estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 23 Related Python libraries 433 23.1 la (larry) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 24 Comparison with R / R libraries 435 24.1 data.frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564 26 Comparison with R / R libraries 565 26.1 Base R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented a collection of useful recipes in pandas (and that we want contributions!). There are several libraries that are now Recommended Dependencies 1.4.1 Selection Choices Starting in 0.11.0, object selection

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 26 Comparison with R / R libraries 625 26.1 Base R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented a collection of useful recipes in pandas (and that we want contributions!). There are several libraries that are now Recommended Dependencies 1.5.1 Selection Choices Starting in 0.11.0, object selection

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 30 Comparison with R / R libraries 777 30.1 Base R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented line client. This submodule now uses httplib2 and the Google apiclient and oauth2client API client libraries which should be more stable and, therefore, reliable than bq.py. See the docs. (GH6937). 1.4.5

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 30 Comparison with R / R libraries 763 30.1 Base R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented line client. This submodule now uses httplib2 and the Google apiclient and oauth2client API client libraries which should be more stable and, therefore, reliable than bq.py. See the docs. (GH6937). 1.3.5

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 31 Comparison with R / R libraries 1145 31.1 Quick Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 31 Comparison with R / R libraries 1143 31.1 Quick Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. 60 Chapter 1. What’s New pandas: powerful Python data analysis toolkit, Release 0.20.2 Previous

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1050 32 Comparison with R / R libraries 1051 32.1 Quick Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1052 32 Comparison with R / R libraries 1053 32.1 Quick Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1181 31 Comparison with R / R libraries 1183 31.1 Quick Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . intended to integrate well within a scientific computing environment with many other 3rd party libraries. Here are just a few of the things that pandas does well: • Easy handling of missing data (represented columns as small integers, rather than floats (GH8725). This should provide an improved memory footprint. Previous behavior: In [1]: pd.get_dummies(['a', 'b', 'a', 'c']).dtypes Out[1]: a float64 b