## How to determine Spearman's correlation in Python
def Snippet_121(): 
    print()
    print(format('How to determine Spearman\'s correlation in Python','*^82'))    
    import warnings
    warnings.filterwarnings("ignore")
    # load libraries
    import matplotlib.pyplot as plt    
    import scipy.stats
    import pandas as pd
    import random
    import seaborn as sns
    # Create empty dataframe
    df = pd.DataFrame()
    # Add columns
    df['x'] = random.sample(range(1, 100), 75)
    df['y'] = random.sample(range(1, 100), 75)
    # View first few rows of data
    print(); print(df.head())
    # Calculate Pearson’s Correlation Coefficient
    def spearmans_rank_correlation(xs, ys):
        # Calculate the rank of x's
        xranks = pd.Series(xs).rank()
        # Caclulate the ranking of the y's
        yranks = pd.Series(ys).rank()
        # Calculate Pearson's correlation coefficient on the ranked versions of the data
        return scipy.stats.pearsonr(xranks, yranks)
    # Show Pearson's Correlation Coefficient
    result = spearmans_rank_correlation(df.x, df.y)[0]
    print()
    print("spearmans_rank_correlation is: ", result)
    # Calculate Spearman’s Correlation Using SciPy
    print("Scipy spearmans_rank_correlation is: ", scipy.stats.spearmanr(df.x, df.y)[0])
    # reg plot
    sns.lmplot('x', 'y', data=df, fit_reg=True)
    plt.show()    
Snippet_121()

****************How to determine Spearman's correlation in Python*****************

    x   y
0  54  17
1  71  16
2  19  71
3  36  69
4  24   9

spearmans_rank_correlation is:  -0.014736842105263158
Scipy spearmans_rank_correlation is:  -0.014736842105263156