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} | 8 | 2019-09-06T11:08:45Z | 2021-07-11T17:39:08Z | null | NONE | null | #### Summary
Thanks for making Pandas I have used it in a lot of projects! But now I have a problem.
I have spent nearly 3 days trying to figure out how to **resample / upsample a Pandas MultiIndex** elegantly and correctly. I have read and tried numerous posts on StackOverflow and GitHub. My conclusion is that **I don't think this is supported very well in Pandas**. Let me explain what I want to do.
#### Background
I am currently building a Python API in collaboration with www.simfin.com that makes it very easy to download and use financial data (share-prices, fundamentals, etc.) for free. This will enable people to conduct and share financial research very easily. It works by downloading bulk-data in CSV files from the SimFin server and loading them in Pandas. The fundamental data such as Income Statements and Balance Sheets is usually indexed by the Ticker and Report Date which creates a Pandas DataFrame with a MultiIndex.
#### Data Example
Let us say we have a Pandas DataFrame `df` with this data:
Revenue Net Income (Common)
Ticker Report Date
AAPL 2007-09-30 24578000000 3495000000
2008-09-30 37491000000 6119000000
2009-09-30 42905000000 8235000000
2010-09-30 65225000000 14013000000
2011-09-30 108249000000 25922000000
2012-09-30 156508000000 41733000000
2013-09-30 170910000000 37037000000
2014-09-30 182795000000 39510000000
2015-09-30 233715000000 53394000000
2016-09-30 215639000000 45687000000
2017-09-30 229234000000 48351000000
2018-09-30 265595000000 59531000000
AMZN 2007-12-31 14835000000 476000000
2008-12-31 19166000000 645000000
2009-12-31 24509000000 902000000
2010-12-31 34204000000 1152000000
2011-12-31 48077000000 631000000
2012-12-31 61093000000 -39000000
2013-12-31 74452000000 274000000
2014-12-31 88988000000 -241000000
2015-12-31 107006000000 596000000
2016-12-31 135987000000 2371000000
2017-12-31 177866000000 3033000000
2018-12-31 232887000000 10073000000
MSFT 2008-06-30 60420000000 17681000000
2009-06-30 58437000000 14569000000
2010-06-30 62484000000 18760000000
2011-06-30 69943000000 23150000000
2012-06-30 73723000000 16978000000
2013-06-30 77849000000 21863000000
2014-06-30 86833000000 22074000000
2015-06-30 93580000000 12193000000
2016-06-30 91154000000 20539000000
2017-06-30 96571000000 25489000000
2018-06-30 110360000000 16571000000
#### Resample a single Ticker (DatetimeIndex)
Let us first resample for a single ticker:
df.loc['MSFT'].resample('D').pad()
This works and the result is:
Revenue Net Income (Common)
Report Date
2008-06-30 60420000000 17681000000
2008-07-01 60420000000 17681000000
2008-07-02 60420000000 17681000000
2008-07-03 60420000000 17681000000
2008-07-04 60420000000 17681000000
#### Resample multiple Tickers (MultiIndex)
Let us now try and resample for all tickers in the DataFrame. The `resample()` function takes an argument `level` which is supposed to work with a MultiIndex DataFrame:
df.resample('D', level='Report Date').pad()
But this apparently doesn't work for upsampling e.g. annual data to daily data, because we get this error message:
ValueError: Upsampling from level= or on= selection is not supported, use .set_index(...) to explicitly set index to datetime-like
One solution is to use `groupby()` (adapted from e.g. #13699):
df.reset_index('Ticker').groupby('Ticker').resample('D').pad()
This works, but it now has duplicated the Ticker both as an index and as a column:
Ticker Revenue Net Income (Common)
Ticker Report Date
AAPL 2007-09-30 AAPL 24578000000 3495000000
2007-10-01 AAPL 24578000000 3495000000
2007-10-02 AAPL 24578000000 3495000000
2007-10-03 AAPL 24578000000 3495000000
2007-10-04 AAPL 24578000000 3495000000
We can avoid one of them by adding the arg `group_keys=False`:
df.reset_index('Ticker').groupby('Ticker', group_keys=False).resample('D').pad()
This works, but now the Ticker is a data-column instead of an index:
Ticker Revenue Net Income (Common)
Report Date
2007-09-30 AAPL 24578000000 3495000000
2007-10-01 AAPL 24578000000 3495000000
2007-10-02 AAPL 24578000000 3495000000
2007-10-03 AAPL 24578000000 3495000000
2007-10-04 AAPL 24578000000 3495000000
To get the original MultiIndex back with both Ticker and Report Date, we need to do:
df.reset_index('Ticker').groupby('Ticker', group_keys=False).resample('D').pad().reset_index().set_index(['Ticker', 'Report Date'])
Which produces the desired result:
Revenue Net Income (Common)
Ticker Report Date
AAPL 2007-09-30 24578000000 3495000000
2007-10-01 24578000000 3495000000
2007-10-02 24578000000 3495000000
2007-10-03 24578000000 3495000000
2007-10-04 24578000000 3495000000
But this is so complicated that nobody can be expected to remember how to do it. So I would have to make a small helper-function that does all of this. But because the resampling method (pad, interpolate, etc.) is invoked through a function call on the groupby-object, my helper-function would get big and awkward if I want to allow different methods of resampling.
#### Conclusion
It appears that upsampling a MultiIndex is not supported very well in Pandas, unless I have misunderstood how it is supposed to work.
I think that by far the most elegant solution would be if the `resample()` function supported the `level` argument for upsampling, because the syntax and semantics would be very similar for upsampling DatetimeIndex and MultiIndex:
# DatetimeIndex
df.loc['MSFT'].resample('D').pad()
# MultiIndex
df.resample('D', level='Report Date').pad()
I have taken a look at the Pandas source-code, but it is complicated and so sparsely documented, that it would take me forever to figure out how everything is connected and how it works, so I don't think I will be able to fix this myself. Is this something you could fix, because it would make it so much easier to upsample DataFrames with a MultiIndex?
Thanks!
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-60-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details> | {
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} | [] | closed | false | null | [] | null | 9 | 2019-09-06T12:53:08Z | 2019-11-06T17:55:29Z | 2019-11-06T17:55:29Z | CONTRIBUTOR | null | - [X] closes #26706
- [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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] | closed | false | null | [] | null | 2 | 2019-09-06T13:40:31Z | 2019-10-01T11:59:04Z | 2019-10-01T11:59:04Z | CONTRIBUTOR | null | From the [user survey](http://dev.pandas.io/pandas-blog/2019-pandas-user-survey.html), the most critical feature request was "Improve scaling to larger datasets".
While we continue to do work within pandas to improve scaling (fewer copies, native string dtype, etc.), we can document a few strategies that are available that may help with scaling.
1. Using efficient dtypes / ensure you don't have object dtypes. Possibly use Categorical for strings, if they have low cardinality. Possibly use lower-precision dtypes.
2. Avoid unnecessary work. When loading data, use `columns=` (csv or parquet) to select only the columns you need. Probably some other examples.
3. Use out of core methods, like `pd.read_csv(..., chunksize=)`, to avoid having to rewrite.
4. Use other libraries. I would of course recommend Dask. But I'm not opposed to a section highlighting Vaex, and possibly Spark (though installing it in our doc environment may be difficult).
Do people have thoughts on this? Any objections to highlighting outside projects like Dask?
Are there other strategies we should mention? | {
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} | 6 | 2019-09-06T14:26:12Z | 2019-09-11T06:34:15Z | 2019-09-11T06:34:15Z | CONTRIBUTOR | null | - [x] further improves #27717 - Currently the 'Contributing to pandas', 'Installing a C compiler' section provides links to pages that DO NOT give information about how to install a C compiler.
Note:
- For Linux, we have recommended to install a group of packages, along with GCC:
``sudo apt-get install build-essential`` and ``yum groupinstall "Development Tools"`` whereas in the [sklearn installation page](https://scikit-learn.org/stable/developers/advanced_installation.html#linux) have recommended to install GCC with ``sudo apt-get install build-essential`` but to install individually for Fedora etc: `sudo yum -y install gcc gcc-c++``
Which would be better?
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https://api.github.com/repos/pandas-dev/pandas/issues/28317 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28317/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28317/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28317/events | https://github.com/pandas-dev/pandas/issues/28317 | 490,387,994 | MDU6SXNzdWU0OTAzODc5OTQ= | 28,317 | Improve DataFrame.select_dtypes scaling to wide data frames | {
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} | 10 | 2019-09-06T15:26:08Z | 2019-09-20T12:42:52Z | 2019-09-20T12:42:52Z | CONTRIBUTOR | null | Running select_dtypes for a variety of lengths.
```python
import numpy as np
import pandas as pd
from timeit import default_timer as tic
ns = [0, 10, 100, 1_000, 10_000]
times = []
for n in ns:
df = pd.DataFrame(np.random.randn(10, n))
t0 = tic()
df.select_dtypes(include='int')
t1 = tic()
times.append([t1 - t0])
df = pd.DataFrame(times, columns=['include'], index=ns)
df.plot()
```
This looks O(n) in the number of columns. I think that can be improved (to whatever set intersection is)
Edit: maybe it's O(log(n)), I never took CS :) | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28318 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28318/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28318/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28318/events | https://github.com/pandas-dev/pandas/issues/28318 | 490,407,559 | MDU6SXNzdWU0OTA0MDc1NTk= | 28,318 | .infer_objects() behavior is unintuitive when used with schemaless inputs | {
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"description": "Unexpected or buggy dtyp... | open | false | null | [] | null | 4 | 2019-09-06T16:07:20Z | 2021-07-11T17:39:57Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
from io import StringIO
df = pd.concat([
pd.DataFrame({'a': ['a', 'b']}),
pd.DataFrame({'a': [1, 'c']}),
pd.DataFrame({'a': [2, 3]})
]).reset_index(drop=True)
#int64
print(df.loc[[2,4,5]].infer_objects().dtypes)
#object
df2 = pd.read_html(StringIO(df.to_html(index=False)))[0]
print(df2.loc[[2,4,5]].infer_objects().dtypes)
#object
df3 = pd.read_csv(StringIO(df.to_csv(None, index=False)))
print(df3.loc[[2,4,5]].infer_objects().dtypes)
#int64
df4 = pd.read_csv(StringIO(df.loc[[2,4,5]].to_csv(None, index=False)))
print(df4.dtypes)
```
#### Problem description
`.infer_objects()` attempts only a soft conversion of columns classified as objects. When you read from a file type that doesn't come with any schema information like HTML tables or CSVs, the type inference system will read numbers in a mixed format column as strings. As a result, even after you filter to just the numeric rows, `.infer_objects()` will leave them as `object` instead of `int64`/`float64`, whereas dumping/rereading them will convert them to numbers.
It would be nice if `.infer_objects()` did something more like rerunning the standard type inference that happens in `pd.read_csv()` or `pd.read_html()` on the DataFrame since the read/write feels hacky, and having to do a whole lot of `pd.to_numeric()` on a wide frame is cumbersome, particularly if it's just a subset of the columns.
#### Expected Output
```
a int64
dtype: object
```
for all above.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.1.final.0
python-bits : 64
OS : Windows
OS-release : 8.1
machine : AMD64
processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 9.0.1
setuptools : 41.0.0
Cython : 0.25.2
pytest : 3.0.7
hypothesis : None
sphinx : 1.5.6
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : 3.7.3
html5lib : 0.999
pymysql : None
psycopg2 : 2.7.3 (dt dec pq3 ext lo64)
jinja2 : 2.9.6
IPython : 5.3.0
pandas_datareader: None
bs4 : 4.6.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 3.7.3
matplotlib : 3.0.0
numexpr : 2.6.2
odfpy : None
openpyxl : 2.4.7
pandas_gbq : None
pyarrow : 0.14.0
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.1.9
tables : 3.2.2
xarray : None
xlrd : 1.0.0
xlwt : 1.2.0
xlsxwriter : 0.9.6
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28319 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28319/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28319/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28319/events | https://github.com/pandas-dev/pandas/pull/28319 | 490,407,579 | MDExOlB1bGxSZXF1ZXN0MzE1MDIwMzA1 | 28,319 | FIX: add additional condition to check invalid type(#28277) | {
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] | closed | false | null | [] | null | 9 | 2019-09-06T16:07:23Z | 2019-09-10T12:19:12Z | 2019-09-10T12:18:41Z | NONE | null | - [ ] closes #28277
- [ ] 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/28320 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28320/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28320/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28320/events | https://github.com/pandas-dev/pandas/pull/28320 | 490,411,461 | MDExOlB1bGxSZXF1ZXN0MzE1MDIzNDU0 | 28,320 | Added cpp files to build clean | {
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} | 2 | 2019-09-06T16:16:58Z | 2020-01-16T00:33:53Z | 2019-09-07T17:43:02Z | MEMBER | null | Looks like the Cython-compiled `window.cpp` hangs around after running `python setup.py clean --all` - this should take care of that
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] | closed | false | null | [] | null | 6 | 2019-09-06T16:30:09Z | 2019-10-29T19:11:19Z | 2019-10-29T19:11:19Z | MEMBER | null | We customize the display of pandas imports in setup.cfg for isort. This is documented in the contributing guide:
https://pandas.pydata.org/pandas-docs/stable/development/contributing.html#import-formatting
To provide an example, isort would naturally order imports to be something like:
```python
from pandas.api import DataFrame
from pandas.core.dtypes.inference import is_list_like
```
Though our customization would do this:
```python
from pandas.core.dtypes.inference import is_list_like
from pandas.api import DataFrame
```
I personally have found the alphabetic rule much easier to grok and am sometimes surprised by the logic of our imports.
