gotchas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 18 rpy2 / R interface 193 18.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 18.3 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 19 Related becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds

gotchas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 18 rpy2 / R interface 193 18.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 18.3 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 19 Related becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds

gotchas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 18 rpy2 / R interface 205 18.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 18.3 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 19 Related becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 17 Trellis plotting interface 347 17.1 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 428 22 rpy2 / R interface 431 22.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 22.4 High-level interface to R estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 23 Related

high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about what’s in the library. CONTENTS 1 pandas: powerful Python the Anaconda distribution is built upon. It is a package manager that is both cross-platform and language agnostic (it can play a similar role to a pip and virtualenv combination). 35 pandas: powerful becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds

high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the overview for more detail about what’s in the library. CONTENTS 1 pandas: powerful Python the Anaconda distribution is built upon. It is a package manager that is both cross-platform and language agnostic (it can play a similar role to a pip and virtualenv combination). Miniconda allows you becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal. pandas is well suited for many different kinds

CategoricalDtype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614 2.12.3 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 2.12 high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New to pandas? Check out the getting started guides. They contain an introduction information and explanation. To the user guide API reference The reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used

CategoricalDtype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641 2.12.3 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 2.12 high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New to pandas? Check out the getting started guides. They contain an introduction information and explanation. To the user guide API reference The reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used

CategoricalDtype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 2.12.3 Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643 2.12 high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Getting started New to pandas? Check out the getting started guides. They contain an introduction information and explanation. To the user guide API reference The reference guide contains a detailed description of the pandas API. The reference describes how the methods work and which parameters can be used

high-performance, easy-to-use data structures and data analysis tools for the Python programming language. See the Package overview for more detail about what’s in the library. CONTENTS 1 pandas: powerful around a numpy.ndarray. PandasArray isn’t especially useful on its own, but it does provide the same interface as any extension array defined in pandas or by a third-party library. In [23]: ser = pd.Series([1 just a thin (no-copy) wrapper around a numpy.ndarray that satisfies the pandas exten- sion array interface. In [28]: pd.array([1, 2, 3]) Out[28]: [1, 2, 3] Length: 3, dtype: int64 On their