0

I need to remove the .0s after floats in a column of a DataFrame, made from a dictionary.

For example, the dictionary might be:

mydict = { "part1" : [1,2,None,4,5] "part2" : [6,7,None,9,10] }

and then when I mydf = pd.DataFrame(mydict), the DataFrame generated is as follows:

part1 part2
0 1.0 6.0
1 2.0 7.0
2 NaN NaN
3 4.0 9.0
4 5.0 10.0

This happens because every single column in a DataFrame must have all objects of the same type. But, I want to have no .0s at the end of my data for look purposes. Obviously, I can't make them integers, due to the lack of an NaN in integers. I also can't make them strings, for the reason of numerical sorting. I also wouldn't want "01","02","03"…"10" for the purpose of looks.

Becuase this project is really serious, the looks matter, so please don't blame me of overthinking looks of data.

Yiding Max Tang
  • 1
  • 1
  • You can find this answer here: https://stackoverflow.com/questions/21291259/convert-floats-to-ints-in-pandas – Senne Dec 31 '22 at 01:22
  • 1
    Would `pd.set_option('precision', 0)` work for you? See also this question: https://stackoverflow.com/questions/37084812/how-to-remove-decimal-points-in-pandas – Nick ODell Dec 31 '22 at 01:22

1 Answers1

0

the comments point out a couple of solutions.

  • i prefer to cast to type .astype('Int64') which retains the NaN as a <NA>.

here is my solution (with your data):

import pandas as pd

mydict = {"part1":[1,2,None,4,5],  "part2":[6,7,None,9,10]}

df = pd.DataFrame(mydict)


df['part2'] = df['part2'].astype('Int64')
print(df)

returns this:

   part1  part2
0    1.0      6
1    2.0      7
2    NaN   <NA>
3    4.0      9
4    5.0     10

You can apply the above to one (or many) columns of your choice.

D.L
  • 4,339
  • 5
  • 22
  • 45