. . . . . . . . . . . . . . . . . . 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)

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

..: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 .....: 39.95 Everyday Italian Giada De Laurentiis 2005 30.00 1 children Harry Potter J K. Rowling 2005 29.99 2 web Learning XML Erik T. Ray 2003 39.95 Read a URL with no options: 322 Chapter 2. User Guide pandas: powerful Rowling 2005 29.99 None 2 web XQuery Kick Start Vaidyanathan Nagarajan 2003 49.99 None 3 web Learning XML Erik T. Ray 2003 39.95 paperback Read in the content of the “books.xml” file and pass it to

..: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 .....: 39.95 Everyday Italian Giada De Laurentiis 2005 30.00 1 children Harry Potter J K. Rowling 2005 29.99 2 web Learning XML Erik T. Ray 2003 39.95 Read a URL with no options: In [321]: df = pd.read_xml("https://www Rowling 2005 29.99 None 2 web XQuery Kick Start Vaidyanathan Nagarajan 2003 49.99 None 3 web Learning XML Erik T. Ray 2003 39.95 paperback Read in the content of the “books.xml” file and pass it to

..: 29.99 .....: .....: .....: .....: Erik T. Ray .....: 2003 .....: 39.95 Everyday Italian Giada De Laurentiis 2005 30.00 1 children Harry Potter J K. Rowling 2005 29.99 2 web Learning XML Erik T. Ray 2003 39.95 Read a URL with no options: In [321]: df = pd.read_xml("https://www Rowling 2005 29.99 None 2 web XQuery Kick Start Vaidyanathan Nagarajan 2003 49.99 None 3 web Learning XML Erik T. Ray 2003 39.95 paperback Read in the content of the “books.xml” file and pass it to

. . . . . . . . . . . . . . . . . . . 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 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. 136 Chapter 1. What’s New pandas:

. . . . . . . . . . . . . . . . . . 1045 31 pandas Ecosystem 1047 31.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047 31.1.1 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 axvlines_kwds to parallel coordinates plot (GH10709) • Option to .info() and .memory_usage() to provide for deep introspection of memory consumption. Note that this can be expensive to compute and therefore is an

. . . . . . . . . . . . . . . . . . 1047 31 pandas Ecosystem 1049 31.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1049 31.1.1 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 axvlines_kwds to parallel coordinates plot (GH10709) • Option to .info() and .memory_usage() to provide for deep introspection of memory consumption. Note that this can be expensive to compute and therefore is an

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 pandas does not count the memory used by values in columns with dtype=object. Passing memory_usage='deep' will enable a more accurate memory usage report, accounting for the full usage of the contained objects is optional as it can be expensive to do this deeper introspection. In [7]: df.info(memory_usage='deep') RangeIndex: 5000 entries, 0 to 4999 Data columns (total 8

. . . . . . . . . . . . . . . . . . 1140 30 pandas Ecosystem 1141 30.1 Statistics and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1141 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 Index(['foo', 'bar', 'baz']) In [9]: index.memory_usage(deep=True) Out[9]: 180 In [10]: index.get_loc('foo') Out[10]: 0 In [11]: index.memory_usage(deep=True) Out[11]: 180 New Behavior: In [8]: index =