**# EXAMPLE OF DATA CLEANING:**

#Here with divide the sum() of the Null Values with the length of the data and sort it

(data.isna().sum()/len(data)).sort_values(ascending=False)

# drop all rows where gender is null because it has a small percentage

data.drop(data[data['GENDER'].isnull()].index, inplace=True)

# here we use .fillna() to fill the value 'U' with Null Values

data['HOMEOWNR'] = data['HOMEOWNR'].fillna('U')

**# EXAMPLE INTERPOLATE:**

# a plot to see the unconnect lines as a null values

data['INCOME'][0:40].plot()
plt.show()

# a plot of interpolate with the method 'linear'

data['INCOME'][0:40].interpolate(method='linear').plot()
plt.show()

# a plot of interpolate with the method 'akima'

points = data['INCOME'].interpolate(method='akima')
sns.histplot(points)
plt.show()