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MDU6SXNzdWUxODc4NDY2Nzc=
| 14,608 |
Feature Request: expose full DOM nodes to converters in html_read
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[] | null | 12 | 2016-11-07T22:23:04Z | 2019-04-29T11:05:51Z | null |
NONE
| null |
With #13461 bringing `converters` in `read_html`, I would be very interested in having the full DOM nodes exposed to them : the added flexibility would make easy to perform tasks like the one described in #13141 (extracting links instead of text, or any other non-displayed information).
The old behavior could be emulated by changing the default converter (currently `None`) to something like
```
def default_converter(tag):
remove_whitespace(tag.text)
```
Would this feature be interesting or is there a reason to stick with the current parsing ?
P.S : I am new to pandas and git-(hub) so please let me know if there is anything wrong in this post.
edit :
The following code emulates the treatement on a random exemple. I would say that in general `first_link`, `links` (and `whitespace`) would be the the most used converters and would worth to be predefined as suggested below.
Sometimes it is interesting to parse input tags like `<input id="hidObj_12546" value="1" type="hidden">` so we may also want `input_id` and `input_value` or even `img_alt`. But if the goal is to just cover typical usecases, I would say that `links` and `whitespace` are sufficient, with maybe another one for documentation purpose.
<details>
```
from __future__ import (print_function)
import pandas as pa
import re
def series_key(tag):
return re.search(r'KEY=(.*)',tag.a['href']).group(1)
def first_link(tag):
return tag.a['href']
def default_conv(tag):
return tag.text #whitespaces should be removed
def changed_read_html(io, match='.+', flavor=None, header=None, index_col=None, skiprows=None,
attrs=None, parse_dates=False, tupleize_cols=False, thousands=',', encoding=None,
decimal='.', converters=None, na_values=None, keep_default_na=True):
original_parse_raw_data = pa.io.html._HtmlFrameParser._parse_raw_data
def new_parse_raw_data(self, rows):
data = [[col for col in self._parse_td(row)] for row in rows]
return data
pa.io.html._HtmlFrameParser._parse_raw_data = new_parse_raw_data
df = pa.read_html(io, match, flavor, header, index_col, skiprows,
attrs, parse_dates, tupleize_cols, thousands, encoding,
decimal, converters, na_values, keep_default_na)
pa.io.html._HtmlFrameParser._parse_raw_data = original_parse_raw_data
return df
converters = {0 : default_conv, 1 : series_key, 2 : default_conv, 3 : default_conv}
ecb_old = pa.read_html('http://sdw.ecb.europa.eu/', match='Selected Indicators for the Euro Area',
attrs = {'class': 'tableopenpage'})
ecb = changed_read_html('http://sdw.ecb.europa.eu/', match='Selected Indicators for the Euro Area',
attrs = {'class': 'tableopenpage'}, converters = converters)
print("actual read_html\n")
print(ecb_old)
print("\nproposed read_html\n")
print(ecb)
```
</details>
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| 2 | 2016-11-08T00:22:31Z | 2017-04-29T07:08:31Z | 2016-11-08T00:30:59Z |
NONE
| null |
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.1
setuptools: 28.7.1
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.0
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
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ENHANCEMENT to_clipboard index=False
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| 3 | 2016-11-08T02:07:10Z | 2016-11-08T13:50:07Z | 2016-11-08T02:44:52Z |
NONE
| null |
#### ENHANCEMENT: to_clipboard(index=False)
Commonly in my work I am pasting a column of data into another program that I have no api for as an input. I cannot have the index index in the input. I will generally use the .to_clipboard() method paste in excel, then copy the column from excel. I would really like to avoid opening another program to solve this problem. Would there be a way to add a keyword argument such that the index is not copied to the clipboard?
```python
import pandas as pd
df = pd.DataFrame({'a':[1,2,3,4]})
df.to_clipboard(index=False)
```
#### Expected Output
_Values in clipboard_
```
a
1
2
3
4
```
_rather than_
```
a
0 1
1 2
2 3
3 4
```
#### Output of ``pd.show_versions()``
<details>
# INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1.post20160901
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
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numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: None
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xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
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BLD: Add missing inline declarations
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| 7 | 2016-11-08T03:52:38Z | 2016-12-31T09:58:11Z | 2016-12-31T05:35:18Z |
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Pandas Keyerror
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| 6 | 2016-11-08T08:04:21Z | 2016-11-08T08:57:51Z | 2016-11-08T08:34:51Z |
NONE
| null |
#### A small, complete example of the issue
```start_time = timeit.default_timer()
np.set_printoptions(suppress=True)
df=pd.read_csv('/Users/sudarshan/Desktop/masterproject/SampleDatasets/dataset3.csv')
print (df.isnull().any())
df.dropna(inplace=True)
print (df.isnull().any())
#df= df.fillna(lambda x: x.median())
just_dummies = df['Flags'].str.get_dummies(sep='|')
dfMod = pd.concat([df, just_dummies], axis=1)
print dfMod.head()
print(dfMod.columns.tolist())
dfMod['A'] = dfMod['A'].astype(float)
dfMod['C'] = dfMod['C'].astype(float)
dfMod['E'] = dfMod['E'].astype(float)
dfMod['F'] = dfMod['F'].astype(float)
dfMod['P'] = dfMod['P'].astype(float)
dfMod['R'] = dfMod['R'].astype(float)
dfMod['S'] = dfMod['S'].astype(float)
print(dfMod.columns.tolist())
#print (df.head())
#le = preprocessing.LabelEncoder()
#le.fit(df['Flags'])
#print le.classes_
#df['Flags']=le.transform(df['Flags'])
#print (df.head())
X = df.as_matrix(columns=['Packets','Bytes','Duration','A','C','E','F','P','R','S'])
X[ ~np.isfinite(X) ] = 0
print "converted into matrix"
print X[0:5,:]
print (np.any(np.isnan(X)))
y = df['Type']
scaler = preprocessing.StandardScaler().fit(X)
standardized_X= Imputer().fit_transform(X)
print standardized_X
```
Traceback (most recent call last):
File "randomforest5changingflags.py", line 36, in <module>
dfMod['C'] = dfMod['C'].astype(float)
File "/Users/sudarshan/anaconda3/lib/python2.7/site-packages/pandas/core/frame.py", line 2056, in __getitem__
return self._getitem_column(key)
File "/Users/sudarshan/anaconda3/lib/python2.7/site-packages/pandas/core/frame.py", line 2063, in _getitem_column
return self._get_item_cache(key)
File "/Users/sudarshan/anaconda3/lib/python2.7/site-packages/pandas/core/generic.py", line 1385, in _get_item_cache
values = self._data.get(item)
File "/Users/sudarshan/anaconda3/lib/python2.7/site-packages/pandas/core/internals.py", line 3515, in get
loc = self.items.get_loc(item)
File "/Users/sudarshan/anaconda3/lib/python2.7/site-packages/pandas/indexes/base.py", line 2091, in get_loc
return self._engine.get_loc(self._maybe_cast_indexer(key))
File "pandas/index.pyx", line 138, in pandas.index.IndexEngine.get_loc (pandas/index.c:4149)
File "pandas/index.pyx", line 160, in pandas.index.IndexEngine.get_loc (pandas/index.c:4013)
File "pandas/src/hashtable_class_helper.pxi", line 732, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13161)
File "pandas/src/hashtable_class_helper.pxi", line 740, in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13115)
KeyError: 'C'
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
</details>
Python 2.7 using pandas 0.19.1rc version
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append / concat category with different categories fails in HDF5
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| 9 | 2016-11-08T08:33:54Z | 2019-10-21T19:34:18Z | null |
NONE
| null |
Hi,
just a remark, I guess it must be fairly complicated to replicate the behaviour from pandas ( appending categoricals, with different categories) into HDF5.
Currently this fails
```
pd1.to_hdf(store_file,"/ISE_nombre_de_tabla",format="table",append=True)
pd2..to_hdf(store_file,"/ISE_nombre_de_tabla",format="table",append=True)
.
.
.
1698 if new_metadata is not None and cur_metadata is not None \
1699 and not array_equivalent(new_metadata, cur_metadata):
-> 1700 raise ValueError("cannot append a categorical with "
1701 "different categories to the existing")
1702
ValueError: cannot append a categorical with different categories to the existing
```
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.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: None
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
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: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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DateOffset should have a property for a "normalized" shortcut string
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[] | null | 2 | 2016-11-08T09:26:57Z | 2016-11-08T09:45:19Z | 2016-11-08T09:45:19Z |
NONE
| null |
Following discussion in https://github.com/pydata/pandas-datareader/pull/257
`DateOffset` should have a property for a "normalized" shortcut string
So for example we should have
```python
to_offset("7Min").normalized_string == "7T"
to_offset("7MIN").normalized_string == "7T"
to_offset("7min").normalized_string == "7T"
```
Maybe something like
```python
@property
def normalized_string(self):
if self.n == 1:
return self.rule_code
else:
return str(self.n) + offset.rule_code
```
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Unable to pass additional arguments to resample().apply()
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| 2 | 2016-11-08T16:46:26Z | 2018-08-22T12:24:24Z | 2018-08-22T12:24:24Z |
NONE
| null |
```python
import pandas as pd
import numpy as np
def stuff(vals, th):
return np.mean(vals)
rng = pd.date_range('1/1/2011', periods=72, freq='H')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
df_res = ts.resample("D").apply(stuff, 10)
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.0
pip: 9.0.0
setuptools: 28.7.1
Cython: 0.20.2
numpy: 1.11.2
scipy: 0.13.2
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.3.1
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: None
jinja2: 2.8
boto: None
pandas_datareader: None
<details>
In this version, it seems to be impossible to apply a function with an additional argument (which is not used in the example above): checking the Pandas 0.19.1 code, what happens is that the grouped argument of the function _groupby_and_aggregate gets the first value of the *args argument passed from the function aggregate, which is clearly wrong.
Traceback (most recent call last):
File "test.py", line 9, in <module>
df_res = ts.resample("D").apply(stuff, 10)
File "/usr/local/lib/python2.7/site-packages/pandas/tseries/resample.py", line 324, in aggregate
**kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/tseries/resample.py", line 405, in _groupby_and_aggregate
result = grouped.apply(how, *args, **kwargs)
File "/usr/local/lib/python2.7/site-packages/pandas/core/groupby.py", line 694, in apply
return self._python_apply_general(f)
File "/usr/local/lib/python2.7/site-packages/pandas/core/groupby.py", line 697, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self._selected_obj,
AttributeError: 'int' object has no attribute 'apply'
</details>
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MDU6SXNzdWUxODgwNjk5OTQ=
| 14,616 |
Feature Request: Keep only these columns (vs. dropping all the ones you don't want)
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[] | null | 5 | 2016-11-08T18:38:04Z | 2021-04-20T13:01:14Z | 2016-11-08T20:58:41Z |
NONE
| null |
Apologies if this has been submitted or considered in the past, I searched through the GitHub issues and couldn't find any information pertaining to this.
The idea is that instead of specifying all of the columns that you wish to delete from a `DataFrame` via the `.drop` method, you specify instead the columns you wish to keep through a `.keep_cols` method - all other columns are deleted. This would save typing in cases where there are many columns, and we only want to keep a small subset of columns. The prime use case here is method chaining, where using `[[` doesn't really work in the middle of many methods being chained together.
-------------------
#### A small, complete example of the issue
```python
import pandas as pd
# Create an example DataFrame
data = [
[1, 'ABC', 4, 10, 6.3],
[2, 'BCD', 10, 9, 11.6],
[3, 'CDE', 7, 4, 10.0],
[4, 'DEF', 7, 10, 5.4],
[5, 'EFG', 2, 9, 5.3],
]
data = pd.DataFrame(data,
columns = ['Id', 'Name', 'Rating1', 'Rating2', 'ThisIsANumber'])
# Just want columns Id and Ratings2
new_data = data.drop(['Name', 'Rating1', 'ThisIsANumber'], axis = 1)
new_data.head()
# ** It would be nice to be able to only specify the columns we want
# ** to keep to save typing - similar to dplyr in R
def keep_cols(DataFrame, keep_these):
"""Keep only the columns [keep_these] in a DataFrame, delete
all other columns.
"""
drop_these = list(set(list(DataFrame)) - set(keep_these))
return DataFrame.drop(drop_these, axis = 1)
new_data = data.pipe(keep_cols, ['Id', 'Rating2'])
new_data.head()
# In this specific example there was not much more typing between
# `.drop` and the `keep_cols` function, but often when a `DataFrame`
# has many columns this is not the case!
```
In this contrived example I created a `keep_cols` function as a rough draft of a `.keep_columns` method to the `DataFrame` object, and used the `.pipe` method to pipe that function to the DataFrame as if it were a method.
I don't think using `[[` cuts if here. Yes, doing `new_data[['Id', 'Rating2]]` would work, but when method chaining, people often want to drop columns somewhere in the middle of a bunch of methods.
Just in case it's helpful, here's a good article demonstrating the power/beauty of method chaining in Pandas: https://tomaugspurger.github.io/modern-1.html.
Thanks!
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| 188,092,021 |
MDU6SXNzdWUxODgwOTIwMjE=
| 14,617 |
Columns and Index share the same numpy object underneath when pd.DataFrame.cov is used
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| 4 | 2016-11-08T20:13:26Z | 2017-02-28T14:28:38Z | 2017-02-28T14:28:37Z |
NONE
| null |
#### A small, complete example of the issue
```python
In [1]: import pandas as pd
import numpy as np
In [2]: df = pd.DataFrame(np.random.randn(4 * 1000).reshape(1000, 4), columns=list("abcd"))
In [3]: c = df.cov()
In [4]: c.index is c.columns
Out[4]: True
In [5]: c.index.name = "ABC"
In [6]: c.columns.name
Out[6]: 'ABC'
```
#### Expected Output
```python
In[7]: c.index is c.columns
False
```
#### Output of ``pd.show_versions()``
<details>
In [8]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.36.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.0
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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to_stata + read_stata results in NaNs (close to double precision limit)
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| 15 | 2016-11-08T21:00:02Z | 2016-11-17T12:54:05Z | 2016-11-17T12:54:05Z |
NONE
| null |
#### Explanation
Saving and loading data as stata results in a lot of NaNs.
I think the code & output is pretty self-explanatory, otherwise please ask.
I've not been able to test this on other systems yet.
If this is somehow expected behaviour, maybe a bigger warning would be in order.
