3 Control of Matplotlib Image Attributes

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Do curso por Nanjing University

Data Processing Using Python

84 classificações

Nanjing University

Data Processing Using Python

84 classificações

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.

Na lição

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

Conheça os instrutores

ZHANG Li
associate professor
Department of Computer Science

Like MATLAB and Excel,

Matplotlib has a set of

default settings allowing attribute customization.

You can almost control all default attributes in Matplotlib,

like the image size,

dots per inch, line width, color, style,

subgraph, coordinate axis and grid attribute.

Question time: Can we change the plot color,

and the type and style of line?

Answer: Absolutely yes.

Because we mentioned the scatter plot before,

which was generated through the third argument of "plot()" function,

Let's look at the two charts below, for example.

One is with a green dotted line,

and the other is with red diamonds.

Think about it.

What is the default color and style of chart before?

Is it blue.

Is the style of line a solid line?

Let's have a guess.

The original default style

may be written like this, "b-".

Yes, indeed.

For a function like "plot()",

each of its argument pair (x,y) may be followed,

by an optional third argument,

which is in the format of string,

to indicate the color and type of line of plot.

Let's have a guess at this chart.

How can it be expressed?

Green, dotted line.

If it's written like "g--", would it be more vivid?

Bingo.

It's really like this.

Let's look at the next one,

a shape of red diamond.

Sure, we can't draw a diamond inside.

Its form is like this, "rD", Quite vivid, right?

All these symbols are borrowed from MATLAB.

Here, a lot of marks and symbols are listed,

like those we're familiar with, 'o', 'D',

and some color symbols,

as well as some line styles.

Let's demonstrate the program,

to see its actual effect.

We still use the line chart to be the baseline,

say, to draw a red dotted line.

Is it like this?

Draw one more line

like green asterisks,

like this.

You guys can try other forms by yourselves.

Where can we find those attributes?

Apart from inquiries on its official website,

we may also use "help()".

For example, let's briefly view

the help information of the "plot()" function.

Are there a lot of contents?

It contains all the attributes

we listed just now.

That's why, like I mentioned before,

we should get used to using the help information.

Besides, many other attributes are also settable,

like, very important words.

What can be added to the plot?

Titles, x axis labels, y axis labels,

which are generated with "title()",

"xlabel()" and "ylabel()", respectively.

The result is like this.

Is the meaning of the plot clearer?

Let's look at

some other attributes.

I suppose you can guess their meanings.

For the function "figure()", say,

there's "figsize=(8,6)".

It must be the size of the figure,

"dpi" means the resolution of the figure.

Next, as we see, in the "plot()" function,

there's an attribute "color".

Previously, we used a single character to represent the color,

like r or g.

Here, as we see, it uses a dedicated argument,

as well as the representation and marking of line style, line width and data value.

The actual effect is like this.

How about the final function. Can you guess its meaning "legend()"?

It has an argument "loc", "upper left".

Yes, the "upper left".

You might already guess it,

"loc" is the abbreviation of "location",

meaning to put the legend to the upper left.

The text of legend is specified by the "label" argument.

As we notice in the actual effect of plot,

here, several curves are

put into the same plot.

We may wonder whether we can draw plots in different areas.

Yes, we can .

In Matplotlib, in a plot, it's possible to plot,

in several areas of a plot, i.e. subplots.

It's realized with the "subplot()" function.

For example, let's look at the forms of three plots.

The first one contains two rows in one column,

the second one, one row in two columns,

the third one, two rows in two columns.

Let's guess the meanings of the arguments of "subplot()" function.

Have you got it?

The first and second arguments represent the quantity of row and column, respectively.

The third argument,

should also be easy to guess.

It's the serial number of the area.

For example, this one

means multiple subplots of one row in two columns.

And, the first, 1 and the second, 2

represents the two areas, respectively.

Well, if no subplot is set,

what is its default form?

Should it be subplot(1,1,1)?

Sure, this statement may be omitted.

Well, let's look at an example.

Plot the monthly mean closing prices of stocks

of the Coca-Cola Company and IBM over the recent year

into one plot.

Here, we may use two such "subplot()" functions,

two rows in one column, and put data into them.

The result is like this.

As for the way to use "subplot()" to draw subplots,

you must have been familiar with it.

You may, based on the actual data,

determine how many subplots are needed.

Besides, the "axes()" function is another way

to draw subplots.

Let's look.

The way of its drawing subplots is quite different from that of "subplot()",

like this.

Quite special, right?

In use, this function is also easy.

It has four arguments:

"left", "bottom", "width", "height".

"left" means its distance from the left boundary (calculated based on the coordinate axis percentage),

"bottom" means its distance form the bottom line (calculated based on the coordinate axis percentage),

and the next two arguments are the width and height of the plot (calculated based on the coordinate axis percentage).

For example, let's look at the second curve.

Its distance, width and height are approximately like this.

Be more careful if you draw subplots with the "axis()" function,

instead of the "subplot()" function,

since if you need to display all the data points,

do well calculate the ranges of arguments.

In my writing here, say,

its' obvious that, we can't see the data points of April.

Well, just now, we introduced how to set some attributes

for some drawn plots in Matplotlib.

As we saw,

the use of many attributes may enrich the plot

or, clearer,

enabling audiences seeing the plot to better understand your intention.

So, when drawing plots,

we should add all necessary attributes.

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