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[SPARK-40012][PYTHON][DOCS] Make pyspark.sql.dataframe examples self-contained (Part 1) #37444

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[SPARK-40012][PYTHON][DOCS] Make pyspark.sql.dataframe examples self-contained (Part 1) #37444

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380149f
adding self-contained examples for pyspark dataframe
Transurgeon Aug 9, 2022
1fed3ca
adding additional self-contained examples for dataframe API
Transurgeon Aug 9, 2022
24c4769
splitting instantion of df to two lines to avoid overflow of example …
Transurgeon Aug 23, 2022
60605d3
Apply suggestions from code review
Transurgeon Aug 25, 2022
1ce9604
adding descriptions and removing duplicate dataframe initialisations
Transurgeon Aug 25, 2022
096ab81
Merge branch 'master' into master
Transurgeon Aug 25, 2022
dfa5726
running python lint and reformat
Transurgeon Aug 26, 2022
aadf3d0
making some additional small detail changes
Transurgeon Aug 26, 2022
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238 changes: 202 additions & 36 deletions python/pyspark/sql/dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -477,9 +477,22 @@ def schema(self) -> StructType:

Examples
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--------
>>> df = spark.createDataFrame(
... [(14, "Tom"), (23, "Alice"),(16, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+

Retrieve the schema of the current DataFrame.

>>> df.schema
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StructType([StructField('age', IntegerType(), True),
StructField('name', StringType(), True)])
StructType([StructField('age', IntegerType(), True),
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Let's remove the space in the end

StructField('name', StringType(), True)])
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Please avoid using tabs

"""
if self._schema is None:
try:
Expand Down Expand Up @@ -731,29 +744,51 @@ def show(self, n: int = 20, truncate: Union[bool, int] = True, vertical: bool =

Examples
--------
>>> df
DataFrame[age: int, name: string]
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
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>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+

Show only top 2 rows.

>>> df.show(2)
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+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
+---+-----+
only showing top 2 rows

Show DataFrame where the maximum number of characters is 3.
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:class:`DataFrame`


>>> df.show(truncate=3)
+---+----+
|age|name|
+---+----+
| 2| Ali|
| 5| Bob|
| 14| Tom|
| 23| Ali|
| 16| Bob|
+---+----+

Show DataFrame vertically.

>>> df.show(vertical=True)
-RECORD 0-----
age | 2
name | Alice
age | 14
name | Tom
-RECORD 1-----
age | 5
name | Bob
age | 23
name | Alice
-RECORD 2-----
age | 16
name | Bob
"""

if not isinstance(n, int) or isinstance(n, bool):
Expand Down Expand Up @@ -992,8 +1027,21 @@ def count(self) -> int:

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
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>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+

Return the number of rows in the :class:`DataFrame`.

>>> df.count()
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2
3
"""
return int(self._jdf.count())

Expand Down Expand Up @@ -1088,8 +1136,21 @@ def take(self, num: int) -> List[Row]:

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+

Return the first 2 rows of the :class:`DataFrame`.

>>> df.take(2)
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[Row(age=2, name='Alice'), Row(age=5, name='Bob')]
[Row(age=14, name='Tom'), Row(age=23, name='Alice')]
"""
return self.limit(num).collect()

Expand All @@ -1115,8 +1176,18 @@ def tail(self, num: int) -> List[Row]:

Examples
--------
>>> df.tail(1)
[Row(age=5, name='Bob')]
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df.tail(2)
[Row(age=23, name='Alice'), Row(age=16, name='Bob')]
"""
with SCCallSiteSync(self._sc):
sock_info = self._jdf.tailToPython(num)
Expand Down Expand Up @@ -1511,6 +1582,19 @@ def distinct(self) -> "DataFrame":

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (23, "Alice")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 23|Alice|
+---+-----+

Return the number of distinct rows in the :class:`DataFrame`

>>> df.distinct().count()
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2
"""
Expand Down Expand Up @@ -1722,8 +1806,17 @@ def dtypes(self) -> List[Tuple[str, str]]:

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"),
... (23, "Alice")], ["age", "name"])
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>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
+---+-----+
>>> df.dtypes
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[('age', 'int'), ('name', 'string')]
[('age', 'bigint'), ('name', 'string')]
"""
return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields]

Expand Down Expand Up @@ -3358,7 +3451,20 @@ def fillna(

Examples
--------
>>> df4.na.fill(50).show()
Fill all null values with 50 when the data type of the column is an integer

>>> df = spark.createDataFrame([(10, 80, "Alice"), (5, None, "Bob"),
... (None, None, "Tom"), (None, None, None)], ["age", "height", "name"])
>>> df.show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
| 5| null| Bob|
|null| null| Tom|
|null| null| null|
+----+------+-----+
>>> df.na.fill(50).show()
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+---+------+-----+
|age|height| name|
+---+------+-----+
Expand All @@ -3368,7 +3474,19 @@ def fillna(
| 50| 50| null|
+---+------+-----+

>>> df5.na.fill(False).show()
Fill all null values with ``False`` when the data type of the column is a boolean

>>> df = spark.createDataFrame([(10, "Alice", None), (5, "Bob", None),
... (None, "Mallory", True)], ["age", "name", "spy"])
>>> df.show()
+----+-------+----+
| age| name| spy|
+----+-------+----+
| 10| Alice|null|
| 5| Bob|null|
|null|Mallory|true|
+----+-------+----+
>>> df.na.fill(False).show()
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+----+-------+-----+
| age| name| spy|
+----+-------+-----+
Expand All @@ -3377,7 +3495,20 @@ def fillna(
|null|Mallory| true|
+----+-------+-----+

