How to profile CSV data before import

Before cleaning or importing a file, understand what is actually inside it. A short profiling pass can reveal sparse columns, duplicate rows, unstable identifiers, mixed types and unexpected value distributions.

1. Start with shape and completeness

Record the number of rows and columns, then measure missing values by column. A dataset with a high overall completeness rate can still contain one critical identifier or business field that is unexpectedly sparse.

2. Measure uniqueness, not just row count

Columns that are expected to identify records should be checked for both missing values and uniqueness. A column with one value per row and no blanks is a candidate key; a sudden drop in uniqueness can indicate duplicates or source-system changes.

3. Infer types and look for mixed values

A column dominated by numbers but containing a few text values deserves attention. Mixed types often expose formatting drift, placeholders such as “N/A”, or records that will fail downstream conversion.

4. Inspect ranges and common values

Minimum and maximum values help reveal outliers in numeric and date fields. For categorical columns, common-value counts can reveal unexpected defaults, status codes or skew.

5. Detect exact duplicate rows

Exact duplicates are not always errors, but they should be visible before import. A duplicated row can inflate totals, create constraint failures or hide a broken upstream extraction process.

6. Use the profile to decide what to clean

Profiling is descriptive. Once the signals are visible, create deliberate transformations rather than applying broad cleanup rules blindly. Trim text where whitespace is accidental, normalize case where casing is not meaningful, and remove duplicates only when the key semantics are clear.

Once you understand the dataset, continue with cleaning and transforming CSV data before import.