You need add parameter errors='coerce' to function to_numeric:

CopyID = pd.to_numeric(ID, errors='coerce')

If ID is column:

Copydf.ID = pd.to_numeric(df.ID, errors='coerce')

but non numeric are converted to NaN, so all values are float.

For int need convert NaN to some value e.g. 0 and then cast to int:

Copydf.ID = pd.to_numeric(df.ID, errors='coerce').fillna(0).astype(np.int64)

Sample:

Copydf = pd.DataFrame({'ID':['4806105017087','4806105017087','CN414149']})
print (df)
              ID
0  4806105017087
1  4806105017087
2       CN414149

print (pd.to_numeric(df.ID, errors='coerce'))
0    4.806105e+12
1    4.806105e+12
2             NaN
Name: ID, dtype: float64

df.ID = pd.to_numeric(df.ID, errors='coerce').fillna(0).astype(np.int64)
print (df)
              ID
0  4806105017087
1  4806105017087
2              0

EDIT: If use pandas 0.25+ then is possible use integer_na:

Copydf.ID = pd.to_numeric(df.ID, errors='coerce').astype('Int64')
print (df)
              ID
0  4806105017087
1  4806105017087
2            NaN

You're trying to compare a scalar with the entire series which raise the ValueError you saw. A simple method would be to cast the boolean series to int:

CopyIn [84]:
df['viz'] = (df['viz'] !='n').astype(int)
df

Out[84]:
   viz  a1_count  a1_mean     a1_std
0    0         3        2   0.816497
1    1         0      NaN        NaN
2    0         2       51  50.000000

You can also use np.where:

CopyIn [86]:
df['viz'] = np.where(df['viz'] == 'n', 0, 1)
df

Out[86]:
   viz  a1_count  a1_mean     a1_std
0    0         3        2   0.816497
1    1         0      NaN        NaN
2    0         2       51  50.000000

Output from the boolean comparison:

CopyIn [89]:
df['viz'] !='n'

Out[89]:
0    False
1     True
2    False
Name: viz, dtype: bool

And then casting to int:

CopyIn [90]:
(df['viz'] !='n').astype(int)

Out[90]:
0    0
1    1
2    0
Name: viz, dtype: int32