Returning multiple values from pandas apply on a DataFrame

I'm using a Pandas DataFrame to do a row-wise t-test as per this example: ``` import numpy as np import pandas as pd df = pd.DataFrame(np.log2(np.randn(1000, 4), columns=["a", "b", "c", "d"]).dropna() ``` Now, suppose I have "a" and "b" as one group, and "c" and "d" at the other, I'm performing the t-test row-wise. This is fairly trivial with pandas, using `apply` with `axis=1`. However, I can either return a DataFrame of the same shape if my function doesn't aggregate, or a Series if it aggregates. Normally I would just output the p-value (so, aggregation) but I would like to generate an additional value based on other calculations (in other words, return two values). I can of course do two runs, aggregating the p-values first, then doing the other work, but I was wondering if there is a more efficient way to do so as the data is reasonably large. As an example of the calculation, a hypothetical function would be: ``` from scipy.stats import ttest_ind def t_test_and_mean(series, first, second): first_group = series[first] second_group = series[second] _, pvalue = ttest_ind(first_group, second_group) mean_ratio = second_group.mean() / first_group.mean() return (pvalue, mean_ratio) ``` Then invoked with ``` df.apply(t_test_and_mean, first=["a", "b"], second=["c", "d"], axis=1) ``` Of course in this case it returns a single Series with the two tuples as value. Instead, my expected output would be a DataFrame with two columns, one for the first result, and one for the second. Is this possible or I have to do two runs for the two calculations, then merge them together?