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https://api.github.com/repos/pandas-dev/pandas/issues/25513 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25513/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25513/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25513/events | https://github.com/pandas-dev/pandas/issues/25513 | 416,251,532 | MDU6SXNzdWU0MTYyNTE1MzI= | 25,513 | DataFrame.to_json silently ignores index parameter for most orients. | {
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} | 20 | 2019-03-01T19:43:00Z | 2021-09-10T11:48:29Z | null | CONTRIBUTOR | null | *see
https://github.com/pandas-dev/pandas/issues/25513#issuecomment-469264925*
#### Code Sample, a copy-pastable example if possible
```python
from __future__ import print_function
import pandas as pd
df = pd.DataFrame.from_records(
[
{'id': 'abc', 'data': 'qwerty'},
{'id': 'def', 'data': 'uiop'}
],
index='id'
)
print(df.to_json(orient='records', index=True))
# Prints out:
[{"data":"qwerty"},{"data":"uiop"}]
series = df.squeeze()
print(series.to_json(orient='records', index=True))
# Prints out:
["qwerty","uiop"]
```
#### Problem description
When creating a `DataFrame` that has two columns, one to be used as an index and another for the data, if you call `.to_json(orient='records')` the index is omitted. I know that in theory I should be using a `Series` for this, but I'm using it to convert a CSV file into JSONL and I don't know what the CSV file is going to look like ahead of time.
In any case, squeezing the `DataFrame` into a `Series` doesn't work either. In fact, the bug in `Series.to_json` is even worse, as it produces an array of strings instead of an array of dictionaries.
This bug is present in master.
#### Expected Output
Expected output is:
```json
[{"id":"abc", "data":"qwerty"},{"id":"def","data":"uiop"}]
```
#### 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: 0.24.1
pytest: 4.3.0
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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} | 5 | 2019-03-01T20:11:25Z | 2019-04-12T17:08:30Z | 2019-04-12T17:08:30Z | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
pd.set_option("display.precision", 3)
A = np.random.rand(3,3)
df_A = pd.DataFrame(A)
B = np.random.rand(3,3)+1j*np.random.rand(3,3)
df_B = pd.DataFrame(B)
print(df_A)
print(df_B)
```
**Output:**
```
0 1 2
0 0.665 0.824 0.953
1 0.341 0.078 0.408
2 0.945 0.411 0.078
0 \
0 (0.2553045397887609+0.8464631779709604j)
1 (0.5724405361700162+0.047300152111596105j)
2 (0.5824766758024993+0.974755917539835j)
1 \
0 (0.3533797280121552+0.20515824034905894j)
1 (0.928312255311234+0.3909443194373995j)
2 (0.2700966337410404+0.9137483492310767j)
2
0 (0.21748538025578568+0.7730350615682122j)
1 (0.2435709287109903+0.46366472945887327j)
2 (0.6531622218102532+0.5102462678094016j)
```
#### Problem description
Display precision (`pd.set_option("display.precision", 3)`) doesn't affect complex float numbers like shown in the example.
#### Expected Output
```
0 1 2
0 0.665 0.824 0.953
1 0.341 0.078 0.408
2 0.945 0.411 0.078
0 1 2
0 0.976+0.935j 0.739+0.851j 0.436+0.734j
1 0.998+0.867j 0.774+0.849j 0.553+0.749j
2 0.405+0.049j 0.965+0.912j 0.292+0.958j
```
#### Output of ``pd.show_versions()``
<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 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 39.0.1
Cython: 0.29
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 7.1.1
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 3.0.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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"description": "Incorrect or improved er... | open | false | null | [] | null | 0 | 2019-03-02T12:05:39Z | 2021-06-27T19:00:47Z | null | MEMBER | null | see also #22705
#### Code Sample, a copy-pastable example if possible
```python
from pandas import DataFrame
from collections import OrderedDict
DataFrame.from_dict(
OrderedDict([('b', 8), ('a', 5), ('a', 6)]))
```
Note: code sample from exisiting test...
https://github.com/pandas-dev/pandas/blob/638ddebaa7da1e569836a5b89593b120dbbf491c/pandas/tests/frame/test_constructors.py#L1436-L1437
#### Problem description
```python-traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-ee89993a8718> in <module>
2 from collections import OrderedDict
3 DataFrame.from_dict(
----> 4 OrderedDict([('b', 8), ('a', 5), ('a', 6)]))
~\Anaconda3\lib\site-packages\pandas\core\frame.py in from_dict(cls, data, orient, dtype, columns)
983 raise ValueError('only recognize index or columns for orient')
984
--> 985 return cls(data, index=index, columns=columns, dtype=dtype)
986
987 def to_dict(self, orient='dict', into=dict):
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
346 dtype=dtype, copy=copy)
347 elif isinstance(data, dict):
--> 348 mgr = self._init_dict(data, index, columns, dtype=dtype)
349 elif isinstance(data, ma.MaskedArray):
350 import numpy.ma.mrecords as mrecords
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _init_dict(self, data, index, columns, dtype)
457 arrays = [data[k] for k in keys]
458
--> 459 return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
460
461 def _init_ndarray(self, values, index, columns, dtype=None, copy=False):
~\Anaconda3\lib\site-packages\pandas\core\frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
7354 # figure out the index, if necessary
7355 if index is None:
-> 7356 index = extract_index(arrays)
7357
7358 # don't force copy because getting jammed in an ndarray anyway
~\Anaconda3\lib\site-packages\pandas\core\frame.py in extract_index(data)
7391
7392 if not indexes and not raw_lengths:
-> 7393 raise ValueError('If using all scalar values, you must pass'
7394 ' an index')
7395
ValueError: If using all scalar values, you must pass an index
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 5 | 2019-03-03T03:00:18Z | 2019-03-20T12:27:02Z | 2019-03-20T12:26:56Z | CONTRIBUTOR | null | - [X] closes #24971
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
The following gives a series containing [1] instead of 1
```
>>> pd.Series(pd.Categorical('A', categories=['A', 'B'])).replace({'A': 1, 'B': 2})
0 [1]
dtype: object
```
This bug occurs because in the process of copying the original categorical block (which is needed as the operation is not inplace), the constructor class for the new object defaults to `ObjectBlock`, whose constructor has a default `ndim` of 2. This PR alters the block `copy` function to specify that the newly constructed block should have the same `ndim` as the block being copied. | {
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} | 2 | 2019-03-03T15:37:03Z | 2019-03-04T07:48:18Z | 2019-03-03T19:51:44Z | NONE | null | #### Code Sample as entered in jupyter notebook with python 3.7
---------------------------------------------------------------------
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
df = pd.read_csv('tstdatapanda.csv', delimiter=',', encoding="utf-8-sig")
X = df['sensex','nifty']
y = df['Last_Trade_Price']
X
--------------------------------------------------------
#### Problem description
i am unable to display X it gives the below error
--------------------------------------------------------------
KeyError Traceback (most recent call last)
D:\python\base.py in get_loc(self, key, method, tolerance)
3077 try:
-> 3078 return self._engine.get_loc(key)
3079 except KeyError:
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: ('sensex', 'nifty')
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
<ipython-input-1-5ee89fa30c8d> in <module>
4 df = pd.read_csv('tstdatapanda.csv', delimiter=',', encoding="utf-8-sig")
5 #df.drop_duplicates(subset=['S', 'T'])
----> 6 X = df['sensex','nifty']
7 y = df['nifty']
8 #X1.drop_duplicates(keep=False)
D:\em\education\python\anaconda\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2686 return self._getitem_multilevel(key)
2687 else:
-> 2688 return self._getitem_column(key)
2689
2690 def _getitem_column(self, key):
D:\em\education\python\anaconda\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
2693 # get column
2694 if self.columns.is_unique:
-> 2695 return self._get_item_cache(key)
2696
2697 # duplicate columns & possible reduce dimensionality
D:\em\education\python\anaconda\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
2487 res = cache.get(item)
2488 if res is None:
-> 2489 values = self._data.get(item)
2490 res = self._box_item_values(item, values)
2491 cache[item] = res
D:\em\education\python\anaconda\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
4113
4114 if not isna(item):
-> 4115 loc = self.items.get_loc(item)
4116 else:
4117 indexer = np.arange(len(self.items))[isna(self.items)]
D:\em\education\python\anaconda\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
3078 return self._engine.get_loc(key)
3079 except KeyError:
-> 3080 return self._engine.get_loc(self._maybe_cast_indexer(key))
3081
3082 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: ('sensex', 'nifty')
----------------------------------------------------------------------------------------------------
#### Output of ``pd.show_versions()``
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 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
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
[tstdatapanda.zip](https://github.com/pandas-dev/pandas/files/2923280/tstdatapanda.zip)
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https://api.github.com/repos/pandas-dev/pandas/issues/25523 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25523/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25523/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25523/events | https://github.com/pandas-dev/pandas/issues/25523 | 416,537,543 | MDU6SXNzdWU0MTY1Mzc1NDM= | 25,523 | ExcelFile class has no attribute 'encoding'. Is it correct? | {
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} | 3 | 2019-03-03T17:00:27Z | 2021-01-19T18:00:56Z | 2021-01-19T18:00:56Z | NONE | null | #### Code Sample
```python
import os
import pandas as pd
directory = '/Data'
# more than 300 files
for d, dirs, files in os.walk(directory):
# i can not use read_excel function because i do not know how many sheets in each xls
# i use instance of ExcelFile
# in fact it returns xlrd.Book instance (because engine: Acceptable values are None or ``xlrd``)
try
current_file = pd.ExcelFile(os.path.join(d,f))
#do not work. My files use cp1251 charset. See more in output
except UnicodeDecodeError:
pass
sheet_names_ = current_file.sheet_names()
for sn in current_file.sheet_names():
# this Data Frame i shoud parse and import to my Pstgresql DB
current_file.parse(sheet_name=sn)
```
#### Problem description
I have a lot of XLS files encoded with cp1251. Each file contains 1 or more sheets. I need to import them into my Postgesql DB. But Pandas can not do it simple. I can load one sheet (list of, but i don`t have it) at a time in any encoding using pandas.read_excel(file,encoding=my_encoding). Or I can load all sheets from all files in UTF encoding using instance of pandas.ExcelFile(file). It looks like some bad recursion.
I can use xlrd to read my files with specific charset to get xlrd.Book instance. Then pass it to pandas.read_excel(). But I do not like this decision.
If pandas.ExcelFile() would have an attribute of the encoding, then it could be passed to any "engine". I saw a new version of excel package. The same problem.
(I dont have MS Excel and can not create "bad" file for test)
Sorry for my english(((
#### Expected Output
#### Output pandas/xlrd Error
<details>
*** No CODEPAGE record, no encoding_override: will use 'ascii'
*** No CODEPAGE record, no encoding_override: will use 'ascii'
Traceback (most recent call last):
File "TableLoader.py", line 13, in <module>
c = ExelLoader()
File "TableLoader.py", line 10, in ExelLoader
my_xls_x = pandas.read_excel('Data/qq.xls')
File "/home/dz/.local/lib/python3.6/site-packages/pandas/util/_decorators.py", line 188, in wrapper
return func(*args, **kwargs)
File "/home/dz/.local/lib/python3.6/site-packages/pandas/util/_decorators.py", line 188, in wrapper
return func(*args, **kwargs)
File "/home/dz/.local/lib/python3.6/site-packages/pandas/io/excel.py", line 350, in read_excel
io = ExcelFile(io, engine=engine)
File "/home/dz/.local/lib/python3.6/site-packages/pandas/io/excel.py", line 653, in __init__
self._reader = self._engines[engine](self._io)
File "/home/dz/.local/lib/python3.6/site-packages/pandas/io/excel.py", line 424, in __init__
self.book = xlrd.open_workbook(filepath_or_buffer)
File "/home/dz/.local/lib/python3.6/site-packages/xlrd/__init__.py", line 157, in open_workbook
ragged_rows=ragged_rows,
File "/home/dz/.local/lib/python3.6/site-packages/xlrd/book.py", line 120, in open_workbook_xls
bk.get_sheets()
File "/home/dz/.local/lib/python3.6/site-packages/xlrd/book.py", line 723, in get_sheets
self.get_sheet(sheetno)
File "/home/dz/.local/lib/python3.6/site-packages/xlrd/book.py", line 714, in get_sheet
sh.read(self)
File "/home/dz/.local/lib/python3.6/site-packages/xlrd/sheet.py", line 820, in read
strg = unpack_string(data, 6, bk.encoding or bk.derive_encoding(), lenlen=2)
File "/home/dz/.local/lib/python3.6/site-packages/xlrd/biffh.py", line 250, in unpack_string
return unicode(data[pos:pos+nchars], encoding)
File "/home/dz/.local/lib/python3.6/site-packages/xlrd/timemachine.py", line 31, in <lambda>
unicode = lambda b, enc: b.decode(enc)
UnicodeDecodeError: 'ascii' codec can't decode byte 0xcd in position 0: ordinal not in range(128)
</details>
#### Output of `pd.show_versions()`
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 32
OS: Linux
OS-release: 4.15.0-45-generic
machine: i686
processor: i686
byteorder: little
LC_ALL: None
LANG: ru_RU.UTF-8
LOCALE: ru_RU.UTF-8
pandas: 0.24.1
pytest: None
pip: 9.0.1
setuptools: 39.0.1
Cython: None
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: 1.2.0
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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} | 7 | 2019-03-03T17:56:04Z | 2019-03-11T11:58:36Z | 2019-03-11T11:58:35Z | CONTRIBUTOR | null | Initial work on #24589
- Removes hard-coded examples from _flex_doc_SERIES
- Adds separate examples for each op (_*_example_SERIES). At this stage I've just copied the examples which were in the _flex_doc_SERIES template (add, sub, mul, div)
- Modifies _make_flex_doc to format the _flex_doc_SERIES template with an example string from op_desc['series_examples']
- adds references to each of the examples to _op_descriptions['series_examples'], allowing them to be picked up in _make_flex_doc
Ops outside not in [add, sub, mul, div] will return their docstring with no examples in this revision.
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} | 7 | 2019-03-03T18:24:01Z | 2019-03-10T21:08:35Z | 2019-03-10T21:08:26Z | CONTRIBUTOR | null | - [X] closes #25172
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Original number of errors: 95
Remaining errors: 0
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} | 6 | 2019-03-03T20:14:11Z | 2019-03-20T01:55:38Z | 2019-03-20T01:55:34Z | CONTRIBUTOR | null | [ci skip]
DatetimeArray was listed twice. PeriodArray was missed. | {
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"description": "Clarification a... | closed | false | null | [] | null | 7 | 2019-03-03T20:46:17Z | 2019-05-07T11:14:41Z | 2019-05-07T11:14:40Z | NONE | null | #### Code Sample, a copy-pastable example if possible
The first line is the problem, the others given for context. My goal in the function this comes from is to remove non-modal ("atypical") items per `key`. The task should, and does, allow multi-modal targets per group (there are other techniques to pick a unique, but arbitrary mode, but I want all of them).
```python
# Your code here
modes = pd.DataFrame(df.groupby('key').value.agg(pd.Series.mode))
df.set_index('key', inplace=True)
df = df.join(modes, rsuffix='_mode')
df = df[df.apply(lambda row:row.value in row.value_mode, axis=1)]
```
The below uses a DataFrame smaller than the one where the problem was encountered. As discussed in a comment below, the precise sampling of the original data and the precise anonymization of groupby keys used has an unpredictable effect on the problem being accidentally fixed or continuing to occur.
```python
>>> import pandas as pd
>>> import gzip
>>> import pickle
>>> df = pickle.load(gzip.open('modal-crash.pkl.gz'))
>>> modes = df.groupby('key').value.agg(pd.Series.mode)
Traceback (most recent call last):
File "<ipython-input-13-4fe1f0dd3977>", line 1, in <module>
modes = df.groupby('key').value.agg(pd.Series.mode)
File "/anaconda3/envs/boldmetrics-analysis/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 778, in aggregate
result = self._aggregate_named(func_or_funcs, *args, **kwargs)
File "/anaconda3/envs/boldmetrics-analysis/lib/python3.7/site-packages/pandas/core/groupby/generic.py", line 909, in _aggregate_named
raise Exception('Must produce aggregated value')
Exception: Must produce aggregated value
```
[modal-crash.pkl.gz](https://github.com/pandas-dev/pandas/files/2923871/modal-crash.pkl.gz)
#### Problem description
In a manner sensitive to the exact data, but not following any obvious pattern I have discerned, the `modes = ...` line can produce 3-4 levels of nested exceptions leading to `Exception: Must produce aggregated value`.
I have checked the "problem" data to make sure there are no missing values in `df.value`. All values are present and are an expected (string) value. This particular task has to do with a target that is a clothing size. E.g. when run correctly, `modes` will resemble this:
```
value
key
ddhhmttmvv [L, M]
dfdavpqthh [S]
ddfadjqqhd [M]
ddvjmatfvm [M]
... 160k more rows ...
```
The code succeeds for **most** initial DataFrames. In fact, adding `df = df.iloc[:-1]` or `df = df.iloc[1:]` makes the operation succeed for every "problem" DataFrame I have encountered.
I have identified a hack/fix to the problem, but it's ad hoc and ugly:
```python
def mode(s):
return list(pd.Series.mode(s))
modes = pd.DataFrame(df.groupby('key').value.agg(mode))
```
#### Expected Output
Expected output shown in the problem description. The DataFrame generated after the filtering is just a subset of rows in the original, but the problem only occurs in finding the mode per group.
#### Output of ``pd.show_versions()``
<details>
% python -c "import pandas; pandas.show_versions()"
/anaconda3/envs/boldmetrics-analysis/lib/python3.7/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use "pip install psycopg2-binary" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.
""")
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
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: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: None
html5lib: None
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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} | 2 | 2019-03-03T23:35:36Z | 2019-03-04T18:54:13Z | 2019-03-04T18:54:13Z | CONTRIBUTOR | null | -[x] fixes 5 small (subject verb agreement, articles, formatting, duplicate conjunction) typos in the doc's user_guide _indexing.rst_ file.
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} | 7 | 2019-03-04T00:01:18Z | 2019-03-07T22:01:37Z | 2019-03-04T18:39:42Z | MEMBER | null | We have several functions getting called inside a `with nogil:` block that aren't currently declared as `nogil`. In the future cython should warn in this type of situation (cython#2879). | {
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###sample code:
import pandas as pd
import re
s = pd.Series(['hello there', 'well hello then'])
print(s)
'''OUTPUT
0 hello there
1 well hello then
dtype: object
'''
s.replace({'hello' : 'goodbye'}, regex=True, inplace=True)
print(s)
'''OUTPUT
0 goodbye there
1 well hello then #<-not replaced
dtype: object
'''
###expected output using re.sub as per series.replace documentation
x = re.sub('hello','goodbye', 'hello there')
print(x)
#out: goodbye there
x = re.sub('hello','goodbye', 'well hello then')
print(x)
#### Problem description
.24 bug where series.replace is no longer working the same as in .23 pandas. In past versions, pandas would replace substrings that appeared in the middle/end of strings, but now it only seems to work if the pattern is at the very beginning.
I saw the docs mention that passing a dict and using regex follows re.sub, but when I tried that, I got the expected result (not the one .24 pandas yielded).
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
see above - should replace sub-strings in middle (not just beginning)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: 3.8.0
pip: 10.0.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.16.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.7.9
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: 2.5.6
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.1.0
lxml.etree: None
bs4: 4.6.3
html5lib: 1.0.1
sqlalchemy: 1.2.11
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
None
</details>
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} | 13 | 2019-03-04T08:59:34Z | 2019-03-30T19:17:32Z | 2019-03-30T19:17:29Z | CONTRIBUTOR | null | closes #25444
closes #15306
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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- [ ] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
```
################################################################################
####################### Docstring (pandas.read_parquet) #######################
################################################################################
Load a parquet object from the file path, returning a DataFrame.
.. versionadded 0.21.0
Parameters
----------
path : str
File path.
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
columns : list, default=None
If not None, only these columns will be read from the file.
.. versionadded 0.21.1
**kwargs
Any additional kwargs are passed to the engine.
Returns
-------
DataFrame
A parquet file is returned as two-dimensional data structure with
labeled axes.
See Also
--------
to_parquet : Write DataFrame to a parquet file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
Examples
--------
>>> pd.read_parquet('data.parquet') # doctest: +SKIP
################################################################################
################################## Validation ##################################
################################################################################
Docstring for "pandas.read_parquet" correct. :)
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https://api.github.com/repos/pandas-dev/pandas/issues/25533 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25533/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25533/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25533/events | https://github.com/pandas-dev/pandas/pull/25533 | 416,784,287 | MDExOlB1bGxSZXF1ZXN0MjU3OTI4NzU0 | 25,533 | BUG: caught typeError in series.at (#25506) | {
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} | 5 | 2019-03-04T12:43:55Z | 2019-03-05T21:11:53Z | 2019-03-05T21:11:49Z | CONTRIBUTOR | null | - [x] closes #25506
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/25534 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25534/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25534/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25534/events | https://github.com/pandas-dev/pandas/issues/25534 | 416,808,589 | MDU6SXNzdWU0MTY4MDg1ODk= | 25,534 | DOC: Make the sidebar index more useful in the docs | {
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} | [] | closed | false | null | [] | null | 1 | 2019-03-04T13:46:03Z | 2019-03-04T14:01:08Z | 2019-03-04T14:01:08Z | NONE | null | My team recently upgraded the version of Pandas we use, and while the improved functionality of the module itself is great, I'm finding the docs annoying to use. Previously the sidebar index showed (if anything) too much; now it shows too little. Compare, for example, the Essential Basic Functionality docs in [0.20.3](http://pandas.pydata.org/pandas-docs/version/0.20/basics.html) vs. [0.24.0](http://pandas.pydata.org/pandas-docs/version/0.24/getting_started/basics.html). The 0.20.3 version is a little long, but makes it fairly easy to navigate to any part of the lengthy page to find the section you need. The index for 0.24.0, only shows links that were already on the front page and is virtually useless for navigating the page I find myself in.
