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

问题描述

我想在我的数据的每一列中找到 NaN 的数量，以便在 NaN 少于某个阈值时我可以删除一列.我看了看，但找不到任何功能.value_counts 是对我来说太慢了，因为大多数值都是不同的，我只对 NaN 计数感兴趣.

I want to find the number of NaN in each column of my data so that I can drop a column if it has fewer NaN than some threshold. I looked but wasn't able to find any function for this. value_counts is too slow for me because most of the values are distinct and I'm only interested in the NaN count.

推荐答案

您可以使用 isna() 方法(或者它的别名 isnull()，它也与较旧的 Pandas 版本 <0.21.0 兼容)和然后求和以计算 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

对于多列，它也有效:

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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