Run doctests.

python/cudf/cudf/__init__.py

@@ -126,3 +126,66 @@
 
 __version__ = get_versions()["version"]
 del get_versions
+
+__all__ = [
+    "BaseIndex",
+    "CategoricalDtype",
+    "CategoricalIndex",
+    "DataFrame",
+    "DateOffset",
+    "DatetimeIndex",
+    "Decimal32Dtype",
+    "Decimal64Dtype",
+    "Float32Index",
+    "Float64Index",
+    "GenericIndex",
+    "Grouper",
+    "Index",
+    "Int16Index",
+    "Int32Index",
+    "Int64Index",
+    "Int8Index",
+    "IntervalDtype",
+    "IntervalIndex",
+    "ListDtype",
+    "MultiIndex",
+    "NA",
+    "RangeIndex",
+    "Scalar",
+    "Series",
+    "StringIndex",
+    "StructDtype",
+    "TimedeltaIndex",
+    "UInt16Index",
+    "UInt32Index",
+    "UInt64Index",
+    "UInt8Index",
+    "api",
+    "concat",
+    "cut",
+    "date_range",
+    "factorize",
+    "from_dataframe",
+    "from_dlpack",
+    "from_pandas",
+    "get_dummies",
+    "interval_range",
+    "isclose",
+    "melt",
+    "merge",
+    "merge_sorted",
+    "pivot",
+    "read_avro",
+    "read_csv",
+    "read_feather",
+    "read_hdf",
+    "read_json",
+    "read_orc",
+    "read_parquet",
+    "read_text",
+    "set_allocator",
+    "testing",
+    "to_datetime",
+    "to_numeric",
+    "unstack",
+]

python/cudf/cudf/api/__init__.py

@@ -1,3 +1,5 @@
 # Copyright (c) 2021, NVIDIA CORPORATION.
 
-from cudf.api import types
+from cudf.api import extensions, types
+
+__all__ = ["extensions", "types"]

python/cudf/cudf/api/extensions/__init__.py

@@ -5,3 +5,9 @@
     register_index_accessor,
     register_series_accessor,
 )
+
+__all__ = [
+    "register_dataframe_accessor",
+    "register_index_accessor",
+    "register_series_accessor",
+]

python/cudf/cudf/core/_base_index.py

@@ -495,7 +495,7 @@ def fillna(self, value, downcast=None):
         >>> import cudf
         >>> index = cudf.Index([1, 2, None, 4])
         >>> index
-        Int64Index([1, 2, null, 4], dtype='int64')
+        Int64Index([1, 2, <NA>, 4], dtype='int64')
         >>> index.fillna(3)
         Int64Index([1, 2, 3, 4], dtype='int64')
         """
@@ -553,7 +553,7 @@ def to_pandas(self):
         >>> type(idx.to_pandas())
         <class 'pandas.core.indexes.numeric.Int64Index'>
         >>> type(idx)
-        <class 'cudf.core.index.GenericIndex'>
+        <class 'cudf.core.index.Int64Index'>
         """
         return pd.Index(self._values.to_pandas(), name=self.name)
 
@@ -942,6 +942,7 @@ def is_interval(self):
         Examples
         --------
         >>> import cudf
+        >>> import pandas as pd
         >>> idx = cudf.from_pandas(
         ...     pd.Index([pd.Interval(left=0, right=5),
         ...               pd.Interval(left=5, right=10)])
@@ -1105,15 +1106,16 @@ def join(
         Examples
         --------
         >>> import cudf
-        >>> lhs = cudf.DataFrame(
-        ...     {"a":[2, 3, 1], "b":[3, 4, 2]}).set_index(['a', 'b']
-        ... ).index
+        >>> lhs = cudf.DataFrame({
+        ...     "a": [2, 3, 1],
+        ...     "b": [3, 4, 2],
+        ... }).set_index(['a', 'b']).index
         >>> lhs
         MultiIndex([(2, 3),
                     (3, 4),
                     (1, 2)],
                    names=['a', 'b'])
-        >>> rhs = cudf.DataFrame({"a":[1, 4, 3]}).set_index('a').index
+        >>> rhs = cudf.DataFrame({"a": [1, 4, 3]}).set_index('a').index
         >>> rhs
         Int64Index([1, 4, 3], dtype='int64', name='a')
         >>> lhs.join(rhs, how='inner')

