Pandas transpose resets decimal rounding

Question

I am defining decimal points carefully by column in a large results table, but when I transpose it so it can fit on a page in my report (using .to_latex), pandas sets the decimals to whatever the largest number is.

Is it possible to keep my decimal precision during the transpose? I would rather not have to define the decimals after the transpose.

(NOTE: I have noticed it does not do this when there is a row of strings in the dataframe before the transpose, so I am thinking it is some kind of datatype definition that I am not understanding?)

Example code:

df = pd.DataFrame(np.random.random([3, 3]),
     columns=['A', 'B', 'C'], index=['first', 'second', 'third'])

df = df.round({'A': 1, 'C': 2})
print(df)
print(df.transpose())

Output:

         A         B     C
first   0.3  0.557432  0.78
second  0.8  0.568175  0.28
third   0.4  0.745703  0.62
      first    second     third
A  0.300000  0.800000  0.400000
B  0.557432  0.568175  0.745703
C  0.780000  0.280000  0.620000

I want:

      first    second     third
A  0.3       0.8       0.4
B  0.557432  0.568175  0.745703
C  0.78      0.28      0.62

cs95 · Accepted Answer · 2017-12-20T13:47:39.413

5

When transposing, it is a possibility that the now transposed rows (as columns) may not be of the same type, or of the same rounding. Pandas tries to remedy this (if possible, from a performance point of view), and so the rounding is reset. If you want to preserve the rounding, convert the dataframe to object type, and then transpose -

df.astype(object).T

     first    second     third
A      0.8       0.7       0.7
B  0.22444  0.475358  0.498084
C     0.17      0.87      0.71

Now, pandas makes no assumptions about object columns, and they are transposed as-is, without any attempt at transforming the data. Keep in mind that data as objects is suicidal in terms of performance, you might as well use python lists at this point, as objects do not offer any performance benefits.

edited Dec 20 '17 at 13:47

answered Dec 20 '17 at 13:34

cs95

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You type so fast LOL – Bharath M Shetty Dec 20 '17 at 13:36
Italics are better for emphasising! – cs95 Dec 20 '17 at 13:47
Yeah, never used one till date. So yeah not it looks a great answer – Bharath M Shetty Dec 20 '17 at 13:48
This works great, except I just noticed that it changes 5.0 to 5, do you know a way to preserve the significant digits? – nicolejane33 Dec 20 '17 at 14:44
@NicoleAndrew that's the annoying thing about an object caste, rounded floats are coerced to ints. I am not aware of a way to circumvent that. If this is for representational purposes, you can caste `df` to a string with `.astype(str)`. – cs95 Dec 20 '17 at 14:46
The string works perfectly! This is the last step before `.to_latex` so strings work fine. – nicolejane33 Dec 20 '17 at 15:00

Pandas transpose resets decimal rounding

1 Answers1