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BLD: require more recent cython
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| 2 | 2016-10-26T22:36:15Z | 2016-12-12T11:48:25Z | 2016-12-12T11:48:25Z |
CONTRIBUTOR
| null |
see comments [here](https://github.com/pandas-dev/pandas/pull/14496#issuecomment-256497307)
we currently require >= 0.19.1. So probably should move that up.
Not super pressing though.
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CI: add conda-forge as lowest priority channel
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| 7 | 2016-10-26T22:41:50Z | 2016-11-18T11:37:18Z | 2016-11-18T11:37:18Z |
CONTRIBUTOR
| null |
xref #14495
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ENH : First attempt to add sparse_pivot
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[] | null | 2 | 2016-10-27T01:07:26Z | 2016-12-17T01:17:03Z | 2016-12-16T23:52:31Z |
NONE
| null |
- [ ] Resolves #14493
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
``` python
sparse_series = pivot_sparse(index=df['a'], columns=df['b'], values=df['c'])
```
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ENH: Construct Panel from 2D structured array
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| 3 | 2016-10-27T01:48:04Z | 2018-07-06T20:21:11Z | 2016-10-27T12:56:57Z |
CONTRIBUTOR
| null |
Right now (0.18.1), I can construct a DataFrame from a "1D" structured array. The NumPy structure field names are used as the Pandas column names.
But I cannot construct a Panel from a structured array:
```
arr = np.zeros((3,5), [('x',float), ('y',bool)])
pd.Panel(arr) # error
```
> ValueError: The number of dimensions required is 3, but the number of dimensions of the ndarray given was 2
One "obvious" way is slow:
```
pd.Panel(dict(enumerate(pd.DataFrame(a) for a in arr)))
```
And an efficient way is verbose:
```
dtyp = np.array(arr.dtype.names)
dim = arr.shape[0], arr.shape[1], dtyp.shape[0]
pd.Panel(pd.DataFrame(arr.ravel()).values.reshape(dim), minor_axis=dtyp)
```
Note that both of the above ways "promote" all values to a single dtype, which in my case (float + bool) becomes object. That's really bad--I need to preserve the original dtypes so that if I do `panel[0].dtypes` matches `arr[0].dtype`. I created #14512 for that problem.
Background for this issue is here: http://stackoverflow.com/questions/40259034/construct-pandas-panel-from-2d-structured-numpy-array
I'm using Pandas 0.18.1.
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| 3 | 2016-10-27T02:51:34Z | 2018-07-06T20:21:11Z | 2016-10-27T09:14:37Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
>>> df = pd.DataFrame({'a':[1,2,3], 'b':[True,False,True]})
>>> df.dtypes
a int64
b bool
dtype: object
>>> pl = pd.Panel({'x':df})
>>> pl.x.dtypes
a object
b object
dtype: object
```
#### Expected Output
I expected the dtypes of `df` and `pl.x` to match, the latter was created directly from the former.
#### Output of `pd.show_versions()`
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-85-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
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| 185,561,159 |
MDU6SXNzdWUxODU1NjExNTk=
| 14,513 |
ImportError: Using URI string without sqlalchemy installed.
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| 5 | 2016-10-27T02:55:01Z | 2016-10-29T13:24:28Z | 2016-10-29T13:24:19Z |
NONE
| null |
#### Having created a new virtual environment and having installed SQLAlchemy and Pandas, I'm having trouble with the `read_sql_query` function which gives me the following error:
`ImportError: Using URI string without sqlalchemy installed.`
The code looks something like this:
``` python
URI_CONN_STRING = 'postgres://user:pass@ip-address:5432/db-name'
df = pd.read_sql_query(sql_query, URI_CONN_STRING, index_col=None, coerce_float=True, params=None, chunksize=None)
```
#### Expected Output
I'd expect to get a data frame with the results of the SQL query
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 15.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 28.6.1
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.2
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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BUG: Don't parse inline quotes in skipped lines
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| 7 | 2016-10-27T03:46:08Z | 2016-10-31T20:40:06Z | 2016-10-31T20:39:59Z |
MEMBER
| null |
Title is self-explanatory. Closes #14459.
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| 185,577,640 |
MDU6SXNzdWUxODU1Nzc2NDA=
| 14,515 |
Pandas 0.19 read_csv with header=[0, 1] on an empty df throws error
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| 7 | 2016-10-27T05:37:46Z | 2016-11-22T11:24:11Z | 2016-11-22T11:24:11Z |
NONE
| null |
#### Pandas 0.19 incorrectly handles empty dataframe files with multi index columns
``` python
import pandas as pd
import tempfile
df = pd.DataFrame.from_records([], columns=['col_1', 'col_2'])
joined_df_in = pd.concat([df, df], keys=['a', 'b'], axis=1)
joined_df_in.reset_index(drop=True, inplace=True)
with tempfile.NamedTemporaryFile(delete=False) as f:
joined_df_in.to_csv(f.name, index=False)
```
What the file looks like
```
a,a,b,b
col_1,col_2,col_1,col_2
```
#### Expected Output
``` python
# in pandas 0.18.1
pd.read_csv(f.name, header=[0,1])
```
yields what we expect, an empty MultiIndex data frame
```
(a, col_1) (a, col_2) (b, col_1) (b, col_2)
```
``` python
# in pandas 0.19
pd.read_csv(f.name, header=[0,1])
```
Throws
```
---------------------------------------------------------------------------
CParserError Traceback (most recent call last)
<ipython-input-10-1051c5f9aa58> in <module>()
----> 1 pd.read_csv(f.name, header=[0,1])
/Users/mik-OD/anaconda/envs/signals/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
643 skip_blank_lines=skip_blank_lines)
644
--> 645 return _read(filepath_or_buffer, kwds)
646
647 parser_f.__name__ = name
/Users/mik-OD/anaconda/envs/signals/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
386
387 # Create the parser.
--> 388 parser = TextFileReader(filepath_or_buffer, **kwds)
389
390 if (nrows is not None) and (chunksize is not None):
/Users/mik-OD/anaconda/envs/signals/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
727 self.options['has_index_names'] = kwds['has_index_names']
728
--> 729 self._make_engine(self.engine)
730
731 def close(self):
/Users/mik-OD/anaconda/envs/signals/lib/python3.5/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
920 def _make_engine(self, engine='c'):
921 if engine == 'c':
--> 922 self._engine = CParserWrapper(self.f, **self.options)
923 else:
924 if engine == 'python':
/Users/mik-OD/anaconda/envs/signals/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
1387 kwds['allow_leading_cols'] = self.index_col is not False
1388
-> 1389 self._reader = _parser.TextReader(src, **kwds)
1390
1391 # XXX
pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:5811)()
pandas/parser.pyx in pandas.parser.TextReader._get_header (pandas/parser.c:8615)()
CParserError: Passed header=[0,1], len of 2, but only 2 lines in file
```
#### Expected Output
#### Output of `pd.show_versions()`
<details>
For pandas 0.81
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.1
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.7.3
boto: 2.40.0
pandas_datareader: None
```
For pandas 0.19
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.1
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
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httplib2: None
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sqlalchemy: 1.1.1
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.7.3
boto: 2.40.0
pandas_datareader: None
```
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MDExOlB1bGxSZXF1ZXN0OTExNDE1MTQ=
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MAINT: Expand lint for *.py
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| 8 | 2016-10-27T06:41:56Z | 2016-10-28T08:38:03Z | 2016-10-28T08:37:55Z |
MEMBER
| null |
Expand lint to include all Python files, excluding the directories `pandas/rpy` (deprecated and soon to be removed) and `pandas/src` (C code).
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Rolling window and np.nanmean outputs only NaN
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| 2 | 2016-10-27T09:29:04Z | 2016-10-27T12:01:15Z | 2016-10-27T10:19:48Z |
NONE
| null |
Hi,
I am having an issue with the rolling apply method combinated with numpy's nanmean method. Here is my isssue:
``` python
import pandas as pd
import numpy as np
>>> df = pd.DataFrame({'A': [np.nan, np.nan, np.nan, 5, np.nan, np.nan]})
>>> df
A
0 NaN
1 NaN
2 NaN
3 5.0
4 NaN
5 NaN
>>> df.rolling(3).apply(np.nanmean)
A
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
```
#### Expected Output
After index 3 I was expecting to see 5 in every line, how come I got only Nan?
Thanks for your help!
#### Output of `pd.show_versions()`
<details>
# Paste the output here
## INSTALLED VERSIONS
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-100-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 24.0.3
Cython: None
numpy: 1.11.2
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxODU2Mjk1OTk=
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ENH: add np.nan* functions to map for groupby/rolling/resample aggregators
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| 4 | 2016-10-27T10:24:12Z | 2021-05-02T00:40:46Z | null |
CONTRIBUTOR
| null |
xref #14517
[here](https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L275) we define mappings for numpy functions when they are used in aggregation functions, e.g.
`df.groupby(...).apply(np.mean)` is mapped to `df.groupby(..).mean()`
these are much more performant when used this way and have more typical pandas semantics.
This issue is about adding the `np.nan*` functions (e.g. `np.nanmean`) and such.
The only thing tricky is that these must be conditionally added based on numpy version, and need testing! (currently tests for this type of mapping is spread across tests for groupby/rolling/resampling), so tests need to be added in various locations, with a possiblity of moving the common parts of this to a single location.
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COMPAT: openpyxl
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| 1 | 2016-10-27T14:48:34Z | 2017-01-21T16:30:07Z | 2017-01-21T16:30:07Z |
MEMBER
| null |
Just noticed in one of the test suites:
```
home/travis/miniconda/envs/pandas/lib/python3.4/site-packages/openpyxl/worksheet/worksheet.py:300: UserWarning: Using a coordinate with ws.cell is deprecated. Use ws[coordinate] instead
warn("Using a coordinate with ws.cell is deprecated. Use ws[coordinate] instead")
```
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MDExOlB1bGxSZXF1ZXN0OTEyMjI5OTc=
| 14,520 |
BUG: don't close user-provided file handles in C parser (GH14418)
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| 1 | 2016-10-27T15:21:19Z | 2016-11-02T11:27:41Z | 2016-11-02T11:27:41Z |
MEMBER
| null |
Closes #14418
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| 3 | 2016-10-27T15:30:24Z | 2016-10-29T11:09:57Z | 2016-10-28T21:07:27Z |
NONE
| null |
The `json` library, `encoder` module, raises a `TypeError` exception, which I think should not be raised. Hence I think the `Series.tolist()` method may be at fault here and not `json` standard library.
``` python
import pandas as pd
import json
s = pd.Series([1, 2, 3])
json.dumps(s.tolist())
```
Gives:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.4/json/__init__.py", line 230, in dumps
return _default_encoder.encode(obj)
File "/usr/lib/python3.4/json/encoder.py", line 192, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/lib/python3.4/json/encoder.py", line 250, in iterencode
return _iterencode(o, 0)
File "/usr/lib/python3.4/json/encoder.py", line 173, in default
raise TypeError(repr(o) + " is not JSON serializable")
TypeError: 1 is not JSON serializable
```
But
``` python
import pandas as pd
import json
s = pd.Series([1, 2, 3])
json.dumps(s.values.tolist())
```
Works:
```
'[1, 2, 3]'
```
I am aware of `Series.to_json()` method, but I need to insert the values first into a nested dictionary which is only later serialized to JSON. That is how I came across this behaviour.
#### Output of `pd.show_versions()`
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.0
nose: None
pip: 8.1.2
setuptools: 20.6.7
Cython: 0.24
numpy: 1.11.0
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: None
boto: None
```
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MDU6SXNzdWUxODU3MDY4NTY=
| 14,522 |
Categorical.searchsorted() uses lexical order instead of the provided categorical order
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| 4 | 2016-10-27T15:45:30Z | 2016-12-30T21:43:21Z | 2016-12-30T21:43:21Z |
NONE
| null |
Hi,
it seems that the searchsorted() method of Categorical series does not take into account the specific order of the categories.
#### An slightly modified example from the API documentation
``` python
x = pd.Categorical(
['cheese', 'apple', 'bread', 'bread', 'milk'],
categories=['cheese', 'milk', 'apple', 'bread'],
ordered=True)
print('Unsorted:', x)
x_sort = x.sort_values()
print('Sorted:', x_sort)
print('Searchsorted apple:', x_sort.searchsorted('apple'))
print('Searchsorted milk:', x_sort.searchsorted('milk'))
```
#### Output
Unsorted: [cheese, apple, bread, bread, milk]
Categories (4, object): [cheese < milk < apple < bread]
Sorted: [cheese, milk, apple, bread, bread]
Categories (4, object): [cheese < milk < apple < bread]
Searchsorted apple: [0]
Searchsorted milk: [5]
As you can see, "apple" is inserted at the beginning (b/c it starts with an 'a') and "milk" is inserted at the end, even though the order of both categories is between "cheese" and "bread".
Unfortunately, the API documentation does not specify is this is the expected behavior, but it seems inconsistent with the fact that .sort_values() does use the categorical order.
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-34-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.0.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: 0.9.2
apiclient: 1.5.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
|
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Adding a list of timedelta to a column does not work for tzlocal() timezones
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| 1 | 2016-10-27T17:56:13Z | 2016-10-28T20:46:56Z | 2016-10-28T20:45:26Z |
NONE
| null |
#### A small, complete example of the issue
When you try to add a list of timedelta to a column that is datetime the behavior changes if the tzinfo is coming from datetime.tz.tzlocal(). In the example below I make two columns of timestamps, on with tz.tzlocal() and the other tz.tzutc() and then try to add a list of timedelta to it. The tzutc() ones behave as expected but the ones with tzloca() only the first row does the addition properly and for the rest the addition is done to unix epoch of 0!
``` python
# Your code here
import datetime
from dateutil import tz
import pandas as pd
dates1 = [datetime.datetime(2013, 9, 20,13,00, tzinfo=tz.tzlocal()) + datetime.timedelta(minutes=100*i) for i in range(10)]
dates2 = [datetime.datetime(2013, 9, 20,13,00, tzinfo=tz.tzutc()) + datetime.timedelta(minutes=100*i) for i in range(10)]
df = pd.DataFrame({"start1": dates1, "start2": dates2})
delta = [datetime.timedelta(seconds=x) for x in range(10)]
df['end1'] = df['start1'] + delta
df['end2'] = df['start2'] + delta
df
```
#### Expected Output
```
start1 start2 \
```
0 2013-09-20 13:00:00-07:00 2013-09-20 13:00:00+00:00
1 2013-09-20 14:40:00-07:00 2013-09-20 14:40:00+00:00
2 2013-09-20 16:20:00-07:00 2013-09-20 16:20:00+00:00
3 2013-09-20 18:00:00-07:00 2013-09-20 18:00:00+00:00
4 2013-09-20 19:40:00-07:00 2013-09-20 19:40:00+00:00
5 2013-09-20 21:20:00-07:00 2013-09-20 21:20:00+00:00
6 2013-09-20 23:00:00-07:00 2013-09-20 23:00:00+00:00
7 2013-09-21 00:40:00-07:00 2013-09-21 00:40:00+00:00
8 2013-09-21 02:20:00-07:00 2013-09-21 02:20:00+00:00
9 2013-09-21 04:00:00-07:00 2013-09-21 04:00:00+00:00
```
end1 end2
```
0 2013-09-20 13:00:00-07:00 2013-09-20 13:00:00+00:00
1 **2013-09-20 13:00:01-07:00** 2013-09-20 14:40:01+00:00
2 **2013-09-20 13:00:02-07:00** 2013-09-20 16:20:02+00:00
3 **2013-09-20 13:00:03-07:00** 2013-09-20 18:00:03+00:00
4 **2013-09-20 13:00:04-07:00** 2013-09-20 19:40:04+00:00
5 **2013-09-20 13:00:05-07:00** 2013-09-20 21:20:05+00:00
6 **2013-09-20 13:00:06-07:00** 2013-09-20 23:00:06+00:00
7 **2013-09-20 13:00:07-07:00** 2013-09-21 00:40:07+00:00
8 **2013-09-20 13:00:08-07:00** 2013-09-21 02:20:08+00:00
9 **2013-09-20 13:00:09-07:00** 2013-09-21 04:00:09+00:00
#### Output of `pd.show_versions()`
<details>
# Paste the output here
## INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.4-100.fc23.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.utf8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 18.0.1
Cython: 0.23.4
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.1
sphinx: 1.2.3
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 0.6.0
tables: None
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 1.8.6
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.4.4
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.41.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxODU3NTgzNDQ=
| 14,524 |
Pandas 0.18.1 and 0.19.0 have different (and confusing) handling of TZ when subtracting Timedelta series vs subtracting single Timedelta
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| 6 | 2016-10-27T19:26:29Z | 2016-10-30T17:29:57Z | 2016-10-29T15:00:31Z |
NONE
| null |
#### A small, complete example of the issue
Apologies if this is already a known issue: I've not found anything similar in my googling, but my google-fu may have let me down.
When adding a single time delta to a time column with non-naive TZs, in pandas 0.19.0 this produces an array which still has the correct timezone. However, if adding an array of time deltas, the timezone is dropped and a dtype of <M8 is returned.
``` python
# Your code here
import pandas as pd
df = pd.DataFrame(pd.date_range(start=pd.Timestamp('2016-01-01 00:00:00+00'), end=pd.Timestamp('2016-01-01 23:59:59+00'), freq='H'))
df.columns = ['a']
df.a.dtype # Both 0.19.0 and 0.18.1 here return datetime64[ns, UTC]
df.a # Here, the +00 is visible.
df['b'] = df['a'].subtract(pd.Timedelta(seconds=60*60))
df['b'].dtype # Here, 0.18.1 returns dtype('<M8[ns]'), while 0.19.0 returns datetime64[ns, UTC]
df.b # Here, 0.19.0 will display the +00 while 0.18.1 will drop the TZ alteration
df['c'] = df['a'].subtract(df['b'] - df['a']) # Here, pandas 0.18.1 Fails due to trying to subtract a known timezone from a timezone-naive datetime.
df.c.dtype # Here, 0.19.0 returns dtype('<M8[ns]')
df.c # Here, 0.19.0 will not show the +00 TZ alteration.
```
#### Expected Output
I am not sure what the expected output should be. From my point of view, subtracting a Timedelta from a TZ specific timestamp should maintain the TZ. This is what appears to have happened between version 0.18.1 and 0.19.0 for single time deltas. However, I also think that the behaviour between the two cases above (single timedeltas and series of timedeltas) should be consistent.
Please clarify if I've understood this correctly!
```
>> df.a.dtype
datetime64[ns, UTC]
>> df.a
0 2016-01-01 00:00:00+00:00
1 2016-01-01 01:00:00+00:00
2 2016-01-01 02:00:00+00:00
3 2016-01-01 03:00:00+00:00
4 2016-01-01 04:00:00+00:00
5 2016-01-01 05:00:00+00:00
6 2016-01-01 06:00:00+00:00
7 2016-01-01 07:00:00+00:00
8 2016-01-01 08:00:00+00:00
9 2016-01-01 09:00:00+00:00
10 2016-01-01 10:00:00+00:00
11 2016-01-01 11:00:00+00:00
12 2016-01-01 12:00:00+00:00
13 2016-01-01 13:00:00+00:00
14 2016-01-01 14:00:00+00:00
15 2016-01-01 15:00:00+00:00
16 2016-01-01 16:00:00+00:00
17 2016-01-01 17:00:00+00:00
18 2016-01-01 18:00:00+00:00
19 2016-01-01 19:00:00+00:00
20 2016-01-01 20:00:00+00:00
21 2016-01-01 21:00:00+00:00
22 2016-01-01 22:00:00+00:00
23 2016-01-01 23:00:00+00:00
Name: a, dtype: datetime64[ns, UTC]
>> df.b.dtype
datetime64[ns, UTC]
>> df.b
0 2015-12-31 23:00:00+00:00
1 2016-01-01 00:00:00+00:00
2 2016-01-01 01:00:00+00:00
3 2016-01-01 02:00:00+00:00
4 2016-01-01 03:00:00+00:00
5 2016-01-01 04:00:00+00:00
6 2016-01-01 05:00:00+00:00
7 2016-01-01 06:00:00+00:00
8 2016-01-01 07:00:00+00:00
9 2016-01-01 08:00:00+00:00
10 2016-01-01 09:00:00+00:00
11 2016-01-01 10:00:00+00:00
12 2016-01-01 11:00:00+00:00
13 2016-01-01 12:00:00+00:00
14 2016-01-01 13:00:00+00:00
15 2016-01-01 14:00:00+00:00
16 2016-01-01 15:00:00+00:00
17 2016-01-01 16:00:00+00:00
18 2016-01-01 17:00:00+00:00
19 2016-01-01 18:00:00+00:00
20 2016-01-01 19:00:00+00:00
21 2016-01-01 20:00:00+00:00
22 2016-01-01 21:00:00+00:00
23 2016-01-01 22:00:00+00:00
Name: b, dtype: datetime64[ns, UTC]
>> df.c.dtype
datetime64[ns, UTC]
>> df.c
0 2016-01-01 01:00:00+00
1 2016-01-01 02:00:00+00
2 2016-01-01 03:00:00+00
3 2016-01-01 04:00:00+00
4 2016-01-01 05:00:00+00
5 2016-01-01 06:00:00+00
6 2016-01-01 07:00:00+00
7 2016-01-01 08:00:00+00
8 2016-01-01 09:00:00+00
9 2016-01-01 10:00:00+00
10 2016-01-01 11:00:00+00
11 2016-01-01 12:00:00+00
12 2016-01-01 13:00:00+00
13 2016-01-01 14:00:00+00
14 2016-01-01 15:00:00+00
15 2016-01-01 16:00:00+00
16 2016-01-01 17:00:00+00
17 2016-01-01 18:00:00+00
18 2016-01-01 19:00:00+00
19 2016-01-01 20:00:00+00
20 2016-01-01 21:00:00+00
21 2016-01-01 22:00:00+00
22 2016-01-01 23:00:00+00
23 2016-01-02 00:00:00+00
Name: c, dtype: datetime64[ns, UTC]
```
#### Output of `pd.show_versions()`
(I have tested in two virtualenvs, the only difference between the two being the python version)
<details>
# Paste the output here
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 28.6.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxODU3OTgyMTE=
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Inconsistent error raising in indexing
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| 2 | 2016-10-27T22:41:30Z | 2019-11-07T22:13:28Z | 2019-11-07T22:13:28Z |
CONTRIBUTOR
| null |
#### A small, complete example of the issue
``` python
x = np.random.randn(2, 2)
y = pd.Series(range(2))
y.loc[range(2)] = x # succeeds
y.loc[:] = x # fails
```
#### Expected Output
Since we're assigning a 2D array to a 1D vector that is smaller than the raveled array, I would expect both assignments to fail.
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ENH: Added optional pickle protocol version argument to pandas.to_pickle()
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[] | null | 4 | 2016-10-28T01:28:01Z | 2017-05-05T10:47:19Z | 2016-12-30T21:22:51Z |
NONE
| null |
- [ ] closes #14488
- [ ] tests added / passed
- [ ] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
… :issue:'14488'
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BUG/ERR: raise correct error when sql driver is not installed
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| 1 | 2016-10-28T09:59:03Z | 2016-10-29T13:24:19Z | 2016-10-29T13:24:19Z |
MEMBER
| null |
Closes #14513
When the driver was not installed, but sqlalchemy itself was, when passing a URI string, you got an error indicating that SQLAlchemy was not installed, instead of the driver not being installed. This was because the import error for the driver was captured as import error for sqlalchemy.
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GH #14499 Panel.ffill ignores axis parameter and fill along axis=1
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[] | null | 6 | 2016-10-28T16:06:47Z | 2017-03-20T13:41:41Z | 2017-03-20T13:41:41Z |
CONTRIBUTOR
| null |
- [x] closes #14499, closes #8251
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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Join on closest value
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| 8 | 2016-10-28T16:57:53Z | 2016-10-31T10:14:59Z | 2016-10-28T21:03:29Z |
CONTRIBUTOR
| null |
Hello,
I am not very familiar with the current discussions, but I was wondering if there was a feature in the pipe to _**join two dataframes on the closest value of a given column**_. It happens quite often that two dataframes contain different data with different timestamps, yet it feels natural to want to join the two dataframes by matching each row on the left dataframe with the _closest_ row in the right dataframe.
If you want me to add more detail please do tell me.
Regards.
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MDU6SXNzdWUxODU5NzU1MjQ=
| 14,530 |
read_json ValueError: Value is too big
|
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| 2 | 2016-10-28T17:17:18Z | 2019-01-07T20:26:29Z | 2016-10-28T21:01:30Z |
NONE
| null |
Loading a json file with large integers (> 2^32), results in "Value is too big". I have tried changing the orient to "records" and also passing in dtype={'id': numpy.dtype('uint64')}. The error is the same.
``` python
import pandas
data = pandas.read_json('''{"id": 10254939386542155531}''')
print(data.describe())
```
#### Expected Output
```
id
count 1
unique 1
top 10254939386542155531
freq 1
```
#### Actual Output (even with dtype passed in)
```
File "./parse_dispatch_table.py", line 34, in <module>
print(pandas.read_json('''{"id": 10254939386542155531}''', dtype=dtype_conversions).describe())
File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 234, in read_json
date_unit).parse()
File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 302, in parse
self._parse_no_numpy()
File "/users/XXX/.local/lib/python3.4/site-packages/pandas/io/json.py", line 519, in _parse_no_numpy
loads(json, precise_float=self.precise_float), dtype=None)
ValueError: Value is too big
```
No problem using read_csv:
``` python
import pandas
import io
print(pandas.read_csv(io.StringIO('''id\n10254939386542155531''')).describe())
```
#### Output using read_csv
```
id
count 1
unique 1
top 10254939386542155531
freq 1
```
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 28.6.0
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTE0MjIxMjQ=
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ENH: merge_asof() has left_index/right_index and left_by/right_by (#14253)
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| 8 | 2016-10-28T17:43:02Z | 2016-12-14T16:12:37Z | 2016-12-14T16:10:42Z |
CONTRIBUTOR
| null |
- [x] closes #14253
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
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MDU6SXNzdWUxODU5OTQwNzE=
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pandas.DataFrame.head does not properly trim MultiIndexes
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| 1 | 2016-10-28T18:47:49Z | 2016-10-28T20:42:05Z | 2016-10-28T20:41:50Z |
NONE
| null |
I'm trying to trim a very big `DataFrame` with a `MultiIndex` using `head`, and I noticed that the new `DataFrame` was still ridicolously big because it kept all the previous data from the entire `MultiIndex`. This doesn't happen if the `DataFrame` has a regular `Index`.
``` python
In [1]: from pandas import DataFrame
In [2]: df = DataFrame({'a': [1, 2, 3, 4, 5, 6], 'b': ['q', 'w', 'e', 'r', 't', 'y'], 'c': ['a', 's', 'd', 'f', 'g', 'h']}).set_index(['a', 'b'])
In [3]: df.head(3)
Out[3]:
c
a b
1 q a
2 w s
3 e d
In [4]: df.head(3).index
Out[4]:
MultiIndex(levels=[[1, 2, 3, 4, 5, 6], [u'e', u'q', u'r', u't', u'w', u'y']],
labels=[[0, 1, 2], [1, 4, 0]],
names=[u'a', u'b'])
```
``` python
#### Expected Output
MultiIndex(levels=[[1, 2, 3], [u'e', u'q', u'w']],
labels=[[0, 1, 2], [1, 2, 0]],
names=[u'a', u'b'])
```
#### Output of `pd.show_versions()`
<details>
# Paste the output here
## INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.1.1
Cython: None
numpy: 1.11.1
scipy: 0.18.0
statsmodels: None
xarray: None
IPython: 5.0.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
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MDExOlB1bGxSZXF1ZXN0OTE0NTUwNjM=
| 14,533 |
BUG: CategoricalDtype could not be sorted
|
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[] | null | 3 | 2016-10-28T21:26:00Z | 2016-11-02T22:11:53Z | 2016-11-02T20:16:49Z |
CONTRIBUTOR
| null |
- [x] closes #14368
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
|
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| 186,064,697 |
MDExOlB1bGxSZXF1ZXN0OTE0ODE4MzU=
| 14,534 |
BUG: pivot_table sometimes returns Series (#4386)
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[] | null | 6 | 2016-10-29T07:24:28Z | 2017-12-20T01:56:42Z | 2016-12-30T02:11:46Z |
MEMBER
| null |
- [x] closes #4386
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
The [`pivot_table` docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html) mentions that a `DataFrame` should be returned. In some cases, a `Series` is returned. I had to also alter some existing tests that expected `pivot_table` to return a `Series`
Any feedback welcomed, thanks!
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| 14,535 |
BUG: Index.insert may not coerced to correct dtype
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[] | null | 6 | 2016-10-29T14:09:42Z | 2017-05-08T09:30:00Z | 2017-05-07T14:03:13Z |
MEMBER
| null |
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
### on current master
```
# NG, must be float dtype
pd.Index([1]).insert(1, 1.0)
# Int64Index([1, 1], dtype='int64')
# NG, must be object dtype
pd.DatetimeIndex(['2011-01-01']).insert(1, 1)
# TypeError: cannot insert DatetimeIndex with incompatible label
```
## after the PR
```
pd.Index([1]).insert(1, 1.0)
# Float64Index([1.0, 1.0], dtype='float64')
pd.DatetimeIndex(['2011-01-01']).insert(1, 1)
# Index([2011-01-01 00:00:00, 1], dtype='object')
```
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| 1 | 2016-10-29T14:22:57Z | 2021-11-15T07:32:29Z | 2016-11-02T13:01:04Z |
MEMBER
| null |
Closes #14357
I first used `nanpercentile` when available (np >= 1.9), but this only can handle numeric data, not datetimes. So therefore included a custom `_nanpercentile`
I also added some tests regarding empty dataframes/series in comments, as these are still failing (but are also failing on 0.19.0)
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| 8 | 2016-10-29T17:56:08Z | 2016-11-07T20:53:03Z | 2016-11-07T20:52:08Z |
CONTRIBUTOR
| null |
- [x] closes #13726
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| 1 | 2016-10-29T23:26:08Z | 2016-10-29T23:42:58Z | 2016-10-29T23:42:46Z |
NONE
| null |
#### A small, complete example of the issue
``` python
import pandas as pd
df = pd.DataFrame(columns=['a', 'b'])
df2 = df.groupby('a').agg({
'a': {'max_a': lambda x: x.max()},
'b': {'count_b': 'count'}
})
print(df2.index)
```
#### Expected Output
``` python
Index([], dtype='object', name='a')
```
(This is the sort of index you would get if it wasn't an empty dataframe, or if the aggregation function was a cythoned builtin string like 'max').
#### Actual Output
``` python
RangeIndex(start=0, stop=0, step=1)
```
Nondeterministic in Python3 (sometimes the expected result, sometimes not, with the same code, on different runs).
Deterministically wrong in Python2 (provided the outer agg dictionary is ordered so that the user-defined function agg appears first in the list of keys).
#### Output of `pd.show_versions()`
<details>
## INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Darwin
OS-release: 15.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.18.1
nose: None
pip: 6.0.8
setuptools: 12.2
Cython: 0.24.1
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: None
pandas_datareader: None
</details>
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COMPAT: Updated method call for _Openpyxl22Write.write_cells() for Openpyxl =>2.4.0 #14519
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| 7 | 2016-10-30T06:51:58Z | 2017-01-14T18:23:56Z | 2017-01-14T18:23:49Z |
NONE
| null |
- [X] closes #14519
- [X] tests added / passed
- [X] passes `git diff upstream/master | flake8 --diff`
- [ ] whatsnew entry
Updated _Openpyxl22Writer.write_cells() to use new coordinate call method wks['coordinate']
instead of wks.cell() in accordance with https://openpyxl.readthedocs.io/en/default/tutorial.html#accessing-one-cell
in the newest release of Openpyxl (2.4.0 and later).
I did not know how to write a true test for this fix since it involves Excel file I/O and pandas does not currently supporting reading in .xlsx as far as I know. The best I could do is use it with openpyxl 2.4.0 install on my machine and passing a small dataframe into an .xlsx. Please let me know if you have any suggestions on how to better test this.
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BUG: fixes #12405 by eliding values index by NaT in MPLPlot._get_xticks
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[] | null | 17 | 2016-10-30T14:16:28Z | 2017-05-20T21:59:27Z | 2017-05-20T21:59:19Z |
CONTRIBUTOR
| null |
- [ ] closes #12405
- [x] tests added / passed
- [x] passes `git diff upstream/master | flake8 --diff`
- [x] whatsnew entry
Proposed solution: elide rows where x value would be `NaT` in [MPLPlot._get_xticks](https://github.com/pandas-dev/pandas/blob/7f5a45c1b388b3f7f309f82bfa0733b7b9980c3a/pandas/tools/plotting.py#L1328) (which seems like a minor misnomer: its getting x values, not what will ultimately be ticks).
Reordering occurs in MPLPlot._get_xticks and it has a block that is conditional on `use_index` and `is_datetype`, so seems like a reasonable place to elide the values. Mutation of `self.data` already occurs there (reordering) and a read through suggests nothing has saved info related to `self.data`'s shape before this point.
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DOC: Simplify the gbq integration testing procedure for contributors
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| 2 | 2016-10-31T01:53:27Z | 2016-10-31T12:23:42Z | 2016-10-31T12:23:39Z |
CONTRIBUTOR
| null |
I've updated the contributing documentation to simplify the steps required to run Google BigQuery integration tests on a forked repository of pandas.
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pd.eval does fails when the same operation in python works fine.
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| 9 | 2016-10-31T05:46:54Z | 2016-11-03T09:26:16Z | 2016-10-31T12:22:00Z |
NONE
| null |
#### A small, complete example of the issue
```python
# Your code here
df = pd.DataFrame(data=[(1,2),(3,4)], columns=['a', 'b'])
pd.eval("sum(df) / 7", local_dict={'sum': np.sum, 'df': df}, engine='numexpr')
*** TypeError: unhashable type: 'numpy.ndarray'
```
#### Expected Output
a 0.571429
b 0.857143
dtype: float64
This works fine when doing that same
sum in python (and it also works fine in pd.eval() using the 'python' engine.
The following from the docs does not seem to apply in this case.
> Series and DataFrame objects are supported and behave as they would with plain ol’ Python evaluation
.
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.5-300.fc23.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_AU.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.1
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 0.8.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0OTE1OTY2OTk=
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BUG: tseries ceil doc fix
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| 1 | 2016-10-31T11:53:48Z | 2016-10-31T12:24:10Z | 2016-10-31T12:24:05Z |
NONE
| null |
just fixes copy-paste doc bug from 4437e9c9e161b884450e8027daa3622f74c4b9e7
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BUG: fix categorical searchsorted bug (#14522)
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| 2 | 2016-10-31T14:15:30Z | 2016-11-02T10:38:25Z | 2016-11-02T10:08:31Z |
NONE
| null |
- [ ] closes #14522
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
the bug is cause by `x.categories.values.searchsorted`, where the search should not be performed.
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| 6 | 2016-10-31T15:43:07Z | 2016-11-03T11:12:19Z | 2016-11-03T11:12:19Z |
MEMBER
| null |
Closes #14298, related to #13660 and #13767
This closes the `info()` issue with CategoricalIndex, but the underlying issue is actually related to Index.append, when appending mixed dtype index objects:
```
In [11]: pd.Index(['a']).append(pd.CategoricalIndex(['a', 'b']))
...
AttributeError: 'Index' object has no attribute '_is_dtype_compat'
```
This error occurs when the calling index is not a Categorical (because of https://github.com/pandas-dev/pandas/blob/v0.19.0/pandas/indexes/base.py#L1439 the Categorical `_is_dtype_compat` method is used anyway).
But the question is, what should the result be of the above expression?
- a `CategoricalIndex` ? -> IMO not, as the calling index is not a CategoricalIndex, the result should also not be one (regardless of what is appended) -> so the intention of the code is IMO also not correct
- an object Index ? -> yes, this is also what 0.18.1 does
But, the more important discussion is *what to do when the calling index is a CategoricalIndex* ? In 0.18 (and in 0.19.0) this either returned a CategoricalIndex or raised an error:
```
In [13]: pd.__version__
Out[13]: u'0.18.1'
In [104]: pd.CategoricalIndex(['a', 'b']).append(pd.Index(['a']))
Out[104]: CategoricalIndex([u'a', u'b', u'a'], categories=[u'a', u'b'], ordered=False, dtype='category')
In [105]: pd.CategoricalIndex(['a', 'b']).append(pd.Index(['c']))
...
TypeError: cannot append a non-category item to a CategoricalIndex
```
But, for the `concat` case we decided to return an object dtyped Index in those cases instead of coercing or raising (see discussion in #13767 and the summary table with rules in https://github.com/pandas-dev/pandas/pull/13767#issuecomment-237806603). So the question is, do think that `append` should follow the same rules as `concat`. Or, do we want to be more flexible for `append`, and let it depend on the type of the calling Index?
cc @sinhrks
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printing Series in Jupyter/IPython opens and closes a terminal window
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[] | null | 6 | 2016-10-31T15:57:49Z | 2017-11-07T19:57:40Z | 2017-11-07T19:57:40Z |
NONE
| null |
When Git for Windows is installed and the option to use Unix tools in the Windows command line is checked, printing a Series in Jupyter QTConsole or Notebook (or Jupyter consoles in IDEs like Spyder) causes a terminal window to quickly pop up and close. Sometimes this happens in other cases where the Series is not necessarily printed, and I have experienced many terminal windows popping up and closing one after the other.
```python
import pandas as pd
pd.Series([1])
```
This seems to be due to [series.py](https://github.com/pandas-dev/pandas/blob/47f117d18a99f8bbaf2ecbc7829d198d304b8c2a/pandas/core/series.py#L1005) calling the `get_terminal_size()` function in [terminal.py](https://github.com/pandas-dev/pandas/blob/47f117d18a99f8bbaf2ecbc7829d198d304b8c2a/pandas/util/terminal.py). That function states that it should only be used when running in a terminal and not in IPython GUIs, but `series.py` is calling it regardless. It then fails to find the terminal size and falls back to calling the `tput` command, which only exists in Windows if Cygwin/Git for Windows is installed, and causes a terminal window to be briefly opened.
Possibly related issue: http://stackoverflow.com/questions/36133447/pandas-printing-tput-unknown-terminal-emacs
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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| 186,382,820 |
MDU6SXNzdWUxODYzODI4MjA=
| 14,547 |
Group-by/apply unexpected output with some operations when as_index=False
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| 6 | 2016-10-31T20:01:52Z | 2019-02-14T05:24:21Z | 2016-11-01T10:02:07Z |
NONE
| null |
#### A small, complete example of the issue
```python
import pandas as pd
import numpy as np
dd = dict(a=np.arange(9), b=np.repeat(np.arange(3), 3))
df = pd.DataFrame(dd)
# Problematic line below
df.groupby('b', as_index=False).apply(np.std)
# The line below has the same problem
df.groupby('b', as_index=False).std()
# When as_index=False is not passed in, it works as expected.
# The following two lines work exactly as expected
# (An example of the group-by/apply working for some operations
# df.groupby('b', as_index=False).apply(np.mean)
# df.groupby('b', as_index=False).mean()
```
#### Expected Output
```
a b
0 0.816497 0.0
1 0.816497 1.0
2 0.816497 2.0
```
#### Actual Output
```
a b
0 0.816497 0.0
1 0.816497 0.0
2 0.816497 0.0
```
When ``as_index=False`` is passed into groupby, finding the standard deviation doesn't work as expected. When ``as_index=True``, everything works as expected.
Finding the mean works as expected in both cases.
I have been able to reproduce the problem also on Linux with the same version of pandas.
