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.

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

try this:

df = pd.DataFrame({'sales': {0: 4.000, 1: 6.454, 2: float('nan'), 3:23.463, 4:5.654}})

df = (df.sales.fillna(0)*1000).astype(int)
#OR
df = (df.sales.fillna(0)*1000).astype(np.int64)

print(df)

output:

0     4000
1     6454
2        0
3    23463
4     5654
Name: sales, dtype: int64

EDIT: if you want converting currency with $ to int64 you can try this:

df = pd.DataFrame({'sales': {0: '$4.000', 1: '$6.454', 2: float('nan'), 3: '$23.463', 4:'$5.654'}})

print(df)

df['sales'] = (((df['sales'].replace('[\$,]', '', regex=True).astype(float)).fillna(0))*1000).astype(np.int64) 

print();print(df)

output:

     sales
0   $4.000
1   $6.454
2      NaN
3  $23.463
4   $5.654

   sales
0   4000
1   6454
2      0
3  23463
4   5654