Question

我有一个包含3列的csv文件。用户，文本和标签。每个用户都有多个文本和标签。我想知道出现频率最高的标签，以确定每个用户的类别。

我试过了：

static

，它为所有用户返回相同的值

    public class AlphabetFacade
    {
        private Dictionary<int, AlphabetFactory> _alphabetFactories = new Dictionary<int, AlphabetFactory>();

        private Character GetChar(string character, int fontSize)
        {
            if (_alphabetFactories.ContainsKey(fontSize)) return _alphabetFactories[fontSize].GetCharacter(character);
            var factoryNew = new AlphabetFactory(fontSize);
            _alphabetFactories.Add(fontSize, factoryNew);
            return factoryNew.GetCharacter(character);
        }
    }

    internal class AlphabetFactory
    {
        private readonly int _fontSize;

        private readonly Dictionary<string, Character> _characters = new Dictionary<string, Character>();

        public AlphabetFactory(int fontSize)
        {
            _fontSize = fontSize;
        }

        public Character GetCharacter(string character)
        {
            if (_characters.ContainsKey(character)) return _characters[character];
            var characterNew = new Character(_fontSize, character);
            _characters.Add(character, characterNew);
            return characterNew;
        }
    }

     class Character
     {
         public Character(int size, string @char)
         {
            Size = size;
            Char = @char;
         }

         public int Size { get; }
         public string Char { get; }
     }

我也试过

for i in df['user'].unique():
    print (df['class'].value_counts())

得到了

4    3062
1    1250
0     393
3     281
2      13
Name: class, dtype: int64

这是示例数据 sample data 请帮忙

Answer 1

要按组计算值，您可以将groupby与pd.value_counts：

一起使用

df = pd.DataFrame([[1, 1], [1, 2], [1, 3], [1, 1], [1, 1], [1, 2],
                   [2, 1], [2, 3], [2, 2], [2, 2], [2, 3], [2, 3]],
                  columns=['user', 'class'])

res = df.groupby('user')['class'].apply(pd.value_counts).reset_index()
res.columns = ['user', 'class', 'count']

print(res)

   user  class   count
0     1      1       3
1     1      2       2
2     1      3       1
3     2      3       3
4     2      2       2
5     2      1       1

计算pandas dataframe对象中每个唯一实体的值

1 个答案: