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} | 4 | 2016-05-27T13:32:58Z | 2016-05-31T14:31:03Z | 2016-05-31T14:31:03Z | NONE | null | issue is explained here:
http://stackoverflow.com/questions/37484738/pandas-dataframe-construction-from-a-list-of-ordereddict-preserving-columns-ord?noredirect=1#comment62465925_37484738
workaround with constructor option "columns" works but not elegant.
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} | 1 | 2016-05-27T18:32:42Z | 2016-05-27T20:02:24Z | 2016-05-27T20:02:12Z | NONE | null | The `name` attribute of `dataframe.columns` does not get populated when rendering the `dataframe.style` property of the dataframe. This behavior occurs regardless of whether I set `styler.columns.name` explicitly.
#### Code Sample, a copy-pastable example if possible
import pandas as pd
test_df = pd.DataFrame({'A':range(4), 'B':range(4)})
test_df.columns.name = 'x_label'
test_df.index.name = 'y_label'
styler = test_df.style
styler.columns.name = 'x_label'
styler.render()
HTML output:
This is from `styler.render()`:
```
<tr>
<th class="blank">
<th class="col_heading level0 col0">A
<th class="col_heading level0 col1">B
</tr>
```
#### Expected Output
```
<tr>
<th class="blank">x_label
<th class="col_heading level0 col0">A
<th class="col_heading level0 col1">B
</tr>
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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https://api.github.com/repos/pandas-dev/pandas/issues/13306 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13306/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13306/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13306/events | https://github.com/pandas-dev/pandas/issues/13306 | 157,302,384 | MDU6SXNzdWUxNTczMDIzODQ= | 13,306 | Hour overflow in tz-aware datetime conversions | {
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} | 10 | 2016-05-27T22:38:17Z | 2016-06-02T18:00:21Z | 2016-06-02T18:00:21Z | NONE | null | Comparison of tz-aware timestamps fails across DST boundaries. The comment in tslib.pyx:3845
```
# TODO: this assumed sortedness :/
```
perhaps implies this is a known problem that was never resolved, so apologies if a new issue is not appropriate.
#### Self contained example
```
import pandas as pd
import numpy as np
# Manifesting issue:
ts_listA = ['2008-05-12 09:50:00-04:00',
'2008-05-12 09:50:18-04:00',
'2008-05-12 09:50:33-04:00',
'2008-05-12 09:50:33-04:00',
'2008-12-12 09:50:35-05:00',
'2008-05-12 09:50:32-04:00',
'2008-05-12 09:49:15-04:00',
'2008-05-12 09:50:49-04:00',
'2008-05-12 09:50:54-04:00']
ts_listB = ['2008-05-12 09:50:34-04:00',
'2008-05-12 09:50:35-04:00',
'2008-05-12 09:50:36-04:00',
'2008-05-12 09:50:40-04:00',
'2008-05-12 09:50:42-04:00',
'2008-05-12 09:50:43-04:00',
'2008-05-12 09:50:55-04:00',
'2008-05-12 09:50:55-04:00',
'2008-05-12 09:50:57-04:00']
df = pd.DataFrame({'dtA' : pd.to_datetime(ts_listA).tz_localize('US/Eastern'),
'dtB' : pd.to_datetime(ts_listB).tz_localize('US/Eastern')})
print (df.dtA - df.dtB) / pd.Timedelta('1 minutes')
# Underlying tslib.tz_convert call - similar problem
asi8 = np.array([1210600200000000000,
1210600218000000000,
1210600233000000000,
1210600233000000000,
1229093435000000000,
1210600232000000000,
1210600155000000000,
1210600249000000000,
1210600254000000000])
tz = pd.Timestamp('2008-05-12 12:00:00').tz_localize('US/Eastern').tz
result = pd.tslib.tz_convert(asi8, 'UTC', tz)
print pd.to_datetime(asi8)
print pd.to_datetime(result)
```
#### Output
Expected output:
```
0 -0.566667
1 -0.283333
2 -0.050000
3 -0.116667
4 308219.883333
5 -0.183333
6 -1.666667
7 -0.100000
8 -0.050000
Name: dtA, dtype: float64
```
Actual output of timedelta computation:
```
0 -0.566667
1 -0.283333
2 -0.050000
3 -0.116667
4 308219.883333
5 -60.183333
6 -61.666667
7 -60.100000
8 -60.050000
Name: dtA, dtype: float64
```
Computed timedelta for rows after the 2008-12-12 date are off by an hour.
Output of tz_convert:
```
DatetimeIndex(['2008-05-12 13:50:00', '2008-05-12 13:50:18',
'2008-05-12 13:50:33', '2008-05-12 13:50:33',
'2008-12-12 14:50:35', '2008-05-12 13:50:32',
'2008-05-12 13:49:15', '2008-05-12 13:50:49',
'2008-05-12 13:50:54'],
dtype='datetime64[ns]', freq=None)
DatetimeIndex(['2008-05-12 09:50:00', '2008-05-12 09:50:18',
'2008-05-12 09:50:33', '2008-05-12 09:50:33',
'2008-12-12 09:50:35', '2008-05-12 08:50:32',
'2008-05-12 08:49:15', '2008-05-12 08:50:49',
'2008-05-12 08:50:54'],
dtype='datetime64[ns]', freq=None)
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-504.1.3.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.1.1
setuptools: 20.3
Cython: None
numpy: 1.8.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
Jinja2: None
```
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} | 5 | 2016-05-27T22:48:21Z | 2016-06-03T15:04:28Z | 2016-06-03T15:04:18Z | CONTRIBUTOR | null | - [x] closes #13006
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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| null | 4 | 2016-05-27T22:48:33Z | 2016-05-27T22:55:53Z | 2016-05-27T22:52:41Z | CONTRIBUTOR | null | I cloned this repo and tried to generate docs:
``` pytb
D:\src\pandas\doc>python make.py html
[NbConvertApp] Converting notebook source/html-styling.ipynb to html
[NbConvertApp] Writing 620631 bytes to source\source/html-styling.html
Traceback (most recent call last):
File "c:\python35\lib\runpy.py", line 170, in _run_module_as_main
"__main__", mod_spec)
File "c:\python35\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Python35\Scripts\jupyter-nbconvert.exe\__main__.py", line 9, in <module>
File "c:\python35\lib\site-packages\jupyter_core\application.py", line 267, in launch_instance
return super(JupyterApp, cls).launch_instance(argv=argv, **kwargs)
File "c:\python35\lib\site-packages\traitlets\config\application.py", line 596, in launch_instance
app.start()
File "c:\python35\lib\site-packages\nbconvert\nbconvertapp.py", line 293, in start
self.convert_notebooks()
File "c:\python35\lib\site-packages\nbconvert\nbconvertapp.py", line 457, in convert_notebooks
self.convert_single_notebook(notebook_filename)
File "c:\python35\lib\site-packages\nbconvert\nbconvertapp.py", line 429, in convert_single_notebook
write_results = self.write_single_notebook(output, resources)
File "c:\python35\lib\site-packages\nbconvert\nbconvertapp.py", line 390, in write_single_notebook
output, resources, notebook_name=notebook_name)
File "c:\python35\lib\site-packages\nbconvert\writers\files.py", line 127, in write
with io.open(dest, 'w', encoding='utf-8') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'source\\source/html-styling.html'
Running Sphinx v1.4.1
Exception occurred while building, starting debugger:
Traceback (most recent call last):
File "D:\src\pandas\pandas\__init__.py", line 25, in <module>
from pandas import hashtable, tslib, lib
ImportError: cannot import name 'hashtable'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "c:\python35\lib\site-packages\sphinx\cmdline.py", line 243, in main
opts.warningiserror, opts.tags, opts.verbosity, opts.jobs)
File "c:\python35\lib\site-packages\sphinx\application.py", line 132, in __init__
confoverrides or {}, self.tags)
File "c:\python35\lib\site-packages\sphinx\config.py", line 289, in __init__
execfile_(filename, config)
File "c:\python35\lib\site-packages\sphinx\util\pycompat.py", line 130, in execfile_
exec_(code, _globals)
File "conf.py", line 16, in <module>
File "D:\src\pandas\pandas\__init__.py", line 31, in <module>
"the C extensions first.".format(module))
ImportError: C extension: 'hashtable' not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace --force' to build the C extensions first.
> d:\src\pandas\pandas\__init__.py(31)<module>()
-> "the C extensions first.".format(module))
(Pdb) q
'rm' is not recognized as an internal or external command,
operable program or batch file.
The system cannot find the path specified.
```
I noticed this exception `ImportError: cannot import name 'hashtable'` maybe 2 years ago, and as it's still here I thought to report it.
FYI, just in case, exception has nothing to do with nbconvert failure
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} | 3 | 2016-05-28T00:50:03Z | 2016-05-31T14:31:05Z | 2016-05-31T14:31:03Z | MEMBER | null | Title is self-explanatory. Closes #13304.
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} | 2 | 2016-05-28T03:17:01Z | 2016-05-28T17:30:48Z | 2016-05-28T17:30:44Z | CONTRIBUTOR | null | - [ ] closes #xxxx
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2 changes to make tests more strict:
- `assert_numpy_array_equal` now checks input is `np.ndarray`
- `assert_almost_equal` now checks inputs are the same class.
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} | 1 | 2016-05-28T04:51:59Z | 2016-05-29T14:45:37Z | 2016-05-29T14:45:37Z | MEMBER | null | - [x] passes `git diff upstream/master | flake8 --diff`
Found docstring example is broken:
- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_stata.html
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} | 12 | 2016-05-28T07:07:58Z | 2016-06-02T18:01:05Z | 2016-06-02T18:00:21Z | CONTRIBUTOR | null | - [x] closes #13306
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Bug: tz-converting tz-aware DateTimeIndex relied on index being sorted for correct results.
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} | 5 | 2016-05-28T22:45:50Z | 2016-05-30T13:44:38Z | 2016-05-30T13:44:38Z | MEMBER | null | In an attempt to squash a Python parser bug in which weirdly-formed floats weren't being checked for `nan`, the bug was traced back to a bug in the `maybe_convert_numeric` function of `pandas/src/inference.pyx`. Added tests for the bug in `test_lib.py` and adjusted the original `nan` tests in `na_values.py` to test all of the engines.
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When the group selection changes, the cache for `_selected_obj` needs to be reset so that `nth`, `head`, and `tail` can return consistent results.
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} | 31 | 2016-05-29T13:19:02Z | 2017-03-09T16:24:19Z | 2017-03-09T15:25:04Z | CONTRIBUTOR | null | closes #11666
My code is not pythonic enough, maybe need some refactor. Any comment is welcome.
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To use the category labels I would expect to say labels=True but instead you have to say labels=None.
It seems illogical to say labels=None when you want labels.
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} | 2 | 2016-05-29T22:33:11Z | 2016-05-30T14:21:44Z | 2016-05-30T14:21:42Z | MEMBER | null | 1) Moved no columns test from CParser-only to `common.py`
2) Moved erroneous placed skiprows tests into their proper place
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} | 6 | 2016-05-29T22:35:20Z | 2016-06-01T11:14:00Z | 2016-06-01T11:10:08Z | MEMBER | null | Fixes bug in which the Python parser failed to detect trailing `NaN` values in rows
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} | 5 | 2016-05-29T23:36:32Z | 2016-05-31T13:14:55Z | 2016-05-31T13:14:26Z | MEMBER | null | Title is self-explanatory.
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} | 5 | 2016-05-30T00:03:29Z | 2016-08-09T10:05:24Z | 2016-07-29T00:20:26Z | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
``` python
obsids = ['ESP_012345_6789', 'ESP_987654_3210']
imgids = ['APF00006np', 'APF0001imm']
data = [4.3, 9.8]
df = pd.DataFrame(dict(obsids=obsids, imgids=imgids, data=data))
df.to_hdf('testdf_no_cats.hdf', 'df',format='t', data_columns=True)
df.obsids = df.obsids.astype('category')
df.imgids = df.imgids.astype('category')
df.to_hdf('testdf_with_cats.hdf', 'df',format='t', data_columns=True)
print("No categories:")
print(pd.read_hdf('testdf_no_cats.hdf', 'df', where='obsids=B'))
print("With categories:")
print(pd.read_hdf('testdf_with_cats.hdf', 'df', where='obsids=B'))
```
### Actual Output:
``` bash
No categories:
Empty DataFrame
Columns: [data, imgids, obsids]
Index: []
With categories:
data imgids obsids
0 4.3 APF00006np ESP_012345_6789
```
#### Expected Output
Empty DataFrame for both cases.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.13.1+5155.g9baf452
nose: None
pip: 8.1.1
setuptools: 21.2.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: 0.7.2
IPython: 4.2.0
sphinx: 1.4.1
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 39 | 2016-05-30T01:53:56Z | 2016-06-02T23:58:48Z | 2016-06-02T23:16:42Z | MEMBER | null | Title is self-explanatory.
xref #12686 - I don't quite understand why these are marked (if at all) as internal to the C engine only, as the benefits for having these options accepted for the Python engine is quite clear based on the documentation I added as well.
Implementation simply just calls the already-written function in `pandas/parsers.pyx` - as it isn't specific to the `TextReader` class, crossing over to grab this function from Cython (instead of duplicating in pure Python) seems reasonable while maintaining that separation between the C and Python engines.
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} | 1 | 2016-05-30T10:54:53Z | 2016-05-31T15:43:14Z | 2016-05-31T15:43:14Z | NONE | null | When using pandas.to_numeric with errors='coerce' and non hashable objects in the series/dataframe pandas returns the same series/dataframe as passed in, instead of coercing the non-numeric objects to nans.
``` python
python -c "import pandas as pd; s = pd.Series([[13], 1.0, 'apple']); print(pd.to_numeric(s, errors='coerce'))"
```
The error is due to this line
https://github.com/pydata/pandas/blob/4de83d25d751d8ca102867b2d46a5547c01d7248/pandas/src/inference.pyx#L572
More generally this will affect any call to `maybe_convert_numeric` that has unhashable objects in the `values` param.
Suggested fix might be to update this check with something like
``` python
if val.__hash__ is not None and val in na_values:
blah
```
As there should never be a case where you would expect an unhashable object to be in a set of na_values
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-22-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 19.0
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 7 | 2016-05-30T16:55:24Z | 2016-05-31T16:23:44Z | 2016-05-31T15:43:14Z | NONE | null | - [x] closes #13324
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 10 | 2016-05-30T17:33:14Z | 2020-08-06T09:45:30Z | 2016-05-30T20:50:20Z | NONE | null | Hello everyone,
I would like to submit one very useful addition to rolling, namely the possibility to compute any statistics over a specific **time range.**
Indeed, my understanding is that `rolling(windows=5).mean()` computes, say, the mean over the last five observations.
Instead, it would be very useful to specify something like `rolling(windows=5,type_windows='time_range').mean() to get the rolling mean over the last 5 days.
So if your data starts on January 1 and then the next data point is on Feb 2nd, then the rolling mean for the Feb 2nb point is NA because there was no data on Jan 29, 30, 31, Feb 1, Feb 2.
I believe this would be very useful in settings where data represents trading data, so most of the time the data points are not equidistant in time. Still, you want to compute rolling metrics that are specified over the same delta.
What do you think?
Thanks!
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} | 3 | 2016-05-30T20:59:31Z | 2016-05-30T21:13:27Z | 2016-05-30T21:07:34Z | NONE | null | I'm sorry if this is the expected behavior, but I think this is odd and may be an issue.
Parsing the dates automatically using `read_csv` (with the keywords `parse_dates` and `date_parser`) is actually slower than reading the file without parsing any dates, manually putting the date columns together and then using `pandas.to_datetime` function on the newly created column to parse the dates.
Basically (assuming you have date and hour in two different columns in the csv) doing this
``` python
df = pd.read_csv("..\\file.csv", parse_dates = [['YYYYMMDD', 'HH']],
index_col = 0,
date_parser=parse)
```
is 3 to 5 times slower than this
``` python
df = pd.read_csv("..\\file.csv")
format = "%Y%m%d %H"
times = pd.to_datetime(df.YYYYMMDD + ' ' + df.HH, format=format)
df.set_index(times, inplace=True)
df = df.drop(['YYYYMMDD','HH'], axis=1)
```
I first found this issue comparing the two answers of [this question](http://stackoverflow.com/questions/11615504/parse-dates-when-yyyymmdd-and-hh-are-in-separate-columns-using-pandas-in-python) and thought it was weird but OK, maybe it was due to some feature in his dataset. But while the person on the answer reported a 3 times time difference between both approaches, when I did the comparison I found that the difference is 5 times for me.
This seems to me like an issue, since using the function's internal options should be faster, or at the least take the same time.
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} | 3 | 2016-05-30T22:11:37Z | 2018-07-14T21:51:10Z | 2018-07-14T20:18:54Z | NONE | null | Dividing a DataFrame by a `numpy.uint64` or `numpy.uint32` scalar causes python to segfault, on both 64-bit Linux and Mac OS X, and both Python 2.7.11 and 3.5.1. There appears to be a minimum DataFrame size to trigger this; the same operation with size 10, instead of 10^5, works fine.
#### Code Sample
```
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': np.ones(10**5, dtype='float64')})
df.values / np.uint64(1) # No problem
df / np.uint64(1) # Segfault!
```
#### Expected Output
The DataFrame divided by 1. The process would continue to run.