#### A small, complete example of the issue
```python
from numpy.random.mtrand import RandomState
from pandas import DataFrame, read_stata
pth = '/tmp/demo.dta'
rs = RandomState(seed=123456789)
data = (2 * rs.rand(1000, 400).astype('float64') - 1) * 1.7976931348623157e+308
colnames = tuple('c{0:03d}'.format(k) for k in range(data.shape[1]))
frame = DataFrame(data=data, columns=colnames)
with open(pth, 'w+') as fh:
frame.to_stata(fh)
with open(pth, 'r') as fh:
frame2 = read_stata(fh)
print(frame2.tail())
```
#### Expected Output
index c000 c001 c002 c003 \
995 995 1.502566e+308 1.019238e+308 -1.169342e+308 6.845363e+307
996 996 -3.418435e+307 -8.113486e+307 2.544741e+306 5.771775e+307
997 997 1.507324e+308 4.610183e+307 -1.016633e+308 -1.632862e+308
998 998 -8.138620e+307 6.312126e+307 -6.557370e+307 6.342690e+307
999 999 -1.179032e+308 1.554709e+308 -1.175680e+308 1.921731e+307
c004 c005 c006 c007 \
995 1.611898e+308 -5.171776e+307 -8.918000e+307 -5.322720e+307
996 3.693405e+307 -1.480267e+308 1.586053e+308 7.489689e+306
997 1.060605e+308 -6.826590e+307 1.644990e+308 -1.379562e+308
998 1.379642e+308 1.005632e+307 -1.206948e+308 -1.198931e+308
999 -5.965607e+307 8.844623e+307 2.727894e+307 -5.433995e+307
c008 c009 ... c390 \
995 -6.580851e+306 1.284482e+308 ... -1.770789e+308
996 -9.312612e+307 -1.778315e+308 ... 7.410784e+307
997 -9.415141e+307 9.058828e+307 ... -5.451829e+305
998 1.651712e+308 4.435415e+307 ... 5.220773e+307
999 -1.747738e+308 -1.603248e+308 ... 1.415798e+307
c391 c392 c393 c394 \
995 7.360232e+307 -3.850417e+307 1.453624e+308 5.690363e+307
996 -6.943490e+307 1.047268e+308 4.026712e+307 9.161669e+305
997 4.406343e+306 1.617739e+308 4.218585e+307 1.573892e+307
998 -2.390131e+307 -6.649416e+307 6.548489e+307 1.000078e+307
999 -1.239203e+308 -5.038284e+307 -1.340608e+307 -1.193758e+308
c395 c396 c397 c398 c399
995 8.371989e+307 3.491895e+307 7.344525e+307 -9.260950e+307 1.032120e+308
996 9.200510e+307 -1.729595e+308 4.021503e+307 2.274318e+307 5.856302e+307
997 -7.624901e+307 -1.206386e+308 -6.164537e+306 -7.634148e+307 -1.462809e+308
998 -9.399560e+307 9.697224e+307 -6.963726e+307 -1.655656e+308 1.513218e+308
999 -1.476121e+308 1.187603e+308 1.402195e+308 -1.584051e+308 -1.232190e+308
[5 rows x 401 columns]
#### Actual Output
index c000 c001 c002 c003 \
995 995 NaN NaN -1.169342e+308 6.845363e+307
996 996 -3.418435e+307 -8.113486e+307 2.544741e+306 5.771775e+307
997 997 NaN 4.610183e+307 -1.016633e+308 -1.632862e+308
998 998 -8.138620e+307 6.312126e+307 -6.557370e+307 6.342690e+307
999 999 -1.179032e+308 NaN -1.175680e+308 1.921731e+307
c004 c005 c006 c007 \
995 NaN -5.171776e+307 -8.918000e+307 -5.322720e+307
996 3.693405e+307 -1.480267e+308 NaN 7.489689e+306
997 NaN -6.826590e+307 NaN -1.379562e+308
998 NaN 1.005632e+307 -1.206948e+308 -1.198931e+308
999 -5.965607e+307 8.844623e+307 2.727894e+307 -5.433995e+307
c008 ... c390 c391 \
995 -6.580851e+306 ... -1.770789e+308 7.360232e+307
996 -9.312612e+307 ... 7.410784e+307 -6.943490e+307
997 -9.415141e+307 ... -5.451829e+305 4.406343e+306
998 NaN ... 5.220773e+307 -2.390131e+307
999 -1.747738e+308 ... 1.415798e+307 -1.239203e+308
c392 c393 c394 c395 \
995 -3.850417e+307 NaN 5.690363e+307 8.371989e+307
996 NaN 4.026712e+307 9.161669e+305 NaN
997 NaN 4.218585e+307 1.573892e+307 -7.624901e+307
998 -6.649416e+307 6.548489e+307 1.000078e+307 -9.399560e+307
999 -5.038284e+307 -1.340608e+307 -1.193758e+308 -1.476121e+308
c396 c397 c398 c399
995 3.491895e+307 7.344525e+307 -9.260950e+307 NaN
996 -1.729595e+308 4.021503e+307 2.274318e+307 5.856302e+307
997 -1.206386e+308 -6.164537e+306 -7.634148e+307 -1.462809e+308
998 NaN -6.963726e+307 -1.655656e+308 NaN
999 NaN NaN -1.584051e+308 -1.232190e+308
[5 rows x 401 columns]
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 9.0.1
setuptools: 26.1.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.2
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
</details>
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| 188,115,663 |
MDExOlB1bGxSZXF1ZXN0OTI4Njc5NjI=
| 14,619 |
removed disabled tag to run clipboard tests on traviss
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[] | null | 1 | 2016-11-08T22:01:50Z | 2016-11-08T23:36:34Z | 2016-11-08T23:36:21Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
@jreback this is pull request without the unicode fix and test_clipboard enabled
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| 188,146,962 |
MDExOlB1bGxSZXF1ZXN0OTI4OTEyMjk=
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COMPAT: Fix indent level bug preventing wrapper function rename
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| 16 | 2016-11-09T01:17:30Z | 2017-03-28T22:44:43Z | 2017-03-28T22:44:42Z |
NONE
| null |
Original code intends to rename the wrapper function f using the provided name, but this isn't happening because code is incorrectly indented an extra level.
```python
Example:
>>> from pandas.core.groupby import GroupBy
>>> GroupBy.sum.__name__
'f'
```
Should be 'sum'.
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dateutil 2.6 gives segfault in normalizing timestamp with datetutil timezone
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| 17 | 2016-11-09T07:07:02Z | 2016-11-12T15:59:51Z | 2016-11-12T15:59:51Z |
NONE
| null |
Newly release dateutil 2.6.0 breaks some of the tests related to the use of dateutil timezones (travis is therefore currently failing)
---
Original report:
#### A small, complete example of the issue
I maintain central installs of miniconda environments that include pandas. My previous environment with pandas 0.19.0, if I did this
```python
python -c "import pandas; pandas.test('fast')"
```
it worked. Now with pandas 0.19.1, it seg faults. Other packages may have been updated in the new environment.
Below are details - first the failure in my ana-1.0.5 environment, it is clearly segfaulting on a test maybe 2/3 the way through? Then the success in my ana-1.0.4 environment, then the pd.get_versions() in the working old environment, and finally in the newer environment where it fails:
<details>
```
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ python -c "import pandas; pandas.test('fast')"
Running unit tests for pandas
pandas version 0.19.1
numpy version 1.11.2
pandas is installed in /reg/g/psdm/sw/conda/inst/miniconda2-dev-rhel7/envs/ana-1.0.5/lib/python2.7/site-packages/pandas
Python version 2.7.12 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
nose version 1.3.7
/reg/g/psdm/sw/conda/inst/miniconda2-dev-rhel7/envs/ana-1.0.5/lib/python2.7/site-packages/nose/importer.py:94: FutureWarning: The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2.
See here for a guide on how to port your code to rpy2: http://pandas.pydata.org/pandas-docs/stable/r_interface.html
mod = load_module(part_fqname, fh, filename, desc)
...................................................................................S...........................S.......S.......S.......S.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S............SS.....................................................................................................................................................................................................S............SS...S................................................................................................................................................................................S............SS..S...............S...................................S.........S.SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.SSSSS.S........SS...SS.SS.....................................................................................................................S.......................S......................................................................SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS..SS..................................................S.............................................................................................................................................................................................................................................................................................................................................................S................................................................................S..SS.....SSSSSSSSSSSSSSSSSSSSSSSSSSS....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S..S...............................................................Segmentation fault (core dumped)
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ (ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ python -c "import pandas; pandas.test('fast')"
-bash: syntax error near unexpected token `pslogin7a:'
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ Running unit tests for pandas
-bash: Running: command not found
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ pandas version 0.19.1
-bash: pandas: command not found
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ numpy version 1.11.2
-bash: numpy: command not found
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ pandas is installed in /reg/g/psdm/sw/conda/inst/miniconda2-dev-rhel7/envs/ana-1.0.5/lib/python2.7/site-packages/pandas
-bash: pandas: command not found
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ Python version 2.7.12 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
-bash: syntax error near unexpected token `[GCC'
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ nose version 1.3.7
-bash: nose: command not found
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ source activate ana-1.0.4
(ana-1.0.4) pslogin7a: ~/rel/slaclab_conda/anarel-test $ python -c "import pandas; pandas.test('fast')"
Running unit tests for pandas
pandas version 0.19.0
numpy version 1.11.2
pandas is installed in /reg/g/psdm/sw/conda/inst/miniconda2-dev-rhel7/envs/ana-1.0.4/lib/python2.7/site-packages/pandas
Python version 2.7.12 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:42:40) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
nose version 1.3.7
/reg/g/psdm/sw/conda/inst/miniconda2-dev-rhel7/envs/ana-1.0.4/lib/python2.7/site-packages/nose/importer.py:94: FutureWarning: The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2.
See here for a guide on how to port your code to rpy2: http://pandas.pydata.org/pandas-docs/stable/r_interface.html
mod = load_module(part_fqname, fh, filename, desc)
..............................................................................................................S.......S.......S.......S..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S............SS..................................................................................................................................................................................................S............SS...S.............................................................................................................................................................................S............SS..S...............S...................................S.........S.SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS.SSSSS.S........SS...SS.SS.....................................................................................................................S.......................S......................................................................SSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS..SS..................................................S.............................................................................................................................................................................................................................................................................................................................................................S................................................................................S..SS.....SSSSSSSSSSSSSSSSSSSSSSSSSSS..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S..S.......................................................................................................................................................................................................S..............................................................S...S..............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S....................................................................................................................................................................................................................................................................................................................SSS.......................................................................................................................................................................................................S..................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S............................................SS.....S...SS.......S.S.......................SS.....S...SS.......S.S........................................................................................................................................................S.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S.....................................S.............S..............................................................................................................................................................................................................................................................................................................................................................................S.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S...........................S.........................................................................................................................................................................................................................................................................................................................................................S........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................S........................................................................................................S.........SS..........................................................................................................................SS..............................................................................................................................................................................................................................................................................................................................................................................S.................................................................................................................................................................................................................................................................................................................................................................................................................................................
----------------------------------------------------------------------
Ran 10625 tests in 701.643s
OK (SKIP=537)
(ana-1.0.4) pslogin7a: ~/rel/slaclab_conda/anarel-test $ python -c "import pandas; pandas.show_versions()"
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.13.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
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: None
pandas_datareader: None
(ana-1.0.4) pslogin7a: ~/rel/slaclab_conda/anarel-test $ source activate ana-1.0.5
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $ python -c "import pandas; pandas.show_versions()"
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.13.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
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: None
pandas_datareader: None
(ana-1.0.5) pslogin7a: ~/rel/slaclab_conda/anarel-test $
```
</details>
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| 188,207,352 |
MDU6SXNzdWUxODgyMDczNTI=
| 14,622 |
Consistent columns naming for candlesticks
|
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| 3 | 2016-11-09T10:01:32Z | 2016-11-13T10:04:25Z | 2016-11-12T16:02:43Z |
NONE
| null |
Resampling timeseries with Pandas resample and ohlc method and Pandas DataReader should output DataFrame with same columns name to provide a consistent columns naming.
Currently this is not consistent.
```python
In [1]: import pandas_datareader as pdr
In [2]: df = pdr.DataReader("IBM", "google")
In [2]: df
Out[2]:
Open High Low Close Volume
Date
2010-01-04 131.18 132.97 130.85 132.45 6155846
2010-01-05 131.68 131.85 130.10 130.85 6842471
2010-01-06 130.68 131.49 129.81 130.00 5605290
2010-01-07 129.87 130.25 128.91 129.55 5840569
2010-01-08 129.07 130.92 129.05 130.85 4197105
... ... ... ... ... ...
2016-11-02 152.48 153.34 151.67 151.95 3074438
2016-11-03 152.51 153.74 151.80 152.37 2878803
2016-11-04 152.40 153.64 151.87 152.43 2470368
2016-11-07 153.99 156.11 153.84 155.72 3706214
2016-11-08 154.56 NaN NaN 155.17 3921944
```
Pandas DataReader uses `Open`, `High`, `Low`, `Close` column name for candlesticks
```python
In [3]: price = df["Close"]
In [4]: price.resample("W").ohlc()
Out[4]:
open high low close
Date
2010-01-10 132.449997 132.449997 129.550003 130.850006
2010-01-17 129.479996 132.309998 129.479996 131.779999
2010-01-24 134.139999 134.139999 125.500000 125.500000
2010-01-31 126.120003 126.330002 122.389999 122.389999
2010-02-07 124.669998 125.660004 123.000000 123.519997
... ... ... ... ...
2016-10-16 157.020004 157.020004 153.720001 154.449997
2016-10-23 154.770004 154.770004 149.630005 149.630005
2016-10-30 150.570007 153.350006 150.570007 152.610001
2016-11-06 153.690002 153.690002 151.949997 152.429993
2016-11-13 155.720001 155.720001 155.720001 155.720001
[358 rows x 4 columns]
```
Pandas uses `open`, `high`, `low`, `close` column name for candlesticks
We should change this on pandas side or on pandas-datareader side.
PS: on pandas_datareader side
https://github.com/pydata/pandas-datareader/issues/260
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| 14,623 |
to_htlm + read_html small errors for floats despite formatter
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[] | null | 3 | 2016-11-09T11:08:07Z | 2016-11-09T12:06:19Z | 2016-11-09T11:44:55Z |
NONE
| null |
#### Description
Storing a double precision float as HTML (`to_html`) and loading it back (`read_html`) loses precision, even though `float_format` has enough precision.
I saw that `float_format` is working, but even with way too many digits, it fails to recover the original number.
In contrast, just calling `float` or `float64` on the string-formatted number works perfectly fine.
#### A small, complete example of the issue
```python
from pandas import DataFrame, read_html
def floatformat(val):
return '{:.16e}'.format(val)
x = 1.18047406523e+307
s = floatformat(x)
y = float(s)
assert x == y
frame = DataFrame(data=[[x]], columns=['a'])
pth = '/tmp/demo.dta'
with open(pth, 'w+') as fh:
frame.to_html(fh, float_format=floatformat)
with open(pth, 'r') as fh:
frame2 = read_html(fh)[0]
assert frame.a[0] == frame2.a[0], floatformat(frame.a[0] - frame2.a[0])
assert x == frame2.a[0]
```
#### Expected Output
Nothing
#### Actual Output
The last two assertions fail:
AssertionError: -4.9896007738367995e+291
#### Output of ``pd.show_versions()``
same as #14618
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| 188,245,555 |
MDU6SXNzdWUxODgyNDU1NTU=
| 14,624 |
IndexError using converters in read_html
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[] | null | 2 | 2016-11-09T13:19:05Z | 2020-05-08T17:33:08Z | null |
NONE
| null |
`read_html` returns a list of DF. Giving a `converters` parameter (see #13461) applies the converters on each DF. Keys of the converters, when being integers, can not be greater than the number of columns minus 1 of the parsed DF (otherwise it raises an IndexError exception in io.parser.PythonParser._convert_data ).
But most of the time, DFs returned by `read_html` are of different sizes. Thus converters are unusable on all columns of index greater `min([len(df.column) for df in pd.read_html(url)])`
#### Example
```python
import pandas
def converter_one(c):
return 1
d = {0 : converter_one, 1 : converter_one}
d2 = {0 : converter_one, 1 : converter_one, 2 : converter_one}
url = 'https://web.archive.org/web/20160419075502/http://clients.rte-france.com/lang/an/visiteurs/vie/prod/prevision_production.jsp'
pandas.read_html(url, converters = d2)
```
#### Expected Output
As in
```python
pandas.read_html(url, converters = d)
```
This issue may also prevent to change `read_html`as proposed in #14608
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
LOCALE: fr_FR.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: 1.0b8
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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| 188,321,825 |
MDU6SXNzdWUxODgzMjE4MjU=
| 14,625 |
to_hdf(mode='a') overwrites HDF table, does not append
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| 2 | 2016-11-09T18:45:30Z | 2019-03-27T23:54:03Z | 2016-11-09T18:54:30Z |
NONE
| null |
This seems to be the same issue as #4584 (https://github.com/pandas-dev/pandas/issues/4584), but the issue seems to persist.
I'm using Pandas 0.19.1. The docs state that for to_hdf(), "mode = 'a' " means "Append; an existing file is opened for reading and writing"
http://pandas.pydata.org/pandas-docs/version/0.19.1/generated/pandas.DataFrame.to_hdf.html
But the following code example shows to_hdf(... mode='a') is overwriting, not appending
```
df1 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df2 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df3 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
df4 = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
for df in (df1, df2, df3, df4):
df.to_hdf('example.h5', 'my_hdf_table', mode='a', format='table')
store = pd.HDFStore('example.h5')
print(store.items)
#prints "/my_hdf_table frame_table (typ->appendable,nrows->100,..."
```
However, using `df.to_hdf('example.h5', 'my_hdf_table2', format='table', append=True)` does result in appending, not overwriting.
Maybe this is a documentation issue not a code issue? Why do we have "mode = a" described as "append" if a separate non-default option "append = True" is still required for appending?
As always, thanks for all the work on a fantastic library.
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TST: indexes/test_base.py:TestIndex.test_format is flaky
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| 1 | 2016-11-09T21:21:39Z | 2016-11-12T16:08:53Z | 2016-11-12T16:08:53Z |
CONTRIBUTOR
| null |
`indexes/test_base.py:TestIndex.test_format` compares `index.format()` and `str(index[0])`, where `index = Index([datetime.now()])`. These won't match if the current timestamp ends with zeros:
```python
In [2]: i = pd.Index([pd.Timestamp('2012-01-01 00:00:00.111111')])
In [3]: j = pd.Index([pd.Timestamp('2012-01-01 00:00:00.111000')])
In [4]: i.format(), [str(i[0])]
Out[4]: (['2012-01-01 00:00:00.111111'], ['2012-01-01 00:00:00.111111'])
In [5]: j.format(), [str(j[0])]
Out[5]: (['2012-01-01 00:00:00.111'], ['2012-01-01 00:00:00.111000'])
```
Same thing for real: https://travis-ci.org/pandas-dev/pandas/jobs/173440088#L1355
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numpy 1.11.0 nonuniform binning
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| 15 | 2016-11-09T21:48:57Z | 2021-05-02T00:45:05Z | null |
NONE
| null |
Binning continuous data into discrete bins is a non-trivial problem, especially when there are features of various scales that one is trying to capture. This [astropy example](http://docs.astropy.org/en/stable/visualization/histogram.html) is much more clear than any I could hope to write. The current implementation of `pandas.cut` does not allow for easy user access to these tools.
