You can use the describe() function to generate for variables in a pandas DataFrame.

To suppress scientific notation in the output of the describe() function, you can use the following methods:

Method 1: Suppress Scientific Notation When Using describe() with One Column

df['my_column'].describe().apply(lambda x: format(x, 'f'))

Method 2: Suppress Scientific Notation When Using describe() with Multiple Columns

df.describe().apply(lambda x: x.apply('{0:.5f}'.format))

The following examples show how to use each method in practice with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'store': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],
                   'sales': [8450550, 406530, 53000, 6000, 2000, 4000, 5400, 6500],
                   'returns':[2212200, 145200, 300, 2500, 700, 600, 800, 1200]})

#view DataFrame
print(df)

  store    sales  returns
0     A  8450550  2212200
1     A   406530   145200
2     A    53000      300
3     A     6000     2500
4     B     2000      700
5     B     4000      600
6     B     5400      800
7     B     6500     1200

Example 1: Suppress Scientific Notation When Using describe() with One Column

If we use the describe() function to calculate descriptive statistics for the sales column, the values in the output will be displayed in scientific notation:

#calculate descriptive statistics for sales column
df['sales'].describe()
count    8.000000e+00
mean     1.116748e+06
std      2.966552e+06
min      2.000000e+03
25%      5.050000e+03
50%      6.250000e+03
75%      1.413825e+05
max      8.450550e+06
Name: sales, dtype: float64

Notice that each of the values in the output are displayed using scientific notation.

We can use the following syntax to suppress scientific notation in the output:

#calculate descriptive statistics for sales column and suppress scientific notation
df['sales'].describe().apply(lambda x: format(x, 'f'))

count          8.000000
mean     1116747.500000
std      2966551.594104
min         2000.000000
25%         5050.000000
50%         6250.000000
75%       141382.500000
max      8450550.000000
Name: sales, dtype: object

Notice that the values in the output are now shown without scientific notation.

Example 2: Suppress Scientific Notation When Using describe() with Multiple Columns

If we use the describe() function to calculate descriptive statistics for each numeric column, the values in the output will be displayed in scientific notation:

#calculate descriptive statistics for each numeric column
df.describe()

               sales	     returns
count	8.000000e+00	8.000000e+00
mean	1.116748e+06	2.954375e+05
std	2.966552e+06	7.761309e+05
min	2.000000e+03	3.000000e+02
25%	5.050000e+03	6.750000e+02
50%	6.250000e+03	1.000000e+03
75%	1.413825e+05	3.817500e+04
max	8.450550e+06	2.212200e+06

Notice that each of the values in the output are displayed using scientific notation.

We can use the following syntax to suppress scientific notation in the output:

#calculate descriptive statistics for numeric columns and suppress scientific notation
df.describe().apply(lambda x: x.apply('{0:.5f}'.format))

                sales	      returns
count	      8.00000	      8.00000
mean	1116747.50000	 295437.50000
std	2966551.59410	 776130.93692
min	   2000.00000	    300.00000
25%	   5050.00000	    675.00000
50%	   6250.00000	   1000.00000
75%	 141382.50000	  38175.00000
max	8450550.00000	2212200.00000

Notice that the values in the output are now shown without scientific notation.

Note that in this example we used 0:.5f to display 5 decimal places in the output.

Feel free to change the 5 to a different number to display a different number of decimal places.

The following tutorials explain how to perform other common operations in pandas: