Pandas DataFrame循环迭代代码运行结果及相关技术咨询

Alright, let's walk through your Pandas code and break down both its logic and why you're getting the output ['hi' 'See you']!

1. 数据合并后的结构 #

First, let's look at what your merged DataFrame df3 actually looks like. When you use pd.concat([df1, df2], axis=1, join='outer'):

• df1 has 8 rows of greetings, while df2 only has 4 rows of farewells
• The join='outer' setting means Pandas will keep all rows from both DataFrames, filling missing values in df2's column with NaN for rows 4-7.

Here's what df3 looks like under the hood:

2. 遍历与匹配逻辑 #

Your loop does a few specific things:

sentence = 'hi' 
for index, row in df3.iterrows(): 
    if index>0:  # Skips the first row (index 0)
        if sentence.lower() in df3.iloc[index,:].values:  # Checks if 'hi' is in the row's values
            print(df3.iloc[index].values)

• You skip the first row (index 0), so we start checking from index 1
• At index 1, the row values are ['hi', 'See you'] — since 'hi' matches sentence.lower(), this row gets printed
• For all subsequent rows (indexes 2-7), none of the values equal 'hi', so nothing else gets printed. That's why you only see the single output line.

3. 可选优化建议 #

If your goal is to find rows containing your target sentence, you can skip the slow iterrows() loop and use Pandas' vectorized operations instead (much more efficient for large datasets):

sentence = 'hi'
# Check if the Greetings column matches (case-insensitive)
match_mask = df3['Greetings'].str.lower() == sentence.lower()
# Get the matching rows
matching_rows = df3[match_mask]
print(matching_rows.values)

If you need to check the entire row (not just the Greetings column) for the sentence, you can use:

match_mask = df3.apply(
    lambda row: sentence.lower() in row.astype(str).str.lower().values,
    axis=1
)
matching_rows = df3[match_mask]
print(matching_rows.values)

内容的提问来源于stack exchange，提问作者Muke888