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| null | 27 | 2015-11-14T19:39:37Z | 2020-01-10T14:08:50Z | 2015-11-14T19:43:52Z | NONE | null | I got this error message. Already installed pandas.
```python
>>> import pandas as pd
Traceback (most recent call last):
File "analyze_tweets.py", line 9, in <module>
import pandas as pd
ImportError: No module named pandas
```
**SOLUTION** (by @datapythonista)
This error happens when Python can't find pandas in the list of available libraries. Python has internally a list of directories where it'll look for packages. These directories can be obtained with `import sys; sys.path`.
In one machine you can (and should) have several Python installations. Meaning that you can have pandas installed in one, and be using another Python installation (this is likely the case here).
In Linux/Mac you can run `which python` and will tell you which is the Python you're using. If it's something like `/usr/bin/python`, you're using the Python from the system, which is not a great practice, and rarely used in the Python community.
If you used Python before you may have used virtual environments and pip. While this is fine for many Python projects (e.g. Django), when using data projects (pandas, numpy, tensorflow...) this is discouraged. It's easy that you have installation errors, and also the libraries can run much slower when using pip.
The widely used solution to this problem is to use conda. You can find simple installation instructions for pandas in this document: https://dev.pandas.io/getting_started.html
For more advanced users, installing miniconda, and then manually install pandas (and any other required package) with conda can be preferred (but avoid this if you're starting, since you'll probably have errors of missing dependencies when using many of the pandas features). | {
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| null | 5 | 2015-11-14T20:51:55Z | 2015-11-29T18:10:53Z | 2015-11-29T18:10:53Z | CONTRIBUTOR | null | {
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} | 7 | 2015-11-14T20:56:22Z | 2015-11-19T02:28:06Z | 2015-11-19T02:28:06Z | NONE | null | closes #11600
Fixed problem with multi-index column level names not propagating into sparse frames or back out to dense on a round trip through sparse. Includes 4 new tests to cover some relevant scenarios. Problem fixed for the conventions to_sparse path, I'm not sure about other paths where something else is passed to SparseDataSeries directly, those would be outside scope of bug.
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} | 3 | 2015-11-14T22:12:57Z | 2015-11-23T01:37:17Z | 2015-11-23T01:20:25Z | MEMBER | null | Closes #11586.
After the PR:
```
import pandas as pd
index = pd.CategoricalIndex(list('aabbca'), categories=list('cabe'))
df = pd.DataFrame({'A' : np.arange(6,dtype='int64')}, index=index)
# OK (not changed)
df.loc[['a', 'b']].index
# CategoricalIndex([u'a', u'a', u'a', u'b', u'b'], categories=[u'c', u'a', u'b', u'e'], ordered=False, dtype='category')
# Fixed to return Categorical Index if value exists in categories
df.loc[['a', 'b', 'e']].index
# CategoricalIndex([u'a', u'a', u'a', u'b', u'b', u'e'], categories=[u'a', u'b', u'e'], ordered=False, dtype='category')
# raise KeyError otherwise (not changed)
df.loc[['a', 'b', 'x']].index
# KeyError: 'a list-indexer must only include values that are in the categories'
```
There are separate paths when `CategoricalIndex` values (codes) are unique and not-unique and both fixed.
Also, `.reindex` intentionally returns normal `Index` when passed values are not `Categorical`, I kept the behavior as it is. If `.reindex` can always return `CategoricalIndex`, above 2 separate fixes are not required (fixing `.reindex` to return `CategoricalIndex` should work both paths).
```
# return normal Index(not changed)
df.index.reindex(['a', 'b'])
# (Index([u'a', u'a', u'a', u'b', u'b'], dtype='object'), array([0, 1, 5, 2, 3]))
# return CategoricalIndex(not changed)
df.index.reindex(pd.Categorical(['a', 'b']))
# (CategoricalIndex([u'a', u'a', u'a', u'b', u'b'], categories=[u'a', u'b'], ordered=False, dtype='category'), array([0, 1, 5, 2, 3]))
```
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| null | 1 | 2015-11-15T06:09:08Z | 2015-11-18T12:08:29Z | 2015-11-18T12:08:23Z | NONE | null | I run the python script that contain Pandas (that one cause the problem) library I got this error:
```
Traceback (most recent call last):
File "test.py", line 2, in <module>
import pandas
ImportError: No module named pandas
```
But if I import from package path to make sure it detects Pandas library, I got this error message
```
Traceback (most recent call last):
File "test.py", line 3, in <module>
import pandas
File "/usr/local/lib/python2.7/site-packages/pandas/__init__.py", line 44, in <module>
from pandas.core.api import *
File "/usr/local/lib/python2.7/site-packages/pandas/core/api.py", line 9, in <module>
from pandas.core.groupby import Grouper
File "/usr/local/lib/python2.7/site-packages/pandas/core/groupby.py", line 16, in <module>
from pandas.core.frame import DataFrame
File "/usr/local/lib/python2.7/site-packages/pandas/core/frame.py", line 41, in <module>
from pandas.core.series import Series
File "/usr/local/lib/python2.7/site-packages/pandas/core/series.py", line 2864, in <module>
import pandas.tools.plotting as _gfx
File "/usr/local/lib/python2.7/site-packages/pandas/tools/plotting.py", line 28, in <module>
import pandas.tseries.converter as conv
File "/usr/local/lib/python2.7/site-packages/pandas/tseries/converter.py", line 7, in <module>
import matplotlib.units as units
File "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py", line 1131, in <module>
rcParams = rc_params()
File "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py", line 975, in rc_params
return rc_params_from_file(fname, fail_on_error)
File "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py", line 1100, in rc_params_from_file
config_from_file = _rc_params_in_file(fname, fail_on_error)
File "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py", line 1018, in _rc_params_in_file
with _open_file_or_url(fname) as fd:
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/contextlib.py", line 17, in __enter__
return self.gen.next()
File "/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py", line 1000, in _open_file_or_url
encoding = locale.getdefaultlocale()[1]
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/locale.py", line 543, in getdefaultlocale
return _parse_localename(localename)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/locale.py", line 475, in _parse_localename
raise ValueError, 'unknown locale: %s' % localename
ValueError: unknown locale: UTF-8
```
I already update my `.bash_profile` and `.bashrc` with these lines but still not working even I already restart the terminal and my machine.
```
export LANG="it_IT.UTF-8"
export LC_COLLATE="it_IT.UTF-8"
export LC_CTYPE="it_IT.UTF-8"
export LC_MESSAGES="it_IT.UTF-8"
export LC_MONETARY="it_IT.UTF-8"
export LC_NUMERIC="it_IT.UTF-8"
export LC_TIME="it_IT.UTF-8"
export LC_ALL="it_IT.UTF-8"
```
I'm also enabled `Set locale environment variables on startup` in the terminal preferences.
[](http://i.stack.imgur.com/Yfo9e.png)
I'm afraid the problem cause because of EI Capitan. Please advice. Thank you.
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| null | 41 | 2015-11-16T00:16:37Z | 2021-05-25T19:32:50Z | 2021-05-25T19:32:49Z | CONTRIBUTOR | null | Follows #10250
For 0.17.1
- [x] Nicer table styling for the pydata.org website
- [x] Include a visual example in 0.17.1.rst
- [x] remove `doc/source/html-styling.html` and find a way to include `doc/source/html-styling.ipynb` in the doc build (should use `--template=basic`)
- [x] update `print_versions`
- [x] update `requirements_all.txt` to include jinja
For 0.18.0 / Future
- [x] sparsify MultiIndex repr (maybe push till 0.18)
- [ ] easy alignment styles #12144
- [ ] Template modification: This is for things like wrapping base64 encoded values in `img` tags, urls, etc. flows into...
- [ ] Break the large template in `Styler.template` into smaller blocks. Let people extend that. We could (maybe) allow users to choose which template to use to render each column/cell with solving the template modification problem
- [ ] Truncated repr
- [ ] hook into `pd.options`, allow setting of default reprs with styles
- [ ] Refactor parts of `Styler` into a `BaseStyler`, maybe add a LaTeX styler (maybe deprecate / replace the `to_html` and `to_latex` methods; Jinja templates are much more pleasant to work with), xref #11700
- [ ] Categoricals / Boolens builtin stylings
will add more as we go.
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} | 2 | 2015-11-16T11:05:38Z | 2015-11-20T13:55:35Z | 2015-11-20T13:55:35Z | CONTRIBUTOR | null | Applying a numpy function, such as `np.round`, to a DataFrame with duplicated column indicies can cause an unrecoverable stack overflow error (Fatal Python error: Cannot recover from stack overflow.), which causes e.g. a ipython kernel to crash. E.g. take the following example, where python crashes at line 4:
``` python
x = pd.DataFrame(np.random.randn(3,3))
y = pd.DataFrame(np.random.randn(3,3))
z = pd.concat((x, y), axis=1)
print(np.round(z))
```
However, removing the duplicate column entries, works as expected:
``` python
...
z = pd.concat((x, y), axis=1, ignore_index=True)
print(np.round(z))
```
---
python 3.5.0, numpy 1.10.1, pandas 0.17.0
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| null | 2 | 2015-11-16T14:48:00Z | 2015-11-16T15:05:53Z | 2015-11-16T15:04:58Z | NONE | null | For negative integer values close to - 2^63, the value is dectected as NaN after cast
Pandas version : 0.17.0
``` python
import pandas
import StringIO
df1 = pandas.read_csv(StringIO.StringIO("0\t-9223372036854775295\n0\t0\n0\t9223372036854775807\n0\t\n"),names=["foo", "bar"], sep="\t")
print df1
df2 = pandas.read_csv(StringIO.StringIO("0\t-9223372036854775296\n0\t0\n0\t9223372036854775807\n0\t\n"),names=["foo", "bar"], sep="\t")
print df2
```
output :
```
foo bar
0 0 NaN
1 0 0.000000e+00
2 0 9.223372e+18
3 0 NaN
float64
foo bar
0 0 -9.223372e+18
1 0 0.000000e+00
2 0 9.223372e+18
3 0 NaN
float64
```
```
pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-30-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.10.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: 0.8
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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} | 1 | 2015-11-16T15:10:23Z | 2015-11-16T16:19:04Z | 2015-11-16T16:18:59Z | CONTRIBUTOR | null | It should be 'default' instead of 'defalt'.
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} | 2 | 2015-11-16T15:34:11Z | 2015-12-25T21:47:39Z | 2015-12-25T21:47:39Z | NONE | null | Hi there,
I'm not sure if this is a pandas/matplotlib or seaborn issue...but since I upgraded to Matplotlib 1.5 using anaconda I can't get the colors of my barplots right anymore, here's an example:
In ipython notebook:
```
import pandas as pd
%matplotlib inline
import seaborn as sns
sns.set_style('whitegrid')
# Works, color as expected in nice muted blue
pd.DataFrame([1,2,3,4,5,6]).plot(linewidth=10)
# Doesn't work, getting matplotlib "eye-cancer-blue"
pd.DataFrame([1,2,3,4,5,6]).plot(kind='bar')
```
I attached a screenshot one with matplotlib 1.4.x and one with 1.5:
Again I know this might well not be a pandas issue but maybe someone has an idea where the problem might be.
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| null | 5 | 2015-11-16T16:32:50Z | 2015-12-30T19:07:12Z | 2015-11-17T20:39:42Z | NONE | null | ERROR: type should be string, got "https://groups.google.com/forum/#!topic/pydata/8Xr7SBmCvbs\n\nBelow is 2 versions of applyshift function (if you comment out some places), in one case it returns tuple and in the other case pandas Series.\n\nThen this function is applied to a large pandas dataframe along each row (axis=1).\n\nWhy is the running time slower about 10 times with pd.Series???\n\n```\ndef applyshift(col1,col2,col3,col4):\n if abs(col1-0)<0.01:\n #return pd.Series(dict(zip(('col2','col3','col4'),\n return (\n 0,0,col4)#)))\n if abs(col1-1)<0.01:\n #return pd.Series(dict(zip(('col2','col3','col4'),\n return (\n col2,0,col4)#)))\n\n%%time\ndf2=df1.apply(lambda row: applyshift(\n row['col1'],row['col2'],row['col3'],row['col4']), axis=1)\n```\n" | {
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https://api.github.com/repos/pandas-dev/pandas/issues/11616 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11616/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11616/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11616/events | https://github.com/pandas-dev/pandas/issues/11616 | 117,163,999 | MDU6SXNzdWUxMTcxNjM5OTk= | 11,616 | groupby UTC timestamp aggregation | {
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} | 2 | 2015-11-16T16:32:52Z | 2015-11-27T13:40:10Z | 2015-11-27T13:40:10Z | NONE | null | Hi all,
I've found an inconsistency between pandas 0.17 and 0.16.2 when aggregating on UTC timestamps. Here is a snippet to reproduce the problem:
``` python
import numpy as np
import pandas as pd
np.random.seed(42)
data = pd.DataFrame({
'factor': np.random.randint(0, 3, size=60),
'time': pd.date_range('01/01/2000 00:00', periods=60, freq='s', tz='UTC')
})
gp = data.groupby('factor')
print(gp['time'].min())
print(gp['time'].max())
```
On 0.16.2 the output seems correct, i.e it returns timestamps:
```
In [1]: %run bug_pandas.py
factor
0 2000-01-01 00:00:01+00:00
1 2000-01-01 00:00:07+00:00
2 2000-01-01 00:00:00+00:00
Name: time, dtype: object
factor
0 2000-01-01 00:00:57+00:00
1 2000-01-01 00:00:54+00:00
2 2000-01-01 00:00:59+00:00
Name: time, dtype: object
```
However on 0.17 it returns timestamps as integers:
```
In [1]: %run bug_pandas.py
factor
0 946684801000000000
1 946684807000000000
2 946684800000000000
Name: time, dtype: int64
factor
0 946684857000000000
1 946684854000000000
2 946684859000000000
Name: time, dtype: int64
```
It should be noted that the problem doesn't appear with `tz=None`.
Thanks for your help,
Alexandre
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} | 10 | 2015-11-16T16:36:29Z | 2017-01-12T22:43:12Z | 2017-01-12T22:43:12Z | NONE | null | xref #14205
The `.loc` method of `DataFrame` with different dtypes yields coerced type **even if the resulting slice does only contain elements from one type**. This happens only when selecting a single row.
