Python Hands-on

What is Python? Everything you need to know

Why the Python programming language shines for data science, machine learning, systems automation, web and API development, and beyond

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A common adage of software development is that 90 percent of the activity for a program tends to be in 10 percent of the code, so optimizing that 10 percent can yield major improvements. With Python, you can selectively convert that 10 percent to C or even assembly, using projects like Cython or Numba. The result is often a program that runs within striking distance of a counterpart written entirely in C, but without being cluttered with C’s memory-micromanagement details.

Developer time typically beats machine time

Or to put it another way: For many tasks, speed of development beats speed of execution.

A given Python program might take six seconds to execute versus a fraction of a second in another language. But it might take only ten minutes for a developer to put that Python program together, versus an hour or more of development time in another language. The amount of time lost in the execution of the Python program is more than gained back by the time saved in the development process.

Obviously, this is less true when you’re writing software that has high-throughput, low-concurrency demands, such as a trading application. But for many real-world applications, in domains ranging from systems management to machine learning, Python will prove to be fast enough.

Plus, the flexibility and pace of development that Python enables may allow for innovation that would be more difficult and time-consuming to achieve in other languages.

When speed of development and programmer comfort are more important than shaving a few seconds off the machine clock, Python may well be the best tool for the job.

Further Python reading

The Python essentials from InfoWorld:

Python for machine learning and deep learning:

Python for data science:

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