Python - 如何计算 Pandas 数据框中列中的 NaN 出现次数？
要计算列中出现NaN的次数，请使用isna().使用sum()来添加值并找到计数。
首先，让我们使用各自的别名导入所需的库-
import pandas as pd import numpy as np
创建一个数据框。我们已经使用np.inf“Units_Sold”列中的Numpy设置了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()
示例
以下是代码-
import pandas as pd
import numpy as np
#创建数据框
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("\nCount of NaN values in column Units_Sold...\n",count)输出结果这将产生以下输出-
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
Count of NaN values in column Units_Sold...
3