Is there an actual value add to the way we do this (avoiding circular imports?) or would we consider getting rid of the customizations and just letting isort do the work
I think @jbrockmendel and @datapythonista might have some insights into historical reasoning | {
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} | 1 | 2019-09-06T17:01:05Z | 2020-01-16T00:33:53Z | 2019-09-07T17:20:03Z | MEMBER | null | We have some old PyString references hanging around in our extension modules. These are a relic of the old Py2 string handling and will be removed in Python 4 I think, so figured worth modernizing
https://docs.python.org/3/howto/cporting.html | {
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} | 1 | 2019-09-06T17:50:14Z | 2019-09-10T12:06:25Z | 2019-09-10T12:06:25Z | NONE | null | The following code reproduces the issue:
```python
import pandas as pd
df = pd.DataFrame(
{
"a": [1, 1, 2, 2],
"b": [
pd.Period("2019Q1"),
pd.Period("2019Q2"),
pd.Period("2019Q1"),
pd.Period("2019Q2"),
],
"x": 1.0,
}
)
df.pivot_table(index="a", columns="b", values="x", margins=True)
```
Output:
```
b 2019Q1 2019Q2 All
a
1 1.0 1.0 1.0
2 1.0 1.0 1.0
All NaN NaN 1.0
```
Expected output:
```
b 2019Q1 2019Q2 All
a
1 1.0 1.0 1.0
2 1.0 1.0 1.0
All 1.0 1.0 1.0
``` | {
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} | 2 | 2019-09-06T22:01:37Z | 2019-09-12T01:10:16Z | 2019-09-07T17:18:54Z | MEMBER | null | Cleans an error message from `groupby.ops`. | {
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} | 11 | 2019-09-06T22:32:14Z | 2019-10-09T21:24:02Z | 2019-09-19T15:46:14Z | CONTRIBUTOR | null | `PyArrowImpl` already correctly opens a non-existent file for writing (https://github.com/pandas-dev/pandas/blob/master/pandas/io/parquet.py#L95), with `engine='fastparquet'` this fails for e.g. a GCS URL (though it looks like S3 is already correct):
```
[nav] In [1]: pd.DataFrame().to_parquet('gs://city_data/test/blah.parquet')
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-1-dde78378baaa> in <module>
----> 1 pd.DataFrame().to_parquet('gs://city_data/test/blah.parquet')
~/venvs/model/lib/python3.7/site-packages/pandas/core/frame.py in to_parquet(self, fname, engine, compression, index, partition_cols, **kwargs)
2215 index=index,
2216 partition_cols=partition_cols,
-> 2217 **kwargs
2218 )
2219
~/venvs/model/lib/python3.7/site-packages/pandas/io/parquet.py in to_parquet(df, path, engine, compression, index, partition_cols, **kwargs)
250 index=index,
251 partition_cols=partition_cols,
--> 252 **kwargs
253 )
254
~/venvs/model/lib/python3.7/site-packages/pandas/io/parquet.py in write(self, df, path, compression, index, partition_cols, **kwargs)
171 kwargs["open_with"] = lambda path, _: path
172 else:
--> 173 path, _, _, _ = get_filepath_or_buffer(path)
174
175 with catch_warnings(record=True):
~/venvs/model/lib/python3.7/site-packages/pandas/io/common.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode)
212
213 return gcs.get_filepath_or_buffer(
--> 214 filepath_or_buffer, encoding=encoding, compression=compression, mode=mode
215 )
216
~/venvs/model/lib/python3.7/site-packages/pandas/io/gcs.py in get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode)
15
16 fs = gcsfs.GCSFileSystem()
---> 17 filepath_or_buffer = fs.open(filepath_or_buffer, mode)
18 return filepath_or_buffer, None, compression, True
<decorator-gen-147> in open(self, path, mode, block_size, acl, consistency, metadata)
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in _tracemethod(f, self, *args, **kwargs)
51 logger.log(logging.DEBUG - 1, tb_io.getvalue())
52
---> 53 return f(self, *args, **kwargs)
54
55
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in open(self, path, mode, block_size, acl, consistency, metadata)
1148 if 'b' in mode:
1149 return GCSFile(self, path, mode, block_size, consistency=const,
-> 1150 metadata=metadata)
1151 else:
1152 mode = mode.replace('t', '') + 'b'
<decorator-gen-150> in __init__(self, gcsfs, path, mode, block_size, acl, consistency, metadata)
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in _tracemethod(f, self, *args, **kwargs)
51 logger.log(logging.DEBUG - 1, tb_io.getvalue())
52
---> 53 return f(self, *args, **kwargs)
54
55
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in __init__(self, gcsfs, path, mode, block_size, acl, consistency, metadata)
1276 raise NotImplementedError('File mode not supported')
1277 if mode == 'rb':
-> 1278 self.details = gcsfs.info(path)
1279 self.size = self.details['size']
1280 else:
<decorator-gen-136> in info(self, path)
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in _tracemethod(f, self, *args, **kwargs)
51 logger.log(logging.DEBUG - 1, tb_io.getvalue())
52
---> 53 return f(self, *args, **kwargs)
54
55
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in info(self, path)
863
864 try:
--> 865 return self._get_object(path)
866 except FileNotFoundError:
867 logger.debug("info FileNotFound at path: %s", path)
<decorator-gen-122> in _get_object(self, path)
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in _tracemethod(f, self, *args, **kwargs)
51 logger.log(logging.DEBUG - 1, tb_io.getvalue())
52
---> 53 return f(self, *args, **kwargs)
54
55
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in _get_object(self, path)
539 raise FileNotFoundError(path)
540
--> 541 result = self._process_object(bucket, self._call('GET', 'b/{}/o/{}', bucket, key).json())
542
543 return result
<decorator-gen-121> in _call(self, method, path, *args, **kwargs)
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in _tracemethod(f, self, *args, **kwargs)
51 logger.log(logging.DEBUG - 1, tb_io.getvalue())
52
---> 53 return f(self, *args, **kwargs)
54
55
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in _call(self, method, path, *args, **kwargs)
482 r = self.session.request(method, path,
483 params=kwargs, json=json, headers=headers, data=data, timeout=self.requests_timeout)
--> 484 validate_response(r, path)
485 break
486 except (HttpError, RequestException, RateLimitException, GoogleAuthError) as e:
~/venvs/model/lib/python3.7/site-packages/gcsfs/core.py in validate_response(r, path)
156
157 if r.status_code == 404:
--> 158 raise FileNotFoundError(path)
159 elif r.status_code == 403:
160 raise IOError("Forbidden: %s\n%s" % (path, msg))
FileNotFoundError: https://www.googleapis.com/storage/v1/b/city_data/o/test%2Fblah.parquet
```
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} | 1 | 2019-09-06T22:39:05Z | 2019-09-07T17:28:23Z | 2019-09-07T17:17:32Z | MEMBER | null | In the branch I've got going that implements block-wise ops I found a bug in Block.split_and_operate. This doesn't fix that bug (since I don't have a test case that is independent of the rest of ops branch), just upstreams the cleanups that go along along with it. Small optimizations. | {
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} | 2 | 2019-09-06T23:48:38Z | 2019-09-07T17:30:41Z | 2019-09-07T17:17:48Z | MEMBER | null | A bunch of what we do in _gen_eval_kwargs is no longer necessary. | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28329 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28329/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28329/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28329/events | https://github.com/pandas-dev/pandas/issues/28329 | 490,562,789 | MDU6SXNzdWU0OTA1NjI3ODk= | 28,329 | DataFrame.corr(method="kendall") calculation is slow | {
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} | 1 | 2019-09-06T23:51:58Z | 2021-01-20T21:08:10Z | 2021-01-20T21:08:10Z | MEMBER | null | ```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1000, 300))
df.corr(method="kendall")
# 21.6 s ± 686 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
`DataFrame.corr(method="kendall")` doesn't scale particularly well, perhaps because it's the only named correlation method that isn't Cythonized at the moment (we just call `kendalltau` from `scipy` repeatedly in a Python for loop: https://github.com/pandas-dev/pandas/blob/master/pandas/core/frame.py#L7454). It may be worthwhile to try to implement something more efficient within `_libs/algos.pyx`.
Relevant discussion: https://github.com/pandas-dev/pandas/pull/28151 | {
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https://api.github.com/repos/pandas-dev/pandas/issues/28330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28330/events | https://github.com/pandas-dev/pandas/issues/28330 | 490,587,647 | MDU6SXNzdWU0OTA1ODc2NDc= | 28,330 | groupby and resample methods do not preserve subclassed data structures | {
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} | 3 | 2019-09-07T03:57:43Z | 2020-05-13T14:52:10Z | 2020-05-13T14:52:10Z | NONE | null | #### Code sample
```python
class MyDataFrame(pd.DataFrame):
@property
def _constructor(self):
return MyDataFrame
dates = pd.date_range('2019', freq='H', periods=1000)
my_df = MyDataFrame(np.arange(len(dates)), index=dates)
print(type(my_df))
# __main__.MyDataFrame (✓)
print(type(my_df.diff()))
# __main__.MyDataFrame (✓)
print(type(my_df.sample(1)))
# __main__.MyDataFrame (✓)
print(type(my_df.rolling('5H').mean()))
# __main__.MyDataFrame (✓)
print(type(my_df.groupby(my_df.index.dayofweek).mean()))
# pandas.core.frame.DataFrame (✘)
print(type(my_df.resample('D').mean()))
# pandas.core.frame.DataFrame (✘)
```
#### Problem description
Originally posted on [SO](https://stackoverflow.com/questions/57796464/pandas-groupby-resample-etc-for-subclassed-dataframe?noredirect=1#comment102088835_57796464).
The intended behaviour for chain-able methods on subclassed data structures is clearly that the operation returns an instance of the subclass (i.e. `MyDataFrame`), rather than the native type (i.e. `DataFrame`). This is the current behaviour for most operations (e.g., slicing, sampling, sorting) but not resample and groupby.
Currently groupby and resample both return explicitly constructed pandas datatypes, e.g. here:
https://github.com/pandas-dev/pandas/blob/ac693331400bce4747c63aa76c53c6e3488933ae/pandas/core/groupby/generic.py#L338
To get the expected behaviour, the intermediary classes (e.g. DataFrameGroupBy) would need to retain information about the calling class so that the appropriate constructor can be used (i.e. one of `_constructor` or `_constructor_sliced` or `_constructor_expanddim`).
Note that operations that use Window and Rolling already appear have the expected behaviour because these assemble their results via a call to `concat` such as this one:
https://github.com/pandas-dev/pandas/blob/171c71611886aab8549a8620c5b0071a129ad685/pandas/core/window.py#L325
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 40.8.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28331 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28331/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28331/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28331/events | https://github.com/pandas-dev/pandas/issues/28331 | 490,602,943 | MDU6SXNzdWU0OTA2MDI5NDM= | 28,331 | DataFrame resample and Grouper ,Why are the results different? | {
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} | 1 | 2019-09-07T07:18:41Z | 2021-07-11T17:40:34Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
data:
```python
idx = pd.date_range('2017-1-1','2018-12-31',freq='M')
df = pd.DataFrame(np.arange(len(idx)),idx)
```
resample:
```python
df.resample('3m',kind='period').sum()
```
use grouper:
```python
df.groupby(pd.Grouper(level=0,freq='3m',kind='period')).sum()
```
#### Problem description
Why are the results different?
#### Expected Output
I hope that the result of using the grouper is the same as the resample method.
thank you!!
#### 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 58 Stepping 9, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.0
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 40.8.0
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : 0.4.0
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : 0.13.0
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.1
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28332 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28332/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28332/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28332/events | https://github.com/pandas-dev/pandas/issues/28332 | 490,637,091 | MDU6SXNzdWU0OTA2MzcwOTE= | 28,332 | Feature request: extend `pandas.DataFrame.replace` | {
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} | 7 | 2019-09-07T13:42:58Z | 2019-09-10T21:02:33Z | 2019-09-07T19:53:22Z | NONE | null | The following code examples explain this feature request.
The following code works:
```python
df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
df.rename(columns=str.lower)
```
however the following ones do not work:
```python
df.rename(columns=str.upper.str.lower)
```
```python
df.rename(columns=str.upper().str.lower())
```
```python
df.rename(columns=str.strip())
```
```python
df.rename(columns=str.replace("A", "C"))
``` | {
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} | 4 | 2019-09-07T14:51:53Z | 2021-09-12T13:20:14Z | null | NONE | null | #### Code Sample
```python
import pandas as pd
animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
'height': [9.1, 6.0, 9.5, 34.0],
'weight': [7.9, 7.5, 9.9, 198.0]})
# (1) - doesn't work
animals.groupby("kind").rolling(1).agg(**{'total weight': pd.NamedAgg(column='weight', aggfunc=sum),
'min_weight': pd.NamedAgg(column='weight', aggfunc=min)})
# (2) - works
animals.groupby("kind").agg(**{'total weight': pd.NamedAgg(column='weight', aggfunc=sum),
'min_weight': pd.NamedAgg(column='weight', aggfunc=min)})
# works, but returns multiindex
animals.groupby("kind").rolling(1).agg({'weight': {'total_weight': 'sum', 'min_weight': 'min'}})
# (3) - this is what I'd want, expected output of (1)
animals.groupby("kind").rolling(1).agg({'weight': {'total_weight': 'sum', 'min_weight': 'min'}}).droplevel(0, axis=1)
```
#### Problem description
Pandas version 0.25 provides new aggregate functionality through [NamedAgg](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html#groupby-aggregation-with-relabeling). I would expect to be able to pass this in after a groupby as a `pd.apply` statement, and also after a groupby _and_ `pd.rolling`.
However, when I try to pass in a dict of `NamedAgg` tuples as shown in (1) in the example code I get the following TypeError: `TypeError: aggregate() missing 1 required positional argument: 'arg'`
Is this expected or am I incorrectly using this functionality? Thanks in advance!
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 18.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.1
pytz : 2018.5
dateutil : 2.7.3
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 0.9.2
psycopg2 : 2.7.7 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.3.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.3.3
tables : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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] | closed | false | null | [] | null | 15 | 2019-09-07T19:01:13Z | 2019-09-13T18:35:12Z | 2019-09-13T18:35:12Z | CONTRIBUTOR | null | In preperation for https://github.com/pandas-dev/pandas/issues/27108
(disallowing duplicates), we need to enhance our metadata propagation.
