np.inf into a string representation, customizable by the inf_rep keyword argu- ment (Excel has no native inf representation) (GH6782) • Replace pandas.compat.scipy.scoreatpercentile with numpy.percentile pandas: powerful Python data analysis toolkit, Release 0.14.0 Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = ’my_nan_rep’ to append, to change the default are running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar

np.inf into a string representation, customizable by the inf_rep keyword argu- ment (Excel has no native inf representation) (GH6782) • Replace pandas.compat.scipy.scoreatpercentile with numpy.percentile pandas: powerful Python data analysis toolkit, Release 0.15.2 Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = ’my_nan_rep’ to append, to change the default are running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar

np.inf into a string representation, customizable by the inf_rep keyword argu- ment (Excel has no native inf representation) (GH6782) • Replace pandas.compat.scipy.scoreatpercentile with numpy.percentile pandas: powerful Python data analysis toolkit, Release 0.15.1 Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = ’my_nan_rep’ to append, to change the default are running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar

np.inf into a string representation, customizable by the inf_rep keyword argu- ment (Excel has no native inf representation) (GH6782) • Replace pandas.compat.scipy.scoreatpercentile with numpy.percentile 540770 -0.370038 1.298390 1.662964 bar [2 rows x 7 columns] Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = ’my_nan_rep’ to append, to change the default are running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar

to_datetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1585 pandas.Index.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1585 pandas.Index.to_series to_datetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1614 pandas.CategoricalIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1614 pandas.CategoricalIndex.to_series to_hierarchical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1643 pandas.MultiIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1644 pandas.MultiIndex.to_series

to_datetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1588 pandas.Index.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1588 pandas.Index.to_series to_datetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1618 pandas.CategoricalIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1618 pandas.CategoricalIndex.to_series to_hierarchical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1648 pandas.MultiIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1648 pandas.MultiIndex.to_series

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1753 34.6.1.106pandas.Index.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1753 34.6.1.107pandas.Index.to_series to_hierarchical . . . . . . . . . . . . . . . . . . . . . . . . . . 1801 34.10.1.125pandas.MultiIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . 1802 34.10.1.126pandas.MultiIndex.to_series to_julian_date . . . . . . . . . . . . . . . . . . . . . . . . . 1836 34.11.1.151pandas.DatetimeIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . 1836 34.11.1.152pandas.DatetimeIndex.to_period

to_datetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1659 34.6.1.104pandas.Index.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1659 34.6.1.105pandas.Index.to_series to_hierarchical . . . . . . . . . . . . . . . . . . . . . . . . . . 1699 34.9.1.124pandas.MultiIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . . . 1700 34.9.1.125pandas.MultiIndex.to_series to_julian_date . . . . . . . . . . . . . . . . . . . . . . . . . 1732 34.10.1.149pandas.DatetimeIndex.to_native_types . . . . . . . . . . . . . . . . . . . . . . . . 1732 34.10.1.150pandas.DatetimeIndex.to_period

302741 0.261551 -0.066342 0.897097 bar [2 rows x 7 columns] Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = ’my_nan_rep’ to append, to change the default are running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar np.array(list(range(10)), ’>i4’) # big endian In [28]: newx = x.byteswap().newbyteorder() # force native byteorder In [29]: s = Series(newx) See the NumPy documentation on byte order for more details

0.705209 0.487465 0.432875 1.248644 1.200735 1.645992 bar Enhancements • HDFStore now can read native PyTables table format tables • You can pass nan_rep = ’my_nan_rep’ to append, to change the default are running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar x = np.array(range(10), ’>i4’) # big endian In [38]: newx = x.byteswap().newbyteorder() # force native byteorder In [39]: s = Series(newx) See the NumPy documentation on byte order for more details