Development support for benchmarking with the Air Speed Velocity library (GH8361) • Added vbench benchmarks for alternative ExcelWriter engines and reading Excel files (GH7171) • Performance improvements regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

Development support for benchmarking with the Air Speed Velocity library (GH8361) • Added vbench benchmarks for alternative ExcelWriter engines and reading Excel files (GH7171) • Performance improvements regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

Development support for benchmarking with the Air Speed Velocity library (GH8361) • Added vbench benchmarks for alternative ExcelWriter engines and reading Excel files (GH7171) • Performance improvements the asv library to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. asv supports both python2 and python3. 236 Chapter regressions. You can run specific benchmarks using the -b flag which takes a regular expression. For example this will only run tests from a pandas/asv_bench/benchmarks/groupby.py file: asv continuous

Development support for benchmarking with the Air Speed Velocity library (GH8361) • Added vbench benchmarks for alternative ExcelWriter engines and reading Excel files (GH7171) • Performance improvements regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

Development support for benchmarking with the Air Speed Velocity library (GH8361) • Added vbench benchmarks for alternative ExcelWriter engines and reading Excel files (GH7171) • Performance improvements regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

flexibility: what can/cannot be done with each tool • Performance: how fast are operations. Hard numbers/benchmarks are preferable • Ease-of-use: Is one tool easier/harder to use (you may have to be the judge of regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

flexibility: what can/cannot be done with each tool • Performance: how fast are operations. Hard numbers/benchmarks are preferable • Ease-of-use: Is one tool easier/harder to use (you may have to be the judge of regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory. upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

flexibility: what can/cannot be done with each tool • Performance: how fast are operations. Hard numbers/benchmarks are preferable 58 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory, upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

flexibility: what can/cannot be done with each tool • Performance: how fast are operations. Hard numbers/benchmarks are preferable 60 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory, upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments

flexibility: what can/cannot be done with each tool • Performance: how fast are operations. Hard numbers/benchmarks are preferable • Ease-of-use: Is one tool easier/harder to use (you may have to be the judge of regressions. pandas is in the process of migrating to asv benchmarks to enable easy monitoring of the performance of critical pandas operations. These benchmarks are all found in the pandas/asv_bench directory, upstream/master HEAD You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments