Find first occurrence of value in dataframe based on another dataframe with a shared column

Question

I am trying to find the first occurrence of a value exceeding a threshold based on another data frame's value with the same identifier. In the examples below, the "Trace" column would be the same identifier. However, the second data frame only includes one value for each "Trace" number. The goal is to find the first occurrence where df1's "Signal" column exceeds 2 times the "Signal" column in df2. Also, I would like to keep the rest of the information in the row that meets the given criteria.

df1 = {"Trace": [1,1,1,1,1,2,2,2,2,2], "Sample": [1,2,3,4,5,1,2,3,4,5], "Signal": [2,3,5,6,1,8,9,5,4,3]}

df2 = {"Trace": [1,2], "Sample": [4,2], "Signal": [2,4]}

For example, in the given case, I want to create a new data frame (df3) containing instances where the "Signal" column in df2 is exceeded by 2 times the original "Signal" in df2 (would be 4 and 6). Therefore the new data frame (df3) would contain "Trace" values of 1 and 2, "Sample" values of 3 and 2, and "Signal" values of 5 and 9.

Any ideas? I have tried using .groupby and .loc, but I just can't seem to get what I want.

Answer 1

import pandas as pd

df1 = pd.DataFrame({
    "Trace": [1,1,1,1,1,2,2,2,2,2],
    "Sample": [1,2,3,4,5,1,2,3,4,5],
    "Signal": [2,3,5,6,1,8,9,5,4,3],
})
df2 = pd.DataFrame({
    "Trace": [1,2],
    "Sample": [4,2],
    "Signal": [2,4]},
)

df3 = df1.merge(
    df2[['Trace', 'Signal']],
    on='Trace'
)
mask = (df3.Signal_x > 2 * df3.Signal_y)
df3 = df3.loc[mask]

mask = ~df3.duplicated('Trace')
df3 = df3.loc[mask]

where the resulting df3 should look as follows:

   Trace  Sample    Signal_x    Signal_y
2      1       3           5           2
6      2       2           9           4