
Checkout Time object
Total Usage object
```
#### Problem description
When I read my Excel- / CSV-File into a DataFrame the last column 'Total Usage' will be interpreted as datetime (even if I try to force it to stay as a string).
This happens if the %H part exceeds 24 hours. For instance if the content of 'Total Usage'
column has following content '54:05:03'
User Name | Feature Name | Tag | Machine Name | Checkout Date | Checkout Time | Total Usage
-- | -- | -- | -- | -- | -- | --
someUser| someFeature | someTag | someMachine | 13.09.2018 | 07:40:00 | 1900-01-02 06:05:03
#### Expected Output
I would like if that columns would stay as string so I've the option to parse it to a timedelta on my own.
User Name | Feature Name | Tag | Machine Name | Checkout Date | Checkout Time | Total Usage
-- | -- | -- | -- | -- | -- | --
someUser| someFeature | someTag | someMachine | 13.09.2018 | 07:40:00 | 54:05:03
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-87-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.22.0
pytest: 3.4.2
pip: 19.0.2
setuptools: 39.0.1
Cython: 0.27.3
numpy: 1.14.3
scipy: 1.0.1
pyarrow: 0.9.0
xarray: 0.10.7
IPython: 6.3.1
sphinx: 1.7.1
patsy: 0.5.0
dateutil: 2.7.2
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.2.2
openpyxl: 2.5.1
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.2.0
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.5
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25880 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25880/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25880/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25880/events | https://github.com/pandas-dev/pandas/issues/25880 | 425,390,749 | MDU6SXNzdWU0MjUzOTA3NDk= | 25,880 | Int64Dtype conversion seems to casts to double first in the Series constructor | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 1 | 2019-03-26T12:33:18Z | 2021-06-27T20:41:38Z | null | NONE | null | Constructing a `Series` with `dtype=Int64Dtype()` suggests that there can be an intermiediate conversion to double if the input contains `NaN`s:
```
import pandas as pd
n = 2**62+5
print(n, int(float(n)))
print(pd.Series([n, float('nan')]))
print(pd.Series([n, float('nan')], dtype=pd.Int64Dtype()))
print(pd.Series([n, float('nan')], dtype=object).astype(pd.Int64Dtype()))
```
In both latter cases we end up with a `Series` with `dtype=Int64` containing integer NaNs. However, in the penultimate case we get the int-float-int converted value of `n` (4611686018427387904), and we only get the exact `Int64` result (4611686018427387909) if we instantiate the `Series` with `dtype=object` and convert afterward.
This doesn't happen if the input doesn't contain any `NaN`s:
```
print(pd.Series([n, n+3], dtype=pd.Int64Dtype()))
```
Expected output is `4611686018427387909` inside the `Series` constructed with `dtype=Int64Dtype()`.
Issue is there in master. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25881 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25881/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25881/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25881/events | https://github.com/pandas-dev/pandas/issues/25881 | 425,416,798 | MDU6SXNzdWU0MjU0MTY3OTg= | 25,881 | .at does not work using a categorical index. | {
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"id": 2822098,
"name": "Indexing",
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"color": "e1... | closed | false | null | [] | null | 1 | 2019-03-26T13:26:08Z | 2019-11-26T14:30:48Z | 2019-11-26T14:30:48Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame({
"x": pd.Categorical(["a", "b", "c"]),
"y": [10, 20, 30]}
).set_index("x")
print(df.index)
print(df.loc["a"]["y"])
print(df.at["a", "y"])
```
#### Problem description
Loc works using a categorical index, at does not.
Both should work.
