How to Remove Rows in Numpy Array that Contains Non-Numeric Values?

Given a NumPy array that may contain invalid values such as NaN, inf, or non-numeric entries, the task is to remove all rows that contain any such value and keep only rows with clean numerical data.

For Example:

Input: [[10.5, 22.5, 3.8], [41, nan, nan]]
Output: [[10.5, 22.5, 3.8]]

Let's explore different methods to remove rows which contains non numeric values in Python.

Using np.isfinite()

This method keeps only those rows where every element is a valid finite number. It works by detecting and removing rows that contain NaN, inf, or -inf in one check.

[[10.5 22.5 3.8]]

Explanation:

• np.isfinite(a) creates a mask where valid numbers = True, invalid values = False.
• .all(axis=1) keeps rows where all values are True.
• a[...] filters and returns only numeric rows

Using ~np.isnan(a).any(axis=1)

This method filters rows by checking if they contain any NaN. Rows without NaN are kept; rows with even one NaN are removed.

[[10.5 22.5 3.8]]

Explanation:

• np.isnan(a) True where elements are NaN.
• .any(axis=1) checks if any column in a row contains NaN.
• ~ flips the mask so that only valid rows remain.

Using np.isreal() + Manual Numeric Check

This method validates each row by checking whether all values are numeric. It works even when the array contains strings or Python objects.

[[10.5 22.5 3.8]]

Explanation:

• str(v) converts each value to a string so the numeric check never errors.
• s.replace('.', '', 1).replace('-', '', 1).isdigit() removes one decimal point and one minus sign, then checks if the rest is numeric.
• Row is valid only if all values pass the numeric check.
• r.astype(float) converts valid rows to float because every value is confirmed numeric.