>>> df4.na.fill({'age': 50, 'name': 'unknown'}).show()
Fill all null values in the 'age' column to 50 and "unknown" in the 'name' column

>>> df = spark.createDataFrame([(10, 80, "Alice"), (5, None, "Bob"),
... (None, None, "Tom"), (None, None, None)], ["age", "height", "name"])
>>> df.show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
| 5| null| Bob|
|null| null| Tom|
|null| null| null|
+----+------+-----+
>>> df.na.fill({'age': 50, 'name': 'unknown'}).show()
+---+------+-------+
|age|height| name|
+---+------+-------+
Expand Down Expand Up @@ -3489,7 +3620,18 @@ def replace( # type: ignore[misc]

Examples
--------
>>> df4.na.replace(10, 20).show()
>>> df = spark.createDataFrame([(10, 80, "Alice"), (5, None, "Bob"),
... (None, None, "Tom"), (None, None, None)], ["age", "height", "name"])
>>> df.show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
| 5| null| Bob|
|null| null| Tom|
|null| null| null|
+----+------+-----+
>>> df.na.replace(10, 20).show()
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+----+------+-----+
| age|height| name|
+----+------+-----+
Expand All @@ -3499,17 +3641,9 @@ def replace( # type: ignore[misc]
|null| null| null|
+----+------+-----+

>>> df4.na.replace('Alice', None).show()
+----+------+----+
| age|height|name|
+----+------+----+
| 10| 80|null|
| 5| null| Bob|
|null| null| Tom|
|null| null|null|
+----+------+----+
Replace all instances of Alice to null

>>> df4.na.replace({'Alice': None}).show()
>>> df.na.replace('Alice', None).show()
+----+------+----+
| age|height|name|
+----+------+----+
Expand All @@ -3519,7 +3653,9 @@ def replace( # type: ignore[misc]
|null| null|null|
+----+------+----+

>>> df4.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
Replace all instances of Alice to 'A' and Bob to 'B' under the name column

>>> df.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
+----+------+----+
| age|height|name|
+----+------+----+
Expand Down Expand Up @@ -4064,11 +4200,31 @@ def drop(self, *cols: "ColumnOrName") -> "DataFrame": # type: ignore[misc]

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df.drop('age').collect()
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[Row(name='Alice'), Row(name='Bob')]
[Row(name='Tom'), Row(name='Alice'), Row(name='Bob')]

>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
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I think we don't need to create a new DataFrame here, since drop() doesn't remove the column in-place.

e.g.

>>> df.drop('age').show()
+-----+
| name|
+-----+
|  Tom|
|Alice|
|  Bob|
+-----+

>>> df.drop(df.age).show()
+-----+
| name|
+-----+
|  Tom|
|Alice|
|  Bob|
+-----+

>>> df.drop(df.age).collect()
[Row(name='Alice'), Row(name='Bob')]
[Row(name='Tom'), Row(name='Alice'), Row(name='Bob')]

>>> df.join(df2, df.name == df2.name, 'inner').drop(df.name).collect()
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I am not sure what these 3 inner joins do exactly. I dont see anywhere an instantiation of df2..

What should I do with these 3 examples?

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I think it's showing a common example that join and drop the join key.

[Row(age=5, height=85, name='Bob')]
Expand Down Expand Up @@ -4100,7 +4256,7 @@ def toDF(self, *cols: "ColumnOrName") -> "DataFrame":
Parameters
----------
cols : str
new column names
new column names. The length of the list needs to be the same as the number of columns in the initial :class:`DataFrame`
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@Transurgeon mind running ./dev/lint-python script and fix the line length, etc?

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yes will do, sorry about that

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Seems like it exceeds the 100 lines, which violates flake8 rule.

starting flake8 test...
flake8 checks failed:
./python/pyspark/sql/dataframe.py:4250:101: E501 line too long (128 > 100 characters)
        """
        Returns a best-effort snapshot of the files that compose this :class:`DataFrame`.
        This method simply asks each constituent BaseRelation for its respective files and
        takes the union of all results. Depending on the source relations, this may not find
        all input files. Duplicates are removed.

        new column names. The length of the list needs to be the same as the number of columns in the initial :class:`DataFrame`

        .. versionadded:: 3.1.0

        Returns
        -------
        list
            List of file paths.

        Examples
        --------
        >>> df = spark.read.load("examples/src/main/resources/people.json", format="json")
        >>> len(df.inputFiles())
        1
        """

                                                                                        ^
1     E501 line too long (128 > 100 characters)

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You can run dev/lint-python to check if the static analysis is passed.

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done


Returns
-------
Expand All @@ -4109,8 +4265,18 @@ def toDF(self, *cols: "ColumnOrName") -> "DataFrame":

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
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>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df.toDF('f1', 'f2').collect()
[Row(f1=2, f2='Alice'), Row(f1=5, f2='Bob')]
[Row(f1=14, f2='Tom'), Row(f1=23, f2='Alice'), Row(f1=16, f2='Bob')]
"""
jdf = self._jdf.toDF(self._jseq(cols))
return DataFrame(jdf, self.sparkSession)
Expand Down