python seaborn library


SEABRON LIBRARY

Introduction:

              Seaborn is a powerful Python data visualization library built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. With Seaborn, users can quickly create a wide variety of plots, including heatmaps, violin plots, and categorical plots, with minimal code. One of Seaborn's strengths is its ability to work seamlessly with pandas DataFrames, making it ideal for exploring and visualizing complex datasets. It also integrates well with other libraries in the scientific Python ecosystem, such as NumPy and SciPy, offering flexibility for data analysis and presentation.

 NOTE:  Seaborn is a Python data visualization library based on Matplotlib, used to make statistical graphics more attractive and informative.

Installation of seaborn package :

Steps to Install Seaborn:

1.    Open Command Prompt:

o   Press Windows + R, type cmd, and hit Enter.

2.    Run the following command to install Seaborn:

                #  - pip install seaborn

                  If you're using Python 3, you might need to use:

               #  - pip3 install seaborn

Additional Dependencies

  • Seaborn requires NumPy, Pandas, and Matplotlib to work correctly. pip will automatically install these if they are not already installed.

3.    Verify Installation: After installation, open a Python terminal or your IDE and run the following to verify the installation:

 

                                program:

           import seaborn as sns

print(sns.__version__)

If no error appears, Seaborn is successfully installed.

it shows version.


Syntax:

Importing Seaborn:

>> import seaborn as sns

 >> import matplotlib.pyplot as plt  # Often used to display the plots

1) Basic Seaborn Plotting Syntax

a)               a)Line Plot

>> sns.lineplot(x='x_column', y='y_column', data=dataframe)

>> plt.show()

b)                b)Scatter Plot

>> sns.scatterplot(x='x_column', y='y_column', data=dataframe)

>> plt.show()

c)                   c)Bar Plot

>> sns.barplot(x='category_column', y='numeric_column', data=dataframe)

>> plt.show()

d)                 d)Histogram / Distribution Plot

>> sns.histplot(data=dataframe['column'], bins=30, kde=True)

>> plt.show()

e)                   e)Box Plot

sns.boxplot(x='category_column', y='numeric_column', data=dataframe)

plt.show()

f)                   f)    Heatmap

>> sns.heatmap(data=correlation_matrix, annot=True, cmap='coolwarm')

>> plt.show()

2) Customization in Seaborn:

       a) Adding Titles and Labels

>> plt.title('Plot Title')

>> plt.xlabel('X Axis Label')

>> plt.ylabel('Y Axis Label')

      b) Setting Plot Style

>> sns.set_style('whitegrid')  # Options: 'whitegrid', 'darkgrid', 'white', 'dark', 'ticks'

     c) Changing Color Palette

>> sns.set_palette('pastel')  # Available options include 'deep', 'muted', 'bright', 'pastel',ect.

      d) Saving a Plot

>> plt.savefig('plot_filename.png')  # Save the plot as a PNG or other format



                 Simple program with output:

>> import matplotlib.pyplot as plt

>> import seaborn  as n

>> n.histplot([0,3,2,3,4,5])

>> plt.show()

           output:


 Code: using distplot function

>> import matplotlib.pyplot as plt

>> import seaborn  as n         #you can take any name for variable

>> n.distplot([0,3,2,3,4,5])

# Here we use distplot  function it may gives the line for easy understanding

>>plt.show()

                  output:

         


 

             Better example:

>> import seaborn as sns

>> import matplotlib.pyplot as plt

>> import pandas as pd

 

# Sample Data

>> data = {'x': [1, 2, 3, 4, 5], 'y': [10, 11, 12, 13, 14]}

>> df = pd.DataFrame(data)

 

# Line Plot Example

>> sns.lineplot(x='x', y='y', data=df)

 

# Customize the plot

>> plt.title("Sample Line Plot")

>> plt.xlabel("X Axis")

>> plt.ylabel("Y Axis")

 

# Show plot

>> plt.show()

                        output:




Video



 



 


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