New APIs for accessing the array backing a Series or Index • A new top-level method for creating arrays • Store Interval and Period data in a Series or DataFrame • Support for joining on two MultiIndexes the returned value would be the actual array, some transformation of it, or one of pandas custom arrays (like Categorical). For example, with PeriodIndex, .values generates a new ndarray of period objects Period('2000-01-04', 'D')], dtype=object) For Series and Indexes backed by normal NumPy arrays, Series.array will return a new arrays. PandasArray, which is a thin (no-copy) wrapper around a numpy.ndarray. PandasArray

the Numba engine can yield significant performance gains if the apply function can operate on numpy arrays and the data set is larger (1 million rows or greater). For more details, see rolling apply documentation extension type dedicated to string data. Previously, strings were typically stored in object-dtype NumPy arrays. (GH29975) Warning: StringDtype is currently considered experimental. The implementation and parts change without warning. The 'string' extension type solves several issues with object-dtype NumPy arrays: 1. You can accidentally store a mixture of strings and non-strings in an object dtype array. A

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1807 3.5 pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1808 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2403 3.16.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2415 3.16.8 pandas split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1807 3.5 pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1808 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2403 3.16.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2415 3.16.8 pandas split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1981 3.5 pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1982 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2676 3.15.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2688 3.15.8 pandas split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1981 3.5 pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1982 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2676 3.15.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2688 3.15.8 pandas split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1915 3.5 pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1916 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2598 3.15.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2610 3.15.8 pandas split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1836 3.5 pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1837 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2447 3.15.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2459 3.15.8 pandas split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1837 3.5 pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1838 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2453 3.15.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2465 3.15.8 pandas split-apply-combine strategy for data analysis. The functions revolve around three data structures in R, a for arrays, l for lists, and d for data.frame. The table below shows how these data structures could be mapped

IO / conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1731 3.5 Pandas arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1732 extensions.ExtensionArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2310 3.16.7 pandas.arrays.PandasArray . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2323 3.16.8 pandas different data types, which comes down to a fundamental differ- ence between pandas and NumPy: NumPy arrays have one dtype for the entire array, while pandas DataFrames have one dtype per column. When you