---
license: mit
task_categories:
- time-series-forecasting
language:
- en
size_categories:
- 100K<n<1M
---


# Timeseries Data Processing

This repository contains a script for loading and processing timeseries data using the `datasets` library and converting it to a pandas DataFrame for further analysis.

## Dataset

The dataset used in this example is `Weijie1996/load_timeseries`, which contains timeseries data with the following features:
- `id`
- `datetime`
- `target`
- `category`

## Requirements

- Python 3.6+
- `datasets` library
- `pandas` library

You can install the required libraries using pip:

```sh
pip install datasets pandas
```

## Usage

The following example demonstrates how to load the dataset and convert it to a pandas DataFrame.

```python
import datasets
import pandas as pd

# Load the dataset
ds = datasets.load_dataset("Weijie1996/load_timeseries", split="train")

# Convert the dataset to a pandas DataFrame
df = ds.to_pandas()

# Display the first few rows of the DataFrame
print(df.head())

# Optional: Display basic info about the DataFrame
print(df.info())
print(df.describe())
```

## Output
``` data
        id            datetime    target category
0  NL_1  2013-01-01 00:00:00  0.117475      60m
1  NL_1  2013-01-01 01:00:00  0.104347      60m
2  NL_1  2013-01-01 02:00:00  0.103173      60m
3  NL_1  2013-01-01 03:00:00  0.101686      60m
4  NL_1  2013-01-01 04:00:00  0.099632      60m
```