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When data has both string and numeric values, the default object dtype keeps them as such, and breaks functions such as Series.sort_values() which is used in the univariate module.

The StringDtype is more appropriate. See Working with text data.

>>> import pandas as pd
>>> s = pd.Series(["b", 2, "a", 1)
>>> s.sort_values()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
...
TypeError: '<' not supported between instances of 'str' and 'int'
>>> s2 = pd.Series(["b", 2, "a", 1], dtype="string")
>>> s2.sort_values()
3    1
1    2
2    a
0    b
dtype: string

The plotting and analysis functions can be made considerably faster by running them concurrently instead of sequentially.

The multiprocessing module can help.

If a column in the data has no values at all, a ValueError: min() arg is an empty sequence is raised.

The program then terminates in the cli, but the GUI freezes.