I can guess that this might be intended because the implementation of `loc` seems to first lookup the row _as a single Series_, doing the coercion and _then_ applying the second (column) indexer.
However, when the column indexer narrows down the selection such that the upcasting would not have been necessary in the first place, it can be very surprising and may even cause bugs (on user-side) if it goes unnoticed. (Like, "I was sure that those column was `int64`").
``` python
>>> import pandas as pd
>>> d = pd.DataFrame(dict(a=[1.23]))
>>> d["b"] = 666 # adding column with int
>>> d.info() # info as expected (column b is int64 - fine)
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1 entries, 0 to 0
Data columns (total 2 columns):
a 1 non-null float64
b 1 non-null int64
dtypes: float64(1), int64(1)
memory usage: 24.0 bytes
>>> d.loc[0,"b"] # UNEXPECTED: returning a single float
666.0
>>> d.ix[0, "b"] # OK: returns a single int
666
>>> d.loc[[0], "b"] # OK
0 666
Name: b, dtype: int64
```
Feel free to close if the behavior s intended. Maybe this this a "bug" or an suggested API change. I dunno.
Perhaps related to #10503, #9519, #9269, #11594 ?
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 15.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.17.0
[...]
```
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} | 4 | 2015-11-16T16:43:55Z | 2015-11-20T16:26:22Z | 2015-11-20T13:55:24Z | CONTRIBUTOR | null | Fixes #11611. The iterator now uses DataFrame.iteritems instead of direct indexing.
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} | 1 | 2015-11-16T20:07:23Z | 2015-11-18T16:49:58Z | 2015-11-18T16:49:58Z | NONE | null | running pandas 0.17, and getting this error when generating a csv, traceback is below. What exactly does "try using the ambigous argument mean"?
df.to_csv("VIP_data.csv", encoding="utf-8", index=False)
---
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-24-222761ffc778> in <module>()
----> 1 df.to_csv("VIP_data.csv", encoding="utf-8", index=False)
/opt/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in to_csv(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, quoting, quotechar, line_terminator, chunksize, tupleize_cols, date_format, doublequote, escapechar, decimal, **kwds)
1287 escapechar=escapechar,
1288 decimal=decimal)
-> 1289 formatter.save()
1290
1291 if path_or_buf is None:
/opt/anaconda/lib/python2.7/site-packages/pandas/core/format.pyc in save(self)
1492
1493 else:
-> 1494 self._save()
1495
1496 finally:
/opt/anaconda/lib/python2.7/site-packages/pandas/core/format.pyc in _save(self)
1592 break
1593
-> 1594 self._save_chunk(start_i, end_i)
1595
1596 def _save_chunk(self, start_i, end_i):
/opt/anaconda/lib/python2.7/site-packages/pandas/core/format.pyc in _save_chunk(self, start_i, end_i)
1607 decimal=self.decimal,
1608 date_format=self.date_format,
-> 1609 quoting=self.quoting)
1610
1611 for col_loc, col in zip(b.mgr_locs, d):
/opt/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in to_native_types(self, slicer, na_rep, date_format, quoting, **kwargs)
1999
2000 from pandas.core.format import _get_format_datetime64_from_values
-> 2001 format = _get_format_datetime64_from_values(values, date_format)
2002
2003 result = tslib.format_array_from_datetime(values.view('i8').ravel(),
/opt/anaconda/lib/python2.7/site-packages/pandas/core/format.pyc in _get_format_datetime64_from_values(values, date_format)
2189 def _get_format_datetime64_from_values(values, date_format):
2190 """ given values and a date_format, return a string format """
-> 2191 is_dates_only = _is_dates_only(values)
2192 if is_dates_only:
2193 return date_format or "%Y-%m-%d"
/opt/anaconda/lib/python2.7/site-packages/pandas/core/format.pyc in _is_dates_only(values)
2143 def _is_dates_only(values):
2144 # return a boolean if we are only dates (and don't have a timezone)
-> 2145 values = DatetimeIndex(values)
2146 if values.tz is not None:
2147 return False
/opt/anaconda/lib/python2.7/site-packages/pandas/util/decorators.pyc in wrapper(_args, *_kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(_args, *_kwargs)
90 return wrapper
91 return _deprecate_kwarg
/opt/anaconda/lib/python2.7/site-packages/pandas/tseries/index.pyc in **new**(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
342 ints = subarr.view('i8')
343 subarr = tslib.tz_localize_to_utc(ints, tz,
--> 344 ambiguous=ambiguous)
345
346 subarr = subarr.view(_NS_DTYPE)
pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:64516)()
AmbiguousTimeError: Cannot infer dst time from Timestamp('2015-11-01 01:00:10'), try using the 'ambiguous' argument
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| null | 3 | 2015-11-16T23:27:04Z | 2015-11-18T22:25:16Z | 2015-11-18T22:25:16Z | NONE | null | Fixed issue [#4964](https://github.com/pydata/pandas/issues/4964) so rolling_apply works on non-numeric columns as well.
I followed instructions [here](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md) but I'm not sure where to add documentation in the [release notes](https://github.com/pydata/pandas/blob/master/CONTRIBUTING.md#documenting-your-code).
Also would like to get feedback on the code and complete any code changes before adding to documentation.
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| null | 1 | 2015-11-17T01:10:49Z | 2015-11-17T14:50:17Z | 2015-11-17T14:50:17Z | NONE | null | Converting percentages to multipliers on this dataframe produces interesting results (in that its occasionally wrong).
``` python
In [1]: df = pd.read_hdf(r'pct_change.h5', 'data')
In [2]: df.head()
Out[2]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ...
2006-05-19 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-22 NaN NaN NaN -0.0613 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-23 NaN NaN NaN -3.9288 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-24 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-25 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
[5 rows x 1200 columns]
In [3]: #((sign * df / 100.0) + 1.0).head()
In [4]: sign = -1
In [5]: sign = -1.0
In [6]: ((sign * df / 100.0) + 1.0).head()
Out[6]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ...
2006-05-19 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-22 NaN NaN NaN 1.000613 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-23 NaN NaN NaN 1.039288 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-24 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-25 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
[5 rows x 1200 columns]
```
... 16 tries later ...
``` python
In [22]: ((sign * df / 100.0) + 1.0).head()
Out[22]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ...
2006-05-19 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-22 NaN NaN NaN 1.000613 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-23 NaN NaN NaN 1.039288 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-24 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-25 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
[5 rows x 1200 columns]
In [23]: ((sign * df / 100.0) + 1.0).head()
Out[23]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ...
2006-05-19 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-22 NaN NaN NaN 1.000613 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-23 NaN NaN NaN 1.039288 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-24 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-25 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
[5 rows x 1200 columns]
In [24]: ((sign * df / 100.0) + 1.0).head()
Out[24]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ...
2006-05-19 0 0 0 0 0 0 ... 0 0 0 0 0 0
2006-05-22 0 0 0 0 0 0 ... 0 0 0 0 0 0
2006-05-23 0 0 0 0 0 0 ... 0 0 0 0 0 0
2006-05-24 0 0 0 0 0 0 ... 0 0 0 0 0 0
2006-05-25 0 0 0 0 0 0 ... 0 0 0 0 0 0
[5 rows x 1200 columns]
In [25]: ((sign * df / 100.0) + 1.0).head()
Out[25]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ...
2006-05-19 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-22 NaN NaN NaN 1.000613 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-23 NaN NaN NaN 1.039288 NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-24 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2006-05-25 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
[5 rows x 1200 columns]
In [26]: ((sign * df / 100.0) + 1.0).head()
Out[26]:
T0 T1 T2 T3 T4 T5 ... T1194 T1195 T1196 T1197 T1198 T1199
date ...
2006-05-19 1 1 1 1 1 1 ... 1 1 1 1 1 1
2006-05-22 1 1 1 1 1 1 ... 1 1 1 1 1 1
2006-05-23 1 1 1 1 1 1 ... 1 1 1 1 1 1
2006-05-24 1 1 1 1 1 1 ... 1 1 1 1 1 1
2006-05-25 1 1 1 1 1 1 ... 1 1 1 1 1 1
[5 rows x 1200 columns]
In [27]: ((sign * df / 100.0) + 1.0).head()
```
#### System:
- Windows 8.1 Pro
``` bash
> conda list | grep pandas
pandas 0.16.2 np19py34_0
> conda list | grep python
ipython 4.0.0 py34_0
ipython-genutils 0.1.0 <pip>
ipython_genutils 0.1.0 py34_0
python 3.4.3 4
python-dateutil 2.4.2 py34_0
> conda update pandas
Fetching package metadata: ....
Solving package specifications: ............
Package plan for installation in environment C:\LocalSoftware\miniconda\envs\general:
The following packages will be downloaded:
package | build
---------------------------|-----------------
pytz-2015.7 | py34_0 167 KB
pandas-0.17.0 | np110py34_0 5.8 MB
------------------------------------------------------------
Total: 6.0 MB
Th
e following packages will be UPDATED:
pandas: 0.16.2-np19py34_0 --> 0.17.0-np110py34_0
pytz: 2015.6-py34_0 --> 2015.7-py34_0
```
Post update - same behavior
Data: https://github.com/telson/pandasbugdata
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} | 9 | 2015-11-17T03:09:55Z | 2015-11-18T11:36:57Z | 2015-11-18T11:36:54Z | CONTRIBUTOR | null | closes #11108
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} | 11 | 2015-11-17T04:17:48Z | 2019-02-05T11:45:10Z | 2016-05-12T13:12:58Z | NONE | null | The quantile function is almost 10 000 times slower than the equivalent percentile function in numpy. See code below:
``` python
import time
import pandas as pd
import numpy as np
q = np.array([0.1,0.4,0.6,0.9])
data = np.random.randn(10000, 4)
df = pd.DataFrame(data, columns=['a', 'b', 'c', 'd'])
time1 = time.time()
pandas_quantiles = df.quantile(q, axis=1)
time2 = time.time()
print 'Pandas took %0.3f ms' % ((time2-time1)*1000.0)
time1 = time.time()
numpy_quantiles = np.percentile(data, q*100, axis=1)
time2 = time.time()
print 'Numpy took %0.3f ms' % ((time2-time1)*1000.0)
print (pandas_quantiles.values == numpy_quantiles).all()
# Output:
# Pandas took 15337.531 ms
# Numpy took 1.653 ms
# True
```
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| null | 4 | 2015-11-17T09:58:59Z | 2015-11-17T11:29:25Z | 2015-11-17T11:26:33Z | CONTRIBUTOR | null | In Pandas 0.17, if I put a `Series` into an `HDFStore` in `table` format, and if this `Series` contains a day light saving transition, then Pandas crashes with an `AmbiguousTimeError`.
Here's the minimal code example to reproduce the bug:
``` python
import pandas as pd
index = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
tz="Europe/London", freq="H")
series = pd.Series(0, index=index)
with pd.HDFStore("test.hdf", mode="w") as store:
store.put("series", series, format='table')
store["series"]
```
And here's the traceback:
``` python
In [4]: ---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-4-5deb3c83fafd> in <module>()
----> 1 __pyfile = open('''/tmp/py7201vZi''');exec(compile(__pyfile.read(), '''/home/jack/temp/pandas_dst_bug.py''', 'exec'));__pyfile.close(); import os; os.remove('''/tmp/py7201vZi''')
/home/jack/temp/pandas_dst_bug.py in <module>()
2 index = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
3 tz="Europe/London", freq="H")
----> 4 series = pd.Series(0, index=index)
5 with pd.HDFStore("test.hdf", mode="w") as store:
6 store.put("series", series, format='table')
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize, name, closed)
1912 return DatetimeIndex(start=start, end=end, periods=periods,
1913 freq=freq, tz=tz, normalize=normalize, name=name,
-> 1914 closed=closed)
1915
1916
/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
234 return cls._generate(start, end, periods, name, freq,
235 tz=tz, normalize=normalize, closed=closed,
--> 236 ambiguous=ambiguous)
237
238 if not isinstance(data, (np.ndarray, Index, ABCSeries)):
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in _generate(cls, start, end, periods, name, offset, tz, normalize, ambiguous, closed)
450
451 if end is not None and end.tz is None:
--> 452 end = end.tz_localize(tz)
453
454 if start and end:
pandas/tslib.pyx in pandas.tslib.Timestamp.tz_localize (pandas/tslib.c:11965)()
pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:64516)()
AmbiguousTimeError: Cannot infer dst time from Timestamp('2013-10-27 01:00:00'), try using the 'ambiguous' argument
```
Everything works fine if:
- we use `format=fixed` instead of `format=table`
- we used `format=table` but the `Series` does not include a DST change
I'm not certain but I expect this bug is related to #11619
I'm 99.999% certain that this bug did not exist in Pandas 0.16.2
```
In [30]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-33-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 1.5.6
setuptools: 15.2
Cython: 0.23.1
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.2
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
```
Finally, just to say another huge thank you to everyone who supports Pandas. It must be a huge amount of work and I am hugely grateful. Pandas is an awesome tool. Thank you.
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} | 13 | 2015-11-17T11:12:13Z | 2015-11-19T09:11:35Z | 2015-11-19T01:10:44Z | CONTRIBUTOR | null | - use [namedtuple._make()](https://docs.python.org/2/library/collections.html#collections.somenamedtuple._make)
Also:
- replace bare `except:` to avoid catching `SystemExit` and `KeyboardInterrupt`
- remove the generator `return` from the `try`-clause to an `else`
- more explicit fallback to regular tuples when `name=None` (docs?)
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} | 1 | 2015-11-17T11:26:22Z | 2015-11-17T13:12:30Z | 2015-11-17T12:18:23Z | CONTRIBUTOR | null | In Pandas 0.17, if I construct a `Series` which contains a day light saving transition, then Pandas crashes with an `AmbiguousTimeError`.
