pandas :检查行是否存在某些值 [英] Pandas: Check if row exists with certain values

问题描述

我有一个这样的二维(或更多)熊猫DataFrame:

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

现在假设我有一个像np.array([2,3])这样的numpy数组，并想检查df中是否有任何与我的数组内容匹配的行.这里的答案显然应该是正确的，但是例如. np.array([1,2])应该返回false，因为没有行同时在A列和B列中都包含2.

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.

确定这很容易，但是现在看不到.

Sure this is easy but don't see it right now.

推荐答案

事实证明这确实很容易，以下是在这里完成的工作:

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

也许有人想出了一个更好的解决方案，它允许直接传递数组和要匹配的列列表.

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

请注意，df['A'] == 2周围的括号不是可选的，因为&运算符的绑定强度与==运算符相同.

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

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