. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 2.4.22 Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 2.5 Indexing and selecting operations 1.4. Tutorials 101 pandas: powerful Python data analysis toolkit, Release 1.3.2 Other considerations Fill Handle Create a series of numbers following a set pattern in a certain set of cells. In 506344 1.4. Tutorials 123 pandas: powerful Python data analysis toolkit, Release 1.3.2 Other considerations Disk vs memory pandas operates exclusively in memory, where a SAS data set exists on disk.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 2.4.22 Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 2.5 Indexing and selecting instead of just a single specified column • It supports more complex join operations Other considerations Fill Handle Create a series of numbers following a set pattern in a certain set of cells. In 506344 1.4. Tutorials 127 pandas: powerful Python data analysis toolkit, Release 1.3.3 Other considerations Disk vs memory pandas operates exclusively in memory, where a SAS data set exists on disk.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 2.4.22 Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 2.5 Indexing and selecting instead of just a single specified column • It supports more complex join operations Other considerations Fill Handle Create a series of numbers following a set pattern in a certain set of cells. In 506344 1.4. Tutorials 127 pandas: powerful Python data analysis toolkit, Release 1.3.4 Other considerations Disk vs memory pandas operates exclusively in memory, where a SAS data set exists on disk.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 2.4.22 Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 2.5 Indexing and selecting instead of just a single specified column • It supports more complex join operations Other considerations Fill Handle Create a series of numbers following a set pattern in a certain set of cells. In 506344 1.4. Tutorials 127 pandas: powerful Python data analysis toolkit, Release 1.4.2 Other considerations Disk vs memory pandas operates exclusively in memory, where a SAS data set exists on disk.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 2.4.22 Performance considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 2.5 Indexing and selecting instead of just a single specified column • It supports more complex join operations Other considerations Fill Handle Create a series of numbers following a set pattern in a certain set of cells. In 506344 1.4. Tutorials 127 pandas: powerful Python data analysis toolkit, Release 1.4.4 Other considerations Disk vs memory pandas operates exclusively in memory, where a SAS data set exists on disk.

instead of just a single specified column • It supports more complex join operations Other considerations Fill Handle Create a series of numbers following a set pattern in a certain set of cells. In Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.5.0rc0 Other considerations Disk vs memory pandas operates exclusively in memory, where a SAS data set exists on disk. 686344 Male No 5.51 2.00 Thur Lunch 2 -11.678278 Yes 5.25 5.15 Sun Dinner 2 -13.506344 Other considerations Disk vs memory pandas and Stata both operate exclusively in memory. This means that the size

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136 24.16 Performance Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136 25 Remote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1220 33.8 Other Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1221 33 some small regression fixes, bug fixes and performance improvements. We recommend that all users upgrade to this version. Highlights include: • Temporarily restore matplotlib datetime plotting functionality

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101 24.15 Performance Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101 25 Remote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1181 33.7 Other Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1182 33 20.x series and includes some small regression fixes and bug fixes. We recommend that all users upgrade to this version. What’s new in v0.20.3 • Bug Fixes – Conversion – Indexing – I/O – Plotting

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1100 24.15 Performance Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1100 25 Remote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1179 33.7 Other Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 33 some small regression fixes, bug fixes and performance improvements. We recommend that all users upgrade to this version. What’s new in v0.20.2 • Enhancements • Performance Improvements • Bug Fixes

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 847 24.14 Performance Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848 25 Remote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 932 33.7 Other Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 934 34 and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version. Warning: pandas >= 0.17.0 will no longer support compatibility with Python version