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Class label colours with parallel coordinates
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| 0 | 2017-04-05T18:45:33Z | 2017-04-14T13:30:24Z | 2017-04-14T13:30:24Z |
CONTRIBUTOR
| null |
#### Problem description
The parallel coordinates function seems to currently assume that the class labels don't have an inherent ordering. I'd like to provide a sort option or a class-to-colour mapping to capture this. My specific use case is situations where the label are quantized buckets and a smooth colour change between buckets make the final plot easy to visualize.
The current function zips the available classes and the colour_values without any sorting.
I've identified where in the current '_parallel_coordinates' function a change should be made and would like to work on this.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.2.45-0.6.wd.865.49.315.metal1.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.3.2
Cython: None
numpy: 1.12.1
scipy: 0.19.0
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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| 15,909 |
DEPR: correct locations to access public json/parser objects in depr message
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| 12 | 2017-04-05T19:44:53Z | 2017-04-09T08:50:21Z | 2017-04-07T19:04:32Z |
MEMBER
| null |
Another one remaining is `pd.parser.na_values`
```
In [2]: pd.parser.na_values
/home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning: pandas.parser.na_values is deprecated.
Please use pandas.io.libparsers.na_values instead.
#!/home/joris/miniconda3/envs/dev/bin/python
Out[2]:
{numpy.bool_: 255,
dtype('bool'): 255,
numpy.uint64: 18446744073709551615,
numpy.int64: -9223372036854775808,
dtype('int16'): -32768,
dtype('uint64'): 18446744073709551615,
numpy.int8: -128,
dtype('int32'): -2147483648,
numpy.uint8: 255,
dtype('int8'): -128,
numpy.object_: nan,
numpy.float64: nan,
numpy.uint32: 4294967295,
dtype('O'): nan,
numpy.int32: -2147483648,
dtype('uint32'): 4294967295,
dtype('uint16'): 65535,
dtype('uint8'): 255,
dtype('int64'): -9223372036854775808,
dtype('float64'): nan,
numpy.uint16: 65535,
numpy.int16: -32768}
```
I don't think we should refer to `libparsers`, but for now it is not imported in `pandas.io.parsers` as alternative. I could add it there, but the question is maybe whether this is actually useful for users? Maybe we should say that it will be removed without giving alternative?
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BUG: read_csv skipfooter fails with invalid quoted line
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| 13 | 2017-04-05T20:09:10Z | 2019-09-03T17:59:06Z | 2017-04-06T13:31:32Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
from pandas.compat import StringIO
pd.read_csv(StringIO('''Date,Value
1/1/2012,100.00
1/2/2012,102.00
"a quoted junk row"morejunk'''), skipfooter=1)
Out[21]
ERROR:root:An unexpected error occurred while tokenizing input
The following traceback may be corrupted or invalid
The error message is: ('EOF in multi-line string', (1, 20))
---------------------------------------------------------------------------
Error Traceback (most recent call last)
<ipython-input-34-d8dff6b9f4a7> in <module>()
2 1/1/2012,100.00
3 1/2/2012,102.00
----> 4 "a quoted junk row" '''), skipfooter=1)
C:\Users\chris.bartak\Documents\python-dev\pandas\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)
651 skip_blank_lines=skip_blank_lines)
652
--> 653 return _read(filepath_or_buffer, kwds)
654
655 parser_f.__name__ = name
C:\Users\chris.bartak\Documents\python-dev\pandas\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
404
405 try:
--> 406 data = parser.read()
407 finally:
408 parser.close()
C:\Users\chris.bartak\Documents\python-dev\pandas\pandas\io\parsers.py in read(self, nrows)
977 raise ValueError('skipfooter not supported for iteration')
978
--> 979 ret = self._engine.read(nrows)
980
981 if self.options.get('as_recarray'):
C:\Users\chris.bartak\Documents\python-dev\pandas\pandas\io\parsers.py in read(self, rows)
2066 def read(self, rows=None):
2067 try:
-> 2068 content = self._get_lines(rows)
2069 except StopIteration:
2070 if self._first_chunk:
C:\Users\chris.bartak\Documents\python-dev\pandas\pandas\io\parsers.py in _get_lines(self, rows)
2717 while True:
2718 try:
-> 2719 new_rows.append(next(source))
2720 rows += 1
2721 except csv.Error as inst:
Error: ',' expected after '"'
```
#### Problem description
This error only happens if the last row has quoting, and is invalid - e.g. delete the `morejunk` above and it does not error.
#### Expected Output
successful parse
pandas 0.19.2
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Produce representative dataframes for benchmarking
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| 0 | 2017-04-05T21:07:45Z | 2017-11-26T17:45:02Z | null |
CONTRIBUTOR
| null |
It would be convenient to have a canonical set of dataframes for use in testing and/or benchmarking. Ideally this would be a set of named dataframes that represented common forms of data like the following:
1. Random floating point data
2. Random integer data
3. Strings with low entropy
4. Strings with high entropy
5. Mostly sorted datetimes
6. ...
These could then be used either within Pandas or in other libraries for benchmarks. Having a consistent set of dataframes would probably aid consistent benchmarking.
Additionally if this was then separately arranged into pytest fixture we could imagine setting things up and tearing things down in a way that made benchmarking more consistent (such as controlling garbage collection), though this may be a separate endeavor. It would be nice to have access to the dataframes outside of the context of PyTest as well
cc @jreback @wesm @cpcloud
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MDU6SXNzdWUyMTk3MjY5Nzg=
| 15,912 |
Pandas dataframe + groupby = failed zooming for x-axis ticks
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| 2 | 2017-04-05T21:54:48Z | 2017-04-06T07:36:04Z | 2017-04-06T07:35:45Z |
NONE
| null |
This was posted onto stackoverflow but it seems more like a bug than anything:
http://stackoverflow.com/questions/43242246/pandas-dataframe-groupby-failed-zooming-for-x-axis-ticks
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MDExOlB1bGxSZXF1ZXN0MTE0NTA3NzI1
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BUG: Standardize malformed row handling in Python engine
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| 3 | 2017-04-06T01:53:36Z | 2017-04-06T14:53:25Z | 2017-04-06T13:31:32Z |
MEMBER
| null |
Wrap `next(source)` when `source` is a file-buffer object to allow for more uniform error message displaying in the Python engine. Closes #15910.
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| 15,914 |
read_excel does not work on excel file binary text or buffered binary text object
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| 2 | 2017-04-06T06:43:08Z | 2020-01-03T11:55:36Z | 2020-01-03T11:55:36Z |
NONE
| null |
Python 3.5, Pandas 0.19.2
I have the following excel file, and I am trying to read its binary content, and then have read_excel read the binary content in, except this is not working, and there may be a bug somewhere, as I have specified the engine for reading.
>>> import pandas as pd
>>> f = open("Test_Prom_Data.xlsx", "rb")
>>> df = pd.read_excel(f.read(), engine = "xlrd")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/alexander/anaconda3/lib/python3.5/site-packages/pandas/io/excel.py", line 191, in read_excel
io = ExcelFile(io, engine=engine)
File "/home/alexander/anaconda3/lib/python3.5/site-packages/pandas/io/excel.py", line 251, in __init__
raise ValueError('Must explicitly set engine if not passing in'
ValueError: Must explicitly set engine if not passing in buffer or path for io.
[Test_Prom_Data.xlsx](https://github.com/pandas-dev/pandas/files/901685/Test_Prom_Data.xlsx)
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DOC: Fix a typo in travis.yml
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DOC: Fix a typo in indexing.rst
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BLD: update merge script to update on github
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[] | null | 15 | 2017-04-06T08:37:15Z | 2017-04-07T18:58:29Z | 2017-04-07T18:58:21Z |
MEMBER
| null |
@jreback @TomAugspurger This adds the ability to checkout a PR, do a small fixup or rebase, and push changes to github.
I now added it as an additional question whether to update or merge, but can also make to separate scripts of it for convenience.
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Fix a typo
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| 1 | 2017-04-06T09:36:14Z | 2017-04-06T11:34:40Z | 2017-04-06T11:34:25Z |
CONTRIBUTOR
| null |
Simple fix.
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DOC: timeseries.rst floating point precision (#15817)
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CONTRIBUTOR
| null |
- [x] closes #15817
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BUG: partial slicing with a Timestamp on PeriodIndex
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| 10 | 2017-04-06T11:39:06Z | 2020-05-11T02:17:43Z | null |
NONE
| null |
An example: a PeriodicIndex with a freq of 300S. First second works.
Remaining portion of the interval raises a KeyError.
```
>>> DR = pd.period_range(datetime.datetime.now(), freq='300S',periods=22)
>>> S = pd.Series( [0.0]*22,index=DR)
>>> now = datetime.datetime.now()
>>> S[now]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas/core/series.py", line 603, in __getitem__
result = self.index.get_value(self, key)
File "/Library/Python/2.7/site-packages/pandas/tseries/period.py", line 757, in get_value
return com._maybe_box(self, self._engine.get_value(s, key),
File "pandas/index.pyx", line 98, in pandas.index.IndexEngine.get_value (pandas/index.c:3557)
File "pandas/index.pyx", line 106, in pandas.index.IndexEngine.get_value (pandas/index.c:3240)
File "pandas/index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas/index.c:4279)
File "pandas/src/hashtable_class_helper.pxi", line 404, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8564)
File "pandas/src/hashtable_class_helper.pxi", line 410, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8508)
KeyError: 1491422454
>>> S.index[0]
Period('2017-04-05 20:00:23', '300S')
>>> S[datetime.datetime(2017,4,5,20,0,23)]
0.0
>>> S[datetime.datetime(2017,4,5,20,0,23,999)]
0.0
```
```
>>> DR = pd.period_range(datetime.datetime.now(), freq='5T',periods=22)
>>> S = pd.Series( [0.0]*22,index=DR)
>>> now = datetime.datetime.now()
>>> S[now]
0.0
>>> S.index[0]
Period('2017-04-05 20:03', '5T')
>>> S[datetime.datetime(2017,4,5,20,3,23,999)]
0.0
>>> S[datetime.datetime(2017,4,5,20,4,23,999)]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Library/Python/2.7/site-packages/pandas/core/series.py", line 603, in __getitem__
result = self.index.get_value(self, key)
File "/Library/Python/2.7/site-packages/pandas/tseries/period.py", line 757, in get_value
return com._maybe_box(self, self._engine.get_value(s, key),
File "pandas/index.pyx", line 98, in pandas.index.IndexEngine.get_value (pandas/index.c:3557)
File "pandas/index.pyx", line 106, in pandas.index.IndexEngine.get_value (pandas/index.c:3240)
File "pandas/index.pyx", line 154, in pandas.index.IndexEngine.get_loc (pandas/index.c:4279)
File "pandas/src/hashtable_class_helper.pxi", line 404, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8564)
File "pandas/src/hashtable_class_helper.pxi", line 410, in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8508)
KeyError: 24857044
```
<details>
pandas.version
u'0.19.2'
</details>
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| 219,878,018 |
MDExOlB1bGxSZXF1ZXN0MTE0NTg2MTUz
| 15,921 |
DOC: Added statsmodels to show_versions
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| 5 | 2017-04-06T12:10:35Z | 2017-05-29T20:30:57Z | 2017-04-06T13:51:17Z |
CONTRIBUTOR
| null |
xref https://github.com/statsmodels/statsmodels/issues/3580
|
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| 1 | 2017-04-06T15:01:58Z | 2017-04-06T16:41:08Z | 2017-04-06T16:41:07Z |
CONTRIBUTOR
| null |
xref #15865
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BUG pd.get_dummies does not propagate `NaN` correctly
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| 18 | 2017-04-06T15:54:55Z | 2017-04-07T00:38:34Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [17]: s
Out[17]:
0 a
1 b
2 c
3 NaN
dtype: object
In [18]: pd.get_dummies(s)
Out[18]:
a b c
0 1 0 0
1 0 1 0
2 0 0 1
3 0 0 0
```
#### Problem description
The current implementation does not propagate `NaN` correctly. `NaN` means the data is missing. So if you turn missing data to all zeros when one-hot encoding, you are asserting that the proper label is none of the labels you have. In reality, the proper label could be one of the labels you have, you just do not know the proper label.