*We need a way for a particiular attribute to deterimine how it's
propagated for a particular method*. Our current method of metadata
propagation lacked two features
1. It only copies an attribute from a source NDFrame to a new NDFrame.
There is no way to propagate metadata from a collection of NDFrames
(say from `pd.concat`) to a new NDFrame.
2. It only and always copies the attribute. This is not always
appropriate when dealing with a collection of input NDFrames, as the
source attributes may differ. The resolution of conflicts will differ
by attribute (for `Series.name` we might throw away the name. For
`Series.allow_duplicates`, any Series disallowing duplicates should
mean the output disallows duplicates)
- [ ] Closes https://github.com/pandas-dev/pandas/issues/8572
- [ ] Update existing calls to `__finalize__ ` to pass through `method`
- [ ] profile / Run ASV
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"color": "0052cc"... | closed | false | null | [] | null | 3 | 2019-09-07T19:49:25Z | 2019-09-07T21:40:12Z | 2019-09-07T21:34:16Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
no code to show it's just the behavior of the csv_read method.
```
#### Problem description
If there's a dataframe that has a bunch of ints but a blank in between for example
> 1
> 2
> 3
>
> 4
pandas will turn it into
> 1.0
> 2.0
> 3.0
>
> 4.0
I may be a little off on remembering the exact decimal (it might be 1.1 but you get the point) | {
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} | 1 | 2019-09-07T21:07:02Z | 2019-09-10T14:16:01Z | 2019-09-10T12:05:09Z | CONTRIBUTOR | null | - [x] closes #28301
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
The old implementation used `arange` to generate codes for a non-multiindex, which ended up creating bad codes if the index was not unique. The fix is just a straightforward factorize. | {
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} | 2 | 2019-09-08T03:25:31Z | 2019-09-10T12:06:25Z | 2019-09-10T12:06:25Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
idx1 = pd.MultiIndex.from_tuples(
[
("x", Period("2019Q1", "Q-DEC")),
("x", Period("2019Q2", "Q-DEC")),
]
)
idx2 = pd.MultiIndex.from_tuples(
[
("x", Period("2019Q1", "Q-DEC")),
("x", Period("2019Q2", "Q-DEC")),
("x", "All"),
]
)
s = pd.Series([1.0, 1.0], index=idx1)
x 2019Q1 1.0
2019Q2 1.0
dtype: float64
# ACTUAL
s.reindex(idx2)
x 2019Q1 NaN
2019Q2 NaN
All NaN
dtype: float64
# EXPECTED
x 2019Q1 1.0
2019Q2 1.0
All NaN
dtype: float64
```
#### Problem description
This is the underlying problem causing https://github.com/pandas-dev/pandas/issues/28323. Reindexing with an index that contains a Period results in `NaN` results.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 5 | 2019-09-08T11:26:51Z | 2019-09-11T17:17:09Z | 2019-09-11T15:51:34Z | CONTRIBUTOR | null | [pyjanitor](https://github.com/ericmjl/pyjanitor) is a quite nice pandas data cleaning library and gives its users some useful method on data frames. It's pipe oriented, so it can be helpful in data cleaning/transformation work.
Adding pyjanitor to the ecosystem page gives it visibility to our users and makes it easier for the users to discover if there is functionailty in pyjanitor, that will help their use cases. | {
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] | closed | false | null | [] | null | 19 | 2019-09-08T12:04:36Z | 2020-10-12T17:32:43Z | 2020-04-10T11:20:26Z | MEMBER | null | **tl;dr No need for review!**
no intention of merging this so using patterns that probably won't be accepted.
PR is for visibility and reference only.
using http://blog.zulip.org/2016/10/13/static-types-in-python-oh-mypy/ as a suitable template for a project plan. resolution of #27568 would come before too much work on adding type hints in general.
There is quite a bit of work involved to enable check-untyped-defs globally so need to keep the phases described in "Fully annotating a large codebase" section as out-of-scope for now.
I do not expect to complete this in the near term, so am using Python 3.6 type annotations for variables (better formatting with black and no need to initialize variables to None) and mypy 0.720 for checking (less false positives)
One of the advantages of enabling check-untyped-defs globally, is the additional checking of PRs.
However, for as long as I can keep this branch in sync with master, this additional checking is effectively being performed.
The goal is to cover as much ground in the shortest possible time. The piecemeal approach of submitting separate PRs was, IMO, not allowing us to see the wood from the trees. The extent of the work involved (and changes required) won't be known till the majority of modules have been covered.
So far, nothing major has surfaced. If latent bugs are discovered could always break bits off and submit for review along the way.
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} | 1 | 2019-09-08T13:39:20Z | 2019-09-08T17:08:23Z | 2019-09-08T17:08:19Z | CONTRIBUTOR | null | Solves:
- Unknown parameters {kwargs}
- Parameter "errors" description should finish with "."
- flake8 error: E126 continuation line over-indented for hanging indent
- flake8 error: E231 missing whitespace after ','
pandas.Series.astype: Unknown parameters {kwargs} in #27976 | {
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} | 13 | 2019-09-08T15:55:59Z | 2020-01-10T14:09:53Z | 2019-09-13T16:11:50Z | CONTRIBUTOR | null | NumPy is considering this in https://github.com/numpy/numpy/pull/14453. I suspect other projects will follow suite.
The tldr is that as long as we ship the cythonized code, our older sdists have no hope of working with newer Pythons. The have to be compiled with a modern enough Cython.
I think this makes even more sense for us. We already require that NumPy is present as a build dependency. Requiring Cython on top of that is not a big deal.
Do we want to do this for 0.25.2? | {
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} | 1 | 2019-09-08T16:59:00Z | 2019-09-09T14:21:23Z | 2019-09-09T11:53:38Z | MEMBER | null | These imports in io.formats.format take about 1.6ms, out of a total of about 470ms and (7.8ms total for formats.format). So its not massive, but it is easy to avoid and we are running out of lower-hanging fruit. | {
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The included code check won't pass until the others in this sequence are merged. | {
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将ndarray转为DataFrame时报错了"No such keys(s): 'display.html.border'",使用的是python3.7,
回滚python3.6才没报错,用的centos7 | {
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} | 3 | 2019-09-08T23:29:01Z | 2019-09-10T20:52:01Z | 2019-09-10T20:19:27Z | MEMBER | null | @WillAyd think there is a compelling reason to keep this as `break` instead of lumping it into the `continue` clause above?
(obviously the TODO comment will be removed/resolved before merge) | {
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} | 1 | 2019-09-08T23:48:01Z | 2019-09-09T14:24:00Z | 2019-09-09T11:57:21Z | MEMBER | null | This is necessary for tightening up a bunch of Exception catching in groupby/apply/resample. Doing this as a separate step because I find the isolation clearer. | {
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} | 1 | 2019-09-09T03:29:36Z | 2019-09-10T14:15:48Z | 2019-09-10T12:06:25Z | CONTRIBUTOR | null | closes #28323
closes #28337
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry
Adds a workaround to allow period indexes to be reindexed with non-period indexes. | {
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} | 10 | 2019-09-09T04:27:47Z | 2019-09-22T09:09:10Z | 2019-09-11T16:00:14Z | NONE | null | #### Below are python codes which could run successfully under Python2.7.14, but failed under Python3.6.5 due to StringIO return an unexpected 'nan' character.
<details>
```python
# -*- coding: utf-8 -*-
import pandas as pd
from pandas.compat import StringIO
html_file_content = """#<table><tr>
<td colspan="16"><div align="right" style="padding-right:3px">注：带的数据表示为发行区间上限，最终值待公告后即时更新  
<a href="http://down.tech.sina.com.cn/content/8255.html" target="_blank"">下载PDF公告阅读器</a></div></td>
</tr>
<tr class="tr_2">
<td><div align="center"><strong><a href="?page=1&cngem=0&orderBy=PaperCode&orderType=asc">证券代码</a></strong></div></td>
<td><div align="center"><strong>申购代码</strong></div></td>
<td><div align="center"><strong><a href="?page=1&cngem=0&orderBy=PaperName&orderType=asc">证券简称</a></strong></div></td>
<td><div align="center"><strong><a href="?page=1&cngem=0&orderBy=NetDate&orderType=asc">上网发行<br />日期↓</a></strong></div></td>
<td><div align="center"><strong><a href="?page=1&cngem=0&orderBy=MarketDate&orderType=asc">上市日期</a></strong></div></td>
<td width="60"><div align="center"><strong>发行数量<br />(万股)</strong></div></td>
<td width="80"><div align="center"><strong>上网发行数量<br />(万股)</strong></div></td>
<td><div align="center"><strong>发行价格(元)</strong></div></td>
<td><div align="center"><strong>市盈率</strong></div></td>
<td><div align="center"><strong>个人申购上限<br />(万股)</strong></div></td>
<td><div align="center"><strong>募集资金<br />(亿元)</strong></div></td>
<td><div align="center"><strong>网上中签率<br />(%)</strong></div></td>
<td><div align="center"><strong>中签号</strong></div></td>
<td><div align="center"><strong>预测</strong></div></td>
<td><div align="center"><strong>公告</strong></div></td>
</tr>
<tr>
<td><div align="center">300717</div></td>
<td><div align="center">300717</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300717/nc.shtml">
华信新材 </a></div>
</td>
<td><div align="center">2017-10-26</div></td>
<td><div align="center">2017-11-06</div></td>
<td><div align="right" style="padding-right:3px">1600 </div></td>
<td><div align="right" style="padding-right:3px">1600</div></td>
<td><div align="right" style="padding-right:3px">14.24</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.60</div></td>
<td><div align="right" style="padding-right:3px">2.278</div></td>
<td><div align="right" style="padding-right:3px">0.01</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300717">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300717.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603856</div></td>
<td><div align="center">732856</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603856/nc.shtml">
东宏股份 </a></div>
</td>
<td><div align="center">2017-10-25</div></td>
<td><div align="center">2017-11-06</div></td>
<td><div align="right" style="padding-right:3px">4933 </div></td>
<td><div align="right" style="padding-right:3px">4440</div></td>
<td><div align="right" style="padding-right:3px">10.89</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.90</div></td>
<td><div align="right" style="padding-right:3px">7.400</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603856">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603856.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">300718</div></td>
<td><div align="center">300718</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300718/nc.shtml">
长盛轴承 </a></div>
</td>
<td><div align="center">2017-10-25</div></td>
<td><div align="center">2017-11-06</div></td>
<td><div align="right" style="padding-right:3px">2500 </div></td>
<td><div align="right" style="padding-right:3px">2250</div></td>
<td><div align="right" style="padding-right:3px">18.56</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.00</div></td>
<td><div align="right" style="padding-right:3px">4.640</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300718">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300718.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603507</div></td>
<td><div align="center">732507</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603507/nc.shtml">
振江股份 </a></div>
</td>
<td><div align="center">2017-10-25</div></td>
<td><div align="center">2017-11-06</div></td>
<td><div align="right" style="padding-right:3px">3141 </div></td>
<td><div align="right" style="padding-right:3px">2827</div></td>
<td><div align="right" style="padding-right:3px">26.25</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.20</div></td>
<td><div align="right" style="padding-right:3px">8.245</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603507">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603507.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">603659</div></td>
<td><div align="center">732659</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603659/nc.shtml">
璞泰来 </a></div>
</td>
<td><div align="center">2017-10-24</div></td>
<td><div align="center">2017-11-03</div></td>
<td><div align="right" style="padding-right:3px">6370 </div></td>
<td><div align="right" style="padding-right:3px">5734</div></td>
<td><div align="right" style="padding-right:3px">16.53</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.80</div></td>
<td><div align="right" style="padding-right:3px">10.530</div></td>
<td><div align="right" style="padding-right:3px">0.04</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603659">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603659.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603937</div></td>
<td><div align="center">732937</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603937/nc.shtml">
丽岛新材 </a></div>
</td>
<td><div align="center">2017-10-23</div></td>
<td><div align="center">2017-11-02</div></td>
<td><div align="right" style="padding-right:3px">5222 </div></td>
<td><div align="right" style="padding-right:3px">4700</div></td>
<td><div align="right" style="padding-right:3px">9.59</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">2.00</div></td>
<td><div align="right" style="padding-right:3px">5.008</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603937">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603937.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">300711</div></td>
<td><div align="center">300711</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300711/nc.shtml">
广哈通信 </a></div>
</td>
<td><div align="center">2017-10-20</div></td>
<td><div align="center">2017-11-01</div></td>
<td><div align="right" style="padding-right:3px">3610 </div></td>
<td><div align="right" style="padding-right:3px">3249</div></td>
<td><div align="right" style="padding-right:3px">8.35</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.40</div></td>
<td><div align="right" style="padding-right:3px">3.014</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300711">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300711.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603912</div></td>
<td><div align="center">732912</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603912/nc.shtml">
佳力图 </a></div>
</td>
<td><div align="center">2017-10-20</div></td>
<td><div align="center">2017-11-01</div></td>
<td><div align="right" style="padding-right:3px">3700 </div></td>
<td><div align="right" style="padding-right:3px">3330</div></td>
<td><div align="right" style="padding-right:3px">8.64</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.40</div></td>
<td><div align="right" style="padding-right:3px">3.197</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603912">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603912.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">603289</div></td>
<td><div align="center">732289</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603289/nc.shtml">
泰瑞机器 </a></div>
</td>
<td><div align="center">2017-10-19</div></td>
<td><div align="center">2017-10-31</div></td>
<td><div align="right" style="padding-right:3px">5100 </div></td>
<td><div align="right" style="padding-right:3px">4590</div></td>
<td><div align="right" style="padding-right:3px">7.83</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">2.00</div></td>
<td><div align="right" style="padding-right:3px">3.993</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603289">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603289.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">300712</div></td>
<td><div align="center">300712</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300712/nc.shtml">
永福股份 </a></div>
</td>
<td><div align="center">2017-10-19</div></td>
<td><div align="center">2017-10-31</div></td>
<td><div align="right" style="padding-right:3px">3502 </div></td>
<td><div align="right" style="padding-right:3px">3152</div></td>
<td><div align="right" style="padding-right:3px">11.82</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.40</div></td>
<td><div align="right" style="padding-right:3px">4.139</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300712">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300712.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">300713</div></td>
