我需要将数据框中的每一列的每一行除以一个特定/设置的行。在这种情况下，我需要将每一行除以每个时间段的收入。我想得到每个帐户的收入百分比。我还想弄清楚如何对任意数量的列进行动态化。
我的当前数据框：

data = {'202112 YTD': {'Gross Margin': 200000,
  'Other (Income) & Expense': -100000,
  'Revenue': 5000000,
  'SG&A Expense': 150000,
  'Segment EBITDA': 200000},
 '202212 YTD': {'Gross Margin': 2850000,
  'Other (Income) & Expense': -338000,
  'Revenue': 6000000,
  'SG&A Expense': 15000,
  'Segment EBITDA': 200000}}

df = pd.DataFrame.from_dict(data)
df

预期输出：

outdata = {'202112 YTD': {'Gross Margin': 0.040,
  'Other (Income) & Expense': -0.020,
  'Revenue': 1,
  'SG&A Expense': 0.030,
  'Segment EBITDA': 0.040},
 '202212 YTD': {'Gross Margin': 0.475,
  'Other (Income) & Expense': -0.056,
  'Revenue': 1,
  'SG&A Expense': 0.003,
  'Segment EBITDA': 0.033}}

outdf = pd.DataFrame.from_dict(outdata)
outdf

帮助将不胜感激。最初的尝试是这样的结构解决方案的例子：

import copy
import pandas as pd

original_table = [
    {'name': 'Alice', 'age': 25, 'gender': 'Female'},
    {'name': 'Bob', 'age': 32, 'gender': 'Male'},
    {'name': 'Charlie', 'age': 40, 'gender': 'Male'},
    {'name': 'Daisy', 'age': 22, 'gender': 'Female'},
    {'name': 'Eve', 'age': 18, 'gender': 'Female'},
]


# Duplicate the table using copy.deepcopy()
duplicate_table = copy.deepcopy(original_table)

# Choose a specific column to divide the rows by
column_name = 'age'
divisor_value = original_table[3][column_name]

# Iterate over the rows in the duplicate table and divide each column by the divisor value
for i, row in enumerate(duplicate_table):
    if column_name in row:
        duplicate_table[i][column_name] = row[column_name] / divisor_value
    else:
        print(f"column: {column_name} not found in table")

# Convert the duplicate table to a DataFrame
duplicate_df = pd.DataFrame(duplicate_table)

# Print the duplicate DataFrame
duplicate_df

duplicate_df