Python - 如何在 Pandas 中用均值填充 NAN 值？
对于均值，请使用该mean()函数。用NaN计算列的平均值，并使用fillna()用平均值填充NaN值。
让我们首先导入所需的库-
import pandas as pd import numpy as np
创建一个包含2列和一些NaN值的DataFrame。我们已经使用numpy输入了这些NaN值np.NaN-
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'],"Units": [100, 150, np.NaN, 80, np.NaN, np.NaN]
}
)使用NaN查找列值的平均值，即此处的Units列。因此，Units列有100、150和80；因此，平均值将为110-
meanVal = dataFrame['Units'].mean()
将NaN替换为其所在列的平均值。上面计算的平均值是110，所以NaN值将替换为110-
dataFrame['Units'].fillna(value=meanVal, inplace=True)
示例
以下是代码-
import pandas as pd
import numpy as np
#CreateDataFrame
dataFrame = pd.DataFrame(
{
"Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'],"Units": [100, 150, np.NaN, 80, np.NaN, np.NaN]
}
)
print"DataFrame ...\n",dataFrame
#findingmeanofthecolumnvalueswithNaNi.e,forUnitscolumnshere
#sotheUnitscolumnhas100,150and80;thereforethemeanwouldne110
meanVal = dataFrame['Units'].mean()
#ReplaceNaNswiththemeanofthecolumnwhereitislocated
#themeancalculatedaboveis110,soNaNvalueswillbereplacedwith110
dataFrame['Units'].fillna(value=meanVal, inplace=True)
print"\nUpdated Dataframe after filling NaN values with mean...\n",dataFrame输出结果这将产生以下输出-
DataFrame ...
Car Units
0 BMW 100.0
1 Lexus 150.0
2 Lexus NaN
3 Mustang 80.0
4 Bentley NaN
5 Mustang NaN
Updated Dataframe after filling NaN values with mean...
Car Units
0 BMW 100.0
1 Lexus 150.0
2 Lexus 110.0
3 Mustang 80.0
4 Bentley 110.0
5 Mustang 110.0