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CI: install sphinx with conda for doc build
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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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2016-02-02T13:59:39Z
2016-02-08T15:26:19Z
2016-02-08T15:26:10Z
MEMBER
null
Start for removing some of the deprecated functions. Other functions to add: `write_frame`, `tquery`, `uquery`
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DOC: improve s3 access doc-strings / docs
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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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ENH: formatting integers in FloatIndex as floats
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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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STYLE: final flake8 fixes, add back check for travis-ci
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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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130,756,535
MDExOlB1bGxSZXF1ZXN0NTgwMjE4MzI=
12,209
DOC: Added deprecation to convert_objects docstring#12052
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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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https://api.github.com/repos/pandas-dev/pandas/issues/12210
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130,787,708
MDU6SXNzdWUxMzA3ODc3MDg=
12,210
pivot_table throws exception with multiple aggregations per column and margins=True
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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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130,804,016
MDU6SXNzdWUxMzA4MDQwMTY=
12,211
str(data_frame) raises an exception when there is a column with both NaT value and Timestamp with timezone info
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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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130,840,328
MDU6SXNzdWUxMzA4NDAzMjg=
12,212
Support for json lines format
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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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130,841,150
MDU6SXNzdWUxMzA4NDExNTA=
12,213
Support Infinity, -Infinity and NaN in read_json
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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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12,214
pd.concat() from list of DataFrames read with pd.read_hdf() without .copy() results in 100X slower concat!
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2016-02-03T00:04:20Z
2016-02-03T03:12:16Z
2016-02-03T03:12:16Z
NONE
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``` 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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12,215
BUG: Strings like '2E' are incorrectly parsed as valid floats
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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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130,893,607
MDExOlB1bGxSZXF1ZXN0NTgwODQ0NjE=
12,216
DEPR: Change boxplot return_type kwarg
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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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12,217
Concatenating two localized datetimes returns NaTs
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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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MDU6SXNzdWUxMzEwMDg3ODA=
12,218
Empty TypeError message with bad column selection
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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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MDExOlB1bGxSZXF1ZXN0NTgxMzE3NDU=
12,219
ERR automatic broadcast for merging different levels, #9455
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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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12,220
DataFrame.idxmax may return modified index/column dtype
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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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12,221
KeyError Iterating on empty HDFStore
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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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12,222
Iterrows making incorrect assumptions about datetime
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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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multi-index display bug
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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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read_csv's na_values dict format cannot parse float type
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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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msgpack reads python 2 string as bytes
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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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12,226
Query on Series
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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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Use correct prop_cycle in the mpl_stylesheet.
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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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131,171,577
MDU6SXNzdWUxMzExNzE1Nzc=
12,228
Pandas read_csv with chunksize stops before end of file
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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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131,233,961
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12,229
BadZipfile: File is not a zip file on Windows not on Mac OS
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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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131,318,477
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12,230
Regarding pd.DataFrame.std() function
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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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131,366,532
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12,231
datetime index list when pd.concat does not infer frequency
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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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131,478,562
MDU6SXNzdWUxMzE0Nzg1NjI=
12,232
issue with StataReader for stata files versions 108 and older
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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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131,562,624
MDExOlB1bGxSZXF1ZXN0NTgzOTEyODA=
12,233
Read very old stata DTA files
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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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131,565,886
MDExOlB1bGxSZXF1ZXN0NTgzOTIyMjk=
12,234
DOC: Remove claims of unbiasedness from doc string for std
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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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131,676,162
MDU6SXNzdWUxMzE2NzYxNjI=
12,235
3.5 builds failing on linux
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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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131,691,514
MDU6SXNzdWUxMzE2OTE1MTQ=
12,236
BUG: read_hdf is not read-threadsafe
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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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131,702,808
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12,237
When reading CSV, float-like strings lacking exponent digits are accepted as floats
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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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131,733,709
MDU6SXNzdWUxMzE3MzM3MDk=
12,238
np.searchsorted fails on Index due to Index.searchsorted not taking a sorter argument.
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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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131,743,678
MDExOlB1bGxSZXF1ZXN0NTg0Nzc5OTM=
12,239
TST: fix some scipy 0.17.0 changes
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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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12,240
Travis build fails on master
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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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12,241
BUG: Timestamp subtraction of NaT with timezones
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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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HDFStore.append fails when appending dataframe with empty string column for which min_itemsize < 8
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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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TST: Skip scipy NaN test on 0.17 for now
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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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BUG: DataFrame.apply fails on timezone aware datetime data
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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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RLS: 0.18.0 schedule
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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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DEPR: removal of deprecation warnings for float indexers
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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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131,918,962
MDExOlB1bGxSZXF1ZXN0NTg1MzUyOTI=
12,247
TST: allow assert_almost_equal to handle pandas instances
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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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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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CLN: Remove testing._skip_if_no_cday
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1
2016-02-07T13:34:19Z
2016-02-08T21:49:08Z
2016-02-08T15:20:29Z
MEMBER
null
NumPy 1.7 or later supports custom business day.
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12,250
Inconsistent results when concatenating parsed csv files with function application on windows
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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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BUG: Ensure data_columns is always a list (i.e. min_itemsize can exte…
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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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ENH: Make HDFStore iterable
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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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BUG: Groupby on categorical column with date and period producing unexpected result
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2016-02-07T20:43:55Z
2016-02-08T18:14:06Z
2016-02-08T15:02:07Z
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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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Assignment to mixed-type DataFrame with float32 coerce to float64
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2016-02-08T02:35:28Z
2021-04-23T00:46:46Z
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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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12,256
DOC: pd.read_csv doc-string clarification #11555
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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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MDU6SXNzdWUxMzIwNjc1NTI=
12,257
use_inf_as_null makes fillna extremely slow
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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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12,258
"ValueError" on specific MultiIndex.from_product initialization(s?)
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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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ValueError: Data type datetime64[ns] not currently understood. Please report an error to the developers.
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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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12,260
BUG: Make Styler ignore None index names to match to_html behaviour
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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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132,194,367
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12,261
sort_index doesn't sort MultiIndex when .loc[] referred to non-existent label on other axis
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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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132,197,599
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12,262
high temporary memory usage on creation of DataFrame from list
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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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132,217,601
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12,263
PEP8: fix sql.py and test_sql.py
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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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132,279,030
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12,264
timezones discarded when calling .values and .unique()
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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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Reading table with chunksize still pumps the memory
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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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ValueError when opening a read-only hdf twice
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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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12,267
Do not raise error when reopening a read-only HDFStore.
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1
2016-02-09T10:58:43Z
2017-03-08T17:01:59Z
2016-02-09T14:49:33Z
MEMBER
null
closes #12266
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added pd as namespace
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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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Respect byteorder in StataReader
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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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Fix #12037 Error when Resampling using pd.tseries.offsets.Nano as period
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2016-02-09T17:02:22Z
2016-02-10T23:06:41Z
2016-02-10T17:37:09Z
CONTRIBUTOR
null
Closes #12037
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Fix #12169 - Resample category data with timedelta index
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3
2016-02-09T17:42:12Z
2016-02-10T23:06:42Z
2016-02-10T17:29:34Z
CONTRIBUTOR
null
closes #12169
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12,272
ENH: Make use of RangeIndex in DatetimeIndex
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4
2016-02-09T21:12:01Z
2021-04-23T00:48:03Z
null
CONTRIBUTOR
null
`DatetimeIndex` looks like it can benefit from `RangeIndex` #11892 . 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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12,273
python-statsmodels required for s390x arch?
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2
2016-02-09T22:37:44Z
2016-02-10T13:02:41Z
2016-02-09T23:59:23Z
NONE
null
The debian/control file has some logic that, in our case on ubuntu, only requires python-statsmodels for 390x. Is it really required for s390x?