On the other hand, the index for the [0.20.3 API Reference doc page](http://pandas.pydata.org/pandas-docs/version/0.20/api.html) is actually longer than the page itself (at least on my browser, due to wrapping) and thus equally useless for navigation, and I find the [0.24.0 version](http://pandas.pydata.org/pandas-docs/version/0.24/reference/index.html) more useful.
Surely there is a happy medium between showing too little in the sidebar index and showing too much. I expect I'll be continuing to refer to the Pandas docs on a daily basis for the foreseeable future, and the sidebar could be making them much easier to navigate than they currently are. | {
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} | 6 | 2019-03-04T14:34:08Z | 2019-03-07T10:10:05Z | 2019-03-05T22:15:21Z | CONTRIBUTOR | null | - [X] closes #13473
- [x] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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https://api.github.com/repos/pandas-dev/pandas/issues/25536 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25536/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25536/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25536/events | https://github.com/pandas-dev/pandas/issues/25536 | 416,944,506 | MDU6SXNzdWU0MTY5NDQ1MDY= | 25,536 | DOC: remove Panel from docs | {
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} | 1 | 2019-03-04T18:55:22Z | 2019-03-10T16:27:26Z | 2019-03-10T16:27:26Z | CONTRIBUTOR | null | this might require multiple PRs. Panel exists in the reference, main docs & in the historical whatsnew notes (these *should* already be code-blocks if its directly referenced) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25537 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25537/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25537/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25537/events | https://github.com/pandas-dev/pandas/pull/25537 | 416,953,133 | MDExOlB1bGxSZXF1ZXN0MjU4MDU4OTYx | 25,537 | fix MacPython / pandas-wheels ci failures | {
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} | 6 | 2019-03-04T19:19:00Z | 2019-03-05T12:22:14Z | 2019-03-05T10:24:47Z | MEMBER | null | https://github.com/pandas-dev/pandas/pull/25483#issuecomment-469366672
@jreback : the regex is getting a bit unreadable.. this may suggest that a more (user friendly|consistent) error message should be raised by `safe_sort`? | {
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- subject verb agreements
- extra conjunctions
- adds articles
- fixes formatting as code markup
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https://api.github.com/repos/pandas-dev/pandas/issues/25539 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25539/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25539/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25539/events | https://github.com/pandas-dev/pandas/issues/25539 | 417,065,297 | MDU6SXNzdWU0MTcwNjUyOTc= | 25,539 | read_excel only loading first 11 rows | {
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```python
# data = read_excel(data.xlsx, nrows=100)
```
#### Problem description
I'm currently trying to open a 46 MB xlsx file with around 1700 rows and 26 columns. Everytime I load the dataset through read_excel, the DataFrame only contains 11 rows. I've tried this on multiple ipython notebooks with the same problem. Even loading specific sizes (nrows) of the dataset only results with 11 rows being loaded. Is this a common bug? Am I missing something? | {
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} | 5 | 2019-03-05T06:50:22Z | 2019-03-10T21:59:17Z | 2019-03-10T21:59:15Z | CONTRIBUTOR | null | BUG: Fix #25481 by fixing the error message in TypeError
- [x] closes #25481
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 13 | 2019-03-05T08:03:21Z | 2019-03-19T04:03:13Z | 2019-03-18T12:35:48Z | CONTRIBUTOR | null | - [x] closes #16607
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I messed up the rebase of the old PR #24844 and started this again on a clean state. Reviewers please review on this PR!
#### From old PR
I found the issue stagnant, but since the fix was already in place, I manually took the code @rosygupta made and applied it onto latest master. Rebase wasn't an option that I found would work since the file in question has been split into multiple. Let me know what else needs to be updated. | {
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```python
import numpy as np
import pandas as pd
# np.__version__ is 1.14.5
# pd.__version__ is 0.24.1
# create list of dicts with ndarrays
ll = []
for ii in range(2):
dd = {0: np.ones(2)}
ll += [dd]
# create data frame
df = pd.DataFrame(ll)
# print(df) looks as expected
# 0
# 0 [1.0, 1.0]
# 1 [1.0, 1.0]
m = df[0].mean() # works as expected
# equivalent to df[0].values.mean(axis=0)
print(m) # array([1., 1.])
v = df[0].values.var(axis=0) # yields expected result array([0., 0.])
v = df[0].var() # raises TypeError: setting an array element with a sequence.
```
#### Problem description
A Pandas series od dtype object can contain numpy.ndarrays. This ist useful to store high-dimensional data in DataFrames.
Calculating the mean of such a series works as expected. Calculating the variance however, yields an error. The calculation is easily performed by inserting .values between the series and the var call, so it is no fundamental problem.
This is the error Traceback of df[0].var()
```
Traceback (most recent call last):
File "<ipython-input-23-be47a51ab53b>", line 1, in <module>
df[0].var()
File "C:\Users\Maksim\WinPython\python-3.6.3.amd64\lib\site-packages\pandas\core\generic.py", line 10976, in stat_func
skipna=skipna, ddof=ddof)
File "C:\Users\Maksim\WinPython\python-3.6.3.amd64\lib\site-packages\pandas\core\series.py", line 3626, in _reduce
return op(delegate, skipna=skipna, **kwds)
File "C:\Users\Maksim\WinPython\python-3.6.3.amd64\lib\site-packages\pandas\core\nanops.py", line 76, in _f
return f(*args, **kwargs)
File "C:\Users\Maksim\WinPython\python-3.6.3.amd64\lib\site-packages\pandas\core\nanops.py", line 138, in f
raise TypeError(e)
TypeError: setting an array element with a sequence.
```
#### Expected Output
I expect
df[0].var()
to yield the same as
df[0].values.var(axis=0)
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.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: en
LOCALE: None.None
pandas: 0.24.1
pytest: 3.2.3
pip: 18.1
setuptools: 39.2.0
Cython: 0.27.2
numpy: 1.14.5
scipy: 1.1.0
pyarrow: 0.7.1
xarray: 0.9.6
IPython: 6.2.1
sphinx: 1.6.5
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: 1.5.1
bottleneck: 1.2.1
tables: None
numexpr: None
feather: 0.4.0
matplotlib: 2.1.0
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: 1.0.2
lxml.etree: 4.1.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.14
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | [] | closed | false | null | [] | null | 1 | 2019-03-05T10:14:39Z | 2019-03-05T14:01:27Z | 2019-03-05T14:01:27Z | NONE | null | This here works when looking for whole words with \b
```python
# Your code here
df = pd.DataFrame({'b' : ['hif das', 'foodas', 'fat', 'cat']})
df[df.b.str.contains(r'\bdas\b')]
```
But this doesn't work:
```python
df[df.eval('b.str.contains(r"\bdas\b")', engine="python", parser="python")]
df.query('b.str.contains(r"\bdas\b")', engine="python")
```
query() and eval() functions are useful for non-programmers writing just sql code and you load it in query(). I don't see any reason why you couldn't use \b inside query or eval, since you can use other characters like | or ^ or \s
Expected Output:
```
b
0 hif das
```
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} | 6 | 2019-03-05T12:38:58Z | 2020-05-03T21:46:02Z | 2020-05-03T21:46:02Z | NONE | null | ```python
pd.Timedelta(3,unit= 'M')
Out[26]: Timedelta('91 days 07:27:18')
pd.Timedelta('3M')
Out[27]: Timedelta('0 days 00:03:00')
```
#### Problem description
I tried instantiating Timedelta object using '3M' which returns Timedelta object with 3 minutes rather than 3Months
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 32
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: English_United States.1252
pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 39.1.0
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: 1.8.4
patsy: 0.5.1
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
</details>
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} | 2 | 2019-03-05T13:09:01Z | 2019-03-07T20:42:29Z | 2019-03-07T20:42:29Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.Series(np.random.random(1000)).plot(logy=True)
pd.Series(np.random.random(1000)).plot(secondary_y=True, logy=True)
plt.show()
```
#### Problem description
When I plot some data and set both the `secondary_y` and `logy` parameters to `True`, the `logy` parameter is not respected. Instead, the secondary y axis still uses linear scaling.
#### Expected Output
The secondary y axis should use logarithmic scaling.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.19.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.24.1
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.8.0
sphinx: 1.8.3
patsy: None
dateutil: 2.7.3
pytz: 2018.9
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
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 | 2019-03-05T13:30:47Z | 2019-05-07T04:19:24Z | 2019-05-07T04:19:24Z | NONE | null | #### Problem description
If I'm trying to load json data thought the Series.read_json considering the following command line :
```python
pd.read_json('{"2019-01-01T11:00:00.000Z":88.352985054,"2019-01-01T12:00:00.000Z":90.091719896,"2019-01-01T13:00:00.000Z":15}', typ='series', orient='index').index
```
I've go the following output
```
DatetimeIndex(['2019-01-01 11:00:00',
'2019-01-01 12:00:00', '2019-01-01 13:00:00'],
dtype='datetime64[ns]', freq=None)
```
The timezone is contained in the 'Z' at the end of the timestamp string. It means that it's a UTC time. More information you can consult the wikipedia page for iso date https://fr.wikipedia.org/wiki/ISO_8601
#### Expected Output
UTC timezone should be mentionned :
```
DatetimeIndex(['2019-01-01 11:00:00',
'2019-01-01 12:00:00', '2019-01-01 13:00:00'],
dtype='datetime64[ns, UTC]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.6.2
Cython: None
numpy: 1.16.1
scipy: None
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: 4.6.3
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | [] | closed | false | null | [] | null | 2 | 2019-03-05T14:04:38Z | 2019-03-05T15:31:10Z | 2019-03-05T15:31:10Z | CONTRIBUTOR | null | If I merge two data frames by columns ignoring the indexes, it seems the column names get lost on the resulting object, being replaced instead by integers. Example:
```python
import numpy as np, pandas as pd
np.random.seed(1)
df1 = pd.DataFrame(np.random.random(size = (10,5)), columns = ['col1', 'col2', 'col3', 'col4', 'col5'])
df2 = pd.DataFrame(np.random.poisson(5, size = (10, 3)), columns = ['cl1', 'cl2', 'cl3'])
df2 = pd.concat([df2, df1], axis = 1, ignore_index = True)
df2.columns
```
Returns:
```RangeIndex(start=0, stop=8, step=1)```
When using `ignore_index = False` however, the column names remain in the merged object:
```python
import numpy as np, pandas as pd
np.random.seed(1)
df1 = pd.DataFrame(np.random.random(size = (10,5)), columns = ['col1', 'col2', 'col3', 'col4', 'col5'])
df2 = pd.DataFrame(np.random.poisson(5, size = (10, 3)), columns = ['cl1', 'cl2', 'cl3'])
df2 = pd.concat([df2, df1], axis = 1, ignore_index = False)
df2.columns
```
Returns:
```Index(['cl1', 'cl2', 'cl3', 'col1', 'col2', 'col3', 'col4', 'col5'], dtype='object')``` | {
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"description": "Related to indexing on s... | open | false | null | [] | null | 3 | 2019-03-05T14:08:26Z | 2021-06-27T19:03:17Z | null | NONE | null | I noticed that `.loc` and `__setitem__` behave very differently when assigning one series to a sub-range of another series:
```python
>>> s = pd.Series(0.0, index=list('abcd'))
>>> s1 = pd.Series(1.0, index=list('ab'))
>>> s2 = pd.Series(2.0, index=list('xy'))
>>> s[['a', 'b']] = s2
>>> s # names of s2 are ignored as expected
a 2.0
b 2.0
c 0.0
d 0.0
dtype: float64
>>> s.loc[['a', 'b']] = s2
>>> s # not expected!!
a NaN
b NaN
c 0.0
d 0.0
dtype: float64
>>> s.loc[['a', 'b']] = s1
>>> s # everything's fine if the indices match
a 1.0
b 1.0
c 0.0
d 0.0
dtype: float64
```
I'm not sure if this is intended behaviour but it seems odd.
