Subsections

- Source code handling tips
  - Lightweight 'version control'
- Effective logging

#
Quick tips

IPython can be used as an improved replacement for the Python prompt, and for that you don't really need to read any more of this manual. But in this section we'll try to summarize a few tips on how to make the most effective use of it for everyday Python development, highlighting things you might miss in the rest of the manual (which is getting long). We'll give references to parts in the manual which provide more detail when appropriate.

The following article by Jeremy Jones provides an introductory tutorial about IPython:
`http://www.onlamp.com/pub/a/python/2005/01/27/ipython.html`

- The TAB key. TAB-completion, especially for attributes, is a convenient way to explore the structure of any object you're dealing with. Simply type `object_name.<TAB>` and a list of the object's attributes will be printed (see sec. 6.5 for more). Tab completion also works on file and directory names, which combined with IPython's alias system allows you to do from within IPython many of the things you normally would need the system shell for.
- Explore your objects. Typing `object_name?` will print all sorts of details about any object, including docstrings, function definition lines (for call arguments) and constructor details for classes. The magic commands `%pdoc`, `%pdef`, `%psource` and `%pfile` will respectively print the docstring, function definition line, full source code and the complete file for any object (when they can be found). If automagic is on (it is by default), you don't need to type the '`%`' explicitly. See sec. 6.4 for more.
- The `%run` magic command allows you to run any python script and load all of its data directly into the interactive namespace. Since the file is re-read from disk each time, changes you make to it are reflected immediately (in contrast to the behavior of `import`). I rarely use `import` for code I am testing, relying on `%run` instead. See sec. 6.2 for more on this and other magic commands, or type the name of any magic command and ? to get details on it. See also sec. 6.9 for a recursive reload command.
  `%run` also has special flags for timing the execution of your scripts (`-t`) and for executing them under the control of either Python's `pdb` debugger (`-d`) or profiler (`-p`). With all of these, `%run` can be used as the main tool for efficient interactive development of code which you write in your editor of choice.
- Use the Python debugger, `pdb` 2. The `%pdb` command allows you to toggle on and off the automatic invocation of an IPython-enhanced `pdb` debugger (with coloring, tab completion and more) at any uncaught exception. The advantage of this is that `pdb` starts inside the function where the exception occurred, with all data still available. You can print variables, see code, execute statements and even walk up and down the call stack to track down the true source of the problem (which often is many layers in the stack above where the exception gets triggered).
  Running programs with `%run` and pdb active can be an efficient to develop and debug code, in many cases eliminating the need for `print` statements or external debugging tools. I often simply put a `1/0` in a place where I want to take a look so that pdb gets called, quickly view whatever variables I need to or test various pieces of code and then remove the `1/0`.
  Note also that ``%run -d`' activates `pdb` and automatically sets initial breakpoints for you to step through your code, watch variables, etc. See Sec. 6.12 for details.
- Use the output cache. All output results are automatically stored in a global dictionary named `Out` and variables named `_1`, `_2`, etc. alias them. For example, the result of input line 4 is available either as `Out[4]` or as `_4`. Additionally, three variables named `_`, `__` and `___` are always kept updated with the for the last three results. This allows you to recall any previous result and further use it for new calculations. See Sec. 6.12 for more.
- Put a '`;`' at the end of a line to supress the printing of output. This is useful when doing calculations which generate long output you are not interested in seeing. The `_*` variables and the `Out[]` list do get updated with the contents of the output, even if it is not printed. You can thus still access the generated results this way for further processing.
- A similar system exists for caching input. All input is stored in a global list called `In` , so you can re-execute lines 22 through 28 plus line 34 by typing `'exec In[22:29]+In[34]'` (using Python slicing notation). If you need to execute the same set of lines often, you can assign them to a macro with the `%macro` `function.` See sec. 6.11 for more.
- Use your input history. The `%hist` command can show you all previous input, without line numbers if desired (option `-n`) so you can directly copy and paste code either back in IPython or in a text editor. You can also save all your history by turning on logging via `%logstart`; these logs can later be either reloaded as IPython sessions or used as code for your programs.
- Define your own system aliases. Even though IPython gives you access to your system shell via the `!` prefix, it is convenient to have aliases to the system commands you use most often. This allows you to work seamlessly from inside IPython with the same commands you are used to in your system shell.
  IPython comes with some pre-defined aliases and a complete system for changing directories, both via a stack (see `%pushd`, `%popd` and `%ds`) and via direct `%cd`. The latter keeps a history of visited directories and allows you to go to any previously visited one.
- Use Python to manipulate the results of system commands. The ``!!`' special syntax, and the `%sc` and `%sx` magic commands allow you to capture system output into Python variables.
- Expand python variables when calling the shell (either via ``!'` and ``!!'` or via aliases) by prepending a `$` in front of them. You can also expand complete python expressions. See sec. 6.7 for more.
- Use profiles to maintain different configurations (modules to load, function definitions, option settings) for particular tasks. You can then have customized versions of IPython for specific purposes. See sec. 7.3 for more.
- Embed IPython in your programs. A few lines of code are enough to load a complete IPython inside your own programs, giving you the ability to work with your data interactively after automatic processing has been completed. See sec. 9 for more.
- Use the Python profiler. When dealing with performance issues, the `%run` command with a `-p` option allows you to run complete programs under the control of the Python profiler. The `%prun` command does a similar job for single Python expressions (like function calls).
- Use the IPython.demo.Demo class to load any Python script as an interactive demo. With a minimal amount of simple markup, you can control the execution of the script, stopping as needed. See sec. 14 for more.

## Source code handling tips

IPython is a line-oriented program, without full control of the terminal. Therefore, it doesn't support true multiline editing. However, it has a number of useful tools to help you in dealing effectively with more complex editing.

The `%edit` command gives a reasonable approximation of multiline editing, by invoking your favorite editor on the spot. IPython will execute the code you type in there as if it were typed interactively. Type `%edit?` for the full details on the edit command.

If you have typed various commands during a session, which you'd like to reuse, IPython provides you with a number of tools. Start by using `%hist` to see your input history, so you can see the line numbers of all input. Let us say that you'd like to reuse lines 10 through 20, plus lines 24 and 28. All the commands below can operate on these with the syntax

  %command 10-20 24 28

where the command given can be:

- `%macro <macroname>`: this stores the lines into a variable which, when called at the prompt, re-executes the input. Macros can be edited later using ``%edit macroname`', and they can be stored persistently across sessions with ``%store macroname`' (the storage system is per-profile). The combination of quick macros, persistent storage and editing, allows you to easily refine quick-and-dirty interactive input into permanent utilities, always available both in IPython and as files for general reuse.
- `%edit`: this will open a text editor with those lines pre-loaded for further modification. It will then execute the resulting file's contents as if you had typed it at the prompt.
- `%save <filename>`: this saves the lines directly to a named file on disk.

While `%macro` saves input lines into memory for interactive re-execution, sometimes you'd like to save your input directly to a file. The `%save` magic does this: its input sytnax is the same as `%macro`, but it saves your input directly to a Python file. Note that the `%logstart` command also saves input, but it logs all input to disk (though you can temporarily suspend it and reactivate it with `%logoff/%logon`); `%save` allows you to select which lines of input you need to save.

### Lightweight 'version control'

When you call `%edit` with no arguments, IPython opens an empty editor with a temporary file, and it returns the contents of your editing session as a string variable. Thanks to IPython's output caching mechanism, this is automatically stored:

  In [1]: %edit

  IPython will make a temporary file named: /tmp/ipython_edit_yR-HCN.py

  Editing... done. Executing edited code...

  hello - this is a temporary file

  Out[1]: "print 'hello - this is a temporary file'\n"

Now, if you call ``%edit -p'`, IPython tries to open an editor with the same data as the last time you used `%edit`. So if you haven't used `%edit` in the meantime, this same contents will reopen; however, it will be done in a new file. This means that if you make changes and you later want to find an old version, you can always retrieve it by using its output number, via ``%edit _NN'`, where `NN` is the number of the output prompt.

Continuing with the example above, this should illustrate this idea:

  In [2]: edit -p

  IPython will make a temporary file named: /tmp/ipython_edit_nA09Qk.py

  Editing... done. Executing edited code...

  hello - now I made some changes

  Out[2]: "print 'hello - now I made some changes'\n"

  In [3]: edit _1

  IPython will make a temporary file named: /tmp/ipython_edit_gy6-zD.py

  Editing... done. Executing edited code...

  hello - this is a temporary file

  IPython version control at work :)

  Out[3]: "print 'hello - this is a temporary file'\nprint 'IPython version control at work :)'\n"

This section was written after a contribution by Alexander Belchenko on the IPython user list.

## Effective logging

A very useful suggestion sent in by Robert Kern follows:

I recently happened on a nifty way to keep tidy per-project log files. I made a profile for my project (which is called "parkfield").

  include ipythonrc

  # cancel earlier logfile invocation:

  logfile ''

  execute import time

  execute __cmd = '/Users/kern/research/logfiles/parkfield-%s.log rotate'

  execute __IP.magic_logstart(__cmd % time.strftime('%Y-%m-%d'))

I also added a shell alias for convenience:

  alias parkfield="ipython -pylab -profile parkfield"

Now I have a nice little directory with everything I ever type in, organized by project and date.

Contribute your own: If you have your own favorite tip on using IPython efficiently for a certain task (especially things which can't be done in the normal Python interpreter), don't hesitate to send it!