python/cudf/cudf/core/column/categorical.py

@@ -61,7 +61,6 @@ class CategoricalAccessor(ColumnMethods):
     --------
     >>> s = cudf.Series([1,2,3], dtype='category')
     >>> s
-    >>> s
     0    1
     1    2
     2    3

python/cudf/cudf/core/dataframe.py

@@ -463,12 +463,12 @@ class DataFrame(IndexedFrame, Serializable, GetAttrGetItemMixin):
     ...     [(t0+ timedelta(seconds=x)) for x in range(n)])
     ... })
     >>> df
-        id                datetimes
-    0    0  2018-10-07T12:00:00.000
-    1    1  2018-10-07T12:00:01.000
-    2    2  2018-10-07T12:00:02.000
-    3    3  2018-10-07T12:00:03.000
-    4    4  2018-10-07T12:00:04.000
+        id            datetimes
+    0    0  2018-10-07 12:00:00
+    1    1  2018-10-07 12:00:01
+    2    2  2018-10-07 12:00:02
+    3    3  2018-10-07 12:00:03
+    4    4  2018-10-07 12:00:04
 
     Build DataFrame via list of rows as tuples:
 
@@ -984,23 +984,34 @@ def __getitem__(self, arg):
 
         Examples
         --------
-        >>> df = DataFrame([('a', list(range(20))),
-        ...                 ('b', list(range(20))),
-        ...                 ('c', list(range(20)))])
-        >>> df[:4]    # get first 4 rows of all columns
+        >>> df = cudf.DataFrame({
+        ...     'a': list(range(10)),
+        ...     'b': list(range(10)),
+        ...     'c': list(range(10)),
+        ... })
+
+        Get first 4 rows of all columns.
+
+        >>> df[:4]
            a  b  c
         0  0  0  0
         1  1  1  1
         2  2  2  2
         3  3  3  3
-        >>> df[-5:]  # get last 5 rows of all columns
-             a   b   c
-        15  15  15  15
-        16  16  16  16
-        17  17  17  17
-        18  18  18  18
-        19  19  19  19
-        >>> df[['a', 'c']] # get columns a and c
+
+        Get last 5 rows of all columns.
+
+        >>> df[-5:]
+           a  b  c
+        5  5  5  5
+        6  6  6  6
+        7  7  7  7
+        8  8  8  8
+        9  9  9  9
+
+        Get columns a and c.
+
+        >>> df[['a', 'c']]
            a  c
         0  0  0
         1  1  1
@@ -1012,8 +1023,17 @@ def __getitem__(self, arg):
         7  7  7
         8  8  8
         9  9  9
-        >>> df[[True, False, True, False]] # mask the entire dataframe,
-        # returning the rows specified in the boolean mask
+
+        Return the rows specified in the boolean mask.
+
+        >>> df[[True, False, True, False, True,
+        ...     False, True, False, True, False]]
+           a  b  c
+        0  0  0  0
+        2  2  2  2
+        4  4  4  4
+        6  6  6  6
+        8  8  8  8
         """
         if _is_scalar_or_zero_d_array(arg) or isinstance(arg, tuple):
             return self._get_columns_by_label(arg, downcast=True)
@@ -1253,10 +1273,12 @@ def memory_usage(self, index=True, deep=False):
         object     40000
         bool        5000
         dtype: int64
+
         Use a Categorical for efficient storage of an object-dtype column with
         many repeated values.
+
         >>> df['object'].astype('category').memory_usage(deep=True)
-        5048
+        5008
         """
         if deep:
             warnings.warn(
@@ -2209,11 +2231,11 @@ def reindex(
         3    3  13.0
         4    4  14.0
         >>> df_new
-           key   val  sum
-        0    0  10.0  NaN
-        3    3  13.0  NaN
-        4    4  14.0  NaN
-        5   -1   NaN  NaN
+           key   val   sum
+        0     0  10.0  <NA>
+        3     3  13.0  <NA>
+        4     4  14.0  <NA>
+        5  <NA>  <NA>  <NA>
         """
 
         if labels is None and index is None and columns is None:
@@ -3685,10 +3707,10 @@ def query(self, expr, local_dict=None):
 
         Examples
         --------
-        >>> import cudf
-        >>> a = ('a', [1, 2, 2])
-        >>> b = ('b', [3, 4, 5])
-        >>> df = cudf.DataFrame([a, b])
+        >>> df = cudf.DataFrame({
+        ...     "a": [1, 2, 2],
+        ...     "b": [3, 4, 5],
+        ... })
         >>> expr = "(a == 2 and b == 4) or (b == 3)"
         >>> df.query(expr)
            a  b
@@ -3704,8 +3726,8 @@ def query(self, expr, local_dict=None):
         >>> df['datetimes'] = data
         >>> search_date = datetime.datetime.strptime('2018-10-08', '%Y-%m-%d')
         >>> df.query('datetimes==@search_date')
-                        datetimes
-        1 2018-10-08T00:00:00.000
+           datetimes
+        1 2018-10-08
 
