converting currency with $ to numbers in Python pandas

You can use the vectorised str methods to replace the unwanted characters and then cast the type to int:

In [81]:
df[df.columns[1:]] = df[df.columns[1:]].apply(lambda x: x.str.replace('$','')).apply(lambda x: x.str.replace(',','')).astype(np.int64)
df

Out[81]:
            state       1st        2nd      3rd
index                                          
0      California  11593820  109264246  8496273
1        New York  10861680   45336041  6317300
2         Florida   7942848   69369589  4697244
3           Texas   7536817   61830712  5736941

dtype change is now confirmed:

In [82]:

df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 4 entries, 0 to 3
Data columns (total 4 columns):
state    4 non-null object
1st      4 non-null int64
2nd      4 non-null int64
3rd      4 non-null int64
dtypes: int64(3), object(1)
memory usage: 160.0+ bytes

Another way:

In [108]:

df[df.columns[1:]] = df[df.columns[1:]].apply(lambda x: x.str[1:].str.split(',').str.join('')).astype(np.int64)
df
Out[108]:
            state       1st        2nd      3rd
index                                          
0      California  11593820  109264246  8496273
1        New York  10861680   45336041  6317300
2         Florida   7942848   69369589  4697244
3           Texas   7536817   61830712  5736941

@EdChum's answer is clever and works well. But since there's more than one way to bake a cake.... why not use regex? For example:

df[df.columns[1:]] = df[df.columns[1:]].replace('[\$,]', '', regex=True).astype(float)

To me, that is a little bit more readable.