<td><div align="center">300713</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300713/nc.shtml">
英可瑞 </a></div>
</td>
<td><div align="center">2017-10-19</div></td>
<td><div align="center">2017-11-01</div></td>
<td><div align="right" style="padding-right:3px">1328 </div></td>
<td><div align="right" style="padding-right:3px">956</div></td>
<td><div align="right" style="padding-right:3px">40.29</div></td>
<td><div align="right" style="padding-right:3px">21.95</div></td>
<td><div align="right" style="padding-right:3px">0.50</div></td>
<td><div align="right" style="padding-right:3px">4.346</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300713">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300713.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603260</div></td>
<td><div align="center">732260</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603260/nc.shtml">
合盛硅业 </a></div>
</td>
<td><div align="center">2017-10-18</div></td>
<td><div align="center">2017-10-30</div></td>
<td><div align="right" style="padding-right:3px">7000 </div></td>
<td><div align="right" style="padding-right:3px">6300</div></td>
<td><div align="right" style="padding-right:3px">19.52</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">2.10</div></td>
<td><div align="right" style="padding-right:3px">13.664</div></td>
<td><div align="right" style="padding-right:3px">0.04</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603260">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603260.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">002910</div></td>
<td><div align="center">002910</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002910/nc.shtml">
庄园牧场 </a></div>
</td>
<td><div align="center">2017-10-18</div></td>
<td><div align="center">2017-10-31</div></td>
<td><div align="right" style="padding-right:3px">4684 </div></td>
<td><div align="right" style="padding-right:3px">4216</div></td>
<td><div align="right" style="padding-right:3px">7.46</div></td>
<td><div align="right" style="padding-right:3px">22.94</div></td>
<td><div align="right" style="padding-right:3px">1.85</div></td>
<td><div align="right" style="padding-right:3px">3.494</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002910">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002910.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603722</div></td>
<td><div align="center">732722</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603722/nc.shtml">
阿科力 </a></div>
</td>
<td><div align="center">2017-10-13</div></td>
<td><div align="center">2017-10-25</div></td>
<td><div align="right" style="padding-right:3px">2170 </div></td>
<td><div align="right" style="padding-right:3px">1953</div></td>
<td><div align="right" style="padding-right:3px">11.24</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">0.80</div></td>
<td><div align="right" style="padding-right:3px">2.439</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603722">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603722.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">603607</div></td>
<td><div align="center">732607</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603607/nc.shtml">
京华激光 </a></div>
</td>
<td><div align="center">2017-10-13</div></td>
<td><div align="center">2017-10-25</div></td>
<td><div align="right" style="padding-right:3px">2278 </div></td>
<td><div align="right" style="padding-right:3px">2050</div></td>
<td><div align="right" style="padding-right:3px">16.04</div></td>
<td><div align="right" style="padding-right:3px">22.93</div></td>
<td><div align="right" style="padding-right:3px">0.90</div></td>
<td><div align="right" style="padding-right:3px">3.655</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603607">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603607.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">002909</div></td>
<td><div align="center">002909</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002909/nc.shtml">
集泰股份 </a></div>
</td>
<td><div align="center">2017-10-13</div></td>
<td><div align="center">2017-10-26</div></td>
<td><div align="right" style="padding-right:3px">3000 </div></td>
<td><div align="right" style="padding-right:3px">2700</div></td>
<td><div align="right" style="padding-right:3px">7.47</div></td>
<td><div align="right" style="padding-right:3px">22.97</div></td>
<td><div align="right" style="padding-right:3px">1.20</div></td>
<td><div align="right" style="padding-right:3px">2.241</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002909">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002909.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">601108</div></td>
<td><div align="center">780108</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh601108/nc.shtml">
财通证券 </a></div>
</td>
<td><div align="center">2017-10-12</div></td>
<td><div align="center">2017-10-24</div></td>
<td><div align="right" style="padding-right:3px">35900 </div></td>
<td><div align="right" style="padding-right:3px">32310</div></td>
<td><div align="right" style="padding-right:3px">11.38</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">10.70</div></td>
<td><div align="right" style="padding-right:3px">0.000</div></td>
<td><div align="right" style="padding-right:3px">0.13</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=601108">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/601108.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">300715</div></td>
<td><div align="center">300715</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300715/nc.shtml">
凯伦股份 </a></div>
</td>
<td><div align="center">2017-10-12</div></td>
<td><div align="center">2017-10-26</div></td>
<td><div align="right" style="padding-right:3px">1800 </div></td>
<td><div align="right" style="padding-right:3px">1800</div></td>
<td><div align="right" style="padding-right:3px">12.50</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.80</div></td>
<td><div align="right" style="padding-right:3px">2.250</div></td>
<td><div align="right" style="padding-right:3px">0.01</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300715">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300715.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">603922</div></td>
<td><div align="center">732922</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603922/nc.shtml">
金鸿顺 </a></div>
</td>
<td><div align="center">2017-10-11</div></td>
<td><div align="center">2017-10-23</div></td>
<td><div align="right" style="padding-right:3px">3200 </div></td>
<td><div align="right" style="padding-right:3px">2880</div></td>
<td><div align="right" style="padding-right:3px">17.54</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.20</div></td>
<td><div align="right" style="padding-right:3px">5.613</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603922">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603922.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">002907</div></td>
<td><div align="center">002907</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002907/nc.shtml">
华森制药 </a></div>
</td>
<td><div align="center">2017-10-11</div></td>
<td><div align="center">2017-10-20</div></td>
<td><div align="right" style="padding-right:3px">4006 </div></td>
<td><div align="right" style="padding-right:3px">3605</div></td>
<td><div align="right" style="padding-right:3px">4.53</div></td>
<td><div align="right" style="padding-right:3px">22.96</div></td>
<td><div align="right" style="padding-right:3px">1.15</div></td>
<td><div align="right" style="padding-right:3px">1.815</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002907">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002907.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">603466</div></td>
<td><div align="center">732466</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603466/nc.shtml">
风语筑 </a></div>
</td>
<td><div align="center">2017-10-10</div></td>
<td><div align="center">2017-10-20</div></td>
<td><div align="right" style="padding-right:3px">3600 </div></td>
<td><div align="right" style="padding-right:3px">3240</div></td>
<td><div align="right" style="padding-right:3px">16.56</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.40</div></td>
<td><div align="right" style="padding-right:3px">5.962</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603466">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603466.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603683</div></td>
<td><div align="center">732683</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603683/nc.shtml">
晶华新材 </a></div>
</td>
<td><div align="center">2017-10-10</div></td>
<td><div align="center">2017-10-20</div></td>
<td><div align="right" style="padding-right:3px">3167 </div></td>
<td><div align="right" style="padding-right:3px">2850</div></td>
<td><div align="right" style="padding-right:3px">9.34</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.10</div></td>
<td><div align="right" style="padding-right:3px">2.958</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603683">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603683.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">002908</div></td>
<td><div align="center">002908</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002908/nc.shtml">
德生科技 </a></div>
</td>
<td><div align="center">2017-10-10</div></td>
<td><div align="center">2017-10-20</div></td>
<td><div align="right" style="padding-right:3px">3334 </div></td>
<td><div align="right" style="padding-right:3px">3001</div></td>
<td><div align="right" style="padding-right:3px">7.58</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.30</div></td>
<td><div align="right" style="padding-right:3px">2.527</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002908">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002908.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">300709</div></td>
<td><div align="center">300709</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300709/nc.shtml">
精研科技 </a></div>
</td>
<td><div align="center">2017-09-29</div></td>
<td><div align="center">2017-10-19</div></td>
<td><div align="right" style="padding-right:3px">2200 </div></td>
<td><div align="right" style="padding-right:3px">1980</div></td>
<td><div align="right" style="padding-right:3px">38.70</div></td>
<td><div align="right" style="padding-right:3px">22.65</div></td>
<td><div align="right" style="padding-right:3px">0.85</div></td>
<td><div align="right" style="padding-right:3px">8.514</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300709">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300709.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">603396</div></td>
<td><div align="center">732396</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603396/nc.shtml">
金辰股份 </a></div>
</td>
<td><div align="center">2017-09-29</div></td>
<td><div align="center">2017-10-18</div></td>
<td><div align="right" style="padding-right:3px">1889 </div></td>
<td><div align="right" style="padding-right:3px">1889</div></td>
<td><div align="right" style="padding-right:3px">19.47</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.80</div></td>
<td><div align="right" style="padding-right:3px">3.678</div></td>
<td><div align="right" style="padding-right:3px">0.01</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603396">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603396.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">300710</div></td>
<td><div align="center">300710</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300710/nc.shtml">
万隆光电 </a></div>
</td>
<td><div align="center">2017-09-29</div></td>
<td><div align="center">2017-10-19</div></td>
<td><div align="right" style="padding-right:3px">1750 </div></td>
<td><div align="right" style="padding-right:3px">1750</div></td>
<td><div align="right" style="padding-right:3px">14.75</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.75</div></td>
<td><div align="right" style="padding-right:3px">2.581</div></td>
<td><div align="right" style="padding-right:3px">0.01</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300710">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300710.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">300708</div></td>
<td><div align="center">300708</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300708/nc.shtml">
聚灿光电 </a></div>
</td>
<td><div align="center">2017-09-28</div></td>
<td><div align="center">2017-10-16</div></td>
<td><div align="right" style="padding-right:3px">6433 </div></td>
<td><div align="right" style="padding-right:3px">5790</div></td>
<td><div align="right" style="padding-right:3px">2.82</div></td>
<td><div align="right" style="padding-right:3px">22.93</div></td>
<td><div align="right" style="padding-right:3px">2.55</div></td>
<td><div align="right" style="padding-right:3px">1.814</div></td>
<td><div align="right" style="padding-right:3px">0.04</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300708">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300708.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603829</div></td>
<td><div align="center">732829</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603829/nc.shtml">
洛凯股份 </a></div>
</td>
<td><div align="center">2017-09-28</div></td>
<td><div align="center">2017-10-17</div></td>
<td><div align="right" style="padding-right:3px">4000 </div></td>
<td><div align="right" style="padding-right:3px">3600</div></td>
<td><div align="right" style="padding-right:3px">7.23</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.60</div></td>
<td><div align="right" style="padding-right:3px">2.892</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603829">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603829.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">002905</div></td>
<td><div align="center">002905</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002905/nc.shtml">
金逸影视 </a></div>
</td>
<td><div align="center">2017-09-28</div></td>
<td><div align="center">2017-10-16</div></td>
<td><div align="right" style="padding-right:3px">4200 </div></td>
<td><div align="right" style="padding-right:3px">3780</div></td>
<td><div align="right" style="padding-right:3px">20.98</div></td>
<td><div align="right" style="padding-right:3px">20.63</div></td>
<td><div align="right" style="padding-right:3px">1.65</div></td>
<td><div align="right" style="padding-right:3px">8.812</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002905">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002905.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603499</div></td>
<td><div align="center">732499</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603499/nc.shtml">
翔港科技 </a></div>
</td>
<td><div align="center">2017-09-27</div></td>
<td><div align="center">2017-10-16</div></td>
<td><div align="right" style="padding-right:3px">2500 </div></td>
<td><div align="right" style="padding-right:3px">2250</div></td>
<td><div align="right" style="padding-right:3px">9.24</div></td>
<td><div align="right" style="padding-right:3px">22.97</div></td>
<td><div align="right" style="padding-right:3px">1.00</div></td>
<td><div align="right" style="padding-right:3px">2.310</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603499">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603499.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">002906</div></td>
<td><div align="center">002906</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002906/nc.shtml">
华阳集团 </a></div>
</td>
<td><div align="center">2017-09-27</div></td>
<td><div align="center">2017-10-13</div></td>
<td><div align="right" style="padding-right:3px">7310 </div></td>
<td><div align="right" style="padding-right:3px">6579</div></td>
<td><div align="right" style="padding-right:3px">13.69</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">2.15</div></td>
<td><div align="right" style="padding-right:3px">10.007</div></td>
<td><div align="right" style="padding-right:3px">0.04</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002906">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002906.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603110</div></td>
<td><div align="center">732110</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603110/nc.shtml">
东方材料 </a></div>
</td>
<td><div align="center">2017-09-26</div></td>
<td><div align="center">2017-10-13</div></td>
<td><div align="right" style="padding-right:3px">2567 </div></td>
<td><div align="right" style="padding-right:3px">2310</div></td>
<td><div align="right" style="padding-right:3px">13.04</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.00</div></td>
