One-hot encoding

Most of the machine learning algorithms can't work with the categorical variables, so usually we want to convert them to the one-hot vectors (statisticians prefer to call them dummy variables). Let's convert first, and then I will explain what this is:

In []: 
features = pd.get_dummies(features, columns = ['color']) 
features.head() 
Out[]: 

So now, instead of one column, color, we have four columns: color_light black, color_pink gold, color_purple polka dot, and color_space gray. The color of each sample is encoded as 1 in the corresponding column. Why do we need this if we could simply replace colors with the numbers from 1 to 4? Well, this is the problem: why to prefer 1 to 4 over the 4 to 1, or powers of 2, or prime numbers? These colors on their own don't carry any quantitative information associated to them. They can't be sorted from the largest to the smallest. If we introduce this information artificially, the machine learning algorithm may attempt to utilize that meaningless information, and we will end up with the classifier that sees regularities where there are none.