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} | 1 | 2016-02-02T13:45:37Z | 2016-02-02T15:36:14Z | 2016-02-02T15:36:14Z | MEMBER | null | Sphinx > 1.3.1 is now available in conda,
so not needed to isntall this separately with pip to get latest version.
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Other functions to add: `write_frame`, `tquery`, `uquery`
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} | 2 | 2016-02-02T15:39:25Z | 2018-11-14T13:58:32Z | 2018-11-14T13:58:16Z | CONTRIBUTOR | null | https://github.com/pydata/pandas/pull/12198
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} | 3 | 2016-02-02T16:42:46Z | 2016-02-06T06:54:47Z | 2016-02-05T14:53:09Z | CONTRIBUTOR | null | Ref issue #12164
Previous Behavior:
``` python
In [2]: s = Series([1,2,3], index=np.arange(3.))
In [3]: s
Out[3]:
0 1
1 2
2 3
dtype: int64
In [4]: s.index
Out[4]: Float64Index([0.0, 1.0, 2.0], dtype='float64')
In [5]: print(s.to_csv(path=None))
0,1
1,2
2,3
```
New behavior
``` python
In [2]: s = Series([1,2,3], index=np.arange(3.))
In [3]: s
Out[3]:
0.0 1
1.0 2
2.0 3
dtype: int64
In [4]: s.index
Out[4]: Float64Index([0.0, 1.0, 2.0], dtype='float64')
In [5]: print(s.to_csv(path=None))
0.0,1
1.0,2
2.0,3
```
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} | 1 | 2016-02-02T17:27:08Z | 2016-02-03T14:06:28Z | 2016-02-03T14:06:28Z | CONTRIBUTOR | null | closes #11928
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} | 1 | 2016-02-02T17:36:47Z | 2016-02-06T21:50:36Z | 2016-02-06T21:50:04Z | CONTRIBUTOR | null | Added deprecation warning to convert_objects docstring
Closes #12052
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} | 14 | 2016-02-02T19:21:36Z | 2021-04-23T00:44:41Z | null | NONE | null | When applying multiple aggregations to columns and setting margins=True I receive a KeyError. I believe that because multiple aggregations are applied the columns become a MultiIndex, which is unexpected when computing margins.
``` python
>>> import pandas as pd
>>> import numpy as np
>>> import random
>>> df = pd.DataFrame({'random1': [random.random() for i in range(10)],
'random2': [random.random() for i in range(10)],
'type': ['duck', 'bird']*5},
index=range(10,20))
>>> df.pivot_table(index='type',
aggfunc={'random1': [np.median, np.mean],
'random2': np.sum},
margins=True)
KeyError Traceback (most recent call last)
<ipython-input> in <module>()
3 aggfunc={'random1': [np.median, np.mean],
4 'random2': np.sum},
----> 5 margins=True)
/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
86 else:
87 kwargs[new_arg_name] = new_arg_value
---> 88 return func(*args, **kwargs)
89 return wrapper
90 return _deprecate_kwarg
/pandas/util/decorators.pyc in wrapper(*args, **kwargs)
86 else:
87 kwargs[new_arg_name] = new_arg_value
---> 88 return func(*args, **kwargs)
89 return wrapper
90 return _deprecate_kwarg
/pandas/tools/pivot.pyc in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna)
145 if margins:
146 table = _add_margins(table, data, values, rows=index,
--> 147 cols=columns, aggfunc=aggfunc)
148
149 # discard the top level
/pandas/tools/pivot.pyc in _add_margins(table, data, values, rows, cols, aggfunc)
189 row_margin[k] = grand_margin[k]
190 else:
--> 191 row_margin[k] = grand_margin[k[0]]
192
193 margin_dummy = DataFrame(row_margin, columns=[key]).T
KeyError: 'random1'
```
The _exact_ same error occurs with
``` python
aggfunc={'random1': {'median': np.median, 'mean': np.mean},
'random2': np.sum}
```
These errors do not occur when margins=False
This is using pandas version 0.15.2. I have not seen any changes which would lead me to believe it is be fixed in master.
Happy to create a PR, although I haven't done one before.
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} | 9 | 2016-02-02T20:16:13Z | 2016-04-30T14:32:55Z | 2016-04-30T14:32:55Z | NONE | null | When you try to print a data frame that has a Timestamp column with both NaT values and Timestamp with timezone, AND the number or rows is more than the display setting - an exception is raised.
```
import pandas as pd
from datetime import datetime
from pytz import UTC
test=pd.DataFrame([{"dt": pd.Timestamp(datetime.now().replace(tzinfo=UTC)), "x": 1}] + [{"dt": pd.NaT, "x": 1}] * 5)
pd.options.display.max_rows = 5
str(test)
```
This is the traceback when running the code above with 0.17.1 (with 0.17.0, the traceback is different, with this error message: AttributeError: 'numpy.ndarray' object has no attribute 'tz_localize')
---
TypeError Traceback (most recent call last)
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/IPython/core/formatters.py in **call**(self, obj)
339 method = _safe_get_formatter_method(obj, self.print_method)
340 if method is not None:
--> 341 return method()
342 return None
343 else:
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/frame.py in _repr_html_(self)
573 return self.to_html(max_rows=max_rows, max_cols=max_cols,
574 show_dimensions=show_dimensions,
--> 575 notebook=True)
576 else:
577 return None
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/frame.py in to_html(self, buf, columns, col_space, colSpace, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, bold_rows, classes, escape, max_rows, max_cols, show_dimensions, notebook)
1529 max_rows=max_rows,
1530 max_cols=max_cols,
-> 1531 show_dimensions=show_dimensions)
1532 # TODO: a generic formatter wld b in DataFrameFormatter
1533 formatter.to_html(classes=classes, notebook=notebook)
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/format.py in **init**(self, frame, buf, columns, col_space, header, index, na_rep, formatters, justify, float_format, sparsify, index_names, line_width, max_rows, max_cols, show_dimensions, **kwds)
378 self.columns = frame.columns
379
--> 380 self._chk_truncate()
381 self.adj = _get_adjustment()
382
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/format.py in _chk_truncate(self)
444 else:
445 row_num = max_rows_adj // 2
--> 446 frame = concat((frame.iloc[:row_num, :], frame.iloc[-row_num:, :]))
447 self.tr_row_num = row_num
448
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
811 verify_integrity=verify_integrity,
812 copy=copy)
--> 813 return op.get_result()
814
815
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/tools/merge.py in get_result(self)
993
994 new_data = concatenate_block_managers(
--> 995 mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy)
996 if not self.copy:
997 new_data._consolidate_inplace()
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
4454 copy=copy),
4455 placement=placement)
-> 4456 for placement, join_units in concat_plan]
4457
4458 return BlockManager(blocks, axes)
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in <listcomp>(.0)
4454 copy=copy),
4455 placement=placement)
-> 4456 for placement, join_units in concat_plan]
4457
4458 return BlockManager(blocks, axes)
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in concatenate_join_units(join_units, concat_axis, copy)
4551 to_concat = [ju.get_reindexed_values(empty_dtype=empty_dtype,
4552 upcasted_na=upcasted_na)
-> 4553 for ju in join_units]
4554
4555 if len(to_concat) == 1:
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in <listcomp>(.0)
4551 to_concat = [ju.get_reindexed_values(empty_dtype=empty_dtype,
4552 upcasted_na=upcasted_na)
-> 4553 for ju in join_units]
4554
4555 if len(to_concat) == 1:
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in get_reindexed_values(self, empty_dtype, upcasted_na)
4799
4800 if self.is_null and not getattr(self.block,'is_categorical',None):
-> 4801 missing_arr = np.empty(self.shape, dtype=empty_dtype)
4802 if np.prod(self.shape):
4803 # NumPy 1.6 workaround: this statement gets strange if all
TypeError: data type not understood
---
TypeError Traceback (most recent call last)
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/IPython/core/formatters.py in **call**(self, obj)
695 type_pprinters=self.type_printers,
696 deferred_pprinters=self.deferred_printers)
--> 697 printer.pretty(obj)
698 printer.flush()
699 return stream.getvalue()
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/IPython/lib/pretty.py in pretty(self, obj)
381 if callable(meth):
382 return meth(obj, self, cycle)
--> 383 return _default_pprint(obj, self, cycle)
384 finally:
385 self.end_group()
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/IPython/lib/pretty.py in _default_pprint(obj, p, cycle)
501 if _safe_getattr(klass, '__repr__', None) not in _baseclass_reprs:
502 # A user-provided repr. Find newlines and replace them with p.break_()
--> 503 _repr_pprint(obj, p, cycle)
504 return
505 p.begin_group(1, '<')
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/IPython/lib/pretty.py in _repr_pprint(obj, p, cycle)
683 """A pprint that just redirects to the normal repr function."""
684 # Find newlines and replace them with p.break_()
--> 685 output = repr(obj)
686 for idx,output_line in enumerate(output.splitlines()):
687 if idx:
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/base.py in **repr**(self)
61 Yields Bytestring in Py2, Unicode String in py3.
62 """
---> 63 return str(self)
64
65
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/base.py in **str**(self)
40
41 if compat.PY3:
---> 42 return self.**unicode**()
43 return self.**bytes**()
44
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/frame.py in **unicode**(self)
539 width = None
540 self.to_string(buf=buf, max_rows=max_rows, max_cols=max_cols,
--> 541 line_width=width, show_dimensions=show_dimensions)
542
543 return buf.getvalue()
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/frame.py in to_string(self, buf, columns, col_space, header, index, na_rep, formatters, float_format, sparsify, index_names, justify, line_width, max_rows, max_cols, show_dimensions)
1478 max_rows=max_rows,
1479 max_cols=max_cols,
-> 1480 show_dimensions=show_dimensions)
1481 formatter.to_string()
1482
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/format.py in **init**(self, frame, buf, columns, col_space, header, index, na_rep, formatters, justify, float_format, sparsify, index_names, line_width, max_rows, max_cols, show_dimensions, **kwds)
378 self.columns = frame.columns
379
--> 380 self._chk_truncate()
381 self.adj = _get_adjustment()
382
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/format.py in _chk_truncate(self)
444 else:
445 row_num = max_rows_adj // 2
--> 446 frame = concat((frame.iloc[:row_num, :], frame.iloc[-row_num:, :]))
447 self.tr_row_num = row_num
448
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/tools/merge.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
811 verify_integrity=verify_integrity,
812 copy=copy)
--> 813 return op.get_result()
814
815
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/tools/merge.py in get_result(self)
993
994 new_data = concatenate_block_managers(
--> 995 mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy)
996 if not self.copy:
997 new_data._consolidate_inplace()
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy)
4454 copy=copy),
4455 placement=placement)
-> 4456 for placement, join_units in concat_plan]
4457
4458 return BlockManager(blocks, axes)
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in <listcomp>(.0)
4454 copy=copy),
4455 placement=placement)
-> 4456 for placement, join_units in concat_plan]
4457
4458 return BlockManager(blocks, axes)
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in concatenate_join_units(join_units, concat_axis, copy)
4551 to_concat = [ju.get_reindexed_values(empty_dtype=empty_dtype,
4552 upcasted_na=upcasted_na)
-> 4553 for ju in join_units]
4554
4555 if len(to_concat) == 1:
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in <listcomp>(.0)
4551 to_concat = [ju.get_reindexed_values(empty_dtype=empty_dtype,
4552 upcasted_na=upcasted_na)
-> 4553 for ju in join_units]
4554
4555 if len(to_concat) == 1:
/Users/uzi/.virtualenvs/pandas/lib/python3.4/site-packages/pandas/core/internals.py in get_reindexed_values(self, empty_dtype, upcasted_na)
4799
4800 if self.is_null and not getattr(self.block,'is_categorical',None):
-> 4801 missing_arr = np.empty(self.shape, dtype=empty_dtype)
4802 if np.prod(self.shape):
4803 # NumPy 1.6 workaround: this statement gets strange if all
TypeError: data type not understood
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| closed | false | null | []
| null | 2 | 2016-02-02T22:29:38Z | 2016-02-02T22:37:14Z | 2016-02-02T22:34:15Z | NONE | null | This is a useful format for streamed applications.
