Matplotlib.pyplot library is most commonly used in Python in the field of machine learning. It helps in plotting the graph of large dataset. Not only this also helps in classifying different dataset. It can plot graph both in 2d and 3d format. It has a feature of legend, label, grid, graph shape, grid and many more that make it easier to understand and classify the dataset.
Seaborn provides a beautiful with different styled graph plotting that make our dataset more distinguishable and attractive.
Installation
To install the package write the below code in terminal of ubuntu/Linux or Window Command prompt.
pip install matplotlib pip install seaborn
Attribute Information about data set:
Attribute Information:
-> sepal length in cm
-> sepal width in cm
-> petal length in cm
-> petal width in cm
-> class:
Iris Setosa
Iris Versicolour
Iris Virginica
Number of Instances: 150
Summary Statistics:
Min Max Mean SD Class Correlation
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
Class Distribution: 33.3% for each of 3 classes.
To get the Iris Data click here.
Plotting graph For IRIS Dataset Using Seaborn Library And matplotlib.pyplot library
Loading data
Python3
import numpy as npimport pandas as pdimport matplotlib.pyplot as pltdata = pd.read_csv("Iris.csv") print (data.head(10)) |
Output:
Plotting Using Matplotlib
Python3
import pandas as pdimport matplotlib.pyplot as pltiris = pd.read_csv("Iris.csv")plt.plot(iris.Id, iris["SepalLengthCm"], "r--")plt.show |
Scatter Plot
Python3
iris.plot(kind ="scatter", x ='SepalLengthCm', y ='PetalLengthCm')plt.grid() |
Plotting using Seaborn
Python3
import seaborn as snsiris = sns.load_dataset('iris')# style used as a theme of graph# for example if we want black# graph with grid then write "darkgrid"sns.set_style("whitegrid")# sepal_length, petal_length are iris# feature data height used to define# Height of graph whereas hue store the# class of iris dataset.sns.FacetGrid(iris, hue ="species", height = 6).map(plt.scatter, 'sepal_length', 'petal_length').add_legend() |