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12,274
DEPR: remove order kw from .factorize(), xref #6930
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2
2016-02-10T00:30:58Z
2016-02-10T03:58:21Z
2016-02-10T03:57:37Z
CONTRIBUTOR
null
xref #6930
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12,275
BUG: option to avoid latex in qtconsole #12182
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2
2016-02-10T00:59:40Z
2016-02-10T18:52:27Z
2016-02-10T18:51:40Z
CONTRIBUTOR
null
closes #12182
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Hdf warning
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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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12,277
API: to_msgpack and read_msgpack encoding defaults to utf-8
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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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132,662,500
MDU6SXNzdWUxMzI2NjI1MDA=
12,278
Performance: .unique / .nunique of categorical series slow on large data set
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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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132,683,900
MDU6SXNzdWUxMzI2ODM5MDA=
12,279
ENH: Conditional mpl formatting
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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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132,692,095
MDU6SXNzdWUxMzI2OTIwOTU=
12,280
Helper class to keep enable autocomplete of the columns in notebooks
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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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12,281
DOC: extract/extractall clarifications
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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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BUG: Series.reindex using TimedeltaIndex/PeriodIndex/DatetimeIndex as index will erase the data to np.NaN
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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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12,283
DOC: show how to use to_xarray then use xarray to perform analysis on Panels
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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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132,770,161
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12,284
BUG: Allow 'apply' to be used with non-numpy-dtype DataFrames
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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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12,285
pandas.test() issue when run from a development instance of pandas
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2
2016-02-10T20:41:58Z
2016-04-18T17:50:53Z
2016-04-18T17:50:53Z
NONE
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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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12,286
Feature suggestion: flexible hierarchical data (json) importer (will implement if interest exists)
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8
2016-02-10T22:58:40Z
2019-10-22T04:21:31Z
null
NONE
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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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12,287
BUG: Fixes KeyError when indexes don't overlap.
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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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RangeIndex copy behaviour differs from other indices
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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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12,289
BUG: provide chunks with progressively numbered (default) indices
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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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Computing time difference with timezone in Series with one row gives wrong result
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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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12,291
ENH: add empty() methods for DataFrame and Series
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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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12,292
read_excel crash with empty Exception
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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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BLD: Windows build failing
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7
2016-02-11T14:30:25Z
2016-02-12T14:46:07Z
2016-02-12T14:46:07Z
CONTRIBUTOR
null
Don't think an issue has been made? It's been failing for a while (`complex128` comparison, `numexpr` related?), but didn't always fail until 81e3303
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BUG: Series.plot() doesn't work with CustomBusinessDay frequency
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4
2016-02-11T15:13:40Z
2016-02-12T14:30:34Z
2016-02-12T14:30:24Z
CONTRIBUTOR
null
Closes #7222 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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12,295
BUG: Refine validation of parameters to RangeIndex.__init__
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2
2016-02-11T15:21:45Z
2016-02-12T15:39:40Z
2016-02-12T14:38:36Z
MEMBER
null
closes #12288
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12,296
Fix #12292 : error when read one empty column from excel file
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6
2016-02-11T17:52:01Z
2016-02-27T18:42:21Z
2016-02-27T15:09:05Z
CONTRIBUTOR
null
Fix #12292 also fix #9002
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Improvements to str_cat
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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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12,298
crosstab is not respecting filtered scope when dtype=category
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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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133,104,609
MDExOlB1bGxSZXF1ZXN0NTkxMDQ1MTg=
12,299
BUG: Make dict iterators real iterators, provide "next()" in Python 2
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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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133,111,670
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12,300
NaT.isoformat() returns 'NaT'.
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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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ERR: stat function kwarg interpretation
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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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12,302
BUG: Fix subtraction with timezone Series
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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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float-cast-overflow: converting NaN to integers in numpy C code triggered by pandas
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2016-02-12T00:43:14Z
2020-04-19T21:25:07Z
2020-04-19T21:25:06Z
NONE
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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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