#### output of `pd.show_versions()` (Linux Python 2)
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-22-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
```
#### output of `pd.show_versions()` (Mac Python 3)
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: 2.8
```
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| null | 3 | 2016-05-31T08:34:49Z | 2018-06-26T09:21:05Z | 2016-05-31T11:44:01Z | NONE | null | Under Ubuntu 16, I am getting the following:
```
>>> import pandas as pd
>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> a = pd.DataFrame(a)
>>> a
0 1
0 1 2
1 3 4
>>> np.transpose(a)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "....../anaconda2/lib/python2.7/site-packages/numpy/core/fromnumeric.py", line 551, in transpose
return transpose(axes)
TypeError: transpose() takes exactly 1 argument (2 given)
>>> a.T
0 1
0 1 3
1 2 4
>>> np.transpose(a.as_matrix())
array([[1, 3],
[2, 4]])
```
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| null | 1 | 2016-05-31T08:58:41Z | 2016-05-31T11:52:00Z | 2016-05-31T11:51:01Z | NONE | null | In RHEL 7.2, with python 2.7.5
```
a = pd.np.random.randn(10, 2)
a.itemset((3, 0), pd.np.NAN)
df = pd.DataFrame(a)
res = df.describe()
```
---
Output:
```
/.../function_base.py:3823: RuntimeWarning: Invalid value encountered in percentile
RuntimeWarning)
0 1
count 8.000000 10.000000
mean -0.406550 -0.220345
std 1.517141 0.791003
min -1.942997 -1.607897
25% NaN -0.683385
50% NaN -0.281060
75% NaN 0.303566
max 2.275532 1.227234
```
---
pd.show_versions:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.5.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.18.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 0.9.8
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 1.5
pytz: 2012d
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.5.2
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
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} | 2 | 2016-05-31T13:02:42Z | 2016-05-31T13:05:58Z | 2016-05-31T13:05:43Z | NONE | null | #### Code Sample, a copy-pastable example if possible
``` python
from StringIO import StringIO
import pandas as pd
from random import random
s = "a,b,c"
df = pd.read_csv(StringIO(s))
f1 = lambda row: random()
new_col1 = df.apply(f1, axis=1)
df["d"] = new_col1
f2 = lambda row: 0 if row["a"] > 10 else 1
new_col2 = df.apply(f2, axis=1)
df["e"] = new_col2
df
```
#### Expected Output
```
Empty DataFrame
Columns: [a, b, c, d, e]
Index: []
```
The problem seems to be that a `Series` `row` is passed to the lambda and that 'a' can't be used to index into it. This is only breaks for an empty `DataFrame`. If it has values the output is as expected.
According to the docs:
> Objects passed to functions are Series objects having index either the DataFrame’s index (axis=0) or the columns (axis=1)
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-21-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 20.7.0
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
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} | 17 | 2016-05-31T13:03:15Z | 2016-08-22T20:35:12Z | 2016-08-22T20:35:12Z | CONTRIBUTOR | null | seems @tacaswell / @mdboom have been busy
https://github.com/matplotlib/matplotlib/releases/tag/v2.0.0b1
we should test with this.
is this going up on a `conda-forge` dev channel? (is there such a thing)?
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} | 0 | 2016-05-31T13:10:12Z | 2016-05-31T13:53:44Z | 2016-05-31T13:53:44Z | CONTRIBUTOR | null | {
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} | 4 | 2016-05-31T16:41:01Z | 2016-06-15T01:48:21Z | 2016-06-15T01:48:21Z | NONE | null | In python3 with certain sets of single precision floating point data pandas groupby is up to ~150 slower than the same data in python2
#### Code Sample, a copy-pastable example if possible
``` python
import numpy as np
import pandas as pd
from numpy.random import random
from time import time
def do_groupby(df):
df.groupby(['a'])['b'].sum()
def main():
tmp1 = (random(10000) * 0.1).astype(np.float32)
tmp2 = (random(10000) * 10.0).astype(np.float32)
tmp = np.concatenate((tmp1, tmp2))
arr = np.repeat(tmp, 100)
df = pd.DataFrame(dict(a=arr, b=arr))
t1 = time()
do_groupby(df)
print("Took: %s" % (time() - t1,))
main()
```
On my machine
python2 this_file.py
The groupby takes around 0.1s
Where as python3 this_file.py
The groupby takes around 11s
With some investigation the discrepancy in run time between python versions varies hugely between the actual data but seems to have the biggest difference when half the data is roughly 100 times smaller than the other half.
Having profiled this, it seems this function is taking almost all the 11s in the python3 version in this method
https://github.com/pydata/pandas/blob/master/pandas/hashtable.pyx#L538
However I have no idea what is causing the run time discrepancy.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-22-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IE.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 21.2.2
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.16.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.5.2
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.9
pymysql: 0.7.4.None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
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} | 14 | 2016-05-31T21:42:16Z | 2017-03-14T12:27:47Z | 2017-03-07T21:13:35Z | CONTRIBUTOR | null | - [x] closes #13247
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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https://api.github.com/repos/pandas-dev/pandas/issues/13338 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13338/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13338/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13338/events | https://github.com/pandas-dev/pandas/issues/13338 | 157,778,115 | MDU6SXNzdWUxNTc3NzgxMTU= | 13,338 | TST: slow computation/test_eval.py tests need adjustment | {
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} | 1 | 2016-05-31T21:46:16Z | 2016-05-31T23:18:16Z | 2016-05-31T23:18:16Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/134204278
I think #13311 broke some of these
here's some partial fixes
```
diff --git a/pandas/computation/tests/test_eval.py b/pandas/computation/tests/test_eval.py
index 4dc1e24..69b8830 100644
--- a/pandas/computation/tests/test_eval.py
+++ b/pandas/computation/tests/test_eval.py
@@ -248,7 +248,7 @@ class TestEvalNumexprPandas(tm.TestCase):
for ex in (ex1, ex2, ex3):
result = pd.eval(ex, engine=self.engine,
parser=self.parser)
- tm.assert_numpy_array_equal(result, expected)
+ tm.assert_almost_equal(result, expected)
def check_simple_cmp_op(self, lhs, cmp1, rhs):
ex = 'lhs {0} rhs'.format(cmp1)
@@ -265,7 +265,8 @@ class TestEvalNumexprPandas(tm.TestCase):
ex = 'lhs {0} rhs'.format(arith1)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
expected = _eval_single_bin(lhs, arith1, rhs, self.engine)
- tm.assert_numpy_array_equal(result, expected)
+
+ tm.assert_almost_equal(result, expected)
ex = 'lhs {0} rhs {0} rhs'.format(arith1)
result = pd.eval(ex, engine=self.engine, parser=self.parser)
nlhs = _eval_single_bin(lhs, arith1, rhs,
@@ -280,8 +281,10 @@ class TestEvalNumexprPandas(tm.TestCase):
# TypeError, AttributeError: series or frame with scalar align
pass
else:
+
+ # direct numpy comparison
expected = self.ne.evaluate('nlhs {0} ghs'.format(op))
- tm.assert_numpy_array_equal(result, expected)
+ tm.assert_numpy_array_equal(result.values, expected)
# modulus, pow, and floor division require special casing
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13339 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13339/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13339/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13339/events | https://github.com/pandas-dev/pandas/pull/13339 | 157,782,198 | MDExOlB1bGxSZXF1ZXN0NzIwOTQxNTU= | 13,339 | TST: computation/test_eval.py tests (slow) | {
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} | 0 | 2016-05-31T22:10:29Z | 2016-05-31T23:18:16Z | 2016-05-31T23:18:16Z | CONTRIBUTOR | null | closes #13338
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} | 22 | 2016-06-01T04:00:04Z | 2016-12-13T23:12:40Z | 2016-12-13T23:11:38Z | NONE | null | - [x] closes #12688
- [x] tests passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry - not needed
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} | 3 | 2016-06-01T13:13:06Z | 2016-11-26T09:13:24Z | 2016-11-26T09:13:05Z | CONTRIBUTOR | null | You can't see anything in the ax1 plot.
