numpy.ndarray.tolist will do it:

a.tolist()

If your data is a pandas series you can call their tolist wrapper with the same result.

In trouble with this, too. I finally find out a somewhat unskillful solution as follows:

def trans(data):
"""
translate numpy.int/float into python native data type
"""
result = []
for i in data.index:
    # i = data.index[0]
    d0 = data.iloc[i].values
    d = []
    for j in d0:
        if 'int' in str(type(j)):
            res = j.item() if 'item' in dir(j) else j
        elif 'float' in str(type(j)):
            res = j.item() if 'item' in dir(j) else j
        else:
            res = j
        d.append(res)
    d = tuple(d)
    result.append(d)
result = tuple(result)
return result

However, it performs poorly when handling data with large number of rows. You will spend some minutes in translate a dataframe with more than 100,000 records.

Solution for pandas 0.24+ for converting numeric with missing values:

df = pd.DataFrame({'column name':[7500000.0,7500000.0, np.nan]})
print (df['column name'])
0    7500000.0
1    7500000.0
2          NaN
Name: column name, dtype: float64

df['column name'] = df['column name'].astype(np.int64)

ValueError: Cannot convert non-finite values (NA or inf) to integer

#http://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
df['column name'] = df['column name'].astype('Int64')
print (df['column name'])
0    7500000
1    7500000
2        NaN
Name: column name, dtype: Int64

I think you need cast to numpy.int64:

df['column name'].astype(np.int64)

Sample:

df = pd.DataFrame({'column name':[7500000.0,7500000.0]})
print (df['column name'])
0    7500000.0
1    7500000.0
Name: column name, dtype: float64

df['column name'] = df['column name'].astype(np.int64)
#same as
#df['column name'] = df['column name'].astype(pd.np.int64)
print (df['column name'])
0    7500000
1    7500000
Name: column name, dtype: int64

If some NaNs in columns need replace them to some int (e.g. 0) by fillna, because type of NaN is float:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})

df['column name'] = df['column name'].fillna(0).astype(np.int64)
print (df['column name'])
0    7500000
1          0
Name: column name, dtype: int64

Also check documentation - missing data casting rules

EDIT:

Convert values with NaNs is buggy:

df = pd.DataFrame({'column name':[7500000.0,np.nan]})

df['column name'] = df['column name'].values.astype(np.int64)
print (df['column name'])
0                7500000
1   -9223372036854775808
Name: column name, dtype: int64

Use val.item() to convert most NumPy values to a native Python type:

import numpy as np

# for example, numpy.float32 -> python float
val = np.float32(0)
pyval = val.item()
print(type(pyval))         # <class 'float'>

# and similar...
type(np.float64(0).item()) # <class 'float'>
type(np.uint32(0).item())  # <class 'int'>
type(np.int16(0).item())   # <class 'int'>
type(np.cfloat(0).item())  # <class 'complex'>
type(np.datetime64(0, 'D').item())  # <class 'datetime.date'>
type(np.datetime64('2001-01-01 00:00:00').item())  # <class 'datetime.datetime'>
type(np.timedelta64(0, 'D').item()) # <class 'datetime.timedelta'>
...

(A related method np.asscalar(val) was deprecated with 1.16, and removed with 1.23).

For the curious, to build a table of conversions of NumPy array scalars for your system:

for name in dir(np):
    obj = getattr(np, name)
    if hasattr(obj, 'dtype'):
        try:
            if 'time' in name:
                npn = obj(0, 'D')
            else:
                npn = obj(0)
            nat = npn.item()
            print('{0} ({1!r}) -> {2}'.format(name, npn.dtype.char, type(nat)))
        except:
            pass

There are a few NumPy types that have no native Python equivalent on some systems, including: clongdouble, clongfloat, complex192, complex256, float128, longcomplex, longdouble and longfloat. These need to be converted to their nearest NumPy equivalent before using .item().

Use .astype.

>>> a = numpy.array([1, 2, 3, 4], dtype=numpy.float64)
>>> a
array([ 1.,  2.,  3.,  4.])
>>> a.astype(numpy.int64)
array([1, 2, 3, 4])

See the documentation for more options.

As others have said, 32-bit versions of numpy still support 64-bit dtypes. But if you really need to convert to int32, you can use the astype function:

>>> import numpy as np
>>> x = np.array([1,2,3], dtype=np.int64)
>>> x
array([1, 2, 3])
>>> x.dtype
dtype('int64')
>>> y = x.astype(np.int32)
>>> y
array([1, 2, 3], dtype=int32)