`numpy` provides a number of simple binning algorithms to capture these features in computationally efficient ways. As done in `astropy`, perhaps we can pass the bin edge calculations directly to `numpy`. To do so, we would need to call `histogram_bin_edges` (available in `numpy` 1.15.1, if not earler). This function isolates the calculation of bin edges.
To utilize `np.histogram_bin_edges`, a user would call
```
data = np.random.normal(100000)
edges = np.histogram_bin_edges(data, bins="auto") # Other strings are acceptable.
cut = pd.cut(data, bins=edges)
```
We can simplify this by replacing one or two lines in [pandas.cut source](https://github.com/pandas-dev/pandas/blob/v0.23.4/pandas/core/reshape/tile.py)
```
bins = np.linspace(mn, mx, bins + 1, endpoint=True)
```
becomes
```
bins = np.histogram_bin_edges(x, bins=bins)
```
so that the user can simply call
```
data = np.random.normal(100000)
cut = pd.cut(data, bins="auto") # Or any of the other strings accepted by numpy
```
and have access to all the techniques available there.
The `histogram_bin_edges` docs are availble [here](https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.histogram_bin_edges.html). The source is [here](https://github.com/numpy/numpy/blob/v1.15.1/numpy/lib/histograms.py).
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MDU6SXNzdWUxODgzODg4NDc=
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to_csv execution unreliable in combination with for loop
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| 3 | 2016-11-10T00:30:51Z | 2016-11-11T08:49:38Z | 2016-11-10T15:23:59Z |
NONE
| null |
I am outputting a single column from a dataframe to a csv. The data, however, is too long for some older downstream applications, so I have the csv add line breaks every `size` items so that individual rows in the output aren't too long.
#### Example
```python
from tkinter import Tk
from tkinter import filedialog
import pandas as pd
import numpy as np
Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing
infilename = filedialog.askopenfilename()# show an "Open" dialog box and return the path to the selected file
data = pd.read_csv(infilename, header=None) #usecols=[0], only get 1st column, specify no header
outfilename = filedialog.asksaveasfile() #get save location
size=50 #number of items per line
col=0
indexes = np.arange(0,len(data),size)#have to use numpy since range is now an immutable type in python 3
indexes = np.append(indexes,[len(data)]) #add the uneven final index
for i in range(len(indexes)-1):
holder = pd.DataFrame(data.iloc[indexes[i]:indexes[i+1],col]).T
holder.to_csv(outfilename, index=False, header=False)
```
#### Expected Output
for input:
A,B,C,D,E,F,G,H,I,J,K #this is actually a column in the input, rendered horizontal for space
Becomes in the file (size=3):
A,B,C
D,E,F
G,H,I
J,K
Despite not throwing any errors, the final loop (with the uneven final index) does not write to the file, even though the information is assigned to `holder` without issue. Also, despite my not setting the `mode` param, acts as `w` and overwrites file if existing on the first loop, then acts as `a` and appends to the file on subsequent loops, indicating some possible statefullness which may be relevant. Since no errors are thrown, I cannot figure out why the final information is not being written.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
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: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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| 188,431,905 |
MDExOlB1bGxSZXF1ZXN0OTMwOTUxMzE=
| 14,629 |
DOC: Ordering during set operation on index (#3123)
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| 1 | 2016-11-10T06:40:35Z | 2016-11-10T08:07:18Z | 2016-11-10T08:07:18Z |
MEMBER
| null |
- [x] closes #3123
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MDU6SXNzdWUxODg0NzM4MzQ=
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pd.read_csv dtype error when there is str"inf" in the float column
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| 1 | 2016-11-10T10:38:49Z | 2016-11-12T16:00:31Z | 2016-11-12T16:00:31Z |
NONE
| null |
#### A small, complete example of the issue
pd.read_csv dtype error when there is string "inf" in one column of the csv file.
part file detail:
31 1.159451e+261
32 8.804630e+291
33 1.355031e+293
34 2.288852e+303
35 inf
36 4.439159e+261
37 1.809108e+282
38 2.257864e+250
39 8.623610e+295
Name: FT64, dtype: float64
df = pd.read_csv(src_path+os.sep+file, dtype=float) : will be **error**
but when i use this code :+1: df = pd.read_csv(src_path+os.sep+file), the csv can be read in. and
the element type is object(str). i can use df.astype(float) to convert the df.
i am just confusing why use read_csv(file, dtype=float) is not useful
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BUG/WIP: segfault manifesting with dateutil=2.6 w.r.t. replace when timezones are present
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| 25 | 2016-11-10T13:18:42Z | 2016-11-12T15:59:51Z | 2016-11-12T15:59:51Z |
CONTRIBUTOR
| null |
closes #14621
This is very close, but a stubborn fall transition issue. any ideas? (note that this does not solve #7825 but should be a simple change after I think)
cc @ischwabacher
cc @rockg
cc @sinhrks
cc @adamgreenhall
cc @pganssle
```
(pandas) [Thu Nov 10 09:14:13 ~/pandas]$ nosetests pandas/tseries/tests/test_offsets.py pandas/tseries/tests/test_timezones.py -s
...................S..S...........................................................................................................................................................................F...........................S..............................................................S...S.......................F................................................................................................................................................
======================================================================
FAIL: test_fallback_plural (pandas.tseries.tests.test_offsets.TestDST)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/tseries/tests/test_offsets.py", line 4900, in test_fallback_plural
expected_utc_offset=hrs_post)
File "/Users/jreback/pandas/pandas/tseries/tests/test_offsets.py", line 4850, in _test_all_offsets
self._test_offset(offset_name=name, offset_n=n, **kwds)
File "/Users/jreback/pandas/pandas/tseries/tests/test_offsets.py", line 4856, in _test_offset
self.assertTrue(get_utc_offset_hours(t) == expected_utc_offset)
AssertionError: False is not true
======================================================================
FAIL: test_ambiguous_flags (pandas.tseries.tests.test_timezones.TestTimeZoneSupportDateutil)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/pandas/pandas/tseries/tests/test_timezones.py", line 524, in test_ambiguous_flags
freq="H"))
AssertionError: Times[28 chars]00', tz='dateutil//usr/share/zoneinfo/Europe/London', freq='H') != Times[28 chars]00', tz='dateutil//usr/share/zoneinfo/Europe/London', freq='H')
----------------------------------------------------------------------
Ran 458 tests in 5.788s
FAILED (SKIP=5, failures=2)
```
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DOC: Outdated matplotlib style setting
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| 5 | 2016-11-10T13:44:10Z | 2016-11-11T08:25:11Z | 2016-11-11T08:25:07Z |
CONTRIBUTOR
| null |
http://pandas.pydata.org/pandas-docs/version/0.19.1/visualization.html
```python
import matplotlib
matplotlib.style.use('ggplot')
```
I think this should be removed since currently the way to do this is:
```python
import matplotlib.pyplot as plt
plt.style.use('ggplot')
```
I can fix this.
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| 188,530,781 |
MDU6SXNzdWUxODg1MzA3ODE=
| 14,633 |
Setting 2D slice of a Panel
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| 3 | 2016-11-10T15:02:01Z | 2018-07-06T20:21:12Z | 2017-07-10T21:04:28Z |
CONTRIBUTOR
| null |
#### Issue
Assigning to a Panel with two vector indexers and one scalar indexer produces unexpected behaviour: the vector indexers are zipped together, rather than the Cartesian product.
#### A small, complete example of the issue
```python
p=pd.Panel(0,[0,1],[0,1],[0,1])
p.loc[[0,1],[0,1],[0]]=21
p.loc[[0,1],[0,1],0]=42
p.loc[[0,1],[0,1],0]
```
#### Actual Output
```
0 1
0 42 21
1 21 42
```
#### Expected Output
```
0 1
0 42 42
1 42 42
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 06b35db1d88e75a9e3f183b58678589cf7715381
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.3-101.fc21.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.0+69.g06b35db
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxODg1NDg3NzI=
| 14,634 |
Pandas to_csv unstable interaction with tkinter filedialog
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| 1 | 2016-11-10T16:07:54Z | 2016-11-12T16:00:17Z | 2016-11-12T16:00:17Z |
NONE
| null |
#### Example
```python
from tkinter import filedialog
import pandas as pd
import numpy as np
data = pd.DataFrame(np.random.randn(13, 5))
outfilename = filedialog.asksaveasfile() #get save location
data.to_csv(outfilename, index=False, header=False, mode='a')
```
Gives an empty or unaltered file with no error messages. Full importation of tkinter and call as `tkinter.filedialog.asksaveasfile()` yields the same result.
#### Expected Output
However, if all references to tkiniter are removed:
```python
import pandas as pd
import numpy as np
data = pd.DataFrame(np.random.randn(13, 5))
outfilename = "C:\\test.txt"
data.to_csv(outfilename, index=False, header=False, mode='a')
```
then the file is written perfectly.
NOTE - if full tkinter or filedialog are imported but not used, then `to_csv` functions normally as well
```python
from tkinter import filedialog
import pandas as pd
import numpy as np
data = pd.DataFrame(np.random.randn(13, 5))
outfilename = "C:\\test.txt"
data.to_csv(outfilename, index=False, header=False, mode='a')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.1
scipy: 0.17.1
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: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
</details>
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BUG: Unable to pass additional arguments to resample().apply()
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| 3 | 2016-11-10T16:20:16Z | 2017-02-27T21:05:51Z | 2017-02-27T16:00:41Z |
NONE
| null |
- closes #14615
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MDU6SXNzdWUxODg1NjkyNDE=
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API: set_axis does not have inplace=False
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| 6 | 2016-11-10T17:25:03Z | 2017-07-24T23:49:21Z | 2017-07-24T23:49:21Z |
NONE
| null |
http://pandas.pydata.org/pandas-docs/version/0.19.1/generated/pandas.DataFrame.set_axis.html
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ENH: Explicit range checking when writing Stata
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| 8 | 2016-11-10T22:51:34Z | 2016-11-17T12:54:41Z | 2016-11-17T12:54:05Z |
CONTRIBUTOR
| null |
- [X] closes #14618
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
Add explicit error checking for out-of-range doubles when writing Stata files
closes #14618
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Fix trailing current date zeros flaky test_format problem
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| 1 | 2016-11-11T14:38:19Z | 2016-11-12T16:08:59Z | 2016-11-12T16:08:53Z |
CONTRIBUTOR
| null |
- [x] closes #14626
- [x] tests added / passed
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`read_msgpack` doesn't read `to_msgpack` if a dataframe contains `tuple`.
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| 1 | 2016-11-11T16:52:55Z | 2018-07-06T20:21:13Z | 2016-11-11T17:21:41Z |
NONE
| null |
Pandas v0.19.0
Python 2.7.12
```
x = pd.DataFrame({'x': [['a', 'b']], 'y': [1]})
pd.read_msgpack(x.to_msgpack())
ValueError: total size of new array must be unchanged
```
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changed return type to unicode to test in travis
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[] | null | 1 | 2016-11-11T21:38:50Z | 2016-11-11T23:10:07Z | 2016-11-11T23:10:07Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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import pandas hanging Flask 0.11.1 / Apache 2.4.18
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| 22 | 2016-11-12T08:44:56Z | 2017-03-15T16:05:50Z | 2017-03-15T16:05:50Z |
NONE
| null |
pandas 0.19.1 is hanging apache on the import in the python script, the website times out. Downgrading to 0.18.1 resolves the issue. Tested this on a fresh EC2 instance Ubuntu 16.04.
Apache log:
- [Sat Nov 12 03:05:18.784672 2016] [core:warn] [pid 23426:tid 139710063925120] AH00045: child process 23563 still did not exit, sending a SIGTERM
- [Sat Nov 12 03:05:20.786946 2016] [core:warn] [pid 23426:tid 139710063925120] AH00045: child process 23563 still did not exit, sending a SIGTERM
- [Sat Nov 12 03:05:22.789238 2016] [core:warn] [pid 23426:tid 139710063925120] AH00045: child process 23563 still did not exit, sending a SIGTERM
- [Sat Nov 12 03:05:24.791547 2016] [core:error] [pid 23426:tid 139710063925120] AH00046: child process 23563 still did not exit, sending a SIGKILL
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
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
</details>
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BUG: iloc misbehavior with pd.Series: sometimes returns pd.Categorical, fixes #14580
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| 5 | 2016-11-12T17:16:24Z | 2016-12-30T21:35:27Z | 2016-12-30T21:35:23Z |
CONTRIBUTOR
| null |
- [x] closes #xxxx
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
closes https://github.com/pandas-dev/pandas/issues/14580
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TST: skipping a gbq test for now
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| 4 | 2016-11-12T17:46:30Z | 2017-02-14T13:31:52Z | 2017-02-14T13:31:52Z |
CONTRIBUTOR
| null |
I am xfailing most of these tests for now till we have the testing sorted: https://github.com/pandas-dev/pandas/commit/d9e75c7e724e5f7449c8c57624ce9395c9ffe11a
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Pandas Keyerror after extracting the file(.csv format) using get_dummies
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| 2 | 2016-11-13T01:55:26Z | 2016-11-15T18:38:17Z | 2016-11-15T18:38:09Z |
NONE
| null |
#### A small, complete example of the issue
import numpy as np
import pandas as pd
import timeit
from sklearn import preprocessing
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report,roc_auc_score
from sklearn.externals import joblib
from sklearn.metrics import (precision_score, recall_score,f1_score)
from sklearn.metrics import confusion_matrix
import os
import psutil
from sklearn.preprocessing import Imputer
start_time = timeit.default_timer()
np.set_printoptions(suppress=True)
df=pd.read_csv('/Users/sudarshan/Desktop/masterproject/SampleDatasets/dataset.csv')
print (df.isnull().any())
df.dropna(inplace=True)
print (df.isnull().any())
#df['flag'] = df['A'].str.extract('([A])', expand=True)
just_dummies = pd.Series(df['flags'])
values = just_dummies.str.get_dummies(sep='|')
#s = df['flags'].apply(pd.Series)
#dfMod= pd.concat([df,pd.DataFrame(s.values.tolist())], axis=1)
dfMod = pd.concat([df, values], axis=1)
print dfMod.head()
print(dfMod.columns.tolist())
dfMod['A'] = dfMod['A'].astype(float)
dfMod['F'] = dfMod['F'].astype(float)
dfMod['P'] = dfMod['P'].astype(float)
dfMod['R'] = dfMod['R'].astype(float)
dfMod['S'] = dfMod['S'].astype(float)
print(dfMod.columns.tolist())
X = df.as_matrix(columns=['packets','bytes','duration','A','F','P','R','S'])
#### Expected Output
output should be if the flag is having a value of FPA it should be processed as separate,like
'F','P','A'
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
</details>
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| 188,954,666 |
MDExOlB1bGxSZXF1ZXN0OTM0NjM4NDQ=
| 14,645 |
Standardize function signatures
|
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| 6 | 2016-11-13T06:19:13Z | 2016-11-26T09:18:30Z | 2016-11-26T09:18:22Z |
MEMBER
| null |
Standardize the following function signatures:
1) `repeat(reps, *args, **kwargs)`
2) `searchsorted(value, side='left', sorter=None)`
Closes #12662.