I realize people use the `dummy_na` option here, but if that option is not passed, then the output should have the `NaN`s put in the right spot.
#### Expected Output
It should propagate the NaNs.
```python
In [18]: pd.get_dummies(s)
Out[18]:
a b c
0 1 0 0
1 0 1 0
2 0 0 1
3 NaN NaN NaN
```
#### Output of ``pd.show_versions()``
<details>
In [19]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.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: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 33.1.1.post20170320
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.5
boto: 2.46.1
pandas_datareader: None
</details>
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BUG: use entire size of DatetimeTZBlock when coercing result (#15855)
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| 3 | 2017-04-06T17:41:46Z | 2017-04-07T21:36:37Z | 2017-04-07T20:37:41Z |
CONTRIBUTOR
| null |
- [x] closes #15855
- [x] tests added / passed
- [x] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
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ENH: Support malformed row handling in Python engine
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| 6 | 2017-04-06T18:28:34Z | 2017-04-07T19:58:05Z | 2017-04-07T19:47:30Z |
MEMBER
| null |
Support `warn_bad_lines` and `error_bad_lines` for the Python engine.
xref #12686 (master tracker)
Inspired by <a href="https://github.com/pandas-dev/pandas/issues/15910#issuecomment-291998838">#15910 (comment)</a>
In addition, the Python parser now raises `pandas.error.ParserError`, which is in line with what the C engine would do.
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NONE
| null |
- [x] closes #15923
- [x] tests added / passed
- [x] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
Closes #15923
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Default iteration behavior of the DataFrame
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| 2 | 2017-04-06T21:04:23Z | 2017-04-06T21:18:04Z | 2017-04-06T21:09:15Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
for x in my_data_frame:
print rep(x)
```
#### Problem description
The current behavior is to print the columns during the normal iteration of a data frame. However, most of the time when actually using a DataFrame we want to iterate over the rows. How useful in data analysis is it to iterate over the columns? In my nearly 2 years of using pandas, not once have I had to do this. If one needs this, ```my_data_frame.columns``` works just fine.
In addition, there is no ```my_data_frame.rows``` to work on. The only good way to iterate over rows is to use iterrows:
```python
for index, row in my_data_frame.iterrows():
print row
```
This seems like something that should be much more obvious than it is, and to even find that answer, one would pretty much need to google and find the stack overflow answer.
Is there any way we could iterate over the rows when using the python ```in``` syntax? I think this is a much more obvious thing to do during iteration than going through the columns for most users.
#### Expected Output
Row output using the python ```in``` keyword.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
loaded rc file /Users/jadolfbr/.matplotlib/matplotlibrc
matplotlib version 1.5.1
verbose.level helpful
interactive is False
platform is darwin
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_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 20.3.1
Cython: None
numpy: 1.11.1
scipy: 0.13.0b1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 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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| 15,928 |
AttributeError when slicing a datetime MultiIndex level
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| 5 | 2017-04-06T21:07:33Z | 2020-10-20T02:08:30Z | 2020-10-20T02:08:30Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: df = pd.read_csv(pd.__path__[0]+'/tests/tools/data/quotes2.csv', parse_dates=['time']).set_index(['ticker', 'time']).sort_index()
In [3]: df.loc['AAPL'].loc[slice('2016-05-25 13:30:00'), :].head()
Out[3]:
bid ask
time
2016-05-25 13:30:00.075 98.55 98.56
2016-05-25 13:30:00.076 98.55 98.56
2016-05-25 13:30:00.076 98.55 98.56
2016-05-25 13:30:00.076 98.55 98.56
2016-05-25 13:30:00.080 98.55 98.56
In [4]: df.loc[('AAPL', slice('2016-05-25 13:30:00')), :].head()
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-4-552cfee2e501> in <module>()
----> 1 df.loc[('AAPL', slice('2016-05-25 13:30:00')), :].head()
/home/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1322 except (KeyError, IndexError):
1323 pass
-> 1324 return self._getitem_tuple(key)
1325 else:
1326 key = com._apply_if_callable(key, self.obj)
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_tuple(self, tup)
833 def _getitem_tuple(self, tup):
834 try:
--> 835 return self._getitem_lowerdim(tup)
836 except IndexingError:
837 pass
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_lowerdim(self, tup)
945 # we may have a nested tuples indexer here
946 if self._is_nested_tuple_indexer(tup):
--> 947 return self._getitem_nested_tuple(tup)
948
949 # we maybe be using a tuple to represent multiple dimensions here
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_nested_tuple(self, tup)
1020
1021 current_ndim = obj.ndim
-> 1022 obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
1023 axis += 1
1024
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1542 # nested tuple slicing
1543 if is_nested_tuple(key, labels):
-> 1544 locs = labels.get_locs(key)
1545 indexer = [slice(None)] * self.ndim
1546 indexer[axis] = locs
/home/nobackup/repo/pandas/pandas/indexes/multi.py in get_locs(self, tup)
2173 # a slice, include BOTH of the labels
2174 indexer = _update_indexer(_convert_to_indexer(
-> 2175 self._get_level_indexer(k, level=i, indexer=indexer)),
2176 indexer=indexer)
2177 else:
/home/nobackup/repo/pandas/pandas/indexes/multi.py in _get_level_indexer(self, key, level, indexer)
2054 # note that the stop ALREADY includes the stopped point (if
2055 # it was a string sliced)
-> 2056 return convert_indexer(start.start, stop.stop, step)
2057
2058 elif level > 0 or self.lexsort_depth == 0 or step is not None:
AttributeError: 'int' object has no attribute 'start'
```
#### Problem description
The two calls should be perfectly equivalent.
By the way, if I swap the levels, I get a different error:
``` python
In [16]: df = pd.read_csv(pd.__path__[0]+'/tests/tools/data/quotes2.csv', parse_dates=['time']).set_index(['time', 'ticker']).sort_index()
In [26]: df.loc[:'2016-05-25 13:30:00'].head()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-a0298286991a> in <module>()
----> 1 df.loc[:'2016-05-25 13:30:00'].head()
/home/nobackup/repo/pandas/pandas/core/indexing.py in __getitem__(self, key)
1325 else:
1326 key = com._apply_if_callable(key, self.obj)
-> 1327 return self._getitem_axis(key, axis=0)
1328
1329 def _is_scalar_access(self, key):
/home/nobackup/repo/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis)
1503 if isinstance(key, slice):
1504 self._has_valid_type(key, axis)
-> 1505 return self._get_slice_axis(key, axis=axis)
1506 elif is_bool_indexer(key):
1507 return self._getbool_axis(key, axis=axis)
/home/nobackup/repo/pandas/pandas/core/indexing.py in _get_slice_axis(self, slice_obj, axis)
1353 labels = obj._get_axis(axis)
1354 indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,
-> 1355 slice_obj.step, kind=self.name)
1356
1357 if isinstance(indexer, slice):
/home/nobackup/repo/pandas/pandas/indexes/base.py in slice_indexer(self, start, end, step, kind)
3226 """
3227 start_slice, end_slice = self.slice_locs(start, end, step=step,
-> 3228 kind=kind)
3229
3230 # return a slice
/home/nobackup/repo/pandas/pandas/indexes/multi.py in slice_locs(self, start, end, step, kind)
1741 # This function adds nothing to its parent implementation (the magic
1742 # happens in get_slice_bound method), but it adds meaningful doc.
-> 1743 return super(MultiIndex, self).slice_locs(start, end, step, kind=kind)
1744
1745 def _partial_tup_index(self, tup, side='left'):
/home/nobackup/repo/pandas/pandas/indexes/base.py in slice_locs(self, start, end, step, kind)
3413 end_slice = None
3414 if end is not None:
-> 3415 end_slice = self.get_slice_bound(end, 'right', kind)
3416 if end_slice is None:
3417 end_slice = len(self)
/home/nobackup/repo/pandas/pandas/indexes/multi.py in get_slice_bound(self, label, side, kind)
1712 if not isinstance(label, tuple):
1713 label = label,
-> 1714 return self._partial_tup_index(label, side=side)
1715
1716 def slice_locs(self, start=None, end=None, step=None, kind=None):
/home/nobackup/repo/pandas/pandas/indexes/multi.py in _partial_tup_index(self, tup, side)
1770 start = start + section.searchsorted(idx, side='left')
1771 else:
-> 1772 return start + section.searchsorted(idx, side=side)
1773
1774 def get_loc(self, key, method=None):
TypeError: unorderable types: int() > slice()
```
... while everything works fine if there is one level only.
#### Expected Output
The same as ``Out[3]:``.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: 4502e82083f4e253630588665a4fc6002c4f32ed
python: 3.5.3.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+743.g4502e8208
pytest: 3.0.6
pip: 9.0.1
setuptools: 33.1.1
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
xarray: 0.9.1
IPython: 5.1.0.dev
sphinx: 1.4.9
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: 0.2.1
</details>
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CLN: algos
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| 2 | 2017-04-06T21:09:42Z | 2017-04-25T10:47:05Z | 2017-04-07T00:16:56Z |
CONTRIBUTOR
| null |
- clean up select_n algos
- clean ensure_data
closes #15903
should make this much simpler going forward. All dtype conversions are now centralized.
Added some doc-strings as well.
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| 220,041,382 |
MDU6SXNzdWUyMjAwNDEzODI=
| 15,930 |
Resetting Index on slice
|
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| 7 | 2017-04-06T21:38:41Z | 2017-04-06T23:42:31Z | 2017-04-06T22:21:21Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# Your code here
df = data_index[data_index['ntorsions'] == 2]
```
#### Problem description
When slicing a dataframe, the index is not reset by default. This becomes an issue if you want to output that dataframe, combine that dataframe with other dataframes (good luck with that), or output the dataframe without two index columns.
Fixing this will not break code in the wild.
#### Expected Output
Index being correct - without the need to manually call reset_index over and over again. This is much more intuitive to end users.
