spyder

Official repository for Spyder - The Scientific Python Development Environment

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Quick Overview

Spyder is an open-source scientific Python development environment. It provides a powerful interactive development environment for the Python language with advanced editing, interactive testing, debugging, and introspection features. Spyder is designed by and for scientists, engineers, and data analysts, integrating with popular scientific packages like NumPy, SciPy, and Matplotlib.

Pros

Integrated development environment specifically tailored for scientific computing and data analysis
Includes a powerful variable explorer and IPython console
Offers advanced editing features like code completion, real-time code analysis, and multi-language editor
Highly customizable with a plugin system for extending functionality

Cons

Can be resource-intensive, especially on older hardware
Learning curve for new users, particularly those not familiar with IDEs
Some users report occasional stability issues or crashes
Updates can sometimes introduce compatibility issues with existing workflows

Getting Started

To install Spyder, you can use conda or pip:

# Using conda (recommended)
conda install spyder

# Using pip
pip install spyder

To launch Spyder after installation:

spyder

For the best experience, it's recommended to install Spyder as part of the Anaconda distribution, which includes many scientific Python packages:

Download and install Anaconda from https://www.anaconda.com/products/distribution
Open Anaconda Navigator
Click on the Spyder icon to launch the IDE

Once Spyder is open, you can start a new Python file, use the IPython console for interactive computing, or explore your variables in the Variable Explorer pane.

Competitor Comparisons

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Pros of VS Code

Broader language support and extensive ecosystem of extensions
More frequent updates and active development from Microsoft
Lightweight and fast performance, even with many extensions

Cons of VS Code

Less specialized for scientific computing and data analysis
Steeper learning curve for setting up a Python development environment

Code Comparison

VS Code (settings.json):

{
  "python.pythonPath": "/path/to/python",
  "python.linting.pylintEnabled": true,
  "python.formatting.provider": "black"
}

Spyder (spyder.ini):

[main]
spyder_pythonpath = /path/to/python
pylint = True
autoformatting = True
autoformatting_provider = autopep8

Summary

VS Code is a versatile, general-purpose code editor with extensive language support and a vast ecosystem of extensions. It offers frequent updates and excellent performance. However, it requires more setup for Python development.

Spyder is specifically designed for scientific computing and data analysis in Python. It provides a more integrated environment out-of-the-box but has a narrower focus and slower development cycle.

The choice between the two depends on the user's specific needs and preferences in their Python development workflow.

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Cons of IntelliJ IDEA Community Edition

Heavier resource usage and slower startup time compared to Spyder
Steeper learning curve for new users
Less focused on scientific computing and data analysis workflows

Code Comparison

Spyder (Python):

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

IntelliJ IDEA (Java):

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public class Main {
    public static void main(String[] args) {
        int[] numbers = {1, 2, 3, 4, 5};
        Arrays.stream(numbers).forEach(System.out::println);
    }
}

While both IDEs support multiple languages, Spyder is more tailored for scientific Python development, whereas IntelliJ IDEA Community Edition offers a broader range of language support and features for general software development.

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Pros of JupyterLab

More flexible and extensible interface with a modular architecture
Better support for interactive data visualization and widgets
Seamless integration with various file formats and data sources

Cons of JupyterLab

Steeper learning curve for beginners compared to Spyder's more traditional IDE layout
Less integrated debugging capabilities out-of-the-box
May require more system resources, especially for large notebooks or multiple open tabs

Code Comparison

Spyder:

# In Spyder, you typically work with .py files
def hello_world():
    print("Hello, World!")

hello_world()

JupyterLab:

# In JupyterLab, you work with notebook cells
%%javascript
alert("Hello, World!")

# Or in Python
print("Hello, World!")

JupyterLab's notebook format allows for mixing code, markdown, and rich output in a single document, while Spyder focuses on traditional script editing. JupyterLab's cell-based execution model is more suited for exploratory data analysis and interactive computing, whereas Spyder's environment is more aligned with traditional software development workflows.

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More extensive plugin ecosystem with a wider range of language support
Highly customizable and hackable, allowing users to modify core functionality
Cross-platform support with consistent experience across operating systems

Cons of Atom

Slower startup time and higher memory usage, especially with many packages
Less integrated scientific computing features out-of-the-box
Steeper learning curve for non-technical users

Code Comparison

Atom (JavaScript):

atom.commands.add('atom-workspace', {
  'custom:hello-world': () => {
    console.log('Hello, World!');
  }
});

Spyder (Python):

from spyder.api.plugins import Plugins, SpyderPluginV2

class HelloWorldPlugin(SpyderPluginV2):
    def on_initialize(self):
        print("Hello, World!")

Both Atom and Spyder are powerful IDEs, but they cater to different audiences. Atom is more general-purpose and extensible, while Spyder focuses on scientific computing and data analysis. Atom's flexibility comes at the cost of performance, whereas Spyder provides a more streamlined experience for Python developers working with data. The code examples demonstrate the different approaches to extending functionality in each IDE.

