Pandas: Check if row exists with certain values

Turns out it is really easy, the following does the job here:

>>> ((df['A'] == 2) & (df['B'] == 3)).any()True>>> ((df['A'] == 1) & (df['B'] == 2)).any()False

Maybe somebody comes up with a better solution which allows directly passing in the array and the list of columns to match.

Note that the parenthesis around df['A'] == 2 are not optional since the & operator binds just as strong as the == operator.

python pandas contains

an easier way is:

a = np.array([2,3])(df == a).all(1).any()

python pandas contains

If you also want to return the index where the matches occurred:

index_list = df[(df['A'] == 2)&(df['B'] == 3)].index.tolist()

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