SQL Tricky Conceptual Interview Questions

pivoting (turning row values into columns)

Got it — you mean pivoting (turning row values into columns).

Here’s a quick breakdown depending on where you want to do it:

1️⃣ In SQL

Use PIVOT or conditional aggregation.

Example table:

SQL (conditional aggregation):

SELECT
    product,
    SUM(CASE WHEN month = 'Jan' THEN sales END) AS Jan,
    SUM(CASE WHEN month = 'Feb' THEN sales END) AS Feb
FROM sales_table
GROUP BY product;

Result:

2️⃣ In PySpark

Use pivot().

from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

data = [
    ("A", "Jan", 100),
    ("A", "Feb", 120),
    ("B", "Jan", 90),
    ("B", "Feb", 150)
]
df = spark.createDataFrame(data, ["product", "month", "sales"])

pivot_df = df.groupBy("product").pivot("month").sum("sales")
pivot_df.show()

3️⃣ In Pandas (Python)

Use pivot_table().

import pandas as pd

data = {
    "product": ["A", "A", "B", "B"],
    "month": ["Jan", "Feb", "Jan", "Feb"],
    "sales": [100, 120, 90, 150]
}
df = pd.DataFrame(data)

pivot_df = df.pivot_table(index="product", columns="month", values="sales", aggfunc="sum")
print(pivot_df)

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