How to use function seaborn in Python? - with practical example

Seaborn is a data visualization library in Python that is built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. In this video, I will show you how to use the seaborn function in Python with two different examples. Example 1: Scatter Plot Step 1: Import the necessary libraries, including seaborn and matplotlib. Step 2: Load a sample dataset from seaborn. Step 3: Create a scatter plot using the seaborn function. Step 4: Customize the plot by adding labels and a title. 
import seaborn as sns
import matplotlib.pyplot as plt

# Load the sample dataset
tips = sns.load_dataset('tips')

# Create a scatter plot
sns.scatterplot(x='total_bill', y='tip', data=tips)

# Add labels and a title
plt.xlabel('Total Bill')
plt.ylabel('Tip')
plt.title('Total Bill vs. Tip Scatter Plot')

plt.show()

Example 2: Box Plot Step 1: Import the necessary libraries, including seaborn and matplotlib. Step 2: Load a sample dataset from seaborn. Step 3: Create a box plot using the seaborn function. Step 4: Customize the plot by adding labels and a title. 
import seaborn as sns
import matplotlib.pyplot as plt

# Load the sample dataset
tips = sns.load_dataset('tips')

# Create a box plot
sns.boxplot(x='day', y='total_bill', data=tips)

# Add labels and a title
plt.xlabel('Day')
plt.ylabel('Total Bill')
plt.title('Total Bill Distribution by Day')

plt.show()

By following these examples, you can start using the seaborn function in Python to create visually appealing and informative plots for your data analysis tasks.

Comments

Post a Comment

Popular posts from this blog

What are the different types of optimization algorithms used in deep learning?

In the first example, we are going to discuss the Stochastic Gradient Descent (SGD) optimization algorithm in deep learning using Python. # Import necessary libraries import numpy as np from sklearn.datasets import make_classification from sklearn.linear_model import SGDClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler # Create a synthetic dataset X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42) # Split the dataset into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Standardize features by removing the mean and scaling to unit variance scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Initialize the SGDClassifier with appropriate parameters sgd_clf = SGDClassifier(loss='log', max_iter=1000, learning_rate='optimal') # Fit the model on the train...
Read more

What are the different evaluation metrics used in machine learning?

In the first example, we are going to demonstrate how to use accuracy as an evaluation metric in machine learning using Python. Here is the code: from sklearn.metrics import accuracy_score y_true = [0, 1, 1, 0, 1] y_pred = [0, 1, 0, 0, 1] accuracy = accuracy_score(y_true, y_pred) print("Accuracy:", accuracy) Step 1: Import the accuracy_score function from sklearn.metrics. Step 2: Define two lists, y_true and y_pred, containing the true and predicted values. Step 3: Calculate the accuracy score by calling the accuracy_score function with y_true and y_pred as arguments. Step 4: Print out the accuracy score. In the second example, we are going to demonstrate how to use precision and recall as evaluation metrics in machine learning using Python. Here is the code: from sklearn.metrics import precision_score, recall_score y_true = [0, 1, 1, 0, 1] y_pred = [0, 1, 0, 0, 1] precision = precision_score(y_true, y_pred) recall = recall_score(y_true, y_pred) print("Precis...
Read more

What is the difference between a module and a package in Python?

In the first example, we are going to explore the concept of a module in Python. A module is a file containing Python code that can define functions, classes, and variables. It allows you to organize your code into reusable components. Here is an example code snippet demonstrating the use of a module in Python: # Create a module named mymodule.py # Define a function inside the module def greet(name): return "Hello, " + name In the code above, we have created a module named mymodule.py that defines a function called greet . This function takes a name as an argument and returns a greeting message. To use this module in another Python script, we can import it using the import keyword. import mymodule message = mymodule.greet("Alice") print(message) When we run the above code, it will import the mymodule module and use the greet function to print a greeting message for the name "Alice". In the second example, we are going to discuss the ...
Read more