#### Output of ``pd.show_versions()``
<details>
> INSTALLED VERSIONS
> ------------------
> commit: None
> python: 3.5.2.final.0
> python-bits: 64
> OS: Darwin
> OS-release: 15.6.0
> machine: x86_64
> processor: i386
> byteorder: little
> LC_ALL: en_US
> LANG: en_US.UTF8
>
> pandas: 0.18.0
> nose: None
> pip: 8.1.2
> setuptools: 28.5.0
> Cython: None
> numpy: 1.11.0
> scipy: 0.17.0
> statsmodels: None
> xarray: None
> IPython: 5.1.0
> sphinx: 1.3.5
> patsy: None
> dateutil: 2.5.3
> pytz: 2016.3
> blosc: None
> bottleneck: None
> tables: None
> numexpr: None
> matplotlib: 1.5.1
> openpyxl: None
> xlrd: 0.9.4
> xlwt: None
> xlsxwriter: None
> lxml: None
> bs4: 4.4.1
> html5lib: None
> httplib2: None
> apiclient: None
> sqlalchemy: None
> pymysql: None
> psycopg2: None
> jinja2: 2.8
> boto: None
</details>
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MDU6SXNzdWUxODY1MDM0ODE=
| 14,548 |
unique(), values discards timezone information in some cases
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| 5 | 2016-11-01T10:34:30Z | 2016-11-02T08:58:30Z | 2016-11-01T10:48:04Z |
NONE
| null |
Not sure if it's an expected behavior. This seems strange to me.
#### A small, complete example of the issue
```python
rng = pd.date_range('1/1/2011', periods=5, freq='D', tz='utc')
rng # Timezone is good
```
>>> DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04',
'2011-01-05'],
dtype='datetime64[ns, UTC]', freq='D')
```python
rng.unique() # Timezone is still good
```
>>> DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04',
'2011-01-05'],
dtype='datetime64[ns, UTC]', freq='D')
```python
rng.values # Timezone lost!
```
>>> array(['2011-01-01T00:00:00.000000000', '2011-01-02T00:00:00.000000000',
'2011-01-03T00:00:00.000000000', '2011-01-04T00:00:00.000000000',
'2011-01-05T00:00:00.000000000'], dtype='datetime64[ns]')
```python
serie = rng.to_series().reset_index()['index']
serie # Timezone is good
```
>>> 0 2011-01-01 00:00:00+00:00
1 2011-01-02 00:00:00+00:00
2 2011-01-03 00:00:00+00:00
3 2011-01-04 00:00:00+00:00
4 2011-01-05 00:00:00+00:00
Name: index, dtype: datetime64[ns, UTC]
```python
serie.unique() # Timezone is lost!
```
>>> array(['2011-01-01T00:00:00.000000000', '2011-01-02T00:00:00.000000000',
'2011-01-03T00:00:00.000000000', '2011-01-04T00:00:00.000000000',
'2011-01-05T00:00:00.000000000'], dtype='datetime64[ns]')
```python
serie.values # Timezone is lost!
```
>>> array(['2011-01-01T00:00:00.000000000', '2011-01-02T00:00:00.000000000',
'2011-01-03T00:00:00.000000000', '2011-01-04T00:00:00.000000000',
'2011-01-05T00:00:00.000000000'], dtype='datetime64[ns]')
#### Expected Output
Outputs of pd.DatetimeIndex.values, pd.Series.unique() and pd.Series.values should be preserve the timezone information.
Note: I did a quick test with Python 3.4.5 and pandas 0.19
only difference is (to be clear, rng.values and serie.values still discard the tzinfo):
```python
serie.unique() # Timezone is good again!! but dtype is object (preserving pd.Timestamp)
```
>>> array([Timestamp('2011-01-01 00:00:00+0000', tz='UTC', freq='D'),
Timestamp('2011-01-02 00:00:00+0000', tz='UTC', freq='D'),
Timestamp('2011-01-03 00:00:00+0000', tz='UTC', freq='D'),
Timestamp('2011-01-04 00:00:00+0000', tz='UTC', freq='D'),
Timestamp('2011-01-05 00:00:00+0000', tz='UTC', freq='D')], dtype=object)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.6.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
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| 14,549 |
Pandas 0.12 is much faster than Pandas 0.18
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| 4 | 2016-11-01T12:27:03Z | 2016-11-01T16:40:15Z | 2016-11-01T14:00:12Z |
NONE
| null |
Hello,
I am running the code below in pandas 0.18 and benchmarking it against pandas 0.12 and the profiling shows that 0.18 performs much slower on this. I put the output in the details section. Could you please advise?
Thank you so very much for your help,
Giuliano
```
from datetime import date, timedelta
from time import time
import pandas as pd
sizes = [500]
base_date = date(2016,10,31)
for n in sizes:
dates = [base_date - timedelta(days = x) for x in range(1, n, 1)]
dates_df = pd.DataFrame({'DATE' : dates, 'key' : 1})
identifiers = range(1, 5000)
identifiers_df = pd.DataFrame({'IDENTIFIER' : identifiers, 'key' : 1})
df = pd.merge(dates_df, identifiers_df, on='key')
df = df.set_index(['DATE', 'IDENTIFIER'])
df = df.sort_index(axis = 0, level = ['DATE', 'IDENTIFIER'], ascending=False)
start_time = time()
for d in dates:
temp = df.loc[d]
end_time = time()
print ('%s %s' % (n, end_time - start_time))
```
<details>
# Output from 0.18
/opt/anaconda/base/envs/pandas0.18/bin/python2.7 /home/gamantini/QA/avoitenok/pandas_migration/testDatesPerformance.py
500 5.26671504974
337459 function calls (331370 primitive calls) in 14.176 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 14.176 14.176 <string>:1(<module>)
1519 0.002 0.000 0.002 0.000 __init__.py:157(iteritems)
1 0.000 0.000 0.000 0.000 __init__.py:22(find_module)
2 0.000 0.000 0.000 0.000 _methods.py:34(_prod)
1027 0.001 0.000 0.023 0.000 _methods.py:37(_any)
7 0.000 0.000 0.000 0.000 _methods.py:40(_all)
6 0.020 0.003 2.945 0.491 algorithms.py:145(factorize)
6 0.000 0.000 0.007 0.001 algorithms.py:616(_get_data_algo)
11 0.000 0.000 0.000 0.000 algorithms.py:807(_get_take_nd_function)
11 0.012 0.001 0.527 0.048 algorithms.py:840(take_nd)
499 0.002 0.000 0.004 0.000 base.py:1016(_convert_slice_indexer)
2002 0.001 0.000 0.004 0.000 base.py:106(_reset_cache)
1 0.000 0.000 0.000 0.000 base.py:1136(_cleanup)
14 0.000 0.000 0.000 0.000 base.py:1143(_engine)
15 0.000 0.000 0.000 0.000 base.py:1146(<lambda>)
1 0.000 0.000 0.000 0.000 base.py:1172(inferred_type)
6 0.000 0.000 0.000 0.000 base.py:1233(__contains__)
16/14 0.001 0.000 0.003 0.000 base.py:124(__new__)
10 0.000 0.000 0.000 0.000 base.py:1247(__getitem__)
1 0.000 0.000 0.000 0.000 base.py:1281(_ensure_compat_append)
1 0.000 0.000 0.000 0.000 base.py:1308(append)
1 0.000 0.000 0.000 0.000 base.py:1326(_ensure_compat_concat)
499 0.005 0.000 0.047 0.000 base.py:1360(take)
4 0.000 0.000 0.000 0.000 base.py:1415(hasnans)
5 0.000 0.000 0.000 0.000 base.py:1507(equals)
1 0.000 0.000 0.000 0.000 base.py:1520(identical)
1 0.000 0.000 0.003 0.003 base.py:169(_box_values)
1 0.000 0.000 0.000 0.000 base.py:1770(_wrap_union_result)
1 0.000 0.000 0.000 0.000 base.py:1774(intersection)
2007 0.013 0.000 0.043 0.000 base.py:1915(get_loc)
2 0.000 0.000 0.000 0.000 base.py:2028(get_indexer)
998 0.002 0.000 0.002 0.000 base.py:218(freqstr)
2 0.000 0.000 0.000 0.000 base.py:2201(_possibly_promote)
1 0.000 0.000 0.000 0.000 base.py:227(inferred_freq)
1 0.000 0.000 0.000 0.000 base.py:2276(_can_reindex)
1 0.000 0.000 0.000 0.000 base.py:2295(reindex)
998 0.001 0.000 0.002 0.000 base.py:2795(_maybe_cast_indexer)
1497 0.001 0.000 0.002 0.000 base.py:2810(_validate_indexer)
1 0.000 0.000 0.000 0.000 base.py:3001(delete)
1 0.000 0.000 0.000 0.000 base.py:3031(drop)
1517 0.006 0.000 0.011 0.000 base.py:309(_simple_new)
2 0.000 0.000 0.000 0.000 base.py:321(_isnan)
2 0.000 0.000 0.000 0.000 base.py:326(hasnans)
3527 0.002 0.000 0.004 0.000 base.py:3381(_ensure_index)
1 0.000 0.000 0.000 0.000 base.py:3412(_get_na_value)
2002 0.003 0.000 0.012 0.000 base.py:3417(_ensure_frozen)
1 0.000 0.000 0.000 0.000 base.py:3425(_ensure_has_len)
2506 0.011 0.000 0.070 0.000 base.py:346(_shallow_copy)
1 0.000 0.000 0.000 0.000 base.py:354(_shallow_copy_with_infer)
5 0.000 0.000 0.000 0.000 base.py:383(is_)
3523 0.003 0.000 0.003 0.000 base.py:403(_reset_identity)
3579 0.002 0.000 0.003 0.000 base.py:409(__len__)
1 0.000 0.000 0.000 0.000 base.py:415(__array__)
4 0.000 0.000 0.000 0.000 base.py:430(dtype)
2542 0.003 0.000 0.006 0.000 base.py:440(values)
10 0.000 0.000 0.000 0.000 base.py:445(get_values)
5 0.000 0.000 0.000 0.000 base.py:490(_coerce_to_ndarray)
2508 0.011 0.000 0.014 0.000 base.py:506(_get_attributes_dict)
2 0.000 0.000 0.000 0.000 base.py:510(view)
6001 0.011 0.000 0.015 0.000 base.py:711(__getitem__)
2002 0.001 0.000 0.002 0.000 base.py:774(_shallow_copy)
1 0.000 0.000 0.000 0.000 base.py:778(_assert_can_do_setop)
2 0.000 0.000 0.000 0.000 base.py:791(nlevels)
1 0.000 0.000 0.000 0.000 base.py:795(_get_names)
2003 0.001 0.000 0.002 0.000 base.py:798(_set_names)
2003 0.004 0.000 0.013 0.000 base.py:806(set_names)
2002 0.002 0.000 0.015 0.000 base.py:865(rename)
8 0.000 0.000 0.000 0.000 base.py:870(_values)
2 0.000 0.000 0.000 0.000 base.py:914(is_monotonic)
1 0.000 0.000 0.000 0.000 base.py:919(is_monotonic_increasing)
10 0.000 0.000 0.000 0.000 base.py:938(is_unique)
499 0.001 0.000 0.005 0.000 base.py:972(_convert_scalar_indexer)
6 0.003 0.001 3.767 0.628 categorical.py:222(__init__)
4 0.010 0.003 2.967 0.742 categorical.py:374(from_array)
8 0.000 0.000 0.000 0.000 categorical.py:436(_get_codes)
6 0.000 0.000 0.003 0.000 categorical.py:470(_validate_categories)
6 0.000 0.000 0.000 0.000 categorical.py:534(_get_categories)
6 0.000 0.000 0.000 0.000 categorical.py:565(set_ordered)
1500 0.002 0.000 0.002 0.000 collections.py:78(__iter__)
4 0.000 0.000 0.000 0.000 common.py:1011(_possibly_cast_to_datetime)
10 0.000 0.000 0.802 0.080 common.py:1111(_possibly_infer_to_datetimelike)
2 0.000 0.000 0.801 0.401 common.py:1147(_try_datetime)
2497 0.005 0.000 0.017 0.000 common.py:1203(is_bool_indexer)
2 0.000 0.000 0.000 0.000 common.py:1226(_default_index)
2 0.000 0.000 0.000 0.000 common.py:1268(_try_sort)
1 0.000 0.000 0.000 0.000 common.py:1276(_count_not_none)
3 0.000 0.000 0.000 0.000 common.py:1277(<genexpr>)
11 0.000 0.000 0.000 0.000 common.py:1380(_asarray_tuplesafe)
1 0.000 0.000 0.000 0.000 common.py:1412(_index_labels_to_array)
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2055 0.006 0.000 0.009 0.000 common.py:1511(_get_dtype_type)
5 0.000 0.000 0.000 0.000 common.py:1532(is_dtype_equal)
4 0.000 0.000 0.000 0.000 common.py:1550(is_integer_dtype)
4 0.000 0.000 0.000 0.000 common.py:1561(is_int_or_datetime_dtype)
1011 0.002 0.000 0.005 0.000 common.py:1567(is_datetime64_dtype)
64 0.000 0.000 0.000 0.000 common.py:1575(is_datetime64tz_dtype)
8 0.000 0.000 0.000 0.000 common.py:1592(is_timedelta64_dtype)
6 0.000 0.000 0.000 0.000 common.py:1602(is_datetime_or_timedelta_dtype)
1 0.000 0.000 0.000 0.000 common.py:165(_isnull_ndarraylike)
6 0.000 0.000 0.000 0.000 common.py:1664(needs_i8_conversion)
1 0.000 0.000 0.000 0.000 common.py:1675(is_string_dtype)
1 0.000 0.000 0.000 0.000 common.py:1680(is_string_like_dtype)
6 0.000 0.000 0.000 0.000 common.py:1686(is_float_dtype)
8 0.000 0.000 0.000 0.000 common.py:1696(is_bool_dtype)
6 0.000 0.000 0.000 0.000 common.py:1705(is_sparse)
58 0.000 0.000 0.001 0.000 common.py:1710(is_datetimetz)
2 0.000 0.000 0.000 0.000 common.py:1717(is_extension_type)
16 0.000 0.000 0.000 0.000 common.py:1731(is_categorical)
70 0.000 0.000 0.001 0.000 common.py:1736(is_categorical_dtype)
1008 0.002 0.000 0.009 0.000 common.py:1745(is_object_dtype)
5019 0.005 0.000 0.008 0.000 common.py:1763(is_list_like)
4 0.000 0.000 0.000 0.000 common.py:1788(is_hashable)
1002 0.001 0.000 0.001 0.000 common.py:1846(_apply_if_callable)
5 0.000 0.000 0.000 0.000 common.py:272(array_equivalent)
2008 0.002 0.000 0.021 0.000 common.py:379(_coerce_indexer_dtype)
2 0.000 0.000 0.000 0.000 common.py:446(_infer_dtype_from_scalar)
8 0.000 0.000 0.001 0.000 common.py:527(_maybe_promote)
5 0.000 0.000 0.000 0.000 common.py:73(isnull)
5 0.000 0.000 0.000 0.000 common.py:94(_isnull_new)
2 0.000 0.000 0.001 0.000 common.py:998(_possibly_convert_platform)
2 0.000 0.000 0.000 0.000 decorators.py:65(wrapper)
27 0.000 0.000 0.000 0.000 dtypes.py:122(construct_from_string)
9 0.000 0.000 0.000 0.000 dtypes.py:159(__init__)
9 0.000 0.000 0.000 0.000 dtypes.py:191(construct_from_string)
134 0.000 0.000 0.001 0.000 dtypes.py:74(is_dtype)
4 0.000 0.000 0.002 0.000 frame.py:1973(__getitem__)
503 0.000 0.000 0.000 0.000 frame.py:199(_constructor)
4 0.000 0.000 0.001 0.000 frame.py:1999(_getitem_column)
505 0.004 0.000 0.011 0.000 frame.py:210(__init__)
4 0.000 0.000 0.001 0.000 frame.py:2331(_box_item_values)
4 0.000 0.000 0.001 0.000 frame.py:2338(_box_col_values)
1 0.000 0.000 0.001 0.001 frame.py:2675(_reindex_axes)
1 0.000 0.000 0.001 0.001 frame.py:2700(_reindex_columns)
1 0.000 0.000 0.001 0.001 frame.py:2738(reindex)
1 0.000 0.000 2.401 2.401 frame.py:2769(set_index)
2 0.000 0.000 0.003 0.001 frame.py:307(_init_dict)
1 0.022 0.022 5.554 5.554 frame.py:3227(sort_index)
1 0.000 0.000 0.000 0.000 frame.py:434(axes)
2 0.000 0.000 0.002 0.001 frame.py:5224(_arrays_to_mgr)
2 0.000 0.000 0.000 0.000 frame.py:5244(extract_index)
2 0.000 0.000 0.001 0.001 frame.py:5521(_homogenize)
2 0.000 0.000 0.000 0.000 frame.py:726(__len__)
1 0.000 0.000 0.000 0.000 frequencies.py:1017(mdiffs)
1 0.000 0.000 0.000 0.000 frequencies.py:1022(ydiffs)
1 0.000 0.000 0.000 0.000 frequencies.py:1026(_infer_daily_rule)
1 0.000 0.000 0.000 0.000 frequencies.py:1062(_get_annual_rule)
1 0.000 0.000 0.000 0.000 frequencies.py:1073(_get_quarterly_rule)
1 0.000 0.000 0.000 0.000 frequencies.py:1084(_get_monthly_rule)
1 0.000 0.000 0.000 0.000 frequencies.py:1127(_maybe_add_count)
1 0.000 0.000 0.000 0.000 frequencies.py:1300(_is_multiple)
2 0.000 0.000 0.000 0.000 frequencies.py:420(to_offset)
1 0.000 0.000 0.000 0.000 frequencies.py:826(infer_freq)
1 0.000 0.000 0.000 0.000 frequencies.py:890(__init__)
1 0.000 0.000 0.000 0.000 frequencies.py:908(deltas)
1 0.000 0.000 0.000 0.000 frequencies.py:916(is_unique)
1 0.000 0.000 0.000 0.000 frequencies.py:924(get_freq)
1 0.000 0.000 0.000 0.000 frequencies.py:968(fields)
2 0.000 0.000 0.000 0.000 fromnumeric.py:2395(prod)
1 0.000 0.000 0.000 0.000 fromnumeric.py:829(argsort)
500 0.002 0.000 0.019 0.000 function.py:36(__call__)
3 0.107 0.036 0.107 0.036 function_base.py:4110(delete)
503 0.001 0.000 0.001 0.000 generic.py:124(_init_mgr)
998 0.001 0.000 0.002 0.000 generic.py:1312(_indexer)
4 0.000 0.000 0.001 0.000 generic.py:1345(_get_item_cache)
4 0.000 0.000 0.000 0.000 generic.py:1359(_set_as_cached)
499 0.001 0.000 0.003 0.000 generic.py:1402(_is_view)
500 0.001 0.000 0.001 0.000 generic.py:1441(_clear_item_cache)
499 0.006 0.000 0.167 0.000 generic.py:1447(_slice)
998 0.003 0.000 0.005 0.000 generic.py:1467(_set_is_copy)
2 0.000 0.000 0.108 0.054 generic.py:1580(__delitem__)
499 0.014 0.000 2.877 0.006 generic.py:1643(xs)
1 0.000 0.000 0.001 0.001 generic.py:1846(drop)
1 0.000 0.000 0.001 0.001 generic.py:2194(reindex)
1 0.000 0.000 0.000 0.000 generic.py:2251(_needs_reindex_multi)
1 0.000 0.000 0.000 0.000 generic.py:2320(_reindex_with_indexers)
1 0.000 0.000 0.000 0.000 generic.py:260(_construct_axes_from_arguments)
504 0.001 0.000 0.001 0.000 generic.py:2641(__finalize__)
4 0.000 0.000 0.000 0.000 generic.py:2658(__getattr__)
2012 0.007 0.000 0.016 0.000 generic.py:2674(__setattr__)
500 0.002 0.000 0.010 0.000 generic.py:2713(_protect_consolidate)
500 0.002 0.000 0.012 0.000 generic.py:2723(_consolidate_inplace)
500 0.003 0.000 0.008 0.000 generic.py:2726(f)
1 0.000 0.000 0.877 0.877 generic.py:2953(copy)
1002 0.004 0.000 0.005 0.000 generic.py:307(_get_axis_number)
2499 0.005 0.000 0.006 0.000 generic.py:320(_get_axis_name)
2498 0.004 0.000 0.014 0.000 generic.py:333(_get_axis)
501 0.001 0.000 0.003 0.000 generic.py:337(_get_block_manager_axis)
2 0.000 0.000 0.000 0.000 generic.py:381(_info_axis)
2 0.000 0.000 0.000 0.000 generic.py:401(ndim)
500 0.002 0.000 0.009 0.000 generic.py:427(_set_axis)
3151 0.003 0.000 0.007 0.000 generic.py:7(_check)
2 0.000 0.000 0.000 0.000 generic.py:844(__contains__)
509 0.002 0.000 0.002 0.000 generic.py:94(__init__)
1 0.115 0.115 0.339 0.339 groupby.py:3978(get_group_index)
1 0.000 0.000 0.000 0.000 groupby.py:4005(_int64_cut_off)
1 0.218 0.218 0.218 0.218 groupby.py:4013(loop)
2 0.005 0.003 0.006 0.003 groupby.py:4043(maybe_lift)
1 0.000 0.000 0.000 0.000 groupby.py:4056(_int64_overflow_possible)
1 0.004 0.004 1.720 1.720 groupby.py:4110(_indexer_from_factorized)
1 0.010 0.010 2.549 2.549 groupby.py:4122(_lexsort_indexer)
1 0.000 0.000 0.030 0.030 groupby.py:4244(_get_group_index_sorter)
1 0.001 0.001 1.348 1.348 groupby.py:4269(_compress_group_index)
1 0.004 0.004 0.237 0.237 groupby.py:4290(_reorder_by_uniques)
998 0.035 0.000 0.102 0.000 index.py:1406(get_loc)
998 0.004 0.000 0.024 0.000 index.py:1477(_get_string_slice)
1999 0.001 0.000 0.001 0.000 index.py:1538(_get_freq)
1 0.000 0.000 0.000 0.000 index.py:1657(inferred_type)
1 0.000 0.000 0.000 0.000 index.py:1663(dtype)
2 0.000 0.000 0.000 0.000 index.py:219(__new__)
1 0.000 0.000 0.000 0.000 index.py:545(_box_func)
499 0.003 0.000 0.003 0.000 index.py:547(<lambda>)
1003 0.009 0.000 0.028 0.000 index.py:570(_simple_new)
2 0.000 0.000 0.000 0.000 index_tricks.py:231(_retval)
2 0.000 0.000 0.000 0.000 index_tricks.py:251(__getitem__)
998/499 0.005 0.000 5.262 0.011 indexing.py:1286(__getitem__)
499 0.006 0.000 2.364 0.005 indexing.py:1355(_has_valid_type)
499 0.001 0.000 0.001 0.000 indexing.py:1407(_get_partial_string_timestamp_match_key)
499 0.006 0.000 5.259 0.011 indexing.py:1431(_getitem_axis)
499 0.001 0.000 0.003 0.000 indexing.py:1498(_has_valid_type)
499 0.003 0.000 0.183 0.000 indexing.py:1571(_get_slice_axis)
499 0.002 0.000 0.188 0.000 indexing.py:1583(_getitem_axis)
499 0.002 0.000 0.009 0.000 indexing.py:183(_convert_scalar_indexer)
499 0.003 0.000 0.012 0.000 indexing.py:189(_convert_slice_indexer)
998 0.001 0.000 0.002 0.000 indexing.py:1890(is_list_like_indexer)
499 0.001 0.000 0.001 0.000 indexing.py:1901(need_slice)
2 0.000 0.000 0.000 0.000 indexing.py:50(__init__)
499 0.004 0.000 2.882 0.006 indexing.py:80(_get_label)
499 0.001 0.000 0.168 0.000 indexing.py:98(_slice)
499 0.001 0.000 0.001 0.000 internals.py:100(is_view)
4 0.000 0.000 0.000 0.000 internals.py:129(internal_values)
3 0.000 0.000 0.000 0.000 internals.py:135(get_values)
2 0.000 0.000 0.000 0.000 internals.py:156(fill_value)
1555 0.001 0.000 0.001 0.000 internals.py:160(mgr_locs)
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1 0.000 0.000 0.000 0.000 internals.py:1665(is_bool)
507 0.002 0.000 0.008 0.000 internals.py:183(make_block_same_class)
517 0.001 0.000 0.001 0.000 internals.py:191(mgr_locs)
499 0.001 0.000 0.001 0.000 internals.py:225(_slice)
499 0.006 0.000 0.016 0.000 internals.py:244(getitem_block)
514 0.002 0.000 0.006 0.000 internals.py:2482(make_block)
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3081 0.001 0.000 0.008 0.000 internals.py:2621(<genexpr>)
1016 0.001 0.000 0.001 0.000 internals.py:2623(ndim)
500 0.002 0.000 0.007 0.000 internals.py:2627(set_axis)
8 0.000 0.000 0.000 0.000 internals.py:264(shape)
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508 0.015 0.000 0.041 0.000 internals.py:2674(_rebuild_blknos_and_blklocs)
21 0.000 0.000 0.000 0.000 internals.py:2695(_get_items)
520 0.000 0.000 0.000 0.000 internals.py:272(dtype)
509 0.003 0.000 0.004 0.000 internals.py:276(ftype)
2 0.000 0.000 0.000 0.000 internals.py:2784(__len__)
5 0.000 0.000 0.000 0.000 internals.py:2799(_verify_integrity)
13 0.000 0.000 0.000 0.000 internals.py:2801(<genexpr>)
1 0.000 0.000 0.877 0.877 internals.py:2811(apply)
1 0.000 0.000 0.000 0.000 internals.py:2871(<genexpr>)
1006 0.001 0.000 0.001 0.000 internals.py:2996(is_consolidated)
505 0.003 0.000 0.006 0.000 internals.py:3004(_consolidate_check)
4 0.000 0.000 0.000 0.000 internals.py:301(iget)
499 0.001 0.000 0.002 0.000 internals.py:3027(is_view)
499 0.009 0.000 0.145 0.000 internals.py:3084(get_slice)
1 0.000 0.000 0.877 0.877 internals.py:3111(copy)
2 0.000 0.000 0.000 0.000 internals.py:3131(<lambda>)
1 0.000 0.000 0.107 0.107 internals.py:314(delete)
500 0.001 0.000 0.002 0.000 internals.py:3260(consolidate)
506 0.004 0.000 0.498 0.001 internals.py:3276(_consolidate_inplace)
4 0.000 0.000 0.000 0.000 internals.py:3283(get)
4 0.000 0.000 0.000 0.000 internals.py:3312(iget)
2 0.000 0.000 0.108 0.054 internals.py:3345(delete)
4 0.000 0.000 0.000 0.000 internals.py:3378(<genexpr>)
2 0.000 0.000 0.012 0.006 internals.py:3560(reindex_indexer)
1 0.000 0.000 0.000 0.000 internals.py:3603(_slice_take_blocks_ax0)
1 0.000 0.000 0.030 0.030 internals.py:3690(take)
4 0.000 0.000 0.000 0.000 internals.py:3778(__init__)
4 0.000 0.000 0.000 0.000 internals.py:3824(_block)
4 0.000 0.000 0.000 0.000 internals.py:3911(internal_values)
2 0.000 0.000 0.001 0.001 internals.py:3996(create_block_manager_from_arrays)
2 0.000 0.000 0.001 0.000 internals.py:4007(form_blocks)
1 0.000 0.000 0.000 0.000 internals.py:4122(_simple_blockify)
2 0.000 0.000 0.000 0.000 internals.py:4136(_multi_blockify)
3 0.000 0.000 0.000 0.000 internals.py:4140(<lambda>)
3 0.000 0.000 0.000 0.000 internals.py:4168(_stack_arrays)
4 0.000 0.000 0.000 0.000 internals.py:4171(_asarray_compat)
3 0.000 0.000 0.000 0.000 internals.py:4177(_shape_compat)
1 0.000 0.000 0.493 0.493 internals.py:4257(_consolidate)
6 0.000 0.000 0.000 0.000 internals.py:4263(<lambda>)
2 0.250 0.125 0.493 0.247 internals.py:4274(_merge_blocks)
4 0.000 0.000 0.000 0.000 internals.py:4301(_extend_blocks)
1 0.000 0.000 0.243 0.243 internals.py:4327(_vstack)
5 0.000 0.000 0.000 0.000 internals.py:4409(_get_blkno_placements)
1 0.000 0.000 0.000 0.000 internals.py:4432(items_overlap_with_suffix)
1 0.000 0.000 0.372 0.372 internals.py:4526(concatenate_block_managers)
3 0.000 0.000 0.012 0.004 internals.py:4550(get_empty_dtype_and_na)
3 0.000 0.000 0.372 0.124 internals.py:4630(concatenate_join_units)
2 0.000 0.000 0.000 0.000 internals.py:4655(get_mgr_concatenation_plan)
4 0.000 0.000 0.000 0.000 internals.py:4734(combine_concat_plans)
3 0.000 0.000 0.000 0.000 internals.py:4827(__init__)
3 0.011 0.004 0.011 0.004 internals.py:4839(needs_filling)
3 0.000 0.000 0.011 0.004 internals.py:4848(dtype)
3 0.000 0.000 0.000 0.000 internals.py:4861(is_null)
3 0.000 0.000 0.360 0.120 internals.py:4890(get_reindexed_values)
2 0.000 0.000 0.000 0.000 internals.py:4947(_fast_count_smallints)
1 0.000 0.000 0.000 0.000 internals.py:4958(_preprocess_slice_or_indexer)
2 0.000 0.000 0.379 0.190 internals.py:581(copy)
514 0.002 0.000 0.004 0.000 internals.py:77(__init__)
6 0.000 0.000 0.000 0.000 internals.py:92(_consolidate_key)
2 0.000 0.000 0.011 0.006 internals.py:975(take_nd)
1 0.000 0.000 0.000 0.000 merge.py:1252(_should_fill)
2 0.000 0.000 0.000 0.000 merge.py:1259(_any)
1 0.000 0.000 0.002 0.002 merge.py:161(__init__)
1 0.000 0.000 0.929 0.929 merge.py:212(get_result)
1 0.003 0.003 0.003 0.003 merge.py:282(_maybe_add_join_keys)
1 0.003 0.003 0.933 0.933 merge.py:30(merge)
1 0.000 0.000 0.553 0.553 merge.py:333(_get_join_info)
1 0.000 0.000 0.002 0.002 merge.py:393(_get_merge_keys)
1 0.000 0.000 0.000 0.000 merge.py:416(<lambda>)
1 0.000 0.000 0.000 0.000 merge.py:418(<lambda>)
1 0.000 0.000 0.000 0.000 merge.py:482(_validate_specification)
1 0.000 0.000 0.458 0.458 merge.py:527(_get_join_indexers)
2 0.000 0.000 0.000 0.000 merge.py:698(_factorize_keys)
1 0.000 0.000 0.000 0.000 merge.py:757(_get_join_keys)
1 0.000 0.000 0.000 0.000 merge.py:761(<lambda>)
3 0.000 0.000 0.000 0.000 missing.py:559(clean_reindex_fill_method)
3 0.000 0.000 0.000 0.000 missing.py:61(clean_fill_method)
1 0.000 0.000 0.019 0.019 multi.py:1010(take)
1 0.000 0.000 0.018 0.018 multi.py:1022(_assert_take_fillable)
499 0.033 0.000 0.111 0.000 multi.py:1155(droplevel)
998 0.001 0.000 0.009 0.000 multi.py:1180(<genexpr>)
14495 0.004 0.000 0.004 0.000 multi.py:131(_get_levels)
1001 0.008 0.000 0.086 0.000 multi.py:134(_set_levels)
3003 0.005 0.000 0.071 0.000 multi.py:148(<genexpr>)
499 0.004 0.000 2.342 0.005 multi.py:1515(get_loc)
499 0.003 0.000 0.005 0.000 multi.py:1534(_maybe_to_slice)
499 0.009 0.000 2.633 0.005 multi.py:1601(get_loc_level)
499 0.008 0.000 0.274 0.001 multi.py:1615(maybe_droplevels)
998 0.029 0.000 4.658 0.005 multi.py:1734(_get_level_indexer)
4503 0.002 0.000 0.002 0.000 multi.py:240(_get_labels)
1001 0.008 0.000 0.030 0.000 multi.py:243(_set_labels)
3003 0.005 0.000 0.019 0.000 multi.py:253(<genexpr>)
2002 0.003 0.000 0.009 0.000 multi.py:433(__len__)
5493 0.028 0.000 0.035 0.000 multi.py:436(_get_names)
14477 0.005 0.000 0.005 0.000 multi.py:437(<genexpr>)
1001 0.010 0.000 0.034 0.000 multi.py:439(_set_names)
1497 0.016 0.000 0.046 0.000 multi.py:510(_get_level_number)
1 0.000 0.000 1.315 1.315 multi.py:535(values)
1001 0.009 0.000 0.160 0.000 multi.py:69(__new__)
1 0.000 0.000 0.009 0.009 multi.py:781(is_lexsorted)
2 0.000 0.000 0.023 0.011 multi.py:793(lexsort_depth)
2 0.000 0.000 2.968 1.484 multi.py:808(from_arrays)
1 0.000 0.000 1.628 1.628 multi.py:852(from_tuples)
3502 0.003 0.000 0.004 0.000 multi.py:941(nlevels)
499 0.002 0.000 2.345 0.005 multi.py:949(__contains__)
998 0.021 0.000 0.206 0.000 multi.py:986(__getitem__)
2 0.000 0.000 0.000 0.000 numeric.py:121(asi8)
4 0.000 0.000 0.000 0.000 numeric.py:126(is_all_dates)
3 0.000 0.000 0.000 0.000 numeric.py:148(ones)
53 0.000 0.000 0.000 0.000 numeric.py:414(asarray)
4 0.000 0.000 0.000 0.000 numeric.py:484(asanyarray)
5 0.000 0.000 0.000 0.000 numeric.py:607(require)
10 0.000 0.000 0.000 0.000 numeric.py:676(<genexpr>)
5 0.000 0.000 0.000 0.000 numeric.py:93(__new__)
4 0.000 0.000 0.000 0.000 numerictypes.py:942(_can_coerce_all)
2 0.000 0.000 0.000 0.000 numerictypes.py:964(find_common_type)
2 0.000 0.000 0.000 0.000 range.py:117(_simple_new)
2 0.000 0.000 0.000 0.000 range.py:139(_validate_dtype)
2 0.000 0.000 0.000 0.000 range.py:42(__new__)
18 0.000 0.000 0.000 0.000 range.py:435(__len__)
4 0.000 0.000 0.000 0.000 range.py:58(_ensure_int)
4 0.000 0.000 0.000 0.000 series.py:120(__init__)
4 0.000 0.000 0.000 0.000 series.py:236(from_array)
4 0.000 0.000 0.000 0.000 series.py:270(_set_axis)
4 0.000 0.000 0.001 0.000 series.py:2787(_sanitize_array)
2 0.000 0.000 0.000 0.000 series.py:2804(_try_cast)
2 0.000 0.000 0.000 0.000 series.py:2875(create_from_value)
4 0.000 0.000 0.000 0.000 series.py:292(_set_subtyp)
4 0.000 0.000 0.000 0.000 series.py:302(name)
4 0.000 0.000 0.000 0.000 series.py:306(name)
4 0.000 0.000 0.000 0.000 series.py:366(_values)
1 0.000 0.000 0.243 0.243 shape_base.py:180(vstack)
2 0.000 0.000 0.000 0.000 shape_base.py:61(atleast_2d)
1 0.000 0.000 0.000 0.000 six.py:180(find_module)
3 0.000 0.000 0.000 0.000 six.py:184(find_module)
1 0.023 0.023 14.176 14.176 testDatesPerformance.py:6(testPerformance)
998 0.013 0.000 0.014 0.000 tools.py:603(parse_time_string)
500 0.006 0.000 0.007 0.000 validators.py:104(_check_for_invalid_keys)
500 0.002 0.000 0.017 0.000 validators.py:120(validate_kwargs)
500 0.007 0.000 0.007 0.000 validators.py:29(_check_for_default_values)
1 0.000 0.000 0.000 0.000 {all}
1004 0.001 0.000 0.001 0.000 {any}
3523 0.002 0.000 0.002 0.000 {built-in method __new__ of type object at 0x7f63038a7f20}
1002 0.000 0.000 0.000 0.000 {callable}
6001 0.002 0.000 0.002 0.000 {function __getitem__ at 0x7f62f7ed9230}
16175/16172 0.017 0.000 0.020 0.000 {getattr}
6711 0.003 0.000 0.003 0.000 {hasattr}
509 0.001 0.000 0.001 0.000 {hash}
50016 0.025 0.000 0.032 0.000 {isinstance}
2230 0.001 0.000 0.001 0.000 {issubclass}
27470/21885 0.010 0.000 0.021 0.000 {len}
21 0.000 0.000 0.000 0.000 {max}
5 0.000 0.000 0.000 0.000 {method 'add' of 'pandas.lib.BlockPlacement' objects}
7 0.000 0.000 0.000 0.000 {method 'all' of 'numpy.ndarray' objects}
1025 0.002 0.000 0.025 0.000 {method 'any' of 'numpy.ndarray' objects}
49 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
8 0.055 0.007 0.055 0.007 {method 'argsort' of 'numpy.ndarray' objects}
12 0.000 0.000 0.000 0.000 {method 'astype' of 'numpy.ndarray' objects}
500 0.000 0.000 0.000 0.000 {method 'clear' of 'dict' objects}
1 0.000 0.000 0.000 0.000 {method 'clear_mapping' of 'pandas.index.IndexEngine' objects}
502 0.000 0.000 0.000 0.000 {method 'copy' of 'dict' objects}
4 0.381 0.095 0.381 0.095 {method 'copy' of 'numpy.ndarray' objects}
1497 0.002 0.000 0.002 0.000 {method 'count' of 'list' objects}
2 0.000 0.000 0.000 0.000 {method 'cumsum' of 'numpy.ndarray' objects}
1 0.000 0.000 0.000 0.000 {method 'delete' of 'pandas.lib.BlockPlacement' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
3 0.000 0.000 0.000 0.000 {method 'extend' of 'list' objects}
4 0.000 0.000 0.000 0.000 {method 'factorize' of 'pandas.hashtable.Int64Factorizer' objects}
1018 0.002 0.000 0.002 0.000 {method 'fill' of 'numpy.ndarray' objects}
3519 0.001 0.000 0.001 0.000 {method 'get' of 'dict' objects}
2 0.000 0.000 0.000 0.000 {method 'get_count' of 'pandas.hashtable.Int64Factorizer' objects}
2 0.000 0.000 0.000 0.000 {method 'get_indexer' of 'pandas.index.IndexEngine' objects}
5 1.934 0.387 1.934 0.387 {method 'get_labels' of 'pandas.hashtable.Int64HashTable' objects}
1 0.264 0.264 0.264 0.264 {method 'get_labels' of 'pandas.hashtable.PyObjectHashTable' objects}
1 1.110 1.110 1.110 1.110 {method 'get_labels_groupby' of 'pandas.hashtable.Int64HashTable' objects}
2994 0.023 0.000 0.023 0.000 {method 'get_loc' of 'pandas.index.DatetimeEngine' objects}
11 0.000 0.000 0.000 0.000 {method 'get_loc' of 'pandas.index.IndexEngine' objects}
1497 0.006 0.000 0.006 0.000 {method 'index' of 'list' objects}
509 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}
1519 0.001 0.000 0.001 0.000 {method 'iteritems' of 'dict' objects}
3 0.000 0.000 0.000 0.000 {method 'keys' of 'dict' objects}
6 0.000 0.000 0.000 0.000 {method 'nonzero' of 'numpy.ndarray' objects}
1 0.000 0.000 0.000 0.000 {method 'partition' of 'str' objects}
16 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}
1497 0.001 0.000 0.001 0.000 {method 'pop' of 'list' objects}
7 0.039 0.006 0.039 0.006 {method 'put' of 'numpy.ndarray' objects}
7 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}
1036 0.021 0.000 0.021 0.000 {method 'reduce' of 'numpy.ufunc' objects}
3 0.000 0.000 0.000 0.000 {method 'reshape' of 'numpy.ndarray' objects}
9 0.000 0.000 0.000 0.000 {method 'search' of '_sre.SRE_Pattern' objects}
1996 4.507 0.002 4.507 0.002 {method 'searchsorted' of 'numpy.ndarray' objects}
2 0.000 0.000 0.000 0.000 {method 'startswith' of 'str' objects}
513 0.733 0.001 0.733 0.001 {method 'take' of 'numpy.ndarray' objects}
5 0.000 0.000 0.000 0.000 {method 'to_array' of 'pandas.hashtable.Int64Vector' objects}
1 0.000 0.000 0.000 0.000 {method 'to_array' of 'pandas.hashtable.ObjectVector' objects}
4 0.000 0.000 0.000 0.000 {method 'transpose' of 'numpy.ndarray' objects}
2507 0.001 0.000 0.001 0.000 {method 'update' of 'dict' objects}
5 0.000 0.000 0.000 0.000 {method 'upper' of 'str' objects}
4 0.000 0.000 0.000 0.000 {method 'values' of 'dict' objects}
6560 0.006 0.000 0.006 0.000 {method 'view' of 'numpy.ndarray' objects}
1009 0.001 0.000 0.001 0.000 {min}
1 0.000 0.000 0.000 0.000 {next}
523 0.115 0.000 0.115 0.000 {numpy.core.multiarray.arange}
1090 0.001 0.000 0.001 0.000 {numpy.core.multiarray.array}
2 0.000 0.000 0.000 0.000 {numpy.core.multiarray.bincount}
3 0.000 0.000 0.000 0.000 {numpy.core.multiarray.can_cast}
5 0.243 0.049 0.243 0.049 {numpy.core.multiarray.concatenate}
3 0.000 0.000 0.000 0.000 {numpy.core.multiarray.copyto}
1043 0.121 0.000 0.121 0.000 {numpy.core.multiarray.empty}
7 0.012 0.002 0.012 0.002 {numpy.core.multiarray.putmask}
2 0.006 0.003 0.006 0.003 {numpy.core.multiarray.where}
2 0.000 0.000 0.000 0.000 {numpy.core.multiarray.zeros}
2008 0.015 0.000 0.015 0.000 {pandas.algos.ensure_int16}
38 0.028 0.001 0.028 0.001 {pandas.algos.ensure_int64}
5 0.000 0.000 0.000 0.000 {pandas.algos.ensure_object}
509 0.007 0.000 0.007 0.000 {pandas.algos.ensure_platform_int}
1 0.030 0.030 0.030 0.030 {pandas.algos.groupsort_indexer}
1 0.000 0.000 0.000 0.000 {pandas.algos.inner_join_indexer_object}
1 0.457 0.457 0.457 0.457 {pandas.algos.inner_join}
5 0.126 0.025 0.126 0.025 {pandas.algos.take_1d_int64_int64}
1 0.013 0.013 0.013 0.013 {pandas.algos.take_1d_object_object}
1 0.000 0.000 0.000 0.000 {pandas.algos.take_2d_axis0_int64_int64}
3 0.239 0.080 0.239 0.080 {pandas.algos.take_2d_axis1_int64_int64}
1 0.014 0.014 0.014 0.014 {pandas.algos.take_2d_axis1_object_object}
2 0.000 0.000 0.000 0.000 {pandas.lib.array_equivalent_object}
4 0.000 0.000 0.000 0.000 {pandas.lib.checknull}
1 1.278 1.278 1.278 1.278 {pandas.lib.fast_zip}
2 0.000 0.000 0.000 0.000 {pandas.lib.get_blkno_indexers}
19 0.000 0.000 0.000 0.000 {pandas.lib.infer_dtype}