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| 14,646 |
maybe a bug in pandas.Timestamp.strptime
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[] | null | 2 | 2016-11-13T06:29:16Z | 2016-11-14T13:29:55Z | 2016-11-14T13:29:55Z |
NONE
| null |
#### A small, complete example of the issue
```python
# Your code here
pd.Timestamp.strptime('20150303', '%Y%m%d')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pandas/tslib.pyx", line 294, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:8967)
TypeError: __new__() takes at most 5 positional arguments (8 given)
```
#### Expected Output
Timestamp('2015-03-03 00:00:00')
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: 1.3.7
pip: 9.0.1
setuptools: 27.3.0
Cython: 0.23.1
numpy: 1.11.1
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.4.6
patsy: 0.4.1
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
I can't trace the code in tslib.c. the result in _strptime.py(462) was still right : (Pdb) year
2015
(Pdb) month
1
(Pdb) day
2
but after the "return" finish, the above Exception occur
</details>
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| 14,647 |
BUG: to_dense is broken
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| 2 | 2016-11-13T07:41:56Z | 2016-11-23T20:40:41Z | 2016-11-23T20:40:40Z |
MEMBER
| null |
As of <a href="https://github.com/pandas-dev/pandas/tree/1d6dbb41b26a39121ec8c4f19f5da78bb0ab4af7/pandas">1d6dbb</a>, the behaviour, documentation, and signatures for `to_dense` are widely inconsistent:
<a href="https://github.com/pandas-dev/pandas/blob/1d6dbb41b26a39121ec8c4f19f5da78bb0ab4af7/pandas/sparse/array.py#L382">SparseArray</a>: documentation is incorrect (we are not converting a `SparseSeries`), and the `fill` parameter is not even respected if you trace the code. Not sure why it was ever there in the first place.
<a href="https://github.com/pandas-dev/pandas/blob/1d6dbb41b26a39121ec8c4f19f5da78bb0ab4af7/pandas/sparse/series.py#L529">SparseSeries</a>: What is the purpose of `sparse_only`? Not only is it not documented, but it also is inconsistent with `SparseArray` behaviour.
<a href="https://github.com/pandas-dev/pandas/blob/1d6dbb41b26a39121ec8c4f19f5da78bb0ab4af7/pandas/sparse/frame.py#L231">SparseDataFrame</a>: Perhaps the most intuitive of them all. Documentation is correct, and it has no unnecessary parameters like the other two do.
I propose that we change the `to_dense` behaviour to match that of `SparseDataFrame`. Take no arguments and just convert to a dense object with no questions asked.
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Cython not found
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[] | null | 1 | 2016-11-13T19:10:00Z | 2016-11-13T19:39:55Z | 2016-11-13T19:39:55Z |
NONE
| null |
I pip instlaled pandas, and did `setup.py build_ext --inplace --force` but when I import pandas from python I get this message
```python
running build_ext
Traceback (most recent call last):
File "setup.py", line 685, in <module>
**setuptools_kwargs)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/core.py", line 151, in setup
dist.run_commands()
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/dist.py", line 953, in run_commands
self.run_command(cmd)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/command/build_ext.py", line 337, in run
self.build_extensions()
File "setup.py", line 374, in build_extensions
self.check_cython_extensions(self.extensions)
File "setup.py", line 371, in check_cython_extensions
""" % src)
Exception: Cython-generated file 'pandas/index.c' not found.
Cython is required to compile pandas from a development branch.
Please install Cython or download a release package of pandas.
```
#### Expected Output
How can I fix this? Thank you!
|
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| 14,649 |
BUG: resampling with NaT in TimedeltaIndex (#13223)
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[] | null | 8 | 2016-11-14T01:46:12Z | 2017-05-07T14:12:40Z | 2017-05-07T14:12:40Z |
NONE
| null |
- [ ] closes #13223
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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DOC: Explain pivot vs. pivot_table (#6950)
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| 3 | 2016-11-14T05:19:33Z | 2016-11-16T09:06:28Z | 2016-11-16T09:06:06Z |
MEMBER
| null |
- [x] closes #6950
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pd.DataFrame.merge raises TypeError when dataframe contains a tz-aware datetime column containing all NaTs
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| 3 | 2016-11-14T08:07:19Z | 2016-11-14T11:56:31Z | 2016-11-14T11:56:01Z |
NONE
| null |
#### A small, complete example of the issue
```python
# removing tz='UTC' below resolves this problem
df1 = pd.DataFrame({'INT':[0,1], 'DATE':pd.DatetimeIndex([np.nan, np.nan], tz='UTC')})
df2 = pd.DataFrame({'INT':[0,1], 'FLOAT':[0.0, 1.0]})
df3 = df1.merge(df2, on='INT')
```
#### Expected Output
Traceback (most recent call last):
File "<ipython-input-77-3a4f3f69414f>", line 3, in <module>
df3 = df1.merge(df2, on='INT')
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/frame.py", line 4330, in merge
suffixes=suffixes, copy=copy, indicator=indicator)
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/tools/merge.py", line 35, in merge
return op.get_result()
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/tools/merge.py", line 210, in get_result
concat_axis=0, copy=self.copy)
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4456, in concatenate_block_managers
for placement, join_units in concat_plan]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4456, in <listcomp>
for placement, join_units in concat_plan]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4553, in concatenate_join_units
for ju in join_units]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4553, in <listcomp>
for ju in join_units]
File "/export/data/anaconda/anaconda3.2.4/lib/python3.5/site-packages/pandas/core/internals.py", line 4801, in get_reindexed_values
missing_arr = np.empty(self.shape, dtype=empty_dtype)
TypeError: data type not understood
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-431.11.2.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.1
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: None
Jinja2: None
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BUG: pandas.cut and negative values
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| 3 | 2016-11-14T12:21:48Z | 2016-11-17T12:49:43Z | 2016-11-17T12:49:43Z |
CONTRIBUTOR
| null |
Here is an example of pandas.cut ran on a pandas.Series with only one positive element and then on a pandas.Series with only one negative element. In the second scenario pandas.cut is not able to insert the single value on the only one bin.
I might be wrong but I expected pandas.cut to behave on negative values the same as with positive
values.
#### A small, complete example of the issue
```python
import pandas as pd
import numpy as np
>>> s = pd.Series([1.])
>>> pd.cut(s, 1)
0 (0.999, 1.001]
dtype: category
Categories (1, object): [(0.999, 1.001]]
>>> s = pd.Series([-1.])
>>> pd.cut(s, 1)
0 NaN
dtype: category
Categories (1, object): [(-0.999, -1.001]]
```
#### Expected Output
```
>>> s = pd.Series([-1.])
>>> pd.cut(s, 1)
0 (-1.001, -0.999]
dtype: category
Categories (1, object): [(-1.001, -0.999]]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-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: 1.5.4
setuptools: 28.0.0
Cython: 0.20.1post0
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
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: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
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jinja2: 2.8
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MDExOlB1bGxSZXF1ZXN0OTM1NTk4MTE=
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DOC: Update Series.mode docstring
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| 4 | 2016-11-14T12:42:29Z | 2016-11-15T08:45:08Z | 2016-11-15T08:28:25Z |
NONE
| null |
- [ ] closes #xxxx
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
Series mode method does not support sort parameter, even though the documentation says so. Neither does the underlying function from pandas.core.algorithms.
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0.19.x
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| 0 | 2016-11-14T14:58:05Z | 2016-11-14T15:58:42Z | 2016-11-14T14:58:44Z |
CONTRIBUTOR
| null |
BUG: pandas/pandas/io/sas/sasreader.py assumes filename_or_buffer input is a string if format=None, which errors if one provides an open File/ SFTPFile object.
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DataFrame.from_dict automatic dtype conversion?
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| 6 | 2016-11-14T15:45:49Z | 2016-11-15T19:10:17Z | 2016-11-14T18:40:55Z |
NONE
| null |
I would expect this syntax to work without problems
```python
import pandas as pd
d = [{'a':'1','b':'2'},{'a':'3','b':'4'}]
pd.DataFrame.from_dict(d, orient='columns', dtype={'a':int,'b':int})
```
#### Expected Output
Expected DataFrame:
```
a b
0 1 2
1 3 4
```
with dtypes:
```
a int64
b int64
dtype: object
```
Instead, the output is
```
/usr/lib/python2.7/dist-packages/numpy/core/_internal.pyc in _makenames_list(adict, align)
24 for fname in fnames:
25 obj = adict[fname]
---> 26 n = len(obj)
27 if not isinstance(obj, tuple) or n not in [2, 3]:
28 raise ValueError("entry not a 2- or 3- tuple")
TypeError: object of type 'type' has no len()
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-68-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: 1.3.4
pip: None
setuptools: 26.1.1
Cython: 0.24.1
numpy: 1.8.2
scipy: 0.14.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.2
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.4.2
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9.2
apiclient: 1.5.3
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.34.0
pandas_datareader: None
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[WIP] set_axis now has inplace=False
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[] | null | 5 | 2016-11-14T17:27:26Z | 2017-02-01T20:53:03Z | 2017-02-01T20:53:03Z |
NONE
| null |
- [ ] closes #14636
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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COMPAT: Cast to string before raise in read_stata
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| 3 | 2016-11-14T18:21:52Z | 2017-04-05T02:07:48Z | 2016-11-14T20:29:25Z |
CONTRIBUTOR
| null |
In `pandas.io.stata::StataReader::_read_old_header` we
try to raise a ValueError.
I'm working on making a test, but this is buried pretty deep.
For now, `typlist` has type `List[int]`. We call
```python
','.join(typlist)
```
which errors on py3 (and python 2 I guess?). This came up in #13856, see https://travis-ci.org/pandas-dev/pandas/jobs/175705122#L1284 for the failure.
@kshedden any memory of writing this code? This is a bare `except` but it looks like the only exception that would trigger this is an `IndexError` when something in `typlist` is out of bounds on `self.TYPE_MAP`. Any hints on writing a test to exercise this code? If not I'll dig in more tonight.
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RFE: a faster way to construct a pandas.Timestamp from an epoch time
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| 10 | 2016-11-14T18:22:51Z | 2016-11-15T22:26:30Z | 2016-11-14T18:50:43Z |
NONE
| null |
`pandas.to_datetime` called with an `int` is too slow for my use case. Basically, I have a loop that sequentially gets an integer from a generator of about 1 000 000 numbers, converts it to `pandas.Timestamp` and passes it to a function. A profiler says that the call of `pandas.to_datetime` takes about 40 % of the total run time of my program.
Compared to `datetime.datetime.fromtimestamp`, it's more than 60 times slower:
```console
$ python -m timeit -n 1000000 -s 'import datetime' 'datetime.datetime.fromtimestamp(30, tz=datetime.timezone.utc)'
1000000 loops, best of 3: 0.889 usec per loop
$ python -m timeit -n 1000000 -s 'import pandas' 'pandas.to_datetime(30, utc=True, unit="s")'
1000000 loops, best of 3: 62.8 usec per loop
```
<details>
```console
$ python -c 'import pandas;pandas.show_versions()'
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 9.0.1
setuptools: 20.7.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.7
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
```
</details>
Can you please provide/document a faster way to instantiate a `pandas.Timestamp` instance from an epoch time?
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| 1 | 2016-11-14T18:56:19Z | 2016-11-14T21:55:45Z | 2016-11-14T21:55:23Z |
CONTRIBUTOR
| null |
https://travis-ci.org/pandas-dev/pandas/jobs/175345297
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Feature Request: `apply` method to `Index` objects
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| 1 | 2016-11-14T21:49:05Z | 2016-11-14T21:58:34Z | 2016-11-14T21:58:17Z |
NONE
| null |
I recently started using `apply` on my `DataFrames` and `Series`. It would be awesome to have this functionality on `Index` objects. I usually have to rename my indices a lot.
```
D_obsv_meta = dict(of-label-mapping)
DF_raw.index.apply(lambda x: D_obsv_meta[x])
```
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read_sql_table add argument skip_chunks
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[] | null | 3 | 2016-11-14T22:34:59Z | 2020-05-16T03:49:46Z | null |
NONE
| null |
`read_sql_table` in pandas is much nicer than using raw SQLAlchemy API and likely has better performance.
But when reading large tables, `chunksize` is unavoidable.
Can I suggest an option to read with `skip_chunks`, like `skip_rows` in `read_csv`?
Right now I'm doing next(chunk), but really do not need all the chunks.
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MDU6SXNzdWUxODk0Nzk2Mzk=
| 14,662 |
Extending Pandas Dataframe Documentation
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| 2 | 2016-11-15T19:11:51Z | 2016-11-15T19:16:51Z | 2016-11-15T19:15:47Z |
NONE
| null |
Hello,
I am looking for documentation on how to extend the pandas dataframe object. Can someone please point me to some documentation on how to do this?
Thank you
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BUG: pandas.cut and negative values #14652
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| 6 | 2016-11-15T20:17:39Z | 2017-02-17T14:30:08Z | 2016-11-17T12:49:43Z |
CONTRIBUTOR
| null |
- [OK ] closes #14652
- [OK] tests passed
- [OK ] passes ``git diff upstream/master | flake8 --diff``
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Spelling Error in docs for to_json
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| 0 | 2016-11-15T23:14:47Z | 2016-11-16T02:09:30Z | 2016-11-16T02:09:19Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_json.html
`default` is spelled `defalut`
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fix ##14664
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| 1 | 2016-11-15T23:18:00Z | 2016-11-16T09:08:29Z | 2016-11-16T02:09:19Z |
CONTRIBUTOR
| null |
- [X] closes #14664
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
This is just a bug fix. Please let me know if you want me to add more. Very simple spelling change.
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| 189,546,258 |
MDU6SXNzdWUxODk1NDYyNTg=
| 14,666 |
EWMA to be Depreciated Reduces Avialable Functionality
|
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| 2 | 2016-11-16T00:16:41Z | 2016-11-16T00:33:53Z | 2016-11-16T00:33:25Z |
NONE
| null |
#### Description
I have DataFrame with a datetime index and two dtype-float columns.
I wanted to use a groupby function with an EWMA such that the EWMA works on each hour of the day separately. I used the below code and all seemed to work as expected. The issue is that a warning message appeared: `FutureWarning: pd.ewm_mean is deprecated for DataFrame and will be removed in a future version, replace with DataFrame.ewm(adjust=True, min_periods=0, ignore_na=False, alpha=0.84).mean()`
My currently working code
```
data.groupby([data.index.hour]).apply(lambda x: pd.ewma(x, alpha=0.84))
```
The new code as kind of suggested
```
data.groupby([data.index.hour]).apply(lambda x: pd.ewm(alpha=0.84).mean(x))
AttributeError: module 'pandas' has no attribute 'ewm'
```
What I also tried first of all (before applying `pd.ewma`)
```
data.groupby([data.index.hour]).ewm(alpha=0.84).mean(x)
AttributeError: Cannot access callable attribute 'ewm' of 'DataFrameGroupBy' objects, try using the 'apply' method
```
Usually, with this kind of thing it is me not using the intuitive pandas functionality correctly. This time it seems that the change to `df.ewm().mean()` (which admittedly does feel much neater) is actually losing functionality or at the least, an intuitive solution to a problem.
I think the perfect solution would be to have `df.groupby().ewm().mean()` working as this seems most intuitive as a a step onward from `df.groupby().rolling().mean()`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 9.0.1
setuptools: 27.3.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: None
xlwt: None
xlsxwriter: 0.9.3
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTM4ODY4Mjc=
| 14,667 |
BUG: Columns and Index share the same numpy object underneath when pd.DataFrame.cov is used
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| 5 | 2016-11-16T01:49:24Z | 2019-04-30T23:15:24Z | 2017-02-27T16:01:26Z |
NONE
| null |
- [ ] closes #14617
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] Index now copies the columns array instead of using the same object in
covariance and correlations stats methods of dataframe
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ENH: add Series & DataFrame .agg/.aggregate
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| 38 | 2016-11-16T02:03:39Z | 2017-04-14T15:39:13Z | 2017-04-14T15:37:04Z |
CONTRIBUTOR
| null |
- to provide convienent function application that mimics the groupby(..).agg/.aggregate
interface
- ``.apply`` is now a synonym for ``.agg/.aggregate``, and will accept dict/list-likes
for aggregations
- Automatic handling of both reductive and transformation functions, e.g.``Series.agg(['min', 'sqrt'])``
- interpretation of string names on series (with a fallback to numpy), e.g. ``sqrt``, ``log``)
closes #1623
closes #14464
custom .describe. I included these issues because its is quite easy to now do custom .describe.
closes #14483
closes #7014
TODO:
- [ ] docs (new section in basics, with linking to groupby, rolling, resample aggregates sections)
- [ ] tests for``Series.agg({'foo' : ['min', 'max']})``
- [ ] update doc-strings
- [ ] example from: https://github.com/pandas-dev/pandas/issues/15386
Series:
```
In [2]: s = Series(range(6))
In [3]: s
Out[3]:
0 0
1 1
2 2
3 3
4 4
5 5
dtype: int64
In [4]: s.agg(['min', 'max'])
Out[4]:
min 0
max 5
dtype: int64
In [5]: s.agg(['sqrt', 'min'])
ValueError: cannot combine transform and aggregation operations
In [6]: s.agg({'foo' : 'min'})
Out[6]:
foo 0
dtype: int64
In [7]: s.agg({'foo' : ['min','max']})
Out[7]:
foo
min 0
max 5
In [8]: s.agg({'foo' : ['min','max'], 'bar' : ['sum', 'mean']})
Out[8]:
foo bar
max 5.0 NaN
mean NaN 2.5
min 0.0 NaN
sum NaN 15.0
```
DataFrame
```
In [8]: df = pd.DataFrame({'A': range(5), 'B': 5})
In [9]: df
Out[9]:
A B
0 0 5
1 1 5
2 2 5
3 3 5
4 4 5
In [10]: df.agg(['min', 'max'])
Out[10]:
A B
min 0 5
max 4 5
In [11]: df.agg({'A': ['min', 'max'], 'B': ['sum', 'max']})
Out[11]:
A B
max 4.0 5.0
min 0.0 NaN
sum NaN 25.0
# df.agg is equivalent to a tranfsorm
In [15]: df.transform([np.sqrt, np.abs, lambda x: x**2])
Out[15]:
A B
sqrt absolute <lambda> sqrt absolute <lambda>
0 0.000000 0 0 2.236068 5 25
1 1.000000 1 1 2.236068 5 25
2 1.414214 2 4 2.236068 5 25
3 1.732051 3 9 2.236068 5 25
4 2.000000 4 16 2.236068 5 25
```
Not sure what I should do in cases like this. could skip, or maybe raise better message.
```
In [16]: df = pd.DataFrame({'A': range(5), 'B': 5, 'C':'foo'})
In [17]: df
Out[17]:
A B C
0 0 5 foo
1 1 5 foo
2 2 5 foo
3 3 5 foo
4 4 5 foo
In [18]: df.transform(['log', 'abs'])
AttributeError: 'str' object has no attribute 'log'
```
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| 189,589,731 |
MDU6SXNzdWUxODk1ODk3MzE=
| 14,669 |
PERF: `dt` accessor has massive performance drain when accessing date, time
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| 9 | 2016-11-16T04:58:57Z | 2016-11-16T22:12:15Z | 2016-11-16T11:20:28Z |
NONE
| null |
#### A small, complete example of the issue
```
import pandas as pd
import numpy as np
df = pd.DataFrame({'tstamp' : pd.date_range('2016-11-15', periods=4*365*60, freq='T'),
'data' : np.random.rand(4*365*60)})
len(df)
## 87600
%%timeit
__ = df['tstamp'].dt.date
## 10 loops, best of 3: 128 ms per loop
%%timeit
__ = df['tstamp'].dt.time
## 10 loops, best of 3: 132 ms per loop
%%timeit
__ = df['tstamp'].dt.dayofyear
## 100 loops, best of 3: 3.04 ms per loop
%%timeit
__ = df['tstamp'].dt.day
## 100 loops, best of 3: 2.83 ms per loop
```
As clearly demonstrated, accessing date and time take a really long time to compute. I do not know what is causing the bottleneck, but some speed-up will be definitely appreciated.
Also, accessing `date` and `time` require more than double the memory that the DataFrame requires. I don't have a memory profiler working, but I can attest that my computer with 30 GB of available RAM (after OS use), can load a massive csv that consumes 10.2 GB in memory as a DataFrame. However, trying to access `date` from that DataFrame raises `MemoryError`. It basically fills up the remaining 19.8 GB of RAM trying to compute the `date` from a timestamp column. The DataFrame in question has 113,587,339 rows, and 5 columns of numeric data, one column of strings, and a column with the datetime stamp similar to the example above.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 189,765,287 |
MDU6SXNzdWUxODk3NjUyODc=
| 14,670 |
Change to_gbq() from stream to batch loading
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| 6 | 2016-11-16T16:43:16Z | 2017-02-26T20:22:45Z | 2017-02-26T20:12:04Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
Recently we have discovered that streaming loads into google tables are best effort but can be up to 90 minute delay. It has no transactional guarantees. As such `to_gbq()` returning is no guarantee of anything.
I propose that we switch the loading from tableData.insertAll() to the batch bigquery load, either using an http upload, or pushing to a cloud bucket. The latter would suck because it requires bucket perms too, but I'm not sure on the size bounds for the http stream version.
However, the atomicity seems important in the case where we next execute a BQ query with the recently uploaded to_gbq() results and worth a tradeoff in terms of time.
At least we would could include an option.
Thoughts ?
(not sure how to label this IO:google)
#### Output of ``pd.show_versions()``
<details>
commit: None
python: 2.7.11.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: 0.18.1
nose: 1.3.7
pip: 9.0.0
setuptools: 25.2.0
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.5.3
pytz: 2015.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.0
sqlalchemy: 1.0.4
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxODk4MjQ4NTY=
| 14,671 |
ERR: usecols fails to raise error if column doesn't exist but is the same length as headers
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| 17 | 2016-11-16T19:55:53Z | 2017-06-04T10:47:15Z | 2017-06-04T10:47:15Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
read_csv doesn't raise a value error if the use cols contains the same number of values as the headers
simple [test_csv.txt](https://github.com/pandas-dev/pandas/files/595756/test_csv.txt) file contains:
A,B
1,2
3,4
It currently will set the other column as NaN values:
A B
0 1 NaN
1 3 NaN
```python
import pandas as pd
file = pd.read_csv('data/test.csv',usecols=['A','C']) # Column C doesn't exist
file
```
#### Expected Output
ValueError("Usecols do not match names.") exception
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
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: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
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xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUxODk4NTEzNzk=
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Add an option not to sort levels in MultiIndex.from_product?
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| 2 | 2016-11-16T21:29:16Z | 2019-10-21T19:34:18Z | null |
MEMBER
| null |
Currently, `from_product` always sorts levels in the resulting MultiIndex. This means that the result does not necessarily have lexsorted labels/codes.
PR #14062 adds an option to not sort levels when calling `from_product`. Compare:
```
In [4]: pd.MultiIndex.from_product([['a', 'b'], [2, 1, 0]], sort_levels=False)
Out[4]:
MultiIndex(levels=[['a', 'b'], [2, 1, 0]],
labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
sortorder=0)
In [5]: pd.MultiIndex.from_product([['a', 'b'], [2, 1, 0]], sort_levels=True)
Out[5]:
MultiIndex(levels=[['a', 'b'], [0, 1, 2]],
labels=[[0, 0, 0, 1, 1, 1], [2, 1, 0, 2, 1, 0]])
```
Using this option yields a few benefits:
1. It's simpler -- resulting levels on the MultiIndex are exactly those you passed in.
2. It's marginally faster -- you don't need to sort the levels.
3. The resulting MultiIndex is always lex-sorted. This is handy if you want to be able to index it efficiently.
The downside is that the result can be a little less intuitive, because levels and labels do not have the same sort order (#14015).
I'm suggesting this option because it was useful for xarray (to fix https://github.com/pydata/xarray/issues/980) and might also be relevant for other advanced users.
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xlrd required for read_excel put not a requirement in setup.py
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| 1 | 2016-11-16T22:24:59Z | 2016-11-16T22:29:54Z | 2016-11-16T22:29:31Z |
NONE
| null |
I feel that requirements, such as 'xlrd' should be part of the setup.py file and installed when we install Pandas. Thanks!
#### A small, complete example of the issue
```python
# Your code here
import pandas as pd
pd.read_excel('srep10775-s2.xls', sheet=1)
```
#### Expected Output
... should be contents of excel file
#### Actual Output
```python
218 def __init__(self, io, **kwds):
219
--> 220 import xlrd # throw an ImportError if we need to
221
222 ver = tuple(map(int, xlrd.__VERSION__.split(".")[:2]))
ImportError: No module named 'xlrd'
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: None
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
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
</details>
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MDExOlB1bGxSZXF1ZXN0OTQwODcyMDI=
| 14,674 |
raise ValueError if usecols has same size as but doesn't exist in headers (#14671)
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[] | null | 6 | 2016-11-17T01:44:37Z | 2017-03-20T13:41:26Z | 2017-03-20T13:41:25Z |
CONTRIBUTOR
| null |
fixes #14671
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TST: Test aggregation over arrays (#3788)
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| 6 | 2016-11-17T06:11:27Z | 2017-12-20T01:57:10Z | 2016-11-22T11:39:12Z |
MEMBER
| null |
- [x] closes #3788
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
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API: Expand DataFrame.astype to allow Categorical(categories, ordered)
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| 5 | 2016-11-17T13:55:54Z | 2017-09-23T18:56:12Z | 2017-09-23T18:56:12Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
This is a proposal to allow something like
```python
df.astype({'A': pd.CategoricalDtype(['a', 'b', 'c', 'd'], ordered=True})
```
Currently, it can be awkward to convert many columns in a DataFrame to a Categorical *with control over the categories and orderedness*. If you just want to use the defaults, it's not so bad with `.astype`:
```python
In [5]: df = pd.DataFrame({"A": list('abc'), 'B': list('def')})
In [6]: df
Out[6]:
A B
0 a d
1 b e
2 c f
In [8]: df.astype({"A": 'category', 'B': 'category'}).dtypes
Out[8]:
A category
B category
dtype: object
```
If you need to control `categories` or `ordered`, your best off with
```python
In [20]: mapping = {'A': lambda x: x.A.astype('category').cat.set_categories(['a', 'b'], ordered=True),
...: 'B': lambda x: x.B.astype('category').cat.set_categories(['d', 'f', 'e'], ordered=False)}
In [21]: df.assign(**mapping)
Out[21]:
A B
0 a d
1 b e
2 NaN f
```
By expanding astype to accept instances of `Categorical`, you remove the need for the `lambda`s and you can do conversions of other types at the same time.
This would mirror the semantics in https://github.com/pandas-dev/pandas/issues/14503
Updated to change `pd.Categorical(...)` to a new/modified `pd.CategoricalDtype(...)` based on the discussion below.
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Regression: `pip wheel pandas` does not work any more with pandas 0.19.0
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| 1 | 2016-11-17T15:27:38Z | 2016-11-17T15:53:15Z | 2016-11-17T15:53:06Z |
NONE
| null |
# A small, complete example of the issue
## Setup
```
% virtualenv venv
New python executable in venv/bin/python2.7
Also creating executable in venv/bin/python
Installing setuptools, pip, wheel...done.
% . venv/bin/activate
% pip list
You are using pip version 7.0.3, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
pip (7.0.3)
setuptools (17.0)
wheel (0.24.0)
hpeters@MAC-025s-MacBook-Pro-2 /tmp % pip install -U pip setuptools wheel
You are using pip version 7.0.3, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Collecting pip
Using cached pip-9.0.1-py2.py3-none-any.whl
Collecting setuptools
Using cached setuptools-28.8.0-py2.py3-none-any.whl
Collecting wheel
Using cached wheel-0.29.0-py2.py3-none-any.whl
Installing collected packages: pip, setuptools, wheel
Found existing installation: pip 7.0.3
Uninstalling pip-7.0.3:
Successfully uninstalled pip-7.0.3
Found existing installation: setuptools 17.0
Uninstalling setuptools-17.0:
Successfully uninstalled setuptools-17.0
Found existing installation: wheel 0.24.0
Uninstalling wheel-0.24.0:
Successfully uninstalled wheel-0.24.0
Successfully installed pip-9.0.1 setuptools-28.8.0 wheel-0.29.0
```
## The issue
One cannot use `pip wheel` to build pandas 0.19.0 and higher:
```
% pip wheel "pandas==0.19.0" --no-binary :all:
Collecting pandas==0.19.0
Using cached pandas-0.19.0.tar.gz
Collecting python-dateutil (from pandas==0.19.0)
Using cached python-dateutil-2.6.0.tar.gz
Collecting pytz>=2011k (from pandas==0.19.0)
Using cached pytz-2016.7.zip
Collecting numpy>=1.7.0 (from pandas==0.19.0)
Using cached numpy-1.11.2.tar.gz
Collecting six>=1.5 (from python-dateutil->pandas==0.19.0)
Using cached six-1.10.0.tar.gz
Building wheels for collected packages: pandas, python-dateutil, pytz, numpy, six
Running setup.py bdist_wheel for pandas ... error
Complete output from command /private/tmp/venv/bin/python2.7 -u -c "import setuptools, tokenize;__file__='/private/var/folders/kn/3fznwwr15z9chdd30p3q4qm80000gp/T/pip-build-RJ3_w0/pandas/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d /var/folders/kn/3fznwwr15z9chdd30p3q4qm80000gp/T/tmpVWnJJSpip-wheel-:
running bdist_wheel
running build
......
running build_ext
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/private/var/folders/kn/3fznwwr15z9chdd30p3q4qm80000gp/T/pip-build-RJ3_w0/pandas/setup.py", line 680, in <module>
**setuptools_kwargs)
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/core.py", line 151, in setup
dist.run_commands()
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/dist.py", line 953, in run_commands
self.run_command(cmd)
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/private/tmp/venv/lib/python2.7/site-packages/wheel/bdist_wheel.py", line 179, in run
self.run_command('build')
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/cmd.py", line 326, in run_command
self.distribution.run_command(command)
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/command/build.py", line 127, in run
self.run_command(cmd_name)
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/cmd.py", line 326, in run_command
self.distribution.run_command(command)
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/usr/local/Cellar/python/2.7.12_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/distutils/command/build_ext.py", line 339, in run
self.build_extensions()
File "/private/var/folders/kn/3fznwwr15z9chdd30p3q4qm80000gp/T/pip-build-RJ3_w0/pandas/setup.py", line 371, in build_extensions
build_ext.build_extensions(self)
File "/private/var/folders/kn/3fznwwr15z9chdd30p3q4qm80000gp/T/pip-build-RJ3_w0/pandas/setup.py", line 129, in build_extensions
raise ImportError('Building pandas requires cython')
ImportError: Building pandas requires cython
----------------------------------------
Failed building wheel for pandas
Running setup.py clean for pandas
Running setup.py bdist_wheel for python-dateutil ... done
Stored in directory: /private/tmp
Running setup.py bdist_wheel for pytz ... done
Stored in directory: /private/tmp
Running setup.py bdist_wheel for numpy ... |^error
Failed building wheel for numpy
Running setup.py clean for numpy
^C Failed cleaning build dir for numpy
Running setup.py bdist_wheel for six ... done
Stored in directory: /private/tmp
Successfully built python-dateutil pytz six
Failed to build pandas numpy
ERROR: Failed to build one or more wheels
```
#### Expected Output
With pandas 0.18 it is possible to build a wheel using `pip wheel`
```
% pip wheel "pandas==0.18.0" --no-binary :all:
Collecting pandas==0.18.0
Downloading pandas-0.18.0.tar.gz (7.1MB)
100% |################################| 7.1MB 169kB/s
Collecting python-dateutil (from pandas==0.18.0)
Downloading python-dateutil-2.6.0.tar.gz (258kB)
100% |################################| 266kB 3.2MB/s
Collecting pytz>=2011k (from pandas==0.18.0)
Downloading pytz-2016.7.zip (497kB)
100% |################################| 501kB 2.3MB/s
Collecting numpy>=1.7.0 (from pandas==0.18.0)
Using cached numpy-1.11.2.tar.gz
Collecting six>=1.5 (from python-dateutil->pandas==0.18.0)
Downloading six-1.10.0.tar.gz
Building wheels for collected packages: pandas, python-dateutil, pytz, numpy, six
Running setup.py bdist_wheel for pandas ... done
....
Successfully built pandas python-dateutil pytz numpy six
```
## Discussion
I believe that this is due to Pandas not spelling out all of its build-time-requirements (like Cython) in the `setup_requires` section.
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| 14,678 |
ERR: fix exception propogation for datetime parsing functions, noted in python 3.6
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| 3 | 2016-11-17T15:58:46Z | 2016-11-17T23:56:04Z | 2016-11-17T17:41:06Z |
CONTRIBUTOR
| null |
closes #14561
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"organizations_url": "https://api.github.com/users/jreback/orgs",
"received_events_url": "https://api.github.com/users/jreback/received_events",
"repos_url": "https://api.github.com/users/jreback/repos",
"site_admin": false,
"starred_url": "https://api.github.com/users/jreback/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/jreback/subscriptions",
"type": "User",
"url": "https://api.github.com/users/jreback"
},
"description": "after 0.19.1",
"due_on": "2016-12-24T08:00:00Z",
"html_url": "https://github.com/pandas-dev/pandas/milestone/45",
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"node_id": "MDk6TWlsZXN0b25lMjEzMTA2OA==",
"number": 45,
"open_issues": 0,
"state": "closed",
"title": "0.19.2",
"updated_at": "2017-01-29T09:53:52Z",
"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/45"
}
| 8 | 2016-11-17T17:34:50Z | 2016-11-25T22:30:18Z | 2016-11-25T22:30:17Z |
CONTRIBUTOR
| null |
after #14678
Bunch of Deprecation Warnings
```
/home/travis/build/jreback/pandas/pandas/io/tests/json/test_pandas.py:974: DeprecationWarning: invalid escape sequence \[
TypeError, '\[unicode\] is not implemented as a table column')
```
only a small number of 3.6 failures remain.
