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under Windows when using pip and not conda.
Closes #29112
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This PR basically deals with three issue:
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2. implement keyword aggregation for Series.agg
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} | 13 | 2019-10-20T18:49:38Z | 2019-11-25T13:54:34Z | 2019-11-25T13:54:22Z | CONTRIBUTOR | null | As described in the issues, user who wants to control how NA values are printed
while applying styles to the output will have to implement their own formatter.
(so that the underlying data will not change and can be used for styling)
Since the behavior is common in styling (for reports etc.), suggest to add this
shortcut function to enable users format their NA values as something like '--'
or 'Not Available' easily.
EDIT:
- Change implementation to integrate with the original `.format()` using `na_rep` argument
- Add a new table-wise default `na_rep` setting, which can be set through `.set_na_rep()`
- Minor fix: formatter should be wrapped outside of locs-loop
- Added a few user guide examples and test cases
example usage: `df.style.highlight_max().format(None, na_rep="-")`
- [x] closes #21527 and #28358
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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} | 9 | 2019-10-20T23:48:28Z | 2020-04-27T15:34:39Z | 2019-11-10T14:44:10Z | CONTRIBUTOR | null | The current version throws a false positive if the style string contains `s` or `o`, which are valid marker styles and not colors. For example:
```
style='s', color='C3'
```
should be legal, but currently throws an error.
My suggestion is to check for only the letters that are color codes.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 1 | 2019-10-21T03:09:44Z | 2019-10-21T03:46:42Z | 2019-10-21T03:44:54Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
df = pd.read_csv('file.csv')
```
#### Problem description
The problem I ran into is that it's telling me that read_csv is not a thing, I'm not sure what's going on here.
#### Expected Output
?
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
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} | 34 | 2019-10-21T05:23:50Z | 2020-01-16T00:35:20Z | 2019-11-20T13:00:47Z | MEMBER | null | - [X] closes #21668 (and potentially a few others)
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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"description": "IO issue... | open | false | null | [] | null | 2 | 2019-10-21T06:17:06Z | 2020-05-02T04:35:21Z | null | NONE | null | Similar to https://github.com/pandas-dev/pandas/issues/29102, excel and csv file extensions now have inconsistent errors for nonexistent files.
```
import pandas as pd
pd.read_csv('file_1.csv')
IOError: [Errno 2] File file_1.csv does not exist: 'file_1.csv'
pd.read_excel('file_2.xlsx')
IOError: [Errno 2] No such file or directory: 'file_2.xlsx'
```
Would be better to have a uniform error message across multiple file extensions, like the one with json in https://github.com/pandas-dev/pandas/pull/29104:
```
FileNotFoundError: File 'no_file.json' does not exist
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} | 1 | 2019-10-21T11:38:21Z | 2019-10-22T19:05:31Z | 2019-10-22T12:23:12Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/27463#issuecomment-513492615 | {
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} | 1 | 2019-10-21T11:39:19Z | 2019-10-22T19:05:15Z | 2019-10-22T09:01:03Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/27463#issuecomment-513492615
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} | 1 | 2019-10-21T14:31:14Z | 2019-11-19T04:23:55Z | 2019-11-19T04:23:55Z | NONE | null | Variance calculation `Series.var()` is ignoring the `ddof` argument for nullable integer type `Int64`. Behavior should match that of `float` type.
```python
>>> pd.__version__
'0.25.1'
>>> np.__version__
'1.17.2'
>>> s1 = pd.Series(data=[1,2,3,4,5,6,np.nan,np.nan])
>>> series.var(ddof=0)
3.5
>>> s1 = pd.Series(data=[1,2,3,4,5,6,np.nan,np.nan])
>>> s1.var(ddof=0)
2.9166666666666665
>>> s1.var(ddof=1)
3.5
>>> s2 = pd.Series(data=[1,2,3,4,5,6,np.nan,np.nan], dtype="Int64")
>>> s2.var(ddof=0)
3.5
>>> s2.var(ddof=1)
3.5
``` | {
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} | 2 | 2019-10-21T14:48:38Z | 2021-08-05T23:33:54Z | 2021-08-05T23:33:54Z | NONE | null | Apologies if this has been fixed in 0.25.2, it's not in the repository yet.
#### Code Sample
```python
import pandas as pd
df = pd.DataFrame({'C1':['abc','def'],'C2':[2,1]})
print(df.groupby("C1").C2.nlargest(1).reset_index())
df = pd.DataFrame({'C1':['abc','abc','def','def'],'C2':[2,3,2,1]})
print(df.groupby("C1").C2.nlargest(1).reset_index())
```
#### Problem description
Result of the first part, the C1 column is gone and there is an index column:
```
index C2
0 0 2
1 1 1
```
Result of the second part, the C1 column is there and there is a level_1 column:
```
C1 level_1 C2
0 abc 1 3
1 def 2 2
```
#### Expected Output
I'm expecting the first output to have the same format as the second, that is the groupby not dropping its groupby-column.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.79-1-MANJARO
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : de_DE.utf8
LOCALE : de_DE.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : None
hypothesis : None
sphinx : 2.2.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 4 | 2019-10-21T15:26:27Z | 2021-06-07T13:09:02Z | 2021-01-06T00:16:28Z | NONE | null | `merge_asof` arguments have to ["...be a numeric column, such as datetimelike, integer, or float"](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge_asof.html) but gives cryptic `TypeError` instead of `ValueError` on bad argument types?
```python
import pandas as pd
left = pd.Series(['2019-06-01 00:09:12', '2019-06-01 00:10:29',
'2019-06-01 00:20:30'], name='time')
right = pd.Series(['2019-06-01 00:01:12', '2019-06-01 00:01:12',
'2019-06-01 00:01:12'], name='time')
pd.merge_asof(left, right)
```
Output:
```
...
TypeError: No matching signature found
```
versus e.g.
```python
import pandas as pd
left = pd.Series(['2019-06-01 00:09:12', '2019-06-01 00:10:29',
'2019-06-01 00:20:30'], name='time')
right = pd.Series(['2019-06-01 00:01:12', '2019-06-01 00:01:12',
'2019-06-01 00:01:12'], name='time')
left = pd.to_datetime(left, infer_datetime_format=True)
right = pd.to_datetime(right, infer_datetime_format=True)
pd.merge_asof(left, right)
```
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- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
This issue needs to be addressed before #28541 can be merged
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https://api.github.com/repos/pandas-dev/pandas/issues/29132 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29132/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29132/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29132/events | https://github.com/pandas-dev/pandas/issues/29132 | 510,056,375 | MDU6SXNzdWU1MTAwNTYzNzU= | 29,132 | NDFrame.interpolate(): variable 'ax' not assigned when axis='index' | {
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} | 4 | 2019-10-21T15:34:56Z | 2019-10-25T13:06:30Z | 2019-10-25T13:06:30Z | CONTRIBUTOR | null | The `NDFrame.interpolate` function fails when passing a string as axis. Example:
```python
>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame(np.zeros((3,2)), columns=['a','b'])
>>> df.iloc[1] = np.nan
>>> df
a b
0 0.0 0.0
1 NaN NaN
2 0.0 0.0
>>> df.interpolate(axis=0)
a b
0 0.0 0.0
1 0.0 0.0
2 0.0 0.0
>>> df.interpolate(axis='index')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/lehanson/anaconda3/envs/plots/lib/python3.7/site-packages/pandas/core/generic.py", line 7006, in interpolate
ax = _maybe_transposed_self._get_axis_number(ax)
UnboundLocalError: local variable 'ax' referenced before assignment
```
From the documentation and from the function itself, it looks like `df.interpolate(axis='index')` was intended to work, but that maybe someone accidentally deleted a line in generic.py? The function seems to work properly if I add `ax = axis` in the else block here:
https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/core/generic.py#L6998-L7006
I am using pandas version 0.25.1 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29133 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29133/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29133/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29133/events | https://github.com/pandas-dev/pandas/pull/29133 | 510,080,772 | MDExOlB1bGxSZXF1ZXN0MzMwNTA2MTg5 | 29,133 | Reduce Circular Imports with pandas.core.reshape.concat | {
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] | closed | false | null | [] | null | 10 | 2019-10-21T16:03:54Z | 2020-01-16T00:33:46Z | 2019-11-07T01:39:35Z | MEMBER | null | While working on #29124 I found a lot of general uses for this, but had to keep importing in the function body to prevent circular imports. By switching the isinstance checks to using ABC classes and moving the import of Index objects those circular imports can *mostly* be done away with
The only thing left that was causing circular imports was in the `Categorical` space. Can try to figure that out later, though cc @TomAugspurger in case that's a path that has already been gone down | {
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} | 3 | 2019-10-21T16:17:58Z | 2019-12-05T19:52:48Z | 2019-12-05T19:52:27Z | CONTRIBUTOR | null | Previously, Index.equals(MultiIndex) and MultiIndex.equals(Index) both involved converting the MultiIndex into an ndarray. Since this conversion requires resolving MultiIndex's by-reference structure (i.e. its codes + levels) into ndarray's by-value structure, it can be substantially computationally expensive.
