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} | 8 | 2015-09-15T13:37:05Z | 2018-08-20T13:03:19Z | 2018-08-20T13:03:19Z | NONE | null | When I run the code below, i get the SettingWithCopyWarning and I am unsure of what I am doing wrong. Is there a way to code without the warning?
```
for chrom, df in grouped:
bins = np.arange(0, 2e6, 1e5)
df.ix[:,'Bins'] = pd.cut(df.Pos, bins)
```
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
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https://api.github.com/repos/pandas-dev/pandas/issues/11105 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11105/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11105/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11105/events | https://github.com/pandas-dev/pandas/issues/11105 | 106,573,323 | MDU6SXNzdWUxMDY1NzMzMjM= | 11,105 | Pandas options data not working | {
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| null | 3 | 2015-09-15T14:50:42Z | 2015-09-15T16:09:53Z | 2015-09-15T15:02:49Z | NONE | null | I am trying to use get_all_data() method to download options data. I am getting following error:
RemoteDataError: Parsed URL 'http://finance.yahoo.com/q/op?s=SPY' has no rootelement
Actual code:
from pandas.io.data import Options
SPYD = Options('spy', 'yahoo')
data = SPYD.get_all_data()
My pandas version is 0.16.2
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} | 1 | 2015-09-15T14:52:00Z | 2017-02-15T18:56:05Z | 2017-02-15T18:56:05Z | CONTRIBUTOR | null | these are left out of `ci/requirements-3.5.txt` as not available yet
- `beautiful-soup`
- `bottleneck`
these are left out of `ci/requirements-3.5.txt` as not available yet
- `blosc` (prob need a new version)
- `nose`, need to use the newest version as a deprecation warning in `inspect`
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| null | 0 | 2015-09-15T15:12:45Z | 2015-09-15T15:13:36Z | 2015-09-15T15:13:36Z | NONE | null | I am trying to use get_all_data() method to download options data. I am getting following error:
RemoteDataError: Parsed URL 'http://finance.yahoo.com/q/op?s=SPY' has no rootelement
Actual code:
from pandas.io.data import Options
SPYD = Options('spy', 'yahoo')
data = SPYD.get_all_data()
My pandas version is 0.16.2
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} | 4 | 2015-09-15T15:25:08Z | 2015-11-18T11:36:54Z | 2015-11-18T11:36:54Z | CONTRIBUTOR | null | And a couple other changes for [PEP479](https://www.python.org/dev/peps/pep-0479/) to avoid warnings. Will submit a PR if others agree.
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| null | 1 | 2015-09-15T15:28:19Z | 2015-09-15T16:10:21Z | 2015-09-15T16:10:20Z | NONE | null | I am trying to use get_all_data() method to download options data. I am getting following error:
RemoteDataError: Parsed URL 'http://finance.yahoo.com/q/op?s=SPY' has no rootelement
Actual code:
from pandas.io.data import Options
SPYD = Options('spy', 'yahoo')
data = SPYD.get_all_data()
My pandas version is 0.16.2
NOTE: I am raising this issue again, as it was closed without showing the fix/solution
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} | 16 | 2015-09-15T15:32:08Z | 2015-09-27T14:19:46Z | 2015-09-27T14:10:24Z | CONTRIBUTOR | null | closes #11094
Adds gbq support for python 3.4
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} | 48 | 2015-09-15T15:33:13Z | 2015-09-22T15:35:37Z | 2015-09-22T11:29:59Z | CONTRIBUTOR | null | install via: `conda install matplotlib -c tacaswell` (only linux-64 ATM)
this is with `pandas-0.17rc1`
```
......F......E..FF...FE..F./home/jreback/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
warnings.warn(self.msg_depr % (key, alt_key))
/home/jreback/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/__init__.py:892: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.
warnings.warn(self.msg_depr % (key, alt_key))
...E.../home/jreback/pandas/pandas/tools/plotting.py:3242: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
"is being cleared", UserWarning)
EF......ES.....E.......E.E.........../home/jreback/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/artist.py:221: MatplotlibDeprecationWarning: This has been deprecated in mpl 1.5, please use the
axes property. A removal date has not been set.
warnings.warn(_get_axes_msg, mplDeprecation, stacklevel=1)
.........FE..E........F.E..E..........E.......................EE.
======================================================================
ERROR: test_bar_colors (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 1810, in test_bar_colors
self._check_colors(ax.patches[::5], facecolors=default_colors[:5])
TypeError: 'NoneType' object has no attribute '__getitem__'
======================================================================
ERROR: test_boxplot_colors (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2860, in test_boxplot_colors
_check_colors(bp, default_colors[0], default_colors[0], default_colors[2])
TypeError: 'NoneType' object has no attribute '__getitem__'
======================================================================
ERROR: test_df_legend_labels (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2478, in test_df_legend_labels
ax = df.plot(legend=True, secondary_y='b')
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3610, in __call__
sort_columns=sort_columns, **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2495, in plot_frame
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1596, in _make_plot
ax = self._get_ax(i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1280, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
ERROR: test_errorbar_plot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 3129, in test_errorbar_plot
df.plot(yerr=df_err)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3610, in __call__
sort_columns=sort_columns, **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2495, in plot_frame
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1609, in _make_plot
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1623, in _plot
lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1241, in _plot
return ax.errorbar(x, y, **kwds)
File "/home/jreback/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/__init__.py", line 1806, in inner
return func(ax, *args, **kwargs)
File "/home/jreback/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/axes/_axes.py", line 2964, in errorbar
raise ValueError("yerr must be a scalar, the same "
ValueError: yerr must be a scalar, the same dimensions as y, or 2xN.
======================================================================
ERROR: test_hist_colors (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2737, in test_hist_colors
self._check_colors(ax.patches[::10], facecolors=default_colors[:5])
TypeError: 'NoneType' object has no attribute '__getitem__'
======================================================================
ERROR: test_kde_colors_and_styles_subplots (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2799, in test_kde_colors_and_styles_subplots
for ax, c in zip(axes, list(default_colors)):
TypeError: 'NoneType' object is not iterable
======================================================================
ERROR: test_line_colors_and_styles_subplots (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2634, in test_line_colors_and_styles_subplots
for ax, c in zip(axes, list(default_colors)):
TypeError: 'NoneType' object is not iterable
======================================================================
ERROR: test_line_lim (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 1768, in test_line_lim
ax = df.plot(secondary_y=True)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3610, in __call__
sort_columns=sort_columns, **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2495, in plot_frame
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1579, in _make_plot
if self._is_ts_plot():
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1572, in _is_ts_plot
return not self.x_compat and self.use_index and self._use_dynamic_x()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1576, in _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1280, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
ERROR: test_subplots_dup_columns (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 1668, in test_subplots_dup_columns
axes = df.plot(subplots=True, secondary_y='a')
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3610, in __call__
sort_columns=sort_columns, **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2495, in plot_frame
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1579, in _make_plot
if self._is_ts_plot():
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1572, in _is_ts_plot
return not self.x_compat and self.use_index and self._use_dynamic_x()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1576, in _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1276, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
ERROR: test_subplots_sharex_axes_existing_axes (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 1651, in test_subplots_sharex_axes_existing_axes
df['C'].plot(ax=axes[0], secondary_y=True)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3436, in __call__
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2526, in plot_series
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1579, in _make_plot
if self._is_ts_plot():
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1572, in _is_ts_plot
return not self.x_compat and self.use_index and self._use_dynamic_x()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1576, in _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1280, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
ERROR: test_df_series_secondary_legend (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 889, in test_df_series_secondary_legend
s.plot(legend=True, secondary_y=True)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3436, in __call__
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2526, in plot_series
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1579, in _make_plot
if self._is_ts_plot():
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1572, in _is_ts_plot
return not self.x_compat and self.use_index and self._use_dynamic_x()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1576, in _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1280, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
ERROR: test_errorbar_plot (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 1113, in test_errorbar_plot
s.plot(yerr=s_err)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3436, in __call__
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2526, in plot_series
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1609, in _make_plot
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1623, in _plot
lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1241, in _plot
return ax.errorbar(x, y, **kwds)
File "/home/jreback/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/__init__.py", line 1806, in inner
return func(ax, *args, **kwargs)
File "/home/jreback/miniconda/envs/pandas/lib/python2.7/site-packages/matplotlib/axes/_axes.py", line 2964, in errorbar
raise ValueError("yerr must be a scalar, the same "
ValueError: yerr must be a scalar, the same dimensions as y, or 2xN.
======================================================================
ERROR: test_hist_secondary_legend (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 852, in test_hist_secondary_legend
df['b'].plot.hist(ax=ax, legend=True, secondary_y=True)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3524, in hist
return self(kind='hist', bins=bins, **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3436, in __call__
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2526, in plot_series
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1948, in _make_plot
ax = self._get_ax(i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1280, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
ERROR: test_ts_area_lim (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 582, in test_ts_area_lim
ax = tz_ts.plot.area(stacked=False, secondary_y=True)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3560, in area
return self(kind='area', **kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3436, in __call__
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2526, in plot_series
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1579, in _make_plot
if self._is_ts_plot():
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1572, in _is_ts_plot
return not self.x_compat and self.use_index and self._use_dynamic_x()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1576, in _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1280, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
ERROR: test_ts_line_lim (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 551, in test_ts_line_lim
ax = self.ts.plot(secondary_y=True)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 3436, in __call__
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2526, in plot_series
**kwds)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 2323, in _plot
plot_obj.generate()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 944, in generate
self._make_plot()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1579, in _make_plot
if self._is_ts_plot():
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1572, in _is_ts_plot
return not self.x_compat and self.use_index and self._use_dynamic_x()
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1576, in _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 1280, in _get_ax
ax = self._maybe_right_yaxis(ax, i)
File "/home/jreback/pandas/pandas/tools/plotting.py", line 976, in _maybe_right_yaxis
new_ax._get_lines.color_cycle = orig_ax._get_lines.color_cycle
AttributeError: '_process_plot_var_args' object has no attribute 'color_cycle'
======================================================================
FAIL: test_area_colors (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2701, in test_area_colors
self._check_colors(handles, linecolors=custom_colors)
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 194, in _check_colors
self.assertEqual(len(collections), len(linecolors))
AssertionError: 10 != 5
======================================================================
FAIL: test_bar_log_no_subplots (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2188, in test_bar_log_no_subplots
tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
File "/home/jreback/pandas/pandas/util/testing.py", line 822, in assert_numpy_array_equal
left.shape, right.shape)
File "/home/jreback/pandas/pandas/util/testing.py", line 791, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array shapes are different
[left]: (4,)
[right]: (2,)
======================================================================
FAIL: test_bar_log_subplots (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2199, in test_bar_log_subplots
tm.assert_numpy_array_equal(ax[0].yaxis.get_ticklocs(), expected)
File "/home/jreback/pandas/pandas/util/testing.py", line 822, in assert_numpy_array_equal
left.shape, right.shape)
File "/home/jreback/pandas/pandas/util/testing.py", line 791, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array shapes are different
[left]: (6,)
[right]: (4,)
======================================================================
FAIL: test_boxplot (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2214, in test_boxplot
self.bp_n_objects * len(numeric_cols))
AssertionError: 21 != 24
======================================================================
FAIL: test_boxplot_vertical (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 2251, in test_boxplot_vertical
self.assertEqual(len(ax.lines), self.bp_n_objects * len(numeric_cols))
AssertionError: 21 != 24
======================================================================
FAIL: test_errorbar_scatter (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 3251, in test_errorbar_scatter
_check_errorbar_color(ax.containers, 'green', has_err='has_yerr')
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 3240, in _check_errorbar_color
self._check_colors(errs, linecolors=[expected] * len(errs))
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 204, in _check_colors
self.assertEqual(result, expected)
AssertionError: Tuples differ: (0.0, 0.0, 1.0, 1.0) != (0.0, 0.5019607843137255, 0.0,...