```
import pylab
import pandas as pd
pylab.ion(); pylab.show()
idx1 = pd.date_range('2015-01-01', periods=3, freq='1M')
idx2 = idx1[:1] + idx1[2:]
series1 = pd.Series(range(len(idx1)), idx1)
series2 = pd.Series(range(len(idx2)), idx2)
# this doesn't work
pylab.figure()
ax1 = pylab.subplot(211)
series1.plot(ax=ax1)
ax2 = pylab.subplot(212, sharex=ax1)
series2.plot(ax=ax2)
# this works
pylab.figure()
ax3 = pylab.subplot(211)
pylab.plot(series1.index, series1.values)
ax4 = pylab.subplot(212, sharex=ax3)
pylab.plot(series2.index, series2.values)
```
Issue seems to be with how its converting xdata
```
for ax in (ax1, ax2, ax3, ax4):
print(ax.get_lines()[0].get_xdata())
[Period('2015-01', 'M') Period('2015-02', 'M') Period('2015-03', 'M')]
[datetime.datetime(2015, 1, 31, 0, 0) datetime.datetime(2015, 3, 31, 0, 0)]
['2015-01-31T00:00:00.000000000+0000' '2015-02-28T00:00:00.000000000+0000'
'2015-03-31T00:00:00.000000000+0000']
['2015-01-31T00:00:00.000000000+0000' '2015-03-31T00:00:00.000000000+0000']
str(ax2.get_xticklabels()[0])
"Text(735636,0,'63273')"
str(ax4.get_xticklabels()[0])
"Text(735630,0,'Feb 01 2015')"
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Linux
OS-release: 3.14.32-pv-ts1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.2
pytz: 2016.3
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.8.5
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
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} | 4 | 2016-06-01T13:19:33Z | 2016-06-07T05:37:50Z | 2016-06-01T13:33:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
pd.to_datetime("06/2015") **# When my system date is 5/31/2016, it returns a string (06/2015)**
#### Expected Output
Timestamp('2015-06-01 00:00:00')
#### output of `pd.show_versions()`
'0.16.2'
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} | 1 | 2016-06-01T13:47:59Z | 2016-06-02T17:12:43Z | 2016-06-02T17:12:43Z | MEMBER | null | Small thing I just noticed in the docs (the note on the other version was not updated when the example was changed from cythonmagic -> Cython)
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} | 3 | 2016-06-01T16:02:23Z | 2016-06-01T18:16:13Z | 2016-06-01T17:31:56Z | NONE | null | #### Code Sample, a copy-pastable example if possible
As opposed to pandas, the default value for `ddof` in standard deviation calculations is 0 in numpy. However, when I use `groupby.agg(np.std)` I get the result for `ddof=1`.
```
import numpy as np
import pandas as pd
prng = np.random.RandomState(0)
df = pd.DataFrame({"A": prng.choice(list("abcdef"), 15),
"B": prng.randint(0, 10, 15)})
df.groupby('A')['B'].agg(np.std)
Out[54]:
A
a 3.511885
b NaN
c 2.828427
d 3.593976
e 1.527525
f 1.414214
Name: B, dtype: float64
```
#### Expected Output
There is a [note ](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.DataFrameGroupBy.agg.html) in the docs that states the default axis is changed but for me ddof change is quite unexpected. So my expected output is this:
```
df.groupby('A')['B'].apply(np.std)
Out[61]:
A
a 2.867442
b 0.000000
c 2.000000
d 3.112475
e 1.247219
f 1.000000
Name: B, dtype: float64
```
#### output of `pd.show_versions()`
INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_GB
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
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xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
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https://api.github.com/repos/pandas-dev/pandas/issues/13345 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13345/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13345/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13345/events | https://github.com/pandas-dev/pandas/issues/13345 | 157,968,074 | MDU6SXNzdWUxNTc5NjgwNzQ= | 13,345 | Working with pd.Period is very slow | {
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| null | 1 | 2016-06-01T17:52:27Z | 2016-06-01T17:59:21Z | 2016-06-01T17:58:24Z | NONE | null | I find working with `pd.Period` very slow. Much much slower than working with `pd.Timestamp` and I was wondering why that is. I have around 2 million records and I'm forced to work with full timestamps even though my data is really on the level of months. Here is an example that I think shows the discrepancy between working with these types.
#### Code Sample, a copy-pastable example if possible
``` python
import pandas as pd
import numpy as np
# create a data frame with 500K dates
dates = pd.date_range('1/1/2011', periods=500000, freq='H')
values = np.random.random(size=len(dates))
df = pd.DataFrame({"Date":dates, "Values":values})
```
Creating the period index is fairly slow
``` python
%%time
df["Period"] = df.Date.dt.to_period(freq="1M")
```
```
Wall time: 1.47 s
```
And using boolean indexing with periods is extremely slow
``` python
%%time
near_future = df[df.Period <= df.Period.iloc[0] + 3]
```
```
Wall time: 7.36 s
```
Especially when compared with using timestamps!
``` python
%%time
near_future = df[df.Date <= df.Date.iloc[0] + pd.Timedelta("90d")]
```
```
Wall time: 0 ns
```
Returning the values of the period is also very slow
``` python
%%time
periods = df.Period.unique()
```
```
Wall time: 17.4 s
```
``` python
%%time
times = df.Date.unique()
```
```
Wall time: 69 ms
```
Unrelated but I also found that I can only do the period comparison on a series in one direction. This throws an error even though all I did was flip the terms in the inequality
``` python
near_future = df[(df.Period.iloc[0] + 3 ) >= df.Period]
```
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-8-f626f4b8a04f> in <module>()
----> 1 near_future = df[(df.Period.iloc[0] + 3 ) >= df.Period]
pandas\src\period.pyx in pandas._period.Period.__richcmp__ (pandas\src\period.c:12753)()
TypeError: Cannot compare type 'Period' with type 'Series'
```
#### Output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 61 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 21.2.2
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
```
``` python
```
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} | 13 | 2016-06-01T19:21:03Z | 2020-10-05T20:52:52Z | 2020-10-05T20:52:52Z | NONE | null | ```
In [1]: import pandas as pd
In [2]: pd.Period(year=2300, month=1, day=1, freq='M').end_time
Out[2]: Timestamp('1715-07-14 00:25:26.290448383')
```
Input year is '2300', output shows '1715'
#### output of `pd.show_versions()`
In [3]: pd.show_versions()
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-25-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 7.1.2
setuptools: 21.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.1
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} | 1 | 2016-06-01T19:30:48Z | 2016-09-13T23:07:43Z | 2016-09-13T23:07:26Z | NONE | null | For consistency with read_csv, and because this would be handy anyway.
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} | 6 | 2016-06-02T17:46:14Z | 2021-05-01T21:45:52Z | null | CONTRIBUTOR | null | #### Code Sample, a copy-pastable example if possible
If I have a Df with a MultiIndex:
```
import numpy.random as nr
nr.seed(0)
d = DataFrame(nr.randint(0, 5, (4, 2)),
columns=pd.MultiIndex.from_product([['L1'], ['A', 'B']]))
d
''' L1
A B
0 4 0
1 3 3
2 3 1
3 3 2'''
```
and want to use the `query` method to get the following subset:
```
d[d.L1.A == 3]
'''
L1
A B
1 3 3
2 3 1
3 3 2'''
```
`query` doesn't seem to know about the column:
```
try:
d.query('L1.A == 3')
except Exception as e:
print(e)
# => name 'L1' is not defined
```
#### Expected Output
```
'''
L1
A B
1 3 3
2 3 1
3 3 2'''
```
Am I referencing the column incorrectly, or is this just not possible at the moment?
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 21.2.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 3.2.3
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
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} | 3 | 2016-06-02T21:51:23Z | 2017-03-23T20:21:12Z | 2016-07-29T00:25:24Z | MEMBER | null | `skipfooter` : "This line <a href="https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L729">here</a> says I am the right choice."
`skip_footer` : "This line <a href="https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L519">here</a> says I am the right choice."
Whatever the case is, something should certainly be done to avoid having duplicate arguments for the same thing, as can be seen <a href="https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L582">here</a>
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} | 23 | 2016-06-02T23:15:57Z | 2016-09-14T10:40:44Z | 2016-09-10T02:25:40Z | NONE | null | closes #13032
- [x] tests added / passed - added test specific to format bug
- [x] passes `pep8radius master --diff`
- [x] whatsnew entry - not needed
Found this bug experimenting with formatters. First pull request to pandas, but I believe guidelines are quite clear. I can explain what was happening in more detail if that is necessary.
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https://api.github.com/repos/pandas-dev/pandas/issues/13351 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13351/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13351/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13351/events | https://github.com/pandas-dev/pandas/pull/13351 | 158,265,190 | MDExOlB1bGxSZXF1ZXN0NzI0MzYxODU= | 13,351 | ENH: Adding json line parsing to pd.read_json #9180 | {
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} | 30 | 2016-06-02T23:56:56Z | 2016-07-24T15:37:50Z | 2016-07-24T14:14:06Z | CONTRIBUTOR | null | - [x] closes #9180
- [x] closed #13356
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 0 | 2016-06-02T23:57:36Z | 2016-07-10T21:12:56Z | 2016-07-10T21:12:56Z | MEMBER | null | Follow-up from #13323. This functionality can be made applicable outside of the parser and therefore should not exist at the parser level.
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} | 2 | 2016-06-03T00:47:45Z | 2021-05-12T13:27:56Z | 2021-05-12T13:27:56Z | NONE | null | There is a strange error happening during pandas.merge when there is a unicode column name followed by a numpy.float64 column name. The error only happens for certain numpy.float64 values. The error is: "UnicodeDecodeError: 'utf8' codec can't decode byte 0x80 in position 5: invalid start byte"
The following code reproduces the error:
```
import pandas as pd
import numpy as np
def my_test(X):
t=pd.DataFrame([[1, 2], [3, 4]])
u=pd.DataFrame([[9, 10], [11, 12]])
t.rename(columns={0:unicode('a'),1:np.float64(X)}, inplace=True)
u.rename(columns={0:unicode('x'),1:unicode('y')}, inplace=True)
pd.merge(u, t, how="inner", left_index=True, right_index=True)
#works fine for 113, but throws an error for 114
my_test(113)
my_test(114)
#print out the numbers up to 200 for which this error occurs:
problem_numbers=[]
for i in range(200):
try:
my_test(i)
except UnicodeDecodeError:
problem_numbers.append(i)
print(problem_numbers)
```
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} | 1 | 2016-06-03T07:56:45Z | 2016-06-18T15:25:15Z | 2016-06-18T15:25:15Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
costs = pd.DataFrame({'item':['bacon','cheese','bacon','cheese'],
'cost':[2.5,4.5,3.2,3.3],
'store':['SuperS','DollarM','SuperS','DollarM'],
'day':['M','M','T','T']})
costs.pivot_table(index="item",columns="day",margins=True, margins_name="Weekly",aggfunc=[np.mean,max])
```
Will ignore the margins_name
#### Expected Output
```
mean max
cost cost store
M T Weekly M T Weekly M T Weekly
item
bacon 2.5 3.20 2.850 2.5 3.2 3.2 SuperS SuperS SuperS
cheese 4.5 3.30 3.900 4.5 3.3 4.5 DollarM DollarM DollarM
All 3.5 3.25 3.375 4.5 3.3 4.5 SuperS SuperS SuperS
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 1.5.6
setuptools: 20.2.2
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.3
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
```
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| null | 10 | 2016-06-03T10:38:00Z | 2016-06-14T14:39:18Z | 2016-06-14T14:39:18Z | MEMBER | null | - [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
`assert_numpy_array_equal`'s docstring is at odds with (and the existence of the `obj` argument clashes with) the fact that it's now strict on its arguments being ndrarrays.
There are [practical reasons](https://github.com/pydata/pandas/pull/13205#discussion_r65358471) to allow other kinds of arguments.
On the other hand, I don't see any reason for being strict, but I assume @sinhrks 's [PR](https://github.com/pydata/pandas/pull/13311) had some rationale behind: so a possible solution is an argument `ensure_ndarray=True` which restores the previous behaviour when set to `False`.
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} | 1 | 2016-06-03T16:39:28Z | 2016-07-24T14:14:06Z | 2016-07-24T14:14:06Z | CONTRIBUTOR | null | the hooks already exist, just need to define the argument and pass thru to `_get_filepath_or_buffer`
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} | 9 | 2016-06-03T19:34:19Z | 2020-06-24T22:25:21Z | 2020-06-24T22:25:21Z | NONE | null | Hi, I'm wondering if the following behavior of pandas._testing.assert_almost_equal is expected:
#### Code Sample, a copy-pastable example if possible
```
from pandas import _testing
_testing.assert_almost_equal(0.000011, 0.000012, check_less_precise=True)
```
#### Expected Output
Expect no output / no AssertionError
#### Actual Output
```
AssertionError Traceback (most recent call last)
<ipython-input-199-ace78e82c603> in <module>()
1 from pandas import _testing
----> 2 _testing.assert_almost_equal(0.000011, 0.000012, check_less_precise=True)
pandas/src/testing.pyx in pandas._testing.assert_almost_equal (pandas/src/testing.c:3887)()
pandas/src/testing.pyx in pandas._testing.assert_almost_equal (pandas/src/testing.c:3653)()
AssertionError: expected 0.00001 but got 0.00001, with decimal 3
```
Note that the numbers differ at decimal 6 and the output suggests they are different at position 3.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.0.2
setuptools: 21.2.1
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: 2.3.3
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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} | 39 | 2016-06-03T19:34:49Z | 2016-06-17T00:10:57Z | 2016-06-17T00:09:32Z | CONTRIBUTOR | null | closes #1870
xref #2941
[Here](http://nbviewer.jupyter.org/gist/jreback/5f089d308750c89b2a7d7446b790c056) is a notebook of example usage and timings
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} | 3 | 2016-06-03T23:07:02Z | 2016-06-05T14:08:29Z | 2016-06-05T14:08:12Z | CONTRIBUTOR | null | - [x] closes #13231
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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} | 3 | 2016-06-04T02:33:30Z | 2016-06-05T13:57:54Z | 2016-06-05T13:57:52Z | MEMBER | null | `buffer_lines` is not respected, as it is determined internally via a heuristic involving `table_width` (see <a href="https://github.com/pydata/pandas/blob/master/pandas/parser.pyx#L527">here</a> for how it is computed).
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} | 34 | 2016-06-04T12:03:11Z | 2017-03-17T01:04:23Z | 2016-06-08T11:32:41Z | CONTRIBUTOR | null | I was looking into #10153 (parsing Categoricals directly) and one thing that seems to be needed
for that is a good way to combine Categoricals. That part alone is complicated enough
so I decided to do a separate PR for it.
This adds a `union_categoricals` function that takes a list of (identical dtyped, unordered)
and combines them without doing a full factorization again, unioning the categories.
This seems like it might be generically useful, does it belong the public api somewhere and/or
as a method a `Categorical`? Maybe as on option on `concat`?
Might also be useful for dask? e.g. https://github.com/dask/dask/issues/1040, cc @mrocklin
An example timing below. Obviously depends on the density of the categories,
but for relatively small numbers of categories, seems to generally be in 5-6x
speed up range vs concatting everything and re-categorizing.
``` python
from pandas.types.concat import union_categoricals
group1 = ['aaaaa', 'bbbbb', 'cccccc', 'ddddddd', 'eeeeeeee']
group2 = group1[3:] + ['fffffff', 'gggggggg', 'ddddddddd']
a = np.random.choice(group1, 1000000).astype('object')
b = np.random.choice(group2, 1000000).astype('object')
a_cat, b_cat = pd.Categorical(a), pd.Categorical(b)
In [10]: %timeit np.concatenate([a,b,a,b,a])
10 loops, best of 3: 82.7 ms per loop
In [12]: %timeit pd.Categorical(np.concatenate([a_cat.get_values(),b_cat.get_values(),
a_cat.get_values(),b_cat.get_values(),
a_cat.get_values()]))
1 loops, best of 3: 344 ms per loop
In [14]: %timeit union_categoricals([a_cat,b_cat,a_cat,b_cat,a_cat])
10 loops, best of 3: 40.9 ms per loop
```
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| null | 6 | 2016-06-04T14:55:11Z | 2016-06-04T21:34:44Z | 2016-06-04T21:34:44Z | NONE | null | I have a very large Data Frame that takes 10.7 GB when serialized with "to_csv", but it takes 6.7 GB when using "to_msgpack". When reading the serialized file with "read_msgpack", I get a type List of length Zero.
The same IO process on 200 MB Data Frame works perfectly, i.e. a Data Frame is returned by the function "read_msgpack".
#### Code Sample, a copy-pastable example if possible
type(df) -> pd.DataFrame
len(df) -> 39227674
df.to_msgpack('foo.msg')
df2 = pd.read_msgpack('foo.msg')
type(df2) -> List
len(df2) -> 0
#### Expected Output
type(df2) -> pd.DataFrame
len(df2) -> 39227674
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-49-virtual
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.1.2
pip: 8.1.2
setuptools: 1.4
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.0.0
sphinx: None
patsy: None
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| null | 4 | 2016-06-04T22:34:36Z | 2016-11-16T22:22:58Z | 2016-11-16T22:22:58Z | NONE | null | - [x ] closes #13354
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
…st #13354
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| null | 7 | 2016-06-04T22:48:03Z | 2017-02-01T20:53:15Z | 2017-02-01T20:53:15Z | CONTRIBUTOR | null | - [x] closes #12351
- [x] tests added / passed (`TestPeriodIndex.test_resample_hourly_business_hourly`)
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
I did this during pandas sprint at PyCon 2016. Hope this close #12351
Resampling with BusinessHour took much more consideration I guess.
Please review this. Any comments are welcome.
