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The describe() function can be used to view summary statistics of a given dataset, including the specified percentiles. This is done by specifying the percentiles parameter of the function to a sequence of desired percentiles, such as (0.25, 0.50, 0.75). This will provide the summary statistics of the dataset at the given percentiles.
You can use the describe() function to generate for variables in a pandas DataFrame.
By default, pandas calculates the 25th, 50th and 75th percentiles for variables.
However you can use the percentiles argument within the describe() function to specify the exact percentiles to calculate.
The following examples show how to use this argument in practice with the following pandas DataFrame:
import pandas as pd
#create DataFrame
df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
'points': [18, 22, 19, 14, 14, 11, 20, 28],
'assists': [5, 7, 7, 9, 12, 9, 9, 4],
'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})
#view DataFrame
print(df)
team points assists rebounds
0 A 18 5 11
1 B 22 7 8
2 C 19 7 10
3 D 14 9 6
4 E 14 12 6
5 F 11 9 5
6 G 20 9 9
7 H 28 4 12
Example 1: Use describe() with Default Percentiles
The following code shows how to use the describe() function to calculate descriptive statistics for each numeric variable in the DataFrame:
#calculate descriptive statistics for each numeric variable
df.describe()
points assists rebounds
count 8.000000 8.00000 8.000000
mean 18.250000 7.75000 8.375000
std 5.365232 2.54951 2.559994
min 11.000000 4.00000 5.000000
25% 14.000000 6.50000 6.000000
50% 18.500000 8.00000 8.500000
75% 20.500000 9.00000 10.250000
max 28.000000 12.00000 12.000000 Notice that the describe() function calculates the 25th, 50th and 75th percentiles for each variable by default.
Example 2: Use describe() with Custom Percentiles
The following code shows how to use the describe() function with the percentiles argument to calculate the 30th, 60th and 90th percentiles for each numeric variable in the DataFrame:
#calculate custom percentiles for each numeric variable
df.describe(percentiles=[.3, .6, .9])
points assists rebounds
count 8.000000 8.00000 8.000000
mean 18.250000 7.75000 8.375000
std 5.365232 2.54951 2.559994
min 11.000000 4.00000 5.000000
30% 14.400000 7.00000 6.200000
50% 18.500000 8.00000 8.500000
60% 19.200000 9.00000 9.200000
90% 23.800000 9.90000 11.300000
max 28.000000 12.00000 12.000000 Notice that the describe() function returns the 30th, 60th and 90th percentiles for each numeric variable.
Note: The describe() function also returns the 50th percentile because this represents the median value for each variable and it is one of the default metrics calculated by the describe() function.
Example 3: Use describe() with No Percentiles
The following code shows how to use the describe() function with the argument percentiles=[] to calculate no percentiles for each numeric variable in the DataFrame:
#calculate no percentiles for each numeric variable
df.describe(percentiles=[])
points assists rebounds
count 8.000000 8.00000 8.000000
mean 18.250000 7.75000 8.375000
std 5.365232 2.54951 2.559994
min 11.000000 4.00000 5.000000
50% 18.500000 8.00000 8.500000
max 28.000000 12.00000 12.000000 Note that the 50th percentile is still included in the output because it represents the for each variable.