Skip to main content

Handle Missing Data

Project description

Handling Missing Data

This package has been created based on Project 3 of course UCS633(DATA ANALYSIS AND VISUALISATION ) Thapar University, Patiala
Nishant Goel  
Roll number: 101703376

Output is a DataFrame with all NaN removal


pip install nishant_missing_data_76

Recommended - test it out in a virtual environment.


pip install nishant_missing_data_76 --upgrade

To use via command line

missing_data_cli_76 in.csv out.csv

Drop rows with NaN missing_data_cli_76 in.csv out.csv DROP 0

Drop columns with NaN missing_data_cli_76 in.csv out.csv DROP 1

Forward filling missing_data_cli_76 in.csv out.csv FILL 0

Backward filling missing_data_cli_76 in.csv out.csv FILL 1

Imputing with mean missing_data_cli_76 in.csv out.csv IMPUTE 0

Imputing with median missing_data_cli_76 in.csv out.csv IMPUTE 1

Imputing with mode missing_data_cli_76 in.csv out.csv IMPUTE 2

To use in .py script

from nishant_miss_data_76 import dropvalue,filler,imputer

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

nishant_miss_data_76-1.2.1.tar.gz (2.6 kB view hashes)

Uploaded source

Built Distribution

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page