Usage

Quinn Helper Functions

import quinn

DataFrame Validations

validate_presence_of_columns()

quinn.validate_presence_of_columns(source_df, ["name", "age", "fun"])

Raises an exception unless source_df contains the name, age, and fun column.

validate_schema()

quinn.validate_schema(source_df, required_schema)

Raises an exception unless source_df contains all the StructFields defined in the required_schema.

validate_absence_of_columns()

quinn.validate_absence_of_columns(source_df, ["age", "cool"])

Raises an exception if source_df contains age or cool columns.

Functions

single_space()

actual_df = source_df.withColumn(
    "words_single_spaced",
    quinn.single_space(col("words"))
)

Replaces all multispaces with single spaces (e.g. changes "this has some" to "this has some".

remove_all_whitespace()

actual_df = source_df.withColumn(
    "words_without_whitespace",
    quinn.remove_all_whitespace(col("words"))
)

Removes all whitespace in a string (e.g. changes "this has some" to "thishassome".

anti_trim()

actual_df = source_df.withColumn(
    "words_anti_trimmed",
    quinn.anti_trim(col("words"))
)

Removes all inner whitespace, but doesn't delete leading or trailing whitespace (e.g. changes " this has some " to " thishassome ".

remove_non_word_characters()

actual_df = source_df.withColumn(
    "words_without_nonword_chars",
    quinn.remove_non_word_characters(col("words"))
)

Removes all non-word characters from a string (e.g. changes "si%$#@!#$!@#mpsons" to "simpsons".

multi_equals()

source_df.withColumn(
    "are_s1_and_s2_cat",
    quinn.multi_equals("cat")(col("s1"), col("s2"))
)

multi_equals returns true if s1 and s2 are both equal to "cat".

approx_equal()

This function takes 3 arguments which are 2 Pyspark DataFrames and one integer values as threshold, and returns the Boolean column which tells if the columns are equal in the threshold.

let the columns be
col1 = [1.2, 2.5, 3.1, 4.0, 5.5]
col2 = [1.3, 2.3, 3.0, 3.9, 5.6]
threshold = 0.2

result = approx_equal(col("col1"), col("col2"), threshold)
result.show()

+-----+
|value|
+-----+
| true|
|false|
| true|
| true|
| true|
+-----+

array_choice()

This function takes a Column as a parameter and returns a PySpark column that contains a random value from the input column parameter

df = spark.createDataFrame([(1,), (2,), (3,), (4,), (5,)], ["values"])
result = df.select(array_choice(col("values")))

The output is :=
+--------------+
|array_choice()|
+--------------+
|             2|
+--------------+

regexp_extract_all()

The regexp_extract_all takes 2 parameters String s and regexp which is a regular expression. This function finds all the matches for the string which satisfies the regular expression.

print(regexp_extract_all("this is a example text message for testing application",r"\b\w*a\w*\b"))

The output is :=
['a', 'example', 'message', 'application']

Where r"\b\w*a\w*\b" pattern checks for words containing letter a

week_start_date()

It takes 2 parameters, column and week_start_day. It returns a Spark Dataframe column which contains the start date of the week. By default the week_start_day is set to "Sun".

For input ["2023-03-05", "2023-03-06", "2023-03-07", "2023-03-08"] the Output is

result = df.select("date", week_start_date(col("date"), "Sun"))
result.show()
+----------+----------------+
|      date|week_start_date |
+----------+----------------+
|2023-03-05|      2023-03-05|
|2023-03-07|      2023-03-05|
|2023-03-08|      2023-03-05|
+----------+----------------+

week_end_date()

It also takes 2 Paramters as Column and week_end_day, and returns the dateframe column which contains the end date of the week. By default the week_end_day is set to "sat"

+---------+-------------+
      date|week_end_date|
+---------+-------------+
2023-03-05|   2023-03-05|
2023-03-07|   2023-03-12|
2023-03-08|   2023-03-12|
+---------+-------------+

uuid5()

This function generates UUIDv5 in string form from the passed column and optionally namespace and optional extra salt. By default namespace is NAMESPACE_DNS UUID and no extra string used to reduce hash collisions.

df = spark.createDataFrame([("lorem",), ("ipsum",)], ["values"])
result = df.select(quinn.uuid5(F.col("values")).alias("uuid5"))
result.show(truncate=False)

The output is :=
+------------------------------------+
|uuid5                               |
+------------------------------------+
|35482fda-c10a-5076-8da2-dc7bf22d6be4|
|51b79c1d-d06c-5b30-a5c6-1fadcd3b2103|
+------------------------------------+

Transformations

snake_case_col_names()

quinn.snake_case_col_names(source_df)

Converts all the column names in a DataFrame to snake_case. It's annoying to write SQL queries when columns aren't snake cased.

sort_columns()

quinn.sort_columns(df=source_df, sort_order="asc", sort_nested=True)

Sorts the DataFrame columns in alphabetical order, including nested columns if sort_nested is set to True. Wide DataFrames are easier to navigate when they're sorted alphabetically.

DataFrame Helpers

column_to_list()

quinn.column_to_list(source_df, "name")

Converts a column in a DataFrame to a list of values.

two_columns_to_dictionary()

quinn.two_columns_to_dictionary(source_df, "name", "age")

Converts two columns of a DataFrame into a dictionary. In this example, name is the key and age is the value.

to_list_of_dictionaries()

quinn.to_list_of_dictionaries(source_df)

Converts an entire DataFrame into a list of dictionaries.

show_output_to_df()

quinn.show_output_to_df(output_str, spark)

Parses a spark DataFrame output string into a spark DataFrame. Useful for quickly pulling data from a log into a DataFrame. In this example, output_str is a string of the form:

+----+---+-----------+------+
|name|age|     stuff1|stuff2|
+----+---+-----------+------+
|jose|  1|nice person|  yoyo|
|  li|  2|nice person|  yoyo|
| liz|  3|nice person|  yoyo|
+----+---+-----------+------+

Schema Helpers

schema_from_csv()

quinn.schema_from_csv("schema.csv")

Converts a CSV file into a PySpark schema (aka StructType). The CSV must contain the column name and type. The nullable and metadata columns are optional.

Here's an example CSV file:

name,type
person,string
address,string
phoneNumber,string
age,int

Here's how to convert that CSV file to a PySpark schema:

schema = schema_from_csv(spark, "some_file.csv")

StructType([
    StructField("person", StringType(), True),
    StructField("address", StringType(), True),
    StructField("phoneNumber", StringType(), True),
    StructField("age", IntegerType(), True),
])

Here's a more complex CSV file:

name,type,nullable,metadata
person,string,false,{"description":"The person's name"}
address,string
phoneNumber,string,TRUE,{"description":"The person's phone number"}
age,int,False

Here's how to read this CSV file into a PySpark schema:

another_schema = schema_from_csv(spark, "some_file.csv")

StructType([
    StructField("person", StringType(), False, {"description": "The person's name"}),
    StructField("address", StringType(), True),
    StructField("phoneNumber", StringType(), True, {"description": "The person's phone number"}),
    StructField("age", IntegerType(), False),
])

print_schema_as_code()

fields = [
    StructField("simple_int", IntegerType()),
    StructField("decimal_with_nums", DecimalType(19, 8)),
    StructField("array", ArrayType(FloatType()))
]
schema = StructType(fields)
printable_schema: str = quinn.print_schema_as_code(schema)

Converts a Spark DataType to a string of Python code that can be evaluated as code using eval(). If the DataType is a StructType, this can be used to print an existing schema in a format that can be copy-pasted into a Python script, log to a file, etc.

For example:

print(printable_schema)

StructType(
    fields=[
        StructField("simple_int", IntegerType(), True),
        StructField("decimal_with_nums", DecimalType(19, 8), True),
        StructField(
            "array",
            ArrayType(FloatType()),
            True,
        ),
    ]
)

Once evaluated, the printable schema is a valid schema that can be used in dataframe creation, validation, etc.

from chispa.schema_comparer import assert_basic_schema_equality

parsed_schema = eval(printable_schema)
assert_basic_schema_equality(parsed_schema, schema) # passes

print_schema_as_code() can also be used to print other DataType objects.

ArrayType

array_type = ArrayType(FloatType())
printable_type: str = quinn.print_schema_as_code(array_type)
print(printable_type)
 ```

 ```
ArrayType(FloatType())
 ```

`MapType`
```python
map_type = MapType(StringType(), FloatType())
printable_type: str = quinn.print_schema_as_code(map_type)
print(printable_type)
 ```

 ```
MapType(
        StringType(),
        FloatType(),
        True,
)
 ```

`IntegerType`, `StringType` etc.
```python
integer_type = IntegerType()
printable_type: str = quinn.print_schema_as_code(integer_type)
print(printable_type)
 ```

 ```
IntegerType()
 ```

## Pyspark Core Class Extensions

import pyspark.sql.functions as F import quinn

### Column Extensions

**isFalsy()**

```python
source_df.withColumn("is_stuff_falsy", quinn.is_falsy(F.col("has_stuff")))

Returns True if has_stuff is None or False.

isTruthy()

source_df.withColumn("is_stuff_truthy", quinn.is_truthy(F.col("has_stuff")))

Returns True unless has_stuff is None or False.

isNullOrBlank()

source_df.withColumn("is_blah_null_or_blank", quinn.is_null_or_blank(F.col("blah")))

Returns True if blah is null or blank (the empty string or a string that only contains whitespace).

isNotIn()

source_df.withColumn("is_not_bobs_hobby", quinn.is_not_in(F.col("fun_thing")))

Returns True if fun_thing is not included in the bobs_hobbies list.

nullBetween()

source_df.withColumn("is_between", quinn.null_between(F.col("age"), F.col("lower_age"), F.col("upper_age")))

Returns True if age is between lower_age and upper_age. If lower_age is populated and upper_age is null, it will return True if age is greater than or equal to lower_age. If lower_age is null and upper_age is populate, it will return True if age is lower than or equal to upper_age.