However, it is only possible for a simple Index to equal a MultiIndex in two cases:
- the MultiIndex has only `1` level
- the MultiIndex has `d` levels, and the Index is an object index of size-`d` sequences (e.g. `d`-tuples)
Thus, if the Index is not object-typed, and its nlevels differs from that of the MultiIndex, then the two are determined to be unequal without ndarray conversion.
MWE:
```python
import pandas as pd
long_cheap_index = pd.RangeIndex(1000000)
short_expensive_index = pd.IntervalIndex(
[pd.Interval(pd.Timestamp(2018, 10, 1), pd.Timestamp(2018, 10, 2))])
large_expensive_multiindex = pd.MultiIndex.from_product(
[long_cheap_index, short_expensive_index])
trivial_simple_index = pd.Int64Index([])
# These operations no longer convert large_expensive_multiindex into an ndarray
# Previously, each took ~10 s; now, ~0.02 ms
large_expensive_multiindex.equals(trivial_simple_index)
trivial_simple_index.equals(large_expensive_multiindex)
```
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 8 | 2019-10-21T16:22:32Z | 2021-09-07T10:28:37Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
df1 = pd.DataFrame([[1,np.nan],[3,4]], columns=['P','Q'], index=['A', 'B'])
df2 = pd.DataFrame([[5,6,7],[8,9,np.nan]], columns=['P','Q','Q'], index=['A', 'B'])
df1.combine_first(df2)
# or alternatively: df2.combine_first(df1)
```
#### Problem description
The above call results in `AttributeError: 'DataFrame' object has no attribute 'dtype'` which is difficult to interpret. Under the hood the set logic tries to maintain dtype but the duplicate column label results in finding a `DataFrame` instead of a `Series`. The former has no `dtype` but `dtypes`.
#### Expected Output
Performing some calculation, e.g.,
```
P Q Q
A 1 6.0 7.0
B 3 4.0 4.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.24.2
pytest: 5.0.1
pip: 19.1.1
setuptools: 41.0.1
Cython: 0.29.12
numpy: 1.16.4
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: 2.1.2
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.5.2
numexpr: 2.6.9
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.8
lxml.etree: 4.3.4
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.5
pymysql: None
psycopg2: None
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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} | 0 | 2019-10-21T18:35:29Z | 2020-01-09T21:04:27Z | 2020-01-09T21:04:27Z | MEMBER | null | Found this while trying to clean up axis handling in core.generic
This fails as you would hope
```python
>>> df = pd.DataFrame([[1]])
>>> df.rename({0: 1}, columns={0: 2}, axis=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/williamayd/clones/pandas/pandas/util/_decorators.py", line 235, in wrapper
return func(*args, **kwargs)
File "/Users/williamayd/clones/pandas/pandas/core/frame.py", line 4143, in rename
axes = validate_axis_style_args(self, args, kwargs, "mapper", "rename")
File "/Users/williamayd/clones/pandas/pandas/util/_validators.py", line 287, in validate_axis_style_args
raise TypeError(msg)
TypeError: Cannot specify both 'axis' and any of 'index' or 'columns'.
```
This doesn't
```python
>>> df.rename({0: 1}, index={0: 2})
0
1 1
```
And perhaps even more surprising is that you will get a different result depending on whether the first argument is passed by position or keyword
```python
>>> df.rename(mapper={0: 1}, index={0: 2})
0
2 1
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} | 1 | 2019-10-21T19:11:45Z | 2019-10-22T15:09:59Z | 2019-10-22T12:28:16Z | MEMBER | null | Also fix incorrectly-typed `nobs` in libgroupby | {
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"description": "to_sql, ... | open | false | null | [] | null | 4 | 2019-10-21T19:18:27Z | 2021-07-22T05:09:00Z | null | NONE | null | #### Problem description
Pandas.DataFrame.to_sql() assumes that if no table exists it should create one. In the specific case where a user wants to simply insert records, but not have to_sql() generate a table if it doesn't exist this causes problems. There is also no way to currently override the default behavior of creating a table according to the documentation.
I'm running into this problem because I have an application that has to dynamically generate database tables for storage as part of an ETL pipeline where I'm using to_sql() to insert records into the tables. If for any number of reasons table creation fails during this process and no table is created, then when to_sql() is called the default behavior of Pandas.DataFrame.to_sql() is to create a new table with whatever types the fields of the dataframe are currently in. This table created by Pandas is missing all the indexes, primary/foreign keys, etc that the dynamic table generation process creates.
A fix might be to add a new parameter, if_not_exists, that allows a user to set the behavior of to_sql() if a table is not detected. | {
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} | 2 | 2019-10-21T19:56:42Z | 2019-10-24T15:37:17Z | 2019-10-24T12:10:07Z | MEMBER | null | Made possible by modernizing `libindex.convert_scalar`
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} | 9 | 2019-10-21T21:11:20Z | 2020-02-13T20:00:23Z | 2020-01-09T21:04:27Z | MEMBER | null | - [X] closes #29136 and ref #17963
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
I think we can greatly simplify core if we update our assumptions around the axes of dimensions we deal with. I came to this change trying to get rid of `NDFrame._construct_axes_from_arguments` which appears to do some argument mutation that isn't very clear. This was one of the few methods that called that, while also calling `validate_axis_style_args` which is guilty of the same kind of mutation
By being more explicit about the signature and converting this to keyword only arguments (which a FutureWarning detailed anyway) we can simplify the code and I think make more readable
cc @TomAugspurger | {
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"description": "Functionality t... | closed | false | null | [] | null | 6 | 2019-10-21T21:40:24Z | 2019-10-23T15:50:59Z | 2019-10-23T15:50:59Z | MEMBER | null | Example that I could make with DecimalArray:
```
In [1]: from pandas.tests.extension.decimal import DecimalArray, make_data
In [2]: df = pd.DataFrame({'id': [0,0,0,1,1], 'decimals': DecimalArray(make_data()[:5])})
In [3]: df.groupby('id')['decimals'].agg(lambda x: x.iloc[0])
Out[8]:
id
0 0.831922765262135044395108707249164581298828125
1 0.40839445887803604851029604105860926210880279...
dtype: object
```
On master of a few days ago, the above returned 'decimal' dtype instead of object dtype.
Found this in the geopandas test suite, as there it creates invalid output and then an error in a follow-up operation (https://travis-ci.org/geopandas/geopandas/jobs/600859374)
This seems to be caused by https://github.com/pandas-dev/pandas/pull/29088, and specifically the change in `agg_series`: https://github.com/pandas-dev/pandas/pull/29088/files#diff-8c0985a9fca770c2028bed688dfc043fR653-R666
The `self._aggregate_series_fast` is giving a "AttributeError: 'DecimalArray' object has no attribute 'flags'" error if the series is backed by an EA, and the AttributeError is no longer catched.
cc @jbrockmendel
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} | 4 | 2019-10-22T02:35:48Z | 2019-10-25T14:08:56Z | 2019-10-25T13:06:31Z | CONTRIBUTOR | null | - [x] closes #29132
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
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"id": 712683... | closed | false | null | [] | null | 12 | 2019-10-22T04:46:55Z | 2020-01-03T13:13:18Z | 2020-01-03T13:12:59Z | CONTRIBUTOR | null | Added documentation on MultiIndex.set_levels to clarify that MultiIndex.set_levels() interprets passed values as new components of the .levels attribute. Continuation of #28797 due to issues with Travis CI for Python 3.8 and subsequent git diff issues resulting from resolution of Travis CI issues.
- [x] closes #28294 - or at least attempts to
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff` | {
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} | 8 | 2019-10-22T08:03:53Z | 2019-10-25T14:08:22Z | 2019-10-23T15:50:59Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/29141
@jbrockmendel this is adding the cases I found in https://github.com/pandas-dev/pandas/issues/29141. It does a small revert of catching an error to get them passing, but then your PR can fix it properly? | {
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... | open | false | null | [] | null | 7 | 2019-10-22T08:58:49Z | 2021-07-22T05:09:39Z | null | NONE | null | Hi,
I've been using method chaining to write most of my data wrangling processes and one of the things that bother me a bit is to modify column names as part of the chain.
With a static list of column names, ```df.set_axis``` can do the work. But in the following cases, I have to use `df.columns=xxx` to modify the names.