First differing element 1:
0.0
0.501960784314
- (0.0, 0.0, 1.0, 1.0)
+ (0.0, 0.5019607843137255, 0.0, 1.0)
======================================================================
FAIL: test_subplots (pandas.tests.test_graphics.TestDataFramePlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 1440, in test_subplots
self._check_legend_labels(ax, labels=[com.pprint_thing(column)])
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 117, in _check_legend_labels
self._check_text_labels(ax.get_legend().get_texts(), labels)
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 241, in _check_text_labels
self.assertEqual(len(labels), len(expected))
AssertionError: 2 != 1
======================================================================
FAIL: test_bar_log (pandas.tests.test_graphics.TestSeriesPlots)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/jreback/pandas/pandas/tests/test_graphics.py", line 649, in test_bar_log
tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
File "/home/jreback/pandas/pandas/util/testing.py", line 822, in assert_numpy_array_equal
left.shape, right.shape)
File "/home/jreback/pandas/pandas/util/testing.py", line 791, in raise_assert_detail
raise AssertionError(msg)
AssertionError: numpy array are different
numpy array shapes are different
[left]: (6,)
[right]: (4,)
----------------------------------------------------------------------
Ran 136 tests in 124.794s
FAILED (SKIP=1, errors=15, failures=8)
```
cc @TomAugspurger @sinhrks @jorisvandenbossche
cc @tacaswell
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https://api.github.com/repos/pandas-dev/pandas/issues/11112 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11112/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11112/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11112/events | https://github.com/pandas-dev/pandas/issues/11112 | 106,584,044 | MDU6SXNzdWUxMDY1ODQwNDQ= | 11,112 | COMPAT/TST: warnings for sql installs on python 3.5 | {
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} | 2 | 2015-09-15T15:39:56Z | 2019-02-03T03:58:37Z | 2019-02-03T03:58:36Z | CONTRIBUTOR | null | These are actually from the current version of `nose` (e.g. it has a deprecation warning in inspect).
currently I have `sqlalchemy` and friends not installing on 3.5 (though they _can_ be), they are commented out in `requirements-3.5.txt`
So can do things like this (e.g. I did this in `test_pytables.py`), until a new `nose` comes out.
e.g. see https://github.com/pydata/pandas/pull/11114
```
expected_warning = Warning if compat.PY35 else PerformanceWarning
with tm.assert_produces_warning(expected_warning=expected_warning, check_stacklevel=False):
....
```
```
(py3.5)bash-3.2$ nosetests pandas/io/tests/test_sql.py
SSS.........................................................................S...........F.F........F..........F...........F.F........F..........F..F...................................F................................S.....................................................S/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2183: FutureWarning: write_frame is deprecated, use to_sql
sql.write_frame(frame, name='test_table', con=self.conn)
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2184: FutureWarning: read_frame is deprecated, use read_sql
result = sql.read_frame("select * from test_table", self.conn)
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2195: FutureWarning: write_frame is deprecated, use to_sql
sql.write_frame(frame2, name='test_table2', con=self.conn)
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2197: FutureWarning: read_frame is deprecated, use read_sql
index_col='Idx')
./Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2110: FutureWarning: read_frame is deprecated, use read_sql
result = sql.read_frame("select * from test", self.conn)
./Users/jreback/miniconda/envs/py3.5/pandas/pandas/util/testing.py:1872: FutureWarning: tquery is deprecated, and will be removed in future versions. You can use ``execute(...).fetchall()`` instead.
_callable(*args, **kwargs)
../Users/jreback/miniconda/envs/py3.5/pandas/pandas/util/testing.py:1872: FutureWarning: write_frame is deprecated, use to_sql
_callable(*args, **kwargs)
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2285: FutureWarning: write_frame is deprecated, use to_sql
flavor='sqlite', if_exists='fail')
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2296: FutureWarning: write_frame is deprecated, use to_sql
flavor='sqlite', if_exists='replace')
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2297: FutureWarning: tquery is deprecated, and will be removed in future versions. You can use ``execute(...).fetchall()`` instead.
self.assertEqual(sql.tquery(sql_select, con=self.conn),
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2300: FutureWarning: write_frame is deprecated, use to_sql
flavor='sqlite', if_exists='replace')
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2301: FutureWarning: tquery is deprecated, and will be removed in future versions. You can use ``execute(...).fetchall()`` instead.
self.assertEqual(sql.tquery(sql_select, con=self.conn),
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2307: FutureWarning: write_frame is deprecated, use to_sql
flavor='sqlite', if_exists='fail')
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2308: FutureWarning: tquery is deprecated, and will be removed in future versions. You can use ``execute(...).fetchall()`` instead.
self.assertEqual(sql.tquery(sql_select, con=self.conn),
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2311: FutureWarning: write_frame is deprecated, use to_sql
flavor='sqlite', if_exists='append')
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2312: FutureWarning: tquery is deprecated, and will be removed in future versions. You can use ``execute(...).fetchall()`` instead.
self.assertEqual(sql.tquery(sql_select, con=self.conn),
./Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2243: FutureWarning: write_frame is deprecated, use to_sql
sql.write_frame(df, con = self.conn, name = 'testkeywords')
../Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2250: FutureWarning: write_frame is deprecated, use to_sql
sql.write_frame(mono_df, con = self.conn, name = 'mono_df')
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2257: FutureWarning: read_frame is deprecated, use read_sql
result = sql.read_frame("select * from mono_df",con_x)
../Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2205: FutureWarning: write_frame is deprecated, use to_sql
sql.write_frame(frame, name='test_table', con=self.conn)
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2206: FutureWarning: tquery is deprecated, and will be removed in future versions. You can use ``execute(...).fetchall()`` instead.
result = sql.tquery("select A from test_table", self.conn)
./Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2223: FutureWarning: write_frame is deprecated, use to_sql
sql.write_frame(frame, name='test_table', con=self.conn)
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2225: FutureWarning: uquery is deprecated, and will be removed in future versions. You can use ``execute(...).rowcount`` instead.
self.assertEqual(sql.uquery(stmt, con=self.conn), 1)
./Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2091: FutureWarning: tquery is deprecated, and will be removed in future versions. You can use ``execute(...).fetchall()`` instead.
sql.tquery(fmt_sql, cur=cur)
/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py:2095: FutureWarning: read_frame is deprecated, use read_sql
result = sql.read_frame("select * from test", con=self.conn)
======================================================================
FAIL: test_legacy_write_frame (test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py", line 587, in test_legacy_write_frame
flavor='sqlite')
File "/Users/jreback/miniconda/envs/py3.5/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/Users/jreback/miniconda/envs/py3.5/pandas/pandas/util/testing.py", line 2021, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['PendingDeprecationWarning'].
======================================================================
FAIL: test_not_reflect_all_tables (test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py", line 906, in test_not_reflect_all_tables
self.assertEqual(len(w), 0, "Warning triggered for other table")
AssertionError: 2 != 0 : Warning triggered for other table
======================================================================
FAIL: test_timedelta (test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py", line 671, in test_timedelta
df.to_sql('test_timedelta', self.conn)
File "/Users/jreback/miniconda/envs/py3.5/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/Users/jreback/miniconda/envs/py3.5/pandas/pandas/util/testing.py", line 2021, in assert_produces_warning
% extra_warnings)
AssertionError: Caused unexpected warning(s): ['PendingDeprecationWarning'].
======================================================================
FAIL: test_warning_case_insensitive_table_name (test_sql.TestSQLApi)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/Users/jreback/miniconda/envs/py3.5/pandas/pandas/io/tests/test_sql.py", line 919, in test_warning_case_insensitive_table_name
self.assertEqual(len(w), 0, "Warning triggered for writing a table")
AssertionError: 1 != 0 : Warning triggered for writing a table
.....
```
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| null | 2 | 2015-09-15T15:49:43Z | 2021-10-16T17:17:26Z | null | CONTRIBUTOR | null | Calling qcut with infinite values in a pandas Series should be a well-defined operation, but it tends to produce wrong results or raise (un-obvious) exceptions. I'm using the following snippet to test:
```
data = range(10) + [np.inf] * n
s = pd.Series(data, index=data)
pd.qcut(s, [0.1, 0.9])
```
When called with n=1, it produces the following result:
```
0.000000 NaN
1.000000 [1, 9]
2.000000 [1, 9]
3.000000 [1, 9]
4.000000 [1, 9]
5.000000 [1, 9]
6.000000 [1, 9]
7.000000 [1, 9]
8.000000 [1, 9]
9.000000 [1, 9]
inf NaN
dtype: category
Categories (1, object): [[1, 9]]
```
I don't think that the 0 value and the inf should get assigned to NaN bins. When called with n=2, it now produces:
```
0.000000 NaN
1.000000 NaN
2.000000 [1.1, inf]
3.000000 [1.1, inf]
4.000000 [1.1, inf]
5.000000 [1.1, inf]
6.000000 [1.1, inf]
7.000000 [1.1, inf]
8.000000 [1.1, inf]
9.000000 [1.1, inf]
inf [1.1, inf]
inf [1.1, inf]
dtype: category
Categories (1, object): [[1.1, inf]]
```
Again, the binning looks suspicious to me... And when called with n >= 3, I get the following exception:
```
TypeError Traceback (most recent call last)
<ipython-input-29-db4904bb94b0> in <module>()
1 data = range(10) + [np.inf] * 3
2 s = pd.Series(data, index=data)
----> 3 pd.qcut(s, [0.1, 0.9])
C:\Anaconda\lib\site-packages\pandas\tools\tile.pyc in qcut(x, q, labels, retbins, precision)
167 bins = algos.quantile(x, quantiles)
168 return _bins_to_cuts(x, bins, labels=labels, retbins=retbins,precision=precision,
--> 169 include_lowest=True)
170
171
C:\Anaconda\lib\site-packages\pandas\tools\tile.pyc in _bins_to_cuts(x, bins, right, labels, retbins, precision, name, include_lowest)
201 try:
202 levels = _format_levels(bins, precision, right=right,
--> 203 include_lowest=include_lowest)
204 except ValueError:
205 increases += 1
C:\Anaconda\lib\site-packages\pandas\tools\tile.pyc in _format_levels(bins, prec, right, include_lowest)
240 levels = []
241 for a, b in zip(bins, bins[1:]):
--> 242 fa, fb = fmt(a), fmt(b)
243
244 if a != b and fa == fb:
C:\Anaconda\lib\site-packages\pandas\tools\tile.pyc in <lambda>(v)
236 def _format_levels(bins, prec, right=True,
237 include_lowest=False):
--> 238 fmt = lambda v: _format_label(v, precision=prec)
239 if right:
240 levels = []
C:\Anaconda\lib\site-packages\pandas\tools\tile.pyc in _format_label(x, precision)
274 return '%d' % (-whole - 1)
275 else:
--> 276 return '%d' % (whole + 1)
277
278 if 'e' in val:
TypeError: %d format: a number is required, not numpy.float64
```
... which doesn't look very related to the cause at first glance. What is happening here is that the value passed to `_format_label()` and then to the `%` operator is a NaN, which is doesn't support.
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} | 0 | 2015-09-15T16:32:37Z | 2015-09-15T23:25:24Z | 2015-09-15T23:25:24Z | CONTRIBUTOR | null | closes #11097
- update install / compat docs for 3.5
- use visit_Call based on the version of python
- skip if html5lib is not installed in test_data
- bug in nose causes deprecation warning in some pytables tests
- remove superfluous socket timeout
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} | 1 | 2015-09-15T16:42:54Z | 2015-09-16T23:04:33Z | 2015-09-16T23:04:33Z | CONTRIBUTOR | null | PR #10691 had a bug in computing the NaN mask in certain cases.
Specifically, when a NaN occurs within `limit` of the beginning of the series and `limit_direction` is set to `'both'`, the interpolation code will erroneously interpolate these NaNs rather than masking them off.
From the PR:
```
In [43]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13])
In [44]: ser.interpolate(limit=1, limit_direction='both')
Out[44]:
0 5
1 5
2 5
3 7
4 NaN
5 11
6 13
dtype: float64
```
This should actually give:
```
Out[44]:
0 NaN
1 5
2 5
3 7
4 NaN
5 11
6 13
dtype: float64
```
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} | 6 | 2015-09-15T16:51:14Z | 2015-10-02T14:13:52Z | 2015-10-02T14:13:52Z | NONE | null | I upgraded from 0.16.2 to 0.17/master and it broke working code.
