Applying regression on each of the columns or rows in a pandas dataframe, without using for loops. There is a similar post about this; Apply formula across pandas rows/ regression line, that does a regression for each of the "rows," however plotting the answer given is wrong. I couldn't comment on it as i do not have enough reputation, the main problem with that is that, it takes the values of the columns but then uses the apply function on each row. Currently I only know how to do each column eg. np.random.seed(1997) df = pd.DataFrame(np.random.randn(10, 4)) first_stats = scipy.stats.linregress(df.index,df[0]) second_stats = scipy.stats.linregress(df.index,df[1]) I was hoping to find an answer without creating a function or for loops, similar to; pandas df.sum(), but instead of sum i want to do a regression that results in slope, intercept, r-value, p-value and standard error. Continue reading...
