README.md

---
language:
- multilingual
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- structure-prediction
- object-detection
- text-mining
- information-retrieval
- other
task_ids:
- other
pretty_name: Mario Maker 2 user likes
---

# Mario Maker 2 user likes
Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)

## Dataset Description
The Mario Maker 2 user likes dataset consists of 105.5 million user likes from Nintendo's online service totaling around 630MB of data. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022.

### How to use it
The Mario Maker 2 user likes dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following code:

```python
from datasets import load_dataset

ds = load_dataset("TheGreatRambler/mm2_user_liked", streaming=True, split="train")
print(next(iter(ds)))

#OUTPUT:
{
 'pid': '14510618610706594411',
 'data_id': 25861713
}
```
Each row is a unique like in the level denoted by the `data_id` done by the player denoted by the `pid`.

You can also download the full dataset. Note that this will download ~630MB:
```python
ds = load_dataset("TheGreatRambler/mm2_user_liked", split="train")
```

## Data Structure

### Data Instances

```python
{
 'pid': '14510618610706594411',
 'data_id': 25861713
}
```

### Data Fields

|Field|Type|Description|
|---|---|---|
|pid|string|The player ID of this user, an unsigned 64 bit integer as a string|
|data_id|int|The data ID of the level this user liked|

### Data Splits

The dataset only contains a train split.

<!-- TODO create detailed statistics -->
<!--
## Dataset Statistics

The dataset contains 115M files and the sum of all the source code file sizes is 873 GB (note that the size of the dataset is larger due to the extra fields). A breakdown per language is given in the plot and table below:

![dataset-statistics](https://huggingface.co/datasets/codeparrot/github-code/resolve/main/github-code-stats-alpha.png)

|    | Language     |File Count| Size (GB)|
|---:|:-------------|---------:|-------:|
|  0 | Java         | 19548190 | 107.70 |
|  1 | C            | 14143113 | 183.83 |
|  2 | JavaScript   | 11839883 |  87.82 |
|  3 | HTML         | 11178557 | 118.12 |
|  4 | PHP          | 11177610 |  61.41 |
|  5 | Markdown     |  8464626 |  23.09 |
|  6 | C++          |  7380520 |  87.73 |
|  7 | Python       |  7226626 |  52.03 |
|  8 | C#           |  6811652 |  36.83 |
|  9 | Ruby         |  4473331 |  10.95 |
| 10 | GO           |  2265436 |  19.28 |
| 11 | TypeScript   |  1940406 |  24.59 |
| 12 | CSS          |  1734406 |  22.67 |
| 13 | Shell        |  1385648 |   3.01 |
| 14 | Scala        |   835755 |   3.87 |
| 15 | Makefile     |   679430 |   2.92 |
| 16 | SQL          |   656671 |   5.67 |
| 17 | Lua          |   578554 |   2.81 |
| 18 | Perl         |   497949 |   4.70 |
| 19 | Dockerfile   |   366505 |   0.71 |
| 20 | Haskell      |   340623 |   1.85 |
| 21 | Rust         |   322431 |   2.68 |
| 22 | TeX          |   251015 |   2.15 |
| 23 | Batchfile    |   236945 |   0.70 |
| 24 | CMake        |   175282 |   0.54 |
| 25 | Visual Basic |   155652 |   1.91 |
| 26 | FORTRAN      |   142038 |   1.62 |
| 27 | PowerShell   |   136846 |   0.69 |
| 28 | Assembly     |    82905 |   0.78 |
| 29 | Julia        |    58317 |   0.29 |
-->

## Dataset Creation

The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.

## Considerations for Using the Data

The dataset contains no harmful language or depictions.