From Machine Learning for Absolute Beginners: A Plain English Introduction:

Contained in each column is a feature. A feature is also known as variable, a dimension or an attribute - but they all mean the same thing.

From here (the supplement file for this book):

  • In NumPy, each dimension is called an axis.
  • The number of axes is called the rank.
    • For example, the above 3x4 matrix is an array of rank 2 (it is 2-dimensional).
    • The first axis has length 3, the second has length 4.
  • An array's list of axis lengths is called the shape of the array.
    • For example, the above matrix's shape is (3, 4).
    • The rank is equal to the shape's length.
  • The size of an array is the total number of elements, which is the product of all axis lengths (eg. 3*4=12)

Question: Is the dataframe dimension completely different not related to the NumPy dimension (just same word but describing different concept)?

I am learning Python and Machine learning but familial with R and R dataframe from statistical perspective

  • 1
    $\begingroup$ I (almost) never hear people refer to a feature as a "dimension" $\endgroup$
    – oW_
    Commented Jan 6, 2020 at 20:26
  • $\begingroup$ @oW_ So you mostly heard (used) in context as is for NumPy? I mean length of the shape. $\endgroup$
    – vasili111
    Commented Jan 6, 2020 at 20:56

1 Answer 1


The dataframe case refers to the linear algebraic notion of a dimension. In the NumPy context, it just means the number of axes or rank.


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