statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 10 Computational tools 211 10.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 10.2 . 215 10.3 Expanding window moment functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 10.4 Exponentially weighted moment functions . . . . . . . . . . . . . . . . . .

Numba instead of Cython. Using the Numba engine can yield significant performance gains if the apply function can operate on numpy arrays and the data set is larger (1 million rows or greater). For more details (GH28250) • Implemented pandas.core.window.Window.var() and pandas.core.window.Window. std() functions (GH26597) • Added encoding argument to DataFrame.to_string() for non-ascii text (GH28766) • Added testing module has been deprecated. Use the public API in pandas.testing documented at Testing functions (GH16232). • pandas.SparseArray has been deprecated. Use pandas.arrays.SparseArray (arrays. SparseArray)

class has gained a pipe() method. This provides a convenient way to apply users’ predefined styling functions, and can help reduce “boilerplate” when using DataFrame styling functionality repeatedly within index was not of a categorical dtype (GH23963). • Series.to_list() and Index.to_list() are now aliases of Series.tolist respectively Index. tolist (GH8826) • The result of SparseSeries.unstack is now ExtensionArray inside of Series (GH20825) • Series.combine() with scalar argument now works for any function type (GH21248) • Series.astype() and DataFrame.astype() now dispatch to ExtensionArray.astype()

. . . . . . . . . 648 2.12.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 2.12.2 Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 933 3.2 General functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935 3 . . . . . . . . . . 1212 3.3.5 Binary operator functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1213 3.3.6 Function application, groupby & window . . . . . . . . .

. . . . . . . . . 649 3.12.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 3.12.2 Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 935 4.2 General functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937 4 . . . . . . . . . . 1214 4.3.5 Binary operator functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1215 4.3.6 Function application, groupby & window . . . . . . . . .

. . . . . . . . . 652 2.12.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 2.12.2 Window Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937 3.2 General functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 939 3 . . . . . . . . . . 1216 3.3.5 Binary operator functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1217 3.3.6 Function application, groupby & window . . . . . . . . .

known as “named aggregation”, for naming the output columns when applying multiple aggregation functions to specific columns (GH18366, GH26512). In [1]: animals = pd.DataFrame({'kind': ['cat', 'dog', and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg namedtuple to make it clearer what the arguments to the function are, but plain tuples are accepted as well groupby objects as well. Because there’s no need for column selection, the values can just be the functions to apply In [5]: animals.groupby("kind").height.agg( ...: min_height="min", ...: max_height="max"

known as “named aggregation”, for naming the output columns when applying multiple aggregation functions to specific columns (GH18366, GH26512). In [1]: animals = pd.DataFrame({'kind': ['cat', 'dog', and the second element is the aggregation function to apply. Pandas provides the pandas.NamedAgg namedtuple to make it clearer what the arguments to the function are, but plain tuples are accepted as well groupby objects as well. Because there’s no need for column selection, the values can just be the functions to apply In [5]: animals.groupby("kind").height.agg( ...: min_height="min", ...: max_height="max"

statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 2.3.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 2.3 . . . . . . . . . 658 2.15.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 2.15.2 Window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 952 3.2 General functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954 3

statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 2.3.6 Function application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 2.3 . . . . . . . . . 658 2.15.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658 2.15.2 Window functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 951 3.2 General functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 954 3