#### Expected Output
CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'], ordered=False, name='x', dtype='category')
10
10
#### Actual output
```python
CategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'], ordered=False, name='x', dtype='category')
10
Traceback (most recent call last):
File "test.py", line 9, in <module>
print(df.at["a", "y"])
File "/rose/temp/shu/pandas/pandas/core/indexing.py", line 2277, in __getitem__
return self.obj._get_value(*key, takeable=self._takeable)
File "/rose/temp/shu/pandas/pandas/core/frame.py", line 2720, in _get_value
return engine.get_value(series._values, index)
File "pandas/_libs/index.pyx", line 81, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 89, in pandas._libs.index.IndexEngine.get_value
File "pandas/_libs/index.pyx", line 129, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index_class_helper.pxi", line 214, in pandas._libs.index.Int8Engine._check_type
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-134-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+321.gb878f5b
pytest: None
pip: 19.0.1
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: 1.8.5
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
Traceback (most recent
</details>
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} | 10 | 2019-03-26T15:55:46Z | 2019-06-25T12:34:26Z | 2019-06-25T12:34:26Z | MEMBER | null | #25844 introduced a blacklist of modules in the mypy.ini config file which currently throw errors when analyzed with mypy. We would ideally be able to whittle these down (save exclusion of the test folder(s), which I think is fine).
I'm not sure there is one easy way to split it up as the degree of difficulty may vary wildly. PRs from the community to clean up one to a few of the blacklisted modules at a time would certainly be welcome!
Here's the list of modules for reference:
- [X] pandas._config.config (#25899)
- [X] pandas._version (#25949)
- [X] pandas.compat (#26251)
- [X] pandas.compat.numpy.function
- [X] pandas.compat.pickle_compat (#25991)
- [X] pandas.core.accessor (#26146)
- [x] pandas.core.api
- [X] pandas.core.apply
- [X] pandas.core.arrays.array_ (#26065)
- [X] pandas.core.arrays.datetimelike (#26065)
- [X] pandas.core.arrays.integer (#26065)
- [X] pandas.core.arrays.interval (#26065)
- [X] pandas.core.arrays.numpy_ (#26065)
- [X] pandas.core.arrays.period (#26065)
- [X] pandas.core.arrays.sparse (#26065)
- [X] pandas.core.arrays.timedeltas (#26065)
- [x] pandas.core.base
- [x] pandas.core.computation.expr
- [x] pandas.core.computation.ops
- [x] pandas.core.computation.pytables
- [x] pandas.core.config (#26048)
- [x] pandas.core.config_init
- [X] pandas.core.dtypes.dtypes (#26028)
- [X] pandas.core.dtypes.missing (#26028)
- [X] pandas.core.frame
- [X] pandas.core.generic
- [X] pandas.core.groupby.generic (#26089)
- [X] pandas.core.groupby.groupby (#26089)
- [X] pandas.core.groupby.ops (#26089)
- [x] pandas.core.indexes.base (#26315)
- [x] pandas.core.indexes.datetimelike
- [ ] pandas.core.indexes.datetimes (#26315)
- [x] pandas.core.indexes.period (#26315)
- [x] pandas.core.indexes.timedeltas (#26315)
- [x] pandas.core.indexing
- [x] pandas.core.internals.blocks
- [x] pandas.core.ops
- [X] pandas.core.panel (DEPRECATED - do not update)
- [x] pandas.core.resample
- [X] pandas.core.reshape.concat (#26019)
- [x] pandas.core.reshape.merge
- [x] pandas.core.reshape.reshape
- [X] pandas.core.reshape.tile (#26019)
- [X] pandas.core.series
- [X] pandas.core.sparse.frame
- [X] pandas.core.strings (#25902)
- [x] pandas.core.util.hashing
- [x] pandas.core.window
- [x] pandas.io.clipboards (#26048)
- [x] pandas.io.feather_format (#25939)
- [x] pandas.io.formats.css (#25900)
- [x] pandas.io.html (#25939)
- [x] pandas.io.json.json (#25939)
- [x] pandas.io.json.normalize (#25939)
- [x] pandas.io.json.table_schema (#25939)
- [x] pandas.io.packers (#25939)
- [x] pandas.io.parquet (#25939)
- [x] pandas.io.pytables
- [x] pandas.io.stata (#25940)
- [X] pandas.plotting._core (#26021)
- [X] pandas.tseries.frequencies (#26014)
- [X] pandas.tseries.holiday (#26014)
- [X] pandas.tseries.offsets (#26014)
- [x] pandas.util._doctools
- [x] pandas.util.testing | {
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"description": "Memory or execu... | open | false | null | [] | null | 11 | 2019-03-26T17:35:43Z | 2019-10-16T18:58:53Z | null | NONE | null | #### Code Sample
```python
import time
import numpy as np
import pandas as pd
nrows, ncols = 1000, 100
# data frame with random values and a key to be grouped by
df = pd.DataFrame(np.random.rand(nrows, ncols))
df["key"] = range(nrows)
numeric_columns = list(range(ncols))
grouping = df.groupby(by="key")
# performance regression in apply()
start = time.time()
grouping[numeric_columns].apply(lambda x: x - x.mean())
end = time.time()
print("[pandas=={}] execution time: {:.4f} seconds".format(pd.__version__, end - start))
# [pandas==0.23.4] execution time: 0.8700 seconds
# [pandas==0.24.0] execution time: 24.3790 seconds
# [pandas==0.24.2] execution time: 23.9600 seconds
```
#### Problem description
The function `GroupBy.apply` is a lot slower (~ 25 times) with version 0.24.0, compared to the 0.23.4 release. The problem still persists in the latest 0.24.2 release.
The code sample above shows this performance regression.
The purpose of the sample is to subtract the group mean from all elements in this group.
The problem does only occur when the lambda for `apply()` returns a data frame.
There are no performance issues with scalar return values, e.g. `lambda x: x.mean()`.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.0
pytest: None
pip: 10.0.1
setuptools: 39.2.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
```
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Haven't used these yet as wanted to agree on type first. I wouldn't be surprised if we start seeing some issues pop up that would require real development changes once these get utilized
@jreback @jbrockmendel | {
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SqlDataReader reader = myCommand.ExecuteReader(CommandBehavior.CloseConnection);
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row_index += 1;
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reader.Close();
conn.Close();
p.Save(); | {
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"default"... | open | false | null | [] | null | 4 | 2019-03-26T21:46:23Z | 2020-06-05T17:28:20Z | null | CONTRIBUTOR | null | Hello,
Creating an empty dataframe with specified columns
```python
pd.__version__
# '0.24.2'
%timeit pd.DataFrame([], columns=['a'])
# 1.57 ms ± 20.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
is way slower than it should be:
```python
%timeit pd.DataFrame([])
# 184 µs ± 1.68 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit pd.DataFrame([[1]], columns=['a'])
# 343 µs ± 3.33 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
``` | {
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} | 13 | 2019-03-26T22:52:35Z | 2019-03-31T12:40:25Z | 2019-03-31T02:03:15Z | CONTRIBUTOR | null | … dict logic branch.
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} | 3 | 2019-03-27T01:43:47Z | 2019-03-27T16:28:12Z | 2019-03-27T11:50:53Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Not needed after dropping Python 2. | {
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} | 1 | 2019-03-27T08:57:40Z | 2019-03-27T15:21:35Z | 2019-03-27T15:21:25Z | NONE | null | Hey! I have the following dataframe:
```python
price
item_name timestamp
item1 2018-10-12 12.2
2018-10-13 14.3
2018-10-14 17.1
item2 2018-10-12 11.4
2018-10-13 15.6
2018-10-14 17.2
item2 2018-10-12 11.5
2018-10-13 17.2
2018-10-14 17.2
```
And I want to convert it to JSON in the following format:
```json
{
"item1":{
"1539302400000": 12.2,
"1539388800000": 14.3,
"1539475200000": 17.1,
},
"item2":{
"1539302400000":11.4,
"1539388800000":15.6,
"1539475200000":17.2,
},
"item3":{
"1539302400000":11.5,
"1539388800000":17.2,
"1539475200000":17.2,
}
}
```
However, I was not able to get json in the desired format.
`df.reset_index().to_json(orient='records')` gives me this:
```json
[
{
"item_name":"item1",
"timestamp":1539302400000,
"price":12.2
},
{
"item_name":"item1",
"timestamp":1539388800000,
"price":14.3
},
{
"item_name":"item1",
"timestamp":1539475200000,
"price":17.1
},
{
"item_name":"item2",
"timestamp":1539302400000,
"price":11.4
},
{
"item_name":"item2",
"timestamp":1539388800000,
"price":15.6
},
{
"item_name":"item2",
"timestamp":1539475200000,
"price":17.2
},
]
```
Can anyone give me a clue how to accomplish that?
