Format / Suppress Scientific Notation from Python

Question

How can one modify the format for the output from a groupby operation in pandas that produces scientific notation for very large numbers. I know how to do string formatting in python but I'm at a loss when it comes to applying it here.

df1.groupby('dept')['data1'].sum()

dept
value1       1.192433e+08
value2       1.293066e+08
value3       1.077142e+08

This suppresses the scientific notation if I convert to string but now I'm just wondering how to string format and add decimals.

sum_sales_dept.astype(str)

Answer 1

Here is another way of doing it, similar to Dan Allan's answer but without the lambda function:

>>> pd.options.display.float_format = '{:.2f}'.format
>>> Series(np.random.randn(3))
0    0.41
1    0.99
2    0.10

or

>>> pd.set_option('display.float_format', '{:.2f}'.format)

Answer 2

You can use round function just to suppress scientific notation for specific dataframe:

df1.round(4)

or you can suppress is globally by:

pd.options.display.float_format = '{:.4f}'.format

Answer 3

Granted, the answer I linked in the comments is not very helpful. You can specify your own string converter like so.

In [25]: pd.set_option('display.float_format', lambda x: '%.3f' % x)

In [28]: Series(np.random.randn(3))*1000000000
Out[28]: 
0    -757322420.605
1   -1436160588.997
2   -1235116117.064
dtype: float64

I'm not sure if that's the preferred way to do this, but it works.

Converting numbers to strings purely for aesthetic purposes seems like a bad idea, but if you have a good reason, this is one way:

In [6]: Series(np.random.randn(3)).apply(lambda x: '%.3f' % x)
Out[6]: 
0     0.026
1    -0.482
2    -0.694
dtype: object

Answer 4

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

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