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 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, 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
featurefloat
- for float dtypesint
- for integer dtypesbool
- for boolean dtypestring_label
- for string dtypes, if there are less than or equal to 30 unique values in a string column in a given splitstring_text
- for string dtypes, if there are more than 30 unique values in a string column in a given splitThis 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
values{
"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 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:
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 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:
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
]
}
}
}