It's basically the records orientation without the brackets and commas after each line.
http://jsonlines.org
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} | 10 | 2016-02-02T22:33:18Z | 2020-01-02T11:10:16Z | 2020-01-02T00:40:41Z | NONE | null | While these special values are not strictly standard conform, most implementations do allow or use them.
For example Google's GSON Library or python's json module in the standard library.
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| null | 5 | 2016-02-03T00:04:20Z | 2016-02-03T03:12:16Z | 2016-02-03T03:12:16Z | NONE | null | ```
pandas: 0.17.1
numpy: 1.9.2
```
I have two example DataFrames written to hdf5 files on disk. I use pd.read_hdf() to I/O them into a list. When I attempt to pd.concat([list,of,frames]) it takes 100X longer if I don't explicitly call .copy() right after the pd.read_hdf(). :confused:
Did something change in the pd.read_hdf() implementation to only reference data on disk instead of actually reading it into memory? This was a really subtle performance issue that threw a wrench in my data flow on the latest pandas stack. Profiling shows the bottleneck is in method 'ravel' of 'numpy.ndarray' (when you don't call copy().) This does not happen in pandas 0.13.1 (I know very old). :anguished:
``` python
import os
import pandas as pd
import numpy as np
rows, cols, skip, frames = 777, 10000, 250, 2
def create_df():
ncols = cols - np.random.randint(0,skip,1,)
nrows = rows - np.random.randint(0,skip,1,)
df = pd.DataFrame(np.random.randn(nrows, ncols), columns=[('SUPER_LONG_COLUMN_NAME_NUMBER_THAT_IS_VERY_LONG_'+ str(i))*2 for i in range(ncols)]).astype(np.float32)
df.iloc[100:25:500,1000:100:2000] = np.nan
df.iloc[:,-10:] = 'TEST_STRING_COL_VALUE'
df.iloc[1:,:100] = np.nan
df.iloc[10:, 100:200] = np.nan
return df[np.random.choice(df.columns, ncols-skip, replace=False)]
%time dfs = [create_df() for i in range(frames)]
Wall time: 508 ms
%%time
for i, df in enumerate(dfs):
df.to_hdf(r'./dfs/{}.h5'.format(i), 'df', mode='w', complib='lzo', complevel=9)
Wall time: 508 ms
%time dfs_read_hdf = [pd.read_hdf(r'./dfs/{}'.format(fn), mode='r') for fn in os.listdir(r'./dfs/')]
Wall time: 247 ms
%time dfs_read_hdf_copy = [pd.read_hdf(r'./dfs/{}'.format(fn), mode='r').copy() for fn in os.listdir(r'./dfs/')]
Wall time: 337 ms
%time dfa = pd.concat(dfs_read_hdf, ignore_index=True)
Wall time: 34.6 s
%time dfa = pd.concat(dfs_read_hdf_copy, ignore_index=True)
Wall time: 304 ms
%prun pd.concat(dfs_read_hdf, ignore_index=True)
```
Here is the %prun of the slow concat. Notice that a whopping 30 seconds is being spent in np.ravel() :exclamation:
```
298220 function calls (298178 primitive calls) in 34.758 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
1431 30.263 0.021 30.263 0.021 {method 'ravel' of 'numpy.ndarray' objects}
955 3.619 0.004 34.144 0.036 internals.py:4461(get_empty_dtype_and_na)
1419 0.277 0.000 0.277 0.000 {pandas.algos.take_2d_axis1_float32_float32}
1423 0.118 0.000 0.144 0.000 common.py:230(_isnull_ndarraylike)
3337 0.031 0.000 0.031 0.000 {method 'reduce' of 'numpy.ufunc' objects}
1910 0.027 0.000 30.476 0.016 internals.py:4767(is_null)
62628 0.024 0.000 0.049 0.000 {isinstance}
1427 0.024 0.000 0.361 0.000 common.py:697(take_nd)
...
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Windows
OS-release: 2008ServerR2
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 17.1.1
Cython: 0.23
numpy: 1.9.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: None
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.4
blosc: None
bottleneck: 1.0.0
tables: 3.2.1
numexpr: 2.4.3
matplotlib: 1.5.0
openpyxl: 2.2.5
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: None
lxml: 3.4.4
bs4: 4.4.0
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.8
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 17 | 2016-02-03T00:12:18Z | 2016-02-09T22:15:54Z | 2016-02-09T22:03:11Z | CONTRIBUTOR | null | A work colleague, David Chase, encountered some surprising behaviour, which can be reduced to the following. The data-frame
```
DataFrame({'x': [2.5], 'y': [42], 'z': ['2E']})
```
does not round-trip correctly. The string '2E' is interpreted as a valid float, but it should not be (according to man strtod(3), which seems a reasonable spec).
This PR changes the three variants of `xstrtod()` to reject a string where no digits follow the 'e' or 'E', and includes tests for this case.
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} | 46 | 2016-02-03T02:51:32Z | 2017-04-05T02:08:35Z | 2016-09-04T10:21:01Z | CONTRIBUTOR | null | Part of https://github.com/pydata/pandas/issues/6581
Deprecation started in https://github.com/pydata/pandas/pull/7096
Changes the default value of `return_type` in DataFrame.boxplot
and DataFrame.plot.box from None to 'axes'.
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} | 2 | 2016-02-03T09:22:52Z | 2016-02-13T01:15:50Z | 2016-02-13T01:15:50Z | NONE | null | Issue:
``` python
import pandas as pd
from datetime import datetime
### Concat'ing two UTC times works
first = pd.DataFrame([[datetime(2016, 1, 1)]])
first[0] = first[0].dt.tz_localize('UTC')
second = pd.DataFrame([[datetime(2016, 1, 2)]])
second[0] = second[0].dt.tz_localize('UTC')
print pd.concat([first, second]) # Works
### Concat'ing two London times doesn't work
first = pd.DataFrame([[datetime(2016, 1, 1)]])
first[0] = first[0].dt.tz_localize('Europe/London')
second = pd.DataFrame([[datetime(2016, 1, 2)]])
second[0] = second[0].dt.tz_localize('Europe/London')
print pd.concat([first, second]) # Doesn't work - returns a DF full of NaTs
### Concat'ing 2+1 London times works
first = pd.DataFrame([[datetime(2016, 1, 1)], [datetime(2016, 1, 2)]])
first[0] = first[0].dt.tz_localize('Europe/London')
second = pd.DataFrame([[datetime(2016, 1, 3)]])
second[0] = second[0].dt.tz_localize('Europe/London')
print pd.concat([first, second]) # Works
### Concat'ing 1+2 London times doesn't work
first = pd.DataFrame([[datetime(2016, 1, 1)]])
first[0] = first[0].dt.tz_localize('Europe/London')
second = pd.DataFrame([[datetime(2016, 1, 2)], [datetime(2016, 1, 3)]])
second[0] = second[0].dt.tz_localize('Europe/London')
print pd.concat([first, second]) # Doesn't work - first row is NaT
```
Dependencies:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Linux
OS-release: 3.17.7-300.fc21.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 19.4
Cython: None
numpy: 1.10.2
scipy: 0.16.0
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 2 | 2016-02-03T12:24:39Z | 2016-02-24T00:05:40Z | 2016-02-24T00:05:40Z | CONTRIBUTOR | null | I was trying to guess, in a dataframe with multi-indexed columns, how to select all columns with a given second-level value.
And then I hit an empty `TypeError` message
```
In [8]: df = pd.DataFrame(columns=['i', 'c', 'x', 'y'], data=[[0, 0, 1, 2], [1, 0, 3, 4], [0, 1, 1, 2], [1, 1, 3, 4]])
In [9]: dg
Out[9]:
x y
c 0 1 0 1
i
0 1 1 2 2
1 3 3 4 4
In [12]: dg = df.pivot_table(index='i', columns='c', values=['x', 'y'])
n [19]: dg['x', 0]
Out[19]:
i
0 1
1 3
Name: (x, 0), dtype: int64
In [20]: dg[:, 0]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-20-7eb22191791d> in <module>()
----> 1 dg[:, 0]
/home/nicolas/Git/pandas/pandas/core/frame.py in __getitem__(self, key)
1977 return self._getitem_frame(key)
1978 elif is_mi_columns:
-> 1979 return self._getitem_multilevel(key)
1980 else:
1981 return self._getitem_column(key)
/home/nicolas/Git/pandas/pandas/core/frame.py in _getitem_multilevel(self, key)
2021
2022 def _getitem_multilevel(self, key):
-> 2023 loc = self.columns.get_loc(key)
2024 if isinstance(loc, (slice, Series, np.ndarray, Index)):
2025 new_columns = self.columns[loc]
/home/nicolas/Git/pandas/pandas/indexes/multi.pyc in get_loc(self, key, method)
1526 key = _values_from_object(key)
1527 key = tuple(map(_maybe_str_to_time_stamp, key, self.levels))
-> 1528 return self._engine.get_loc(key)
1529
1530 # -- partial selection or non-unique index
/home/nicolas/Git/pandas/pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4005)()
135 util.set_value_at(arr, loc, value)
136
--> 137 cpdef get_loc(self, object val):
138 if is_definitely_invalid_key(val):
139 raise TypeError
/home/nicolas/Git/pandas/pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3617)()
137 cpdef get_loc(self, object val):
138 if is_definitely_invalid_key(val):
--> 139 raise TypeError
140
141 if self.over_size_threshold and self.is_monotonic_increasing:
TypeError:
```
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} | 28 | 2016-02-03T12:42:05Z | 2016-04-25T14:38:24Z | 2016-04-25T14:38:03Z | CONTRIBUTOR | null | I'm adding tests to close #9455 after pull request #12158 solved the issue
But I confess I'm a bit surprised by the result:
```
n [2]: df1 = DataFrame(columns=['a', 'b'], data=[[0, 1]])
In [3]: df2a = DataFrame(columns=['c', 'd'], data=[[2, 3]])
In [4]: df2b = DataFrame(columns=['c', 'e'], data=[[4, 5]])
In [5]: df2 = concat([df2a, df2b], keys=['l', 'r'], axis=1)
In [6]: df2.index.name = 'a'
In [7]: df2 = df2.reset_index()
In [8]: df1
Out[8]:
a b
0 0 1
In [9]: df2
Out[9]:
a l r
c d c e
0 0 2 3 4 5
In [10]: merge(df1, df2, on='a')
pandas/tools/merge.py:467: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
self.right = self.right.drop(right_drop, axis=1)
Out[10]:
a b (l, c) (l, d) (r, c) (r, e)
0 0 1 2 3 4 5
```
Is it all right ?
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| null | 1 | 2016-02-03T13:02:26Z | 2016-02-03T13:39:33Z | 2016-02-03T13:39:33Z | NONE | null | In some circumstances, `idxmax` may return a Series with unexpected `dtype`:
``` python
>>> df = pandas.DataFrame(data=[[0.0, 1.0], [0.0, 1.0]], columns=[1,2], index=[1,2])
>>> df.idxmax(axis=0)
1 1
2 1
dtype: int64
```
``` python
>>> df = pandas.DataFrame(data=[[-inf, 1.0], [-inf, 1.0]], columns=[1,2], index=[1,2])
>>> df.idxmax(axis=0)
1 NaN
2 1
dtype: float64
```
In the second case, `dtype` is changed from `int64` to `float64` (even though both `columns` and `index` only contains `int64`.
I understand that `float64` is needed to actually store `NaN` in the second case but I find it confusing (and possible error-prone if we combine it with a subsequent call to `df.loc`, for example).
But maybe this is the expected behavior...
