use astype(np.int64)

s = pd.Series(['', 8.00735e+09, 4.35789e+09, 6.10644e+09])
mask = pd.to_numeric(s).notnull()
s.loc[mask] = s.loc[mask].astype(np.int64)
s

0              
1    8007350000
2    4357890000
3    6106440000
dtype: object

Try

df['Column'] = df['Column'].str.replace('\d+', '')

this will remove all digits in the column

Here's a function to format your numbers the way you want them:

def formatNumber(num):
  if num % 1 == 0:
    return int(num)
  else:
    return num

For example:

formatNumber(3.11111)

returns

3.11111

formatNumber(3.0)

returns

3

You seem to be confusing objects with their representations. This DataFrame holds floats. You can represent these how you like (as strings) but when you change display options the objects remain the same. Converting to a list sends these float objects into a python list which has it's own representation of floats (unrelated to pandas) and will always display the .0.

You can't change the formatting of a python list. It is inbuilt. You could convert these floats to ints, by doing

list(map(int, sample_df['gpid']))[0:2]
[10498310800133, 767838527881217]

but be aware you are making new objects, not simply changing their representations. This means any non-integer floats (ie. doesn't end in .0) will be converted to integers.

To convert the pandas values to strings, do

sample_df['gpid'] = sample_df['gpid'].apply(lambda f: format(f, '.0f'))

Output:

              gpid
0   10498310800133
1  767838527881217

If want convert integers and floats numbers to strings with no trailing 0 use this with map or apply:

df = pd.DataFrame({'col1':[1.00, 1, 0.5, 1.50]})

df['new'] = df['col1'].map('{0:g}'.format)
#alternative solution
#df['new'] = df['col1'].apply('{0:g}'.format)
print (df)
   col1  new
0   1.0    1
1   1.0    1
2   0.5  0.5
3   1.5  1.5

print (df['new'].apply(type))
0    <class 'str'>
1    <class 'str'>
2    <class 'str'>
3    <class 'str'>
Name: new, dtype: object

You could use %g to achieve this:

'%g'%(3.140)

or, with Python ≥ 2.6:

'{0:g}'.format(3.140)

or, with Python ≥ 3.6:

f'{3.140:g}'

From the docs for format: g causes (among other things)

insignificant trailing zeros [to be] removed from the significand, and the decimal point is also removed if there are no remaining digits following it.

You have a few options...

1) convert everything to integers.

df.astype(int)
          <=35  >35
Cut-off            
Calcium      0    1
Copper       1    0
Helium       0    8
Hydrogen     0    1

2) Use round:

>>> df.round()
          <=35  >35
Cut-off            
Calcium      0    1
Copper       1    0
Helium       0    8
Hydrogen     0    1

but not always great...

>>> (df - .2).round()
          <=35  >35
Cut-off            
Calcium     -0    1
Copper       1   -0
Helium      -0    8
Hydrogen    -0    1

3) Change your display precision option in Pandas.

pd.set_option('precision', 0)

>>> df
          <=35  >35
Cut-off            
Calcium      0    1
Copper       1    0
Helium       0    8
Hydrogen     0    1