. . . . . . . . . . . . . . . . . . 1176 30 pandas Ecosystem 1177 30.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1177 30.1.1 Statsmodels general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS Bug in to_json() where several conditions (including objects with unprintable symbols, objects with deep recursion, overlong labels) caused segfaults instead of raising the appropriate exception (GH14256)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913 2.24.2 Use efficient datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914 2.24.3 NaN 0.007207 B 1.552825 NaN C NaN 1.018601 2.1.9 Time series pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency con- version (e.g., converting reader.get_chunk(5) .....: Specifying the parser engine Under the hood pandas uses a fast and efficient parser implemented in C as well as a Python implementation which is currently more feature-complete

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 951 2.24.2 Use efficient datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 953 2.24.3 NaN 0.007207 B 1.552825 NaN C NaN 1.018601 2.1.9 Time series pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency con- version (e.g., converting reader.get_chunk(5) .....: Specifying the parser engine Under the hood pandas uses a fast and efficient parser implemented in C as well as a Python implementation which is currently more feature-complete

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 952 2.24.2 Use efficient datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954 2.24.3 NaN 0.007207 B 1.552825 NaN C NaN 1.018601 2.1.9 Time series pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency con- version (e.g., converting reader.get_chunk(5) .....: Specifying the parser engine Under the hood pandas uses a fast and efficient parser implemented in C as well as a Python implementation which is currently more feature-complete

better handle NumPy ufuncs applied to Series backed by extension arrays (GH23293). • Keyword argument deep has been removed from ExtensionArray.copy() (GH27083) 1.6.19 Other • Removed unused C functions -1.092905 B -0.201151 NaN C NaN 0.570683 3.2.9 Time series pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency con- version (e.g., converting analysis toolkit, Release 0.25.1 Specifying the parser engine Under the hood pandas uses a fast and efficient parser implemented in C as well as a Python implementation which is currently more feature-complete

. . . . . . . . . . . . . . . . . . . 895 30 pandas Ecosystem 897 30.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 897 30.2 Visualization general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS with dupli- cates. This is a container around a Categorical (introduced in v0.15.0) and allows efficient indexing and storage of an index with a large number of duplicated elements. Prior to 0.16.1, setting

general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS flat files (CSV, delimited, Excel 2003), and saving and loading pandas objects from the fast and efficient PyTables/HDF5 format. • Memory-efficent “sparse” versions of the standard data structures for storing to a particular application than the ones provided in pandas. For example, we plan to add a more efficient datetime index which leverages the new numpy.datetime64 dtype in the relatively near future. From

general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS flat files (CSV, delimited, Excel 2003), and saving and loading pandas objects from the fast and efficient PyTables/HDF5 format. • Memory-efficent “sparse” versions of the standard data structures for storing to a particular application than the ones provided in pandas. For example, we plan to add a more efficient datetime index which leverages the new numpy.datetime64 dtype in the relatively near future. From

. . . . . . . . . . . . . . . . . . . 622 25 Pandas Ecosystem 623 25.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623 25.2 Visualization general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. See the package overview for more detail about what’s in the library. 2 CONTENTS including: • Comparison with SQL, which should be useful for those familiar with SQL but still learning pandas. • Comparison with R, idiom translations from R to pandas. • Enhancing Performance, ways

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 960 2.24.2 Use efficient datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 962 2.24.3 -0.872606 B -0.765135 NaN C NaN -0.845175 2.1.9 Time series pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency con- version (e.g., converting ..: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 .....: 39.95