. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 17.3 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining one of the most important, was a complete review of how integer indexes are handled with regard to label-based indexing. Here is an example: In [904]: s = Series(randn(10), index=range(0, 20, 2)) In [905]:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 17.3 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining one of the most important, was a complete review of how integer indexes are handled with regard to label-based indexing. Here is an example: In [905]: s = Series(randn(10), index=range(0, 20, 2)) In [906]:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 17.3 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining one of the most important, was a complete review of how integer indexes are handled with regard to label-based indexing. Here is an example: In [935]: s = Series(randn(10), index=range(0, 20, 2)) In [936]:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423 21.3 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining groupby("val1").apply(func) 0 1 2 3 val1 1 0.5 -0.5 7.5 -7.5 • Raise on iloc when boolean indexing with a label based indexer mask e.g. a boolean Series, even with integer labels, will raise. Since iloc is purely

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 10.6 Selection By Label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 10.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 23.4 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 10.6 Selection By Label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 10.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614 23.4 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 12.6 Selection By Label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 12.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764 27.4 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332 12.6 Selection By Label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 12.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 750 27.4 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 13.5 Selection By Label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 13.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 886 28.4 Label-based slicing conventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets • Intuitive merging and joining

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 1.27.3 API tweaks regarding label-based slicing . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 1.27.4 Changes to Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 6.3.2 Selection by Label . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 6.3.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 9.1.3 Vectorized operations and label alignment with Series . . . . . . . . . . . . . . . . . . . . . 416 9.1.4 Name attribute . . . .