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-16-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.24.2
pytest: None
pip: 9.0.1
setuptools: 40.8.0
Cython: None
numpy: 1.15.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: 1.7.6
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: 0.9.2
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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"description": "Arithmetic, Comparison, ... | open | false | null | [] | null | 3 | 2019-03-27T10:22:49Z | 2021-06-28T02:05:26Z | null | NONE | null | When multiplying two Series with overlapping MultiIndices and a _single_ entry, pandas drops levels from the MultiIndex. This doesn't happen when either Series contains multiple entries.
Using pandas 0.24.1 and Numpy 1.16.2
#### This works fine
```python
# Create Series s1, with a scope over {T, N} and set two values
index1 = pd.MultiIndex.from_product([[], []], names=['T', 'N'])
s1 = pd.Series(index=index1)
s1['T.1A', 'N.0'] = 0.5
s1['T.1B', 'N.0'] = 0.5
# Create Series s2 with a scope over {N, M} and set a single value
index2 = pd.MultiIndex.from_product([[], []], names=['N', 'M'])
s2 = pd.Series(index=index2)
s2['N.0', 'M.0'] = 0.5
# When multiplying s1 and s2 pandas will align the Series using the index.
# Note that the index has 3 levels: {N, T, M}
print(s1 * s2)
# Prints:
# N T M
# N.0 T.1A M.0 0.25
# T.1B M.0 0.25
# dtype: float64
```
#### This doesn't return the expected index levels
```python
# Create Series s1, with a scope over {T, N} and set a single value
index1 = pd.MultiIndex.from_product([[], []], names=['T', 'N'])
s1 = pd.Series(index=index1)
s1['T.1A', 'N.0'] = 0.5
# Create Series s2 with a scope over {N, M} and set a single value
index2 = pd.MultiIndex.from_product([[], []], names=['N', 'M'])
s2 = pd.Series(index=index2)
s2['N.0', 'M.0'] = 0.5
# Multiply s1 and s2. Correctly yields 0.25. But where did index level 'M' go!?
print(s1 * s2)
# Prints:
# T N
# T.1A N.0 0.25
# dtype: float64
```
#### Problem description
Index level 'M' is dropped in the result. It appears the index from the first Series, `s1` is carried over?
#### Expected Output
I'd expect the result to have an index that is essentially the union of the indices from `s1` and `s2`.
```
# N T M
# N.0 T.1A M.0 0.25
# dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.utf-8
LANG: None
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.6.2
Cython: None
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 5 | 2019-03-27T14:49:54Z | 2019-04-04T02:02:33Z | 2019-04-04T02:02:27Z | NONE | null | Hi,
When using GroupBy.apply on function that applies pandas.DataFrame.sort_values() **with inplace=True** and also **data-frame slicing**, the first group is evaluated N times (N is the original number of the groups).
See the following code and output:
```python
>>> import numpy as np
>>> import pandas as pd
>>>
>>> A = np.repeat('a', 8)
>>> B = np.repeat([1, 2, 3, 4], 2)
>>> data = pd.DataFrame.from_dict({'A': A, 'B': B})
>>> print(data)
A B
0 a 1
1 a 1
2 a 2
3 a 2
4 a 3
5 a 3
6 a 4
7 a 4
>>>
>>> def func(group_data, sort, slice):
... if sort:
... group_data.sort_values('A', inplace=True)
... if slice:
... group_data = group_data[:]
... return group_data
...
>>> B_groups = data.groupby('B')
>>> out_sort_slice = B_groups.apply(lambda x: func(x, sort=True, slice=True))
>>> out_sort_no_slice = B_groups.apply(lambda x: func(x, sort=True, slice=False))
>>> out_no_sort_slice = B_groups.apply(lambda x: func(x, sort=False, slice=True))
>>> out_no_sort_no_slice = B_groups.apply(lambda x: func(x, sort=False, slice=False))
>>>
>>> print(out_sort_slice)
A B
B
1 0 a 1
1 a 1
2 0 a 1
1 a 1
3 0 a 1
1 a 1
4 0 a 1
1 a 1
>>> print(out_sort_no_slice)
A B
0 a 1
1 a 1
2 a 2
3 a 2
4 a 3
5 a 3
6 a 4
7 a 4
>>> print(out_no_sort_slice)
A B
B
1 0 a 1
1 a 1
2 2 a 2
3 a 2
3 4 a 3
5 a 3
4 6 a 4
7 a 4
>>> print(out_no_sort_no_slice)
A B
0 a 1
1 a 1
2 a 2
3 a 2
4 a 3
5 a 3
6 a 4
7 a 4
```
As you can see, out_sort_no_slice, out_no_sort_slice and out_no_sort_no_slice get the expected outputs, while the output of out_sort_slice contains the first group 4 times. while debugging, you can see the first group evaluated 4 times inside func.
Notice that the data is already sorted by field 'A' and the slicing part keeps all the data in the data-frame.
I'm using Anaconda Python 3.7 with Pandas 0.24.2 on Mac.
Thanks! | {
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} | 8 | 2019-03-27T15:10:36Z | 2019-04-01T22:27:46Z | 2019-04-01T22:27:46Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.read_csv('file.csv', index_col=0, parse_dates=True)
while True:
df['close'].rolling(4000).max()
```
#### Problem description
Memory leak which shuts down my application. This occurs in pandas 0.24.2 but not in pandas 0.23.4. My 16 GB memory gets filled in a few hours of running this code. file.csv is attached in zip, the memory leak might only occur on certain data.
[file.csv.zip](https://github.com/pandas-dev/pandas/files/3013836/file.csv.zip)
#### Expected Output
No memory leaks.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 38.4.0
Cython: None
numpy: 1.14.5
scipy: None
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.2.14
pymysql: None
psycopg2: 2.7.6.1 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-03-27T15:13:50Z | 2020-01-16T00:34:15Z | 2019-03-27T20:44:44Z | MEMBER | null | Workaround for #25875 to get CI passing - I kept intact the working part of the test and split off the failing piece into a separate test, which may be more explicit anyway
Haven't been able to reproduce this locally so plan to either keep the original issue open or create a new one for a more permanent fix, which may require a total refactor of the test
@gfyoung | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25895 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25895/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25895/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25895/events | https://github.com/pandas-dev/pandas/pull/25895 | 426,084,512 | MDExOlB1bGxSZXF1ZXN0MjY1MDIzMjk4 | 25,895 | Implement helper method to get char* buffer from Python objects | {
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} | 7 | 2019-03-27T17:12:36Z | 2019-04-01T14:31:27Z | 2019-03-29T15:26:20Z | CONTRIBUTOR | null | - [x] closes N/A
- [x] tests added - `test_parsers_iso8601_leading_space`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
This is a follow-up PR for #25754 which adds a utility method for getting internal `char *` buffer from `unicode` or `bytes` Python object and switch at least some of usages to it.
Its advantages over what is built-in in Cython is saving at least one extra memory allocation (Cython internally calls Python C API that creates a new `char *` copy which is not needed for most `unicode` objects which are internally stored in `utf8` encoding), and it also obtains the length in one call.
cc @jbrockmendel | {
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import numpy as np
import pandas as pd
df = pd.DataFrame({"a": ["x", "x", "x", "y", "y", "y"],
"b": [np.nan, 2.0, 1.0, 2.0, np.nan, 1.0]})
df
# a b
# 0 x NaN
# 1 x 2.0
# 2 x 1.0
# 3 y 2.0
# 4 y NaN
# 5 y 1.0
df.groupby("a")["b"].cummax()
# 0 NaN
# 1 2.0
# 2 2.0
# 3 2.0
# 4 NaN
# 5 2.0
# Name: b, dtype: float64
```
#### Problem description
In the example above we're taking the cumulative max of column `b` grouped by `a`, which should have the same result for rows 1 and 4 since both are taking the max of `np.nan` and `2.0` (order shouldn't matter). It seems like the max might be `np.nan` whenever the most recent value in the window is `np.nan` since we go back to the correct value in the next row.