         Using local_dict:
 
@@ -3716,9 +3738,9 @@ def query(self, expr, local_dict=None):
         >>> df['datetimes'] = data
         >>> search_date2 = datetime.datetime.strptime('2018-10-08', '%Y-%m-%d')
         >>> df.query('datetimes==@search_date',
-        ...         local_dict={'search_date':search_date2})
-                        datetimes
-        1 2018-10-08T00:00:00.000
+        ...          local_dict={'search_date': search_date2})
+           datetimes
+        1 2018-10-08
         """
         # can't use `annotate` decorator here as we inspect the calling
         # environment.
@@ -4173,18 +4195,23 @@ def info(
         dtypes: float64(1), int64(1), object(1)
         memory usage: 130.0+ bytes
 
-        Pipe output of DataFrame.info to buffer instead of sys.stdout,
-        get buffer content and writes to a text file:
+        Pipe output of DataFrame.info to a buffer instead of sys.stdout and
+        print buffer contents:
 
         >>> import io
         >>> buffer = io.StringIO()
         >>> df.info(buf=buffer)
-        >>> s = buffer.getvalue()
-        >>> with open("df_info.txt", "w",
-        ...           encoding="utf-8") as f:
-        ...     f.write(s)
-        ...
-        369
+        >>> print(buffer.getvalue())
+        <class 'cudf.core.dataframe.DataFrame'>
+        RangeIndex: 5 entries, 0 to 4
+        Data columns (total 3 columns):
+         #   Column     Non-Null Count  Dtype
+        ---  ------     --------------  -----
+         0   int_col    5 non-null      int64
+         1   text_col   5 non-null      object
+         2   float_col  5 non-null      float64
+        dtypes: float64(1), int64(1), object(1)
+        memory usage: 130.0+ bytes
 
         The `memory_usage` parameter allows deep introspection mode, specially
         useful for big DataFrames and fine-tune memory optimization:
@@ -5745,7 +5772,7 @@ def stack(self, level=-1, dropna=True):
         Examples
         --------
         >>> import cudf
-        >>> df = cudf.DataFrame({'a':[0,1,3], 'b':[1,2,4]})
+        >>> df = cudf.DataFrame({'a': [0, 1, 3], 'b': [1, 2, 4]})
         >>> df.stack()
         0  a    0
            b    1
@@ -6068,8 +6095,11 @@ def explode(self, column, ignore_index=False):
         Examples
         --------
         >>> import cudf
-        >>> cudf.DataFrame(
-                {"a": [[1, 2, 3], [], None, [4, 5]], "b": [11, 22, 33, 44]})
+        >>> df = cudf.DataFrame({
+        ...     "a": [[1, 2, 3], [], None, [4, 5]],
+        ...     "b": [11, 22, 33, 44],
+        ... })
+        >>> df
                    a   b
         0  [1, 2, 3]  11
         1         []  22

python/cudf/cudf/core/groupby/groupby.py

@@ -1256,9 +1256,10 @@ class DataFrameGroupBy(GroupBy, GetAttrGetItemMixin):
     --------
     >>> import cudf
     >>> import pandas as pd
-    >>> df = cudf.DataFrame({'Animal': ['Falcon', 'Falcon',
-    ...                               'Parrot', 'Parrot'],
-    ...                    'Max Speed': [380., 370., 24., 26.]})
+    >>> df = cudf.DataFrame({
+    ...     'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
+    ...     'Max Speed': [380., 370., 24., 26.],
+    ... })
     >>> df
        Animal  Max Speed
     0  Falcon      380.0
@@ -1272,10 +1273,10 @@ class DataFrameGroupBy(GroupBy, GetAttrGetItemMixin):
     Parrot       25.0
 
     >>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
-    ... ['Captive', 'Wild', 'Captive', 'Wild']]
+    ...           ['Captive', 'Wild', 'Captive', 'Wild']]
     >>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
     >>> df = cudf.DataFrame({'Max Speed': [390., 350., 30., 20.]},
-            index=index)
+    ...     index=index)
     >>> df
                     Max Speed
     Animal Type