Iris Dataset in Python
The iris dataset is a toy dataset provided by many libraries of Python, such as sci-kit-learn, and it is often used in machine learning and data science because of its simple understanding and well-defined. The iris dataset contains the species of three flowers: Setosa, Versicolor, or Virginica.
There are 150 samples in the iris dataset of three species of iris. There are 4 columns in the iris dataset: the first column shows sepal length, the second column shows the sepal width, the third column shows petal length, the fourth column shows the petal width, and one more column is a target column shows the class of each flower base on the four properties of the flower.
The three species of iris look similar, but the difference is the measurements can be used to classify them. This data set is an example of supervised learning (that contains labels). The input variables are sepal length and width and petal length and petal width; each row shows an input variable or observation. The output variables in the iris dataset are Iris-Setosa, Iris-versicolor, or iris-virginica. Each column shows a class label.
Iris Data Set in Python:
To see the iris dataset in Python, first import the dataset from the scikit-learn library or you can download the structured iris dataset from various platforms like Kaggle.
Code:
from sklearn.datasets import load_iris
iris = load_iris()
iris.keys()
Output:
dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])
The dataset consists of some keys, which can be used for accessing specific data. Let;s say you want to get the data about the length and width of the flower using iris['data'].
Code:
iris.data
Output:
array([[5.1, 3.5, 1.4, 0.2],
[4.9, 3. , 1.4, 0.2], [4.7, 3.2, 1.3, 0.2], [4.6, 3.1, 1.5, 0.2], [5. , 3.6, 1.4, 0.2], [5.4, 3.9, 1.7, 0.4], [4.6, 3.4, 1.4, 0.3], [5. , 3.4, 1.5, 0.2], [4.4, 2.9, 1.4, 0.2], [4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2], [4.8, 3.4, 1.6, 0.2], [4.8, 3. , 1.4, 0.1], [4.3, 3. , 1.1, 0.1], [5.8, 4. , 1.2, 0.2], [5.7, 4.4, 1.5, 0.4], [5.4, 3.9, 1.3, 0.4], [5.1, 3.5, 1.4, 0.3], [5.7, 3.8, 1.7, 0.3], [5.1, 3.8, 1.5, 0.3], [5.4, 3.4, 1.7, 0.2], [5.1, 3.7, 1.5, 0.4], [4.6, 3.6, 1. , 0.2], [5.1, 3.3, 1.7, 0.5], [4.8, 3.4, 1.9, 0.2], [5. , 3. , 1.6, 0.2], [5. , 3.4, 1.6, 0.4], [5.2, 3.5, 1.5, 0.2], [5.2, 3.4, 1.4, 0.2], [4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2], [5.4, 3.4, 1.5, 0.4], [5.2, 4.1, 1.5, 0.1], [5.5, 4.2, 1.4, 0.2], [4.9, 3.1, 1.5, 0.2], [5. , 3.2, 1.2, 0.2], [5.5, 3.5, 1.3, 0.2], [4.9, 3.6, 1.4, 0.1], [4.4, 3. , 1.3, 0.2], [5.1, 3.4, 1.5, 0.2], [5. , 3.5, 1.3, 0.3], [4.5, 2.3, 1.3, 0.3], [4.4, 3.2, 1.3, 0.2], [5. , 3.5, 1.6, 0.6], [5.1, 3.8, 1.9, 0.4], [4.8, 3. , 1.4, 0.3], [5.1, 3.8, 1.6, 0.2], [4.6, 3.2, 1.4, 0.2], [5.3, 3.7, 1.5, 0.2], [5. , 3.3, 1.4, 0.2], [7. , 3.2, 4.7, 1.4], [6.4, 3.2, 4.5, 1.5], [6.9, 3.1, 4.9, 1.5], [5.5, 2.3, 4. , 1.3], [6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3], [6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3, 1. ], [6.6, 2.9, 4.6, 1.3], [5.2, 2.7, 3.9, 1.4], [5. , 2. , 3.5, 