. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 899 31 Comparison with R / R libraries 901 31.1 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 setting an empty range using DataFrame.loc (GH9596) • Bug in hiding ticklabels with subplots and shared axes when adding a new plot to an existing grid of axes (GH9158) • Bug in transform and filter when

4 X python-dateutil 2.6.1 X bottleneck 1.2.1 numexpr 2.6.2 pytest (dev) 4.0.2 For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported. 20 Chapter 1 version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries. (GH26104) • Fixed memory leak in DataFrame.to_json() when dealing with numeric data (GH24889)

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

4 X python-dateutil 2.6.1 X bottleneck 1.2.1 numexpr 2.6.2 pytest (dev) 4.0.2 For optional libraries the general recommendation is to use the latest version. The following table lists the lowest version version per library that is currently being tested throughout the development of pandas. Optional libraries below the lowest tested version may still work, but are not considered supported. 20 Chapter 1 version 0.10.0 of the pandas-gbq library as well as the google-cloud-bigquery-storage and fastavro libraries. (GH26104) • Fixed memory leak in DataFrame.to_json() when dealing with numeric data (GH24889)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916 2.24.4 Use other libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 917 2.25 Sparse environment is like a virtualenv that allows you to specify a specific version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This encounter an ImportError, it usually means that Python couldn’t find pandas in the list of available libraries. Python internally has a list of directories it searches through, to find packages. You can obtain

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954 2.24.4 Use other libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956 2.25 Sparse data environment is like a virtualenv that allows you to specify a specific version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This encounter an ImportError, it usually means that Python couldn’t find pandas in the list of available libraries. Python internally has a list of directories it searches through, to find packages. You can obtain