forked from Open-CT/opendata
92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
import pandas as pd
|
|
import json
|
|
import numpy as np
|
|
import ast
|
|
from datetime import datetime
|
|
import plotly.graph_objs as go
|
|
from plotly.offline import plot
|
|
import plotly.offline as offline
|
|
import plotly.figure_factory as ff
|
|
from pandas.core.indexes import interval
|
|
import re
|
|
# 2021-07-16T19:31:13+08:00
|
|
def get_interval_per_row(index, df):
|
|
row_data = df.loc[index,:]
|
|
start_time = row_data['start_time']
|
|
if start_time != start_time:
|
|
return -1
|
|
start_time = datetime.strptime(str(start_time),"%Y-%m-%dT%H:%M:%S+08:00")
|
|
|
|
expire_time = row_data['expire_time']
|
|
if expire_time != expire_time:
|
|
return -1
|
|
expire_time = datetime.strptime(str(expire_time),"%Y-%m-%dT%H:%M:%S+08:00")
|
|
|
|
stop_time = row_data['stop_time']
|
|
if stop_time != stop_time:
|
|
return -1
|
|
stop_time = datetime.strptime(str(stop_time),"%Y-%m-%dT%H:%M:%S+08:00")
|
|
|
|
total_minu = (stop_time - start_time).seconds / 60.0
|
|
return total_minu
|
|
|
|
def remove_str_per_row(data_per_row):
|
|
frame_list = ast.literal_eval(data_per_row)
|
|
frame_dic_list = []
|
|
for index in range(len(frame_list)):
|
|
temp = json.loads(frame_list[index])
|
|
if 'frame' in temp.keys():
|
|
if 'data' in temp.keys():
|
|
frame_dic_list.append(list(temp['frame']['data'].values()))
|
|
else:
|
|
frame_dic_list.append(list(temp['frame'].values()))
|
|
else:
|
|
frame_dic_list.append(temp)
|
|
return frame_dic_list
|
|
df_main = pd.read_csv('./20210717_2/data/data.csv')
|
|
time_minu_list = []
|
|
drop_index_list = []
|
|
for row in range(len(df_main)):
|
|
interval_minu = get_interval_per_row(row, df_main)
|
|
if interval_minu == -1:
|
|
drop_index_list.append(row)
|
|
else:
|
|
time_minu_list.append(interval_minu)
|
|
df_main = df_main.drop(drop_index_list)
|
|
if 'interval_minutes' not in df_main.columns:
|
|
df_main.insert(len(df_main.columns), 'interval_minutes', time_minu_list)
|
|
grouped_main = df_main.groupby('contest_id')
|
|
df_contest_list = [tup[1] for tup in list(grouped_main)]
|
|
df_contest_name_list = [tup[0] for tup in list(grouped_main)]
|
|
df_res_1 = df_contest_list[0] # 智能计算
|
|
df_res_2 = df_contest_list[1] # 问题解决
|
|
x1 = []
|
|
x2 = []
|
|
|
|
for row in range(len(df_res_1)):
|
|
print(row)
|
|
interval = df_res_1.iloc[row, 17]
|
|
if interval != -1:
|
|
x1.append(interval)
|
|
|
|
for row in range(len(df_res_2)):
|
|
interval = df_res_2.iloc[row, 17]
|
|
if interval != -1:
|
|
x2.append(interval)
|
|
|
|
|
|
layout={"title": "学生用时分布",
|
|
"xaxis_title": "学生用时，单位秒",
|
|
"yaxis_title": "学生个数",
|
|
# x轴坐标倾斜60度
|
|
"xaxis": {"tickangle": 60}
|
|
}
|
|
|
|
#数据组
|
|
hist_data=[x1,x2]
|
|
|
|
group_labels=['智能计算','问题解决']
|
|
|
|
fig=ff.create_distplot(hist_data,group_labels,bin_size=10,histnorm = 'probability')
|
|
fig['layout'].update(xaxis = dict(range = [0,100]))
|
|
plot(fig,filename='./plot/时间分布直方图.html') |