Multiple regression (OLS-based) on panel data including with fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols function effect (intercept): In [219]: fe_model = ols(y=volume, x={’return’ : np.abs(rets)}, .....: entity_effects=True) --------------------------------------------------------------------------- NameError Traceback 2b0e6684> in () ----> 1 fe_model = ols(y=volume, x={’return’ : np.abs(rets)}, 2 entity_effects=True) NameError: name ’volume’ is not defined In [220]: fe_model -----------------------------

Multiple regression (OLS-based) on panel data including with fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols function effect (intercept): In [219]: fe_model = ols(y=volume, x={’return’ : np.abs(rets)}, .....: entity_effects=True) In [220]: fe_model Out[220]: -------------------------Summary of Regression Analysis----- toolkit, Release 0.7.1 In [221]: fe_model = ols(y=volume, x={’return’ : np.abs(rets)}, .....: entity_effects=True, intercept=False) In [222]: fe_model Out[222]: -------------------------Summary of Regression

Multiple regression (OLS-based) on panel data including with fixed-effects (also known as entity or individual effects) or time-effects. Both kinds of linear models are accessed through the ols function effect (intercept): In [219]: fe_model = ols(y=volume, x={’return’ : np.abs(rets)}, .....: entity_effects=True) In [220]: fe_model Out[220]: -------------------------Summary of Regression Analysis----- toolkit, Release 0.7.2 In [221]: fe_model = ols(y=volume, x={’return’ : np.abs(rets)}, .....: entity_effects=True, intercept=False) In [222]: fe_model Out[222]: -------------------------Summary of Regression

empty containers (GH5740) • df.to_csv will now return a string of the CSV data if neither a target path nor a buffer is provided (GH6061) • pd.infer_freq() will now raise a TypeError if given an invalid the docs. In [39]: path = ’test.h5’ In [40]: dfq = DataFrame(randn(10,4), ....: columns=list(’ABCD’), ....: index=date_range(’20130101’,periods=10)) ....: In [41]: dfq.to_hdf(path,’dfq’,format=’table’ data_columns=True) Use boolean expressions, with in-line function evaluation. In [42]: read_hdf(path,’dfq’, ....: where="index>Timestamp(’20130104’) & columns=[’A’, ’B’]") ....: Out[42]: A B 2013-01-05

dtypes Out[113]: female bool fitness int64 dtype: object • Series.to_csv() now returns a string when path=None, matching the behaviour of DataFrame.to_csv() (GH8215). • read_hdf now raises IOError when a truncated (GH7972). • Bug in groupby where callable objects without name attributes would take the wrong path, and produce a DataFrame instead of a Series (GH7929) • Bug in groupby error message when a DataFrame alignment with TimeOps and non-unique indexes (GH8363) • Bug in GroupBy.filter() where fast path vs. slow path made the filter return a non scalar value that appeared valid but wasn’t (GH7870). • Bug in

dtypes Out[113]: female bool fitness int64 dtype: object • Series.to_csv() now returns a string when path=None, matching the behaviour of DataFrame.to_csv() (GH8215). • read_hdf now raises IOError when a truncated (GH7972). • Bug in groupby where callable objects without name attributes would take the wrong path, and produce a DataFrame instead of a Series (GH7929) • Bug in groupby error message when a DataFrame alignment with TimeOps and non-unique indexes (GH8363) • Bug in GroupBy.filter() where fast path vs. slow path made the filter return a non scalar value that appeared valid but wasn’t (GH7870). • Bug in

returned. (GH9450) # Returns the 1st and 4th sheet, as a dictionary of DataFrames. pd.read_excel('path_to_file.xls',sheetname=['Sheet1',3]) • Allow Stata files to be read incrementally with an iterator; dtypes Out[108]: female bool fitness int64 dtype: object • Series.to_csv() now returns a string when path=None, matching the behaviour of DataFrame.to_csv() (GH8215). • read_hdf now raises IOError when a truncated (GH7972). • Bug in groupby where callable objects without name attributes would take the wrong path, and produce a DataFrame instead of a Series (GH7929) • Bug in groupby error message when a DataFrame

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 910 21.11.6 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 911 22 Visualization detecting the terminal size. This fix only applies to python 3 (GH16496) • Bug in using pathlib.Path or py.path.local objects with io functions (GH16291) • Bug in Index.symmetric_difference() on two equal In [21]: url = 'https://github.com/{repo}/raw/{branch}/{path}'.format( ....: repo = 'pandas-dev/pandas', ....: branch = 'master', ....: path = 'pandas/tests/io/parser/data/salaries.csv.bz2', ....:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821 22.11.6 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 822 23 Visualization raising a NonExistentTimeError (GH13057) • .to_hdf/read_hdf() now accept path objects (e.g. pathlib.Path, py.path.local) for the file path (GH11773) • The pd.read_csv() with engine='python' has gained support using standard operator like + or - is recommended, because standard operators use more efficient path (GH13980) • Bug in operations on NaT returning float instead of datetime64[ns] (GH12941) • Bug in

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823 22.11.6 Side Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 824 xv 23 raising a NonExistentTimeError (GH13057) • .to_hdf/read_hdf() now accept path objects (e.g. pathlib.Path, py.path.local) for the file path (GH11773) • The pd.read_csv() with engine='python' has gained support using standard operator like + or - is recommended, because standard operators use more efficient path (GH13980) • Bug in operations on NaT returning float instead of datetime64[ns] (GH12941) • Bug in