Explore statistics over split data

Datasets Server provides a /statistics endpoint for fetching some basic statistics precomputed for a requested dataset. This will get you a quick insight on how the data is distributed.

The /statistics endpoint requires three query parameters:

• dataset: the dataset name, for example glue
• config: the configuration name, for example cola
• split: the split name, for example train

Let’s get some stats for glue dataset, cola config, train split:

import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://datasets-server.huggingface.co/statistics?dataset=glue&config=cola&split=train"
def query():
    response = requests.get(API_URL, headers=headers)
    return response.json()
data = query()

The response JSON contains three keys:

• num_examples - number of samples in a split or number of samples in the first chunk of data if dataset is larger than 5GB (see partial field below).
• statistics - list of dictionaries of statistics per each column, each dictionary has three keys: column_name, column_type, and column_statistics. Content of column_statistics depends on a column type, see Response structure by data types for more details
• partial - true if statistics are computed on the first 5 GB of data, not on the full split, false otherwise.

{
  "num_examples": 8551,
  "statistics": [
    {
      "column_name": "idx",
      "column_type": "int",
      "column_statistics": {
        "nan_count": 0,
        "nan_proportion": 0,
        "min": 0,
        "max": 8550,
        "mean": 4275,
        "median": 4275,
        "std": 2468.60541,
        "histogram": {
          "hist": [
            856,
            856,
            856,
            856,
            856,
            856,
            856,
            856,
            856,
            847
          ],
          "bin_edges": [
            0,
            856,
            1712,
            2568,
            3424,
            4280,
            5136,
            5992,
            6848,
            7704,
            8550
          ]
        }
      }
    },
    {
      "column_name": "label",
      "column_type": "class_label",
      "column_statistics": {
        "nan_count": 0,
        "nan_proportion": 0,
        "no_label_count": 0,
        "no_label_proportion": 0,
        "n_unique": 2,
        "frequencies": {
          "unacceptable": 2528,
          "acceptable": 6023
        }
      }
    },
    {
      "column_name": "sentence",
      "column_type": "string_text",
      "column_statistics": {
        "nan_count": 0,
        "nan_proportion": 0,
        "min": 6,
        "max": 231,
        "mean": 40.70074,
        "median": 37,
        "std": 19.14431,
        "histogram": {
          "hist": [
            2260,
            4512,
            1262,
            380,
            102,
            26,
            6,
            1,
            1,
            1
          ],
          "bin_edges": [
            6,
            29,
            52,
            75,
            98,
            121,
            144,
            167,
            190,
            213,
            231
          ]
        }
      }
    }
  ],
  "partial": false
}

Response structure by data type

Currently, statistics are supported for strings, float and integer numbers, and the special datasets.ClassLabel feature type of the datasets library.

column_type in response can be one of the following values:

• class_label - for datasets.ClassLabel feature
• float - for float dtypes
• int - for integer dtypes
• bool - for boolean dtype
• string_label - for string dtypes, if there are less than or equal to 30 unique values in a string column in a given split
• string_text - for string dtypes, if there are more than 30 unique values in a string column in a given split

class_label

This type represents categorical data encoded as ClassLabel feature. The following measures are computed:

• number and proportion of null values
• number and proportion of values with no label
• number of unique values (excluding null and no label)
• value counts for each label (excluding null and no label)

{
  "column_name": "label",
  "column_type": "class_label",
  "column_statistics": {
    "nan_count": 0,
    "nan_proportion": 0,
    "no_label_count": 0,
    "no_label_proportion": 0,
    "n_unique": 2,
    "frequencies": {
      "unacceptable": 2528,
      "acceptable": 6023
    }
  }
}

float

The following measures are returned for float data types:

• minimum, maximum, mean, and standard deviation values
• number and proportion of null values
• histogram with 10 bins

{
  "column_name": "clarity",
  "column_type": "float",
  "column_statistics": {
    "nan_count": 0,
    "nan_proportion": 0,
    "min": 0,
    "max": 2,
    "mean": 1.67206,
    "median": 1.8,
    "std": 0.38714,
    "histogram": {
      "hist": [
        17,
        12,
        48,
        52,
        135,
        188,
        814,
        15,
        1628,
        2048
      ],
      "bin_edges": [
        0,
        0.2,
        0.4,
        0.6,
        0.8,
        1,
        1.2,
        1.4,
        1.6,
        1.8,
        2
      ]
    }
  }
}

int

The following measures are returned for integer data types:

• minimum, maximum, mean, and standard deviation values
• number and proportion of null values
• histogram with less than or equal to 10 bins

{
    "column_name": "direction",
    "column_type": "int",
    "column_statistics": {
        "nan_count": 0,
        "nan_proportion": 0.0,
        "min": 0,
        "max": 1,
        "mean": 0.49925,
        "median": 0.0,
        "std": 0.5,
        "histogram": {
            "hist": [
                50075,
                49925
            ],
            "bin_edges": [
                0,
                1,
                1
            ]
        }
    }
}

bool

The following measures are returned for bool data type:

• number and proportion of null values
• value counts for 'True' and 'False' values

{
  "column_name": "penalty",
  "column_type": "bool",
  "column_statistics":
    {
        "nan_count": 3,
        "nan_proportion": 0.15,
        "frequencies": {
            "False": 7,
            "True": 10
        }
    }
}

string_label

If string column has less than or equal to 30 unique values within the requested split, it is considered to be a category. The following measures are returned:

• number and proportion of null values
• number of unique values (excluding null)
• value counts for each label (excluding null)

{
  "column_name": "answerKey",
  "column_type": "string_label",
  "column_statistics": {
    "nan_count": 0,
    "nan_proportion": 0,
    "n_unique": 4,
    "frequencies": {
      "D": 1221,
      "C": 1146,
      "A": 1378,
      "B": 1212
    }
  }
}

string_text

If string column has more than 30 unique values within the requested split, it is considered to be a column containing texts and response contains statistics over text lengths. The following measures are computed:

• minimum, maximum, mean, and standard deviation of text lengths
• number and proportion of null values
• histogram of text lengths with 10 bins

{
  "column_name": "sentence",
  "column_type": "string_text",
  "column_statistics": {
    "nan_count": 0,
    "nan_proportion": 0,
    "min": 6,
    "max": 231,
    "mean": 40.70074,
    "median": 37,
    "std": 19.14431,
    "histogram": {
      "hist": [
        2260,
        4512,
        1262,
        380,
        102,
        26,
        6,
        1,
        1,
        1
      ],
      "bin_edges": [
        6,
        29,
        52,
        75,
        98,
        121,
        144,
        167,
        190,
        213,
        231
      ]
    }
  }
}