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README.md

Quinn

Pyspark helper methods to maximize developer productivity.

Quinn validates DataFrames, extends core classes, defines DataFrame transformations, and provides SQL functions.

quinn

Setup

Quinn is uploaded to PyPi and can be installed with this command:

pip install quinn

Pyspark Core Class Extensions

from quinn.extensions import *

Column Extensions

isFalsy()

source_df.withColumn("is_stuff_falsy", F.col("has_stuff").isFalsy())

Returns True if has_stuff is None or False.

isTruthy()

source_df.withColumn("is_stuff_truthy", F.col("has_stuff").isTruthy())

Returns True unless has_stuff is None or False.

isNullOrBlank()

source_df.withColumn("is_blah_null_or_blank", F.col("blah").isNullOrBlank())

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", F.col("fun_thing").isNotIn(bobs_hobbies))

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

nullBetween()

source_df.withColumn("is_between", F.col("age").nullBetween(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.

SparkSession Extensions

create_df()

spark.create_df(
    [("jose", "a"), ("li", "b"), ("sam", "c")],
    [("name", StringType(), True), ("blah", StringType(), True)]
)

Creates DataFrame with a syntax that's less verbose than the built-in createDataFrame method.

DataFrame Extensions

transform()

source_df\
    .transform(lambda df: with_greeting(df))\
    .transform(lambda df: with_something(df, "crazy"))

Allows for multiple DataFrame transformations to be run and executed.

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".

exists()

source_df.withColumn(
    "any_num_greater_than_5",
    quinn.exists(lambda n: n > 5)(col("nums"))
)

nums contains lists of numbers and exists() returns True if any of the numbers in the list are greater than 5. It's similar to the Python any function.

forall()

source_df.withColumn(
    "all_nums_greater_than_3",
    quinn.forall(lambda n: n > 3)(col("nums"))
)

nums contains lists of numbers and forall() returns True if all of the numbers in the list are greater than 3. It's similar to the Python all function.

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".

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(source_df, "asc")

Sorts the DataFrame columns in alphabetical order. 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.

Contributing

We are actively looking for feature requests, pull requests, and bug fixes.

Any developer that demonstrates excellence will be invited to be a maintainer of the project.

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