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} | 2 | 2016-06-04T22:51:02Z | 2021-05-01T22:02:32Z | null | MEMBER | null | #### Code Sample, a copy-pastable example if possible
```
In [2]: c = pd.CategoricalIndex(['3z53', '3z53', 'LoJG', 'LoJG', 'LoJG', 'N503'])
In [3]: s1 = pd.Series(2, index=c)
In [4]: s2 = pd.Series(2, index=c[:-1])
In [5]: s1*s2
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-7426096cfe41> in <module>()
----> 1 s1*s2
/home/pietro/nobackup/repo/pandas/pandas/core/ops.py in wrapper(left, right, name, na_op)
647 else:
648 index, lidx, ridx = left.index.join(rindex, how='outer',
--> 649 return_indexers=True)
650
651 if lidx is not None:
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in join(self, other, how, level, return_indexers)
2524 if not self.is_unique and not other.is_unique:
2525 return self._join_non_unique(other, how=how,
-> 2526 return_indexers=return_indexers)
2527 elif not self.is_unique or not other.is_unique:
2528 if self.is_monotonic and other.is_monotonic:
/home/pietro/nobackup/repo/pandas/pandas/indexes/base.py in _join_non_unique(self, other, how, return_indexers)
2616 join_index = self.values.take(left_idx)
2617 mask = left_idx == -1
-> 2618 np.putmask(join_index, mask, other._values.take(right_idx))
2619
2620 join_index = self._wrap_joined_index(join_index, other)
TypeError: putmask() argument 1 must be numpy.ndarray, not Categorical
```
#### Expected Output
No error
#### output of `pd.show_versions()`
```
In [6]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.0-2-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
pandas: 0.18.1+102.g0c6226c
nose: 1.3.7
pip: 1.5.6
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.16.0
statsmodels: 0.8.0.dev0+0fac349
xarray: None
IPython: 5.0.0.dev
sphinx: 1.3.1
patsy: 0.3.0-dev
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: 1.1.0dev
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: None
bs4: 4.4.0
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.0
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: 0.2.1
```
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} | 3 | 2016-06-04T23:19:03Z | 2016-06-07T02:02:56Z | 2016-06-05T13:51:02Z | NONE | null | It also seems that the texts of the `doc/README.rst` and the `doc/source/contributing.rst` have lots of overlap and duplication. Not sure if there are any plans in consolidating these two files but I thought to mention that here 😄
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| null | 1 | 2016-06-05T07:41:07Z | 2016-06-05T14:20:42Z | 2016-06-05T14:20:32Z | NONE | null | In `df.apply(f, axis=1)` , it means applying `f` on rows. However, In `df.drop([column_name], axis=1)`, it means dropping columns.
So, what is the trick to determine in which cases `axis=1` means functioning on rows while in other cases it means functioning on columns ?
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| null | 4 | 2016-06-05T14:23:02Z | 2016-06-05T18:06:10Z | 2016-06-05T18:06:10Z | CONTRIBUTOR | null | Corrected a small typo found when reviewing the script.
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| null | 1 | 2016-06-05T20:11:55Z | 2016-06-05T20:46:40Z | 2016-06-05T20:46:32Z | CONTRIBUTOR | null | Does Pandas come with any "sample" dataframes ready to be imported? Otherwise, does the library provide any utilities to _quickly_ create them randomly? (aside from the usual np. random utils). I'm thinking of examples with timestamps, categorical and numerical data.
Alternatively, are there any recommended packages that can help users load "sample" dataframes/series/panels?
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| null | 10 | 2016-06-05T20:55:14Z | 2021-05-01T22:07:16Z | null | NONE | null | In the following examples there are three different merge behaviours when it comes to handling NaNs.
They are all based on pd.merge(..., how="left", ...)
The difference depends on:
##### 1) Whether we are merging using an index or a column.
##### 2) Whether the column keys we are merging on are the same value or not (i.e. if left_on = right_on).
Arguably, if we specify "left" as the merging criterion, the desired behaviour is to have NaNs in the columns coming from the right dataframe where there is no match between the left and right dataframes' key columns (see first merge in example below, 'd' and 'e' columns).
The problem is, if we are merging on left's index, the NaNs get filled with the index values from the left dataframe even if the names of the two columns don't match ('c' and 'd' in the example). We are thus led to believe there was a perfect match between the index of the left dataframe and the "key" column of the right dataframe ('d' here).
#### Gotchas:
###### -There is something puzzling going on with the new indices of the resulting dataframe (when merging on index).
###### -Type casting occurs when merging on index, perhaps suggesting NaNs are explicitly filled in a second step.
#### Proposed behaviour:
Maybe it is simply a matter of removing this NaN filling step.
##### Better yet, the "key" column in the merged dataframe should perhaps bear the name of left's index not of the "right_on" key (provided we used left's index to merge). I.e. in the second merge of the example, the 'd' column should be called 'c'.
This is really the source of the confusion when the two names are different. When they are the same the "no NaN" behaviour is arguably legitimate.
Also it might be worthwhile to cast the final column back to the original dtype if there are no NaNs.
Maybe this is not really an issue though, more something to be aware of. I would be interested in hearing any motivation behind this behaviour.
##### Looking forward to reading your thoughts!
### Code Sample:
```
df1 = pd.DataFrame(columns=['a','b','c'], data=[[1,2,3],[4,5,6],[7,8,9],[10,11,12]])
df1_c_index = df1.set_index('c')
df2 = pd.DataFrame(columns=['d','e'], data=[[3,14],[6,15],[13,16]])
print 'df1', '\n', df1, '\n'
print 'df1_c_index', '\n', df1_c_index, '\n'
print 'df2', '\n', df2, '\n'
print "pd.merge(df1, df2, how='left', left_on='c', right_on='d')", '\n'
print pd.merge(df1, df2, how='left', left_on='c', right_on='d'), '\n'
print "pd.merge(df1_c_index, df2, how='left', left_index=True, right_on='d')", '\n'
print pd.merge(df1_c_index, df2, how='left', left_index=True, right_on='d'), '\n'
df2.rename(columns={'d':'c'}, inplace=True)
print 'df1', '\n', df1, '\n'
print 'df1_c_index', '\n', df1_c_index, '\n'
print 'df2', '\n', df2, '\n'
print "pd.merge(df1, df2, how='left', left_on='c', right_on='c')", '\n'
print pd.merge(df1, df2, how='left', left_on='c', right_on='c'), '\n'
print "pd.merge(df1_c_index, df2, how='left', left_index=True, right_on='c')", '\n'
print pd.merge(df1_c_index, df2, how='left', left_index=True, right_on='c'), '\n'
```
### Output:
### output of `pd.show_versions()`:
#### INSTALLED VERSIONS
---
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 21.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: 0.7.2
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
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} | 14 | 2016-06-05T22:02:12Z | 2016-06-06T23:16:54Z | 2016-06-06T23:14:50Z | MEMBER | null | 1) Documented and deprecate `as_recarray`
2) Added `as_recarray` functionality to Python engine
3) Fixed bug in C engine in which `usecols` was not being respected in combination with `as_recarray`
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} | 10 | 2016-06-05T23:16:34Z | 2017-09-08T13:04:30Z | 2016-11-25T21:21:03Z | MEMBER | null | On `master` (<a href="https://github.com/pydata/pandas/commit/b722222f5ea760a3f3df4d063309949eb4956674">b722222</a>):
``` python
>>> data = 'a,,b\n1,,a\n2,,"2,,b"'
>>> read_csv(StringIO(data), sep=',,', engine='python')
...
ValueError: Expected 2 fields in line 3, saw 3
```
I expect this command to work, but because no parsing is done on quoted fields as can be seen <a href="https://github.com/pydata/pandas/blob/master/pandas/io/parsers.py#L1847">here</a>, an extra field is produced, breaking the parser. Note that this does not affect the C parser because multi-char delimiters are not supported. Similar to what we saw in #10911 and #12775, but unless we want to write the `tokenizer.c` code in Python, a similar fix does not seem trivial.
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} | 6 | 2016-06-06T00:05:25Z | 2016-07-20T22:04:54Z | 2016-07-20T22:04:50Z | CONTRIBUTOR | null | New PR for what was started in #12086.
closes #7271
By passing a dict of {column name/column index: dtype}, multiple columns can be cast to different data types in a single command. Now users can do:
`df = df.astype({'my_bool', 'bool', 'my_int': 'int'})`
or:
`df = df.astype({0, 'bool', 1: 'int'})`
instead of:
```
df['my_bool'] = df.my_bool.astype('bool')
df['my_int'] = df.my_int.astype('int')
```
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} | 2 | 2016-06-06T02:35:12Z | 2016-06-06T12:10:51Z | 2016-06-06T12:10:41Z | CONTRIBUTOR | null | I don't know the exact intended phrasing here, but I think the writer might have meant "Provide"?
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} | 2 | 2016-06-06T08:56:35Z | 2016-06-06T12:09:45Z | 2016-06-06T12:09:42Z | MEMBER | null | So I wasn't 100% correct when I said that `float_precision` was documented <a href="https://github.com/pydata/pandas/issues/12686#issuecomment-222684918">here<a/>. It was well documented internally for `TextParser` and in a section for `io.rst`, but it wasn't listed formally in the parameters for the `read_csv` documentation.
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} | 9 | 2016-06-06T10:13:23Z | 2019-10-21T19:52:16Z | null | NONE | null | `>>> import pandas`
`>>> import io`
`>>> data = """A`
`20150908`
`20150909`
`"""`
`>>> t=pandas.read_csv(io.StringIO(data))`
`>>> t.dtypes`
`A int64`
`dtype: object`
`>>> t=pandas.read_csv(io.StringIO(data),parse_dates={'date':['A']},keep_date_col=True)`
`>>> t.dtypes`
`date datetime64[ns]`
`A object`
`dtype: object`
The second time, the datatype of the column 'A' should also be int64.
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} | 5 | 2016-06-06T13:14:08Z | 2021-05-26T02:09:32Z | 2021-05-26T02:09:32Z | MEMBER | null | I was answering a question on conditional formatting of html on [SO](http://stackoverflow.com/questions/37638402/pandas-html-output-conditional-formatting-highlight-cell-if-value-in-range/37647100?noredirect=1#comment62774932_37647100).
