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} | 5 | 2015-01-19T17:12:20Z | 2015-01-19T17:28:21Z | 2015-01-19T17:24:42Z | NONE | null | This could be either a bug, or an error in the documentation, or just my fault (hopefully the third reason).
Pandas 0.15.2 documentation explains how to create one plot with two axes (http://pandas.pydata.org/pandas-docs/dev/visualization.html?highlight=visualization#plotting-on-a-secondary-y-axis). For example:
``` python
import numpy as np,pandas as pd,matplotlib.pyplot as plt
x=pd.DataFrame(range(100))**.5
y=x+np.random.normal(size=(x.shape[0],1))
# Does not work ()
x.plot()
y.plot(secondary_y=True,style='g:')
plt.show()
```
The problem is, two plots are generated, rather than only one with two axes.
This alternative would work:
``` python
# Works (1 figure, 2 axes)
fig,ax=plt.subplots()
ax2=ax.twinx()
x.plot(ax=ax)
y.plot(ax=ax2,style='g:')
plt.show()
```
Question: Does the example in the documentation actually generate a plot with 2 axes? What I get is two separate plots. This seems to be a bug. If this is not a bug, then I would suggest the example in the documentation is modified to explain the second alternative.
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| null | 1 | 2015-01-19T17:51:58Z | 2015-01-22T21:29:00Z | 2015-01-22T21:28:30Z | CONTRIBUTOR | null | My first stab at this issue. Would wait for comments and inputs. This is the behavior without the fix.
```
In [4]: df_1 = pd.DataFrame({"Row":[0,1,1], "EmptyCol":np.nan, "NumberCol":[1,2,3]})
In [5]: df_2 = pd.DataFrame(columns = df_1.columns)
In [6]: df_concat = pd.concat([df_1, df_2], axis=0)
In [7]: df_1.dtypes
Out[7]:
EmptyCol float64
NumberCol int64
Row int64
dtype: object
In [8]: df_2.dtypes
Out[8]:
EmptyCol object
NumberCol object
Row object
dtype: object
In [9]: df_concat.dtypes
Out[9]:
EmptyCol object
NumberCol float64
Row float64
dtype: object
In [10]: df_concat
Out[10]:
EmptyCol NumberCol Row
0 NaN 1 0
1 NaN 2 1
2 NaN 3 1
In [11]: df_1
Out[11]:
EmptyCol NumberCol Row
0 NaN 1 0
1 NaN 2 1
2 NaN 3 1
In [12]: df_2
Out[12]:
Empty DataFrame
Columns: [EmptyCol, NumberCol, Row]
Index: []
```
Seeing this after the fix.
```
In [3]: df_1 = pd.DataFrame({"Row":[0,1,1], "EmptyCol":np.nan, "NumberCol":[1,2,3]})
In [4]: df_2 = pd.DataFrame(columns = df_1.columns)
In [5]: df_concat = pd.concat([df_1, df_2], axis=0)
In [6]: df_1.dtypes
Out[6]:
EmptyCol float64
NumberCol int64
Row int64
dtype: object
In [7]: df_2.dtypes
Out[7]:
EmptyCol object
NumberCol object
Row object
dtype: object
In [8]: df_concat.dtypes
Out[8]:
EmptyCol float64
NumberCol float64
Row float64
dtype: object
In [9]: df_1
Out[9]:
EmptyCol NumberCol Row
0 NaN 1 0
1 NaN 2 1
2 NaN 3 1
In [10]: df_2
Out[10]:
Empty DataFrame
Columns: [EmptyCol, NumberCol, Row]
Index: []
In [11]: df_concat
Out[11]:
EmptyCol NumberCol Row
0 NaN 1 0
1 NaN 2 1
2 NaN 3 1
```
However this is causing `test_partial_setting_mixed_dtype` test to fail because after the fix dtypes change for df.
```
In [2]: df = DataFrame(columns=['A','B'])
In [3]: df.loc[0] = Series(1,index=range(4))
In [4]: df1 = DataFrame(columns=['A','B'],index=[0])
In [5]: df.dtypes
Out[5]:
A float64
B float64
dtype: object
In [6]: df1.dtypes
Out[6]:
A object
B object
dtype: object
```
whereas before the fix, the test was happy as all dtypes were same.
```
In [2]: df = DataFrame(columns=['A','B'])
In [3]: df.loc[0] = Series(1,index=range(4))
In [4]: df1 = DataFrame(columns=['A','B'],index=[0])
In [5]: df.dtypes
Out[5]:
A object
B object
dtype: object
In [6]: df1.dtypes
Out[6]:
A object
B object
dtype: object
```
Is it expected to preserve the test behavior?
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} | 2 | 2015-01-19T18:03:13Z | 2021-01-01T20:41:44Z | 2021-01-01T20:41:44Z | NONE | null | ```
In [386]: h = pd.DataFrame({'a':[1,2,1,np.nan,1], 'b':[1,2,3,3,2], 'c':[2,3,1,4,2]})
In [387]: gh=h.groupby(['a', 'b'])
In [388]: gh.groups.keys()
Out[388]: [(1.0, 2), (nan, 3), (1.0, 3), (1.0, 1), (2.0, 2)]
In [389]: gh.indices.keys()
Out[389]: [(1.0, 2), (1.0, 3), (2.0, 3), (1.0, 1), (2.0, 2)] # Incorrect
```
The tuple (2.0, 3) should not be here.
The problem goes away when there are no NAs
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| null | 3 | 2015-01-19T23:12:39Z | 2015-01-20T12:53:41Z | 2015-01-20T11:15:12Z | CONTRIBUTOR | null | Pandas requires dateutil 1.5. This version was released in 2010. There is also a dateutil 2.x series that is currentlu updated. It would be nice if pandas supports datautil 2.x as linux distributions are strating to move away from dateutil 1.5
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https://api.github.com/repos/pandas-dev/pandas/issues/9306 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9306/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9306/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9306/events | https://github.com/pandas-dev/pandas/issues/9306 | 54,838,165 | MDU6SXNzdWU1NDgzODE2NQ== | 9,306 | BUG: API Change with skiprows from v0.14.1 to v0.15.2 | {
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| null | 5 | 2015-01-20T02:58:14Z | 2015-01-23T00:32:57Z | 2015-01-23T00:32:57Z | NONE | null | Whereas this worked in v0.14.1:
```
In [1]: import pandas as pd
In [2]: from StringIO import StringIO
In [3]: pd.__version__
Out[3]: '0.14.1'
In [4]: data = '#header\n\na,b,c\n1,2,3\n4,5,6'
In [5]: df = pd.read_csv(StringIO(data), skiprows=2, index_col='a')
In [6]: df
Out[6]:
b c
a
1 2 3
4 5 6
```
In v0.15.2 and the current dev it doesn't. There is a workaround, which requires you to explicitly use `header`.
```
In [1]: import pandas as pd
In [2]: pd.__version__
Out[2]: '0.15.2-103-gfda5012'
In [3]: from StringIO import StringIO
In [4]: data = '#header\n#header\na,b,c\n1,2,3\n4,5,6'
In [5]: df = pd.read_csv(StringIO(data), skiprows=2, index_col='a')
In [6]: df
Out[6]:
b c
a
1 2 3
4 5 6
In [7]: data = '#header\n\na,b,c\n1,2,3\n4,5,6'
In [8]: df = pd.read_csv(StringIO(data), skiprows=2, index_col='a')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-8-26f3ae16e644> in <module>()
----> 1 df = pd.read_csv(StringIO(data), skiprows=2, index_col='a')
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/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)
463 skip_blank_lines=skip_blank_lines)
464
--> 465 return _read(filepath_or_buffer, kwds)
466
467 parser_f.__name__ = name
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
249 return parser
250
--> 251 return parser.read()
252
253 _parser_defaults = {
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in read(self, nrows)
708 raise ValueError('skip_footer not supported for iteration')
709
--> 710 ret = self._engine.read(nrows)
711
712 if self.options.get('as_recarray'):
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in read(self, nrows)
1177 values = data.pop(i)
1178 else:
-> 1179 values = data.pop(self.index_col[i])
1180
1181 values = self._maybe_parse_dates(values, i,
KeyError: 'a'
In [9]: df = pd.read_csv(StringIO(data), skiprows=2, index_col='a', header=1) # Need to specify header kwarg to make this work
In [10]: df
Out[10]:
b c
a
1 2 3
4 5 6
In [11]: data = '\n#header\na,b,c\n1,2,3\n4,5,6'
In [12]: df = pd.read_csv(StringIO(data), skiprows=2, index_col='a')
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-12-26f3ae16e644> in <module>()
----> 1 df = pd.read_csv(StringIO(data), skiprows=2, index_col='a')
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/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)
463 skip_blank_lines=skip_blank_lines)
464
--> 465 return _read(filepath_or_buffer, kwds)
466
467 parser_f.__name__ = name
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in _read(filepath_or_buffer, kwds)
249 return parser
250
--> 251 return parser.read()
252
253 _parser_defaults = {
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in read(self, nrows)
708 raise ValueError('skip_footer not supported for iteration')
709
--> 710 ret = self._engine.read(nrows)
711
712 if self.options.get('as_recarray'):
/home/tmaloney/vedev/pandas-test-03/lib/python2.7/site-packages/pandas-0.15.2_103_gfda5012-py2.7-linux-x86_64.egg/pandas/io/parsers.pyc in read(self, nrows)
1177 values = data.pop(i)
1178 else:
-> 1179 values = data.pop(self.index_col[i])
1180
1181 values = self._maybe_parse_dates(values, i,
KeyError: 'a'
In [13]: df = pd.read_csv(StringIO(data), skiprows=2, index_col='a', header=1) # Need to specify header kwarg to make this work
In [14]: df
Out[14]:
b c
a
1 2 3
4 5 6
In [15]: df = pd.read_csv(StringIO(data), skiprows=2)
In [16]: df
Out[16]:
Empty DataFrame
Columns: []
Index: [(a, b, c), (1, 2, 3), (4, 5, 6)]
In [17]: df = pd.read_csv(StringIO(data), skiprows=2, header=0)
In [18]: df
Out[18]:
Empty DataFrame
Columns: []
Index: [(a, b, c), (1, 2, 3), (4, 5, 6)]
In [19]: df = pd.read_csv(StringIO(data), skiprows=2, header=1)
In [20]: df
Out[20]:
a b c
0 1 2 3
1 4 5 6
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9307 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9307/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9307/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9307/events | https://github.com/pandas-dev/pandas/pull/9307 | 54,845,996 | MDExOlB1bGxSZXF1ZXN0Mjc2NTUxNjg= | 9,307 | Fix plotting memory leak | {
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} | 14 | 2015-01-20T05:41:04Z | 2015-06-02T19:30:49Z | 2015-04-19T23:29:18Z | CONTRIBUTOR | null | This PR resolves #9003 (and explains matplotlib/matplotlib#3892). The root cause of the memory leak is a reference cycle between `MPLPlot` objects and the `AxesSubplot` objects they create.
Specifically, a `plotf` function object is stored in `ax._plot_data` for the purposes of potentially redrawing if the data needs to be resampled. This would be fine if this were a top-level function; however, these are all nested functions that make use of `self`. This means that by `plotf` pulls in `self.ax` and `self.axes`, which point to the `AxesSubplot` that `plotf` is being attached to. We therefore have a reference cycle:
```
AxesSubplot -> AxesSubplot._plot_data -> plotf -> self -> self.ax -> AxesSubplot
```
In order to make the objects collectable, we need to either explicitly break a link or replace it with a weakref. Weakrefs don't work as `AxesSubplot` and `MPLPlot` have the same lifetime. Just not using `_plot_data` prevents the leak but breaks functionality.
The final option as I see it is to change `plotf` to not depend on `self`. This works but involves a fair amount of modifications. I elected to make each of the `plotf`s top-level functions to make the lack of `self`-dependency explicit. This also required making several other functions `classmethods` and moving some data from `MPLPlot` to the `AxesSubplot` object.
The key assumption being made by this change is that either `MPLPlot` objects are discarded immediately after use _or_ we don't want any modifications to the `MPLPlot` (e.g., adding errors post-plotting) to be reflected if redrawing. I believe both cases are true but this patch has the potential for behavioral changes if `MPLPlot` objects are regularly being retained and modified.
I've also added a memory-leak test to prevent a regression; the test fails as expected if applied without the other commits in this patch.
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} | 20 | 2015-01-20T07:28:48Z | 2018-01-21T09:03:00Z | 2015-02-16T12:35:24Z | CONTRIBUTOR | null | closes #9144
closes #8445
---
Here's the results from testing the vbenches related to DataFrames (I also added 6 vbenches).
