metadata

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:

pip install datasets pandas

Usage

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

from datasets import load_dataset

ds = load_dataset("Weijie1996/load_timeseries", split='train')
# Print the category of the dataset

print(set(ds['category']))
print(set(ds['id']))

# Filter the dataset that category is 15m and id is GE_1
ds = ds.filter(lambda x: x['category'] == '30m' and x['id'] == 'GE_1') 

# Transform the dataset to a pandas dataframe
df = ds.to_pandas()

Output

        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