# Machine learning dataframe dimension concept vs NumPy dimension

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

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