axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 9.3.2 From dict of DataFrame objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 9.3.3 From DataFrame using to_panel . . . . . . 446 10.4.5 Comparing if objects are equivalent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 10.4.6 Comparing array-like objects . . . . . . . . . . . . . . . . . . . with another object . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 10.7.2 Aligning objects with each other with align . . . . . . . . . . . . . . . . . . . . . . . . . 467 10.7.3 Filling

axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.2 From dict of DataFrame objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 9.3.3 From DataFrame using to_panel . . . . . . 448 10.4.5 Comparing if objects are equivalent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 10.4.6 Comparing array-like objects . . . . . . . . . . . . . . . . . . . with another object . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468 10.7.2 Aligning objects with each other with align . . . . . . . . . . . . . . . . . . . . . . . . . 469 10.7.3 Filling

axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 8.3.2 From dict of DataFrame objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 8.3.3 From DataFrame using to_panel . . . . . . . 498 9.4.5 Comparing if objects are equivalent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 9.4.6 Comparing array-like objects . . . . . . . . . . . . . . . . . . . with another object . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 9.7.2 Aligning objects with each other with align . . . . . . . . . . . . . . . . . . . . . . . . . 526 9.7.3 Filling while

axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 8.3.2 From dict of DataFrame objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 8.3.3 From DataFrame using to_panel . . . . . . . 496 9.4.5 Comparing if objects are equivalent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 9.4.6 Comparing array-like objects . . . . . . . . . . . . . . . . . . . with another object . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524 9.7.2 Aligning objects with each other with align . . . . . . . . . . . . . . . . . . . . . . . . . 524 9.7.3 Filling while

Integration with Apache Parquet file format . . . . . . . . . . . . . . . . . . . . . 8 1.2.1.2 infer_objects type conversion . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2.1.3 Improved warnings CategoricalDtype for specifying categoricals . . . . . . . . . . . . . . . . . 11 1.2.1.7 GroupBy objects now have a pipe method . . . . . . . . . . . . . . . . . . . . . 12 1.2.1.8 Categorical.rename_categories axis labels . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 8.3.2 From dict of DataFrame objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510 8.3.3 From DataFrame using to_panel

agg() with a dictionary when renaming). A similar approach is now available for Series groupby objects as well. Because there’s no need for column selection, the values can just be the functions to apply allows to still see the full content of relatively small objects (e.g. df.head(20) shows all 20 rows), while giving a brief repr for large objects. To restore the previous behaviour of a single threshold transition times (GH25017) • DataFrame.at_time() and Series.at_time() now support datetime.time objects with time- zones (GH24043) • DataFrame.pivot_table() now accepts an observed parameter which is

agg() with a dictionary when renaming). A similar approach is now available for Series groupby objects as well. Because there’s no need for column selection, the values can just be the functions to apply allows to still see the full content of relatively small objects (e.g. df.head(20) shows all 20 rows), while giving a brief repr for large objects. To restore the previous behaviour of a single threshold transition times (GH25017) • DataFrame.at_time() and Series.at_time() now support datetime.time objects with time- zones (GH24043) • DataFrame.pivot_table() now accepts an observed parameter which is

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 10.22 Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 . . . . . . . . . . . . . . . . . 375 14 Merge, join, and concatenate 377 14.1 Concatenating objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 14.2 Database-style . . . . . . . . . 395 15 Reshaping and Pivot Tables 399 15.1 Reshaping by pivoting DataFrame objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 15.2 Reshaping by stacking and

arrays (like Categorical). For example, with PeriodIndex, .values generates a new ndarray of period objects each time. In [18]: idx.values Out[18]: array([Period('2000-01-01', 'D'), Period('2000-01-02', 'D') data. Warning: For backwards compatibility, Series.values continues to return a NumPy array of objects for Interval and Period data. We recommend using Series.array when you need the array of data stored renaming for more details. 1.1.9 Other Enhancements • merge() now directly allows merge between objects of type DataFrame and named Series, without the need to convert the Series object into a DataFrame

scalars, and data types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2067 3.5.1 Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2067 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2166 3.6 Index objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2194 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3016 4.9.1 Storing pandas DataFrame objects in Apache Parquet format . . . . . . . . . . . . . . . . . 3016 4.10 Policies . . . . . . . . .