How to determine Spearmans correlation in Python?
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# How to determine Spearmans correlation in Python?

This recipe helps you determine Spearmans correlation in Python

0
In [2]:
```## 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")

import matplotlib.pyplot as plt
import scipy.stats
import pandas as pd
import random
import seaborn as sns

# Create empty dataframe
df = pd.DataFrame()

df['x'] = random.sample(range(1, 100), 75)
df['y'] = random.sample(range(1, 100), 75)

# View first few rows of data

# 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  94  78
1  14  72
2  72  45
3  13  97
4  49  49

spearmans_rank_correlation is:  0.0745945945945946
Scipy spearmans_rank_correlation is:  0.0745945945945946
```

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