Count String Values in a Numeric Column Pandas

Use to_numeric with errors='coerce' for convert non numeric to NaNs and create mask by isna:

mask = pd.to_numeric(df['Hours_Worked'], errors='coerce').isna()
#oldier pandas versions
#mask = pd.to_numeric(df['Hours_Worked'], errors='coerce').isnull()    

Then count Trues values by sum:

a = mask.sum()
print (a)
2

And filter by boolean indexing:

df1 = df[mask]
print (df1)
    Name Hours_Worked
3  Billy            T
4  Sarah            A

Detail:

print (mask)
1    False
2    False
3     True
4     True
5    False
Name: Hours_Worked, dtype: bool

Another way for check numeric:

def check_num(x):
    try:
        float(x)
        return False        
    except ValueError:
        return True

mask = df['Hours_Worked'].apply(check_num)