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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 @@ -363,9 +363,18 @@ def schema(self) -> StructType:

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df.schema
StructType([StructField('age', IntegerType(), True),
StructField('name', StringType(), True)])
StructType([StructField('age', LongType(), True), StructField('name', StringType(), True)])
"""
if self._schema is None:
try:
Expand Down Expand Up @@ -571,29 +580,42 @@ 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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@HyukjinKwon HyukjinKwon Aug 23, 2022

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ditto for indentation

>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 2|Alice|
| 5| Bob|
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df.show(2)
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
+---+-----+
only showing top 2 rows
>>> df.show(truncate=3)
+---+----+
|age|name|
+---+----+
| 2| Ali|
| 5| Bob|
| 14| Tom|
| 23| Ali|
| 16| Bob|
+---+----+
>>> 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 @@ -798,8 +820,18 @@ def count(self) -> int:

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
... (16, "Bob")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
| 16| Bob|
+---+-----+
>>> df.count()
2
3
"""
return int(self._jdf.count())

Expand Down Expand Up @@ -862,8 +894,18 @@ 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|
+---+-----+
>>> df.take(2)
[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 @@ -878,8 +920,18 @@ 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 @@ -1179,6 +1231,16 @@ 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|
+---+-----+
>>> df.distinct().count()
2
"""
Expand Down Expand Up @@ -1375,8 +1437,17 @@ def dtypes(self) -> List[Tuple[str, str]]:

Examples
--------
>>> df = spark.createDataFrame([(14, "Tom"),
... (23, "Alice")], ["age", "name"])
>>> df.show()
+---+-----+
|age| name|
+---+-----+
| 14| Tom|
| 23|Alice|
+---+-----+
>>> df.dtypes
[('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 @@ -2743,7 +2814,20 @@ 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"])
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@HyukjinKwon HyukjinKwon Aug 23, 2022

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intentation

>>> df.show()
+----+------+-----+
| age|height| name|
+----+------+-----+
| 10| 80|Alice|
| 5| null| Bob|
|null| null| Tom|
|null| null| null|
+----+------+-----+
>>> df.na.fill(50).show()
+---+------+-----+
|age|height| name|
+---+------+-----+
Expand All @@ -2753,7 +2837,19 @@ 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()
+----+-------+-----+
| age| name| spy|
+----+-------+-----+
Expand All @@ -2762,7 +2858,20 @@ 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 @@ -2869,7 +2978,18 @@ 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()
+----+------+-----+
| age|height| name|
+----+------+-----+
Expand All @@ -2879,17 +2999,20 @@ 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 = 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('Alice', None).show()
+----+------+----+
| age|height|name|
+----+------+----+
Expand All @@ -2899,7 +3022,20 @@ 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 = spark.createDataFrame([(10, 80, "Alice"), (5, None, "Bob"),
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@dcoliversun dcoliversun Aug 24, 2022

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Why do we need to create duplicate dataframe here? If we don't need it, better to delete

>>> 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('Alice', None).show()
+----+------+----+
| age|height|name|
+----+------+----+
|  10|    80|null|
|   5|  null| Bob|
|null|  null| Tom|
|null|  null|null|
+----+------+----+

>>> df.show()
+----+------+-----+
| age|height| name|
+----+------+-----+
|  10|    80|Alice|
|   5|  null|  Bob|
|null|  null|  Tom|
|null|  null| null|
+----+------+-----+

>>> df.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
+----+------+----+
| age|height|name|
+----+------+----+
|  10|    80|   A|
|   5|  null|   B|
|null|  null| Tom|
|null|  null|null|
+----+------+----+

... (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(['Alice', 'Bob'], ['A', 'B'], 'name').show()
+----+------+----+
| age|height|name|
+----+------+----+
Expand Down Expand Up @@ -3356,11 +3492,31 @@ 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()
[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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@itholic itholic Aug 26, 2022

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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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@Transurgeon Transurgeon Aug 23, 2022

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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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@HyukjinKwon HyukjinKwon Aug 23, 2022

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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 @@ -3393,12 +3549,22 @@ 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 DataFrame

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