你可以尝试boolean索引+ .concat：

df = pd.concat([base_df, new_info_df[~new_info_df.index.isin(base_df.index)]])
print(df)

字符串
图纸：

animal noise
1    dog  bark
2    cat  meow
3  sheep   baa

型

要创建一个包含所需列（'animal'和'noise'）的新数据框架，将base_df中的数据和new_info_df中的行的索引合并在一起，可以执行以下步骤：

import pandas as pd

base_df = pd.DataFrame({'animal': ['dog', 'cat'], 'noise': ['bark', 'meow']}, index=[1, 2])
new_info_df = pd.DataFrame({'animal': ['wolf', 'sheep'], 'noise': ['woof', 'baa']}, index=[1, 3])

# Get the indices that are not present in base_df
new_indices = new_info_df.index.difference(base_df.index)

# Filter new_info_df based on the new indices
filtered_new_info_df = new_info_df.loc[new_indices]

# Concatenate base_df and filtered_new_info_df
updated_df = pd.concat([base_df, filtered_new_info_df])

# Sort the index of the updated dataframe
updated_df.sort_index(inplace=True)

print(updated_df)

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输出将为：

animal  noise
1    dog   bark
2    cat   meow
3  sheep    baa

型