Machine learning dataframe dimension concept vs NumPy dimension 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
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.