Why do Strings in Pandas DataFrames Appear as Objects Even After Conversion?

Why are Strings in a DataFrame Stored as Objects?

Despite explicitly converting DataFrame columns containing strings to the string data type, Python's Pandas library may still report them as objects. This discrepancy arises due to NumPy's underlying data structures.

NumPy uses ndarrays to store arrays of data, with each element in an ndarray having a fixed number of bytes. For integers (int64) and floating-point numbers (float64), each element occupies 8 bytes. However, strings have variable lengths, making it impractical to store them directly in an ndarray.

To accommodate this, Pandas uses object ndarrays to store pointers to objects. These objects contain the actual string values. As a result, object ndarrays have an indeterminate size and are represented as the "object" data type.

Example:

Consider an int64 array containing four 64-bit integers and an object array containing four pointers to three string objects:

int64 array: | 1 | 2 | 3 | 4 |
object array: | pointer to "hello" | pointer to "world" | pointer to "!" |

Visualization:

+---------+-----------+
| int64    |  object   |
|---------+-----------|
| 1        | hello     |
| 2        | world     |
| 3        | !         |
| 4        | null      |
+---------+-----------+

In this representation, the int64 array occupies a fixed amount of space, with each element being 8 bytes. On the other hand, the object array stores pointers to objects of varying sizes, hence the "object" data type.

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