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--- |
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license: mit |
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task_categories: |
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- time-series-forecasting |
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language: |
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- en |
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size_categories: |
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- 1M<n<1B |
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tags: |
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- finance |
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--- |
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# Timeseries Data Processing |
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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. |
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## Dataset |
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The dataset used in this example is `Weijie1996/load_timeseries`, which contains timeseries data with the following features: |
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- `id` |
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- `datetime` |
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- `target` |
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- `category` |
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## Requirements |
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- Python 3.6+ |
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- `datasets` library |
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- `pandas` library |
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You can install the required libraries using pip: |
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```sh |
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python -m pip install "dask[complete]" # Install everything |
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``` |
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## Usage |
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The following example demonstrates how to load the dataset and convert it to a pandas DataFrame. |
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```python |
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import dask.dataframe as dd |
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# read parquet file |
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df = dd.read_parquet("hf://datasets/Weijie1996/load_timeseries/30m_resolution_ge/ge_30m.parquet") |
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# change to pandas dataframe |
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df = df.compute() |
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``` |
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## Output |
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``` data |
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id datetime target category |
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0 NL_1 2013-01-01 00:00:00 0.117475 60m |
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1 NL_1 2013-01-01 01:00:00 0.104347 60m |
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2 NL_1 2013-01-01 02:00:00 0.103173 60m |
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3 NL_1 2013-01-01 03:00:00 0.101686 60m |
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4 NL_1 2013-01-01 04:00:00 0.099632 60m |
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``` |
