To merge two dataframes df1 and df2 based on columns df1['A'] and df2['B'], you can use the following syntax:
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df = pd.merge(df1, df2, on=['A', 'B'])

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This will create a new dataframe, df, that contains all of the rows from df1 and df2, where the values in the 'A' column of df1 match the values in the 'B' column of df2.

For example, let's say we have two dataframes, df1 and df2, that look like this:
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df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [2, 3, 4], 'C': [7, 8, 9]})

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If we merge these two dataframes on the 'A' column, we will get the following dataframe:
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df = pd.merge(df1, df2, on='A')
print(df)

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Code snippet

   A  B  C
0  2  5  7
1  3  6  8
2  4  9

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As you can see, the new dataframe contains all of the rows from df1 and df2, where the values in the 'A' column match. The values in the 'B' column from df1 are preserved, and the values in the 'C' column from df2 are added.
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