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9,302
Plotting secondary axis
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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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9,303
BUG 9188: concat of all-nan with empty frame produces object dtype
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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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9,304
additional keys in groupby indices when NAs are present
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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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9,305
RFE: support dateutil 2.x
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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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9,306
BUG: API Change with skiprows from v0.14.1 to v0.15.2
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2015-01-23T00:32:57Z
2015-01-23T00:32:57Z
NONE
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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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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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9,308
BUG: 0/frame numeric ops buggy (GH9144)
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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 | 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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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MDU6SXNzdWU1NDg5Mzg4MA==
9,309
BUG: Timedelta repr does not show nanoseconds
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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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9,310
DOC: update install.rst, required version of dateutil is 1.5 or higher G...
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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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BUG: first() changes datetime64 data
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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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54,913,681
MDU6SXNzdWU1NDkxMzY4MQ==
9,312
BUG date_range for Annual interval gives end of year instead of start
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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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9,313
ENH date_range yearly frequency flag 'A' is confusing, 'Y' should be accepted
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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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ENH date_range should accept dateutil frequency constants
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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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bug: read_csv and german umlaut (ä,ö,ü) in filename
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2015-01-20T18:57:25Z
2015-02-11T00:05:33Z
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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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MDExOlB1bGxSZXF1ZXN0Mjc3MDUyNzg=
9,316
Doc: API docstrings for indexers (GH6920)
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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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9,317
_metadata has bizzare behaviour in python 3.3
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2015-01-20T20:00:53Z
2015-01-22T11:57:55Z
2015-01-22T11:57:55Z
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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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DOC: delete removed Timedelta properties (see GH9257) from API overview
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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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9,319
df[column].dtype in {int, float} does not evaluate correctly
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1
2015-01-21T01:03:21Z
2015-01-21T02:56:24Z
2015-01-21T02:56:24Z
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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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DataFrame.to_hdf append feature does behave properly
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2015-01-21T05:44:25Z
2017-06-08T08:26:21Z
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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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ENH: plot method accessors
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MEMBER
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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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54,988,082
MDExOlB1bGxSZXF1ZXN0Mjc3NDEwMDY=
9,322
ENH/DOC: reimplement Series delegates/accessors using descriptors
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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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MDU6SXNzdWU1NDk5MDM4Ng==
9,323
_repr_html_ returns None
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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: ![image](https://cloud.githubusercontent.com/assets/10627907/5833104/1239147c-a147-11e4-8991-4f363490ec35.png) On 0.14.1, running the above I get `0.14.1, False` and `df` results in this: ![image](https://cloud.githubusercontent.com/assets/10627907/5833089/d315dbfe-a146-11e4-8197-95b37af9add9.png)
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55,006,442
MDU6SXNzdWU1NTAwNjQ0Mg==
9,324
Panel to_frame should have which axis are columns and index in the new DF
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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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9,325
Bug in indexing time series with datetime object?
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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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55,029,013
MDU6SXNzdWU1NTAyOTAxMw==
9,326
Performance regression in .ffill()
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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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9,327
read_hdf does not work with auto_close argument
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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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9,328
OMP: Error #134: Cannot set thread affinity mask.
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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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9,329
Partial indexing of MultiIndex fails with empty tuple
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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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9,330
False negative on .equals() after read_hdf()
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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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9,331
BUG: don't sort unique values from categoricals
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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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MDExOlB1bGxSZXF1ZXN0Mjc4MjA5MDM=
9,332
FIX: Fixed some issues around vb_suite
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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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9,333
Fix docs in groupby.tail
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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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55,160,561
MDU6SXNzdWU1NTE2MDU2MQ==
9,334
Windows wheel package (.whl) on Pypi
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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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9,335
Fix missing text on Index documentation page.
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2015-01-22T15:16:03Z
2015-03-05T23:29:47Z
2015-03-05T23:29:43Z
CONTRIBUTOR
null
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9,336
Problem mixing datetime64 with other types in DataFrame column
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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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MDU6SXNzdWU1NTE3MDA3Mw==
9,337
Problem indexing into first 2-levels of 3-level Index
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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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MDExOlB1bGxSZXF1ZXN0Mjc4NjU2NjE=
9,338
GH 9016: Bitwise operation weirdness
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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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9,339
Add lag parameter to autocorrelation
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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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9,340
API: Interpolate at new values
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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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9,341
to_datetime does not handle timezones correctly for timestamps
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2015-01-22T23:09:06Z
2015-01-22T23:17:37Z
2015-01-22T23:16:44Z
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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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9,342
convert_objects(convert_dates='coerce') is not coercing to date
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2015-01-22T23:48:22Z
2015-01-22T23:51:45Z
2015-01-22T23:51:22Z
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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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9,343
Series.str should only be defined for strings, not all Series with dtype=object
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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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9,344
Groupby behaves differently when using levels and list of column keys
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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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9,345
BUG: Fixes GH9311 groupby on datetime64
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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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9,346
DOC/TST: is pd.unique and the order it returns API?
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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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55,338,519
MDExOlB1bGxSZXF1ZXN0Mjc5NTg5MjQ=
9,347
Categorical: don't sort the categoricals if Categorical(..., ordered=False)
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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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9,348
Fix gbq client to only return results when jobCompleted is True.
