Introduction to Seaborn
Seaborn 
Seaborn is a Python visualization library based on matplotlib that provides a highlevel interface for drawing attractive statistical graphics. It is built on top of matplotlib and closely integrated with pandas data structures, making it an excellent tool for exploring and visualizing datasets. 
Key Features 
Simplified syntax for creating complex visualizations. 
Builtin themes and color palettes to improve the aesthetics of plots. 
Support for a wide range of statistical plots for exploring relationships in data. 
Seamless integration with pandas DataFrames for easy data manipulation and visualization. 
Capabilities for both univariate and multivariate visualizations. 
Integration with matplotlib for finetuning and customization. 
Getting Started 
Install Seaborn using pip: pip install seaborn. 
Import Seaborn in your Python script or Jupyter Notebook: import seaborn as sns. 
Load your data into pandas DataFrame if not already in one. 
Start exploring your data using Seaborn's highlevel plotting functions. 
Installing Seaborn
Using pip 

Using conda 

Verify Installation 

Loading Data
Using Pandas 
import pandas as pd df = pd.read_csv('filename.csv') # Load CSV file

Viewing Data 
df.head() # View first few rows

Understanding Data 
df.info() # Summary of DataFrame df.describe() # Descriptive statistics

Handling Missing Data 
df.dropna() # Drop rows with missing values df.fillna(value) # Fill missing values

Loading Builtin Datasets 
import seaborn as sns df = sns.load_dataset('dataset_name')

Basic Plotting Functions
sns.scatterplot(x, y, data)

Create a scatter plot to visualize the relationship between two variables. 
sns.lineplot(x, y, data)

Generate a line plot to show trends in data over continuous intervals. 
sns.barplot(x, y, data)

Construct a bar plot to display the distribution of categorical data. 

Plot the frequency of unique values in a categorical variable. 
sns.boxplot(x, y, data)

Draw a box plot to summarize the distribution of a continuous variable within different levels of a categorical variable. 
sns.violinplot(x, y, data)

Create a violin plot to visualize the distribution of a continuous variable across different categories. 

Generate a histogram to display the distribution of a single variable. 
Customizing Plots
Changing Colors 
Use the color parameter to specify colors for elements such as lines, markers, and bars. Seaborn also provides color palettes (palette parameter) for different visualizations. 
Adjusting Line Styles and Markers 
Control the style of lines with the linestyle parameter and markers with the marker parameter. Options include solid lines (''), dashed lines (''), and various marker shapes ('o', 's', 'D', etc.). 
Setting Plot Size 
Use the plt.figure(figsize=(width, height)) function to specify the size of your plot. Adjust the width and height values as needed to achieve the desired dimensions. 
Adding Titles and Labels 
Set the title of your plot with plt.title() and label the axes with plt.xlabel() and plt.ylabel(). Provide informative titles and labels to make your plots more understandable. 
Changing Font Sizes 
Customize font sizes for titles, labels, and ticks using parameters such as fontsize or by accessing individual text elements. 
Adjusting Axis Limits 
Control the range of values displayed on the x and y axes using plt.xlim() and plt.ylim() functions. Set appropriate limits to focus on specific regions of interest in your data. 
Adding Grid Lines 
Use plt.grid(True) to display grid lines on your plot, aiding in data interpretation. 
Adding Legends 
Include a legend to distinguish between multiple elements in your plot using the plt.legend() function. Customize the legend labels and placement for clarity. 
Saving Plots
Syntax 
import seaborn as sns # Create your plot here sns.savefig("filename.extension")

Example 
import seaborn as sns import matplotlib.pyplot as plt # Create a scatter plot sns.scatterplot(x='x', y='y', data=data) # Save the plot as a PNG file plt.savefig("scatter_plot.png")