#### Expected Output
```
# 0 NaN
# 1 2.0
# 2 2.0
# 3 2.0
# 4 2.0
# 5 2.0
# Name: b, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 18.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.2
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.1
scipy: 1.2.1
pyarrow: 0.12.0
xarray: None
IPython: 7.3.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25897 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25897/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25897/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25897/events | https://github.com/pandas-dev/pandas/issues/25897 | 426,177,857 | MDU6SXNzdWU0MjYxNzc4NTc= | 25,897 | BUG: DatetimeIndex from datetime.datetime shifted by n minutes | {
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} | 2 | 2019-03-27T20:51:42Z | 2019-03-27T22:55:23Z | 2019-03-27T22:55:11Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import datetime as dt
import pytz
tz = pytz.timezone('America/New_York')
data1 = pd.DataFrame([
{'date': dt.datetime(2019, 1, 1, tzinfo=tz), 'value': 10},
{'date': dt.datetime(2019, 1, 1, tzinfo=tz), 'value': 10},
{'date': dt.datetime(2019, 1, 2, tzinfo=tz), 'value': 20}
]).set_index('date')
print(data1.index)
# DatetimeIndex(['2018-12-31 23:56:00-05:00', '2018-12-31 23:56:00-05:00',
# '2019-01-01 23:56:00-05:00'],
# dtype='datetime64[ns, America/New_York]', name='date', freq=None)
#
data2 = pd.DataFrame([
{'date': pd.Timestamp(year=2019, month=1, day=1, tzinfo=tz), 'value': 10},
{'date': pd.Timestamp(year=2019, month=1, day=1, tzinfo=tz), 'value': 10},
{'date': pd.Timestamp(year=2019, month=1, day=2, tzinfo=tz), 'value': 20}
]).set_index('date')
print(data2.index)
# DatetimeIndex(['2019-01-01 00:00:00-05:00', '2019-01-01 00:00:00-05:00',
# '2019-01-02 00:00:00-05:00'],
# dtype='datetime64[ns, America/New_York]', name='date', freq=None)
#
```
#### Problem description
Creating index from python datetime object shifts time by n minutes depending on chosen timezone
#### Expected Output
Output for index created from datetime object should be same as created from Timestamp
#### Similar Issues
https://github.com/pandas-dev/pandas/issues/1676
https://github.com/pandas-dev/pandas/issues/1790
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: ac318d26cdbe3d10030f4863b84a06e938da9bba
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+327.gac318d26c
pytest: None
pip: 9.0.1
setuptools: 39.0.1
Cython: 0.29.6
numpy: 1.16.2
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 3 | 2019-03-28T02:21:13Z | 2019-04-01T11:51:04Z | 2019-04-01T11:51:04Z | MEMBER | null | Part of #25882
Current errors are as follows:
```sh
pandas/_config/config.py:60: error: Need type annotation for '_deprecated_options'
pandas/_config/config.py:61: error: Need type annotation for '_registered_options'
pandas/_config/config.py:62: error: Need type annotation for '_global_config'
```
Should add annotations and remove this module from mypy.ini - think this is straightforward so community PR is certainly welcome! | {
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} | 4 | 2019-03-28T02:37:56Z | 2019-03-29T02:50:17Z | 2019-03-28T21:29:10Z | MEMBER | null | There was a seemingly unused class variable which was failing mypy due to a lack of annotation. On closer inspection however it doesn't appear that this was even used, so I figured deletion makes things simpler.
I suppose if this was subclassed it could have some effect but this isn't part of the exposed API
@jnothman if you have any thoughts or objections | {
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} | 7 | 2019-03-28T03:39:42Z | 2019-04-16T12:17:42Z | 2019-04-16T12:17:42Z | MEMBER | null | Part of #25882
Current errors are as follows:
```sh
pandas/core/generic.py:116: error: Need type annotation for '_accessors'
pandas/core/generic.py:119: error: Need type annotation for '_metadata'
```
Both of these are containers which should contain strings, so just need proper annotation and this module can be removed from mypy.ini | {
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} | 0 | 2019-03-28T04:05:14Z | 2019-04-03T20:31:40Z | 2019-04-03T20:31:40Z | MEMBER | null | Pushing PR shortly | {
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} | 6 | 2019-03-28T04:07:05Z | 2019-04-05T16:57:57Z | 2019-03-29T12:20:43Z | MEMBER | null | - [X] xref #25725
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Removes the following:
- `string_types`
- `text_types`
- `binary_types`
- `string_and_binary_types`
- `strlen`
- only used once and in PY3 is equivalent to `len`, so just directly called `len`
- `east_asian_len`
- only called by `EastAsianTextAdjustment.len` in `pandas/io/formats/format.py`
- directly implemented as `EastAsianTextAdjustment.len` instead | {
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} | 4 | 2019-03-28T04:25:52Z | 2020-01-16T00:34:14Z | 2019-04-03T20:31:41Z | MEMBER | null | - [x] closes #25902
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"color": "370f... | closed | false | null | [] | null | 2 | 2019-03-28T05:11:11Z | 2019-04-01T18:44:01Z | 2019-04-01T18:44:01Z | NONE | null | #### Code Sample, a copy-pastable example if possible
Given the code sample below:
```python
data_df = pd.DataFrame([{'col_a': '1', 'col_b': '16.5%', 'col_c': 'test'},
{'col_a': '2.2', 'col_b': '15.3', 'col_c': 'another_test'}
])
type_dict = {'col_a': 'float64', 'col_b': 'float64', 'col_c': 'object}
data_df = data_df.astype(dtype=type_dict, errors='ignore')
```
#### Problem description
Pandas will raise the error `ValueError: could not convert string to float: '16.5%'` despite the fact that the `errors='ignore'` parameter was passed in. This is probable due to the implementation of `astype`, as can be seen in `pandas.core.generic.py` lines 5658 - 5680
```python
if is_dict_like(dtype):
if self.ndim == 1: # i.e. Series
if len(dtype) > 1 or self.name not in dtype:
raise KeyError('Only the Series name can be used for '
'the key in Series dtype mappings.')
new_type = dtype[self.name]
return self.astype(new_type, copy, errors, **kwargs)
elif self.ndim > 2:
raise NotImplementedError(
'astype() only accepts a dtype arg of type dict when '
'invoked on Series and DataFrames. A single dtype must be '
'specified when invoked on a Panel.'
)
for col_name in dtype.keys():
if col_name not in self:
raise KeyError('Only a column name can be used for the '
'key in a dtype mappings argument.')
results = []
for col_name, col in self.iteritems():
if col_name in dtype:
results.append(col.astype(dtype[col_name], copy=copy))
else:
results.append(results.append(col.copy() if copy else col))
```
#### Expected Output
One would expect the `pd.DataFrame` to successfully type cast, leaving the `col_b` as an `np.object` type
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https://api.github.com/repos/pandas-dev/pandas/issues/25906 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25906/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25906/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25906/events | https://github.com/pandas-dev/pandas/issues/25906 | 426,325,288 | MDU6SXNzdWU0MjYzMjUyODg= | 25,906 | groupby-apply got different output with for loop in same action | {
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"id": ... | closed | false | null | [] | null | 2 | 2019-03-28T06:45:10Z | 2019-03-28T12:42:32Z | 2019-03-28T12:42:16Z | NONE | null | #### data csv download link
https://mega.nz/#!XjZWyYDT!EezOrZJglcwqytA5bIgc6-VoiEMGB7R0Tu8CG-QjV5w
#### Problem description
I have a Stock Time Series, I only take 4 ts_code(stock code) and other 3 useful columns to recurrent problem，you can download throgh link.
