Data manipulation
* Data (set) operations
* option: change current/add new dataset
* cut selected x/y-range/outside selected x/y-range
* geometric transform(x/y): translation/scaling (factor/to range)/rotation(?) (see xmgrace)
* evaluate expression on data set (see xmgrace)
* sort data
* Detrend (remove constant/linear fit, see scipy.detrend)
* Baseline removal (how to define baseline?)
* Spike removal (killspikes.m)
* Feature extraction (see xmgrace)
Data cleaning
* https://github.com/rhiever/datacleaner
* drop any row with a missing value
* replace missing values with the mode (for categorical variables) or median (for continuous variables) on a column-by-column basis
* encode non-numerical variables (e.g., categorical variables with strings) with numerical equivalents
* https://github.com/NathanEpstein/Dora
* impute the missing values (using the average of each column)
* scale the values of the input variables (center to mean and scale to unit variance)
* extract an ordinal feature through one-hot encoding
* [[ https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf | Introduction to data cleaning with R ]]