Missing data is a problem that arises in data science when some data contained in rows or columns may be missing or unavailable for some samples in a dataset.

Missing data is a problem that arises in data science when some data contained in rows or columns may be missing or unavailable for some samples in a dataset. This can occur from non-response, input errors, or lack of information. Remedies for missing data include dropping them (ie using df.dropna() in pandas) or some form of imputation. Popular imputation methods include mean imputation.