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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