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.0.9-boot2docker
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: None
pip: 1.5.4
setuptools: 3.3
Cython: None
numpy: 1.10.4
scipy: 0.13.3
statsmodels: None
IPython: 4.0.1
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 2 | 2016-02-03T15:07:21Z | 2016-02-11T02:55:17Z | 2016-02-11T02:55:17Z | MEMBER | null | ```
list(pd.HDFStore("non_existing_store.hdf"))
```
should, I think, return `[]`, instead it yields:
```
KeyError: 'No object named 0 in the file'
```
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} | 4 | 2016-02-03T16:10:53Z | 2019-05-18T14:16:55Z | 2019-05-18T14:16:55Z | NONE | null | This is a very obscure bug in a corner-case, but it's still a bug which I ran into. It's easiest to understand by seeing it in action. I'm running **pandas version 0.17.1**.
```
>>> import pandas as pd
>>> import datetime
>>> df = pd.DataFrame({'a': [datetime.datetime(2015,1,1)], 'b': [None], 'c': [None], 'd': [''], 'e': [[]], 'f': [set()], 'g': [{}]})
>>> df
a b c d e f g
0 2015-01-01 None None [] {} {}
```
The key to causing the bug is a row which starts with a datetime obj followed by at least two None's. Following that is any number of empty strings, empty lists, empty sets, or empty dicts. Indexing into the DataFrame by row works fine.
```
>>>> df.iloc[0]
a 2015-01-01 00:00:00
b None
c None
d
e []
f {}
g {}
Name: 0, dtype: object
```
Iterating over the rows using iterrows causes a weird behavior of converting to a datetime dtype with NaT's. **This is the bug:**
```
>>> df.iterrows().next()
(0, a 2015-01-01
b NaT
c NaT
d NaT
e NaT
f NaT
g NaT
Name: 0, dtype: datetime64[ns])
```
The following properties **avoid the issue** entirely:
1. Having less than two None's following the datetime.
2. Having any of the items after the two None's be "truthy".
3. Including either a zero or a False in the items after the None's.
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} | 1 | 2016-02-03T17:01:14Z | 2017-02-24T20:18:20Z | 2017-02-24T20:18:20Z | NONE | null | Thanks for great tool!
When printing a long DataFrame with multi-index which are result of set_index(..., append=True),
displaying index values look strange.
``` py
import pandas as pd
def func(n):
x = [
[['a1-%d' % i, 'a2-%d' % i] for i in range(n)],
[['b1-%d' % i, 'b2-%d' % i] for i in range(n)],
]
y = pd.concat([pd.DataFrame(a) for a in x], keys=['X', 'Y']).set_index(1, append=True)
print(y)
func(5) # no problem
func(50) # bug!
```
When DataFrames are short (in this case, 10 rows), there is no problem.
However, when DataFrames is long (in this case, 100 rows) and omitted middle-part on printing, some columns of index is not correct.
```
0
1
X 0 a2-0 a1-0
1 a2-1 a1-1
2 a2-2 a1-2
3 a2-3 a1-3
4 a2-4 a1-4
Y 0 b2-0 b1-0
1 b2-1 b1-1
2 b2-2 b1-2
3 b2-3 b1-3
4 b2-4 b1-4
0
1
X a2-0 a2-0 a1-0
a2-1 a2-1 a1-1
a2-2 a2-2 a1-2
a2-3 a2-3 a1-3
a2-4 a2-4 a1-4
a2-5 a2-5 a1-5
a2-6 a2-6 a1-6
a2-7 a2-7 a1-7
a2-8 a2-8 a1-8
a2-9 a2-9 a1-9
... ...
Y b2-40 b2-40 b1-40
b2-41 b2-41 b1-41
b2-42 b2-42 b1-42
b2-43 b2-43 b1-43
b2-44 b2-44 b1-44
b2-45 b2-45 b1-45
b2-46 b2-46 b1-46
b2-47 b2-47 b1-47
b2-48 b2-48 b1-48
b2-49 b2-49 b1-49
[100 rows x 1 columns]
```
result of pd.show_versions() is as follows.
```
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Linux
OS-release: 2.6.32-279.22.1.el6.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.5
Cython: 0.23.4
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.2.6
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 10 | 2016-02-03T17:21:54Z | 2018-01-08T23:14:59Z | 2016-08-21T19:34:41Z | NONE | null | Minor issue regarding `read_csv`'s `na_values` argument in `dict` format. I note that the list format works fine when the NA value is given as a float-type (which is often the intuitive choice), e.g.:
``` python
co2 = pd.read_csv("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
comment = "#", delim_whitespace = True,
names = ["year", "month", "decimal_date", "average", "interpolated", "trend", "days"],
na_values =[-99.99, -1])
```
However, the dict format is more appropriate for this classic data set, since different columns are defining different NA values. Unfortunately, this fails with an error about float type:
``` python
co2 = pd.read_csv("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
comment = "#", delim_whitespace = True,
names = ["year", "month", "decimal_date", "average", "interpolated", "trend", "days"],
na_values = {"decimal_date" : -99.99, "days" : -1})
```
and the NA value must be given as a string; which feels all kinds of wrong here:
``` python
co2 = pd.read_csv("ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt",
comment = "#", delim_whitespace = True,
names = ["year", "month", "decimal_date", "average", "interpolated", "trend", "days"],
na_values = {"decimal_date" : "-99.99", "days" : "-1"})
```
Thanks for all the `pandas` awesomeness,
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} | 2 | 2016-02-03T17:54:06Z | 2017-02-03T15:30:51Z | 2017-02-03T15:29:59Z | CONTRIBUTOR | null | This maybe is just a doc warning - but caught me by surprise. I would prefer the `read_csv` behavior, but it would be backwards incompatible.
``` python
In [57]: pd.read_csv('path/doesn_exist')
---------------------------------------------------------------------------
IOError: File path/doesn_exist does not exist
In [56]: pd.read_msgpack('path/doesn_exist')
Out[56]: [112, 97, 116, 104, 47, 100, 111, 101, 115, 110, 95, 101, 120, 105, 115, 116]
In [59]: pd.read_msgpack(u'path/doesn_exist')
---------------------------------------------------------------------------
ValueError: path_or_buf needs to be a string file path or file-like
```
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| null | 3 | 2016-02-03T19:09:04Z | 2016-02-03T20:54:55Z | 2016-02-03T19:36:04Z | CONTRIBUTOR | null | I often find myself building a chain of logic, and then wanting to slice over some expression. Here's a contrived example:
``` python
In [12]: pd.Series(range(10)).mul(5).pipe(lambda x: x**2).pipe(lambda x: x-500)
Out[12]:
0 -500
1 -475
2 -400
3 -275
4 -100
5 125
6 400
7 725
8 1100
9 1525
dtype: int64
```
...and then having to assign to a separate variable and slice using that. Which is not the worst affliction that could strike western civilization, but can disrupt the flow in a repl a little.
``` python
In [13]: s=pd.Series(range(10)).mul(5).pipe(lambda x: x**2).pipe(lambda x: x-500)
In [14]: s[s>200]
Out[14]:
6 400
7 725
8 1100
9 1525
dtype: int64
```
Instead, would it be reasonable to extend query or filter to do this. For example:
`pd.Series(range(10)).mul(5).pipe(lambda x: x**2).pipe(lambda x: x-500).query('>200')`
or
`pd.Series(range(10)).mul(5).pipe(lambda x: x**2).pipe(lambda x: x-500).filter(lambda x: x>200)`
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} | 3 | 2016-02-03T19:35:18Z | 2016-02-06T19:49:54Z | 2016-02-06T19:49:44Z | NONE | null | Fixes https://github.com/pydata/pandas/issues/11727
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} | 0 | 2016-02-03T22:15:36Z | 2019-10-21T19:52:15Z | null | CONTRIBUTOR | null | I have a massive csv file (54 GB, ~2.7 billion records) that I'm trying to process in chunks using chunksize. The csv reader stopped before hitting the end of the file. Upon further investigation, I found that it stopped after reading 2147483648 rows, which, is (2**32)/2. I assume there's an overflow happening somewhere inside the cython code for read_csv.
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| null | 3 | 2016-02-04T04:02:07Z | 2017-08-07T14:45:59Z | 2016-02-05T17:29:20Z | NONE | null | When trying to run code on my computer that my partner wrote on his computer, I get this error when I run
``` python
def read_excel(file):
xl = pd.ExcelFile(file)
df = xl.parse(0)
return df
```
Here's the Error messages:
File "C:\Users\wjayn_000\Documents\GitHub\tippecanoe\app\views\lodging.py", line 19, in lodging_output
hotels = read_excel(f)
File "C:\Users\wjayn_000\Documents\GitHub\tippecanoe\app\views\functions.py", line 7, in read_excel
xl = pd.ExcelFile(file)
File "C:\Python27\lib\site-packages\pandas\io\excel.py", line 212, in **init**
self.book = xlrd.open_workbook(file_contents=data)
File "C:\Python27\lib\site-packages\xlrd__init__.py", line 399, in open_workbook
zf = zipfile.ZipFile(timemachine.BYTES_IO(file_contents))
File "C:\Python27\lib\zipfile.py", line 770, in **init**
self._RealGetContents()
File "C:\Python27\lib\zipfile.py", line 811, in _RealGetContents
raise BadZipfile, "File is not a zip file"
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| null | 5 | 2016-02-04T11:25:46Z | 2016-02-05T05:48:35Z | 2016-02-05T02:26:24Z | NONE | null | It looks like it calculates an adjusted value of std, i.e. a squared deviation from the mean, (x_i - x_bar) *\* 2, divided by n-1 instead of n.
It is a reasonable measure for cases when n <= 50 allowing to avoid underestimation of std. But in cases when n > 50 actually there is no difference what number is used in the denominator, n or n-1. I guess you need to take this into account.
Many thanks for your excellent piece of work.
B.R., Andre Logunov, Russia
https://plus.google.com/u/1/
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| null | 6 | 2016-02-04T14:36:38Z | 2016-02-10T19:21:45Z | 2016-02-05T17:25:44Z | CONTRIBUTOR | null | The cookbook [Data In/Out](http://pandas-docs.github.io/pandas-docs-travis/cookbook.html#data-in-out) points to: [how to read in multiple files, appending to create a single dataframe](http://stackoverflow.com/questions/25210819/speeding-up-data-import-function-pandas-and-appending-to-dataframe/25210900#25210900).
When using the suggested procedure on 2 df with freq '10T' the index is without the freq after the concat.
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} | 4 | 2016-02-04T21:24:54Z | 2016-02-08T15:18:59Z | 2016-02-08T15:18:59Z | NONE | null | I am having an issue with the StataReader class, which is found in stata.py ("pandas/io/stata.py").
I have pandas: 0.17.1.
The following is the python code I am trying to run:
```
import sys
reload(sys).setdefaultencoding('utf-8')
import pandas as pd
from pandas.io import stata
sr=stata.StataReader(fileName)
```
where fileName is a stata file.
The following code is part of the _read_old_header method(which starts on line 1184) of the StataReader class in stata.py, which gets called during the initialization of a StataReader object:
```
if self.format_version > 108:
typlist = [ord(self.path_or_buf.read(1))
for i in range(self.nvar)]
else:
typlist = [
self.OLD_TYPE_MAPPING[
self._decode_bytes(self.path_or_buf.read(1))
] for i in range(self.nvar)
]
```
I have no errors when my stata files are newer than version 108, but with files that are version 105, there seems to be a bug in _decode_bytes. The above code passes in self and only one additional argument to _decode_bytes, the string that is returned by path_or_buf.read(1).
Here is the the method _decode_bytes (line 896):
```
def _decode_bytes(self, str, errors=None):
if compat.PY3 or self._encoding is not None:
return str.decode(self._encoding, errors)
else:
return str
```
When no third argument is passed in (as is the case when it is called by _read_old_header), the argument "errors" is set to None. Here is where the error is thrown. The error is:
```
TypeError: decode() argument 2 must be string, not None
```
That is the issue: the decode method of the string class is expecting the second argument to not be a None type, but _decode_bytes passes in errors as None by default.