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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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9,349
pd.to_timedelta returns Timedelta(0)
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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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https://api.github.com/repos/pandas-dev/pandas/issues/9350
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55,385,082
MDExOlB1bGxSZXF1ZXN0Mjc5ODI0NTQ=
9,350
Changed uin8 to uint8 in response to issue #9266
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14
2015-01-24T21:16:23Z
2015-07-15T12:42:57Z
2015-07-15T12:42:57Z
NONE
null
closes #9266
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MDU6SXNzdWU1NTM4NzQyMA==
9,351
DataFrame.hist() does not get along with matplotlib.pyplot.tight_layout()
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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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9,352
ENH: StringMethods now supports ljust and rjust
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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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9,353
to_timedelta returns an object of different type depending of Python version
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5
2015-01-25T20:42:01Z
2015-01-25T20:51:37Z
2015-01-25T20:50:10Z
NONE
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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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9,354
PERF: nunique perf can be improved by using len(unique) rather than value_counts
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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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9,355
Value counts base
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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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9,356
Add various display types for pivot table such as Excel
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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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Deprecating the trellis rplot module (GH3445)
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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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CLN: Bring pandas up to date with pandas-datareader
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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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FEATURE: Load recursive dictionaries
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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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MDExOlB1bGxSZXF1ZXN0MjgwOTA2NjA=
9,360
BUG: Fix buffer overflows in tokenizer.c that caused python to segfault with certain
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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
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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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9,362
TST add a test for repeat() method with MultiIndex, referenced in #9361
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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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55,644,496
MDU6SXNzdWU1NTY0NDQ5Ng==
9,363
assignment with dataframe.loc fails (in some cases)
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1
2015-01-27T17:01:43Z
2015-01-27T17:14:39Z
2015-01-27T17:14:30Z
NONE
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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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PERF: nunique perf improved by using len(unique) rather than value_counts
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6
2015-01-27T21:47:57Z
2015-01-28T11:08:35Z
2015-01-28T06:25:53Z
NONE
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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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9,365
Use KDTrees to support nearest neighbor queries/joins on MultiIndexes?
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2015-01-28T07:49:55Z
2021-04-12T05:03:49Z
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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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MDU6SXNzdWU1NTc4NTc1OA==
9,366
DataFrame modification operations should return number of rows modified
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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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9,367
Natural name convention broken for "size" in non-interactive mode.
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2015-01-28T20:51:40Z
2015-01-28T22:34:46Z
2015-01-28T21:11:05Z
NONE
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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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9,368
Multiplying Together Series with MultiIndexes
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2015-01-29T01:37:25Z
2015-01-30T13:39:00Z
2015-01-30T13:38:42Z
NONE
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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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9,369
BUG: binary comparison of numpy.int/float and Series
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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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9,370
Support label based slicing even for unsorted indexes?
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2015-01-29T06:34:31Z
2015-01-29T10:08:49Z
2015-01-29T10:08:49Z
MEMBER
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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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9,371
Performance degradation in __setitem__ from 0.12 to 0.15.2
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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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55,949,113
MDU6SXNzdWU1NTk0OTExMw==
9,372
to_excel doesn't support dateutil.relativedelta
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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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9,373
Errors while unstacking the DataFrame with MultiIndex
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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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9,374
BUG: inserting a Categorical with the wrong length into a DataFrame is allowed
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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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9,375
Reading Panel4D stored in HDF5
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2015-01-30T04:09:19Z
2015-01-31T11:26:26Z
2015-01-30T13:26:23Z
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``` 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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read_csv: date_parser called once with arrays and then many times with strings (from each single row) as arguments
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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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9,377
DOC: Clarify how date_parser is called (GH9376)
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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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9,378
New data format: ROOT files
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17
2015-01-30T15:52:28Z
2019-12-22T16:56:23Z
2019-12-22T16:56:23Z
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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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9,379
pandas.read_csv() won't read back in complex number dtypes from pandas.DataFrame.to_csv()
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5
2015-01-30T18:31:55Z
2019-10-21T19:34:16Z
null
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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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9,380
bug in groupby when key space exceeds int64 bounds
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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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56,120,358
MDU6SXNzdWU1NjEyMDM1OA==
9,381
DataFrame.from_records do not support record have sub array?
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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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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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9,383
DOC: further clean-up of removed timedelta attributes in whatsnew docs
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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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9,384
example/finance.py throws error
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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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9,385
GH 9384: example/finance.py throws error
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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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ENH: Add StringMethod.find and rfind
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9
2015-02-01T10:13:26Z
2015-03-31T13:31:20Z
2015-02-16T13:39:17Z
MEMBER
null
Derived from #9111.
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2015-02-01T10:31:06Z
2015-02-04T13:26:40Z
2015-02-04T13:18:33Z
MEMBER
null
Derived from #9111. Fixed docstring of `str_pad` which is inprecise.
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TST: Cleanup offsets.Tick tests
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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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example/regressions.py throws error
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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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GH 9389: fix for example/regressions.py error
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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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9,391
Status of the examples
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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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9,392
Gh 9391: delete examples folder
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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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9,393
to_sql double quoting in create index
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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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9,394
Pandas creates a large number of unnecessary threads
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7
2015-02-02T05:01:39Z
2015-02-03T03:29:20Z
2015-02-03T03:11:40Z
NONE
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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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9,395
MAINT: get rid of some compiler warnings
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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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9,396
BUG?: div method with timedelta
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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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9,397
Shortcut for getting examples of the first few groups from a GroupBy?
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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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PERF: improves performance and memory usage of DataFrame.duplicated
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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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BUG: Exception raised for a duplicate MultiIndex level name
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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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MDU6SXNzdWU1NjM3NjM2Ng==
9,400
Improve error message in plotting.py's _plot
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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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TST/DOC: Add procedure for TestPickle
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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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