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Robust data visualization and reporting capabilities with R Markdown
Extensive package management and project organization features

Cons of RStudio

Heavier resource usage, potentially slower on less powerful machines
Steeper learning curve for beginners due to more complex interface
Primarily focused on R, with Python support as a secondary feature

Code Comparison

RStudio (R):

library(ggplot2)
ggplot(mtcars, aes(x = mpg, y = wt)) +
  geom_point() +
  theme_minimal()

Spyder (Python):

import matplotlib.pyplot as plt
import seaborn as sns
sns.scatterplot(x='mpg', y='weight', data=mtcars)
plt.show()

Both IDEs offer powerful data analysis and visualization capabilities, but RStudio excels in R-specific features and integrated reporting, while Spyder provides a more Python-centric environment with scientific computing tools. RStudio's interface is more comprehensive but potentially overwhelming for beginners, whereas Spyder offers a simpler, more focused experience for Python developers and data scientists.

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README

Some source files and icons may be under other authorship/licenses; see NOTICE.txt.

Project status

Build status

Try Spyder online

:point_left: Click on this link to run the latest Spyder version in your browser.

:point_left: Click on this link to check the next Spyder 5 version.

:point_left: Click on this link to test changes in our master branch.

How Spyder looks

Screenshot of Spyder's main window

Help support Spyder, the community-developed scientific IDE!

Spyder development is made possible by contributions from our global user community, along with organizations like NumFOCUS and Quansight. There are numerous ways you can help, many of which don't require any programming. If you'd like to make a donation to help fund further improvements, we're on OpenCollective.

Thanks for all you do to make the Spyder project thrive! More details

Overview

Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It offers a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package.

Beyond its many built-in features, its abilities can be extended even further via its plugin system and API. Furthermore, Spyder can also be used as a PyQt5 extension library, allowing you to build upon its functionality and embed its components, such as the interactive console, in your own software.

For more general information about Spyder and to stay up to date on the latest Spyder news and information, please check out our new website.

Core components

Editor

Work efficiently in a multi-language editor with a function/class browser, real-time code analysis tools (pyflakes, pylint, and pycodestyle), automatic code completion (jedi and rope), horizontal/vertical splitting, and go-to-definition.
Interactive console

Harness the power of as many IPython consoles as you like with full workspace and debugging support, all within the flexibility of a full GUI interface. Instantly run your code by line, cell, or file, and render plots right inline with the output or in interactive windows.
Documentation viewer

Render documentation in real-time with Sphinx for any class or function, whether external or user-created, from either the Editor or a Console.
Variable explorer

Inspect any variables, functions or objects created during your session. Editing and interaction is supported with many common types, including numeric/strings/bools, Python lists/tuples/dictionaries, dates/timedeltas, Numpy arrays, Pandas index/series/dataframes, PIL/Pillow images, and more.
Development tools

Examine your code with the static analyzer, trace its execution with the interactive debugger, and unleash its performance with the profiler. Keep things organized with project support and a built-in file explorer, and use find in files to search across entire projects with full regex support.

Documentation

You can read the Spyder documentation online on the Spyder Docs website.

Installation

For a detailed guide to installing Spyder, please refer to our installation instructions.

The easiest way to install Spyder on any of our supported platforms is to download it as part of the Anaconda distribution, and use the conda package and environment manager to keep it and your other packages installed and up to date.

If in doubt, you should always install Spyder via this method to avoid unexpected issues we are unable to help you with; it generally has the least likelihood of potential pitfalls for non-experts, and we may be able to provide limited assistance if you do run into trouble.

Other installation options exist, including:

The WinPython distribution for Windows
The MacPorts project for macOS
Your distribution's package manager (i.e. apt-get, yum, etc) on Linux
The pip package manager, included with most Python installations

However, we lack the resources to provide individual support for users who install via these methods, and they may be out of date or contain bugs outside our control, so we recommend the Anaconda version instead if you run into issues.

Troubleshooting

Before posting a report, please carefully read our Troubleshooting Guide and search the issue tracker for your error message and problem description, as the great majority of bugs are either duplicates, or can be fixed on the user side with a few easy steps. Thanks!

Contributing and Credits

Spyder was originally created by Pierre Raybaut, and is currently maintained by Carlos CÃ³rdoba and an international community of volunteers.

You can join usâeveryone is welcome to help with Spyder! Please read our contributing instructions to get started!

Certain source files are distributed under other compatible permissive licenses and/or originally by other authors. The icons for the Spyder 3 theme are derived from Font Awesome 4.7 (Â© 2016 David Gandy; SIL OFL 1.1). Most Spyder 2 theme icons are sourced from the Crystal Project icon set (Â© 2006-2007 Everaldo Coelho; LGPL 2.1+). Other Spyder 2 icons are from Yusuke Kamiyamane (Â© 2013 Yusuke Kamiyamane; CC-BY 3.0), the FamFamFam Silk icon set (Â© 2006 Mark James; CC-BY 2.5), and the KDE Oxygen icons (Â© 2007 KDE Artists; LGPL 3.0+).

See NOTICE.txt for full legal information.

Running from a git clone

Please see the instructions in our Contributing guide to learn how to do run Spyder after cloning its repo from Github.

Dependencies

Important Note: Most or all of the dependencies listed below come with Anaconda and other scientific Python distributions, so you don't need to install them separately in those cases.