Returns a bucketized column, with a bucket index assigned to each input.


  • x: A numeric input Tensor or SparseTensor whose values should be mapped to buckets. For a SparseTensor only non-missing values will be included in the quantiles computation, and the result of bucketize will be a SparseTensor with non-missing values mapped to buckets.
  • num_buckets: Values in the input x are divided into approximately equal-sized buckets, where the number of buckets is num_buckets. This is a hint. The actual number of buckets computed can be less or more than the requested number. Use the generated metadata to find the computed number of buckets.
  • epsilon: (Optional) Error tolerance, typically a small fraction close to zero. If a value is not specified by the caller, a suitable value is computed based on experimental results. For num_buckets less than 100, the value of 0.01 is chosen to handle a dataset of up to ~1 trillion input data values. If num_buckets is larger, then epsilon is set to (1/num_buckets) to enforce a stricter error tolerance, because more buckets will result in smaller range for each bucket, and so we want the boundaries to be less fuzzy. See analyzers.quantiles() for details.
  • weights: (Optional) Weights tensor for the quantiles. Tensor must have the same shape as x.
  • name: (Optional) A name for this operation.


A Tensor of the same shape as x, with each element in the returned tensor representing the bucketized value. Bucketized value is in the range [0, actual_num_buckets). Sometimes the actual number of buckets can be different than num_buckets hint, for example in case the number of distinct values is smaller than num_buckets, or in cases where the input values are not uniformly distributed.


  • ValueError: If value of num_buckets is not > 1.