Here's the minimal code example to reproduce the bug:
``` python
import pandas as pd
index = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
tz="Europe/London", freq="H")
series = pd.Series(0, index=index)
```
And here's the traceback:
``` python
In [4]: ---------------------------------------------------------------------------
AmbiguousTimeError Traceback (most recent call last)
<ipython-input-4-5deb3c83fafd> in <module>()
----> 1 __pyfile = open('''/tmp/py7201vZi''');exec(compile(__pyfile.read(), '''/home/jack/temp/pandas_dst_bug.py''', 'exec'));__pyfile.close(); import os; os.remove('''/tmp/py7201vZi''')
/home/jack/temp/pandas_dst_bug.py in <module>()
2 index = pd.date_range("2013-10-26 23:00", "2013-10-27 01:00",
3 tz="Europe/London", freq="H")
----> 4 series = pd.Series(0, index=index)
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in date_range(start, end, periods, freq, tz, normalize, name, closed)
1912 return DatetimeIndex(start=start, end=end, periods=periods,
1913 freq=freq, tz=tz, normalize=normalize, name=name,
-> 1914 closed=closed)
1915
1916
/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
87 else:
88 kwargs[new_arg_name] = new_arg_value
---> 89 return func(*args, **kwargs)
90 return wrapper
91 return _deprecate_kwarg
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in __new__(cls, data, freq, start, end, periods, copy, name, tz, verify_integrity, normalize, closed, ambiguous, dtype, **kwargs)
234 return cls._generate(start, end, periods, name, freq,
235 tz=tz, normalize=normalize, closed=closed,
--> 236 ambiguous=ambiguous)
237
238 if not isinstance(data, (np.ndarray, Index, ABCSeries)):
/usr/local/lib/python2.7/dist-packages/pandas/tseries/index.pyc in _generate(cls, start, end, periods, name, offset, tz, normalize, ambiguous, closed)
450
451 if end is not None and end.tz is None:
--> 452 end = end.tz_localize(tz)
453
454 if start and end:
pandas/tslib.pyx in pandas.tslib.Timestamp.tz_localize (pandas/tslib.c:11965)()
pandas/tslib.pyx in pandas.tslib.tz_localize_to_utc (pandas/tslib.c:64516)()
AmbiguousTimeError: Cannot infer dst time from Timestamp('2013-10-27 01:00:00'), try using the 'ambiguous' argument
```
I'm not certain but I expect this bug is related to #11619
This bug report follows on from #11624
I'm 99.999% certain that this bug did not exist in Pandas 0.16.2
```
In [30]: show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-33-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 1.5.6
setuptools: 15.2
Cython: 0.23.1
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.2
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.3.2
html5lib: 0.999
httplib2: 0.9
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
```
Finally, just to say another huge thank you to everyone who supports Pandas. It must be a huge amount of work and I am hugely grateful. Pandas is an awesome tool. Thank you.
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} | 0 | 2015-11-17T11:38:13Z | 2015-11-17T12:18:23Z | 2015-11-17T12:18:23Z | CONTRIBUTOR | null | TST: some tests for datetime tz aware serialized to/from csv/hdf
closes #11626
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| null | 6 | 2015-11-17T13:22:53Z | 2015-11-17T15:24:17Z | 2015-11-17T15:24:17Z | NONE | null | Sometimes DataFrame.add method fails when adding two dataframes.
I have create the snippet below to reproduce. It will fail sooner or later (may take a while, 100000 or more iterations).
I have reproduced the problem always in pandas 0.17.0 and windows x64 (linux x64 seems fine). Using both anaconda and winpython, under different versions of numpy (1.9 & 1.10) and python 3.4 & 3.5
Other pandas versions work ok.
``` python
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from random import randint
print("pandas version:", pd.__version__)
print("numpy version:", np.__version__)
NROWS = 10000
NCOLS = 10
COLUMNS = [c+1 for c in range(NCOLS)]
def equalf(a, b, rtol=1e-05, atol=1e-08):
return abs(a - b) <= (atol + rtol * abs(b))
def create_df():
"""
:return: dataframe with indexes somewhat overlapping
"""
nrows = randint(1, NROWS)
offset = randint(0, NROWS)
m = np.random.randn(nrows, NCOLS)
return pd.DataFrame(data=m, index=[i for i in range(offset, offset + nrows)], columns=COLUMNS)
if __name__ == '__main__':
pd.set_option('display.width', 1024)
df0 = create_df()
for n in range(1000000):
if not n % 1000: print(n)
df0_checksum = df0.values.sum()
df1 = create_df()
df1_checksum = df1.values.sum()
df = df0.add(df1, fill_value=0) # sometimes doesnt add correctly !!!!!!
df_checksum = df.values.sum()
if not equalf(df_checksum, df0_checksum + df1_checksum):
print("error")
print(df0, df0_checksum)
print(df1, df1_checksum)
print(df, df_checksum)
print("df, df0 + df1 =", df_checksum, ",", df0_checksum + df1_checksum)
assert equalf(df_checksum, df0_checksum + df1_checksum)
df0 = df
```
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} | 10 | 2015-11-17T16:35:11Z | 2016-05-20T14:16:17Z | 2016-05-20T14:16:17Z | NONE | null | pandas 0.17.0, python 2.7.10:
> > > import pandas as pd
> > > p = pd.Timestamp(2015, 11, 12)
> > > p
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "pandas\tslib.pyx", line 339, in pandas.tslib.Timestamp.**repr** (pandas\tslib.c:9660)
> > > AttributeError: 'int' object has no attribute 'freqstr'
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} | 2 | 2015-11-17T22:45:59Z | 2020-06-15T13:20:34Z | 2020-06-15T13:20:34Z | CONTRIBUTOR | null | Particularly now that `shift` only works on datetime-like indexes (https://github.com/pydata/pandas/pull/11211)
They look almost the same, although not exactly:
``` python
In [11]: df=pd.DataFrame(pd.np.random.rand(5,2), index=pd.date_range(periods=5, start='2000'))
In [12]: df
Out[12]:
0 1
2000-01-01 0.640148 0.781291
2000-01-02 0.261649 0.652372
2000-01-03 0.642422 0.734348
2000-01-04 0.582657 0.601868
2000-01-05 0.848645 0.078437
In [13]: df.shift()
Out[13]:
0 1
2000-01-01 NaN NaN
2000-01-02 0.640148 0.781291
2000-01-03 0.261649 0.652372
2000-01-04 0.642422 0.734348
2000-01-05 0.582657 0.601868
In [14]: df.tshift()
Out[14]:
0 1
2000-01-02 0.640148 0.781291
2000-01-03 0.261649 0.652372
2000-01-04 0.642422 0.734348
2000-01-05 0.582657 0.601868
2000-01-06 0.848645 0.078437
In [15]: df.shift(freq='D')
Out[15]:
0 1
2000-01-02 0.640148 0.781291
2000-01-03 0.261649 0.652372
2000-01-04 0.642422 0.734348
2000-01-05 0.582657 0.601868
2000-01-06 0.848645 0.078437
In [16]: df.tshift(freq='D')
Out[16]:
0 1
2000-01-02 0.640148 0.781291
2000-01-03 0.261649 0.652372
2000-01-04 0.642422 0.734348
2000-01-05 0.582657 0.601868
2000-01-06 0.848645 0.078437
```
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} | 1 | 2015-11-17T23:21:52Z | 2018-09-11T02:58:11Z | 2015-11-18T11:48:36Z | CONTRIBUTOR | null | I spotted two failing tests with errors similar to what described @chris-b1 in #10631:
```
Attribute "dtype" are different
[left]: int32
[right]: int64
```
This PR fixes this on windows.
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} | 7 | 2015-11-18T01:10:01Z | 2016-05-18T13:22:36Z | 2016-05-18T13:22:36Z | NONE | null | I want to convert a DataFrame to SparseDataFrame before pivoting it (when it gets really sparse, see also [this discussion](http://stackoverflow.com/questions/31661604/efficiently-create-sparse-pivot-tables-in-pandas) ). I have a textual key, which I need to keep ("chr"):
```
df = pd.DataFrame( list(zip([3,2,4,1,5,3,2],
["chr1", "chr1", "chr1", "chr1", "chr2", "chr2", "chr3"],
[100,100, 100, 200, 1,3,1],
[True, True, True, False, True, False, True],
[-1,0,1,3, 0,2,1])) ,
columns = ["counts", "chr", "pos", "strand", "distance"])
df.iloc[:,1:].dtypes
Out[]:
chr object
pos int64
strand bool
distance int64
dtype: object
```
For this small table it works well with regular `DataFrame`:
```
pd.pivot_table(df, index= [ "chr", "pos"], columns= ["strand","distance"], values= "counts").fillna(0)
strand False True
distance 2 3 -1 0 1
chr pos
chr1 100 0 0 3 2 4
200 0 1 0 0 0
chr2 1 0 0 0 5 0
3 3 0 0 0 0
chr3 1 0 0 0 0 2
```
But I need to do it on much larger matrices. So I tried to do following trick:
```
dfpiv = pd.pivot_table(pd.SparseDataFrame(df), index= [ "chr", "pos"], columns= ["strand","distance"], values= "counts")
```
but I am getting:
```
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```
Are there any plans to include a functionality option into `pivot` function for automatic conversion into SparseDataFrame?
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} | 0 | 2015-11-18T03:15:27Z | 2015-11-18T11:52:12Z | 2015-11-18T11:52:12Z | CONTRIBUTOR | null | Partially closes https://github.com/pydata/pandas/issues/11610#issuecomment-157197629
- CSS update
- whatsnew example
- print_versions
- requirements_all
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} | 8 | 2015-11-18T09:31:34Z | 2017-07-20T12:31:42Z | 2017-07-20T12:31:42Z | MEMBER | null | From SO: http://stackoverflow.com/questions/33775560/how-to-group-categorical-values-in-pandas
```
import pandas as pd
df = pd.DataFrame()
df['A'] = ['C1', 'C1', 'C2', 'C2', 'C3', 'C3']
df['B'] = [1,2,3,4,5,6]
df['A'] = df.loc[:,'A'].astype('category')
df2 = df[0:3]
result = df2.groupby(by='A')['B'].nunique()
```
This worked in 0.16.2, but is broken in 0.17.0 (it gives _ValueError: Wrong number of items passed 2, placement implies 3_)
It is related with the fact that not all categories are present in the actual values (due to the slicing).
Probably related with new nunique implementation in 0.16.2 (https://github.com/pydata/pandas/pull/10894, https://github.com/pydata/pandas/pull/11079)
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| null | 1 | 2015-11-18T11:15:47Z | 2018-02-13T11:06:02Z | 2018-02-13T11:06:02Z | NONE | null | I have a dataframe that looks like this:
| Index | IssueId | AppName | IssueStatus | IssueDescription | IssueCreatedTime |
| --- | --- | --- | --- | --- | --- |
| 9965 | 239578 | abc | New | 'Rewards' | 2015-11-15 14:25:40 |
| 9966 | 219878 | abc | Resolved | '\u0414' | 2015-10-27 13:35:03 |
| 9967 | 239580 | abc | New | 'I'm still having problems with the game I log out of Facebook and now it will not let me back in saying that my farm is already register with a Facebook account.' | 2015-11-15 14:28:07 |
| 9968 | 132141 | abc | In Progress | 'Where do you get capers' | 2015-07-08 00:20:40 |
| 9969 | 209387 | abc | Resolved | '3.3.0' | 2015-10-18 11:32:08 |
If I do this, the '3.3.0' gets recognized as a date for some reason, and I get the following:
```
ex2 = ex.groupby('IssueId').apply(lambda x: x.iloc[-1])
```
| IssueId | AppName | IssueStatus | IssueDescription | IssueCreatedTime |
| --- | --- | --- | --- | --- |
| 132141 | abc | In Progress | | 2015-07-08 00:20:40 |
| 209387 | abc | Resolved | 2000-03-03 00:00:00 | 2015-10-18 11:32:08 |
| 219878 | abc | Resolved | | 2015-10-27 13:35:03 |
| 239578 | abc | New | | 2015-11-15 14:25:40 |
| 239580 | abc | New | | 2015-11-15 14:28:07 |
If I drop the column 'IssueCreatedTime', everything is normal again. Is this a bug? How can I suppress this behavior?
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} | 2 | 2015-11-18T11:33:18Z | 2015-11-20T00:40:57Z | 2015-11-20T00:40:57Z | NONE | null | This used to work OK in version 0.15 but stopped working in versions 0.16+.
``` python
import pandas as pd
df = pd.DataFrame({0: ['a', 'b', 'c'],
1: ['aa', 'bb', 'cc']})
df['test'] = 'txt'
df.to_csv('/home/temp/df.csv', columns=['test', 0, 1])
```
The following output is produced (values from columns 0 and 1 are missing):
```
,test,0,1
0,txt,,
1,txt,,
2,txt,,
```
Replacing 'test' with an integer value or skipping the columns argument works fine.
## INSTALLED VERSIONS
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-238.9.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.17.0
...
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} | 12 | 2015-11-18T12:17:46Z | 2015-11-20T15:18:49Z | 2015-11-20T00:59:32Z | CONTRIBUTOR | null | The following example should explain:
``` python
Python 2.7.10 |Continuum Analytics, Inc.| (default, Oct 19 2015, 18:04:42)
Type "copyright", "credits" or "license" for more information.
IPython 4.0.0 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-52-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
In [4]: df = pd.DataFrame({'one': np.full(10, 0, dtype=np.int8)})
In [5]: df
Out[5]:
one
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
In [6]: df.dtypes
Out[6]:
one int8
dtype: object
In [7]: df.loc[1, 'one'] = 6
In [8]: df
Out[8]:
one
0 0
1 6
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
In [9]: df.dtypes
Out[9]:
one int8
dtype: object
In [10]: df.one = np.int8(7)
In [11]: df.dtypes
Out[11]:
one int64
dtype: object
In [12]: df
Out[12]:
one
0 7
1 7
2 7
3 7
4 7
5 7
6 7
7 7
8 7
9 7
```
So it is cast to the correct dtype if a slice of the column is changed but setting the whole column changes the dtype even when explicitly set to `np.int8`
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} | 1 | 2015-11-18T13:19:31Z | 2015-11-18T21:34:56Z | 2015-11-18T21:34:56Z | MEMBER | null | Closes #11599
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} | 13 | 2015-11-18T14:27:07Z | 2016-01-29T14:40:50Z | 2016-01-29T14:40:50Z | CONTRIBUTOR | null | I guess it will be clearer with an example. First, let's prepare the dataframe:
```
In [2]: df = pd.DataFrame(columns=['a','b','c','d'], data=[[1,'b1','c1',3], [1,'b2','c2',4]])
In [3]: df = df.pivot_table(index='a', columns=['b','c'], values='d').reset_index()
In [4]: df
Out[28]:
b a b1 b2
c c1 c2
0 1 3 4
```
Now, the exception raised:
```
In [5]: df.groupby('a').mean()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-29-a830c6135818> in <module>()
----> 1 df.groupby('a').mean()
/home/nicolas/Git/pandas/pandas/core/groupby.py in mean(self)
764 self._set_selection_from_grouper()
765 f = lambda x: x.mean(axis=self.axis)
--> 766 return self._python_agg_general(f)
767
768 def median(self):
/home/nicolas/Git/pandas/pandas/core/groupby.py in _python_agg_general(self, func, *args, **kwargs)
1245 output[name] = self._try_cast(values[mask], result)
1246
-> 1247 return self._wrap_aggregated_output(output)
1248
1249 def _wrap_applied_output(self, *args, **kwargs):
/home/nicolas/Git/pandas/pandas/core/groupby.py in _wrap_aggregated_output(self, output, names)
3529 def _wrap_aggregated_output(self, output, names=None):
3530 agg_axis = 0 if self.axis == 1 else 1
-> 3531 agg_labels = self._obj_with_exclusions._get_axis(agg_axis)
3532
3533 output_keys = self._decide_output_index(output, agg_labels)
/home/nicolas/Git/pandas/pandas/core/groupby.py in __getattr__(self, attr)
557
558 raise AttributeError("%r object has no attribute %r" %
--> 559 (type(self).__name__, attr))
560
561 def __getitem__(self, key):
AttributeError: 'DataFrameGroupBy' object has no attribute '_obj_with_exclusions'
```
Maybe I'm doing something wrong, and it's not a bug, but then the exception raised should definitely be more explicit than a reference to an internal attribute :-)
This attribute, by the way, is (only) referenced in one file and in issue #5264. It might be connected, but the discussion is a bit long and technical.
I'll try to have a look at what's going on.
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} | 0 | 2015-11-18T16:17:40Z | 2015-11-18T16:49:58Z | 2015-11-18T16:49:58Z | CONTRIBUTOR | null | closes #11619
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} | 5 | 2015-11-18T22:49:17Z | 2017-06-01T22:12:15Z | 2017-06-01T22:12:15Z | CONTRIBUTOR | null | Sometimes it's handy to have access to a distinct integer for each group. For example, using the (internal) grouper:
```
>>> df = pd.DataFrame({"a": list("xyyzxy"), "b": list("ab"*3), "c": range(6)})
>>> df["group_id"] = df.groupby(["a","b"]).grouper.group_info[0]
>>> df
a b c group_id
0 x a 0 0
1 y b 1 2
2 y a 2 1
3 z b 3 3
4 x a 4 0
5 y b 5 2
```
This can be achieved in a number of ways but none of them are particularly elegant, esp. if we're grouping on multiple keys and/or Series. Accordingly, after a brief discussion on gitter, I propose a new method `transform("enumerate")` which returns a Series of integers from 0 to ngroups-1 matching the order the groups will be iterated in. In other words, we'll simply be applying the following map:
```
>>> m = {k: i for i, (k,g) in enumerate(df.groupby(["a","b"]))}
>>> m
{('x', 'a'): 0, ('y', 'b'): 2, ('y', 'a'): 1, ('z', 'b'): 3}
```
(Note this is only to shows the desired behaviour, and wouldn't be how it'd be implemented!)
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} | 1 | 2015-11-19T02:02:07Z | 2015-11-21T21:41:14Z | 2015-11-19T03:13:22Z | CONTRIBUTOR | null | on master:
``` python
In [1]: np.random.seed(2718281)
In [2]: n = 1 << 20
In [3]: ix = tm.makeCategoricalIndex(n, n // 10)
In [4]: %timeit ix.remove_unused_categories()
1 loops, best of 3: 527 ms per loop
```
on branch:
``` python
In [4]: %timeit ix.remove_unused_categories()
1 loops, best of 3: 216 ms per loop
```
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} | 8 | 2015-11-19T03:27:19Z | 2015-11-20T01:41:08Z | 2015-11-20T00:59:20Z | CONTRIBUTOR | null | closes #11638
Please review.
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} | 6 | 2015-11-19T12:19:03Z | 2020-09-18T21:59:24Z | 2020-09-18T21:59:24Z | NONE | null | Hello,
Please consider the following code :
```
import pandas as pd
import numpy as ny
dates = pd.date_range("2015-01-01", periods=10, freq="D")
ts = pd.TimeSeries(data=range(10), index=dates, dtype=ny.float64)
ts_mean = pd.rolling_mean(ts, 5)
print(ts)
2015-01-01 0
2015-01-02 1
2015-01-03 2
2015-01-04 3
2015-01-05 4
2015-01-06 5
2015-01-07 6
2015-01-08 7
2015-01-09 8
2015-01-10 9
Freq: D, dtype: float64
print(ts_mean)
2015-01-01 NaN
2015-01-02 NaN
2015-01-03 NaN
2015-01-04 NaN
2015-01-05 2
2015-01-06 3
2015-01-07 4
2015-01-08 5
2015-01-09 6
2015-01-10 7
Freq: D, dtype: float64
```
For the last date (2015-01-10), you should obtain 7, which corresponds to [5, 6, 7, 8, 9] mean value.
Now, please replace the 2015-01-03 value by -9+33 extreme value.
```
dates = pd.date_range("2015-01-01", periods=10, freq="D")
ts = pd.TimeSeries(data=range(10), index=dates, dtype=ny.float64)
ts[2] = -9e+33
print(ts)
2015-01-01 0.000000e+00
2015-01-02 1.000000e+00
2015-01-03 -9.000000e+33
2015-01-04 3.000000e+00
2015-01-05 4.000000e+00
2015-01-06 5.000000e+00
2015-01-07 6.000000e+00
2015-01-08 7.000000e+00
2015-01-09 8.000000e+00
2015-01-10 9.000000e+00
Freq: D, dtype: float64
```
And compute rolling_mean again :
```
ts_mean = pd.rolling_mean(ts, 5)
print(ts_mean)
2015-01-01 NaN
2015-01-02 NaN
2015-01-03 NaN
2015-01-04 NaN
2015-01-05 -1.800000e+33
2015-01-06 -1.800000e+33
2015-01-07 -1.800000e+33
2015-01-08 0.000000e+00
2015-01-09 1.000000e+00
2015-01-10 2.000000e+00
Freq: D, dtype: float64
```
As you can see, from the 2015-01-08, computation returns an incorrect result i.e [1, 2, 3] instead of [5, 6, 7]. The extreme value has introduced some perturbations in following date computation.
Best regards,
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} | 0 | 2015-11-19T13:18:00Z | 2015-11-19T13:38:45Z | 2015-11-19T13:38:45Z | CONTRIBUTOR | null | change in function locations, very minor corrections for functions like `DataFrame.sum`
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} | 4 | 2015-11-19T14:47:45Z | 2015-11-19T15:45:44Z | 2015-11-19T15:41:04Z | NONE | null | Please, consider the following code:
```
In [0]: import pandas as pd
In [1]: my_data = [{'TICKET_NUMBER' : '001', 'DATE' : '2015-11-11 11:15'},
{'TICKET_NUMBER' : '002', 'DATE' : '2015-11-12 14:32'},
{'TICKET_NUMBER' : '003', 'DATE' : '2015-11-13 15:33'},
{'TICKET_NUMBER' : '004', 'DATE' : '2015-11-14 11:34'},
{'TICKET_NUMBER' : '005', 'DATE' : '2015-11-15 17:35'},
{'TICKET_NUMBER' : '006', 'DATE' : '2015-11-16 19:36'},
{'TICKET_NUMBER' : '007', 'DATE' : '2015-11-17 12:37'},
{'TICKET_NUMBER' : '008', 'DATE' : '2015-11-18 15:38'}]
In [2]: df
Out [2]: DATE TICKET_NUMBER
0 2015-11-11 11:15 001
1 2015-11-12 14:32 002
2 2015-11-13 15:33 003
3 2015-11-14 11:34 004
4 2015-11-15 17:35 005
5 2015-11-11 11:15 006
6 2015-11-16 19:36 007
7 2015-11-18 15:38 008
In [3]: df = pd.DataFrame(my_data)
In [4]: df.DATE = pd.to_datetime(df.DATE)
In [5]: df[df.DATE<='2015-11-13']
Out [5]: DATE TICKET_NUMBER
0 2015-11-11 11:15 001
1 2015-11-12 14:32 002
```
The above code should print rows with dates "LESS THAN OR EQUAL TO" 2015-11-13 (indexes 0,1 AND 2), but it only shows rows with dates "LESS THAN" 2015-11-13 (that is, indexes 0 and 1, not 3).
Remarks:
(1) Filtering "MORE THAN OR EQUAL TO" some date (instead of LESS) works as expected.
(2) Removing the HOUR in the DATE makes everything work fine. This works as expected:
```
In [1]: my_data = [{'TICKET_NUMBER' : '001', 'DATE' : '2015-11-11'},
{'TICKET_NUMBER' : '002', 'DATE' : '2015-11-12'},
{'TICKET_NUMBER' : '003', 'DATE' : '2015-11-13'},
{'TICKET_NUMBER' : '004', 'DATE' : '2015-11-14'},
{'TICKET_NUMBER' : '005', 'DATE' : '2015-11-15'},
{'TICKET_NUMBER' : '006', 'DATE' : '2015-11-16'},
{'TICKET_NUMBER' : '007', 'DATE' : '2015-11-17'},
{'TICKET_NUMBER' : '008', 'DATE' : '2015-11-18'}]
```
(3) Setting the HOUR to 00:00 also makes it work as expected.
(4) pd.installed_versions()
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
LANG: es_ES.UTF-8
pandas: 0.16.2
nose: 1.3.4
Cython: 0.22
numpy: 1.9.3
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.3
openpyxl: 1.8.5
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xlsxwriter: 0.6.7
lxml: 3.4.2
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.9.9
pymysql: None
psycopg2: None
```
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| null | 2 | 2015-11-19T19:31:36Z | 2015-11-19T19:54:03Z | 2015-11-19T19:53:46Z | NONE | null | ``` python
import pandas as pd
## two timepoints in UTC flanking the US/Eastern DST shift
date_strings = ['2015-11-01T05:00:00Z', '2015-11-01T07:00:00Z']
S = pd.Series(date_strings)
## Convert to US/Eastern
S = pd.to_datetime(S).apply(lambda x: x.tz_localize('UTC').tz_convert('US/Eastern'))
S.dt.dayofweek
## AttributeError: Can only use .dt accessor with datetimelike values
## Note: the problem does not arise if the US/Eastern version of the times are on different days
## e.g. if date_strings = ['2015-11-01T03:00:00Z', '2015-11-01T07:00:00Z']
```
Using python 3.4 and pandas 0.17.0.
When a Series contains two time-zone aware datetimes on the 'same day' in the local time zone on either side of the Fall 2015 daylight savings time shift, calling .dt produces an attribute error.
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} | 4 | 2015-11-19T20:02:39Z | 2015-11-20T01:41:16Z | 2015-11-20T00:40:45Z | CONTRIBUTOR | null | This is a fix to Bug #11637.
Error Description : incorrect output when a mix of integer and string column names passed as columns parameter in to_csv().
[format.py](https://github.com/pydata/pandas/blob/master/pandas/core/format.py#L1330) is the cause of this discrepancy.
Please review .
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} | 9 | 2015-11-19T20:39:41Z | 2015-11-20T15:19:21Z | 2015-11-20T12:43:54Z | CONTRIBUTOR | null | cc @jorisvandenbossche
cc @cpcloud
cc @shoyer
cc @wesm
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} | 1 | 2015-11-19T21:34:09Z | 2015-11-19T21:40:19Z | 2015-11-19T21:40:15Z | CONTRIBUTOR | null | Numexpr was hosted on Google code, but moved to Github.
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} | 3 | 2015-11-19T22:14:22Z | 2015-11-20T00:38:08Z | 2015-11-20T00:38:08Z | NONE | null | From SO (http://stackoverflow.com/questions/33814223/strange-error-in-pandas-indexing-with-range-when-length-1-000-000)
Pandas raises a ValueError when assigning multiple values to a Series (or DataFrame) using range(x) where x > 1. This error is raised only when its length is one million or larger.
``` python
import pandas as pd
for x in [5, 999999, 1000000]:
s = pd.Series(index=range(x))
print('series length =', len(s))
# assigning value with range(1), always works
s.loc[range(1)] = 42
# reading values with range(x>1), always works
_ = s.loc[range(2)]
# assigning values with range(x>1), fails only when len >= 1 million
s.loc[range(2)] = 42
```
Output:
``` python
series length = 5
series length = 999999
series length = 1000000
Traceback (most recent call last):
File "<stdin>", line 9, in <module>
File "/home/nekobon/.env_exp/lib/python3.4/site-packages/pandas/core/indexing.py", line 114, in __setitem__
indexer = self._get_setitem_indexer(key)
File "/home/nekobon/.env_exp/lib/python3.4/site-packages/pandas/core/indexing.py", line 109, in _get_setitem_indexer
return self._convert_to_indexer(key, is_setter=True)
File "/home/nekobon/.env_exp/lib/python3.4/site-packages/pandas/core/indexing.py", line 1042, in _convert_to_indexer
return labels.get_loc(obj)
File "/home/nekobon/.env_exp/lib/python3.4/site-packages/pandas/core/index.py", line 1692, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas/index.pyx", line 137, in pandas.index.IndexEngine.get_loc (pandas/index.c:3979)
File "pandas/index.pyx", line 145, in pandas.index.IndexEngine.get_loc (pandas/index.c:3680)
File "pandas/index.pyx", line 464, in pandas.index._bin_search (pandas/index.c:9124)
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
Tested on pandas 0.17.0 and python 3.4.