```
Invoked with :
--ncalls: 10
--repeats: 10
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_float_div_by_zero | 1.5656 | 23.4006 | 0.0669 |
frame_float_floor_by_zero | 3.1077 | 24.2786 | 0.1280 |
groupby_frame_nth_none | 1.4918 | 2.1483 | 0.6944 |
groupby_frame_nth_any | 4.0633 | 5.4162 | 0.7502 |
dataframe_resample_max_string | 1.3970 | 1.5892 | 0.8791 |
stat_ops_frame_mean_int_axis_1 | 3.4506 | 3.9006 | 0.8846 |
stat_ops_frame_mean_float_axis_1 | 3.6501 | 4.1200 | 0.8859 |
dataframe_resample_max_numpy | 1.4134 | 1.5861 | 0.8911 |
frame_reindex_upcast | 5.6926 | 6.3748 | 0.8930 |
stat_ops_frame_sum_int_axis_1 | 3.2424 | 3.6136 | 0.8973 |
dataframe_resample_min_numpy | 1.4320 | 1.5937 | 0.8985 |
stat_ops_frame_mean_float_axis_0 | 3.5722 | 3.9361 | 0.9075 |
frame_ctor_dtindex_Nanox2 | 0.8544 | 0.9404 | 0.9086 |
frame_dropna_axis1_any | 17.6609 | 19.4360 | 0.9087 |
frame_ctor_dtindex_Hourx1 | 0.8695 | 0.9554 | 0.9101 |
frame_ctor_dtindex_Secondx1 | 0.8586 | 0.9384 | 0.9150 |
frame_ctor_dtindex_Microx2 | 0.8564 | 0.9341 | 0.9168 |
dataframe_resample_mean_string | 1.8672 | 2.0302 | 0.9197 |
frame_ctor_dtindex_Nanox1 | 0.8526 | 0.9242 | 0.9225 |
stat_ops_level_frame_sum_multiple | 4.7174 | 5.0987 | 0.9252 |
frame_ctor_dtindex_Hourx2 | 0.8645 | 0.9342 | 0.9254 |
frame_xs_row | 0.0242 | 0.0261 | 0.9269 |
frame_ctor_dtindex_BMonthBeginx1 | 1.0908 | 1.1755 | 0.9280 |
stat_ops_frame_sum_float_axis_0 | 3.6003 | 3.8713 | 0.9300 |
eval_frame_mult_python_one_thread | 12.8115 | 13.7715 | 0.9303 |
stat_ops_frame_sum_int_axis_0 | 3.2616 | 3.4839 | 0.9362 |
eval_frame_mult_python | 12.8203 | 13.6336 | 0.9403 |
dataframe_resample_min_string | 1.4186 | 1.5066 | 0.9416 |
frame_shift_axis0 | 7.3947 | 7.8502 | 0.9420 |
stat_ops_frame_sum_float_axis_1 | 3.5332 | 3.7481 | 0.9427 |
frame_ctor_dtindex_BYearEndx2 | 1.0797 | 1.1452 | 0.9428 |
frame_fillna_many_columns_pad | 4.3826 | 4.6457 | 0.9434 |
frame_ctor_dtindex_Secondx2 | 0.8635 | 0.9153 | 0.9434 |
frame_get_dtype_counts | 0.0601 | 0.0635 | 0.9467 |
frame_dropna_axis1_all | 34.1749 | 36.0852 | 0.9471 |
frame_float_equal | 2.1541 | 2.2727 | 0.9478 |
dataframe_resample_mean_numpy | 1.8673 | 1.9664 | 0.9496 |
append_frame_single_mixed | 1.2900 | 1.3544 | 0.9525 |
eval_frame_add_python | 13.0182 | 13.6672 | 0.9525 |
eval_frame_and_python_one_thread | 23.5453 | 24.5942 | 0.9574 |
frame_drop_dup_inplace | 1.8290 | 1.9081 | 0.9585 |
groupby_frame_cython_many_columns | 2.4763 | 2.5806 | 0.9596 |
frame_get_numeric_data | 0.0697 | 0.0725 | 0.9612 |
eval_frame_and_python | 23.6922 | 24.6444 | 0.9614 |
frame_mask_bools | 8.5507 | 8.8782 | 0.9631 |
frame_shift_axis_1 | 11.5263 | 11.9162 | 0.9673 |
eval_frame_chained_cmp_python | 68.9274 | 71.2359 | 0.9676 |
frame_multi_and_st | 20.8511 | 21.5107 | 0.9693 |
eval_frame_add_python_one_thread | 11.9996 | 12.3749 | 0.9697 |
frame_mask_floats | 5.7624 | 5.9394 | 0.9702 |
frame_multi_and_no_ne | 21.2904 | 21.9065 | 0.9719 |
frame_apply_axis_1 | 45.9807 | 47.2289 | 0.9736 |
join_dataframe_integer_2key | 3.5381 | 3.6330 | 0.9739 |
frame_ctor_nested_dict_int64 | 48.7418 | 50.0032 | 0.9748 |
eval_frame_chained_cmp_python_one_thread | 67.2400 | 68.9405 | 0.9753 |
frame_to_csv_mixed | 404.9059 | 414.6069 | 0.9766 |
groupby_frame_singlekey_integer | 1.4465 | 1.4805 | 0.9770 |
frame_dropna_axis1_all_mixed_dtypes | 137.6919 | 140.7975 | 0.9779 |
frame_insert_500_columns_end | 65.0207 | 66.4810 | 0.9780 |
frame_drop_dup_na_inplace | 1.6683 | 1.7045 | 0.9788 |
frame_iteritems | 17.1694 | 17.5347 | 0.9792 |
stat_ops_level_frame_sum | 2.1278 | 2.1730 | 0.9792 |
reindex_frame_level_align | 0.6034 | 0.6153 | 0.9807 |
frame_count_level_axis0_mixed_dtypes_multi | 73.6246 | 75.0608 | 0.9809 |
dataframe_reindex | 0.2569 | 0.2615 | 0.9824 |
frame_reindex_axis1 | 44.7410 | 45.5110 | 0.9831 |
groupby_frame_median | 5.1782 | 5.2658 | 0.9834 |
frame_to_csv_date_formatting | 6.7534 | 6.8611 | 0.9843 |
frame_fancy_lookup_all | 11.3160 | 11.4933 | 0.9846 |
frame_ctor_dtindex_DateOffsetx2 | 0.7447 | 0.7562 | 0.9848 |
join_dataframe_integer_key | 1.2000 | 1.2166 | 0.9863 |
frame_reindex_columns | 0.2247 | 0.2276 | 0.9872 |
frame_from_records_generator | 41.6522 | 42.1751 | 0.9876 |
frame_ctor_dtindex_QuarterBeginx2 | 0.9039 | 0.9145 | 0.9884 |
frame_sort_index_by_columns | 24.9708 | 25.2553 | 0.9887 |
join_dataframe_index_single_key_bigger | 8.9800 | 9.0787 | 0.9891 |
frame_apply_ref_by_name | 8.7795 | 8.8757 | 0.9892 |
stat_ops_frame_mean_int_axis_0 | 3.2407 | 3.2742 | 0.9898 |
indexing_dataframe_boolean | 82.9152 | 83.7444 | 0.9901 |
frame_add | 3.7720 | 3.8059 | 0.9911 |
frame_ctor_dtindex_BYearBeginx2 | 1.0778 | 1.0870 | 0.9915 |
frame_drop_duplicates_na | 14.2417 | 14.3591 | 0.9918 |
frame_ctor_dtindex_CBMonthBeginx1 | 2.2563 | 2.2738 | 0.9923 |
frame_ctor_dtindex_Weekx2 | 0.7799 | 0.7859 | 0.9924 |
join_dataframe_index_single_key_small | 8.2365 | 8.2989 | 0.9925 |
frame_dropna_axis1_any_mixed_dtypes | 124.3344 | 125.2386 | 0.9928 |
frame_ctor_list_of_dict | 42.8818 | 43.1860 | 0.9930 |
frame_ctor_dtindex_YearEndx2 | 0.8680 | 0.8737 | 0.9935 |
append_frame_single_homogenous | 0.8964 | 0.9022 | 0.9936 |
groupby_frame_apply | 22.1934 | 22.3310 | 0.9938 |
frame_mult_no_ne | 3.8051 | 3.8274 | 0.9942 |
frame_nonunique_equal | 7.3191 | 7.3515 | 0.9956 |
frame_ctor_dtindex_BMonthBeginx2 | 1.0844 | 1.0890 | 0.9958 |
frame_apply_lambda_mean | 3.8462 | 3.8609 | 0.9962 |
frame_ctor_nested_dict | 46.5123 | 46.6852 | 0.9963 |
frame_count_level_axis1_mixed_dtypes_multi | 61.2177 | 61.4444 | 0.9963 |
frame_ctor_dtindex_BMonthEndx2 | 0.9260 | 0.9288 | 0.9970 |
frame_html_repr_trunc_mi | 22.1488 | 22.2066 | 0.9974 |
frame_multi_and | 21.2545 | 21.3085 | 0.9975 |
frame_add_st | 3.7661 | 3.7741 | 0.9979 |
frame_ctor_dtindex_BYearBeginx1 | 1.0904 | 1.0926 | 0.9980 |
join_dataframe_index_multi | 13.3128 | 13.3311 | 0.9986 |
frame_getitem_single_column | 12.7089 | 12.7029 | 1.0005 |
frame_object_equal | 7.3267 | 7.3224 | 1.0006 |
frame_from_records_generator_nrows | 0.5950 | 0.5940 | 1.0016 |
frame_to_string_floats | 15.5732 | 15.5458 | 1.0018 |
frame_add_no_ne | 3.8073 | 3.7999 | 1.0019 |
frame_ctor_dtindex_CBMonthBeginx2 | 1.9428 | 1.9389 | 1.0020 |
frame_ctor_dtindex_BusinessDayx2 | 0.8365 | 0.8348 | 1.0020 |
frame_ctor_dtindex_BDayx2 | 0.8400 | 0.8381 | 1.0023 |
frame_float_mod | 2.4775 | 2.4717 | 1.0024 |
frame_reindex_axis0 | 42.1150 | 42.0129 | 1.0024 |
frame_drop_duplicates | 13.3927 | 13.3401 | 1.0039 |
sparse_frame_constructor | 3.7919 | 3.7743 | 1.0047 |
frame_iloc_big | 0.1020 | 0.1015 | 1.0049 |
frame_interpolate_some_good_infer | 1.8705 | 1.8610 | 1.0051 |
indexing_dataframe_boolean_st | 85.3268 | 84.8837 | 1.0052 |
frame_dropna_axis0_any | 18.0998 | 18.0034 | 1.0054 |
frame_count_level_axis1_multi | 57.8269 | 57.5086 | 1.0055 |
indexing_dataframe_boolean_rows | 0.2319 | 0.2306 | 1.0056 |
indexing_dataframe_boolean_rows_object | 0.3911 | 0.3880 | 1.0080 |
frame_dropna_axis0_any_mixed_dtypes | 125.2823 | 124.2290 | 1.0085 |
frame_repr_wide | 8.4213 | 8.3428 | 1.0094 |
frame_apply_pass_thru | 2.7872 | 2.7612 | 1.0094 |
frame_dtypes | 0.0732 | 0.0725 | 1.0099 |
frame_to_html_mixed | 120.9245 | 119.7213 | 1.0101 |
frame_ctor_dtindex_DateOffsetx1 | 0.7530 | 0.7452 | 1.0105 |
frame_ctor_dtindex_Dayx1 | 0.8844 | 0.8746 | 1.0112 |
groupby_frame_apply_overhead | 5.2761 | 5.2173 | 1.0113 |
frame_ctor_dtindex_Millix1 | 0.9011 | 0.8910 | 1.0113 |
frame_count_level_axis0_multi | 43.2266 | 42.7371 | 1.0115 |
reindex_frame_level_reindex | 0.6016 | 0.5946 | 1.0117 |
frame_ctor_dtindex_BQuarterBeginx1 | 1.1092 | 1.0958 | 1.0122 |
frame_reindex_both_axes | 13.9118 | 13.7180 | 1.0141 |
join_dataframe_index_single_key_bigger_sort | 11.0337 | 10.8757 | 1.0145 |
frame_ctor_dtindex_Weekx1 | 0.7509 | 0.7395 | 1.0154 |
frame_ctor_dtindex_BMonthEndx1 | 0.9711 | 0.9547 | 1.0172 |
indexing_dataframe_boolean_no_ne | 87.0329 | 85.4171 | 1.0189 |
frame_fancy_lookup | 2.0553 | 2.0135 | 1.0208 |
frame_mult_st | 3.8758 | 3.7959 | 1.0210 |
frame_repr_tall | 12.1078 | 11.8041 | 1.0257 |
frame_insert_100_columns_begin | 24.3260 | 23.6877 | 1.0269 |
frame_ctor_dtindex_QuarterEndx2 | 1.0204 | 0.9927 | 1.0279 |
frame_iteritems_cached | 0.3542 | 0.3440 | 1.0297 |
frame_ctor_dtindex_Easterx2 | 0.9328 | 0.9050 | 1.0307 |
frame_interpolate | 64.3447 | 62.4263 | 1.0307 |
frame_html_repr_trunc_si | 17.4936 | 16.9715 | 1.0308 |
frame_mult | 3.9272 | 3.8050 | 1.0321 |
frame_dropna_axis0_all_mixed_dtypes | 142.3560 | 137.7486 | 1.0334 |
frame_from_series | 0.0670 | 0.0648 | 1.0338 |
frame_apply_np_mean | 4.2445 | 4.0938 | 1.0368 |
frame_interpolate_some_good | 1.0546 | 1.0159 | 1.0381 |
frame_ctor_dtindex_MonthBeginx1 | 0.9428 | 0.9080 | 1.0383 |
frame_ctor_dtindex_Minutex1 | 0.8931 | 0.8599 | 1.0386 |
frame_constructor_ndarray | 0.0554 | 0.0532 | 1.0408 |
frame_ctor_dtindex_BQuarterEndx2 | 1.0568 | 1.0139 | 1.0423 |
frame_ctor_dtindex_QuarterEndx1 | 1.0460 | 1.0035 | 1.0423 |
frame_getitem_single_column2 | 12.9310 | 12.3968 | 1.0431 |
frame_ctor_dtindex_MonthBeginx2 | 0.9435 | 0.9042 | 1.0435 |
frame_ctor_dtindex_Microx1 | 0.9086 | 0.8704 | 1.0439 |
frame_ctor_dtindex_CustomBusinessDayx2 | 0.8865 | 0.8490 | 1.0441 |
frame_ctor_dtindex_CustomBusinessDayx1 | 0.8898 | 0.8517 | 1.0447 |
frame_to_csv2 | 82.5006 | 78.9100 | 1.0455 |
frame_ctor_dtindex_BQuarterBeginx2 | 1.1443 | 1.0945 | 1.0455 |
frame_ctor_dtindex_BQuarterEndx1 | 1.0723 | 1.0249 | 1.0462 |
frame_ctor_dtindex_Dayx2 | 0.9089 | 0.8669 | 1.0485 |
frame_ctor_dtindex_CDayx1 | 0.8952 | 0.8536 | 1.0487 |
frame_ctor_dtindex_Easterx1 | 0.9384 | 0.8919 | 1.0521 |
frame_ctor_dtindex_YearBeginx1 | 0.8962 | 0.8514 | 1.0526 |
frame_float_div | 4.8441 | 4.6001 | 1.0530 |
frame_reindex_both_axes_ix | 14.6171 | 13.8707 | 1.0538 |
frame_ctor_dtindex_YearEndx1 | 0.9240 | 0.8754 | 1.0555 |
frame_ctor_dtindex_QuarterBeginx1 | 0.9843 | 0.9323 | 1.0558 |
frame_ctor_dtindex_BDayx1 | 0.8610 | 0.8153 | 1.0561 |
frame_ctor_dtindex_MonthEndx2 | 0.9667 | 0.9151 | 1.0564 |
frame_ctor_dtindex_Millix2 | 0.9122 | 0.8625 | 1.0576 |
frame_to_csv | 96.1645 | 90.8887 | 1.0580 |
frame_ctor_dtindex_CDayx2 | 0.9058 | 0.8557 | 1.0585 |
frame_boolean_row_select | 0.1848 | 0.1741 | 1.0615 |
frame_loc_dups | 0.7166 | 0.6750 | 1.0616 |
frame_assign_timeseries_index | 0.5794 | 0.5451 | 1.0629 |
frame_ctor_dtindex_Minutex2 | 0.9103 | 0.8556 | 1.0639 |
frame_ctor_dtindex_CBMonthEndx1 | 3.0158 | 2.8304 | 1.0655 |
frame_apply_user_func | 57.2865 | 53.6327 | 1.0681 |
frame_ctor_dtindex_CBMonthEndx2 | 3.0658 | 2.8691 | 1.0686 |
frame_ctor_dtindex_BusinessDayx1 | 0.8704 | 0.8101 | 1.0744 |
frame_ctor_dtindex_YearBeginx2 | 0.9055 | 0.8417 | 1.0758 |
frame_ctor_dtindex_MonthEndx1 | 0.9801 | 0.9096 | 1.0775 |
frame_iloc_dups | 0.1877 | 0.1736 | 1.0813 |
frame_dropna_axis0_all | 31.8504 | 29.3697 | 1.0845 |
frame_fillna_inplace | 8.8447 | 8.0231 | 1.1024 |
frame_ctor_dtindex_BYearEndx1 | 1.2137 | 1.0994 | 1.1040 |
frame_isnull | 0.6357 | 0.5477 | 1.1606 |
frame_xs_mi_ix | 2.3058 | 1.9489 | 1.1831 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [e33f3bc] : BUG: Fix #9144 #8445 Fix how core.common._fill_zeros handles div and mod by zero
Base [76195fb] : Merge pull request #9498 from jreback/consist
```
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} | 5 | 2015-01-20T15:09:02Z | 2020-04-03T18:53:37Z | 2020-04-03T18:53:37Z | MEMBER | null | Not sure this is intended, but `Timestamp` and `Timedelta` displays nanoseconds information inconsistently.
