Search is not available for this dataset
id
string | datetime
timestamp[ns] | target
float64 | category
string |
---|---|---|---|
GE_1 | 2015-05-21T15:45:00 | 0.157 | 15m |
GE_1 | 2015-05-21T16:00:00 | 0.273 | 15m |
GE_1 | 2015-05-21T16:15:00 | 0.311 | 15m |
GE_1 | 2015-05-21T16:30:00 | 0.28 | 15m |
GE_1 | 2015-05-21T16:45:00 | 0.265 | 15m |
GE_1 | 2015-05-21T17:00:00 | 0.446 | 15m |
GE_1 | 2015-05-21T17:15:00 | 0.231 | 15m |
GE_1 | 2015-05-21T17:30:00 | 0.187 | 15m |
GE_1 | 2015-05-21T17:45:00 | 0.164 | 15m |
GE_1 | 2015-05-21T18:00:00 | 0.161 | 15m |
GE_1 | 2015-05-21T18:15:00 | 0.164 | 15m |
GE_1 | 2015-05-21T18:30:00 | 0.138 | 15m |
GE_1 | 2015-05-21T18:45:00 | 0.12 | 15m |
GE_1 | 2015-05-21T19:00:00 | 0.15 | 15m |
GE_1 | 2015-05-21T19:15:00 | 0.18 | 15m |
GE_1 | 2015-05-21T19:30:00 | 0.113 | 15m |
GE_1 | 2015-05-21T19:45:00 | 0.137 | 15m |
GE_1 | 2015-05-21T20:00:00 | 0.133 | 15m |
GE_1 | 2015-05-21T20:15:00 | 0.137 | 15m |
GE_1 | 2015-05-21T20:30:00 | 0.12 | 15m |
GE_1 | 2015-05-21T20:45:00 | 0.12 | 15m |
GE_1 | 2015-05-21T21:00:00 | 0.182 | 15m |
GE_1 | 2015-05-21T21:15:00 | 0.063 | 15m |
GE_1 | 2015-05-21T21:30:00 | 0.115 | 15m |
GE_1 | 2015-05-21T21:45:00 | 0.082 | 15m |
GE_1 | 2015-05-21T22:00:00 | 0.073 | 15m |
GE_1 | 2015-05-21T22:15:00 | 0.08 | 15m |
GE_1 | 2015-05-21T22:30:00 | 0.08 | 15m |
GE_1 | 2015-05-21T22:45:00 | 0.08 | 15m |
GE_1 | 2015-05-21T23:00:00 | 0.078 | 15m |
GE_1 | 2015-05-21T23:15:00 | 0.069 | 15m |
GE_1 | 2015-05-21T23:30:00 | 0.101 | 15m |
GE_1 | 2015-05-21T23:45:00 | 0.072 | 15m |
GE_1 | 2015-05-22T00:00:00 | 0.08 | 15m |
GE_1 | 2015-05-22T00:15:00 | 0.078 | 15m |
GE_1 | 2015-05-22T00:30:00 | 0.062 | 15m |
GE_1 | 2015-05-22T00:45:00 | 0.08 | 15m |
GE_1 | 2015-05-22T01:00:00 | 0.067 | 15m |
GE_1 | 2015-05-22T01:15:00 | 0.083 | 15m |
GE_1 | 2015-05-22T01:30:00 | 0.087 | 15m |
GE_1 | 2015-05-22T01:45:00 | 0.073 | 15m |
GE_1 | 2015-05-22T02:00:00 | 0.088 | 15m |
GE_1 | 2015-05-22T02:15:00 | 0.07 | 15m |
GE_1 | 2015-05-22T02:30:00 | 0.072 | 15m |
GE_1 | 2015-05-22T02:45:00 | 0.08 | 15m |
GE_1 | 2015-05-22T03:00:00 | 0.068 | 15m |
GE_1 | 2015-05-22T03:15:00 | 0.092 | 15m |
GE_1 | 2015-05-22T03:30:00 | 0.098 | 15m |
GE_1 | 2015-05-22T03:45:00 | 0.082 | 15m |
GE_1 | 2015-05-22T04:00:00 | 0.125 | 15m |
GE_1 | 2015-05-22T04:15:00 | 0.088 | 15m |
GE_1 | 2015-05-22T04:30:00 | 0.143 | 15m |
GE_1 | 2015-05-22T04:45:00 | 0.117 | 15m |
GE_1 | 2015-05-22T05:00:00 | 0.153 | 15m |
GE_1 | 2015-05-22T05:15:00 | 0.176 | 15m |
GE_1 | 2015-05-22T05:30:00 | 0.266 | 15m |
GE_1 | 2015-05-22T05:45:00 | 0.419 | 15m |
GE_1 | 2015-05-22T06:00:00 | 0.459 | 15m |
GE_1 | 2015-05-22T06:15:00 | 0.56 | 15m |
GE_1 | 2015-05-22T06:30:00 | 1.019 | 15m |
GE_1 | 2015-05-22T06:45:00 | 1.046 | 15m |
GE_1 | 2015-05-22T07:00:00 | 1.068 | 15m |
GE_1 | 2015-05-22T07:15:00 | 0.805 | 15m |
GE_1 | 2015-05-22T07:30:00 | 1.544 | 15m |
GE_1 | 2015-05-22T07:45:00 | 1.645 | 15m |
GE_1 | 2015-05-22T08:00:00 | 2.473 | 15m |
GE_1 | 2015-05-22T08:15:00 | 2.046 | 15m |
GE_1 | 2015-05-22T08:30:00 | 1.987 | 15m |
GE_1 | 2015-05-22T08:45:00 | 1.718 | 15m |
GE_1 | 2015-05-22T09:00:00 | 1.674 | 15m |
GE_1 | 2015-05-22T09:15:00 | 1.69 | 15m |
GE_1 | 2015-05-22T09:30:00 | 0.82 | 15m |
GE_1 | 2015-05-22T09:45:00 | 1.208 | 15m |
GE_1 | 2015-05-22T10:00:00 | 1.278 | 15m |
GE_1 | 2015-05-22T10:15:00 | 1.088 | 15m |
GE_1 | 2015-05-22T10:30:00 | 0.779 | 15m |
GE_1 | 2015-05-22T10:45:00 | 1.162 | 15m |
GE_1 | 2015-05-22T11:00:00 | 1.537 | 15m |
GE_1 | 2015-05-22T11:15:00 | 1.742 | 15m |
GE_1 | 2015-05-22T11:30:00 | 1.762 | 15m |
GE_1 | 2015-05-22T11:45:00 | 1.217 | 15m |
GE_1 | 2015-05-22T12:00:00 | 0.346 | 15m |
GE_1 | 2015-05-22T12:15:00 | 0.442 | 15m |
GE_1 | 2015-05-22T12:30:00 | 0.697 | 15m |
GE_1 | 2015-05-22T12:45:00 | 0.69 | 15m |
GE_1 | 2015-05-22T13:00:00 | 0.348 | 15m |
GE_1 | 2015-05-22T13:15:00 | 0.94 | 15m |
GE_1 | 2015-05-22T13:30:00 | 1.143 | 15m |
GE_1 | 2015-05-22T13:45:00 | 1.429 | 15m |
GE_1 | 2015-05-22T14:00:00 | 1.35 | 15m |
GE_1 | 2015-05-22T14:15:00 | 0.918 | 15m |
GE_1 | 2015-05-22T14:30:00 | 0.979 | 15m |
GE_1 | 2015-05-22T14:45:00 | 1.318 | 15m |
GE_1 | 2015-05-22T15:00:00 | 1.231 | 15m |
GE_1 | 2015-05-22T15:15:00 | 0.754 | 15m |
GE_1 | 2015-05-22T15:30:00 | 0.475 | 15m |
GE_1 | 2015-05-22T15:45:00 | 0.584 | 15m |
GE_1 | 2015-05-22T16:00:00 | 0.529 | 15m |
GE_1 | 2015-05-22T16:15:00 | 0.313 | 15m |
GE_1 | 2015-05-22T16:30:00 | 0.403 | 15m |
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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
librarypandas
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
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