[Python] Pandas smarter conditional merge for two dataframes

Here I tried to find the shortest possible example code for a conditional merge of two dataframes. There I try to merge exact rows based on a condition. Currently I have to iterate over both dataframes for that.

import pandas as pd

df1 = pd.DataFrame([
['a', 'Test1', 2995],
['b', 'Test2', 3995],
['c', 'Test3', 5000]],
columns=['ID', 'Name', 'Value'])
df2 = pd.DataFrame([
['1', 1, 2],
['2', 3, 4],
['3', 99, 100]],
columns=['ID', 'Start', 'Context'])

df_test = pd.DataFrame(columns=['ID', 'Name', 'Value','Start'])
i = 0
for index, row1 in df1.iterrows():
for index, row2 in df2.iterrows():
if row1['Value'] + 25 > row2['Start'] * 1000 and row1['Value']< row2['Start'] * 1000:
df_test.loc = row1
df_test.loc['Start'] = row2['Start']
i += 1


And it works so the dft_test is

ID Name Value Start
0 a Test1 2995 3


Is there a smarter way doing that?

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