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} | 0 | 2015-11-19T22:47:48Z | 2015-11-20T00:38:08Z | 2015-11-20T00:38:08Z | CONTRIBUTOR | null | closes #11652
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| null | 6 | 2015-11-20T00:19:47Z | 2015-11-20T19:51:50Z | 2015-11-20T01:26:45Z | NONE | null | I'm having trouble with an install of pandas 0.17.0 not raising errors CParserErrors when encountering a badly formatted csv.
```
>>> import pandas as pd
>>> import StringIO
>>> dataframe = pd.read_csv(StringIO.StringIO('group,"this\nreally,error\ni,hope\n'), sep=',', header=None, skiprows=0, nrows=None, index_col=False, skipinitialspace=True)
Exception pandas.parser.CParserError: CParserError('Error tokenizing data. C error: EOF inside string starting at line 0',) in 'pandas.parser.TextReader._tokenize_rows' ignored
>>> dataframe
Empty DataFrame
Columns: [0]
Index: []
```
I'd expect an error to be raised instead of returning an empty Dataframe, an error like:
```
>>> Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 491, in parser_f
return _read(filepath_or_buffer, kwds)
File "/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 268, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 583, in __init__
self._make_engine(self.engine)
File "/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 724, in _make_engine
self._engine = CParserWrapper(self.f, **self.options)
File "/venv/lib/python2.7/site-packages/pandas/io/parsers.py", line 1093, in __init__
self._reader = _parser.TextReader(src, **kwds)
File "pandas/parser.pyx", line 509, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4897)
File "pandas/parser.pyx", line 709, in pandas.parser.TextReader._get_header (pandas/parser.c:7427)
File "pandas/parser.pyx", line 820, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8736)
File "pandas/parser.pyx", line 1732, in pandas.parser.raise_parser_error (pandas/parser.c:22105)
pandas.parser.CParserError: Error tokenizing data. C error: EOF inside string starting at line 0
```
I installed pandas via pip 7.1.2 in a virtualenv and other environment information for the test that fails is:
```
Ubuntu 14.04.1 LTS (GNU/Linux 3.13.0-44-generic x86_64)
$ uname -a
Linux 3.13.0-44-generic #73-Ubuntu SMP Tue Dec 16 00:22:43 UTC 2014 x86_64 x86_64 x86_64 GNU/Linux
$ python
Python 2.7.10 (default, Nov 19 2015, 23:31:54)
[GCC 4.8.4] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-44-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.0
nose: 1.3.4
pip: 7.1.2
setuptools: 18.2
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.1
statsmodels: 0.6.1
IPython: None
sphinx: None
patsy: 0.3.0
dateutil: 2.2
pytz: 2014.7
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.4.6
matplotlib: None
openpyxl: 2.1.0
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.8.0
pymysql: None
psycopg2: 2.5.4 (dt dec pq3 ext)
```
Is there something I'm doing wrong or not setting up in the environment correctly? Also is this the appropriate place to ask this question? Thanks for any help!
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} | 16 | 2015-11-20T10:57:04Z | 2016-08-04T11:43:56Z | 2016-08-04T11:43:55Z | NONE | null | I noticed that the styling doesn't quite work with `MultiIndex` data frames. For example if we have a dataframe with two cross validated scores and take their `mean`and `sem` using `groupby` to get a `MultiIndex` dataframe
```
import itertools
import pandas as pd
import numpy as np
jobs = itertools.product(['a', 'b', 'c', 'd'], np.arange(1e-4, 1e-3, .0003), range(10))
rows = []
for v, v2, itr in jobs:
rows.append({'param_1': v, 'param_2': v2, 'iter': itr,
'score_1': np.random.randint(0, 100, size=(1,))[0],
'score_2': np.random.rand(1, )[0]})
df_multi = pd.DataFrame(rows)
agg = df_multi.groupby(by=['param_1', 'param_2'])[['score_1', 'score_2']].agg(['mean', 'sem'])
agg.style.highlight_max()
```
where as without the styling the index names are preserved and the `score_1` and `score_2` column headers span 2 columns which improves legibility.
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} | 7 | 2015-11-20T11:07:38Z | 2021-09-09T14:13:05Z | 2021-09-09T14:13:04Z | NONE | null | There's a problem in the way `.set_precision` currently works as it is being applied to all table cells indiscriminately. For instance a dataframe with means and standard errors cross validated across some parameter values the `set_precision` will easily hide a lot of detail in the results.
The second parameter (2nd index) is not all zeros, they're just values in the range `[1e-4, 1e-3]`. Also while for the `mean` columns a precision of 2 is fine, for the standard error 4 might be more appropriate. So having a `subset=` keyword argument also for `.set_precision` would be great.
---
code for making the df
```
import itertools
import pandas as pd
import numpy as np
jobs = itertools.product(['a', 'b', 'c', 'd'], np.arange(1e-4, 1e-3, .0003), range(10))
rows = []
for v, v2, itr in jobs:
rows.append({'param_1': v, 'param_2': v2, 'iter': itr,
'score_1': np.random.randint(0, 100, size=(1,))[0],
'score_2': np.random.rand(1, )[0]})
df_multi = pd.DataFrame(rows)
agg = df_multi.groupby(by=['param_1', 'param_2'])[['score_1', 'score_2']].agg(['mean', 'sem'])
```
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| null | 2 | 2015-11-20T13:18:16Z | 2015-11-20T14:03:10Z | 2015-11-20T13:57:29Z | CONTRIBUTOR | null | I think I have a similar to issue https://github.com/pydata/pandas/issues/10933.
In my case the problem manifests only in PyCharm when I try to debug some code. I've already tried to force building the c dependencies using `python setup.py build_ext --inplace -f` but that didn't help.
The error I get is:
```
/Users/grossi/anaconda/envs/pandas_dev/bin/python3.4 "/Applications/PyCharm CE.app/Contents/helpers/pydev/pydevd.py" --multiproc --qt-support --client 127.0.0.1 --port 62593 --file "/Applications/PyCharm CE.app/Contents/helpers/pycharm/noserunner.py" /Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/tests/test_indexing.py
Testing started at 21:45 ...
Connected to pydev debugger (build 143.595)
pydev debugger: process 23639 is connecting
Error
Traceback (most recent call last):
File "/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/__init__.py", line 7, in <module>
from pandas import hashtable, tslib, lib
ImportError: dlopen(/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so, 2): Symbol not found: _PyCObject_Type
Referenced from: /Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so
Expected in: dynamic lookup
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/unittest/case.py", line 58, in testPartExecutor
yield
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/unittest/case.py", line 577, in run
testMethod()
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/failure.py", line 39, in runTest
raise self.exc_val.with_traceback(self.tb)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/loader.py", line 418, in loadTestsFromName
addr.filename, addr.module)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/importer.py", line 47, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/importer.py", line 94, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/imp.py", line 245, in load_module
return load_package(name, filename)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/imp.py", line 217, in load_package
return methods.load()
File "<frozen importlib._bootstrap>", line 1220, in load
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/__init__.py", line 13, in <module>
"extensions first.".format(module))
ImportError: C extension: dlopen(/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so, 2): Symbol not found: _PyCObject_Type
Referenced from: /Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so
Expected in: dynamic lookup
not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
E
======================================================================
ERROR: Failure: ImportError (C extension: dlopen(/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so, 2): Symbol not found: _PyCObject_Type
Referenced from: /Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so
Expected in: dynamic lookup
not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/__init__.py", line 7, in <module>
from pandas import hashtable, tslib, lib
ImportError: dlopen(/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so, 2): Symbol not found: _PyCObject_Type
Referenced from: /Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so
Expected in: dynamic lookup
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/failure.py", line 39, in runTest
raise self.exc_val.with_traceback(self.tb)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/loader.py", line 418, in loadTestsFromName
addr.filename, addr.module)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/importer.py", line 47, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/site-packages/nose/importer.py", line 94, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/imp.py", line 245, in load_module
return load_package(name, filename)
File "/Users/grossi/anaconda/envs/pandas_dev/lib/python3.4/imp.py", line 217, in load_package
return methods.load()
File "<frozen importlib._bootstrap>", line 1220, in load
File "<frozen importlib._bootstrap>", line 1200, in _load_unlocked
File "<frozen importlib._bootstrap>", line 1129, in _exec
File "<frozen importlib._bootstrap>", line 1471, in exec_module
File "<frozen importlib._bootstrap>", line 321, in _call_with_frames_removed
File "/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/__init__.py", line 13, in <module>
"extensions first.".format(module))
ImportError: C extension: dlopen(/Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so, 2): Symbol not found: _PyCObject_Type
Referenced from: /Users/grossi/PycharmProjects/pandas-IamGianluca/pandas/hashtable.so
Expected in: dynamic lookup
not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
----------------------------------------------------------------------
Ran 1 test in 0.006s
FAILED (errors=1)
Process finished with exit code 1
```
My python version is:
```
Python 3.4.3 |Continuum Analytics, Inc.| (default, Oct 20 2015, 14:27:51)
[GCC 4.2.1 (Apple Inc. build 5577)] on darwin
```
Interestingly I can run all nosetests without a problem from the command line.
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} | 7 | 2015-11-20T13:24:45Z | 2020-04-10T02:46:15Z | 2020-04-10T02:46:14Z | CONTRIBUTOR | null | This multiple-assignment indexing broke in numpy 1.10 because I think it accidently broadcast certain things.
```
df = DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
df[['A','B']] = [[1,1,1,1],[2,2,2,2]]
```
e.g. fails in 1.10, but works in 1.9
http://stackoverflow.com/questions/33818388/python-pandas-updating-multiple-columns-using-lists-failed-in-v-0-17
Here's a patch that papers this over, but only if its a single block.
I think the right way to do this is to detect this in `core/indexing.py` that we are doing a list-like on the lhs and have a multi-dim on the rhs, then assign them one at a time. This will fix the dtype conversion issue as well. (as my patch below fails on this). Also makes the indexing code _slightly_ simpler and less special casey.
```
diff --git a/pandas/core/internals.py b/pandas/core/internals.py
index 1b08140..0254e3e 100644
--- a/pandas/core/internals.py
+++ b/pandas/core/internals.py
@@ -717,7 +717,18 @@ class Block(PandasObject):
block = block.convert(numeric=False)
return block
- except (ValueError, TypeError) as detail:
+ except ValueError as detail:
+
+ # broadcasting of list-of-list can be incorrect
+ # in older numpy versions & works
+ # but fixed in numpy >= 1.10
+ if (values.ndim == arr_value.ndim) and (
+ values[indexer].shape == arr_value.T.shape):
+ values[indexer] = arr_value.T
+ else:
+ raise
+
+ except TypeError as detail:
raise
except Exception as detail:
pass
diff --git a/pandas/tests/test_frame.py b/pandas/tests/test_frame.py
index 0f6c6f0..11d100d 100644
--- a/pandas/tests/test_frame.py
+++ b/pandas/tests/test_frame.py
@@ -213,6 +213,34 @@ class CheckIndexing(object):
expected = Series(['1', '2'], df.columns, name=1)
assert_series_equal(result, expected)
+ def test_setitem_list_multiple(self):
+ # regression from 0.16.2
+ df = DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
+ 'B': ['B0', 'B1', 'B2', 'B3'],
+ 'C': ['C0', 'C1', 'C2', 'C3'],
+ 'D': ['D0', 'D1', 'D2', 'D3']},
+ index=[0, 1, 2, 3])
+ df[['A','B']] = [[1,1,1,1],[2,2,2,2]]
+ expected = DataFrame({'A': Series([1]*4,dtype=object),
+ 'B': Series([2]*4,dtype=object),
+ 'C': ['C0', 'C1', 'C2', 'C3'],
+ 'D': ['D0', 'D1', 'D2', 'D3']},
+ index=[0, 1, 2, 3])
+ assert_frame_equal(df, expected)
+
+ df = DataFrame({'A': -1,
+ 'B': -2,
+ 'C': ['C0', 'C1', 'C2', 'C3'],
+ 'D': ['D0', 'D1', 'D2', 'D3']},
+ index=[0, 1, 2, 3])
+ df[['A','B']] = [[1,1,1,1],[2,2,2,2]]
+ expected = DataFrame({'A': 1,
+ 'B': 2,
+ 'C': ['C0', 'C1', 'C2', 'C3'],
+ 'D': ['D0', 'D1', 'D2', 'D3']},
+ index=[0, 1, 2, 3])
+ assert_frame_equal(df, expected)
+
def test_setitem_list_not_dataframe(self):
data = np.random.randn(len(self.frame), 2)
self.frame[['A', 'B']] = data
```
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} | 1 | 2015-11-20T13:45:15Z | 2015-11-20T13:56:08Z | 2015-11-20T13:56:04Z | CONTRIBUTOR | null | Addresses [this comment](https://github.com/pydata/pandas/issues/11610#issuecomment-158326899) about the formatting being unclear.
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- warning in the whatsnew
- warning in the Styler docstring
- removes generated HTML version of the notebook, that's built as part of the doc build.
Building the docs now to double check, I'll merge when those are good.
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} | 2 | 2015-11-20T15:33:55Z | 2015-11-20T16:48:23Z | 2015-11-20T16:48:23Z | CONTRIBUTOR | null | Comments in https://github.com/pydata/pandas/pull/11660#issuecomment-158427446
@jorisvandenbossche
@jreback
I think we're ok to merge.
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} | 0 | 2015-11-20T15:58:22Z | 2015-11-20T16:38:49Z | 2015-11-20T16:38:49Z | CONTRIBUTOR | null | points to master.
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} | 18 | 2015-11-20T17:32:33Z | 2017-11-11T21:00:37Z | 2017-11-11T21:00:37Z | CONTRIBUTOR | null | I'll propose a
`cache_datetime=False` keyword as an addition to `read_csv` and `pd.to_datetime`
this would use a lookup cache (a dict will probably work), to map datetime strings to Timestamp objects. For repeated dates this will lead to some dramatic speedups.
Care must be taken if a `format` kw is provided (in `to_datetime` as the cache will have to be exposed). This would be an optional (and default `False`) as I think if you have unique dates this could modestly slow down things (but can be revisted if needed).
This _might_ need also want to accept a list of column names (like `parse_dates`) to enable per-column caching (e.g. you might want to apply to a column, but not the index of example).
possibly we could overload `parse_dates='cache'` to mean this as well
trivial example
```
In [1]: pd.DataFrame({'A' : ['20130101 00:00:00']*10000}).to_csv('test.csv',index=True)
In [14]: def parser(x):
....: uniques = pd.Series(pd.unique(x))
....: d = pd.to_datetime(uniques)
....: d.index = uniques
....: return Series(x).map(d).values
....:
In [3]: df1 = pd.read_csv('test.csv',index_col=0, parse_dates=['A'])
In [4]: df2 = pd.read_csv('test.csv',index_col=0, parse_dates=['A'], date_parser=parser)
In [17]: %timeit pd.read_csv('test.csv',index_col=0, parse_dates=['A'])
1 loops, best of 3: 969 ms per loop
In [18]: %timeit pd.read_csv('test.csv',index_col=0, parse_dates=['A'], date_parser=parser)
100 loops, best of 3: 5.31 ms per loop
In [7]: df1.equals(df2)
Out[7]: True
```
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} | 14 | 2015-11-20T17:45:15Z | 2017-03-13T01:42:24Z | 2017-03-09T15:25:04Z | NONE | null | Right now, `read_csv()` has a `compression` option, which allows the user to pass in a gzipped or bz2-compressed CSV file directly into Pandas to be read. It would be great if `read_pickle()` supported the same option. Pickles actually compress surprisingly well; I have a 567M Pandas pickle (resulting from `DataFrame.to_pickle()`) that packs down to 45M with `pigz --best`. An order of magnitude difference in size is pretty significant. This makes storing static pickles long-term as gzipped archives a very attractive option. Workflow would be made easier if Pandas could natively handle my `dataframe.pickle.gz` files in the same way it does compressed CSV files.
More generally, a `compression` option should probably be allowed for most `read_*` methods. Many of the `read_*` methods involve formats that compress very well.
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| null | 16 | 2015-11-20T18:05:27Z | 2016-03-12T17:47:24Z | 2016-03-12T17:47:24Z | NONE | null | closes #11656
I found some extra time behind the sofa and started working on this. I made quite a few additions but by default everything should work as it did.
- just calling `.set_precision` with an `int` will truncate all columns to that many decimal places as before
- precision can be set per column as an extra `dict` to `.set_precision`
- the precision values can be either `int`s or valid python `str.format` strings
- I wanted to add this to allow formats like `{:.2e}` and possibly some string/date manipulations
- the `.set_precision` should perhaps be called `set_format` (or `format_cells`) as a result
- all formatting can be reset by calling `.set_precision()` (no args)
_what's missing?_
- the documentation needs to be updated to reflect these changes (if you're happy with what's here I'll move on to updating the docs)
- should leave the index alone and just format the data columns
- tests?
- support for `pd.IndexSlice` for subsets?
---
```
import itertools
import pandas as pd
import numpy as np
jobs = itertools.product(['a', 'b', 'c', 'd'], np.arange(1e-4, 1e-3, .0003), range(10))
rows = []
for v, v2, itr in jobs:
rows.append({'param_1': v, 'param_2': v2, 'iter': itr,
'score_1': np.random.randint(0, 100, size=(1,))[0],
'score_2': np.random.rand(1, )[0]})
df_multi = pd.DataFrame(rows)
agg = df_multi.groupby(by=['param_1', 'param_2'])[['score_1', 'score_2']].agg(['mean', 'sem'])
```
---
printing with just the defaults
crazy awesome new formatting (forget that the `kwarg` is called `column_formats` that's been changed to `subsets`)
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| null | 5 | 2015-11-20T18:11:39Z | 2015-12-18T18:14:37Z | 2015-11-20T18:17:35Z | NONE | null | Dataframe.duplicated() and .drop_duplicates() are flagging rows as duplicates when they are in fact distinct.
This was the smallest dataset I could make to recreate the issue, but I've seen this issue on DataFrames of any size:
``` python
>>> import pandas as pd
>>> json_data = '{"Col1":{"0":"S2#OaGwWII","1":")A9$rw3W_I","2":"2Ra+*_RWII","3":"2RA`4kRWII","4":"2R=K*_RWII"},' \
'"Col2":{"0":141105144406,"1":141107294517,"2":141106133624,"3":141108219194,"4":141106133614}}'
>>> df = pd.read_json(json_data)
>>> print(df)
Col1 Col2
0 S2#OaGwWII 141105144406
1 )A9$rw3W_I 141107294517
2 2Ra+*_RWII 141106133624
3 2RA`4kRWII 141108219194
4 2R=K*_RWII 141106133614
>>> df.duplicated(keep=False)
0 False
1 False
2 True
3 False
4 True
dtype: bool
```
It also seems to depend on row order / column order; this behavior can be changed by shuffling / sampling rows or columns, e.g.:
``` python
>>> df[['Col2', 'Col1']].duplicated(keep=False)
0 False
1 False
2 False
3 False
4 False
dtype: bool
```
I only see this behavior on 0.17.0, while 0.16.2 is fine. More details about each environment are below:
#### pandas 0.17.0 / python 3.4.3 (failing)
```
>>> pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-400.1.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.17.0
nose: 1.3.4
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.4.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: 0.6.6.None
psycopg2: 2.6 (dt dec pq3 ext)
```
#### pandas 0.17.0 / python 3.5 (failing)
```
>>> pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-348.18.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 1.8.5
xlrd: 0.9.4
xlwt: None
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: 0.6.7.None
psycopg2: None
```
#### pandas 0.16.2 python 3.5 (passing)
```
>>> pd.util.print_versions.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-348.18.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.16.2
nose: None
Cython: None
numpy: 1.10.1
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
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
```
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| null | 4 | 2015-11-20T20:17:40Z | 2019-01-21T14:06:31Z | 2019-01-21T14:06:31Z | NONE | null | From what I can see `read_sql_table` has everything necessary to connect to an Oracle database using an SQLAlchemy connection. Unfortunately, it doesn't support `oracle_resolve_synonyms`. http://docs.sqlalchemy.org/en/latest/dialects/oracle.html#synonym-dblink-reflection That means it's not possible to use synonyms for table names, which I think is pretty common in oracle databases.
It would be cool to add this. How hard would that be? I can work on a PR.
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} | 7 | 2015-11-20T21:27:24Z | 2019-11-03T14:59:06Z | 2019-11-03T14:59:06Z | NONE | null | I'm trying to calculate the mean of all the columns of a DataFrame but it looks like having a value in the B column of row 6 prevents from calculating the mean on the C column. Possible bug? (pandas: 0.17.1)
``` python
import pandas as pd
from decimal import Decimal
d = [
{'A': 2, 'B': None, 'C': Decimal('628.00')},
{'A': 1, 'B': None, 'C': Decimal('383.00')},
{'A': 3, 'B': None, 'C': Decimal('651.00')},
{'A': 2, 'B': None, 'C': Decimal('575.00')},
{'A': 4, 'B': None, 'C': Decimal('1114.00')},
{'A': 1, 'B': 'TEST', 'C': Decimal('241.00')},
{'A': 2, 'B': None, 'C': Decimal('572.00')},
{'A': 4, 'B': None, 'C': Decimal('609.00')},
{'A': 3, 'B': None, 'C': Decimal('820.00')},
{'A': 5, 'B': None, 'C': Decimal('1223.00')}
]
df = pd.DataFrame(d)
In : df
Out:
A B C
0 2 None 628.00
1 1 None 383.00
2 3 None 651.00
3 2 None 575.00
4 4 None 1114.00
5 1 TEST 241.00
6 2 None 572.00
7 4 None 609.00
8 3 None 820.00
9 5 None 1223.00
```
dtypes are equivalent:
```
In : df.dtypes
Out:
A int64
B object
C object
dtype: object
In : df.head(5).dtypes
Out:
A int64
B object
C object
dtype: object
```
But calling mean on the dataframe does not work when row 6 is present:
``` python
# no mean for C column: row 6 is present
In : df.mean()
Out:
A 2.7
dtype: float64
# mean for C column when row 6 is left out of the DF
In : df.head(5).mean()
Out:
A 2.4
B NaN
C 670.2
dtype: float64
# no mean for C column when row 6 is part of the DF
In : df.head(6).mean()
Out:
A 2.166667
dtype: float64
```
Also, it works when I explicitely leave out column B for calculating the mean:
``` python
In : df[['A','B','C']].mean()
Out:
A 2.7
dtype: float64
In : df[['A','C']].mean()
Out:
A 2.7
C 681.6
dtype: float64
```
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| null | 3 | 2015-11-22T00:37:58Z | 2015-11-22T18:35:09Z | 2015-11-22T02:31:36Z | CONTRIBUTOR | null | The asv benchmarks fails to build due to [PyTables PyPI package name being `tables` and not `pytables`](https://pypi.python.org/pypi/tables). This PR fixes the issue and the asv benchmarks now builds correctly.
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} | 7 | 2015-11-22T03:18:43Z | 2015-11-27T14:43:04Z | 2015-11-27T13:40:00Z | CONTRIBUTOR | null | closes #11616
closes #11682
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} | 0 | 2015-11-22T10:07:03Z | 2015-11-22T10:09:15Z | 2015-11-22T10:09:15Z | MEMBER | null | Some minor fixes that went into the 0.17.1 docs
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| null | 1 | 2015-11-22T10:14:00Z | 2015-11-22T23:22:04Z | 2015-11-22T23:21:57Z | NONE | null | _Summary_
Attempting to read a JSON file that has been written with orient="split" and a multi index is failing.
Pandas '0.17.0' on Python 3.5.0.
_Observations_
It works with orient=columns
It works when there is no multiindex
The output seems ok, the issue is probably on the decoding
_Test case_
import pandas as pd
df=pd.DataFrame([[1,2,3,4],[3,4,5,6],[7,8,9,10]],columns=["a","b","c","d"]).groupby(["a","b"]).sum()
df.to_json("t.json",orient="split")
pd.read_json("t.json",orient="split")
_Error message_
Traceback (most recent call last):
File "<ipython-input-6-32e343951e76>", line 1, in <module>
pd.read_json("t.json",orient="split")
File "C:\Users\mchevann\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\json.py", line 210, in read_json
date_unit).parse()
File "C:\Users\mchevann\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\json.py", line 278, in parse
self._parse_no_numpy()
File "C:\Users\mchevann\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\io\json.py", line 502, in _parse_no_numpy
self.obj = DataFrame(dtype=None, **decoded)
File "C:\Users\mchevann\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 277, in **init**
dtype=dtype)
File "C:\Users\mchevann\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py", line 5102, in _arrays_to_mgr
return create_block_manager_from_arrays(arrays, arr_names, axes)
File "C:\Users\mchevann\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py", line 3805, in create_block_manager_from_arrays
construction_error(len(arrays), arrays[0].shape, axes, e)
File "C:\Users\mchevann\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py", line 3771, in construction_error
passed,implied))
ValueError: Shape of passed values is (2, 3), indices imply (2, 2)
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https://api.github.com/repos/pandas-dev/pandas/issues/11675 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11675/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11675/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11675/events | https://github.com/pandas-dev/pandas/issues/11675 | 118,275,779 | MDU6SXNzdWUxMTgyNzU3Nzk= | 11,675 | apply loop adds an imaginary row | {
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| null | 1 | 2015-11-22T17:19:22Z | 2015-11-22T18:36:45Z | 2015-11-22T18:36:29Z | NONE | null | The following code illustrates the issue:
``` python
import pandas
from pandas import Timestamp
test_data = {'A': {Timestamp('2015-05-05 19:59:09'): 90.659, Timestamp('2015-05-22 19:59:02'): 90.810},
'B': {Timestamp('2015-05-05 19:59:09'): 100.0, Timestamp('2015-05-22 19:59:02'): 100.0}}
def disp(row):
print('processing row:\n%s' % row)
return pandas.Series({'test': 1})
test_data_df = pandas.DataFrame(test_data)
print(test_data_df.apply(disp, axis=1))
```
Since there are only 2 rows, "processing row" should only appear twice, but instead it appears 3 times. In fact it is called twice for the first row: the first time with the index timestamp localized, the second time with no localization.
## Execution result
```
processing row:
A 90.659
B 100.000
Name: 2015-05-05T21:59:09.000000000+0200, dtype: float64
processing row:
A 90.659
B 100.000
Name: 2015-05-05 19:59:09, dtype: float64
processing row:
A 90.81
B 100.00
Name: 2015-05-22 19:59:02, dtype: float64
test
2015-05-05 19:59:09 1
2015-05-22 19:59:02 1
```
## INSTALLED VERSIONS
```
commit: None
python: 3.4.3.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
pandas: 0.17.0
nose: None
pip: 7.1.2
setuptools: 18.4
Cython: None
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: None
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.4.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.7.7
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
pandas version: None
```
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https://api.github.com/repos/pandas-dev/pandas/issues/11676 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11676/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11676/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11676/events | https://github.com/pandas-dev/pandas/issues/11676 | 118,290,782 | MDU6SXNzdWUxMTgyOTA3ODI= | 11,676 | Can't get it to read a csv | {
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| null | 4 | 2015-11-22T21:43:56Z | 2015-11-22T21:51:32Z | 2015-11-22T21:45:34Z | NONE | null | I am getting this error. Kindly help.
> > > import pandas as pd
> > > df= pd.read_csv('FBI-CRIME11.csv')
> > > Traceback (most recent call last):
> > > File "<stdin>", line 1, in <module>
> > > File "/Users/ajinkyasirsikar/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py", line 491, in parser_f
> > > return _read(filepath_or_buffer, kwds)
> > > File "/Users/ajinkyasirsikar/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py", line 268, in _read
> > > parser = TextFileReader(filepath_or_buffer, **kwds)
> > > File "/Users/ajinkyasirsikar/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py", line 583, in __init__
> > > self._make_engine(self.engine)
> > > File "/Users/ajinkyasirsikar/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py", line 724, in _make_engine
> > > self._engine = CParserWrapper(self.f, **self.options)
> > > File "/Users/ajinkyasirsikar/anaconda/envs/py35/lib/python3.5/site-packages/pandas/io/parsers.py", line 1093, in __init__
> > > self._reader = _parser.TextReader(src, **kwds)
> > > File "pandas/parser.pyx", line 350, in pandas.parser.TextReader.__cinit__ (pandas/parser.c:3229)
> > > File "pandas/parser.pyx", line 583, in pandas.parser.TextReader._setup_parser_source (pandas/parser.c:6042)
> > > OSError: File b'FBI-CRIME11.csv' does not exist
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| null | 5 | 2015-11-22T23:40:42Z | 2016-01-20T14:11:40Z | 2016-01-20T14:11:40Z | CONTRIBUTOR | null | This is the start of an effort to address compression in `read_*` methods (at the moment, just `read_pickle`, as per #11666. So far, I've factored out the compression handling in the `_read` function of `pandas.io.parsers` and used that in the pickling read routine.
Tests on the way, along with actual testing that it works, docs and (if we want), I can also put something similar in the other `read_*` methods too.
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} | 2 | 2015-11-23T03:35:51Z | 2015-12-12T00:26:48Z | 2015-12-10T20:59:56Z | MEMBER | null | It's not a bug on pandas side, but CSS `linear-gradient` with `0%` width seems not be rendered properly using specific browsers.
Below is the output using Mac OSX 10.10.5 / Firefox.
The fix simply exclude `linear-gradient` if its width is 0.
After the fix (same environment)
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} | 7 | 2015-11-23T14:37:50Z | 2018-06-28T10:23:02Z | 2018-06-28T10:23:02Z | NONE | null | addtl example / repro in #13908
Hello,
I encounter some problems using the data provided here: [data.txt](https://github.com/pydata/pandas/files/41644/data.txt)
When I was trying to use **.loc** on my **DateTimeIndex**, I go t the following error
``` python
KeyError: "['2015-03-01T02:00:00.000000000+0100'] not in index"
```
Notice that the error occurs only if we have the DST changing time in the DateTimeIndex
While debugging I've found that the error comes from the _convert_to_indexer method at:
``` python
# indexing.py
mask = check == -1
if mask.any():
raise KeyError('%s not in index' % objarr[mask]) <------------------------------------
```
Here is the copy-paste example using the data on the .txt attached:
``` python
data = pd.read_csv("data.txt"), parse_dates={'time':['Date']})
data.set_index('time', inplace=True)
data.index = data.index.tz_localize('Europe/Paris', ambiguous='infer').tz_convert('UTC')
for i in range(1, len(data)):
try:-aware
data.loc[data.index[:i], 'value'] = -1
except Exception as e:
print i,e
break
```
Other important fact: the error occurs for pandas version 0.17.0 and 0.17.1. However, I've managed to make it work with an older version of pandas (**0.16.2**)
I hope this is explicit enough.
**UPDATE**
``` python
In[19]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-33-generic
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: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.1
statsmodels: None
IPython: 4.0.0
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.6
matplotlib: 1.5.0
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
```
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} | 8 | 2015-11-23T16:19:54Z | 2017-05-31T08:17:50Z | 2017-05-31T08:17:43Z | NONE | null | I am getting the following warning every time I import Pandas in any script
```
/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
warnings.warn(self.msg_depr % (key, alt_key))
```
One example would be
``` python
>>> import pandas
/usr/local/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
warnings.warn(self.msg_depr % (key, alt_key))
```
I am running on OSX a "brewed" Python 2.7 with pandas==0.17.1 and matplotlib==1.5.0.
Thanks in advance
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} | 13 | 2015-11-23T17:38:21Z | 2015-12-27T17:25:29Z | 2015-12-27T17:24:32Z | CONTRIBUTOR | null | Fix issue #11553
Two things:
1) I've created a `Float64Index._format_native_types` method which is a copy-paste of `FloatBlock.to_native_types`. I would have preferred to call the latter directly, but I'm not sure what the `placement` parameter of the `FloatBlock` constructor means. I guess I doesn't really matters, since I could put whatever value and it should work (I think), and my hesitation a bit unfounded, but I don't know if it would be really a clean solution. Maybe someone can think of a more elegant way?
2) Since a `Float64Index` containing only NaNs collapses when part of a multi-index, its NaNs values would not be converted using `na_rep`, so I had to hack a solution. I put a comment in the relevant part. I'm not quite convinced myself of the elegance of the solution, though.
What do you think?
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} | 2 | 2015-11-23T21:17:53Z | 2015-11-27T13:40:22Z | 2015-11-27T13:40:22Z | CONTRIBUTOR | null | I've encountered a bug in pandas 0.16.2, where when using broadcasting to assign a datetime.datetime value to a whole column of a DataFrame, the timezone info is lost. Here is an example:
``` python
In [1]: import pandas, datetime, pytz
In [2]: df = pandas.DataFrame({'a': [1,2,3]})
In [3]: dt = datetime.datetime.now(pytz.utc)
In [4]: dt.tzinfo
Out[4]: <UTC>
In [5]: df['b'] = dt
In [6]: df
Out[6]:
a b
0 1 2015-11-23 21:02:54.562175
1 2 2015-11-23 21:02:54.562175
2 3 2015-11-23 21:02:54.562175
In [7]: df['b'][0].tzinfo
```
Note how `dt` has a timezone attached, but the values in the 'b' column don't. The problem only occurs when broadcasting a scalar datetime column, not when assigning an array or series. Also, the problem only occurs when using the builtin datetime.datetime class, not pandas's Timestamp class.
I've tracked the problem down to the pandas.core.common._infer_dtype_from_scalar function, which is called during the assignment. It contains this code for handling scalar date times:
``` python
elif isinstance(val, (np.datetime64, datetime)) and getattr(val,'tz',None) is None:
val = lib.Timestamp(val).value
dtype = np.dtype('M8[ns]')
```
The problem is that the Timestamp.value property returns an integer value which doesn't contain the timezone information, so the timezone is lost. The reason this problem occurs for datetime.datetime, but not for pandas.Timestamp, is because the code is looking for the 'tz' attribute, which is specific to Timestamp. If the gettattr call was changed to look at the 'tzinfo' attribute instead, this code would work correctly for both pandas.Timestamp and datetime.datetime values. So a fix for this code which works for both datetime and Timestamp would be:
``` python
elif isinstance(val, (np.datetime64, datetime)) and getattr(val,'tzinfo',None) is None:
val = lib.Timestamp(val).value
dtype = np.dtype('M8[ns]')
```
I checked and this bug still exists in the latest version of the pandas source. Nevertheless here is the output of show_versions() on my machine:
``` python
In [8]: pandas.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.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: 0.16.2
nose: 1.3.7
Cython: 0.23.2
numpy: 1.9.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.1.0
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2012c
bottleneck: None
tables: 3.2.1.1
numexpr: 2.4.4
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.4
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
```
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| null | 2 | 2015-11-23T22:34:54Z | 2020-05-16T03:45:19Z | null | NONE | null | Pandas read SQL functions internally convert params to a list in pandas.io.sql._convert_params().
However on pymssql, params must be a tuple or dictionary. Stacktrace below.
```
Traceback (most recent call last):
File "/Users/simon/.virtualenvs/rfcapital/lib/python3.4/site-packages/pandas/io/sql.py", line 1539, in execute
cur.execute(*args)
File "pymssql.pyx", line 447, in pymssql.Cursor.execute (pymssql.c:6274)
File "_mssql.pyx", line 998, in _mssql.MSSQLConnection.execute_query (_mssql.c:10085)
File "_mssql.pyx", line 1029, in _mssql.MSSQLConnection.execute_query (_mssql.c:9964)
File "_mssql.pyx", line 1146, in _mssql.MSSQLConnection.format_and_run_query (_mssql.c:11010)
File "_mssql.pyx", line 1168, in _mssql.MSSQLConnection.format_sql_command (_mssql.c:11219)
File "_mssql.pyx", line 1799, in _mssql._substitute_params (_mssql.c:18045)
ValueError: 'params' arg (<class 'list'>) can be only a tuple or a dictionary.
```
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| null | 1 | 2015-11-24T00:40:33Z | 2015-11-24T13:20:45Z | 2015-11-24T13:15:45Z | NONE | null | pandas 0.17.1
```
import pandas as pd
from io import StringIO
sio=StringIO("""
A
2
nan
""")
df = pd.read_csv(sio,dtype={'A':str})
print(type(df.A[0]),df.A[0])
print(type(df.A[1]),df.A[1])
print()
df=df.astype(str)
print(type(df.A[0]),df.A[0])
print(type(df.A[1]),df.A[1])
```
prints:
```
<class 'str'> 2
<class 'float'> nan
<class 'str'> 2
<class 'str'> nan
```
---
`read_csv`,even I've specified the dtype to str , reads column A into a mixed object dtype (str + float nan) .
`astype` is as expected as it interpreting the `nan` into string `'nan'` thus it results in pure str dtype.
These two semantic is not consistent .
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| null | 1 | 2015-11-24T06:42:27Z | 2015-11-24T06:51:33Z | 2015-11-24T06:51:33Z | NONE | null | Hi Pandas Community,
```
I found out one potential bug about drop_duplicates producing inconsistent result, with only a switch order of two columns. My Pandas version is 0.17.0. The data set is in Walmart - TripType classification question. https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/data?train.csv.zip
Following is my code:
```
```
import pandas as pd
df = pd.read_csv('train.csv')
df_meta = df[['VisitNumber', 'TripType', 'Weekday']].drop_duplicates()
print df_meta.shape
```
Return result (95622, 3)
```
import pandas as pd
df = pd.read_csv('train.csv')
df_meta = df[['TripType', 'VisitNumber', 'Weekday']].drop_duplicates()
print df_meta.shape
```
Return result (95674, 3)
The only difference is switching the order of 'TripType' and 'VisitNumber'.
Please keep me updated if and when this bug would be fixed. Thanks!
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| null | 3 | 2015-11-24T08:31:01Z | 2015-12-23T19:09:00Z | 2015-12-23T19:09:00Z | CONTRIBUTOR | null | Following references are missing
- rpy2
- psycopg (at least testing needs it)
- pymysql (at least testing needs it)
- matplotlib
perhaps there are more.
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| null | 2 | 2015-11-24T11:14:57Z | 2015-12-06T04:24:18Z | 2015-12-06T04:23:08Z | CONTRIBUTOR | null | ```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.3.0-lh-nvidia
machine: x86_64
processor: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: 1.2.8
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.0
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: None
bs4: 4.4.1
html5lib: 0.9999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.9
pymysql: 0.6.7.None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
``` python
======================================================================
FAIL: test_legacy_write_frame (pandas.io.tests.test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 589, in test_legacy_write_frame
flavor='sqlite')
File "/usr/lib64/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/util/testing.py", line 2060, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['PendingDeprecationWarning'].
======================================================================
FAIL: test_not_reflect_all_tables (pandas.io.tests.test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 913, in test_not_reflect_all_tables
self.assertEqual(len(w), 0, "Warning triggered for other table")
AssertionError: 2 != 0 : Warning triggered for other table
======================================================================
FAIL: test_timedelta (pandas.io.tests.test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 673, in test_timedelta
df.to_sql('test_timedelta', self.conn)
File "/usr/lib64/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/util/testing.py", line 2060, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['PendingDeprecationWarning'].
======================================================================
FAIL: test_warning_case_insensitive_table_name (pandas.io.tests.test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 926, in test_warning_case_insensitive_table_name
self.assertEqual(len(w), 0, "Warning triggered for writing a table")
AssertionError: 1 != 0 : Warning triggered for writing a table
======================================================================
FAIL: test_legacy_write_frame (pandas.io.tests.test_sql.TestSQLApiConn)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 589, in test_legacy_write_frame
flavor='sqlite')
File "/usr/lib64/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/util/testing.py", line 2060, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['PendingDeprecationWarning'].
======================================================================
FAIL: test_not_reflect_all_tables (pandas.io.tests.test_sql.TestSQLApiConn)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 913, in test_not_reflect_all_tables
self.assertEqual(len(w), 0, "Warning triggered for other table")
AssertionError: 2 != 0 : Warning triggered for other table
======================================================================
FAIL: test_timedelta (pandas.io.tests.test_sql.TestSQLApiConn)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 673, in test_timedelta
df.to_sql('test_timedelta', self.conn)
File "/usr/lib64/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/util/testing.py", line 2060, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['PendingDeprecationWarning'].
======================================================================
FAIL: test_warning_case_insensitive_table_name (pandas.io.tests.test_sql.TestSQLApiConn)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 926, in test_warning_case_insensitive_table_name
self.assertEqual(len(w), 0, "Warning triggered for writing a table")
AssertionError: 1 != 0 : Warning triggered for writing a table
======================================================================
FAIL: test_bigint_warning (pandas.io.tests.test_sql.TestSQLiteAlchemy)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 1666, in test_bigint_warning
self.assertEqual(len(w), 0, "Warning triggered for other table")
AssertionError: 2 != 0 : Warning triggered for other table
======================================================================
FAIL: test_bigint_warning (pandas.io.tests.test_sql.TestSQLiteAlchemyConn)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/var/tmp/portage/dev-python/pandas-0.17.1/work/pandas-0.17.1-python3_5/lib/pandas/io/tests/test_sql.py", line 1666, in test_bigint_warning
self.assertEqual(len(w), 0, "Warning triggered for other table")
AssertionError: 2 != 0 : Warning triggered for other table
----------------------------------------------------------------------
```
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| null | 2 | 2015-11-24T11:58:23Z | 2015-11-24T12:03:14Z | 2015-11-24T12:03:06Z | CONTRIBUTOR | null | zoneinfo.gettz() seems to have problems (1 & 2) on system which do not install
the zoninfo tarball (e.g. Debian, Gentoo and Fedora) but rely on the system
zoneinfo files. This results in test failures (3 & 4)
tz.gettz() doesn't suffer from this problem.
