offset, a monotonically increasing sequence number • Within a partition, all messages are totally ordered but there is no ordering guarantee across partitions 28 29 Failure handling • The broker

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894 29.3 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894 29.4 Converting data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously 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

categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544 21.4 Ordered or not... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755 28 rpy2 / R interface 757 28.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757 28.2 Converting data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously

. . . . . . . . . . . . . . . . . . . . 769 28 rpy2 / R interface 771 28.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 771 28.2 Converting data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously 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 data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 10.4.7 Combining overlapping data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 10.4.8 General DataFrame Combine handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 668 17.9.3 Grouping with ordered factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 668 17.9.4 Grouping with . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701 18.3.1 Merging Ordered Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701 18.3.2 Merging

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450 10.4.7 Combining overlapping data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 10.4.8 General DataFrame Combine handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 670 17.9.3 Grouping with ordered factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 670 17.9.4 Grouping with . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703 18.3.1 Merging Ordered Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703 18.3.2 Merging

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500 9.4.7 Combining overlapping data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 9.4.8 General DataFrame Combine handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 16.9.3 Grouping with ordered factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 16.9.4 Grouping with . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 786 17.3.1 Merging Ordered Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 786 17.3.2 Merging

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498 9.4.7 Combining overlapping data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 9.4.8 General DataFrame Combine handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745 16.9.3 Grouping with ordered factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745 16.9.4 Grouping with . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 17.3.1 Merging Ordered Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 17.3.2 Merging

. . . . . . . . . . . . . . . . . . . . 620 24 rpy2 / R interface 621 24.1 Transferring R data sets into Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 24.2 Converting data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously 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