<td><div align="right" style="padding-right:3px">3.347</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603110">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603110.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">002903</div></td>
<td><div align="center">002903</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002903/nc.shtml">
宇环数控 </a></div>
</td>
<td><div align="center">2017-09-26</div></td>
<td><div align="center">2017-10-13</div></td>
<td><div align="right" style="padding-right:3px">2500 </div></td>
<td><div align="right" style="padding-right:3px">2250</div></td>
<td><div align="right" style="padding-right:3px">12.78</div></td>
<td><div align="right" style="padding-right:3px">16.44</div></td>
<td><div align="right" style="padding-right:3px">1.00</div></td>
<td><div align="right" style="padding-right:3px">3.195</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002903">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002903.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603103</div></td>
<td><div align="center">732103</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603103/nc.shtml">
横店影视 </a></div>
</td>
<td><div align="center">2017-09-22</div></td>
<td><div align="center">2017-10-12</div></td>
<td><div align="right" style="padding-right:3px">5300 </div></td>
<td><div align="right" style="padding-right:3px">4770</div></td>
<td><div align="right" style="padding-right:3px">15.45</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.50</div></td>
<td><div align="right" style="padding-right:3px">8.188</div></td>
<td><div align="right" style="padding-right:3px">0.04</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603103">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603103.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">300707</div></td>
<td><div align="center">300707</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300707/nc.shtml">
威唐工业 </a></div>
</td>
<td><div align="center">2017-09-22</div></td>
<td><div align="center">2017-10-10</div></td>
<td><div align="right" style="padding-right:3px">1965 </div></td>
<td><div align="right" style="padding-right:3px">1965</div></td>
<td><div align="right" style="padding-right:3px">15.09</div></td>
<td><div align="right" style="padding-right:3px">22.99</div></td>
<td><div align="right" style="padding-right:3px">1.95</div></td>
<td><div align="right" style="padding-right:3px">2.965</div></td>
<td><div align="right" style="padding-right:3px">0.01</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300707">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300707.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">300705</div></td>
<td><div align="center">300705</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz300705/nc.shtml">
九典制药 </a></div>
</td>
<td><div align="center">2017-09-21</div></td>
<td><div align="center">2017-10-10</div></td>
<td><div align="right" style="padding-right:3px">2934 </div></td>
<td><div align="right" style="padding-right:3px">2641</div></td>
<td><div align="right" style="padding-right:3px">10.37</div></td>
<td><div align="right" style="padding-right:3px">22.97</div></td>
<td><div align="right" style="padding-right:3px">1.15</div></td>
<td><div align="right" style="padding-right:3px">3.043</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=300705">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/300705.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">601086</div></td>
<td><div align="center">780086</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh601086/nc.shtml">
国芳集团 </a></div>
</td>
<td><div align="center">2017-09-20</div></td>
<td><div align="center">2017-09-29</div></td>
<td><div align="right" style="padding-right:3px">16000 </div></td>
<td><div align="right" style="padding-right:3px">14400</div></td>
<td><div align="right" style="padding-right:3px">3.16</div></td>
<td><div align="right" style="padding-right:3px">22.92</div></td>
<td><div align="right" style="padding-right:3px">4.80</div></td>
<td><div align="right" style="padding-right:3px">5.056</div></td>
<td><div align="right" style="padding-right:3px">0.07</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=601086">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/601086.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">002902</div></td>
<td><div align="center">002902</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sz002902/nc.shtml">
铭普光磁 </a></div>
</td>
<td><div align="center">2017-09-20</div></td>
<td><div align="center">2017-09-29</div></td>
<td><div align="right" style="padding-right:3px">3500 </div></td>
<td><div align="right" style="padding-right:3px">3150</div></td>
<td><div align="right" style="padding-right:3px">14.13</div></td>
<td><div align="right" style="padding-right:3px">22.90</div></td>
<td><div align="right" style="padding-right:3px">1.40</div></td>
<td><div align="right" style="padding-right:3px">4.946</div></td>
<td><div align="right" style="padding-right:3px">0.02</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=002902">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/002902.phtml" target="_blank">查看</a>
</td>
</tr>
<tr>
<td><div align="center">603367</div></td>
<td><div align="center">732367</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603367/nc.shtml">
辰欣药业 </a></div>
</td>
<td><div align="center">2017-09-19</div></td>
<td><div align="center">2017-09-29</div></td>
<td><div align="right" style="padding-right:3px">10000 </div></td>
<td><div align="right" style="padding-right:3px">9000</div></td>
<td><div align="right" style="padding-right:3px">11.66</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">3.00</div></td>
<td><div align="right" style="padding-right:3px">11.660</div></td>
<td><div align="right" style="padding-right:3px">0.05</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603367">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603367.phtml" target="_blank">查看</a>
</td>
</tr>
<tr class="tr_2">
<td><div align="center">603378</div></td>
<td><div align="center">732378</div></td>
<td><div align="center">
<a target="_blank" href="http://finance.sina.com.cn/realstock/company/sh603378/nc.shtml">
亚士创能 </a></div>
</td>
<td><div align="center">2017-09-19</div></td>
<td><div align="center">2017-09-28</div></td>
<td><div align="right" style="padding-right:3px">4900 </div></td>
<td><div align="right" style="padding-right:3px">4410</div></td>
<td><div align="right" style="padding-right:3px">12.94</div></td>
<td><div align="right" style="padding-right:3px">22.98</div></td>
<td><div align="right" style="padding-right:3px">1.90</div></td>
<td><div align="right" style="padding-right:3px">6.341</div></td>
<td><div align="right" style="padding-right:3px">0.03</div></td>
<td><div align="center"><a class="clickme" href="javascript:void(0)">查看</a></div></td>
<td><div align="center">
<a target="_blank" href="http://biz.finance.sina.com.cn/qmx/stockreports.php?symbol=603378">
预测
</a></div>
</td>
<td style="text-align:center">
<a href="http://money.finance.sina.com.cn/corp/go.php/vCB_AllBulletin/stockid/603378.phtml" target="_blank">查看</a>
</td>
</tr>
</table>"""
io_content = StringIO(html_file_content)
print("io_content:", io_content)
df_all = pd.read_html(StringIO(html_file_content), skiprows=[0, 1])
print("df_all:")
print(df_all)
```
</details>
#### Problem description
See below result after program executing, there is an extra 'nan' returned unexpectedly. Is there some experts could offer any suggestions why this could happen?
#### Returned Output
<details>
Results in Python2:
<img width="614" alt="image" src="https://user-images.githubusercontent.com/4705587/64503305-055c3a80-d2fd-11e9-99f8-eba6669462dc.png">
However results in Python3:
<img width="608" alt="image" src="https://user-images.githubusercontent.com/4705587/64503285-e52c7b80-d2fc-11e9-8f22-23cf787e8797.png">
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} | 0 | 2019-09-09T05:18:35Z | 2019-09-11T18:57:28Z | 2019-09-11T18:57:07Z | CONTRIBUTOR | null | We currently have https://github.com/pandas-dev/pandas/blob/df3d9b2cdfe2ebab3a5b22e6f5359a393e519af4/ci/deps/azure-windows-36.yaml#L2-L4 for the requirements in the Windows tests. This is sadly a known source for problems as there are conflicting packages in both channels, e.g. boost from `defaults` and `conda-forge` is in different packages (`libboost` vs `boost-cpp`) and thus both can be installed while one is overriding the other (for boost this is fixed but there are several other packages). See also https://conda-forge.org/docs/user/tipsandtricks.html#multiple-channels for another explanation. The wrong order is probably a source of issues like https://github.com/pandas-dev/pandas/issues/23180 | {
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} | 10 | 2019-09-09T05:20:01Z | 2019-09-26T06:40:45Z | 2019-09-11T18:57:08Z | CONTRIBUTOR | null | - [x] closes #28356
- [ ] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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"description": "__repr__... | closed | false | null | [] | null | 6 | 2019-09-09T08:15:27Z | 2021-02-25T18:44:53Z | 2021-02-25T18:44:53Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
"bar", "bar", "bar", "bar"],
"B": ["one", "one", "one", "two", "two",
"one", "one", "two", "two"],
"C": ["small", "large", "large", "small",
"small", "large", "small", "small",
"large"],
"D": [1, 2, 2, 3, 3, 4, None, 6, 7],
"E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
table = pd.pivot_table(df, values='D', index=['A','B'], columns=['C'], aggfunc=np.sum, fill_value='-')
df.fillna('-').style.highlight_max()
table.style.hightlight_max()
```
#### Problem description
`pandas.DataFrame.style.highlight_*` does not work on column where `nan` has been replaced by string using `pandas.DataFrame.fillna()` or `pandas.PivotTable(fill_value=)`
#### Expected Output
Expected that highlight will still work on column when `fillna` or `fill_value` get strings.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.0-27-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.1
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : 2.6.3
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : None
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : None
</details>
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} | [] | closed | false | null | [] | null | 1 | 2019-09-09T11:46:22Z | 2019-09-09T14:17:17Z | 2019-09-09T14:17:17Z | NONE | null | #### Code Sample, a copy-pastable example if possible
[root@rc14e00h003 recon]# python
Python 2.7.5 (default, Jun 20 2019, 20:27:34)
[GCC 4.8.5 20150623 (Red Hat 4.8.5-36)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib64/python2.7/site-packages/pandas/__init__.py", line 42, in <module>
import pandas.core.config_init
File "/usr/lib64/python2.7/site-packages/pandas/core/config_init.py", line 13, in <module>
import pandas.core.config as cf
File "/usr/lib64/python2.7/site-packages/pandas/core/config.py", line 807, in <module>
is_text = is_instance_factory((str, bytes))
File "/usr/lib64/python2.7/site-packages/pandas/core/config.py", line 778, in is_instance_factory
from pandas.core.common import pprint_thing
File "/usr/lib64/python2.7/site-packages/pandas/core/common.py", line 21, in <module>
from pandas.core.dtypes import CategoricalDtype, CategoricalDtypeType, DatetimeTZDtype, DatetimeTZDtypeType
ImportError: cannot import name CategoricalDtype
```python
Can we still post this? as in the documentation page it says pandas is only supported with python 3.6
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"default"... | open | false | null | [] | null | 2 | 2019-09-09T11:57:23Z | 2019-09-09T12:34:13Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import time
import pandas as pd
a = pd.Series([1, 1], index=pd.DatetimeIndex([pd.Timestamp('2019-04-01T01:00:00+01:00'),
pd.Timestamp('2019-04-07T01:00:00+01:00')]))
a = a.resample('1s').sum() # ~500k items
# same TZ
b = pd.Series([1], index=pd.DatetimeIndex([pd.Timestamp('2019-04-01T02:00:00+01:00')]))
b = b.resample('1s').sum() # 1 item
# different TZ
c = pd.Series([1], index=pd.DatetimeIndex([pd.Timestamp('2019-04-01T02:00:00+02:00')]))
c = c.resample('1s').sum() # 1 item
start = time.time()
pd.DataFrame({'A': a, 'B': b})
print('same TZ:', time.time() - start)
start = time.time()
pd.DataFrame({'A': a, 'C': c})
print('different TZ:', time.time() - start)
```
#### Problem description
DataFrame is constructed from 2 series. The performance degrades significantly when series have indexes with different time zones. The code from above returns:
```
same TZ: 0.037207841873168945
different TZ: 8.304282188415527
```
#### Expected Output
It's expected that performance is the same.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.7.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-58-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : None
pytest : 4.4.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.6.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : None
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
</details>
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} | 6 | 2019-09-09T18:12:46Z | 2019-09-12T17:05:05Z | 2019-09-12T17:00:26Z | MEMBER | null | - [x] closes #28080
- [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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... | open | false | null | [] | null | 4 | 2019-09-09T18:45:52Z | 2021-01-02T23:51:00Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
from pandas.util import hash_pandas_object
x = pd.Series([np.nan])
print(x.data.hex())
expected = hash_pandas_object(x)
x1 = np.array([-np.sqrt(-1.)])
print(x1.data.hex())
hashed_x1 = hash_pandas_object(pd.Series(x1))
assert expected[0] == hashed_x1.values[0]
x2 = np.array([np.sqrt(-1.)])
print(x2.data.hex())
hashed_x2 = hash_pandas_object(pd.Series(x2))
assert expected[0] == hashed_x2.values[0]
```
#### Problem description
For the array x2 the nan value results in a different hash what is not expected.
#### Expected Output
```python
# To fix the test for x2 one could do a fillna
# x2.fillna(np.nan)
x2 = np.array([np.sqrt(-1.)])
print(x2.data.hex())
x2 = pd.Series(x2)
x2 = x2.fillna(np.nan)
hashed_x2 = hash_pandas_object(x2)
assert expected[0] == hashed_x2.values[0]
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4.final.0
python-bits: 64
OS: Linux
OS-release: 5.2.8-1-MANJARO
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.24.2
pytest: 5.0.1
pip: 19.0.3
setuptools: 41.0.1
Cython: 0.29.13
numpy: 1.17.0
scipy: 1.3.0
pyarrow: None
xarray: None
IPython: 7.6.1
sphinx: None
patsy: None
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
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https://api.github.com/repos/pandas-dev/pandas/issues/28365 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28365/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28365/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28365/events | https://github.com/pandas-dev/pandas/issues/28365 | 491,361,940 | MDU6SXNzdWU0OTEzNjE5NDA= | 28,365 | pandas.DataFrame.boxplot(by="class", ...) doesn't plot correctly | {
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**pandas=0.24.2**
I uploaded the jupyter notebook file on [my GitHub account](https://github.com/yuriikoval1997/pandas0.24.2-has-a-problem-with-cyrillic/blob/master/pandas%20bugs.ipynb), so here I will be short.
For some reason, pandas.DataFrame.boxplot(by="class") method doesn't work correctly with Cyrillic letters in the table.
```python
for col in list(dataSetCyrillic.columns[:-1]):
fig, ax = pyplot.subplots(figsize=(6, 6))
dataSetCyrillic.boxplot(column=[col],
by="class",
ax=ax,
widths=0.5)
tmp = map(lambda x: x.replace(" ", "\n", 1), dataSetCyrillic["class"].unique())
ax.set_xticklabels(tmp, fontsize="20")
ax.set_yticklabels(ax.get_yticks(), fontsize="xx-large")
ax.set_title(label=col, fontsize="xx-large", fontstyle="oblique", fontweight="bold")
pyplot.xlabel("")
pyplot.suptitle("")
fig.tight_layout()
``` | {
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} | 2 | 2019-09-10T08:33:28Z | 2019-09-10T18:22:36Z | 2019-09-10T18:22:10Z | NONE | null | #### Code Sample
```python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(1000, 300))
#Using Spearman method: On my computer about 12.19 seconds
df.corr(method = 'spearman')
#Using detour: On my computer about 0.72 seconds
rank_df = df.rank()
rank_df.corr(method = 'pearson')
```
#### Problem description
I found that when I calculate Spearman correlation via the detour method it's way faster. The output (correlation coefficients) differs slightly (minimal) when using data containing nan-values. I'd like to understand this difference in output, and maybe the Spearman method can be faster by re-using the current Pearson implementation or something.
