. . . . . . . . . . . . . . . . . . . . 189 18 rpy2 / R interface 193 18.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 18.2 Calling the broader goal of 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 or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary

. . . . . . . . . . . . . . . . . . . . 189 18 rpy2 / R interface 193 18.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 18.2 Calling the broader goal of 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 or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary

. . . . . . . . . . . . . . . . . . . . 201 18 rpy2 / R interface 205 18.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 18.2 Calling the broader goal of 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 or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary

. . . . . . . . . . . . . . . . . . . . 428 22 rpy2 / R interface 431 22.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 22.2 Converting the broader goal of 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 or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary

. . . . . . . . . . . . . . . . . . . . 560 24 rpy2 / R interface 561 24.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 24.2 Converting the broader goal of 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 or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary

. . . . . . . . . . . . . . . . . . . . 620 24 rpy2 / R interface 621 24.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 24.2 Converting the broader goal of 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 or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary

install these libraries, as they provide speed improvements, especially when working with large data sets. 2.4.2 Optional dependencies Pandas has many optional dependencies that are only used for specific the broader goal of 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 or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 9.4.7 Combining overlapping data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 9.4.8 General DataFrame Combine . . . . . . . . . . . . . . . . . . . 1174 29 rpy2 / R interface 1175 29.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175 29.2 Converting the broader goal of 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500 9.4.7 Combining overlapping data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 9.4.8 General DataFrame Combine . . . . . . . . . . . . . . . . . . . 1138 29 rpy2 / R interface 1139 29.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1139 29.2 Converting the broader goal of 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498 9.4.7 Combining overlapping data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 9.4.8 General DataFrame Combine . . . . . . . . . . . . . . . . . . . 1136 29 rpy2 / R interface 1137 29.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 29.2 Converting the broader goal of 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