Spark-df-Cleaner 0.0.4

spark dataframe cleaner

SparkCleaner Documentation

Overview

Disclaimer: this software is not stable yet

SparkCleaner is a Python library for data cleaning using PySpark. It provides various cleaning strategies, a pipeline for applying these strategies, and a logging mechanism for tracking errors and issues.

Cleaning Strategies

1. DropDuplicatesStrategy

   • Purpose: Removes duplicate rows based on specified columns.
   • Method: clean(df: DataFrame) -> DataFrame
   • Logs: Logs errors for rows identified as duplicates.

2. DropMissingValuesStrategy

   • Purpose: Removes rows with missing values in specified columns.
   • Method: clean(df: DataFrame) -> DataFrame
   • Logs: Logs errors for missing values in specified columns.

3. FilterNegativeValuesStrategy

   • Purpose: Filters out rows where specified columns have non-positive values.
   • Method: clean(df: DataFrame) -> DataFrame
   • Logs: Logs errors for negative values in specified columns.

4. ValidateColumnTypesStrategy

   • Purpose: Ensures columns have the expected data types.
   • Method: clean(df: DataFrame) -> DataFrame
   • Logs: Logs errors for type mismatches.

5. ValidateDatesStrategy

   • Purpose: Validates dates in specified columns against a given format.
   • Method: clean(df: DataFrame) -> DataFrame
   • Logs: Logs errors for invalid dates.

6. ValidateRegexStrategy

   • Purpose: Ensures column values match specified regex patterns.
   • Method: clean(df: DataFrame) -> DataFrame
   • Logs: Logs errors for values not matching the regex.

7. FilteringStrategy

   • Purpose: Filters rows based on specified boolean conditions.
   • Method: clean(df: DataFrame) -> DataFrame

8. FillNaStrategy

   • Purpose: Replaces missing values with specified default values.
   • Method: clean(df: DataFrame) -> DataFrame

Cleaning Pipeline

• Class: CleaningPipeline
• Methods:
  • add_strategy(strategy: BaseCleaningStrategy | list[BaseCleaningStrategy]): Adds a cleaning strategy or list of strategies.
  • set_dataframe(df: DataFrame): Sets the DataFrame to be cleaned and adds an __index column.
  • run() -> DataFrame: Applies all strategies to the DataFrame and returns the cleaned DataFrame.
  • get_report() -> str: Returns a JSON report of all logged errors.

Logging

• Class: Logger
• Purpose: Records errors encountered during the cleaning process.
• Method: log_error(index, column, message): Logs errors with details.

Usage Example

from SparkCleaner import *
from pyspark.sql import SparkSession
from pyspark.sql.types import StringType, IntegerType

data = [
    (1, "Alice", 30, "2024-07-01", "[e-mail]"),
    (2, "Bob", None, "2024-07/02", "bob@example"), # null in age col
    (3, "Charlie", 25, "invalid_date", None), # empty email and invalid date
    (4, "David", -5, "2024-07-04", "[e-mail]"),
    (5, "Eve", 22, "2024-07-05", "[e-mail]"),
    (5, "Eve", 22, "2024-07-05", "[e-mail]"),
    (5, "Eve", '22', "05-07-2024", "[e-mail]"),  #string value in age column
]
schema = ["id", "name", "age", "date", "email"]

spark = SparkSession.builder \
    .appName("DataCleaningTests") \
    .master("local[*]") \
    .getOrCreate()

df = spark.createDataFrame(data, schema=schema)
cleaning_pipeline = CleaningPipeline()
strategies_pipeline =[
    DropDuplicatesStrategy(columns=df.columns),
    ValidateRegexStrategy(columns=["email"], patterns={'email': '^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$'}),
    DropMissingValuesStrategy(columns=["age"]),
    FilterNegativeValuesStrategy(columns=["age"]),
    ValidateColumnTypesStrategy(columns=df.columns, 
            expected_types={
                'age': IntegerType(), 
                'email': StringType()
                # add more if needed
    }),
    ValidateDatesStrategy(columns=["date"], date_format='yyyy-MM-dd'),
]


cleaning_pipeline.add_strategy(strategy=strategies_pipeline)
cleaning_pipeline.set_dataframe(df = df)

cleaned_df = cleaning_pipeline.run()
errors_report = cleaning_pipeline.get_report()