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
Top answer 1 of 3
64
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
2 of 3
22
Since pandas 0.17.1 you can set the displayed numerical precision by modifying the style of the particular data frame rather than setting the global option:
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame(np.random.randn(5, 3), columns=list('ABC'))
df
df.style.set_precision(2)
It is also possible to apply column specific styles
df.style.format({
'A': '{:,.1f}'.format,
'B': '{:,.3f}'.format,
})
Homedutech
homedutech.com › faq › python › how-to-remove-decimal-points-in-pandas.html
How to remove decimal points in pandas
import pandas as pd # Sample DataFrame ... Series" Description: This code snippet illustrates how to remove decimal places from a Pandas Series using the astype() function to convert the data type to integer....
Top answer 1 of 2
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
2 of 2
1
You can solve this by setting the pandas option, precision to 0.
import pandas as pd
df = pd.DataFrame(data={"col": [24.00, 2.00, 3.00]})
print(df)
col
0 24.0
1 2.0
2 3.0
pd.set_option('precision',0)
print(df)
col
0 24
1 2
2 3
Reddit
reddit.com › r/learnpython › how to remove decimals in float?
r/learnpython on Reddit: How to remove decimals in float?
April 15, 2023 -
I need to remove decimals from float to get 6 characters after the dot WITHOUT rounding For example I have 0.00655379 and I need to get 0.006553
Top answer 1 of 10
14
If this is just for printing... f'{my_float:.6f}. If you actually need that level of precision, you're likely to run into rounding issues, but you could try something like this: int(my_float * (10**6)) / (10**6)
2 of 10
7
This might be a case that you should consider using the decimal library vs float, floats are notorious bad for precision, just try print(0.1+0.2) Using Decimal: In []: from decimal import Decimal, ROUND_DOWN d = Decimal.from_float(0.00655379) # ideally you just start with a decimal print(d) Out[]: Decimal('0.0065537900000000003097877510072066797874867916107177734375') Wow, that looks worse - but this is one of the issues with floats. But you can easily convert to what ever decimal places you want with whatever rounding semantics you want, e.g.: In []: precision = Decimal('0.000001') d.quantize(precision, rounding=ROUND_DOWN) Out[]: Decimal('0.006553')
CodeProject
codeproject.com › Questions › 5291723 › How-do-I-remove-decimals-from-a-pandas-data-frame
How do I remove decimals from a pandas data frame index
September 1, 2021 - Do not try and find the page. That’s impossible. Instead only try to realise the truth - For those who code; Updated: 1 Jul 2007
Reddit
reddit.com › r/learnpython › how to remove decimal points from values ?
r/learnpython on Reddit: how to remove decimal points from values ?
December 5, 2023 -
my boyfriend has moved his excel table to python but it has added .0 to his values (eg 160 becomes 160.0) is there anyway to fix this and remove decimals ?
GitHub
github.com › beepscore › pandas_decimal
GitHub - beepscore/pandas_decimal: Using Pandas with Python Decimal for accurate currency arithmetic · GitHub
def int_by_removing_decimal(self, a_float): """ removes decimal separator. removing decimal from a_float with n decimal places multiplies by 10 ** n :a_float: a Python float e.g. 1.23 :return: an integer e.g.
Author beepscore
Javatpoint
javatpoint.com › how-to-remove-decimal-in-python
How to Remove Decimal in Python - Javatpoint
How to Remove Decimal in Python with python, tutorial, tkinter, button, overview, entry, checkbutton, canvas, frame, environment set-up, first python program, operators, etc.
Java2Blog
java2blog.com › home › python › how to remove decimals in python
How to Remove Decimals in Python - Java2Blog
October 2, 2023 - In the above example, we convert ... that can calculate complex mathematical calculations. We can remove decimal places in Python using the truncate() function from the math library....
Top answer 1 of 3
4
Use a function and apply to whole column:
In [94]:
df = pd.DataFrame({'Movies':['Save the last dance', '2012.0']})
df
Out[94]:
Movies
0 Save the last dance
1 2012.0
[2 rows x 1 columns]
In [95]:
def trim_fraction(text):
if '.0' in text:
return text[:text.rfind('.0')]
return text
df.Movies = df.Movies.apply(trim_fraction)
In [96]:
df
Out[96]:
Movies
0 Save the last dance
1 2012
[2 rows x 1 columns]
2 of 3
0
Here is hint for you ,
In case of Valid number ,
a="2012.0"
try:
a=float(a)
a=int(a)
print a
except:
print a
Output:
2012
In case of String like "Dance with Me"
a="Dance with Me"
try:
a=float(a)
a=int(a)
print a
except:
print a
Output:
Dance with Me
Saturn Cloud
saturncloud.io › blog › how-to-remove-decimal-points-in-pandas-a-guide-for-data-scientists
How to Remove Decimal Points in Pandas A Guide for Data Scientists | Saturn Cloud Blog
December 23, 2023 - This function can be a built-in function, a user-defined function, or a lambda function. To remove decimal points using the apply() function, you can create a lambda function that rounds each value to 0 decimal places.
Top answer 1 of 9
73
You can call int() on the end result:
>>> int(2.0)
2
2 of 9
36
When a number as a decimal it is usually a float in Python.
If you want to remove the decimal and keep it an integer (int). You can call the int() method on it like so...
>>> int(2.0)
2
However, int rounds down so...
>>> int(2.9)
2
If you want to round to the nearest integer you can use round:
>>> round(2.9)
3.0
>>> round(2.4)
2.0
And then call int() on that:
>>> int(round(2.9))
3
>>> int(round(2.4))
2
Top answer 1 of 3
4
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
2 of 3
1
If - as your comment suggests - the data just all needs to be multiplied by 100...
df['columnName'] = df['columnName'].apply(lambda x: x*100)
Top answer 1 of 2
38
The DataFrame round method should work...
import numpy as np
import pandas as pd
some_numbers = np.random.ranf(5)
df = pd.DataFrame({'random_numbers':some_numbers})
rounded_df = df.round(decimals=2)
2 of 2
1
import numpy as np
import pandas as pd
#fileName
f=001.csv
#Load File to Df
Urbs_Data=pd.read_csv(f,header=None)
#Round of all the numeric values to the specified decimal value
Urbs_Data= Urbs_Data.round(decimals=3)
This rounding off will be applied on all the Numeric columns
YouTube
youtube.com › watch
How to Remove Decimal Places and Spaces from Data in Pandas DataFrame - YouTube
Learn how to effectively remove decimal places and unnecessary spaces from numeric data in a Pandas DataFrame. Follow our step-by-step guide to clean your da...
Published October 2, 2025 Views 0
Top answer 1 of 3
5
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')
2 of 3
1
You can convert to float before converting to int and then str:
df.columns = df.columns.values.astype(float).astype(int).astype(str)
print(df.columns)
Index(['2006', '2007', '2008', '2009'], dtype='object')
Seems long-winded, but at least we are working with the underlying NumPy array.
CopyProgramming
copyprogramming.com › howto › python-how-to-remove-decimal-number-using-python
How to Remove Decimal Numbers in Python: Complete Guide 2026 - Remove decimal numbers in python complete guide
December 30, 2025 - Python's built-in round() function removes or reduces decimal places while rounding to the nearest value.
GeeksforGeeks
geeksforgeeks.org › how-to-remove-all-decimals-from-a-number-using-python
How to remove all decimals from a number using Python? - GeeksforGeeks
March 27, 2025 - Explanation: This code converts floating-point numbers to integers using the int() function, which removes the decimal part. It then prints the converted values and shows the data types of the original and converted numbers using type(). ... ...
Pandas
pandas.pydata.org › pandas-docs › stable › reference › api › pandas.DataFrame.round.html
pandas.DataFrame.round — pandas 3.0.1 documentation
Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored. ... Additional keywords have no effect but might be accepted for compatibility with numpy. ... Additional keywords have no effect but might be accepted for compatibility with numpy. ... A DataFrame ...