0.16.2:
``` python
In [2]: import pandas as pd
...: from StringIO import StringIO
...: x="""foo,bar
...: 2015-09-14,True
...: 2015-09-15,
...: """
...: df=pd.read_csv(StringIO(x),sep=',').convert_objects('coerce')
...: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 2 columns):
foo 2 non-null datetime64[ns]
bar 1 non-null object
dtypes: datetime64[ns](1), object(1)
memory usage: 48.0+ bytes
```
0.17/master:
``` python
import pandas as pd
from StringIO import StringIO
x="""foo,bar
2015-09-14,True
2015-09-15,
"""
df=pd.read_csv(StringIO(x),sep=',').convert_objects('coerce')
df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 2 columns):
foo 2 non-null datetime64[ns]
bar 0 non-null datetime64[ns]
dtypes: datetime64[ns](2)
memory usage: 48.0 bytes
/home/ambk/work/pandas/pandas/core/generic.py:2584: FutureWarning: The use of 'coerce' as an input is deprecated. Instead set coerce=True.
FutureWarning)
```
booleans get cast to datetimes now?! Usually deprecation means "avoid in new code" and not that your working code will break, otherwise it wouldn't be a deprecation but a breaking change. So that's not good, but ok, let's follow the helpful hint:
``` python
In [3]: import pandas as pd
...: from StringIO import StringIO
...: x="""foo,bar
...: 2015-09-14,True
...: 2015-09-15,
...: """
...: df=pd.read_csv(StringIO(x),sep=',').convert_objects(coerce=True)
...: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2 entries, 0 to 1
Data columns (total 2 columns):
foo 2 non-null object
bar 1 non-null float64
dtypes: float64(1), object(1)
memory usage: 48.0+ bytes
```
**wut?**, booleans become floats and almost-datetimes aren't converted at all anymore?
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https://api.github.com/repos/pandas-dev/pandas/issues/11117 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/11117/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/11117/comments | https://api.github.com/repos/pandas-dev/pandas/issues/11117/events | https://github.com/pandas-dev/pandas/issues/11117 | 106,636,775 | MDU6SXNzdWUxMDY2MzY3NzU= | 11,117 | read_clipboard() Windows to Linux | {
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| null | 5 | 2015-09-15T20:15:16Z | 2015-09-17T17:27:39Z | 2015-09-17T17:27:39Z | NONE | null | I have an IPython (Jupyter) Notebook running on a webserver (Ubuntu) and am trying to read_clipboard() from a Windows computer. I have xclip installed on the ubuntu machine. Here is the code:
```
import pandas as pd
from pandas import Series,DataFrame
test = pd.read_clipboard()
print test
```
It appears that the clipboard is empty and Pandas is trying to parse the empty clipboard. The error I receive is the following:
```
ValueError
Traceback (most recent call last)
<ipython-input-3-4f7527f57c80> in <module>()
2 import pandas as pd
3 from pandas import Series,DataFrame
----> 4 test = pd.read_clipboard()
5 print test
/usr/local/lib/python2.7/dist-packages/pandas/io/clipboard.pyc in read_clipboard(**kwargs)
49 kwargs['sep'] = '\s+'
50
---> 51 return read_table(StringIO(text), **kwargs)
52
53
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in parser_f(filepath_or_buffer, sep, dialect, compression, doublequote, escapechar, quotechar, quoting, skipinitialspace, lineterminator, header, index_col, names, prefix, skiprows, skipfooter, skip_footer, na_values, na_fvalues, true_values, false_values, delimiter, converters, dtype, usecols, engine, delim_whitespace, as_recarray, na_filter, compact_ints, use_unsigned, low_memory, buffer_lines, warn_bad_lines, error_bad_lines, keep_default_na, thousands, comment, decimal, parse_dates, keep_date_col, dayfirst, date_parser, memory_map, float_precision, nrows, iterator, chunksize, verbose, encoding, squeeze, mangle_dupe_cols, tupleize_cols, infer_datetime_format, skip_blank_lines)
472 skip_blank_lines=skip_blank_lines)
473
--> 474 return _read(filepath_or_buffer, kwds)
475
476 parser_f.__name__ = name
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
248
249 # Create the parser.
--> 250 parser = TextFileReader(filepath_or_buffer, **kwds)
251
252 if (nrows is not None) and (chunksize is not None):
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in __init__(self, f, engine, **kwds)
564 self.options['has_index_names'] = kwds['has_index_names']
565
--> 566 self._make_engine(self.engine)
567
568 def _get_options_with_defaults(self, engine):
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in _make_engine(self, engine)
703 def _make_engine(self, engine='c'):
704 if engine == 'c':
--> 705 self._engine = CParserWrapper(self.f, **self.options)
706 else:
707 if engine == 'python':
/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.pyc in __init__(self, src, **kwds)
1070 kwds['allow_leading_cols'] = self.index_col is not False
1071
-> 1072 self._reader = _parser.TextReader(src, **kwds)
1073
1074 # XXX
pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:4814)()
**ValueError: No columns to parse from file**
```
I've tried using xsel with no luck so I don't think that's the issue. I tried to use read_clipboard on the try.jupyter.com site as well and it shutdown the kernel. Any clarity on this subject would be nice. Below is the full version string of Pandas and dependencies:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-48-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.1
Cython: None
numpy: 1.9.2
scipy: 0.13.3
statsmodels: 0.5.0
IPython: 4.0.0
sphinx: None
patsy: 0.2.1
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: 3.1.1
numexpr: 2.2.2
matplotlib: 1.3.1
openpyxl: 1.7.0
xlrd: 0.9.2
xlwt: 0.7.5
xlsxwriter: None
lxml: None
bs4: 4.2.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
```
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| null | 4 | 2015-09-15T22:59:19Z | 2015-09-16T00:37:19Z | 2015-09-16T00:16:58Z | NONE | null | ```
df.head()
OSymbol
0 .GGP:150918:C:18.0
1 .GGP:150918:C:19.0
2 .GGP:150918:C:20.0
3 .GGP:150918:C:21.0
4 .GGP:150918:C:22.0
df['OSymbol'].str.replace(".0", "")
0 .GGP:1918:C:18
1 .GGP:1918:C:19
2 .GGP:1918:C:
3 .GGP:1918:C:21
4 .GGP:1918:C:22
```
Look at 20 strike... should be 20.0 > 20 , instead all of 20.0 is removed...
Pandas version 0.16.2
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} | 3 | 2015-09-15T23:01:11Z | 2021-04-18T23:44:29Z | null | CONTRIBUTOR | null | Setup:
```
import pandas as pd
df = pd.DataFrame({True: [1,2,5,9],
False: [6,1,13,8]})
```
`df[True]` and `df[False]` work fine; but `df[[True,False]]` throws an exception:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-a28f55cd0379> in <module>()
----> 1 df[[True,False]]
/Users/username/pandasenv/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
1772 if isinstance(key, (Series, np.ndarray, Index, list)):
1773 # either boolean or fancy integer index
-> 1774 return self._getitem_array(key)
1775 elif isinstance(key, DataFrame):
1776 return self._getitem_frame(key)
/Users/username/pandasenv/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_array(self, key)
1809 elif len(key) != len(self.index):
1810 raise ValueError('Item wrong length %d instead of %d.' %
-> 1811 (len(key), len(self.index)))
1812 # _check_bool_indexer will throw exception if Series key cannot
1813 # be reindexed to match DataFrame rows
ValueError: Item wrong length 2 instead of 4.
```
On the other hand, this works fine:
```
df = pd.DataFrame({'True': [1,2,5,9],
'False': [6,1,13,8]})
df[['True','False']]
```
Probably relevant to: https://github.com/pydata/pandas/issues/9595
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} | 1 | 2015-09-16T01:58:17Z | 2015-09-22T14:16:59Z | 2015-09-17T01:00:29Z | CONTRIBUTOR | null | since it must not e.g. if there is ~ in some directory name along the path (awkward but possible/allowed).
Better to check either new path is absolute. Removed 1 test which had no value
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} | 33 | 2015-09-16T04:27:04Z | 2015-09-24T23:37:55Z | 2015-09-24T23:02:30Z | CONTRIBUTOR | null | Removed the `bq` command line module from `test_gbq.py`, as it doesn't support python 3. In order to do this, I had to create functions for `create_dataset()`, `delete_dataset()` and `dataset_exists()`.
This change is required for #11110
At the same time, I also implemented the following list functions: `list_dataset()` and `list_table()`.
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} | 0 | 2015-09-16T11:34:30Z | 2015-09-16T23:01:46Z | 2015-09-16T23:01:46Z | CONTRIBUTOR | null | https://travis-ci.org/pydata/pandas/pull_requests
Recently the Python 2.7 builds on travis have been failing with the same error message.
```
Traceback (most recent call last):
File "setup.py", line 566, in <module>
**setuptools_kwargs)
File "/home/travis/miniconda/envs/pandas/lib/python2.7/distutils/core.py", line 151, in setup
dist.run_commands()
File "/home/travis/miniconda/envs/pandas/lib/python2.7/distutils/dist.py", line 953, in run_commands
self.run_command(cmd)
File "/home/travis/miniconda/envs/pandas/lib/python2.7/distutils/dist.py", line 972, in run_command
cmd_obj.run()
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/Cython/Distutils/build_ext.py", line 163, in run
_build_ext.build_ext.run(self)
File "/home/travis/miniconda/envs/pandas/lib/python2.7/distutils/command/build_ext.py", line 337, in run
self.build_extensions()
File "setup.py", line 305, in build_extensions
build_ext.build_extensions(self)
File "setup.py", line 88, in build_extensions
numpy_incl = pkg_resources.resource_filename('numpy', 'core/include')
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/setuptools-18.1-py2.7.egg/pkg_resources/__init__.py", line 1160, in resource_filename
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/setuptools-18.1-py2.7.egg/pkg_resources/__init__.py", line 435, in get_provider
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/numpy/__init__.py", line 137, in <module>
import add_newdocs
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/numpy/add_newdocs.py", line 9, in <module>
from numpy.lib import add_newdoc
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/numpy/lib/__init__.py", line 13, in <module>
from polynomial import *
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/numpy/lib/polynomial.py", line 17, in <module>
from numpy.linalg import eigvals, lstsq, inv
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/numpy/linalg/__init__.py", line 48, in <module>
from linalg import *
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/numpy/linalg/linalg.py", line 23, in <module>
from numpy.linalg import lapack_lite
ImportError: libgfortran.so.1: cannot open shared object file: No such file or directory
```
```
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "pandas/__init__.py", line 16, in <module>
"extensions first.".format(module))
ImportError: C extension: hashtable not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
nosetests --exe -A slow and not network and not disabled pandas --doctest-tests --with-xunit --xunit-file=/tmp/nosetests.xml
E
======================================================================
ERROR: Failure: ImportError (C extension: hashtable not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/nose/loader.py", line 418, in loadTestsFromName
addr.filename, addr.module)
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/nose/importer.py", line 47, in importFromPath
return self.importFromDir(dir_path, fqname)
File "/home/travis/miniconda/envs/pandas/lib/python2.7/site-packages/nose/importer.py", line 94, in importFromDir
mod = load_module(part_fqname, fh, filename, desc)
File "/home/travis/build/pydata/pandas/pandas/__init__.py", line 16, in <module>
"extensions first.".format(module))
ImportError: C extension: hashtable not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext --inplace' to build the C extensions first.
```
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} | 16 | 2015-09-16T11:42:39Z | 2019-06-09T11:53:21Z | 2019-06-09T11:48:38Z | CONTRIBUTOR | null | Hi everyone,
I just stumbled across this odd behaviour:
For a daily time series, I would expect `resample('B', how='last')` to be equivalent to selecting rows whose weekday is not Saturday or Sunday. However, `resample` groups together Fridays, Saturdays and Sundays and hence we get Sunday's value on Friday.
In the time series context, I guess most people would expect `resample('B', ...)` to bin Saturdays, Sundays and Mondays, so that **no looking into the future** occurs.
Am I missing something?
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} | 4 | 2015-09-16T18:03:12Z | 2015-09-16T23:04:13Z | 2015-09-16T23:04:10Z | CONTRIBUTOR | null | Fixes issue #11115.
This change fixes up a couple edge cases for the computation of invalid NaN indexes for interpolation limits.
I've also added a test explicitly for the reported bug (and similar behaviors at the opposite end of the series) in #11115.
