Introduction
Visualization allows us to extract valuable insights from data through effective plotting. Python offers many libraries for data visualization. It offers a simple way to create informative statistical graphics. This article will focus on the seaborn vs matplotlib library of Python.
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What is Seaborn in Python?
Seaborn in python is one of the useful libraries, which can be used for Data Visualization. There are multiple libraries available in python for Data Visualization and seaborn is one of them. This blog Seaborn vs Matplotlib will explain you the features and the plot type of both the libraries.
Now Let’s understand the difference between Matplolib and Seaborn
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Seaborn Vs Matplolib
seaborn vs matplotlib: matplotlib is one of the Data Visualization library like seaborn and there are following differences in matplotlib and seaborn:
| Feature | Matplotlib | Seaborn |
|---|---|---|
| Purpose | General-purpose plotting library. Ideal for basic and highly customized plots. | Built on top of Matplotlib, designed for statistical data visualization with ease. |
| Ease of Use | More low-level, requires more code for complex visualizations. | High-level interface, making complex plots simpler to create with less code. |
| Aesthetics | Basic styling; customization requires manual adjustments. | Provides aesthetically pleasing defaults and various built-in themes like darkgrid. |
| Customization | Highly customizable with detailed control over every aspect of the plot. | Offers less control but more attractive defaults and simpler customization options. |
| Types of Plots | Supports basic charts (line, scatter, bar, histogram, etc.). | Specializes in statistical plots (box, violin, pair plots, heatmaps, etc.). |
| Data Handling | Works with NumPy arrays, lists, and requires manual setup for DataFrames. | Works natively with Pandas DataFrames, making it easier to plot from DataFrame columns. |
| Learning Curve | Steeper learning curve for beginners due to its detailed settings. | Easier for beginners to create complex visualizations quickly. |
| Dependencies | Standalone; does not require other libraries for basic usage. | Depends on Matplotlib for rendering plots. |
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Let’s go through frequently used types of plots in matplotlib library:
| Plot Type | Description | Matplotlib Function | Use Case |
|---|---|---|---|
| Line Plot | Displays data as a series of points connected by straight lines. | plt.plot() | Visualizing trends over time or a sequence. |
| Scatter Plot | Displays individual data points using dots in a two-dimensional space. | plt.scatter() | Showing the relationship or correlation between two variables. |
| Bar Chart | Represents categorical data with rectangular bars. | plt.bar() / plt.barh() | Comparing quantities across different categories. |
| Histogram | Represents the distribution of numerical data by grouping data into bins. | plt.hist() | Showing the frequency distribution of a continuous variable. |
| Pie Chart | Displays the proportion of categories as slices of a circle. | plt.pie() | Showing the percentage share of each category in a whole. |
| Box Plot | Displays the distribution of data based on quartiles, showing the median, spread, and outliers. | plt.boxplot() | Summarizing the spread and skewness of numerical data. |
| Violin Plot | Combines aspects of box plots and kernel density plots to show distribution. | plt.violinplot() | Comparing the distribution of a dataset across multiple categories. |
| Area Plot | Displays the cumulative value over time as an area under a curve. | plt.fill_between() | Showing the cumulative total or change over time. |
| Heatmap | Displays data in a matrix format with varying colors to represent values. | plt.imshow() | Visualizing data matrices, such as correlation matrices. |
| Error Bar Plot | Displays error or uncertainty in data points along with the actual data values. | plt.errorbar() | Indicating the uncertainty or variability in data points. |
| Stacked Bar Chart | Stacks bars on top of each other to show sub-groups within each category. | plt.bar() with stacked=True | Comparing part-to-whole relationships across categories. |
| Step Plot | Displays data as a series of steps, useful for discrete changes. | plt.step() | Representing changes over time, especially for discrete intervals. |
| Quiver Plot | Displays velocity vectors as arrows in a grid. | plt.quiver() | Visualizing vector fields such as wind speed and direction. |
| Contour Plot | Shows 3D data in 2D by using contours (lines of constant values). | plt.contour() / plt.contourf() | Visualizing three-dimensional data with color gradients and contour lines. |
| 3D Plot | Displays 3D data points in a three-dimensional space. | Axes3D.plot() from mpl_toolkits.mplot3d | Visualizing three-dimensional relationships between variables. |
| Polar Plot | Plots data in polar coordinates, with an angle and radius. | plt.polar() | Visualizing data with a radial pattern, such as wind directions. |
| Hexbin Plot | Displays a density plot in hexagonal bins, useful for large datasets. | plt.hexbin() | Showing the density of points in a two-dimensional space. |
| Stem Plot | Displays data as lines originating from a baseline to emphasize discrete data points. | plt.stem() | Highlighting individual data points, often used in signal processing. |
Let’s go through frequently used types of plots in seaborn library:
| Line Plot | Displays data as a series of points connected by a line, frequently used for time series data. | sns.lineplot() | Tracking changes over time or displaying trends. |
| Bar Plot | Shows the average value of a variable for different categories using rectangular bars. | sns.barplot() | Comparing means or sums across categories. |
| Count Plot | Displays the count of observations in each categorical bin using bars. | sns.countplot() | Visualizing the frequency of categorical data. |
| Histogram | Represents the distribution of a single continuous variable by dividing it into bins. | sns.histplot() | Understanding the frequency distribution of a variable. |
| KDE Plot | Displays the probability density of a continuous variable using a smooth curve. | sns.kdeplot() | Visualizing the distribution of data with a smoothed probability density function. |
| Box Plot | Displays the distribution of a dataset based on quartiles, highlighting outliers. | sns.boxplot() | Summarizing data spread, identifying outliers, and comparing distributions across categories. |
| Violin Plot | Combines box plot and KDE to show the distribution of data with its probability density. | sns.violinplot() | Comparing distributions across multiple categories with more detail than a box plot. |
| Swarm Plot | Displays individual data points while avoiding overlap to show distribution in categories. | sns.swarmplot() | Visualizing individual data points along with their distribution. |
| Strip Plot | Similar to a swarm plot but places points in a single line along each category. | sns.stripplot() | Showing individual data points within categories without adjusting for overlap. |
| Pair Plot | Used to create a grid of scatter plots and histograms for each pair of variables in a dataset. | sns.pairplot() | Exploring pairwise relationships in a dataset, especially useful for Exploratory Data Analysis (EDA ). |
| Heatmap | Displays data as a matrix with different color shades representing values. | sns.heatmap() | Visualizing correlation matrices, or any data where the relationship between two dimensions matters. |
| FacetGrid | Maps data subsets onto multiple plots using a grid layout, allowing for comparison across facets. | sns.FacetGrid() | Visualizing complex datasets by creating multiple plots across different conditions or variables. |
| Joint Plot | Combines scatter plot and histogram/KDE plot to show the relationship between two variables along with marginal distributions. | sns.jointplot() | Exploring the relationship between two variables along with their distributions. |
| Reg Plot | Fits and plots a linear regression model between two variables. | sns.regplot() | Understanding linear relationships between two variables with a regression line. |
| Cat Plot | Allows for plotting different types of categorical plots (e.g., bar, strip, box) in a grid layout. | sns.catplot() | Creating categorical plots with options to choose plot type (like bar, box, strip) flexibly. |
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Conclusion
I hope this blog gives you a better understanding of Seaborn vs Matplotlib in Python.
- Matplotlib is a library used for creating a wide range of data visualizations with many customization choices.
- Seaborn, built on top of Matplotlib, simplifies the process of making attractive, statistical plots with a more user-friendly interface and better default visuals.
- Matplotlib is great for precise and customized plots, while Seaborn in Python is better for quick and visually attractive statistical analysis.
- Seaborn excels in exploratory data analysis (EDA), especially with complex datasets.
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