-> At end of slice, call reset_index(drop = True) on the returned dataframe or current dataframe if you are slicing in-place.
#### Output of ``pd.show_versions()``
<details>
loaded rc file /Users/jadolfbr/.matplotlib/matplotlibrc
matplotlib version 1.5.1
verbose.level helpful
interactive is False
platform is darwin
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_US.UTF-8
pandas: 0.18.1
nose: 1.3.7
pip: 9.0.1
setuptools: 20.3.1
Cython: None
numpy: 1.11.1
scipy: 0.13.0b1
statsmodels: 0.6.1
xarray: None
IPython: 4.1.2
sphinx: None
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 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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| 15,931 |
DEPR: deprecate relableling dicts in groupby.agg
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| 27 | 2017-04-07T02:44:53Z | 2017-11-30T20:22:28Z | 2017-04-13T10:18:04Z |
CONTRIBUTOR
| null |
pre-curser to #14668
This is basically in the whatsnew, but:
```
In [1]: df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
...: 'B': range(5),
...: 'C':range(5)})
...: df
...:
Out[1]:
A B C
0 1 0 0
1 1 1 1
2 1 2 2
3 2 3 3
4 2 4 4
```
This is good; multiple aggregations on a dataframe with a dict-of-lists
```
In [2]: df.groupby('A').agg({'B': ['sum', 'max'],
...: 'C': ['count', 'min']})
...:
Out[2]:
B C
sum max count min
A
1 3 2 3 0
2 7 4 2 3
```
This is a dict on a grouped Series -> deprecated
```
In [3]: df.groupby('A').B.agg({'foo': 'count'})
FutureWarning: using a dictionary on a Series for aggregation
is deprecated and will be removed in a future version
Out[3]:
foo
A
1 3
2 2
```
Further this has to go as well, a nested dict that does renaming.
Note once we fix https://github.com/pandas-dev/pandas/issues/4160 (renaming with a level); the following becomes almost trivial to rename in-line.
```
In [4]: df.groupby('A').agg({'B': {'foo': ['sum', 'max']},
'C': {'bar': ['count', 'min']}})
FutureWarning: using a dictionary on a Series for aggregation
is deprecated and will be removed in a future version
Out[4]:
foo bar
sum max count min
A
1 3 2 3 0
2 7 4 2 3
```
Note: I will fix this message (as it doesn't actually apply here)
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| 2 | 2017-04-07T02:55:59Z | 2017-04-07T12:04:54Z | 2017-04-07T12:03:55Z |
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| 2 | 2017-04-07T04:06:09Z | 2017-04-07T12:09:00Z | 2017-04-07T12:08:57Z |
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MDExOlB1bGxSZXF1ZXN0MTE0NzQxMTY0
| 15,934 |
BUG: Correct Timestamp localization with tz near DST (#11481)
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| 4 | 2017-04-07T04:19:44Z | 2017-12-20T02:00:29Z | 2017-04-08T21:59:06Z |
MEMBER
| null |
- [x] closes #11481, #15777
- [x] tests added / passed
- [x] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
Seemed that the `Timestamp` constructor with a string incorrectly localized a naive time with `tz_convert_single` instead of `tz_localize_to_utc` which is similar to how `DatetimeIndex` localizes.
Now the `Timestamp` will raise an error if an ambiguous combination of string and timezone is passed. Should this be the default behavior?
I had to slightly change 2 existing tests. `test_timestamp_to_datetime_tzoffset` used a now ambiguous timestamp and `test_setitem_with_tz_dst` had an incorrect expected timestamp.
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ENH: Minor change to parallel_coordinates (#15908)
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| 10 | 2017-04-07T05:17:44Z | 2017-04-14T13:30:30Z | 2017-04-14T13:30:24Z |
CONTRIBUTOR
| null |
Add option to sort class lables, add to test
- [x] closes #15908
- [x] tests added / passed
- [x] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
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| 7 | 2017-04-07T13:10:40Z | 2017-04-16T18:28:33Z | 2017-04-16T18:28:33Z |
MEMBER
| null |
Currently, you get the following message when you try to access one of the `pandas.lib` functions:
```
In [1]: pd.lib.Timedelta
/home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning: pandas.lib.Timedelta is deprecated.
Please use pandas._libs.lib.Timedelta instead.
#!/home/joris/miniconda3/envs/dev/bin/python
Out[1]: pandas._libs.tslib.Timedelta
In [1]: pd.lib.is_bool
/home/joris/miniconda3/envs/dev/bin/ipython:1: FutureWarning: pandas.lib.is_bool is deprecated.
Please use pandas._libs.lib.is_bool instead.
#!/home/joris/miniconda3/envs/dev/bin/python
Out[1]: <function pandas._libs.lib.is_bool>
```
(apart from `infer_dtypes`, for which there is already another redirect).
We shouldn't recommend to use `pandas._lib.lib`.
For some there is a public alternative (like `Timestamp`, `Timedelta`), others are probably just private and shouldn't have an alternative.
We could list all of them as `removals` and `moved` in `lib = _DeprecatedModule(..)`, but that will be a long list. Or we could just don't provide an alternative import path by default, and only include some specific ones for which there is an alternative.
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BUG: MemoryError on reading big HDF5 files
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| 17 | 2017-04-07T13:15:16Z | 2019-10-21T19:52:17Z | null |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
store = pd.get_store('big1.h5')
i = 0
for df in store.select('/MeasurementSamples', chunksize=100):
i += 1
print(i)
store.close()
```
Result:
```
Traceback (most recent call last):
File "memerror.py", line 6, in <module>
for df in store.select(experiment, chunksize=100):
File "/home/chwalisz/Code/ext_tools/pandas/pandas/io/pytables.py", line 721, in select
return it.get_result()
File "/home/chwalisz/Code/ext_tools/pandas/pandas/io/pytables.py", line 1409, in get_result
self.coordinates = self.s.read_coordinates(where=self.where)
File "/home/chwalisz/Code/ext_tools/pandas/pandas/io/pytables.py", line 3652, in read_coordinates
coords = self.selection.select_coords()
File "/home/chwalisz/Code/ext_tools/pandas/pandas/io/pytables.py", line 4718, in select_coords
return np.arange(start, stop)
MemoryError
Closing remaining open files:big1.h5...done
```
#### Problem description
I'm not able to iterate over the chunks of file when the index array is to big and cannot fit into memory. I can also mention that I'm able to view the data with `ViTables` (that use `PyTables` internally to load data).
I'm using more less following code to create file (writing to it long enough to have 20GB of data).
```python
import tables as tb
class FreqSample(tb.IsDescription):
tsf = tb.Int64Col(dflt=-1) # [us] TSF value ticks in micro seconds
timestamp = tb.Int64Col() # [ns] Epoch time
frequency = tb.Float64Col()
power = tb.Float64Col()
h5filters = tb.Filters(complib='blosc', complevel=5)
h5file = tb.open_file(fname, mode="a",
title=title,
filters=h5filters)
tab = h5file.create_table('/Measurement', 'a', FreqSample)
try:
while True:
row = tab.row
row['tsf'] = 1
row['timestamp'] = 2
row['frequency'] = 3
row['power'] = 4
row.append()
except:
pass
tab.autoindex = True
tab.flush()
h5file.close()
```
#### Expected Output
I would expect the above code prints number of chunks.
#### Output of ``pd.show_versions()``
<details>
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.10-040910-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+739.g7b82e8b
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.4
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.2
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.8
pymysql: None
psycopg2: None
jinja2: 2.9.5
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
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MDU6SXNzdWUyMjAyMTE3NDk=
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BUG: invalid construction from repr of dt-aware index
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| 1 | 2017-04-07T13:20:31Z | 2018-09-21T23:49:17Z | 2018-09-21T23:49:17Z |
CONTRIBUTOR
| null |
thought we had an issue about this....
The data is converted from string and is tz-aware, then the dtype convert is *also* applied.
```
In [1]: dt = pd.date_range('20160101', periods=1, tz='US/Eastern')
In [2]: dt
Out[2]: DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq='D')
In [3]: r = DatetimeIndex(['2016-01-01 00:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq='D')
In [4]: r
Out[4]: DatetimeIndex(['2016-01-01 05:00:00-05:00'], dtype='datetime64[ns, US/Eastern]', freq='D')
In [5]: dt.equals(r)
Out[5]: False
```
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| 20 | 2017-04-07T13:21:42Z | 2017-04-09T17:22:28Z | 2017-04-09T15:28:52Z |
CONTRIBUTOR
| null |
closes #9346
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| 1 | 2017-04-07T14:02:46Z | 2017-04-07T15:23:01Z | 2017-04-07T15:21:05Z |
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[] | null | 2 | 2017-04-07T17:31:33Z | 2017-04-08T00:40:39Z | 2017-04-07T22:42:30Z |
MEMBER
| null |
Adds documentation for all internal functions in `types/common.py`
In addition, caught a bug in which some functions calling `_get_dtype` were not catching the `TypeError`. Documented those functions along the way too.
Partially addresses #15895.
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MDU6SXNzdWUyMjAyOTczNTY=
| 15,942 |
to_datetime drops 00:00:00 from output
|
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| 5 | 2017-04-07T18:47:58Z | 2017-04-07T19:51:32Z | 2017-04-07T18:57:15Z |
NONE
| null |
####
```python
import pandas as pd
print (pd.__version__)
data = pd.DataFrame({'Mbps':[10.0], 'Region':['NA'], 'CID':['gsdfd']})
x1 = pd.to_datetime('2017-02-02 00:00:00', format='%Y-%m-%d %H:%M:%S')
data['time1'] = x1
print (x1)
print (data.time1)
x2 = pd.to_datetime('2017-02-02 00:00:05', format='%Y-%m-%d %H:%M:%S')
data['time2'] = x2
print (x2)
print (data.time2)
```
#### Problem description
Regardless of the format specified, to_datetime drops the hours,mins, and seconds when they are all zeros (i.e 00:00:00).
#### Expected Output
```
0.19.2
2017-02-02 00:00:00
0 2017-02-02 00:00:00
Name: time1, dtype: datetime64[ns]
2017-02-02 00:00:05
0 2017-02-02 00:00:05
Name: time2, dtype: datetime64[ns]
```
#### Actual Output
```
0.19.2
2017-02-02 00:00:00
0 2017-02-02
Name: time1, dtype: datetime64[ns]
2017-02-02 00:00:05
0 2017-02-02 00:00:05
Name: time2, dtype: datetime64[ns]
```
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MDU6SXNzdWUyMjAzMDgwODE=
| 15,943 |
HDFStore - raising an exception when complevel > 0 and complib is None
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| 5 | 2017-04-07T19:34:30Z | 2017-06-13T23:20:59Z | 2017-06-13T23:20:59Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
HDFStore = pd.HDFStore('mydata.ph5', mode='w', complevel=9)
```
#### Problem description
Hi,
It seems when `complib is None`, `pd.HDFStore` does not use any compression even though a user supplies `complevel > 0`. If there is no good default option for `complib`, then in my opinion, `HDFStore` should raise an exception when `(complib is None) and (complevel > 0)` instead. This is because clearly the user wants compression but it cannot be done - I think explicitly raising an exception is better than silently ignoring the option.
