Pandas: Why is default column type for numeric float?

It's not possible for Pandas to store NaN values in integer columns.

This makes float the obvious default choice for data storage, because as soon as missing value arises Pandas would have to change the data type for the entire column. And missing values arise very often in practice.

As for why this is, it's a restriction inherited from Numpy. Basically, Pandas needs to set aside a particular bit pattern to represent NaN. This is straightforward for floating point numbers and it's defined in the IEEE 754 standard. It's more awkward and less efficient to do this for a fixed-width integer.

Update

Exciting news in pandas 0.24. IntegerArray is an experimental feature but might render my original answer obsolete. So if you're reading this on or after 27 Feb 2019, check out the docs for that feature.

The why is almost certainly to do with flexibility and speed. Just because Pandas has only seen an integer in that column so far doesn't mean that you're not going to try to add a float later, which would require Pandas to go back and change the type for all that column. A float is the most robust/flexible numeric type.

There's no global way to override that behaviour (that I'm aware of), but you can use the astype method to modify an individual DataFrame.

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.astype.html