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:

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.

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

        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