You could use a function like this:

def nan_ints(df, convert_strings=False, subset=None):
    types = ["int64", "float64"]
    if subset is None:
        subset = list(df)
    if convert_strings:
        types.append("object")
    for col in subset:
        if df[col].dtype in types:
            df[col] = (
                df[col].astype(float, errors="ignore").astype("Int64", errors="ignore")
            )
    return df

It iterates through each column and coverts it to an Int64 if it is a int. If it's a float it will convert to a Int64 only if all of the values in the column could be converted to ints other than the NaN's. I've given you the option to convert strings to Int64 as well with the convert_strings argument.

df1 = pd.DataFrame({'a':[1.1,2,3,1],
                  'b':[1,2,3,np.nan],
                  'c':['1','2','3',np.nan],
                  'd':[3,2,1,np.nan]})


nan_ints(df1,convert_strings=True,subset=['b','c'])
df1.info()

Will return the following:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 4 columns):
a    4 non-null float64
b    3 non-null Int64
c    3 non-null Int64
d    3 non-null float64
dtypes: Int64(2), float64(2)
memory usage: 216.0 bytes

if you are going to use this on every DataFrame you could add the function to a module and import it every time you want to use pandas. from my_module import nan_ints Then just use it with something like: nan_ints(pd.read_csv(path))

Note: Nullable integer data type is New in version 0.24.0. Here is the documentation.

There are several major differences. The first is that python integers are flexible-sized (at least in python 3.x). This means they can grow to accommodate any number of any size (within memory constraints, of course). The numpy integers, on the other hand, are fixed-sized. This means there is a maximum value they can hold. This is defined by the number of bytes in the integer (int32 vs. int64), with more bytes holding larger numbers, as well as whether the number is signed or unsigned (int32 vs. uint32), with unsigned being able to hold larger numbers but not able to hold negative number.

So, you might ask, why use the fixed-sized integers? The reason is that modern processors have built-in tools for doing math on fixed-size integers, so calculations on those are much, much, much faster. In fact, python uses fixed-sized integers behind-the-scenes when the number is small enough, only switching to the slower, flexible-sized integers when the number gets too large.

Another advantage of fixed-sized values is that they can be placed into consistently-sized adjacent memory blocks of the same type. This is the format that numpy arrays use to store data. The libraries that numpy relies on are able to do extremely fast computations on data in this format, in fact modern CPUs have built-in features for accelerating this sort of computation. With the variable-sized python integers, this sort of computation is impossible because there is no way to say how big the blocks should be and no consistentcy in the data format.

That being said, numpy is actually able to make arrays of python integers. But rather than arrays containing the values, instead they are arrays containing references to other pieces of memory holding the actual python integers. This cannot be accelerated in the same way, so even if all the python integers fit within the fixed integer size, it still won't be accelerated.

None of this is the case with Python 2. In Python 2, Python integers are fixed integers and thus can be directly translated into numpy integers. For variable-length integers, Python 2 had the long type. But this was confusing and it was decided this confusion wasn't worth the performance gains, especially when people who need performance would be using numpy or something like it anyway.