Supported File Formats 
PNG (Portable Network Graphics) JPG/JPEG (Joint Photographic Experts Group) PDF (Portable Document Format) SVG (Scalable Vector Graphics) and more 
Categorical Plots
barplot() 
Displays the central tendency and confidence interval of numeric variables across different categories. Useful for comparing the mean or aggregate statistic of numeric data for each category. 
countplot() 
Shows the count of observations in each category using bars. Suitable for exploring the distribution of categorical variables. 
boxplot() 
Visualizes the distribution of quantitative data across different levels of one or more categorical variables. Useful for identifying outliers and comparing distributions. 
violinplot() 
Combines the benefits of a box plot and a kernel density plot. Provides information about the distribution of data within each category. 
stripplot() and swarmplot() 
Scatterplots for categorical data. Show individual data points along with a categorical variable. Swarmplot avoids overlapping points by adjusting them along the categorical axis. 
pointplot() 
Represents the point estimates and confidence intervals using lines. Useful for visualizing the relationship between two categorical variables. 
factorplot() (deprecated, use catplot() instead) 
A versatile function that can create different types of categorical plots based on the kind parameter. Offers a convenient way to explore relationships between variables. 
catplot() 
Replaces factorplot and serves as a general plot function for categorical data. Supports various plot types such as stripplot, swarmplot, boxplot, etc., through the kind parameter. 


Distribution Plots
Distribution Plots 
Distribution plots in Seaborn allow you to visualize the distribution of a dataset. These plots help you understand the underlying distribution of your data, including its central tendency, spread, and skewness. 
Histograms 
sns.histplot(data, x='column'): Plot a histogram of the specified column in the dataset. Customize with parameters like bins, kde, color, and alpha. 
Kernel Density Estimation (KDE) Plots 
sns.kdeplot(data, x='column'): Generate a smooth estimate of the probability density function. Additional parameters include bw_method, fill, and common_norm. 
Rug Plots 
sns.rugplot(data, x='column'): Plot a line for each data point along the xaxis. Useful for visualizing individual data points in combination with other plots. 
Cumulative Distribution Function (CDF) 
sns.ecdfplot(data, x='column'): Plot the empirical cumulative distribution function. Helps to visualize the cumulative proportion of data points. 
Joint Distribution Plots 
sns.jointplot(data=data, x='x_column', y='y_column', kind='kind'): Plot the joint distribution of two variables along with their marginal distributions. kind parameter can be set to scatter, kde, hist, hex, or reg for different visualizations. 
Pair Plots 
sns.pairplot(data): Create pairwise plots for all numerical columns in the dataset. Offers a quick overview of relationships between multiple variables. 
Violin Plots 
sns.violinplot(data=data, x='x_column', y='y_column'): Visualize the distribution of a numeric variable for different categories. Provides insights into both the distribution and the probability density at different values. 
Box Plots 
sns.boxplot(data=data, x='x_column', y='y_column'): Summarize the distribution of a numeric variable for different categories using quartiles. Helps to identify outliers and compare distributions between categories. 
Swarm Plots 
sns.swarmplot(data=data, x='x_column', y='y_column'): Show each data point along with the distribution. Useful for small to moderatesized datasets. 
ViolinSwarm Combination 
Combining violin and swarm plots can provide a comprehensive view of the distribution and individual data points. 
Regression Plots
Regression Plots 
Regression plots in Seaborn are useful for visualizing relationships between variables and fitting regression models to the data. Seaborn provides several functions for creating regression plots, allowing you to explore linear relationships, examine residuals, and detect outliers. 
lmplot() 
Used for plotting linear models. Syntax: sns.lmplot(x, y, data, ...). Displays scatter plot with a linear regression line. Useful for visualizing the relationship between two variables and assessing the fit of a linear model. 
regplot() 
Similar to lmplot() but can be used in more general contexts. Syntax: sns.regplot(x, y, data, ...). Produces scatter plot with a regression line. Offers additional customization options compared to lmplot(). 
residplot() 
Used for plotting the residuals of a linear regression. Syntax: sns.residplot(x, y, data, ...). Helps to diagnose the fit of the regression model by plotting the difference between observed and predicted values. Useful for identifying patterns or heteroscedasticity in residuals. 
Additional Parameters 
order: Specifies the order of the polynomial regression (default is 1 for linear). scatter_kws: Additional keyword arguments passed to the scatterplot function. line_kws: Additional keyword arguments passed to the line plot function. ci: Confidence interval size for the regression estimate. truncate: Truncates the regression line at the data limits. 
Example 
import seaborn as sns import matplotlib.pyplot as plt # Load sample data tips = sns.load_dataset("tips") # Create a regression plot sns.lmplot(x="total_bill", y="tip", data=tips) # Show the plot plt.show()

Matrix Plots
Matrix Plots 
Matrix plots in Seaborn are useful for visualizing data in matrix form, typically with heatmapstyle representations. 
Heatmaps 
Use sns.heatmap() to create a colored matrix plot, with each cell representing the value of a variable in the dataset. Ideal for displaying correlation matrices or any twodimensional data. 
Cluster Maps 
sns.clustermap() creates a hierarchical clustering heatmap. It's handy for exploring relationships between variables by grouping similar ones together. 
Pair Plots 
Although not strictly matrix plots, sns.pairplot() generates a matrix of scatterplots and histograms for quick visualization of relationships between multiple variables in a dataset. 
Customization 
Seaborn allows extensive customization of matrix plots, including adjusting color schemes, annotating cells with values, and tweaking axes. 
Time Series Plots
Time Series Plots 
Time series plots in Seaborn are useful for visualizing data over time. Seaborn provides several functions to create informative time series plots. 
seaborn.lineplot(x, y, data)

Creates a line plot of y vs. x with optional data argument. Ideal for visualizing trends and patterns over time. 
seaborn.relplot(x, y, data, kind='line')

Offers a highlevel interface to create various plot types, including line plots for time series data. Use the kind parameter to specify the plot type (default is 'line'). 
seaborn.scatterplot(x, y, data)

Plots individual data points as scatter points. Suitable for visualizing relationships between variables over time. 
seaborn.tsplot(data, time, unit, value)

Deprecated since Seaborn version 0.9. Use other functions for time series visualization. 
seaborn.linearmodels.Tsplot( data, time, unit, value)

Visualizes time series data with confidence intervals. Suitable for comparing multiple time series. 
Style and Aesthetics
Seaborn Styles 
seaborn.set_style(style=None): Set the aesthetic style of the plots. Styles include: 'darkgrid', 'whitegrid', 'dark', 'white', and 'ticks'. 
Color Palettes 
seaborn.color_palette(palette=None, n_colors=None, desat=None): Set the color palette for plots. Builtin palettes: 'deep', 'muted', 'bright', 'pastel', 'dark', 'colorblind', etc. Custom palettes can be created using seaborn.color_palette(). 
Contexts 
seaborn.set_context(context=None, font_scale=1, rc=None): Set the context parameters for the plot. Contexts control the scale of plot elements. Contexts include: 'paper', 'notebook', 'talk', and 'poster'. 
Plot Aesthetics 
seaborn.despine(fig=None, ax=None, top=True, right=True, left=False, bottom=False, offset=None, trim=False): Remove axes spines from the plot. seaborn.set_palette(palette, n_colors=None, desat=None, color_codes=False): Set the color palette for the current seaborn context. seaborn.set_context(context=None, font_scale=1, rc=None): Set the plotting context parameters. 
Other Aesthetic Tweaks 
seaborn.set(): Set aesthetic parameters in one step. seaborn.reset_defaults(): Restore default seaborn parameters. seaborn.set_theme(): Set the default seaborn theme. 
Saving Aesthetic Settings 
seaborn.axes_style(style=None, rc=None): Return a dictionary of parameters or use in a with statement to temporarily set the style. seaborn.plotting_context(context=None, font_scale=1, rc=None): Return a dictionary of parameters or use in a with statement to temporarily set the context. 
