Suppressing scientific notation in pandas?

Question

I have a DataFrame in pandas where some of the numbers are expressed in scientific notation (or exponent notation) like this:

                  id        value
id              1.00    -4.22e-01
value          -0.42     1.00e+00
percent        -0.72     1.00e-01
played          0.03    -4.35e-02
money          -0.22     3.37e-01
other            NaN          NaN
sy             -0.03     2.19e-04
sz             -0.33     3.83e-01

And the scientific notation makes what should be an easy comparison, needlessly difficult. I assume it's the 21900 value that's screwing it up for the others. I mean 1.0 is encoded. ONE!

This doesn't work:

np.set_printoptions(supress=True)

And pandas.set_printoptions doesn't implement suppress either, and I've looked all at pd.describe_options() in despair, and pd.core.format.set_eng_float_format() only seems to turn it on for all the other float values, with no ability to turn it off.

Dd you fix the typo in `np.set_printoptions(suppress=True)` - two p's in suppress? — smci, Nov 16 '16 at 14:50

citynorman · Answer 1 · 2018-03-28T21:09:45.813

78

quick temporary: df.round(4)

global: pd.options.display.float_format = '{:20,.2f}'.format

edited Mar 28 '18 at 21:09

answered Dec 07 '16 at 16:52

citynorman

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What does the {:20} mean? – Moondra Apr 28 '17 at 00:24
1

try this experiment: `print('{:20,.8f}'.format(12333344445676.0123456789))`, then adjust the 20 to 40 and see what happens and I think you'll have your answer. you can use this same style formatter on numbers in a print statement. – TMWP Jul 26 '17 at 04:12
Fwiw you might also want to [suppress the data type output](https://stackoverflow.com/questions/24295451/suppress-descriptive-output-when-printing-pandas-dataframe) – citynorman Oct 06 '17 at 20:58
1

This is the only really working solution for me. Works like a charm in Jupyter. – Bouncner Feb 15 '18 at 08:09
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Agree with @Bouncner, I also tried many solutions but found that only this solution can print a specific number of decimal points for float values in pandas as expected. – Good Will Apr 22 '18 at 21:00
@Moondra {:20} specifies the total width of the printed output, including the decimal portion. This needs not be specified. So, {:,.2f} can be used to specify that the commas have to be printed and the 2 decimal points. – Siraj Samsudeen Jan 18 '21 at 04:14
Great! For scientific notation, use `'{:e}'.format` – Eduardo Reis May 10 '21 at 18:30

score 13 · Accepted Answer · answered Jul 20 '13 at 21:11

Your data is probably object dtype. This is a direct copy/paste of your data. read_csv interprets it as the correct dtype. You should normally only have object dtype on string-like fields.

In [5]: df = read_csv(StringIO(data),sep='\s+')

In [6]: df
Out[6]: 
           id     value
id       1.00 -0.422000
value   -0.42  1.000000
percent -0.72  0.100000
played   0.03 -0.043500
money   -0.22  0.337000
other     NaN       NaN
sy      -0.03  0.000219
sz      -0.33  0.383000

check if your dtypes are object

In [7]: df.dtypes
Out[7]: 
id       float64
value    float64
dtype: object

This converts this frame to object dtype (notice the printing is funny now)

In [8]: df.astype(object)
Out[8]: 
           id     value
id          1    -0.422
value   -0.42         1
percent -0.72       0.1
played   0.03   -0.0435
money   -0.22     0.337
other     NaN       NaN
sy      -0.03  0.000219
sz      -0.33     0.383

This is how to convert it back (astype(float)) also works here

In [9]: df.astype(object).convert_objects()
Out[9]: 
           id     value
id       1.00 -0.422000
value   -0.42  1.000000
percent -0.72  0.100000
played   0.03 -0.043500
money   -0.22  0.337000
other     NaN       NaN
sy      -0.03  0.000219
sz      -0.33  0.383000

This is what an object dtype frame would look like

In [10]: df.astype(object).dtypes
Out[10]: 
id       object
value    object
dtype: object

Actually the column was int64, that had then been df.corr() 'd which returns all float64s — user1244215, Jul 23 '13 at 03:36
if you have NaN in the column it could NOT have been int64; only float64 or object — Jeff, Jul 23 '13 at 08:39
they may have started out as ``Int64`` but they are ``float64`` by definition. However, if they were actually object to begin with, then they still might be ``object`` — Jeff, Jul 23 '13 at 21:56

score 2 · Answer 3 · answered Dec 04 '17 at 17:29

2

If you would like to use the values as formated string in a list, say as part of csvfile csv.writier, the numbers can be formated before creating a list:

df['label'].apply(lambda x: '%.17f' % x).values.tolist()

answered Dec 04 '17 at 17:29

evil242

31
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score 1 · Answer 4 · answered Nov 19 '20 at 22:07

Try this which will give you scientific notation only for large and very small values (and adds a thousands separator unless you omit the ","):

pd.set_option('display.float_format', lambda x: '%,g' % x)

Or to almost completely suppress scientific notation without losing precision, try this:

pd.set_option('display.float_format', str)

score 1 · Answer 5 · answered Mar 31 '21 at 23:55

1

quick fix without rounding:

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

answered Mar 31 '21 at 23:55

Reihan_amn

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Suppressing scientific notation in pandas?

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