I've been very confused about how python axes are defined, and whether they refer to a DataFrame's rows or columns. Consider the code below: >>> df = pd.DataFrame([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]], columns=["col1", "col2", "col3", "col4"]) >>> df col1 col2 col3 col4 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 So if we call df.mean(axis=1), we'll get a mean across the rows: >>> df.mean(axis=1) 0 1 1 2 2 3 However, if we call df.drop(name, axis=1), we actually drop a column, not a row: >>> df.drop("col4", axis=1) col1 col2 col3 0 1 1 1 1 2 2 2 2 3 3 3 Can someone help me understand what is meant by an "axis" in pandas/numpy/scipy? A side note, DataFrame.mean just might be defined wrong. It says in the documentation for DataFrame.mean that axis=1 is supposed to mean a mean over the columns, not the rows... Continue reading...
