如何均衡两个数据帧？

import pandas as pd

# 示例数据帧
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8], 'D': [9, 10]})

# 确定目标行数
target_rows = max(len(df1), len(df2))

# 扩展较小的数据帧以达到目标行数
if len(df1) < target_rows:
    df1 = pd.concat([df1] * (target_rows // len(df1) + 1)).reset_index(drop=True)
if len(df2) < target_rows:
    df2 = pd.concat([df2] * (target_rows // len(df2) + 1)).reset_index(drop=True)

# 截断较大的数据帧以达到目标行数
df1 = df1.head(target_rows)
df2 = df2.head(target_rows)

print("Balanced df1:")
print(df1)
print("Balanced df2:")
print(df2)

import pandas as pd

# 示例数据帧
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9], 'D': [10, 11, 12], 'E': [13, 14, 15]})

# 确定目标列数
target_columns = max(len(df1.columns), len(df2.columns))

# 扩展较小的数据帧以达到目标列数
if len(df1.columns) < target_columns:
    for col in range(target_columns - len(df1.columns)):
        df1[f'new_col_{col}'] = None
if len(df2.columns) < target_columns:
    for col in range(target_columns - len(df2.columns)):
        df2[f'new_col_{col}'] = None

print("Balanced df1:")
print(df1)
print("Balanced df2:")
print(df2)