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} | 11 | 2019-09-10T09:44:53Z | 2019-09-12T17:44:56Z | 2019-09-12T12:45:22Z | CONTRIBUTOR | null | … (#28299)
- [x] closes #28299
- [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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} | 13 | 2019-09-10T14:29:25Z | 2019-10-23T13:51:01Z | 2019-09-12T20:57:00Z | MEMBER | null | Adding custom conversion of IntegerArray to an Arrow array, which makes that this can also be written to parquet.
Currently it is only one way, for read_parquet it will come back as int or float (depending on presence of missing values), but fixing this is also being discussed (https://issues.apache.org/jira/browse/ARROW-2428).
The tests currently require the master version of Arrow, which we don't test. I can assure that the tests pass locally for me, but is this something we want to merge without coverage on CI?
(in principle we could add Arrow master in eg the numpy-dev build or another one, but that is a bit more work, so not sure that is worth the currently limited number of tests we have relying on arrow)
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https://api.github.com/repos/pandas-dev/pandas/issues/28369 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28369/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28369/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28369/events | https://github.com/pandas-dev/pandas/issues/28369 | 491,715,768 | MDU6SXNzdWU0OTE3MTU3Njg= | 28,369 | Apply not available for all windows type | {
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} | 5 | 2019-09-10T14:38:09Z | 2020-05-08T19:50:22Z | 2020-05-08T19:50:22Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': range(1000)})
df.rolling(5, center=True, win_type='triang').apply(lambda x: np.nanstd(x), raw=False)
```
#### Problem description
The above produces an error, stating that the `Window` class produces by the rolling function does not have an `apply` attribute.
If I remove the `win_type` attribute, the computation goes fine and returns a result:
```
A
0 NaN
1 NaN
2 1.414214
3 1.414214
4 1.414214
... ...
995 1.414214
996 1.414214
997 1.414214
998 NaN
999 NaN
```
#### Expected Output
I expect the `apply` attribute to be available to any `win_type` (I did not see any restrictions in the documentation of `pd.DataFrame.rolling` (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html), or the documentation of `pd.DataFrame.rolling.apply` (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.window.Rolling.apply.html))
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.6.8.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 : fr_FR.UTF-8
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.2
pytz : 2018.9
dateutil : 2.8.0
pip : 19.1.1
setuptools : 40.8.0
Cython : 0.29.6
pytest : 4.3.1
hypothesis : None
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.5
lxml.etree : 4.3.2
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10
IPython : 7.4.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.3.2
matplotlib : 3.0.3
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.1
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.2.1
sqlalchemy : 1.3.1
tables : 3.5.1
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.5
</details>
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} | 24 | 2019-09-10T15:33:31Z | 2020-01-09T08:34:28Z | 2020-01-09T08:34:23Z | MEMBER | null | Related to https://github.com/pandas-dev/pandas/pull/28368, but now for Period and Interval for which we define extension types to store them with metadata in arrow.
Still needs some more tests and fixing corner cases.
We probably also want to consolidate the pyarrow import checking somewhat.
I think a main question is how to deal with the import of pyarrow. The way I did it now makes that it is no longer a lazy import (as it was currently for the parquet functionality). | {
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} | 10 | 2019-09-10T16:35:01Z | 2021-07-11T17:42:28Z | null | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
cat_df = pd.DataFrame()
cat_df['letters'] = ['a', 'b', 'c']
cat_df['letters'] = cat_df['letters'].astype('category')
cat_df['nums'] = [0, 1, 2]
cat_df['nums'] = cat_df['nums'].astype('category')
cat_df['count'] = 1
# when I set an index, I don't get all the columns!
cat_df.iloc[:2].pivot_table(aggfunc=sum, columns=['nums'], values='count', index='letters')
```
#### Problem description
I would expect the default behavior of `pivot_table` to yield precisely 1 column per category of a categorical grouper when pivoting over a single categorical column. This was not the case when I passed in an index -- I only ended up with the observed categories for columns, even when passing `observed=False` explicitly!
I am working on an application where I want to pivot with an index and group by multiple columns (e.g. column A with categories a1, a2, ..., column B with categories b1, b2, ..., etc.), and I was surprised to discover that if any combination of categories (e.g. a1, b1, ...) didn't appear in the same row, I would not get a corresponding column for it.
As an aside, I would guess that the correct behavior would be to have all the nans in the output from above by 0, but that is seems important.
#### Expected Output
```
nums 0 1 2
letters
a 1.0 0.0 0.0
b 0.0 1.0 0.0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 4.14.133-88.112.amzn1.x86_64
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 68 | 2019-09-10T16:41:14Z | 2021-07-12T18:32:51Z | 2021-07-12T13:10:45Z | MEMBER | null | - [x] closes #15079
- [x] xref: #11053, #28177
- [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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} | 21 | 2019-09-10T18:43:58Z | 2019-09-14T08:24:49Z | 2019-09-13T16:11:48Z | CONTRIBUTOR | null | * Adds pyproject.toml to support this.
* Bumps minimum Cython version to the version supporting Python 3.8.
Closes https://github.com/pandas-dev/pandas/issues/20775
xref https://github.com/pandas-dev/pandas/issues/27435 (check if closes)
xref https://github.com/pandas-dev/pandas/pull/25227, https://github.com/pandas-dev/pandas/pull/20718, https://github.com/pandas-dev/pandas/pull/16745 | {
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} | 13 | 2019-09-10T20:00:11Z | 2021-02-24T15:11:11Z | 2020-02-02T22:29:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
S3 paths work for reading and writing CSV. However, I am not able to write json files using the to_json method. Reading json from an S3 path seems to work just fine.
```python
df.to_json(s3uri, orient='values')
```
#### Problem description
```python
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
in
1 final_users['user_id'].to_json(s3_path + 'users.json',
----> 2 orient='values')
~/anaconda3/envs/qa-tool/lib/python3.7/site-packages/pandas/core/generic.py in to_json(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index)
2271 default_handler=default_handler,
2272 lines=lines, compression=compression,
-> 2273 index=index)
2274
2275 def to_hdf(self, path_or_buf, key, **kwargs):
~/anaconda3/envs/qa-tool/lib/python3.7/site-packages/pandas/io/json/json.py in to_json(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines, compression, index)
66
67 if isinstance(path_or_buf, compat.string_types):
---> 68 fh, handles = _get_handle(path_or_buf, 'w', compression=compression)
69 try:
70 fh.write(s)
~/anaconda3/envs/qa-tool/lib/python3.7/site-packages/pandas/io/common.py in _get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text)
425 elif is_text:
426 # Python 3 and no explicit encoding
--> 427 f = open(path_or_buf, mode, errors='replace', newline="")
428 else:
429 # Python 3 and binary mode
FileNotFoundError: [Errno 2] No such file or directory: 's3://bucket/folder/foo.json'
```
#### Expected Output
None. Expected output is no error and the file is written to the s3 bucket.
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit: None
pandas: 0.24.2
pytest: None
pip: 19.2.2
setuptools: 41.0.1
Cython: None
numpy: 1.16.4
scipy: 1.3.1
pyarrow: None
xarray: None
IPython: 7.7.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.1.0
openpyxl: 2.6.2
xlrd: 1.2.0
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: 0.2.1
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
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https://api.github.com/repos/pandas-dev/pandas/issues/28376 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28376/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28376/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28376/events | https://github.com/pandas-dev/pandas/issues/28376 | 491,892,748 | MDU6SXNzdWU0OTE4OTI3NDg= | 28,376 | How to fix your data returning Nan instead of float values in Python 3 for categorical data | {
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After grouping by, I tried to see what the data looks like and this is what I get. what can I do?
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
happiness_dataset= pd.read_csv('happiness-cantril-ladder.csv')
### Lets explore the dataset ####
happiness_dataset.info()
happiness_dataset.describe()
happiness_dataset.columns = ['Country','Country_code','Year','Happiness_level'] ### rename the columns
happiness_dataset['Happiness_level'] = pd.to_numeric(happiness_dataset['Happiness_level'],errors='coerce')
happiness_dataset.info()
groupby_country_v= pd.DataFrame(happiness_dataset.groupby('Country')['Happiness_level'].mean())
print(groupby_country_v)
This is what I get when I print the groupby_country_v
Happiness_level
Country
Afghanistan NaN
Albania NaN
Algeria NaN
Angola NaN
Argentina NaN
Armenia NaN
Australia NaN
Austria NaN
Azerbaijan NaN
Bahrain NaN
Bangladesh NaN
Belarus NaN
Belgium NaN
Belize NaN
Benin NaN
Bhutan NaN
Bolivia NaN
Bosnia and Herzegovina NaN
Botswana NaN
Brazil NaN
Bulgaria NaN
Burkina Faso NaN
Burundi NaN
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"description": "IO issue... | open | false | null | [] | null | 2 | 2019-09-10T21:04:36Z | 2020-05-02T04:33:59Z | null | CONTRIBUTOR | null | Split from https://github.com/pandas-dev/pandas/pull/27899. How should we handle options to be passed to the `open` call (like `encoding`, `errors`).
A few options
* pass through `**kwargs`
* a "storage_options" kwarg that's a dict with some specification
Want to thing about things like S3, etc. too. | {
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} | 14 | 2019-09-10T21:28:00Z | 2019-09-17T12:59:25Z | 2019-09-17T12:59:25Z | MEMBER | null | ```python-traceback
>>> import pandas.util.testing as tm
>>> tm.makeObjectSeries()
...
t1Q4jcW3nW 2000-02-09
ElZCr3WTz2 2000-02-10
p43Nqg7u4D 2000-02-11
dtype: datetime64[ns]
```
@jbrockmendel and I were both surprised that calling `makeObjectSeries` returns a Series with a datetime dtype. I think it would be more logical for this function to actually return an object dtype Series | {
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] | open | false | null | [] | null | 2 | 2019-09-11T01:24:19Z | 2019-09-11T16:28:40Z | null | CONTRIBUTOR | null | Related to #28333 and #26513
There are several helper methods to help with named aggregations, that are found in [`pandas.core.groupby.generic`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/generic.py), namely:
- [`_is_multi_agg_with_relabel`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/generic.py#L1704)
- [`_normalize_keyword_aggregation`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/generic.py#L1729)
- [`_make_unique`](https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/generic.py#L1793)
If this functionality gets standardized across aggregations other than just `groupby`, it would make sense to have these moved out of this file.
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] | closed | false | null | [] | null | 2 | 2019-09-11T01:41:10Z | 2019-09-11T17:31:59Z | 2019-09-11T17:30:23Z | NONE | null | #### This is probably some configuration issue on my end.
```python
Traceback (most recent call last):
File ".\utils.py", line 70, in <module>
sql_to_hdf5(chunked_dfs)
File ".\utils.py", line 27, in sql_to_hdf5
store = pd.HDFStore(file_path)
File "C:\Users\J68531\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\io\pytables.py", line 484, in __init__
tables = import_optional_dependency("tables")
File "C:\Users\J68531\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\compat\_optional.py", line 99, in import_optional_dependency
version = _get_version(module)
File "C:\Users\J68531\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\compat\_optional.py", line 48, in _get_version
raise ImportError("Can't determine version for {}".format(module.__name__))
ImportError: Can't determine version for tables
```
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} | 0 | 2019-09-11T02:15:40Z | 2019-09-13T00:56:41Z | 2019-09-13T00:56:41Z | MEMBER | null | Looking at the Cython source there is a big deprecation warning about doing `from cpython cimport ...`
https://github.com/cython/cython/blob/cd031618700bf8293818729ee07bfc855fb424a2/Cython/Includes/cpython/__init__.pxd#L125
We tend to import things that way, so I think would be nice to clean up our imports to be absolute from cpython.
I think this is a great issue to pick up for those looking to get more familiar with Cython, so labeling as good first issue
@jbrockmendel | {
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} | 13 | 2019-09-11T05:35:00Z | 2019-10-22T01:31:31Z | 2019-10-22T01:31:31Z | NONE | null | #### Code Sample, a copy-pastable example if possible
import string, pandas
p_csv = pandas.read_csv(my_dir/myFile), index_col=0)
sepanames = sorted(p_csv["SEPARATOR"].unique())
for i in range(0, 14):
print(i)
col = p_csv.columns.get_loc("SEPARATOR") + 1 + i
p_csv.insert(col, "SEPARATOR_" + sepanames[i].upper(), p_csv["SEPARATOR"].apply(lambda x: int(x == sepanames[i])))
p_csv.to_csv("my_dir/new_file.csv")
''' p_csv= pandas.read_csv(("/myDir/myFile.csv"), index_col=0)
pandas.get_dummies(p_csv, prefix="SEPARATOR_", columns="SEPARATOR")
p_csv.to_csv("/myDir/myNew.csv")'''
FILES:
https://www.amazon.de/clouddrive/share/h37d1hqtrj5SrZTvKdrs9gXltVKUgo8Is9BxL8WH7Sf
#### Problem description
This is all of my code. The quoted part is what I first tried, but after 20 minutes it ended through a sigkill without any result. The required files are available for download. I would think, that my code does the equivalent in this very case and it works just fine in under about a minute.
Pandas Version: 25.1.0
Pandas git version: '171c71611886aab8549a8620c5b0071a129ad685'
#### Expected Output
No error and changed csv file | {
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} | 7 | 2019-09-11T09:54:31Z | 2020-09-22T23:21:38Z | 2020-09-22T23:21:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
cat_type = pd.CategoricalDtype(categories=["a", "b", "c"])
s1_cat = pd.Series(["a", "b", "c"], dtype=cat_type)
s2_cat = pd.Series([np.nan, "a", "b"], dtype=cat_type)
s1_cat != s2_cat # expected all True
```
Output:
```
0 False
1 True
2 True
dtype: bool
```
#### Problem description
I would expect anything to compare `!=` to NaN.
