In Python, we can use the pandas.DataFrame.describe() function to get the statistical summary of data in a dataset. In this article, we will use pandas to read from a CSV file and then, print the statistical summary of the dataset using the pandas.DataFrame.describe() function.

We can use the following Python code for that purpose:

import pandas

data = pandas.read_csv("iris.csv")
print(data.describe(include="all"))

Here, we first import the pandas module. Then, we read the CSV file named “iris.csv”. The CSV file contains various information, such as the sepal length, sepal width, petal length, petal width, and species of flowers. Out of these five columns, the first four columns contain floating point values and the last column contains strings.

We want to print the statistical summary of all the columns including the categorical columns. So, we pass the argument include=”all” in the pandas.DataFrame.describe() function.

The output of the above program will be:

        sepal_length  sepal_width  petal_length  petal_width species
count     150.000000   150.000000    150.000000   150.000000     150
unique           NaN          NaN           NaN          NaN       3
top              NaN          NaN           NaN          NaN  setosa
freq             NaN          NaN           NaN          NaN      50
mean        5.843333     3.057333      3.758000     1.199333     NaN
std         0.828066     0.435866      1.765298     0.762238     NaN
min         4.300000     2.000000      1.000000     0.100000     NaN
25%         5.100000     2.800000      1.600000     0.300000     NaN
50%         5.800000     3.000000      4.350000     1.300000     NaN
75%         6.400000     3.300000      5.100000     1.800000     NaN
max         7.900000     4.400000      6.900000     2.500000     NaN

Here, count specifies the number of rows. Unique specifies the number of unique values in a categorical column. It contains NaN …