preface
Today I choose a simple and random analysis of some data of the college entrance examination ~~~
The development tools
Python version: 3.6.4
Related modules:
Pyecharts module;
And some of the modules that come with Python.
Environment set up
Install Python and add it to the environment variable, and the PIP will install the appropriate module.
Pyecharts module installation can refer to:
Python simply analyzes WeChat friends
“Serious analysis”
First of all, let’s take a look at the trend of the total number of people who have applied for and been admitted since the resumption of the gaokao (1977) :
It does seem that there are more and more student parties.
But that doesn’t seem to give you an intuitive sense of what percentage of students are admitted each year, right? OK, let’s see it visually:
It seems that getting into college is becoming “easier” to say is not groundless, the total enrollment rate is high terrible ~~~
What about the provinces?
Because the statistical standard of the college entrance examination of each province is not the same as that of the final admission number, some are only statistical undergraduate course, some are all statistical, in order to avoid the unfair comparison that the statistical standard is different and brings, we only analyze the number of the college entrance examination of each province.
From 2010 to this year (2018), the distribution chart of the number of Gaokao examinees in each province is as follows:
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The number of college entrance examination students in Henan is really unique.
So how is the number of universities distributed among the provinces? Taking the number of public universities as the statistical standard, the distribution would look something like this:
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Emmm. Beijing and Jiangsu ranked first and second, respectively. If you think about it, it must be T_T
What about the distribution of 985&211 universities?
“That’s it. Love is over.” He fell silent when he saw this.
With the province as the X axis, the year as the Y axis, and the number of examinees in that year as the Z axis, let’s have a more intuitive look at the change of the number of examinees in each province each year:
The order of provinces in the figure above looks like this:
Beijing, sichuan, shaanxi, jiangxi, jilin, ningxia, guangxi, Inner Mongolia, gansu, Tibet, fujian, Shanghai, guangdong, shandong, zhejiang, henan, anhui, jiangsu, hebei, heilongjiang, hunan, hubei, shanxi, yunnan, guizhou, hainan, liaoning, qinghai, xinjiang, chongqing, tianjin, Taiwan because there is no data, so not to join.
The number of gaokao candidates in Henan is really terrible.
EMMM, because the available data is not much, and then analysis is probably a fancy graphics game, think or forget it. As for personal views, it is better not to publish them. After all, everyone’s Hamlet is different.
After reading this article, friends like to click like support, pay attention to me every day to share Python data crawler cases, the next article to share is Python simple analysis of Chrome browser browsing history
All done~ complete source code see personal profile or private message access to related files.