```
======================================================================
ERROR: pandas.io.tests.test_pickle.TestPickle.test_round_trip_current
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 52, in try_read
return pkl.load(fh)
File "pandas/tslib.pyx", line 407, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:10434)
ts = convert_to_tsobject(ts_input, tz, unit, 0, 0)
File "pandas/tslib.pyx", line 1542, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:28780)
raise TypeError('Cannot convert input to Timestamp')
TypeError: Cannot convert input to Timestamp
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 57, in try_read
return pc.load(fh, encoding=encoding, compat=False)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
File "pandas/tslib.pyx", line 407, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:10434)
ts = convert_to_tsobject(ts_input, tz, unit, 0, 0)
File "pandas/tslib.pyx", line 1542, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:28780)
raise TypeError('Cannot convert input to Timestamp')
TypeError: Cannot convert input to Timestamp
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 65, in read_pickle
return try_read(path)
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 62, in try_read
return pc.load(fh, encoding=encoding, compat=True)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
File "pandas/tslib.pyx", line 407, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:10434)
ts = convert_to_tsobject(ts_input, tz, unit, 0, 0)
File "pandas/tslib.pyx", line 1542, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:28780)
raise TypeError('Cannot convert input to Timestamp')
TypeError: Cannot convert input to Timestamp
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 52, in try_read
return pkl.load(fh)
File "pandas/tslib.pyx", line 407, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:10434)
ts = convert_to_tsobject(ts_input, tz, unit, 0, 0)
File "pandas/tslib.pyx", line 1542, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:28780)
raise TypeError('Cannot convert input to Timestamp')
TypeError: Cannot convert input to Timestamp
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 57, in try_read
return pc.load(fh, encoding=encoding, compat=False)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
File "pandas/tslib.pyx", line 407, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:10434)
ts = convert_to_tsobject(ts_input, tz, unit, 0, 0)
File "pandas/tslib.pyx", line 1542, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:28780)
raise TypeError('Cannot convert input to Timestamp')
TypeError: Cannot convert input to Timestamp
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/miniconda/envs/pandas/lib/python3.6/site-packages/nose/case.py", line 198, in runTest
self.test(*self.arg)
File "/home/travis/build/jreback/pandas/pandas/io/tests/test_pickle.py", line 239, in test_round_trip_current
result = pd.read_pickle(path)
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 68, in read_pickle
return try_read(path, encoding='latin1')
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 62, in try_read
return pc.load(fh, encoding=encoding, compat=True)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
File "pandas/tslib.pyx", line 407, in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:10434)
ts = convert_to_tsobject(ts_input, tz, unit, 0, 0)
File "pandas/tslib.pyx", line 1542, in pandas.tslib.convert_to_tsobject (pandas/tslib.c:28780)
raise TypeError('Cannot convert input to Timestamp')
TypeError: Cannot convert input to Timestamp
======================================================================
ERROR: test_transactions (pandas.io.tests.test_sql.TestSQLiteFallback)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/sql.py", line 1401, in execute
cur.execute(*args)
sqlite3.OperationalError: no such table: test_trans
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/tests/test_sql.py", line 2004, in test_transactions
self._transaction_test()
File "/home/travis/build/jreback/pandas/pandas/io/tests/test_sql.py", line 476, in _transaction_test
res = self.pandasSQL.read_query('SELECT * FROM test_trans')
File "/home/travis/build/jreback/pandas/pandas/io/sql.py", line 1436, in read_query
cursor = self.execute(*args)
File "/home/travis/build/jreback/pandas/pandas/io/sql.py", line 1413, in execute
raise_with_traceback(ex)
File "/home/travis/build/jreback/pandas/pandas/compat/__init__.py", line 339, in raise_with_traceback
raise exc.with_traceback(traceback)
File "/home/travis/build/jreback/pandas/pandas/io/sql.py", line 1401, in execute
cur.execute(*args)
pandas.io.sql.DatabaseError: Execution failed on sql 'SELECT * FROM test_trans': no such table: test_trans
======================================================================
ERROR: test_roundtrip_pickle (pandas.tseries.tests.test_offsets.TestCustomBusinessMonthBegin)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_offsets.py", line 1980, in test_roundtrip_pickle
_check_roundtrip(self._object())
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_offsets.py", line 1977, in _check_roundtrip
unpickled = self.round_trip_pickle(obj)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 96, in round_trip_pickle
pd.to_pickle(obj, path)
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 20, in to_pickle
pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)
TypeError: can't pickle numpy.busdaycalendar objects
======================================================================
ERROR: test_roundtrip_pickle (pandas.tseries.tests.test_offsets.TestCustomBusinessMonthEnd)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_offsets.py", line 1980, in test_roundtrip_pickle
_check_roundtrip(self._object())
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_offsets.py", line 1977, in _check_roundtrip
unpickled = self.round_trip_pickle(obj)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 96, in round_trip_pickle
pd.to_pickle(obj, path)
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 20, in to_pickle
pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)
TypeError: can't pickle numpy.busdaycalendar objects
======================================================================
ERROR: test_partial_slicing_with_multiindex (pandas.tseries.tests.test_timeseries.TestSlicing)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_timeseries.py", line 4971, in test_partial_slicing_with_multiindex
self.assertRaises(KeyError, f)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2380, in assertRaises
_callable(*args, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_timeseries.py", line 4969, in f
df_multi.loc[('2013-06-19', 'ACCT1', 'ABC')]
File "/home/travis/build/jreback/pandas/pandas/core/indexing.py", line 1309, in __getitem__
return self._getitem_tuple(key)
File "/home/travis/build/jreback/pandas/pandas/core/indexing.py", line 800, in _getitem_tuple
self._has_valid_tuple(tup)
File "/home/travis/build/jreback/pandas/pandas/core/indexing.py", line 150, in _has_valid_tuple
if not self._has_valid_type(k, i):
File "/home/travis/build/jreback/pandas/pandas/core/indexing.py", line 1409, in _has_valid_type
if key not in ax:
File "/home/travis/build/jreback/pandas/pandas/indexes/multi.py", line 1042, in __contains__
self.get_loc(key)
File "/home/travis/build/jreback/pandas/pandas/indexes/multi.py", line 1643, in get_loc
loc = self._get_level_indexer(key, level=0)
File "/home/travis/build/jreback/pandas/pandas/indexes/multi.py", line 1917, in _get_level_indexer
i = labels.searchsorted(loc, side='left')
TypeError: '>' not supported between instances of 'int' and 'slice'
======================================================================
ERROR: test_pickle (pandas.tseries.tests.test_timeseries.TestTimeSeries)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 52, in try_read
return pkl.load(fh)
TypeError: __new__() takes exactly one argument (2 given)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 57, in try_read
return pc.load(fh, encoding=encoding, compat=False)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
TypeError: __new__() takes exactly one argument (2 given)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 65, in read_pickle
return try_read(path)
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 62, in try_read
return pc.load(fh, encoding=encoding, compat=True)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
TypeError: __new__() takes exactly one argument (2 given)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 52, in try_read
return pkl.load(fh)
TypeError: __new__() takes exactly one argument (2 given)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 57, in try_read
return pc.load(fh, encoding=encoding, compat=False)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
TypeError: __new__() takes exactly one argument (2 given)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tseries/tests/test_timeseries.py", line 2219, in test_pickle
p = self.round_trip_pickle(NaT)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 97, in round_trip_pickle
return pd.read_pickle(path)
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 68, in read_pickle
return try_read(path, encoding='latin1')
File "/home/travis/build/jreback/pandas/pandas/io/pickle.py", line 62, in try_read
return pc.load(fh, encoding=encoding, compat=True)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 117, in load
return up.load()
File "/home/travis/miniconda/envs/pandas/lib/python3.6/pickle.py", line 1050, in load
dispatch[key[0]](self)
File "/home/travis/build/jreback/pandas/pandas/compat/pickle_compat.py", line 21, in load_reduce
stack[-1] = func(*args)
TypeError: __new__() takes exactly one argument (2 given)
======================================================================
ERROR: Test case for when the object being moved has exactly one reference.
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/test_util.py", line 326, in test_exactly_one_ref
as_stolen_buf = move_into_mutable_buffer(b[:-3])
pandas.util._move.BadMove: b'test'
======================================================================
FAIL: test_argsort (pandas.tests.indexes.test_base.TestMixedIntIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1789, in test_argsort
result = idx.argsort()
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1784, in argsort
return result.argsort(*args, **kwargs)
TypeError: '>' not supported between instances of 'str' and 'int'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1789, in test_argsort
result = idx.argsort()
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2452, in __exit__
return self.handle_success(exc_type, exc_value, traceback)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2465, in handle_success
raise_with_traceback(e, traceback)
File "/home/travis/build/jreback/pandas/pandas/compat/__init__.py", line 339, in raise_with_traceback
raise exc.with_traceback(traceback)
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1789, in test_argsort
result = idx.argsort()
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1784, in argsort
return result.argsort(*args, **kwargs)
AssertionError: "unorderable types" does not match "'>' not supported between instances of 'str' and 'int'"
======================================================================
FAIL: test_numpy_argsort (pandas.tests.indexes.test_base.TestMixedIntIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1799, in test_numpy_argsort
result = np.argsort(idx)
File "/home/travis/miniconda/envs/pandas/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 913, in argsort
return argsort(axis, kind, order)
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1784, in argsort
return result.argsort(*args, **kwargs)
TypeError: '>' not supported between instances of 'str' and 'int'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1799, in test_numpy_argsort
result = np.argsort(idx)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2452, in __exit__
return self.handle_success(exc_type, exc_value, traceback)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2465, in handle_success
raise_with_traceback(e, traceback)
File "/home/travis/build/jreback/pandas/pandas/compat/__init__.py", line 339, in raise_with_traceback
raise exc.with_traceback(traceback)
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1799, in test_numpy_argsort
result = np.argsort(idx)
File "/home/travis/miniconda/envs/pandas/lib/python3.6/site-packages/numpy/core/fromnumeric.py", line 913, in argsort
return argsort(axis, kind, order)
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1784, in argsort
return result.argsort(*args, **kwargs)
AssertionError: "unorderable types" does not match "'>' not supported between instances of 'str' and 'int'"
======================================================================
FAIL: test_order (pandas.tests.indexes.test_base.TestMixedIntIndex)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1780, in test_order
idx.order()
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1730, in order
ascending=ascending)
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1710, in sort_values
_as = self.argsort()
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1784, in argsort
return result.argsort(*args, **kwargs)
TypeError: '>' not supported between instances of 'str' and 'int'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1780, in test_order
idx.order()
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2452, in __exit__
return self.handle_success(exc_type, exc_value, traceback)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2465, in handle_success
raise_with_traceback(e, traceback)
File "/home/travis/build/jreback/pandas/pandas/compat/__init__.py", line 339, in raise_with_traceback
raise exc.with_traceback(traceback)
File "/home/travis/build/jreback/pandas/pandas/tests/indexes/test_base.py", line 1780, in test_order
idx.order()
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1730, in order
ascending=ascending)
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1710, in sort_values
_as = self.argsort()
File "/home/travis/build/jreback/pandas/pandas/indexes/base.py", line 1784, in argsort
return result.argsort(*args, **kwargs)
AssertionError: "unorderable types" does not match "'>' not supported between instances of 'str' and 'int'"
======================================================================
FAIL: test_basic_indexing (pandas.tests.series.test_indexing.TestSeriesIndexing)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/jreback/pandas/pandas/tests/series/test_indexing.py", line 1356, in test_basic_indexing
self.assertRaises(IndexError, s.__setitem__, 5, 0)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2380, in assertRaises
_callable(*args, **kwargs)
File "/home/travis/build/jreback/pandas/pandas/util/testing.py", line 2450, in __exit__
raise AssertionError("{0} not raised.".format(name))
AssertionError: IndexError not raised.
----------------------------------------------------------------------
Ran 10330 tests in 439.233s
FAILED (SKIP=724, errors=7, failures=4)
```
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MDU6SXNzdWUxOTAxMjUzMjg=
| 14,680 |
merge_asof tolerance is not working in both directions
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| 4 | 2016-11-17T18:25:29Z | 2017-01-19T18:52:31Z | 2016-11-17T19:13:57Z |
NONE
| null |
#### A small, complete example of the issue
```python
from io import StringIO
from pandas import Timedelta, merge_asof, read_csv, to_datetime
file1 = '''DATE_TIME,Label
2015-03-02 08:36:51,0
2015-03-02 08:36:54,1'''
df1 = read_csv(StringIO(file1))
df1.DATE_TIME = to_datetime(df1.DATE_TIME)
print(df1)
file2 = '''DATE_TIME,OtherData
2015-03-02 08:36:50,0
2015-03-02 08:36:51,1
2015-03-02 08:36:52,2
2015-03-02 08:36:53,3
2015-03-02 08:36:54,4
2015-03-02 08:36:55,5
2015-03-02 08:36:56,6
2015-03-02 08:36:57,7
2015-03-02 08:36:58,8
2015-03-02 08:36:59,9
2015-03-02 08:37:00,10'''
df2 = read_csv(StringIO(file2))
df2.DATE_TIME = to_datetime(df2.DATE_TIME)
print(df2)
print(merge_asof(df2, df1, on='DATE_TIME'))
print(merge_asof(df2, df1, on='DATE_TIME', tolerance=Timedelta(seconds=1)))
print(merge_asof(df2, df1, on='DATE_TIME', tolerance=Timedelta(seconds=5)))
```
#### Current Output
```
DATE_TIME Label
0 2015-03-02 08:36:51 0
1 2015-03-02 08:36:54 1
DATE_TIME OtherData
0 2015-03-02 08:36:50 0
1 2015-03-02 08:36:51 1
2 2015-03-02 08:36:52 2
3 2015-03-02 08:36:53 3
4 2015-03-02 08:36:54 4
5 2015-03-02 08:36:55 5
6 2015-03-02 08:36:56 6
7 2015-03-02 08:36:57 7
8 2015-03-02 08:36:58 8
9 2015-03-02 08:36:59 9
10 2015-03-02 08:37:00 10
DATE_TIME OtherData Label
0 2015-03-02 08:36:50 0 NaN
1 2015-03-02 08:36:51 1 0.0
2 2015-03-02 08:36:52 2 0.0
3 2015-03-02 08:36:53 3 0.0
4 2015-03-02 08:36:54 4 1.0
5 2015-03-02 08:36:55 5 1.0
6 2015-03-02 08:36:56 6 1.0
7 2015-03-02 08:36:57 7 1.0
8 2015-03-02 08:36:58 8 1.0
9 2015-03-02 08:36:59 9 1.0
10 2015-03-02 08:37:00 10 1.0
DATE_TIME OtherData Label
0 2015-03-02 08:36:50 0 NaN
1 2015-03-02 08:36:51 1 0.0
2 2015-03-02 08:36:52 2 0.0
3 2015-03-02 08:36:53 3 NaN
4 2015-03-02 08:36:54 4 1.0
5 2015-03-02 08:36:55 5 1.0
6 2015-03-02 08:36:56 6 NaN
7 2015-03-02 08:36:57 7 NaN
8 2015-03-02 08:36:58 8 NaN
9 2015-03-02 08:36:59 9 NaN
10 2015-03-02 08:37:00 10 NaN
DATE_TIME OtherData Label
0 2015-03-02 08:36:50 0 NaN
1 2015-03-02 08:36:51 1 0.0
2 2015-03-02 08:36:52 2 0.0
3 2015-03-02 08:36:53 3 0.0
4 2015-03-02 08:36:54 4 1.0
5 2015-03-02 08:36:55 5 1.0
6 2015-03-02 08:36:56 6 1.0
7 2015-03-02 08:36:57 7 1.0
8 2015-03-02 08:36:58 8 1.0
9 2015-03-02 08:36:59 9 1.0
10 2015-03-02 08:37:00 10 NaN
```
#### Expected Output
```
DATE_TIME Label
0 2015-03-02 08:36:51 0
1 2015-03-02 08:36:54 1
DATE_TIME OtherData
0 2015-03-02 08:36:50 0
1 2015-03-02 08:36:51 1
2 2015-03-02 08:36:52 2
3 2015-03-02 08:36:53 3
4 2015-03-02 08:36:54 4
5 2015-03-02 08:36:55 5
6 2015-03-02 08:36:56 6
7 2015-03-02 08:36:57 7
8 2015-03-02 08:36:58 8
9 2015-03-02 08:36:59 9
10 2015-03-02 08:37:00 10
DATE_TIME OtherData Label
0 2015-03-02 08:36:50 0 NaN
1 2015-03-02 08:36:51 1 0.0
2 2015-03-02 08:36:52 2 0.0
3 2015-03-02 08:36:53 3 0.0
4 2015-03-02 08:36:54 4 1.0
5 2015-03-02 08:36:55 5 1.0
6 2015-03-02 08:36:56 6 1.0
7 2015-03-02 08:36:57 7 1.0
8 2015-03-02 08:36:58 8 1.0
9 2015-03-02 08:36:59 9 1.0
10 2015-03-02 08:37:00 10 1.0
DATE_TIME OtherData Label
0 2015-03-02 08:36:50 0 **0.0**
1 2015-03-02 08:36:51 1 0.0
2 2015-03-02 08:36:52 2 0.0
3 2015-03-02 08:36:53 3 NaN
4 2015-03-02 08:36:54 4 1.0
5 2015-03-02 08:36:55 5 1.0
6 2015-03-02 08:36:56 6 NaN
7 2015-03-02 08:36:57 7 NaN
8 2015-03-02 08:36:58 8 NaN
9 2015-03-02 08:36:59 9 NaN
10 2015-03-02 08:37:00 10 NaN
DATE_TIME OtherData Label
0 2015-03-02 08:36:50 0 **0.0**
1 2015-03-02 08:36:51 1 0.0
2 2015-03-02 08:36:52 2 0.0
3 2015-03-02 08:36:53 3 0.0
4 2015-03-02 08:36:54 4 1.0
5 2015-03-02 08:36:55 5 1.0
6 2015-03-02 08:36:56 6 1.0
7 2015-03-02 08:36:57 7 1.0
8 2015-03-02 08:36:58 8 1.0
9 2015-03-02 08:36:59 9 1.0
10 2015-03-02 08:37:00 10 NaN
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.1
nose: None
pip: 9.0.0
setuptools: 28.0.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.0
openpyxl: 2.3.5
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
None
</details>
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MDExOlB1bGxSZXF1ZXN0OTQyNDc4ODM=
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COMPAT: remove some deprecation warnings in 3.6
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| 1 | 2016-11-17T20:51:25Z | 2016-11-18T01:04:14Z | 2016-11-18T01:04:14Z |
CONTRIBUTOR
| null |
partial on #14679
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AmbiguousTimeError on groupby when including a DST change
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| 2 | 2016-11-17T22:22:17Z | 2016-11-22T11:29:39Z | 2016-11-22T11:29:39Z |
NONE
| null |
#### A small, complete example of the issue
```python
#!/usr/bin/env python
import pandas as pd
df=pd.DataFrame([1477786980,1477790580],columns=['ts'])
df['date']=pd.to_datetime(df.ts, unit='s').dt.tz_localize('UTC').dt.tz_convert('Europe/Madrid')
df.set_index('date', inplace=True)
dfo = df.groupby(pd.TimeGrouper('5min'))
```
#### Expected Output
```
ts
date
2016-10-30 02:20:00+02:00 1
2016-10-30 02:25:00+02:00 0
2016-10-30 02:30:00+02:00 0
2016-10-30 02:35:00+02:00 0
2016-10-30 02:40:00+02:00 0
2016-10-30 02:45:00+02:00 0
2016-10-30 02:50:00+02:00 0
2016-10-30 02:55:00+02:00 0
2016-10-30 02:00:00+01:00 0
2016-10-30 02:05:00+01:00 0
2016-10-30 02:10:00+01:00 0
2016-10-30 02:15:00+01:00 0
2016-10-30 02:20:00+01:00 1
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 28.6.1
Cython: 0.25.1
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: 1.4.8
patsy: None
dateutil: 2.4.2
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: None
openpyxl: 2.2.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
The above code raises an `AmbiguousTimeError` exception, when grouping by a time-date series including a DST change. In the above example the unix timestamps are for the recent DST change in Europe.
The stack trace is:
```
Traceback (most recent call last):
File "./t.py", line 7, in <module>
dfo = df.groupby(pd.TimeGrouper('5min'))
File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 3984, in groupby
**kwargs)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 1501, in groupby
return klass(obj, by, **kwds)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 370, in __init__
mutated=self.mutated)
File "/usr/local/lib/python2.7/dist-packages/pandas/core/groupby.py", line 2382, in _get_grouper
binner, grouper, obj = key._get_grouper(obj)
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.py", line 1062, in _get_grouper
r._set_binner()
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.py", line 237, in _set_binner
self.binner, self.grouper = self._get_binner()
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.py", line 245, in _get_binner
binner, bins, binlabels = self._get_binner_for_time()
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.py", line 660, in _get_binner_for_time
return self.groupby._get_time_bins(self.ax)
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.py", line 1118, in _get_time_bins
base=self.base)
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.py", line 1262, in _get_range_edges
closed=closed, base=base)
File "/usr/local/lib/python2.7/dist-packages/pandas/tseries/resample.py", line 1326, in _adjust_dates_anchored
return (Timestamp(fresult).tz_localize(first_tzinfo),
File "pandas/tslib.pyx", line 621, in pandas.tslib.Timestamp.tz_localize (pandas/tslib.c:13694)
File "pandas/tslib.pyx", line 4308, in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:74816)
pytz.exceptions.AmbiguousTimeError: Cannot infer dst time from Timestamp('2016-10-30 02:20:00'), try using the 'ambiguous' argument
```
Code works if the series does not include a DST change (e.g. one day earlier):
```python
#!/usr/bin/env python
import pandas as pd
df=pd.DataFrame([1477700580,1477704180],columns=['ts'])
df['date']=pd.to_datetime(df.ts, unit='s').dt.tz_localize('UTC').dt.tz_convert('Europe/Madrid')
df.set_index('date', inplace=True)
dfo = df.groupby(pd.TimeGrouper('5min'))
print dfo.count()
```
gets:
```
ts
date
2016-10-29 02:20:00+02:00 1
2016-10-29 02:25:00+02:00 0
2016-10-29 02:30:00+02:00 0
2016-10-29 02:35:00+02:00 0
2016-10-29 02:40:00+02:00 0
2016-10-29 02:45:00+02:00 0
2016-10-29 02:50:00+02:00 0
2016-10-29 02:55:00+02:00 0
2016-10-29 03:00:00+02:00 0
2016-10-29 03:05:00+02:00 0
2016-10-29 03:10:00+02:00 0
2016-10-29 03:15:00+02:00 0
2016-10-29 03:20:00+02:00 1
```
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BUG: Avoid AmbiguousTimeError on groupby
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| 6 | 2016-11-17T22:48:08Z | 2016-11-22T11:30:09Z | 2016-11-22T11:29:39Z |
NONE
| null |
closes #14682
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TST: more 3.6 test fixing
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| 1 | 2016-11-18T01:47:23Z | 2016-11-18T11:14:34Z | 2016-11-18T11:14:34Z |
CONTRIBUTOR
| null |
xref #14679
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BUG: fix pickling of Custom offsets in 3.6
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| 1 | 2016-11-18T02:04:51Z | 2016-11-18T11:08:03Z | 2016-11-18T11:08:03Z |
CONTRIBUTOR
| null |
xref #14679
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DEPR: Patch to_dense behaviour for sparse.
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| 6 | 2016-11-18T05:29:16Z | 2016-11-23T20:40:41Z | 2016-11-23T20:40:41Z |
MEMBER
| null |
Patches the following for `to_dense`:
1) Fix `SparseArray.to_dense` documentation to refer to `SparseArray` and not `SparseSeries`.
2) Deprecate the fill parameter in `SparseArray.to_dense`, as that parameter was not being respected.
3) Deprecate the sparse_only parameter in `SparseSeries.to_dense`, as that parameter is inconsistent with the `to_dense` API we want, which is no parameters.
Closes #14647.
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combine_first throws ValueError: Cannot convert NA to integer
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| 1 | 2016-11-18T07:24:12Z | 2016-12-16T09:43:42Z | 2016-12-16T09:43:42Z |
NONE
| null |
I do not understand why there is a need to convert NA to integer if the result does not have NAs. Perhaps the combine_first algo needs to do it under the hood?
#### A small, complete example of the issue
```python
from pandas import DataFrame
DataFrame({'a': [0, 1, 3, 5]}).combine_first(DataFrame({'a': [1, 4]}))
Traceback (most recent call last):
File "/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-16-12b973b1b150>", line 1, in <module>
pd.DataFrame({'a': [0, 1, 3, 5]}).combine_first(pd.DataFrame({'a': [1, 4]}))
File "/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py", line 3787, in combine_first
return self.combine(other, combiner, overwrite=False)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py", line 3714, in combine
otherSeries = otherSeries.astype(new_dtype)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/generic.py", line 3054, in astype
raise_on_error=raise_on_error, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/internals.py", line 3168, in astype
return self.apply('astype', dtype=dtype, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/internals.py", line 3035, in apply
applied = getattr(b, f)(**kwargs)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/internals.py", line 462, in astype
values=values, **kwargs)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/internals.py", line 505, in _astype
values = _astype_nansafe(values.ravel(), dtype, copy=True)
File "/usr/local/lib/python3.4/dist-packages/pandas/types/cast.py", line 531, in _astype_nansafe
raise ValueError('Cannot convert NA to integer')
ValueError: Cannot convert NA to integer
```
#### Expected Output
```
a
0 0
1 1
2 3
3 5
```
It does work when at least one item is a float:
```python
DataFrame({'a': [0.0, 1, 3, 5]}).combine_first(DataFrame({'a': [1, 4]}))
a
0 0.0
1 1.0
2 3.0
3 5.0
```
I am aware that integer series cannot have NAs but there is no need to introduce NAs here. I do like it that the series is not upcasted to float silently though.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 1.5.4
setuptools: 3.3
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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assign a dataframe to part of another dataframe, warning A value is trying to be set on a copy of a slice from a DataFrame
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| 2 | 2016-11-18T07:31:27Z | 2016-11-23T12:25:57Z | 2016-11-23T12:25:57Z |
NONE
| null |
#### A small, complete example of the issue
when i assign a dataframe to part of another dataframe, a warning occured :
D:\Program Files\Anaconda3\lib\site-packages\pandas\core\indexing.py:465: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
my code like this:
df.iloc[:, ::2] = df.iloc[:,::2].fillna(method="ffill", axis=1)
this worked ,but there is a warning , so i change the code like this:
price = df.iloc[:,::2].copy()
price.fillna(method="ffill", axis=1, inplace=True)
price.fillna(method="bfill", axis=1, inplace=True)
df.loc[:, price.columns.tolist()] = price
still work, and still warning.
i'm wonder in the http://pandas-docs.github.io/pandas-docs-travis/indexing.html, there is example:
df.loc[:,['B', 'A']] = df[['A', 'B']]
this code is much the same like my code, but there is no warning at all.
i doubt that in my code ,my df is a large dataframe, so maybe somthing i don't know in the low level process of pandas .
```python
# Your code here
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
</details>
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COMPAT: pickle compat for Timestamp in py3.6
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| 0 | 2016-11-18T11:27:43Z | 2016-11-18T12:23:19Z | 2016-11-18T12:23:19Z |
CONTRIBUTOR
| null |
BUG: fix unorderable exception types in py3.6
xref #14679
xref http://bugs.python.org/issue28730
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MDU6SXNzdWUxOTAzMjg5MjY=
| 14,690 |
Nit: In the documentation of DataFrame.any, the 'axis' argument is missing
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| 3 | 2016-11-18T14:15:37Z | 2016-11-18T15:07:17Z | 2016-11-18T14:57:44Z |
NONE
| null |
The [current online documentation of `pandas.DataFrame.any`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.any.html) omits the `axis` keyword, as e.g. [described for concat](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html).
However, the following works:
```python
pd.DataFrame([[True, False]]).any(axis=1)
```
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| 14,691 |
indexing with df.ix for slicing is inclusive for stop index
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[] | null | 1 | 2016-11-18T18:58:31Z | 2016-11-18T19:02:00Z | 2016-11-18T19:01:50Z |
NONE
| null |
#### A small, complete example of the issue
```python
df = pd.DataFrame({'this': np.arange(10), 'that': np.arange(10,20)})
df.iloc[:5, :]
df.ix[:5, :]
```
df.iloc and df.ix should return the same output. However, it appears that .ix is inclusive on the high end of the slice. This behavior is inconsistent with indexing in Python.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.0
nose: 1.3.4
pip: 8.1.2
setuptools: 20.7.0
Cython: 0.22
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: 0.9.2
apiclient: 1.4.2
sqlalchemy: 0.9.9
pymysql: None
psycopg2: 2.6 (dt dec pq3 ext)
jinja2: 2.7.3
boto: 2.36.0
</details>
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BUG: HDF5 Files cannot be read concurrently
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| 1 | 2016-11-18T21:16:24Z | 2016-11-18T22:32:54Z | 2016-11-18T22:32:54Z |
NONE
| null |
#### A small, complete example of the issue
```
import pandas as pd
import numpy as np
from multiprocessing import Pool
import warnings
# To avoid natural name warnings
warnings.filterwarnings('ignore')
def init(hdf_store):
global hdf_buff
hdf_buff = hdf_store
def reader(name):
df = hdf_buff[name]
return (name, df)
def main():
# Creating the store
with pd.HDFStore('storage.h5', 'w') as store:
for i in range(100):
df = pd.DataFrame(np.random.rand(5,3), columns=list('abc'))
store.append(str(i), df, index=False, expectedrows=5)
# Reading concurrently with one connection
with pd.HDFStore('storage.h5', 'r') as store:
with Pool(4, initializer=init, initargs=(store,)) as p:
ret = pd.concat(dict(p.map(reader, [str(i) for i in range(100)])))
if __name__ == '__main__':
main()
```
The above code either fails loudly with the following error:
```
tables.exceptions.HDF5ExtError: HDF5 error back trace
File "H5Dio.c", line 173, in H5Dread
can't read data
File "H5Dio.c", line 554, in H5D__read
can't read data
File "H5Dchunk.c", line 1856, in H5D__chunk_read
error looking up chunk address
File "H5Dchunk.c", line 2441, in H5D__chunk_lookup
can't query chunk address
File "H5Dbtree.c", line 998, in H5D__btree_idx_get_addr
can't get chunk info
File "H5B.c", line 340, in H5B_find
unable to load B-tree node
File "H5AC.c", line 1262, in H5AC_protect
H5C_protect() failed.
File "H5C.c", line 3574, in H5C_protect
can't load entry
File "H5C.c", line 7954, in H5C_load_entry
unable to load entry
File "H5Bcache.c", line 143, in H5B__load
wrong B-tree signature
End of HDF5 error back trace
```
Or with the following error:
```
multiprocessing.pool.RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/kartik/miniconda3/lib/python3.5/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/kartik/miniconda3/lib/python3.5/multiprocessing/pool.py", line 44, in mapstar
return list(map(*args))
File "/home/kartik/Documents/Code/Scripts/Benchmarking and optimization stuff/HDF_Concurrent.py", line 13, in reader
df = hdf_buff[name]
File "/home/kartik/miniconda3/lib/python3.5/site-packages/pandas/io/pytables.py", line 461, in __getitem__
return self.get(key)
File "/home/kartik/miniconda3/lib/python3.5/site-packages/pandas/io/pytables.py", line 677, in get
raise KeyError('No object named %s in the file' % key)
KeyError: 'No object named 7 in the file'
"""
```
But in this case, `object 7` clearly exists in the table. Any help?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-47-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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BUG: Fix for json lines issue with backslashed quotes
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| 6 | 2016-11-19T00:08:29Z | 2016-11-22T11:43:16Z | 2016-11-21T16:56:27Z |
CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
This is an additional fix to:
https://github.com/pandas-dev/pandas/pull/14429
https://github.com/pandas-dev/pandas/pull/14391
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ENH: Allow updating GBQ table schemas
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CONTRIBUTOR
| null |
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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BUG: Fix move_into_mutable_buffer for python 3.6.
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| 3 | 2016-11-19T02:00:45Z | 2016-11-22T11:13:59Z | 2016-11-22T11:13:59Z |
CONTRIBUTOR
| null |
In python 3.6, the CALL_FUNCTION handling was updated. One change is
that when calling a C function from a python function python now counts
the reference owned by the argument tuple. This means that move was
always seeing objects with two references instead of the expected
one.
