# Getting Started with Python Plotting: Line Plots and More Name
Rajiv Chandra

Published on Data visualization is an integral part of data analysis and data science. It aids in understanding the underlying patterns in the data. Python provides a plethora of libraries that can be used to create simple to complex data visualizations. Among the popular Python plotting libraries are Matplotlib, Seaborn, Plotly, Bokeh, Altair, Pygal, and pandas. This beginner's guide to Python plotting libraries will introduce you to these libraries and their usage.

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## Introduction to Python Plotting Libraries

Let's explore some of the top Python plotting libraries.

### PyGWalker

PyGWalker is an Open Source Python Project that can help speed up the data analysis and visualization workflow directly within a Jupyter Notebook-based environments.

PyGWalker (opens in a new tab) turns your Pandas Dataframe (or Polars Dataframe) into a visual UI where you can drag and drop variables to create graphs with ease. Simply use the following code:

``````pip install pygwalker
import pygwalker as pyg
gwalker = pyg.walk(df)``````

You can run PyGWalker right now with these online notebooks:

### Matplotlib

Matplotlib (opens in a new tab) is a versatile plotting library for Python that allows the creation of a wide variety of plots, such as line graphs, bar charts, and scatter plots. Matplotlib offers extensive customization options for designing and formatting plots.

Here is a simple example of creating a line graph with Matplotlib:

``````import matplotlib.pyplot as plt

# data
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

# create line plot
plt.plot(x, y)

# show plot
plt.show()``````

### Seaborn

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for creating attractive and informative statistical graphics. Seaborn works well with pandas DataFrames, simplifying the process of plotting data directly from CSV files.

### Plotly

Plotly is a Python graphing library that makes interactive, publication-quality graphs. You can create line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, and 3D charts using Plotly.

### Bokeh

Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Bokeh can create versatile, data-driven graphics, and interactive, application-like plots on your browser.

### Altair

Altair is a declarative statistical visualization library for Python. With Altair, you provide a high-level specification of the plot, and the library takes care of the rest.

### Pygal

Pygal is a Python module for creating SVG (Scalable Vector Graphics) graphs/charts. Pygal, like Plotly and Bokeh, creates interactive plots that can be embedded in a web browser.

### Pandas

Pandas, primarily known for data manipulation, also includes simple data visualization capabilities. It's built on Matplotlib and works directly with DataFrame objects.

## Comparing Python Plotting Libraries

Choosing the best plotting library depends on your specific needs—your project, the complexity of the data, and the type of visualization you want to create. Here are some key differences:

1. Matplotlib: Best for simple, quick plots, or customizing plots in detail.
2. Seaborn: Best for statistical plots, and built on Matplotlib for additional customization.
3. Plotly: Best for interactive plots.
4. Bokeh: Similar to Plotly but best for web-based interactive visualizations.
5. Altair: Best for creating declarative visualizations.
6. Pygal: Best for creating SVGs.
7. Pandas: Great for quick, simple plots using DataFrame objects.

## Using the Code Snippets

To use the

source code snippets provided for each library, you need to install the library first. For example, to install Matplotlib, you would use the following pip command:

``pip install matplotlib``

Once installed, you can copy the code snippet into your Python environment to run it.

## Data Visualization Types in Python

Python plotting libraries allow us to create a wide variety of visualizations. Some common ones are:

1. Line Graph: Perfect for representing changes over a period of time.
2. Bar Chart: Great for comparing quantities of different categories.
3. Scatter Plot: Ideal for observing the relationship between two variables.
4. Histogram: Shows the frequency distribution of continuous data.

Here are some Python data visualization tutorials for further study:

1. Line Graphs: Creating Line Graphs with Python
2. Bar Charts: Python Bar Charts: A Simple Guide
3. Scatter Plots: Scatter Plots in Python: An Easy Tutorial
4. Histograms: Making Histograms with Matplotlib

To use these data visualization techniques, you need to learn to work with these libraries. This Python Modin tutorial can help you get started.

## Advanced Python Data Visualization Techniques

For those interested in advanced Python data visualization techniques, libraries like PyGWalker, Seaborn and Plotly offer more complex plotting capabilities. These include heatmaps, boxplots, violin plots, and pair plots. These are particularly useful in data science for exploring datasets and communicating results.

## Conclusion

Data visualization in Python has come a long way thanks to libraries like PyGWalker, Matplotlib, Seaborn, Plotly, Bokeh, Altair, Pygal, and pandas. Whether you're a beginner just getting started with simple data visualization in Python or an experienced data scientist seeking advanced visualization techniques, there's a library for you.

If you're looking for an easier way to create and edit data visualizations, consider trying VizGPT (opens in a new tab), a chat interface for creating and editing data visualizations. With features such as natural language to data visualization, step-by-step exploration of data through chat-based interaction, and the ability to upload your own CSV dataset, VizGPT is a useful tool for any data analyst. (opens in a new tab)

## FAQs

1. What are the top Python plotting libraries?

The top Python plotting libraries are Matplotlib, Seaborn, Plotly, Bokeh, Altair, Pygal, and pandas.

2. How do I compare and choose the best plotting library for my needs?

Choosing the best library depends on your specific needs—your project, the complexity of the data, and the type of visualization you want to create.

3. What data visualizations can I create with Python?

Python allows the creation of various types of visualizations like line graphs, bar charts, scatter plots, and histograms. More complex plots can be created with advanced libraries like Seaborn and Plotly.

4. What is the difference between Matplotlib, Seaborn, Plotly, Bokeh, Altair, Pygal, and pandas?

Each library has unique features: Matplotlib is best for simple plots, Seab

orn is good for statistical plots, Plotly and Bokeh for interactive plots, Altair for declarative visualizations, Pygal for creating SVGs, and pandas for quick plots from data frames.

5. How do I use the source code snippets provided for each library?

To use the code snippets, you need to install the respective library using pip. Once installed, you can copy the code snippet into your Python environment to run it.

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