Working with Arrays of Strings And Bytes

While NumPy is primarily a numerical library, it is often convenient to work with NumPy arrays of strings or bytes. The two most common use cases are:

Working with data loaded or memory-mapped from a data file, where one or more of the fields in the data is a string or bytestring, and the maximum length of the field is known ahead of time. This often is used for a name or label field.

Using NumPy indexing and broadcasting with arrays of Python strings of unknown length, which may or may not have data defined for every value.

For the first use case, NumPy provides the fixed-width numpy.void, numpy.str_ and numpy.bytes_ data types. For the second use case, numpy provides numpy.dtypes.StringDType. Below we describe how to work with both fixed-width and variable-width string arrays, how to convert between the two representations, and provide some advice for most efficiently working with string data in NumPy.

Before NumPy 2.0, the fixed-width numpy.str_, numpy.bytes_, and numpy.void data types were the only types available for working with strings and bytestrings in NumPy. For this reason, they are used as the default dtype for strings and bytestrings, respectively: