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 

You need to re-assign dataframe (which is, what I suppose your error is):

>>> import pandas as pd
>>> df = pd.DataFrame(data={"col": [24.00, 2.00, 3.00]})
>>> df.dtypes
col    float64
dtype: object
>>> df
    col
0  24.0
1   2.0
2   3.0
>>> df=df.astype(int)
>>> df
   col
0   24
1    2
2    3
>>> df.dtypes
col    int32
dtype: object

Use astype with replace:

df = pd.DataFrame({'ID':[805096730.0,805096730.0]})

df['ID'] = df['ID'].astype(str).replace('\.0', '', regex=True)
print (df)
          ID
0  805096730
1  805096730

Or add parameter dtype:

df = pd.read_excel(file, dtype={'ID':str})

Regex101:

df["column1"] = df["column1"].str.replace(r"(\.)(?=.*\.)", "", regex=True)
print(df)

Prints:

      column1
0    27356.10
1       25689
2     2345.00
3         NaN
4    12325.70
5  1234575.00

Try the below:

m=(df.dtypes=='float')
df.loc[:,m]=df.loc[:,m].astype(int)
print(df)

  query  qstart  qend  name  number strand
0     A       2  1064  None       0      +
1     B       2  1076  None       0      +
2     C       2  1064  None       0      +
3     D       0   741  None       0      +

Since df_A is a dataframe, you can fillna and then convert the column to int.

df_A['col1'] = df_A['col1'].fillna(0).astype(int) 

Since you are getting the error invalid literal for int() with base 10: with the above code, it means that there are some non-numeric values in your data which can not be converted to int. Use pd.to_numeric to coerce those values to NaN and then use the above code.

df_A['col1'] = pd.to_numeric(df_A['col1'], errors = 'coerce')
df_A['col1'] = df_A['col1'].fillna(0).astype(int)

You can convert the type with .astype

In [312]: df.columns = df.columns.astype(int)

In [313]: df
Out[313]:
  2006 2007 2008 2009
0  foo  foo  bar  bar
1  foo  foo  bar  bar

Or use .map and convert to string type.

In [338]: df.columns.map('{:g}'.format)
Out[338]: Index(['2006', '2007', '2008', '2009'], dtype='object')

In [319]: df.columns.map(int)
Out[319]: Int64Index([2006, 2007, 2008, 2009], dtype='int64')

You can try using as df['col'] = (df['col']*100).astype(int) as below:

df = pd.DataFrame({'col': [1.10, 2.20, 3.30, 4.40]})
df['col'] = (df['col']*100).astype(int)
print(df)

Output:

   col
0  110
1  220
2  330
3  440