Normality Test for Big Data
Project description
Empirical Normality Test
A normality test based on empirical rule for big data
Installation
pip install big-data-normality
Get started
How to get normality test for dataset with this lib:
# Library import
from big_data_normality.empirical_normality_test import EmpiricalNormalityTest
# This line of code will allow shorter imports
from big_data_normality import EmpiricalNormalityTest
# Instantiate a EmpiricalNormalityTest object
"""Firstly, import the excel file, then run the library
P.S. Excel file must include name of each column. Like that;
name1 name2 name3 name4 ...
73,36 72,64 68,45 66,52 ...
78,97 67,04 60,85 70,96 ...
... ... ... ... ...
"""
import os
import pandas as pd
path = r'C:\data'
os.chdir(path)
df = pd.read_excel("my_dataset.xlsx")
print(EmpiricalNormalityTest(df))
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for big_data_normality-0.1.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7366f4abc4895d51f5ad7a202eac3a273aed347ea40de4ec249ab2117d903f58 |
|
MD5 | 150558362af6d544fa4ff54c6bb70124 |
|
BLAKE2b-256 | 4f5b115b8636bfd21e1f8d8ffd547279bd1caf63d4fb511e79b41aaa2872b1b4 |