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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  
COE17
Roll number: 101703376

Output is a DataFrame with all NaN removal

Installation

pip install nishant_missing_data_76

Recommended - test it out in a virtual environment.

Upgrade

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
df=pd.read_csv('file1.csv')
nd=dropvalue(df,0)
nd=dropvalue(df,1)
nd=imputer(df,0)//mean
nd=imputer(df,1)//median
nd=imputer(df,2)//mode
nd=filler(df,along=0)
nd=filler(df,along=1)

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Files for nishant-miss-data-76, version 1.2.1
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