I'm on pandas v. 0.24.1
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} | 3 | 2019-03-05T14:23:28Z | 2021-11-04T00:40:28Z | 2021-11-04T00:40:28Z | NONE | null | In the docs we see:
but in fact the default behaviour of `str.split()` is more complicated than using `str.split(" ")` as detailed in the [python 3 documentation](https://docs.python.org/3/library/stdtypes.html#str.split). Perhaps linking to this explanation of the splitting algorithm would be preferable or writing something slightly more verbose would be preferable? Even saying "consecutive whitespace" would probably be enough.
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} | 4 | 2019-03-05T16:53:45Z | 2019-03-07T02:21:14Z | 2019-03-07T02:21:08Z | CONTRIBUTOR | null | Add docstring entry for using `axis=None`with `style.background_gradient`, which was added in #21259. Also reword to be in numerical order and consistent with how `highlight_max` and `highligh_min` defines column-wise and row-wise.
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | [] | closed | false | null | [] | null | 5 | 2019-03-05T18:17:52Z | 2019-03-05T21:28:40Z | 2019-03-05T21:28:40Z | NONE | null | #### Code Sample
```python
>>> s = pd.Series(range(5))
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
```
#### Problem description
This is a purely design-related issue!
I think that the behavior of the `DataFrame.where()` is potentially confusing, and can easily be misunderstood. The core of the issue is the use of the `other` parameter.
The behavior when `other` is set by the user can be confusing,
```python
>>> s = pd.Series(range(5))
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
```
Reading `s.where(s > 1, 10)`, I think the logical way to read that, or to use a function called `.where()`, is:
> Replace values in `s` where `s > 1` is `True` with `10`
While the proper use of `other` may make sense if you're fully familiar with the default behavior of `.where()`, it can be confusing to those reading it for the first time.
I think this boils down to the function being,
> Replace values where the condition is **False**
Instead of,
> Replace values where the condition is **True**
which I think is the expected behavior based on the function name.
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
#### Expected Output
Changing the behavior of `.where()` to be:
> Replace values where the condition is **True**
This would then give,
```python
>>> s = pd.Series(range(5))
>>> s.where(s > 1, 10)
0 0
1 1
2 10
3 10
4 10
dtype: int64
```
So, to replicate the output of the initial snippet,
```python
>>> s = pd.Series(range(5))
>>> s.where(s < 2, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
```
#### Output of ``pd.show_versions()``
Not applicable | {
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} | 5 | 2019-03-05T18:30:15Z | 2019-03-07T09:50:08Z | 2019-03-06T22:35:56Z | MEMBER | null | - [x] closes #25555
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
I don't think there is currently an issue for this. will open new and then add whatsnew. | {
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} | 24 | 2019-03-05T18:37:00Z | 2019-03-28T17:50:31Z | 2019-03-28T12:48:15Z | CONTRIBUTOR | null | #25227 is trying to add `pyproject.toml` which is necessary for the whole `pip`-machinery to work smoothly post v.19.0. However, this seems to be running into problems with a numpy version bump, and mood there was to bump numpy requirement to 1.13.x.
The bare minimum for the bump is implemented in that PR, but a version bump requires getting rid of a bunch of compat code (and CI adjustments), otherwise the cruft just accumulates (it took me three PRs to clean out until 1.12).
Scipy is goint to [bump to 1.13.3](https://github.com/pandas-dev/pandas/pull/25227#issuecomment-464128057) with the next release (due to cython issues, I believe), so I thought I'll directly take it to 1.13.3 here.
I tried redistributing the CI jobs as best as I could. Here's the before/after:
**EDIT: updated pins in https://github.com/pandas-dev/pandas/pull/25554#issuecomment-475881206**
CI job: line number | before | this PR
-------|-----------------------------|-------------------
`ci/deps/azure-27-compat.yaml:10:` | `numpy=1.12.0` | `numpy=1.13.3`
`ci/deps/azure-27-locale.yaml:10:` | `numpy=1.12.0` | `numpy=1.14.*`
`ci/deps/azure-36-locale_slow.yaml:16:` | `numpy` | `numpy=1.15.*`
`ci/deps/azure-37-locale.yaml:15:` | `numpy` | `numpy`
`ci/deps/azure-37-numpydev.yaml:18:` | `numpy` | `numpy`
`ci/deps/azure-macos-35.yaml:14:` | `numpy=1.12.0` | `numpy=1.13.3`
`ci/deps/azure-windows-27.yaml:15:` | `numpy=1.12*` | `numpy=1.13.3`
`ci/deps/azure-windows-36.yaml:12:` | `numpy=1.14*` | `numpy=1.15.*`
`ci/deps/travis-27.yaml:20:` | `numpy=1.13*` | `numpy=1.14.*`
`ci/deps/travis-36-doc.yaml:23:` | `numpy=1.13*` | `numpy`
`ci/deps/travis-36-locale.yaml:15:` | `numpy` | `numpy`
`ci/deps/travis-36-slow.yaml:12:` | `numpy` | `numpy`
`ci/deps/travis-36.yaml:17:` | `numpy` | `numpy=1.15.*`
`ci/deps/travis-37.yaml:10:` | `numpy` | `numpy`
Matplotlib needs to be bumped as well (since current min 2.0.0 cannot be resolved by conda together with numpy 1.13.3), but it's only a tiny bump to ~2.0.2~ ~2.1.0~ ~2.1.1~ 2.2.2. In any case, the version spread should probably start to reflect that there's a bunch of mpl 3.0.x about, and so I also changed the spread a bit here:
CI job: line number | before | this PR
-------------|-------|-----------
`ci/deps/azure-27-locale.yaml:9:` | `matplotlib=2.0.0` | `matplotlib=2.2.2`
`ci/deps/azure-36-locale_slow.yaml:13:` | `matplotlib` | `matplotlib=3.0.*`
`ci/deps/azure-37-locale.yaml:12:` | `matplotlib` | `matplotlib`
`ci/deps/azure-macos-35.yaml:11:` | `matplotlib=2.2.0` | `matplotlib=2.2.3`
`ci/deps/azure-windows-27.yaml:13:` | `matplotlib=2.0.1` | `matplotlib=2.2.4`
`ci/deps/azure-windows-36.yaml:10:` | `matplotlib` | `matplotlib`
`ci/deps/travis-27.yaml:16:` | `matplotlib=2.2.2` | `matplotlib=2.2.*`
`ci/deps/travis-36-doc.yaml:17:` | `matplotlib` | `matplotlib`
`ci/deps/travis-36-locale.yaml:12:` | `matplotlib` | `matplotlib=3.0.1`
`ci/deps/travis-36-slow.yaml:10:` | `matplotlib` | `matplotlib`
`ci/deps/travis-36.yaml:14:` | `matplotlib` | `matplotlib=3.0.0`
It's probably wishful thinking that everything will pass straight away (especially when mpl versions change, see the code in `pandas/tests/plotting/test_datetimelike.py`), but oh well...
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} | 1 | 2019-03-05T18:52:40Z | 2019-03-06T22:35:55Z | 2019-03-06T22:35:55Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
import numpy as np
from pandas import DataFrame
df = DataFrame(np.random.randn(6, 4))
df.dropna(axis=3)
```
#### Problem description
0.25.0.dev0+198.gd10bbce34
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\frame.py", line 4575, in dropna
axis = self._get_axis_number(axis)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\generic.py", line 361, in _get_axis_number
.format(axis, type(cls)))
ValueError: No axis named 3 for object type <class 'type'>
```
0.23.4:
```python-traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-5ab8608d1d61> in <module>
2 from pandas import DataFrame
3 df = DataFrame(np.random.randn(6, 4))
----> 4 df.dropna(axis=3)
~\Anaconda3\lib\site-packages\pandas\core\frame.py in dropna(self, axis, how, thresh, subset, inplace)
4274 axis=ax)
4275 else:
-> 4276 axis = self._get_axis_number(axis)
4277 agg_axis = 1 - axis
4278
~\Anaconda3\lib\site-packages\pandas\core\generic.py in _get_axis_number(self, axis)
373 pass
374 raise ValueError('No axis named {0} for object type {1}'
--> 375 .format(axis, type(self)))
376
377 def _get_axis_name(self, axis):
ValueError: No axis named 3 for object type <class 'pandas.core.frame.DataFrame'>
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 5 | 2019-03-05T20:18:48Z | 2019-03-07T14:25:00Z | 2019-03-07T14:24:56Z | CONTRIBUTOR | null | Closes #25403
~@jbrockmendel suggested changing the def from `object[:]` to `ndarray[object]`. This unfortunately had the same runtime error about readonly buffers. So as a workaround for now (until Cython has const memoryviews), how about unsetting and then `re-setting` the writability of the input array?~ | {
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} | 1 | 2019-03-05T21:37:46Z | 2019-03-20T12:28:44Z | 2019-03-20T12:28:44Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
##Working:
divmod(a,b)
##Fails:
a.divmod(b)
```
#### Problem description
divmod(series_a,series_b) works as expected, but series_a.divmod(b) returns the following error:
```
>>> a.divmod(b)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/danlaw/Projects/pandas/pandas/core/ops.py", line 1892, in flex_wrapper
return self._binop(other, op, level=level, fill_value=fill_value)
File "/Users/danlaw/Projects/pandas/pandas/core/series.py", line 2522, in _binop
result = self._constructor(result, index=new_index, name=name)
File "/Users/danlaw/Projects/pandas/pandas/core/series.py", line 250, in __init__
.format(val=len(data), ind=len(index)))
ValueError: Length of passed values is 2, index implies 4
```
#### Expected Output
```python
(a 0.0
b 0.0
c NaN
d NaN
e NaN
dtype: float64, a 1.0
b 1.0
c NaN
d NaN
e NaN
dtype: float64)
```
#### Output of ``pd.show_versions()``
<details>
commit: 221be3b4adde0f45927803b1c593b56d4678faeb
python: 3.7.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.25.0.dev0+200.g221be3b4a
pytest: 4.3.0
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.5
numpy: 1.16.2
scipy: 1.2.1
pyarrow: 0.11.1
xarray: 0.11.3
IPython: 7.3.0
sphinx: 1.8.4
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: 3.0.2
openpyxl: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.2.1
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 2 | 2019-03-06T01:28:15Z | 2019-03-06T18:01:26Z | 2019-03-06T04:17:06Z | MEMBER | null | - [X] xref https://github.com/pandas-dev/pandas/pull/25498#issuecomment-469672734
- [X] tests added / passed
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
Skipping this test as it's failing on 32bit due to #23440. Note that this is consistent with #23442 where a few other tests that are being skipped for the same reason.
cc @jreback | {
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} | 3 | 2019-03-06T01:34:53Z | 2019-03-06T18:01:06Z | 2019-03-06T09:13:12Z | MEMBER | null | A few small things I noticed reading over the whatsnew entries.
