Detect outliers from pandas dataframe using various statistical tools
Project description
Outlier Detection
Detect outliers from pandas dataframe using various statistical tools
INSTALLATION AND USAGE
!pip install outlier-detection
from outlier_detection import detect_outliers_using_iqr
import pandas as pd
# Pandas Dataframe
data = pd.read_csv('titanic.csv')
# Detect outliers using IQR Method
# On overall data
detect_outliers_using_iqr(df, 'Fare')
# Based on factors data
detect_outliers_using_iqr(data, 'Fare', is_factor=True, factor='Sex')
Github Repository: https://github.com/bilalProgTech/outlier-detection.git
Tutorial Data Credit: https://www.kaggle.com/c/titanic
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
File details
Details for the file outlier_detection-1.0.6-py3-none-any.whl.
File metadata
- Download URL: outlier_detection-1.0.6-py3-none-any.whl
- Upload date:
- Size: 10.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.2.0 pkginfo/1.6.0 requests/2.25.1 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.51.0 CPython/3.7.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 09ffae1f17645cb5d8ff3b50448d1c801edbefd767170713f336c6a4267249d8 |
|
| MD5 | 10b747a6f4a6d37d8baef31d89052603 |
|
| BLAKE2b-256 | 9a8767c6af9559864c5cf54b8877094d1caeb10b986c2f5959edb539bfc6db3e |
