Format / Suppress Scientific Notation from Python Pandas Aggregation Results

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

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 2

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 3

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 4

If you want to style the output of a data frame in a jupyter notebook cell, you can set the display style on a per-dataframe basis:

df = pd.DataFrame({'A': np.random.randn(4)*1e7}) df.style.format("{:.1f}") 

See the documentation here.

Answer 5

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()