sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa
Unique species: 
 ['setosa' 'versicolor' 'virginica']

So, there are three unique species ‘setosa’, ‘versicolor’, and ‘virginica’ in the species column of the DataFrame. Now, we want to find out how many records contain ‘setosa’, ‘versicolor’, and ‘virginica’ in the species column, respectively. We can use the value_counts() function in the pandas Python library to find out the same.

import pandas

df = pandas.read_csv("iris.csv")
print(df.head())

print("Unique species: \n", df["species"].unique())

print("Count of each unique values: \n", df["species"].value_counts())

The output will be:

   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa
Unique species: 
 ['setosa' 'versicolor' 'virginica']
Count of each unique values: 
 setosa        50
versicolor    50
virginica     50
Name: species, dtype: int64

So, there are 50 records that have “setosa” in the species column of the DataFrame, and so on.