使用Pandas apply函数时DataFrame首行数据为何被重复打印两次？

Great question! The double print of your first row occurs because pandas runs your function twice on the first row: once as a "trial run" to infer the structure of your function's output, and again when applying the function to all rows in the DataFrame.

Here's a detailed breakdown: #

• When you call df.apply(func, axis=1), pandas needs to know what kind of output your function produces (a scalar, Series, list, etc.) to correctly construct the final result.
• To figure this out, pandas first runs your function on the first row of the DataFrame. This is the first time you see the print output for row 0.
• Once pandas confirms the output type, it then runs the function on every row of the DataFrame—including the first row again. That’s why you see the print output for row 0 a second time.
• Rows 1 and 2 are only processed once during the full application, so their print outputs appear once each.

How to Prevent the Double Print #

If you want to skip the trial run, explicitly tell pandas what output structure to expect using the result_type parameter. Since your function returns a Series (squaring each element of the input row), use result_type='expand':

df[["a","b"]].apply(func, axis=1, result_type='expand')

This tells pandas to expand the function’s output into a DataFrame, eliminating the need for the initial test run. Now your print statements will execute only once per row.

Content of the question originates from Stack Exchange, asked by Bhuvan M