Python 3.6 also removed a copy in the argument tuple when *unpacking
functions. This means that if a user does:
tuple = (create_string(),)
move_into_mutable_buffer(*tuple)
where create_string() creates a string object with one reference then we
will fail to raise a BadMove even though the user could later retrieve
that string with tuple[0]. There is no way to detect this case so this
patch adds a warning to the docstring advising against star unpacking.
xref: #14679
I played around with removing the extra reference that was added in 3.6 but it looks like playing with borrowed refs everywhere will be a bit tricky. This change should clear things up for 3.6 while continuing to work for older versions. In 3.6 you __could__ get a shared mutable string from this but you need to try pretty hard for it.
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Fix segfault on trying to free an invalid pointer in tokenizer.c
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[] | null | 15 | 2016-11-19T05:47:34Z | 2017-07-20T23:14:13Z | 2017-02-16T17:54:53Z |
NONE
| null |
This fixes a segmentation fault on an out of memory error in `tokenizer.c` (encountered when loading large JSON files).
The gist of the error is that we're trying to `free()` a pointer that points to a read-only area of memory holding the error message string literal, as illustrated by the example below:
```c
#include<stdio.h>
#include<stdlib.h>
typedef struct Readonly {
char *message;
} Readonly;
int main() {
Readonly readonly;
// This works
readonly.message = (char*) malloc(200);
sprintf(readonly.message, "This goes into normal memory\n");
printf(readonly.message);
// This fails as we try to free in a read-only memory region.
readonly.message = "This goes into a read-only data section";
// Segfault here
free(readonly.message);
}
```
I will try to add tests that exercise this code path and verify that this solution is correct.
|
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BUG: Categorical.searchsorted(): use provided categorical order
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| 6 | 2016-11-20T00:47:52Z | 2017-01-03T17:18:11Z | 2016-12-30T21:43:21Z |
CONTRIBUTOR
| null |
- [x] closes #14522
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Previously, it used lexical order instead of the provided categorical
order.
Tests updated accordingly.
Closes https://github.com/pandas-dev/pandas/issues/14522
Documentation also needs to be updated.
Could you please review? If it's OK I'll append doc update to this pull request.
Thanks!
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| 5 | 2016-11-20T02:47:15Z | 2016-11-22T14:10:04Z | 2016-11-22T14:10:04Z |
CONTRIBUTOR
| null |
closes #14676
This adds a new top-level class `pd.CategoricalType` for representing the *type* information on a `Categorical`, *without the values*. Users can pass this anywhere they would previously pass `'category'` if they need to specify non-default values for `categories` or `ordered` (you can still use `'category'` of course):
```python
In [1]: from pandas import *
In [2]: paste
>>> t = CategoricalType(categories=['b', 'a'], ordered=True)
>>> s = Series(['a', 'a', 'b', 'b', 'a'])
>>> c = s.astype(t)
>>> c
## -- End pasted text --
Out[2]:
0 a
1 a
2 b
3 b
4 a
dtype: category
Categories (2, object): [b < a]
```
This is the simplest possible change for now. A bigger change would be to make `c.dtype` return a `CategoricalType` instead of '`category`'. We could probably do that in a way that's backwards compatible, but I'll need to think on it a bit.
Other places this should work
- `select_dtypes` (currently returns all categoricals; should just be ones with same `CategoricalType`?)
- `read_csv`: need to implement, see https://github.com/pandas-dev/pandas/issues/14503
- `Series(..., dtype=...`)
- probably others.
Implementation-wise I need to document, more tests, clean up some things like the repr. But I wanted to get this out there for discussion. @janschulz you might be interested.
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MDU6SXNzdWUxOTA1Mzc1OTk=
| 14,699 |
test_quotechar_unicode on Debian jessie (stable)
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| 7 | 2016-11-20T03:48:06Z | 2016-12-12T11:48:25Z | 2016-12-12T11:48:25Z |
CONTRIBUTOR
| null |
Seems to happen only with python3 (passes on python2)
```
======================================================================
ERROR: test_quotechar_unicode (pandas.io.tests.parser.test_parsers.TestCParserHighMemory)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/parser/quoting.py", line 152, in test_quotechar_unicode
result = self.read_csv(StringIO(data), quotechar=u('\u0394'))
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/parser/test_parsers.py", line 59, in read_csv
return read_csv(*args, **kwds)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 645, in parser_f
return _read(filepath_or_buffer, kwds)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 388, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 729, in __init__
self._make_engine(self.engine)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 922, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 1389, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas/parser.pyx", line 411, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4130)
File "pandas/parser.pyx", line 588, in pandas.parser.TextReader._set_quoting (pandas/parser.c:6307)
OverflowError: value too large to convert to char
======================================================================
ERROR: test_quotechar_unicode (pandas.io.tests.parser.test_parsers.TestCParserLowMemory)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/parser/quoting.py", line 152, in test_quotechar_unicode
result = self.read_csv(StringIO(data), quotechar=u('\u0394'))
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/tests/parser/test_parsers.py", line 77, in read_csv
return read_csv(*args, **kwds)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 645, in parser_f
return _read(filepath_or_buffer, kwds)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 388, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 729, in __init__
self._make_engine(self.engine)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 922, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/build/pandas-0.19.1/debian/tmp/usr/lib/python3/dist-packages/pandas/io/parsers.py", line 1389, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas/parser.pyx", line 411, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4130)
File "pandas/parser.pyx", line 588, in pandas.parser.TextReader._set_quoting (pandas/parser.c:6307)
OverflowError: value too large to convert to char
```
FWIW also happens on ubuntu 15.04 . Passes on later releases.
Advice on where/how to dig would be appreciated
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-rc2+
machine: x86_64
processor:
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.4
pip: None
setuptools: 20.10.1
Cython: 0.21.1
numpy: 1.8.2
scipy: 0.14.0
statsmodels: None
xarray: None
IPython: None
sphinx: 1.2.3
patsy: None
dateutil: 2.2
pytz: 2012c
blosc: None
bottleneck: None
tables: 3.2.1
numexpr: 2.4.3
matplotlib: 1.4.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTQ1MTg0ODM=
| 14,700 |
CLN: remove bare excepts
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[] | null | 13 | 2016-11-20T13:50:40Z | 2016-12-19T11:49:11Z | 2016-12-19T11:49:11Z |
CONTRIBUTOR
| null |
- [x] closes #14554
- [ ] tests added / passed: updated test_indexing.py
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
Errors in indexing no longer forced into KeyError
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MDU6SXNzdWUxOTA2NjM2MzE=
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groupby boxplot doesn't work if multiple by's are used
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[] | null | 2 | 2016-11-21T09:39:54Z | 2021-11-15T01:08:51Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
data = pd.DataFrame({'A': [1, 1, 2, 2], 'B': ['a', 'b', 'a', 'b'], 'value': [1, 2, 3, 4]})
data.groupby(['A', 'B']).boxplot()
```
#### Problem description
This code fails with an obscure error message from the internals:
```
KeyError Traceback (most recent call last)
<ipython-input-118-d3ffaf6e9196> in <module>()
----> 1 data.groupby(['A', 'B']).boxplot()
/home/languitar/miniconda2/envs/monitoring/lib/python2.7/site-packages/pandas/tools/plotting.pyc in boxplot_frame_groupby(grouped, subplots, column, fontsize, rot, grid, ax, figsize, layout, **kwds)
3109 rot=rot, grid=grid, **kwds)
3110 ax.set_title(pprint_thing(key))
-> 3111 ret.loc[key] = d
3112 fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1,
3113 right=0.9, wspace=0.2)
/home/languitar/miniconda2/envs/monitoring/lib/python2.7/site-packages/pandas/core/indexing.pyc in __setitem__(self, key, value)
137 else:
138 key = com._apply_if_callable(key, self.obj)
--> 139 indexer = self._get_setitem_indexer(key)
140 self._setitem_with_indexer(indexer, value)
141
/home/languitar/miniconda2/envs/monitoring/lib/python2.7/site-packages/pandas/core/indexing.pyc in _get_setitem_indexer(self, key)
124
125 try:
--> 126 return self._convert_to_indexer(key, is_setter=True)
127 except TypeError as e:
128
/home/languitar/miniconda2/envs/monitoring/lib/python2.7/site-packages/pandas/core/indexing.pyc in _convert_to_indexer(self, obj, axis, is_setter)
1227 mask = check == -1
1228 if mask.any():
-> 1229 raise KeyError('%s not in index' % objarr[mask])
1230
1231 return _values_from_object(indexer)
```
There is no hint in the documentation that this could be an illegal operation.
#### Expected Output
It should just print a boxplot for all groups, or if this really doesn't work, give a real hint why.
#### Output of ``pd.show_versions()``
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.8.8-1-ARCH
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
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
```
</details>
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| 190,756,995 |
MDU6SXNzdWUxOTA3NTY5OTU=
| 14,702 |
DOC: pd.read_msgpack not in API index
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| 3 | 2016-11-21T15:54:55Z | 2017-01-29T09:53:52Z | 2016-11-29T23:33:47Z |
CONTRIBUTOR
| null |
If I have not overlooked anything, it seams that `pd.read_msgpack` does not show up in the [API reference index](http://pandas.pydata.org/pandas-docs/version/0.19.1/genindex.html#R).
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pandas-qt appears to broken for python 3.x
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| 3 | 2016-11-21T18:08:29Z | 2016-11-22T16:39:37Z | 2016-11-22T16:39:16Z |
CONTRIBUTOR
| null |
In the pandas [faq](https://github.com/pandas-dev/pandas/blob/master/doc/source/faq.rst) contains the following statement;
> "There is no support for such visualization in pandas. However, the external package pandas-qt does provide this functionality."
I have tested pandas-qt on in several python environments and I could not get pandas-qt to install. I have logged the this in the [pandas-qt issues](https://github.com/datalyze-solutions/pandas-qt/issues/17). So far none of the contributors have commented on this issue. The last commit on on the repo was two months ago leading me to believe that this project has gone dormant.
So my suggestion is to change the above statement from the FAQ to read;
> "There is no support for such visualization in pandas."
If anyone out there knows of a functional PyQt viewer for pandas please let me know.
Thanks!
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read_csv(...,mangle_dupe_cols=True) causes silent data loss for certain column names. Request introduction of mangle_dupe_cols_str
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[] | null | 6 | 2016-11-21T21:25:11Z | 2021-05-02T00:47:06Z | null |
NONE
| null |
Lets say we have data with both column and row headers. Lets say this dataset also simply outputs a ```0``` in that csv file for the cell at the intersection of the column and row headers (A1 in excel notation, or cell (0,0) in the csv file). Additionally, both ```"0"``` and ```"0.1"``` are valid column names:
RH\\CH|-0.1|0|0.1
---|---|---|---
10.0|123|456|789
20.0|012|345|678
Thus, "RH\\CH" is replaced by ```"0"``` on export (of which I have no control).
```python
import pandas as pd
from cStringIO import StringIO
data = '0,-0.1,0,0.1\n10.0,123,456,789\n20.0,012,345,678'
df = pd.read_csv(StringIO(data),index_col=0,header=0)
print(df)
-0.1 0.1 0.1
0
10.0 123 789 789
20.0 12 678 678
```
The name mangling will change the duplicate ```"0"``` column containing data to ```"0.1"```, thus if a real data column has the name ```"0.1"```, this data will be copied (?) back to the mangled duplicate ```"0"``` column. Additionally, if the true ```"0.1"``` data name is missing, then this name mangling is frustrating as there is no obvious way to determine if the now present ```"0.1"``` column is a duplicate ```"0"``` that has been mangled or is a real ```"0.1"``` data series.
I propose the addition of a ```mangle_dupe_cols_str``` keyword option that defaults to ```'.'``` to preserve the current behavior. However, it can be passed as a kwarg to ```read_csv``` in cases where the period name mangling could result in further duplicate columns.
In https://github.com/pandas-dev/pandas/blob/v0.19.1/pandas/io/parsers.py#L2109-L2111:
```python
if cur_count > 0:
this_columns[i] = '%s.%d' % (col, cur_count)
counts[col] = cur_count + 1
```
should be adapted to
```python
if cur_count > 0:
this_columns[i] = '%s%s%d' % (col, self.mangle_dupe_cols_str, cur_count)
counts[col] = cur_count + 1
```
and lines 1042-1043 in https://github.com/pandas-dev/pandas/blob/v0.19.1/pandas/io/parsers.py#L1042-L1043 changed to:
```python
self.mangle_dupe_cols = kwds.get('mangle_dupe_cols', True)
self.mangle_dupe_cols_str = kwds.get('mangle_dupe_cols_str', '.')
self.infer_datetime_format = kwds.pop('infer_datetime_format', False)
```
#### Expected Output
Then the corrected output would be:
```python
>>> data = '0,-0.1,0,0.1\n10.0,123,456,789\n20.0,012,345,678'
>>> df = pd.read_csv(StringIO(data),index_col=0,header=0,mangle_dupe_cols_str='__')
>>> print(df)
-0.1 0__1 0.1
0
10.0 123 456 789
20.0 12 345 678
```
where subsequent operations could identify any columns with the mangle_dupe_cols_str, if needed. The current behavior silently causes data loss for certain column names.
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: None
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxOTA4NTE4NjE=
| 14,705 |
pd.HDFStore doesn't accept pathlib objects
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| 5 | 2016-11-21T22:17:25Z | 2018-04-14T18:15:44Z | 2018-04-14T18:15:40Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
In [1]: from pathlib import Path
In [2]: pd.HDFStore(Path('store.h5'))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-2-7a495ee2d608> in <module>()
----> 1 pd.HDFStore(Path('store.h5'))
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/io/pytables.py in __init__(self, path, mode, complevel, complib, fletcher32, **kwargs)
446 self._fletcher32 = fletcher32
447 self._filters = None
--> 448 self.open(mode=mode, **kwargs)
449
450 @property
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/pandas/pandas/io/pytables.py in open(self, mode, **kwargs)
585
586 try:
--> 587 self._handle = tables.open_file(self._path, self._mode, **kwargs)
588 except (IOError) as e: # pragma: no cover
589 if 'can not be written' in str(e):
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/tables/file.py in open_file(filename, mode, title, root_uep, filters, **kwargs)
316
317 # Finally, create the File instance, and return it
--> 318 return File(filename, mode, title, root_uep, filters, **kwargs)
319
320 openFile = previous_api(open_file)
/Users/tom.augspurger/Envs/py3/lib/python3.5/site-packages/tables/file.py in __init__(self, filename, mode, title, root_uep, filters, **kwargs)
782
783 # Now, it is time to initialize the File extension
--> 784 self._g_new(filename, mode, **params)
785
786 # Check filters and set PyTables format version for new files.
tables/hdf5extension.pyx in tables.hdf5extension.File._g_new (tables/hdf5extension.c:3781)()
tables/utilsextension.pyx in tables.utilsextension.encode_filename (tables/utilsextension.c:5345)()
TypeError: Expected bytes, got PosixPath
```
#### Expected Output
same as str for path
xref https://github.com/pandas-dev/pandas/issues/11033 for the original Pathlib implementation
xref https://github.com/pandas-dev/pandas/issues/13823 for fspath
|
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| 190,880,264 |
MDU6SXNzdWUxOTA4ODAyNjQ=
| 14,706 |
HTML rendering of dataframes no longer working
|
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[] | null | 6 | 2016-11-22T01:02:52Z | 2016-11-22T09:46:37Z | 2016-11-22T08:50:22Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import pandas as pd
arrays = [np.hstack([ ['one']*3, ['two']*3]), ['Dog', 'Bird', 'Cat']*2]
columns = pd.MultiIndex.from_arrays(arrays, names=['foo', 'bar'])
df = pd.DataFrame(np.zeros((3,6)),columns=columns,index=pd.date_range('20000103',periods=3))
df
```
#### Problem description
The HTML table of a dataframe is no longer rendering in the most recent versions. I am not sure if this is an issue that should be solved in Pandas or IPython but the symptom is in pandas. The shortest way to a reproduction of the issues is to install a fresh build of Canopy and run the above code. I have talked with the Canopy people and they have desire to see this fixed but no bandwidth to do it themselves. Some more info is on the related stack overflow question http://stackoverflow.com/questions/37041696/html-not-rendering-properly-with-canopy-1-7-1-3323-ipython-4-1-2
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| 190,904,548 |
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| 14,707 |
BUG: _nsorted incorrect with duplicated values in index (#13412)
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| 10 | 2016-11-22T04:26:16Z | 2017-12-20T01:57:28Z | 2016-12-06T11:35:03Z |
MEMBER
| null |
- [X] closes #13412
- [X] tests added / passed
- [X] passes ``git diff upstream/master | flake8 --diff``
- [X] whatsnew entry
`DataFrame.nlargest` and `DataFrame.nsmallest` does not behave correctly when there's duplicate values in the index.
Any feedback is appreciated!
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