- `Timestamp` shows nanoseconds when it isn't 0
```
dt = pd.Timestamp(datetime.datetime.now())
h = pd.Timedelta(hours=1)
dt
#2015-01-21 00:01:05.260660
dt + offsets.Nano()
#2015-01-21 00:01:05.260660001
```
- But `Timedelta` doesn't.
```
pd.Timedelta(nanoseconds=1)
#0 days 00:00:00.000000
pd.Timedelta(nanoseconds=1).nanoseconds
#1
```
I think `Timestamp` behavior is prefferable to avoid any misunderstanding.
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} | 1 | 2015-01-20T15:12:16Z | 2015-01-20T20:03:54Z | 2015-01-20T15:14:55Z | CONTRIBUTOR | null | Just show in the docs that required version of dateutil is >= 1.5 (#9305)
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} | 19 | 2015-01-20T16:52:32Z | 2015-02-14T03:10:28Z | 2015-02-14T03:10:28Z | CONTRIBUTOR | null | I have a dataframe that contains a datetime64 column that seems to be losing some digits after a groupby.first(). I know that under the hood these are stored at nanosecond precision, but I need them to remain equal, since I'm merging back onto the original frame later on. Is this expected behavior?
``` python
import pandas as pd
from pandas import Timestamp
data = [
{'a': 1,
'dateCreated': Timestamp('2011-01-20 12:50:28.593448')},
{'a': 1,
'dateCreated': Timestamp('2011-01-15 12:50:28.502376')},
{'a': 1,
'dateCreated': Timestamp('2011-01-15 12:50:28.472790')},
{'a': 1,
'dateCreated': Timestamp('2011-01-15 12:50:28.445286')}]
df = pd.DataFrame(data)
```
Output is:
``` python
In [6]: df
Out[6]:
a dateCreated
0 1 2011-01-20 12:50:28.593448
1 1 2011-01-15 12:50:28.502376
2 1 2011-01-15 12:50:28.472790
3 1 2011-01-15 12:50:28.445286
In [7]: df.groupby('a').first()
Out[7]:
dateCreated
a
1 2011-01-20 12:50:28.593447936
```
When I compare the datetime64 in the first row to the datetime64 after the groupby.first(), the two are not equal.
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| null | 14 | 2015-01-20T17:33:49Z | 2015-01-26T01:24:00Z | 2015-01-25T23:15:41Z | CONTRIBUTOR | null | When Creating an Annual date range from the start of a year, the actual dates generated are for the END of that year, and do not include the datetime provided. You would expect to get a range starting from `start_date` and a series of subsequent dates one year apart
``` python
pd.date_range(datetime.datetime(2006, 1, 1, 0,0,0), periods=2, freq='A')
<class 'pandas.tseries.index.DatetimeIndex'>
[2006-12-31, 2007-12-31]
Length: 2, Freq: A-DEC, Timezone: None
```
For reference, using `dateutils.rrule` gives intuitive behavior
``` python
list(rrule(YEARLY, count=2, dtstart=datetime.datetime(2006, 1, 1, 0,0,0)))
[datetime.datetime(2006, 1, 1, 0, 0), datetime.datetime(2007, 1, 1, 0, 0)]
```
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} | 5 | 2015-01-20T17:38:29Z | 2017-07-19T02:51:58Z | 2017-07-19T02:51:58Z | CONTRIBUTOR | null | to get a yearly frequency from date range the flag is actually `freq="A"` (for "Annual" i guess) rather than `freq="Y"` for yearly.
It would seem more consistent if it accepted "Y" as well/instead of "A", following the pattern:
hour -> hourly -> H
day -> daily -> D
week -> weekly -> W
year -> yearly -> Y
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"url": "https://api.github.com/repos/pandas-dev/pandas/milestones/32"
} | 4 | 2015-01-20T17:41:15Z | 2020-05-23T04:29:14Z | null | CONTRIBUTOR | null | Currently date_range accepts string flags for `freq=`.
dateutil (which is already a pandas dependency) has constants for `YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY` which are used in the dateutil equivalent of date_range, `rrule`.
it would be a nice feature if these were accepted as well as the string arguments
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https://api.github.com/repos/pandas-dev/pandas/issues/9315 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9315/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9315/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9315/events | https://github.com/pandas-dev/pandas/issues/9315 | 54,923,638 | MDU6SXNzdWU1NDkyMzYzOA== | 9,315 | bug: read_csv and german umlaut (ä,ö,ü) in filename | {
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| null | 11 | 2015-01-20T18:57:25Z | 2015-02-11T00:05:33Z | 2015-01-23T15:09:46Z | NONE | null | The following code
```
import pandas
df = pandas.read_csv('C:\\exampleä\\test.csv')
```
thows an `OSError: File b'C:\\example\xc3\xa4\\test.csv' does not exist`.
This `df = pandas.read_excel('C:\exampleä\test.xlsx', 'test')` and this `open(r'C:\exampleä\test.csv', newline='')` works perfectly.
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} | 2 | 2015-01-20T19:35:04Z | 2015-03-05T23:31:16Z | 2015-03-05T23:30:27Z | MEMBER | null | WIP
Closes #6920
For now, I just copied the relevant parts of the tutorial docs (in indexing.rst).
I should also include `__getitem__` for `[]`
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| null | 10 | 2015-01-20T20:00:53Z | 2015-01-22T11:57:55Z | 2015-01-22T11:57:55Z | NONE | null | See this post: http://stackoverflow.com/questions/28041762/pandas-metadata-of-dataframe-persistence-error?noredirect=1#comment44479369_28041762
Users using python 3.3 are reporting that when "non _metadata" attributes are copied they propagate to the new copied dataframe. Furtheremore, when persisting to json they are able to access _metadata and non _metadata attributes from the file when loading via read_json method even though the json file has no contents which leads me to believe data is floating around somewhere. I can't replicate this on my system as I use python 2.7.5 my _metadata behaviour is consistent with my expectations (see the post). Assuming there are no other issues I can think of easy ways to write helper functions for json and hdf5 to persist the _metadata however now it appears to me that there may be other things to look into. This is my first ever bug report, am I a real programmer now?
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} | 3 | 2015-01-20T20:20:35Z | 2015-01-21T08:43:49Z | 2015-01-21T08:43:49Z | MEMBER | null | Related to #9257. The removed TimedeltaProperties were not removed from the api.rst overview.
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| null | 1 | 2015-01-21T01:03:21Z | 2015-01-21T02:56:24Z | 2015-01-21T02:56:24Z | NONE | null | Comparing dtypes of DataFrame columns works fine for a list of possible dtypes, but not for a set of possible dtypes. For example:
``` python
In [1]: sample_df = pd.DataFrame(
[[1,2,3],[4,5,6],[7,8,9],[10,11,12]],
columns=['a', 'b', 'c']
)
In [2]: sample_df.dtypes
Out[2]:
a int64
b int64
c int64
dtype: object
In [3]: [sample_df[c].dtype in [int] for c in sample_df.columns]
Out[3]: [True, True, True]
In [4]: [sample_df[c].dtype in {int} for c in sample_df.columns]
Out[4]: [False, False, False]
```
What is the cause of this behaviour? I would expect `[sample_df[c].dtype in {int} for c in sample_df.columns]` to evaluate to `[True, True, True]` as well.
I am using pandas version 0.15.0.
``` python
pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
pandas: 0.15.0
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: 0.9
apiclient: None
rpy2: 2.4.2
sqlalchemy: 0.8.2
pymysql: None
psycopg2: 2.5.3 (dt dec pq3 ext)
```
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| null | 10 | 2015-01-21T05:44:25Z | 2017-06-08T08:26:21Z | null | NONE | null | I create a DataFrame and save it to disk as an hdf5 file, as follows:
df.to_hdf(fname, 'table')
This behaves as expected. But when I try to append to the existing hdf5 file,
df.to_hdf(fname, 'table',append=True)
pandas returns an error:
ValueError: Can only append to Tables
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} | 32 | 2015-01-21T07:09:09Z | 2015-09-11T19:53:48Z | 2015-09-11T04:42:33Z | MEMBER | null | Fixes #9124
This PR adds plotting sub-methods like `df.plot.scatter()` as an alternative to using `df.plot(kind='scatter')`.
I've added meaningful function signatures and documentation for a few of these methods, but I would greatly appreciate help to fill in the rest -- this is a lot of documentation to assemble/reconstruct! The entire point of this PR, of course, is to have better introspection and docstrings.
Todo list:
- [x] Basic docstrings/signatures
- [x] `area`
- [x] `line`
- [x] `bar`
- [x] `barh`
- [x] `box`
- [x] `hexbin`
- [x] `hist`
- [x] `kde`/`density`
- [x] `pie`
- [x] `scatter`
- [x] Write tests for the methods
- [x] Fix groupby plots (tests currently failing)
- [x] Plotting docs
- [x] API docs
- [x] Release notes
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} | 32 | 2015-01-21T07:51:49Z | 2015-03-10T06:47:48Z | 2015-01-25T20:06:58Z | MEMBER | null | Fixes #9184
This PR fixes the API docs to use `Series.str` and `Series.dt` instead of `StringMethods` and `DatetimeProperties`.
It will need a rebase once #9318 is merged.
CC @jorisvandenbossche @jreback
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| null | 7 | 2015-01-21T08:26:13Z | 2015-01-23T17:09:28Z | 2015-01-21T09:30:47Z | NONE | null | In previous versions, the Spyder IDE displayed HTML for DataFrames in its iPython console. In 0.15.2 they are no longer displayed.
I think this may be to do with `DataFrame._repr_html_()`
If I run the following on 0.15.2:
```
import pandas as pd
import numpy as np
print('pandas version ', pd.__version__)
rng = pd.date_range(start='2014-01-01', periods=5, freq='D')
df = pd.DataFrame({'y':np.random.normal(size=len(rng))}, index=rng)
ret = df._repr_html_()
print(None == ret)
```
it prints `0.15.2, True`
and viewing the `DataFrame` with `df` results in the text rendering:

On 0.14.1, running the above I get `0.14.1, False`
and `df` results in this:

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} | 4 | 2015-01-21T11:22:02Z | 2018-07-06T20:21:06Z | 2017-07-10T21:06:48Z | NONE | null | to_frame should have more control (be explicit) over what axis becomes index and the columns in the DF.
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| null | 2 | 2015-01-21T14:51:24Z | 2015-01-21T16:52:25Z | 2015-01-21T16:52:25Z | CONTRIBUTOR | null | Hi I have encountered a very strange issue in one of my programs.
I have a dataframe with a
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2007-01-02, ..., 2014-12-15]
Length: 2914, Freq: None, Timezone: None
```
When I try to slice the DataFrame then this does not work:
```
df2[dt.date(2007,1,1):]
```
while this works:
```
df2["2007-01-01":]
```
I tried to reproduce this issue with a newly constructed DataFrame but was not able to do so but I can also not find any differences in Index type etc. between the new DataFrame and the problematic one.
You can find the pickled dataframe [here](http://s000.tinyupload.com/index.php?file_id=00026126527367673509)
Please see the following example for more detail:
```
In [1]:
import pandas as pd
import numpy as np
import datetime as dt
from pandas.util.print_versions import show_versions
show_versions()
```
```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-24-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2
nose: None
Cython: None
numpy: 1.9.1
scipy: None
statsmodels: None
IPython: 2.3.1
sphinx: None
patsy: None
dateutil: 2.2
pytz: 2014.10
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.6.4
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
```
In [2]:
dates=[dt.datetime(2013,1,2), dt.datetime(2013,1,3), dt.datetime(2013,1,4), dt.datetime(2013,1,5)]
dft = pd.DataFrame(np.random.randn(4),dates)
print dft.index
dft[dt.date(2013,1,1):]
```
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-01-02, ..., 2013-01-05]
Length: 4, Freq: None, Timezone: None
0
2013-01-02 0.930665
2013-01-03 1.829505
2013-01-04 0.313932
2013-01-05 -0.769223
```
```
In [3]:
import pandas.io.pickle as pickle
df2 = pickle.read_pickle("test_df.pic")['SWI_001']
print df2.index
print df2["2007-01-01":]
df2[dt.date(2007,1,1):]
```
```
<class 'pandas.tseries.index.DatetimeIndex'>
[2007-01-02, ..., 2014-12-15]
Length: 2914, Freq: None, Timezone: None
2007-01-02 36
2007-01-03 24
2007-01-04 27
2007-01-05 27
2007-01-06 27
2007-01-07 25
2007-01-08 27
2007-01-09 48
2007-01-10 48
2007-01-11 48
2007-01-12 17
2007-01-13 21
2007-01-14 9
2007-01-15 9
2007-01-16 37
...
2014-12-01 127
2014-12-02 127
2014-12-03 127
2014-12-04 127
2014-12-05 127
2014-12-06 127
2014-12-07 127
2014-12-08 127
2014-12-09 127
2014-12-10 127
2014-12-11 127
2014-12-12 127
2014-12-13 127
2014-12-14 127
2014-12-15 127
Name: SWI_001, Length: 2914
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-3-440ba88bb691> in <module>()
3 print df2.index
4 print df2["2007-01-01":]
----> 5 df2[dt.date(2007,1,1):]
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
547 key = _check_bool_indexer(self.index, key)
548
--> 549 return self._get_with(key)
550
551 def _get_with(self, key):
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/series.pyc in _get_with(self, key)
552 # other: fancy integer or otherwise
553 if isinstance(key, slice):
--> 554 indexer = self.index._convert_slice_indexer(key, typ='getitem')
555 return self._get_values(indexer)
556 elif isinstance(key, ABCDataFrame):
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/index.pyc in _convert_slice_indexer(self, key, typ)
716 if typ == 'getitem':
717 return self._convert_slice_indexer_getitem(
--> 718 key, is_index_slice=is_index_slice)
719
720 # convert the slice to an indexer here
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/index.pyc in _convert_slice_indexer_getitem(self, key, is_index_slice)
664 if self.is_integer() or is_index_slice:
665 return key
--> 666 return self._convert_slice_indexer(key)
667
668 def _convert_slice_indexer(self, key, typ=None):
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/index.pyc in _convert_slice_indexer(self, key, typ)
738 else:
739 try:
--> 740 indexer = self.slice_indexer(start, stop, step)
741 except Exception:
742 if is_index_slice:
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/tseries/index.pyc in slice_indexer(self, start, end, step)
1335
1336 try:
-> 1337 return Index.slice_indexer(self, start, end, step)
1338 except KeyError:
1339 # For historical reasons DatetimeIndex by default supports
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/index.pyc in slice_indexer(self, start, end, step)
2025 This function assumes that the data is sorted, so use at your own peril
2026 """
-> 2027 start_slice, end_slice = self.slice_locs(start, end, step=step)
2028
2029 # return a slice
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/index.pyc in slice_locs(self, start, end, step)
2139 start_slice = None
2140 if start is not None:
-> 2141 start_slice = self.get_slice_bound(start, 'left')
2142 if start_slice is None:
2143 start_slice = 0
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_slice_bound(self, label, side)
2077
2078 try:
-> 2079 slc = self.get_loc(label)
2080 except KeyError:
2081 if self.is_monotonic_increasing:
/home/pydev/.virtualenvs/pytimesheets/local/lib/python2.7/site-packages/pandas/tseries/index.pyc in get_loc(self, key)
1271 return self._engine.get_loc(stamp)
1272 except (KeyError, ValueError):
-> 1273 raise KeyError(key)
1274
1275 def _maybe_cast_slice_bound(self, label, side):
KeyError: datetime.date(2007, 1, 1)
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9326 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9326/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9326/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9326/events | https://github.com/pandas-dev/pandas/issues/9326 | 55,029,013 | MDU6SXNzdWU1NTAyOTAxMw== | 9,326 | Performance regression in .ffill() | {
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| null | 5 | 2015-01-21T14:57:44Z | 2015-01-22T01:09:06Z | 2015-01-22T01:09:06Z | CONTRIBUTOR | null | Under pandas 0.12:
``` python
In [4]: from pandas import *
In [5]: from numpy.random import randn
In [6]: In [14]: df = DataFrame({'one' : Series(randn(3), index=['a', 'b', 'c']),
....: 'two' : Series(randn(4), index=['a', 'b', 'c', 'd']),
....: 'three' : Series(randn(3), index=['b', 'c', 'd'])})
In [7]: df
Out[7]:
one three two
a -0.899073 NaN 2.164487
b 0.142272 0.158407 -1.387754
c 0.583869 1.063867 0.542845
d NaN 0.739295 1.527532
In [8]: df.ffill()
Out[8]:
one three two
a -0.899073 NaN 2.164487
b 0.142272 0.158407 -1.387754
c 0.583869 1.063867 0.542845
d 0.583869 0.739295 1.527532
In [9]: %timeit df.ffill()
10000 loops, best of 3: 64.4 µs per loop
```
With pandas 0.15:
``` python
In [7]: %timeit df.ffill()
The slowest run took 7.81 times longer than the fastest. This could mean that an intermediate result is being cached
10000 loops, best of 3: 101 µs per loop
```
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| null | 3 | 2015-01-21T16:02:45Z | 2015-01-21T16:08:02Z | 2015-01-21T16:04:51Z | NONE | null | When trying to create DataFrame from HDF Store with `auto_close=True` or `False` it breaks with error:
```
somedf = pd.DataFrame({'a':[1,2], 'b':[3,4]})
somedf.to_hdf('hdf_file', 'somekey', format='t', mode='w')
newdf = pd.read_hdf('hdf_file', 'somekey', auto_close=True)
newdf
```
error:
```
d:\Anaconda\envs\py2k\lib\site-packages\pandas\io\pytables.pyc in <lambda>(store, auto_close)
303
304 f = lambda store, auto_close: store.select(
--> 305 key, auto_close=auto_close, **kwargs)
306
307 if isinstance(path_or_buf, string_types):
TypeError: select() got multiple values for keyword argument 'auto_close'
```
The same without `auto_close` argument works fine:
```
newdf = pd.read_hdf('hdf_file', 'somekey')
newdf
```
yielding:
```
a b
0 1 3
1 2 4
```
pandas 0.15.2, pytables 3.1.1 on Win8.1x64
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| null | 5 | 2015-01-21T17:16:43Z | 2016-02-18T19:42:37Z | 2015-01-22T23:36:46Z | NONE | null | This issue has been reported in the past, however there were some problems reproducing it. (https://github.com/pydata/pandas/issues/6720). I'm providing some code that hopefully helps catch the bug (finally!).
The error appears to be related to the division of a large Series or DataFrame that was built by pd.read_hdf(). To see this, please run the following sample code:
``` python
import pandas as pd,numpy as np
# Works
df=pd.DataFrame(np.arange(10000).reshape(-1,4),columns=['a','b','c','d'])
df.to_hdf('hdf5.h5','test')
df=pd.read_hdf('hdf5.h5','test')
x=(df['a']-df['b'])/(df['c']-df['d'])
print 'Done for 10000'
# Does not work
df=pd.DataFrame(np.arange(100000).reshape(-1,4),columns=['a','b','c','d'])
df.to_hdf('hdf5.h5','test')
df=pd.read_hdf('hdf5.h5','test')
x=(df['a']-df['b'])/(df['c']-df['d'])
print 'Done for 100000'
```
The first operation is carried out, however the second one produces this error when the division is attempted.
``` python
OMP: Error #134: Cannot set thread affinity mask.
OMP: System error #87: The parameter is incorrect.
```
The issue can be avoided by:
1) operating with Numpy arrays, e.g.
``` python
x=(df['a']-df['b']).values/(df['c']-df['d']).values
```
2) or by setting
``` pyhton
pd.computation.expressions.set_use_numexpr(False)
```
3) or by creating the DataFrame from pd.read_csv().
I'm running Pandas 0.15.2-1, Numpy 1.8.1-2. Enthought's Canopy 1.5.0.2717.
Thanks
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https://api.github.com/repos/pandas-dev/pandas/issues/9329 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9329/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9329/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9329/events | https://github.com/pandas-dev/pandas/issues/9329 | 55,064,565 | MDU6SXNzdWU1NTA2NDU2NQ== | 9,329 | Partial indexing of MultiIndex fails with empty tuple | {
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| null | 2 | 2015-01-21T19:23:06Z | 2015-01-23T02:14:09Z | 2015-01-23T02:14:09Z | NONE | null | Indexing the entire MultiIndex works with the empty tuple, and partial indexing works with non-empty tuples, but partial indexing with an empty tuple doesn't. Here's a repro:
```
import pandas as pd
import numpy as np
import itertools
ix_str = pd.MultiIndex.from_tuples(list(itertools.product([('a')], ['a','b','c'])))
ix_tup = pd.MultiIndex.from_tuples(list(itertools.product([()], ['a','b','c'])))
df_str = pd.DataFrame(np.random.rand(10, 3), columns=ix_str)
print df_str[('a')].columns # Index([u'a', u'b', u'c'], dtype='object')
df_tup = pd.DataFrame(np.random.rand(10, 3), columns=ix_tup)
print df_tup[(), 'a'].index # Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')
print df_tup[()].columns
```
PS Thanks for maintaining Pandas, it's awesome!
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https://api.github.com/repos/pandas-dev/pandas/issues/9330 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9330/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9330/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9330/events | https://github.com/pandas-dev/pandas/issues/9330 | 55,078,406 | MDU6SXNzdWU1NTA3ODQwNg== | 9,330 | False negative on .equals() after read_hdf() | {
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} | 10 | 2015-01-21T21:18:00Z | 2015-04-05T23:10:37Z | 2015-04-05T23:10:37Z | NONE | null | I have strange results from `.equals` appearing when DataFrame is written to HDF Store and then read back:
```
import pandas as pd
df = pd.DataFrame({'B':[1,2], 'A':[str('x'), str('y')]}) # str() - just to be sure this is not linked to unicode
print 'df:'
print df
df.to_hdf('hdf_file', 'key', format='t', mode='w')
df_out = pd.read_hdf('hdf_file', 'key')
print '\ndf_out:'
print df_out
print '\ndf equals df_out:', df.equals(df_out)
print '\ndf_out equals df:', df_out.equals(df)
print '\ndf.shape == df_out.shape:', df.shape == df_out.shape
print '\narray_equivalent(df.values, df_out.values):', pd.core.common.array_equivalent(df.values, df_out.values)
print '\ndf.index equals df_out.index:', df.index.equals(df_out.index)
print '\ndf.columns equals df_out.columns:', df.columns.equals(df_out.columns)
for col in df.columns:
print '\ndf.{0} equals df_out.{0}: {1}'.format(col, df[col].equals(df_out[col]))
```
output:
```
df:
A B
0 x 1
1 y 2
df_out:
A B
0 x 1
1 y 2
df equals df_out: False
df_out equals df: False
df.shape == df_out.shape: True
array_equivalent(df.values, df_out.values): True
df.index equals df_out.index: True
df.columns equals df_out.columns: True
df.A equals df_out.A: True
df.B equals df_out.B: True
```
The interesting thing is that if DataFrame is initialized with different columns "order" in the dictionary the results are ALL True (i.e. correct):
```
df = pd.DataFrame({'A':[1,2], 'B':[str('x'),str('y')]}) # in the code above
```
will give:
```
df equals df_out: True
df_out equals df: True
```
I have seen similar issues ([#8437](https://github.com/pydata/pandas/issues/8437) and [#7605](https://github.com/pydata/pandas/issues/7605)), which are marked as closed, but seeing this strange results... might be something different?
python 2.7.9, pandas 0.15.2
My apologies in advance for potential duplicate.
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} | 20 | 2015-01-22T02:36:03Z | 2015-02-13T00:42:40Z | 2015-02-13T00:42:02Z | MEMBER | null | This should resolve the inconsistency @mwaskom reported in #9148.
CC @jreback @TomAugspurger @JanSchulz
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} | 4 | 2015-01-22T06:29:13Z | 2015-01-22T23:55:04Z | 2015-01-22T23:54:43Z | NONE | null | I'm new to vb_suite, and ran following command according to [this document](https://github.com/pydata/pandas/wiki/Performance-Testing).
```
./test_perf.sh -b master -t HEAD
```
But I got some errors (`InvalidGitRepositoryError` and Benchmark duplication errors), and finally I got tests run by fixing following issues.
- VB_DIR pointed wrong directory
- temporary variable `bmark` in groupby was collected as Benchmark
- indexing module had same name ('series_getitem_scalar') Benchmarks
I'm not so familier with vb_suite, and any feedbacks are welcome ! Thanks.
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} | 3 | 2015-01-22T11:06:37Z | 2015-02-04T14:57:51Z | 2015-02-04T07:34:03Z | CONTRIBUTOR | null | The old docs is wrong where head() & tail() return the same result.
Change input of the example to see group data clearer.
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} | 5 | 2015-01-22T14:41:41Z | 2015-01-26T09:09:59Z | 2015-01-25T22:43:51Z | NONE | null | Please make Windows wheel packages and put them on Pypi.
Currently it is possible to download Windows wheel packages for pandas here: http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas
It would be great if the wheels were directly available on the Pypi server https://pypi.python.org/pypi/ so that they can be installed with pip.
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} | 2 | 2015-01-22T15:39:56Z | 2018-05-29T01:34:34Z | 2018-05-29T01:34:34Z | NONE | null | Given a DataFrame with a column of type datetime64, there is a problem assigning values of other types to slice of said column:
in [148]:
```
import datetime as dt
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.random((3, 2)), columns = ['a', 'b' ])
df['c'] = pd.NaT
df.c
Out[148]:
0 NaT
1 NaT
2 NaT
Name: c, dtype: datetime64[ns]
```
It doesn't mind this:
```
In[185]:df.loc[0, 'c'] = pd.datetime.today()
df.c
Out[185]:
0 2015-01-22 10:56:49.763999
1 NaT
2 NaT
```
Now try assigning values of a another type:
```
In [186]: df.loc[0, 'c'] = 1
AttributeError: 'int' object has no attribute 'view'
In[187]: df.loc[[0, 1], 'c'] = 'a', 'b'
AttributeError: 'tuple' object has no attribute 'view'
```
Now, the problem runs deeper than this. I didn't actually come across it when trying to assign mixed types to a single column. I was actually assigning values to a block of a DataFrame which included the time column. Here is an example:
```
In [252]:
A = pd.DataFrame([[2, pd.datetime(2013, 1, 1)], [3, pd.datetime(2014, 1, 1)]])
In[253]:
df.loc[[0, 1], ['a', 'c']] = A.values
AttributeError: 'list' object has no attribute 'view'
```
Note that the following works:
```
In[254]:
df.loc[[0, 1], ['a', 'c']] = A
```
So when using the array A.values, the dtype is forced to object which then causes the problem. However, in the case where A has a different index than df, using A.values is necessary to assign the values. Maybe I need to be more careful but the error message doesn't help and it took me a long time to understand the problem. If I try to assign values to a slice of column 'c' using the corresponding slice of A.values I get a different error, which is more informative:
```
In[255]:
df.loc[[0, 1], 'c'] = A.values[:, 1]
TypeError: Cannot change data-type for object array.
```
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} | 2 | 2015-01-22T15:55:49Z | 2020-03-31T04:37:10Z | 2020-03-31T04:37:10Z | NONE | null | First create a DataFrame with a 3-level index and then index into it using a single tuple from the outer two levels:
```
import pandas as pd
import itertools
ix = pd.MultiIndex.from_product([range(3), pd.date_range('20140101', '20140102'), list('ab')])
df = pd.DataFrame(0, index = ix, columns = ['A', 'B'])
outer_levels = ix.droplevel(2).tolist()
df.loc[outer_levels[0]]
A B
a 0 0
b 0 0
```
No problems there. Now try the whole outer level:
df.loc[outer_levels]
```
(0, 2014-01-01 00:00:00) NaN NaN
(0, 2014-01-01 00:00:00) NaN NaN
(0, 2014-01-02 00:00:00) NaN NaN
......
```
Or a slice:
```
df.loc[outer_levels[2:4]].count().sum()
0
```
The order of the levels (i.e. datatypes) doesn't see to matter:
```
for p in itertools.permutations(range(3)):
df = pd.DataFrame(0, index = ix.reorder_levels(p), columns = ['A', 'B'])
outer_levels = df.index.droplevel(2).tolist()
print test.loc[outer_levels].count().sum()
0
0
.....
```
Also, there is no problem indexing into the two outer levels using the same indexer if the 3rd level is dropped:
```
df.reset_index(2).loc[outer_levels[2:4]]
level_2 A B
0 2014-01-02 a 0 0
2014-01-02 b 0 0
2014-01-02 a 0 0
2014-01-02 b 0 0
```
Pandas version is 0.15.2
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} | 11 | 2015-01-22T18:30:00Z | 2015-02-05T16:54:48Z | 2015-02-05T11:37:26Z | CONTRIBUTOR | null | Series now supports bitwise op for integral types.
I have made the changes in wrapper() itself instead of na_op() because it looked to me like wrapper is controlling the input and output fill and dtype. Once that is taken care of, na_op() seems to be doing the right thing by itself and so I did not have to change anything to get back the expected behavior. Happy to change if things need to be altered toward better design etc.
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} | 2 | 2015-01-22T18:44:10Z | 2015-01-29T11:17:51Z | 2015-01-29T11:17:51Z | CONTRIBUTOR | null | Add lag parameter to autocorrelation, default to lag-1 autocorrelation
so existing code will work unchanged.
This is effectively reopening issue #9192
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} | 36 | 2015-01-22T22:10:34Z | 2021-04-12T05:03:37Z | null | NONE | null | First time I used the .interpolate() method I thought that it receives a new index and then interpolates on it, similar to [scipy.interpolate.interp1d](http://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d)
From scipy web:
``` python
from scipy import interpolate
x = np.arange(0, 10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y)
xnew = np.arange(0,9, 0.1)
ynew = f(xnew) # use interpolation function returned by `interp1d`
```
Later I saw the .reindex() method, so I understood that this role is done by .reindex(). However .reindex() is not really doing a powerful interpolation, just extending the current values using the [method](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.reindex.html) keyword.
The current way to achieve it (joining previous and new index and then using .reindex()), in version 0.15.0,
``` python
index_joined = df.index.join(new_index, how='outer')
df.reindex(index=index_joined).interpolate().reindex(new_index)
```
A simpler syntax could be accepting the methods from [.interpolate()](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.interpolate.html) into the 'method' keyword of .reindex():
``` python
df.reindex(index=new_index, method='linear')
```
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| null | 1 | 2015-01-22T23:09:06Z | 2015-01-22T23:17:37Z | 2015-01-22T23:16:44Z | NONE | null | It seems that the `to_datetime` function always assumes that a timestamp is in local time and converts it to UTC. This does not seem to be the case with converting from strings.
This code:
``` python
from datetime import datetime
import time
import pandas as pd
now = datetime.now()
ts = time.mktime(now.timetuple())
print ts
print now.strftime('%Y-%m-%d %H:%M')
print pd.to_datetime(ts, unit='s')
print pd.to_datetime(ts, unit='s', utc=True)
print pd.to_datetime(now.strftime('%Y-%m-%d %H:%M'))
```
Give this result (pandas 0.15.1):
```
1421967906.0
2015-01-22 18:05
2015-01-22 23:05:06
2015-01-22 23:05:06+00:00
2015-01-22 18:05:00
```
Note that when `to_datetime` is converting a timestamp five hours are added no matter the `utc=` arg value.
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| null | 1 | 2015-01-22T23:48:22Z | 2015-01-22T23:51:45Z | 2015-01-22T23:51:22Z | NONE | null | Shouldn't `convert_objects(convert_dates='coerce')` be coercing a series of strings to NaTs?
``` python
df = pd.DataFrame({'date': ['a', 'b', 'c', 'd']})
df.convert_objects(convert_dates='coerce')
date
0 a
1 b
2 c
3 d
```
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} | 1 | 2015-01-23T08:04:35Z | 2016-08-02T10:05:27Z | 2016-08-02T10:05:27Z | MEMBER | null | #9322 will make `Series.str` raise an exception if it is accessed on Series instances with non-object dtype. In principle, the exception should really be raised for any non-strictly string-like data, but that's not practical until pandas has a true string dtype to use (currently we abuse `np.object_` for this purpose).
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} | 2 | 2015-01-23T14:24:50Z | 2015-02-10T14:52:04Z | 2015-02-10T14:52:04Z | NONE | null | When grouping by several levels of a MultiIndex, groupby evaltuates all possible combinations of the groupby keys. When grouping by column name, it only evaluates what exist in the DataFrame. Also, this behavior does not exist in 0.14.1, but does in all final releases from 0.15.0 on.
This may be a new feature, not a bug, but I couldn't find anything in the [docs](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby), open or closed issues, etc. (closest was Issue #8138). If this is the intended behavior, it would be nice to have in the docs.
``` python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(12).reshape(-1, 3))
df.index = pd.MultiIndex.from_tuples([(1, 1), (1, 2), (3, 4), (5, 6)])
idx_names = ['x', 'y']
df.index.names = idx_names
# Adds nan's for (x, y) combinations that aren't in the data
by_levels = df.groupby(level=idx_names).mean()
# This does not add missing combinations of the groupby keys
by_columns = df.reset_index().groupby(idx_names).mean()
print by_levels
print by_columns
# This passes in 0.14.1, but not >=0.15.0 final
assert by_levels.equals(by_columns)
```
## INSTALLED VERSIONS
commit: None
python: 2.7.7.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.15.2
nose: 1.3.4
Cython: 0.20.1
numpy: 1.9.1
scipy: 0.15.1
statsmodels: 0.7.0.dev-161a0f8
IPython: 2.3.0
sphinx: 1.2.2
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.9
bottleneck: 0.8.0
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.5
lxml: 3.3.5
bs4: 4.3.1
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.4
pymysql: None
psycopg2: None
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} | 42 | 2015-01-23T16:36:04Z | 2015-02-14T04:48:51Z | 2015-02-14T03:10:28Z | CONTRIBUTOR | null | datetime64 columns were changing at the nano-second scale when
applying a groupby aggregator.
closes #9311
closes #6620
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} | 22 | 2015-01-23T20:57:00Z | 2017-04-09T15:28:52Z | 2017-04-09T15:28:52Z | MEMBER | null | Looking at the API docs, neither the function `pd.unique` nor the order of the unique values from `unique` is mentioned. I would like to:
1. Add `pd.unique` to API > General functions > Data manipulations
2. Note that `Series.unique()` and `unique()` return values in order of appearance
3. Add unit tests to verify the "order of appearance" nature `unique` (untested directly AFAICT, though I'm sure it's relied on implicitly)
Any objections? If not, I'll put together a PR.
This lack of documentation has caused some hesitation for relying on this functionality in Seaborn: https://github.com/mwaskom/seaborn/issues/361
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} | 18 | 2015-01-23T21:58:53Z | 2016-04-03T18:40:32Z | 2015-03-07T23:14:32Z | CONTRIBUTOR | null | In https://github.com/mwaskom/seaborn/issues/361 it was discussed that lexicographical sorting the categories is only appropiate if an order is specified/implied. If this is explicitly not done, e.g. with `Categorical(..., ordered=False)` then the order should be taken from the order of appearance, similar to the current `Series.unique()` implementation.
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| null | 1 | 2015-01-23T23:15:05Z | 2015-01-23T23:40:41Z | 2015-01-23T23:40:28Z | NONE | null | When polling for a long-running gbq job to determine if it is complete, we should only return results once `query_results['jobCompleted']` is `True`, not just when the `jobCompleted` key exists. Otherwise, the gbq client thinks results exist and it will start attempting to parse the results, leading to a weird KeyError:
```
/Library/Python/2.7/site-packages/pandas/io/gbq.pyc in read_gbq(query, project_id, index_col, col_order, reauth)
368▓
369 connector = GbqConnector(project_id, reauth = reauth)
--> 370 schema, pages = connector.run_query(query)
371 dataframe_list = []
372 while len(pages) > 0:
/Library/Python/2.7/site-packages/pandas/io/gbq.pyc in run_query(self, query)
192 jobId=job_reference['jobId']).execute()
193▓
--> 194 total_rows = int(query_reply['totalRows'])
195 result_pages = list()
196 seen_page_tokens = list()
KeyError: 'totalRows'
```
This simple patch accounts for the case where `query_results['jobCompleted']` is `False`.
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| null | 1 | 2015-01-24T17:55:40Z | 2015-01-24T18:06:37Z | 2015-01-24T18:06:12Z | CONTRIBUTOR | null | Hello,
I noticed:
```
In [1]: pd.to_timedelta('00:00:00.1')
Out[1]: Timedelta('0 days 00:00:00.100000')
```
but
```
In [2]: pd.to_timedelta('0.1')
Out[2]: Timedelta('0 days 00:00:00')
```
same for
```
In [3]: pd.to_timedelta(0.1)
Out[3]: Timedelta('0 days 00:00:00')
```
I was expecting it returns `Timedelta('0 days 00:00:00.100000')`
Kind regards
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} | 19 | 2015-01-24T22:34:13Z | 2017-03-13T23:04:39Z | 2017-03-13T23:04:39Z | CONTRIBUTOR | null | When `tight_layout()` is called after `DataFrame.hist()` it raises `AttributeError: 'NoneType' object has no attribute 'is_bbox` if any of subplots is empty (number of charts with histograms is smaller than number os subplots).
I.e. the following code:
```
pd.DataFrame({'a':np.random.randn(100),
'b':np.random.randn(100),
}).hist()
plt.tight_layout()
```
works fine, but this code:
```
pd.DataFrame({'a':np.random.randn(100),
'b':np.random.randn(100),
'c':np.random.randn(100),
}).hist()
plt.tight_layout()
```
raises `AttributeError`.
My versions:
pandas: 0.15.2
numpy: 1.9.1
scipy: 0.14.1
IPython: 2.3.1
matplotlib: 1.4.2
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} | 3 | 2015-01-25T13:12:41Z | 2015-01-29T23:28:30Z | 2015-01-29T11:12:01Z | MEMBER | null | Derived from #9111.
- Add `StringMethods.ljust` and `StringMethods.rjust` to be consistent with standard `str`.
- Add `fillchar` option to `StringMethods.pad` and `StringMethods.center` to be consistent with standard `str.center`
- https://docs.python.org/2/library/string.html#string.center
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| null | 5 | 2015-01-25T20:42:01Z | 2015-01-25T20:51:37Z | 2015-01-25T20:50:10Z | NONE | null | Hello,
```
import pandas as pd
from datetime import timedelta
td=pd.to_timedelta('00:00:15')
```
Python 2
```
td
Timedelta('0 days 00:00:15')
isinstance(td, timedelta)
True
```
but with Python 3
```
td
numpy.timedelta64(15000000000,'ns')
isinstance(td, timedelta)
False
```
Kind regards
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} | 3 | 2015-01-25T23:33:12Z | 2015-01-28T11:09:07Z | 2015-01-28T11:09:00Z | CONTRIBUTOR | null | xref #7784
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} | 1 | 2015-01-26T01:23:36Z | 2015-05-09T16:06:25Z | 2015-05-09T16:06:25Z | CONTRIBUTOR | null | rebase of #6632.
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} | 4 | 2015-01-26T06:43:31Z | 2015-04-07T12:03:04Z | 2015-04-04T19:07:54Z | NONE | null | Excel offers several types of values display for a pivot table (ratio of row, ratio of column, ratio of total, difference with, cumulated sum ...)
This ticket aims to implement this option in the pivot table API. I implemented the three types cited above for a start.
I chose "display_value" for the argument name. One can find too generic, so i'm opened to suggestions.
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} | 13 | 2015-01-26T09:50:07Z | 2015-03-05T08:58:32Z | 2015-03-05T08:55:49Z | MEMBER | null | Closes #3445
---
Start for #3445. Then the current examples should get a seaborn or ggplot alternative.
Questions:
- explicitely refer to seaborn and ggplot as alternatives? (as there may be other packages that could feel neglected that way?)
- I now raise the warning on import of the module (`import pandas.tools.rplot as rplot`). The only thing is that the warning will be raised in the tests this way
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} | 2 | 2015-01-26T16:03:18Z | 2015-03-07T21:19:39Z | 2015-03-05T23:28:23Z | CONTRIBUTOR | null | Per: pydata/pandas-datareader#15
Fixes #9010, #9026
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| null | 3 | 2015-01-26T20:42:04Z | 2015-02-04T21:14:15Z | 2015-02-04T12:08:38Z | NONE | null | I have fought with trying to load recursive dictionaries (with labels that have depth) for about an hour now, and [this seems to be the solution](http://stackoverflow.com/questions/28157564/please-explain-odd-behavior-with-pandas-dataframes-of-multi-level-index)
```
import pandas as pd
test = {'a': tuple(range(10)),
'b': tuple(range(10, 20)),
'c': {'0': tuple(range(10)),
'1': tuple(range(30, 40))
}
}
def dict_depth(d, depth=0):
if not isinstance(d, dict) or not d:
return depth
return max(dict_depth(v, depth+1) for k, v in d.items())
import collections
def flatten(d, parent_key=()):
items = []
for k, v in d.items():
new_key = parent_key + (k,)
if isinstance(v, collections.MutableMapping):
items.extend(flatten(v, new_key).items())
else:
items.append((new_key, v))
return dict(items)
def df_dict(dic, filler=''):
'''Convert a general dictionary into a data frame (even nested ones)'''
make_len = dict_depth(dic)
out = {key + (filler,) * (make_len - len(key)): value for (key, value) in flatten(dic).items()}
return pd.DataFrame.from_dict(out)
df_dict(test) # gives a nice value when using an ipython notebook
```
Is it possible that this can be included in the general loading mechanism? I can't see why it shouldn't be.
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} | 8 | 2015-01-27T08:19:25Z | 2015-02-05T11:34:52Z | 2015-02-05T11:34:48Z | CONTRIBUTOR | null | closes #9205
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https://api.github.com/repos/pandas-dev/pandas/issues/9361 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9361/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9361/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9361/events | https://github.com/pandas-dev/pandas/issues/9361 | 55,606,484 | MDU6SXNzdWU1NTYwNjQ4NA== | 9,361 | bug in repeat() method with MultiIndex - fixed in 0.15 | {
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} | 4 | 2015-01-27T11:31:00Z | 2015-01-27T17:15:45Z | 2015-01-27T17:15:45Z | CONTRIBUTOR | null | Hi,
I found a bug in version 0.14, which has since been fixed in 0.15. I thought I'd report it because I couldn't find a mention of it and it might have been fixed silently. Also I couldn't find a specific test for this bug (as far as I could see).
Here is a test that fails with the bug:
``` python
import pandas as pd
print(pd.version.version)
def test_multiindex_repeat():
idx = pd.Index([1, 2, 3, 4])
m_idx = pd.MultiIndex.from_tuples([(1, 2), (3, 4),
(5, 6), (7, 8)])
data = ['a', 'b', 'c', 'd']
df = pd.Series(data, index=idx)
m_df = pd.Series(data, index=m_idx)
assert df.repeat(3).shape == (3 * len(data),)
assert m_df.repeat(3).shape == (3 * len(data),)
test_multiindex_repeat()
```
With pandas 0.14, here is the backtrace:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-11-7fcd65f0e025> in <module>()
----> 1 assert m_df.repeat(3).shape == (3 * len(data),)
/home/guillaume/anaconda/envs/pelican/lib/python2.7/site-packages/pandas/core/series.pyc in repeat(self, reps)
705 new_values = self.values.repeat(reps)
706 return self._constructor(new_values,
--> 707 index=new_index).__finalize__(self)
708
709 def reshape(self, *args, **kwargs):
/home/guillaume/anaconda/envs/pelican/lib/python2.7/site-packages/pandas/core/series.pyc in __init__(self, data, index, dtype, name, copy, fastpath)
229 raise_cast_failure=True)
230
--> 231 data = SingleBlockManager(data, index, fastpath=True)
232
233 generic.NDFrame.__init__(self, data, fastpath=True)
/home/guillaume/anaconda/envs/pelican/lib/python2.7/site-packages/pandas/core/internals.pyc in __init__(self, block, axis, do_integrity_check, fastpath)
3028 block = make_block(block,
3029 placement=slice(0, len(axis)),
-> 3030 ndim=1, fastpath=True)
3031
3032 self.blocks = [block]
/home/guillaume/anaconda/envs/pelican/lib/python2.7/site-packages/pandas/core/internals.pyc in make_block(values, placement, klass, ndim, dtype, fastpath)
1835
1836 return klass(values, ndim=ndim, fastpath=fastpath,
-> 1837 placement=placement)
1838
1839
/home/guillaume/anaconda/envs/pelican/lib/python2.7/site-packages/pandas/core/internals.pyc in __init__(self, values, ndim, fastpath, placement)
1231 super(ObjectBlock, self).__init__(values, ndim=ndim,
1232 fastpath=fastpath,
-> 1233 placement=placement)
1234
1235 @property
/home/guillaume/anaconda/envs/pelican/lib/python2.7/site-packages/pandas/core/internals.pyc in __init__(self, values, placement, ndim, fastpath)
72 raise ValueError('Wrong number of items passed %d,'
73 ' placement implies %d' % (
---> 74 len(self.values), len(self.mgr_locs)))
75
76 @property
ValueError: Wrong number of items passed 12, placement implies 4
```
Shall I make a PR adding this test to test_multiindex or is it not worth the (minimal) work?
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} | 3 | 2015-01-27T11:56:17Z | 2015-01-27T17:27:43Z | 2015-01-27T17:15:26Z | CONTRIBUTOR | null | This was fixed in 0.15, presumably by #7891
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| null | 1 | 2015-01-27T17:01:43Z | 2015-01-27T17:14:39Z | 2015-01-27T17:14:30Z | NONE | null | This is probably by design but I find this very unintuitive...
More context:
``` python
import pandas as pd
import numpy as np
data = pd.DataFrame(np.zeros(2), columns=['A'])
data.loc[:].A = 'HELLO'
print data
data = pd.DataFrame(np.zeros(2), columns=['A'])
data.A.loc[:] = 'HELLO'
print data
data = pd.DataFrame(np.zeros(2), columns=['A'])
data.loc[:1].A = 'HELLO' # <-- FAILS
print data
data = pd.DataFrame(np.zeros(2), columns=['A'])
data.A.loc[:1] = 'HELLO'
print data
```
Output:
```
A
0 HELLO
1 HELLO
A
0 HELLO
1 HELLO
A
0 0
1 0
A
0 HELLO
1 HELLO
```
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} | 6 | 2015-01-27T21:47:57Z | 2015-01-28T11:08:35Z | 2015-01-28T06:25:53Z | NONE | null | Before:
```
In [1]: s=pandas.Series(range(100)*1000)
In [2]: %timeit s.nunique()
1000 loops, best of 3: 1.43 ms per loop
```
After:
```
In [1]: s=pandas.Series(range(100)*1000)
In [4]: %timeit s.nunique()
1000 loops, best of 3: 440 µs per loop
```
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| null | 4 | 2015-01-28T07:49:55Z | 2021-04-12T05:03:49Z | null | MEMBER | null | If we're willing to construct KDTrees when necessary, we can support efficient nearest neighbor queries even in multiple dimensions (i.e., on a MultiIndex) and even for unsorted indexes.
For an example of what you can do with this, take a look at this question I just answered on SO: http://stackoverflow.com/a/28186940/809705
See also: 1d nearest neighbor queries (#8845) and an implementation for sorted indexes (#9258).
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| null | 8 | 2015-01-28T17:14:10Z | 2015-02-04T13:29:42Z | 2015-01-29T10:51:28Z | NONE | null | For situations where an operation on a DataFrame causes a modification to the data in that DataFrame - it would be extremely useful to know how many rows are affected by the modification.
From a debugging, data flow audit perspective - having this "modified rows" value would avoid an extra data query to determine those rows in their "new state".
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| null | 4 | 2015-01-28T20:51:40Z | 2015-01-28T22:34:46Z | 2015-01-28T21:11:05Z | NONE | null | If there is a column named "size", the member syntax return the NumPy .size property instead of the contents of the column:
```
$ python -c "import pandas; df=pandas.DataFrame({'size':range(3)}); print df.size; print df['size']"
```
prints
```
3
0 0
1 1
2 2
Name: size, dtype: int64
```
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| null | 2 | 2015-01-29T01:37:25Z | 2015-01-30T13:39:00Z | 2015-01-30T13:38:42Z | NONE | null | I have a question regarding multiplying two Series that have MultiIndexes. In pandas, I can multiply these two Series and it makes sense:
``` python
>>> iterables = [
['A', 'B'],
['foo', 'bar', 'baz', 'qux'],
['one', 'two', 'three']
]
>>> s1 = pd.Series(range(12), index=pd.MultiIndex.from_product(iterables[1:3],
names=['first', 'second']))
>>> s1
first second
foo one 0
two 1
three 2
bar one 3
two 4
three 5
baz one 6
two 7
three 8
qux one 9
two 10
three 11
dtype: int64
>>> s2 = pd.Series(range(4), index=pd.MultiIndex.from_product([iterables[1]],
names=['first']))
>>> s2
first
foo 0
bar 1
baz 2
qux 3
dtype: int64
>>> s2 * s1
first second
foo one 0
two 0
three 0
bar one 3
two 4
three 5
baz one 12
two 14
three 16
qux one 27
two 30
three 33
dtype: int64
```
I want to do the same thing with deeper MultiIndexes, but it raises an exception:
``` python
>>> s1 = pd.Series(range(24),index=pd.MultiIndex.from_product(iterables,
names=['first', 'second', 'third']))
>>> s1
first second third
A foo one 0
two 1
three 2
bar one 3
two 4
three 5
baz one 6
two 7
three 8
qux one 9
two 10
three 11
B foo one 12
two 13
three 14
bar one 15
two 16
three 17
baz one 18
two 19
three 20
qux one 21
two 22
three 23
dtype: int64
>>> s2 = pd.Series(range(8),index=pd.MultiIndex.from_product(iterables[:2],
names=['first', 'second']))
>>> s2
first second
A foo 0
bar 1
baz 2
qux 3
B foo 4
bar 5
baz 6
qux 7
dtype: int64
>>> s2 * s1 # raises an exception
```
Shouldn't I be able to multiply these two Series together without raising an exception? Am I doing something wrong?
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} | 14 | 2015-01-29T05:35:07Z | 2018-10-26T10:59:59Z | 2018-10-25T23:55:55Z | NONE | null | This only happens with the numpy object is on the left. It doesn't matter if it's an int or a float. This error does not get raised with DataFrames.
After more poking around, It looks like this actually comres from a change in numpy, between versions 1.8.2 and 1.9.0.
``` python
import pandas as pd
import numpy as np
s = pd.Series(np.arange(4))
arr = np.arange(4)
right = s < arr[0]
left = arr[0] > s
```
Running this yields
``` python
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
bug_lhs_numpy.py in <module>()
6
7 right = s < arr[0]
----> 8 left = arr[0] > s
C:\Anaconda\lib\site-packages\pandas-0.14.1_236_g989a51b-py2.7-win-amd64.egg\pandas\core\ops.py
ther)
555 return NotImplemented
556 elif isinstance(other, (pa.Array, pd.Series, pd.Index)):
--> 557 if len(self) != len(other):
558 raise ValueError('Lengths must match to compare')
559 return self._constructor(na_op(self.values, np.asarray(other)),
TypeError: len() of unsized object
In [2]: type(arr[0])
Out[2]: numpy.int32
```
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| null | 2 | 2015-01-29T06:34:31Z | 2015-01-29T10:08:49Z | 2015-01-29T10:08:49Z | MEMBER | null | By sorting the index under the hood, we could support label based slicing even for unordered indexes.
Does this seem like a good idea? Here is what the implementation would look like, roughly:
``` python
def slice_indexer_anyorder(idx, start=None, end=None):
# preprocessing takes time O(n * log(n)) and space 2 * n
# (these variables could be cached on the index)
ordering = idx.argsort()
reordered = idx.take(ordering)
# query complexity is O(log(n) + k * log(k)),
# where k is the size of the result
start, end = reordered.slice_locs(start, end)
return np.sort(ordering[start:end])
```
The main potential for confusion here is that this _conflicts_ with the current behavior for `.loc`, which is to sort all values with labels _between_ the locations of start and end if start and end are found in the index. So adding this to `.loc` would require deprecating the existing usage.
CC @immerrr @jreback @jorisvandenbossche
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| null | 10 | 2015-01-29T10:34:17Z | 2015-01-29T20:48:47Z | 2015-01-29T10:45:10Z | CONTRIBUTOR | null | ```
In [1]: import pandas as pd
In [2]: print pd.__version__
0.12.0
In [3]: s = pd.Series({'AAPL': 20.})
In [4]: %timeit s['AAPL'] = 25.
1000000 loops, best of 3: 1.29 µs per loop
```
VS
```
In [1]: import pandas as pd
In [2]: print pd.__version__
0.15.2
In [3]: s = pd.Series({'AAPL': 20.})
In [4]: %timeit s['AAPL'] = 25.
100000 loops, best of 3: 6.05 µs per loop
```
Slow down of ~470%. Is this expected? Any way to speed this up?
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} | 6 | 2015-01-29T20:03:24Z | 2019-01-21T05:26:28Z | 2019-01-21T04:41:39Z | CONTRIBUTOR | null | Hello,
to_excel doesn't support `dateutil.relativedelta`
```
import pandas as pd
from dateutil.relativedelta import relativedelta
df = pd.DataFrame([relativedelta(years=2)])
df.to_excel('out.xls')
```
raises `Exception: Unexpected data type <type 'instance'>`
```
df.to_excel('out.xlsx')
```
raises `AttributeError: relativedelta instance has no attribute '__float__'`
Not sure if it's a Pandas issue, an XLS writer issue or a dateutil issue.
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| null | 1 | 2015-01-29T22:51:47Z | 2015-01-30T10:33:39Z | 2015-01-29T23:05:07Z | NONE | null | Dear Pandas community, this is a duplicate of SO's [question](http://stackoverflow.com/questions/28226074/unstack-pandas-dataframe-with-multiindex). I have seen a lots of similar questions across the web, but unfortunately I was not able to find the answers that could help me.
What I'm trying to do is just to reshape the DataFrame with MultiIndex via unstack() method. Here is it:
```
val
item indicator
0 Расположение: Минское шоссе /Минское шоссе
Направление: Запад
Площадь: 1200 м²
Стоимость: 1 007 259 000 руб.
1 Расположение: Переделкино /Минское шоссе
Направление: Запад
Площадь: 850 м²
Стоимость: 973 683 700 руб.
2 Расположение: Бородки /Минское шоссе
Направление: Запад
Площадь: 860 м²
Стоимость: 786 669 600 руб.
```
The desired output:
```
Расположение: Направление: Площадь: Стоимость:
0 ... ... ... ...
1 ... ... ... ...
2 ... ... ... ...
```
I tried to use the unstack() according to the [manual](http://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-by-stacking-and-unstacking), but with no success:
```
In [6]: combined.unstack('indicator')
...
ValueError: Index contains duplicate entries, cannot reshape
```
Any help will be appreciated.
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} | 2 | 2015-01-30T00:33:48Z | 2019-11-16T22:00:33Z | 2019-11-16T22:00:33Z | MEMBER | null | This leaves the DataFrame in a very weird/buggy state:
```
In [33]: cat = pd.Categorical.from_codes([0, 1, 1, 0, 1, 2], ['a', 'b', 'c'])
In [34]: df = pd.DataFrame()
In [35]: df['bar'] = range(10)
In [36]: df['foo'] = cat
In [37]: df
Out[37]:
bar foo
0 0 a
1 1 b
2 2 b
3 3 a
4 4 b
5 5 c
6 6
7 7
8 8
9 9
In [38]: df.foo.shape
Out[38]: (6,)
```
I was expecting something like:
```
In [49]: df['foo'] = np.array(cat)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-49-2c5dea325c23> in <module>()
----> 1 df['foo'] = np.array(cat)
/Users/shoyer/dev/pandas/pandas/core/frame.py in __setitem__(self, key, value)
2108 else:
2109 # set column
-> 2110 self._set_item(key, value)
2111
2112 def _setitem_slice(self, key, value):
/Users/shoyer/dev/pandas/pandas/core/frame.py in _set_item(self, key, value)
2185
2186 self._ensure_valid_index(value)
-> 2187 value = self._sanitize_column(key, value)
2188 NDFrame._set_item(self, key, value)
2189
/Users/shoyer/dev/pandas/pandas/core/frame.py in _sanitize_column(self, key, value)
2258 elif (isinstance(value, Index) or is_sequence(value)):
2259 from pandas.core.series import _sanitize_index
-> 2260 value = _sanitize_index(value, self.index, copy=False)
2261 if not isinstance(value, (np.ndarray, Index)):
2262 if isinstance(value, list) and len(value) > 0:
/Users/shoyer/dev/pandas/pandas/core/series.py in _sanitize_index(data, index, copy)
2562
2563 if len(data) != len(index):
-> 2564 raise ValueError('Length of values does not match length of '
2565 'index')
2566
ValueError: Length of values does not match length of index
```
Tested on master.
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| null | 3 | 2015-01-30T04:09:19Z | 2015-01-31T11:26:26Z | 2015-01-30T13:26:23Z | NONE | null | ```
pd.__version__ ### '0.15.2'
store = pd.HDFStore('synthetic_networks.h5',mode='w', table=True)
controls = pd.Panel4D(labels=['a', 'b', 'c'],
items=arange(100),
major_axis=arange(121),
minor_axis=arange(121),
dtype='float64')
store.append('controls', controls)
store.close()
store_emp = pd.HDFStore('synthetic_networks.h5',mode='r', table=True)
controls = store_emp['controls']
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-4-b8e1db9431a6> in <module>()
1 store_emp = pd.HDFStore('synthetic_networks2.h5',mode='r', table=True)
----> 2 controls = store_emp['controls']
/home/jeffrey_alstott/anaconda3/lib/python3.4/site-packages/pandas/io/pytables.py in __getitem__(self, key)
407
408 def __getitem__(self, key):
--> 409 return self.get(key)
410
411 def __setitem__(self, key, value):
/home/jeffrey_alstott/anaconda3/lib/python3.4/site-packages/pandas/io/pytables.py in get(self, key)
617 if group is None:
618 raise KeyError('No object named %s in the file' % key)
--> 619 return self._read_group(group)
620
621 def select(self, key, where=None, start=None, stop=None, columns=None,
/home/jeffrey_alstott/anaconda3/lib/python3.4/site-packages/pandas/io/pytables.py in _read_group(self, group, **kwargs)
1265 s = self._create_storer(group)
1266 s.infer_axes()
-> 1267 return s.read(**kwargs)
1268
1269
/home/jeffrey_alstott/anaconda3/lib/python3.4/site-packages/pandas/io/pytables.py in read(self, where, columns, **kwargs)
3701
3702 lp = DataFrame(new_values, index=new_index, columns=lp.columns)
-> 3703 objs.append(lp.to_panel())
3704
3705 # create the composite object
/home/jeffrey_alstott/anaconda3/lib/python3.4/site-packages/pandas/core/frame.py in to_panel(self)
1062 if (not isinstance(self.index, MultiIndex) or # pragma: no cover
1063 len(self.index.levels) != 2):
-> 1064 raise NotImplementedError('Only 2-level MultiIndex are supported.')
1065
1066 if not self.index.is_unique:
NotImplementedError: Only 2-level MultiIndex are supported.
```
I assume this is a known issue but I can't find any analysis of it. Am I doing something wrong?
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} | 7 | 2015-01-30T08:25:22Z | 2015-02-01T14:58:46Z | 2015-02-01T14:58:46Z | CONTRIBUTOR | null | Pandas version info:
```
>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: EN
pandas: 0.15.2
nose: 1.3.4
Cython: 0.21
numpy: 1.9.1
scipy: 0.15.0
statsmodels: 0.5.0
IPython: 2.3.1
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
Consider the following script, containing data to be loaded, a date parsing function, and `pd.read_csv()`:
``` python
from __future__ import print_function
import StringIO
import pandas as pd
import datetime as dt
text = '''
YYYY DOY HR MN 1 2 3 4 5 6 7
2013 1 0 0 71 1 100 5571 0 99999.9 999.9
2013 1 0 1 99 999 999 999999 999999 99999.9 999.9
2013 1 0 2 71 5 100 5654 19 -348.2 9.1
2013 1 0 3 71 1 100 5647 0 -350.6 9.5
2013 1 0 4 71 1 100 5693 0 -351.2 9.4
'''
def parse_date(year, doy, hour, minute):
print(year, type(year))
try: # array arguments
year = year.astype('datetime64[Y]')
doy = doy.astype('datetime64[D]')
hour = hour.astype('datetime64[h]')
minute = minute.astype('datetime64[m]')
return year + doy + hour + minute # return None also gives the same final DataFrame
except: # string arguments
year = int(year)
doy = int(doy)
hour = int(hour)
minute = int(minute)
return dt.datetime(year, 1, 1, hour, minute) + dt.timedelta(doy - 1)
data = pd.read_csv(StringIO.StringIO(text), delim_whitespace=True,
date_parser=parse_date, parse_dates=[[0, 1, 2, 3]], index_col=0)
```
The `print` statement in `parse_date` outputs
```
['2013' '2013' '2013' '2013' '2013'] <type 'numpy.ndarray'>
2013 <type 'str'>
2013 <type 'str'>
2013 <type 'str'>
2013 <type 'str'>
2013 <type 'str'>
```
With `parse_date` as written above, the DataFrame is loaded properly, even when the first `return` statement in `parse_date` is changed to `return None`:
```
>>> data
1 2 3 4 5 6 7
YYYY_DOY_HR_MN
2013-01-01 00:00:00 71 1 100 5571 0 99999.9 999.9
2013-01-01 00:01:00 99 999 999 999999 999999 99999.9 999.9
2013-01-01 00:02:00 71 5 100 5654 19 -348.2 9.1
2013-01-01 00:03:00 71 1 100 5647 0 -350.6 9.5
2013-01-01 00:04:00 71 1 100 5693 0 -351.2 9.4
```
It strikes me as odd that the date parser is called with both whole columns and individual rows. I could not find any documentation regarding this behaviour. It immediately raises two questions in my mind:
- Why is the date parser called with both whole columns as arguments, and then called with strings from every single row as arguments? Is this a bug? It seems pointless to require the user to design for both kinds of arguments, and apparently not even use the return value from the array call.
- Wouldn't it be much better performance wise to only call the date parser once with the arrays (whole columns) as arguments, enabling the user to process the whole index in one go if possible?
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} | 18 | 2015-01-30T11:13:27Z | 2015-02-01T14:58:46Z | 2015-02-01T14:58:46Z | CONTRIBUTOR | null | closes #9376
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| null | 17 | 2015-01-30T15:52:28Z | 2019-12-22T16:56:23Z | 2019-12-22T16:56:23Z | NONE | null | Hi,
I've recently written a tiny python package for loading/saving ROOT files as pandas DataFrames: [root_pandas](https://github.com/ibab/root_pandas).
ROOT is the main data format used by particle physicists.
Do you think it might be worth adding `read_root` and `to_root` functions to pandas itself?
This could convince a lot of physicists to give pandas a try.
The dependencies it would add could be handled like it's currently done with hdf5.
ROOT performs well in comparison with hdf5 and could be a useful addition to pandas, even ignoring the fact that it is very popular in physics.
If there's interest, I would polish my code and create a pull request.
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} | 5 | 2015-01-30T18:31:55Z | 2019-10-21T19:34:16Z | null | NONE | null | How can I read back in dataframes from CSV files which I export using `to_csv()` that have complex numbers?
test case:
```
data = pd.DataFrame([1+2j,2+3j,3+4j],columns=['a'])
print 'a='
print data['a']
print 'a*2='
print data['a']*2
filename = 'testcase1.csv'
data.to_csv(filename)
print "\nReadback..."
data2 = pd.read_csv(filename)
print data2['a']
print data2['a']*2
```
output:
```
a=
0 (1+2j)
1 (2+3j)
2 (3+4j)
Name: a, dtype: complex128
a*2=
0 (2+4j)
1 (4+6j)
2 (6+8j)
Name: a, dtype: complex128
Readback...
0 (1+2j)
1 (2+3j)
2 (3+4j)
Name: a, dtype: object
0 (1+2j)(1+2j)
1 (2+3j)(2+3j)
2 (3+4j)(3+4j)
Name: a, dtype: object
```
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} | 1 | 2015-01-31T01:34:08Z | 2015-02-07T13:07:06Z | 2015-01-31T16:23:42Z | CONTRIBUTOR | null | closes https://github.com/pydata/pandas/issues/9096
also improves performance when there is `int64` overflow; complete groupby benchmarks [here](https://gist.github.com/behzadnouri/e142fbd65f41357a7360).
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
groupby_int64_overflow | 637.6967 | 3021.1190 | 0.2111 |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [d6e3cb7] : bug in groupby when key space exceeds int64 bounds
Base [391f46a] : BUG: allow for empty SparseSeries SparsePanel constructors (GH9272)
```
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| null | 2 | 2015-01-31T10:15:49Z | 2017-10-23T13:12:11Z | 2015-01-31T16:13:55Z | NONE | null | The code is like below, which i thought it should work, but it doesn't, it raise an exception saying `Data must be 1-dimensional`. Is this by design or an defect? if by design how should i walk around this? pandas version is 0.15.1
``` py
dt_type = np.dtype([
('k', 'i4'),
('v', 'i4', 2)
])
dt = np.array([(123457803, (1, 2))], dtype=dt_type)
print(dt)
df = pd.DataFrame.from_records(dt)
print(df)
```
``` exception
File "/home/aulphar/Documents/dev/proj/FIS/tools/data_exportor.py", line 90, in main
df = pd.DataFrame.from_records(dt)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py", line 877, in from_records
columns)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py", line 4684, in _arrays_to_mgr
arrays = _homogenize(arrays, index, dtype)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py", line 4998, in _homogenize
raise_cast_failure=False)
File "/usr/local/lib/python3.4/dist-packages/pandas/core/series.py", line 2684, in _sanitize_array
raise Exception('Data must be 1-dimensional')
Exception: Data must be 1-dimensional
```
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https://api.github.com/repos/pandas-dev/pandas/issues/9382 | https://api.github.com/repos/pandas-dev/pandas | https://api.github.com/repos/pandas-dev/pandas/issues/9382/labels{/name} | https://api.github.com/repos/pandas-dev/pandas/issues/9382/comments | https://api.github.com/repos/pandas-dev/pandas/issues/9382/events | https://github.com/pandas-dev/pandas/issues/9382 | 56,137,026 | MDU6SXNzdWU1NjEzNzAyNg== | 9,382 | Don't make dropping missing rows a default behavior for HDF append()? | {
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} | 8 | 2015-01-31T19:55:54Z | 2015-07-31T22:37:48Z | 2015-07-31T22:37:48Z | CONTRIBUTOR | null | Hi All,
At the moment, the default behavior for the HDF append() function ( docs: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.HDFStore.append.html?highlight=append#pandas.HDFStore.append ) is to silently drop all rows that are all NaN except for the index.
As I understand it from a PyData exchange with Jeff, the reason is that people working with panels often have sparse datasets, so this is a very reasonable default.
However, while I appreciate the appeal for time-series analysis, I think this is a dangerous default. The main reason is that the assumption is that if an index has a value but the columns do not, there is no meaningful data in the row. But while true in a time series context -- where it's easy to reconstruct the index values that are dropped -- if indexes contain information like userIDs, sensor codes, place names, etc., the index itself is meaningful, and not easy to reconstruct. Thus the default behavior is potentially deleting user data without a warning.
Given the trade-off between a default that may lead to inefficient storage (dropna = False) and one that potentially erases user data (dropna = True), I think we should error on the side of data preservation.
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} | 2 | 2015-01-31T23:02:42Z | 2015-02-02T22:11:59Z | 2015-02-02T22:11:48Z | MEMBER | null | Follow-up #9318 (and that one from #9257).
whatsnew v0.15.0 was giving some errors now, so made it into a code-block
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| null | 0 | 2015-02-01T00:22:14Z | 2015-02-03T22:44:59Z | 2015-02-03T22:44:59Z | CONTRIBUTOR | null | When I run example/finance.py from current master I see the below error.
```
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/finance.py:485: MatplotlibDeprecationWarning: This function has been deprecated in 1.4 in favor of `quotes_historical_yahoo_ochl`, which maintains the original argument order, or `quotes_historical_yahoo_ohlc`, which uses the open-high-low-close order. This function will be removed in 1.5
mplDeprecation)
Traceback (most recent call last):
File "examples/finance.py", line 38, in <module>
goog = getQuotes('GOOG', startDate, endDate)
File "examples/finance.py", line 22, in getQuotes
quotes = fin.quotes_historical_yahoo(symbol, start, end)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/finance.py", line 489, in quotes_historical_yahoo
ochl=True)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/finance.py", line 621, in _quotes_historical_yahoo
fh = fetch_historical_yahoo(ticker, date1, date2, cachename)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/matplotlib/finance.py", line 431, in fetch_historical_yahoo
with contextlib.closing(urlopen(url)) as urlfh:
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 154, in urlopen
return opener.open(url, data, timeout)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 437, in open
response = meth(req, response)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 550, in http_response
'http', request, response, code, msg, hdrs)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 475, in error
return self._call_chain(*args)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 409, in _call_chain
result = func(*args)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/urllib2.py", line 558, in http_error_default
raise HTTPError(req.get_full_url(), code, msg, hdrs, fp)
urllib2.HTTPError: HTTP Error 404: Not Found
```
Here is `pd.show_versions()` output
```
In [2]: pd.show_versions()
INSTALLED VERSIONS
------------------
commit: 9f439f0e53409fd48e5486a3acf84b844f200270
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 14.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
pandas: 0.15.2-118-g6ba5846
nose: 1.3.4
Cython: 0.21.2
numpy: 1.9.1
scipy: 0.14.1
statsmodels: 0.6.1
IPython: 2.3.1
sphinx: 1.2.3
patsy: None
dateutil: 2.4.0
pytz: 2014.10
bottleneck: 0.8.0
tables: None
numexpr: 2.4
matplotlib: 1.4.2
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
rpy2: 2.4.2
sqlalchemy: 0.9.8
pymysql: None
psycopg2: None
```
Updating the stock symbol GOOG to GOOGL fixes this apparently.
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| null | 1 | 2015-02-01T00:24:16Z | 2015-02-09T06:39:09Z | 2015-02-01T18:22:46Z | CONTRIBUTOR | null | This fixes the HTTP Error 404 thrown by examples/finance.py
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} | 1 | 2015-02-01T10:40:53Z | 2015-02-05T13:39:22Z | 2015-02-05T11:28:38Z | MEMBER | null | - Move `Ticks` related tests to under `TestTicks`.
- Added some comprehensive test cases.
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| null | 3 | 2015-02-01T16:08:00Z | 2015-02-03T22:44:53Z | 2015-02-03T22:44:53Z | CONTRIBUTOR | null | When I try to run `python example/regressions.py` it throws this error
```
Traceback (most recent call last):
File "examples/regressions.py", line 12, in <module>
dateRange = DatetimeIndex(start, periods=N)
File "/Users/vyomkesh/code/pandas/pandas/util/decorators.py", line 88, in wrapper
return func(*args, **kwargs)
File "/Users/vyomkesh/code/pandas/pandas/tseries/index.py", line 240, in __new__
data = list(data)
TypeError: 'datetime.datetime' object is not iterable
```
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| null | 0 | 2015-02-01T16:11:59Z | 2015-02-09T06:39:19Z | 2015-02-01T18:22:24Z | CONTRIBUTOR | null | closes #9389
Looks like using the date_range method fixes the error.
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} | 4 | 2015-02-01T16:51:51Z | 2015-02-03T22:44:34Z | 2015-02-03T22:44:34Z | MEMBER | null | @tvyomkesh opened some issues (#9384, #9389) on errors in the examples (and some PRs to fix this). This raised the following question to me:
Apart from fixing the examples, I think we should think for a moment what we want to do with these examples. Why do we want them? What is their function?
And depending on that, do we want to update/extend them? Or maybe move them to the docs?
I personally never looked at these examples. And they are also quite outdated. Eg using the ols from pandas is somewhat (unofficially) deprecated for the use of statsmodels, and the finance module in matplotlib is deprecated starting from matplotlib 1.4. And to get such data, I think pandas-datareader should be used.
@jreback @TomAugspurger @shoyer
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} | 1 | 2015-02-01T18:28:18Z | 2015-02-05T16:52:20Z | 2015-02-03T22:44:34Z | CONTRIBUTOR | null | closes #9391
Deleting examples dir as this should be part of documentation
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} | 10 | 2015-02-01T19:49:18Z | 2015-02-08T11:26:44Z | 2015-02-08T11:26:44Z | CONTRIBUTOR | null | I have table names that include `_`, `.` and `-`, up until Pandas .14 I used to quote the table name like this:
``` python
def quote_identifier(s, errors="ignore"):
encodable = s.encode("utf-8", errors).decode("utf-8")
nul_index = encodable.find("\x00")
if nul_index >= 0:
error = UnicodeEncodeError("NUL-terminated utf-8", encodable,
nul_index, nul_index + 1, "NUL not allowed")
error_handler = codecs.lookup_error(errors)
replacement, _ = error_handler(error)
encodable = encodable.replace("\x00", replacement)
return "\"" + encodable.replace('"', '""') + "\""
```
then call:
``` python
pd_sql.to_sql(group, quote_identifier(name), con, if_exists='replace')
```
But since 0.15 if I do not quote `CREATE TABLE` (or `DROP TABLE`) will give me an error:
``` text
OperationalError: near "-": syntax error
```
And if I do quote `CREATE INDEX` will give me an error:
``` text
OperationalError: near ""_b.m_test_01-30"": syntax error
```
The problem is that in `CREATE INDEX ix_{tbl}_{cnames} ON {tbl} ({cnames_br})` CREATE INDEX thinks it is the only one trying to prepend a namespace with `_` and thus does not check if `{tbl}` is already quoted.
The proper fix should do what `quote_identifier` does with `"` and escape them, or just use `quote_identifier` and then prepend `ix_`.
This is the query that it tries and gives error:
``` sql
CREATE INDEX ix_"_b.m_test_01-30"_index ON "_b.m_test_01-30" ([index])
```
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| null | 7 | 2015-02-02T05:01:39Z | 2015-02-03T03:29:20Z | 2015-02-03T03:11:40Z | NONE | null | Simply importing pandas creates a huge number of threads:
```
$ gdb python
GNU gdb (Ubuntu 7.7.1-0ubuntu5~14.04.2) 7.7.1
Copyright (C) 2014 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type "show copying"
and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<http://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
<http://www.gnu.org/software/gdb/documentation/>.
For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from python...Reading symbols from /usr/lib/debug//usr/bin/python2.7...done.
done.
(gdb) run
Starting program: /usr/bin/python
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
[New Thread 0x7ffff42b6700 (LWP 19093)]
[New Thread 0x7ffff3ab5700 (LWP 19094)]
[New Thread 0x7ffff12b4700 (LWP 19095)]
[New Thread 0x7fffeeab3700 (LWP 19096)]
[New Thread 0x7fffec2b2700 (LWP 19097)]
[New Thread 0x7fffe9ab1700 (LWP 19098)]
[New Thread 0x7fffe72b0700 (LWP 19099)]
[Thread 0x7ffff42b6700 (LWP 19093) exited]
[Thread 0x7fffe72b0700 (LWP 19099) exited]
[Thread 0x7ffff3ab5700 (LWP 19094) exited]
[Thread 0x7fffec2b2700 (LWP 19097) exited]
[Thread 0x7ffff12b4700 (LWP 19095) exited]
[Thread 0x7fffe9ab1700 (LWP 19098) exited]
[Thread 0x7fffeeab3700 (LWP 19096) exited]
[New Thread 0x7fffe72b0700 (LWP 19103)]
[New Thread 0x7fffe9ab1700 (LWP 19104)]
[New Thread 0x7fffec2b2700 (LWP 19105)]
[New Thread 0x7fffeeab3700 (LWP 19106)]
[New Thread 0x7ffff3cd1700 (LWP 19107)]
[New Thread 0x7ffff12b4700 (LWP 19108)]
[New Thread 0x7fffde6e8700 (LWP 19109)]
[New Thread 0x7fffddee7700 (LWP 19110)]
[New Thread 0x7fffdac85700 (LWP 19111)]
[New Thread 0x7fffda484700 (LWP 19112)]
[New Thread 0x7fffd9c83700 (LWP 19113)]
[New Thread 0x7fffd9482700 (LWP 19114)]
[New Thread 0x7fffd8c81700 (LWP 19115)]
[New Thread 0x7fffd8480700 (LWP 19116)]
[New Thread 0x7fffd7c7f700 (LWP 19117)]
[New Thread 0x7fffd747e700 (LWP 19118)]
>>>
```
Setting OMP_NUM_THREADS=1 and NUMEXPR_NUM_THREADS=1 reduces the number of threads created, but it looks like a bunch of blosc threads are still being created.
More seriously, simply importing a small pandas component also creates a ton of threads:
```
$ export OMP_NUM_THREADS=1
$ export NUMEXPR_NUM_THREADS=1
$ gdb python
GNU gdb (Ubuntu 7.7.1-0ubuntu5~14.04.2) 7.7.1
Copyright (C) 2014 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type "show copying"
and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<http://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
<http://www.gnu.org/software/gdb/documentation/>.
For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from python...Reading symbols from /usr/lib/debug//usr/bin/python2.7...done.
done.
(gdb) run
Starting program: /usr/bin/python
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
Python 2.7.6 (default, Mar 22 2014, 22:59:56)
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from pandas.tslib import Timestamp
[New Thread 0x7fffecca9700 (LWP 19238)]
[New Thread 0x7fffec4a8700 (LWP 19239)]
[New Thread 0x7fffebca7700 (LWP 19240)]
[New Thread 0x7fffeb4a6700 (LWP 19241)]
[New Thread 0x7fffeaca5700 (LWP 19242)]
[New Thread 0x7fffea4a4700 (LWP 19243)]
[New Thread 0x7fffe9ca3700 (LWP 19244)]
[New Thread 0x7fffe94a2700 (LWP 19245)]
```
This is kind of wasteful, and it makes it difficult to optimize thread handling on multicore systems using thread pinning or other scheduling techniques.
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} | 9 | 2015-02-02T16:50:50Z | 2015-07-03T09:06:00Z | 2015-07-02T19:09:35Z | CONTRIBUTOR | null | - uninitialized pointer
- unused variables
- unused function
- ISO C prototypes
- `cimport *`
- useless cast before `snprintf`
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} | 1 | 2015-02-02T18:41:55Z | 2016-12-18T20:22:25Z | 2016-12-18T20:22:25Z | CONTRIBUTOR | null | Not sure if this is a bug or not.
``` python
import datetime
In [24]: s = pd.Series(pd.to_datetime(['2015-01-01', '2015-02-02']))
In [25]: s
Out[25]:
0 2015-01-01
1 2015-02-02
dtype: datetime64[ns]
In [26]: s.diff() / datetime.timedelta(1)
Out[26]:
0 NaN
1 32
dtype: float64
In [27]: s.diff().div(datetime.timedelta(1))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-27-1197779902ae> in <module>()
----> 1 s.diff().div(datetime.timedelta(1))
C:\Miniconda\lib\site-packages\pandas\core\ops.pyc in flex_wrapper(self, other,
level, fill_value, axis)
716 level=level, fill_value=fill_value)
717 else:
--> 718 return self._constructor(op(self.values, other),
719 self.index).__finalize__(self)
720
TypeError: ufunc true_divide cannot use operands with types dtype('<m8[ns]') and
dtype('O')
```
Works with our Timedelta though, as suggested by the error message:
``` python
In [28]: s.diff().div(pd.Timedelta(1, unit='D'))
Out[28]:
0 NaN
1 32
dtype: float64
```
It at least seems strange that `/` worked while `.div` didn't.
This is the released 0.15.2, and python 2.
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} | 7 | 2015-02-02T21:22:23Z | 2021-04-12T05:06:24Z | null | MEMBER | null | For visualization/testing purposes, I'm often interested in looking at the first example group(s) from a groupby operation.
Is there a convenient shortcut for this? The best I could come up with is `pd.concat([x for _, x in itertools.islice(group, 3)])` which seemed awkward to me.
Note that this is a _different_ use-case from `.first()`/`.head()`, which returns the first example from each group -- here I want full examples of the first few groups.
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} | 7 | 2015-02-03T00:12:12Z | 2015-03-08T22:34:02Z | 2015-03-03T00:49:35Z | CONTRIBUTOR | null | on master:
```
In [1]: np.random.seed(2718281)
In [2]: n = 1 << 20
In [3]: t = pd.date_range('2015-01-01', freq='S', periods=n // 64)
In [4]: xs = np.random.randn(n // 64).round(2)
In [5]: df = DataFrame({'a':np.random.randint(- 1 << 8, 1 << 8, n),
...: 'b':np.random.choice(t, n),
...: 'c':np.random.choice(xs, n)})
In [6]: %timeit df.duplicated()
1 loops, best of 3: 8.03 s per loop
In [7]: %memit df.duplicated()
peak memory: 461.79 MiB, increment: 356.10 MiB
```
on branch:
```
In [6]: %timeit df.duplicated()
1 loops, best of 3: 259 ms per loop
In [7]: %memit df.duplicated()
peak memory: 154.62 MiB, increment: 49.36 MiB
```
benchmarks:
```
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
frame_duplicated | 308.4060 | 7809.8300 | 0.0395 |
frame_drop_duplicates | 16.1637 | 28.8030 | 0.5612 |
frame_drop_duplicates_na | 17.1696 | 28.3937 | 0.6047 |
multiindex_duplicated | 136.1593 | 141.6107 | 0.9615 |
series_drop_duplicates_int | 1.1793 | 1.1443 | 1.0306 |
series_drop_duplicates_string | 0.7900 | 0.7540 | 1.0479 |
-------------------------------------------------------------------------------
Test name | head[ms] | base[ms] | ratio |
-------------------------------------------------------------------------------
Ratio < 1.0 means the target commit is faster then the baseline.
Seed used: 1234
Target [76459d2] : performance improvement in DataFrame.duplicated
Base [ef48c6f] : Merge pull request #9377 from cmeeren/patch-1
DOC: Clarify how date_parser is called (GH9376)
```
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} | 3 | 2015-02-03T02:43:17Z | 2021-04-12T05:05:32Z | null | CONTRIBUTOR | null | The following two tests seem inconsistent. Why does the first test for a `KeyError` while the second tests for a `ValueError`? Also, the first error message, `Level foo not found`, doesn't seem correct.
`test_index.py`:
```
def test_duplicate_names(self):
self.index.names = ['foo', 'foo']
assertRaisesRegexp(KeyError, 'Level foo not found',
self.index._get_level_number, 'foo')
```
`test_frame.py`:
```
def test_unstack_non_unique_index_names(self):
idx = MultiIndex.from_tuples([('a', 'b'), ('c', 'd')],
names=['c1', 'c1'])
df = DataFrame([1, 2], index=idx)
with tm.assertRaises(ValueError):
df.unstack('c1')
with tm.assertRaises(ValueError):
df.T.stack('c1')
```
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} | 2 | 2015-02-03T13:20:06Z | 2015-02-08T13:35:47Z | 2015-02-08T13:35:47Z | CONTRIBUTOR | null | This a minor enhancement proposal. At the moment I cannot submit a pull request. I will probably have time to create one during the next week.
This is a snippet from `tools/plotting.py`: https://github.com/pydata/pandas/blob/master/pandas/tools/plotting.py#L2269-2283
``` python
def _plot(data, x=None, y=None, subplots=False,
ax=None, kind='line', **kwds):
kind = _get_standard_kind(kind.lower().strip())
if kind in _all_kinds:
klass = _plot_klass[kind]
else:
raise ValueError('Invalid chart type given %s' % kind)
from pandas import DataFrame
if kind in _dataframe_kinds:
if isinstance(data, DataFrame):
plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax,
kind=kind, **kwds)
else:
raise ValueError('Invalid chart type given %s' % kind)
```
Which results in following error message:
```
C:\Anaconda3\lib\site-packages\pandas\tools\plotting.py in plot_series(series, label, kind, use_index, rot, xticks, yticks, xlim, ylim, ax, style, grid, legend, logx, logy, secondary_y, **kwds)
2231 klass = _plot_klass[kind]
2232 else:
-> 2233 raise ValueError('Invalid chart type given %s' % kind)
2234
2235 """
ValueError: Invalid chart type given hist
```
I would suggest using the format string `"Invalid chart type given: '%s'"` instead.
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} | 6 | 2015-02-03T13:41:38Z | 2015-02-15T01:33:36Z | 2015-02-11T22:58:13Z | MEMBER | null | Added procedure to `TestPickle`. Based on #9291, I think updating `setup.py` is likely to be ommitted.
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