Comparing the categorical series with `==` works as expected:
```python
s1_cat == s2_cat # expect all False
```
Output:
```
0 False
1 False
2 False
dtype: bool
```
Element-wise comparison seems to work fine:
```python
for left, right in zip(s1_cat, s2_cat):
print(left != right) # expect all True
```
Output:
```
True
True
True
```
This also works as expected with other data types, e.g.:
```python
s1_int = pd.Series([1, 2, 3])
s2_int = pd.Series([np.nan, 1, 2])
s1_int != s2_int # expect all True
```
gives
```
0 True
1 True
2 True
dtype: bool
```
Apologies if this is a duplicate; I have found various issues about categorical types and missing values, but not about comparing them in this way.
#### Expected Output
```python
s1_cat != s2_cat # expected all True
```
```
0 True
1 True
2 True
dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Darwin
OS-release : 16.7.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
</details>
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} | 6 | 2019-09-11T10:19:00Z | 2021-07-12T03:16:43Z | null | CONTRIBUTOR | null | #### Code
```python
import pandas as pd
s1 = pd.Series([1, 2, 3, 4], dtype='Int64')
s2 = pd.Series([1, 2, float('nan'), 3, 4], dtype='Int64')
cs1 = s1.cumsum()
cs2 = s1.cumsum()
print(str(s1.dtype), str(cs1.dtype))
print(str(s2.dtype), str(cs2.dtype))
```
#### Output
```
Int64 object
Int64 object
```
#### Expected Output
```
Int64 Int64
Int64 Int64
```
#### Problem description
After an operation like a cumulative sum on a column/series with an `Int64` dtype, the dtype of the result is downcast to `object`. The contents (integers and nans) of the result still qualify for it to have an `Int64` dtype.
`cummax`, `cummin` and `cumprod` have the same behaviour.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.0.13-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.utf8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.0
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.1.0
Cython : 0.29.13
pytest : 5.1.0
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 : 2.10.1
IPython : 7.7.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : None
tables : None
xarray : 0.12.3
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
import pandas as pd
s = pd.Series([1, 2, 3, 4], dtype='Int64')
cs = s.cumsum()
print(str(s.dtype))
print(str(cs.dtype))
import pandas as pd
</details>
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} | 1 | 2019-09-11T13:40:25Z | 2019-09-12T12:50:33Z | 2019-09-12T12:50:30Z | CONTRIBUTOR | null | test to ensure OutOfBoundsDatetime exception is raised when calling
pd.to_datetime with out-of-bounds date strings
As discussed briefly in #28367
- [x] closes #9107 ? the issue is no longer truly reproducible, this PR adds a test around the new behaviour
- [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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} | [] | closed | false | null | [] | null | 1 | 2019-09-11T15:26:07Z | 2019-09-11T15:34:53Z | 2019-09-11T15:34:53Z | NONE | null | Please keep the to_msgpack function. I need this function to save data to redis for data persistence. Thank you. | {
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Change the EA interface to require `size` instead of `__len__`.
Doing this separately from #27142 makes it much simpler to explain to downstream authors what is changing for them. After this, the rest of the changes necessary for (limited) 2D support should not affect downstream authors. | {
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```python
import pandas as pd
d = {('East',1):{'Home':'MIL','Away':'BOS','Winner':'MIL'},
('East',2):{'Home':'PHI','Away':'IND','Winner':'PHI'},
('East',3):{'Home':'MIL','Away':'PHI','Winner':'PHI'},
('West',1):{'Home':'HOU','Away':'UTA','Winner':'HOU'},
('West',2):{'Home':'LAC','Away':'LAL','Winner':'LAC'},
('West',3):{'Home':'HOU','Away':'LAC','Winner':'HOU'},
(None,1):{'Home':'HOU','Away':'PHI','Winner':'PHI'}}
df = pd.DataFrame.from_dict(d, orient='index')
print(df)
```
#### Output
Home Away Winner
East 1 MIL BOS MIL
2 PHI IND PHI
3 MIL PHI PHI
West 1 HOU UTA HOU
2 LAC LAL LAC
3 HOU LAC HOU
NaN 1 NaN NaN NaN
#### Problem description
When constructing a DataFrame from a dictionary with tuple keys, values for all columns are set to NaN if the index tuple contains None.
I only started encountering this bug when I upgraded from pandas 0.22 to pandas 0.25.
Possibly related: #19993
#### Expected Output
Home Away Winner
East 1 MIL BOS MIL
2 PHI IND PHI
3 MIL PHI PHI
West 1 HOU UTA HOU
2 LAC LAL LAC
3 HOU LAC HOU
NaN 1 HOU PHI PHI
#### 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 45 Stepping 7, GenuineIntel
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : None.None
pandas : 0.25.1
numpy : 1.14.3
pytz : 2017.3
dateutil : 2.7.3
pip : 10.0.1
setuptools : 39.0.1
Cython : 0.28.2
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.2.4
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : 4.6.3
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.2.4
matplotlib : 3.0.3
numexpr : None
odfpy : None
openpyxl : 2.4.9
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.1.0
sqlalchemy : 1.1.15
tables : None
xarray : None
xlrd : 1.1.0
xlwt : None
xlsxwriter : None
None
</details>
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} | 6 | 2019-09-11T19:14:33Z | 2019-09-12T12:54:05Z | 2019-09-12T12:49:22Z | CONTRIBUTOR | null | In preperation for Python 3.8, we need to ensure we compile the sdist
with new enough versions of Cython. This is the latest current release.
xref https://github.com/pandas-dev/pandas/issues/28341 | {
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} | 71 | 2019-09-11T22:13:09Z | 2020-09-08T16:44:37Z | 2020-09-03T03:00:00Z | CONTRIBUTOR | null | This adds a property to NDFrame to disallow duplicate labels. This fixes a vexing issue with using pandas for ETL pipelines, where accidentally introducing duplicate labels can lead to confusing downstream behavior (e.g. `NDFrame.__getitem__` not reducing dimensionality).
When set (via the construction with `allow_duplicate_labels=False` or afterward via `.allows_duplicate_labels=False`), the presence of duplicate labels causes a DuplicateLabelError exception to be raised:
```pytb
In [3]: df = pd.DataFrame({"A": [1, 2]}, index=['a', 'a'], allow_duplicate_labels=False)
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
<ipython-input-3-1c8833763dfc> in <module>
----> 1 df = pd.DataFrame({"A": [1, 2]}, index=['a', 'a'], allow_duplicate_labels=False)
~/sandbox/pandas/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy, allow_duplicate_labels)
493
494 NDFrame.__init__(
--> 495 self, mgr, fastpath=True, allow_duplicate_labels=allow_duplicate_labels
496 )
497
~/sandbox/pandas/pandas/core/generic.py in __init__(self, data, axes, copy, dtype, allow_duplicate_labels, fastpath)
202
203 if not self.allows_duplicate_labels:
--> 204 self._maybe_check_duplicate_labels()
205
206 def _init_mgr(self, mgr, axes=None, dtype=None, copy=False):
~/sandbox/pandas/pandas/core/generic.py in _maybe_check_duplicate_labels(self, force)
240 if force or not self.allows_duplicate_labels:
241 for ax in self.axes:
--> 242 ax._maybe_check_unique()
243
244 @property
~/sandbox/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
540 # TODO: position, value, not too large.
541 msg = "Index has duplicates."
--> 542 raise DuplicateLabelError(msg)
543
544 # --------------------------------------------------------------------
DuplicateLabelError: Index has duplicates.
```
This property is preserved through operations (using `_metadata` and `__finalize__`).
```pytb
In [4]: df = pd.DataFrame(index=['a', 'A'], allow_duplicate_labels=False)
In [5]: df.rename(str.upper)
---------------------------------------------------------------------------
DuplicateLabelError Traceback (most recent call last)
<ipython-input-5-17c8fb0b7c7f> in <module>
----> 1 df.rename(str.upper)
...
~/sandbox/pandas/pandas/core/indexes/base.py in _maybe_check_unique(self)
540 # TODO: position, value, not too large.
541 msg = "Index has duplicates."
--> 542 raise DuplicateLabelError(msg)
543
544 # --------------------------------------------------------------------
DuplicateLabelError: Index has duplicates.
```
---
### API design questions
- [ ] Do we want to be positive or negative?
```python
pd.Series(..., allow_duplicate_labels=True/False)
pd.Series(..., disallow_duplicate_labels=False/True)
pd.Series(..., require_unique_labels=False/True)
```
- [ ] In my proposal, the argument is different from the property. Do we like that? The rational is the argument is making a statement about what *to* do, while the property is making a statement about what *is* allowed.
```python
In [7]: s = pd.Series(allow_duplicate_labels=False)
In [8]: s.allows_duplicate_labels
Out[8]: False
```
- [ ] I'd like a method-chainable way of saying "duplicates aren't allowed." Some options
```python
# two methods.
s.disallow_duplicate().allow_duplicate()
```
I dislike that in combination with `.allows_duplicates`, since we'd have a property and a method that only differ by an `s`. Perhaps something like
```python
s.update_metdata(allows_duplicates=False)
```
But do people know that "allows_duplicates" is considered metadata?
---
TODO:
- [ ] many, many more tests
- [ ] Propagate metadata in more places (groupby, rolling, etc.)
- [ ] Backwards compatibility in `__finalize__` (we may be changing when we call it, and what we pass)
*Apologies for using the PR for discussion, but we needed a way to compare this to https://github.com/pandas-dev/pandas/pull/28334.* | {
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} | 1 | 2019-09-11T22:13:52Z | 2019-09-12T15:05:15Z | 2019-09-12T12:52:48Z | MEMBER | null | With this, L823-849 is not-Series-specific, so the next step will be to refactor this out to array_ops and we'll be able to to share it between the Series op and the upcoming block-wise op.
Change to `fill_bool` makes it work on `ndarray` instead of `Series`.
`other = lib.item_from_zerodim` is added for consistency with all our other ops, shouldn't make a difference since we don't do an explicit length check. | {
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} | 3 | 2019-09-12T02:29:36Z | 2020-07-06T23:27:27Z | 2020-07-06T23:27:27Z | NONE | null | #### Code Sample
```python
import pandas as pd
idx1 = pd.Categorical(['A1', 'A2', 'A2'], categories=['A1', 'A2'])
idx2 = pd.Categorical(['A2', 'A1', 'A1'], categories=['A1', 'A2'])
tmp1 = pd.DataFrame(np.random.randn(6).reshape(3, 2), index=idx1)
tmp2 = pd.DataFrame(np.random.randn(6).reshape(3, 2), index=idx2)
tmp1 * tmp2
```
The result:
```python
In [1]: tmp1
Out[1]:
0 1
A1 -0.880693 0.794644
A2 -0.671629 1.145027
A2 0.305152 0.379116
In [2]: tmp2
Out[2]:
0 1
A2 0.434122 -0.966894
A1 -0.162195 0.499271
A1 0.840569 0.043832
In [3]: tmp1 * tmp2
Out[3]:
0 1
0 0.142844 0.396742
0 -0.740283 0.034831
1 -0.291569 -1.107120
1 0.132473 -0.366565
In [4]: pd.__version__
Out[4]: '0.24.2'
```
#### Problem description
When getting multiplication of two DataFrames with CategoricalIndex based on the same categories, the result will convert the CategoricalIndex to the Int one. The problem may be from dataframe alignment.
```python
In [7]: tmp1_align, tmp2_align =tmp1.align(tmp2)
In [8]: tmp1_align
Out[8]:
0 1
0 -0.880693 0.794644
0 -0.880693 0.794644
1 -0.671629 1.145027
1 0.305152 0.379116
```
I think the simple binary operations should preserve the index type for categorical index with the same categories. However, it doesn't. Then I find sometimes the operation preserve the categorical index type but convert to object plain-text index which cost large memory. So I am confused about it and haven't found any explaination yet.
#### Expected Output
The result with categorical index like this:
```python
0 1
A1 0.142844 0.396742
A1 -0.740283 0.034831
A2 -0.291569 -1.107120
A2 0.132473 -0.366565
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3.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: en
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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} | 4 | 2019-09-12T03:23:28Z | 2019-09-13T00:57:00Z | 2019-09-13T00:56:42Z | CONTRIBUTOR | null | - [x] closes #28382
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
Apologies if anything is not ideal, as this is my first pull request here. Any suggestions or comments are much appreciated! | {
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} | 7 | 2019-09-12T04:56:42Z | 2020-01-06T15:41:43Z | 2020-01-06T13:29:36Z | MEMBER | null | - [X] closes #23563
- [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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} | 5 | 2019-09-12T06:24:43Z | 2019-10-01T18:43:42Z | 2019-09-12T12:51:16Z | CONTRIBUTOR | null | There's `np.random.np.random` in /doc/source/user_guide/computation.rst, which I believe is a typo. But the weird thing is there's actually `np.random.np` in numpy (1.16.4), but not in numpy (1.17.2). That's maybe why the doc build passed before. While I'm trying to build the doc locally with numpy 1.17.2, it failed.
