🌸 Iris Data Analysis with Seaborn

This repository contains a Jupyter notebook for analyzing the famous Iris dataset using the Seaborn library. The goal is to demonstrate how to load, visualize, and analyze data with Seaborn and pandas.

Requirements

Make sure you have the following libraries installed:

• pandas
• seaborn
• matplotlib
• numpy
• scikitlearn

You can install these libraries using pip:

pip install pandas  
pip install seaborn  
pip install matplotlib  
pip install numpy  
pip install scikit-learn

Introduction

This Jupyter notebook contains various code blocks that perform different tasks for data analysis and visualization. Below, we explain each of the code blocks present in the Seaborniris.ipynb file.

Importing Libraries

First, we import the necessary libraries for data analysis and visualization.

# For working with DataFrames and data manipulation  
import pandas as pd

# For statistical visualizations    
import seaborn as sns

 # For creating plots    
import matplotlib.pyplot as plt

# For numerical operations 
import numpy as np

# To access datasets and tools from scikit-learn      
from sklearn import datasets  \

Generating Data

We create a sequence of x values ranging from -5 to 5, with 100 equally spaced points.

Loading the Dataset

We load the Iris dataset using the load_iris function from Scikit-learn and convert it into a pandas DataFrame.

# Load the Iris dataset
iris_data = load_iris()

# Convert to DataFrame
iris = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)
iris['target'] = iris_data.target

Visualizing the Data

We visualize the data using the Seaborn library. First, we configure the style of the plots.

# Configure the style of the plots
sns.set(style="whitegrid")

Exemple:

sns.pairplot(df, hue='target')
plt.show()

Pairplot

We visualize the distribution of the features with a pairplot.

We visualize the data using the Seaborn library. First, we configure the style of the plots.

sns.pairplot(iris, hue='target')
plt.show()

Boxplot

We create a boxplot of the sepal length by species.

plt.figure(figsize=(10, 6))
sns.boxplot(x='target', y='sepal length (cm)', data=iris, color='b')
plt.title('Boxplot of Sepal Length by Species')
plt.xlabel('Species')
plt.ylabel('Sepal Length (cm)')
plt.show()

Exploratory Data Analysis

We perform exploratory data analysis to better understand the features and the distribution of the classes.

Descriptive Statistics

# Descriptive statistics
print(iris.describe())

Class Countv

# Count of each class
print(iris['target'].value_counts())

Generating Data

We create a sequence of x values ranging from -5 to 5, with 100 equally spaced points.

x = np.linspace(-5, 5, 100)

Calculating Derivatives

We calculate the derivatives of a function f4 at each point in x using two different approaches: a function derivada and a function f4_prime_exato. The results are stored in the lists y2 and _y3, respectively.

y2 = []
y3 = []
for xx in x:
  y2.append(derivada(f4, xx))
  y3.append(f4_prime_exato(xx))

Plotting the Results

We use the matplotlib library to plot the results of the calculated derivatives. The solid line (-) represents the values calculated by the derivada function, while the dashed line (--) represents the values calculated by the f4_prime_exato function.

plt.plot(x, y2, '-', x, y3, '--')
plt.show()

##Running the Notebook

To run the notebook, you can use Jupyter Notebook or JupyterLab. Execute the following command to start Jupyter Notebook:

jupyter notebook

Open the Seaborniris .ipynb file and run the cells to see the results.