A common problem faced by statistical institutes is that some data of otherwise responding units may be missing. This is referred to as item non-response. Item-nonresponse is usually treated by imputing the missing data. The problem of imputing missing data is complicated by the fact that statistical data often have to satisfy so-called edit rules, which for numerical data usually take the form of linear restrictions. A further complication is that numerical data sometimes have to sum up to known totals. Standard imputation methods for numerical data as described in the literature generally do not take such linear edit restrictions on the data or known totals into account. In this paper we develop simple imputation methods that satisfy edits and preserve known totals. These methods are based on well-known hot deck approaches. Extension of our methods to other types of imputation, such as regression imputation or predictive mean matching, is straightforward.