Bar Graph plot using different Python Libraries

#!/usr/bin/env python

# coding: utf-8

# # 1. Using Matplotlib library

# In[1]:

import matplotlib.pyplot as plt

# Sample data

categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']

values = [10, 25, 15, 30]

# Create a bar graph

plt.bar(categories, values)

# Adding labels and title

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Bar Graph Example')

# Show the graph

plt.show()

#clcoding.com

# # 2. Using Seaborn library

# In[2]:

import seaborn as sns

import matplotlib.pyplot as plt

# Sample data

categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']

values = [10, 25, 15, 30]

# Create a bar plot using Seaborn

sns.barplot(x=categories, y=values)

# Adding labels and title

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Bar Plot Example')

# Show the plot

plt.show()

#clcoding.com

# # 3. Using Plotly library

# In[3]:

import plotly.express as px

# Sample data

categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']

values = [10, 25, 15, 30]

# Create an interactive bar graph using Plotly

fig = px.bar(x=categories, y=values, labels={'x': 'Categories', 'y': 'Values'}, title='Bar Graph Example')

# Show the plot

fig.show()

#clcoding.com

# # 4. Using Bokeh library

# In[4]:

from bokeh.plotting import figure, show

from bokeh.io import output_notebook

# Sample data

categories = ['Category 1', 'Category 2', 'Category 3', 'Category 4']

values = [10, 25, 15, 30]

# Create a bar graph using Bokeh

p = figure(x_range=categories, title='Bar Graph Example', x_axis_label='Categories', y_axis_label='Values')

p.vbar(x=categories, top=values, width=0.5)

# Show the plot in a Jupyter Notebook (or use output_file for standalone HTML)

output_notebook()

show(p)

#clcoding.com

# In[ ]: