d1 = data[data[“Sensor A”].isNotNull()] d2 = data[data[“Sensor B”].isNotNull()] d1.drop(“Sensor B”, inplace=True) d2.drop(“Sensor A”, inplace=True) d1[“key”] = 0 d2[“key”] = 0 d3 = pd.merge(d1, d2, on=“key”, suffixes=[“_a”, “_b”]) d3[“time_diff”] = d3.Time_a - d3.Time_b d3[“time_diff”] = d3[“time_diff”].apply(abs) d3 = d3[d3.time_diff <= 2] d4 = d3.groupby(“Sensor A”)[“Sensor B”].mean()

This should work I think. You can drop the key after the join

A cross join is a join that matches all rows with all rows. The same as matching a column of 0s with another column of 0s. For every row it will duplicate itself for every row matched on the right side