In order to avoid the problem of false assumed relationships present with LabelEncoder, you can use OneHotEncoder to transform a column into a series of columns where each column represents the binary choice of 'did the original column contain value x'. This prevents the creation of false ordinal relationship. However, OneHotEncoder only takes columns of integer values greater than 1, but you can easily transform categorical variables using LabelEncoder or pd.get_dummies

sklearn.preprocessing.OneHotEncoder

The package category_encoders (https://contrib.scikit-learn.org/category_encoders/) implements most mainstream categorical variables encoders in Python, including One-hot encoder, Binary encoder, Hashing encoder, etc. 

University of Michigan - Ann Arbor

https://online.stat.psu.edu/stat200/lesson/1/1.1/1.1.1

Categorical vs. Quantitative Data

https://www.coursera.org/lecture/data-analysis-with-python/turning-categorical-variables-into-quantitative-variables-in-python-7w5xB

Turning Categorical Data to Quantitative Data

Categorical Variables Encoding in Python

Categorical variable: data represent groups
Examples: rankings, binary outcomes
Formal definition: Names or labels (i.e., categories) with no logical order or with a logical order but inconsistent differences between groups (e.g., rankings), also known as qualitative.

Quantitative variable: data represent amounts
Examples: height, weight, or age
Formal definition: Numerical values with magnitudes that can be placed in a meaningful order with consistent intervals, also known as numerical.

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