Casting full DataFrame.

df = df.astype(str).astype(float)

For single column. IDs is the name of the column.

df["IDs"] = df.IDs.astype(str).astype(float)

Test implementation

from pprint import pprint
import bson
df = pd.DataFrame()
y = []
for i in range(1,6):
    i = i *2/3.5
    y.append(bson.decimal128.Decimal128(str(i)))
pprint(y)
df["D128"] = y
df["D128"] = df.D128.astype(str).astype(float)
print("\n", df)

Output:

[Decimal128('0.5714285714285714'),
 Decimal128('1.1428571428571428'),
 Decimal128('1.7142857142857142'),
 Decimal128('2.2857142857142856'),
 Decimal128('2.857142857142857')]

        D128
0  0.571429
1  1.142857
2  1.714286
3  2.285714
4  2.857143

I think you need converters:

import pandas as pd
import io
import decimal as D

temp = u"""a,b,c,d
           1,1,1,1.0"""

# after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), 
                 dtype={'a': int, 'b': float}, 
                 converters={'c': D.Decimal, 'd': D.Decimal})

print (df)
       a    b  c    d
    0  1  1.0  1  1.0

for i, v in df.iterrows():
    print(type(v.a), type(v.b), type(v.c), type(v.d))

    <class 'int'> <class 'float'> <class 'decimal.Decimal'> <class 'decimal.Decimal'>

This can be modified by changing the print options for floats, however it will modify how every float datatype is printed

pd.set_option('display.float_format', '{:.10f}'.format)

Keep in mind that this is only the way it's printed. The value is stored in the dataframe, with every decimal.

On the other hand, you can restrict decimals by:

df.Value = df.Value.round(4)

But this will round depending the fifth decimal. Last option would be to use np.ceil or np.floor but since this wont support decimals, an approach with multiplication and division is requierd:

precision = 4
df['Value_ceil'] = np.ceil(df.Value * 10**precision) / (10**precision)
df['Value_floor'] = np.floor(df.Value * 10**precision) / (10**precision)

To modify the float output do this:

df= pd.DataFrame(range(5), columns=['a'])
df.a = df.a.astype(float)
df

Out[33]:

          a
0 0.0000000
1 1.0000000
2 2.0000000
3 3.0000000
4 4.0000000

pd.options.display.float_format = '{:,.0f}'.format
df

Out[35]:

   a
0  0
1  1
2  2
3  3
4  4

The problem is for the .iloc it assign the value and did not change the column type

l = df.columns[2:]
df[l] = df[l].astype(int)
df
     0    1  2  3   4
0  1.1  2.1  3  4   5
1  6.1  7.1  8  9  10