Pandas: Check if row exists with certain values
contains, pandas, python
Solution
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.
Problem
I have a two dimensional (or more) pandas DataFrame like this: ``` >>> import pandas as pd >>> df = pd.DataFrame([[0,1],[2,3],[4,5]], columns=['A', 'B']) >>> df A B 0 0 1 1 2 3 2 4 5 ``` Now suppose I have a numpy array like `np.array([2,3])` and want to check if there is any row in `df` that matches with the contents of my array. Here the answer should obviously true but eg. `np.array([1,2])` should return false as there is no row with both 1 in column A and 2 in column B. Sure this is easy but don't see it right now.