1 0.000 0.000 0.000 0.000 {pandas.lib.is_bool_array}
11 0.000 0.000 0.000 0.000 {pandas.lib.is_bool}
1 0.000 0.000 0.000 0.000 {pandas.lib.is_complex}
1006 0.000 0.000 0.000 0.000 {pandas.lib.is_float}
2007 0.001 0.000 0.001 0.000 {pandas.lib.is_integer}
3 0.006 0.002 0.006 0.002 {pandas.lib.is_lexsorted}
1 0.000 0.000 0.000 0.000 {pandas.lib.is_timedelta_array}
1 0.000 0.000 0.000 0.000 {pandas.lib.isnullobj}
1516 0.008 0.000 0.008 0.000 {pandas.lib.isscalar}
4 0.000 0.000 0.000 0.000 {pandas.lib.list_to_object_array}
1 0.000 0.000 0.003 0.003 {pandas.lib.map_infer}
2 0.001 0.000 0.001 0.000 {pandas.lib.maybe_convert_objects}
1 0.073 0.073 0.073 0.073 {pandas.lib.tuples_to_object_array}
2023 0.004 0.000 0.004 0.000 {pandas.lib.values_from_object}
2 0.801 0.400 0.801 0.400 {pandas.tslib.array_to_datetime}
1 0.000 0.000 0.000 0.000 {pandas.tslib.build_field_sarray}
1 0.000 0.000 0.000 0.000 {pandas.tslib.is_timestamp_array}
1003 0.002 0.000 0.002 0.000 {pandas.tslib.maybe_get_tz}
3 0.000 0.000 0.000 0.000 {pandas.tslib.unique_deltas}
8 0.000 0.000 0.000 0.000 {range}
2 0.000 0.000 0.000 0.000 {setattr}
1002 0.004 0.000 0.013 0.000 {sorted}
6 0.000 0.000 0.000 0.000 {sum}
2 0.000 0.000 0.000 0.000 {time.time}
Process finished with exit code 0
# Output from 0.12
/opt/epd/7.3-2_pandas0.12/bin/python2.7 /home/gamantini/QA/avoitenok/pandas_migration/testDatesPerformance.py
500 0.138274908066
91488 function calls (90977 primitive calls) in 2.079 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 2.079 2.079 <string>:1(<module>)
4 0.000 0.000 0.000 0.000 _methods.py:20(_prod)
11 0.000 0.000 0.011 0.001 _methods.py:24(_any)
1 0.000 0.000 0.000 0.000 _methods.py:28(_all)
2 0.008 0.004 0.309 0.154 algorithms.py:105(factorize)
2 0.000 0.000 0.000 0.000 algorithms.py:298(_get_data_algo)
1 0.000 0.000 0.000 0.000 arraysetops.py:281(in1d)
1 0.000 0.000 0.000 0.000 arraysetops.py:388(setdiff1d)
2 0.000 0.000 0.000 0.000 arraysetops.py:90(unique)
1000 0.000 0.000 0.000 0.000 base.py:45(_constructor)
2 0.000 0.000 0.000 0.000 categorical.py:111(_set_levels)
2 0.000 0.000 0.000 0.000 categorical.py:119(_get_levels)
2 0.000 0.000 0.000 0.000 categorical.py:71(__init__)
2 0.001 0.000 0.310 0.155 categorical.py:87(from_array)
2 0.000 0.000 0.001 0.000 common.py:1138(_possibly_convert_platform)
4 0.000 0.000 0.000 0.000 common.py:1167(_possibly_cast_to_datetime)
1504 0.001 0.000 0.003 0.000 common.py:1232(_is_bool_indexer)
2 0.000 0.000 0.000 0.000 common.py:1292(_try_sort)
3 0.000 0.000 0.000 0.000 common.py:1369(split_ranges)
16 0.000 0.000 0.000 0.000 common.py:1468(_asarray_tuplesafe)
1 0.000 0.000 0.000 0.000 common.py:1496(_index_labels_to_array)
3 0.000 0.000 0.000 0.000 common.py:1511(_maybe_make_list)
2 0.000 0.000 0.000 0.000 common.py:1517(is_bool)
1502 0.001 0.000 0.001 0.000 common.py:1521(is_integer)
5 0.000 0.000 0.000 0.000 common.py:1525(is_float)
2 0.000 0.000 0.000 0.000 common.py:1542(is_integer_dtype)
4 0.000 0.000 0.000 0.000 common.py:1552(_is_int_or_datetime_dtype)
4 0.000 0.000 0.000 0.000 common.py:1561(is_datetime64_dtype)
2 0.000 0.000 0.000 0.000 common.py:1583(is_float_dtype)
4 0.000 0.000 0.000 0.000 common.py:1611(is_list_like)
8 0.000 0.000 0.000 0.000 common.py:431(_get_take_nd_function)
8 0.013 0.002 0.204 0.025 common.py:462(take_nd)
2 0.000 0.000 0.000 0.000 common.py:677(_infer_dtype_from_scalar)
5 0.000 0.000 0.000 0.000 common.py:728(_maybe_promote)
1 0.000 0.000 0.000 0.000 frame.py:1722(as_matrix)
504 0.001 0.000 0.039 0.000 frame.py:1986(__getitem__)
499 0.000 0.000 0.033 0.000 frame.py:2008(_getitem_slice)
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MDU6SXNzdWUxODY1NDgyOTU=
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Pandas replace with string and integers - incorrect behavior?
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| 2 | 2016-11-01T14:31:32Z | 2016-11-01T16:34:22Z | 2016-11-01T16:34:06Z |
NONE
| null |
I encountered a potentially incorrect behavior of pandas replace with strings and integers. If the dataframe has both 0 (integer) and '0' (strings) then replace '0' affects both strings and integers. Here's how it goes:
`In [1]: df = pd.DataFrame({'numbers' : [0, 1, 2, 0], 'strings' : ['0', 1, 2, '0']})`
To check that it's indeed the correct setup:
```
In [2]: df.dtypes
Out [2]:
numbers int64
strings object
dtype: object
```
And check individual values:
```
In [3]: type(df['numbers'][0])
Out[3]: numpy.int64
In [4]: type(df['strings'][0])
Out[4]: str
```
Now, do replace:
```
In [5]: df.replace(to_replace='0', value=np.NaN, inplace=True)
In [6]: df.head()
Out[6]:
numbers strings
0 NaN NaN
1 1 1
2 2 2
3 NaN NaN
```
As you can see, it replaced both strings and integers, however should have worked only on the strings. If we try same on integers, it works correctly:
```
In [7]: df = pd.DataFrame({'numbers' : [0, 1, 2, 0], 'strings' : ['0', 1, 2, '0']})
...: df.replace(to_replace=0, value=np.NaN, inplace=True)
...: print df.head()
Out [7]:
numbers strings
0 NaN 0
1 1 1
2 2 2
3 NaN 0
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.8.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 3.6
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 4.0.3
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 1.0b8
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 186,558,152 |
MDExOlB1bGxSZXF1ZXN0OTE4MDA0ODI=
| 14,551 |
PERF: casting loc to labels dtype before searchsorted
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| 3 | 2016-11-01T15:08:03Z | 2016-11-02T15:16:15Z | 2016-11-02T15:16:15Z |
MEMBER
| null |
Intrigued by the profiling results of the below example (Multi-index `loc` indexing, based on the example in https://github.com/pandas-dev/pandas/issues/14549), where `searchsorted` seemed to take the majority of the computation time.
And it seems that `searchsorted` casts both inputs (in this case `labels` and `loc`) to a common dtype, and the `labels` of the MultiIndex were in this case `int16`, while `loc` (output from `Index.get_loc`) is a python int.
By casting `loc` to the dtype of `labels`, the specific example gets a ca 20 x speed improvement
```
df = pd.DataFrame({'a': np.random.randn(500*5000)}, index=pd.MultiIndex.from_product([date_range("2014-01-01", periods=500), range(5000)]))
dt = pd.Timestamp('2015-01-01')
%timeit df.loc[dt]
```
On master:
```
In [3]: %timeit df.loc[dt]
The slowest run took 5.70 times longer than the fastest. This could mean that an intermediate result is being cached.
100 loops, best of 3: 9.39 ms per loop
```
with this PR:
```
In [3]: %timeit df.loc[dt]
The slowest run took 122.51 times longer than the fastest. This could mean that an intermediate result is being cached.
1000 loops, best of 3: 422 µs per loop
```
Just putting it here as a showcase.
Actual PR probably needs some more work (other places where this can be done, can loc ever be out of bound for that dtype?, benchmarks, ..)
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| 186,559,799 |
MDU6SXNzdWUxODY1NTk3OTk=
| 14,552 |
strange error with Pandas-0.19.0
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| 15 | 2016-11-01T15:14:27Z | 2016-11-02T12:57:15Z | 2016-11-02T12:57:15Z |
CONTRIBUTOR
| null |
Just to raise eyebrows, not sure if it's related to pandas-0.19 or to a bug of myself.
It is ok under Pandas-0.18.1 (and seems not related to Python-3.6)
#### A small, complete example of the issue
```python
# in Ipython
#Pandas
import pandas as pd
import numpy as np
idx = pd.date_range('2000', '2005', freq='d', closed='left')
datas = pd.DataFrame({'Color': [ 'green' if x> 1 else 'red' for x in np.random.randn(len(idx))],
'Measure': np.random.randn(len(idx)), 'Year': idx.year},
index=idx)
#datas.head()
# checking baresql
from __future__ import print_function, unicode_literals, division # line needed only if Python2.7
from baresql import baresql
bsql = baresql.baresql(connection="sqlite:///.baresql.db")
bsqldf = lambda q: bsql.df(q, dict(globals(),**locals()))
bsqldf("select Color, Year from datas$$")
```
#### Expected Output
this:
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.beta.3
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 23 Stepping 10, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 28.7.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.8.0rc1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: 1.4.4
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
</details>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas\index.c:11091)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.Int64HashTable.get_item (pandas\hashtable.c:8120)()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\indexes\base.py in get_loc(self, key, method, tolerance)
2103 try:
-> 2104 return self._engine.get_loc(key)
2105 except KeyError:
pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas\index.c:11241)()
pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas\index.c:11147)()
pandas/index.pyx in pandas.index.DatetimeEngine._date_check_type (pandas\index.c:11311)()
KeyError: 'next'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas\index.c:11091)()
pandas/src/hashtable_class_helper.pxi in pandas.hashtable.Int64HashTable.get_item (pandas\hashtable.c:8120)()
TypeError: an integer is required
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\tseries\index.py in get_loc(self, key, method, tolerance)
1410 try:
-> 1411 return Index.get_loc(self, key, method, tolerance)
1412 except (KeyError, ValueError, TypeError):
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\indexes\base.py in get_loc(self, key, method, tolerance)
2105 except KeyError:
-> 2106 return self._engine.get_loc(self._maybe_cast_indexer(key))
2107
pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas\index.c:11241)()
pandas/index.pyx in pandas.index.DatetimeEngine.get_loc (pandas\index.c:11147)()
pandas/index.pyx in pandas.index.DatetimeEngine._date_check_type (pandas\index.c:11311)()
KeyError: 'next'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
ValueError: Error parsing datetime string "next" at position 0
The above exception was the direct cause of the following exception:
SystemError Traceback (most recent call last)
<ipython-input-7-526f069d545f> in <module>()
----> 1 bsqldf('''select Color, Year from datas$$ ''')
<ipython-input-2-7f10bad21e85> in <lambda>(q)
3 from baresql import baresql
4 bsql = baresql.baresql(connection="sqlite:///.baresql.db")
----> 5 bsqldf = lambda q: bsql.df(q, dict(globals(),**locals()))
6
7 users = ['Alexander', 'Billy', 'Charles', 'Danielle', 'Esmeralda', 'Franz', 'Greg']
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\baresql\baresql.py in df(self, q, env)
506 def df(self, q, env):
507 "same as .cursor , but returns a pandas dataframe"
--> 508 cur = self.cursor( q, env)
509 result = None
510 rows = cur.fetchall()
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\baresql\baresql.py in cursor(self, q, env)
483 pre_q = "DROP TABLE IF EXISTS %s" % table_sql.join(self.delimiters)
484 cur = self._execute_sql (pre_q)
--> 485 self._write_table( table_sql, df, self.conn)
486 #multiple sql must be executed one by one
487 for q_single in self.get_sqlsplit(sql, True) :
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\baresql\baresql.py in _write_table(self, tablename, df, conn)
450 to_sql(df, name = tablename, con = self.conn, flavor = self.engine)
451 else:
--> 452 to_sql(df, name = tablename, con = self.conn)
453
454
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in to_sql(frame, name, con, flavor, schema, if_exists, index, index_label, chunksize, dtype)
468 pandas_sql.to_sql(frame, name, if_exists=if_exists, index=index,
469 index_label=index_label, schema=schema,
--> 470 chunksize=chunksize, dtype=dtype)
471
472
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in to_sql(self, frame, name, if_exists, index, index_label, schema, chunksize, dtype)
1498 table = SQLiteTable(name, self, frame=frame, index=index,
1499 if_exists=if_exists, index_label=index_label,
-> 1500 dtype=dtype)
1501 table.create()
1502 table.insert(chunksize)
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in __init__(self, *args, **kwargs)
1261 # (this is what sqlalchemy does)
1262 sqlite3.register_adapter(time, lambda _: _.strftime("%H:%M:%S.%f"))
-> 1263 super(SQLiteTable, self).__init__(*args, **kwargs)
1264
1265 def sql_schema(self):
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in __init__(self, name, pandas_sql_engine, frame, index, if_exists, prefix, index_label, schema, keys, dtype)
560 if frame is not None:
561 # We want to initialize based on a dataframe
--> 562 self.table = self._create_table_setup()
563 else:
564 # no data provided, read-only mode
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in _create_table_setup(self)
1297 """
1298 column_names_and_types = \
-> 1299 self._get_column_names_and_types(self._sql_type_name)
1300
1301 pat = re.compile('\s+')
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in _get_column_names_and_types(self, dtype_mapper)
755 dtype_mapper(self.frame.iloc[:, i]),
756 False)
--> 757 for i in range(len(self.frame.columns))
758 ]
759
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in <listcomp>(.0)
755 dtype_mapper(self.frame.iloc[:, i]),
756 False)
--> 757 for i in range(len(self.frame.columns))
758 ]
759
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in _sql_type_name(self, col)
1338 return dtype[col.name]
1339
-> 1340 col_type = self._get_notnull_col_dtype(col)
1341 if col_type == 'timedelta64':
1342 warnings.warn("the 'timedelta' type is not supported, and will be "
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\io\sql.py in _get_notnull_col_dtype(self, col)
846 col_for_inference = col
847 if col.dtype == 'object':
--> 848 notnulldata = col[~isnull(col)]
849 if len(notnulldata):
850 col_for_inference = notnulldata
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\core\series.py in __getitem__(self, key)
634 raise
635
--> 636 if is_iterator(key):
637 key = list(key)
638
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\types\inference.py in is_iterator(obj)
35 def is_iterator(obj):
36 # python 3 generators have __next__ instead of next
---> 37 return hasattr(obj, 'next') or hasattr(obj, '__next__')
38
39
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
2740 return object.__getattribute__(self, name)
2741 else:
-> 2742 if name in self._info_axis:
2743 return self[name]
2744 return object.__getattribute__(self, name)
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\tseries\base.py in __contains__(self, key)
232 def __contains__(self, key):
233 try:
--> 234 res = self.get_loc(key)
235 return is_scalar(res) or type(res) == slice or np.any(res)
236 except (KeyError, TypeError, ValueError):
C:\WinPython\basedir36\build\winpython-64bit-3.6.x.0\python-3.6.0b3.amd64\lib\site-packages\pandas\tseries\index.py in get_loc(self, key, method, tolerance)
1417
1418 try:
-> 1419 stamp = Timestamp(key, tz=self.tz)
1420 return Index.get_loc(self, stamp, method, tolerance)
1421 except (KeyError, ValueError):
pandas/tslib.pyx in pandas.tslib.Timestamp.__new__ (pandas\tslib.c:9932)()
pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas\tslib.c:25231)()
pandas/tslib.pyx in pandas.tslib.convert_str_to_tsobject (pandas\tslib.c:26851)()
pandas/src/datetime.pxd in datetime._string_to_dts (pandas\tslib.c:87106)()
SystemError: <class 'str'> returned a result with an error set
```
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| 186,652,326 |
MDU6SXNzdWUxODY2NTIzMjY=
| 14,553 |
Adding (Insert or update if key exists) option to `.to_sql`
|
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| false | null |
[] | null | 55 | 2016-11-01T21:27:33Z | 2021-04-12T08:41:48Z | null |
NONE
| null |
Suppose you have an existing SQL table called `person_age`, where `id` is the primary key:
```python
age
id
1 18
2 42
```
and you also have new data in a `DataFrame` called `extra_data`
```python
age
id
2 44
3 95
```
then it would be useful to have an option on `extra_data.to_sql()` that allows to pass the DataFrame to SQL with an `INSERT` or `UPDATE` option on the rows, based on the `primary key`.
In this case, the `id=2` row would get updated to `age=44` and the `id=3` row would get added
#### Expected Output
```python
age
id
1 18
2 44
3 95
```
#### (Maybe) helpful code references
+ [Use `merge` from SQLAlchemy](http://stackoverflow.com/questions/7889183/sqlalchemy-insert-or-update-example)?
+ The query: '''INSERT or REPLACE into person_age (id, age) values (?,?,?) ''' in [this question](http://stackoverflow.com/questions/23574614/appending-pandas-dataframe-to-sqlite-table-by-primary-key)
I looked at `pandas` `sql.py` sourcecode to come up with a solution, but I couldn't follow.
#### Code to replicate the example above
(Apologies for mixing `sqlalchemy` and `sqlite`
```python
import pandas as pd
from sqlalchemy import create_engine
import sqlite3
conn = sqlite3.connect('example.db')
c = conn.cursor()
c.execute('''DROP TABLE IF EXISTS person_age;''')
c.execute('''
CREATE TABLE person_age
(id INTEGER PRIMARY KEY ASC, age INTEGER NOT NULL)
''')
conn.commit()
conn.close()
##### Create original table
engine = create_engine("sqlite:///example.db")
sql_df = pd.DataFrame({'id' : [1, 2], 'age' : [18, 42]})
sql_df.to_sql('person_age', engine, if_exists='append', index=False)
#### Extra data to insert/update
extra_data = pd.DataFrame({'id' : [2, 3], 'age' : [44, 95]})
extra_data.set_index('id', inplace=True)
#### extra_data.to_sql() with row update or insert option
expected_df = pd.DataFrame({'id': [1, 2, 3], 'age': [18, 44, 95]})
expected_df.set_index('id', inplace=True)
```
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| 14,554 |
CLN/BUG: remove bare excepts
|
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| 7 | 2016-11-01T23:51:17Z | 2016-12-19T23:46:27Z | 2016-12-19T23:46:27Z |
NONE
| null |
Using KeyError may prevent the system to show system error, and thus make the error hard to find
```python
df = pd.DataFrame({'a':[1,]}, index=['a'])
def bla(a):
return bla(df.loc[['a'], ['a']])
bla(1)
```
#### Expected Output
Max recursion limit exception. Got key error
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: None
setuptools: None
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 14,555 |
array_equivalent has disappeared from pandas.core.common in version 0.19, is this a mistake?
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| 9 | 2016-11-02T01:10:18Z | 2019-12-11T07:55:35Z | 2019-12-11T07:55:20Z |
NONE
| null |
#### A small, complete example of the issue
```python
# Your code here
import pandas
import pandas.core.common as com
print com.array_equivalent
```
#### Expected Output
```python
# expect something like:
# <function array_equivalent at 0x000000000445A198>
# But I get
```
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-fc6fc07906df> in <module>()
----> 1 print com.array_equivalent
AttributeError: 'module' object has no attribute 'array_equivalent'
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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| 14,556 |
floats and ints missing as acceptable arguments for pandas.to_datetime in API Reference
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| 6 | 2016-11-02T07:07:48Z | 2016-12-12T11:10:35Z | 2016-12-12T11:10:35Z |
NONE
| null |
#### A small, complete example of the issue
http://pandas.pydata.org/pandas-docs/version/0.19.0/generated/pandas.to_datetime.html says:
> **arg** : _string, datetime, list, tuple, 1-d array, Series_
While http://pandas.pydata.org/pandas-docs/version/0.19.0/timeseries.html#epoch-timestamps says that `int`s and `float`s are accepted as well.
#### Expected Output
`int` and `float` to be in the list
#### Output of ``pd.show_versions()``
```
pandas: 0.19.0
```
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 7.1.0
setuptools: 18.0.1
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
</details>
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| 186,732,682 |
MDU6SXNzdWUxODY3MzI2ODI=
| 14,557 |
IndexError when indexing into a Series containing a UTC datetime by name
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| 1 | 2016-11-02T07:49:34Z | 2016-11-02T09:16:02Z | 2016-11-02T07:52:26Z |
NONE
| null |
#### A small, complete example of the issue
```python
import pandas; import pytz; import datetime; pandas.Series([pytz.UTC.localize(datetime.datetime(2000,1,1))], index=['idx1'])['idx1']
/usr/lib/python2.7/dist-packages/pandas/tseries/base.py:100: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
val = getitem(key)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/dist-packages/pandas/core/series.py", line 557, in __getitem__
result = self.index.get_value(self, key)
File "/usr/lib/python2.7/dist-packages/pandas/core/index.py", line 1780, in get_value
return s[key]
File "/usr/lib/python2.7/dist-packages/pandas/tseries/base.py", line 100, in __getitem__
val = getitem(key)
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
```
#### Expected Output
datetime.datetime(2000, 1, 1, 0, 0, tzinfo=<UTC>)
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C
pandas: 0.17.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.7.0
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 2.4.1
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2014.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.1
openpyxl: 2.3.0
xlrd: 0.9.4
xlwt: 0.7.5
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
</details>
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| 186,735,411 |
MDU6SXNzdWUxODY3MzU0MTE=
| 14,558 |
read_table with dtype=object and an int converter still returns float64 if NaN present
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| 5 | 2016-11-02T08:08:14Z | 2016-11-02T09:49:21Z | 2016-11-02T09:49:21Z |
NONE
| null |
#### A small, complete example of the issue
```python
>>> import io, pandas
>>> csvfile = io.StringIO('a\nN/A\n1\n')
>>> converters = {'a': lambda x: float('nan') if x == 'N/A' else int(x)}
>>> pandas.read_table(csvfile, dtype=object, converters=converters)
a
0 NaN
1 1.0
```
#### Expected Output
```python
a
0 NaN
1 1
```
#### Output of ``pd.show_versions()``
```
pandas: 0.19.0
```
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 7.1.0
setuptools: 18.0.1
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
</details>
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| 14,559 |
series.apply(pandas.to_datetime, convert_dtype=False) still converts dtype
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| 4 | 2016-11-02T08:46:30Z | 2016-11-02T11:00:33Z | 2016-11-02T09:11:47Z |
NONE
| null |
#### A small, complete example of the issue
```python
>>> import pandas
>>> s = pandas.Series({'a': '2012-05-01 00:00:00'})
>>> s.apply(pandas.to_datetime, convert_dtype=False)
a 2012-05-01
dtype: datetime64[ns]
```
#### Expected Output
```python
a 2012-05-01
dtype: object
```
#### Output of ``pd.show_versions()``
```
pandas: 0.19.0
```
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 7.1.0
setuptools: 18.0.1
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
</details>
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MDU6SXNzdWUxODY3NjY5NTM=
| 14,560 |
pandas.Timestamp not in API Reference
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| 2 | 2016-11-02T10:44:49Z | 2016-11-02T11:44:19Z | 2016-11-02T11:17:28Z |
NONE
| null |
#### A small, complete example of the issue
`pandas.Timestamp` cannot be found at http://pandas.pydata.org/pandas-docs/version/0.19.0/api.html
#### Expected Output
`pandas.Timestamp` to be there
#### Output of ``pd.show_versions()``
```
pandas: 0.19.0
```
<details>
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 7.1.0
setuptools: 18.0.1
Cython: None
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
```
</details>
#### Additional info
1. It's a bit inconvenient for a newcomer to read [Time Series / Date functionality](http://pandas.pydata.org/pandas-docs/version/0.19.0/timeseries.html) over and over again in order to find out the API of the class.
2. I can get rid of messages such as `WARNING: po:obj reference target not found: pandas.Timestamp` coming from Sphinx.
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Invalid datetime string conversion giving SystemError with Python 3.6.0b3
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| 4 | 2016-11-02T12:56:16Z | 2016-11-17T17:41:06Z | 2016-11-17T17:41:06Z |
MEMBER
| null |
From #14552, using Python 3.6.0b3 and pandas 0.19.0:
```
In [12]: pd.Timestamp('invalid')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
ValueError: Error parsing datetime string "invalid" at position 0
The above exception was the direct cause of the following exception:
SystemError Traceback (most recent call last)
<ipython-input-12-91abde127db1> in <module>()
----> 1 pd.Timestamp('invalid')
pandas/tslib.pyx in pandas.tslib.Timestamp.__new__ (pandas/tslib.c:9932)()
pandas/tslib.pyx in pandas.tslib.convert_to_tsobject (pandas/tslib.c:25231)()
pandas/tslib.pyx in pandas.tslib.convert_str_to_tsobject (pandas/tslib.c:26851)()
pandas/src/datetime.pxd in datetime._string_to_dts (pandas/tslib.c:87106)()
SystemError: <class 'str'> returned a result with an error set
```
gives a SystemError (which gives problems in the indexing code etc), while with python < 3.6, you get a ValueError:
```
In [56]: pd.Timestamp('next')
...
series of "During handling of the above exception, another exception occurred:"
...
/home/joris/scipy/pandas/pandas/tslib.pyx in pandas.tslib.convert_str_to_tsobject (pandas/tslib.c:27281)()
1537 ts, dayfirst=dayfirst, yearfirst=yearfirst)
1538 except Exception:
-> 1539 raise ValueError("could not convert string to Timestamp")
1540
1541 return convert_to_tsobject(ts, tz, unit, dayfirst, yearfirst)
ValueError: could not convert string to Timestamp
```
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MDU6SXNzdWUxODY3OTQ3OTg=
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CLN: move nanpercentile functionality to nanops
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[] | null | 2 | 2016-11-02T13:03:28Z | 2019-12-25T23:20:41Z | 2019-12-25T23:20:41Z |
MEMBER
| null |
See comment https://github.com/pandas-dev/pandas/pull/14536#discussion_r85660330
Move the `_nanpercentile` functionality used in `quantile` in `internals.py` (https://github.com/pandas-dev/pandas/blob/52f31d470d779204e4c1388cdb56351c68332c3f/pandas/core/internals.py#L1319-L1393) to `nanops.py`.
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| 3 | 2016-11-02T13:15:50Z | 2020-01-04T17:57:30Z | 2020-01-04T17:57:30Z |
NONE
| null |
There is an issue with the markers in the legend when working with the built-in matplotlib plot-wrapper in pandas. The problem is also in earlier versions and the only workaround I found is using matplotlib directly.
#### A small, complete example of the issue
```python
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': [1, 2, 3, 4, 5, 6],
'B': [2, 4, 1, 3, 2, 4],
'C': [3, 3, 2, 6, 4, 2],
'X': [1, 2, 3, 4, 5, 6]})
fig, ax = plt.subplots()
for kind in 'ABC':
df.plot('X', kind, label=kind, ax=ax, style='.')
plt.show()
```
#### Expected Output
The markers in the legend are missing, except for the last label. It however works if I don't use `style=...`, but the default line-plot, like in this example:
```python
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': [1, 2, 3, 4, 5, 6],
'B': [2, 4, 1, 3, 2, 4],
'C': [3, 3, 2, 6, 4, 2],
'X': [1, 2, 3, 4, 5, 6]})
fig, ax = plt.subplots()
for kind in 'ABC':
df.plot('X', kind, label=kind, ax=ax)
plt.show()
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 25.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxODY4MDI0Mjc=
| 14,564 |
BUG: inconsistencies/errors in quantile on empty DataFrame
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| 3 | 2016-11-02T13:36:59Z | 2021-05-19T12:53:57Z | 2021-05-19T12:53:57Z |
MEMBER
| null |
In PR https://github.com/pandas-dev/pandas/pull/14536, I added some tests in comments, as they currently fail or give inconsistent results:
**1. Empty frame with float dtype:**
```
df = DataFrame(columns=['a', 'b'], dtype='float64')
df.quantile(0.5)
df.quantile(0.5, axis=1)
```
In 0.18.1, this gives NaNs or empty frame depending on the axis (which is correct I think):
```
In [10]: df.quantile(0.5)
Out[10]:
a NaN
b NaN
dtype: float64
In [9]: df.quantile(0.5, axis=1)
Out[9]:
Empty DataFrame
Columns: []
Index: []
```
But on master, the `axis=1` case errors (df.quantile(0.5) also gives NaNs):
master:
```
In [8]: df.quantile(0.5, axis=1)
...
ValueError: need at least one array to concatenate
```
**2. Empty frame with int dtype**
```
df = DataFrame(columns=['a', 'b'], dtype='int64')
df.quantile(0.5)
```
Opposed to float dtype giving a series of NaNs, with integers it gives an empty frame in 0.18.1:
```
In [11]: df.quantile(0.5)
Out[11]:
Empty DataFrame
Columns: []
Index: []
```
and on master also raises the ValueError as for float with `axis=1`:
```
In [14]: df.quantile(0.5)
...
ValueError: need at least one array to concatenate
```
**3. Empty frame with datetime values**
```
df = DataFrame(columns=['a', 'b'], dtype='datetime64')
df.quantile(0.5, numeric_only=False)
```
On 0.18.1 / master, it gives a series of NaNs, where this should be NaTs:
```
In [13]: df.quantile(0.5, numeric_only=False)
Out[13]:
a NaN
b NaN
Name: 0.5, dtype: float64
```
**4. Frame with only only datetime columns but without `only_numeric=False`**
```
df = DataFrame({'a': pd.to_datetime(['2010', '2011']), 'b': [0, 5], 'c': pd.to_datetime(['2011', '2012'])})
df[['a', 'c']].quantile(.5)
```
On 0.18.1, this gives an empty frame
```
In [8]: df[['a', 'c']].quantile(.5)
Out[8]:
Empty DataFrame
Columns: []
Index: []
```
while on master this raises the same ValueError as above:
```
In [7]: df[['a', 'c']].quantile(.5)
...
ValueError: need at least one array to concatenate
```
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PERF: better use of searchsorted for indexing performance
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| 7 | 2016-11-02T15:20:04Z | 2019-02-24T03:40:08Z | 2019-02-24T03:40:08Z |
MEMBER
| null |
Follow-up on https://github.com/pandas-dev/pandas/pull/14551
When searchsorted is used (eg as `labels.searchsorted(loc)`), it is important that `loc` is first cast to the same dtype as `labels` for performance reasons.
PR https://github.com/pandas-dev/pandas/pull/14551 fixed one such example, but further actions:
- check the other usages of `searchsorted` for this pattern
- add benchmarks to track the improvement
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MDU6SXNzdWUxODY4Mzc0Mjk=
| 14,566 |
Pandas can read first 216 lines, but not first 217 lines.
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[] | null | 8 | 2016-11-02T15:41:03Z | 2021-10-15T18:12:04Z | null |
NONE
| null |
Posted on Stackoverflow: [Tested](http://stackoverflow.com/questions/40290262/pandas-fails-to-read-past-216th-line-of-jagged-text-file/40290886#40290886) on Python 2.7, 3.5 - Pandas 0.18.1.
Given the following file: https://gist.github.com/alexanderwhatley/e07af297b1a10cd5cb57c7b75ee7f229, I tried reading the first 216 lines which worked, and then the first 217 lines, which failed. Since 216 does not seem to be a special number, it seems like this may be an issue with Pandas, and not with the file.....
`>>> df = pd.read_table("test.txt", names = range(2000), nrows = 216)
>>> df = pd.read_table("test.txt", names = range(2000), nrows = 217)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/alexwhatley/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py", line 562, in parser_f
return _read(filepath_or_buffer, kwds)
File "/Users/alexwhatley/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py", line 321, in _read
return parser.read(nrows)
File "/Users/alexwhatley/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py", line 815, in read
ret = self._engine.read(nrows)
File "/Users/alexwhatley/anaconda3/lib/python3.5/site-packages/pandas/io/parsers.py", line 1314, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 805, in pandas.parser.TextReader.read (pandas/parser.c:8748)
File "pandas/parser.pyx", line 839, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:9208)
File "pandas/parser.pyx", line 881, in pandas.parser.TextReader._read_rows (pandas/parser.c:9731)
File "pandas/parser.pyx", line 868, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:9602)
File "pandas/parser.pyx", line 1865, in pandas.parser.raise_parser_error (pandas/parser.c:23325)
pandas.io.common.CParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file.`
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MDExOlB1bGxSZXF1ZXN0OTE5OTc3MTc=
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DEPR: add deprecation warning for com.array_equivalent
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| 2 | 2016-11-02T15:49:51Z | 2016-11-03T09:36:10Z | 2016-11-03T09:36:10Z |
MEMBER
| null |
xref https://github.com/pandas-dev/pandas/issues/14555
pandas.core.common.array_equivalent was removed without deprecation warning.
This commits adds it back to the core.common namespace with deprecation warning
We should still decide on the usefulness to add to the public API (in this commit I didn't add any pointer to alternative import location)
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MDU6SXNzdWUxODY4NTkyMjc=
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Cannot understand ValueError for pandas.HDFStore.select(), HDF5 from Pytables
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[] | null | 4 | 2016-11-02T16:58:14Z | 2016-11-02T22:01:19Z | 2016-11-02T22:01:15Z |
NONE
| null |
This is a standard Python error, but I don't understand it in this context:
I have an HDF5 object I create with Pytables. Printing out the information in Python with `>>> store`, I see the following information
import pandas as pd
store = pd.HDFStore("file.h5")
>>> store
<class 'pandas.io.pytables.HDFStore'>
File path: /path/pathname/file.h5
given_key frame_table [0.0.0] (typ->generic,nrows->877129127,ncols->6,indexers->[index],dc->[COL1, COL2, COL3, COL4, COL5, COL6])
So, there are 6 columns and 877 million rows. I indexed on COL1 and COl6.
Now, I'm getting errors when I try to select values.
df = store.select(key ="given_key", columns = "COL1", start=0, stop=1)
This outputs the error:
ValueError: Shape of passed values is (1, 1), indices imply (1, 877129127)
So I try this:
df = store.select(key ="given_key", start=0, stop=1)
I get the same ValueError
ValueError: Shape of passed values is (1, 1), indices imply (1, 877129127)
What does this mean? Is there an error in terms of how I created this HDF5 object?
Let's say I want a value which I know exists in `COL1`
df = store.select("given_key", where = "COL1 == 'abc'")
Here's the error I get:
ValueError: Shape of passed values is (1, 1231), indices imply (1, 877129127)
It looks like it has accessed 1231 rows with this query value (which makes sense) but I cannot....get it.
How can I fix this?
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BUG: Fix justification for DataFrame.to_string(index=False)
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[] | null | 5 | 2016-11-02T18:09:23Z | 2017-02-01T20:52:54Z | 2017-02-01T20:52:54Z |
NONE
| null |
I believe this fixes #13032. I'm a new contributor, so please let me know if I've missed any steps.
|
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MDU6SXNzdWUxODY5MTA4MjA=
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Reading bz2, zip, and xz-compressed files from URL fails
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| 3 | 2016-11-02T20:26:55Z | 2016-12-13T23:13:06Z | 2016-12-13T23:11:38Z |
CONTRIBUTOR
| null |
Previously, we've discussed reading tables form gzip-compressed URLs (see https://github.com/pandas-dev/pandas/issues/8685 and https://github.com/pandas-dev/pandas/pull/10649). This is an essential feature as data storage increasingly migrates to the cloud.
I now realize that the previous work only added support for gzipped URLs and not bz2, zip, or xz compression. However, [the documentation](http://pandas.pydata.org/pandas-docs/version/0.19.0/generated/pandas.read_csv.html) doesn't reflect this. See [this notebook](https://github.com/dhimmel/iris/blob/1418a9abe7608c3c1e821138c120e05aa8116ecb/pandas-bug.ipynb) to see the errors that are generated.
So :+1: for allowing URL locations for all supported compression formats.
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ENH: return an OrderedDict from read_excel with sheetname=None
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| 2 | 2016-11-03T09:47:21Z | 2016-11-11T10:00:51Z | 2016-11-11T10:00:32Z |
CONTRIBUTOR
| null |
- [x] closes #9930
- [x] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
|
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| 187,030,508 |
MDU6SXNzdWUxODcwMzA1MDg=
| 14,572 |
Floating point accuracy problems in `Timestamp.round`
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| 4 | 2016-11-03T11:07:37Z | 2016-11-03T13:58:26Z | 2016-11-03T11:15:15Z |
NONE
| null |
This is the same problem as described in #14440 but with Timestamp, and the fix should be similar.
Probable source of error:
https://github.com/pandas-dev/pandas/blob/093aa8231eae92ff6cf7ef9564d62289b458aaff/pandas/tslib.pyx#L438
#### A small, complete example of the issue
```python
In [1] import pandas as pd
In [2]: ts=pd.Timestamp('2016-03-06 18:43:45.950000')
In [3]: ts.floor('10L')
Out [3]: Timestamp('2016-03-06 18:43:45.950000128')
```
#### Expected Output
Timestamp('2016-03-06 18:43:45.950000')
#### Output of ``pd.show_versions()``
INSTALLED VERSIONS
------------------
commit: 093aa8231eae92ff6cf7ef9564d62289b458aaff
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0+63.g093aa82
nose: 1.3.7
pip: 9.0.0
setuptools: 28.7.1
Cython: 0.25.1
numpy: 1.11.2
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
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| 14,573 |
DOC: update whatsnew/release notes for 0.19.1
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| 0 | 2016-11-03T11:46:48Z | 2016-11-03T14:17:42Z | 2016-11-03T14:17:42Z |
MEMBER
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| 187,041,639 |
MDU6SXNzdWUxODcwNDE2Mzk=
| 14,574 |
value_counts failed on unique list values
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| 2 | 2016-11-03T12:04:05Z | 2020-11-19T00:45:10Z | 2016-11-03T17:04:11Z |
NONE
| null |
#### When value_counts on different list value, it's ok:
```python
ser1 = pd.Series([[1], [2], [2]])
ser1.value_counts()
[2] 2
[1] 1
dtype: int64
```
#### But, when value_counts on unique list value, it's failed:
```python
ser2 = pd.Series([[1], [1], [1]])
ser2.value_counts()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-64-845402fb8b57> in <module>()
1 ser2 = pd.Series([[1], [1], [1]])
----> 2 ser2.value_counts()
/home/centos/anaconda2/lib/python2.7/site-packages/pandas/core/base.pyc in value_counts(self, normalize, sort, ascending, bins, dropna)
945 from pandas.core.algorithms import value_counts
946 result = value_counts(self, sort=sort, ascending=ascending,
--> 947 normalize=normalize, bins=bins, dropna=dropna)
948 return result
949
/home/centos/anaconda2/lib/python2.7/site-packages/pandas/core/algorithms.pyc in value_counts(values, sort, ascending, normalize, bins, dropna)
276 else:
277 # ndarray path. pass original to handle DatetimeTzBlock
--> 278 keys, counts = _value_counts_arraylike(values, dropna=dropna)
279
280 from pandas import Index, Series
/home/centos/anaconda2/lib/python2.7/site-packages/pandas/core/algorithms.pyc in _value_counts_arraylike(values, dropna)
346 values = com._ensure_object(values)
347 mask = com.isnull(values)
--> 348 keys, counts = htable.value_count_object(values, mask)
349 if not dropna and mask.any():
350 keys = np.insert(keys, 0, np.NaN)
pandas/hashtable.pyx in pandas.hashtable.value_count_object (pandas/hashtable.c:18970)()
pandas/hashtable.pyx in pandas.hashtable.value_count_object (pandas/hashtable.c:18705)()
TypeError: unhashable type: 'list'
```
#### I tried 1.18.1 and 1.19.0
#### Output of ``pd.show_versions()``, 1.18.1
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.36.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
#### Output of ``pd.show_versions()``, 1.19.0
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-45-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
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Update GitHub org from pydata to pandas-dev
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| 8 | 2016-11-03T14:22:42Z | 2016-11-04T11:33:26Z | 2016-11-04T11:33:12Z |
CONTRIBUTOR
| null |
It looks like the repo moved owners from `pydata` to `pandas-dev`. This pull request updates the repository to refer to `pandas-dev/pandas` rather than `pydata/pandas`.
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] tests added / passed
- [ ] whatsnew entry
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| 22 | 2016-11-03T15:38:34Z | 2016-12-14T10:58:02Z | 2016-12-13T23:11:38Z |
CONTRIBUTOR
| null |
Work in progress. Picks up where #13340 left off.
closes #12688
closes #13340
closes #14570
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| 1 | 2016-11-03T15:43:17Z | 2016-11-03T15:53:35Z | 2016-11-03T15:53:28Z |
NONE
| null |
The following code fails
> df = pd.DataFrame(columns=['a','b','c'])
> df.groupby('a',as_index=False).agg({'b':'nunique'})
with an exception
> Traceback (most recent call last):
> File "/Users/ilangadassi/Documents/workspace/python/MyTests/test1.py", line 75, in <module>
> testDfGroup()
> File "/Users/ilangadassi/Documents/workspace/python/MyTests/test1.py", line 71, in testDfGroup
> df.groupby('a',as_index=False).agg({'b':'nunique'})
> File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 3774, in aggregate
> return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
> File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 3272, in aggregate
> result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
> File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 549, in _aggregate
> result = _agg(arg, _agg_1dim)
> File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 500, in _agg
> result[fname] = func(fname, agg_how)
> File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 483, in _agg_1dim
> return colg.aggregate(how, _level=(_level or 0) + 1)
> File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 2660, in aggregate
> return getattr(self, func_or_funcs)(*args, **kwargs)
> File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 3023, in nunique
> res = out if ids[0] != -1 else out[1:]
> IndexError: index 0 is out of bounds for axis 0 with size 0
>
- [ ] closes #xxxx
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
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| 5 | 2016-11-03T16:03:29Z | 2016-11-07T11:48:49Z | 2016-11-03T17:08:16Z |
NONE
| null |
#### A small, complete example of the issue
```python
# Your code here
df = pd.DataFrame(columns=['a','b','c'])
df.groupby('a',as_index=False).agg({'b':'nunique'})
```
#### Expected Output
We shouldn't get an Exception
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
i get the following output
Traceback (most recent call last):
File "/Users/ilangadassi/Documents/workspace/python/MyTests/test1.py", line 75, in <module>
testDfGroup()
File "/Users/ilangadassi/Documents/workspace/python/MyTests/test1.py", line 71, in testDfGroup
df.groupby('a',as_index=False).agg({'b':'nunique'})
File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 3774, in aggregate
return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 3272, in aggregate
result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 549, in _aggregate
result = _agg(arg, _agg_1dim)
File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 500, in _agg
result[fname] = func(fname, agg_how)
File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/base.py", line 483, in _agg_1dim
return colg.aggregate(how, _level=(_level or 0) + 1)
File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 2660, in aggregate
return getattr(self, func_or_funcs)(*args, **kwargs)
File "/Users/ilangadassi/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py", line 3023, in nunique
res = out if ids[0] != -1 else out[1:]
IndexError: index 0 is out of bounds for axis 0 with size 0
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| 187,143,627 |
MDU6SXNzdWUxODcxNDM2Mjc=
| 14,579 |
Non-breaking spaces are printed as whitespace, but handled internally as `\xa0`
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[] | null | 8 | 2016-11-03T17:53:08Z | 2020-05-16T21:00:22Z | null |
CONTRIBUTOR
| null |
If a column name contains a non-breaking space, _pandas_ will print it as normal whitespace, but represent it internally as `\xa0`. So, if you print out the column names, then copy/paste a name into a `.loc[]`, you will get a `KeyError`.
```python
import pandas as pd
df = pd.DataFrame(data={"Space column": [1, 2, 3, 4, 5],
"Non-breaking\xa0space column": [1, 2, 3, 4, 5]})
print(df.columns)
# Index(['Non-breaking space column', 'Space column'], dtype='object')
df.loc[:, "Non-breaking space column"]
# ... KeyError: 'the label [Non-breaking space column] is not in the [columns]' ...
```
For obvious reasons, this error is not helpful for diagnosing the actual problem, because all you can do to check the key is print out the column names again...
Of course, it's not just a problem for column names, but for all strings.
```python
s = pd.Series(data=["Foo bar", "Bar\xa0foo"])
print(s)
# 0 Foo bar
# 1 Bar foo
# dtype: object
print(s.str.contains("Bar foo"))
# 0 False
# 1 False
# dtype: bool
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None</details>
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MDU6SXNzdWUxODcxOTI1NDA=
| 14,580 |
BUG: iloc misbehavior with pd.Series: sometimes returns pd.Categorical instead
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| 2 | 2016-11-03T21:16:37Z | 2016-12-30T21:35:23Z | 2016-12-30T21:35:23Z |
NONE
| null |
#### A small, complete example of the issue
```python
# Your code here
>>> type(pd.Series([1,2,3]).astype('category').iloc[0:1])
<class 'pandas.core.series.Series'>
>>> type(pd.Series([1,2,3]).astype('category').iloc[np.array([0, 1])])
<class 'pandas.core.categorical.Categorical'>
```
#### Expected Output
Both should return a pandas Series object
#### Output of ``pd.show_versions()``
<details>
pandas version = 0.18
</details>
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| 187,204,846 |
MDU6SXNzdWUxODcyMDQ4NDY=
| 14,581 |
Feature request: aggregation without MultiIndex columns
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| 5 | 2016-11-03T22:12:55Z | 2016-11-04T19:58:22Z | 2016-11-03T22:35:34Z |
NONE
| null |
Currently the group-by-aggregation in pandas will create MultiIndex columns if there are multiple operation on the same column. However, this introduces some friction to reset the column names for fast filter and join. (If all operations could be chained together, analytics would be smoother)
```python
df = pd.DataFrame([
['A', 1],
['A', 2],
['A', 3],
['B', 4],
['B', 5],
['B', 6]
], columns=['Key', 'Value'])
df1 = df.groupby('Key') \
.agg({
'Value': {'V1': 'sum', 'V2': 'count'}
})
df1.columns = df1.columns.droplevel() # This line introduce friction
df1.query('V1 > V2')
```
#### Expected Output
It would be great if there is a simple alias function for columns (like the pyspark's implementation), such as
```python
# Just one approach, there may be others more appropriate
df.groupby('Key') \
.agg(
pd.Series.sum('Value').alias('V1'),
pd.Series.count('Value').alias('V2')
) \
.query('V1 > V2')
```
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MDExOlB1bGxSZXF1ZXN0OTIyNjc2ODM=
| 14,582 |
BUG: Improve Error Message for Multi-Char Sep + Quotes in Python Engine
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| 18 | 2016-11-04T00:53:47Z | 2016-12-14T16:53:59Z | 2016-11-25T21:21:03Z |
MEMBER
| null |
If there is a field counts mismatch, check whether a multi-char separator was used in conjunction with quotes. Currently, that setup is not respected for the Python engine and can result in improper line breaks. This PR appends that to the error message if that is a possible explanation.
Closes #13374.
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Added errors{'raise','ignore'} for keys not found in meta for json_normalize
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| 22 | 2016-11-04T01:56:51Z | 2016-12-13T23:21:52Z | 2016-12-13T23:21:47Z |
CONTRIBUTOR
| null |
Continuation of https://github.com/pandas-dev/pandas/pull/14505
Added errors{'raise','ignore'} for keys not found in meta for json_normalize
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Enhancement to guess optimal/compressed datatypes for a dataframe
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[] | null | 2 | 2016-11-04T08:42:44Z | 2019-12-28T05:46:15Z | 2019-12-28T05:46:14Z |
NONE
| null |
In many cases we read tabular data from some source modify it, and write it out to another data destination. In this transfer we have an opportunity to tighten the data representation a bit, for example by changing dtypes or using categoricals.
Often people do this by hand. It might be nice for us to do some of this work for them based on a single pass through the data. This might involve finding min/max of all integer columns, the approximate number of unique elements of all object dtype and integer columns, etc. and returning an appropriate dtype dict that we could optionally pass into an asdtype method.
Originially inspired by [issue from dask.
](https://github.com/dask/dask/issues/1744)
#### A small, complete example of the issue
```python
df = pd.DataFrame({'x': [1, 2, 3], 'y': ['a', 'b', 'c']})
```
#### Expected Output
In [5]: df.dtypes
Out[5]:
x int64
y object
dtype: object
ln [6]: compressed_dtypes(df)
x uint8
y object
dtype: object
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ERR: tz_localize ambiguous ='infer'
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[] | null | 3 | 2016-11-04T10:04:06Z | 2016-11-09T10:36:48Z | 2016-11-09T10:36:02Z |
NONE
| null |
Hello,
a problem concerning DST with change time. My df has a index for the year 2013 each 15 minutes.
```python
df.index.tz_localize('Europe/Zurich',ambiguous = 'infer')
```
output:
AmbiguousTimeError: 2013-10-27 02:00:00
The input DateTimeindex is:
...
'2013-10-27 01:30:00', '2013-10-27 01:45:00',
'2013-10-27 02:00:00', '2013-10-27 02:15:00',
'2013-10-27 02:30:00', '2013-10-27 02:45:00',
'2013-10-27 03:00:00', '2013-10-27 02:15:00',
'2013-10-27 02:30:00', '2013-10-27 02:45:00',
'2013-10-27 03:00:00', '2013-10-27 03:15:00',
'2013-10-27 03:30:00', '2013-10-27 03:45:00',
...
I would like an output DateTimeindex like:
output:
...
'2013-10-27 01:30:00+02:00', '2013-10-27 01:45:00+02:00',
'2013-10-27 02:00:00+02:00', '2013-10-27 02:15:00+02:00',
'2013-10-27 02:30:00+02:00', '2013-10-27 02:45:00+02:00',
'2013-10-27 03:00:00+02:00', '2013-10-27 02:15:00+01:00',
'2013-10-27 02:30:00+01:00', '2013-10-27 02:45:00+01:00',
'2013-10-27 03:00:00+01:00', '2013-10-27 03:15:00+01:00',
'2013-10-27 03:30:00+01:00', '2013-10-27 03:45:00+01:00',
...
Thank you
```python
pd.show_versions()
```
<details>
output:
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_CH.UTF-8
LOCALE: fr_CH.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.2
setuptools: 21.2.1
Cython: None
numpy: 1.11.2
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxODczNDczMzk=
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API: Index.append behaviour with categoricals
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| 1 | 2016-11-04T14:28:37Z | 2020-11-29T15:59:57Z | 2020-11-29T15:59:57Z |
MEMBER
| null |
Follow-up of https://github.com/pandas-dev/pandas/pull/14545.
We had a long discussion on what the behaviour of `concat` should be when you have categorical data: https://github.com/pandas-dev/pandas/pull/13767. In the end, for 0.19.0, we changed the behaviour of raising an error when categories didn't match to returning object dtyped data (only data with identical categories and ordered attributed gives a categorical as result). The table below is a summary of the changes between 0.18.1 and 0.19.0:
**For categorical Series:**
| left | right | append/concat 0.18 | append/concat 0.19.0 |
|---------|-------------------------------------------|---------|------------------------------|
| category | category (identical categories) | category | category |
| category | category (different categories) | error | object |
| category | not category | category | object |
| category | not category (different categories) | category with NaNs | object |
However, we didn't change behaviour of `append` for Indexes (the above append is for series):
**For `CategoricalIndex`:**
| left | right | append 0.18 | append 0.19.0 | append 0.19.1 |
|---------|-------------------------------------------|---------|------------------------------|----|
| category | category (identical categories) | category | category | category |
| category | category (different categories) | error | error | error |
| category | not category | category | category | category |
| category | not category (with other values) | error | error | error |
| not category | category (with other values) | object | error | object
The last line, i.e. the case where the calling Index is not a CategoricalIndex, changed by accident in 0.19.0, and it is this that I corrected for in PR #14545 for 0.19.1.
Questions:
* Do we want the same behaviour for `Index.append` as we now have for `Series.append` with categorical data? This means that the column in the table above becomes 'object' apart from the first row.
* Do we want to make an exception for the case where the values in the 'right' correspond with the categories? (so that `pd.CategoricalIndex(['a', 'b', 'c']).append(pd.Index(['a']))`keeps working)
Changing this to always return object dtype unless for categoricals with indentical categories is easy, but gives a few failures in our test suite. Namely, in some indexing tests (indexing a DataFrame with a CategoricalIndex) there are changes in behaviour because indexing with a non-existing value in the index was performed using `CategoricalIndex.append()`. But this we can workaround in the indexing code of course.
cc @janschulz @sinhrks
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| 187,366,999 |
MDExOlB1bGxSZXF1ZXN0OTIzNjM4NDY=
| 14,587 |
TST: Create compressed salary testing data
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| 18 | 2016-11-04T15:43:04Z | 2016-12-04T18:33:13Z | 2016-11-11T08:53:49Z |
CONTRIBUTOR
| null |
Create compressed versions of the salary dataset for testing #14576.
Rename `salary.table.csv` to `salaries.tsv` because the dataset is tab rather than comma delimited. Remove the word table because it's implied by the extension. Rename `salary.table.gz` to `salaries.tsv.gz`, since compressed files should append to not strip the original extension.
Created new files by running the following commands:
```sh
cd pandas/io/tests/parser/data
bzip2 --keep salaries.tsv
xz --keep salaries.tsv
zip salaries.tsv.zip salaries.tsv
```
There will be CI testing failures since the modified files are not yet in master or on amazon S3. @jorisvandenbossche how do we deal with this?
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PERF: asv for select_dtypes
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| 6 | 2016-11-04T16:57:29Z | 2020-10-05T17:41:30Z | 2020-10-05T17:41:30Z |
NONE
| null |
Why is select_dtypes so slow?
`%timeit [col for col in df.columns if np.issubdtype(df[col].dtype, np.number)]`
453 microsecs per loop
`%timeit df.select_dtypes(include=[np.number])`
4.58 secs per loop
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Series.argmax and Series.idxmax ignore numpy.inf values
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| 1 | 2016-11-04T21:42:23Z | 2016-11-04T22:25:59Z | 2016-11-04T22:25:02Z |
NONE
| null |
#### A small, complete example of the issue
```python
>>> series = pd.Series({'a':3, 'b':7, 'c':np.inf, 'd':6})
>>> series.argmax() # should be c
'b'
>>> series.idxmax() # should be c
'b'
>>> series.max() # this works properly
inf
>>> series.index[np.argmax(np.array(series))] # this work-around works
'c'
```
#### Expected Output
```python
>>> series = pd.Series({'a':3, 'b':7, 'c':np.inf, 'd':6})
>>> series.argmax()
'c'
>>> series.idxmax()
'c'
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
```python
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 23.0.0
Cython: 0.24
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: 0.9.2
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None
```
</details>
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| 14,590 |
IndexableSkiplist - Segmentation fault - 💥
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[] | null | 6 | 2016-11-04T22:16:24Z | 2016-11-04T23:12:14Z | 2016-11-04T22:57:33Z |
NONE
| null |
**python**: 3.5.2
**pandas**: 0.19.1
**system**: Mac v 10.12.1
```python
>>> from pandas._window import IndexableSkiplist
>>> sl = IndexableSkiplist()
>>> len(sl)
0
>>> list(sl)
```
**Segmentation fault: 11** :collision:
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ENH: add fill_value to resample
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| 18 | 2016-11-05T03:38:35Z | 2017-01-21T22:51:23Z | 2017-01-21T22:51:23Z |
CONTRIBUTOR
| null |
- [x] closes #3715
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
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Setting DataFrame of Python objects and MultiIndex columns wth single-element NDFrame inserts list
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| 2 | 2016-11-05T11:03:46Z | 2020-01-21T01:06:10Z | 2020-01-21T01:06:10Z |
MEMBER
| null |
#### A small, complete example of the issue
```python
In [2]: t = pd.DataFrame('a', index=range(2),
columns=pd.MultiIndex.from_product([range(2), range(2)]))
In [3]: t.loc[0, [(0,1)]] = t.loc[0, [(0,1)]]
In [4]: t
Out[4]:
0 1
0 1 0 1
0 a [a] a a
1 a a a a
```
The same happens when providing, rather than a list of indices, a mask with only one ``True`` value.
The above line is an useless example, but this is a problem in in-place operations.
