20.5 Attributes ..... 536 20.6 TimedeltaIndex ..... 538 20.7 Resampling ..... 540 21 Categorical Data ..... 541 21.1 Object Creation ..... 541 21.2 Description ..... 544 21.3 Working with in coercing Categorical to a records array, e.g. df.to_records () (GH8626) • Bug in Categorical not created properly with Series.to_frame() (GH8626) • Bug in coercing in类型 of a Categorical of a passed passed pd.Categorical (this now raises TypeError correctly), (GH8626) • Bug in cut/qcut when using Series and retbins=True (GH8589) • Bug in writing Categorical columns to an SQL database with to_sql (GH8624)

(In this case, classes are 0, 1, 2 and so on until 9) inputs. We use the sparse variant of the categorical cross entropy loss function so that we can use the index of the correct class for each example ========================] - 3s 6ms/step - loss: 0.1729 - sparse_categorical_accuracy: 0.9500 - val_loss: 0.0753 - val_sparse_categorical_accuracy: 0.9789 Epoch 2/15 469/469 [======================== ========================] - 2s 5ms/step - loss: 0.0570 - sparse_categorical_accuracy: 0.9825 - val_loss: 0.0462 - val_sparse_categorical_accuracy: 0.9855 Epoch 3/15 469/469 [========================

denote missing values for more. 1.5.8 By default Categorical.min() now returns the minimum instead of np.nan When Categorical contains np.nan, Categorical.min() no longer return np.nan by default (skipna=True) (skipna=True) (GH25303) pandas 0.25.x In [1]: pd.Categorical([1, 2, np.nan], ordered=True).min() Out[1]: nan pandas 1.0.0 In [47]: pd.Categorical([1, 2, np.nan], ordered=True).min() Out[47]: 1 1.5.9 Default (GH4205) • Categorical.take_nd() and CategoricalIndex.take_nd() are deprecated, use Categorical.take() and CategoricalIndex.take() instead (GH27745) • The parameter numeric_only of Categorical.min() and

Experimental NA scalar to denote missing values . . . . . . . . . . . . . . . . . . . . . . . 547 2.11 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . integer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1869 3.5.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1874 3.5 that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. It is similar to a spreadsheet, a SQL table or the data. frame in R

Numeric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.5.8 Categorical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.5.9 String 7 GroupBy objects now have a pipe method . . . . . . . . . . . . . . . . . . . . . 12 1.2.1.8 Categorical.rename_categories accepts a dict-like . . . . . . . . . . . . 13 1.2.1.9 Other Enhancements . Numeric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 1.2.7.9 Categorical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 1.2.7.10 PyPy

Disallowing Duplicate Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632 2.12 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . integer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2056 3.5.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2061 3.5 that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. It is similar to a spreadsheet, a SQL table or the data. frame in R

Disallowing Duplicate Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631 2.12 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . integer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2056 3.5.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2061 3.5 that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. It is similar to a spreadsheet, a SQL table or the data. frame in R

Disallowing Duplicate Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 2.12 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . integer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1990 3.5.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1995 3.5 that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. It is similar to a spreadsheet, a SQL table or the data. frame in R

Experimental NA scalar to denote missing values . . . . . . . . . . . . . . . . . . . . . . . 537 2.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . integer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1797 3.5.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1802 3.5 index=list(range(4)), dtype='float32'), ...: 'D': np.array([3] * 4, dtype='int32'), ...: 'E': pd.Categorical(["test", "train", "test", "train"]), ...: 'F': 'foo'}) ...: In [10]: df2 Out[10]: A B C D E F 0

Experimental NA scalar to denote missing values . . . . . . . . . . . . . . . . . . . . . . . 539 3.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . integer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1795 4.5.8 Categorical data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1800 4.5 like min on object-dtype columns (GH31522) • Fixed regression in .groupby() aggregations with categorical dtype using Cythonized reduction functions (e.g. first) (GH31450) • Fixed regression in GroupBy