pandas distinction between str and object types

Numpy's string dtypes aren't python strings.

Therefore, pandas deliberately uses native python strings, which require an object dtype.

First off, let me demonstrate a bit of what I mean by numpy's strings being different:

In [1]: import numpy as npIn [2]: x = np.array(['Testing', 'a', 'string'], dtype='|S7')In [3]: y = np.array(['Testing', 'a', 'string'], dtype=object)

Now, 'x' is a numpy string dtype (fixed-width, c-like string) and y is an array of native python strings.

If we try to go beyond 7 characters, we'll see an immediate difference. The string dtype versions will be truncated:

In [4]: x[1] = 'a really really really long'In [5]: xOut[5]:array(['Testing', 'a reall', 'string'],      dtype='|S7')

While the object dtype versions can be arbitrary length:

In [6]: y[1] = 'a really really really long'In [7]: yOut[7]: array(['Testing', 'a really really really long', 'string'], dtype=object)

Next, the |S dtype strings can't hold unicode properly, though there is a unicode fixed-length string dtype, as well. I'll skip an example, for the moment.

Finally, numpy's strings are actually mutable, while Python strings are not. For example:

In [8]: z = x.view(np.uint8)In [9]: z += 1In [10]: xOut[10]:array(['Uftujoh', 'b!sfbmm', 'tusjoh\x01'],      dtype='|S7')

For all of these reasons, pandas chose not to ever allow C-like, fixed-length strings as a datatype. As you noticed, attempting to coerce a python string into a fixed-with numpy string won't work in pandas. Instead, it always uses native python strings, which behave in a more intuitive way for most users.