standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures get_option('mode.sim_interactive') Out[11]: False In [12]: pd.set_option('mode.sim_interactive', True) In [13]: pd.get_option('mode.sim_interactive') Out[13]: True Note: that the option ‘mode.sim_interactive’ is mostly

serialization, orient='table', that uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here • Experimental support for exporting styled DataFrames standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures

serialization, orient='table', that uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here • Experimental support for exporting styled DataFrames standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures

standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures get_option(’mode.sim_interactive’) Out[11]: False In [12]: pd.set_option(’mode.sim_interactive’, True) In [13]: pd.get_option(’mode.sim_interactive’) Out[13]: True Note: that the option ‘mode.sim_interactive’ is mostly

standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures get_option(’mode.sim_interactive’) Out[11]: False In [12]: pd.set_option(’mode.sim_interactive’, True) In [13]: pd.get_option(’mode.sim_interactive’) Out[13]: True Note: that the option ‘mode.sim_interactive’ is mostly

serialization, orient='table', that uses the Table Schema spec and that gives the possibility for a more interactive repr in the Jupyter Notebook, see here 36 Chapter 1. What’s New pandas: powerful Python data standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures

standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures get_option('mode.sim_interactive') Out[11]: False In [12]: pd.set_option('mode.sim_interactive', True) In [13]: pd.get_option('mode.sim_interactive') Out[13]: True Note: that the option ‘mode.sim_interactive’ is mostly

standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures get_option('mode.sim_interactive') Out[11]: False In [12]: pd.set_option('mode.sim_interactive', True) In [13]: pd.get_option('mode.sim_interactive') Out[13]: True Note: that the option ‘mode.sim_interactive’ is mostly

standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures use chained assignment, The default is warn mode.sim_interactive : [default: False] [currently: False]: boolean Whether to simulate interactive mode for purposes of testing mode.use_inf_as_null : [default:

standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc write code without doing any explicit data alignment grants immense freedom and flexibility in interactive data analysis and research. The integrated data alignment features of the pandas data structures df.head(), which displays the first N (default 5) rows of the DataFrame. This is often used in interactive work (e.g. Jupyter notebook or terminal) - the equivalent in SAS would be: 3.5. Comparison with