timeseries-shaper 0.0.0.8.1

timeseries-shaper filters, transforms and abstracts your timeseries dataframe

timeseries-shaper

Timeseries-Shaper is a Python library for efficiently filtering and preprocessing time series data using pandas. It provides a set of tools to handle various data transformations, making data preparation tasks easier and more intuitive.

Features

Load

• Load Parquet: Load parquet files for further processing

Transform

Filter

• Filter Missing Values: Quickly filter out or fill missing values in your time series data.
• Boolean Filters: Apply boolean logic to filter data based on specific conditions.
• Integer and Double Filters: Perform numeric operations and filters specific to integer and double data types.
• String Filters: Manipulate and filter data based on string operations.

Calculation

Descriptive Statistics

• Boolean Stats:
• Numeric Stats:
• String Stats:
• Timeseries Stats:

Installation

Install timeseries-shaper using pip:

pip install timeseries-shaper

Useage

Here is a quick example to get you started:

import pandas as pd
from timeseries_shaper.filters import IntegerFilter, StringFilter

# Sample DataFrame
data = {
    'value_integer': [1, 2, None, 4, 5],
    'value_string': ['apple', 'banana', None, 'cherry', 'date']
}
df = pd.DataFrame(data)

# Initialize the filter object
integer_filter = IntegerFilter(df)
string_filter = StringFilter(df)

# Apply filters
filtered_integers = integer_filter.filter_value_integer_not_match(2)
filtered_strings = string_filter.filter_value_string_not_match('banana')

print(filtered_integers)
print(filtered_strings)

Documentation

For full documentation, visit GitHub Pages or check out the docstrings in the code.

Contributing

Contributions are welcome! For major changes, please open an issue first to discuss what you would like to change.

Please ensure to update tests as appropriate.

License

Distributed under the MIT License. See LICENSE for more information.

Development

• Generate new pdocs: .\generate_docs.sh

• Install package locally: pip install -e .

• Run tests locally with pytest: pytest ./tests

• Build package for upload: python setup.py sdist bdist_wheel

• Upload build package to pypi: twine upload dist/* --verbose --skip-existing