pyEasyTrend

A simple entrypoint to perform a trend analysis in Python.

Repository

PyPI

Documentation

Installation

pyEasyTrend can be installed using pip:

pip install pyEasyTrend

or downloading / cloning the repository and, from the root folder of the project, running:

python setup.py install

Updating the package

To update the package via pip, you can use:

pip install --user --upgrade pyeasytrend

Documentation

You can check the full documentation online. Alternatively, a copy of the documentation is available in the docs folder

Example

import pyeasytrend #import the library

#Load some sample data and create a dataframe
data = {'StudyTime':[24, 44, 21, 45, 54, 26, 57, 34, 33, 12, 17, 21, 58, 41, 29, 55, 42, 40, 21, 9, 39, 30, 17, 31, 51, 42, 30, 3, 20, 21, 4, 16, 26, 6, 18, 50, 60, 13, 23, 13, 3, 35, 38, 51, 12, 35, 7, 42, 20, 41, 37, 56, 19, 57, 12, 49, 15, 6, 43, 7, 40, 12, 35, 4, 46, 29, 6, 38, 36, 33, 21, 33, 50, 54, 25, 38, 48, 17, 28, 48, 16, 50, 24, 15, 40, 54, 40, 42, 2, 20, 24, 21, 37, 15, 52, 36, 5, 7, 29, 21],
        'Score':[18, 47, 21, 60, 80, 18, 100, 28, 41, 7, 12, 17, 82, 45, 33, 94, 41, 55, 9, 6, 53, 24, 13, 35, 62, 43, 33, 2, 17, 10, 0, 7, 14, 0, 14, 72, 94, 7, 14, 3, 0, 43, 39, 80, 5, 39, 4, 43, 14, 37, 39, 80, 16, 94, 7, 55, 13, 2, 45, 6, 55, 7, 35, 0, 69, 18, 0, 45, 43, 27, 11, 37, 67, 82, 16, 41, 74, 10, 19, 55, 14, 60, 18, 7, 55, 64, 37, 60, 2, 10, 17, 14, 30, 6, 69, 32, 2, 1, 32, 10]}
df = pd.DataFrame(data)

#Analyze the data using up to a Quartic model (y = ax^4 + bx^3 + cx^2 + dx + q, maxDegree = 4), and generate a visual representation of the analysis (visualize = True)
results = pyeasytrend.analyzeTrend(df.StudyTime, df.Score, maxDegree=4, visualize=True)

#Put the results in a pandas Table
pyeasytrend.tablifyResults(results)

Order	R2	SSE	F	pvalue	AIC	BIC
1	0.907946	6498.52	966.59	1.11022e-16	703.204	705.809
2	0.952677	3340.75	91.6874	1.11022e-15	638.666	643.876
3	0.952691	3339.76	0.0283989	0.866529	640.636	648.452

This and other examples can also be found in the Tutorial folder in the form of Jupyter Notebook.

Requirements

Numpy
Scipy
Pandas
Matplotlib

Contacts

Feel free to contact me for questions, suggestions or to give me advice as well at: giulio001@e.ntu.edu.sg

Gabrock94/pyEasyTrend