NumPy or Pandas: Keeping array type as integer while having a NaN value

This capability has been added to pandas (beginning with version 0.24): https://pandas.pydata.org/pandas-docs/version/0.24/whatsnew/v0.24.0.html#optional-integer-na-support

At this point, it requires the use of extension dtype Int64 (capitalized), rather than the default dtype int64 (lowercase).

This is not a solution for all cases, but mine (genomic coordinates) I've resorted to using 0 as NaN

a3['MapInfo'] = a3['MapInfo'].fillna(0).astype(int)

This at least allows for the proper 'native' column type to be used, operations like subtraction, comparison etc work as expected

In case you are trying to convert a float (1.143) vector to integer (1), and that vector has NAs, converting it to the new 'Int64' dtype will give you an error. In order to solve this you have to round the numbers and then do ".astype('Int64')"

s1 = pd.Series([1.434, 2.343, np.nan])
#without round() the next line returns an error 
s1.astype('Int64')
#cannot safely cast non-equivalent float64 to int64
##with round() it works
s1.round().astype('Int64')
0      1
1      2
2    NaN
dtype: Int64

My use case is that I have a float series that I want to round to int, but when you do .round() still has decimals, you need to convert to int to remove decimals.

Pandas v0.24+

Functionality to support NaN in integer series will be available in v0.24 upwards. There's information on this in the v0.24 "What's New" section, and more details under Nullable Integer Data Type.

Pandas v0.23 and earlier

In general, it's best to work with float series where possible, even when the series is upcast from int to float due to inclusion of NaN values. This enables vectorised NumPy-based calculations where, otherwise, Python-level loops would be processed.

The docs do suggest : "One possibility is to use dtype=object arrays instead." For example:

s = pd.Series([1, 2, 3, np.nan])

print(s.astype(object))

0      1
1      2
2      3
3    NaN
dtype: object

For cosmetic reasons, e.g. output to a file, this may be preferable.

Pandas v0.23 and earlier: background

NaN is considered a float. The docs currently (as of v0.23) specify the reason why integer series are upcasted to float:

In the absence of high performance NA support being built into NumPy from the ground up, the primary casualty is the ability to represent NAs in integer arrays.

This trade-off is made largely for memory and performance reasons, and also so that the resulting Series continues to be “numeric”.

The docs also provide rules for upcasting due to NaN inclusion:

Typeclass   Promotion dtype for storing NAs
floating    no change
object      no change
integer     cast to float64
boolean     cast to object

New for Pandas v1.00 +

You do not (and can not) use numpy.nan any more. Now you have pandas.NA.

Please read: https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html

IntegerArray is currently experimental. Its API or implementation may change without warning.

Changed in version 1.0.0: Now uses pandas.NA as the missing value rather than numpy.nan.

In Working with missing data, we saw that pandas primarily uses NaN to represent missing data. Because NaN is a float, this forces an array of integers with any missing values to become floating point. In some cases, this may not matter much. But if your integer column is, say, an identifier, casting to float can be problematic. Some integers cannot even be represented as floating point numbers.

If there are blanks in the text data, columns that would normally be integers will be cast to floats as float64 dtype because int64 dtype cannot handle nulls. This can cause inconsistent schema if you are loading multiple files some with blanks (which will end up as float64 and others without which will end up as int64

This code will attempt to convert any number type columns to Int64 (as opposed to int64) since Int64 can handle nulls

import pandas as pd
import numpy as np

#show datatypes before transformation
mydf.dtypes

for c in mydf.select_dtypes(np.number).columns:
    try:
        mydf[c] = mydf[c].astype('Int64')
        print('casted {} as Int64'.format(c))
    except:
        print('could not cast {} to Int64'.format(c))

#show datatypes after transformation
mydf.dtypes

This is now possible, since pandas v 0.24.0

pandas 0.24.x release notes Quote: "Pandas has gained the ability to hold integer dtypes with missing values.

I know that OP has asked for NumPy or Pandas only, but I think it is worth mentioning polars as an alternative that supports the requested feature.

In Polars any missing values in an integer column are simply null values and the column remains an integer column.

See Polars - User Guide > Coming from Pandas for more info.