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} | 4 | 2016-02-05T05:53:06Z | 2016-02-08T15:19:29Z | 2016-02-08T15:18:59Z | CONTRIBUTOR | null | This should close #12232, although the issue may resurface for files containing double values (I can't determine the old type code for doubles).
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} | 6 | 2016-02-05T06:12:00Z | 2016-02-06T19:46:50Z | 2016-02-06T19:46:50Z | CONTRIBUTOR | null | xref #12230
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} | 2 | 2016-02-05T15:46:02Z | 2016-09-28T10:25:14Z | 2016-09-28T10:25:14Z | CONTRIBUTOR | null | `scipy` is not install `libgfortran`, see issue [here](https://github.com/conda/conda/issues/2028)
log [here](https://travis-ci.org/pydata/pandas/jobs/107252670)
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} | 17 | 2016-02-05T16:45:56Z | 2019-10-29T12:18:22Z | null | CONTRIBUTOR | null | xref: https://github.com/pydata/pandas/issues/2397
xref #14263 (example)
I _think_ that we should protect the file open/closes with a lock to avoid this problem.
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| null | 2 | 2016-02-05T17:27:29Z | 2016-08-21T02:41:54Z | 2016-08-21T02:41:54Z | CONTRIBUTOR | null | The data-frame
``` python
DataFrame({'x': [2.5], 'y': [42], 'z': ['2E']})
```
does not round-trip correctly via CSV. The string `'2E'` is interpreted as a valid float, but it should not be (according to man strtod(3), which seems a reasonable spec).
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} | 1 | 2016-02-05T19:33:55Z | 2016-03-17T23:46:50Z | 2016-03-17T23:46:50Z | NONE | null | ``` python
import numpy, pandas
ix = pandas.Float64Index([1,2,3])
numpy.searchsorted(ix, 2.5)
```
leads to
```
TypeError
..../site-packages/numpy/core/fromnumeric.pyc in searchsorted(a, v, side, sorter)
1086 except AttributeError:
1087 return _wrapit(a, 'searchsorted', v, side, sorter)
-> 1088 return searchsorted(v, side, sorter)
TypeError: searchsorted() takes at most 3 arguments (4 given)
```
The reason is that the interface of Index.searchsorted(self, key, side='left') doesn't match numpy.ndarray.searchsorted(self, v, side='left', sorter=None) and numpy.searchsorted is implemented as
```
def searchsorted(a, v, side='left', sorter=None):
try:
searchsorted = a.searchsorted
except AttributeError:
return _wrapit(a, 'searchsorted', v, side, sorter)
return searchsorted(v, side, sorter)
```
This is pandas 0.17.1 (but the relevant code seems to appear in master) and numpy 1.10.4 .
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} | 2 | 2016-02-05T20:09:44Z | 2016-02-06T19:34:23Z | 2016-02-06T19:34:23Z | CONTRIBUTOR | null | partially on #12235
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| null | 6 | 2016-02-06T07:08:00Z | 2016-02-06T19:38:46Z | 2016-02-06T19:38:41Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/builds/107252660
This happens since the last commit, which was merging a pull request of mine... but Travis was green for me, and beside I can't see no connection between that:
```
ERROR: test_nankurt (pandas.tests.test_nanops.TestnanopsDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/build/pydata/pandas/pandas/tests/test_nanops.py", line 141, in check_results
tm.assert_almost_equal(targ, res)
File "pandas/src/testing.pyx", line 58, in pandas._testing.assert_almost_equal (pandas/src/testing.c:3809)
cpdef assert_almost_equal(a, b, bint check_less_precise=False,
File "pandas/src/testing.pyx", line 156, in pandas._testing.assert_almost_equal (pandas/src/testing.c:2830)
raise_assert_detail(obj, msg, a, b)
File "/home/travis/build/pydata/pandas/pandas/util/testing.py", line 842, in raise_assert_detail
raise AssertionError(msg)
AssertionError: Iterable are different
Second object is iterable, first isn't
[left]: nan
[right]: [[nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan], [nan, nan, nan, nan, nan]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/travis/build/pydata/pandas/pandas/tests/test_nanops.py", line 453, in test_nankurt
allow_str=False, allow_date=False, allow_tdelta=False)
File "/home/travis/build/pydata/pandas/pandas/tests/test_nanops.py", line 233, in check_funs
**kwargs)
File "/home/travis/build/pydata/pandas/pandas/tests/test_nanops.py", line 222, in check_fun
targarnanval, **kwargs)
File "/home/travis/build/pydata/pandas/pandas/tests/test_nanops.py", line 184, in check_fun_data
self.check_results(targ, res, axis)
File "/home/travis/build/pydata/pandas/pandas/tests/test_nanops.py", line 144, in check_results
if targ.dtype == 'm8[ns]':
AttributeError: ("'float' object has no attribute 'dtype'", 'axis: 0 of 2', 'skipna: False', 'kwargs: {}', 'testar: arr_float_nan', 'targar: arr_float', 'targarnan: arr_float_nan')
```
and my commit, which was about the formatting of float strings.
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} | 3 | 2016-02-06T14:03:34Z | 2016-02-11T23:43:23Z | 2016-02-11T23:35:54Z | MEMBER | null | Closes #11718.
- Ops with `NaT` and `Timestamp` with `tz` results in `NaT`
- Ops with `NaT` and `DatetimeIndex` / `TimedeltaIndex` results in corresponding `Index` filled with `NaT`.
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} | 4 | 2016-02-06T15:17:52Z | 2018-11-01T17:06:53Z | 2018-11-01T17:06:53Z | CONTRIBUTOR | null | Reproduced as follows:
```
In [3]: store = pd.HDFStore('teststore.h5', 'w')
In [4]: chunk = pd.DataFrame({'V1':['a','b','c','d','e'], 'data':np.arange(5)})
In [5]: store.append('df', chunk, min_itemsize={'V1': 4})
In [6]: chunk = pd.DataFrame({'V1':['', ''], 'data': [3, 5]})
In [7]: store.append('df', chunk, min_itemsize={'V1': 4})
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-7-c9bafa18ead0> in <module>()
----> 1 store.append('df', chunk, min_itemsize={'V1': 4})
/Users/amcpherson/Anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in append(self, key, value, format, append, columns, dropna, **kwargs)
905 kwargs = self._validate_format(format, kwargs)
906 self._write_to_group(key, value, append=append, dropna=dropna,
--> 907 **kwargs)
908
909 def append_to_multiple(self, d, value, selector, data_columns=None,
/Users/amcpherson/Anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in _write_to_group(self, key, value, format, index, append, complib, encoding, **kwargs)
1250
1251 # write the object
-> 1252 s.write(obj=value, append=append, complib=complib, **kwargs)
1253
1254 if s.is_table and index:
/Users/amcpherson/Anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in write(self, obj, axes, append, complib, complevel, fletcher32, min_itemsize, chunksize, expectedrows, dropna, **kwargs)
3755 self.create_axes(axes=axes, obj=obj, validate=append,
3756 min_itemsize=min_itemsize,
-> 3757 **kwargs)
3758
3759 for a in self.axes:
/Users/amcpherson/Anaconda/lib/python2.7/site-packages/pandas/io/pytables.pyc in create_axes(self, axes, obj, validate, nan_rep, data_columns, min_itemsize, **kwargs)
3432 self.values_axes.append(col)
3433 except (NotImplementedError, ValueError, TypeError) as e:
-> 3434 raise e
3435 except Exception as detail:
3436 raise Exception(
ValueError: Trying to store a string with len [8] in [V1] column but
this column has a limit of [4]!
Consider using min_itemsize to preset the sizes on these columns
```
Does not raise unless all values in a column are empty strings. A workaround is to set min_itemsize to 8 or higher.
Version information:
```
In [10]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.2
scipy: 0.16.1
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: None
```
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} | 1 | 2016-02-06T17:31:31Z | 2016-02-06T19:32:09Z | 2016-02-06T19:31:59Z | CONTRIBUTOR | null | Working around https://github.com/pydata/pandas/issues/12240 which we'll leave open until I (or someone more knowledgeable) can properly fix these.
This skips the following tests when on scipy 0.17
- test_nansem
- test_nanskew
- test_nankurk
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} | 1 | 2016-02-06T18:21:59Z | 2016-02-12T13:42:33Z | 2016-02-12T13:42:33Z | MEMBER | null | ```
In [29]: df = pd.DataFrame({'dt': pd.date_range("2015-01-01", periods=3, tz='Europe/Brussels')})
In [30]: df
Out[30]:
dt
0 2015-01-01 00:00:00+01:00
1 2015-01-02 00:00:00+01:00
2 2015-01-03 00:00:00+01:00
In [31]: df.apply(lambda x: x)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-31-9cd68f0fd3ff> in <module>()
----> 1 df.apply(lambda x: x)
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\frame.py in apply(self, func, a
xis, broadcast, raw, reduce, args, **kwds)
4029 if reduce is None:
4030 reduce = True
-> 4031 return self._apply_standard(f, axis, reduce=reduce)
4032 else:
4033 return self._apply_broadcast(f, axis)
c:\users\vdbosscj\scipy\pandas-joris\pandas\core\frame.py in _apply_standard(sel
f, func, axis, ignore_failures, reduce)
4076 # Create a dummy Series from an empty array
4077 index = self._get_axis(axis)
-> 4078 empty_arr = np.empty(len(index), dtype=values.dtype)
4079 dummy = Series(empty_arr, index=self._get_axis(axis),
4080 dtype=values.dtype)
TypeError: data type not understood
```
(from [SO](http://stackoverflow.com/questions/35241673/pandas-df-apply-typeerror-data-type-not-understood#35244563))
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} | 19 | 2016-02-06T19:18:21Z | 2016-03-12T19:19:12Z | 2016-03-12T16:18:52Z | CONTRIBUTOR | null | so I am thinking of a RC1, friday feb 12th & release on or about friday feb 26th assuming nothing major comes up.
any objections
@jorisvandenbossche @shoyer @wesm @cpcloud @TomAugspurger @sinhrks
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} | 13 | 2016-02-06T20:09:15Z | 2016-02-15T20:51:19Z | 2016-02-13T13:34:34Z | CONTRIBUTOR | null | raise a `TypeError` instead, xref #4892
closes #11836
similar to numpy in 1.11 [here](https://github.com/numpy/numpy/pull/6271)
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} | 1 | 2016-02-07T00:59:28Z | 2016-02-27T05:39:12Z | 2016-02-12T15:04:32Z | MEMBER | null | Closes #11584.
Because many existing tests expects `assert_almost_equal` to compare `pandas` instances, it looks better to call appropriate test functions rather than raise.
I've also fixed some failure cases caused by the change (comparing `Series` and `list`, etc).
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| null | 5 | 2016-02-07T12:06:12Z | 2016-02-11T06:22:08Z | 2016-02-08T13:48:11Z | MEMBER | null | This fixes #12221 and #10381 . Same source file, but let me know if you want separate PRs.
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| null | 2 | 2016-02-07T15:08:49Z | 2016-02-07T15:26:10Z | 2016-02-07T15:26:10Z | NONE | null | I am puzzled with the following problem. I have a set of csv files, which I parse iterativly. Before collecting the dataframes in a list, I apply some function (as simple as `tmp_df*2`) to each of the `tmp_df`. It all worked perfectly fine at first glance, until I've realized I have inconsistencies with the results from run to run. Strangly, I don't not observe inconsistencies like this one when I don't manipulate the parsed data (simply set `with_function=False` ). I have managed to re-create the problem, it should run on win and ux. I've tested on win8.1 facing the problem when with_function=True (typically after 1-5 runs), on ux it runs without problems. with_function=False runs without differences for win and ux. I can also reject the hypothesis that it is related to int or float issue as also the simulated int are different. Here is the discussion on SO http://stackoverflow.com/questions/35252460/inconsistent-results-when-concatenating-parsed-csv-files
```
import pandas as pd
import numpy as np
from pathlib import Path
from tempfile import gettempdir
def simulate_csv_data(tmp_dir,num_files=5):
""" simulate a csv files
:param tmp_dir: Path, csv files are saved to
:param num_files: int, how many csv files to simulate
:return:
"""
rows = 20000
columns = 5
np.random.seed(1282)
for file_num in range(num_files):
file_path = tmp_dir.joinpath(''.join(['df_', str(file_num), '.csv']))
simulated_df = pd.DataFrame(np.random.standard_normal((rows, columns)))
simulated_df['some_int'] = np.random.randint(0,100)
simulated_df.to_csv(str(file_path))
def get_csv_data(tmp_dir,num_files=5, with_function=True):
""" Collect various csv files and return a concatenated dfs
:param tmp_dir: Path, csv files are saved to
:param num_files: int, how many csv files to simulate
:param with_function: Bool, apply function to tmp_dataframe
:return:
"""
data_list = list()
for file_num in range(num_files):
# current file path
file_path = tmp_dir.joinpath(''.join(['df_', str(file_num), '.csv']))
# load csv_file
tmp_df = pd.read_csv(str(file_path), dtype=np.float64)
# replace infs by na
tmp_df.replace([np.inf, -np.inf], np.nan, inplace=True)
# apply function to tmp_dataframe
if with_function:
tmp_df = tmp_df*2
data_list.append(tmp_df)
df = pd.concat(data_list, ignore_index=True)
df.reset_index(inplace=True)
return df
def main():
# INPUT ----------------------------------------------
num_files = 5
with_function = True
max_comparisons = 50
# ----------------------------------------------------
tmp_dir = gettempdir()
# use temporary "non_existing" dir for new file
tmp_csv_folder = Path(tmp_dir).joinpath('csv_files_sdfs2eqqf')
# if exists already don't simulate data/files again
if tmp_csv_folder.exists() is False:
tmp_csv_folder.mkdir()
print('Simulating temp files...')
simulate_csv_data(tmp_csv_folder, num_files)
print('Getting benchmark data frame...')
df1 = get_csv_data(tmp_csv_folder, num_files, with_function)
df_is_same = True
count_runs = 0
# Run until different df is found or max runs exceeded
print('Comparing data frames...')
while df_is_same:
# get another data frame
df2 = get_csv_data(tmp_csv_folder, num_files, with_function)
count_runs += 1
# compare data frames
if df1.equals(df2) is False:
df_is_same = False
print('Found unequal df after {} runs'.format(count_runs))
# print out a standard deviations (arbitrary example)
print('Std Run1: \n {}'.format(df1.std()))
print('Std Run2: \n {}'.format(df2.std()))
if count_runs > max_comparisons:
df_is_same = False
print('No unequal df found after {} runs'.format(count_runs))
print('Delete the following folder if no longer needed: "{}"'.format(
str(tmp_csv_folder)))
if __name__ == '__main__':
main()
```
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} | 6 | 2016-02-07T15:50:43Z | 2016-03-12T20:17:03Z | 2016-03-12T17:52:30Z | MEMBER | null | Fixed docs on min_itemsize
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| null | 10 | 2016-02-07T15:51:05Z | 2016-11-24T12:50:07Z | 2016-11-16T22:28:04Z | MEMBER | null | …nd it)
closes #10381
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} | 2 | 2016-02-07T15:51:57Z | 2016-02-11T06:21:42Z | 2016-02-11T02:55:17Z | MEMBER | null | closes #12221
HDFStore is not an iterator - but being iterable, it can return an iterator of itself (i.e. of `.keys()`).
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| null | 2 | 2016-02-07T20:43:55Z | 2016-02-08T18:14:06Z | 2016-02-08T15:02:07Z | NONE | null | http://stackoverflow.com/questions/35236880/groupby-on-categorical-column-with-date-and-period-producing-unexpected-result
The issue below was created in Python 2.7.11 with Pandas 0.17.1 (and 0.17.1+311.g70f79ce)
When grouping a categorical column with both a period and date column, unexpected rows appear in the grouping.
```
df = pd.DataFrame({'date': pd.date_range('2015-12-29', '2016-1-3'),
'val1': [1] * 6,
'val2': range(6),
'cat1': ['a', 'b', 'c'] * 2,
'cat2': ['A', 'B', 'C'] * 2})
df['cat1'] = df.cat1.astype('category')
df['month'] = [d.to_period('M') for d in df.date]
```
Grouping the month and date with a regular series (e.g. cat2) works as expected:
```
>>> df.groupby(['month', 'date', 'cat2']).sum().unstack()
val1 val2
cat2 A B C A B C
month date
2015-12 2015-12-29 1 NaN NaN 0 NaN NaN
2015-12-30 NaN 1 NaN NaN 1 NaN
2015-12-31 NaN NaN 1 NaN NaN 2
2016-01 2016-01-01 1 NaN NaN 3 NaN NaN
2016-01-02 NaN 1 NaN NaN 4 NaN
2016-01-03 NaN NaN 1 NaN NaN 5
```
But grouping on a categorical produces unexpected results. You'll notice in the index that the extra dates do not correspond to the grouped month.
```
>>> df.groupby(['month', 'date', 'cat1']).sum().unstack()
val1 val2
cat1 a b c a b c
month date
2015-12 2015-12-29 1 NaN NaN 0 NaN NaN
2015-12-30 NaN 1 NaN NaN 1 NaN
2015-12-31 NaN NaN 1 NaN NaN 2
2016-01-01 NaN NaN NaN NaN NaN NaN # <<< Extraneous row.
2016-01-02 NaN NaN NaN NaN NaN NaN # <<< Extraneous row.
2016-01-03 NaN NaN NaN NaN NaN NaN # <<< Extraneous row.
2016-01 2015-12-29 NaN NaN NaN NaN NaN NaN # <<< Extraneous row.
2015-12-30 NaN NaN NaN NaN NaN NaN # <<< Extraneous row.
2015-12-31 NaN NaN NaN NaN NaN NaN # <<< Extraneous row.
2016-01-01 1 NaN NaN 3 NaN NaN
2016-01-02 NaN 1 NaN NaN 4 NaN
2016-01-03 NaN NaN 1 NaN NaN 5
```
Grouping the categorical by month periods or dates works fine, but not when both are combined as in the example above.
This behavior originated in the 0.15.0 update. Prior to that, this was the output:
```
>>> df.groupby(['month', 'date', 'cat1']).sum().unstack()
val1 val2
cat1 a b c a b c
month date
2015-12 2015-12-29 1 NaN NaN 0 NaN NaN
2015-12-30 NaN 1 NaN NaN 1 NaN
2015-12-31 NaN NaN 1 NaN NaN 2
2016-01 2016-01-01 1 NaN NaN 3 NaN NaN
2016-01-02 NaN 1 NaN NaN 4 NaN
2016-01-03 NaN NaN 1 NaN NaN 5
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0+untagged.6557.g123cc73.dirty
nose: 1.3.7
pip: 8.0.2
setuptools: 19.1.1
Cython: 0.22
numpy: 1.10.2
scipy: 0.16.1
statsmodels: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 1.5
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.1.1
numexpr: 2.4.4
matplotlib: 1.5.0
openpyxl: 2.0.4
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.3.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.10
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
jinja2: 2.8
```
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} | 6 | 2016-02-08T02:35:28Z | 2021-04-23T00:46:46Z | null | NONE | null | Assignment to mixed-type DataFrame with float32 numpy array coerce to float64
```
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame([[1, 2, 'a'], [3, 4, 'b']], dtype=np.float32)
>>> A = df.ix[:, 0:1].values
>>> df.ix[:, 0:1] = A
>>> df[0].dtype
dtype('float64')
```
Shouldn't the result be `dtype('float32')`?
See also http://stackoverflow.com/questions/35230388/why-does-pandas-coerce-my-numpy-float32-to-float64
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} | 7 | 2016-02-08T06:33:20Z | 2016-02-12T14:06:46Z | 2016-02-12T03:07:36Z | CONTRIBUTOR | null | closes #11555
Updated IO Tools documentation for read_csv() and read_table() to be consistent with the doc-string,
also reordered keywords to group them more logically. Also updated merging.rst docs for concat.
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| null | 3 | 2016-02-08T06:56:34Z | 2019-08-22T10:07:21Z | null | CONTRIBUTOR | null | Setting `mode.use_inf_as_null=True` makes `fillna` extremely slow (about 14sec).
The most of time is spent on `self._data.fillna` (core/generic.py:2833)
```
import pandas as pd
pd.set_option("mode.use_inf_as_null", True) #+/-inf -> None
s=pd.read_msgpack(r"D:\slow_fillna.msgpack", encoding='utf-8')
s2=s.fillna("<Н/Д>")
```
[slow_fillna.msgpack.gz](https://github.com/pydata/pandas/files/121211/slow_fillna.msgpack.gz)
```
python: 3.4.3.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: en
pandas: 0.17.1
nose: 1.3.7
pip: 7.1.2
setuptools: 18.7.1
Cython: 0.23.4
numpy: 1.9.3
```
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} | 1 | 2016-02-08T13:41:47Z | 2016-09-27T10:36:44Z | 2016-09-27T10:36:44Z | MEMBER | null | Probably my favourite bug so far:
```
In [2]: pd.Series(index=pd.MultiIndex.from_product([[], [0,1], []], names=['var', 'per', 'extr']))
Out[2]: Series([], dtype: float64)
In [3]: pd.Series(index=pd.MultiIndex.from_product([[], [0,1,2,3], []], names=['var', 'per', 'extr']))
Out[3]: Series([], dtype: float64)
In [4]: pd.Series(index=pd.MultiIndex.from_product([[], [0,1,2], []], names=['var', 'per', 'extr']))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-4-82a0e40cf602> in <module>()
----> 1 pd.Series(index=pd.MultiIndex.from_product([[], [0,1,2], []], names=['var', 'per', 'extr']))
/home/nobackup/repo/pandas/pandas/indexes/multi.py in from_product(cls, iterables, sortorder, names)
928 categoricals = [Categorical.from_array(it, ordered=True)
929 for it in iterables]
--> 930 labels = cartesian_product([c.codes for c in categoricals])
931
932 return MultiIndex(levels=[c.categories for c in categoricals],
/home/nobackup/repo/pandas/pandas/tools/util.py in cartesian_product(X)
37 return [np.tile(np.repeat(np.asarray(com._values_from_object(x)), b[i]),
38 np.product(a[i]))
---> 39 for i, x in enumerate(X)]
40
41
/home/nobackup/repo/pandas/pandas/tools/util.py in <listcomp>(.0)
37 return [np.tile(np.repeat(np.asarray(com._values_from_object(x)), b[i]),
38 np.product(a[i]))
---> 39 for i, x in enumerate(X)]
40
41
/home/pietro/.local/lib/python3.4/site-packages/numpy/core/fromnumeric.py in repeat(a, repeats, axis)
395 except AttributeError:
396 return _wrapit(a, 'repeat', repeats, axis)
--> 397 return repeat(repeats, axis)
398
399
ValueError: negative dimensions are not allowed
```
This is with Python 3, doesn't happen with Python 2. I will try to investigate a bit later.
