Pre processing module

prepro.missing( data = "" , min_ratio = 0.0 , values = "" , method = "mean" , drop_max = "no")

Parameters:
(value parameter is mandatory)
data:
    Input data for the processing
min_ratio:
    the minimum level of missing values having column only preprocessed
    example min_ratio=0.5 ,
    only less than 50%missing values columns are only will be preprocessed
*values:
    Giving input as which columns are need to be processed
    example values="all" #for all missing values to be preprocessed ,
    values=["age","fare"] #for particular missing values to be preprocessed
method:
    method it has be used for filling missing values
    methods are : mean , median , mode
drop_max:
    it will delete the column which has been having more than ratio you have
    mentioned in min_ratio , so for that you must mention the min_ratio
    example
    missing(data=ds,min_ratio=0.21,drop_max="yes")
    #it will delete all the missing columns which is higher than min_ratio

>>from prepro import prepro
>>import pandas as pd
>>ds=pd.read_excel("titanic3.xls")

>>pro=prepro.missing(data=ds,min_ratio=0.21,
method="mean",values="all",drop_max="yes")
----------------------------------------------
Missing Values by colunm 
  age     0.200917
 fare    0.000764
 body    0.907563
 dtype: float64
 ----------------------------------------------
 >>pro.isnull().mean()#body column has been dropped , 
 age and fare has been updated
 ----------------------------------------------
 pclass       0.000000
 survived     0.000000
 name         0.000000
 sex          0.000000
 age          0.000000
 sibsp        0.000000
 parch        0.000000
 ticket       0.000000
 fare         0.000000
 cabin        0.774637
 embarked     0.001528
 boat         0.628724
 home.dest    0.430863
 dtype: float64
 ----------------------------------------------
 
 """