python - Pandas - 在 pandas.DataFrame() 中强制使用具有大量缺失值的数据的自动数据类型-6ren

python - Pandas - 在 pandas.DataFrame() 中强制使用具有大量缺失值的数据的自动数据类型

转载作者：行者123 更新时间：2023-12-01 07:15:11

当从如下列表的列表创建数据框时，所有值都获取类型object - 尽管大多数都是存储为字符串的int/floats。有没有办法强制执行数值的正确类型(int/float)？

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['05:17:56.008', '', '', '28', '', '10', '22', '3', '3', '9', '922', '1', '3'], ['05:17:56.008', '', '', '', '', '', '22', '', '3', '73', '922', '2', ''], ['05:17:56.008', '', '', '', '', '', '17', '', '3', '121', '922', '5', ''], ['05:17:56.008', '', '', '', '', '', '17', '', '3', '185', '922', '6', ''], ['05:17:56.011', '', '', '', '', '', '22', '', '3', '217', '922', '7', ''], ['05:17:56.011', '', '', '', '', '', '22', '', '3', '121', '922', '11', ''], ['05:17:56.011', '', '', '', '', '', '22', '', '3', '89', '922', '12', ''], ['05:17:56.020', '', '', '', '', '', '17', '', '3', '105', '923', '5', ''], ['05:17:56.020', '', '', '', '', '', '17', '', '3', '185', '923', '6', ''], ['05:17:56.020', '', '', '', '', '', '22', '', '3', '25', '923', '7', ''], ['05:17:56.020', '', '', '', '', '', '31', '', '3', '112', '923', '10', ''], ['05:17:56.023', '', '', '', '', '', '31', '', '3', '112', '923', '11', ''], ['05:17:56.023', '', '', '', '', '', '22', '', '3', '217', '923', '12', ''], ['05:17:56.023', '', '', '', '', '', '22', '', '3', '217', '923', '13', ''], ['05:17:56.023', '', '', '', '', '', '22', '', '3', '217', '923', '15', ''], ['05:17:56.023', '', '', '', '', '', '22', '', '3', '217', '923', '16', ''], ['05:17:56.023', '', '', '', '', '', '22', '', '3', '217', '923', '17', ''], ['05:17:56.023', '', '', '', '', '', '22', '', '3', '217', '923', '18', ''], ['05:17:56.027', '', '', '', '', '', '18', '', '3', '57', '924', '0', ''], ['05:17:56.027', '', '', '', '', '', '22', '', '3', '41', '924', '1', ''], ['05:17:56.027', '', '', '', '', '', '22', '', '3', '185', '924', '2', ''], ['05:17:56.027', '', '', '', '', '', '22', '', '3', '185', '924', '3', ''], ['05:17:56.027', '', '', '', '', '', '17', '', '3', '73', '924', '5', ''], ['05:17:56.027', '', '', '', '', '', '17', '', '3', '217', '924', '6', ''], ['05:17:56.028', '268', '4', '28', '-67.68', '13', '', '2', '', '', '', '', '2'], ['05:17:56.039', '', '', '', '', '', '22', '', '3', '217', '925', '3', ''], ['05:17:56.039', '', '', '', '', '', '17', '', '3', '217', '925', '5', ''], ['05:17:56.039', '', '', '', '', '', '17', '', '3', '217', '925', '6', ''], ['05:17:56.039', '', '', '', '', '', '22', '', '3', '217', '925', '7', ''], ['05:17:56.039', '', '', '', '', '', '18', '', '3', '57', '925', '10', ''], ['05:17:56.043', '', '', '', '', '', '22', '', '3', '73', '925', '16', '']]
colNames = ["Time","Col2","Col3","Col4","Col5","Col6","Col7","Col8","Col9","Col10","Col11","Col12","Col13"]
df = pd.DataFrame(colVals, columns=colNames)
df.dtypes
Out[88]: 
Time     object
Col2     object
Col3     object
Col4     object
Col5     object
Col6     object
Col7     object
Col8     object
Col9     object
Col10    object
Col11    object
Col12    object
Col13    object
dtype: object

最佳答案

您可以通过 DataFrame.set_index 将 Time 列转换为索引，然后如果可能的话转换为 timedeltas 并将所有列转换为数字 to_numeric使用 errors='coerce' 将非数值转换为 NaN:

df = df.set_index('Time')
df.index = pd.to_timedelta(df.index)
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

如果还需要区分整数和 float ，可以使用 try- except 创建自定义函数:

colVals = [['05:17:55.703', '', '', '', '', '', '21', '', '3', '89', '891', '11', ''], 
           ['05:17:55.703', '', '', '', '', '', '21', '', '3', '217', '891', '12', ''], 
           ['05:17:55.703', '', '', '', '', '', '21', '', '3', '217', '891', '13', '']]
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.index = pd.to_timedelta(df.index)

def func(x):
    try:
        return x.astype(float).astype(int)
    except:
        return pd.to_numeric(x, errors='coerce')

df = df.apply(func)
print (df.dtypes)
Col2     float64
Col3     float64
Col4     float64
Col5     float64
Col6     float64
Col7       int32
Col8     float64
Col9       int32
Col10      int32
Col11      int32
Col12      int32
Col13    float64
dtype: object

关于python - Pandas - 在 pandas.DataFrame() 中强制使用具有大量缺失值的数据的自动数据类型，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58012458/

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python - Pandas - 在 pandas.DataFrame() 中强制使用具有大量缺失值的数据的自动数据类型