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Editor to share with you how the Python data processing library Pandas is born, I believe most people do not know much about it, so share this article for your reference, I hope you will learn a lot after reading this article, let's go to know it!

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Pandas1.0 recently launched. But do you know how it was born?

The story can start in many places, but let's start in 1954. Two things were important to the birth of Pandas in 1954.

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On March 1, 1954, the United States tested a high-yield thermonuclear bomb on Bikini Atoll. Lithium deuteride (LiD) is a fuel. Natural lithium consists of two different isotopes, lithium 6 and lithium 7. Lithium deuteride 6 is considered to be a good hydrogen bomb fuel, and it is used as a moderating substance in the reaction on the assumption that lithium deuteride 7 is completely inert. This assumption turned out to be extremely wrong-the substance that was supposed to moderate the reaction ended up as fuel. Resulting in what was supposed to be a 6 million-ton explosion turned into a 15 million-ton explosion.

The consequences of this experiment are far-reaching in time and space. The crew of a Japanese fishing boat suffered severe radiation poisoning and many people in the Marshall Islands suffered long-term damage from radioactive dust. It gives people an idea of what radioactive dust is, and that it is not safe to be outside of a nuclear explosion, which can affect people hundreds of miles away. Perhaps the most striking and relevant to our story is the 1963 Limited Nuclear-Test-Ban Treaty (Limited Test Ban Treaty), which ended nuclear tests in the atmosphere, under water and in space. Bravo Castle clearly shows how dangerous these experiments are, how widespread the damage is, and it is almost impossible to guarantee that there will be no major mistakes.

Of course, this means investing heavily in computer technology to simulate nuclear explosions, rather than relying too much on experiments. The technology is in its infancy, but the task of simulating nuclear weapons will drive scientific computing to this day. This pursuit of computers and software is the fundamental source of Silicon Valley.

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"most of my work is due to laziness. I don't like writing programs, so when I wrote programs to calculate missile trajectories on IBM 701s, I started to write a program system to make programming easier." -John Bacchus.

This thought process should be familiar to any Pandas user-it is essentially the power of Pandas, R, SQL, and any other declarative programming framework that tries to keep you focused on math and data manipulation. Fortran (the compound word for "formula conversion") is a tool for writing scientific programs, which may seem lengthy compared to the equivalent Python code, but it is certainly more expressive than assembly. You can follow the code here, but not necessarily in the case of a bunch of arithmetic codes. It also has the characteristics of the oldest programming language still in use. It was developed in 1950, but its first project was run in 1954.

Fortran is not just the spiritual forerunner of scientific computing. The Fortran packages used for matrix operations, such as BLAS and LAPACK, are under the hood of pandas. Translations on your computer are not necessarily written in Fortran (C by default). But it's an option, the source of C, and if you really need performance, it's usually what you want.

It is worth noting that BLAS, LAPACK and most other parts of the Fortran scientific ecosystem are still products of the military-industrial complex. Or at least the available open source software. Organizations like the Department of Energy, DARPA and NSF (with defense authorization) provide funding and working hours to keep these packages up to date. This is part of the reason why it is still used. However, the Fortran language is only half of our story. Pandas is not just about fast manipulation-it's also about syntax.

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Well, Fortran is an advance in assembly, but it's not what NumPy or Pandas code really looks like. To do this, we need an array-based language with good vectorization syntax. I'm going to talk about APL because it's the most incredible. I'm sure Wes McKinney said it was Pandas's inspiration.

There is a saying that there are two programming languages, one starting "from computer up" and the other "from math down". Fortran is relatively friendly, but it still makes you think about things like pre-allocating memory. APL comes from a mathematician named Kenneth E. Iverson, who came up with a symbol for manipulating arrays. Eventually they wrote an implementation in Fortran, which became a real programming language.

APL is concise and expressive, which makes matrix operation a first-class citizen. Of course, you have to learn a bunch of strange symbols, you need a custom keyboard, and you have to internalize the grammar that contains the concept of "adverbs". However, if all you do is manipulate the data, it's not that bad. It's also closer to mathematical symbols, which is good if you have a background in this area.

The above is all the contents of the article "how did the data processing library Pandas in Python come into being?" Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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