def yingyu_anwsering_situation(self):
tixing = {
"阅读理解(必做)": [
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40
],
"完形填空(必做)": [
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56,
57, 58, 59, 60
],
"语法填空(必做)": [61, 62, 63, 64, 65, 66, 67, 68, 69, 70],
"短文改错(必做)": [71, 72, 73, 74, 75, 76, 77, 78, 79, 80],
"写作题(必做)": [81]
}
zsbk = {
"应用文阅读(人与社会)(必做)": [21, 22, 23],
"记叙文阅读(人与自我)(必做)": [24, 25, 26, 27],
"说明文阅读(人与社会)(必做)": [28, 29, 30, 31],
"说明性议论文阅读(人与社会)(必做)": [32, 33, 34, 35],
"说明文阅读(人与自然)(必做)": [36, 37, 38, 39, 40],
"动词(必做)": [41, 45, 51, 55, 56, 60],
"名词(必做)": [42, 43, 46, 50, 52, 53, 57, 66, 76, 77],
"形容词(必做)": [44, 47, 48, 59, 68],
"动词短语搭配(必做)": [49, 54],
"副词(必做)": [58, 62, 75, 79],
"主从复合句(必做)": [61, 72, 63],
"介词(必做)": [64, 67, 71, 78],
"非谓语动词(必做)": [64, 67, 71, 78],
"时态(必做)": [65],
"冠词(必做)": [69, 73],
"句子成分(必做)": [70],
"连词(必做)": [74],
"零冠词(必做)": [80],
"应用文写作(必做)": [81],
}
khnl = {
"提取与理解(必做)": [21, 22, 23, 24, 28, 29, 30, 34, 71, 76, 80],
"理解与推断(必做)": [
25, 26, 27, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 58, 59, 60, 63, 65, 74, 79
],
"理解与判断(必做)": [31, 32, 36, 38, 39, 73, 77],
"理解与概括(必做)": [33, 35, 37],
"总结与归纳(必做)": [37, 40],
"理解与分析(必做)": [61, 64, 67, 70, 72, 78],
"辨认与运用(必做)": [62],
"提取与辨认词性(必做)": [66],
"比较与分析(必做)": [68],
"分析运用冠词(必做)": [69],
"辨认与分析(必做)": [75],
"分析与综合(必做)": [81],
}
"""
原始分分析
"""
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
if not os.path.exists("省级报告"):
os.makedirs("省级报告")
if not os.path.exists("省级报告/英语考生答题水平分析"):
os.makedirs("省级报告/英语考生答题水平分析")
lk_ks_ids, wk_ks_ids = pr.judge_ks_wenli(self.__cursor, self.__ks_ids)
output_file = "省级报告/英语考生答题水平分析/原始分概括(英语).xlsx"
writer = pd.ExcelWriter(output_file)
# 全省
result = pr.km_total_grade_analysis(self.__cursor, self.__ks_ids,
"101")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别考生比较(英语)", index=False)
# 文科
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
result = pr.km_total_grade_analysis(self.__cursor, wk_ks_ids, "101")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别文科考生比较(英语)", index=False)
# 理科
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
result = pr.km_total_grade_analysis(self.__cursor, lk_ks_ids, "101")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别理科考生比较(英语)", index=False)
writer.save()
writer.close()
"""
结构分析
"""
output_file = "省级报告/英语考生答题水平分析/结构分析(英语).xlsx"
writer = pd.ExcelWriter(output_file)
# 主客观分析
df = pd.DataFrame(
data=None, columns=["主客观题", "题数", "平均分", "标准差", "难度", "区分度", "信度"])
results = pr.zkg_situation(self.__cursor, "101")
"""
需要手动更改
"""
results[0].insert(1, 40.00)
results[1].insert(1, 21.00)
results[2].insert(1, 61.00)
for result in results:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生客观题得分(英语)", index=False)
# 全省各题型得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in tixing.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "101", i))
result = pr.structural_st_analysis(self.__cursor, "101", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各题型得分情况(英语)", index=False)
# 全省各知识板块得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in zsbk.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "101", i))
result = pr.structural_st_analysis(self.__cursor, "101", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各知识板块得分情况(英语)", index=False)
# 全省各考核能力得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in khnl.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "101", i))
result = pr.structural_st_analysis(self.__cursor, "101", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各考核能力得分情况(英语)", index=False)
writer.save()
writer.close()
"""
单题分析
"""
output_file = "省级报告/英语考生答题水平分析/单题分析(英语).xlsx"
writer = pd.ExcelWriter(output_file)
# 获取所有题号
st = []
for value in zsbk.values():
st.extend(value)
df = pd.DataFrame(data=None,
columns=["题号", "分值", "平均分", "标准差", "难度", "区分度"])
for i in st:
st_ids = pr.get_st_ids(self.__cursor, "101", i)
result = pr.singlel_st_analysis(self.__cursor, "101", st_ids)
result.insert(0, i)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="考生单题作答情况(英语)", index=False)
writer.save()
writer.close()
"""
各市情况分析
"""
output_file = "省级报告/英语考生答题水平分析/各市情况分析(英语).xlsx"
writer = pd.ExcelWriter(output_file)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "101", 0, 0)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "101", i, 0)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市考生成绩比较(英语)", index=False)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "101", 0, 2)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "101", i, 2)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市文科考生成绩比较(英语)", index=False)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "101", 0, 1)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "101", i, 1)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市理科考生成绩比较(英语)", index=False)
writer.save()
writer.close()
pr.get_single_km_picture(self.__cursor, self.__ks_ids, "101",
"省级报告/英语考生答题水平分析/全省考生单科成绩分布(英语).png")
pr.get_single_km_picture(self.__cursor, wk_ks_ids, "101",
"省级报告/英语考生答题水平分析/全省文科考生单科成绩分布(英语).png")
pr.get_single_km_picture(self.__cursor, lk_ks_ids, "101",
"省级报告/英语考生答题水平分析/全省理科考生单科成绩分布(英语).png")
return True
def yuwen_anwsering_situation(self):
tixing = {
"选择题(必做)": [1, 2, 3, 4, 5, 7, 10, 11, 12, 14, 17, 18, 19],
"简答题(必做)": [6, 8, 9, 15],
"翻译题(必做)": [13],
"压缩题(必做)": [16, 20],
"作文题(必做)": [22]
}
zsbk = {
"论述类文本阅读(必做)": [1, 2, 3],
"实用类文本阅读(必做)": [4, 5, 6],
"文学类文本阅读(必做)": [7, 8, 9],
"文言文阅读(必做)": [10, 11, 12, 13],
"古代诗歌阅读(必做)": [14, 15],
"古诗文阅读(必做)": [16],
"语言文字应用(必做)": [17, 18, 19, 20, 21],
"写作(必做)": [22]
}
khnl = {
"理解(必做)": [1, 10, 11, 13],
"分析综合(必做)": [2, 3, 4, 5, 6, 7, 8, 12],
"鉴赏评价(必做)": [9, 14, 15],
"识记(必做)": [16],
"表达应用(必做)": [17, 18, 19, 20, 21, 22]
}
"""
原始分分析
"""
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
if not os.path.exists("省级报告"):
os.makedirs("省级报告")
if not os.path.exists("省级报告/语文考生答题水平分析"):
os.makedirs("省级报告/语文考生答题水平分析")
lk_ks_ids, wk_ks_ids = pr.judge_ks_wenli(self.__cursor, self.__ks_ids)
output_file = "省级报告/语文考生答题水平分析/原始分概括(语文).xlsx"
writer = pd.ExcelWriter(output_file)
# 全省
result = pr.km_total_grade_analysis(self.__cursor, self.__ks_ids,
"001")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别考生比较(语文)", index=False)
# 文科
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
result = pr.km_total_grade_analysis(self.__cursor, wk_ks_ids, "001")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别文科考生比较(语文)", index=False)
# 理科
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
result = pr.km_total_grade_analysis(self.__cursor, lk_ks_ids, "001")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别理科考生比较(语文)", index=False)
writer.save()
writer.close()
"""
结构分析
"""
output_file = "省级报告/语文考生答题水平分析/结构分析(语文).xlsx"
writer = pd.ExcelWriter(output_file)
# 主客观分析
df = pd.DataFrame(
data=None, columns=["主客观题", "题数", "平均分", "标准差", "难度", "区分度", "信度"])
results = pr.zkg_situation(self.__cursor, "001")
results[0].insert(1, 13.00)
results[1].insert(1, 9.00)
results[2].insert(1, 22.00)
for result in results:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生客观题得分(语文)", index=False)
# 全省各题型得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in tixing.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "001", i))
result = pr.structural_st_analysis(self.__cursor, "001", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各题型得分情况(语文)", index=False)
# 全省各知识板块得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in zsbk.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "001", i))
result = pr.structural_st_analysis(self.__cursor, "001", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各知识板块得分情况(语文)", index=False)
# 全省各考核能力得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in khnl.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "001", i))
result = pr.structural_st_analysis(self.__cursor, "001", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各考核能力得分情况(语文)", index=False)
writer.save()
writer.close()
"""
单题分析
"""
output_file = "省级报告/语文考生答题水平分析/单题分析(语文).xlsx"
writer = pd.ExcelWriter(output_file)
# 获取所有题号
st = []
for value in zsbk.values():
st.extend(value)
df = pd.DataFrame(data=None,
columns=["题号", "分值", "平均分", "标准差", "难度", "区分度"])
for i in st:
st_ids = pr.get_st_ids(self.__cursor, "001", i)
result = pr.singlel_st_analysis(self.__cursor, "001", st_ids)
result.insert(0, i)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="考生单题作答情况(语文)", index=False)
writer.save()
writer.close()
"""
各市情况分析
"""
output_file = "省级报告/语文考生答题水平分析/各市情况分析(语文).xlsx"
writer = pd.ExcelWriter(output_file)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "001", 0, 0)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "001", i, 0)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市考生成绩比较(语文)", index=False)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "001", 0, 2)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "001", i, 2)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市文科考生成绩比较(语文)", index=False)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "001", 0, 1)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "001", i, 1)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市理科考生成绩比较(语文)", index=False)
writer.save()
writer.close()
pr.get_single_km_picture(self.__cursor, self.__ks_ids, "001",
"省级报告/语文考生答题水平分析/全省考生单科成绩分布(语文).png")
pr.get_single_km_picture(self.__cursor, wk_ks_ids, "001",
"省级报告/语文考生答题水平分析/全省文科考生单科成绩分布(语文).png")
pr.get_single_km_picture(self.__cursor, lk_ks_ids, "001",
"省级报告/语文考生答题水平分析/全省理科考生单科成绩分布(语文).png")
return True
def likeshuxue_anwsering_situation(self):
tixing = {
"选择题(必做)": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
"填空题(必做)": [13, 14, 15, 16],
"解答题(必做)": [17, 18, 19, 20, 21],
"解答题(选做1)": [22],
"解答题(选做2)": [23]
}
zsbk = {
"集合(必做)": [1],
"复数(必做)": [2],
"函数(必做)": [3, 5],
"相等关系与不等关系(必做)": [4],
"概率(必做)": [6, 15],
"平面向量(必做)": [7],
"程序框图(必做)": [8],
"数列(必做)": [9, 14],
"解析几何(必做)": [10, 16, 19],
"三角函数(必做)": [11],
"立体几何(必做)": [12, 18],
"导数(必做)": [13],
"解三角形(必做)": [17],
"函数与导数(必做)": [20],
"概率与统计(必做)": [21],
"坐标系与参数方程(选做1)": [22],
"不等式选讲(选做2)": [23]
}
khnl = {
"数学运算(必做)": [1, 7, 9, 13, 14],
"综合能力(必做)": [2, 4, 5, 10, 12, 16, 17, 18, 19, 20, 21],
"逻辑推理(必做)": [3, 8, 11, 15],
"数学建模(必做)": [6],
"综合能力(选做1)": [22],
"综合能力(选做2)": [23]
}
"""
原始分分析
"""
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
if not os.path.exists("省级报告"):
os.makedirs("省级报告")
if not os.path.exists("省级报告/理科数学考生答题水平分析"):
os.makedirs("省级报告/理科数学考生答题水平分析")
lk_ks_ids, wk_ks_ids = pr.judge_ks_wenli(self.__cursor, self.__ks_ids)
output_file = "省级报告/理科数学考生答题水平分析/原始分概括(理科数学).xlsx"
writer = pd.ExcelWriter(output_file)
# 理科
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
result = pr.km_total_grade_analysis(self.__cursor, lk_ks_ids, "002")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别考生比较(理科数学)", index=False)
writer.save()
writer.close()
"""
结构分析
"""
output_file = "省级报告/理科数学考生答题水平分析/结构分析(理科数学).xlsx"
writer = pd.ExcelWriter(output_file)
# 主客观分析
df = pd.DataFrame(
data=None, columns=["主客观题", "题数", "平均分", "标准差", "难度", "区分度", "信度"])
results = pr.zkg_situation(self.__cursor, "002")
"""
需要手动更改
"""
results[0].insert(1, 12.00)
results[1].insert(1, 11.00)
results[2].insert(1, 23.00)
for result in results:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生客观题得分(理科数学)", index=False)
# 全省各题型得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in tixing.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "002", i))
result = pr.structural_st_analysis(self.__cursor, "002", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各题型得分情况(理科数学)", index=False)
# 全省各知识板块得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in zsbk.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "002", i))
result = pr.structural_st_analysis(self.__cursor, "002", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各知识板块得分情况(理科数学)", index=False)
# 全省各考核能力得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in zsbk.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "002", i))
result = pr.structural_st_analysis(self.__cursor, "002", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各考核能力得分情况(理科数学)", index=False)
writer.save()
writer.close()
"""
单题分析
"""
output_file = "省级报告/理科数学考生答题水平分析/单题分析(理科数学).xlsx"
writer = pd.ExcelWriter(output_file)
# 获取所有题号
st = []
for value in zsbk.values():
st.extend(value)
df = pd.DataFrame(data=None,
columns=["题号", "分值", "平均分", "标准差", "难度", "区分度"])
for i in st:
st_ids = pr.get_st_ids(self.__cursor, "002", i)
result = pr.singlel_st_analysis(self.__cursor, "002", st_ids)
result.insert(0, i)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="考生单题作答情况(理科数学)", index=False)
writer.save()
writer.close()
"""
各市情况分析
"""
output_file = "省级报告/理科数学考生答题水平分析/各市情况分析(理科数学).xlsx"
writer = pd.ExcelWriter(output_file)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "002", 0, 1)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "002", i, 1)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市考生成绩比较(理科数学)", index=False)
writer.save()
writer.close()
pr.get_single_km_picture(self.__cursor, lk_ks_ids, "002",
"省级报告/理科数学考生答题水平分析/全省考生单科成绩分布(理科数学).png")
return True
def likezonghe_anwsering_situation(self):
tixing = {
"单项选择题(必做)":
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
"多项选择题(必做)": [19, 20, 21],
"非选择题(必做)": [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32],
"选做题_物理(选做1)": [33],
"选做题_物理(选做2)": [34],
"选做题_化学(选做1)": [35],
"选做题_化学(选做2)": [36],
"选做题_生物(选做1)": [37],
"选做题_生物(选做2)": [38]
}
"""
原始分分析
"""
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
if not os.path.exists("省级报告"):
os.makedirs("省级报告")
if not os.path.exists("省级报告/理科综合考生答题水平分析"):
os.makedirs("省级报告/理科综合考生答题水平分析")
lk_ks_ids, wk_ks_ids = pr.judge_ks_wenli(self.__cursor, self.__ks_ids)
output_file = "省级报告/理科综合考生答题水平分析/原始分概括(理科综合).xlsx"
writer = pd.ExcelWriter(output_file)
# 理科
df = pd.DataFrame(data=None,
columns=["维度", "人数", "比率", "平均分", "标准差", "差异系数"])
result = pr.km_total_grade_analysis(self.__cursor, lk_ks_ids, "005")
result.insert(0, "总计")
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各类别考生比较(理科综合)", index=False)
writer.save()
writer.close()
"""
结构分析
"""
output_file = "省级报告/理科综合考生答题水平分析/结构分析(理科综合).xlsx"
writer = pd.ExcelWriter(output_file)
# 主客观分析
df = pd.DataFrame(
data=None, columns=["主客观题", "题数", "平均分", "标准差", "难度", "区分度", "信度"])
results = pr.zkg_situation(self.__cursor, "005")
"""
手动改
"""
results[0].insert(1, 21.00)
results[1].insert(1, 17.00)
results[2].insert(1, 38.00)
for result in results:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生客观题得分(理科综合)", index=False)
# 全省各题型得分情况
df = pd.DataFrame(data=None,
columns=["题型", "题号", "分值", "平均分", "标准差", "难度"])
for key, value in tixing.items():
st = []
for i in value:
st.extend(pr.get_st_ids(self.__cursor, "005", i))
result = pr.structural_st_analysis(self.__cursor, "005", st)
result.insert(0, value)
result.insert(0, key)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="全省考生各题型得分情况(理科综合)", index=False)
writer.save()
writer.close()
"""
单题分析
"""
output_file = "省级报告/理科综合考生答题水平分析/单题分析(理科综合).xlsx"
writer = pd.ExcelWriter(output_file)
# 获取所有题号
st = []
for value in tixing.values():
st.extend(value)
df = pd.DataFrame(data=None,
columns=["题号", "分值", "平均分", "标准差", "难度", "区分度"])
for i in st:
st_ids = pr.get_st_ids(self.__cursor, "005", i)
result = pr.singlel_st_analysis(self.__cursor, "005", st_ids)
result.insert(0, i)
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="考生单题作答情况(理科综合)", index=False)
writer.save()
writer.close()
"""
各市情况分析
"""
output_file = "省级报告/理科综合考生答题水平分析/各市情况分析(理科综合).xlsx"
writer = pd.ExcelWriter(output_file)
df = pd.DataFrame(
data=None,
columns=["城市代码", "地市名称", "人数", "比率", "平均分", "标准差", "差异系数"])
df.loc[len(df)] = pr.city_single_km(self.__cursor, "005", 0, 1)
for i in self.__city_ids:
result = pr.city_single_km(self.__cursor, "005", i, 1)
if result[2] > 0:
df.loc[len(df)] = result
df.to_excel(writer, sheet_name="各市考生成绩比较(理科综合)", index=False)
writer.save()
writer.close()
pr.get_single_km_picture(self.__cursor, lk_ks_ids, "005",
"省级报告/理科综合考生答题水平分析/全省考生单科成绩分布(理科综合).png")
return True