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes `black pandas`
- [ ] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
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} | 3 | 2019-09-12T07:00:36Z | 2020-11-24T13:33:06Z | 2020-11-24T13:33:06Z | MEMBER | null | #### Code Sample, a copy-pastable example if possible
Construction without specifying a dtype results in `object` dtype for `Interval` and `Period`:
```python-traceback
In [2]: pd.Series(pd.Interval(0, 1), index=range(3))
Out[2]:
0 (0, 1]
1 (0, 1]
2 (0, 1]
dtype: object
In [3]: pd.Series(pd.Period("2019Q1", freq="Q"), index=range(3))
Out[3]:
0 2019Q1
1 2019Q1
2 2019Q1
dtype: object
```
This looks okay for a tz-aware `Timestamp`:
```python-traceback
In [4]: pd.Series(pd.Timestamp("2019", tz="US/Eastern"), index=range(3))
Out[4]:
0 2019-01-01 00:00:00-05:00
1 2019-01-01 00:00:00-05:00
2 2019-01-01 00:00:00-05:00
dtype: datetime64[ns, US/Eastern]
```
Specifying a dtype raises for `Interval`, `Period` and tz-aware `Timestamp`:
```python-traceback
In [5]: pd.Series(pd.Interval(0, 1), index=range(3), dtype=pd.IntervalDtype("int64"))
---------------------------------------------------------------------------
TypeError: IntervalArray(...) must be called with a collection of some kind, (0, 1] was passed
In [6]: pd.Series(pd.Period("2019Q1", freq="Q"), index=range(3), dtype=pd.PeriodDtype("Q"))
---------------------------------------------------------------------------
ValueError: Buffer has wrong number of dimensions (expected 1, got 0)
In [7]: pd.Series(pd.Timestamp("2019", tz="US/Eastern"), index=range(3), dtype=pd.DatetimeTZDtype(tz="US/Eastern"))
---------------------------------------------------------------------------
TypeError: 'Timestamp' object is not iterable
```
Both of the above patterns appear to be working fine when using scalars that correspond to non-extensions dtypes (e.g. numeric, tz-naive, timedelta).
#### Problem description
The `Series` constructor is not correctly inferring the dtype from scalar `Interval`/`Period` objects when a dtype isn't specified, and raises for `Interval`/`Period`/tz-aware `Timestamp` when a dtype is specified.
#### Expected Output
I'd expect the `Series` to be constructed with the proper dtype.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit : 261c3a6673304f2f59f7cb2c1b285c3bdd2ff92e
python : 3.7.3.final.0
python-bits : 64
OS : Linux
OS-release : 4.19.14-041914-generic
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.0+332.g261c3a667
numpy : 1.16.4
pytz : 2019.1
dateutil : 2.8.0
pip : 19.1.1
setuptools : 41.0.1
Cython : 0.29.10
pytest : 4.6.2
hypothesis : 4.23.6
sphinx : 1.8.5
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.3.3
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : 4.7.1
bottleneck : 1.2.1
fastparquet : 0.3.0
gcsfs : None
lxml.etree : 4.3.3
matplotlib : 3.1.0
numexpr : 2.6.9
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : 0.11.1
pytables : None
s3fs : 0.2.1
scipy : 1.2.1
sqlalchemy : 1.3.4
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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} | 1 | 2019-09-12T08:23:00Z | 2019-09-13T14:15:23Z | 2019-09-13T14:15:23Z | MEMBER | null | The doc build on Azure is currently failing, with an SSL certificate error in one of the examples that download from an url.
This is probably an issue with the latest openssl from Anaconda, see https://github.com/ContinuumIO/anaconda-issues/issues/11274
But opening just to have an issue to refer to. We can certainly ignore the failure for now when merging PRs, but in that case be careful that it is actually only this problem that is causing the red CI. | {
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} | 2 | 2019-09-12T08:24:42Z | 2019-10-21T19:34:22Z | null | NONE | null | Hello team,
Here is a simple working exemple of value assignment on a dataframe:
```
df = pd.DataFrame({'A': [np.nan, np.nan, 'b']})
df['A'].loc[[0, 1]] = ['a', np.nan]
df.notna()
Out[126]:
A
0 True
1 False
2 True
```
However, if we use loc to assign the entire column with an array including NaNs, all values are converted to strings, breaking notna():
```
df = pd.DataFrame({'A': [np.nan, np.nan, 'b']})
df['A'].loc[[0, 1, 2]] = ['a', np.nan, np.nan]
df['A'].notna()
Out[133]:
0 True
1 True
2 True
Name: A, dtype: bool
``` | {
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} | 1 | 2019-09-12T08:34:58Z | 2019-09-12T09:55:36Z | 2019-09-12T09:54:45Z | MEMBER | null | Temporary solution for https://github.com/pandas-dev/pandas/issues/28402 | {
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} | 1 | 2019-09-12T09:55:18Z | 2019-09-13T14:15:28Z | 2019-09-13T14:15:23Z | MEMBER | null | Reverts pandas-dev/pandas#28404
Closes https://github.com/pandas-dev/pandas/issues/28402 | {
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} | 10 | 2019-09-12T10:25:07Z | 2019-09-18T12:35:04Z | 2019-09-18T12:35:04Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
```python
In [6]: s = pd.Series([1, 0, None], dtype='category')
In [7]: s
Out[7]:
0 1
1 0
2 NaN
dtype: category
Categories (2, int64): [0, 1]
In [8]: s.astype(int)
Out[8]:
0 1
1 0
2 -9223372036854775808 # <- this is unexpected
dtype: int64
```
#### Problem description
When converting categorical series back into Int column, it converts `NaN` to incorect integer negative value.
#### Expected Output
I would expect that `NaN` in category converts to `NaN` in `IntX`(nullable integer) or `float`.
When trying to use `d.astype('Int8')`, I get an error `dtype not understood`
#### Output of ``pd.show_versions()``
```
In [147]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.final.0
python-bits : 64
OS : Linux
OS-release : 5.2.13-arch1-1-ARCH
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 0.25.1
numpy : 1.17.2
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.3
setuptools : 41.2.0
Cython : None
pytest : 5.1.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 7.8.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : 2.7.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : 3.5.2
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
```
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} | 2 | 2019-09-12T11:37:33Z | 2019-09-18T14:02:12Z | 2019-09-18T14:02:12Z | MEMBER | null | mypy error: `pandas\core\sparse\frame.py:94:25: error: Item "SparseArray" of "Union[SparseSeries, SparseArray]" has no attribute "index"`
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.__version__
'0.25.0+332.g261c3a667'
>>> sa = pd.SparseArray([1,2,3])
>>> sa
[1, 2, 3]
Fill: 0
IntIndex
Indices: array([0, 1, 2])
>>> pd.DataFrame(sa)
0
0 1
1 2
2 3
>>> pd.SparseDataFrame(sa)
__main__:1: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.
See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\sparse\frame.py", line 94, in __init__
index = data.index
AttributeError: 'SparseArray' object has no attribute 'index'
>>>
```
#### Problem description
The docstring for SparseDataFrame states that the types accepted for the data argument of the constructor are the same types as can be passed to DataFrame.
the DataFrame constructor accepts a SparseArray, the SparseDataFrame constructor raises `AttributeError: 'SparseArray' object has no attribute 'index'`
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https://api.github.com/repos/pandas-dev/pandas/issues/28408 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/28408/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/28408/comments | https://api.github.com/repos/pandas-dev/pandas/issues/28408/events | https://github.com/pandas-dev/pandas/issues/28408 | 492,751,169 | MDU6SXNzdWU0OTI3NTExNjk= | 28,408 | Series.dt.round fails if fraction is exactly 0.5 | {
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} | [] | closed | false | null | [] | null | 5 | 2019-09-12T11:39:00Z | 2019-09-16T12:03:25Z | 2019-09-12T19:00:38Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```python
rng = pd.date_range('1/1/2018 11:59:00.050', periods=10, freq='100ms')
rng
>>>DatetimeIndex(['2018-01-01 11:59:00.050000', '2018-01-01 11:59:00.150000',
'2018-01-01 11:59:00.250000', '2018-01-01 11:59:00.350000',
'2018-01-01 11:59:00.450000', '2018-01-01 11:59:00.550000',
'2018-01-01 11:59:00.650000', '2018-01-01 11:59:00.750000',
'2018-01-01 11:59:00.850000', '2018-01-01 11:59:00.950000'],
dtype='datetime64[ns]', freq='100L')
rng.round('100ms')
>>>DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 11:59:00.200000',
'2018-01-01 11:59:00.200000', '2018-01-01 11:59:00.400000',
'2018-01-01 11:59:00.400000', '2018-01-01 11:59:00.600000',
'2018-01-01 11:59:00.600000', '2018-01-01 11:59:00.800000',
'2018-01-01 11:59:00.800000', '2018-01-01 11:59:01'],
dtype='datetime64[ns]', freq=None)
```
#### Problem description
If the time fraction is exactly 0.5, rounding half up/half down is applied depending on the digit left of it (even/odd). So, '11:59:00.350' would be rounded half up whilst '11:59:00.450' is rounded half down.
#### Expected Output
Expected output would be either:
```python
>>>DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 11:59:00.100000',
'2018-01-01 11:59:00.200000', '2018-01-01 11:59:00.300000',
'2018-01-01 11:59:00.400000', '2018-01-01 11:59:00.500000',
'2018-01-01 11:59:00.600000', '2018-01-01 11:59:00.700000',
'2018-01-01 11:59:00.800000', '2018-01-01 11:59:00.900000'],
dtype='datetime64[ns]', freq=None)
```
or:
```python
>>> DatetimeIndex(['2018-01-01 11:59:00.100000', '2018-01-01 11:59:00.200000',
'2018-01-01 11:59:00.300000', '2018-01-01 11:59:00.400000',
'2018-01-01 11:59:00.500000', '2018-01-01 11:59:00.600000',
'2018-01-01 11:59:00.700000', '2018-01-01 11:59:00.800000',
'2018-01-01 11:59:00.900000', '2018-01-01 11:59:01'],
dtype='datetime64[ns]', freq=None)
```
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
INSTALLED VERSIONS
------------------
commit : None
python : 3.7.4.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 : en
LOCALE : None.None
pandas : 0.25.1
numpy : 1.16.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.2.2
setuptools : 41.0.1
Cython : 0.29.13
pytest : 5.0.1
hypothesis : None
sphinx : 2.1.2
blosc : None
feather : None
xlsxwriter : 1.1.8
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : 1.2.1
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : 2.7.0
odfpy : None
openpyxl : 2.6.2
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.7
tables : 3.5.2
xarray : 0.12.1
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.1.8
</details>
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"... | closed | false | null | [] | null | 15 | 2019-09-12T11:45:43Z | 2020-09-24T09:56:13Z | 2020-09-24T09:56:13Z | MEMBER | null | Related to #26804 (and indirectly related to #15862, #15008 if we standardize I/O connectors as part of this)
The recent survey shows that several of our I/O connectors (Stata, SPSS, SAS and GBQ) are rarely used (from the chart I'd say around 3% of respondents said they used them):
While afaik all them are just wrappers around other libraries, and don't contain much code to maintain, I see few main immediate advantages:
- Allow an ecosystem of third-party modules
- Reduce the complexity and time of the CI
- Not commit to the connector API
**I/O ecosystem**
While the number of widely used formats is quite limited, the number of possible I/O connectors is huge. Several new connectors have been requested in the past, like markdown (#11052), avro (#11752), docx (#22518), and there are surely plenty of specialized and internal formats that didn't make sense to propose adding to pandas, but that people would appreciate having and using the same standard API (`pandas.read_*` / `DataFrame.to_*`) we have. And there may also be alternative versions of the connectors we provide (see for example this lightning talk by @dutc dicussing about an unsafe `pandas.read_csv`: https://youtu.be/QkQ5HHEu1b4?t=1554).
**CI**
For the CI, those modules include dependencies that need to be downloaded for every build. We also have files in `pandas/tests/io/data` that eventually we may need to update. For GBQ in particular, we have to store the password for the account we use to test, and those tests fail from time to time for connectivity problems. For the clipboard (not proposing to move to a third-party yet, but is a good candidate too), we require to emulate an X system in our tests (need to check if we finally fixed the tests, but they weren't running for a while because of problems on the CI set up).
And of course our test suite is taking some extra time to test all these connectors.
**API changes**
Being part of our public API, I think we need to commit to the API of those connectors, in the same way as we do with the rest of our API. But, for whatever reason it may make sense to discontinue some of these connectors (e.g. Google releasing a replacement for GBQ...), or make important changes to their parameters, and probably it makes sense to have different release cycles for them.
This also applies to connectors not yet in pandas, but that we may include if we keep the policy of adding to our code base the connectors for all the formats / services we consider relevant.
I know timing is not great, but because of this API commitment, I'd personally remove Stata, SPSS, SAS and GBQ before pandas 1.0. | {
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"de... | closed | false | null | [] | null | 0 | 2019-09-12T12:23:29Z | 2019-09-13T01:05:32Z | 2019-09-13T01:05:32Z | MEMBER | null | mypy error:
```
pandas\core\series.py:2733:25: error: Unsupported operand types for + ("List[Any]" and "Tuple[Any, ...]")
pandas\core\series.py:2733:25: note: Right operand is of type "Union[List[Any], Tuple[Any, ...]]"
```
#### Code Sample, a copy-pastable example if possible
```python
>>> import pandas as pd
>>> pd.__version__
'0.25.0+332.g261c3a667'
>>>
>>> ser = pd.Series([1,2,3])
>>>
>>> ser
0 1
1 2
2 3
dtype: int64
>>>
>>> ser.append(ser)
0 1
1 2
2 3
0 1
1 2
2 3
dtype: int64
>>>
>>> ser.append([ser,ser])
0 1
1 2
2 3
0 1
1 2
2 3
0 1
1 2
2 3
dtype: int64
>>>
>>> ser.append((ser,ser))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\simon\OneDrive\code\pandas-simonjayhawkins\pandas\core\series.py", line 2733, in append
to_concat = [self] + to_append
TypeError: can only concatenate list (not "tuple") to list
```
#### Problem description
The docstring for Series.append states `to_append : Series or list/tuple of Series`. Appending a tuple of Series raises `TypeError: can only concatenate list (not "tuple") to list` | {
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} | 2 | 2019-09-12T12:59:38Z | 2019-09-12T17:13:46Z | 2019-09-12T17:13:41Z | CONTRIBUTOR | null | {
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} | 1 | 2019-09-12T14:41:37Z | 2019-09-13T01:05:38Z | 2019-09-13T01:05:32Z | CONTRIBUTOR | null | - [x] closes #28410
- [x] tests added / passed
- [x] passes `black pandas`
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
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