IPython Console

The IPython Console allows you to execute commands and enter, interact with and visualize data inside any number of fully featured IPython interpreters. Each console is executed in a separate process, allowing you to run scripts, interrupt execution and restart or terminate a shell without affecting the others or Spyder itself, and easily test your code in a clean environment without disrupting your primary session.

Spyder IPython Console with code, inline plots, and the In prompt

Connecting to a console

Spyder can launch new IPython instances itself, through “Open an IPython console” under the Consoles menu, the IPython Console pane menu or its context menu (Ctrl-T by default), to take advantage of the full suite of Spyder’s features. Each console implements a robust two-process IPython session, with a lightweight front-end interface connected to a full kernel back end. You can also connect to external local and remote kernels, including those managed by QtConsole sessions or the Jupyter Notebook, through the Connect to an existing kernel dialog under the same menus External kernels still support many (though not all) of Spyder’s advanced capabilities.

Spyder IPython Console as above, with the options menu open

Connect to an external kernel

Note

If on Windows and connecting to a remote machine over ssh, you’ll need to install the paramiko python package first, e.g. with conda install paramiko if using Anaconda.

To connect to an external kernel,

  1. Launch an IPython kernel on the local or remote host if one is not already running. If using Spyder 3.3.0 or later, you’ll need to do so with python -m spyder_kernels.console (after you’ve first installed spyder-kernels on the host with <conda/pip> install spyder-kernels). If using a version of Spyder before 3.3.0, ipython kernel should work to launch the kernel, albeit without certain Spyder-specific features.

  2. Copy the connection file (runtime-dir/kernel-pid.json) to the machine you’re running Spyder on (if remote) or note its location (if local).

    You can get the location of runtime-dir by starting an IPython interpreter in the same Python environment as the kernel and running:

    import jupyter_core
    jupyter_core.paths.jupyter_runtime_dir()
    
  3. Click Connect to an existing kernel from the Console menu or the IPython Console pane’s “Gear” menu.

  4. Browse for or enter the path to the connection file from the previous step. If you’re connecting to a local kernel, click Ok and Spyder should connect to the kernel; if a remote kernel, proceed to the final step.

  5. If connecting to a remote kernel over ssh, check the appropriate box and type the full hostname you’re connecting to (in the form username@hostname:port-number). The port number is the one on which the SSH daemon (sshd) is running, typically 22 unless you or your administrator has configured it otherwise. Then, enter either username ‘s password on the remote machine, or your user SSH keyfile (typically .perm) (only one is needed to connect), and press Ok.

Connect to kernel dialog, requesting path and connection details

For more technical details about connecting to remote IPython kernels, see the Connecting to a remote kernel page in the IPython Cookbook. Just remember to enter the appropriate details into Spyder’s Connect to an existing kernel dialog instead of launching a new frontend on the client with --existing.

Supported features

Any IPython Console in Spyder, internally or externally created, supports additional features including:

Spyder IPython Console, with a popup list of code completion guesses
  • Automatic code completion
  • Real-time function calltips
  • Debugging toolbar integration for launching the debugger and controlling execution flow

Spyder-created consoles support even more advanced capabilities, such as:

  • The Variable Explorer, with GUI-based editors for many built-in and third-party Python objects
  • Full GUI integration with the enhanced IPython debugger, ipdb, including viewing and setting normal and conditional breakpoints interactively in any file, a Breakpoints pane, and following along with execution flow in the in the Editor (see the Debugging documentation for more details)
  • The User Module Reloader, which can automatically re-import modified packages and files
  • Inline display of Matplotlib graphics, if the Inline backend is selected under Preferences ‣ IPython console ‣ Graphics ‣ Graphics backend

For information on the features, commands and capabilities built into IPython itself, see the IPython documentation.

Using UMR to reload changed modules

When working with scripts and modules in an interactive session, Python only loads a module from its source file once, the first time it is import``ed. During this first ``import, the bytecode (.pyc file) is generated if necessary and the imported module object is cached in sys.modules. If you subsequently re-import the module anytime in the same session without Spyder, this cached code object will be used even if its source code (.py{w} file) has changed in the meantime. While efficient for final production code, this behavior is often undesired when working interactively, such as when analyzing data or testing your own modules. In effect, you’re left with no way to update or modify any already-imported modules, aside from manually removing the relevant .pyc files, or restarting the console entirely.

Fortunately, in Spyder, there’s an easy solution: the User Module Reloader (UMR), a Spyder-exclusive feature that, when enabled, automatically reloads modules right in the existing IPython shell whenever they are modified and re-imported, without any of the downsides of the above workarounds. Even better, Spyder also loads the %autoreload magic by default into any kernels it starts, allowing changes in already imported modules to be automatically picked up the as soon as the modified file is saved, without any additional user action. With UMR enabled, you can test complex applications within the same IPython interpreter without having to restart it every time you make a change, saving large amounts of manual tedium and long restart times. Or, if you’re analyzing data step by step using your own custom libraries, you can easily add or tweak a function in the latter and see the results reflected in the former, all without the overhead of reloading the data and re-running your whole script to restore your session to the same point.

UMR is enabled by default, and will do its work automatically without user intervention, although it will provide you with a red Reloaded modules: message in the console listing the files it has refreshed when it activates. If desired, you can turned it and the message on and off, and prevent specific modules from being reloaded, under Preferences ‣ Python interpreter ‣ User Module Reloader (UMR).