-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
73 lines (55 loc) · 2.1 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
import json
import pandas as pd
import random
import os
headers = ['category', 'correct', 'timestamp']
def load_questions():
with open('./questions.json') as f:
return json.load(f)
def generate_test(categories):
questions = load_questions()
print(categories,'\n\n\n')
filtered_questions = [q for q in questions if q['category'] in categories]
return random.sample(filtered_questions, 2)
'''
def save_user_data(data):
df = pd.DataFrame(data)
df.to_csv('user_data/user_data.csv', mode='a', header=False, index=False)
'''
def load_user_data():
return pd.read_csv('user_data/user_data.csv')
def delete_user_data():
open('user_data/user_data.csv', 'w').close()
file_path = 'user_data/user_data.csv'
# Check if file exists and is not empty
if not os.path.exists(file_path) or os.path.getsize(file_path) == 0:
# Create a DataFrame with headers if the file does not exist or is empty
df = pd.DataFrame(columns=headers)
new_data_df = pd.DataFrame(columns=headers)
# Append new data
df = pd.concat([df, new_data_df], ignore_index=True)
# Save DataFrame to CSV
df.to_csv(file_path, index=False)
def get_statistics():
df = load_user_data()
stats = df.describe()
return stats
def get_category_stats():
df = load_user_data()
category_stats = df.groupby('category').mean()
return category_stats
def save_user_data(question, correct, timestamp):
file_path = 'user_data/user_data.csv'
# Check if file exists and is not empty
if not os.path.exists(file_path) or os.path.getsize(file_path) == 0:
# Create a DataFrame with headers if the file does not exist or is empty
df = pd.DataFrame(columns=headers)
else:
# Read the existing data
df = pd.read_csv(file_path)
# Convert the new data to DataFrame
new_data_df = pd.DataFrame([[question["category"], correct, timestamp]], columns=headers)
# Append new data
df = pd.concat([df, new_data_df], ignore_index=True)
# Save DataFrame to CSV
df.to_csv(file_path, index=False)