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https://api.github.com/repos/pandas-dev/pandas/issues/25560 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25560/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25560/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25560/events | https://github.com/pandas-dev/pandas/issues/25560 | 417,601,233 | MDU6SXNzdWU0MTc2MDEyMzM= | 25,560 | IOError: File "/home/jasims/Documents/sample.csv" does not exist | {
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} | 3 | 2019-03-06T03:07:42Z | 2019-03-06T16:18:17Z | 2019-03-06T12:51:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python code
import ipywidgets as widgets
import fileplot
path=widgets.Text(
value=raw_input("Enter the Path Csv File You Want to Plot here : "),
#placeholder="File Path",
#descrition="String:",
#disabled=False
)
button=widgets.Button(
description='Plot',
disabled=False,
button_style='', # 'success', 'info', 'warning', 'danger' or ''
tooltip='Click me',
icon='check'
)
def on_button_clicked(b):
#fileplot.load_file(np)
fileplot.load_file(path.value)
display(path,button)
button.on_click(on_button_clicked)
```
#### Problem description
Enter the Path Csv File You Want to Plot here : "/home/jasims/Documents/sample.csv"
---------------------------------------------------------------------------
IOError Traceback (most recent call last)
<ipython-input-5-d33edd47674b> in on_button_clicked(b)
26 def on_button_clicked(b):
27 #fileplot.load_file(np)
---> 28 fileplot.load_file(path.value)
29
30 display(path,button)
/home/jasims/Downloads/starkit/starkit/filebox/fileplot.pyc in load_file(filename)
6
7 def load_file(filename):
----> 8 df=pd.read_csv(filename, sep="delimeter", header=None)
9 df.plot(x=df.columns[0],y=df.columns[1])
10 plt.show()
/home/jasims/anaconda3/envs/starkit/lib/python2.7/site-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)
676 skip_blank_lines=skip_blank_lines)
677
--> 678 return _read(filepath_or_buffer, kwds)
679
680 parser_f.__name__ = name
/home/jasims/anaconda3/envs/starkit/lib/python2.7/site-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
438
439 # Create the parser.
--> 440 parser = TextFileReader(filepath_or_buffer, **kwds)
441
442 if chunksize or iterator:
/home/jasims/anaconda3/envs/starkit/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
785 self.options['has_index_names'] = kwds['has_index_names']
786
--> 787 self._make_engine(self.engine)
788
789 def close(self):
/home/jasims/anaconda3/envs/starkit/lib/python2.7/site-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
1012 def _make_engine(self, engine='c'):
1013 if engine == 'c':
-> 1014 self._engine = CParserWrapper(self.f, **self.options)
1015 else:
1016 if engine == 'python':
/home/jasims/anaconda3/envs/starkit/lib/python2.7/site-packages/pandas/io/parsers.pyc in __init__(self, src, **kwds)
1706 kwds['usecols'] = self.usecols
1707
-> 1708 self._reader = parsers.TextReader(src, **kwds)
1709
1710 passed_names = self.names is None
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()
pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()
IOError: File "/home/jasims/Documents/sample.csv" does not exist
i have tried changing the file location and name but no luck i have given full paht than also same error
**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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} | 2 | 2019-03-06T04:17:34Z | 2019-03-06T09:15:41Z | 2019-03-06T09:15:41Z | NONE | null | Backport PR #25558: TST: Skip IntervalTree construction overflow test on 32bit | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25562 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25562/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25562/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25562/events | https://github.com/pandas-dev/pandas/issues/25562 | 417,642,701 | MDU6SXNzdWU0MTc2NDI3MDE= | 25,562 | value error when multiply two large numbers | {
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```python
# Your code here
```
#### Problem description
data
Out[24]:
code volume
0 000559 7400
1 000559 3500
2 002415 4000
3 002415 5200
4 600622 4300
5 000559 9340
6 000559 9200
7 600622 3700
8 600622 11700
9 600622 3800
10 600622 22800
11 600622 4200
12 002415 4000
13 000559 21600
14 000559 21900
15 002415 6900
16 002415 4000
17 002415 2200
18 000559 21900
19 002415 4000
20 600622 6000
21 002415 4000
22 600622 3300
23 600622 2400
24 000559 10900
25 600622 3900
26 600622 4000
27 600622 6200
28 000559 5100
29 600622 13900
.. ... ...
32 002415 4000
33 600622 29500
34 600519 2800
35 000559 10900
36 000559 5200
37 000559 10900
38 000559 15051
39 600622 3200
40 600622 2400
41 600622 2800
42 000559 8100
43 600622 14000
44 600622 2400
45 000559 21900
46 600622 10600
47 600622 14700
48 600622 5700
49 600622 5900
50 000559 21900
51 600519 2100
52 000559 10900
53 000559 11000
54 600622 14700
55 600622 4500
56 600622 9200
57 600622 14200
58 000559 21900
59 000559 21900
60 600622 14700
61 600622 5900
data.volume * 1000000
Out[26]:
0 -1189934592
1 -794967296
2 -294967296
3 905032704
4 5032704
5 750065408
6 610065408
7 -594967296
8 -1184901888
9 -494967296
10 1325163520
11 -94967296
12 -294967296
13 125163520
14 425163520
15 -1689934592
16 -294967296
17 -2094967296
18 425163520
19 -294967296
20 1705032704
21 -294967296
22 -994967296
23 -1894967296
24 -1984901888
25 -394967296
26 -294967296
27 1905032704
28 805032704
29 1015098112
32 -294967296
33 -564771072
34 -1494967296
35 -1984901888
36 905032704
37 -1984901888
38 -2128869184
39 -1094967296
40 -1894967296
41 -1494967296
42 -489934592
43 1115098112
44 -1894967296
45 425163520
46 2010065408
47 1815098112
48 1405032704
49 1605032704
50 425163520
51 2100000000
52 -1984901888
53 -1884901888
54 1815098112
55 205032704
56 610065408
57 1315098112
58 425163520
59 425163520
60 1815098112
61 1605032704
When a positive number multiply with a very large number, the result become negative. I think this is caused by data error. The numbers should be changed to double before calculate.
However I have only few knowledge about C language. Can anybody fig this problem/
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] | closed | false | null | [] | null | 2 | 2019-03-06T09:45:22Z | 2019-03-06T14:34:58Z | 2019-03-06T14:34:57Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import networkx as nx
# Create a graph object with 2 edges and 3 nodes
dg1 = nx.DiGraph()
dg1.add_edge(1,2)
dg1.add_edge(3,2)
# Create a graph object with 2 edges and 3 nodes
dg2 = nx.DiGraph()
dg2.add_edge(1,2)
dg2.add_edge('a',2)
l = [dg1, dg2]
# Print their type name
print(list(map(type, l)))
# Create a df to store the graphs
df=pd.DataFrame({'a': [0, 1]})
df['dg'] = l
# Mask selecting, here, got incorrect series object (actually it's the nodes of the graph)
print(type(df['dg'][df['a']==1]))
print(type(df['dg'].loc[df['a']==1]))
# Non-mask selecting, here, got correct graph object
print(type(df['dg'][1]))
print(type(df['dg'].values[1]))
print(type(df['dg'].loc[1]))
```
#### Problem description
Mask selecting got the incorrect objects.
Environment:
platform : win-64
user-agent : conda/4.6.1 requests/2.18.4 CPython/3.6.5 Windows/10 Windows/10.0.16299
pandas 0.24.1 py36ha925a31_0
networkx 2.2 py36_1
#### Expected Output
Mask selecting and non-mask selecting should return the same objects as the originally stored ones.
#### 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 45 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.15.4
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: 2.6.0
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.1
bs4: 4.7.1
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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https://api.github.com/repos/pandas-dev/pandas/issues/25565 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25565/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25565/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25565/events | https://github.com/pandas-dev/pandas/issues/25565 | 417,729,277 | MDU6SXNzdWU0MTc3MjkyNzc= | 25,565 | Applying numpy functions on .values ranges from 20x faster than pandas functions to similar timing | {
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"default"... | closed | false | null | [] | null | 5 | 2019-03-06T10:26:28Z | 2019-03-07T12:31:01Z | 2019-03-06T11:54:53Z | NONE | null | I've made a simple 10'000 row Series of random floats and run some numpy vs pandas functions on it. The timings are given below (using the Ipython console):
```
import pandas as pd
import numpy as np
randSeries = pd.Series( data=np.random.ranf( 10000 ) )
# Min
%timeit randSeries.min( skipna=False )
# 74.2 us
%timeit randSeries.values.min()
# 5.6 us
%timeit randSeries.to_numpy().min()
# 9.2 us
%timeit randSeries.min()
# 99.6 us
%timeit np.nanmin( randSeries.values )
# 11.8 us
%timeit np.nanmin( randSeries.to_numpy() )
# 15.7 us
# Mean
# NB: .mean has an optimized cython implementation
%timeit randSeries.mean( skipna=False )
# 90.1 us
%timeit randSeries.values.mean()
# 8.6 us
%timeit randSeries.to_numpy().mean()
# 12.5 us
%timeit randSeries.mean()
# 37.6 us
%timeit np.nanmean( randSeries.values )
# 43.5 us
%timeit np.nanmean( randSeries.to_numpy() )
# 48.3 us
# Median
%timeit randSeries.median( skipna=False )
# 253 us
%timeit np.median( randSeries.values )
# 113 us
%timeit np.median( randSeries.to_numpy() )
# 119 us
%timeit randSeries.median()
# 110 us
%timeit np.nanmedian( randSeries.values )
# 124 us
%timeit np.nanmedian( randSeries.to_numpy() )
# 131 us
# Isnan
%timeit randSeries.isna()
# 64 us
%timeit np.isnan( randSeries.values )
# 3 us
%timeit np.isnan( randSeries.to_numpy() )
# 6.6 us
```
My conclusion is that if I know that the Series contains numerical values, I'd better use numpy functions, especially if I know there are no NaNs. It would always be much faster or similar to pandas ones. Also, `.to_numpy` gives a small overhead, which becomes significant for very fast operations.
The question is then: could that choice be done internally in pandas?
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.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: en
LOCALE: None.None
pandas: 0.24.1
pytest: 3.5.0
pip: 9.0.3
setuptools: 39.0.1
Cython: 0.28.1
numpy: 1.16.1
scipy: 1.2.1
pyarrow: 0.9.0
xarray: 0.10.2
IPython: 6.2.1
sphinx: 1.7.2
patsy: 0.5.0
dateutil: 2.7.2
pytz: 2018.3
blosc: 1.5.1
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: 1.0.2
lxml.etree: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.5
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: 0.1.4
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
NB: my version of numpy is provided here https://www.lfd.uci.edu/~gohlke/ and includes the MKL library | {
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} | [] | closed | false | null | [] | null | 2 | 2019-03-06T11:10:30Z | 2019-03-06T15:34:16Z | 2019-03-06T15:34:16Z | NONE | null | ```
In: df.shape
Out: (125298, 6)
In: len(interval_index)
Out: 8958
In: len(df['position_start'])
Out: 125298
In: pd.cut(df['position_start'], interval_index)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-18-ec9733045e49> in <module>
----> 1 pd.cut(df['position_start'], interval_index)
~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/tile.py in cut(x, bins, right, labels, retbins, precision, include_lowest, duplicates)
235
236 return _postprocess_for_cut(fac, bins, retbins, x_is_series,
--> 237 series_index, name)
238
239
~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/tile.py in _postprocess_for_cut(fac, bins, retbins, x_is_series, series_index, name)
496 """
497 if x_is_series:
--> 498 fac = Series(fac, index=series_index, name=name)
499
500 if not retbins:
~/anaconda3/lib/python3.7/site-packages/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
260 'Length of passed values is {val}, '
261 'index implies {ind}'
--> 262 .format(val=len(data), ind=len(index)))
263 except TypeError:
264 pass
ValueError: Length of passed values is 125719, index implies 125298
```
I might be missing something, but it seems to me something isn't right.
I tried to google the error message but nothing useful came out.
I tried to reproduce the error on a minimal case to provide runnable code but I couldn't get this same error. In other ways my df is behaving as expected.
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: 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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} | 12 | 2019-03-06T11:11:38Z | 2019-05-30T16:48:39Z | 2019-05-30T13:15:19Z | MEMBER | null | follow-on from #25550 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25568 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25568/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25568/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25568/events | https://github.com/pandas-dev/pandas/pull/25568 | 417,830,306 | MDExOlB1bGxSZXF1ZXN0MjU4NzQwNDQ3 | 25,568 | BLD: Fixed pip install with no numpy | {
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} | 6 | 2019-03-06T14:26:47Z | 2019-09-12T19:23:00Z | 2019-03-06T22:39:50Z | CONTRIBUTOR | null | Closes #25193
Still testing this locally and on https://github.com/pandas-dev/pandas-ci/pull/6 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25569 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25569/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25569/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25569/events | https://github.com/pandas-dev/pandas/pull/25569 | 417,837,512 | MDExOlB1bGxSZXF1ZXN0MjU4NzQ2MDU2 | 25,569 | REF: add custom Exception for safe_sort | {
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] | closed | false | null | [] | null | 15 | 2019-03-06T14:41:27Z | 2019-03-29T12:31:18Z | 2019-03-29T12:31:18Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/25537#issuecomment-469425015
custom `Exception` added primarily to allow more targeted testing, but also to distinguish sort failures and bad input to `safe_sort` | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25570 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25570/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25570/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25570/events | https://github.com/pandas-dev/pandas/issues/25570 | 417,843,165 | MDU6SXNzdWU0MTc4NDMxNjU= | 25,570 | to_string() not easily reversible for multi-index DataFrames | {
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"description": "__repr__... | open | false | null | [] | null | 8 | 2019-03-06T14:52:19Z | 2021-06-27T19:38:44Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas
import numpy as np
from itertools import product
df1 = pandas.DataFrame(product(['a', 'b'], range(3)), columns=['idx1', 'idx2'])
df2 = pandas.DataFrame(np.random.rand(6, 2), columns=['col1', 'col2'])
df = pandas.concat([df1, df2], axis=1)
df.set_index(['idx1','idx2'], inplace=True)
with open('test.dat', 'w') as file:
file.write(df.to_string())
df_read = pandas.read_csv('test.dat')
print(df.values)
print(df_read.values)
df.equals(df_read) # -> False
```
#### Problem description
I wanted to save a pandas DataFrame in human readable form, that is, as a text file with nice vertical alignment. The `to_string` function achieves precisely this, whereas `to_csv` does not.
I have saved data like this before, and it works just fine when one does not save the index. In this case it can be loaded via `pandas.read_csv(file, sep=r'\s+')`. I tried using the `index_cols` and `header` parameters but nothing seems to work properly.
I also made a [StackExchange thread](https://stackoverflow.com/questions/55023981/pands-read-multi-index-dataframe-reverse-to-string).
#### Expected Output
```python
True
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.18.0-15-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
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: 1.8.4
patsy: 0.5.1
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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https://api.github.com/repos/pandas-dev/pandas/issues/25571 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25571/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25571/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25571/events | https://github.com/pandas-dev/pandas/issues/25571 | 417,863,419 | MDU6SXNzdWU0MTc4NjM0MTk= | 25,571 | tricky timestamp conversion | {
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"description": "Clarifica... | closed | false | null | [] | null | 20 | 2019-03-06T15:28:44Z | 2020-05-29T16:30:50Z | 2019-10-20T23:47:22Z | NONE | null | Hello there, its me the bug hunter again :)
I have this massive 200 million rows dataset, and I encountered some very annoying behavior. I wonder if this is a bug.
I load my csv using
```
mylog = pd.read_csv('/mydata.csv',
names = ['mydatetime', 'var2', 'var3', 'var4'],
dtype = {'mydatetime' : str},
skiprows = 1)
```
and the `datetime` column really look like regular timestamps (tz aware)
```
mylog.mydatetime.head()
Out[22]:
0 2019-03-03T20:58:38.000-0500
1 2019-03-03T20:58:38.000-0500
2 2019-03-03T20:58:38.000-0500
3 2019-03-03T20:58:38.000-0500
4 2019-03-03T20:58:38.000-0500
Name: mydatetime, dtype: object
```
Now, I take extra care in converting these string into proper timestamps:
`mylog['mydatetime'] = pd.to_datetime(mylog['mydatetime'] ,errors = 'coerce', format = '%Y-%m-%dT%H:%M:%S.%f%z', infer_datetime_format = True, cache = True)`
That takes a looong time to process, but seems OK. The output is
```
mylog.mydatetime.head()
Out[23]:
0 2019-03-03 20:58:38-05:00
1 2019-03-03 20:58:38-05:00
2 2019-03-03 20:58:38-05:00
3 2019-03-03 20:58:38-05:00
4 2019-03-03 20:58:38-05:00
Name: mydatetime, dtype: object
```
What is puzzling is that so far I thought I had full control of my `dtypes`. However, running the simple
```
mylog['myday'] = pd.to_datetime(mylog['mydatetime'].dt.date, errors = 'coerce')
File "pandas/_libs/tslib.pyx", line 537, in pandas._libs.tslib.array_to_datetime
ValueError: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True
```
The only way I was able to go past this error was by running
`mylog['myday'] = pd.to_datetime(mylog['mydatetime'].apply(lambda x: x.date()))
`
Is this a bug? Before upgrading to `24.1` I was not getting the `tz` error above. What do you think? I cant share the data but I am happy to try some things to help you out!
Thanks! | {
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Example: `pd.read_json(filename, lines=True, dtype={'ColumnName':'category'})`.
fyi #21892
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} | 2 | 2019-03-06T19:15:20Z | 2019-03-06T21:26:52Z | 2019-03-06T21:26:47Z | CONTRIBUTOR | null | Changed from '**his** can be an expensive operation...' to '**this** can be an expensive operation...'
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} | 4 | 2019-03-06T19:22:37Z | 2019-03-13T15:28:32Z | 2019-03-13T15:28:32Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
dti = pd.DatetimeIndex([], name='my_dti', freq='H')
print(dti.round('D')) # Name is preserved
print(dti.snap('D')) # Name is lost
```
#### Problem description
The DatetimeIndex.snap method loses the name of the DatetimeIndex which is a different behaviour than DatetimeIndex.round (or floor, ceil, etc.) that preserves the name of the DatetimeIndex.
#### Expected Output
DatetimeIndex([], dtype='datetime64[ns]', name='my_dti', freq='D')
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
pandas: 0.24.1
pytest: 4.0.0
pip: 19.0.3
setuptools: 40.4.3
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.3.0
sphinx: None
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.2.1
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.18
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: 0.9.0
pandas_datareader: None
gcsfs: None
</details>
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} | 8 | 2019-03-06T21:08:31Z | 2019-03-09T04:05:28Z | 2019-03-08T21:52:27Z | CONTRIBUTOR | null | No code changes, just moved a few comments, and fixed a few broken types.
This patch allows stubgen to run over the pandas codebase without generating errors | {
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I have tried my best to create as little changes to the documentation of each individual function. Although I have tried to get rid of all style errors, I do still receive some errors from the `validate_docstrings.py`:
```
Series
2 Errors found:
Parameters {**kwargs} not documented
Unknown parameters {errors, copy, inplace, level}
```
```
Panel
2 Errors found:
Parameters {minor_axis, items, **kwargs, major_axis} not documented
Unknown parameters {items, major_axis, minor_axis, copy, errors, inplace, level}
```
Help is appreciated! | {
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} | 2 | 2019-03-06T22:40:01Z | 2019-03-07T14:27:21Z | 2019-03-07T14:27:21Z | NONE | null | Backport PR #25568: BLD: Fixed pip install with no numpy | {
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} | 4 | 2019-03-06T23:15:45Z | 2019-03-07T02:22:27Z | 2019-03-07T02:22:20Z | CONTRIBUTOR | null | - [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 0 | 2019-03-07T01:44:24Z | 2019-03-11T16:09:27Z | 2019-03-11T16:09:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index, dtype='Int64')
>>> series.resample('3T').sum()
AttributeError: 'IntegerArray' object has no attribute 'size'
```
#### Problem description
Pandas series with an Int64 dtype can not be resampled, instead raising an AttributeError because they have no size method. If the series is accessed as part of a DataFrame, e.g. 'df.resample('3T').series.mean()', it is successful.
It can also be fixed by adding a 'size' method to the IntegerArray class, e.g.
```python
def size(self):
return self._data.size()
```
#### Expected Output
2000-01-01 00:00:00 1
2000-01-01 00:03:00 4
2000-01-01 00:06:00 7
Freq: 3T, dtype: Int64
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: 4.1.1
pip: 18.1
setuptools: 40.2.0
Cython: 0.29.2
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: 1.8.3
patsy: 0.5.1
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: 1.2.15
pymysql: None
psycopg2: 2.7.7 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.2.0
fastparquet: 0.1.6
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/25581 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25581/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25581/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25581/events | https://github.com/pandas-dev/pandas/issues/25581 | 418,099,788 | MDU6SXNzdWU0MTgwOTk3ODg= | 25,581 | Inconsistent behavior when using GroupBy and pandas.Series.mode | {
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"id": 7... | open | false | null | [] | null | 4 | 2019-03-07T02:30:31Z | 2021-07-11T08:24:44Z | null | NONE | null | #### Code Sample
```python
import pandas
# Works great
df1 = pandas.DataFrame([[20,'A'],[20,'B'],[10,'C']])
gb1 = df1.groupby(0).agg(pandas.Series.mode)
display(gb1)
# Exception: Must produce aggregated value
df2 = pandas.DataFrame([[20,'A'],[20,'B'],[30,'C']])
gb2 = df2.groupby(0).agg(pandas.Series.mode)
display(gb2)
```
#### Problem Description
As it seems, the above code works great for **df1**, returning the following result:
```
1
0
10 C
20 [A, B]
```
(where C is a *str*, and [A, B] is a *numpy.ndarray*)
However, it doesn't work for **df2**, throwing the following exception:
```
...
C:\ProgramData\Anaconda3\envs\py36\lib\site-packages\pandas\core\groupby\generic.py in _aggregate_named(self, func, *args, **kwargs)
907 output = func(group, *args, **kwargs)
908 if isinstance(output, (Series, Index, np.ndarray)):
--> 909 raise Exception('Must produce aggregated value')
910 result[name] = self._try_cast(output, group)
911
Exception: Must produce aggregated value
```
It looks like the order of the processing of the *agg function* affects pandas error checking; if the first result (GroupBy row) returns an *numpy.ndarray* - the above exception is thrown, but if the first result return a str/scalar - the processing continues and suppresses further such exceptions.
In my opinion, item may be related to #2656 or one of its root causes ("fast apply vs. old pathway" as they put it), and to #24016 (which fails to accept *numpy.ndarray* as a return value). However, in our case - where *pandas.Series.mode* sometimes returns a scalar and sometimes a *numpy.ndarray*, it is more illusive and more confusing and therefore inconsistent (I spent ~2 hours debugging this trying to understand why so many of my *agg function* calls work but only one doesn't).
#### Expected Output
```
1
0
20 [A, B]
30 C
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.1
setuptools: 40.4.3
Cython: None
numpy: 1.15.2
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.12
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
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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import pandas as pd
aval = ['A','B','C']
bval = ['[0-10)','[10-20)','[20-30)']
df = pd.DataFrame({'A':aval, 'B':bval})
#case1
#df.loc[:,'num'] = df['B'].str.extract( r'^\[([0-9]{1,2})' )#fails
#case2
#df['num'] = df['B'].str.extract( r'^\[([0-9]{1,2})' )#works
#case3
df.loc[:,'num']=''
df.loc[:,'num'] = df['B'].str.extract( r'^\[([0-9]{1,2})' ) #works
print(df)
```
#### Problem description
Assigning the extraction of groups to a column fails if using the .loc method. The column is filled with NaN, regardless of if there is a match or not. Pre-filling the column with empty strings 'fixes' it.
This behavior is strange, and I spent a long time trying to figure out what is wrong, as it does not raise an error or anything.
### case 1 output
```
A B num
0 A [0-10) NaN
1 B [10-20) NaN
2 C [20-30) NaN
```
#### Expected case 1 Output
```
A B num
0 A [0-10) 0
1 B [10-20) 10
2 C [20-30) 20
```
#### Output of ``pd.show_versions()``
<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 142 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.1
pytest: 4.0.2
pip: 18.1
setuptools: 40.6.3
Cython: 0.29.2
numpy: 1.15.4
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.2.0
sphinx: 1.8.2
patsy: 0.5.0
dateutil: 2.7.5
pytz: 2018.7
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: None
matplotlib: 2.2.3
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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```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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- [x] closes #25580
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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- [x] closes #25575
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- [x] tests added / passed
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} | 4 | 2019-03-07T09:51:04Z | 2019-03-18T02:58:40Z | 2019-03-15T11:06:06Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
s = pd.Series([ 4, 5, 3 ,2 ], dtype="UInt32")
s.astype("int32").plot() # succeeds
s.plot() # fails
```
fails with
```
Traceback (most recent call last):
File "test_plot.py", line 7, in <module>
s.plot()
File "/nix/store/biq309lxqwpl1nfap4h3s58wcxd134xq-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/plotting/_core.py", line 2740, in __call__
**kwds)
File "/nix/store/biq309lxqwpl1nfap4h3s58wcxd134xq-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/plotting/_core.py", line 1995, in plot_series
**kwds)
File "/nix/store/biq309lxqwpl1nfap4h3s58wcxd134xq-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/plotting/_core.py", line 1798, in _plot
plot_obj.generate()
File "/nix/store/biq309lxqwpl1nfap4h3s58wcxd134xq-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/plotting/_core.py", line 251, in generate
self._make_plot()
File "/nix/store/biq309lxqwpl1nfap4h3s58wcxd134xq-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/plotting/_core.py", line 993, in _make_plot
**kwds)
File "/nix/store/biq309lxqwpl1nfap4h3s58wcxd134xq-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/plotting/_core.py", line 1008, in _plot
lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
File "/nix/store/biq309lxqwpl1nfap4h3s58wcxd134xq-python3.7-pandas-0.24.1/lib/python3.7/site-packages/pandas/plotting/_core.py", line 598, in _plot
return ax.plot(*args, **kwds)
File "/nix/store/xgfsbxa29szyi30hl48hac1gihic3z3f-python3.7-matplotlib-3.0.3/lib/python3.7/site-packages/matplotlib/__init__.py", line 1810, in inner
return func(ax, *args, **kwargs)
File "/nix/store/xgfsbxa29szyi30hl48hac1gihic3z3f-python3.7-matplotlib-3.0.3/lib/python3.7/site-packages/matplotlib/axes/_axes.py", line 1611, in plot
for line in self._get_lines(*args, **kwargs):
File "/nix/store/xgfsbxa29szyi30hl48hac1gihic3z3f-python3.7-matplotlib-3.0.3/lib/python3.7/site-packages/matplotlib/axes/_base.py", line 393, in _grab_next_args
yield from self._plot_args(this, kwargs)
File "/nix/store/xgfsbxa29szyi30hl48hac1gihic3z3f-python3.7-matplotlib-3.0.3/lib/python3.7/site-packages/matplotlib/axes/_base.py", line 378, in _plot_args
ncx, ncy = x.shape[1], y.shape[1]
IndexError: tuple index out of range
```
#### Problem description
When using the new type UInt32, plotting won't work.
#### 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: 4.20.12
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: None.None
pandas: 0+unknown
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.1
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
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: 2.6.0
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: None
lxml.etree: 4.3.1
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.2.14
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 6 | 2019-03-07T11:49:30Z | 2019-03-20T12:51:11Z | 2019-03-20T12:28:45Z | CONTRIBUTOR | null | When for binary operator returns more than one `Series` as result, instantiate `Series` for every element in the result tuple.
- [x] closes #25557
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2019-03-07T12:55:21Z | 2019-03-08T08:25:39Z | 2019-03-07T21:59:40Z | MEMBER | null | - [x] closes #5289
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ n/a] whatsnew entry
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} | 9 | 2019-03-07T13:59:34Z | 2019-03-15T11:11:46Z | 2019-03-15T11:06:06Z | CONTRIBUTOR | null | Cast `ExtensionArray`(eg. `IntegerArray`) to `np.ndarray` before send to matplotlib to plot, fixes #25587.
- [x] closes #25587
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 21 | 2019-03-07T14:59:36Z | 2019-03-12T20:39:28Z | 2019-03-12T20:25:26Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/25409
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} | 0 | 2019-03-07T16:01:45Z | 2019-06-26T12:18:55Z | 2019-06-26T12:18:55Z | CONTRIBUTOR | null | I'm having to duplicate this in https://github.com/pandas-dev/pandas-ci/pull/6. Would be good to put this in one place (hypothesis, pytest). | {
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} | 2 | 2019-03-07T16:17:18Z | 2021-07-28T06:39:51Z | 2021-07-28T06:39:51Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.DataFrame(index=[1, 2], columns = list('ab'))
# Single assignment on existing column
df.loc[1, 'a'] = 1
# Multiple assignment on existing column
df.loc[2, ['a', 'b']] = [1, 2]
# Single assignment on new column
df.loc[3, 'c'] = 3
# Multiple assignment on single new column
df.loc[[1, 2], 'l'] = [6, 7]
# Multiple assignment on multiple new column
# Surprisingly fails
try:
df.loc[3, ['d', 'e']] = [6, 7]
print(df)
except KeyError:
print("Error")
```
#### Problem description
The behaviour of loc is surprising:
- When assigning, new columns and index are created as required.
- You can assign multiple index and columns
- BUT, if you try to create a column or index while doing multiple assignment, new columns and index are NOT created as required, and you get a KeyError.
Futhermore, using the indexing operators `[]` directly will create the required columns.
#### Expected Output
```
a b c l d e
1 1 NaN NaN 6.0 NaN NaN
2 1 2 NaN 7.0 NaN NaN
3 NaN NaN 3.0 NaN 6.0 7.0
```
#### 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: 0.24.1
pytest: 4.2.0
pip: 19.0.2
setuptools: 40.8.0
Cython: 0.28.5
numpy: 1.15.2
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: 7.0.1
sphinx: 1.8.1
patsy: None
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: 4.2.5
bs4: None
html5lib: 1.0.1
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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} | 4 | 2019-03-07T18:12:01Z | 2020-01-24T01:07:44Z | 2020-01-24T01:07:44Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# the following code is obtained from the documentation
# https://pandas.pydata.org/pandas-docs/stable/development/extending.html
import pandas as pd
class SubclassedSeries(pd.Series):
@property
def _constructor(self):
return SubclassedSeries
@property
def _constructor_expanddim(self):
return SubclassedDataFrame
class SubclassedDataFrame(pd.DataFrame):
@property
def _constructor(self):
return SubclassedDataFrame
@property
def _constructor_sliced(self):
return SubclassedSeries
# create a class instance as in the example of the documentation
df = SubclassedDataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
>>> df
A B C
0 1 4 7
1 2 5 8
2 3 6 9
# this works just fine
>>> type(df)
<class '__main__.SubclassedDataFrame'>
# slicing also works fine
>>> sliced2 = df['A']
>>> sliced2
0 1
1 2
2 3
Name: A, dtype: int64
>>> type(sliced2)
<class '__main__.SubclassedSeries'>
# however, the sum operation returns a pandas.Series, not SubclassedSeries
>>> sliced3 = df.sum()
>>> sliced3
0 1
1 2
2 3
Name: A, dtype: int64
>>> type(sliced3)
<class 'pandas.core.series.Series'>
```
#### Problem description
In our project, we extend pandas as described in the documentation and implement our own kind of DataFrame and Series, similar to the geopandas project (if you apply sum on their DataFrame, the same problem appears). If you want to use _reduce operations like sum, it is important that the correct SubclassedSeries is returned. Otherwise, inheritance from pandas.DataFrames is not possible.
#### Expected Output
```python
>>> type(sliced3)
<class '__main__.SubclassedSeries'>
```
I think I can provide a possible fix of this problem: The relevant code is contained in core/frame.py just before the return statement of the _reduce function:
```python
# this is the code in core/frame.py:
def _reduce(...):
# .... left out
if constructor is not None:
result = Series(result, index=labels)
return result
# I suggest the following change:
def _reduce(...):
# .... left out
if constructor is None:
result = Series(result, index=labels)
else:
result = constructor(result, index=labels)
# alternative (since constructor will create a SubclassedDataFrame):
result = self._constructor_sliced(result, index=labels)
return result
```
#### 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: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
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: 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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} | 2 | 2019-03-07T20:42:39Z | 2019-03-08T07:22:13Z | 2019-03-08T07:22:13Z | NONE | null | Backport PR #25586: BUG: secondary y axis could not be set to log scale (#25545) | {
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"color": "ea91a4",... | closed | false | null | [] | null | 12 | 2019-03-08T05:23:48Z | 2019-09-18T20:20:27Z | 2019-09-18T20:20:27Z | MEMBER | null | Per pandas-dev chat today opening up a master tracker for TODOs to improve typing.
