Python Pandas - 用常量值填充缺失的列值 (NaN)
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热门推荐
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