keyset#
A KeySet specifies a list of values for one or more columns.
They are used as input to the
groupby()
method to build
group-by queries. An introduction to KeySets can be found in the
Group-by queries tutorial.
Classes#
- class KeySet#
Bases:
abc.ABC
A class containing a set of values for specific columns.
An introduction to KeySet initialization and manipulation can be found in the Group-by queries tutorial.
Warning
If a column has null values dropped or replaced, then Analytics will raise an error if you use a KeySet that contains a null value for that column.
Note
The
from_dict()
andfrom_dataframe()
methods are the preferred way to construct KeySets. Directly constructing KeySets skips checks that guarantee the uniqueness of output rows, and__init__
methods are not guaranteed to work the same way between releases.# Create a KeySet from a dictionary.
Create a KeySet from a dataframe.
Return the dataframe associated with this KeySet.
KeySet[col, col, ...]
returns a KeySet with those columns only.Override equality.
Returns the KeySet’s schema.
A product (
KeySet * KeySet
) returns the cross-product of both KeySets.Return the list of columns used by this KeySet.
Filter this KeySet using some condition.
Get the size of this KeySet.
Caches the KeySet’s dataframe in memory.
Removes the KeySet’s dataframe from memory and disk.
- classmethod from_dict(domains)#
Create a KeySet from a dictionary.
The
domains
dictionary should map column names to the desired values for those columns. The KeySet returned is the cross-product of those columns. Duplicate values in the column domains are allowed, but only one of the duplicates is kept.Example
>>> domains = { ... "A": ["a1", "a2"], ... "B": ["b1", "b2"], ... } >>> keyset = KeySet.from_dict(domains) >>> keyset.dataframe().sort("A", "B").toPandas() A B 0 a1 b1 1 a1 b2 2 a2 b1 3 a2 b2
- Parameters:
domains (Mapping[str, Union[Iterable[Optional[str]], Iterable[Optional[int]], Iterable[Optional[datetime.date]]]]) –
- Return type:
- classmethod from_dataframe(dataframe)#
Create a KeySet from a dataframe.
This DataFrame should contain every combination of values being selected in the KeySet. If there are duplicate rows in the dataframe, only one copy of each will be kept.
When creating KeySets with this method, it is the responsibility of the caller to ensure that the given dataframe remains valid for the lifetime of the KeySet. If the dataframe becomes invalid, for example because its Spark session is closed, this method or any uses of the resulting dataframe may raise exceptions or have other unanticipated effects.
- Parameters:
dataframe (pyspark.sql.DataFrame) –
- Return type:
- abstract dataframe()#
Return the dataframe associated with this KeySet.
This dataframe contains every combination of values being selected in the KeySet, and its rows are guaranteed to be unique as long as the KeySet was constructed safely.
- Return type:
- abstract __getitem__(columns)#
KeySet[col, col, ...]
returns a KeySet with those columns only.The returned KeySet contains all unique combinations of values in the given columns that were present in the original KeySet.
Example
>>> domains = { ... "A": ["a1", "a2"], ... "B": ["b1", "b2"], ... "C": ["c1", "c2"], ... "D": [0, 1, 2, 3] ... } >>> keyset = KeySet.from_dict(domains) >>> a_b_keyset = keyset["A", "B"] >>> a_b_keyset.dataframe().sort("A", "B").toPandas() A B 0 a1 b1 1 a1 b2 2 a2 b1 3 a2 b2 >>> a_b_keyset = keyset[["A", "B"]] >>> a_b_keyset.dataframe().sort("A", "B").toPandas() A B 0 a1 b1 1 a1 b2 2 a2 b1 3 a2 b2 >>> a_keyset = keyset["A"] >>> a_keyset.dataframe().sort("A").toPandas() A 0 a1 1 a2
- __eq__(other)#
Override equality.
Two KeySets are equal if their dataframes contain the same values for the same columns (in any order).
Example
>>> keyset1 = KeySet.from_dict({"A": ["a1", "a2"]}) >>> keyset2 = KeySet.from_dict({"A": ["a1", "a2"]}) >>> keyset3 = KeySet.from_dict({"A": ["a2", "a1"]}) >>> keyset1 == keyset2 True >>> keyset1 == keyset3 True >>> different_keyset = KeySet.from_dict({"B": ["a1", "a2"]}) >>> keyset1 == different_keyset False
- abstract schema()#
Returns the KeySet’s schema.
Example
>>> domains = { ... "A": ["a1", "a2"], ... "B": [0, 1, 2, 3], ... } >>> keyset = KeySet.from_dict(domains) >>> schema = keyset.schema() >>> schema {'A': ColumnDescriptor(column_type=ColumnType.VARCHAR, allow_null=True, allow_nan=False, allow_inf=False), 'B': ColumnDescriptor(column_type=ColumnType.INTEGER, allow_null=True, allow_nan=False, allow_inf=False)}
- Return type:
- __mul__(other)#
A product (
KeySet * KeySet
) returns the cross-product of both KeySets.Example
>>> keyset1 = KeySet.from_dict({"A": ["a1", "a2"]}) >>> keyset2 = KeySet.from_dict({"B": ["b1", "b2"]}) >>> product = keyset1 * keyset2 >>> product.dataframe().sort("A", "B").toPandas() A B 0 a1 b1 1 a1 b2 2 a2 b1 3 a2 b2
- abstract filter(condition)#
Filter this KeySet using some condition.
This method accepts the same syntax as
pyspark.sql.DataFrame.filter()
: valid conditions are those that can be used in a WHERE clause in Spark SQL. Examples of valid conditions include:age < 42
age BETWEEN 17 AND 42
age < 42 OR (age < 60 AND gender IS NULL)
LENGTH(name) > 17
favorite_color IN ('blue', 'red')
Example
>>> domains = { ... "A": ["a1", "a2"], ... "B": [0, 1, 2, 3], ... } >>> keyset = KeySet.from_dict(domains) >>> filtered_keyset = keyset.filter("B < 2") >>> filtered_keyset.dataframe().sort("A", "B").toPandas() A B 0 a1 0 1 a1 1 2 a2 0 3 a2 1 >>> filtered_keyset = keyset.filter(keyset.dataframe().A != "a1") >>> filtered_keyset.dataframe().sort("A", "B").toPandas() A B 0 a2 0 1 a2 1 2 a2 2 3 a2 3
- Parameters:
condition (Union[pyspark.sql.Column, str]) –
- Return type:
- cache()#
Caches the KeySet’s dataframe in memory.
- Return type:
None
- uncache()#
Removes the KeySet’s dataframe from memory and disk.
- Return type:
None