This Python code snippet calculates the **"completeness percentage"** (the percentage...

This Python code snippet calculates the "completeness percentage" (the percentage of non-null values) for each column in a Spark DataFrame (df) and stores the results in a dictionary called completeness_dict. Here's a detailed explanation:

Step-by-Step Breakdown:

1. Iterating Over Columns:

   • The for col_name in df.columns iterates over all column names in the DataFrame df.

2. Calculate Non-Null Row Count for the Column:

   • The count(col(col_name)) computes the number of non-NULL (non-missing) values in the current column (col_name) using Spark's count and col functions.

3. Total Row Count:

   • total_count is assumed to be the total number of rows in the DataFrame (df). It is calculated elsewhere in the code and used in this snippet.

4. Percentage Calculation:

   • The proportion of non-null values in the column is calculated as (count(col(col_name)) / total_count) * 100. This gives the percentage of non-NULL values in the column relative to the total number of rows.

5. Rounding:

   • The percentage is rounded to two decimal places with round(..., 2).

6. Assigning to Dictionary:

   • If total_count > 0, the calculated percentage value is saved in the completeness_dict dictionary, with the column name as the key.
   • If total_count == 0 (i.e., DataFrame is empty), the completeness for each column is set to 0.0.

7. Conversion and Retrieval:

   • The .collect()[0][0] extracts the single value from the Spark DataFrame result (since the select returns a DataFrame). This ensures only the scalar value is stored in the dictionary, not a Spark Row object.

8. Final Structure:

   • completeness_dict contains a mapping of column names to their completeness percentages. For example:

     {
         "column1": 98.5,  # 98.5% non-null values
         "column2": 100.0, # 100% non-null values
         "column3": 0.0    # 0% non-null values, or empty DataFrame
     }
     

Purpose:

The code is often used in data quality assessments to measure the completeness of data in a Spark DataFrame, ensuring that columns have a sufficient proportion of valid (non-null) values.