I suspect the fix should not be very complicated, given that everything works smoothly if we have numbers rather than Python objects (the bug instead arises both if we assign a cell of a numbers-only DF to a cell of a DF of objects, _and_ if we do the opposite)
#### Expected Output
Just the original ``t``.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 7a2bcb6605bacea858ec14cfac424898deb568b3
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.7.0-1-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.utf8
LOCALE: it_IT.UTF-8
pandas: 0.19.0+67.g7a2bcb6.dirty
nose: 1.3.7
pip: 8.1.2
setuptools: 28.0.0
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.8.0.dev0+f80669e
xarray: None
IPython: 5.1.0.dev
sphinx: 1.4.8
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2015.7
blosc: None
bottleneck: 1.2.0dev
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.3
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: None
bs4: 4.5.1
html5lib: 0.999
httplib2: 0.9.1
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: 0.2.1
</details>
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MDU6SXNzdWUxODc0OTgwNTc=
| 14,593 |
Swapping levels does not seem to work as advertised (nevermind, one needs to sortlevels as well)
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[] | null | 0 | 2016-11-05T13:48:20Z | 2016-11-05T13:54:11Z | 2016-11-05T13:53:59Z |
CONTRIBUTOR
| null |
Consider this dataframe:
```
spend impressions actions CPA
item_type A Foo P Foo P Foo P Foo
date
2016-10-21 3926.88 1228.58 14121218.00 266168.00 2.00 7.00 1963.44 175.51
2016-10-22 4455.42 1633.45 16243562.00 341964.00 2.00 1.00 2227.71 1633.45
2016-10-23 1606.02 678.07 4846722.00 198431.00 1.00 3.00 1606.02 226.02
2016-10-24 1586.00 494.29 4493480.00 158689.00 4.00 2.00 396.50 247.14
2016-10-25 1583.77 481.33 3021006.00 127491.00 4.00 0.00 395.94 inf
2016-10-26 3145.96 528.68 5077469.00 130744.00 2.00 3.00 1572.98 176.23
2016-10-27 3163.02 1061.60 4577752.00 188609.00 4.00 2.00 790.76 530.80
2016-10-28 4091.34 1097.26 5834854.00 179609.00 0.00 2.00 inf 548.63
2016-10-29 3661.87 866.31 5269912.00 145719.00 2.00 1.00 1830.93 866.31
2016-10-30 3260.80 945.99 4681901.00 161267.00 1.00 2.00 3260.80 472.99
2016-10-31 2466.62 1165.83 3693572.00 204602.00 4.00 1.00 616.66 1165.83
2016-11-01 1184.63 1084.31 2509393.00 196103.00 1.00 1.00 1184.63 1084.31
2016-11-02 1118.49 949.56 2262660.00 165717.00 2.00 1.00 559.24 949.56
2016-11-03 1002.56 1004.88 1678139.00 169733.00 2.00 2.00 501.28 502.44
```
We have:
```
> df.columns
MultiIndex(levels=[['spend', 'impressions', 'actions', 'CPA'], ['A', 'Foo']],
labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
names=[None, 'item_type'])
```
when I do:
```python
fsa_all.swaplevel(axis=1)
```
I expect to get the columns organized as follows:
```
A Foo
spend impressions actions CPA spend impressions actions CPA
item_type ...
```
but instead I get:
```
item_type A Foo A Foo A Foo A Foo
spend spend impressions impressions actions actions CPA CPA
...
```
Is this expected?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 9.0.0
setuptools: 28.7.1
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.2
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.2.0-b1
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
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MDU6SXNzdWUxODc1MzI3MDc=
| 14,594 |
DOC: indexing with combination of row slice and column label
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| 3 | 2016-11-06T00:17:16Z | 2016-11-07T19:57:34Z | 2016-11-07T19:57:34Z |
NONE
| null |
What is the proper way to set value to a column ('widenGT'), for a range of rows (3:7)? How do I write code (specifically for the problem below) that does not generate the SettingWithCopyWarning: "A value is trying to be set on a copy of a slice from a DataFrame?".
In my production code, the behavior are not consistent, value are not set sometimes!!!
#### A small, complete example of the issue
```python
import pandas as pd
import numpy as np
print pd.show_versions()
df = pd.DataFrame(np.ones((10,2)), columns = ['A', 'B'])
print df
df['newColumn'] = 0
# set values using column label, and range of integer indexes
df.newColumn.iloc[3:7] = 2
print df
# set values using column label, and condition on another column
df.loc[df['newColumn'] == 2, 'A'] = 3
print df
```
#### Expected Output
```
A B newColumn
0 1.0 1.0 0
1 1.0 1.0 0
2 1.0 1.0 0
3 1.0 1.0 2
4 1.0 1.0 2
5 1.0 1.0 2
6 1.0 1.0 2
7 1.0 1.0 0
8 1.0 1.0 0
9 1.0 1.0 0
A B newColumn
0 1.0 1.0 0
1 1.0 1.0 0
2 1.0 1.0 0
3 3.0 1.0 2
4 3.0 1.0 2
5 3.0 1.0 2
6 3.0 1.0 2
7 1.0 1.0 0
8 1.0 1.0 0
9 1.0 1.0 0
```
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-63-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
None
</details>
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| 14,595 |
Read_HDF with nrows=[small-number] takes Much Time and RAM
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| 9 | 2016-11-06T00:51:58Z | 2016-11-06T21:33:29Z | 2016-11-06T02:02:07Z |
NONE
| null |
Using Python 3.5.2 and Pandas 0.18.1
I find that sampling a large HDF5 file using the nrows parameter takes far more RAM and time than sampling the first n rows by pulling the first n indices using a where statement.
Minimal Code Example:
```
store = pd.HDFStore('data_path/hdf_store.h5')
select_indices = np.arange(1000)
time1 = time.time()
train_date_hdf = store.select('table_name', where=select_indices)
time2=time.time()
print(time2-time1)
time1 = time.time()
train_date_hdf2 = pd.read_hdf(store, 'table_name', nrows=1000)
time2=time.time()
print(time2-time1)
```
Output is:
> 0.10559678077697754
> 84.43739724159241
In addition to the much longer time taken by using the nrows parameter, this small sample of a large table also spikes my RAM from 20% to 97% on my machine, which has 8GB of RAM. Whereas reading 1000 rows by selecting 1000 rows by index with a where clause doesn't even use 1% of my RAM.
Would there be a downside to having nrows=n be implemented by pulling the first n indices in a where statement by default?
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| 14,596 |
Fix parse empty df
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| 7 | 2016-11-06T02:13:00Z | 2016-11-22T11:24:11Z | 2016-11-22T11:24:11Z |
CONTRIBUTOR
| null |
- [x] closes #14515
- [x] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [x] whatsnew entry
This commit fixes a bug where `read_csv` failed when given a file with a multiindex header and empty content. Because pandas reads index names as a separate line following the header lines, the reader looks for the line with index names in it. If the content of the dataframe is empty, the reader will choke. This bug surfaced after https://github.com/pandas-dev/pandas/issues/6618 stopped writing an extra line after multiindex columns, which led to a situation where pandas could write CSV's that it couldn't then read.
This commit changes that behavior by explicitly checking if the index name row exists, and processing it correctly if it doesn't.
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DOC: Tweak error message to make it slightly more readable / easier to parse
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| 1 | 2016-11-06T18:01:40Z | 2016-11-11T09:03:25Z | 2016-11-11T09:03:19Z |
CONTRIBUTOR
| null |
- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes ``git diff upstream/master | flake8 --diff``
- [ ] whatsnew entry
|
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MDU6SXNzdWUxODc1ODk4OTU=
| 14,598 |
Can't combine (add) two indexes
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| 1 | 2016-11-06T21:18:46Z | 2016-11-07T06:00:41Z | 2016-11-07T05:59:43Z |
NONE
| null |
I would like to interpolate a Series at new values (which itself doesn't appear to be supported), which can be accomplished by reindexing and interpolating. An example of this is
```python
s = pd.Series(np.random.rand(6), index= np.arange(20,140,20))
idx = pd.Index(np.arange(20,140,10))
s.reindex(s.index + idx).interpolate()
```
But the operation `s.index + idx` results in `ValueError: cannot evaluate a numeric op with unequal lengths`.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en_US
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.11.0
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5.2
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
</details>
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BUG in clipboard (linux, python2) with unicode and separator (GH13747)
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| 56 | 2016-11-07T05:45:03Z | 2016-11-18T22:54:12Z | 2016-11-18T22:42:37Z |
CONTRIBUTOR
| null |
- [x] closes #13747, closes #14362, closes #12807, closes #12529
- [x] tests added / passed pandas/io/tests/test_clipboard.py for OSX
- [x] passes ``git diff upstream/master | flake8 --diff``
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Concatenation of category value counts mixes up the index order
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| 11 | 2016-11-07T09:24:01Z | 2020-01-21T01:06:09Z | 2020-01-21T01:06:09Z |
NONE
| null |
Let's say we have two `Series` s1 and s2, which can be the output of the `pd.value_counts()` function, and we want to combine them into one `DataFrame`
```python
s1 = pd.Series([39,6,4], index=pd.CategoricalIndex(['female','male','unknown']))
s2 = pd.Series([2,152,2,242,150], index=pd.CategoricalIndex(['f','female','m','male','unknown']))
pd.DataFrame([s1,s2])
```
The result is
female male unknown f m
0 NaN 39.0 NaN 6.0 4.0
1 2.0 152.0 2.0 242.0 150.0
where the order of categories in the first row is changed.
And the current workaround is
pd.DataFrame([pd.Series(s1.values,index=s1.index.astype(list)),pd.Series(s2.values,index=s2.index.astype(list))])
which gives the correct result
f female m male unknown
0 NaN 39.0 NaN 6.0 4.0
1 2.0 152.0 2.0 242.0 150.0
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None
</details>
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MDU6SXNzdWUxODc2OTk0NTQ=
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DOC: min_itemsize for HDFStore append for encoded strings
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| 6 | 2016-11-07T12:28:22Z | 2019-10-21T19:52:16Z | null |
NONE
| null |
I'm confused about how to preset min_itemsizes for appending to an HDFStore. Say DataFrame a and b in the MWE below is user-provided, so it can contain any character and the encoding is unknown. Appending a works, but appending b fails even though:
```python
In [4]: len('香')
Out[4]: 1
```
So far I simply used str.len().max() on the string columns to the the numbers for min_itemsize, but this does not work in the example here. This MWE is of course simplified, but I guess I'm wondering:
- how does pytables come up with the string length?
- how should I determine the string length? Considering the encoding is unknown, but pytables assumes some encoding / pytables converts the strings to some other object?
In this toy example I could encode the string as utf-8 to get the correct length, but this isn't a general approach:
```python
In [5]: len('香'.encode('utf-8'))
Out[5]: 3
```
#### MWE:
```python
import pandas as pd
a = pd.DataFrame([['a', 'b']], columns = ['A', 'B'])
b = pd.DataFrame([['香', 'b']], columns = ['A', 'B'])
store = pd.HDFStore('/tmp/tmpstore')
store.append('df', a, min_itemsizes={'A': 1, 'B': 1})
store.append('df', b, min_itemsizes={'A': 1, 'B': 1}) # fails
```
#### Expected Output
ValueError: Trying to store a string with len [3] in [values_block_0] column but
this column has a limit of [1]!
Consider using min_itemsize to preset the sizes on these columns
Closing remaining open files:/tmp/tmpstore...done
#### Output of ``pd.show_versions()``
<details>INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.28-2-MANJARO
machine: x86_64
processor:
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_DE.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.0
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.23.5
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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| 187,709,024 |
MDU6SXNzdWUxODc3MDkwMjQ=
| 14,602 |
Total seconds of pandas.NaT should become NaN
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| 7 | 2016-11-07T13:16:16Z | 2018-07-06T20:21:12Z | 2016-11-12T15:47:18Z |
NONE
| null |
I see
```python
In[2]: pandas.tslib.Timedelta.total_seconds(pandas.NaT)
Out[2]: -9223372036.854776
```
#### Expected Output
```python
Out[2]: nan
```
#### Output of ``pd.show_versions()``:
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 42 Stepping 7, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 25.1.6
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.8.0rc1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: 1.4.1
bottleneck: 1.1.0
tables: 3.2.3
numexpr: 2.6.1
matplotlib: 1.5.2
openpyxl: 2.4.0
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.0.14
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
</details>
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| 187,761,449 |
MDU6SXNzdWUxODc3NjE0NDk=
| 14,603 |
All PRs are failing because AppVeyor is reporting a syntax error
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[] | null | 3 | 2016-11-07T16:39:15Z | 2016-11-12T15:46:46Z | 2016-11-12T15:46:42Z |
CONTRIBUTOR
| null |
AppVeyor tests are all failing with a syntax error:
```python
Using c:\python27_64\envs\_build\lib\site-packages
Finished processing dependencies for pandas==0.19.0+69.g270600c
Compiling C:\Python27_64\envs\_build\Lib\site-packages\pip\_vendor\html5lib\treewalkers\etree.py ...
File "C:\Python27_64\envs\_build\Lib\site-packages\pip\_vendor\html5lib\treewalkers\etree.py", line 13
from pip._vendor. import string_types
^
SyntaxError: invalid syntax
```
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| 14,604 |
df.reset_index() fails with a multi index with Nan's
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| 1 | 2016-11-07T20:00:10Z | 2016-11-07T20:03:38Z | 2016-11-07T20:03:03Z |
NONE
| null |
I believe this is the same issue as [#6322 ](https://github.com/pandas-dev/pandas/issues/6322).
The code below returns the error: `IndexError: cannot do a non-empty take from an empty axes.`
```
import pandas as pd
import numpy as np
import random
import string
# Create 12 random strings 3 char long
rndm_strgs = [''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(3)) for i in range(12)]
rndm_strgs[0] = None
rndm_strgs[5] = None
# Make Dataframe
df = pd.DataFrame({'A' : list('pandasisgood'),
'B' : np.nan,
'C' : rndm_strgs,
'D' : np.random.rand(12)})
# Set an Index -> Columns have Nans
df_w_idx = df.set_index(['A','B','C'])
df_w_idx.reset_index()
```
To get the result I wanted:
```
for clmNm in df_w_idx.index.names:
print(clmNm)
df_w_idx[clmNm] = df_w_idx.index.get_level_values(clmNm)
df_w_idx = df_w_idx.reset_index(drop=True).copy()
df_w_idx
```
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| 1 | 2016-11-07T20:42:10Z | 2018-07-06T20:21:12Z | 2016-11-07T21:02:05Z |
NONE
| null |
When the returned dataframe(or series) have the same length of the original dataframe. The index for grouped keys are missing. For example:
a = pd.DataFrame({'l':['a','a','b','b','c'],'x':[1,2,3,4,5]})
a.groupby('l')['x'].apply(lambda r: r)
output:
0 1
1 2
2 3
3 4
4 5
Notice the key 'l' is missing in the output.
This cause some problem if one is not sure whether the returned dataframe or sereires will have the same length.
Best regards
#### A small, complete example of the issue
```python
# Your code here
```
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
</details>
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Error reading an empty CSV with known column names and dtype 'category'
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| 5 | 2016-11-07T20:50:31Z | 2016-12-10T11:02:54Z | 2016-12-10T11:02:54Z |
NONE
| null |
Hello, I've found a corner case where specifying `category` dtypes in `pd.read_csv` causes an error when it ought to return an empty dataframe.
#### A small, complete example of the issue
```python
pd.read_csv(StringIO(''), names=['a'], dtype={'a': 'object'}) #works
pd.read_csv(StringIO(''), names=['a'], dtype={'a': 'category'}) #breaks
```
#### Expected Output
```
---------------------------------------------------------------------------
StopIteration Traceback (most recent call last)
/Users/dspitz/miniconda3/lib/python3.5/site-packages/pandas/io/parsers.py in read(self, nrows)
1506 try:
-> 1507 data = self._reader.read(nrows)
1508 except StopIteration:
pandas/parser.pyx in pandas.parser.TextReader.read (pandas/parser.c:10364)()
pandas/parser.pyx in pandas.parser.TextReader._read_low_memory (pandas/parser.c:11033)()
StopIteration:
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-13-819a0c4e04cb> in <module>()
----> 1 pd.read_csv(StringIO(''), header=None, names=['a'], dtype={'a': 'category'})
/Users/dspitz/miniconda3/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
643 skip_blank_lines=skip_blank_lines)
644
--> 645 return _read(filepath_or_buffer, kwds)
646
647 parser_f.__name__ = name
/Users/dspitz/miniconda3/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
398 return parser
399
--> 400 data = parser.read()
401 parser.close()
402 return data
/Users/dspitz/miniconda3/lib/python3.5/site-packages/pandas/io/parsers.py in read(self, nrows)
936 raise ValueError('skipfooter not supported for iteration')
937
--> 938 ret = self._engine.read(nrows)
939
940 if self.options.get('as_recarray'):
/Users/dspitz/miniconda3/lib/python3.5/site-packages/pandas/io/parsers.py in read(self, nrows)
1513 index, columns, col_dict = _get_empty_meta(
1514 names, self.index_col, self.index_names,
-> 1515 dtype=self.kwds.get('dtype'))
1516
1517 if self.usecols is not None:
/Users/dspitz/miniconda3/lib/python3.5/site-packages/pandas/io/parsers.py in _get_empty_meta(columns, index_col, index_names, dtype)
2803 col_dict = dict((col_name,
2804 np.empty(0, dtype=dtype.get(col_name, np.object)))
-> 2805 for col_name in columns)
2806
2807 return index, columns, col_dict
/Users/dspitz/miniconda3/lib/python3.5/site-packages/pandas/io/parsers.py in <genexpr>(.0)
2803 col_dict = dict((col_name,
2804 np.empty(0, dtype=dtype.get(col_name, np.object)))
-> 2805 for col_name in columns)
2806
2807 return index, columns, col_dict
TypeError: data type "category" not understood
```
Appears the problem is in `_get_empty_meta()` where the `category` dtype is passed along to `np.empty`. I don't know the idiomatic way to construct an empty Categorical series but that is what needs to happen.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: None
pip: 8.1.1
setuptools: 20.3
Cython: 0.24.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.3
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: 0.6.7.None
psycopg2: 2.6.1 (dt dec pq3 ext)
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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tz_localize fails in 0.19.1 even with ambiguous='infer'
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| 9 | 2016-11-07T21:21:20Z | 2016-11-07T22:09:38Z | 2016-11-07T21:51:15Z |
CONTRIBUTOR
| null |
Consider this timeseries:
```
0 2016-11-06 01:19:54.000
1 2016-11-06 01:34:52.000
2 2016-11-06 01:35:36.000
3 2016-11-06 01:28:25.000
4 2016-11-06 01:59:32.000
5 2016-11-06 01:26:53.000
6 2016-11-06 01:52:44.000
7 2016-11-06 01:07:09.000
8 2016-11-06 01:35:03.000
9 2016-11-06 01:11:19.000
10 2016-11-06 01:31:28.000
11 2016-11-06 01:10:16.000
12 2016-11-06 01:04:55.000
13 2016-11-06 01:07:16.000
14 2016-11-06 01:09:35.000
15 2016-11-06 01:11:50.000
16 2016-11-06 01:15:03.000
17 2016-11-06 01:03:23.000
18 2016-11-06 01:20:27.000
19 2016-11-06 01:17:20.000
20 2016-11-06 01:14:44.000
21 2016-11-06 01:14:32.000
22 2016-11-06 01:20:23.000
23 2016-11-06 01:31:55.000
24 2016-11-06 01:32:45.000
25 2016-11-06 01:32:52.716
26 2016-11-06 01:00:55.000
27 2016-11-06 01:03:52.262
28 2016-11-06 01:19:59.000
29 2016-11-06 01:32:36.000
30 2016-11-06 01:48:56.000
31 2016-11-06 01:49:06.000
32 2016-11-06 01:29:11.170
33 2016-11-06 01:29:51.745
34 2016-11-06 01:30:09.560
35 2016-11-06 01:38:12.432
36 2016-11-06 01:45:23.631
37 2016-11-06 01:51:52.046
38 2016-11-06 01:52:36.318
39 2016-11-06 01:10:58.000
Name: ts, dtype: datetime64[ns]
```
When I try to do:
```
> temp.dt.tz_localize('US/Eastern', ambiguous='infer')
```
I get:
```
AmbiguousTimeError: There are 13 dst switches when there should only be 1.
```
Output of **`pd.show_versions()`**
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.0
setuptools: 27.1.0.post20161104
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.2
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.2.0-b1
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
```
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