Question

我有一个字符串，如下所示：

public class Geometria
{
    public int Id { get; set; }

    public string Componente { get; set; }

    [Required]
    [Range(0, float.MaxValue)]
    [Display(Name = "Tolerância Inferior")]
    public float ToleranciaInferior { get; set; }

    [Required]
    [Range(0, float.MaxValue)]
    [ToleranciaLessOrEqualThan("ToleranciaInferior", ErrorMessage = "Tolerância Superior tem de ser maior que a Tolerância Inferior")]
    [Display(Name = "Tolerância Superior")]
    public float ToleranciaSuperior { get; set; }

}

public class ToleranciaLessOrEqualThanAttribute : ValidationAttribute, IClientModelValidator
{
    private readonly string _comparisonProperty;

    public ToleranciaLessOrEqualThanAttribute(string comparisonProperty)
    {
        _comparisonProperty = comparisonProperty;
    }

    protected override ValidationResult IsValid(object value, ValidationContext validationContext)
    {
        ErrorMessage = ErrorMessageString;
        var currentValue = (float)value;

        var property = validationContext.ObjectType.GetProperty(_comparisonProperty);

        if (property == null)
            throw new ArgumentException("Property with this name not found");

        var comparisonValue = (float)property.GetValue(validationContext.ObjectInstance);

        if (currentValue <= comparisonValue)
            return new ValidationResult(ErrorMessage);

        return ValidationResult.Success;
    }

    public void AddValidation(ClientModelValidationContext context)
    {
        if (context == null)
        {
            throw new ArgumentNullException(nameof(context));
        }

        var error = FormatErrorMessage(context.ModelMetadata.GetDisplayName());
        context.Attributes.Add("data-val", "true");
        context.Attributes.Add("data-val-tolerancialessorequalthan", error);
    }
}

将其转换为具有以下格式的熊猫数据框的最简单方法是什么？我正在寻找创建具有5列的数据框，第一列的标题可以用“实体”填充。第一列包含实体的名称。

Answer 1

您可以尝试以下方法：

import pandas as pd
s1 = "entity precision recall f1-score support B-EXPERIENCE 0.578 0.488 0.529 244 I-EXPERIENCE 0.648 0.799 0.716 399 L-EXPERIENCE 0.850 0.697 0.766 244 U-EXPERIENCE 0.000 0.000 0.000 9 B-LANGUAGE 0.000 0.000 0.000 1 I-LANGUAGE 0.000 0.000 0.000 1 L-LANGUAGE 0.000 0.000 0.000 1 U-LANGUAGE 0.788 0.904 0.842 292 B-PROGRAMMING 0.480 0.433 0.455 141 I-PROGRAMMING 0.524 0.328 0.404 67 L-PROGRAMMING 0.261 0.255 0.258 141 U-PROGRAMMING 0.904 0.825 0.862 2010 micro_avg 0.785 0.746 0.765 3550 macro_avg 0.419 0.394 0.403 3550 weighted_avg 0.787 0.746 0.763 3550"

s = pd.Series(s1.split(' '))
df = pd.DataFrame(s[5:].to_numpy().reshape(-1,5), columns=s[:5])

输出：

           entity precision recall f1-score support
0    B-EXPERIENCE     0.578  0.488    0.529     244
1    I-EXPERIENCE     0.648  0.799    0.716     399
2    L-EXPERIENCE     0.850  0.697    0.766     244
3    U-EXPERIENCE     0.000  0.000    0.000       9
4      B-LANGUAGE     0.000  0.000    0.000       1
5      I-LANGUAGE     0.000  0.000    0.000       1
6      L-LANGUAGE     0.000  0.000    0.000       1
7      U-LANGUAGE     0.788  0.904    0.842     292
8   B-PROGRAMMING     0.480  0.433    0.455     141
9   I-PROGRAMMING     0.524  0.328    0.404      67
10  L-PROGRAMMING     0.261  0.255    0.258     141
11  U-PROGRAMMING     0.904  0.825    0.862    2010
12      micro_avg     0.785  0.746    0.765    3550
13      macro_avg     0.419  0.394    0.403    3550
14   weighted_avg     0.787  0.746    0.763    3550

详细信息：

使用split使用空格作为定界符来分割字符串，因此要求更改列标题的命名以从列标题中删除空格。

使用构造函数创建pd.Series，然后使用构造函数和索引切片创建pd.DataFrame。 to_numpy创建一个numpy数组，然后reshape使用-1表示行数，5表示列数。

