Alternative to irregular nested np.where clauses

I’m struggling to simplify my irregular nested np.where clauses. Is there a way to make the code more readable?

    df["COL"] = np.where(
    (df["A1"] == df["B1"]) & (df["A1"].notna()),
    np.where(
        (df["A1"] == df["C"]),
        np.where(
            (df["A"] == df["B"]) & df["A"].notna() & (df["A"] != df["A1"]),
            "Text1",
            df["A1"]
        ),
        "Text2"
    ),
    np.where(
        (df["A"] == df["B"]) & (df["A"].notna()),
        np.where(
            (df["A"] == df["C"]),
            df["A"],
            "Text1"
        ),
        np.where(
            (df["C"].notna()), 
            df["C"],
            "Text3"
        )
    )
)

Answer

Using np.select as suggested by @sammywemmy:

# Create boolean masks
m1 = (df["A1"] == df["B1"]) & (df["A1"].notna())
m11 = (df["A1"] == df["C"])
m12 = (df["A"] == df["B"]) & (df["A"].notna())
m111 = (df["A"] == df["B"]) & df["A"].notna() & (df["A"] != df["A1"])
m121 = (df["A"] == df["C"])
m122 = (df["C"].notna())

# Combine them
condlist = [m1 & m11 & m111,
            m1 & m11 & ~m111,
            m1 & ~m11,
            ~m1 & m12 & m121,
            ~m1 & m12 & ~m121
            ~m1 & ~m12 & m122,
            ~m1 & ~m12 & ~m122]

# Values for each combination
choicelist = ["Text1", df["A1"], "Text2", df["A"], "Text1", df["C"], "Text3"]

out = np.select(condlist, choicelist)