1. ], [5.9, 3. , 4.2, 1.5], [6. , 2.2, 4. , 1. ], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3], [6.7, 3.1, 4.4, 1.4], [5.6, 3. , 4.5, 1.5], [5.8, 2.7, 4.1, 1. ], [6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9, 1.1], [5.9, 3.2, 4.8, 1.8], [6.1, 2.8, 4. , 1.3], [6.3, 2.5, 4.9, 1.5], [6.1, 2.8, 4.7, 1.2], [6.4, 2.9, 4.3, 1.3], [6.6, 3. , 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3. , 5. , 1.7], [6. , 2.9, 4.5, 1.5], [5.7, 2.6, 3.5, 1. ], [5.5, 2.4, 3.8, 1.1], [5.5, 2.4, 3.7, 1. ], [5.8, 2.7, 3.9, 1.2], [6. , 2.7, 5.1, 1.6], [5.4, 3. , 4.5, 1.5], [6. , 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5], [6.3, 2.3, 4.4, 1.3], [5.6, 3. , 4.1, 1.3], [5.5, 2.5, 4. , 1.3], [5.5, 2.6, 4.4, 1.2], [6.1, 3. , 4.6, 1.4], [5.8, 2.6, 4. , 1.2], [5. , 2.3, 3.3, 1. ], [5.6, 2.7, 4.2, 1.3], [5.7, 3. , 4.2, 1.2], [5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3, 1.3], [5.1, 2.5, 3. , 1.1], [5.7, 2.8, 4.1, 1.3], [6.3, 3.3, 6. , 2.5], [5.8, 2.7, 5.1, 1.9], [7.1, 3. , 5.9, 2.1], [6.3, 2.9, 5.6, 1.8], [6.5, 3. , 5.8, 2.2], [7.6, 3. , 6.6, 2.1], [4.9, 2.5, 4.5, 1.7], [7.3, 2.9, 6.3, 1.8], [6.7, 2.5, 5.8, 1.8], [7.2, 3.6, 6.1, 2.5], [6.5, 3.2, 5.1, 2. ], [6.4, 2.7, 5.3, 1.9], [6.8, 3. , 5.5, 2.1], [5.7, 2.5, 5. , 2. ], [5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3, 2.3], [6.5, 3. , 5.5, 1.8], [7.7, 3.8, 6.7, 2.2], [7.7, 2.6, 6.9, 2.3], [6. , 2.2, 5. , 1.5], [6.9, 3.2, 5.7, 2.3], [5.6, 2.8, 4.9, 2. ], [7.7, 2.8, 6.7, 2. ], [6.3, 2.7, 4.9, 1.8], [6.7, 3.3, 5.7, 2.1], [7.2, 3.2, 6. , 1.8], [6.2, 2.8, 4.8, 1.8], [6.1, 3. , 4.9, 1.8], [6.4, 2.8, 5.6, 2.1], [7.2, 3. , 5.8, 1.6], [7.4, 2.8, 6.1, 1.9], [7.9, 3.8, 6.4, 2. ], [6.4, 2.8, 5.6, 2.2], [6.3, 2.8, 5.1, 1.5], [6.1, 2.6, 5.6, 1.4], [7.7, 3. , 6.1, 2.3], [6.3, 3.4, 5.6, 2.4], [6.4, 3.1, 5.5, 1.8], [6. , 3. , 4.8, 1.8], [6.9, 3.1, 5.4, 2.1], [6.7, 3.1, 5.6, 2.4], [6.9, 3.1, 5.1, 2.3], [5.8, 2.7, 5.1, 1.9], [6.8, 3.2, 5.9, 2.3], [6.7, 3.3, 5.7, 2.5], [6.7, 3. , 5.2, 2.3], [6.3, 2.5, 5. , 1.9], [6.5, 3. , 5.2, 2. ], [6.2, 3.4, 5.4, 2.3], [5.9, 3. , 5.1, 1.8]])
To see the target in the iris data, use
Code:
iris.target
Output:
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
Converting the Dataset into a Pandas Dataframe:
To convert the iris data into a dataframe, make the data frame and add the iris.data as the instances, and the target is the target variable
Code:
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.DataFrame(iris.data, columns = iris.feature_names)
df['target'] = iris.target
df.head(10)
Output:
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
0 5.1 3.5 1.4 0.2 0.0
1 4.9 3.0 1.4 0.2 0.0
2 4.7 3.2 1.3 0.2 0.0
3 4.6 3.1 1.5 0.2 0.0
4 5.0 3.6 1.4 0.2 0.0
5 5.4 3.9 1.7 0.4 0.0
6 4.6 3.4 1.4 0.3 0.0
7 5.0 3.4 1.5 0.2 0.0
8 4.4 2.9 1.4 0.2 0.0
9 4.9 3.1 1.5 0.1 0.0
Here the data frame contains the length and width of sepals and petals consisting of the target column, which is the numerical representation of classes of Iris Flowers; for target 0, the class is Sentosa; for target 1, the class is Versicolor; for target 2, the class is Virginia.
