如何正确合并两个Pandas DataFrame？含Code校验与合并验证

1. Check if All Unique Codes in df1 Exist in df2 #

Want to confirm every unique code in df1 has a match in df2? You can use set operations to compare the unique values in the Code column of both DataFrames—super straightforward:

# Grab unique codes from each DataFrame
df1_codes = set(df1['Code'].unique())
df2_codes = set(df2['Code'].unique())

# Check if all df1 codes are present in df2
all_codes_exist = df1_codes.issubset(df2_codes)
print(all_codes_exist)  # This will output True for your data

In your case, df1's unique codes are A, B, and C—all of which are in df2, so this check returns True.

2. Merging the DataFrames & Verifying Your Code #

Your code df3 = df1.merge(df2, on='Code', how='left') is correct for what you probably want: keeping every row from df1 and adding the corresponding unit price from df2. Let's look at what this code would produce:

That said, your desired df3 includes a row with Code D which isn't present in df1. A left merge only keeps rows from the left DataFrame (df1), so that D row won't show up here. If that D row was a typo in your desired output, then your code is perfect. If you actually want to include rows from df2 that don't have matches in df1, you'd switch to how='right'—but that would also add rows for E from df2 (with NaN values for Sales and Store).

Quick Recap #

• Your left merge code works exactly as intended if you want to retain all rows from df1 with their matching unit prices.
• The D row in your desired output isn't in df1, so it won't appear in a left merge. Double-check if that's a mistake in your expected result or if you need a different merge type.

内容的提问来源于stack exchange，提问作者Ahamed Moosa