Correlation analysis (Pearson, Spearman) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the Pearson correlation coefficient between two lists.

Data Analysis Python

import scipy.stats as stats
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
pearson_corr, p_value = stats.[1](x, y)
print(pearson_corr)

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Apearsonr

Bkendalltau

Clinregress

Dspearmanr

Attempts:

3 left

💡 Hint

Common Mistakes

Using Spearman correlation function instead of Pearson.

Confusing the function name with linear regression.

The pearsonr function from scipy.stats calculates the Pearson correlation coefficient.

2fill in blank

medium

Complete the code to calculate the Spearman correlation coefficient between two lists.

Data Analysis Python

import scipy.stats as stats
x = [10, 20, 30, 40, 50]
y = [5, 15, 25, 35, 45]
spearman_corr, p_value = stats.[1](x, y)
print(spearman_corr)

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Apearsonr

Bspearmanr

Ckendalltau

Dlinregress

Attempts:

3 left

💡 Hint

Common Mistakes

Using Pearson correlation function instead of Spearman.

Trying to use linear regression function.

The spearmanr function calculates the Spearman rank correlation coefficient, which measures monotonic relationships.

3fill in blank

hard

Fix the error in the code to compute Pearson correlation correctly.

Data Analysis Python

import scipy.stats as stats
x = [1, 3, 5, 7, 9]
y = [2, 4, 6, 8, 10]
correlation, p = stats.pearsonr([1], y)
print(correlation)

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Apearsonr

By

Cstats

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the wrong variable as the first argument.

Passing the function name instead of data.

The first argument to pearsonr should be the first data list, here x.

4fill in blank

hard

Fill both blanks to create a dictionary of Spearman correlations for each pair of columns in the DataFrame.

Data Analysis Python

import pandas as pd
import scipy.stats as stats

data = {'A': [1, 2, 3, 4], 'B': [4, 3, 2, 1], 'C': [2, 3, 4, 5]}
df = pd.DataFrame(data)

spearman_corrs = {col1 + '-' + col2: stats.[1](df[col1], df[col2])[0] for col1 in df.columns for col2 in df.columns if col1 != [2]
print(spearman_corrs)

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Aspearmanr

Bpearsonr

Ckendalltau

Dcol2

Attempts:

3 left

💡 Hint

Common Mistakes

Using Pearson correlation instead of Spearman.

Comparing a column with itself causing redundant pairs.

Use spearmanr to calculate Spearman correlation. The condition excludes pairs where columns are the same by checking col1 != col2.

5fill in blank

hard

Fill all three blanks to create a DataFrame showing Pearson correlation coefficients between columns.

Data Analysis Python

import pandas as pd
import scipy.stats as stats

data = {'X': [10, 20, 30], 'Y': [15, 25, 35], 'Z': [20, 30, 40]}
df = pd.DataFrame(data)

pearson_matrix = pd.DataFrame({
    [1]: [stats.pearsonr(df[[3]], df[[2]])[0] for col in df.columns]
    for [3] in df.columns
})
print(pearson_matrix)

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Acol

Crow

Dcol_name

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for both loops causing confusion.

Passing wrong variables to the correlation function.

The outer loop variable is the column name used as keys in the dictionary (col_name), the inner loop variable is col, and the second argument to pearsonr is df[col].