#### Output of ``pd.show_versions()``
<details>
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.9.1
IPython: 5.1.0
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
</details>
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BUG: Resampling PeriodIndex-ed to multiple of frequencies not working as expected
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| 1 | 2017-04-07T19:59:52Z | 2017-09-28T14:29:44Z | 2017-04-07T20:04:14Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [1]: import numpy as np
In [2]: import pandas as pd
In [3]: s = pd.Series([2017, 2018], index=pd.period_range('2017', freq='A', periods=2))
In [4]: s.resample('2Q', kind='period').ffill()
Warning: multiple of frequency -> timestamps
Out[4]:
2017-03-31 2017
2017-09-30 2017
2018-03-31 2018
Freq: 2Q-DEC, dtype: int64
In [7]: s2 = pd.Series(np.arange(12), index=pd.period_range('2017-01', freq='M', periods=12))
In [8]: s2.resample('2Q', kind='period').mean()
Warning: multiple of frequency -> timestamps
Out[8]:
2017-03-31 1.0
2017-09-30 5.5
2018-03-31 10.0
Freq: 2Q-DEC, dtype: float64
```
To compare with, results for resampling to base frequency (no multiples) returning PeriodIndex-ed and covering the full original time span:
<details>
```python
In [5]: s.resample('Q', kind='period').ffill()
Out[5]:
2017Q1 2017
2017Q2 2017
2017Q3 2017
2017Q4 2017
2018Q1 2018
2018Q2 2018
2018Q3 2018
2018Q4 2018
Freq: Q-DEC, dtype: int64
In [9]: s2.resample('Q', kind='period').mean()
Out[9]:
2017Q1 1
2017Q2 4
2017Q3 7
2017Q4 10
Freq: Q-DEC, dtype: int64
```
</details>
#### Problem description
* I'd expect resampling PeriodIndex-ed series/dataframes would return a PeriodIndex-ed result by default, even more when given```kind='period'```.
* Moreover, I'd expect the original full time span to be covered by the resampling result (upsampling A->2Q would return 2 periods per year, downsampling M->2Q would return 2 periods per 12 months).
As indicated by the warning message, resampling to multiple of frequencies falls back to timestamp-based resampling. Both work fine when resampling to a "base" frequency without any multiple.
#### Expected Output
```python
In [4]: s.resample('2Q', kind='period').ffill()
Out[4]:
2017Q1 2017
2017Q3 2017
2018Q1 2018
2018Q3 2018
Freq: 2Q-DEC, dtype: int64
In [8]: s2.resample('2Q', kind='period').mean()
Out[8]:
2017Q1 2.5
2017Q3 8.5
Freq: 2Q-DEC, dtype: float64
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 33.1.1.post20170320
Cython: None
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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BUG: Validate the skipfooter parameter in read_csv
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| 7 | 2017-04-07T20:21:29Z | 2017-04-08T16:35:56Z | 2017-04-08T13:24:44Z |
MEMBER
| null |
Previously, the `skipfooter` parameter was assumed to be an integer, but that was not checked.
xref <a href="https://github.com/pandas-dev/pandas/pull/15925#discussion_r110283317">#15925 (comment)</a>
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MAINT: Refactor Python engine empty line funcs
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| 4 | 2017-04-07T20:33:47Z | 2017-04-08T16:35:49Z | 2017-04-08T13:25:24Z |
MEMBER
| null |
The Python engine's `_empty` and `_check_empty` methods were uninformative and undocumented.
This commit renames them to `_is_line_empty` and `_remove_empty_lines` respectively and provides appropriate documentation.
xref <a href="https://github.com/pandas-dev/pandas/pull/15925#discussion_r110377233">#15925 (comment)</a>
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ENH: add groupby(..).sort_values()
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| 11 | 2017-04-07T21:10:00Z | 2021-05-08T01:56:02Z | null |
CONTRIBUTOR
| null |
might be useful to expose this as a top-level groupby method for intra-group sorting. Anyone have a good usecase for this? We could make this *much* faster that the current impl (not that this is an issue, though potentially it could be).
```
In [10]: df = DataFrame({'A': [1, 1, 2, 2, 2], 'B': [2, 1, 3, 3, 1], 'C':[1, 1, 2, 1, 1]})
In [11]: df
Out[11]:
A B C
0 1 2 1
1 1 1 1
2 2 3 2
3 2 3 1
4 2 1 1
In [12]: df.groupby('A').apply(lambda x: x.sort_values(['B', 'C']))
Out[12]:
A B C
A
1 1 1 1 1
0 1 2 1
2 4 2 1 1
3 2 3 1
2 2 3 2
```
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COMPAT: 32bit compat on indexing for MI.remove_unused_levels
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| 1 | 2017-04-08T13:43:06Z | 2017-04-08T15:56:35Z | 2017-04-08T14:32:09Z |
CONTRIBUTOR
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TST: clean up series/frame api tests inheritance a bit
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| 3 | 2017-04-08T14:46:33Z | 2017-04-10T12:12:13Z | 2017-04-10T12:12:01Z |
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np.read_csv C parser uses _try_int64 when converting np.uint64 values
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| 6 | 2017-04-08T17:39:23Z | 2017-04-08T18:20:56Z | 2017-04-08T18:20:56Z |
NONE
| null |
#### Code Sample
```python
#!/usr/bin/python
import numpy as np
import pandas as pd
df = pd.read_csv("test.data", sep=" ",
names=["opcode", "msg_id", "verb", "time_ns", "src_port", "key"],
dtype={"opcode":np.uint32, "msg_id":np.uint64, "time_ns":np.uint64},
error_bad_lines=False,
skip_blank_lines=True
)
print df
print "value ", df.ix[2, "time_ns"]
print "max u43", np.iinfo(np.int64).max
print "max u64", np.iinfo(np.uint64).max
"""
> cat test.data
5 49175 8 6369116884441 121 user5353376006837317496
5 87 8 6369116913924 120 user5020027490740379661
5 16476 5 9636911691583032832 1208 user2127153374092823212
5 49216 8 6369116917983 122 user3650346306886706056
5 64 8 6369117018969 123 user4432592802498709959
"""
```
#### Problem description
In the example above, I am specifying time_ns column to be of type np.uint64, however, it causes an overflow issue when reading the test.data file on the row[2]. As it follows from the trace, int64 is used instead of unsigned int64 which triggers the issue.
A work around that works is to use python parser and convert uint64 column manually as follows:
```python
df = pd.read_csv("test.data", sep=" ",
names=["opcode", "msg_id", "verb", "time_ns", "src_port", "key"],
skip_blank_lines=True,
engine="python"
)
df["time_ns"] = df.apply(lambda x: np.uint64(x["time_ns"]), axis=1)
```
#### Actual Output
> Traceback (most recent call last):
File "./test.py", line 9, in <module>
skip_blank_lines=True
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 645, in parser_f
return _read(filepath_or_buffer, kwds)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 400, in _read
data = parser.read()
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 938, in read
ret = self._engine.read(nrows)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1507, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 846, in pandas.parser.TextReader.read (pandas/parser.c:10364)
File "pandas/parser.pyx", line 868, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:10640)
File "pandas/parser.pyx", line 945, in pandas.parser.TextReader._read_rows (pandas/parser.c:11677)
File "pandas/parser.pyx", line 1047, in pandas.parser.TextReader._convert_column_data (pandas/parser.c:13111)
File "pandas/parser.pyx", line 1091, in pandas.parser.TextReader._convert_tokens (pandas/parser.c:13856)
File "pandas/parser.pyx", line 1145, in pandas.parser.TextReader._convert_with_dtype (pandas/parser.c:14775)
File "pandas/parser.pyx", line 1749, in pandas.parser._try_int64 (pandas/parser.c:24581)
OverflowError: Overflow
#### Expected Output
opcode msg_id verb time_ns src_port \
0 5 49175 8 6369116884441 121
1 5 87 8 6369116913924 120
2 5 16476 5 9636911691583032832 1208
3 5 49216 8 6369116917983 122
4 5 64 8 6369117018969 123
key
0 user5353376006837317496
1 user5020027490740379661
2 user2127153374092823212
3 user3650346306886706056
4 user4432592802498709959
value 9636911691583032832
max u43 9223372036854775807
max u64 18446744073709551615
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: C
LOCALE: None.None
pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 34.3.3
Cython: None
numpy: 1.12.1
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.4.3
matplotlib: 1.5.3
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: None
apiclient: None
sqlalchemy: None
pymysql: 0.7.2.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTE0OTU2MzI0
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DOC: Cleanup for nbsphinx output
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| 10 | 2017-04-08T18:47:55Z | 2017-05-29T20:43:57Z | 2017-04-08T20:58:17Z |
CONTRIBUTOR
| null |
Followup to https://github.com/pandas-dev/pandas/pull/15581
Using the `nbsphinx: hidden` metadata to hide the ouptut, so
readers don't see matplotlib's fc-list warning.
Make the tables monospaced in CSS.
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TST: Add test decorators for redirecting stdout and stderr
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| 3 | 2017-04-08T20:00:58Z | 2017-04-08T22:01:08Z | 2017-04-08T21:58:32Z |
MEMBER
| null |
Add testing decorators for redirecting `stdout` and `stderr`.
xref <a href="https://github.com/pandas-dev/pandas/pull/15925#discussion_r110283696">#15925 (comment)</a>
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BUG: Empty DataFrame.style.render raises IndexError
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| 1 | 2017-04-08T22:08:25Z | 2017-05-23T16:52:45Z | 2017-05-23T16:52:45Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [6]: pd.DataFrame().style.render()
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-6-345e1b37074e> in <module>()
----> 1 pd.DataFrame().style.render()
/Users/taugspurger/Envs/dask-dev/lib/python3.6/site-packages/pandas/formats/style.py in render(self)
416 """
417 self._compute()
--> 418 d = self._translate()
419 # filter out empty styles, every cell will have a class
420 # but the list of props may just be [['', '']].
/Users/taugspurger/Envs/dask-dev/lib/python3.6/site-packages/pandas/formats/style.py in _translate(self)
252 "is_visible": True})
253
--> 254 for c in range(len(clabels[0])):
255 cs = [COL_HEADING_CLASS, "level%s" % r, "col%s" % c]
256 cs.extend(cell_context.get(
IndexError: list index out of range
```
#### Problem description
We assume that there's at least one column (and probably row)
#### Expected Output
Should match the output of an empty DataFrame in the notebook
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MDExOlB1bGxSZXF1ZXN0MTE0OTYzNjk0
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ENH: Style blocks
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| 27 | 2017-04-08T22:25:20Z | 2017-05-29T20:44:01Z | 2017-04-15T13:59:00Z |
CONTRIBUTOR
| null |
ENH: Add blocks to Styler template
This will make subclassing the Styler and extending
the templates easier.