/cc @jreback
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| null | 3 | 2015-09-16T18:53:50Z | 2015-09-16T20:13:03Z | 2015-09-16T20:13:03Z | NONE | null | Dear gurus,
I am trying to convert a DataFrame to dict and then to json. However, json.dumps or simplejson.dumps kept reporting error "TypeError: _ is not JSON serializable". I created a small code to reproduce the problem. Just copy and paste to run those codes:
df1=pd.DataFrame([{'t1':1,'t2':0}])
dict1=df1.to_dict(orient='records')
simplejson.dumps(dict1)
dict2=[{'t1':1,'t2':0}]
simplejson.dumps(dict2)
My execution resulted in :
> > > df1=pd.DataFrame([{'t1':1,'t2':0}])
> > > df1
> > > Out[17]:
> > > t1 t2
> > > 0 1 0
> > > dict1=df1.to_dict(orient='records')
> > > dict1
> > > Out[19]: [{'t1': 1, 't2': 0}]
> > > simplejson.dumps(dict1)
Traceback (most recent call last):
File "C:\Anaconda27\lib\site-packages\IPython\core\interactiveshell.py", line 3035, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-20-f990fefe9c5f>", line 1, in <module>
simplejson.dumps(dict1)
File "C:\Anaconda27\lib\site-packages\simplejson__init__.py", line 354, in dumps
return _default_encoder.encode(obj)
File "C:\Anaconda27\lib\site-packages\simplejson\encoder.py", line 262, in encode
chunks = self.iterencode(o, _one_shot=True)
File "C:\Anaconda27\lib\site-packages\simplejson\encoder.py", line 340, in iterencode
return _iterencode(o, 0)
File "C:\Anaconda27\lib\site-packages\simplejson\encoder.py", line 239, in default
raise TypeError(repr(o) + " is not JSON serializable")
TypeError: 0 is not JSON serializable
> > > dict2=[{'t1':1,'t2':0}]
> > > simplejson.dumps(dict2)
> > > Out[22]: '[{"t2": 0, "t1": 1}]'
I can not figure out why dict1 can not be converted to json, but dict2 can be.
can anyone help?
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} | 18 | 2015-09-16T19:55:07Z | 2020-04-14T05:20:26Z | 2020-04-14T05:20:26Z | CONTRIBUTOR | null | http://stackoverflow.com/questions/32553207/values-missing-when-loaded-from-pandas-hdf5-file/32587108?noredirect=1#comment53034458_32587108
e.g.
if I
```
df.to_hdf('foo.h5','df',encoding='utf-8')
```
then
```
pd.read_hdf('foo.h5','df')
```
should pick up the encoding for each table (unless the user overrides by passing in `encoding=...`
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} | 0 | 2015-09-16T21:02:57Z | 2015-09-16T23:01:46Z | 2015-09-16T23:01:46Z | CONTRIBUTOR | null | closes #11122
dependency resolution in the `pandas` channel is a bit challenging as the current script tries to install all deps up front before building. But `statsmodels` requires pandas, so a current version is installed (e.g. the rc), and several of the deps are changed.
So fixed, but installing only the build deps, compiling, then install in the run deps.
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} | 5 | 2015-09-16T22:33:38Z | 2016-04-26T13:30:42Z | 2016-04-26T13:30:42Z | CONTRIBUTOR | null | These names exist already in `pd.tslib` (as the below weedays calc), should just create them as a variable so can import it directly here.
```
In [1]: import calendar
In [25]: s = pd.Series(list(range(10)), pd.date_range('2015-09-01', '2015-09-10'))
In [26]: s2 = s.to_frame('value').assign(weekday=Series(s.index.weekday).map(Series(weekdays)).values)
In [27]: weekdays = [calendar.day_name[i].lower() for i in range(7)]
In [28]: s2 = s.to_frame('value').assign(weekday=Series(s.index.weekday).map(Series(weekdays)).values)
In [29]: s2
Out[29]:
value weekday
2015-09-01 0 tuesday
2015-09-02 1 wednesday
2015-09-03 2 thursday
2015-09-04 3 friday
2015-09-05 4 saturday
2015-09-06 5 sunday
2015-09-07 6 monday
2015-09-08 7 tuesday
2015-09-09 8 wednesday
2015-09-10 9 thursday
```
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} | 8 | 2015-09-16T23:13:18Z | 2015-09-17T11:45:05Z | 2015-09-17T11:45:05Z | CONTRIBUTOR | null | Related to #10160 / PR https://github.com/pydata/pandas/pull/10269
```
In [2]: from datetime import timedelta
...: from pandas import *
...: td_as_int = [1, 2, 3, 4]
...:
...: def create_data(constructor):
...: return dict((i, {constructor(s): 2*i}) for i, s in enumerate(td_as_int))
...:
...: data_timedelta64 = create_data(lambda x: np.timedelta64(x, 'D'))
...: data_timedelta = create_data(lambda x: timedelta(days=x))
...:
...: result_timedelta64 = DataFrame(data_timedelta64)
...: result_timedelta = DataFrame(data_timedelta)
...:
In [3]: result_timedelta64
Out[3]:
0 1 2 3
1 days 0 NaN NaN NaN
2 days NaN 2 NaN NaN
3 days NaN NaN 4 NaN
4 days NaN NaN NaN 6
In [4]: result_timedelta
Out[4]:
0 1 2 3
1 days NaN NaN NaN NaN
2 days NaN NaN NaN NaN
3 days NaN NaN NaN NaN
4 days NaN NaN NaN NaN
```
This seems to happen because `Timedelta` doesn't have hash equality with `datetime.timedelta` It could easily be worked around here, but I was wondering if there's any reason not to make `Timedelta` work like `Timestamp` and match hashes if above a certain resolution?
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} | 2 | 2015-09-16T23:38:24Z | 2021-04-18T23:46:14Z | null | CONTRIBUTOR | null | Docs currently explicitly mention float64 as the type of columns for `expanding_max` and `expanding_min`, and indeed, it does a conversion to float64 for other types:
```
In [6]: x = pd.date_range('2008/01/01', periods=3).to_series()
In [7]: x
Out[7]:
2008-01-01 2008-01-01
2008-01-02 2008-01-02
2008-01-03 2008-01-03
Freq: D, dtype: datetime64[ns]
In [8]: pd.expanding_min(x)
Out[8]:
2008-01-01 1.199146e+18
2008-01-02 1.199146e+18
2008-01-03 1.199146e+18
Freq: D, dtype: float64
In [9]: pd.expanding_max(x)
Out[9]:
2008-01-01 1.199146e+18
2008-01-02 1.199232e+18
2008-01-03 1.199318e+18
Freq: D, dtype: float64
```
It would probably be a good idea to implement this for other supported dtypes. Not all expanding functions will work with any dtype, but at least things like max/min for orderable types are pretty straightforward.
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} | 0 | 2015-09-16T23:46:48Z | 2015-09-17T00:28:58Z | 2015-09-17T00:28:58Z | CONTRIBUTOR | null | xref #https://github.com/blaze/dask/issues/693
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} | 2 | 2015-09-17T00:19:12Z | 2015-09-25T09:13:57Z | 2015-09-25T09:13:57Z | CONTRIBUTOR | null | we need to make the API's more explicit/consistent.
```
create_table
delete_table
table_exists
```
should these be
```
table_create
table_delete
table_exists
```
cc @parthea
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} | 22 | 2015-09-17T01:34:35Z | 2015-09-22T21:33:35Z | 2015-09-22T21:33:35Z | CONTRIBUTOR | null | closes #11116
This is a lot more noisy that the current impl. It pretty much raises on the old-style input if it had `coerce` in it. This is correct as the actual behavior of `coerce` has changed (in that it now it much more strict).
errror on old-style input
- raise a ValueError for df.convert_objects('coerce')
- raise a ValueError for df.convert_objects(convert_dates='coerce') (and convert_numeric,convert_timedelta)
```
In [1]: data = """foo,bar
...: 2015-09-14,True
...: 2015-09-15,
...: """
In [2]: df = pd.read_csv(StringIO(data),sep=',')
In [3]: df
Out[3]:
foo bar
0 2015-09-14 True
1 2015-09-15 NaN
```
```
In [4]: df.convert_objects('coerce')
ValueError: The use of 'coerce' as an input is deprecated. Instead set coerce=True.
In [5]: df.convert_objects(coerce=True)
ValueError: coerce=True was provided, with no options for conversions.excatly one of 'datetime', 'numeric' or 'timedelta' must be True when when coerce=True.
In [8]: df.convert_objects(convert_dates='coerce')
/Users/jreback/miniconda/bin/ipython:1: FutureWarning: the 'convert_dates' keyword is deprecated, use 'datetime' instead
ValueError: The use of 'coerce' as an input is deprecated. Instead set coerce=True.
```
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} | 6 | 2015-09-17T01:34:38Z | 2015-09-17T22:31:12Z | 2015-09-17T11:45:05Z | CONTRIBUTOR | null | Closes #11129
Per discussion in issue this adds similar hash equality to `_Timedelta` / `datetime.timedelta`
that exists for `_Timestamp` / `datetime.datime`.
I tried inlining `_Timedelta._ensure_components` and it actually hurt performance a bit, so I left it
as a plain `def` but did build a seperate `_has_ns` check for hashing.
Adds a little overhead to hashing:
```
In [1]: tds = pd.timedelta_range('1 day', periods=100000)
# PR
In [2]: %timeit [hash(td) for td in tds]
1 loops, best of 3: 810 ms per loop
# Master
In [2]: %timeit [hash(td) for td in tds]
1 loops, best of 3: 765 ms per loop
```
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} | 3 | 2015-09-17T14:35:38Z | 2015-09-17T15:36:08Z | 2015-09-17T15:36:06Z | MEMBER | null | Follow-up for #10890. Deprecate `Series.is_timeseries`.
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} | 2 | 2015-09-17T18:51:21Z | 2020-03-12T04:52:29Z | 2020-03-12T04:52:29Z | NONE | null | It seems like the `plot` function jumbles up the colors when columns in a DataFrame have the same name:
``` python
pd.concat([pd.DataFrame({'b': [0, 1, 0], 'a': [1, 2, 3]}), pd.DataFrame({'a': [2, 4, 6]})], axis=1).plot();
```
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| null | 13 | 2015-09-17T19:48:58Z | 2015-09-18T05:09:01Z | 2015-09-17T23:51:07Z | CONTRIBUTOR | null | Using
'0.17.0rc1+31.g9be2180'
and a column of tuples like so:
``` python
df.BINNING_SET.head()
TIME_OF_INT
2014-10-18 07:36:19.926570 (0, 1, 319, 11)
2014-10-18 07:36:19.926570 (0, 1, 441, 11)
2014-10-18 07:36:59.675040 (52, 4, 89, 115)
2014-10-18 07:36:59.675040 (2, 34, 96, 115)
2014-10-18 07:55:27.922290 (52, 4, 89, 115)
Name: BINNING_SET, dtype: object
```
I can't seem to filter on any tuple like so:
``` python
df.BINNING_SET==df.BINNING_SET.iloc[0]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-435fa8bdd1a7> in <module>()
----> 1 df.BINNING_SET==df.BINNING_SET.iloc[0]
/Users/klay6683/miniconda3/lib/python3.4/site-packages/pandas-0.17.0rc1+31.g9be2180-py3.4-macosx-10.5-x86_64.egg/pandas/core/ops.py in wrapper(self, other, axis)
724 other = np.asarray(other)
725
--> 726 res = na_op(values, other)
727 if isscalar(res):
728 raise TypeError('Could not compare %s type with Series'
/Users/klay6683/miniconda3/lib/python3.4/site-packages/pandas-0.17.0rc1+31.g9be2180-py3.4-macosx-10.5-x86_64.egg/pandas/core/ops.py in na_op(x, y)
642 if isinstance(y, (np.ndarray, pd.Series)):
643 if not is_object_dtype(y.dtype):
--> 644 result = lib.vec_compare(x, y.astype(np.object_), op)
645 else:
646 result = lib.vec_compare(x, y, op)
pandas/lib.pyx in pandas.lib.vec_compare (pandas/lib.c:14361)()
ValueError: Arrays were different lengths: 41994 vs 4
```
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} | 1 | 2015-09-17T19:55:56Z | 2015-09-18T09:14:24Z | 2015-09-17T23:45:20Z | CONTRIBUTOR | null | Tests for the bug in GH #9735, which was previously fixed in GH #10023.
