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.

an easier way is:

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

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

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

To find rows where a single column equals a certain value:

df[df['column name'] == value]

To find rows where multiple columns equal different values, Note the inner ():

df[(df["Col1"] == Value1 & df["Col2"] == Value2 & ....)]