text: #Understanding variables
  #Cleaning Dataset
  #Analysing Relationship b/w Variables
  
  import pandas as pd
  import numpy as np
  
  df=pd.read_csv("indian_liver_patient.csv")
  
  print(df)
  
  print(df.shape) #View the no.of columns and rows
  
  print(df.head()) #It will print first 5 datas
  
  print(df.tail()) #It will print Last 5 datas
  
  print(df.columns) #print the column names
  
  print(df.describe()) #Print the statistical value like minimum value,Mean,standard deviation
  
  print(df.isnull().sum()) #Show all the null values
  
  #print(df.fillna(1,inplace=True)) #fill all the null value as 1
  
  print(df.dropna(inplace=True)) #Remove the null value and the data will changed as 579 from 583
  
  print(df.shape)
  
  
  # We have to change catagorial value to binary
  
  print(df.Gender.unique()) #To find the unique values like male,female
  
  print(df.nunique(axis=0)) #axis=1 -->Row vise & axis=0 -->column vise
  
  
  print(df.Gender.value_counts()) #To print how many male and female present 
  
  import seaborn as sns
  #outlayers- e.g 50,45,35,80,90,500,1000
  
  print(sns.boxplot(x=df['Direct_Bilirubin']))
  
  #To remove outlayers
  
  df_clean=df[df['Direct_Bilirubin'].between(0,1.5)]
  
  print(df_clean.shape)
  
  print(sns.boxplot(x=df_clean['Direct_Bilirubin']))
  
  
  
  
  