---
metadata:
  - name: generator
    content: Diplodoc Platform v5.52.0
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_datasets_monotonic2.md
  - href: en/concepts/python-reference_datasets_monotonic2.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

# monotonic2

<!-- source: en/_includes/work_src/reusage-python/datasets__monotonic2.md -->
Load the Yandex dataset with monotonic constraints. This dataset does not contain categorical features.
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This dataset can be used for regression.

The contents of columns depends on the name or on the pattern of the name of the corresponding column:

- `Target`(the first column) — Target values.

- `MonotonicNeg*` — Monotonic negative numerical features.

    If values of such features decrease, then the prediction value must not decrease. Thus, if there are two objects $x_{1}$ and $x_{2}$ with all features being equal except for a monotonic negative feature $MNeg$, such that $x_{1}[MNeg] > x_{2}[MNeg]$, then the following inequality must be met for predictions:

    $f(x_{1}) \leq f(x_{2})$

- `MonotonicPos*` — Monotonic positive numerical features.

    If values of such features decrease, then the prediction value must not increase. Thus, if there are two objects $x_{1}$ and $x_{2}$ with all features being equal except for a monotonic positive feature $MPos$, such that $x_{1}[MPos] > x_{2}[MPos]$, then the following inequality must be met for predictions:

    $f(x_{1}) \geq f(x_{2})$


## Method call format {#method-call}

```python
monotonic2()
```

## 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).
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## Usage examples {#usage-examples}

```python
from catboost.datasets import monotonic2
monotonic2_train, monotonic2_test = monotonic2()

print(monotonic2_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 -->


```no-highlight
   Target  MonotonicNeg0  MonotonicPos0  MonotonicPos1  MonotonicNeg1
0     0.0            NaN            NaN       0.010356       0.032638
1     0.0            NaN            NaN       0.010356       0.032638
```