I think it is important to point out that this is not an example for an ordinal encoding of variables. Sex, Blood and Study should all not have an ordinal scale (and was also not suggested by the person, who asked the question). Ordinal data has a ranking (see e.g. https://en.wikipedia.org/wiki/Ordinal_data) Those examples here do not have a ranking.

In the case that your variable is a target variable you can use the LabelEncoder.(https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)

Then you can do something like:

from sklearn.preprocessing import LabelEncoder

for col in ["Sex","Blood", "Study"]:
    df[col] = LabelEncoder().fit_transform(df[col])

If your variables are features you should use the Ordinalencoder for accomplishing this. (See comments to my answer).

The naming for the Ordinalencoder is quite unfortunate as "ordinal" is seen from a mathematical and not a statistical naming perspective.

More on the difference between ordinal- and labelencoder in sklearn: https://datascience.stackexchange.com/questions/39317/difference-between-ordinalencoder-and-labelencoder

As for differences in OrdinalEncoder and LabelEncoder implementation, the accepted answer mentions the shape of the data:

• OrdinalEncoder is for 2D data with the shape (n_samples, n_features)
• LabelEncoder is for 1D data with the shape (n_samples,)

Maybe that's why the top-voted answer suggests OrdinalEncoder is for the "features" (often a 2D array), whereas LabelEncoder is for the "target variable" (often a 1D array).

That's also why a OrdinalEncoder would get an error if trying to fit on 1D data: OrdinalEncoder().fit(['a','b'])

ValueError: Expected 2D array, got 1D array instead:

Another difference between the encoders is the name of their learned parameter;

• LabelEncoder learns classes_
• OrdinalEncoder learns categories_

Notice the differences when fitting LabelEncoder vs OrdinalEncoder, and the differences in the values of the learned parameters.

• LabelEncoder.fit(...) accepts a 1D array; LabelEncoder.classes_ is 1D
• OrdinalEncoder.fit(...) accepts a 2D array; OrdinalEncoder.categories_ is 2D.

    LabelEncoder().fit(['a','b']).classes_
    # >>> array(['a', 'b'], dtype='<U1')
    
    OrdinalEncoder().fit([['a'], ['b']]).categories_
    # >>> [array(['a', 'b'], dtype=object)]

This is consistent with the idea that

• OrdinalEncoder is for your X aka your input features (2D)
• LabelEncoder is for your y aka your target variables (1D) (also mentioned here):

LabelEncoder should be used to encode target values, i.e. y, and not the input X.

Other encoders that work in 2D, including OneHotEncoder, also use the property categories_

More info here about the dtype <U1 (little-endian , Unicode, 1 byte; i.e. a string with length 1)

EDIT

In the comments to my answer, Piotr disagrees with my answer; but Piotr points out the difference between ordinal encoding and label encoding more generally (vs differences in their implementation). Piotr's right about the general definitions/usages:

• Ordinal encoding should be used for ordinal variables (where order matters, like cold, warm, hot);
• vs Label encoding should be used for non-ordinal (aka nominal) variables (where order doesn't matter, like blonde, brunette)

This is a good point, but this question asks about the sklearn classes/implementation. If you want ordinal encoding like Piotr describes (i.e. where order is preserved); you must do the ordinal encoding yourself (neither OrdinalEncoder nor LabelEncoder can infer the order! See the OrdinalEncoder constructor parameter called categories).

As for implementation it seems like LabelEncoder and OrdinalEncoder have consistent behavior as far as the chosen integers. They both assign integers based on alphabetical order. For example:

OrdinalEncoder().fit_transform([['cold'],['warm'],['hot']]).reshape((1,3))
# >>> array([[0., 2., 1.]])

LabelEncoder().fit_transform(['cold','warm','hot'])
# >>> array([0, 2, 1], dtype=int64)

Notice how both encoders assigned integers in alphabetical order 'c'<'h'<'w'.

But this part is important: Notice how neither encoder got the "real" order correct (i.e. the real order should reflect the temperature, where order is 'cold'<'warm'<'hot'; 0<1<2). If the encoders used the "real" order, the value 'warm' would have been assigned the integer 1 (instead of the integer 2)

In the blog post referenced by Piotr, the author does not even use OrdinalEncoder(). To achieve ordinal encoding the author does it manually: maps each temperature to a "real" order integer, using a dictionary like {'cold':0, 'warm':1, 'hot':2}:

Refer to this code using Pandas, where first we need to assign the real order of the variable through a dictionary... Though its very straight forward but it requires coding to tell ordinal values and what is the actual mapping from text to integer as per the order.

In other words, if you're wondering whether to use OrdinalEncoder, please note OrdinalEncoder may not actually provide "ordinal encoding" the way you expect!

EDIT @Magnus Persson pointed out that the OrdinalEncoder class accepts an argument called categories, which you can use to determine/assign the resulting order.

OrdinalEncoder(categories=[['cold','warm','hot']])
    .fit_transform([['hot'],['warm'],['warm'],['cold']])
    .reshape((1,-1))[0]

# Output is:
# >>> array([[2., 1., 1., 0.]])    

EDIT @lbcommer pointed out that there is a Python library category_encoders, which has an OrdinalEncoder class. Note how even that class constructor has a mapping argument so you can choose the resulting order:

the value of ‘mapping’ should be a dictionary of ‘original_label’ to ‘encoded_label’.... example mapping: {‘col’: ‘col1’, ‘mapping’: {None: 0, ‘a’: 1, ‘b’: 2}}, {‘col’: ‘col2’, ‘mapping’: {None: 0, ‘x’: 1, ‘y’: 2}}