1 https://github.com/dateutil/dateutil/issues/8
2 https://github.com/dateutil/dateutil/issues/11
3 https://github.com/pydata/pandas/issues/9059
4 https://github.com/pydata/pandas/issues/8639
Signed-off-by: Justin Lecher [email protected]
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} | 1 | 2015-11-24T12:04:44Z | 2015-11-24T13:09:25Z | 2015-11-24T13:09:22Z | CONTRIBUTOR | null | `import imp; imp.load_module("Jinja2", *imp.find_module("Jinja2"))`
raises `ImportError`, where as the lowercase version works fine.
Signed-off-by: Justin Lecher [email protected]
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} | 1 | 2015-11-24T13:07:34Z | 2020-05-31T03:23:24Z | 2015-11-29T18:03:36Z | CONTRIBUTOR | null | closes #4314
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} | 2 | 2015-11-24T13:47:25Z | 2021-04-21T05:02:23Z | null | NONE | null | `import pandas as pd`
`import numpy as np`
`index = pd.date_range('1-Jan-2016',periods=12,freq='MS')`
`a = pd.DataFrame(np.ones((12,5)),index)`
`b = pd.Series(4.,index)`
`print a.multiply(b,axis=0)`
out:
0 1 2 3 4
2016-01-01 4 4 4 4 4
2016-02-01 4 4 4 4 4
2016-03-01 4 4 4 4 4
2016-04-01 4 4 4 4 4
2016-05-01 4 4 4 4 4
2016-06-01 4 4 4 4 4
2016-07-01 4 4 4 4 4
2016-08-01 4 4 4 4 4
2016-09-01 4 4 4 4 4
2016-10-01 4 4 4 4 4
2016-11-01 4 4 4 4 4
2016-12-01 4 4 4 4 4
`print b.multiply(a,axis=0)`
out: ValueError: operands could not be broadcast together with shapes (12,) (12,5)
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} | 4 | 2015-11-24T16:47:07Z | 2016-02-12T20:42:51Z | 2016-02-12T20:42:51Z | NONE | null | When using the new CSS styling, there doesn't seem to be a way to add formatters for the numbers. So trying to make the number data display as $10,000 when using any styling options currently seems off the table.
Documented here:
http://stackoverflow.com/questions/33875937/apply-number-formatting-to-pandas-html-css-styling
Related to this PR: https://github.com/pydata/pandas/pull/11667
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} | 3 | 2015-11-24T16:52:53Z | 2016-02-01T21:38:25Z | 2016-02-01T21:38:25Z | NONE | null | This edge case appears when concatenating a timezone aware datetime series with another that is filled with only pd.NaT. This works if the second series is only partially filled with pd.NaT. It also works if the second series is another timezone (in that case the resulting Series is timezone unaware).
``` python
import pandas as pd
pd.__version__
x = pd.Series( pd.date_range('20151124 08:00', '20151124 09:00', freq = '1h') )
y = pd.Series( pd.date_range('20151124 10:00', '20151124 11:00', freq = '1h') )
pd.concat([x,y]) # Works
y[:] = pd.NaT
pd.concat([x,y]) # Works
x = pd.Series( pd.date_range('20151124 08:00', '20151124 09:00', freq = '1h', tz = "US/Eastern") )
y = pd.Series( pd.date_range('20151124 10:00', '20151124 11:00', freq = '1h', tz = "US/Eastern") )
pd.concat([x,y])
y[0] = pd.NaT
pd.concat([x,y]) # Works
y[:] = pd.NaT
pd.concat([x,y]) # Fails
```
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| null | 3 | 2015-11-24T17:41:33Z | 2015-11-25T16:10:40Z | 2015-11-25T15:23:35Z | NONE | null | I've recently upgraded to Python 3.5 and the newest version of pandas `pandas (0.17.1)`, but this broke the package for me. I'm on Mac OS X 10.9.5. What can I do?
```
cls@clsmba ~> python3
Python 3.5.0 (default, Sep 23 2015, 04:41:33)
[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.5/site-packages/pandas/__init__.py", line 44, in <module>
from pandas.core.api import *
File "/usr/local/lib/python3.5/site-packages/pandas/core/api.py", line 9, in <module>
from pandas.core.groupby import Grouper
File "/usr/local/lib/python3.5/site-packages/pandas/core/groupby.py", line 17, in <module>
from pandas.core.frame import DataFrame
File "/usr/local/lib/python3.5/site-packages/pandas/core/frame.py", line 41, in <module>
from pandas.core.series import Series
File "/usr/local/lib/python3.5/site-packages/pandas/core/series.py", line 2909, in <module>
import pandas.tools.plotting as _gfx
File "/usr/local/lib/python3.5/site-packages/pandas/tools/plotting.py", line 28, in <module>
import pandas.tseries.converter as conv
File "/usr/local/lib/python3.5/site-packages/pandas/tseries/converter.py", line 7, in <module>
import matplotlib.units as units
File "/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py", line 1131, in <module>
rcParams = rc_params()
File "/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py", line 975, in rc_params
return rc_params_from_file(fname, fail_on_error)
File "/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py", line 1100, in rc_params_from_file
config_from_file = _rc_params_in_file(fname, fail_on_error)
File "/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py", line 1018, in _rc_params_in_file
with _open_file_or_url(fname) as fd:
File "/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/contextlib.py", line 59, in __enter__
return next(self.gen)
File "/usr/local/lib/python3.5/site-packages/matplotlib/__init__.py", line 1000, in _open_file_or_url
encoding = locale.getdefaultlocale()[1]
File "/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/locale.py", line 559, in getdefaultlocale
return _parse_localename(localename)
File "/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/locale.py", line 487, in _parse_localename
raise ValueError('unknown locale: %s' % localename)
ValueError: unknown locale: UTF-8
```
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} | 8 | 2015-11-24T20:18:45Z | 2015-12-18T18:26:10Z | 2015-12-18T18:26:10Z | CONTRIBUTOR | null | This PR is meant to fix issue #11193
The issue prevent, when creating a new `Index` object based on a passed `Index` or `DatetimeIndex` object, to inherit the existing `Index` name if a new name is not provided.
I've also created two unit test which test the new behaviour on both cases (passing an `Index` or `DatetimeIndex` object) and added a comment in the whatsnew documentation.
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} | 4 | 2015-11-24T20:45:14Z | 2015-12-10T12:47:58Z | 2015-12-10T12:17:20Z | MEMBER | null | Closes #11488.
Based on the type check added in #11588, the change affects to fewer functions than I originally thought.
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} | 7 | 2015-11-24T21:41:56Z | 2016-04-21T23:42:43Z | 2015-12-01T12:10:28Z | NONE | null | The new version of rolling_median from 0.17.1 fails with:
``` python
pandas\algos.pyx in pandas.algos.roll_median_c (pandas\algos.c:36408)()
MemoryError: skiplist_insert failed
```
when called with series containing large number of values. In comparison, the previous version of 0.17.0 was robust and never showed this error.
On my win-64 computer, I can routinely trigger the error by the following command:
``` python
import numpy as np
import pandas as pd
n=50000
pd.rolling_median(pd.Series(np.random.randn(n)), window=2, center=False)
```
while
``` python
import numpy as np
import pandas as pd
n=5000
pd.rolling_median(pd.Series(np.random.randn(n)), window=2, center=False)
```
works fine.
The actual point at which the error begins appears to be non-deterministic but is somewhere around n=35,000.
Versions are as follows:
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
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} | 3 | 2015-11-24T22:07:52Z | 2015-12-02T15:04:44Z | 2015-12-02T15:04:43Z | CONTRIBUTOR | null | This is in 0.17.1. Suppose we have a frame with mixed data types. Suppose also we want to replace a string value with something else. If the frame has only string columns, or maybe string and int columns, this works fine. However, if the frame has string and datetime columns, this raises an exception. Example:
```
>>> pandas.DataFrame([('-', pandas.to_datetime('20150101')), ('a', pandas.to_datetime('20150102')), ('b', pandas.to_datetime('20150103'))], columns=['a', 'b']).replace('-', numpy.nan)
---------------------------------------------------------------------------
UnboundLocalError Traceback (most recent call last)
<ipython-input-11-bce7dc78fc44> in <module>()
----> 1 pandas.DataFrame([('-', pandas.to_datetime('20150101')), ('a', pandas.to_datetime('20150102')), ('b', pandas.to_datetime('20150103'))], columns=['a', 'b']).replace('-', numpy.nan)
/.../pandas/core/generic.pyc in replace(self, to_replace, value, inplace, limit, regex, method, axis)
3108 elif not com.is_list_like(value): # NA -> 0
3109 new_data = self._data.replace(to_replace=to_replace, value=value,
-> 3110 inplace=inplace, regex=regex)
3111 else:
3112 msg = ('Invalid "to_replace" type: '
/.../pandas/core/internals.pyc in replace(self, **kwargs)
2868
2869 def replace(self, **kwargs):
-> 2870 return self.apply('replace', **kwargs)
2871
2872 def replace_list(self, src_list, dest_list, inplace=False, regex=False, mgr=None):
/.../pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
2821
2822 kwargs['mgr'] = self
-> 2823 applied = getattr(b, f)(**kwargs)
2824 result_blocks = _extend_blocks(applied, result_blocks)
2825
/.../pandas/core/internals.pyc in replace(self, to_replace, value, inplace, filter, regex, convert, mgr)
605
606 # we can't process the value, but nothing to do
--> 607 if not mask.any():
608 return self if inplace else self.copy()
609
UnboundLocalError: local variable 'mask' referenced before assignment
```
And the requisite show_versions:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-348.2.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.1.0
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 8 | 2015-11-25T11:31:27Z | 2015-11-29T17:32:18Z | 2015-11-29T17:32:18Z | NONE | null | This used to work fine in previous versions but appears to be broken in 0.17.1
The following code:
```
import pandas as pd
df = pd.DataFrame({'A': [], 'B': []})
gb = df.groupby('A') .size()
```
Produces this error:
```
ValueError: minlength must be positive
```
In v 0.16.2 the same code produced an empty DataFrame. We'd really like to upgrade to 0.17.1 but heavily rely on this functionality so have to hold the upgrade. Checking for empty DataFrame is not going to work for us either as there are too many places where it can actually be empty.
If you can suggest any workaround in the meantime so we could upgrade that would be appreciated.
##
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.18-238.9.1.el5
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US
pandas: 0.16.2
...
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} | 16 | 2015-11-25T13:37:40Z | 2021-08-25T20:34:06Z | null | CONTRIBUTOR | null | Implement `to_html` / notebook repr based on `.style`.
prob need to expand this to take a `use` argument (to select the style, needs to be 'classic' for a while, to replicate the current `.to_html` one).
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} | 7 | 2015-11-25T17:40:52Z | 2020-03-31T05:30:33Z | 2020-03-31T05:30:23Z | CONTRIBUTOR | null | Hello,
`interpolate` doesn't work with `NaT`
see http://stackoverflow.com/questions/33921795/fill-timestamp-nat-with-a-linear-interpolation/33922824#33922824
Here is a trivial example to show the situation:
```
s = pd.Series(pd.date_range('2015-01-01' , '2015-01-30'), name='t')
s[3], s[4], s[5] = pd.NaT, pd.NaT, pd.NaT
s[13], s[14], s[15] = pd.NaT, pd.NaT, pd.NaT
print(s)
0 2015-01-01
1 2015-01-02
2 2015-01-03
3 NaT
4 NaT
5 NaT
6 2015-01-07
7 2015-01-08
8 2015-01-09
9 2015-01-10
10 2015-01-11
11 2015-01-12
12 2015-01-13
13 NaT
14 NaT
15 NaT
16 2015-01-17
17 2015-01-18
18 2015-01-19
19 2015-01-20
20 2015-01-21
21 2015-01-22
22 2015-01-23
23 2015-01-24
24 2015-01-25
25 2015-01-26
26 2015-01-27
27 2015-01-28
28 2015-01-29
29 2015-01-30
Name: t, dtype: datetime64[ns]
print(s.interpolate())
0 2015-01-01
1 2015-01-02
2 2015-01-03
3 NaT
4 NaT
5 NaT
6 2015-01-07
7 2015-01-08
8 2015-01-09
9 2015-01-10
10 2015-01-11
11 2015-01-12
12 2015-01-13
13 NaT
14 NaT
15 NaT
16 2015-01-17
17 2015-01-18
18 2015-01-19
19 2015-01-20
20 2015-01-21
21 2015-01-22
22 2015-01-23
23 2015-01-24
24 2015-01-25
25 2015-01-26
26 2015-01-27
27 2015-01-28
28 2015-01-29
29 2015-01-30
Name: t, dtype: datetime64[ns]
assert s.interpolate().isnull().sum() == 0
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-150-8a59e397a174> in <module>()
----> 1 assert s.interpolate().isnull().sum() == 0
AssertionError:
```
Kind regards
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} | 1 | 2015-11-25T18:21:28Z | 2015-11-25T18:24:11Z | 2015-11-25T18:24:04Z | CONTRIBUTOR | null | {
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| null | 3 | 2015-11-25T18:47:47Z | 2015-11-30T01:31:06Z | 2015-11-30T01:31:06Z | NONE | null | Hi,
I'm noticing a problem with the xlsx files created by `ExcelWriter` when the engine is set to `"xlsxwriter"` and `options={'constant_memory': True})` kwarg is passed.
No exceptions are raised, but the resulting file will have all rows empty but the last one. Indexes are still written as usual, but all rows are blank (except for the last).
Code to reproduce this bug:
``` python
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(4,4))
df
# 0 1 2 3
# 0 0.342306 -0.408126 -1.146106 -0.968315
# 1 1.046181 0.579583 0.137602 -1.181519
# 2 2.198670 0.420310 0.827958 -1.130635
# 3 0.887260 0.985287 -0.349063 1.277966
writer = pd.ExcelWriter('test.xlsx', engine='xlsxwriter', options={'constant_memory': True})
df.to_excel(writer)
writer.save()
```
As mentioned, the result is a mostly empty Excel "table":
``` python
pd.read_excel('test.xlsx')
# 0 1 2 3
# 0 NaN NaN NaN NaN
# 1 NaN NaN NaN NaN
# 2 NaN NaN NaN NaN
# 3 0.887260 0.985287 -0.349063 1.277966
```
I'm using:
`pandas==0.17.0`
`XlsxWriter==0.7.0`
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