本文介绍了如何选择包含大于阈值的所有行?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
请求很简单:我想选择所有包含大于阈值的行。
The request is simple: I want to select all rows which contain a value greater than a threshold.
如果我这样做,则:
df[(df > threshold)]
我得到了这些行,但是低于该阈值的值只是 NaN
。如何避免选择这些行?
I get these rows, but values below that threshold are simply NaN
. How do I avoid selecting these rows?
推荐答案
绝对不需要双重转置-您可以简单地调用沿布尔矩阵上的列索引(提供1或'columns'
)。
There is absolutely no need for the double transposition - you can simply call any
along the column index (supplying 1 or 'columns'
) on your Boolean matrix.
df[(df > threshold).any(1)]
示例
>>> df = pd.DataFrame(np.random.randint(0, 100, 50).reshape(5, 10))
>>> df
0 1 2 3 4 5 6 7 8 9
0 45 53 89 63 62 96 29 56 42 6
1 0 74 41 97 45 46 38 39 0 49
2 37 2 55 68 16 14 93 14 71 84
3 67 45 79 75 27 94 46 43 7 40
4 61 65 73 60 67 83 32 77 33 96
>>> df[(df > 95).any(1)]
0 1 2 3 4 5 6 7 8 9
0 45 53 89 63 62 96 29 56 42 6
1 0 74 41 97 45 46 38 39 0 49
4 61 65 73 60 67 83 32 77 33 96
按自己的答案进行移调只是不必要的性能损失。
Transposing as your self-answer does is just an unnecessary performance hit.
df = pd.DataFrame(np.random.randint(0, 100, 10**8).reshape(10**4, 10**4))
# standard way
%timeit df[(df > 95).any(1)]
1 loop, best of 3: 8.48 s per loop
# transposing
%timeit df[df.T[(df.T > 95)].any()]
1 loop, best of 3: 13 s per loop
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