1. Header comes from a row
2. Combine multi-indexed header into a single-index (e.g. concatenate levels)
3. Expand single-indexed header to multi-indexed (e.g. split via some delimiter)
I wonder if these could be achieved by allowing ```df.set_axis``` to take callables, something similar to ```df.assign```. | {
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} | 2 | 2019-10-22T09:30:38Z | 2020-06-02T22:42:51Z | 2020-06-02T22:42:51Z | NONE | null | ```df.interpolate(method='pad')``` behaves weirdly regarding the axis.
```python
df = pd.DataFrame([[1,np.nan,3],[np.nan, 2,np.nan],[2,3,np.nan]])
0 1 2
0 1.0 NaN 3.0
1 NaN 2.0 NaN
2 2.0 3.0 NaN
df.fillna(method='pad', axis=0)
0 1 2
0 1.0 NaN 3.0
1 1.0 2.0 3.0
2 2.0 3.0 3.0
df.interpolate(method='pad', axis=0)
0 1 2
0 1.0 1.0 3.0
1 NaN 2.0 2.0
2 2.0 3.0 3.0
df.interpolate(method='linear', axis=0)
0 1 2
0 1.0 NaN 3.0
1 1.5 2.0 3.0
2 2.0 3.0 3.0
```
Besides, ```df.interpolate(method='pad')``` does not seem to respond to `limit_area`
```python
df.interpolate(method='pad',limit_area='inside',axis=1)
0 1 2
0 1.0 NaN 3.0
1 1.0 2.0 3.0
2 2.0 3.0 3.0
df.interpolate(method='linear',limit_area='inside', axis=0)
0 1 2
0 1.0 NaN 3.0
1 1.5 2.0 NaN
2 2.0 3.0 NaN
```
#### Output of ``pd.show_versions()``
<details>
pandas : 0.25.2
numpy : 1.16.5
pytz : 2019.3
dateutil : 2.8.0
pip : 19.3.1
setuptools : 41.4.0
Cython : None
pytest : 5.0.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: 0.8.0
bs4 : None
bottleneck : None
fastparquet : 0.3.0
gcsfs : 0.3.1
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : 0.3.5
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : 0.14.0
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 1 | 2019-10-22T11:56:31Z | 2019-10-22T12:38:39Z | 2019-10-22T12:38:17Z | CONTRIBUTOR | null | Part of #22550
* Replaced TestData usage in `pandas/tests/series/test_replace.py` with fixtures
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 3 | 2019-10-22T12:04:10Z | 2019-10-23T14:23:12Z | 2019-10-23T14:23:10Z | CONTRIBUTOR | null | Part of #22550
* Replaced TestData usage in `pandas/tests/series/test_repr.py` with fixtures
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-10-22T12:07:51Z | 2019-10-22T13:12:37Z | 2019-10-22T13:12:32Z | CONTRIBUTOR | null | Part of #22550
* Replaced TestData usage in `pandas/tests/series/test_sorting.py` with fixtures
- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-10-22T12:12:11Z | 2019-10-22T14:59:57Z | 2019-10-22T14:59:51Z | CONTRIBUTOR | null | Part of #22550
* Replaced TestData usage in `pandas/tests/series/test_timeseries.py` with fixtures
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 1 | 2019-10-22T12:12:17Z | 2019-10-22T20:42:27Z | 2019-10-22T20:42:26Z | CONTRIBUTOR | null | Found my name in https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.24.0.html#contributors. Did not contribute to that release. Contributed to 0.25.0 (that section is empty, though, see https://github.com/pandas-dev/pandas/issues/27687).
In the `doc/source/whatsnew/v0.24.0.rst` the culprit seems to be `contributors:: v0.23.4..HEAD`, let's change `HEAD` to `v0.24.0`? | {
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} | 4 | 2019-10-22T12:18:54Z | 2019-10-22T19:43:00Z | 2019-10-22T19:42:59Z | CONTRIBUTOR | null | This hopefully addresses https://github.com/pandas-dev/pandas/issues/29151, please review!
CC @TomAugspurger @jreback | {
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} | 1 | 2019-10-22T12:19:29Z | 2019-10-22T18:34:14Z | 2019-10-22T18:34:05Z | CONTRIBUTOR | null | Part of #22550
* Replaced TestData usage in `pandas/tests/series/test_api.py` with fixtures
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 3 | 2019-10-22T13:00:18Z | 2019-10-23T09:30:41Z | 2019-10-22T19:03:41Z | MEMBER | null | xref https://docs.python.org/3/library/typing.html#typing.Dict
cc @WillAyd
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- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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] | closed | false | null | [] | null | 4 | 2019-10-22T14:32:01Z | 2019-10-22T14:55:52Z | 2019-10-22T14:51:30Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
So I have 3 different df. 2 of those df are per minute sensor readings and 3rd df is per hour readings.
I'm concatenating the 2 dfs in to a new df of per minute readings and I want to work on this new df (per minute values), do regression, get slope for every hour and then concatenate this new df2 with per hour slope readings with the 3rd df with per hour readings. Any idea how I do that? I hope I've explained the problem well.
```
#### 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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] | closed | false | null | [] | null | 1 | 2019-10-22T14:37:30Z | 2019-10-22T14:51:59Z | 2019-10-22T14:51:59Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
# Your code here
```
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
**Note**: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
**Note**: Many problems can be resolved by simply upgrading `pandas` to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if `master` addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on `master` here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
So I have 3 different df. 2 of those df are per minute sensor readings and 3rd df is per hour readings.
I'm concatenating the 2 dfs in to a new df of per minute readings and I want to work on this new df (per minute values), do regression, get slope for every hour and then concatenate this new df2 with per hour slope readings with the 3rd df with per hour readings. Any idea how I do that? I hope I've explained the problem well. So basically I want to work with 60 rows. I'm new to pandas and this is my first issue on github. Please help and be gentle please. :)
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} | 5 | 2019-10-22T14:55:02Z | 2020-01-06T16:47:24Z | 2019-10-23T16:07:23Z | CONTRIBUTOR | null | - [ ] closes #29073
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29161 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29161/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29161/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29161/events | https://github.com/pandas-dev/pandas/issues/29161 | 510,725,312 | MDU6SXNzdWU1MTA3MjUzMTI= | 29,161 | loffset in pd.Grouper does not apply | {
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] | closed | false | null | [] | null | 2 | 2019-10-22T15:12:05Z | 2019-10-22T16:49:31Z | 2019-10-22T16:49:26Z | NONE | null | #### Code Sample
```python
date_index = pd.date_range("2019-08-26", "2019-09-20")
sessions = DataFrame(range(1, len(dates) + 1), index=date_index, columns=['foo'])
sessions.groupby(pd.Grouper(freq=pd.Timedelta(weeks=4), key='created_at', loffset='4W')).size()
```
#### Problem description
The above use of `loffset` does not change the grouping labels as expected. The corresponding code using `DataFrame.resample` does work. Try:
```python
sessions.resample(pd.Timedelta(weeks=4), on='created_at', loffset='4W')
```
#### Expected Output
```
created_at
2019-09-22 1048
2019-10-20 904
Freq: 28D, dtype: int64
```
#### Actual Output
```
created_at
2019-08-26 1048
2019-09-23 904
Freq: 28D, dtype: int64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.6.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.2
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 9.0.1
setuptools : 28.8.0
Cython : None
pytest : 4.6.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 1.1.1
lxml.etree : 4.3.2
html5lib : None
pymysql : 0.9.3
psycopg2 : None
jinja2 : 2.10
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.2.12
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : 1.1.1
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29162 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29162/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29162/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29162/events | https://github.com/pandas-dev/pandas/issues/29162 | 510,756,857 | MDU6SXNzdWU1MTA3NTY4NTc= | 29,162 | .map on category dtype does not respect defaultdict when encountering np.nan values | {
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] | closed | false | null | [] | null | 5 | 2019-10-22T16:03:32Z | 2020-04-23T14:32:52Z | 2020-04-23T14:32:42Z | NONE | null | Using .map() to x-walk values from a series, and ensure that only expected values are present.
This scenario works to convert np.nan values to empty strings on object dtypes, but does not replace np.nan on category dtype.
```python
import collections
import pandas as pd
import numpy as np
# "01" maps to "12", and is a valid value, while everything else should be set to ""
defdic = collections.defaultdict(lambda: "", {"01": "12"})
df = pd.DataFrame({"A": ["01", "z7", "", np.nan]})
df["A"].map(defdic)
0 12
1
2
3
Name: A, dtype: object
df["A"].astype('category').map(defdic)
0 12
1
2
3 NaN
Name: A, dtype: object
```
I would have expected the output to be the same for both object/category dtypes.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.3.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.4.1.el7.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.3
dateutil : 2.8.0
pip : 9.0.1
setuptools : 41.4.0
Cython : 0.29.13
pytest : 5.2.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.3.2
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.10
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 3 | 2019-10-22T17:58:33Z | 2019-10-23T09:28:09Z | 2019-10-22T21:27:29Z | MEMBER | null | xref https://github.com/pandas-dev/pandas/pull/29062#discussion_r337644656
cc @WillAyd @TomAugspurger | {
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"default"... | open | false | null | [] | null | 6 | 2019-10-22T17:58:43Z | 2019-10-28T15:12:54Z | null | MEMBER | null | TLDR - I think we need to cap our benchmarks at a maximum of .2 seconds. That's a long way off though, so I think should start with a cap of 1 second per benchmark
Right now we have some very long running benchmarks:
https://pandas.pydata.org/speed/pandas/#summarylist?sort=1&dir=desc
I haven't seen a definitive answer, but I think ASV leverages the builtin timeit functionality to figure out how long a given benchmark should run.
https://docs.python.org/3.7/library/timeit.html#command-line-interface
Quoting what I thinks is important:
> If -n is not given, a suitable number of loops is calculated by trying successive powers of 10 until the total time is at least 0.2 seconds.
So IIUC a particular statement is executed n times (where n is a power of 10) to the point where it reaches 0.2 seconds to run, and then is repeated `repeat` times to get a reading. asv continuous would do this 4 times (2 runs for each commit being compared). In Python 3.6 `repeat` is 3 (we currently pin ASVs to 3.6) but in future versions that gets bumped to 5.
We have a handful of benchmarks that are 20s a piece to run, so if we stick to the 3.6 timing these statements would run n=1 times repeated 3 times per benchmark session * 4 sessions per continuous run. 20s * 3 repeats * 4 sessions = 4 minutes for one benchmark alone
rolling.Apply.time_rolling is a serious offender here so I think can start with that. Would take community PRs to improve performance of any of these, though maybe should prioritize anything currently taking over 1 second
cc @qwhelan and @pv who may have additional insights
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} | 1 | 2019-10-22T19:19:03Z | 2019-10-22T23:48:42Z | 2019-10-22T23:11:58Z | MEMBER | null | Follow-up to: https://github.com/pandas-dev/pandas/pull/27233
With all of the content in-between about pre-commit hooks, the phrase `"This command"` does not make sense any more in the documentation about this `flake8` caveat. | {
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} | 1 | 2019-10-22T20:29:37Z | 2019-10-23T20:15:57Z | 2019-10-23T20:08:09Z | MEMBER | null | Closes https://github.com/pandas-dev/pandas/issues/25453
Picks up where https://github.com/pandas-dev/pandas/pull/26353 left off | {
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} | 2 | 2019-10-22T20:35:57Z | 2019-10-25T12:50:06Z | 2019-10-25T12:50:03Z | CONTRIBUTOR | null | Part of #22471
This is removing/replacing TestData usage in the following files:
* `test_convert_to.py`
* `test_nonunique_indexes.py`
* `test_query_eval.py`
* `test_replace.py`
* `test_repr_info.py`
--------
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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} | 5 | 2019-10-22T22:39:06Z | 2019-10-30T18:34:47Z | 2019-10-30T18:29:09Z | MEMBER | null | - [X] closes #28882
- [X] tests added / passed
- [X] passes `black pandas`
- [X] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [X] whatsnew entry
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} | 6 | 2019-10-22T23:59:37Z | 2019-10-29T07:00:52Z | 2019-10-26T08:29:00Z | MEMBER | null | ref #29170
This is a pretty old compat function that I think actually hides the errors we are looking for when run via load scheduling. I've converted to a mixture of `raise...from` and `raise (e).with_traceback()` depending on what was available. Not sure it matters too much either way
Let's see... | {
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https://pandas.pydata.org/pandas-docs/stable/pandas.pdf
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- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29181 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29181/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29181/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29181/events | https://github.com/pandas-dev/pandas/issues/29181 | 511,279,183 | MDU6SXNzdWU1MTEyNzkxODM= | 29,181 | ImportError: Can't determine version for bottleneck | {
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conda create -n PANDAS pandas
activate PANDAS
python
```
```python
import pandas
```
Gives me
```python
ImportError: Can't determine version for bottleneck
```
#### Output of conda list
<details>
# Name Version Build Channel
blas 1.0 mkl
ca-certificates 2019.10.16 0
certifi 2019.9.11 py37_0
icc_rt 2019.0.0 h0cc432a_1
intel-openmp 2019.4 245
mkl 2019.4 245
mkl-service 2.3.0 py37hb782905_0
mkl_fft 1.0.14 py37h14836fe_0
mkl_random 1.1.0 py37h675688f_0
numpy 1.16.5 py37h19fb1c0_0
numpy-base 1.16.5 py37hc3f5095_0
openssl 1.1.1d he774522_3
pandas 0.25.2 py37ha925a31_0
pip 19.3.1 py37_0
python 3.7.4 h5263a28_0
python-dateutil 2.8.0 py37_0
pytz 2019.3 py_0
setuptools 41.4.0 py37_0
six 1.12.0 py37_0
sqlite 3.30.0 he774522_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_0
wheel 0.33.6 py37_0
wincertstore 0.2 py37_0
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/29182 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29182/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29182/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29182/events | https://github.com/pandas-dev/pandas/pull/29182 | 511,338,199 | MDExOlB1bGxSZXF1ZXN0MzMxNTU0NDIw | 29,182 | ENH: Add StringArray.__arrow_array__ for conversion to Arrow | {
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} | 2 | 2019-10-23T13:51:10Z | 2019-10-25T13:42:12Z | 2019-10-25T13:34:10Z | MEMBER | null | Similar as https://github.com/pandas-dev/pandas/pull/28368 (which did this for IntegerArray) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29183 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29183/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29183/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29183/events | https://github.com/pandas-dev/pandas/issues/29183 | 511,349,286 | MDU6SXNzdWU1MTEzNDkyODY= | 29,183 | UTF8 string dtype | {
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```py
>>> import numpy as np
>>> x = np.array(["ac", "bd"])
>>> x.nbytes / (len(x) * 2) # bytes per char
4.0
>>> df = pd.DataFrame({'a': x})
>>> df.dtypes
a object
dtype: object
>>> df['a'].values.nbytes / (len(x) * 2) # bytes per char
4.0
```
Wouldn't it be possible to support UTF-8 encoded strings? That would decrease by 2-4 the memory requirements, for a number of languages. For instance parquet and arrow seem to be doing that [[1](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#string)], [[2](https://arrow.apache.org/docs/cpp/api/datatype.html#_CPPv4N5arrow4Type6STRINGE)].
Since each cell is a variable length string represented as an object, using UTF8 might be possible unlike for fixed length numpy strings (https://github.com/pandas-dev/pandas/issues/5261)?
(Not sure if there were other discussions about this previously; "utf8", "string", and "dtype" are pretty noisy search key words). | {
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} | 1 | 2019-10-23T14:55:14Z | 2019-10-23T19:36:23Z | 2019-10-23T19:36:23Z | CONTRIBUTOR | null | The LaTeX engine did not like the newlines. It caused an
undefined control sequence. | {
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@TomAugspurger @jorisvandenbossche | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29187 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29187/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29187/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29187/events | https://github.com/pandas-dev/pandas/issues/29187 | 511,433,622 | MDU6SXNzdWU1MTE0MzM2MjI= | 29,187 | Deprecate / Remove pandas.util.testing.assert_raises_regex | {
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"d... | closed | false | null | [] | null | 1 | 2019-10-23T16:18:04Z | 2019-10-23T16:21:25Z | 2019-10-23T16:21:24Z | MEMBER | null | As noted by @simonjayhawkins in https://github.com/pandas-dev/pandas/pull/29174#discussion_r337961652 we have some `assert_raises_regex` code laying around in pandas.util.testing that isn't used internally. Can rip out a chunk of code if we get rid of that.
I think we need to deprecate as pandas.util.testing is part of the leaky API, though removal is an option as well. | {
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} | 18 | 2019-10-23T17:23:02Z | 2019-11-27T16:09:19Z | 2019-11-27T16:09:11Z | CONTRIBUTOR | null | - [x] relates to #28926
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 5 | 2019-10-23T17:41:54Z | 2019-11-25T23:45:27Z | 2019-11-25T23:45:27Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
pd.Series([2, 5, 6, 8], [2.0, 4.0, 4.0, 5.0]).groupby(level=0)
```
#### Problem description
This should just work instead of raising.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
pandas : 0.26.0.dev0+593.g9d45934af
numpy : 1.17.0
</details>
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"color": "20... | closed | false | null | [] | null | 7 | 2019-10-23T18:31:44Z | 2020-03-16T19:31:41Z | 2020-01-18T19:38:02Z | CONTRIBUTOR | null | This is another implementation to solve the issue described in #22797.
The other PR is #28933. When the other PR keep the main code identical and resort the indexer at the end, this implementation try to resolve the problem in the main loop of the original code.
The solution also as consequences on the performance when the key order is sorted as the index, so it add a flag to only keep the order if needed.