REF: Move template to its own file
Use Environment and PackageLoader to load it
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BUG: Fix MultiIndex names handling in pd.concat
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| 2 | 2017-04-09T14:20:46Z | 2017-04-10T16:39:01Z | 2017-04-10T12:10:24Z |
CONTRIBUTOR
| null |
This is a fix attempt for issue #15787.
The discrepancy between definition and corresponding implementation of so-called non-none names in function _get_consensus_names leads to this bug.
- [x] closes #15787
- [x] tests added / passed
- [ ] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [x] whatsnew entry
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| 1 | 2017-04-09T14:34:43Z | 2017-04-09T14:56:07Z | 2017-04-09T14:40:13Z |
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DOC: update api.rst for Categorical accessors / methods
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| 6 | 2017-04-09T17:49:55Z | 2017-04-10T12:16:39Z | 2017-04-10T12:16:39Z |
CONTRIBUTOR
| null |
we have a ``Categorical`` section, but this mainly describes ``.cat``. Maybe split this out into a separate section to document the actual ``Categorical`` methods / accessors (even though it is *slightly* duplicative.
http://pandas-docs.github.io/pandas-docs-travis/api.html#categorical
also need to add other public methods (that are not available on ``.cat``). This is actually a big list, things like:
``isnull``, ``unique``, ``map``, ``value_counts``, etc.
These are all Series like methods / accessors.
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DOC: suppress some doc build warnings
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| 0 | 2017-04-09T18:07:40Z | 2017-04-09T18:24:05Z | 2017-04-09T18:24:05Z |
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| 7 | 2017-04-09T18:36:48Z | 2017-04-10T08:09:47Z | 2017-04-09T20:15:01Z |
NONE
| null |
I am trying to find a clean, concise way of setting the values of a multi-dimensional, multi-indexed dataframe, using data of lower dimensionality. In this case, I am trying to use two-dimensional data to set values in a four-dimensional dataframe.
Unfortunately, the syntax I am using only works with 2D data if the keys I'm using are already in the dataframe's index/column. But empty dataframes do not have any keys (yet) in their indices/columns.
For single points (0D), this is not a problem. Pandas just adds the missing key(s) appropriately and sets the value. For anything else, the key must be already there, it seems, as the below code shows.
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
import numpy as np
# Create an empty 2-level mux (multi-index) for the index.
# The first level is run number ('r'). The second is x-axis values ('x').
mux = pd.MultiIndex(levels=[[]]*2,labels=[[]]*2,names=['r','x'])
# Create an empty 2-level mux for the column
# The first level is parameter value ('p'). The second is y-axis values ('y').
mux2 = pd.MultiIndex(levels=[[]]*2,labels=[[]]*2,names=['p','y'])
# Create the empty multi-indexed and multi-columned dataframe
df = pd.DataFrame(index=mux,columns=mux2)
# run number 0 (r=0), using parameter value 1.024 (p=1.024)...
# ... produces 2D data on an x-y grid.
data = np.array([[1,2,3],[4,5,6]])
ys = np.array([0,1,2])
xs = np.array([0,1])
# Now we want to set values in the 4D dataframe with our 2D data. Throws error.
df.loc[(0,list(xs)),(1.024,list(ys))] = data
Traceback (most recent call last):
KeyError: 0
```
But single points work fine.
```python
# Single points automatically result in new keys
df.loc[(0,xs[0]),(1.024,ys[0])] = 1
df.loc[(0,xs[0]),(1.024,ys[1])] = 2
df.loc[(0,xs[1]),(1.024,ys[0])] = 3
df.loc[(0,xs[1]),(1.024,ys[1])] = 4
# Keys are now found, and this now works.
df.loc[(0,list(xs[0:2])),(1.024,list(ys[0:2]))] = ((5,6),(7,8))
# But this does not work. '1' is not currently a key.
df.loc[(1,list(xs[0:2])),(1.024,list(ys[0:2]))] = ((1,2),(3,4))
Traceback (most recent call last):
KeyError: 1L
```
#### Problem description
It seems the default behavior for setting single points (that is, auto-creation of keys) is different than the behavior of setting multiple points (no auto-creation of keys). This seems pretty arbitrary from my outsider perspective; not sure why the behavior shouldn't be identical.
If there is another way of accomplishing this, I would love to hear about it. But perhaps the point behavior should be extended to multiple dimensions.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 33.1.0.post20170122
Cython: 0.25.2
numpy: 1.10.4
scipy: 0.17.1
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 5.2.2
sphinx: 1.5.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.45.0
pandas_datareader: 0.2.1
</details>
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sort_values not implemented for Panel
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| 2 | 2017-04-09T23:03:30Z | 2017-05-31T23:10:24Z | 2017-05-31T23:10:24Z |
CONTRIBUTOR
| null |
#### Code Sample, a copy-pastable example if possible
```python
import pandas as pd
pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2']).sort_values(by=0)
---------------------------------------------------------------------------
AbstractMethodError Traceback (most recent call last)
<ipython-input-16-f14e23e389bd> in <module>()
1 import pandas as pd
----> 2 pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2']).sort_values(by=0)
/home/gpepe/software/anaconda3/envs/science/lib/python3.6/site-packages/pandas/core/generic.py in sort_values(self, by, axis, ascending, inplace, kind, na_position)
2010 def sort_values(self, by, axis=0, ascending=True, inplace=False,
2011 kind='quicksort', na_position='last'):
-> 2012 raise AbstractMethodError(self)
2013
2014 _shared_docs['sort_index'] = """
AbstractMethodError: This method must be defined in the concrete class of Panel
```
#### Problem description
It seems like the sort_values() method is not implemented in Panels, but it is not clear from the raised error message. There seems to be a page on this in the API reference: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Panel.sort_values.html. i would find it highly useful to have such a method.
#### Expected Output
Sorted panel
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 3.16.0-4-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_GB.utf8
LOCALE: en_GB.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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| 15,961 |
FYI, if you build pandas release binaries with 3.6.1, they'll be broken on 3.6.0
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| 3 | 2017-04-10T03:11:08Z | 2017-05-02T04:46:53Z | 2017-04-20T22:33:36Z |
NONE
| null |
As a heads up: https://bugs.python.org/issue29943
Two possible workarounds:
* Make sure to use 3.6.0 if building release binaries (this way they'll work on 3.6.1 as well)
* add a `#ifdef PySlice_GetIndicesEx #undef PySlice_GetIndicesEx #endif` to `pandas/_libs/src/compat_helper.h` (but make sure to only enable this on 3.6, or you'll eventually get broken again
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| 15,962 |
Option for showing length of series
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| 4 | 2017-04-10T05:28:33Z | 2017-04-11T21:43:39Z | 2017-04-10T12:05:39Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
pd.Series(range(10))
```
#### Problem description
There is a option `display.show_dimensions` which allow show the dimension of a `DataFrame`, while it would be useful if the length of a `Series` can be set to show when the index is not a range list.
#### Expected Output
```
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
dtype: int64
[10 elements/items/or something else...]
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 16.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 34.3.3
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: 0.9.1
IPython: 5.3.0
sphinx: 1.5.3
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: None
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.5
xlrd: None
xlwt: None
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.5.3
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.7
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: 0.3.0.post
</details>
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IO/SAS (sas7bdat) deleted observations are not filtered out
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| 5 | 2017-04-10T09:32:57Z | 2019-10-21T19:52:17Z | null |
CONTRIBUTOR
| null |
#### Problem description
I filled a table, then deleted two observations (rows).
[datetime.sas7bdat](https://yadi.sk/d/h1En7L9M3GpDCV)
Pandas reads 5 rows with `read_sas`:
```
Date1 Date2 DateTime DateTimeHi \
0 1960-01-06 1960-01-04 1677-09-21 00:12:44 1677-09-21 00:12:43.145226
1 1960-01-03 1960-01-05 2262-04-11 23:47:16 1960-01-01 00:00:00.000000
2 NaT NaT NaT NaT
3 1960-01-06 1960-01-04 1677-09-21 00:12:44 2262-04-11 23:47:16.854774
4 1960-01-01 1960-01-01 NaT 1960-01-01 00:00:00.000000
Taiw
0 1912-01-01
1 1960-01-02
2 NaT
3 1912-01-01
4 1960-01-01
```
#### Expected Output
DataFrame with 3 rows instead of 5 rows (w/o index no.2 and no.4)
#### Output of ``pd.show_versions()``
last commit is cd24fa9 (ENH: add origin to to_datetime)
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
LOCALE: None.None
pandas: 0+unknown
pytest: 3.0.6
pip: 9.0.1
setuptools: 34.3.2
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.19.0
xarray: 0.9.1
IPython: 5.3.0
sphinx: 1.5.2
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: 1.5.0
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.5
lxml: 3.7.3
bs4: 4.5.3
html5lib: 1.0b10
sqlalchemy: 1.1.6
pymysql: None
psycopg2: None
jinja2: 2.9.5
s3fs: 0.0.9
pandas_gbq: None
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTE1MDg3NDQ1
| 15,964 |
DOC: remove Categorical.unique refs from doc-strings
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| 0 | 2017-04-10T12:16:32Z | 2017-04-10T12:16:39Z | 2017-04-10T12:16:39Z |
CONTRIBUTOR
| null |
closes #15957
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TST/DEPR: fix bunch of Panel deprecation warnings
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| 2 | 2017-04-10T12:49:42Z | 2017-04-11T10:39:21Z | 2017-04-11T10:29:31Z |
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BUG: comparing TZ-aware timestamps to a scalar results in exception
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| 3 | 2017-04-10T14:03:08Z | 2017-11-08T20:01:39Z | 2017-11-08T20:01:39Z |
CONTRIBUTOR
| null |
This works as expected:
```
pd.DataFrame({'test': [pd.Timestamp('2012-01-01 13:00:00'),
pd.Timestamp('2012-01-01 13:00:00')]}) == -1
```
However, this raises an exception:
```
pd.DataFrame({'test': [pd.Timestamp('2012-01-01 13:00:00+00:00'),
pd.Timestamp('2012-01-01 13:00:00+00:00')]}) == -1
```
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read_excel skiprows
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| 4 | 2017-04-10T14:14:55Z | 2018-02-14T21:09:04Z | 2018-02-14T21:08:53Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
skip = [0] + [i for i in range(3, 81)]
df = pd.read_excel('excel_file.xls', skiprows=skip)
```
#### Problem description
The dataframe only returns the header from row 1 and row 2 is missing.
Note: changing skip to [0] + [i for i in range(4, 81)] returns a the dataframe with two rows as expected. The problem occurs when I only want one row.
#### Expected Output
A dataframe where row 1 is the header and row 2 is the first row entry.
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.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: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.6
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTE1MTM3MTc0
| 15,968 |
DOC: Fix typos in doc style.ipynb
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| 2 | 2017-04-10T16:11:37Z | 2017-04-11T11:37:16Z | 2017-04-11T11:37:02Z |
CONTRIBUTOR
| null |
Simple typo fix.
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ERR: Most consistent error handling when passing win_type='freq' in rolling
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| 8 | 2017-04-10T20:14:53Z | 2020-12-23T14:27:49Z | 2020-12-23T14:27:49Z |
CONTRIBUTOR
| null |
Working on https://github.com/dask/dask/issues/2190 (df.rolling('5s') for dask), and I think these should all be equivalent.
```python
In [26]: import pandas as pd
In [27]: import numpy as np
In [31]: from pandas.tseries.frequencies import to_offset
In [28]: s = pd.Series(range(10), index=pd.date_range('2017', freq='s', periods=10))
```
```python
>>> s.rolling('2s') # Case 1: correct
>>> s.rolling(window=2000000000, min_periods=1, win_type='freq') # Case 2
>>> s.rolling(window=to_offset('2s'), min_periods=1, win_type='freq') # Case 3
>>> s.rolling(window=pd.Timedelta('2s'), min_periods=1, win_type='freq') # Same as 3
```
I don't *think* there are any parsing ambiguities.
Currently we have
```python
# Case 1
In [33]: s.rolling('2s')
Out[33]: Rolling [window=2000000000,min_periods=1,center=False,win_type=freq,axis=0]
```
```python
# Case 2
In [35]: s.rolling(window=2000000000, min_periods=1, win_type='freq')
```
```pytb
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-a24a29dff0ab> in <module>()
----> 1 s.rolling(window=2000000000, min_periods=1, win_type='freq')
/Users/taugspurger/.virtualenvs/dask-dev/lib/python3.6/site-packages/pandas/core/generic.py in rolling(self, window, min_periods, freq, center, win_type, on, axis)
5502 min_periods=min_periods, freq=freq,
5503 center=center, win_type=win_type,
-> 5504 on=on, axis=axis)
5505
5506 cls.rolling = rolling
/Users/taugspurger/.virtualenvs/dask-dev/lib/python3.6/site-packages/pandas/core/window.py in rolling(obj, win_type, **kwds)
1795
1796 if win_type is not None:
-> 1797 return Window(obj, win_type=win_type, **kwds)
1798
1799 return Rolling(obj, **kwds)
/Users/taugspurger/.virtualenvs/dask-dev/lib/python3.6/site-packages/pandas/core/window.py in __init__(self, obj, window, min_periods, freq, center, win_type, axis, on, **kwargs)
76 self.win_type = win_type
77 self.axis = obj._get_axis_number(axis) if axis is not None else None
---> 78 self.validate()
79
80 @property
/Users/taugspurger/.virtualenvs/dask-dev/lib/python3.6/site-packages/pandas/core/window.py in validate(self)
505 raise ValueError('Invalid win_type {0}'.format(self.win_type))
506 if getattr(sig, self.win_type, None) is None:
--> 507 raise ValueError('Invalid win_type {0}'.format(self.win_type))
508 else:
509 raise ValueError('Invalid window {0}'.format(window))
ValueError: Invalid win_type freq
```
```python
# Case 3
In [36]: s.rolling(window=to_offset('2s'), min_periods=1, win_type='freq')
```
```pytb
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-36-93e14a8d05f6> in <module>()
----> 1 s.rolling(window=to_offset('2s'), min_periods=1, win_type='freq')
/Users/taugspurger/Envs/dask-dev/lib/python3.6/site-packages/pandas/core/generic.py in rolling(self, window, min_periods, freq, center, win_type, on, axis)
5502 min_periods=min_periods, freq=freq,
5503 center=center, win_type=win_type,
-> 5504 on=on, axis=axis)
5505
5506 cls.rolling = rolling
/Users/taugspurger/Envs/dask-dev/lib/python3.6/site-packages/pandas/core/window.py in rolling(obj, win_type, **kwds)
1795
1796 if win_type is not None:
-> 1797 return Window(obj, win_type=win_type, **kwds)
1798
1799 return Rolling(obj, **kwds)
/Users/taugspurger/Envs/dask-dev/lib/python3.6/site-packages/pandas/core/window.py in __init__(self, obj, window, min_periods, freq, center, win_type, axis, on, **kwargs)
76 self.win_type = win_type
77 self.axis = obj._get_axis_number(axis) if axis is not None else None
---> 78 self.validate()
79
80 @property
/Users/taugspurger/Envs/dask-dev/lib/python3.6/site-packages/pandas/core/window.py in validate(self)
507 raise ValueError('Invalid win_type {0}'.format(self.win_type))
508 else:
--> 509 raise ValueError('Invalid window {0}'.format(window))
510
511 def _prep_window(self, **kwargs):
ValueError: Invalid window <2 * Seconds>
```
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MDU6SXNzdWUyMjA3NjQ1Mjc=
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diff breaks when float64 column cast to float32
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[] | null | 7 | 2017-04-10T20:42:46Z | 2017-04-11T00:50:53Z | 2017-04-10T21:37:16Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
# x is a DataFrame
(x['time'].iloc[0],
x['time'].dtype,
np.allclose(x['time'], x['time'].astype('float32')),
np.allclose(x['time'].diff(), x['time'].astype('float32').diff(), equal_nan=True))
# (1490702404.462925, dtype('float64'), True, False)
```
#### Problem description
After I cast a float64 column to float32, diff exhibits strange behavior. The call results in many 0. values and some 128. values, which does not match the underlying data in any way I can understand.
#### Expected Output
```python
# (1490702404.462925, dtype('float64'), True, True)
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.4.1
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
boto: None
pandas_datareader: None
</details>
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BUG: Fix Empty DataFrame.style.render raises IndexError
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| 5 | 2017-04-10T21:07:28Z | 2017-05-30T21:42:39Z | 2017-05-30T21:42:34Z |
NONE
| null |
Fix attempt for Issue #15953
Handles DataFrames and Series with no rows or columns
using `pd.DataFrame().empty` or `pd.Series().empty`
```python
In [1]: import pandas as pd
In [2]: pd.DataFrame().style.render()
Out[2]: '\n <style type="text/css" >\n \n \n </style>\n\n <table id="T_de3b3e8c_1e30_11e7_9abb_a8667f1b25b4" None>\n \n\n <thead>\n \n </thead>\n <tbody>\n \n </tbody>\n </table>\n '
```
- [ ] closes #15953
- [ ] tests added / passed
- [ ] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [ ] whatsnew entry
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Crosstab support for setting the margins name
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| 0 | 2017-04-11T10:39:55Z | 2017-05-26T14:43:13Z | 2017-05-26T14:43:13Z |
NONE
| null |
`pandas.pivot_table` already provides the parameter `margins_name ` . `pandas.crosstab` currently does not.
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ENH: Crosstab support for setting the margins name #15972
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| 4 | 2017-04-11T11:23:12Z | 2017-05-30T21:44:46Z | 2017-05-30T21:44:39Z |
NONE
| null |
closes #15972
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BUG: show series length in repr when truncated (GH15962)
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| 4 | 2017-04-11T13:05:17Z | 2017-04-18T08:44:46Z | 2017-04-18T08:34:37Z |
MEMBER
| null |
Also follow the display.show_dimensions option.
- [x] closes #15962, closes #7117
- [x] tests added / passed
- [ ] passes ``git diff upstream/master --name-only -- '*.py' | flake8 --diff``
- [ ] whatsnew entry
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Typo in documentation of pandas.DatetimeIndex.freq
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| 3 | 2017-04-11T13:05:24Z | 2017-04-11T14:49:09Z | 2017-04-11T14:49:04Z |
NONE
| null |
Just a small typo at http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.freq.html#pandas.DatetimeIndex.freq
It says "get/set the _frequncy_ of the Index" instead of "get/set the _frequency_ of the Index"
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MDU6SXNzdWUyMjA5NTc4NjI=
| 15,976 |
groupby.transform() fails when performed on column of numpy arrays
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| 2 | 2017-04-11T13:48:25Z | 2017-04-11T15:04:37Z | 2017-04-11T15:03:54Z |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df = pd.DataFrame.from_dict({"A":[1, 1, 2, 2],
"B":[np.array([1, 2, 3, 4]),
np.array([5, 6, 7, 8]),
np.array([1, 2, 3, 4]),
np.array([5, 6, 7, 8])]})
def mean_of_arrays(x):
"""
Returns the mean of the two arrays.
"""
print((x.iloc[0] + x.iloc[1])/2)
return (x.iloc[0] + x.iloc[1])/2
df.groupby(["A"])["B"].transform(mean_of_arrays)
# Print [3, 4, 5, 6], [3, 4, 5, 6], as expected...
# But returns [3, 4, 3, 4]
```
#### Problem description
When a column contains a list of numpy arrays, and a "transform" operation is completed on those lists,
the lists are not returned by the transform operation. Instead, the n-th element of the list is return, where n is the number of elements for each group.
#### Expected Output
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.4
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
boto: 2.46.1
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTE1Mzk1NzE0
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typo: comverted -> converted
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| 3 | 2017-04-11T19:25:52Z | 2017-04-11T20:46:31Z | 2017-04-11T20:22:11Z |
NONE
| null |
only one character changed
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merge multi-index with a multi-index
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| 1 | 2017-04-11T23:55:29Z | 2017-04-12T07:22:22Z | 2017-04-11T23:57:48Z |
CONTRIBUTOR
| null |
#6360
Allow the merging of multi-indexed objects with different number of levels.
During tests I receive the following error: pandas/pandas/core/internals.py:4800: ComplexWarning: Casting complex values to real discards the imaginary part
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Multi index operations
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| 1 | 2017-04-12T00:04:25Z | 2017-04-12T07:21:57Z | 2017-04-12T00:05:23Z |
CONTRIBUTOR
| null |
#6360
Allow the merging of multi-indexed objects with different number of levels.
During tests I receive the following error: pandas/pandas/core/internals.py:4800: ComplexWarning: Casting complex values to real discards the imaginary part
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[] | null | 6 | 2017-04-12T00:10:24Z | 2017-04-25T23:40:25Z | 2017-04-25T23:33:26Z |
CONTRIBUTOR
| null |
#6360
Allow the merging of multi-indexed objects with different number of levels.
During tests I receive the following error: pandas/pandas/core/internals.py:4800: ComplexWarning: Casting complex values to real discards the imaginary part
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MDExOlB1bGxSZXF1ZXN0MTE1NDYxMzgz
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DOC: Fix typo in timeseries.rst
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| 1 | 2017-04-12T04:22:27Z | 2017-06-03T12:47:29Z | 2017-04-12T07:21:15Z |
CONTRIBUTOR
| null |
Simple typo fix
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MDU6SXNzdWUyMjEyNDQyMDM=
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Period converted to overflowing Timestamp in DataFrame.to_csv
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| 4 | 2017-04-12T12:21:28Z | 2017-04-13T22:11:33Z | 2017-04-13T22:11:33Z |
NONE
| null |
#### Code Sample
```python
import pandas
dates = ["1990-01-01", "2000-01-01", "3005-01-01"]
index = pandas.PeriodIndex(dates, freq="D")
df = pandas.DataFrame([4, 5, 6], index=index)
print(df)
df.to_csv("bug.csv")
```
#### Problem description
I have some data with dates in the far future (e.g. 4000-01-01). Following the documentation I am using `pandas.Period`: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#representing-out-of-bounds-spans
This works, until I come to export the dataframe to a CSV (demonstrated in the code above). Printing the dataframe works as expected:
> 0
> 1990-01-01 4
> 2000-01-01 5
> 3005-01-01 6
But the CSV files looks like this:
> ,0
> 1990-01-01 00:00:00.000000000,4
> 2000-01-01 00:00:00.000000000,5
> 1835-11-23 00:50:52.580896768,6
It looks like the `Period` has been converted to a `Timestamp` and silently overflowed.
Related to #13346, but I think worth a distinct issue.
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 33.1.0.post20170122
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: 2.46.1
pandas_datareader: None
</details>
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MDExOlB1bGxSZXF1ZXN0MTE1NTUxNDgy
| 15,983 |
BUG:read_json()
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| 1 | 2017-04-12T14:10:16Z | 2017-04-12T16:02:00Z | 2017-04-12T16:01:44Z |
NONE
| null |
I use pandas to read a json file, but I got an AttributeError, here is my code and error info:
<pre lang="python">import pandas as pd
import numpy as np
import xgboost as xgb
import matplotlib.pyplot as plt
#% matplotlib inline
import seaborn as sns
sns.set(style="whitegrid", color_codes=True)
sns.set(font_scale=1)
train = pd.read_json(train.json)
plt.figure(figsize=(8, 10))
plt.scatter(range(train.shape[0]), train["price"].values,color='purple')
plt.title("Distribution of Price")
plt.show()
runfile('/Users/trembleguy/Desktop/rental ap/plotSomething.py', wdir='/Users/trembleguy/Desktop/rental ap')
Traceback (most recent call last):
File "<ipython-input-14-5beeb67a8bb9>", line 1, in <module>
runfile('/Users/trembleguy/Desktop/rental ap/plotSomething.py', wdir='/Users/trembleguy/Desktop/rental ap')
File "/anaconda/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
execfile(filename, namespace)
File "/anaconda/lib/python3.6/site-packages/spyder/utils/site/sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "/Users/trembleguy/Desktop/rental ap/plotSomething.py", line 10, in <module>
train = pd.read_json(train.json)
File "/anaconda/lib/python3.6/site-packages/pandas/core/generic.py", line 2744, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'json'</pre>
I have used read_json() to read json without this error, but this time not.
I delete the .pyc file but didn't work, I think it's a bug.
if not a bug, please tell me the right way to read json, thanks!
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MDExOlB1bGxSZXF1ZXN0MTE1NTk0OTgx
| 15,984 |
BUG: Don't overflow PeriodIndex in to_csv
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| 10 | 2017-04-12T17:29:24Z | 2017-04-13T22:13:29Z | 2017-04-13T22:11:33Z |
MEMBER
| null |
Don't overflow time-stamp objects if being used as index in `to_csv`.
Closes #15982.
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TST: allow the network decorator to catch ssl certificate test failures
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| 2 | 2017-04-12T18:29:00Z | 2017-04-12T19:31:41Z | 2017-04-12T19:29:50Z |
CONTRIBUTOR
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ENH: support pendulum objects
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| 10 | 2017-04-12T18:50:57Z | 2020-10-02T13:50:22Z | 2020-01-01T15:56:55Z |
NONE
| null |
```python
import pendulum
import pandas
pandas.date_range(start=pendulum.now(), end=pendulum.tomorrow())
[1] 56829 segmentation fault ipython
```
#### Problem description
As `pendulum` claims to inherit from `datetime.datetime`, I wouldn't expect a segfaut to happen, as `date_range` always worked find with `datetime` objects
From [the documentation](https://pendulum.eustace.io/docs/#introduction):
> The Pendulum class is a drop-in replacement for the native datetime class (it is inherited from it).
I could file an issue on the pendulum bug tracker as well, but that's pandas who's segfaulting, so I think you guys would have better tools to understand what's going on.
#### Expected Output
```
DatetimeIndex(['2017-04-12 20:54:52.305104', '2017-04-13 20:54:52.305104'], dtype='datetime64[ns]', freq='D')
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: fr_BE.UTF-8
LOCALE: fr_BE.UTF-8
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 34.4.1
Cython: None
numpy: 1.12.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.9
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.6
boto: None
pandas_datareader: None
</details>
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| 15,987 |
DEPR: Deprecate generic timestamp dtypes
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| 16 | 2017-04-12T19:39:24Z | 2017-04-14T13:50:54Z | 2017-04-14T13:06:46Z |
MEMBER
| null |
We only use the nanosecond frequency, and `numpy` doesn't even handle generic timestamp `dtype` well internally with its `ndarray` object.
xref <a href="https://github.com/pandas-dev/pandas/issues/15524#issuecomment-282922120">#15524 (comment)</a>.
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MDU6SXNzdWUyMjE0MjA1MDU=
| 15,988 |
When using to_sql(), continue if duplicate primary keys are detected?
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[] | null | 24 | 2017-04-12T23:42:58Z | 2021-05-18T19:57:38Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
```python
df.to_sql('TableNameHere', engine, if_exists='append', chunksize=900, index=False)
```
#### Problem description
I am trying to append a large DataFrame to a SQL table. Some of the rows in the DataFrame are duplicates of those in the SQL table, some are not. But `to_sql()` completely stops executing if even one duplicate is detected.
It would make sense for `to_sql(if_exists='append')` to merely warn the user which rows had duplicate keys and just continue to add the new rows, not completely stop executing. For large datasets, you will likely have duplicates but want to ignore them.
Maybe add an argument to ignore duplicates and keep executing? Perhaps an additional `if_exists` option like `'append_skipdupes'`?
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: English_United States.1252
pandas: 0.19.2
nose: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.12.0
scipy: None
statsmodels: None
xarray: None
IPython: 5.3.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.5
boto: None
pandas_datareader: None
</details>
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DOC: Demo incorporating pytest into test classes
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| 3 | 2017-04-13T03:03:41Z | 2017-04-14T02:13:25Z | 2017-04-13T22:47:39Z |
MEMBER
| null |
Title is self-explanatory. Also changes CI GitHub UI photo to illustrate what most users (who are non-owners) will see (i.e. no "squash and merge").
<a href="https://github.com/pandas-dev/pandas/pull/15987#discussion_r111292547">#15987 (comment)</a>
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TST: Converting to Pytest Idiom
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| 20 | 2017-04-13T05:09:55Z | 2017-08-16T15:46:55Z | 2017-05-04T02:38:13Z |
MEMBER
| null |
Our testing framework is a literal hodgepodge of different 3rd-party framework standards (i.e. `nosetest`, `pytest`, `unittest`) that needs to be cleaned up.
A couple of points are clear:
* Use `assert` instead of `self.assert*...`
* When possible, replace `for` loops with `pytest.mark.parametrize`
* All new test classes should inherit from `object` instead of `tm.TestCase`
However, we also have this massive testing module helper `testing.py` that is populated with tons of useful comparison methods as well as wrappers around basic `assert` methods that are used inconsistently throughout the code-base. <b>Thus, the question is whether or not we want to keep using those OR remove them entirely and replace them with basic `assert` statements</b>.
The advantage with using `tm.assert` statements is that they come directly with error messages instead of an empty `AssertionError`, which is what I don't like about raw `assert` statements that are considered more idiomatic with `pytest`.
Thus, it's a question of greater clarity (i.e. our error messages will be more useful by default when we get test failures) vs. idiomatic (raw `assert` with no wrapping is the idiomatic way to go but risks presenting less helpful failure messages).
<strike>Another point I wanted to bring up is with regards to the introduction of decorators in #15952 to capture `stdout` and `stderr`. `pytest` has fixtures to capture those, but incorporating it into the code-base is very difficult due to our heavy reliance still on `unittest.TestCase`, which causes incorporation to fail. The question is: <b>do we eventually want to use the `pytest` fixture or continue using the decorator?</b></strike>
Thoughts?
<hr>
From the discussion that follows, here are the functions that should be removed and replaced:
- [x] `self (tm).assertRaises` --> `pytest.raises`
- [x] (<b>NO ACTION</b>) `self (tm).assertRaisesRegexp` -->
~~~python
with pytest.raises(cls) as exc_info:
...
exc_info.match(regex)
~~~
Per discussion in <a href="https://github.com/pandas-dev/pandas/pull/16089#issuecomment-296316294">#16089 (comment)</a>, we are leaving as is.
- [x] `self.assert*` (examples follow)
* `self.assertIs`
* `self.assertEqual`
* ...and many others
- [x] `self.assert_*` (examples follow)
* `self.assert_numpy_array_equal`
* `self.assert_series_equal`
* `self.assert_frame_equal`
* `self.assert_index_equal`
* ...and many others
- [x] `tm.assert_equal`
- [x] `tm.assertIs(Not)`
- [x] `tm.assert(Not)In`
- [x] `tm.assertIs(Not)None`
- [x] `tm.assert(Not)IsInstance`
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Inconsistent handling of nans in min/max functions
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| 3 | 2017-04-13T06:34:10Z | 2017-04-13T10:02:26Z | 2017-04-13T10:02:19Z |
NONE
| null |
#### Code Sample
```python
In [49]: min(pd.Series([pd.np.nan, 1, 2, pd.np.nan]))
Out[49]: nan
In [50]: min(pd.Series([1, 2, pd.np.nan]))
Out[50]: 1.0
```
#### Problem description
Inconsistent handling of nans depending on whether the series starts with a nan.
#### Expected Output
Either 1.0 in both cases or nan in both cases
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-66-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.19.2
nose: 1.3.7
pip: 8.1.2
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.3
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
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apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.8
boto: 2.42.0
pandas_datareader: None
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TST: use checkstack level as per comments in groupby.agg with dicts depr testing
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| 3 | 2017-04-13T11:24:17Z | 2017-04-13T11:54:40Z | 2017-04-13T11:41:04Z |
CONTRIBUTOR
| null |
as per @jorisvandenbossche comments
https://github.com/pandas-dev/pandas/pull/15931#discussion_r111355878
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Fast way to exclude columns
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| 1 | 2017-04-14T03:30:59Z | 2017-04-14T11:43:25Z | 2017-04-14T11:43:14Z |
NONE
| null |
It would be great to be able to exclude/slice columns like you can with np arrays.
```
df = pd.DataFrame(np.random.rand(4,4), columns = list('abcd'))
df[-('b')] #neat!
```
instead of
```
df.drop('b', axis=1)
df.ix[:, df.columns != 'b']
```
etc
see
http://stackoverflow.com/questions/29763620/how-to-select-all-columns-except-one-column-in-pandas-using-ix
http://stackoverflow.com/questions/14940743/selecting-excluding-sets-of-columns-in-pandas
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No implicit casting of int -> float on full indexing of multiindex
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| 3 | 2017-04-14T10:36:05Z | 2018-01-28T11:53:06Z | 2018-01-28T11:53:06Z |
MEMBER
| null |
#### Code Sample, a copy-pastable example if possible
```python
In [2]: mi = pd.MultiIndex.from_product([[0., 1.], ['begin', 'end']])
In [3]: mi.get_loc(1)
Out[3]: slice(2, 4, None)
In [4]: mi.get_indexer_for([(1, 'end')])
Out[4]: array([3])
In [5]: mi.get_loc((1, 'end'))
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-5-eb387fc9b362> in <module>()
----> 1 mi.get_loc((1, 'end'))
/home/pietro/nobackup/repo/pandas/pandas/indexes/multi.py in get_loc(self, key, method)
1962 key = _values_from_object(key)
1963 key = tuple(map(_maybe_str_to_time_stamp, key, self.levels))
-> 1964 return self._engine.get_loc(key)
1965
1966 # -- partial selection or non-unique index
/home/pietro/nobackup/repo/pandas/pandas/_libs/index.pyx in pandas._libs.index.MultiIndexEngine.get_loc (pandas/_libs/index.c:13171)()
/home/pietro/nobackup/repo/pandas/pandas/_libs/index.pyx in pandas._libs.index.MultiIndexEngine.get_loc (pandas/_libs/index.c:13018)()
/home/pietro/nobackup/repo/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.MultiIndexHashTable.get_item (pandas/_libs/hashtable.c:23625)()
/home/pietro/nobackup/repo/pandas/pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.MultiIndexHashTable.get_item (pandas/_libs/hashtable.c:23578)()
KeyError: (1, 'end')
```
#### Problem description
We implicitly cast if indexing only one level, and if asking a list of (complete) keys: we should do the same for a single complete key.
#### Expected Output
``` python
In [6]: mi.get_loc((1., 'end'))
Out[6]: 3
```
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.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+783.gcd35d22a0.dirty
pytest: 3.0.6
pip: 9.0.1
setuptools: 33.1.1
Cython: 0.25.2
numpy: 1.12.0
scipy: 0.18.1
xarray: 0.9.1
IPython: 5.1.0.dev
sphinx: 1.4.9
patsy: 0.3.0-dev
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: 3.7.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
pandas_gbq: None
pandas_datareader: 0.2.1
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How can np.nan be the representation of None when np.nan evaluates to `True`
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| 1 | 2017-04-14T15:35:01Z | 2017-04-14T16:19:02Z | 2017-04-14T16:18:49Z |
NONE
| null |
In reference to @jreback 's answer on [this related issue](https://github.com/pandas-dev/pandas/issues/10871), it seems a bit counter-intuitive for me that `np.nan` evaluates to `True` while `None` does not.
```python
In[1]: not np.nan
Out[1]: False
In[2]: not None
Out[2]: True
```
This proved to be a source of trouble in some of my code, as building a dataframe object using inputs containing `None` turns them into `np.nan` which means they go from evaluating to `False`, to evaluating to `True`. How does this not prevent `np.nan` from being the representation of `None`?
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Table Schema bombs with MultiIndex
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| 13 | 2017-04-14T15:52:26Z | 2017-04-25T23:55:20Z | 2017-04-25T23:55:20Z |
CONTRIBUTOR
| null |
Using the new (ok, yet to be released) table schemaized with the generated MultiIndex as mentioned in #15379, I noticed that it creates a traceback (and falls back on HTML)
#### Code Sample
```python
import pandas as pd
import numpy as np
pd.options.display.html.table_schema = True
midx = pd.MultiIndex.from_product([['A', 'B'], ['a', 'b', 'c']])
df = pd.DataFrame(np.random.randn(5, len(midx)), columns=midx)
df
```
#### Problem description
<details>
<summary>Full Traceback</summary>
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/indexes/multi.py in _convert_can_do_setop(self, other)
2513 try:
-> 2514 other = MultiIndex.from_tuples(other)
2515 except:
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/indexes/multi.py in from_tuples(cls, tuples, sortorder, names)
1128 elif isinstance(tuples, list):
-> 1129 arrays = list(lib.to_object_array_tuples(tuples).T)
1130 else:
TypeError: Argument 'rows' has incorrect type (expected list, got FrozenList)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.6/site-packages/IPython/core/formatters.py in __call__(self, obj)
880 method = get_real_method(obj, self.print_method)
881 if method is not None:
--> 882 method()
883 return True
884
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/core/generic.py in _ipython_display_(self)
138 latex = self._repr_latex_() if hasattr(self, '_repr_latex_') else None
139 html = self._repr_html_() if hasattr(self, '_repr_html_') else None
--> 140 table_schema = self._repr_table_schema_()
141 # We need the inital newline since we aren't going through the
142 # usual __repr__. See
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/core/generic.py in _repr_table_schema_(self)
156 if config.get_option("display.html.table_schema"):
157 data = self.head(config.get_option('display.max_rows'))
--> 158 payload = json.loads(data.to_json(orient='table'),
159 object_pairs_hook=collections.OrderedDict)
160 return payload
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/core/generic.py in to_json(self, path_or_buf, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines)
1232 force_ascii=force_ascii, date_unit=date_unit,
1233 default_handler=default_handler,
-> 1234 lines=lines)
1235
1236 def to_hdf(self, path_or_buf, key, **kwargs):
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/io/json/json.py in to_json(path_or_buf, obj, orient, date_format, double_precision, force_ascii, date_unit, default_handler, lines)
44 obj, orient=orient, date_format=date_format,
45 double_precision=double_precision, ensure_ascii=force_ascii,
---> 46 date_unit=date_unit, default_handler=default_handler).write()
47
48 if lines:
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/io/json/json.py in __init__(self, obj, orient, date_format, double_precision, ensure_ascii, date_unit, default_handler)
141 # TODO: Do this timedelta properly in objToJSON.c See GH #15137
142 if ((obj.ndim == 1) and (obj.name in set(obj.index.names)) or
--> 143 len(obj.columns & obj.index.names)):
144 msg = "Overlapping names between the index and columns"
145 raise ValueError(msg)
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/indexes/base.py in __and__(self, other)
2046
2047 def __and__(self, other):
-> 2048 return self.intersection(other)
2049
2050 def __or__(self, other):
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/indexes/multi.py in intersection(self, other)
2447 """
2448 self._assert_can_do_setop(other)
-> 2449 other, result_names = self._convert_can_do_setop(other)
2450
2451 if self.equals(other):
/Users/kylek/code/src/github.com/pandas-dev/pandas/pandas/indexes/multi.py in _convert_can_do_setop(self, other)
2514 other = MultiIndex.from_tuples(other)
2515 except:
-> 2516 raise TypeError(msg)
2517 else:
2518 result_names = self.names if self.names == other.names else None
TypeError: other must be a MultiIndex or a list of tuples
```
</details>
The key line in there is `expected list, got FrozenList` from `arrays = list(lib.to_object_array_tuples(tuples).T)`
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CLN: move .formats
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| 0 | 2017-04-14T16:42:24Z | 2017-04-14T16:49:04Z | 2017-04-14T16:49:04Z |
CONTRIBUTOR
| null |
xref #13634
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CLN: move top-level dirs
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| 14 | 2017-04-14T17:29:21Z | 2017-04-16T08:59:41Z | 2017-04-15T23:54:46Z |
CONTRIBUTOR
| null |
xref #13634
- [x] ``from pandas.types.concat import union_categoricals`` ?
- ``pandas.types`` -> ``pandas.core.typed``
- ``pandas.computation`` -> ``pandas.core.computation``
- ``pandas.sparse`` -> ``pandas.core.sparse``
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Series.map should return default dictionary values rather than NaN
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| 7 | 2017-04-14T18:11:49Z | 2017-04-15T13:46:44Z | 2017-04-15T13:46:44Z |
CONTRIBUTOR
| null |
`collections.Counter` and `collections.defaultdict` both have default values. However, [`pandas.Series.map`](https://github.com/pandas-dev/pandas/blob/614a48e3e6640b5694fd1ed6cbe56a760e89dd50/pandas/core/series.py#L2042-L2145) does not respect these defaults and instead returns missing values.
The issue is illustrated below:
```python
import pandas
from collections import Counter, defaultdict
input = pandas.Series(range(5))
counter = Counter()
counter[1] += 1
output = input.map(counter)
expected = series.map(lambda x: counter[x])
pandas.DataFrame({
'input': input,
'output': output,
'expected': expected,
})
```
Here's the output:
```
expected input output
0 0 0 NaN
1 1 1 1.0
2 0 2 NaN
3 0 3 NaN
4 0 4 NaN
```
The workaround is rather easy (`lambda x: dictionary[x]`) and shouldn't be to hard to implement. Are people on board with the change? Is there a performance concern with looking up each key independently?
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groupby fails when elements are lists
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| 1 | 2017-04-14T18:46:37Z | 2017-04-14T20:59:28Z | 2017-04-14T19:10:47Z |
NONE
| null |
```
import pandas as pd
df = pd.DataFrame ({'a' : [[1,2,3]]})
g = df.groupby ('a')
g.groups
TypeError: unhashable type: 'list'
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DEPR: more deprecation warnings
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| 1 | 2017-04-14T19:28:36Z | 2017-04-14T20:11:54Z | 2017-04-14T19:55:20Z |
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Support dicts with default values in series.map
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| 11 | 2017-04-14T19:43:47Z | 2017-04-15T23:10:32Z | 2017-04-15T13:46:44Z |
CONTRIBUTOR
| null |
Closes https://github.com/pandas-dev/pandas/issues/15999
Still a work in progress.
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| 10 | 2017-04-14T20:11:15Z | 2017-04-23T03:02:50Z | 2017-04-16T14:05:18Z |
MEMBER
| null |
Closes #9895
This just exposes a few of our testing functions publicly in `pandas.testing`.
- Alternative is to move the full testing module to `pandas.testing` (the actual code), question is then however how to only expose a subset of it publicly.
- I now only included the 3 most obvious ones (frame, series, index). Are there other ones that people would want to use? (the sparse ones?)
- We have quite a lot of keywords to those assert_* functions. We could op to only expose a subset of those publicly (redefining in pandas.testing instead of importing and only passing subset of the kwargs through)
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TST: test addl feather dtypes
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| 3 | 2017-04-14T21:14:49Z | 2017-04-15T00:22:54Z | 2017-04-14T21:31:32Z |
CONTRIBUTOR
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CLN/API: move plotting funcs to pandas.plotting
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| 8 | 2017-04-14T23:53:22Z | 2017-04-16T08:18:46Z | 2017-04-15T16:02:25Z |
MEMBER
| null |
closes #12548, rebase of #13579
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| 1 | 2017-04-15T00:10:31Z | 2017-04-15T02:03:56Z | 2017-04-15T02:02:51Z |
CONTRIBUTOR
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open
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[] | null | 6 | 2017-04-15T02:58:46Z | 2020-04-05T00:21:03Z | null |
NONE
| null |
#### Code Sample, a copy-pastable example if possible
with pd.HDFStore(r'e:\网易历史资金.h5') as f:
#### Problem description
all went on well before update,i got this problem after update python3.52 to python3.6 and conda update --all
#### Expected Output
the filename should be 网易历史资金.h5 but i got 缃戞槗鍘嗗彶璧勯噾.h5
#### Output of ``pd.show_versions()``
<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: 1.5.4
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.5.3
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
boto: 2.46.1
pandas_datareader: None
</details>
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