Type checking is not the only option to do what you want, but definitely one of the easiest:

import numpy as np

def to_str(var):
    if type(var) is list:
        return str(var)[1:-1] # list
    if type(var) is np.ndarray:
        try:
            return str(list(var[0]))[1:-1] # numpy 1D array
        except TypeError:
            return str(list(var))[1:-1] # numpy sequence
    return str(var) # everything else

EDIT: Another easy way, which does not use type checking (thanks to jtaylor for giving me that idea), is to convert everything into the same type (np.array) and then convert it to a string:

import numpy as np

def to_str(var):
    return str(list(np.reshape(np.asarray(var), (1, np.size(var)))[0]))[1:-1]

Example use (both methods give same results):

>>> to_str(1.) #float
'1.0'
>>> to_str([1., 1., 1.]) #list
'1.0, 1.0, 1.0'
>>> to_str(np.ones((1,3))) #np.array
'1.0, 1.0, 1.0'

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.

Simply use str():

import numpy as np

x = np.array([1, 2, 3, 4])

print(type(x))

print(type(str(x)))

Yields:

<class 'numpy.ndarray'>
<class 'str'>

z_as_int64 = numpy.int64(z)

It's that simple. Make sure you have a good reason, though - there are a few good reasons to do this, but most of the time, you can just use a regular int directly.

Update the column names as string and try.

df.columns = df.columns.astype(str)

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().

Solution was:

import pandas as pd 
ts = pd.to_datetime(str(date)) 
d = ts.strftime('%Y.%m.%d')