---
metadata:
  - name: generator
    content: Diplodoc Platform v5.52.0
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_datasets_msrank_10k.md
  - href: en/concepts/python-reference_datasets_msrank_10k.md
    type: text/markdown
    title: Markdown version
  - href: ../llms.txt
    type: text/markdown
    title: llms.txt
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# msrank_10k

<!-- source: en/_includes/work_src/reusage-python/datasets__msrank_10k.md -->
Load a smaller version of the [Microsoft Learning to Rank Dataset](https://www.microsoft.com/en-us/research/project/mslr/). This dataset is a shrunk version of the [msrank](https://catboost.ai/docs/en/concepts/python-reference_datasets_msrank.md) dataset.
<!-- endsource: en/_includes/work_src/reusage-python/datasets__msrank_10k.md -->


The training dataset contains 10000 objects. Each object is described by 138 columns. The first column contains the label value, the second one contains the identifier of the object's group (`GroupId`). All other columns contain features.

The validation dataset contains 10000 objects. The structure is identical to the training dataset.

## Method call format {#method-call}

```python
msrank_10k()
```

## Type of return value {#type-of-return-value}

<!-- source: en/_includes/work_src/reusage-python/datasets__output.md -->
A two pandas.DataFrame tuple (for train and validation datasets).
<!-- endsource: en/_includes/work_src/reusage-python/datasets__output.md -->


## Usage examples {#usage-examples}

```
from catboost.datasets import msrank_10k
msrank_10k_train, msrank_10k_test = msrank_10k()

print(msrank_10k_train.head(3))

```

<!-- source: en/_includes/work_src/reusage-common-phrases/example-output.md -->
The output of this example:
<!-- endsource: en/_includes/work_src/reusage-common-phrases/example-output.md -->


```bash
   0      1    2    3    4    5    6    7    8         9   10   11   12   13   14   ...       123        124        125        126  127  128       129  130  131    132  133  134  135  136  137
0  2.0    1    3    3    0    0    3  1.0  1.0  0.000000  0.0  1.0  156    4    0  ...  -4.474452 -23.634899 -28.119826 -13.581932    3   62  11089534    2  116  64034   13    3    0    0  0.0
1  2.0    1    3    0    3    0    3  1.0  0.0  1.000000  0.0  1.0  406    0    5  ... -24.041386  -5.143860 -28.119826 -11.411068    2   54  11089534    2  124  64034    1    2    0    0  0.0
2  0.0    1    3    0    2    0    3  1.0  0.0  0.666667  0.0  1.0  146    0    3  ... -24.041386 -14.689844 -28.119826 -11.436378    3   45         3    1  124   3344   14   67    0    0  0.0

[3 rows x 138 columns]

```