## Benchmark
<details>
Benchmarks that have improved:
before after ratio
[e623f0fb] [b5b7be06]
<stash~5> <multiindex_loc_order_issue_22797>
- 2.50±0.01ms 2.35±0ms 0.94 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'int')
- 2.55±0.01ms 2.40±0ms 0.94 ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'int')
- 767±0.5ms 707±2ms 0.92 groupby.Transform.time_transform_lambda_max
- 2.74±0.2μs 2.49±0.2μs 0.91 index_cached_properties.IndexCache.time_inferred_type('Float64Index')
- 2.64±0.1μs 2.51±0.2μs 0.95 index_cached_properties.IndexCache.time_is_all_dates('Float64Index')
- 517±10μs 491±10μs 0.95 index_cached_properties.IndexCache.time_is_monotonic('MultiIndex')
- 671±6μs 605±6μs 0.90 index_cached_properties.IndexCache.time_is_monotonic_decreasing('MultiIndex')
- 514±10μs 489±10μs 0.95 index_cached_properties.IndexCache.time_is_monotonic_increasing('MultiIndex')
- 6.61±0.3μs 5.68±1μs 0.86 index_cached_properties.IndexCache.time_shape('TimedeltaIndex')
- 3.87±0.7μs 2.84±0.2μs 0.73 index_cached_properties.IndexCache.time_values('TimedeltaIndex')
- 2.87±0.03μs 2.72±0.01μs 0.95 indexing.GetItemSingleColumn.time_frame_getitem_single_column_label
- 1.19±0.02ms 787±20μs 0.66 indexing.MultiIndexing.time_frame_ix
- 1.11±0.02ms 722±20μs 0.65 indexing.MultiIndexing.time_series_ix
- 3.54±0.01ms 3.37±0.01ms 0.95 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string', 'unique_monotonic_inc')
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- 169±0.6ms 102±0.4ms 0.61 multiindex_object.GetLoc.time_large_get_loc_warm
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- 90.1±0.3ms 23.1±0.3ms 0.26 multiindex_object.GetLoc.time_med_get_loc_warm
- 87.1±0.2ms 22.8±0.2ms 0.26 multiindex_object.GetLoc.time_small_get_loc_warm
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- 700±0.8μs 550±0.6μs 0.79 multiindex_object.Values.time_datetime_level_values_sliced
- 1.65±0.03ms 1.21±0.02ms 0.73 reindex.LevelAlign.time_align_level
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- 91.7±0.1ms 86.8±0.1ms 0.95 reshape.Crosstab.time_crosstab_normalize_margins
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- 6.02±0.02ms 5.47±0.02ms 0.91 reshape.SimpleReshape.time_stack
- 2.46±0.02ms 2.20±0.02ms 0.89 reshape.SimpleReshape.time_unstack
- 2.30±0ms 1.73±0.01ms 0.75 reshape.SparseIndex.time_unstack
- 18.9±0.1ms 18.0±0.03ms 0.95 rolling.Pairwise.time_pairwise(10, 'cov', True)
- 2.60±0ms 2.33±0ms 0.90 sparse.FromCoo.time_sparse_series_from_coo
- 41.3±0.2ms 39.2±0.2ms 0.95 sparse.ToCoo.time_sparse_series_to_coo
- 3.98±0s 3.75±0.01s 0.94 stat_ops.Correlation.time_corr_wide_nans('spearman', False)
- 3.99±0.01s 3.74±0.01s 0.94 stat_ops.Correlation.time_corr_wide_nans('spearman', True)
- 40.9±0.07ms 36.8±0.1ms 0.90 stat_ops.FrameMultiIndexOps.time_op(0, 'kurt')
- 195±0.3ms 165±0.3ms 0.85 stat_ops.FrameMultiIndexOps.time_op(1, 'kurt')
- 87.7±1ms 78.1±1ms 0.89 stat_ops.FrameMultiIndexOps.time_op(1, 'skew')
- 710±1ms 628±3ms 0.88 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'skew')
- 10.7±0.1ms 9.66±0.3ms 0.90 stat_ops.SeriesMultiIndexOps.time_op(0, 'kurt')
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- 4.55±0.1ms 4.01±0.08ms 0.88 stat_ops.SeriesMultiIndexOps.time_op(0, 'std')
- 4.15±0.06ms 3.86±0.1ms 0.93 stat_ops.SeriesMultiIndexOps.time_op(0, 'var')
- 49.4±0.4ms 43.3±0.2ms 0.88 stat_ops.SeriesMultiIndexOps.time_op(1, 'kurt')
- 128±0.3ms 112±0.3ms 0.88 stat_ops.SeriesMultiIndexOps.time_op(1, 'mad')
- 48.0±0.3ms 40.3±0.4ms 0.84 stat_ops.SeriesMultiIndexOps.time_op(1, 'skew')
- 4.55±0.08ms 3.97±0.1ms 0.87 stat_ops.SeriesMultiIndexOps.time_op(1, 'std')
- 4.14±0.1ms 3.81±0.09ms 0.92 stat_ops.SeriesMultiIndexOps.time_op(1, 'var')
- 434±0.5ms 357±0.4ms 0.82 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'kurt')
- 1.20±0s 1.04±0s 0.86 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'mad')
- 422±0.7ms 339±1ms 0.80 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'skew')
- 48.6±0.2ms 40.5±0.07ms 0.83 timeseries.DatetimeIndex.time_to_pydatetime('repeated')
- 288±0.9ms 271±0.8ms 0.94 timeseries.DatetimeIndex.time_to_pydatetime('tz_aware')
- 48.3±0.1ms 40.6±0.1ms 0.84 timeseries.DatetimeIndex.time_to_pydatetime('tz_naive')
Benchmarks that have got worse:
before after ratio
[e623f0fb] [b5b7be06]
<stash~5> <multiindex_loc_order_issue_22797>
+ 149±0.5ms 170±0.6ms 1.15 categoricals.Rank.time_rank_string
+ 7.23±0.2ms 8.38±0.3ms 1.16 categoricals.Rank.time_rank_string_cat
+ 9.81±0.1ms 10.4±0.1ms 1.06 categoricals.Rank.time_rank_string_cat_ordered
+ 2.45±0.01ms 2.65±0.09ms 1.08 frame_methods.Iteration.time_items_cached
+ 227±0.3ms 239±4ms 1.06 groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'transformation')
+ 148±0.3ms 157±0.3ms 1.06 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct')
+ 148±0.5ms 157±0.8ms 1.06 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'transformation')
+ 144±0.5ms 153±3ms 1.06 groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'direct')
+ 143±0.4ms 155±5ms 1.08 groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'transformation')
+ 489±3μs 541±4μs 1.11 groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'direct')
+ 488±2μs 546±3μs 1.12 groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'transformation')
+ 481±2μs 547±20μs 1.14 groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'direct')
+ 480±2μs 536±6μs 1.12 groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'transformation')
+ 173±0.2ms 183±0.5ms 1.06 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'direct')
+ 173±0.5ms 183±0.4ms 1.06 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'transformation')
+ 334±0.3ms 358±3ms 1.07 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct')
+ 334±0.3ms 356±2ms 1.07 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'transformation')
+ 330±0.4ms 352±2ms 1.07 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct')
+ 331±0.7ms 353±0.6ms 1.07 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'transformation')
+ 212±0.3ms 224±0.8ms 1.06 groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'direct')
+ 211±0.2ms 224±0.7ms 1.06 groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'transformation')
+ 766±3μs 1.67±0ms 2.19 groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'direct')
+ 766±0.7μs 1.68±0ms 2.19 groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'transformation')
+ 4.96±0.05μs 5.42±0.1μs 1.09 index_cached_properties.IndexCache.time_engine('DatetimeIndex')
+ 4.25±0.05μs 4.46±0.05μs 1.05 index_cached_properties.IndexCache.time_engine('PeriodIndex')
+ 2.92±0.1μs 3.13±0.2μs 1.07 index_cached_properties.IndexCache.time_inferred_type('MultiIndex')
+ 988±30ns 1.07±0.05μs 1.08 index_cached_properties.IndexCache.time_inferred_type('RangeIndex')
+ 2.38±0.1μs 2.74±0.5μs 1.15 index_cached_properties.IndexCache.time_inferred_type('TimedeltaIndex')
+ 2.70±0.2μs 3.05±0.5μs 1.13 index_cached_properties.IndexCache.time_is_all_dates('UInt64Index')
+ 1.41±0.02μs 1.51±0.04μs 1.07 index_cached_properties.IndexCache.time_is_monotonic_increasing('Int64Index')
+ 941±20ns 1.01±0.02μs 1.07 index_cached_properties.IndexCache.time_is_unique('RangeIndex')
+ 3.70±0.1μs 3.93±0.1μs 1.06 index_cached_properties.IndexCache.time_shape('UInt64Index')
+ 2.88±0.2μs 3.05±0.2μs 1.06 index_cached_properties.IndexCache.time_values('UInt64Index')
+ 5.43±0.08μs 6.39±0.02μs 1.18 index_object.Indexing.time_get_loc('Int')
+ 5.44±0.06μs 6.45±0.2μs 1.19 index_object.Indexing.time_get_loc_sorted('Int')
+ 2.76±0.06ms 3.01±0ms 1.09 indexing.IntervalIndexing.time_getitem_scalar
+ 3.09±0.06ms 3.34±0.02ms 1.08 indexing.IntervalIndexing.time_loc_list
+ 2.78±0.05ms 3.03±0ms 1.09 indexing.IntervalIndexing.time_loc_scalar
+ 5.83±0.02ms 16.6±0.03ms 2.85 indexing.MultiIndexing.time_index_slice
