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Python Pandas - 用常量值填充缺失的列值 (NaN)

2023-06-03 00:59:03 518

使用该fillna()方法并使用参数value在其中为所有缺失值设置一个常量值。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

创建一个包含2列的DataFrame。我们已经使用Numpynp.NaN设置了NaN值-

dataFrame = pd.DataFrame(
   {
      "Car": ['BMW', 'Lexus', 'Lexus', 'Mustang', 'Bentley', 'Mustang'],"Units": [100, 150, np.NaN, 80, np.NaN, np.NaN]
   }
)

使用NaN为列值放置一个常量值，即此处的Units列-

constVal = 200

将NaN替换为常量值，即200-

dataFrame['Units'].fillna(value=constVal, 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

#placingaconstantvalueforthecolumnvalueswithNaNi.e,forUnitscolumnshere
constVal = 200

#ReplaceNaNswiththeconstantvaluei.e200
dataFrame['Units'].fillna(value=constVal, inplace=True)
print"\nUpdated Dataframe after filling NaN values with constant values...\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 constant values...
       Car   Units
0      BMW   100.0
1    Lexus   150.0
2    Lexus   200.0
3  Mustang    80.0
4  Bentley   200.0
5  Mustang   200.0

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Python Pandas - 用常量值填充缺失的列值 (NaN)

示例

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