Data pipelines & processing

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, count

def check_null_rate(df, threshold=0.2, columns=None):
    """
    Check null rate for specified columns (or all) against a threshold.
    Returns columns that exceed the threshold.
    """
    cols = columns or df.columns
    total…

import pandas as pd
import numpy as np

def find_missing_values_summary(datasets):
    """Analyze missing values across multiple datasets (dict of name: DataFrame)."""
    summary = {}
    for name, df in datasets.items():
        missing_count = df.isnull().sum()
        total_rows = len(df)
        missing_pct = (mi…