new_contents: import warnings
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  import scipy.stats
  
  warnings.filterwarnings("ignore")
  
  sns.set(color_codes=True)
  # Reading the data where low_memory=False increases the program efficiency
  data= pd.read_csv("gapminder.csv", low_memory=False)
  # setting variables that you will be working with to numeric
  data['breastcancerper100th']= data['breastcancerper100th'].convert_objects(convert_numeric=True)
  data['femaleemployrate']= data['femaleemployrate'].convert_objects(convert_numeric=True)
  data['alcconsumption']= data['alcconsumption'].convert_objects(convert_numeric=True)
  
  #print "Showing missing data coulmn-wise"
  #print data.isnull().sum()
  # Create a copy of the original dataset as sub5 by using the copy() method
  sub6=data.copy()
  sub6_clean=sub6.dropna()
  
  print("\nAssociation between alcohol consumption and breast cancer per 100th\n")
  print(scipy.stats.pearsonr(sub6_clean['alcconsumption'], sub6_clean['breastcancerper100th']))
  
  # using scatter plot the visulaize quantitative variable.
  scat2= sns.regplot(x='alcconsumption', y='breastcancerper100th', data=sub6_clean)
  plt.xlabel('Alcohol consumption in liters')
  plt.ylabel('Breast cancer per 100th person')
  plt.title('Scatterplot for the Association between Alcohol Consumption and Breast Cancer 100th person')
  
subject: Create pearson_Correlation.py