Can You Make a Command-Line Sensor CSV Summarizer?
From the earlier reference collection. Read the current software blog.
A focused CSV tool can report the minimum, maximum, and number of usable readings while keeping missing data visible.
Define what counts as a reading
A sensor CSV summarizer should make a small promise and keep it: for a chosen numeric column, report its minimum, maximum, and sample count. Missing values need their own accounting, because treating an empty cell as zero changes the result and can make a broken export look plausible.
Start by asking for a file path and a column name, or accept them as command-line arguments. Read the header first and fail with a useful message if the requested column is absent. Preserve each raw cell long enough to distinguish an empty value from a numeric zero. A row with an empty measurement increments the missing count; a valid 0 remains a real sample.
Parse cautiously and summarize
Convert nonempty cells to numbers, but decide how to handle malformed entries explicitly. A practical policy is to count blank cells as missing and report invalid nonblank values separately with their row numbers. Avoid silently dropping N/A, error, or a truncated string, since those values may indicate a collection or export problem.
Suppose the selected column contains 4.2, an empty cell, -1.0, and 0. The valid sample count is three, the minimum is -1.0, the maximum is 4.2, and the missing count is one. If every value is missing, print that no numeric summary is available instead of inventing minimum or maximum values. This example exposes common bugs around zero and empty input.
Keep the output useful at a terminal
A concise report might show the input filename, selected column, numeric sample count, missing count, invalid count, minimum, and maximum. Include the unit only if it is present in trusted metadata or supplied by the user; a column named temperature does not establish whether values are Celsius or Fahrenheit.
When processing a large file, update the current count, minimum, and maximum as each row arrives rather than storing every measurement. The calculation requires only a few running values. For reproducibility, use a consistent numeric format and state whether the reported count excludes missing and invalid cells.
Treat input quirks as part of the interface
CSV files can contain quoted commas, a byte order mark, or different newline conventions. Use a CSV parser rather than splitting lines on commas. If the file has duplicate headers, require a column position or reject the ambiguity with a clear explanation. If a row has fewer fields than the header, identify it instead of accidentally reading the wrong column.
A good ending for this project is a predictable exit status: success when the file was read and summarized, a distinct failure for unreadable input or a missing column, and an informative message when there are simply no samples. That makes the tool useful both interactively and in a scheduled data pipeline.