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https://github.com/fabianacampanari/iris-dataanalysis-seaborn-

🌸 The provided code snippet is a Python script that uses matplotlib to plot the numerical and exact derivatives of a function f4 over a range of values. The script generates a sequence of values x from -5 to 5, calculates the derivatives using two different methods, and then plots the results for comparison.
https://github.com/fabianacampanari/iris-dataanalysis-seaborn-

iris-dataset jupyter-notebook machine-learning matplotlib numpy pandas pyplot python-lambda python3 scikit-learn seaborn seaborn-plots

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🌸 The provided code snippet is a Python script that uses matplotlib to plot the numerical and exact derivatives of a function f4 over a range of values. The script generates a sequence of values x from -5 to 5, calculates the derivatives using two different methods, and then plots the results for comparison.

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README 

        





#  
 🌸 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:



```sh

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.



```python

# 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.



```python



# 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.



```python

# Configure the style of the plots

sns.set(style="whitegrid")

```



Exemple:



```python

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.



```python

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

plt.show()

```







## Boxplot



We create a boxplot of the sepal length by species.



```python

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



```python

# Descriptive statistics

print(iris.describe())

 ```



## Class Countv



```python

# 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.



```python

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.



```python

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.



```python

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:



```python

jupyter notebook

```



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