Suppressing scientific notation in pandas?

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.

quick temporary: df.round(4)

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

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

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)

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()

quick fix without rounding:

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

I tried all the options like

1. pd.options.display.float_format = '{:.4f}'.format
2. pd.set_option('display.float_format', str)
3. pd.set_option('display.float_format', lambda x: f'%.{len(str(x%1))-2}f' % x)
4. pd.set_option('display.float_format', lambda x: '%.3f' % x)

but nothing worked for me.

so while assigning the variable / value (var1) to a variable (say num1) I used round(val,5).

num1 = round(var1,5)

This is a crude method as you have to use this round function in each assignment. But this ensures you control on how it happens and get what you want.