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[MINOR][DOCS] Match several documentation changes in Scala to R/Python #17429

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[MINOR][DOCS] Match several documentation changes in Scala to R/Python #17429

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5f0ebdd
Match Scala/Python/R changes
HyukjinKwon Mar 25, 2017
33f1328
Remove potentially confusing doctest in array_contains in Python
HyukjinKwon Mar 26, 2017
d21a9bf
Add the test in python/pyspark/sql/tests.py instead
HyukjinKwon Mar 26, 2017
d05aba5
Rename variables
HyukjinKwon Mar 26, 2017
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6 changes: 3 additions & 3 deletions R/pkg/R/functions.R
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Original file line number Diff line number Diff line change
Expand Up @@ -2632,8 +2632,8 @@ setMethod("date_sub", signature(y = "Column", x = "numeric"),

#' format_number
#'
#' Formats numeric column y to a format like '#,###,###.##', rounded to x decimal places,
#' and returns the result as a string column.
#' Formats numeric column y to a format like '#,###,###.##', rounded to x decimal places
#' with HALF_EVEN round mode, and returns the result as a string column.
#'
#' If x is 0, the result has no decimal point or fractional part.
#' If x < 0, the result will be null.
Expand Down Expand Up @@ -3548,7 +3548,7 @@ setMethod("row_number",

#' array_contains
#'
#' Returns true if the array contain the value.
#' Returns null if the array is null, true if the array contains the value, and false otherwise.
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@felixcheung felixcheung Mar 25, 2017

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for null, we need to be more careful - null in JVM should show up as NA in R.
also, should true be TRUE and false be FALSE to match R type?

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@HyukjinKwon HyukjinKwon Mar 25, 2017 •
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Yea, I agree with being careful. For this PR, I just followed the others. I skimmed again and it seems we have not used the notation for None, True and False in functions.py, and NA, TRUE and FALSE in functions.R.

I can grep and replace.

#'
#' @param x A Column
#' @param value A value to be checked if contained in the column
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8 changes: 4 additions & 4 deletions python/pyspark/sql/functions.py
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Expand Up @@ -1327,8 +1327,8 @@ def encode(col, charset):
@since(1.5)
def format_number(col, d):
"""
Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places,
and returns the result as a string.
Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places
with HALF_EVEN round mode, and returns the result as a string.

:param col: the column name of the numeric value to be formatted
:param d: the N decimal places
Expand Down Expand Up @@ -1675,8 +1675,8 @@ def array(*cols):
@since(1.5)
def array_contains(col, value):
"""
Collection function: returns True if the array contains the given value. The collection
elements and value must be of the same type.
Collection function: returns null if the array is null, true if the array contains the
given value, and false otherwise.
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@HyukjinKwon HyukjinKwon Mar 25, 2017 •
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Other documentation in this file use true rather than True. So, I matach this to true. I am willing to sweep if anyone feels this should be fixed.
The reason I removed The collection elements and value must be of the same type is it seems we can provide other types that are implicitly castable.
This is not documented in Scala/R too. So, I instead provided a doctest as an example below in the Python documentation.

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@felixcheung felixcheung Mar 25, 2017

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like my other comment, probably should say True when in Python, @holdenk?

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@HyukjinKwon HyukjinKwon Mar 25, 2017 •
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I am willing to grep and replace too. Please let me know @holdenk.

FWIW, If deciding this takes a while and holds off this PR, I would like to ask to merge this as is if you and @holdenk do not strongly feel about this.


:param col: name of column containing array
:param value: value to check for in array
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8 changes: 8 additions & 0 deletions python/pyspark/sql/tests.py
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Expand Up @@ -1129,6 +1129,14 @@ def test_rand_functions(self):
rndn2 = df.select('key', functions.randn(0)).collect()
self.assertEqual(sorted(rndn1), sorted(rndn2))

def test_array_contains_function(self):
from pyspark.sql.functions import array_contains

df = self.spark.createDataFrame([(["1", "2", "3"],), ([],)], ['data'])
actual = df.select(array_contains(df.data, 1).alias('b')).collect()
# The value argument can be implicitly castable to the element's type of the array.
self.assertEqual([Row(b=True), Row(b=False)], actual)

def test_between_function(self):
df = self.sc.parallelize([
Row(a=1, b=2, c=3),
Expand Down