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| null | 3 | 2015-09-18T00:50:32Z | 2015-09-18T14:18:34Z | 2015-09-18T14:18:26Z | CONTRIBUTOR | null | Whether or not I can create a SparseSeries seems to depend on the length of the series:
``` python
pd.SparseSeries([1.] * 10) # works
pd.SparseSeries([1.] * 100) # error
```
I've copied the error below:
```
.../miniconda/lib/python2.7/site-packages/pandas/core/format.pyc in __init__(self, series, buf, length, header, na_rep, name, float_format, dtype, max_rows)
--> 148 self._chk_truncate()
149
150 def _chk_truncate(self):
.../miniconda/lib/python2.7/site-packages/pandas/core/format.pyc in _chk_truncate(self)
159 else:
160 row_num = max_rows // 2
--> 161 series = concat((series.iloc[:row_num], series.iloc[-row_num:]))
162 self.tr_row_num = row_num
163 self.tr_series = series
.../miniconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
752 keys=keys, levels=levels, names=names,
753 verify_integrity=verify_integrity,
--> 754 copy=copy)
755 return op.get_result()
756
.../miniconda/lib/python2.7/site-packages/pandas/tools/merge.pyc in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
803 for obj in objs:
804 if not isinstance(obj, NDFrame):
--> 805 raise TypeError("cannot concatenate a non-NDFrame object")
806
807 # consolidate
TypeError: cannot concatenate a non-NDFrame object
```
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} | 5 | 2015-09-18T01:56:09Z | 2015-10-09T13:21:28Z | 2015-10-09T13:21:28Z | CONTRIBUTOR | null | This bug fix addresses issue #7124. If `to_latex` is called with option `header=False`, the output should not contain a `\midrule` which is used to separate the column headers from the data.
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} | 7 | 2015-09-18T02:10:10Z | 2015-10-16T23:33:49Z | 2015-10-16T23:29:05Z | CONTRIBUTOR | null | Resolve issue where gbq authentication on remote servers fails silently.
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} | 1 | 2015-09-18T12:06:01Z | 2015-09-20T19:21:37Z | 2015-09-20T19:21:37Z | CONTRIBUTOR | null | I think this can be fixed by just padding tokens in the inference step. The dates _do_ parse, just always fall back to the slow path, even with `infer_datetime_format=True`
```
In [22]: from pandas.tseries.tools import _guess_datetime_format
In [23]: _guess_datetime_format('02/02/2014')
Out[23]: '%m/%d/%Y'
In [24]: _guess_datetime_format('2/2/2014')
```
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| null | 4 | 2015-09-18T12:36:12Z | 2015-09-18T20:56:14Z | 2015-09-18T15:40:00Z | CONTRIBUTOR | null | I understand what `object` dtype supporting couldn't be the right option, but it is quite feasible to implement support for `Categorical`.
Right now, all columns of `DataFrame` which aren't numeric are dropped before visualization procedure of `scatter_matrix`.
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} | 24 | 2015-09-18T17:29:52Z | 2017-11-28T11:37:59Z | 2015-09-22T14:12:37Z | CONTRIBUTOR | null | closes #10125
I've added preliminary tests for openpyxl >= 2.2 styles. Unfortunately, I don't know how to get a whole test class to be skipped so tests will only run with either the Openpyxl20 or Openpyxl22 test class disabled, depending upon which version is installed. I hope this can be fixed fairly easily. Some code somewhere is still calling `openpyxl.styles.Style()` which needs changing. The aggregate Style object is an irrelevancy in client code.
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} | 8 | 2015-09-18T19:55:26Z | 2015-09-22T14:02:31Z | 2015-09-22T11:29:59Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/11111 (nice issue number)
Testing with tox locally I get 1 failure using either matplotlib 1.4 or 1.5. I may have messed up my tox config though. Still need to cleanup a bunch of stuff, I just threw in changes to get each test passing as I went.
release notes et. al coming tonight or tomorrow. Are we making any changes to the build matrix to run a matplotlib 1.5?
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} | 2 | 2015-09-18T22:53:15Z | 2015-09-20T20:40:27Z | 2015-09-20T19:21:37Z | CONTRIBUTOR | null | Closes #11142
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} | 1 | 2015-09-18T23:07:27Z | 2015-09-20T19:03:42Z | 2015-09-20T18:53:34Z | CONTRIBUTOR | null | ```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_transform_series2 | 6.3464 | 167.3844 | 0.0379 |
groupby_transform_multi_key3 | 99.4797 | 1078.2626 | 0.0923 |
groupby_transform_multi_key1 | 10.9547 | 98.5500 | 0.1112 |
groupby_transform_multi_key2 | 9.0899 | 67.2680 | 0.1351 |
groupby_transform_series | 5.4360 | 30.5537 | 0.1779 |
groupby_transform_multi_key4 | 39.9816 | 187.1406 | 0.2136 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [0470a10] : PERF: improves performance in SeriesGroupBy.transform
Base [7b9fe14] : revers import .pandas_vb_common
```
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} | 21 | 2015-09-19T00:31:15Z | 2015-10-16T22:07:42Z | 2015-10-16T22:07:39Z | NONE | null | closes #10442
fix for the bug: Convert datetimelike index to strings with astype(str)
going to send pull request for test
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} | 15 | 2015-09-19T18:45:31Z | 2016-01-15T01:02:47Z | 2016-01-04T02:17:23Z | CONTRIBUTOR | null | closes #9297
closes #8664
This is just a proof of concept at this point - the idea is to allow multiple assignments within an eval block, and also include an `inplace` argument (currently default True to match old behavior, although maybe that should be changed) for chaining.
```
In [15]: df = pd.DataFrame({'a': np.linspace(0, 100), 'b': range(50)})
In [16]: df.eval("""
...: c = a * 2
...: d = c / 2.0
...: e = a + b + c""")
In [17]: df.head()
Out[17]:
a b c d e
0 0.000000 0 0.000000 0.000000 0.000000
1 2.040816 1 4.081633 2.040816 7.122449
2 4.081633 2 8.163265 4.081633 14.244898
3 6.122449 3 12.244898 6.122449 21.367347
4 8.163265 4 16.326531 8.163265 28.489796
In [18]: df.eval("""
...: f = e * 2
...: g = f * 1.5""", inplace=False).head()
Out[18]:
a b c d e f g
0 0.000000 0 0.000000 0.000000 0.000000 0.000000 0.000000
1 2.040816 1 4.081633 2.040816 7.122449 14.244898 21.367347
2 4.081633 2 8.163265 4.081633 14.244898 28.489796 42.734694
3 6.122449 3 12.244898 6.122449 21.367347 42.734694 64.102041
4 8.163265 4 16.326531 8.163265 28.489796 56.979592 85.469388
```
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} | 12 | 2015-09-19T19:31:05Z | 2015-11-13T16:41:57Z | 2015-11-13T16:41:57Z | CONTRIBUTOR | null | axis option was not correctly set on fillna in pct_change
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| null | 3 | 2015-09-19T19:45:45Z | 2015-09-21T14:16:41Z | 2015-09-21T14:16:41Z | NONE | null | We ran a profiler comparing both the .ix and .loc methods for pandas, in both versions, and got substantially slower results when using 0.16.2. Why is that?
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} | 1 | 2015-09-19T22:57:21Z | 2015-09-20T19:03:47Z | 2015-09-20T18:54:14Z | CONTRIBUTOR | null | on master:
``` python
In [1]: n = 100000
In [2]: np.random.seed(2718281)
In [3]: dr = date_range('2015-09-19', periods=n, freq='T')
In [4]: ts = Series(np.random.choice(n, n), index=np.random.choice(dr, n))
In [5]: %timeit ts.resample('5T', how='size')
1 loops, best of 3: 538 ms per loop
```
on branch:
``` python
In [5]: %timeit ts.resample('5T', how='size')
10 loops, best of 3: 27.6 ms per loop
```
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} | 3 | 2015-09-19T23:01:47Z | 2015-09-26T00:27:01Z | 2015-09-20T18:52:13Z | MEMBER | null | ```
import pandas as pd
idx = pd.DatetimeIndex(['2011-01-01', pd.NaT, '2011-01-03'], tz='Asia/Tokyo')
s = pd.Series(idx)
s
#0 2011-01-01 00:00:00+09:00
#1 NaT
#2 2011-01-03 00:00:00+09:00
# dtype: datetime64[ns, Asia/Tokyo]
# NG, the result must be DatetimeTZBlock
s.fillna(pd.Timestamp('2011-01-02', tz='Asia/Tokyo'))
# TypeError: putmask() argument 1 must be numpy.ndarray, not DatetimeIndex
# NG, it should be object dtype, as the same as when Series creation with mixed tz
s.fillna(pd.Timestamp('2011-01-02'))
# TypeError: putmask() argument 1 must be numpy.ndarray, not DatetimeIndex
```
Also, found existing `DatetimeBlock` doesn't handle tz properly. Closes #7095.
```
idx = pd.DatetimeIndex(['2011-01-01', pd.NaT, '2011-01-03'])
s = pd.Series(idx)
# OK, result must be DatetimeBlock
s.fillna(pd.Timestamp('2011-01-02'))
#0 2011-01-01
#1 2011-01-02
#2 2011-01-03
# dtype: datetime64[ns]
# NG, it should be object dtype, as the same as when Series creation with mixed tz
s.fillna(pd.Timestamp('2011-01-02', tz='Asia/Tokyo'))
#0 2011-01-01 00:00:00
#1 2011-01-01 15:00:00
#2 2011-01-03 00:00:00
# dtype: datetime64[ns]
# NG, unable to fill different dtypes
s.fillna('AAA')
# ValueError: Error parsing datetime string "AAA" at position 0
```
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xref #8790
Adds new `indicator` keyword to `DataFrame.merge` as well
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} | 2 | 2015-09-20T16:39:58Z | 2015-09-21T02:35:57Z | 2015-09-21T02:35:53Z | CONTRIBUTOR | null | closes #11086, and a typo
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} | 3 | 2015-09-21T00:13:11Z | 2015-09-24T02:16:41Z | 2015-09-21T14:17:23Z | CONTRIBUTOR | null | Part of #11084
http://pydata.github.io/pandas/#frame_ctor.frame_ctor_nested_dict_int64.time_frame_ctor_nested_dict_int64
```
before after ratio
[d28fd70 ] [48c588 ]
- 325.06ms 72.25ms 0.22 frame_ctor.frame_ctor_nested_dict.time_frame_ctor_nested_dict
- 364.35ms 106.67ms 0.29 frame_ctor.frame_ctor_nested_dict_int64.time_frame_ctor_nested_dict_int64
```
Two changes to get back to the old performance:
- skip attempting to box `Timedeltas`/`Timestamps` if not a `DatetimeIndex`/`TimedeltaIndex`
- only convert index to object once
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} | 4 | 2015-09-21T00:17:25Z | 2015-11-07T03:42:03Z | 2015-10-18T13:46:13Z | MEMBER | null | Minor improvement when `dropna` actually does nothing. If OK for 0.17, I'll add a release note.
```
import numpy as np
import pandas as pd
s = pd.Series(np.random.randint(0, 1000, 50000000))
# before
%timeit s.dropna()
#1 loops, best of 3: 1.58 s per loop
# after
%timeit s.dropna()
#1 loops, best of 3: 568 ms per loop
```
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| null | 1 | 2015-09-21T07:18:57Z | 2015-09-21T11:53:25Z | 2015-09-21T11:53:25Z | CONTRIBUTOR | null | I just built a Python 3.5 wheel for Pandas 0.16.2 using the good ol' MacPython travis setup:
https://travis-ci.org/MacPython/pandas-wheels/builds/81332167
The Python 3.5 wheel is at the usual http://wheels.scipy.org location:
http://wheels.scipy.org/pandas-0.16.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Would someone mind uploading this to pypi? I'm happy to do that if someone gives me permission (`matthew.brett` on pypi).
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} | 0 | 2015-09-21T12:22:23Z | 2015-09-21T12:22:28Z | 2015-09-21T12:22:28Z | MEMBER | null | Removes some warnings
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| null | 7 | 2015-09-21T16:37:16Z | 2015-10-15T08:34:51Z | 2015-09-21T16:59:41Z | CONTRIBUTOR | null | ``` python
>>> import warnings
>>> warnings.simplefilter('error')
>>> import pandas as pd
>>> pd.__version__
u'0.17.0rc1'
>>> df = pd.DataFrame({'A': list('aabb'), 'B': [0, 1, 0, 1], 'C': range(4)})
>>> gf = df.groupby(['A', 'B']).sum()
>>> gf
C
A B
a 0 0
1 1
b 0 2
1 3
>>> gf.unstack()
Traceback (most recent call last):
..., line 4274, in _reference_duplicate_name
return np.sum(name == np.asarray(self.names)) > 1
DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.