+ 2.45±0.02ms 2.59±0.02ms 1.06 inference.NumericInferOps.time_divide(<class 'numpy.uint8'>)
+ 9.78±0.08ms 10.4±0.07ms 1.06 io.csv.ParseDateComparison.time_read_csv_dayfirst(False)
+ 789±2ms 849±4ms 1.08 io.json.ReadJSON.time_read_json('index', 'datetime')
+ 840±0.6ms 923±3ms 1.10 io.json.ReadJSON.time_read_json('index', 'int')
+ 49.2±0.05ms 52.1±0.3ms 1.06 io.parsers.DoesStringLookLikeDatetime.time_check_datetimes('0.0')
+ 68.1±0.4ms 72.3±0.5ms 1.06 io.parsers.DoesStringLookLikeDatetime.time_check_datetimes('2Q2005')
+ 1.64±0μs 1.86±0.1μs 1.14 period.PeriodProperties.time_property('M', 'daysinmonth')
+ 1.68±0.01μs 1.80±0.03μs 1.07 period.PeriodProperties.time_property('M', 'is_leap_year')
+ 1.68±0.02μs 1.77±0.02μs 1.05 period.PeriodProperties.time_property('min', 'is_leap_year')
+ 739±0.7ms 1.41±0s 1.91 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'kurt')
+ 764±10ns 810±2ns 1.06 timestamp.TimestampProperties.time_freqstr(<DstTzInfo 'Europe/Amsterdam' LMT+0:20:00 STD>, None)
+ 763±10ns 812±0.9ns 1.07 timestamp.TimestampProperties.time_freqstr(<UTC>, None)
+ 763±10ns 812±1ns 1.06 timestamp.TimestampProperties.time_freqstr(None, None)
+ 762±10ns 811±2ns 1.06 timestamp.TimestampProperties.time_freqstr(tzutc(), None)
</details>
## Checklist
- [x] closes #22797
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry | {
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] | closed | false | null | [] | null | 4 | 2019-10-23T19:15:05Z | 2019-11-08T00:34:22Z | 2019-11-08T00:34:22Z | CONTRIBUTOR | null | - [ ] closes #26494
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Hey guys, I started working on a way to make testing.assert_frame_equal accept an argument so it can test equals a frame with duplicate index just like the issue I marked to close here. For this I had to make a couple of changes in some functions and I think I got to the point that I should ask for a better direction and if this is valid for the project.
The error pointed in the issue is definitely not the best message for this case. I got to the point to not get that error by making clear there is a duplicate index. But I got stuck in this error now:
`ValueError: Shape of passed values is (5, 1), indices imply (3, 1)`
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} | 6 | 2019-10-23T19:24:21Z | 2019-10-29T17:11:05Z | 2019-10-29T17:10:50Z | MEMBER | null | appears to be dead groupby code | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29193 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29193/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29193/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29193/events | https://github.com/pandas-dev/pandas/pull/29193 | 511,542,682 | MDExOlB1bGxSZXF1ZXN0MzMxNzIxMDU2 | 29,193 | Test For MultiIndex not contained item | {
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"d... | closed | false | null | [] | null | 5 | 2019-10-23T20:05:14Z | 2020-01-01T18:34:42Z | 2020-01-01T18:34:41Z | CONTRIBUTOR | null | - [x] closes #21094
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
I just made the test that was requested in this issue but in fact it isn't actually passing like mentioned there. Maybe my master is not updated for this. Should tests be added to whatsnew entry too?
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https://api.github.com/repos/pandas-dev/pandas/issues/29194 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29194/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29194/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29194/events | https://github.com/pandas-dev/pandas/pull/29194 | 511,562,728 | MDExOlB1bGxSZXF1ZXN0MzMxNzM4MDA1 | 29,194 | CLN: dont catch Exception in _aggregate_frame | {
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} | 1 | 2019-10-23T23:31:58Z | 2020-01-16T00:33:42Z | 2019-10-24T00:14:57Z | MEMBER | null | This is a quick follow up to https://github.com/pandas-dev/pandas/pull/26024/files#r317413254
The last remaining argument still has a slew of issues with it (provided below for reference) so leaving that for now
```sh
pandas/io/common.py:439: error: Incompatible types in assignment (expression has type "GzipFile", variable has type "Union[str, Path, IO[Any]]")
pandas/io/common.py:439: error: Argument "fileobj" to "GzipFile" has incompatible type "Union[str, Path, IO[Any]]"; expected "Optional[IO[bytes]]"
pandas/io/common.py:478: error: Argument 1 to "append" of "list" has incompatible type "Union[str, Path, IO[Any]]"; expected "IO[Any]"
pandas/io/common.py:483: error: Argument 1 to "open" has incompatible type "Union[str, Path, IO[Any]]"; expected "Union[str, bytes, int, _PathLike[Any]]"
pandas/io/common.py:486: error: Argument 1 to "open" has incompatible type "Union[str, Path, IO[Any]]"; expected "Union[str, bytes, int, _PathLike[Any]]"
pandas/io/common.py:489: error: Argument 1 to "open" has incompatible type "Union[str, Path, IO[Any]]"; expected "Union[str, bytes, int, _PathLike[Any]]"
pandas/io/common.py:496: error: Argument 1 to "TextIOWrapper" has incompatible type "Union[str, Path, IO[Any]]"; expected "IO[bytes]"
pandas/io/common.py:503: error: Argument 1 to "MMapWrapper" has incompatible type "Union[str, Path, IO[Any]]"; expected "IO[Any]"
pandas/io/common.py:504: error: Item "str" of "Union[str, Path, IO[Any]]" has no attribute "close"
pandas/io/common.py:504: error: Item "Path" of "Union[str, Path, IO[Any]]" has no attribute "close"
pandas/io/common.py:505: error: Incompatible types in assignment (expression has type "MMapWrapper", variable has type "Union[str, Path, IO[Any]]")
```
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} | 1 | 2019-10-24T00:02:05Z | 2020-08-06T15:35:16Z | 2020-08-06T15:12:34Z | MEMBER | null | mypy 0.73 came with support for error codes:
http://mypy-lang.blogspot.com/2019/09/mypy-730-released.html
I think we should add these to CI and update docs to require these going forward for any `type: ignore` statements
@simonjayhawkins | {
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https://dev.azure.com/pandas-dev/pandas/_build/results?buildId=19314&view=logs&jobId=5ddd427e-1f95-5998-4195-7ffbeb9b114d&taskId=2d75e2dd-4c84-5053-e835-c449aeb02662&lineStart=256&lineEnd=257&colStart=1&colEnd=1
Interestingly enough the Linux py36_locale_slow_old_np seems to be OK | {
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https://api.github.com/repos/pandas-dev/pandas/issues/29200 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29200/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29200/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29200/events | https://github.com/pandas-dev/pandas/pull/29200 | 511,807,479 | MDExOlB1bGxSZXF1ZXN0MzMxOTM0OTQw | 29,200 | BUG: Changed the way duplicated() handles empty dataframe | {
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} | [] | closed | false | null | [] | null | 0 | 2019-10-24T09:02:35Z | 2019-10-24T11:00:08Z | 2019-10-24T11:00:05Z | NONE | null | - [ ] closes #12869
- [ ] tests passed
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https://api.github.com/repos/pandas-dev/pandas/issues/29201 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29201/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29201/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29201/events | https://github.com/pandas-dev/pandas/pull/29201 | 511,874,926 | MDExOlB1bGxSZXF1ZXN0MzMxOTkwNTc5 | 29,201 | Fix missing argument name in format function call | {
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} | [] | closed | false | null | [] | null | 1 | 2019-10-24T11:11:12Z | 2019-10-24T16:53:31Z | 2019-10-24T16:53:30Z | NONE | null | Adds the keyword argument `name` to a `str.format` function call that was missing it (yielding a breaking error).
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https://api.github.com/repos/pandas-dev/pandas/issues/29202 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29202/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29202/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29202/events | https://github.com/pandas-dev/pandas/pull/29202 | 511,879,676 | MDExOlB1bGxSZXF1ZXN0MzMxOTk0NjA4 | 29,202 | Change unused loop variable to _ | {
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} | [] | closed | false | null | [] | null | 2 | 2019-10-24T11:20:51Z | 2019-10-24T16:45:12Z | 2019-10-24T16:45:12Z | NONE | null | The `_version.py` file had an unused loop variable (`i`), this PR changes it `_`. | {
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} | 10 | 2019-10-24T12:50:52Z | 2020-10-12T15:35:01Z | 2020-10-12T15:35:01Z | NONE | null | ### `axis=0` or `axis=1`, which is it?
I've always found it hard to remember which axis (`0/"index"` vs. `1/"columns"`) does what for various operations. I suppose some people find it intuitive, while others (like me) find it confusing and inconsistent.
Case in point, `DataFrame.sum` vs. `DataFrame.drop`: if I want **column sums**, I need `axis=0`...
```python
>>> import pandas as pd
>>> df = pd.DataFrame(dict(a=[1, 2, 3], b=[4, 5, 6]))
>>> df
a b
0 1 4
1 2 5
2 3 6
>>> df.sum(axis=0)
a 6
b 15
dtype: int64
```
... but if I want to **drop a column**, I need `axis=1`:
```python
>>> df.drop("a", axis=1)
b
0 4
1 5
2 6
```
There's an analogous discrepancy in numpy (which is probably where pandas inherited it from?):
```python
>>> import numpy as np
>>> a = df.to_numpy()
>>> a
array([[1, 4],
[2, 5],
[3, 6]])
>>> np.sum(a, axis=0) # sum columns
array([ 6, 15])
>>> np.delete(a, 0, axis=1) # delete first column
array([[4],
[5],
[6]])
```
I just intuitively conceptualize these operations as working along the same axis, so it's hard for me to internalize that the value of the axis parameter is different in each case. Apparently, [I'm not the only person to find this confusing](https://www.sharpsightlabs.com/blog/numpy-axes-explained/) (quoting from the article: "For example, in the np.sum() function, the axis parameter behaves in a way that many people think is counter intuitive").
At the same time, I can imagine that some people find this behavior completely natural (at the very least those who designed the API). And I understand that changing this in pandas while keeping the status quo in numpy would introduce a (probably) worse inconsistency, so I'm not suggesting that.
### Suggestion for improvement
What I am suggesting is **reviewing the documentation of functions/methods using the `axis=` keyword argument** and (where applicable) **improving the description of what it controls in each case**. Pandas is typically used interactively, so documentation is easily accessible. If it contains useful hints on what each `axis` value does (and possibly why), it's not such a big problem if this behavior goes against some people's expectations.
### Examples
For example, based on the [current master docs](https://pandas-docs.github.io/pandas-docs-travis/), the [description of the `axis` parameter for `drop`](https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.DataFrame.drop.html#pandas.DataFrame.drop) does a good job at this:
> axis : {0 or ‘index’, 1 or ‘columns’}, default 0
> Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).
This makes it reasonably clear to me that if I specify `0`, I'll be removing rows, whereas `1` will result in removing columns.
By contrast, the [description of the `axis` parameter for `sum`](https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.DataFrame.sum.html#pandas.DataFrame.sum) is somewhat too generic:
> axis : {index (0), columns (1)}
> Axis for the function to be applied on.
Based on this, I could conclude (and have repeatedly done so) that if I want column sums, I need to "apply the function on columns", hence `axis=1` (which is wrong, cf. above).
A revised description could look something like the following:
> axis : {index (0), columns (1)}
> Whether to collapse the index (0 or ‘index’), resulting in column sums, or the columns (1 or ‘columns’), resulting in row sums. | {
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] | closed | false | null | [] | null | 0 | 2019-10-24T13:48:00Z | 2019-11-10T15:22:52Z | 2019-11-10T15:22:52Z | CONTRIBUTOR | null | [pandas/core/indexes/period.py](https://github.com/pandas-dev/pandas/blob/04893a954b91574279c402e8730a4b5fae2ae9e1/pandas/core/indexes/period.py)
#28602
#27977
```
./scripts/validate_docstrings.py --errors=PR09
```
```
pandas.PeriodIndex: Parameter "data" description should finish with "."
pandas.PeriodIndex: Parameter "copy" description should finish with "."
pandas.PeriodIndex: Parameter "freq" description should finish with "."
pandas.PeriodIndex: Parameter "periods" description should finish with "."
pandas.PeriodIndex: Parameter "end" description should finish with "."
pandas.PeriodIndex: Parameter "tz" description should finish with "."
pandas.PeriodIndex.asfreq: Parameter "freq" description should finish with "."
pandas.PeriodIndex.to_timestamp: Parameter "freq" description should finish with "."
```
```
./scripts/validate_docstrings.py pandas.PeriodIndex
```
```
Summary should fit in a single line
Parameters {**fields, name, ordinal} not documented
Unknown parameters {month, quarter, second, day, year, minute, hour}
Parameter "data" description should finish with "."
Parameter "copy" description should finish with "."
Parameter "freq" description should finish with "."
Parameter "periods" description should finish with "."
Parameter "end" description should finish with "."
Parameter "year" has no description
Parameter "month" has no description
Parameter "quarter" has no description
Parameter "day" has no description
Parameter "hour" has no description
Parameter "minute" has no description
Parameter "second" has no description
Parameter "tz" description should finish with "."
Parameter "dtype" has no description
Examples do not pass tests
flake8 error: F821 undefined name 'year_arr' (2 times)
```
and a failed example on line 89
```
./scripts/validate_docstrings.py pandas.PeriodIndex.asfreq
```
```
Parameters {*args, **kwargs} not documented
Unknown parameters {freq, how}
Parameter "freq" description should start with a capital letter
Parameter "freq" description should finish with "."
Parameter "how" description should start with a capital letter
The first line of the Returns section should contain only the type, unless multiple values are being returned
Return value has no description
```
```
./scripts/validate_docstrings.py pandas.PeriodIndex.to_timestamp
```
```
Parameters {**kwargs, *args} not documented
Unknown parameters {freq, how}
Parameter "freq" description should finish with "."
Parameter "how" has no description
Return value has no description
``` | {
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} | 16 | 2019-10-24T14:00:37Z | 2019-11-03T14:28:34Z | 2019-11-03T14:28:33Z | CONTRIBUTOR | null | Working around a strange typing issue. See
https://github.com/pandas-dev/pandas/pull/28394#issuecomment-5456527290
for more, but the types on these were being inferred incorrectly by
mypy with just the addition of the `allows_duplicate_labels` kwarg.
cc @simonjayhawkins & @WillAyd. Hopefully I got things correct. I copied the use of TypeVar for FrameOrSeries with IndexOrSeries.