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| null | 4 | 2016-02-08T14:45:49Z | 2016-07-21T10:59:35Z | 2016-07-21T10:59:35Z | NONE | null | I do what I am told to do ;-)
I have a big dataframe with the following columns
```
daily_news.dtypes
Out[11]:
stock_ric object
obs_day datetime64[ns]
next_dayfirst datetime64[ns]
sentimentmean float32
sentimentcount int64
companyNamefirst object
timestamp_localfirst datetime64[ns]
tickerfirst object
text object
dtype: object
```
and I try to save it using pd.to_stata
`daily_news.to_stata( "mypath/data.dta",convert_dates= {'obs_day' : 'td'})`
and I get
```
daily_news.to_stata( "python_news.dta",convert_dates= {'obs_day' : 'td'})
Traceback (most recent call last):
File "<ipython-input-9-3d9426807f61>", line 1, in <module>
daily_news.to_stata( "python_news.dta",convert_dates= {'obs_day' : 'td'})
File "C:\Users\m1hxb02\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.py", line 1456, in to_stata
write_index=write_index)
File "C:\Users\m1hxb02\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\io\stata.py", line 1859, in __init__
self._prepare_pandas(data)
File "C:\Users\m1hxb02\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\io\stata.py", line 2043, in _prepare_pandas
self.fmtlist.append(_dtype_to_default_stata_fmt(dtype, data[col]))
File "C:\Users\m1hxb02\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\io\stata.py", line 1802, in _dtype_to_default_stata_fmt
"Please report an error to the developers." % dtype)
ValueError: Data type datetime64[ns] not currently understood. Please report an error to the developers.
```
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} | 9 | 2016-02-08T15:37:34Z | 2016-03-04T13:35:00Z | 2016-03-03T19:06:28Z | CONTRIBUTOR | null | Addresses https://github.com/pydata/pandas/pull/12090#issuecomment-180695902 by making the `Styler` behaviour match regular `to_html` behaviour.
This PR is based from #12162.
##### New behaviour
<img width="596" alt="screen shot 2016-02-08 at 10 35 23 am" src="https://cloud.githubusercontent.com/assets/3064019/12890011/b183e81e-ce4f-11e5-9b9f-c021fcb33c5a.png">
cc @TomAugspurger
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} | 3 | 2016-02-08T16:46:28Z | 2020-01-04T17:25:15Z | 2020-01-04T17:25:15Z | MEMBER | null | ```
In [2]: df = pd.DataFrame(0, columns=[], index=pd.MultiIndex.from_product([[], []]))
In [3]: df.loc['b', '2'] = 1
In [4]: df.loc['a', '3'] = 1
In [5]: df.sort_index()
Out[5]:
2 3
b 1.0 NaN
a NaN 1.0
In [6]: df.sort_index().index.is_monotonic
Out[6]: False
```
Am I missing anything obvious?!
(Notice that this is not related to any of the two axes being initialized as empty)
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| null | 3 | 2016-02-08T16:56:35Z | 2016-02-09T20:51:05Z | 2016-02-09T20:50:59Z | NONE | null | When I create a DataFrame from a list of arrays, it temporarily uses a lot more memory and is slower than if I convert the list to an array first (if you want to test this, make sure you have enough memory or change the size of `a`):
``` python
In [1]: import numpy as np
...: import pandas as pd
In [2]: a = np.random.rand(10000000, 10)
In [3]: print(a.nbytes / 1024 / 1024)
762.939453125
In [4]: a = list(a)
In [5]: %time b = pd.DataFrame(np.array(a)) # used memory goes up 840 MB
Wall time: 3.83 s
In [6]: %time c = pd.DataFrame(a) # used memory goes up 5 GB, then drops back to 840 MB above previous value
Wall time: 26.6 s
```
The same problem exists when creating a DataFrame from a list of lists, but definitely not as bad:
``` python
In [1]: import numpy as np
...: import pandas as pd
In [2]: a = np.random.rand(10000000, 10)
In [3]: a = [list(i) for i in a]
In [4]: %time b = pd.DataFrame(np.array(a)) # used memory goes up 840 MB
Wall time: 9.73 s
In [5]: %time c = pd.DataFrame(a) # used memory goes up 1.3 GB, then drops back to 840 MB above previous value
Wall time: 8.52 s
```
Versions:
``` text
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.1.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.17.1
nose: 1.3.7
pip: 8.0.2
setuptools: 19.6.2
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: 0.6.1
IPython: 4.0.3
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.4.6
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.5.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.11
pymysql: None
psycopg2: None
Jinja2: 2.8
```
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} | 1 | 2016-02-08T18:07:31Z | 2016-02-08T22:17:21Z | 2016-02-08T21:13:52Z | MEMBER | null | Master is failing due to my PR https://github.com/pydata/pandas/pull/12205
Strange that it did not fail on travis before merging ?
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} | 2 | 2016-02-08T22:48:02Z | 2016-02-09T07:56:19Z | 2016-02-08T22:52:44Z | NONE | null | Calling `values` to get a column of timestamps seems to be dropping the timezone whether it is a standard column or an index. `unique` also has problems for df columns, though indexes appear to work. Running 0.17.1 through conda.
```
>>> from pytz import timezone
>>> import pandas as pd
>>> EASTERN = timezone('US/Eastern')
>>> df = pd.DataFrame({'Time': ['2016-02-08T13:55:00', '2016-02-08T13:56:00'],
... 'Value': [1, 2]})
>>> df.Time = pd.to_datetime(df.Time)
>>> df.Time = df.Time.dt.tz_localize(EASTERN)
>>> df.Time[0]
Timestamp('2016-02-08 13:55:00-0500', tz='US/Eastern')
>>> df.Time.values[0]
numpy.datetime64('2016-02-08T10:55:00.000000000-0800')
>>> df.Time.unique()
array(['2016-02-08T10:55:00.000000000-0800',
'2016-02-08T10:56:00.000000000-0800'], dtype='datetime64[ns]')
>>> df = df.set_index('Time')
>>> df.index[0]
Timestamp('2016-02-08 13:55:00-0500', tz='US/Eastern')
>>> df.index.values[0]
numpy.datetime64('2016-02-08T10:55:00.000000000-0800')
>>> df.index.unique()
DatetimeIndex(['2016-02-08 13:55:00-05:00', '2016-02-08 13:56:00-05:00'], dtype='datetime64[ns, US/Eastern]', name=u'Time', freq=None)
```
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} | 12 | 2016-02-09T09:39:47Z | 2020-10-02T13:14:56Z | 2016-02-09T10:47:25Z | CONTRIBUTOR | null | I'm trying to migrate database tables from MySQL to SQL Server:
``` python
import pandas as pd
from sqlalchemy import create_engine
my_engine = create_engine("mysql+pymysql://root:pass@localhost/gen")
ms_engine = create_engine('mssql+pyodbc://localhost/gen?driver=SQL Server')
for table_name in ['topics', 'fiction', 'compact']:
for table in pd.read_sql_query('SELECT * FROM %s' % table_name,
my_engine,
chunksize=100000):
table.to_sql(name=table_name, con=ms_engine, if_exists='append')
```
I thought that using chunksize would release the memory, but it's just growing up.
I tried also garbage collector, but it has no effect.
Maybe my expectations were wrong?
_I'm using Python 3.5.1 with pandas 0.17.1 and all latest packages, although I tried also Python 2.7 with pandas 0.16 and same results_
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| null | 5 | 2016-02-09T10:37:27Z | 2016-02-09T15:12:12Z | 2016-02-09T14:49:26Z | MEMBER | null | ```
In [3]: with pd.HDFStore('/tmp/file_I_can_only_read.hdf') as store:
...: store['test'] = pd.Series(range(3))
...:
In [4]: os.chmod('/tmp/file_I_can_only_read.hdf', 0o400)
In [5]: store1 = pd.HDFStore('/tmp/file_I_can_only_read.hdf')
Opening /tmp/file_I_can_only_read.hdf in read-only mode
In [6]: store2 = pd.HDFStore('/tmp/file_I_can_only_read.hdf')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-6-5b173bb4a3e8> in <module>()
----> 1 store2 = pd.HDFStore('/tmp/file_I_can_only_read.hdf')
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in __init__(self, path, mode, complevel, complib, fletcher32, **kwargs)
402 self._fletcher32 = fletcher32
403 self._filters = None
--> 404 self.open(mode=mode, **kwargs)
405
406 @property
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in open(self, mode, **kwargs)
563 hdf_version=tables.get_hdf5_version()))
564
--> 565 raise e
566
567 except (Exception) as e:
/home/pietro/nobackup/repo/pandas/pandas/io/pytables.py in open(self, mode, **kwargs)
538
539 try:
--> 540 self._handle = tables.open_file(self._path, self._mode, **kwargs)
541 except (IOError) as e: # pragma: no cover
542 if 'can not be written' in str(e):
/usr/lib/python3/dist-packages/tables/file.py in open_file(filename, mode, title, root_uep, filters, **kwargs)
308 "The file '%s' is already opened, but "
309 "in read-only mode. Please close it before "
--> 310 "reopening in append mode." % filename)
311 # 'w' means that we want to destroy existing contents
312 elif mode == 'w':
ValueError: The file '/tmp/file_I_can_only_read.hdf' is already opened, but in read-only mode. Please close it before reopening in append mode.
```
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| null | 1 | 2016-02-09T10:58:43Z | 2017-03-08T17:01:59Z | 2016-02-09T14:49:33Z | MEMBER | null | closes #12266
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} | 6 | 2016-02-09T12:37:03Z | 2016-03-14T10:47:30Z | 2016-03-13T16:16:59Z | CONTRIBUTOR | null | more consistent with code in the rest of the document
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| null | 5 | 2016-02-09T14:08:21Z | 2016-03-12T17:53:16Z | 2016-03-12T17:53:16Z | NONE | null | Partially fix #11282 for s390x
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| null | 2 | 2016-02-09T17:02:22Z | 2016-02-10T23:06:41Z | 2016-02-10T17:37:09Z | CONTRIBUTOR | null | Closes #12037
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Use case 1: Evenly-spaced time-series analysis
Use case 2: I sometimes (ab)use `date_range` like a PY3 `range` in a for loop, which I think is quite neat. It would be cooler if it were lazily evaluated.
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} | 7 | 2016-02-10T09:13:59Z | 2016-04-23T13:05:58Z | 2016-04-23T13:05:57Z | CONTRIBUTOR | null | In accordance with #12268 this is now a separate PR.
I think that this warning could be useful either way for no non-daily hdf user:
- Telling that string method is preferred
- Adding a warning
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} | 3 | 2016-02-10T10:31:03Z | 2016-02-10T16:20:58Z | 2016-02-10T16:20:49Z | CONTRIBUTOR | null | closes #12170
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} | 13 | 2016-02-10T10:36:56Z | 2017-12-01T00:34:55Z | 2016-02-23T14:57:16Z | NONE | null | I've noticed that `Series.unique` and `Series.nunique` when used with a categorical series can be slow on large dataset. Presumably its not utilising the shortcuts:
```
Series.unique = Series.cat.categories
Series.nunique = len(Series.cat.categories)
```
Heres an example in iPhython:
```
s = pd.Series(np.random.choice(['a','b','c'], 100000000)).astype('category')
%time s.nunique()
896 ms
%time len(s.cat.categories)
55.1 µs
```
Its significantly slower indicating its not using the above shortcut.
pd.show_versions()
```
pandas: 0.17.1
Cython: 0.23.4
numpy: 1.10.1
IPython: 4.0.1
```
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| null | 3 | 2016-02-10T12:17:34Z | 2021-04-23T00:50:04Z | null | CONTRIBUTOR | null | In line with
- [ENH: Conditional HTML Formatting](https://github.com/pydata/pandas/pull/10250)
- [Support for Conditional HTML Formatting](http://pandas.pydata.org/pandas-docs/version/0.17.1/whatsnew.html#whatsnew-0171-style)
- [Style](http://pandas-docs.github.io/pandas-docs-travis/style.html)
add the same possibility for mpl tables.
Proof of concept:
[Conditional formatting for 2- or 3-scale coloring of cells of a table](http://stackoverflow.com/questions/17748570/conditional-formatting-for-2-or-3-scale-coloring-of-cells-of-a-table)
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https://api.github.com/repos/pandas-dev/pandas/issues/12280 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/12280/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/12280/comments | https://api.github.com/repos/pandas-dev/pandas/issues/12280/events | https://github.com/pandas-dev/pandas/issues/12280 | 132,692,095 | MDU6SXNzdWUxMzI2OTIwOTU= | 12,280 | Helper class to keep enable autocomplete of the columns in notebooks | {
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| null | 10 | 2016-02-10T12:53:21Z | 2016-02-10T17:07:39Z | 2016-02-10T15:07:43Z | NONE | null | When working on the notebook I am always annoyed that column names do not autocomplete in all scenarios.
`dataframe = pd.DataFrame({'COL1':[1,2,3],'COL2':[2,3,4]})`
When typing
`dataframe['CO <--- The notebook will suggest COL1 and COL2 when pressing TAB`
but im lost when using
`pd.pivot_table(index='CO <--- No chance`
My workaround is the following class, which creates an object that binds itself to a given DataFrame and keeps track of the columns
```
class Columns:
import threading
def __init__(self, df):
self._df = df
self._update()
def _update(self):
self.__dict__.update({d:d for d in self._df.columns.tolist()})
threading.Timer(3, self._update).start()
```
So now i use the object to autocomplete columns
```
C= Columns(dataframe)
pd.pivot_table(index=C.CO <--- Yay! Autocomplete works again
```
Do you know of any better solution? And if not... maybe something like this can be included out-of-the-box in pandas for notebook users?
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} | 1 | 2016-02-10T13:47:37Z | 2016-02-10T16:22:59Z | 2016-02-10T16:22:36Z | CONTRIBUTOR | null | This PR clarifies the new documentation for extract and extractall.
It was requested by @jreback in https://github.com/pydata/pandas/pull/11386#issuecomment-182105142
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} | 1 | 2016-02-10T15:09:34Z | 2016-02-10T15:17:14Z | 2016-02-10T15:17:14Z | CONTRIBUTOR | null | When we want to reindex an existing Series using TimedeltaIndex/PeriodIndex/DatetimeIndex,
the result will have np.NaN as data
``` python
In [1]: import pandas as pd
In [2]: # Case of `TimedeltaIndex`
In [3]: a = pd.Series(['A'] * 3)
In [4]: t_d_idx = pd.to_timedelta([0, 10, 20], unit='s')
In [5]: type(t_d_idx)
Out[5]: pandas.tseries.tdi.TimedeltaIndex
In [6]: a
Out[6]:
0 A
1 A
2 A
dtype: object
In [7]: a.reindex(index=t_d_idx)
Out[7]:
00:00:00 NaN
00:00:10 NaN
00:00:20 NaN
dtype: object
In [8]: # Case of `PeriodIndex`
In [9]: b = pd.Series(['B'] * 3)
In [10]: p_idx = pd.period_range('2010-01', '2010-03', freq='M')
In [11]: type(p_idx)
Out[11]: pandas.tseries.period.PeriodIndex
In [12]: b
Out[12]:
0 B
1 B
2 B
dtype: object
In [13]: b.reindex(index=p_idx)
Out[13]:
2010-01 NaN
2010-02 NaN
2010-03 NaN
Freq: M, dtype: object
In [14]: # Case of `DatetimeIndex`
In [15]: c = pd.Series(['C'] * 3)
In [16]: d_t_idx = pd.DatetimeIndex(start='2016-1-1', end='2016-4-1', freq='M')
In [17]: type(d_t_idx)
Out[17]: pandas.tseries.index.DatetimeIndex
In [18]: c
Out[18]:
0 C
1 C
2 C
dtype: object
In [19]: c.reindex(index=d_t_idx)
Out[19]:
2016-01-31 NaN
2016-02-29 NaN
2016-03-31 NaN
Freq: M, dtype: object
```
Even when you want to reindex a Series which already have DatetimeIndex as index
``` Python
In [20]: ts = pd.Series(
....: range(3),
....: index=pd.DatetimeIndex(
....: start='2016-2-1',
....: end='2016-2-22',
....: freq='W'
....: )
....: )
In [21]: type(ts.index)
Out[21]: pandas.tseries.index.DatetimeIndex
In [22]: ts
Out[22]:
2016-02-07 0
2016-02-14 1
2016-02-21 2
Freq: W-SUN, dtype: int64
In [23]: ts.reindex(index=d_t_idx)
Out[23]:
2016-01-31 NaN
2016-02-29 NaN
2016-03-31 NaN
Freq: M, dtype: float64
```
Is this what we expect Series.index should behave?
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} | 9 | 2016-02-10T17:42:04Z | 2017-04-12T10:32:06Z | 2017-04-12T10:32:06Z | CONTRIBUTOR | null | I think we need a section to address this, not really sure where should put it. Also want to link from the doc-string.
also prob need some examples in the doc-string.
xref #11972
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} | 24 | 2016-02-10T17:47:21Z | 2016-02-12T14:27:12Z | 2016-02-12T13:42:08Z | MEMBER | null | Addresses issue in #12244 in which a non-numpy-dtype for DataFrame.values causes a `TypeError` to be thrown in the `reduce == True` case for `DataFrame.apply`. Resolved by first passing `DataFrame.values` through `Series` initialization and taking its `values` attribute, which is an `ndarray` and hence will have a valid `dtype`. Note that the output of `apply` will still have the original `dtype`.
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} | 2 | 2016-02-10T20:41:58Z | 2016-04-18T17:50:53Z | 2016-04-18T17:50:53Z | NONE | null | as described in https://github.com/pydata/pandas/pull/11913#issuecomment-181632169,
when using a development pandas instance, if `pandas.test()` is called while the working directory of the python interpreter is within the pandas repository, it fails to detect any tests, at least on windows:
```
[pandas_dev] C:\Users\jahfe_000\Documents\GitHub\pandas>python
Python 2.7.11 |Continuum Analytics, Inc.| (default, Jan 29 2016, 14:26:21) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> import pandas
>>> pandas.test()
Running unit tests for pandas
pandas version 0.17.1+311.g70f79ce
numpy version 1.10.4
pandas is installed in pandas
Python version 2.7.11 |Continuum Analytics, Inc.| (default, Jan 29 2016, 14:26:21) [MSC v.1500 64 bit (AMD64)]
nose version 1.3.7
----------------------------------------------------------------------
Ran 0 tests in 0.000s
OK
<nose.result.TextTestResult run=0 errors=0 failures=0>
>>>
```
if using `python setup.py develop`, a fix is to browse to a folder outside of the repo:
```
[pandas_dev] C:\Users\jahfe_000\Documents\GitHub\pandas>cd ..
[pandas_dev] C:\Users\jahfe_000\Documents\GitHub>python
Python 2.7.11 |Continuum Analytics, Inc.| (default, Jan 29 2016, 14:26:21) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> import pandas
>>> pandas.test()
Running unit tests for pandas
pandas version 0.17.1+311.g70f79ce
numpy version 1.10.4
pandas is installed in c:\users\jahfe_000\documents\github\pandas\pandas
Python version 2.7.11 |Continuum Analytics, Inc.| (default, Jan 29 2016, 14:26:21) [MSC v.1500 64 bit (AMD64)]
nose version 1.3.7
C:\Anaconda\envs\pandas_dev\lib\site-packages\matplotlib\backends\qt_compat.py:84: ImportWarning: Not importing directory 'C:\Anaconda\envs\pandas_dev\sip': missing __init__.py
import sip
....................................S..............S..............S..............S.................F.........................................................................................................................................................
```
This seems to be due to the fact that `pandas.__file__` returns a relative path from the package root when accessed from a folder located within the package, and a relative path when accessed from any other directory.
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| null | 8 | 2016-02-10T22:58:40Z | 2019-10-22T04:21:31Z | null | NONE | null | Hi there,
I recently wrote a very flexible module for flattening hierarchical (json) data into CSV: https://github.com/tkluck/Text-CSV-Flatten.
It encodes the exact semantics of the flattening by a pattern string. For example, the pattern `.<index>.*` flattens in the same way as `orient="records"`. The pattern `.*.<index>` flattens in the same way as `orient="columns"`.
The module is quite new and has already been very useful for me: I maintain an internal reporting tool for my employer, in which we expose hierarchical data. This module allows my users to download it as CSV with a minimum of effort on my side (no boilerplate) and on theirs.
I realized that the same thing might be very useful for Pandas. Not only does the pattern flatten the hierarchical data into rows and columns, it also encodes which columns are supposed to be an index. In CSV, this information in lost in the output format, but in a DataFrame, that remains meaningful.
This module fulfils a very similar function to what `io.json.read_json`, `io.json.json_normalize` and `io.json.nested_to_record` do. In fact, I think it can reproduce any of their semantics by just tweaking the pattern string.
I wouldn't mind at all to spend some time on adding this to Pandas. If I do that, would you be interested in merging it into your next version?
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} | 7 | 2016-02-11T06:00:43Z | 2016-02-15T06:43:33Z | 2016-02-12T03:02:26Z | CONTRIBUTOR | null | Closes #10291
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} | 5 | 2016-02-11T09:24:39Z | 2016-02-12T14:38:36Z | 2016-02-12T14:38:36Z | MEMBER | null | It is my understanding that the `copy` parameter of `RangeIndex` is present virtually only for API compatibility with other indices: however the behaviour for `copy=False` differs from other indices in what follows:
```
In [2]: i = pd.Int64Index(range(10))
In [3]: i.name = 'original'
In [4]: j = pd.Int64Index(i)
In [5]: j.name
In [6]: j.name = 'copy'
In [7]: i.name
Out[7]: 'original'
In [8]: i = pd.RangeIndex(10)
In [9]: i.name = 'original'
In [10]: j = pd.RangeIndex(i)
In [11]: j.name
Out[11]: 'original'
In [12]: j.name = 'copy'
In [13]: i.name
Out[13]: 'copy'
```
In other words, since `RangeIndex` doesn't have any data to copy, the value of the `copy` parameter should be irrelevant. For homogeneity with other indices, we should always act as if `copy=True`. The docs should also be changed to state that `copy` parameter has no effect and is present only for API compatibility. I'll push a PR if you agree.
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} | 14 | 2016-02-11T09:45:34Z | 2016-07-29T00:15:32Z | 2016-07-29T00:15:15Z | MEMBER | null | closes #12185
Notice the test I fix was indeed wrong - I had written that line as a workaround waiting for this fix.
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} | 6 | 2016-02-11T11:13:31Z | 2016-02-12T13:44:41Z | 2016-02-12T13:39:08Z | NONE | null | When there are only one row containing timestamps with timezone in DataFrame, computing time difference between columns gives incorrect result - always '0 days':
```
In [133]: import pandas as pd
In [134]: df = pd.DataFrame(columns=['s', 'f'])
In [135]: df.s = [pd.Timestamp('2016-02-08 13:43:14.605000', tz='America/Sao_Paulo')]
In [136]: df.f = [pd.Timestamp('2016-02-10 13:43:14.605000', tz='America/Sao_Paulo')]
In [137]: df
Out[137]:
s f
0 2016-02-08 13:43:14.605000-02:00 2016-02-10 13:43:14.605000-02:00
In [138]: df.f - df.s # should give '2 days'
Out[138]:
0 0 days
Name: f, dtype: timedelta64[ns]
In [139]: df.s - df.f # should give '-2 days'
Out[139]:
0 0 days
Name: s, dtype: timedelta64[ns]
```
If there are more than one row, result is correct:
```
In [140]: import pandas as pd
In [141]: df = pd.DataFrame(columns=['s', 'f'])
In [142]: df.s = [pd.Timestamp('2016-02-08 13:43:14.605000', tz='America/Sao_Paulo'), pd.Timestamp('2016-02-08 13:43:14.605000', tz='America/Sao_P aulo')]
In [143]: df.f = [pd.Timestamp('2016-02-10 13:43:14.605000', tz='America/Sao_Paulo'), pd.Timestamp('2016-02-11 15:43:14.605000', tz='America/Sao_P aulo')]
In [144]: df
Out[144]:
s f
0 2016-02-08 13:43:14.605000-02:00 2016-02-10 13:43:14.605000-02:00
1 2016-02-08 13:43:14.605000-02:00 2016-02-11 15:43:14.605000-02:00
In [145]: df.f - df.s
Out[145]:
0 2 days 00:00:00
1 3 days 02:00:00
Name: f, dtype: timedelta64[ns]
```
One row containing timestamps in naive datetime also gives correct result:
```
In [147]: import pandas as pd
In [148]: df = pd.DataFrame(columns=['s', 'f'])
In [149]: df.s = [pd.Timestamp('2016-02-08 13:43:14.605000')]
In [150]: df.f = [pd.Timestamp('2016-02-10 13:43:14.605000')]
In [151]: df
Out[151]:
s f
0 2016-02-08 13:43:14.605 2016-02-10 13:43:14.605
In [152]: df.f - df.s
Out[152]:
0 2 days
dtype: timedelta64[ns]
```
Installed versions:
```
In [162]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-49-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.17.1
nose: None
pip: 8.0.2
setuptools: 18.2
Cython: None
numpy: 1.10.4
scipy: None
statsmodels: None
IPython: 4.0.3
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.2
apiclient: 1.4.2
sqlalchemy: None
pymysql: None
psycopg2: 2.6.1 (dt dec pq3 ext lo64)
Jinja2: None
```
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} | 30 | 2016-02-11T11:18:36Z | 2019-11-18T02:56:00Z | 2016-02-11T12:02:49Z | MEMBER | null | Added `empty()` methods to the `Series` and `DataFrame` classes analogous to the `empty()` function in the `numpy` library that can also accept `scipy` duck-type `dtypes` in addition to `numpy` `dtypes`.
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} | 3 | 2016-02-11T12:52:35Z | 2016-02-27T15:09:05Z | 2016-02-27T15:09:05Z | NONE | null | If read one empty column from excel file, read_excel raise Exception without error message.
Small test case: content of excel file:
| A | B | C | D |
| --- | --- | --- | --- |
| | 1 | 100 | f |
| | 2 | 200 | f |
| | 3 | 300 | f |
| | 4 | 400 | f |
Run:
```
import pandas as pd
pd.read_excel("test_excel_.xls", parse_cols=[0], header=None)
```
Result:
```
File "/home/.../python2.7/site-packages/pandas/io/excel.py", line 170, in read_excelskip_footer=skip_footer, converters=converters, **kwds)
File "/home/.../python2.7/site-packages/pandas/io/excel.py", line 436, in _parse_excel**kwds)
File "/home/.../python2.7/site-packages/pandas/io/parsers.py", line 1346, in TextParserreturn TextFileReader(*args, **kwds)
File "/home/.../python2.7/site-packages/pandas/io/parsers.py", line 590, in __init__self._make_engine(self.engine)
File "/home/.../python2.7/site-packages/pandas/io/parsers.py", line 737, in _make_engineself._engine = klass(self.f, **self.options)
File "/home/.../python2.7/site-packages/pandas/io/parsers.py", line 1452, in __init__self.columns, self.num_original_columns = self._infer_columns()
File "/home/.../python2.7/site-packages/pandas/io/parsers.py", line 1735, in _infer_columnsline = self._buffered_line()
File "/home/.../python2.7/site-packages/pandas/io/parsers.py", line 1793, in _buffered_linereturn self._next_line()
File "/home/.../python2.7/site-packages/pandas/io/parsers.py", line 1817, in _next_lineraise StopIteration
```
Python 2.7.7
pandas 0.17.1
xlrd 0.9.4
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Missing freqstr 'C' in `_period_code_map`
I found `pandas/tseries/tests/test_frequencies.py:TestFrequencyCode.test_freq_code` cover this change.
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also fix #9002
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} | 7 | 2016-02-11T21:26:45Z | 2016-02-12T16:52:07Z | 2016-02-12T15:01:29Z | CONTRIBUTOR | null | closes #11435
closes #11334.
Modified behavior of str_cat to ignore NaN values in the Series by default.
Added some doc examples to make the behavior with nan more clear, and catch an error condition when a sep is passed instead of an others (array) as an argument and print a more intuitive error message.
This is a retry of PR #12000.
Before, if NaN values were present in the Series, calling `cat()` on it would return NaN, unlike the rest of the `Series.str.` API. This behavior has been amended to ignore NaN values by default.
(#11435)
A new, friendlier ValueError was also added to protect against the mistake of supplying the `sep` as an arg, rather than a kwarg.
(#11334)
``` python
>>> Series(['a','b',np.nan,'c']).str.cat(sep=' ')
'a b c'
>>> Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?')
'a b ? c'
>>> Series(['a','b',np.nan,'c']).str.cat(' ')
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-338c379049e9> in <module>()
----> 1 Series(['a','b',np.nan,'c']).str.cat(' ')
[...]
ValueError: Did you mean to supply a `sep` keyword?
```
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} | 15 | 2016-02-11T22:10:34Z | 2016-05-13T10:10:04Z | 2016-05-13T00:21:24Z | NONE | null | First of all, I have to admit that I have some trouble finding a catchy title for this issue. Better suggestions are welcome...
There is some issue with crosstab, when applied on a DataFrame which has been derived from a DataFrame consisting of categorized columns.
Let's consider the following example df:
```
import pandas as pd
df = pd.DataFrame({'col0':list('abbabcabcab'), 'col1':[1,1,1,1,2,3,2,2,3,3,1],
'crit':[1,1,1,1,1,0,0,0,0,0,0]})
```
We apply some filter according to some criterion which happens to eliminate all occurrences of 'c' in 'col0' (and '3' in 'col1'). Then we build a crosstab:
```
df_filtered = df[df.crit == 1]
pd.crosstab(df_filtered.col0, df_filtered.col1)
```
The result is as expected:
```
col1 1 2
col0
a 2 0
b 2 1
```
Now we try the same on categorized columns:
```
for col in df.columns:
df[col] = df[col].astype('category')
df_filtered = df[df.crit == 1]
pd.crosstab(df_filtered.col0, df_filtered.col1
```
In this case, value 'c' and '3' are again listed, although they shouldn't be:
```
col1 1 2 3
col0
a 2 0 0
b 2 1 0
c 0 0 0
```
My guess is that the reason for this behavior can be found in the lookup-table of the category, which does not account for values that are not represented any longer:
`df_filtered.col0.cat.categories`
Output:
`Index(['a', 'b', 'c'], dtype='object'`
So, either this lookup-table has to be fixed upon filtering, or pd.crosstab has to respect this condition.
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} | 12 | 2016-02-11T22:29:45Z | 2016-03-03T21:51:50Z | 2016-03-03T21:34:52Z | MEMBER | null | Currently, `compat.itervalues` and friends are not real iterators (under Python 3): compare
```
In [3]: next(six.itervalues({1:2}))
Out[3]: 2
```
with
```
In [4]: next(pd.compat.itervalues({1:2}))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-177f5d3e9571> in <module>()
----> 1 next(pd.compat.itervalues({1:2}))
TypeError: 'dict_values' object is not an iterator
```
This PR fixes this (it drops support for `**kwargs`: although it is supported by `six`, I fail to see what's the utility, and it's anyway apparently not used in the `pandas` codebase - but if I'm missing something, it is trivial to reintroduce it) and provides `next()` in Python 2.
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} | 5 | 2016-02-11T23:06:54Z | 2016-02-15T22:57:28Z | 2016-02-15T22:57:14Z | CONTRIBUTOR | null | NaT.isoformat() returning 'NaT' allows users to use Timestamp to
rehydrate any _Timestamp object from its isoformat. This means that it
is safe to use _Timestamp.isoformat() as a serialization format for
timestamps where NaT is a valid value.
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} | 13 | 2016-02-11T23:53:20Z | 2016-02-14T17:09:02Z | 2016-02-14T17:09:02Z | NONE | null | `df.max(axi=1)` does not throw an error and evaluates to `df.max()`
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} | 4 | 2016-02-12T00:40:08Z | 2019-11-18T02:57:31Z | 2016-02-12T06:37:21Z | MEMBER | null | Addresses issue in #12290 with timezone `Series` subtraction in which single element `Series`
objects containing tz-aware objects would return a timedelta of zero, even though it visually could not be the case.
The bug was traced to the conversion of the contained timezones to UTC, in which the method call was somehow returning `NaT`, even though attempts to replicate that behaviour were unsuccessful. This new
method call fixes the issue and is in some ways more intuitive given the comment above the conversions.
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} | 21 | 2016-02-12T00:43:14Z | 2020-04-19T21:25:07Z | 2020-04-19T21:25:06Z | NONE | null | Converting floating-point value NaN to any integer data type is an undefined behavior in C. However, it actually happens in numpy extension module, which is probably caused by incorrect usage of it from pandas. If the former is built with Clang+UBSan (http://clang.llvm.org/docs/UndefinedBehaviorSanitizer.html), there are error reports indicating the issue.
Now, this is somewhat tricky to reproduce, because it involves building NumPy C code with UBSan. The following instructions should work on Ubuntu 14.04
- Get fresh enough Clang (e.g. 3.8.0rc2 from http://llvm.org/pre-releases/3.8.0/rc2)
- Build NumPy with Clang and float-cast-overflow detection
```
git clone git://github.com/numpy/numpy.git
cd numpy
CC=clang CXX=clang++ LDSHARED=clang CFLAGS="-fsanitize=float-cast-overflow" python setup.py install
```
- Fetch latest pandas
- Export ASan runtime library to provide UBSan implementation, setup runtime flags for sanitizers:
```
export ASAN_OPTIONS=detect_leaks=0
export UBSAN_OPTIONS=print_stacktrace=1
export LD_PRELOAD=/lib/clang/3.9.0/lib/linux/libclang_rt.asan-x86_64.so
```
- Build pandas
```
cd pandas
python setup.py build_ext --inplace
python setup.py install
```
- Run tests from the test suite triggering the issue
```
nosetests pandas/tests/test_groupby.py:TestGroupBy.test_agg_nested_dicts
numpy/core/src/multiarray/lowlevel_strided_loops.c.src:865:17: runtime error: value nan is outside the range of representable values of type 'long'
#0 0x7fc581bffe41 in _cast_double_to_long /usr/local/google/numpy/numpy/core/src/multiarray/lowlevel_strided_loops.c.src:865:17
#1 0x7fc581b7faf7 in raw_array_assign_array /usr/local/google/numpy/numpy/core/src/multiarray/array_assign_array.c:96:9
#2 0x7fc581b80355 in PyArray_AssignArray /usr/local/google/numpy/numpy/core/src/multiarray/array_assign_array.c:351:13
#3 0x7fc581c19a80 in array_astype /usr/local/google/numpy/numpy/core/src/multiarray/methods.c:832:13
#4 0x49968c in PyEval_EvalFrameEx (/usr/bin/python2.7+0x49968c)
nosetests pandas/tseries/tests/test_resample.py:TestResample.test_custom_grouper
numpy/core/src/multiarray/lowlevel_strided_loops.c.src:867:22: runtime error: value nan is outside the range of representable values of type 'long'
#0 0x7f04ba194da1 in _aligned_cast_double_to_long /usr/local/google/numpy/numpy/core/src/multiarray/lowlevel_strided_loops.c.src:867:22
#1 0x7f04ba13d5d2 in PyArray_CastRawArrays /usr/local/google/numpy/numpy/core/src/multiarray/dtype_transfer.c:3843:5
#2 0x7f04ba11485f in PyArray_AssignRawScalar /usr/local/google/numpy/numpy/core/src/multiarray/array_assign_scalar.c:248:13
#3 0x7f04ba11f28a in PyArray_FillWithScalar /usr/local/google/numpy/numpy/core/src/multiarray/convert.c:464:19
#4 0x7f04ba1af2a0 in array_fill /usr/local/google/numpy/numpy/core/src/multiarray/methods.c:150:9
#5 0x49968c in PyEval_EvalFrameEx (/usr/bin/python2.7+0x49968c)
```
It seems that pandas are using "np.nan" to aggressively: by calling astype(<integer_type>) on arrays that can contain NaNs, and in calling fill(np.nan) on np.empty() arrays of integral types.
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