id
string
datetime
unknown
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

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
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