```
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} | 4 | 2015-09-21T19:11:29Z | 2015-11-14T15:04:01Z | 2015-11-14T15:04:01Z | CONTRIBUTOR | null | Currently (in master), `Series.ptp()` is just implemented using `np.ptp()` and so the method will return `nan` for any Series that has one or more missing values:
```
>>> s = pd.Series([5, 0, np.nan, -3, 2])
>>> s.ptp()
nan
```
It is simple to write `s.max() - s.min()` instead, but the `ptp()` result is surprising as most pandas methods are designed to handle missing data gracefully. I think most users would expect the `ptp()` method to ignore `NaN`.
If there is any agreement as to whether `ptp()` should be changed, I would like to work on a pull request!
---
Extending the idea, it might be useful to have both `DataFrame.ptp()` and `groupby.ptp()` methods.
For this example DataFrame...
```
df = pd.DataFrame({'a': [1, 2, 2, 1, 1],
'b': [3, 11, 72, 46, 32],
'c': [1.2, 6.7, 13.9, np.nan, -7.7],
'd': ['v', 'w', 'x', 'y', 'z']})
```
...I would expect the following behaviour:
```
>>> df.ptp()
a 1
b 69
c 12.7
dtype: float64
>>> df.ptp(axis=1)
0 2.0
1 9.0
2 70.0
3 45.0
4 39.7
dtype: float64
>>> df.groupby('a').ptp()
b c
a
1 43 8.9
2 61 7.2
```
Again, if there is any consensus from the community on whether these additional methods should be added, I'd be happy to work on the pull request.
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} | 3 | 2015-09-21T22:24:41Z | 2021-04-18T23:49:51Z | 2021-04-18T23:49:51Z | CONTRIBUTOR | null | When passing a `level` argument and `numeric_only=True` to `max` and `min`, non-numeric columns are not dropped in the result:
```
>>> df = pd.DataFrame({'a': [1,1,2,2], 'b': [1,4,2,6],
'c': [6,3,2,3], 'd': ['w','x','y','z']})
>>> df.set_index(['a', 'b'])
>>> df
c d
a b
1 1 6 w
4 3 x
2 2 2 y
6 3 z
>>> df.max(level=0, numeric_only=True)
c d
a
1 6 x
2 3 z
```
I would have expected that column 'd' would not be included here, as is the case if the `level` argument is omitted:
```
>>> df.max(numeric_only=True)
c 6
dtype: int64
```
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} | 19 | 2015-09-22T10:25:00Z | 2021-03-26T18:18:18Z | 2018-10-31T19:40:44Z | NONE | null | Hi,
I have encountered a dataset where the C-engine read_csv has problems. I am unsure of the exact issue but I have narrowed it down to a single row which I have [pickled and uploaded it to dropbox](https://www.dropbox.com/s/cm6gz5nv2679dvi/faulty_row.pkl?dl=0). If you obtain the pickle try the following:
``` python
df = pd.read_pickle('faulty_row.pkl')
df.to_csv('faulty_row.csv', encoding='utf8', index=False)
df.read_csv('faulty_row.csv', encoding='utf8')
```
I get the following exception:
```
CParserError: Error tokenizing data. C error: Buffer overflow caught - possible malformed input file.
```
If you try and read the CSV using the python engine then no exception is thrown:
``` python
df.read_csv('faulty_row.csv', encoding='utf8', engine='python')
```
Suggesting that the issue is with read_csv and not to_csv. The versions I using are:
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-28-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
IPython: 3.2.1
patsy: 0.3.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
```
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} | 5 | 2015-09-22T16:00:13Z | 2020-05-08T19:28:17Z | null | CONTRIBUTOR | null | Currently the exponential functions, such as `pd.ewma`, use a `min_periods` argument to ensure there's enough data to specify generate a valid value. While this works well for the rolling functions, it's not effective for exponential functions because points have weight forever, albeit ever decreasing:
``` python
In [4]: series=pd.Series(range(200))
In [5]: series[20:190]=pd.np.nan
In [6]: pd.ewma(series, span=10, min_periods=15)
Out[6]:
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
10 NaN
11 NaN
12 NaN
13 NaN
14 10.277660
15 11.172348
16 12.080052
17 12.999407
18 13.929141
19 14.868084
20 14.868084
21 14.868084
22 14.868084
23 14.868084
24 14.868084
25 14.868084
26 14.868084
27 14.868084
28 14.868084
29 14.868084
...
170 14.868084
171 14.868084
172 14.868084
173 14.868084
174 14.868084
175 14.868084
176 14.868084
177 14.868084
178 14.868084
179 14.868084
180 14.868084
181 14.868084
182 14.868084
183 14.868084
184 14.868084
185 14.868084
186 14.868084
187 14.868084
188 14.868084
189 14.868084
190 190.000000
191 190.550000
192 191.132890
193 191.748020
194 192.394502
195 193.071240
196 193.776953
197 194.510212
198 195.269468
199 196.053089
dtype: float64
```
I think what we want is to have a `min_weight` argument, so if you specify 0.5, it needs 50% of the weight in order to calculate a value. For rolling functions, this would be equivalent to `min_periods` being half of `window`.
What are people's thoughts?
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} | 1 | 2015-09-22T16:25:27Z | 2015-09-24T01:20:51Z | 2015-09-24T01:20:47Z | CONTRIBUTOR | null | also enhanced skip test msg a bit
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} | 4 | 2015-09-22T16:28:36Z | 2016-07-19T13:06:23Z | 2016-07-19T13:06:23Z | CONTRIBUTOR | null | Debian stretch/sid, cython 0.22.1-2, pandas v0.17.0rc1-92-gc6bcc99
```
$> locale
LANG=en_US.UTF-8
LANGUAGE=
LC_CTYPE="en_US.UTF-8"
LC_NUMERIC="en_US.UTF-8"
LC_TIME="en_US.UTF-8"
LC_COLLATE="en_US.UTF-8"
LC_MONETARY="en_US.UTF-8"
LC_MESSAGES="en_US.UTF-8"
LC_PAPER="en_US.UTF-8"
LC_NAME="en_US.UTF-8"
LC_ADDRESS="en_US.UTF-8"
LC_TELEPHONE="en_US.UTF-8"
LC_MEASUREMENT="en_US.UTF-8"
LC_IDENTIFICATION="en_US.UTF-8"
LC_ALL=
```
`nosetest pandas` leads to
```
$> nosetests -s -v --failed pandas --pdb
#1982 test_invalid_index_types (pandas.tseries.tests.test_frequencies.TestFrequencyInference) ... > /usr/lib/python2.7/dist-packages/dateutil/parser.py(310)parse()
-> res, skipped_tokens = self._parse(timestr, **kwargs)
(Pdb) p timestr
u'\u05e4\u05d0\u05e3\u05df\u05e1\u05da\u05e8\u05e1\u05e7\u05df'
(Pdb) self._parse(timestr, **kwargs)
(Pdb) up
> /usr/lib/python2.7/dist-packages/dateutil/parser.py(748)parse()
-> return DEFAULTPARSER.parse(timestr, **kwargs)
(Pdb) p timestr
u'\u05e4\u05d0\u05e3\u05df\u05e1\u05da\u05e8\u05e1\u05e7\u05df'
(Pdb) p DEFAULTPARSER
<dateutil.parser.parser object at 0x7f79dcacf090>
*(Pdb) p timestr.encode()
*** UnicodeEncodeError: UnicodeEncodeError('ascii', u'\u05e4\u05d0\u05e3\u05df\u05e1\u05da\u05e8\u05e1\u05e7\u05df', 0, 10, 'ordinal not in range(128)')
*(Pdb) p timestr.encode('utf-8')
'\xd7\xa4\xd7\x90\xd7\xa3\xd7\x9f\xd7\xa1\xd7\x9a\xd7\xa8\xd7\xa1\xd7\xa7\xd7\x9f'
(Pdb)
ERROR
#3617 test_constructor_compound_dtypes (pandas.tests.test_frame.TestDataFrame) ... > /usr/lib/python2.7/dist-packages/dateutil/parser.py(310)parse()
-> res, skipped_tokens = self._parse(timestr, **kwargs)
(Pdb)
ERROR
======================================================================
ERROR: test_invalid_index_types (pandas.tseries.tests.test_frequencies.TestFrequencyInference)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tests/test_frequencies.py", line 565, in test_invalid_index_types
self.assertRaises(ValueError, lambda : frequencies.infer_freq(i))
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/testing.py", line 1880, in assertRaises
_callable(*args, **kwargs)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tests/test_frequencies.py", line 565, in <lambda>
self.assertRaises(ValueError, lambda : frequencies.infer_freq(i))
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/frequencies.py", line 855, in infer_freq
index = pd.DatetimeIndex(index)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/index.py", line 315, in __new__
subarr = tools.to_datetime(data, box=False)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tools.py", line 276, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tools.py", line 395, in _to_datetime
return _convert_listlike(arg, box, format)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tools.py", line 372, in _convert_listlike
require_iso8601=require_iso8601)
File "pandas/tslib.pyx", line 1828, in pandas.tslib.array_to_datetime (pandas/tslib.c:34125)
cpdef array_to_datetime(ndarray[object] values, errors='raise',
File "pandas/tslib.pyx", line 2017, in pandas.tslib.array_to_datetime (pandas/tslib.c:33760)
raise
File "pandas/tslib.pyx", line 2011, in pandas.tslib.array_to_datetime (pandas/tslib.c:33658)
oresult[i] = parse_datetime_string(val, dayfirst=dayfirst,
File "pandas/tslib.pyx", line 1602, in pandas.tslib.parse_datetime_string (pandas/tslib.c:27245)
dt = parse_date(date_string, default=_DEFAULT_DATETIME,
File "/usr/lib/python2.7/dist-packages/dateutil/parser.py", line 748, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "/usr/lib/python2.7/dist-packages/dateutil/parser.py", line 310, in parse
res, skipped_tokens = self._parse(timestr, **kwargs)
TypeError: 'NoneType' object is not iterable
======================================================================
ERROR: test_constructor_compound_dtypes (pandas.tests.test_frame.TestDataFrame)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tests/test_frame.py", line 14395, in test_constructor_compound_dtypes
f('M8[ns]')
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tests/test_frame.py", line 14384, in f
columns=["A", "B", "C"], dtype=dtype)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/frame.py", line 264, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/frame.py", line 5216, in _to_arrays
dtype=dtype)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/frame.py", line 5299, in _list_to_arrays
coerce_float=coerce_float)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/frame.py", line 5366, in _convert_object_array
arrays = [ convert(arr) for arr in content ]
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/frame.py", line 5363, in convert
arr = com._possibly_cast_to_datetime(arr, dtype)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/core/common.py", line 1998, in _possibly_cast_to_datetime
value = to_datetime(value, errors=errors)._values
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/util/decorators.py", line 89, in wrapper
return func(*args, **kwargs)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tools.py", line 276, in to_datetime
unit=unit, infer_datetime_format=infer_datetime_format)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tools.py", line 395, in _to_datetime
return _convert_listlike(arg, box, format)
File "/home/yoh/deb/gits/pkg-exppsy/pandas/pandas/tseries/tools.py", line 372, in _convert_listlike
require_iso8601=require_iso8601)
File "pandas/tslib.pyx", line 1828, in pandas.tslib.array_to_datetime (pandas/tslib.c:34125)
cpdef array_to_datetime(ndarray[object] values, errors='raise',
File "pandas/tslib.pyx", line 2017, in pandas.tslib.array_to_datetime (pandas/tslib.c:33760)
raise
File "pandas/tslib.pyx", line 2011, in pandas.tslib.array_to_datetime (pandas/tslib.c:33658)
oresult[i] = parse_datetime_string(val, dayfirst=dayfirst,
File "pandas/tslib.pyx", line 1602, in pandas.tslib.parse_datetime_string (pandas/tslib.c:27245)
dt = parse_date(date_string, default=_DEFAULT_DATETIME,
File "/usr/lib/python2.7/dist-packages/dateutil/parser.py", line 748, in parse
return DEFAULTPARSER.parse(timestr, **kwargs)
File "/usr/lib/python2.7/dist-packages/dateutil/parser.py", line 310, in parse
res, skipped_tokens = self._parse(timestr, **kwargs)
TypeError: 'NoneType' object is not iterable
----------------------------------------------------------------------
Ran 2 tests in 59.805s
```
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} | 3 | 2015-09-22T17:51:32Z | 2015-09-27T14:28:49Z | 2015-09-27T14:28:49Z | CONTRIBUTOR | null | I am seeing test failures (both local and Travis) due to
https://github.com/pydata/pandas/blob/master/pandas/tests/test_internals.py#L667
which uses `Timestamp`, not `pd.Timestamp`. Is this expected?
