Handle Missing Data By Either Dropping Rows/Columns, Forward/Backward Filling or Imputing with Mean, Median or Mode
Project description
Library for Handling Missing Data
PROJECT 3, UCS633 - Data Analysis and Visualization
Navkiran Singh
COE17
Roll number: 101703365
Takes two inputs - filename of input csv, intended filename of output csv.
Two optional arguments - which must be provided together or left out.
Resulting csv is saved with the name you provide.
Installation
pip install missing_data_navkiran
Recommended - test in a virtual environment.
Use via command line
Defaults are drop NaN with parameter along = 0 (drops rows containing NaN)
missing_data_navkiran_cli in.csv out.csv
Drop rows with NaN
missing_data_navkiran_cli in.csv out.csv DROP 0
Drop columns with NaN
missing_data_navkiran_cli in.csv out.csv DROP 1
Forward filling
missing_data_navkiran_cli in.csv out.csv FILL 0
Backward filling
missing_data_navkiran_cli in.csv out.csv FILL 1
Imputing with mean
missing_data_navkiran_cli in.csv out.csv IMPUTE 0
Imputing with median
missing_data_navkiran_cli in.csv out.csv IMPUTE 1
Imputing with mode
missing_data_navkiran_cli in.csv out.csv IMPUTE 2
First argument after outcli is the input csv filename from which the dataset is extracted. The second argument is for storing the final dataset after processing.
Use in .py script
from missing_data_navkiran import dropval,filler,impute
input_df = pd.read_csv('in.csv')
axis = 0
output_df = dropval(input_df,along=0)
axis = 1
output_df = dropval(input_df,along=1)
backward-filling
output_df = filler(input_df,0)
forward-filling
output_df = filler(input_df,1)
Mean
output_df = impute(input_df,0)
Median
output_df = impute(input_df,1)
Mode
output_df = impute(input_df,2)
There are also stand alone functions to fill numerical data and fill categorical data.
from missing_data_navkiran import fill_numerical,fill_categorical
fill_numerical(input_df,list_of_numerical_columns)
fill_categorical(input_df,list_of_categorical_columns)
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