如何将单层索引DataFrame添加到多层索引DataFrame（Pandas/Python）

Let's walk through how to add your single-index DataFrame into the specific Date='20170120' group of your multi-index DataFrame. First, let's fix the data formatting from your example to create valid Pandas objects, since the original input was a bit messy.

Step 1: Reconstruct the Original DataFrames #

First, let's build the multi-index DataFrame and the single-index DataFrame as per your description:

import pandas as pd

# Original multi-index DataFrame
multi_df = pd.DataFrame(
    data={
        'Name': ['a', 'b'],
        'Buying_Date': ['20170101', '20180101'],
        'Buying_Price': [5, 6],
        'Buying_Qty': [7, 8]
    },
    index=pd.MultiIndex.from_tuples(
        [('20140117', 'none'), ('20170120', 'none')],
        names=['Date', 'Code']
    )
)

# Single-index DataFrame to add
single_df = pd.DataFrame(
    data={
        'Name': ['abcd', 'efgh'],
        'Code': ['af', 'bf'],
        'Buying_Date': ['20170101', '20180101'],
        'Buying_Price': [5, 6],
        'Buying_Qty': [7, 8]
    }
)

Step 2: Add the Target Multi-Index Level to the Single-Index DataFrame #

We need to assign the Date='20170120' value as the top-level index for all rows in the single-index DataFrame, then convert it to a multi-index matching the original structure:

# Add the 'Date' level to the single DataFrame
single_df_with_multi = single_df.set_index([pd.Series(['20170120']*len(single_df), name='Date'), 'Code'])

Step 3: Combine the Two DataFrames #

Use pd.concat() to merge the original multi-index DataFrame with the modified single-index DataFrame. This will append the new rows under the 20170120 Date group:

# Concatenate the DataFrames
result_df = pd.concat([multi_df, single_df_with_multi])

Step 4: Verify the Result #

If you print result_df, you'll get exactly the structure you wanted:

print(result_df)

Output:

Name Buying_Date  Buying_Price  Buying_Qty
Date     Code                                               
20140117 none        a    20170101             5           7
20170120 none        b    20180101             6           8
         af       abcd    20170101             5           7
         bf       efgh    20180101             6           8

Key Notes #

• Make sure the column names of both DataFrames match exactly (they do in your example, which is good!).
• When setting the multi-index for the single DataFrame, we explicitly set the index names (Date and Code) to match the original multi-index, so concat can align them correctly.
• If you want to sort the index after concatenation, you can use result_df.sort_index() to keep the groups organized.

内容的提问来源于stack exchange，提问作者Vince