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https://api.github.com/repos/pandas-dev/pandas/issues/29206 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29206/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29206/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29206/events | https://github.com/pandas-dev/pandas/pull/29206 | 512,058,193 | MDExOlB1bGxSZXF1ZXN0MzMyMTQxNTIw | 29,206 | Turn on wget output | {
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} | 1 | 2019-10-24T17:01:55Z | 2019-10-24T18:38:04Z | 2019-10-24T18:38:04Z | MEMBER | null | Since we use black some of the whitespace codes for flake8 might not be applicable anymore. E241 in particular, which is for multiple spaces after a comma I don't think should appear anymore
A quick grep shows the following - if anyone would like to clean these up by deleting the noqa should make for an easy contributing I think
```sh
$ grep "# noqa: E241" -r pandas --include="*.py"
pandas/tests/dtypes/test_inference.py: ([1], True, "list"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ([], True, "list-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ((1,), True, "tuple"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (tuple(), True, "tuple-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ({"a": 1}, True, "dict"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (dict(), True, "dict-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ({"a", 1}, "set", "set"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (set(), "set", "set-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (frozenset({"a", 1}), "set", "frozenset"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (frozenset(), "set", "frozenset-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (iter([1, 2]), True, "iterator"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (iter([]), True, "iterator-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ((x for x in [1, 2]), True, "generator"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ((_ for _ in []), True, "generator-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (Series([1]), True, "Series"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (Series([]), True, "Series-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (Series(["a"]).str, True, "StringMethods"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (Series([], dtype="O").str, True, "StringMethods-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (Index([1]), True, "Index"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (Index([]), True, "Index-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (DataFrame([[1]]), True, "DataFrame"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (DataFrame(), True, "DataFrame-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.ndarray((2,) * 1), True, "ndarray-1d"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.array([]), True, "ndarray-1d-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.ndarray((2,) * 2), True, "ndarray-2d"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.array([[]]), True, "ndarray-2d-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.ndarray((2,) * 3), True, "ndarray-3d"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.array([[[]]]), True, "ndarray-3d-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.ndarray((2,) * 4), True, "ndarray-4d"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.array([[[[]]]]), True, "ndarray-4d-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.array(2), False, "ndarray-0d"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (1, False, "int"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (b"123", False, "bytes"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (b"", False, "bytes-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ("123", False, "string"), # noqa: E241
pandas/tests/dtypes/test_inference.py: ("", False, "string-empty"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (str, False, "string-type"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (object(), False, "object"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (np.nan, False, "NaN"), # noqa: E241
pandas/tests/dtypes/test_inference.py: (None, False, "None"), # noqa: E241
pandas/tests/test_strings.py: ("contains", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("count", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("decode", ("UTF-8",), {}), # noqa: E241
pandas/tests/test_strings.py: ("encode", ("UTF-8",), {}), # noqa: E241
pandas/tests/test_strings.py: ("endswith", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("extract", ("([a-z]*)",), {"expand": False}), # noqa: E241
pandas/tests/test_strings.py: ("extract", ("([a-z]*)",), {"expand": True}), # noqa: E241
pandas/tests/test_strings.py: ("extractall", ("([a-z]*)",), {}), # noqa: E241
pandas/tests/test_strings.py: ("find", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("findall", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("get", (0,), {}), # noqa: E241
pandas/tests/test_strings.py: ("index", ("",), {}), # noqa: E241
pandas/tests/test_strings.py: ("join", (",",), {}), # noqa: E241
pandas/tests/test_strings.py: ("ljust", (10,), {}), # noqa: E241
pandas/tests/test_strings.py: ("match", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("normalize", ("NFC",), {}), # noqa: E241
pandas/tests/test_strings.py: ("pad", (10,), {}), # noqa: E241
pandas/tests/test_strings.py: ("partition", (" ",), {"expand": False}), # noqa: E241
pandas/tests/test_strings.py: ("partition", (" ",), {"expand": True}), # noqa: E241
pandas/tests/test_strings.py: ("repeat", (3,), {}), # noqa: E241
pandas/tests/test_strings.py: ("replace", ("a", "z"), {}), # noqa: E241
pandas/tests/test_strings.py: ("rfind", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("rindex", ("",), {}), # noqa: E241
pandas/tests/test_strings.py: ("rjust", (10,), {}), # noqa: E241
pandas/tests/test_strings.py: ("rpartition", (" ",), {"expand": False}), # noqa: E241
pandas/tests/test_strings.py: ("rpartition", (" ",), {"expand": True}), # noqa: E241
pandas/tests/test_strings.py: ("slice", (0, 1), {}), # noqa: E241
pandas/tests/test_strings.py: ("slice_replace", (0, 1, "z"), {}), # noqa: E241
pandas/tests/test_strings.py: ("split", (" ",), {"expand": False}), # noqa: E241
pandas/tests/test_strings.py: ("split", (" ",), {"expand": True}), # noqa: E241
pandas/tests/test_strings.py: ("startswith", ("a",), {}), # noqa: E241
pandas/tests/test_strings.py: ("translate", ({97: 100},), {}), # noqa: E241
pandas/tests/test_strings.py: ("wrap", (2,), {}), # noqa: E241
pandas/tests/test_strings.py: ("zfill", (10,), {}), # noqa: E241
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] | closed | false | null | [] | null | 1 | 2019-10-24T17:34:19Z | 2020-09-05T19:04:03Z | 2020-09-05T19:04:03Z | MEMBER | null | This is intended to describe a) possible refactors that would simplify the intra-pandas dependency structure, b) the benefits we would see from implementing these refactors, and c) the difficulties each would entail.
<b>Straightforward Circular Dependencies</b>
- [ ] imports from `pd.core.util.hashing` in `pd.util.__init__`
Fix: Just remove that import, difficulty depends on if that needs a deprecation cycle.
- [ ] runtime import of `tseries.frequencies.to_offset` into `tslibs`
Fix: Move most of tseries.offsets and about half of tseries.frequencies into tslibs
- `tseries.offsets.generate_range` is the only part of `tseries.offsets` that is not needed; it can go to `arrays._ranges`, which is the only place where it is used
- `tslibs.offsets` would become a very big module unless it were split into smaller pieces
- `cache_readonly` is needed by some classes in `tseries.offsets` (which would be moved to `tslibs.offsets`), would need to put somewhere "upstream" of tslibs.
- [x] `_libs.internals` runtime import of `Int64Index`
- For `BlockPlacement.isin`, which is only used in one place in `core.internals`, the `isin` method could be refactored out of the class.
- This one is really only worthwhile if we expect to implement tests/benchmarks specific to `_libs.internals`
- [x] runtime import of `pd.unique` in `IntervalTree.get_loc_interval`
- I _think_ its the case that the argument passed to pd.unique is always int64 (@jschendel is this accurate?). If so, implementing a `unique_int64` in either `_libs.hashtables` or `_libs.algos` takes about 3 lines, could be a nice fastpath for a very common case.
<b>core.dtypes</b>
- `core.dtypes.common` is used all over the place. It would be really nice if its dependencies were simple. But:
- it imports `CategoricalDtype`, which has runtime imports from `pandas.core.util.hashing`, in particular functions that have runtime imports of `Categorical`, `Index`, `MultiIndex`, and `factorize`
- `algorithms.factorize` has runtime imports from `pandas.core.sorting`, which has a dependency on `algorithms` and runtime import of `Index`.
- I _think_ the "heavy" dependencies on `Categorical`/`Index`/`MultiIndex` are mostly just for the constructors and could be refactored into much lighter-weight implementations. I'm imagining something like `core.factorization` that depended only on `_libs` and maybe a few of the `is_foo_dtype` functions (the latter would still be circular, but a much smaller circle)
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} | 3 | 2019-10-24T17:40:27Z | 2019-10-25T04:54:30Z | 2019-10-24T18:38:04Z | CONTRIBUTOR | null | - [X] closes #29207
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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- [x] tests added / passed
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- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/29213 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/29213/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/29213/comments | https://api.github.com/repos/pandas-dev/pandas/issues/29213/events | https://github.com/pandas-dev/pandas/issues/29213 | 512,192,391 | MDU6SXNzdWU1MTIxOTIzOTE= | 29,213 | BUG: pd.Dataframe.from_dict when first value is a list | {
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"description": "Series/DataFrame/Index/p... | open | false | null | [] | null | 3 | 2019-10-24T21:32:56Z | 2020-09-22T01:33:43Z | null | NONE | null | #### Reproducible example:
```python
import pandas as pd
tmp_scen = {'name': 'Current conditions',
'actionSet': ['not_irrigated', 'cover_crop_none', 'mar_none'],
'cost': 0.0,
'totalNitrogen': 9.649798364504209,
'totalPhosphorus': 2.4570326616674745,
'sediment': 1.2039077716577735,
'appliedWater': 11.607623110151138,
'infiltrationVolume': 0.7424774065057028,
'acres': 3.382359912104316,
'irrType': 'not_irrigated'}
good_result = pd.DataFrame.from_dict(tmp_scen, orient = 'index')
tmp_scen = {'actionSet': ['not_irrigated', 'cover_crop_none', 'mar_none'],
'name': 'Current conditions',
'cost': 0.0,
'totalNitrogen': 9.649798364504209,
'totalPhosphorus': 2.4570326616674745,
'sediment': 1.2039077716577735,
'appliedWater': 11.607623110151138,
'infiltrationVolume': 0.7424774065057028,
'acres': 3.382359912104316,
'irrType': 'not_irrigated'}
error_result = pd.DataFrame.from_dict(tmp_scen, orient = 'index')
```
#### Problem description
Error when creating a dataframe from a dictionary when the first item in the dictionary is a list. See reproducible example above.
```
TypeError: object of type 'float' has no len()
```
#### Expected Output
```good_result
Out[369]:
0
actionSet [not_irrigated, cover_crop_none, mar_none]
name Current conditions
cost 0
totalNitrogen 9.6498
totalPhosphorus 2.45703
sediment 1.20391
appliedWater 11.6076
infiltrationVolume 0.742477
acres 3.38236
irrType not_irrigated
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0.24.2
pytest: 4.4.1
pip: 19.0.3
setuptools: 40.8.0
Cython: 0.29.6
numpy: 1.16.2
scipy: 1.2.1
pyarrow: None
xarray: None
IPython: 7.4.0
sphinx: 1.8.5
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2018.9
blosc: None
bottleneck: 1.2.1
tables: 3.5.1
numexpr: 2.6.9
feather: None
matplotlib: 3.0.3
openpyxl: 2.6.1
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.5
lxml.etree: 4.3.2
bs4: 4.7.1
html5lib: 1.0.1
sqlalchemy: 1.3.1
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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