This code snippet processes a dataset (`df`) and creates new...

This code snippet processes a dataset (df) and creates new weighted and aggregated columns based on existing columns in the DataFrame. Here's a breakdown of what this code is doing:

---

1. Extract column names:

col_name = [ col_name[12:] for col_name in df.columns if col_name.startswith("rd_ins_life_") ]

This line extracts column names from df.columns that start with "rd_ins_life_". For those columns, it removes the prefix "rd_ins_life_" (i.e., takes the substring starting from the 12th character). This creates a list, col_name, of common meaningful suffixes shared by columns representing life and non-life insurance.

---

2. Initialize new columns:

new_columns = []

An empty list new_columns is created to track the new column names that are added to the DataFrame.

---

3. Iterate over col_name and calculate new columns:

for column in col_name :

The code loops through each of the suffixes in col_name. For each suffix:

Create column references:

col_life = (f"rd_ins_life_{column}")
col_nonlife = (f"rd_ins_nonlife_{column}")
weight_life = df.weight_life
weight_nonlife = df.weight_nonlife

• col_life refers to the full name of the life insurance column (rd_ins_life_<suffix>).
• col_nonlife refers to the full name of the non-life insurance column (rd_ins_nonlife_<suffix>).
• weight_life and weight_nonlife are weights for life and non-life aggregated columns, assumed to exist in the dataset as df.weight_life and df.weight_nonlife.

Compute the new column value:

new_col = when(col(col_life).isNull() & col(col_nonlife).isNull(),
               lit(None)
              ).otherwise( coalesce(col(col_life),lit(0))*col("weight_life") +
                          coalesce(col(col_nonlife),lit(0))*col("weight_nonlife")
)

• Check for null values: If both col_life and col_nonlife are null, the new column is NULL (i.e., missing).
• Fallback to zero: If either column is null, it is replaced with 0 using the coalesce function.
• Weighted sum: If life or non-life column values exist, their product with their respective weights is computed and summed.

Rename and update the DataFrame:

new_col_name = f"rd_ins_{column}"

This defines the name for the new computed column as rd_ins_<suffix> (combining life and non-life insurance data).

if new_col_name in df.columns:
    # df = df.drop(new_col_name)
    df = df.withColumn(new_col_name, (round(new_col)).cast(StringType()))
    new_columns.append(new_col_name)

• If a column with the name new_col_name already exists, it is overwritten.
  • (Note: The line to drop the column is commented out.)
• The new weighted sum column is added to the DataFrame using withColumn.
• The resulting column is rounded to the nearest integer using round, cast to a string, and added to the DataFrame.
• The column name is appended to the new_columns list for tracking.

---

Summary:

This code:

1. Extracts column suffixes for columns starting with "rd_ins_life_".
2. Combines these columns (rd_ins_life_ and rd_ins_nonlife_ variants) into weighted and aggregated columns.
3. Adds these new weighted columns (rd_ins_<suffix>) to the DataFrame.
4. Rounds and casts the new column values to strings.
5. Tracks the new columns in the new_columns list.

---

Example:

Assume df.columns contains:

['rd_ins_life_a', 'rd_ins_nonlife_a', 'rd_ins_life_b', 'rd_ins_nonlife_b', 'weight_life', 'weight_nonlife']

Steps:

1. col_name = ['a', 'b'] (suffixes extracted).
2. For each suffix a and b:
   • Compute new columns rd_ins_a and rd_ins_b using the weighted sum of life and nonlife columns.
3. Add these columns to the DataFrame with rounded, string-cast values.

The resulting DataFrame will include the new columns: ['rd_ins_a', 'rd_ins_b'].