import matplotlib.pyplot as plt
import numpy as np

# Data
libraries = [
    "NumPy", "Pandas", "Matplotlib", "Seaborn", "SciPy", "Scikit-learn", "TensorFlow", "Keras",
    "PyTorch", "Statsmodels", "XGBoost", "LightGBM", "CatBoost", "NLTK", "SpaCy", "Gensim",
    "Plotly", "Bokeh", "Dash", "H2O.ai", "PyCaret", "Dask", "Orange3"
]

# Parameters for circular layout
num_libs = len(libraries)
angles = np.linspace(0, 2 * np.pi, num_libs, endpoint=False).tolist()

# Plot
fig, ax = plt.subplots(figsize=(12, 12), subplot_kw={'projection': 'polar'})
bars = ax.bar(angles, np.ones(num_libs), width=0.3, bottom=2.5, color='skyblue', edgecolor='black')

# Add library names and URLs
for bar, angle, lib in zip(bars, angles, libraries):
    rotation = np.rad2deg(angle)
    alignment = 'left' if angle < np.pi else 'right'
    ax.text(angle, bar.get_height() + 3.0, lib, rotation=rotation, ha=alignment, va='center', fontsize=12, color='black')

# Customize plot
ax.set_yticklabels([])
ax.set_xticks([])
ax.spines['polar'].set_visible(False)
plt.show()

# Output

Data Science Repository

Welcome to the Data Science Repository! This repository is designed to help you learn Python for data science and develop the essential skills needed to succeed as a data scientist. From data manipulation to machine learning, you'll gain the knowledge required to excel in this field.

Track Overview

This track is a comprehensive journey through Python for data science. It consists of various libraries and tools to import, clean, manipulate, visualize data, and build predictive models. Here's an overview of the contents in this repository:

➤ ⭐Python Essentials

#	Project	Link
1	Introduction to Python	Open
2	Intermediate Python	Open

➤ ⭐Data Manipulation and Visualization

#	Project	Link
1	Data Manipulation with pandas	Open
2	Joining Data with pandas	Open
3	Introduction to Statistics in Python	Open
4	Introduction to Data Visualization with Matplotlib	Open
5	Introduction to Data Visualization with Seaborn	Open
6	Python-data-science-toolbox-(part-1)	Open
7	Python-data-science-toolbox-(part-2)	Open
8	Intermediate Data Visualization with Seaborn	Open

➤ ⭐ Exploratory Data Analysis (EDA) and Statistics

#	Project	Link
1	Exploratory Data Analysis in Python Part - 1	Open
2	Exploratory Data Analysis in Python Part - 2	Open
3	Working with Categorical Data in Python	Open
4	Data Communication Concepts	Open

➤ ⭐ Data Importing and Cleaning

#	Project	Link
1	Introduction to Importing Data in Python-(part-1)	Open
2	Intermediate Importing Data in Python-(part-2)	Open
3	Cleaning Data in Python [Part - 1]	Open
4	Cleaning Data in Python [Part - 2]	Open
5	Working with Dates and Times in Python	Open

➤ ⭐ Advanced Topics

#	Project	Link
1	Writing Functions in Python	Open
2	Introduction to Regression with statsmodels in Python	Open
3	Sampling in Python	Open
4	Hypothesis Testing in Python	Open
5	Statistical-Thinking-in-Python-[Part -1]	Open
6	Statistical-Thinking-in-Python-[Part -2]	Open
7	Supervised Learning with scikit-learn	Open
8	Unsupervised Learning in Python	Open
9	Cluster Analysis in Python	Open
10	Machine Learning with Tree-Based Models in Python	Open
11	Preprocessing for Machine Learning	Open
12	Developing Python Packages	Open
13	Machine Learning for Business	Open
14	Introduction to SQL	Open
15	Intermediate SQL	Open
16	Joining Data in SQL	Open
17	Introduction to Git	Open

➤ ⭐ Projects

In addition to the comprehensive learning materials, this repository offers various projects to apply and reinforce your data science skills. Here is a list of the projects available:

#	Project	Link
1	Analyzing TV Data
2	Investigating Netflix Movies
3	What and Where are the World's Oldest Businesses
4	Google Play Store Apps and Reviews
5	The GitHub History of the Scala Language
6	A Visual History of Nobel Prize Winners
7	Dr. Semmelweis and the Discovery of Handwashing
8	Predicting Credit Card Approvals
9	School Budgeting with Machine Learning in Python
10	Analyzing Police Activity with pandas
11	Exploring NYC Public School Test Result Scores
12	Analyzing Crime in Los Angeles
13	Preparing Data for Customer Analytics Modeling
14	Modeling Car Insurance Claim Outcomes(EDA)
	Modeling Car Insurance Claim Outcomes(ML)
	Modeling Car Insurance Claim Outcomes(GB-RF)
	Modeling Car Insurance Claim Outcomes(Ensemble_Method)
15	Hypothesis Testing Soccer Matches
16	Predictive Modeling for Agriculture
17	Clustering Antarctic Penguin Species
18	Predicting Movie Rental Durations
19	🔜