如何计算pandas DataFrame列中的NaN值 [英] How to count the NaN values in a column in pandas DataFrame

问题描述

我有数据，想要在其中找到NaN的数字，因此，如果它小于某个阈值，我将删除此列.我看了一下，但是找不到任何功能.有 value_counts ，但它会对我来说很慢，因为大多数值是不同的，并且我只希望计数NaN.

I have data, in which I want to find number of NaN, so that if it is less than some threshold, I will drop this columns. I looked, but didn't able to find any function for this. there is value_counts, but it would be slow for me, because most of values are distinct and I want count of NaN only.

推荐答案

您可以使用 isna() 方法(或别名isnull()，它也与较早的熊猫版本<0.21.0兼容)，然后求和以计算NaN值.对于一列:

You can use the isna() method (or it's alias isnull() which is also compatible with older pandas versions < 0.21.0) and then sum to count the NaN values. For one column:

In [1]: s = pd.Series([1,2,3, np.nan, np.nan])

In [4]: s.isna().sum()   # or s.isnull().sum() for older pandas versions
Out[4]: 2

对于几列，它也可以工作:

For several columns, it also works:

In [5]: df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})

In [6]: df.isna().sum()
Out[6]:
a    1
b    2
dtype: int64

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