So the OP was using `to_html` but wanted conditional formatting. Therefore, I pointed to `Styler` to do this more easily than using the `formatters` argument in `to_html`, but when you have a `Styler` object, there is no longer a `to_html` method to write the result to a html file.
Of course you can write the result of `render` manually to a file, but would this ability be a desired enhancement?
- Maybe we can add a `to_file` method to `Styler`? (`to_html` seems a bit specific, if we also want to use the Styler for other formats such as latex in the future)
- Alternatively, we could add a `style` option in `to_html` where you can pass a Styler object?
Related to #11610
@TomAugspurger
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} | 0 | 2016-06-06T13:15:42Z | 2016-06-06T14:33:23Z | 2016-06-06T14:33:23Z | CONTRIBUTOR | null | makes it a little simpler to iterate on pieces of the cython code.
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} | 11 | 2016-06-06T14:21:05Z | 2016-06-08T11:24:06Z | 2016-06-08T11:24:04Z | MEMBER | null | Title is self-explanatory.
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} | 3 | 2016-06-06T18:08:11Z | 2016-06-08T15:27:07Z | 2016-06-08T15:27:07Z | CONTRIBUTOR | null | Code to replicate error:
```
import pandas as pd
s = pd.Series(["a13a23", "b13", "c13"], index=["A", "B", "C"])
s.str.extractall("[ab](\d\d)")
```
Note that the regex `[ab](\d)` from the documentation page works, whereas `[ab](\d\d)` above doesn't. It seems that any captured group with a length of > 1 causes this error.
Though playing with this a bit more, the following regex's all seem to work correctly without error:
```
([ab])(\d\d)
()[ab](\d+)
(a13)(\d\d)
```
I've reproduced the issue in both versions 0.18.0 and 0.18.1. I'll admit I've not checked against the master branch though.
Note: I posted this to the mailing list, but haven't had any responses - thus I assume this is a bug.
I'm unsure what the underlying cause is here (maybe it doesn't like the first regex character not being within a capture group?).
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} | 3 | 2016-06-07T00:19:52Z | 2016-06-21T10:09:29Z | 2016-06-21T10:09:29Z | CONTRIBUTOR | null | ```
In [171]: s = pd.Series(range(3))
In [172]: s.rolling(-1) # doesn't raise
Out[172]: Rolling [window=-1,center=False,axis=0]
In [173]: s.rolling(-1).mean() # Odd, indirect error
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-173-cda5b8dd0812> in <module>()
----> 1 s.rolling(-1).mean()
/home/mike/modernpandas/local/lib/python2.7/site-packages/pandas/core/window.pyc in mean(self, **kwargs)
885 @Appender(_shared_docs['mean'])
886 def mean(self, **kwargs):
--> 887 return super(Rolling, self).mean(**kwargs)
888
889 @Substitution(name='rolling')
/home/mike/modernpandas/local/lib/python2.7/site-packages/pandas/core/window.pyc in mean(self, **kwargs)
651
652 def mean(self, **kwargs):
--> 653 return self._apply('roll_mean', 'mean', **kwargs)
654
655 _shared_docs['median'] = dedent("""
/home/mike/modernpandas/local/lib/python2.7/site-packages/pandas/core/window.pyc in _apply(self, func, name, window, center, check_minp, how, **kwargs)
558 result = np.apply_along_axis(calc, self.axis, values)
559 else:
--> 560 result = calc(values)
561
562 if center:
/home/mike/modernpandas/local/lib/python2.7/site-packages/pandas/core/window.pyc in calc(x)
553
554 def calc(x):
--> 555 return func(x, window, min_periods=self.min_periods)
556
557 if values.ndim > 1:
/home/mike/modernpandas/local/lib/python2.7/site-packages/pandas/core/window.pyc in func(arg, window, min_periods)
540 # GH #12373: rolling functions error on float32 data
541 return cfunc(com._ensure_float64(arg),
--> 542 window, minp, **kwargs)
543
544 # calculation function
pandas/algos.pyx in pandas.algos.roll_mean (pandas/algos.c:28921)()
pandas/algos.pyx in pandas.algos._check_minp (pandas/algos.c:19103)()
ValueError: min_periods must be >= 0
In [174]: pd.Series([]).rolling(-1).mean() # Never raises
Out[174]: Series([], dtype: float64)
In [175]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-71-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.0.2
setuptools: 20.1.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: 1.3.2
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
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| null | 3 | 2016-06-07T10:41:47Z | 2016-06-07T12:30:10Z | 2016-06-07T12:07:17Z | NONE | null | I cannot find the `Series.nonzeros()` method in the [API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html#series) documentation-page for `pandas <= 0.18.1`.
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| null | 1 | 2016-06-07T14:50:40Z | 2016-06-07T16:54:12Z | 2016-06-07T16:53:58Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
s1 = pd.Series([np.nan, np.nan, np.nan])
print s1.fillna(False)
```
#### Expected Output
```
0 False
1 False
2 False
dtype: bool
```
but the output is:
```
0 0.0
1 0.0
2 0.0
dtype: float64
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.1
pip: 8.1.1
setuptools: 20.3.1
Cython: None
numpy: 1.11.0
scipy: 0.13.3
statsmodels: 0.5.0
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.2.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: 0.7.2.None
psycopg2: None
jinja2: None
boto: 2.40.0
```
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} | 40 | 2016-06-07T15:09:41Z | 2016-07-29T00:25:31Z | 2016-07-29T00:25:25Z | MEMBER | null | Title is self-explanatory.
Closes gh-13349 and partially undoes this <a href="https://github.com/pydata/pandas/commit/9ceea2fd46e79f37269a32de8c140cddf90eda13">commit</a> back in `v0.9.0`. With such a massive API now, having duplicate arguments makes managing it way less practical.
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} | 1 | 2016-06-07T16:49:29Z | 2016-06-07T16:51:47Z | 2016-06-07T16:51:31Z | NONE | null | `.describe()` is not ignoring the `Nan` values at percentiles, so it gives `Nan`:
http://pandas.pydata.org/pandas-docs/stable/basics.html#summarizing-data-describe
Before 0.18 it was working as expected:
http://pandas.pydata.org/pandas-docs/version/0.17.0/basics.html#summarizing-data-describe
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} | 5 | 2016-06-07T17:26:20Z | 2016-06-07T22:18:39Z | 2016-06-07T22:18:39Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/jobs/135897420
cc @gfyoung
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} | 1 | 2016-06-07T17:48:20Z | 2016-07-26T22:49:35Z | 2016-07-26T22:49:35Z | NONE | null | ``` python
>>> import pandas as pd
>>> df1 = pd.DataFrame({'entity_id': [101, 102]})
>>> df2 = pd.Series([None, None], index=[101, 102]).astype('timedelta64[D]').to_frame('days')
>>> print(df2); print(df2.dtypes)
days
101 NaT
102 NaT
days timedelta64[D]
dtype: object
>>> df1.merge(df2, left_on='entity_id', right_index=True)
...
File "/Users/brian/src/pandas/pandas/core/common.py", line 95, in isnull
return _isnull(obj)
File "/Users/brian/src/pandas/pandas/core/common.py", line 105, in _isnull_new
return _isnull_ndarraylike(obj)
File "/Users/brian/src/pandas/pandas/core/common.py", line 196, in _isnull_ndarraylike
result = np.isnan(values)
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
### Expected output:
```
entity_id days
0 101 NaT
1 102 NaT
```
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 22.0.5
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.1
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.7.3
boto: 2.38.0
pandas_datareader: None
(It also appears on master)
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} | 1 | 2016-06-07T18:24:03Z | 2016-06-07T20:22:47Z | 2016-06-07T20:22:30Z | NONE | null | #### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame(dict(a=[0,0,0,1,1,1,2,2,2], b=[0,2,1,3,5,4,7,6,8]))
df
def f(x):
x['c'] = 1
return x.sort_values('b')
df.groupby('a', as_index=False).apply(f).reset_index(drop=True)
```
#### Expected Output
```
pd.DataFrame(dict(a=[0, 0, 0, 1, 1, 1, 2, 2, 2],
b=[0, 1, 2, 3, 4, 5, 6, 7, 8],
c=[1, 1, 1, 1, 1, 1, 1, 1, 1]))
```
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-87-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 20.7.0
Cython: None
numpy: 1.11.0
scipy: 0.16.0
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: None
openpyxl: 2.2.2
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
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} | 1 | 2016-06-07T19:41:05Z | 2016-06-07T19:43:28Z | 2016-06-07T19:43:11Z | CONTRIBUTOR | null | x = pandas.DataFrame(np.nan, index=['a', 'b'], columns=['c', 'd'])
print x.sum()
I expected to see all NaN's in the resulting Series. However, if i have bottleneck installed i get a Series of all zeros instead of all NaNs.
This is with pandas 0.18.1.
Thanks for any comments or suggestions
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} | 9 | 2016-06-07T19:41:20Z | 2016-06-07T22:19:06Z | 2016-06-07T22:18:39Z | MEMBER | null | Traces back to bug in NumPy `v1.7.1` in which the `array_equivalent` method could not compare NumPy arrays with complicated dtypes. As `pandas` relies on this function to check NumPy array equality during testing, this commit adds a fallback method for doing so.
Closes #13388.
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} | 10 | 2016-06-07T22:11:53Z | 2017-11-08T20:25:47Z | 2017-11-08T20:25:47Z | NONE | null | I've confirmed the error does not occur in Pandas 0.16 on a similar machine.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
df = pd.DataFrame({'x': ['a', 'a', 'b'],
'y': [pd.Timestamp('2016-05-07 20:09:25+00:00'), pd.Timestamp('2016-05-07 20:09:29+00:00'), pd.Timestamp('2016-05-07 20:09:29+00:00')]})
df.groupby('x').count()
```
#### Observed output
---
```
ValueError Traceback (most recent call last)
<ipython-input-5-3119045de5b1> in <module>()
2 df = pd.DataFrame({'x': ['a', 'a', 'b'],
3 'y': [pd.Timestamp('2016-05-07 20:09:25+00:00'), pd.Timestamp('2016-05-07 20:09:29+00:00'), pd.Timestamp('2016-05-07 20:09:29+00:00')]})
----> 4 print df.groupby('x').count()
/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.pyc in count(self)
3754 blk = map(make_block, map(counter, val), loc)
3755
-> 3756 return self._wrap_agged_blocks(data.items, list(blk))
3757
3758
pandas/lib.pyx in pandas.lib.count_level_2d (pandas/lib.c:23068)()
ValueError: Buffer has wrong number of dimensions (expected 2, got 1)
```
#### Expected Output
```
x y
a 2
b 1
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-32-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.1
pip: 8.1.2
setuptools: 21.2.1
Cython: None
numpy: 1.11.0
scipy: 0.16.0
statsmodels: 0.5.0
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.2.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.5.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: 0.5.8
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.36.0
pandas_datareader: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/13394 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/13394/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/13394/comments | https://api.github.com/repos/pandas-dev/pandas/issues/13394/events | https://github.com/pandas-dev/pandas/pull/13394 | 159,050,127 | MDExOlB1bGxSZXF1ZXN0NzI5NjcxODg= | 13,394 | BUG: Fix groupby with "as_index" for categorical multi #13204 | {
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} | 20 | 2016-06-07T23:50:50Z | 2016-07-03T23:28:58Z | 2016-07-03T23:28:31Z | CONTRIBUTOR | null | - [x] closes #13204
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Fixes a bug that returns all nan's for groupby(as_index=False) with
multiple column groupers containing a categorical one (#13204).
---
Also:
fixes an internal bug in the string representation of `Grouping`.
``` python
mi = pd.MultiIndex.from_arrays([list("AAB"), list("aba")])
df = pd.DataFrame([[1,2,3]], columns=mi)
gr = df.groupby(df[('A', 'a')])
gr.grouper.groupings
...
TypeError: not all arguments converted during string formatting
```
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} | 28 | 2016-06-08T01:15:07Z | 2016-08-29T12:29:04Z | 2016-08-29T12:29:04Z | MEMBER | null | This should also be noted in the docstring for the method.
Currently, it sometimes returns numpy arrays:
```
>>> pd.DatetimeIndex(['2000-01-01', '2000-01-02']).unique()
DatetimeIndex(['2000-01-01', '2000-01-02'], dtype='datetime64[ns]', freq=None)
>>> pd.Index([0, 1, 2]).unique()
array([0, 1, 2])
```
Most of the work here is probably writing comprehensive tests to check each index type.
xref: https://github.com/pydata/pandas/pull/13361/files/17209f92330c5e949934aec9dea039b35faf6e40#r66179418
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| null | 1 | 2016-06-08T08:43:28Z | 2016-06-08T08:44:04Z | 2016-06-08T08:44:04Z | NONE | null | #### Code Sample, a copy-pastable example if possible
First, relevant imports:
``` python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
```
This doesn't work:
``` python
pd.DataFrame(np.random.randn(100, 3)).hist()
plt.tight_layout()
```
It raises the following error:
```
AttributeError: 'NoneType' object has no attribute 'is_bbox'
```
I suppose this happens because it makes 2x2 plot grid but uses only three subplots. If I use all subplots, then it works:
``` python
pd.DataFrame(np.random.randn(100, 4)).hist()
plt.tight_layout()
```
#### Expected Output
Expected to get tight layout with 2x2 subplot grid.
It should be possible to make this work, because the following works with with matplotlib:
``` python
plt.figure()
plt.subplot(2, 2, 1)
plt.plot([1,2,3])
plt.subplot(2, 2, 2)
plt.plot([4,5,6])
plt.subplot(2, 2, 3)
plt.plot([7,8,9])
plt.tight_layout()
```
#### output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.5.4-gnu-1
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_DK.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.2.1
Cython: 0.24
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: 1.4.2
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
```
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} | 4 | 2016-06-08T12:35:01Z | 2016-06-08T15:27:11Z | 2016-06-08T15:27:07Z | CONTRIBUTOR | null | - [X] closes #13382
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [X] whatsnew entry
Note that I had to fix it based on this [thread](http://stackoverflow.com/questions/2111759/whats-the-best-practice-for-handling-single-value-tuples-in-python), rather than directly with `[x for x in ['ab']]` as this broke previous tests.
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} | 26 | 2016-06-08T13:18:20Z | 2016-07-02T19:31:58Z | 2016-07-02T19:31:58Z | CONTRIBUTOR | null | Although the requirement in the doc says that the input can be a file like object, it doesn't work with objects from tempfile. On Windows, they can't be reopened, so I need to pass the object itself.
#### Code Sample, a copy-pastable example if possible
```
import pandas as pd
from tempfile import TemporaryFile
new_file = TemporaryFile("w+")
dataframe = pd.read_table(new_file, skiprows=3, header=None, sep=r"\s*")
```
#### Expected Output
Not an exception!
#### output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 3.5.1.final.0
python-bits: 64
pandas: 0.18.0
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| null | 11 | 2016-06-08T13:51:13Z | 2016-07-26T10:51:50Z | 2016-07-26T10:51:50Z | CONTRIBUTOR | null | see [here](http://pythonpaste.org/tempita/)
might replace some of the manual code paths in `generate_code.py`
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| null | 1 | 2016-06-08T14:39:00Z | 2021-05-01T22:09:02Z | null | NONE | null | Instead of just histograms or kde, i would like to be able to ask scatter_matrix to put the name of the series in the diagonal panels.
Below is an example of what i did "manually". just notice how the diagonal panel is used to give the name of the series (in that case i also renamed the series to something descriptive before plotting, but that is the responsibility of the user of course)
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} | 5 | 2016-06-08T15:09:59Z | 2020-05-02T04:04:37Z | 2020-05-02T04:04:37Z | CONTRIBUTOR | null | xref https://github.com/pydata/pandas/pull/13351#issuecomment-224614415
We use `io/common/_get_handle` and `io/common/get_filepath_or_buffer` in various places in most of the io modules, need to unify encoding handling to a somewhat simpler interface (1 function).
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} | 3 | 2016-06-08T18:46:37Z | 2016-06-09T13:31:57Z | 2016-06-08T18:50:26Z | NONE | null | I've noticed performance differences between column and row wise function applications under windows 8.1. The problem most likely roots back the memory storage of the numpy arrays, which are per default 'C' like, hence causing for all column based pandas operations signifcant performance differences vs row wise operations.Under linux the problem is not present (or beeing taken care of explicity). If this is hard to fix in windows, I would argue to change the default behaviour to 'Fortran' like memory storaging (column by column), as arguably most time series data is rather of type many rows vs few columns.
#### Code Sample, a copy-pastable example if possible
```
import numpy as np
import pandas as pd
num_sim = 1000
f = np.array(np.random.standard_normal((num_sim,num_sim)), order='F')
c = np.array(np.random.standard_normal((num_sim,num_sim)), order='C')
df_f = pd.DataFrame(f)
df_c = pd.DataFrame(c)
```
#### Expected Output
I expect similiar performance of colum or row wise operations.
```
%timeit df_f.mean()
100 loops, best of 3: 1.9 ms per loop
%timeit df_f.mean(axis=1)
100 loops, best of 3: 10.3 ms per loop
%timeit df_c.mean()
100 loops, best of 3: 10.2 ms per loop
%timeit df_c.mean(axis=1)
100 loops, best of 3: 1.86 ms per loop
```
#### output of `pd.show_versions()`
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 8.1
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.0
setuptools: 20.2.2
Cython: 0.24
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.1
dateutil: 2.5.0
pytz: 2016.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: 0.6.2.None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: 0.2.0
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