Let;s add the names of species in the data frame by adding one more column with the names of different species.
Code:
species = []
for i in range(len(iris['target'])):
if iris['target'][i] == 0:
species.append("setosa")
elif iris['target'][i] == 1:
species.append('versicolor')
else:
species.append('virginica')
df['species'] = species
df.head()
Output:
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target species
0 5.1 3.5 1.4 0.2 0 setosa
1 4.9 3.0 1.4 0.2 0 setosa
2 4.7 3.2 1.3 0.2 0 setosa
3 4.6 3.1 1.5 0.2 0 setosa
4 5.0 3.6 1.4 0.2 0 setosa
5 5.4 3.9 1.7 0.4 0 setosa
6 4.6 3.4 1.4 0.3 0 setosa
7 5.0 3.4 1.5 0.2 0 setosa
8 4.4 2.9 1.4 0.2 0 setosa
9 4.9 3.1 1.5 0.1 0 setosa
To know the size of each species in the iris data set, use
Code:
df.groupby('species').size()
Output:
species
setosa 50
versicolor 50
virginica 50
dtype: int64
This shows that there are 150 values, out of which 50 are setosa, 50 are versicolor, and 50 are virginica.
To know the statistical representation of the iris data, use
Code:
df.describe()
Output:
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
count 150.000000 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.057333 3.758000 1.199333 1.000000
std 0.828066 0.435866 1.765298 0.762238 0.819232
min 4.300000 2.000000 1.000000 0.100000 0.000000
25% 5.100000 2.800000 1.600000 0.300000 0.000000
50% 5.800000 3.000000 4.350000 1.300000 1.000000
75% 6.400000 3.300000 5.100000 1.800000 2.000000
max 7.900000 4.400000 6.900000 2.500000 2.000000
The described method gives the following statistical measure: count, which tells us about the count of each feature, which tells us about the mean of data in each variable, std means standard deviation, min means minimum value in each attribute of the data set, 25% is the 1st quartile of the iris data, 50% median, 75% means the 3rd quartile and max is the maximum value in the data.
Plotting the Dataset:
It is a great way to see and visualize any data, which makes it easy to understand. We can plot the iris data with the help of the matplotlib library.
Code:
import matplotlib.pyplot as plt
setosa = df[df.species == "setosa"]
versicolor = df[df.species=='versicolor']
virginica = df[df.species=='virginica']
fig, ax = plt.subplots()
fig.set_size_inches(13, 7) # adjusting the length and width of plot
# lables and scatter points
ax.scatter(setosa['petal length (cm)'], setosa['petal width (cm)'], label="Setosa", facecolor="blue")
ax.scatter(versicolor['petal length (cm)'], versicolor['petal width (cm)'], label="Versicolor", facecolor="green")
ax.scatter(virginica['petal length (cm)'], virginica['petal width (cm)'], label="Virginica", facecolor="red")
ax.set_xlabel("petal length (cm)")
ax.set_ylabel("petal width (cm)")
ax.grid()
ax.set_title("Iris petals")
ax.legend()
plt.show()
Output: