6 Applications of Python into Science and Engineering Fields

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En provenance du cours de Nanjing University

Data Processing Using Python

84 notes

Nanjing University

Data Processing Using Python

84 notes

This course (The English copy of "用Python玩转数据" <https://www.coursera.org/learn/hipython/home/welcome>) is mainly for non-computer majors. It starts with the basic syntax of Python, to how to acquire data in Python locally and from network, to how to present data, then to how to conduct basic and advanced statistic analysis and visualization of data, and finally to how to design a simple GUI to present and process data, advancing level by level. This course, as a whole, based on Finance data and through establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance and robustness of Python. Also, it discusses the fast, convenient and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Similarly, it may also be flexibly applied into other fields. The course has been updated. Updates in the new version are : 1) the whole course has moved from Python 2.x to Python 3.x 2) Added manual webpage fetching and parsing. Web API is also added. 3) Improve the content order and enrich details of some content especially for some practice projects.

À partir de la leçon

Python Data Statistics and Visualization

Welcome to learn Module 04 “Python Data Statistics and Visualization”! In this module, I will show you, over the entire process of data processing, the unique advantages of Python in data processing and analysis, and use many cases familiar to and loved by us to learn about and master methods and characteristics. After learning this module, you can fast and effectively mine your desired or expected or unknown results from a large amount of data, and can also present those data in various images. In addition, the data statistics modes of all third party packages in Python are extraordinarily and surprisingly strong, but we, as average persons, can still understand and possess them.

2 Basics of Matplotlib Plotting7:40

3 Control of Matplotlib Image Attributes9:44

4 Plotting with pandas6:34

5 Data Access4:10

6 Applications of Python into Science and Engineering Fields7:03

7 Applications into Humanities and Social Sciences Fields7:45

Rencontrer les enseignants

ZHANG Li
associate professor
Department of Computer Science

One major reason for the increasing popularity of Python is that

it has many powerful modules and libraries,

which may solve numerous professional application problems.

For example, the SciPy library and NumPy

may solve mathematical problems,

while BioPython may solve biological problems.

Python has quite convenient toolkits

to calculate for some mathematical problems.

Like the above mentioned math library or SciPy.

Here's a simple case.

Well, let's have a look first.

We use the "linspace()" function

to generate a dataset,

which is an arithmetic progression from –PI to PI.

Then, we calculate their sine values and cosine values

and use the "plot()" function in the pylab module for plotting,

adding some titles and labels as well.

Finally,, we get such a plot.

Have you got it?

It's quite easy.

"sin()" and "cos()" are very fundamental functions in math.

For more complex operations,

say, fast Fourier transform to

a dataset,

there must be a corresponding function.

For example, we can utilize the "fft()" function in SciPy.

There are a group of functions for Fourier transform,

which can meet our needs.

They are just two small examples,

Previously, we also mentioned some applications

in NumPy and the SciPy library.

You guys might still remember that

Python well supports various mathematical applications.

And of course, math is extensively applied even in the fields of humanities or social sciences.

Apart from solving math problems,

Python is also capable of solving problems in professional fields like image and audio.

Take image processing for example,

A common Python image processing library is

PIL, which has been replaced by Pillow

as well as OpenCV and Skimage.

PIL is the most fundamental image processing library in Python.

This software package provides very fundamental capabilities of image processing

like changing an image size,

rotating an image

and conversion of image formats,

image enhancement etc.

OpenCV is an image processing library with better performances.

It contains a ton of algorithms and functions.

Its Python interface is basically identical to its C language interface.

So, if any of you have experience in using OpenCV,

recommend OpenCV to you.

And Skimage is an image processing toolkit

for serving SciPy.

It's not frequently used in daily work.

Take a fundamental image processing library.

Pillow, for example,

Look at a very simple example.

First, let's import such a module, image,

and then use the "open()" function to open an image.

This is an image object,

The most important class in Pillow is image.

Then, we output some of its attributes

like the size, format and pattern.

Again, we save this image as 2.png

and then open the second image.

Set a size.

What's the effect of the "thumbnail()" function?

It's not the fingernail

but a reduced-size version of pictures.

It is to create a thumbnail of the second image

rotate(45) means to rotate for an angle of 45 degrees.

Turn it counterclockwise.

Next, paste the rotated image onto the first image.

This is put at the position of upper left.

Can you think of

what the effect of this program is?

Let's demonstrate the program.

Run it.

It outputs a series of attributes of the image.

Let's look at im1.

It adds a watermark of image onto the original image.

Is your guess correct?

That's all for the basic use of Pillow.

How do you feel it?

It seems not as difficult as we imagined, right?

Apart from in such professional fields as image and audio,

Python also owns special libraries

in academic fields like biology, medicine and astronomy.

Let's look at an bioinformation library: Biopython.

Biopython comes from Biopython which is an international association

of developers of Python tools for computational molecular biology.

Its main capabilities include: analyzing bioinformatics files

into data structures utilizable to Python,

and, for example, processing common online database codes of bioinformatics,

as well as providing some common interfaces for bioinformation programs.

Look at an example.

The most important object in biology is sequence.

We may use the "seq()" function to create a corresponding sequence object.

It's also possible, through this attribute

to view the attribute of sequence object.

The return result

shows that, it is a sequence object composed of alphabet.

It means, it has yet to be designated to be a DNA

or a protein sequence.

Then, let's look at what this is.

It is called Arabidopsis thaliana,

a kind of grass.

This is its chromosome arrangement diagram of transfer RNA.

All the above information on Biopython comes from its official website.

Actually, I didn't quite get it ^_^.

Here, we only showed a simple case.

If you major in biology,

you may give Biopython a try.

Sure, for other majors.

I believe you can also find some corresponding Python toolkits .

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