Help and documentation in IPython

In my daily workflow the most transformative contributions are discussed here. To find an unknown answer when a person ask help to his friend or a family member when a computer gets a problem it is a less matter. Such as mailing list thread, online documentation and stack overflow contain more information when searchable web resources are same in data science. To effectively find the information you don’t know that through web search engine or another means you should use every possible situation and more about learning as it contains less effective practitioner for data science about memorizing the command or tool.

To shorten the gap between the type of documentation and the user and search is the other most useful function of IPython. Through Ipython we can find the required information, for answering complicated questions web searches plays a role.

Keybord shortcuts in IPython shell:

  • For fast navigation while typing commands the IPython shell provides number of keyboard shortcuts. IPython doesnot provide shortcuts by itself it provides by depending on your system configuration like GNU Readline library.

IPython magic commands:

  • In this we will discuss the enhancements that IPython add on the top of the syntax of python which are known as magic commands. In data analysis to solve various common problems these magic commands are designed. These are prefixed by the percentage character.

  • Line magics, Cell magics are the two types of magic commands

  • Line magics operate on a single line of the input and denoted by the single percent prefix whereas cell magics operate on multiple lines of input and denoted by the double percentage prefix.

Input and Output History:

  • To obtain the output of previous commands IPython exposes the several ways as string versions commands themselves.

IPython and shell commands:

  • Between the system command line tools and the multiple windows to access command line tools one of the frustration is the need to switch when working with the standard python interpreter.

  • One of the frustrations is the need to switch between the system command line tools and multiple windows to access python tools. With the exclamation point anything appearing after the ! on a line will be executed by the system command line not by the python kernel.

Quick introduction to the shell:

  • We will offer a quick introduction here for the uninitiated as to use the shell, command for a full intro is beyond the scope of the chapter. The way to interact with the computer textually is shell.

  • The question might be asked by the one who is unfamiliar with the shell is that many results can be found by clicking on the icons and menus, why would you bother this? Then the shell user replies with the another question why need of clicking icons and menus when you can accomplish results easily by typing.

  • Though the learning curve can intimidate the average computer user, It becomes clear that the shell offers more control of advanced tasks when moving beyond basic tasks, while it sound like a typical tech preference Impasse.


  • Python debugger is the standard python tool for interactive debugging. In order to see what causes more error the debugger let the user step through the code line by line. Ipython debugger is the enhanced version of the Ipython. Both the debuggers are used many ways.

  • In Python %debug magic command is the most convenient interface to debugging . At the point of the exception it will automatically open an interactive debugging if you call it after hitting an exception.

Profiling and timing code:

There are often trade-offs that you can make between various implementations in the process of creating data processing pipelines and developing codes. It can be a counter productive to worry about such things when early in developing your algorithm.

It can be useful to dig into its efficiency a bit once when you have your code working. It is used sometimes to check the execution time for a set of commands or a given command. Other times it is useful to determine the complicated series of operation where the bottleneck lies and into a multiline process. For this kind of timing and profiling of code IPython provides access to a wide array of functionality.

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