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} | 12 | 2015-09-22T18:48:51Z | 2015-10-20T17:49:06Z | 2015-10-20T17:49:06Z | NONE | null | Not sure if this is the correct approach to the problem
closes #10949.
Assuming it is I will go ahead and add some documentation for this enhancement. Let me know if you have any feedback.
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} | 12 | 2015-09-22T20:31:53Z | 2015-11-14T23:16:58Z | 2015-11-14T15:04:01Z | CONTRIBUTOR | null | Closes [GH11163](https://github.com/pydata/pandas/issues/11163).
`Series.ptp` will now ignore missing values when calculating the peak to peak difference.
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version of _convert
Adds to_numeric for directly converting numeric data
closes #11116
closes #11133
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| null | 22 | 2015-09-22T23:23:08Z | 2017-04-24T06:42:54Z | 2015-09-25T12:07:57Z | NONE | null | Following a suggestion on [StackOverflow](http://stackoverflow.com/questions/32720514/segmentation-fault-11-during-pandas-to-csv-operation), I'm posting this here:
I'm working on Mac OS 10.10.5, using a Python 2.7 virtualenv and pandas installed via pip.
I have a Pandas dataFrame (called 'pivot') which is 13843 rows by 40 columns. Most of the data cells are single characters (A, B, C or D).
I am trying to export this to a csv file using the command:
```
pivot.to_csv(path_or_buf="test.csv",sep=",", encoding='utf-8')
```
The output CSV gets to row 8743, which is truncated after the 8th column, and the program crashes out with a Segmentation fault: 11
It shouldn't be a memory problem, as my Mac has 16Gb RAM.
Result of pandas.show_versions() :
## INSTALLED VERSIONS
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
pandas: 0.16.2
nose: None
Cython: None
numpy: 1.9.2
scipy: None
statsmodels: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.4
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
Here's a sample of the dataFrame I'm trying to export. Note that the first two columns are indexes.
| CandidateRegistrationID | ExamDate | 301 | 302 | ... | 331 | 332 | 333 | 334 | 340 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| abc123 | 2015-01-18 | B | A | ... | C | C | B | A | B |
| abc124 | 2015-02-16 | C | A | ... | C | C | B | A | B |
The minimum reproducing sample is a dataFrame of size: pivot.iloc[0:8774, 00:40]
Anything with fewer rows than that exports OK. The number of columns makes no difference. The segmentation fault appears even with one column.
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} | 2 | 2015-09-23T03:15:23Z | 2015-09-24T07:11:23Z | 2015-09-23T17:03:53Z | CONTRIBUTOR | null | Closes #11119
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} | 1 | 2015-09-23T11:35:04Z | 2015-09-23T23:19:53Z | 2015-09-23T11:53:01Z | CONTRIBUTOR | null | Fixed a typo where I had `header=False`
Side note, it might be nice to change the actual behavior, not the first time it's tripped me up. I guess there already is an issue, #6113
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| null | 12 | 2015-09-23T17:29:37Z | 2016-01-11T14:15:44Z | 2016-01-11T13:45:22Z | CONTRIBUTOR | null | closes #11119
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} | 10 | 2015-09-23T20:49:39Z | 2021-04-19T00:36:01Z | 2021-04-19T00:36:00Z | NONE | null | I use both Stata and Pandas. Many Stata users save variable labels to describe the columns in a clearer way than the names. Running this in Stata
```
sysuse auto.dta
describe
```
gives something like
| variable name | storage type | variable label |
| --- | --- | --- |
| make | str18 | Make and Model |
| price | int | Price |
| mpg | int | Mileage (mpg) |
For me (maybe for others too) it would be useful to have an optional field in a DataFrame with a column label dictionary. The keys would be the columns (not necessarily all of them) and the values the string labels.
This is used in the pandas.io.stata.StataReader field `variable_labels`(see [the docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.stata.StataReader.variable_labels.html)], that allows you to import these labels when one reads in a Stata `.dta` file.
I know I could just carry around a dictionary with this information, but I think it's cleaner and less error prone to set it and save it within a DataFrame.
Additionally, storing this would allow doing a cycle on Stata/Pandas without loss of information, since the `to_stata` would check if this field exists. (`to_stata` might already have the option to pass the `variable_labels` dictionary as an option, but I didn't see it documented at least)
My coding prowess is quite limited, but I'd be happy to at least write test code and help out if somebody starts out.
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} | 6 | 2015-09-23T22:56:20Z | 2015-09-26T13:15:50Z | 2015-09-25T12:19:03Z | CONTRIBUTOR | null | closes #10161
xref https://github.com/pydata/pandas/issues/10161#issuecomment-142701582
``` python
In [5]: df = DataFrame(np.random.randn(5000, 100).astype(str))
In [6]: %timeit df.duplicated()
1 loops, best of 3: 151 ms per loop
In [7]: %timeit df.T.duplicated()
1 loops, best of 3: 1.39 s per loop
```
part of this is because of taking the transpose (maybe cache locality). i.e. below performs better even though the shape is the same as `df.T` in above:
``` python
In [8]: df = DataFrame(np.random.randn(100, 5000).astype(str))
In [9]: %timeit df.duplicated()
1 loops, best of 3: 965 ms per loop
```
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} | 8 | 2015-09-23T23:24:17Z | 2015-10-23T20:30:33Z | 2015-10-23T20:30:33Z | CONTRIBUTOR | null | Currently, passing a list of `namedtuple`s (or SQLAlchemy results, which is our specific case) to `DataFrame.from_records` doesn't resolve the field names.
I think it would make sense to, at least if the names are all the same. Happy to do a PR if people agree.
``` python
In [17]: from collections import namedtuple
In [18]: Record = namedtuple('Record',('date','value'))
In [19]: records = [Record(d, v) for (d, v) in zip(pd.date_range('2000',freq='D', periods=20), range(20))]
In [20]: records
Out[20]:
[Record(date=Timestamp('2000-01-01 00:00:00', offset='D'), value=0),
Record(date=Timestamp('2000-01-02 00:00:00', offset='D'), value=1),
...
Record(date=Timestamp('2000-01-19 00:00:00', offset='D'), value=18),
Record(date=Timestamp('2000-01-20 00:00:00', offset='D'), value=19)]
In [21]: pd.DataFrame.from_records(records)
Out[21]:
0 1
0 2000-01-01 0
1 2000-01-02 1
...
18 2000-01-19 18
19 2000-01-20 19
```
Desired behavior:
``` python
Out[21]:
date value
0 2000-01-01 0
1 2000-01-02 1
...
18 2000-01-19 18
19 2000-01-20 19
```
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} | 6 | 2015-09-24T00:13:08Z | 2015-09-26T15:17:44Z | 2015-09-24T11:32:00Z | CONTRIBUTOR | null | closes #6113
Passing `header=True|False` to `read_csv` (and brethren), `read_excel`, and `read_html` will now raise a `TypeError`, rather than being coerced to an int.
I had thought about converting `False` to `None` but thought that could break someone's code in a very subtle way if they were somehow depending on the old behavior. But happy to change that if it seems better.
If you want to push this in for 0.17 I can add a doc-note or this could easily wait.
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} | 1 | 2015-09-24T08:54:12Z | 2015-09-25T12:11:12Z | 2015-09-25T12:11:12Z | MEMBER | null | @kshedden `XportReader` is not top-level API, so it was erroring in the doc build.
I now removed it from api.rst (the `TextFileReader` what you get back with chunksize for read_csv is also not included), but I can also adapt it to use `.. currentmodule:: pandas.io.sas` so it builds correctly if you want.
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} | 0 | 2015-09-24T09:33:39Z | 2015-09-24T21:58:27Z | 2015-09-24T21:58:27Z | MEMBER | null | Fixes mainly some linking errors
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} | 3 | 2015-09-24T09:55:45Z | 2015-10-02T22:03:02Z | 2015-10-02T22:03:01Z | NONE | null | Hi guys,
Working with pandas is great, however I might have notice a bug while grouping a 2 rows DataFrame by time and columns:
``` python
>>> import pandas as pd
>>> import numpy as np
>>> from datetime import datetime
>>> freq = 's'
>>> t1 = np.datetime64(datetime.utcnow(), freq)
>>> index = pd.date_range(start=t1, periods=2, freq=freq)
# DatetimeIndex(['2015-09-24 08:55:27', '2015-09-24 08:55:28'], dtype='datetime64[ns]', freq='S', tz=None)
>>> df = pd.DataFrame([['A', 10], ['B', 15]], columns=['metric', 'values'], index=index)
# metric values
#2015-09-24 08:55:27 A 10
#2015-09-24 08:55:28 B 15
>>> grouped = df.groupby([pd.Grouper(level=0, freq=freq), 'metric'])
# here the grouping should output something similar to the input DataFrame,
# since each rows are already individual groups reguarding the parameters of the groupby function.
>>> grouped.mean()
# values
# <pandas.tseries.resample.TimeGrouper object at ... 10
# metric 15
#
# notice how the index is broken : a new TimeGrouper object is the first index values,
# while the second value is the name of the columns used to create the second group...
# now let's try to add another row : a new second, a new metric
>>> df_2 = pd.DataFrame([['C', 0]], columns=df.columns, index=[df.index.shift(-1, freq)[0]])
>>> df_2 = df_2.append(df)
# metric values
#2015-09-24 08:55:26 C 0
#2015-09-24 08:55:27 A 10
#2015-09-24 08:55:28 B 15
>>> grouped = df_2.groupby([pd.Grouper(level=0, freq=freq), 'metric'])
>>> grouped.mean()
# values
# metric
#2015-09-24 08:55:26 C 0
#2015-09-24 08:55:27 A 10
#2015-09-24 08:55:28 B 15
# work as expected with 3 rows !
# let's try with 1 row :
>>> df_2.iloc[0:1].groupby([pd.Grouper(level=0, freq=freq), 'metric']).mean()
# values
# metric
#2015-09-24 08:55:26 C 0
# work as expected too !
```
I have tried to group by key, instead of level, or to use another frequency for aggregating (using freq = 's' while building the dataframe, then aggregate with freq='T'), but the result is the same.
Did I miss something ?
Please, not that using the resampling API provide the expected result, but i think the grouping API should provide consistent results :
``` python
>>> df.groupby(['metric']).resample(how='mean', freq=freq)
# values
# metric
# A 2015-09-24 08:55:27 10
# B 2015-09-24 08:55:28 15
```
Here are the dependencies I have installed with pandas (working on Ubuntu 12.04.5 LTS):
``` python
>>> from pandas.util.print_versions import show_versions
>>> show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Linux
OS-release: 3.5.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: fr_FR.UTF-8
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.15.1
statsmodels: None
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
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} | 4 | 2015-09-24T11:30:54Z | 2015-11-13T22:28:09Z | 2015-11-13T22:27:29Z | CONTRIBUTOR | null | xref #11145
- [ ] change ci/requirements
- [ ] remove checks for '0+unknown' in version checks (bug already fixed in matplotlib)
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} | 0 | 2015-09-24T11:31:17Z | 2015-10-27T13:18:42Z | 2015-10-27T13:18:42Z | CONTRIBUTOR | null | can revert #11262 when conda packages are available
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} | 14 | 2015-09-25T10:39:13Z | 2019-06-21T14:19:43Z | 2019-06-21T14:19:43Z | NONE | null | I created a file using pytables, and now I would like to read it into pandas. My (naive) expectation was that these two tools were compatible. But I am getting an error
``` python
pd.read_hdf(output_fname, '/floats/trajectories', start=0, stop=10)
```
gives
```
ValueError: Shape of passed values is (1, 10), indices imply (1, 753664023)
```
The file is 44 GB, so I can't really post it. I would be happy to post the `h5dump --head` metadata if that would help.