- Convert existing type hints to Python 3 syntax (#25790)
- Configure CI for type checking (assuming mypy?)
- Define base typing module for pandas (array_like, dt_like, etc...) (#25791)
- Various PRs to start adding types (by module? Maybe good sprint material)
Glancing the Python docs here's what I see for "new in version X.X.X" which we may want to consider:
**3.5.2**
- [NewType](https://docs.python.org/3/library/typing.html#newtype)
- [Type](https://docs.python.org/3/library/typing.html#typing.Type)
- [DefaultDict](https://docs.python.org/3/library/typing.html#typing.DefaultDict)
- [Text](https://docs.python.org/3/library/typing.html#typing.Text)
- [TYPE_CHECKING](https://docs.python.org/3/library/typing.html#typing.TYPE_CHECKING)
**3.5.3**
- [ClassVar](https://docs.python.org/3/library/typing.html#typing.ClassVar)
**3.5.4**
- [AsyncGenerator](https://docs.python.org/3/library/typing.html#typing.AsyncGenerator)
- [NoReturn](https://docs.python.org/3/library/typing.html#typing.NoReturn)
**3.6**
- [Collection](https://docs.python.org/3/library/typing.html#typing.Collection)
- [ContextManager](https://docs.python.org/3/library/typing.html#typing.ContextManager)
- [AsyncContextManager](https://docs.python.org/3/library/typing.html#typing.AsyncContextManager)
**3.6.1**
- [Deque](https://docs.python.org/3/library/typing.html#typing.Deque)
- [Counter](https://docs.python.org/3/library/typing.html#typing.Counter)
- [ChainMap](https://docs.python.org/3/library/typing.html#typing.ChainMap)
**3.7.2**
- [OrderedDict](https://docs.python.org/3/library/typing.html#typing.OrderedDict) | {
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} | 4 | 2019-03-08T09:19:08Z | 2019-03-14T09:18:26Z | 2019-03-12T17:03:16Z | CONTRIBUTOR | null | Also affects to_latex midrule position
- [x] closes #16718
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Fixes the 4th example in the above issue. | {
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```python
import pandas as pd
import numpy as np
s = pd.Series( np.arange(100), index= np.arange(100) )
pd.read_json(s.to_json()) # >> ValueError: If using all scalar values, you must pass an index
pd.read_json(s.to_json(orient="split"), orient="split") # >> ValueError: JSON data had unexpected key(s): name
```
#### Problem description
Things are failing, while expected behavior is an identity-operation (even though with some information -loss).
I saw some issues which are possibly related on the issue-board; wanted to let you know that this is already failing at the basic level with default arguments
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.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: 0.24.1
pytest: None
pip: 18.1
setuptools: 40.6.3
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: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 2 | 2019-03-08T10:59:07Z | 2019-04-28T15:30:22Z | 2019-04-28T15:30:22Z | MEMBER | null | Until now, pep8speaks used independent settings, and not the setuptools standard `setup.cfg`. This has been addressed in the latest version of pep8speaks, and we shouldn't need to repeat our configuration anymore: https://github.com/pandas-dev/pandas/blob/master/.pep8speaks.yml#L10
More info about the problem can be found here: https://github.com/OrkoHunter/pep8speaks/issues/95#issuecomment-470887715
Not sure how pep8speaks is integrated, but we should upgrade to the latest version if it's not automatic, and we should remove the duplicated configuration in `.pep8speaks.yml` (which is already present in `setup.cfg`).
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https://api.github.com/repos/pandas-dev/pandas/issues/25606 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25606/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25606/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25606/events | https://github.com/pandas-dev/pandas/issues/25606 | 418,745,879 | MDU6SXNzdWU0MTg3NDU4Nzk= | 25,606 | Scatter plot incorrect when 2 columns have the same name | {
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] | closed | false | null | [] | null | 4 | 2019-03-08T11:19:37Z | 2019-03-13T16:11:59Z | 2019-03-13T05:25:26Z | CONTRIBUTOR | null | ```python
s1 = pd.Series(range(5), name="x")
s2 = pd.Series(range(10, 15), name="x") # The following would work with "y" instead
pd.concat([s1, s2], axis=1).plot.scatter(x=0, y=1)
```
#### Problem description
The first column should be plotted against the second one.
Instead, it is as if the two columns were concatenated (into a 10 elements series), with the resulting new column being plotted against itself.
Everything works if the second series is named differently from the first one.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-642.6.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.21.1
pytest: 3.2.3
pip: 9.0.1
setuptools: 28.8.0
Cython: 0.27.3
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.4
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.2.1
numexpr: 2.6.5
feather: None
matplotlib: 2.1.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
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] | closed | false | null | [] | null | 7 | 2019-03-08T13:15:35Z | 2019-04-20T17:43:37Z | 2019-04-20T17:43:37Z | NONE | null | - [x] closes #25594
- [x] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Starting in 0.21.0, using .loc or [] with a list with one or more missing labels, is deprecated. In the doc, only getting is shown:
https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-with-list-with-missing-labels-is-deprecated
As argued in #25594, this should not be the case for setting. This PR is therefore adding the possibility of adding index and columns when setting with array-like index.
If the axis is a MultiIndex, the expected result is not trivial, and is therefore not covered in this PR. | {
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} | 3 | 2019-03-08T17:24:31Z | 2019-03-10T21:36:39Z | 2019-03-10T21:36:39Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.DataFrame().to_html(classes=True)
```
#### Problem description
```python-traceback
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\frame.py", line 2212, in to_html
formatter.to_html(classes=classes, notebook=notebook, border=border)
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\io\formats\format.py", line 729, in to_html
html = Klass(self, classes=classes, border=border).render()
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\io\formats\html.py", line 146, in render
self._write_table()
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\io\formats\html.py", line 167, in _write_table
.format(typ=type(self.classes)))
AssertionError: classes must be list or tuple, not <class 'bool'>
```
#### Expected Output
```python-traceback
TypeError: classes must be a string, list or tuple, not <class 'bool'>
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 1 | 2019-03-08T17:52:18Z | 2019-03-10T16:24:56Z | 2019-03-10T16:24:56Z | MEMBER | null | As a follow up to #25231 some exceptions are getting raised when building the docs as a result of calls to makePanel lingering around. Here's at least one section where this is occurring:
http://pandas-docs.github.io/pandas-docs-travis/getting_started/dsintro.html#deprecate-panel
Would want to remove these references and possibly the entire section surrounding to clean up doc build | {
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https://api.github.com/repos/pandas-dev/pandas/issues/25610 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25610/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25610/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25610/events | https://github.com/pandas-dev/pandas/issues/25610 | 418,967,013 | MDU6SXNzdWU0MTg5NjcwMTM= | 25,610 | DataFrameGroupBy.ffill with Duplicate Column Labels ValueError | {
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} | 3 | 2019-03-08T21:37:20Z | 2020-09-13T22:18:22Z | 2020-09-13T22:18:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df1 = pd.DataFrame({'field1': [1, 2, 3, 4],
'field2': [1, 2, 3, 4],
'field3': [1, 2, 3, 4]
})
df2 = pd.DataFrame({'field1': [1, 2, np.nan, 4],
})
same_col = pd.concat([df1, df2], axis=1)
print(same_col)
# field1 field2 field3 field1
# 0 1 1 1 1.0
# 1 2 2 2 2.0
# 2 3 3 3 NaN
# 3 4 4 4 4.0
print(same_col.ffill())
# field1 field2 field3 field1
# 0 1 1 1 1.0
# 1 2 2 2 2.0
# 2 3 3 3 2.0
# 3 4 4 4 4.0
for k, v in same_col.groupby(by=['field2']):
print(v.ffill())
# field1 field2 field3 field1
# 0 1 1 1 1.0
# field1 field2 field3 field1
# 1 2 2 2 2.0
# field1 field2 field3 field1
# 2 3 3 3 NaN
# field1 field2 field3 field1
# 3 4 4 4 4.0
same_col.groupby(by=['field2']).ffill()
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
```
#### Problem description
A _DataFrameGroupBy.ffill_ with 2 or more column with the same name produce an error:
`ValueError: Buffer has wrong number of dimensions (expected 1, got 2)`
Pandas 0.22.0 did not have this bud. It seems that it was introduced recently. Or if this is an expected behaviour, it must be consistent with the behaviour of a _DataFrame.ffill_ with 2 or more column with the same name and have a meaningful error.
#### Expected Output
```
# field1 field2 field3 field1
# 0 1 1 1 1.0
# 1 2 2 2 2.0
# 2 3 3 3 NaN
# 3 4 4 4 4.0
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.1.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: en_GB.UTF-8
pandas: 0.24.1
pytest: None
pip: 10.0.1
setuptools: 39.1.0
Cython: None
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: 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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} | 5 | 2019-03-08T23:04:07Z | 2020-03-02T23:25:16Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
>>> pd.Timestamp(np.datetime64('2019-01-01', '6h'))
Timestamp('1978-03-02 20:00:00')
```
#### Problem description
The `pd.Timestamp` constructor gives the wrong result when converting a `np.datetime64` that uses multiples of a standard unit. In the example above, I create a datetime64 with units of `6h` but the conversion appears to assume units of `s`.
This happens with units of `6h` but not with units of `h`:
```python
>>> pd.Timestamp(np.datetime64('2019-01-01', 'h'))
Timestamp('2019-01-01 00:00:00')
```
#### Expected Output
Pandas should either perform a correct conversion or raise a value error:
```python
>>> pd.Timestamp(np.datetime64('2019-01-01', '6h'))
Timestamp('2019-01-01 00:00:00')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-134-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.24.1
pytest: None
pip: 19.0.3
setuptools: 40.8.0
Cython: None
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: 0.11.3
IPython: None
sphinx: 1.8.4
patsy: None
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: None
feather: None
matplotlib: 3.0.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: 4.3.2
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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https://api.github.com/repos/pandas-dev/pandas/issues/25612 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/25612/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/25612/comments | https://api.github.com/repos/pandas-dev/pandas/issues/25612/events | https://github.com/pandas-dev/pandas/pull/25612 | 418,998,684 | MDExOlB1bGxSZXF1ZXN0MjU5NjM5NzY5 | 25,612 | DOC: Remove makePanel from docs (#25609) | {
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} | 5 | 2019-03-08T23:36:43Z | 2019-03-10T16:25:03Z | 2019-03-10T16:24:56Z | CONTRIBUTOR | null | - [x] closes #25609
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
As described in #25609, I removed the tm.makePanel() calls from the docs, along with some of the surrounding sections (a few "Deprecate Panel" sections). I did not replace these calls with an equivalent manual creation of a Panel.
If desired, I could create a panel manually in the docs with something like this:
```
cols = ['Item' + c for c in string.ascii_uppercase[:K - 1]]
data = {c: tm.makeTimeDataFrame() for c in cols}
p = Panel.fromDict(data)
```
but this PR is currently as requested in the issue with the sections removed. | {
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