def create_question(
self,
category: non_null(non_blank(str)),
question: non_null(non_blank(str)),
q_type: non_null(non_blank(str)) = 'range'
) -> Union[BooleanMarkQuestion, RangeMarkQuestion]:
if q_type == 'range':
return RangeMarkQuestionService.create(category, question)
return BooleanMarkQuestionService.create(category, question)
def create(cls, employee_id: non_negative(non_null(int))) -> model:
feedback = (Feedback.objects.select_related(
'user', 'personal_satisfaction', 'team_satisfaction',
'company_satisfaction').order_by('-id').filter(
user_id=employee_id).first())
# TODO: add report generation logic
return
def foo(
a: non_blank(),
b: non_null(),
c: non_empty(),
d: no_whitespaces(),
e: non_negative(),
f: strongly_typed(List),
):
return a, b, c, d, e, f
def __init__(
self,
name: non_blank(str),
description: non_blank(str),
dtype: non_blank(DataType) = None,
from_column: non_blank(str) = None,
transformation: non_null(TransformComponent) = None,
) -> None:
self.name = name
self.description = description
self.transformation = transformation
self.dtype = dtype
self.from_column = from_column
def create(
cls, name: non_null(non_blank(str)), surname: non_null(non_blank(str)),
position: non_null(non_blank(str)), level: non_null(non_blank(str)),
email: non_null(non_blank(str)), pay: non_negative(non_null(int))
) -> User:
validate_email(email)
return cls.model.objects.create(name=name,
surname=surname,
position=position,
hire_date=date.today(),
level=level,
email=email,
pay=pay)
def pivot(
dataframe: DataFrame,
group_by_columns: non_blank(List[str]),
pivot_column: non_blank(str),
agg_column: non_blank(str),
aggregation: non_null(Callable),
mock_value: non_null(object) = None,
mock_type: non_null(object) = None,
with_forward_fill: non_null(bool) = False,
):
"""Defines a pivot transformation.
Attributes:
dataframe: dataframe to be pivoted.
group_by_columns: list of columns' names to be grouped.
pivot_column: column to be pivoted.
agg_column: column to be aggregated by pivoted category.
aggregation: desired spark aggregation function to be performed.
An example: spark_agg(col_name). See docs for all spark_agg:
https://spark.apache.org/docs/2.3.1/api/python/_modules/pyspark/sql/functions.html
mock_value: value used to make a difference between true nulls resulting from
the aggregation and empty values from the pivot transformation.
mock_type: mock_value data type (compatible with spark).
with_forward_fill: applies a forward fill to null values after the pivot
operation.
Example:
>>> dataframe.orderBy("ts", "id", "amenity").show()
+---+---+-------+-----+
| id| ts|amenity| has|
+---+---+-------+-----+
| 1| 1| fridge|false|
| 1| 1| oven| true|
| 1| 1| pool|false|
| 2| 2|balcony|false|
| 1| 3|balcony| null|
| 1| 4| oven| null|
| 1| 4| pool| true|
| 1| 5|balcony| true|
+---+---+-------+-----+
>>> pivoted = pivot(dataframe, ["id", "ts"], "amenity", "has", functions.first)
>>> pivoted.orderBy("ts", "id").show()
+---+---+-------+------+----+-----+
| id| ts|balcony|fridge|oven| pool|
+---+---+-------+------+----+-----+
| 1| 1| null| false|true|false|
| 2| 2| false| null|null| null|
| 1| 3| null| null|null| null|
| 1| 4| null| null|null| true|
| 1| 5| true| null|null| null|
+---+---+-------+------+----+-----+
But, sometimes, you would like to keep the last values that some feature has
assumed from previous modifications. In this example, amenity "oven" for the
id=1 was set to null and "pool" was set to true at ts=4. All other amenities
should then be kept to their actual state at that ts. To do that, we will use
a technique called forward fill:
>>> pivoted = pivot(
... dataframe,
... ["id", "ts"],
... "amenity",
... "has",
... functions.first,
... with_forward_fill=True
...)
>>> pivoted.orderBy("ts", "id").show()
+---+---+-------+------+----+-----+
| id| ts|balcony|fridge|oven| pool|
+---+---+-------+------+----+-----+
| 1| 1| null| false|true|false|
| 2| 2| false| null|null| null|
| 1| 3| null| false|true|false|
| 1| 4| null| false|true| true|
| 1| 5| true| false|true| true|
+---+---+-------+------+----+-----+
Great! Now every amenity that didn't have been changed kept it's state. BUT,
the force change to null for amenity "oven" on id=1 at ts=4 was ignored during
forward fill. If the user wants to respect this change, it must provide a mock
value and type to be used as a signal for "true nulls". In other words, we want
to forward fill only nulls that were created by the pivot transformation.
In this example, amenities only assume boolean values. So there is no mock
values for a boolean. It is only true or false. So users can give a mock value
of another type (for which the column can be cast to). Check this out:
>>> pivoted = pivot(
... dataframe,
... ["id", "ts"],
... "amenity",
... "has",
... functions.first,
... with_forward_fill=True,
... mock_value=-1,
... mock_type="int"
...)
>>> pivoted.orderBy("ts", "id").show()
+---+---+-------+------+----+-----+
| id| ts|balcony|fridge|oven| pool|
+---+---+-------+------+----+-----+
| 1| 1| null| false|true|false|
| 2| 2| false| null|null| null|
| 1| 3| null| false|true|false|
| 1| 4| null| false|null| true|
| 1| 5| true| false|null| true|
+---+---+-------+------+----+-----+
During transformation, this method will cast the agg_column to mock_type
data type and fill all "true nulls" with the mock_value. After pivot and forward
fill are applied, all new pivoted columns will then return to the original type
with all mock values replaced by null.
"""
agg_column_type = None
if mock_value is not None:
if mock_type is None:
raise AttributeError(
"When proving a mock value, users must inform the data type,"
" which should be supported by Spark.")
agg_column_type = dict(dataframe.dtypes).get(agg_column)
dataframe = dataframe.withColumn(
agg_column,
functions.col(agg_column).cast(mock_type)).fillna(
{agg_column: mock_value})
pivoted = (dataframe.groupBy(*group_by_columns).pivot(pivot_column).agg(
aggregation(agg_column)))
new_columns = [c for c in pivoted.columns if c not in group_by_columns]
if with_forward_fill:
for c in new_columns:
pivoted = forward_fill(
dataframe=pivoted,
partition_by=group_by_columns[:-1],
order_by=group_by_columns[-1],
fill_column=c,
)
if mock_value is not None:
for c in new_columns:
pivoted = pivoted.withColumn(
c,
functions.when(
functions.col(c) != mock_value,
functions.col(c)).cast(agg_column_type),
)
return pivoted
def create(cls, category: non_null(non_blank(str)),
question: non_null(non_blank(str))):
return cls.model.objects.create(question_string=question,
category=category)
def guinea_pig(front: str, back: non_null(str)):
result = None
if front:
result = front + '-' + back
return result