The dataset viewer 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 nyu-mll/glue
config
: the subset name, for example cola
split
: the split name, for example train
Let’s get some stats for nyu-mll/glue
dataset, cola
subset, train
split:
import requests
headers = {"Authorization": f"Bearer {API_TOKEN}"}
API_URL = "https://datasets-server.huggingface.co/statistics?dataset=nyu-mll/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 detailspartial
- 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
}
Currently, statistics are supported for strings, float and integer numbers, lists, audio and image data 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 which represents categorical datafloat
- for float data typesint
- for integer data typesbool
- for boolean data typestring_label
- for string data types being treated as categories (see below)string_text
- for string data types if they do not represent categories (see below)list
- for lists of any other data types (including lists)audio
- for audio dataimage
- for image dataThis type represents categorical data encoded as ClassLabel
feature. The following measures are computed:
null
valuesnull
and no label
)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
}
}
}
The following measures are returned for float data types:
null
and NaN
values (NaN
values are treated as null
){
"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
]
}
}
}
The following measures are returned for integer data types:
null
values{
"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
]
}
}
}
The following measures are returned for bool data type:
null
values'True'
and 'False'
values{
"column_name": "penalty",
"column_type": "bool",
"column_statistics":
{
"nan_count": 3,
"nan_proportion": 0.15,
"frequencies": {
"False": 7,
"True": 10
}
}
}
If the proportion of unique values in a string column within requested split is lower than or equal to 0.2 and the number of unique values is lower than 1000, or if the number of unique values is lower or equal to 10 (independently of the proportion), it is considered to be a category. The following measures are returned:
null
valuesnull
)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
}
}
}
If string column does not satisfy the conditions to be treated as a string_label
, it is considered to be a column containing texts and response contains statistics over text lengths. The following measures are computed:
null
values{
"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
]
}
}
}
For lists, the distribution of their lengths is computed. The following measures are returned:
null
values{
"column_name": "chat_history",
"column_type": "list",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"min": 1,
"max": 3,
"mean": 1.01741,
"median": 1.0,
"std": 0.13146,
"histogram": {
"hist": [
11177,
196,
1
],
"bin_edges": [
1,
2,
3,
3
]
}
}
}
Note that dictionaries of lists are not supported.
For audio data, the distribution of audio files durations is computed. The following measures are returned:
null
values{
"column_name": "audio",
"column_type": "audio",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0,
"min": 1.02,
"max": 15,
"mean": 13.93042,
"median": 14.77,
"std": 2.63734,
"histogram": {
"hist": [
32,
25,
18,
24,
22,
17,
18,
19,
55,
1770
],
"bin_edges": [
1.02,
2.418,
3.816,
5.214,
6.612,
8.01,
9.408,
10.806,
12.204,
13.602,
15
]
}
}
}
For image data, the distribution of images widths is computed. The following measures are returned:
null
values{
"column_name": "image",
"column_type": "image",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"min": 256,
"max": 873,
"mean": 327.99339,
"median": 341.0,
"std": 60.07286,
"histogram": {
"hist": [
1734,
1637,
1326,
121,
10,
3,
1,
3,
1,
2
],
"bin_edges": [
256,
318,
380,
442,
504,
566,
628,
690,
752,
814,
873
]
}
}
}