python - 如何优化包含 for 循环和数据框中 2000 万行的函数

Question

我有一个 pandas 数据框 df，如下所示:

student_id   category_id  count
1              111        10
2              111        5
3              222        8
4              333        5
5              111        6

同样，我有 2000 万行。

我想计算每个student_id 的评分。例如，让我们考虑一个category_id“111”。此类别中有 3 个 Student_id 1、2 和 5。 Student_id 1 有 10 个计数，student_id 2 有 5 个计数，student_id 5 有 6 个计数。 每个student_id对category_id的评分由以下公式计算:

(count per student_id / total number of counts per category_id) * 5 

对于学生 ID 1 -> 10/21 * 5 = 2.38

对于学生 ID 2 -> 5/21 *5 = 1.19

对于学生 ID 5 -> 6/21 * 5 = 1.43

下面是我已经必须计算的函数:

countPerStudentID = datasetPandas.groupby('student_id').agg(list)
countPerCategoryID = datasetPandas.groupby('category_id').agg(list)

studentIDMap = dict()
def func1(student_id):
    if student_id in studentIDMap:
        return studentIDMap[student_id]
    runningSum = 0
    countList = countPerStudentID.loc[student_id, 'count']
    for count in countList:
        runningSum += count
    studentIDMap[student_id] = runningSum
    return studentIDMap[student_id]

#Similar to the above function
categoryIDMap = dict()
def func2(category_id):
    if category_id in categoryIDMap:
        return categoryIDMap[category_id]
    runningSum = 0
    countList = countPerCategoryID.loc[category_id, 'count']
    for count in countList:
        runningSum += count
    categoryIDMap[category_id] = runningSum
    return categoryIDMap[category_id]

最后我从下面调用这两个函数:

#Calculating rating category-wise
rating = []
for index, row in df.iterrows():

    totalCountPerCategoryID = func1(row['category_id'])
    totalCountPerStudentID = func2(row['student_id'])

    rating.append((totalCountPerStudentID / totalCountPerCategoryID) * 5)

df['rating'] = rating

所需输出:

student_id   category_id  count   rating
1              111        10       2.38
2              111        5        1.19
3              222        8         5
4              333        5         5 
5              111        6        1.43

由于数据量巨大，运行起来需要很长时间。我想知道如何优化这个计算

提前致谢

Answer 1

您不需要循环，这是一个 groupby 案例:

df['rating'] = df['count']/df.groupby('category_id')['count'].transform('sum') * 5

输出:

   student_id  category_id  count    rating
0           1          111     10  2.380952
1           2          111      5  1.190476
2           3          222      8  5.000000
3           4          333      5  5.000000
4           5          111      6  1.428571