Which command provides a quick, initial view of basic stats for numeric columns?

Which command provides a quick, initial view of basic stats for numeric columns?

• A. df.select('col').describe().show()
• B. df.describe().show()
• C. df.describe().collect()
• D. df.describe('col1')

Answer: (B)

To get a quick, high‑level view of basic statistics for numeric columns, use describe() to compute summary metrics and show() to display them. The command df.describe().show() runs the statistical summary across all numeric columns in the DataFrame and prints a concise table with metrics like count, mean, stddev, min, and max for each column. This provides an immediate overview without having to inspect each column separately. Why other options aren’t as suitable: describing without selecting specific columns gives a broad snapshot for every numeric column at once, which is exactly what you want for a quick initial view; describing a single column would limit the output to just that column, not the whole numeric set; collecting the results brings them to the driver but doesn’t print them automatically, so you’d need extra steps to view them.

To get a quick, high‑level view of basic statistics for numeric columns, use describe() to compute summary metrics and show() to display them. The command df.describe().show() runs the statistical summary across all numeric columns in the DataFrame and prints a concise table with metrics like count, mean, stddev, min, and max for each column. This provides an immediate overview without having to inspect each column separately.

Why other options aren’t as suitable: describing without selecting specific columns gives a broad snapshot for every numeric column at once, which is exactly what you want for a quick initial view; describing a single column would limit the output to just that column, not the whole numeric set; collecting the results brings them to the driver but doesn’t print them automatically, so you’d need extra steps to view them.