# Chapter 12. Structured Data: NumPy’s Structured Arrays

While often our data can be well represented by a homogeneous array of values, sometimes this is not the case. This chapter demonstrates the use of NumPy’s structured arrays and record arrays, which provide efficient storage for compound, heterogeneous data. While the patterns shown here are useful for simple operations, scenarios like this often lend themselves to the use of Pandas `DataFrame`s, which we’ll explore in Part III.

``In` `[``1``]:` `import` `numpy` `as` `np``

Imagine that we have several categories of data on a number of people (say, name, age, and weight), and we’d like to store these values for use in a Python program. It would be possible to store these in three separate arrays:

````In` `[``2``]:` `name` `=` `[``'Alice'``,` `'Bob'``,` `'Cathy'``,` `'Doug'``]`
`age` `=` `[``25``,` `45``,` `37``,` `19``]`
`weight` `=` `[``55.0``,` `85.5``,` `68.0``,` `61.5``]````

But this is a bit clumsy. There’s nothing here that tells us that the three arrays are related; NumPy’s structured arrays allow us to do this more naturally by using a single structure to store all of this data.

Recall that previously we created a simple array using an expression like this:

``In` `[``3``]:` `x` `=` `np``.``zeros``(``4``,` `dtype``=``int``)``

We can similarly create a structured array using a compound data type specification:

````In` `[``4``]:` `# Use a compound data type for structured arrays`
`data` `=` `np``.``zeros``(``4``,` `dtype``=``{``'names'``:(``'name'``,` `'age'``,` `'weight'``),`
`'formats'``:(``'U10'``,` `'i4'``,` `'f8'``)})`
`print``(``data``.``dtype``)`
`Out``[``4``]:` `[(``'name'``,` `'<U10'``),` `(``'age'``,` `'<i4'``),` `(``'weight'``,` `'<f8'``)]````

Here `'U10'` translates ...

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