k-means clustering multi column data in python
You don't need to reformat anything.
Each row is a 60 dimensional vector of continous values with a comparable scale (coordinates), as needed for k-means.
You can just run k-means on this.
But assuming that the measurements were taken in sequence, you may observe a strong correlation between rows, so I wouldn't expect the data to cluster extremely well, unless you set up the use to do and hold certain poses.
I Have data-set for which consist 2000 lines in a text file.
Each line represents x,y,z (3D coordinates location) of 20 skeleton joint points  of human body (eg: head, shoulder center, shoulder left, shoulder right,......, elbow left, elbow right). I want to do k-means clustering of this data.
Data is separated by  'spaces ', each joint is represented by 3 values (Which represents x,y,z coordinates). Like head and shoulder center represented by 
.0255...  .01556600 1.3000...  .0243333 .010000 .1.3102000 .... 
So basically I have 60 columns in each row, which which represents 20 joints and each joins consist of three points.  
My question is how do I format or use this data for k-means clustering,