Handling Missing Data in Python | Dataford Interview Questions

Can you explain how you handle missing values, duplicates, and data reshaping using Python libraries like Pandas and NumPy?

Translate this data-cleaning task into PostgreSQL using raw_metric_readings and metric_catalog. Keep missing metric values as NULL, retain only the latest duplicate reading, and produce the requested wide result.

Output

One row per device and reading timestamp
Columns: device_id, reading_time, temperature_c, humidity_pct, and power_w
Include only timestamps with at least one recognized target metric, ordered by device_id and reading_time ascending

Schema

raw_metric_readings

Column	Type	Description
reading_idPK	INT	Unique identifier for the raw reading
device_id	VARCHAR(20)	Identifier of the AMD device
reading_time	TIMESTAMP	Timestamp represented by the reading
metric_name	VARCHAR(40)	Name of the recorded metric
metric_value	DECIMAL(10,2)	Numeric metric value, which may be missing
ingested_at	TIMESTAMP	Time the row was ingested

metric_catalog

Column	Type	Description
metric_namePK	VARCHAR(40)	Recognized metric name
display_name	VARCHAR(80)	Human-readable metric label

Your question is Handling Missing Data in Python. Start with the requirements and the two tables in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem