问根据计算将Pandas列展开为分隔行

EN

import pandas as pd
df = pd.DataFrame(columns=['ID', 'Divide', 'Object', 'List'], data=[ ['A, B', 2, 20, [0, 5]], ['C, D', 2, 40, [10, 15, 35]], ['E, F', 2, 20, [11, 15]], ['G', 1, 10, [1, 5]], ['H', 1, 10, ''], ['I, J', 2, 20, ''] ])

def split_list(lst, limit):
    l1 = list()
    l2 = list()
    for e in lst:
        if e <= limit:
            l1.append(e)
        else:
            l2.append(e)
    return l1, l2

df['ID'] = df['ID'].str.split(', ')
df['Limit'] = df['Object'] / df['Divide']
df['List'] = df.apply(lambda row: dict(zip(row['ID'], split_list(row['List'], row['Limit']))), axis=1)
df = df.explode('ID')
df['List'] = df.apply(lambda row: row['List'].get(row['ID']), axis=1)

print(df)


# Out[192]:
#   ID  Divide  Object      List  Limit
# 0  A       2      20    [0, 5]   10.0
# 0  B       2      20        []   10.0
# 1  C       2      40  [10, 15]   20.0
# 1  D       2      40      [35]   20.0
# 2  E       2      20        []   10.0
# 2  F       2      20  [11, 15]   10.0
# 3  G       1      10    [1, 5]   10.0
# 4  H       1      10        []   10.0
# 5  I       2      20        []   10.0
# 5  J       2      20        []   10.0

import pandas as pd

df1 = pd.DataFrame(columns=['ID', 'Divide', 'Object', 'List'],
                   data=[['A, B', 2, 20, [0, 5]],
                         ['C, D', 2, 40, [10, 15, 35]],
                         ['E, F', 2, 20, [11, 15]],
                         ['G', 1, 10, [1, 5]],
                         ['H', 1, 10, ''],
                         ['I, J', 2, 20, '']])

# Split and Explode ID
df1['ID'] = df1['ID'].str.split(', ')

# Group By Each ID and set index so that First and Second IDs are tracked
df1 = df1.explode('ID') \
    .groupby(level=0) \
    .apply(lambda x: x.reset_index()) \
    .droplevel(0)
# Calculate Cap For Later
df1['cap'] = df1['Object'] // df1['Divide'] - 1


def split_lists(g):
    # If more than 1 row and non-empty list
    if len(g) > 1 and not g['List'].empty:
        # Check if is the First ID
        if g['level_0'].iloc[0] == 0:
            # Filter Less Than Equal To Cap
            g['List'] = g['List'][g['List'] <= g['cap']]
        else:
            # Filter Greater Than Cap
            g['List'] = g['List'][g['List'] > g['cap']]
    return g


# Explode Lists Group By ID filter using function
# Regroup and convert back to lists
df2 = df1 \
    .explode('List') \
    .reset_index() \
    .groupby('ID') \
    .apply(split_lists) \
    .groupby('ID')['List'] \
    .apply(lambda x: x.dropna().tolist())

# Drop Extra Columns from df1 and merge back
out = df1.drop(columns=['List', 'index', 'cap']) \
    .merge(df2, left_on='ID', right_index=True, how='left') \
    .reset_index(drop=True)

print(out)

  ID  Divide  Object      List
0  A       2      20    [0, 5]
1  B       2      20        []
2  C       2      40  [10, 15]
3  D       2      40      [35]
4  E       2      20        []
5  F       2      20  [11, 15]
6  G       1      10    [1, 5]
7  H       1      10        []
8  I       2      20        []
9  J       2      20        []

   index ID  Divide  Object          List  cap
0      0  A       2      20        [0, 5]    9
1      0  B       2      20        [0, 5]    9
0      1  C       2      40  [10, 15, 35]   19
1      1  D       2      40  [10, 15, 35]   19
0      2  E       2      20      [11, 15]    9
1      2  F       2      20      [11, 15]    9
0      3  G       1      10        [1, 5]    9
0      4  H       1      10                  9
0      5  I       2      20                  9
1      5  J       2      20                  9

ID
A      [0, 5]
B          []
C    [10, 15]
D        [35]
E          []
F    [11, 15]
G      [1, 5]
H          []
I          []
J          []
Name: List, dtype: object