Correlation with corr() in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this correlation calculation?

Given the following DataFrame, what is the output of df.corr()?

Data Analysis Python

import pandas as pd
import numpy as np

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

A {'A': {'A': 1.0, 'B': -1.0, 'C': 0.0}, 'B': {'A': -1.0, 'B': 1.0, 'C': 0.0}, 'C': {'A': 0.0, 'B': 0.0, 'C': 1.0}}

B

     A    B    C
A  1.0 -1.0  0.0
B -1.0  1.0  0.0
C  0.0  0.0  1.0

C

     A    B    C
A  1.0  1.0  0.0
B  1.0  1.0  0.0
C  0.0  0.0  1.0

D

     A    B    C
A  1.0 -1.0  1.0
B -1.0  1.0 -1.0
C  1.0 -1.0  1.0

💡 Hint

Remember that correlation measures how two variables move together. Negative means opposite directions.

Column A and B are perfectly negatively correlated (-1.0). Column C does not correlate with A or B (0.0). The diagonal is always 1.0 because a variable correlates perfectly with itself.

❓ data_output

intermediate

How many pairs have correlation above 0.5?

Using the DataFrame below, how many unique pairs of columns have a correlation greater than 0.5?

Data Analysis Python

import pandas as pd
import numpy as np
np.random.seed(0)
data = {'X': np.random.rand(10), 'Y': np.random.rand(10), 'Z': np.random.rand(10)}
df = pd.DataFrame(data)
corr_matrix = df.corr()
# Count pairs with correlation > 0.5 (excluding self-correlation)
count = 0
for i in corr_matrix.columns:
    for j in corr_matrix.columns:
        if i < j and corr_matrix.loc[i,j] > 0.5:
            count += 1
print(count)

A 0

B 1

C 2

D 3

💡 Hint

Random numbers usually have low correlation unless seeded or constructed.

The random columns X, Y, Z are independent. Their correlations are close to zero, so no pairs exceed 0.5.

🔧 Debug

advanced

What error does this code raise?

What error will this code raise when trying to compute correlation?

Data Analysis Python

import pandas as pd
data = {'A': [1, 2, 3], 'B': ['x', 'y', 'z']}
df = pd.DataFrame(data)
print(df.corr())

A TypeError: unsupported operand type(s) for -: 'str' and 'int'

B ValueError: could not convert string to float: 'x'

C Empty DataFrame with columns and index but no data

D No error, 1x1 correlation matrix with value 1.0

💡 Hint

Correlation only works on numeric columns; non-numeric columns are ignored.

Pandas automatically excludes non-numeric columns when computing correlation. So no error occurs, but columns with strings are ignored. The result is a correlation matrix for numeric columns only.

🚀 Application

advanced

Which column pair has the strongest positive correlation?

Given the DataFrame below, which pair of columns has the strongest positive correlation?

Data Analysis Python

import pandas as pd
data = {'P': [1, 2, 3, 4, 5], 'Q': [2, 4, 6, 8, 10], 'R': [5, 4, 3, 2, 1], 'S': [1, 3, 1, 7, 9]}
df = pd.DataFrame(data)
corr = df.corr()
print(corr)

A Q and R

B P and R

C P and Q

D P and S

💡 Hint

Look for pairs where one column is a perfect multiple of the other.

Column Q is exactly 2 times column P, so their correlation is 1.0 (perfect positive). Other pairs have lower or negative correlation.

🧠 Conceptual

expert

What does a correlation value of 0 indicate?

In the context of the corr() method, what does a correlation value of 0 between two variables mean?

A The two variables have no linear relationship

B The two variables are independent in all ways

C The two variables have a perfect positive linear relationship

D The two variables have a perfect negative linear relationship

💡 Hint

Correlation measures linear relationships only.

A correlation of 0 means no linear relationship exists between the variables. However, they might still have a non-linear relationship or be dependent in other ways.