2017 上海市综合评价招生参考数据

2017_Gaokao_Data.md

数据来源: 阳光高考

上海交通大学 2017 年综合评价物理专业组 已录取考生高考成绩与面试分数的关系

上海交通大学 2017 年综合评价物理专业组 已录取考生面试成绩分布

上海交通大学 2017 年综合评价所有专业组 已录取考生高考成绩与面试分数的关系

上海交通大学 2017 年综合评价所有专业组 已录取考生面试成绩分布

复旦大学 2017 年综合评价所有专业组 已录取考生高考成绩与面试分数的关系

复旦大学 2017 年综合评价所有专业组 已录取考生面试成绩分布

复旦大学 2017 年综合评价物理专业组 已录取考生高考成绩与面试分数的关系

复旦大学 2017 年综合评价物理专业组 已录取考生面试成绩分布

复旦大学 2017 年综合评价物理专业组 已录取考生综合评价成绩和高考成绩的关系

上海交通大学 2017 年综合评价物理专业组 已录取考生综合评价成绩和高考成绩的关系

上海交通大学 2017 年综合评价所有专业组 同高考成绩下考生最终录取率

复旦大学 2017 年综合评价所有专业组 同高考成绩下考生最终录取率

index.js

const fs = require('fs')
const _ = require('lodash')

const __process_file = '2017fdu.admitted'

fs.readFile(`${__dirname}/${__process_file}.html`, 'utf-8', (err, data) => {
  const _reg = /<td.*?>(.*?)<\/td>/g
  let __data = []
  let match = _reg.exec(data);
  while (match != null) {
    __data.push(match[1])
    match = _reg.exec(data)
  }
  __data.splice(13, 1) //sjtuadmitted 13, otherwise 10
  let __csv = _.chain(__data)
    .chunk(13) //sjtuadmitted 13, otherwise 10
    .map(
      row => _.reduce(row, (a, b) => `${a},${b}`)
    )
    .reduce((a, b) => `${a}\n${b}`)
    .value()
  fs.writeFile(`${__dirname}/${__process_file}.csv`, __csv, err => console.log('succeed!'))
})

main.py

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from collections import Counter
import math

FILE_PRE='2018sjtu'
ALL_STUDENTS=pd.read_csv(FILE_PRE + '.admitted.csv', ',')
QULIFIED_STUDENTS=pd.read_csv(FILE_PRE + '.qualified.csv', ',')

ALL_STUDENTS['A'] = ALL_STUDENTS['test_score']
# GAOKAO / 660 * 0.6 + INTERVIEW / 100 * 0.3 + 0.1 = ALL / 660
# => INTERVIEW = ((ALL / 660) - GAOKAO / 660 * 0.6 - 0.1) / 0.3 * 100

# GAOKAO / 660 * 0.6 + INTERVIEW / 100 * 0.3 + 0.1 = ALL / 1000
# => INTERVIEW = ((ALL / 1000) - GAOKAO / 660 * 0.6 - 0.1) / 0.3 * 100

ALL = ALL_STUDENTS['comprehensive_score']
GAOKAO = ALL_STUDENTS['test_score']
ALL_STUDENTS['interview_score'] = ((ALL / 660) - GAOKAO / 660 * 0.6 - 0.1) / 0.3 * 100 # SJTU

# ALL_STUDENTS['interview_score'] = ((ALL / 1000) - GAOKAO / 660 * 0.6 - 0.1) / 0.3 * 100 # FDU


def draw_scatter(students, x_label, y_label, ax):
    x=students[x_label]
    y=students[y_label]

    c = Counter(zip(x,y))

    s = [15*c[(xx,yy)] for xx,yy in zip(x,y)]

    students.plot(kind='scatter', x=x_label, y=y_label, s=s, ax=ax)

def draw_bar(students, label, ax):
    series = pd.Series(np.bincount(students[label].astype(np.int64)))
    series = series[series > 0]
    yint = range(min(series), math.ceil(max(series))+1)
    plt.yticks(yint)
    series.plot.bar(color='#1f77b4', ax=ax)

def new_plot():
    return plt.subplots(figsize=(12,9))

def draw(students, title, identifier):
    fig, ax = new_plot()
    draw_scatter(students, 'test_score', 'comprehensive_score', ax=ax)
    plt.xlabel('Gaokao Score')
    plt.ylabel('Comprehensive Score')
    plt.title(title)
    fig.savefig(identifier + 'test_comprehensive_scatter.png')

    fig, ax = new_plot()
    draw_scatter(students, 'test_score', 'interview_score', ax=ax)
    plt.ylabel('Interview Score')
    plt.xlabel('Comprehensive Score')
    plt.title(title)
    fig.savefig(identifier + 'test_interview_scatter.png')

    fig, ax = new_plot()
    draw_bar(students, 'interview_score', ax=ax)
    plt.xlabel('Interview Score')
    plt.ylabel('Count')
    plt.title(title)
    fig.savefig(identifier + 'interview_hist.png')

    fig, ax = new_plot()
    draw_bar(students, 'comprehensive_score', ax=ax)
    plt.xlabel('Comprehensive Score')
    plt.ylabel('Count')
    plt.title(title)
    fig.savefig(identifier + 'comprehensive_hist.png')

draw(ALL_STUDENTS[ALL_STUDENTS['test_subject'] == '校本部物理专业组'], 'SJTU Physics Major Group', 'physics_')
draw(ALL_STUDENTS, 'SJTU All Major Group', 'all_')
draw(ALL_STUDENTS[ALL_STUDENTS['major'] == '工科试验班类'], 'SJTU Physics Major Group', 'gk_')

series = pd.Series(np.bincount(ALL_STUDENTS['test_score'], minlength=700) / np.bincount(QULIFIED_STUDENTS['test_score'], minlength=700))
series = series[series > 0]
fig, ax = new_plot()
series.plot.bar(color='#1f77b4', ax=ax)
plt.xlabel('Gaokao Score')
plt.ylabel('Percent')
plt.title('SJTU Comprehensive Examination')
fig.savefig('all_hist.png')