File size: 1,334 Bytes
661a2a1 6c806b5 4248e51 0d7fdda 60a05fe 0d7fdda 60a05fe 0d7fdda 60a05fe 0d7fdda 60a05fe 0d7fdda 8676df5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
---
license: mit
task_categories:
- time-series-forecasting
language:
- en
size_categories:
- 1M<n<1B
tags:
- finance
---
# 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
python -m pip install "dask[complete]" # Install everything
```
## Usage
The following example demonstrates how to load the dataset and convert it to a pandas DataFrame.
```python
import dask.dataframe as dd
# read parquet file
df = dd.read_parquet("hf://datasets/Weijie1996/load_timeseries/30m_resolution_ge/ge_30m.parquet")
# change to pandas dataframe
df = df.compute()
```
## 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
``` |