strftime * total_seconds – Period Frequency Enhancement – Support for SAS XPORT files – Support for Math Functions in .eval() – Changes to Excel with MultiIndex – Google BigQuery Enhancements – Display Alignment details. Support for Math Functions in .eval() eval() now supports calling math functions (GH4893) df = pd.DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin multi-index with xs, method #2 In [72]: index = list(itertools.product(['Ada','Quinn','Violet'],['Comp','Math','Sci'])) In [73]: headr = list(itertools.product(['Exams','Labs'],['I','II'])) In [74]: indx =

SAS XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Changes to Excel strftime * total_seconds – Period Frequency Enhancement – Support for SAS XPORT files – Support for Math Functions in .eval() – Changes to Excel with MultiIndex – Google BigQuery Enhancements – Display details. Support for Math Functions in .eval() eval() now supports calling math functions (GH4893) df = pd.DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin

SAS XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Changes to Excel strftime * total_seconds – Period Frequency Enhancement – Support for SAS XPORT files – Support for Math Functions in .eval() – Changes to Excel with MultiIndex – Google BigQuery Enhancements – Display chunksize=10000) do_something(df) See the docs for more details. Support for Math Functions in .eval() eval() now supports calling math functions (GH4893) 1.6. v0.17.0 (October 9, 2015) 103 pandas: powerful

SAS XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 1.10.1.7 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . 151 1.10.1.8 Changes to Excel strftime * total_seconds – Period Frequency Enhancement – Support for SAS XPORT files – Support for Math Functions in .eval() – Changes to Excel with MultiIndex – Google BigQuery Enhancements – Display 10.1.7 Support for Math Functions in .eval() eval() now supports calling math functions (GH4893) df = pd.DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin

SAS XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 1.9.1.7 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . 149 1.9.1.8 Changes to Excel strftime * total_seconds – Period Frequency Enhancement – Support for SAS XPORT files – Support for Math Functions in .eval() – Changes to Excel with MultiIndex – Google BigQuery Enhancements – Display 9.1.7 Support for Math Functions in .eval() eval() now supports calling math functions (GH4893) df = pd.DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin

SAS XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 1.12.1.7 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . 180 1.12.1.8 Changes to Excel strftime * total_seconds – Period Frequency Enhancement – Support for SAS XPORT files – Support for Math Functions in .eval() – Changes to Excel with MultiIndex – Google BigQuery Enhancements – Display 12.1.7 Support for Math Functions in .eval() eval() now supports calling math functions (GH4893) df = pd.DataFrame({'a': np.random.randn(10)}) df.eval("b = sin(a)") The support math functions are sin

multi-index with xs, method #2 In [72]: index = list(itertools.product([’Ada’,’Quinn’,’Violet’],[’Comp’,’Math’,’Sci’])) In [73]: headr = list(itertools.product([’Exams’,’Labs’],[’I’,’II’])) In [74]: indx = Release 0.15.2 Ada Comp 70 71 72 73 Math 71 73 75 74 Sci 72 75 75 75 Quinn Comp 73 74 75 76 Math 74 76 78 77 Sci 75 78 78 78 Violet Comp 76 77 78 79 Math 77 79 81 80 Sci 78 81 81 81 In [78]: 76 77 78 79 Math 77 79 81 80 Sci 78 81 81 81 In [80]: df.loc[(All,’Math’),All] Out[80]: Exams Labs I II I II Student Course Ada Math 71 73 75 74 Quinn Math 74 76 78 77 Violet Math 77 79 81 80

multi-index with xs, method #2 In [72]: index = list(itertools.product([’Ada’,’Quinn’,’Violet’],[’Comp’,’Math’,’Sci’])) In [73]: headr = list(itertools.product([’Exams’,’Labs’],[’I’,’II’])) In [74]: indx = Release 0.15.1 Ada Comp 70 71 72 73 Math 71 73 75 74 Sci 72 75 75 75 Quinn Comp 73 74 75 76 Math 74 76 78 77 Sci 75 78 78 78 Violet Comp 76 77 78 79 Math 77 79 81 80 Sci 78 81 81 81 In [78]: 76 77 78 79 Math 77 79 81 80 Sci 78 81 81 81 In [80]: df.loc[(All,’Math’),All] Out[80]: Exams Labs I II I II Student Course Ada Math 71 73 75 74 Quinn Math 74 76 78 77 Violet Math 77 79 81 80

api. tips.to_csv('tips2.csv') Data operations Operations on columns In the DATA step, arbitrary math expressions can be used on new or existing columns. data tips; set tips; total_bill = total_bill Release 1.1.1 tips.to_stata('tips2.dta') Data operations Operations on columns In Stata, arbitrary math expressions can be used with the generate and replace commands on new or existing columns. The drop high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance

api. tips.to_csv('tips2.csv') Data operations Operations on columns In the DATA step, arbitrary math expressions can be used on new or existing columns. data tips; set tips; total_bill = total_bill Release 1.1.0 tips.to_stata('tips2.dta') Data operations Operations on columns In Stata, arbitrary math expressions can be used with the generate and replace commands on new or existing columns. The drop high performance functions written directly in Python. With a few annotations, array-oriented and math-heavy Python code can be just-in-time compiled to native machine instructions, similar in performance