#### read data
```
df = pd.read_csv('stock.csv', index_col='trade_date')
df.index = pd.to_datetime(df.index)
```
#### problem code
```
dtday = df.groupby('ts_code').apply(lambda x: (x.MA1 > x.MA2) & (x.MA2 > x.MA3))
```
#### right code
```
l = []
for k,v in df.groupby('ts_code'):
v2 = (v.MA1 > v.MA2) & (v.MA2 > v.MA3)
v2.name = k
l.append(v2)
dtday2 = pd.concat(l, axis=1).fillna(False).T
```
#### result 1
```
print(dtday.sum(axis=1))
ts_code
000001.SZ 87
002624.SZ 62
002625.SZ 36
600519.SH 28
dtype: int64
```
#### result 2
```
print(dtday2.sum(axis=1))
000001.SZ 87
002624.SZ 2
002625.SZ 44
600519.SH 39
dtype: int64
```
you can plot the any stock line to verificate
```
df.loc[df['ts_code'] == '002625.SZ', ['MA1', 'MA2', "MA3"]].plot()
```
I only want to count how many day the MA1 > MA2 > MA3, I think that 2 code are exactly the same，but the result show NO.
why?
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.30-1-MANJARO
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.2
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: None
sqlalchemy: 1.3.1
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | [] | closed | false | null | [] | null | 2 | 2019-03-28T11:02:24Z | 2019-03-28T11:24:24Z | 2019-03-28T11:24:24Z | CONTRIBUTOR | null | As documented in #25871, groupby() on an ordered Categorical messes up category order when 'observed=True' is specified.
Specifically, group labels will be ordered by first occurrence (as for an unordered Categorical), but grouped aggregation results will retain the Categorical's order.
The fix is a modified subset of #25173, which fixes a related case, but has not been merged yet.
- [x] closes #25871
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 16 | 2019-03-28T11:29:16Z | 2019-04-05T09:33:48Z | 2019-04-05T00:27:11Z | CONTRIBUTOR | null | As documented in #25871, groupby() on an ordered Categorical messes up category order when 'observed=True' is specified.
Specifically, group labels will be ordered by first occurrence (as for an unordered Categorical), but grouped aggregation results will retain the Categorical's order.
The fix is a modified subset of #25173, which fixes a related case, but has not been merged yet.
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- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 17 | 2019-03-28T13:01:24Z | 2019-04-16T12:21:01Z | 2019-04-16T12:17:42Z | CONTRIBUTOR | null | Also remove this module from mypy.ini
- [x] closes #25901
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 7 | 2019-03-28T13:50:47Z | 2019-03-28T17:05:01Z | 2019-03-28T16:54:57Z | CONTRIBUTOR | null | see https://github.com/pandas-dev/pandas/pull/25554#issuecomment-477602786
added back the gbq dep and this is now failing. | {
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} | 4 | 2019-03-28T14:13:54Z | 2019-05-28T10:48:54Z | 2019-04-02T17:07:57Z | NONE | null | #### Code Sample
```python
newspapers = {'Jim':{'Mon':15,'Tue':17,'Wed':21,'Thu':16,'Fri':19},\
'Tony':{'Mon':8,'Tue':15,'Wed':11,'Thu':16,'Fri':13}, \
'Colin':{'Mon':13,'Tue':17,'Wed':19,'Thu':17,'Fri':20} \
}
newspapers_df = pd.DataFrame(newspapers)
print(newspapers_df)
```
#### Problem description
I am using a recent download of Anaconda with Python 3.7.1 & pandas 0.23.4
The [pandas doc](https://pandas-docs.github.io/pandas-docs-travis/getting_started/dsintro.html) says:
> When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order
I instantiate a pandas DataFrame from a dict with no index passed, and print outputs:
#### Expected Output
```python
Jim Tony Colin
Mon 15 8 13
Tue 17 15 17
Wed 21 11 19
Thu 16 16 17
Fri 19 13 20
```
#### Output of `0.23.4``
```python
Jim Tony Colin
Fri 19 13 20
Mon 15 8 13
Thu 16 16 17
Tue 17 15 17
Wed 21 11 19
``` | {
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} | 5 | 2019-03-28T14:15:54Z | 2019-03-28T17:48:21Z | 2019-03-28T16:54:58Z | CONTRIBUTOR | null | Should close #25910, see https://github.com/pandas-dev/pandas/issues/25910#issuecomment-477611959. @jreback | {
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