For single column:
contrib_df["AMNT"].describe().apply(lambda x: format(x, 'f'))
For entire DataFrame (as suggested by @databyte )
df.describe().apply(lambda s: s.apply('{0:.5f}'.format))
For whole DataFrame (as suggested by @Jayen):
contrib_df.describe().apply(lambda s: s.apply(lambda x: format(x, 'g')))
As the function describe returns a data frame, what the above function does is, it simply formats each row to the regular format.
I wrote this answer because I was having a though, in my mind, that was
** It’s pointless to get the count of 95 as 95.00000e+01**
Also in our regular format its easier to compare.
Before applying the above function we were getting
count 9.500000e+01
mean 5.621943e+05
std 2.716369e+06
min 4.770000e+02
25% 2.118160e+05
50% 2.599960e+05
75% 3.121170e+05
max 2.670423e+07
Name: salary, dtype: float64
After applying, we get
count 95.000000
mean 562194.294737
std 2716369.154553
min 477.000000
25% 211816.000000
50% 259996.000000
75% 312117.000000
max 26704229.000000
Name: salary, dtype: object