For various reasons, there are generally missing values in low voltage transformer districts, and overall data quality is poor. This limits lean management for a distribution network. Traditional data imputation methods ignore the periodicity and temporality of data and the imputation accuracy is relatively low. This paper proposes an imputation method based on imaging encoding and a generative adversarial network. First, a method for one-dimensional time series encoding to a two-dimension image is introduced, the original signals are transformed to Gramian angular field images. Second, using the powerful advantages of a convolutional neural network in image feature extraction, a generative adversarial network model is constructed. Combining the dual constraints of pixel and similarity loss, the quality of generated images is improved. The overall process is driven by data, does not need the distribution hypothesis of prior knowledge and explicit physical modeling. Lastly, experimental results show that this method could accurately impute missing values in a transformer district. This work is supported by the Science and Technology Project of State Grid Corporation of China (No. 52020116000G).