如何合并存在互斥缺失行的两个表？Pandas合并方法咨询

Got it, the issue here is that you're using a left join which only preserves rows from the left table (Table1), but you need a full outer join to keep all unique Column_name entries from both tables. Plus, you'll want to rename the Value columns first to avoid conflicts and clearly distinguish which values come from each table. Here's a step-by-step solution:

Step 1: Prepare Your DataFrames #

First, let's recreate your tables (skip this if you already have them loaded):

import pandas as pd

# Table 1 data
table1 = pd.DataFrame({
    'Column_name': ['K1', 'K2', 'K4', 'H1', 'H3', 'H6'],
    'Value': [13, 25, 46, 56, 26, 56]
})

# Table 2 data
table2 = pd.DataFrame({
    'Column_name': ['K1', 'K2', 'K3', 'H2', 'H4'],
    'Value': [65, 31, 71, 56, 46]
})

Step 2: Rename Value Columns #

Rename the Value columns in each table to make their source clear:

table1 = table1.rename(columns={'Value': 'Value1'})
table2 = table2.rename(columns={'Value': 'Value2'})

Step 3: Perform Full Outer Join #

Use how='outer' in pd.merge() to retain all Column_name entries from both tables:

merged_table = pd.merge(table1, table2, on='Column_name', how='outer')

Step 4: Optional - Format the Output #

If you want the result sorted by Column_name (matching your desired output) and replace NaN with empty strings for cleaner display:

# Sort by Column_name and reset index
merged_table = merged_table.sort_values('Column_name').reset_index(drop=True)
# Replace NaN with empty strings
merged_table = merged_table.fillna('')

Final Output #

Running the above code will give you exactly the table you wanted:

Key Explanation #

• how='outer': Unlike a left join, this keeps every unique Column_name from both tables, filling in missing values with NaN (which we can replace with empty strings if needed).
• Renaming Value columns: Prevents column name collisions and makes it obvious which values came from Table1 vs Table2.

内容的提问来源于stack exchange，提问作者Arron