将列转换为int并强制NaN

Question

给定如下的数据帧

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colNames = ["Time","Col2","Col3","Col4","Col5","Col6","Col7","Col8","Col9","Col10","Col11","Col12","Col13"]
df = pd.DataFrame(colVals, columns=colNames)
df = df.set_index('Time')
df = df.apply(pd.to_numeric, errors='coerce')


    print (df.dtypes)
    Col2     float64
    Col3     float64
    Col4     float64
    Col5     float64
    Col6     float64
    Col7     float64
    Col8     float64
    Col9     float64
    Col10    float64
    Col11    float64
    Col12    float64
    Col13    float64
    dtype: object

正如您在这里可能注意到的，所有列都已转换为float类型。有没有办法将某些列转换为int？你知道吗

尝试了以下操作

type_dict = {'Col2': int}
df.astype(dtype_dict)

这将抛出错误ValueError: Cannot convert non-finite values (NA or inf) to integer

阅读Nullable integer data type并尝试

dtype_dict = {'Col2': 'Int64'}
df.astype(dtype_dict)

这会引发错误ValueError: Cannot convert non-finite values (NA or inf) to integer

Answer 1

在将行转换为int之前，必须用0填充na

你可以这样做

df[["Col2","Col3","Col4","Col5","Col6"]] = df[["Col2","Col3","Col4","Col5","Col6"]].fillna(0).astype(np.int32, copy=False)

display(df)