(Cross post with blaze/dask#747.)
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} | 13 | 2015-09-25T20:20:29Z | 2015-11-01T18:44:44Z | 2015-09-25T23:51:09Z | MEMBER | null | ```
In [1]: df = pd.DataFrame({"id":[1,2,3,4,5,6], "grade":['a', 'b', 'b', 'a', 'a', 'e']})
In [2]: df.groupby("grade").size()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-3-d8e387418f9d> in <module>()
----> 1 df.groupby("grade").size()
/home/joris/scipy/pandas/pandas/core/groupby.pyc in size(self)
818
819 """
--> 820 return self.grouper.size()
821
822 sum = _groupby_function('sum', 'add', np.sum)
/home/joris/scipy/pandas/pandas/core/groupby.pyc in size(self)
1380 """
1381 ids, _, ngroup = self.group_info
-> 1382 out = np.bincount(ids[ids != -1], minlength=ngroup)
1383 return Series(out, index=self.result_index)
1384
TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe'
In [4]: pd.__version__
Out[4]: '0.17.0rc1+108.g3fb802a'
```
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| null | 4 | 2015-09-25T21:10:52Z | 2015-09-25T21:58:55Z | 2015-09-25T21:58:55Z | NONE | null | I might make a pull request sometime next week with an implementation - it shouldn't be too difficult.
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} | 0 | 2015-09-25T22:57:53Z | 2015-09-25T23:51:09Z | 2015-09-25T23:51:09Z | CONTRIBUTOR | null | closes #11189
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} | 2 | 2015-09-26T00:50:51Z | 2015-12-26T00:28:22Z | 2015-12-26T00:28:22Z | MEMBER | null | Because `frequencies.get_offset` may be broken by user operation because of its cache.
https://github.com/pydata/pandas/blob/master/pandas/tseries/frequencies.py#L515
```
import pandas as pd
x = pd.tseries.frequencies.get_offset('D')
x.__dict__
# {'normalize': False, 'kwds': {}, 'n': 1, '_offset': datetime.timedelta(1), '_named': 'D', '_use_relativedelta': False}
# modify its property
x.n = 5
# NG, modified instance is returned
y = pd.tseries.frequencies.get_offset('D')
y.__dict__
# {'normalize': False, 'kwds': {}, 'n': 5, '_offset': datetime.timedelta(1), '_named': 'D', '_use_relativedelta': False}
```
It should return a copy of the cache.
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} | 12 | 2015-09-26T03:02:37Z | 2015-12-18T18:26:22Z | 2015-12-18T18:26:22Z | MEMBER | null | ```
import pandas as pd
idx = pd.Index([1, 2 ,3], name='x')
pd.Index(idx)
# Int64Index([1, 2, 3], dtype='int64')
```
A point is whether to reset existing Index name in below case. Current impl can't distinguish whether name is not passed, or passed `None` explicitly.
```
pd.Index(idx, name=None)
# -> should reset name?
```
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| null | 3 | 2015-09-26T18:14:11Z | 2015-09-26T18:32:56Z | 2015-09-26T18:29:34Z | NONE | null | Consider an arbitrary DataFrame:
``` python
df = pd.DataFrame([1,2,4,35,356,3425,24,24,12,24,6,7,8,8,4], columns=['one'])
df['two'] = np.log(df['one'])
df['three'] = np.log10(df['one'])
df['four'] = np.exp(df['one'])
df['five'] = df['two'] * df['three']
```
This gives:
```
In [8]: df
Out[8]:
one two three four five
0 1 0.000000 0.000000 2.718282e+00 0.000000
1 2 0.693147 0.301030 7.389056e+00 0.208658
2 4 1.386294 0.602060 5.459815e+01 0.834632
3 35 3.555348 1.544068 1.586013e+15 5.489699
4 356 5.874931 2.551450 4.062895e+154 14.989592
5 3425 8.138857 3.534661 inf 28.768096
6 24 3.178054 1.380211 2.648912e+10 4.386386
7 24 3.178054 1.380211 2.648912e+10 4.386386
8 12 2.484907 1.079181 1.627548e+05 2.681665
9 24 3.178054 1.380211 2.648912e+10 4.386386
10 6 1.791759 0.778151 4.034288e+02 1.394260
11 7 1.945910 0.845098 1.096633e+03 1.644485
12 8 2.079442 0.903090 2.980958e+03 1.877923
13 8 2.079442 0.903090 2.980958e+03 1.877923
14 4 1.386294 0.602060 5.459815e+01 0.834632
```
Now shift columns:
```
In [9]: df.shift(axis=1)
Out[9]:
one two three four five
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN
10 NaN NaN NaN NaN NaN
11 NaN NaN NaN NaN NaN
12 NaN NaN NaN NaN NaN
13 NaN NaN NaN NaN NaN
14 NaN NaN NaN NaN NaN
```
It appears this issue has been around for over a year:
http://stackoverflow.com/questions/23105197/shift-entire-column-on-a-pandas-dataframe
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} | 3 | 2015-09-27T13:06:51Z | 2019-10-06T02:15:15Z | 2019-10-06T02:15:15Z | CONTRIBUTOR | null | `nosetests pandas/tests/test_internals.py:TestBlockManager.test_insert` runs successfully even when I tweak the `yield` statement to something false, such as `yield self.assertIs, 1, 2`.
This is because generator tests are not discovered inside subclasses of `TestCase`.
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} | 1 | 2015-09-27T13:11:46Z | 2015-09-27T14:28:49Z | 2015-09-27T14:28:49Z | CONTRIBUTOR | null | closes #11170
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} | 20 | 2015-09-27T15:46:20Z | 2015-10-04T17:01:35Z | 2015-09-30T22:05:27Z | CONTRIBUTOR | null | Replace the `**kwds` in `read_excel` with the actual list of supported keyword args. This doesn't
change any functionality, just nicer for interactive use. Also a bit of clarification on the `thousands`
arg in the docstring.
Additionally, `chunksize` was a parameter in the `ExcelFile.parse` signature, but didn't do anything (xref #8011). I removed this and raise `NotImplementedError` if passed, which is potentially breaking.
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} | 24 | 2015-09-27T20:07:20Z | 2017-06-11T23:08:56Z | 2017-06-11T23:08:56Z | NONE | null | xref #15091
xref #3707
Dear pandas team
My environment is:
```
numpy==1.9.3
pandas==0.16.2
```
On it, may it be possible that the bug [#10172](https://github.com/pydata/pandas/pull/10172) has been re-introduced? For instance, with a csv file (`mini.csv`) with three int columns generated as per the code below:
``` python
import random
def contents(f, sc1=10, sc2=1000, cnt=10000):
for i in range(cnt):
zone = random.choice(range(10))
val1 = random.randint(0, zone * sc1)
val2 = random.randint(0, zone * sc2)
f.write("%d,%d,%d\n" % (zone, val1, val2))
with open("mini.csv", "w") as f:
f.write("zipcode,sqft,price\n")
contents(f, sc2=1000000, cnt=50000)
```
Once we load the file into pandas
``` python
import pandas
mini = pandas.read_csv("mini.csv")
mini.groupby('zipcode')[['price']].mean()
```
results in an the price mean being an int64
```
price
zipcode
0 0
1 499960
2 1005490
3 1465088
4 2001135
5 2495200
6 2993253
7 3569320
8 4076548
9 4416133
```
but if we add an extra column to the selection,
``` python
mini.groupby('zipcode')[['price','sqft']].mean()
```
The price mean is a float64
```
price sqft
zipcode
0 0.000000 0.000000
1 499960.563138 5.000400
2 1005490.239062 9.928459
3 1465088.765919 14.922507
4 2001135.222045 20.148962
5 2495200.657097 25.160088
6 2993253.872691 29.624297
7 3569320.017926 35.089428
8 4076548.696706 39.605981
9 4416133.246409 44.851756
```
Thanks in advance
Cristobal
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| null | 1 | 2015-09-28T15:43:34Z | 2015-09-28T17:49:16Z | 2015-09-28T17:49:05Z | NONE | null | Hi,
is this a expected behavior when using apply on axis 0?( apply iterates twice the first column a)
Thanks!
``` python
import pandas as pd
import numpy as np
pd.show_versions()
df3 = pd.DataFrame(np.random.randn(10, 5),columns=['a', 'b', 'c', 'd', 'e'])
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.16.2
nose: 1.3.7
Cython: 0.22.1
numpy: 1.9.2
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 4.0.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.4
bottleneck: 1.0.0
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.5
pymysql: None
psycopg2: None
```
``` python
def f(x):
print(x.name)
return x
df3.apply(f,0)
```
```
a
a
b
c
d
e
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>a</th>
<th>b</th>
<th>c</th>
<th>d</th>
<th>e</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1.726737</td>
<td>-0.759505</td>
<td>-0.847297</td>
<td>-0.753917</td>
<td>-0.308126</td>
</tr>
<tr>
<th>1</th>
<td>0.944636</td>
<td>0.111687</td>
<td>-1.918813</td>
<td>0.609801</td>
<td>-0.800732</td>
</tr>
<tr>
<th>2</th>
<td>-1.452284</td>
<td>-0.311735</td>
<td>0.526410</td>
<td>0.486475</td>
<td>0.183785</td>
</tr>
<tr>
<th>3</th>
<td>1.159549</td>
<td>-0.691184</td>
<td>-0.671914</td>
<td>0.411232</td>
<td>-0.759693</td>
</tr>
<tr>
<th>4</th>
<td>-1.102834</td>
<td>-0.442751</td>
<td>-2.195439</td>
<td>1.057525</td>
<td>-1.611347</td>
</tr>
<tr>
<th>5</th>
<td>-0.628189</td>
<td>0.088928</td>
<td>-0.725967</td>
<td>-0.728804</td>
<td>-0.060732</td>
</tr>
<tr>
<th>6</th>
<td>-0.908476</td>
<td>1.002714</td>
<td>-0.038057</td>
<td>1.407736</td>
<td>-0.750645</td>
</tr>
<tr>
<th>7</th>
<td>2.446902</td>
<td>1.916245</td>
<td>1.000570</td>
<td>-1.045217</td>
<td>1.621034</td>
</tr>
<tr>
<th>8</th>
<td>0.890660</td>
<td>2.575584</td>
<td>-0.362869</td>
<td>-1.074129</td>
<td>-0.271362</td>
</tr>
<tr>
<th>9</th>
<td>1.430119</td>
<td>-0.315331</td>
<td>-0.768683</td>
<td>-1.719671</td>
<td>-1.754809</td>
</tr>
</tbody>
</table>
</div>
``` python
```
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} | 12 | 2015-09-29T03:56:45Z | 2019-10-21T20:07:56Z | null | NONE | null | `Index.get_loc` raises unexpected `KeyError` when `data` is a 2-dimensional `numpy.ndarray` with non-unique, monotonically increasing values, i.e.,
```
>>> import numpy as np
>>> import pandas as pd
>>> ix = pd.Index(np.array([["A"], ["A"], ["B"]]))
>>> ix.get_loc(ix[0])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python2.7/site-packages/pandas/core/index.py", line 1572, in get_loc
return self._engine.get_loc(_values_from_object(key))
File "pandas/index.pyx", line 134, in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)
File "pandas/index.pyx", line 149, in pandas.index.IndexEngine.get_loc (pandas/index.c:3651)
File "pandas/index.pyx", line 169, in pandas.index.IndexEngine._get_loc_duplicates (pandas/index.c:3990)
KeyError: ('A',)
```
But it works correctly when the `Index` is either unique, i.e.,
```
>>> ix = pd.Index(np.array([["A"], ["B"], ["C"]]))
>>> ix.get_loc(ix[0])
0
```
or non-monotonic, i.e.,
```
>>> ix = pd.Index(np.array([["A"], ["B"], ["A"]]))
>>> ix.get_loc(ix[0])
array([ True, False, True], dtype=bool)
```
p.s. `pandas.__version__` 0.16.2; `numpy.__version__` 1.9.3
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} | 1 | 2015-09-29T19:06:15Z | 2015-10-01T10:45:52Z | 2015-10-01T10:45:44Z | CONTRIBUTOR | null | Fixes issue #11185
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