Answer 2

我将使用numpy重塑：

data = np.array(string.split())
data = data.reshape(len(data)//5, 5)
df = pd.DataFrame(data[1:], columns=data[0]).set_index('entity').rename_axis('')
print(df)

给予：

              precision recall f1-score support

B-EXPERIENCE      0.578  0.488    0.529     244
I-EXPERIENCE      0.648  0.799    0.716     399
L-EXPERIENCE      0.850  0.697    0.766     244
U-EXPERIENCE      0.000  0.000    0.000       9
B-LANGUAGE        0.000  0.000    0.000       1
I-LANGUAGE        0.000  0.000    0.000       1
L-LANGUAGE        0.000  0.000    0.000       1
U-LANGUAGE        0.788  0.904    0.842     292
B-PROGRAMMING     0.480  0.433    0.455     141
I-PROGRAMMING     0.524  0.328    0.404      67
L-PROGRAMMING     0.261  0.255    0.258     141
U-PROGRAMMING     0.904  0.825    0.862    2010
micro_avg         0.785  0.746    0.765    3550
macro_avg         0.419  0.394    0.403    3550
weighted_avg      0.787  0.746    0.763    3550

Answer 3

如果您要调整最后三个条目中的字符串并删除空格（例如，用短划线代替），则以下代码将起作用，并且还可以扩展到更多行：

my_list = string.split(' ') # split the string along the whitespaces

my_dict = {}
num_cols = 5
# convert the string to a dictionary with appropriate keys
for i in range(0,num_cols):
    my_dict.update({my_list[i]:my_list[num_cols+i::num_cols]})

# Convert dict to pandas DataFrame
df = pd.DataFrame(my_dict)

>> pd.DataFrame(df)
           entity precision recall f1-score support
0    B-EXPERIENCE     0.578  0.488    0.529     244
1    I-EXPERIENCE     0.648  0.799    0.716     399
2    L-EXPERIENCE     0.850  0.697    0.766     244
3    U-EXPERIENCE     0.000  0.000    0.000       9
4      B-LANGUAGE     0.000  0.000    0.000       1
5      I-LANGUAGE     0.000  0.000    0.000       1
6      L-LANGUAGE     0.000  0.000    0.000       1
7      U-LANGUAGE     0.788  0.904    0.842     292
8   B-PROGRAMMING     0.480  0.433    0.455     141
9   I-PROGRAMMING     0.524  0.328    0.404      67
10  L-PROGRAMMING     0.261  0.255    0.258     141
11  U-PROGRAMMING     0.904  0.825    0.862    2010
12      micro-avg     0.785  0.746    0.765    3550
13      macro-avg     0.419  0.394    0.403    3550
14   weighted-avg     0.787  0.746    0.763    3550

Answer 4

另一种方法是用yield将字符串分成5个均匀的列表，这会返回到上一次迭代时剩下的状态：

cols = string.split()[:5]
vals = string.split()[5:]

# Define function to make evenly chunks of your words
def divide_chunks(l, n): 

    for i in range(0, len(l), n):  
        yield l[i:i + n]

现在我们可以定义数据框：

df = pd.DataFrame(list(divide_chunks(vals, 5)), columns=cols)

输出：

           entity precision recall f1-score support
0    B-EXPERIENCE     0.578  0.488    0.529     244
1    I-EXPERIENCE     0.648  0.799    0.716     399
2    L-EXPERIENCE     0.850  0.697    0.766     244
3    U-EXPERIENCE     0.000  0.000    0.000       9
4      B-LANGUAGE     0.000  0.000    0.000       1
5      I-LANGUAGE     0.000  0.000    0.000       1
6      L-LANGUAGE     0.000  0.000    0.000       1
7      U-LANGUAGE     0.788  0.904    0.842     292
8   B-PROGRAMMING     0.480  0.433    0.455     141
9   I-PROGRAMMING     0.524  0.328    0.404      67
10  L-PROGRAMMING     0.261  0.255    0.258     141
11  U-PROGRAMMING     0.904  0.825    0.862    2010
12      micro_avg     0.785  0.746    0.765    3550
13      macro_avg     0.419  0.394    0.403    3550
14   weighted_avg     0.787  0.746    0.763    3550

如何将字符串转换为数据框，并指定列数？

4 个答案: