Python – 如何在Pandas Dataframe中计算列中NaN的数量？

要计算列中NaN的数量，请使用isna（）。使用sum（）将值相加并找到计数。

首先，让我们导入所需的库及其别名 –

import pandas as pd
import numpy as np

创建DataFrame。我们在“Units_Sold”列中使用Numpy np.inf设置NaN值 –

dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],"Units_Sold": [ 100, np.NaN, 150, np.NaN, 200, np.NaN]
})

计算“Units_Sold”列中的NaN值 –

dataFrame["Units_Sold"].isna().sum()

更多Pandas文章，请阅读：Pandas教程

示例

以下是代码 –

import pandas as pd
import numpy as np

# creating dataframe
dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],"Units_Sold": [ 100, np.NaN, 150, np.NaN, 200, np.NaN]
})

print("DataFrame...\n",dataFrame)

# count NaN values from column "Units_Sol"
count = dataFrame["Units_Sold"].isna().sum()

print("\n计算“Units_Sold”列中NaN值的数量...\n",count)

输出

这将产生以下输出 –

Dataframe...
        Car    Cubic_Capacity    Reg_Price    Units_Sold
0      BMW              2000          7000          100.0
1     Lexus             1800          1500           NaN
2     Tesla             1500          5000          150.0
3   Mustang             2500          8000           NaN
4  Mercedes             2200          9000          200.0
5    Jaguar             3000          6000           NaN

计算“Units_Sold”列中NaN值的数量...
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