Correlation with corr() in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the correlation matrix of the DataFrame.

Data Analysis Python

correlation_matrix = df.[1]()

Drag options to blanks, or click blank then click option'

Amean

Bcorr

Csum

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'mean()' instead of 'corr()' which calculates averages, not correlations.

Using 'sum()' which adds values, not related to correlation.

The corr() method calculates the correlation matrix between numerical columns in a DataFrame.

2fill in blank

medium

Complete the code to calculate the correlation between columns 'A' and 'B' in the DataFrame.

Data Analysis Python

correlation = df['A'].[1](df['B'])

Drag options to blanks, or click blank then click option'

Acorr

Bmean

Csum

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'mean()' which calculates average, not correlation.

Using 'sum()' which adds values, not correlation.

The corr() method can be used on a Series to find correlation with another Series.

3fill in blank

hard

Fix the error in the code to calculate correlation matrix correctly.

Data Analysis Python

correlation_matrix = df.[1]

Drag options to blanks, or click blank then click option'

Acorr()

Bcorr

Ccorr[]

Dcorr{}

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after 'corr' causing no calculation.

Using brackets or braces which are invalid syntax here.

The corr() method must be called with parentheses to execute and return the correlation matrix.

4fill in blank

hard

Fill both blanks to create a dictionary of correlations for columns with correlation greater than 0.5.

Data Analysis Python

high_corr = {col: df['target'].[1](df[col]) for col in df.columns if df['target'].[2] col > 0.5}

Drag options to blanks, or click blank then click option'

Acorr

B==

C>

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '>' which checks for equality, not greater than.

Using 'mean' instead of 'corr' which does not calculate correlation.

Use corr to calculate correlation and > to filter columns with correlation greater than 0.5.

5fill in blank

hard

Fill all three blanks to create a filtered DataFrame with columns having correlation above 0.7 with 'score'.

Data Analysis Python

corr_matrix = df.[1]()
high_corr_cols = [col for col in corr_matrix['score'] if corr_matrix['score'][col] [2] 0.7]
filtered_df = df[[3]]

Drag options to blanks, or click blank then click option'

Acorr

B>

Chigh_corr_cols

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'mean' instead of 'corr' for correlation matrix.

Using '<' instead of '>' for filtering.

Using wrong variable name instead of the filtered column list.

Calculate correlation matrix with corr(), filter columns with correlation greater than 0.7, and select those columns from the DataFrame.

Practice

1. What does the corr() function in pandas do?

easy

A. Counts the number of missing values in a DataFrame

B. Calculates the correlation between numeric columns in a DataFrame

C. Sorts the DataFrame by a specific column

D. Filters rows based on a condition

Solution

1. Step 1: Understand the purpose of corr()

   The corr() function calculates how numeric columns relate to each other in a DataFrame.

2. Step 2: Compare with other options

   Counting missing values, sorting, or filtering are different functions like isna(), sort_values(), and loc.

3. Final Answer:

   Calculates the correlation between numeric columns in a DataFrame -> Option B

4. Quick Check:

   corr() finds relationships [OK]

Hint: Remember: corr() measures how numbers move together [OK]

Common Mistakes:

• Confusing corr() with counting or sorting functions
• Thinking corr() works on non-numeric data
• Assuming corr() changes the DataFrame structure

2. Which of the following is the correct way to calculate correlation between columns in a pandas DataFrame named df?

easy

A. df.corr()

B. df.correlate()

C. df.corrrelation()

D. df.correlation()

Solution

1. Step 1: Recall the exact method name

   The correct pandas method to calculate correlation is corr().

2. Step 2: Check each option

   Options A, B, and C are misspelled or incorrect method names.

3. Final Answer:

   df.corr() -> Option A

4. Quick Check:

   Use df.corr() for correlation [OK]

Hint: Remember exact method name: corr() not correlation [OK]

Common Mistakes:

• Misspelling the method name
• Using non-existent pandas methods
• Confusing corr() with other functions

3. Given the following DataFrame df:

   A  B  C
0  1  2  5
1  2  4  7
2  3  6  9
3  4  8  11

What is the output of df.corr()?

medium

A. {'A': {'A': 1.0, 'B': 1.0, 'C': 1.0}, 'B': {'A': 1.0, 'B': 1.0, 'C': 1.0}, 'C': {'A': 1.0, 'B': 1.0, 'C': 1.0}}

B. {'A': {'A': 1.0, 'B': 0.5, 'C': 0.5}, 'B': {'A': 0.5, 'B': 1.0, 'C': 0.5}, 'C': {'A': 0.5, 'B': 0.5, 'C': 1.0}}

C. {'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}}

D. Error: corr() cannot be applied to this DataFrame

Solution

1. Step 1: Understand the data pattern

   Columns A, B, and C increase perfectly linearly: B = 2*A, C = 2*A + 3.

2. Step 2: Calculate correlation values

   Since all columns are perfectly linearly related, correlation between any two is 1.0, and correlation with itself is 1.0.

3. Final Answer:

   {'A': {'A': 1.0, 'B': 1.0, 'C': 1.0}, 'B': {'A': 1.0, 'B': 1.0, 'C': 1.0}, 'C': {'A': 1.0, 'B': 1.0, 'C': 1.0}} -> Option A

4. Quick Check:

   Perfect linear relation means correlation = 1 [OK]

Hint: Perfect linear columns have correlation 1 [OK]

Common Mistakes:

• Assuming correlation less than 1 for perfect linear data
• Confusing correlation with covariance
• Thinking corr() raises error on numeric data

4. You run the code below but get an error:

import pandas as pd
df = pd.DataFrame({'X': [1, 2, 3], 'Y': ['a', 'b', 'c']})
print(df.corr(numeric_only=False))

What is the cause of the error?

medium

A. The corr() method is misspelled

B. The DataFrame is empty

C. The DataFrame has non-numeric columns which corr() cannot process

D. The DataFrame columns have missing values

Solution

1. Step 1: Analyze DataFrame columns

   Column 'Y' contains strings, which are non-numeric.

2. Step 2: Understand corr() requirements

   corr() only works on numeric columns; non-numeric columns cause it to ignore or error depending on pandas version.

3. Final Answer:

   The DataFrame has non-numeric columns which corr() cannot process -> Option C

4. Quick Check:

   corr() needs numeric data [OK]

Hint: Check for non-numeric columns before using corr() [OK]

Common Mistakes:

• Assuming corr() works on strings
• Thinking misspelling caused error
• Ignoring data types in DataFrame

5. You have a DataFrame df with columns height, weight, and age. You want to find which two variables have the strongest positive correlation. Which code snippet correctly finds this?

hard

A. corr_matrix = df.corr()
max_corr = corr_matrix.where(corr_matrix < 1).max().max()

B. corr_matrix = df.corr()
max_corr = corr_matrix.where(corr_matrix != 1).min().min()

C. corr_matrix = df.corr()
max_corr = corr_matrix.max().max()

D. corr_matrix = df.corr()
max_corr = corr_matrix.where(corr_matrix != 1).max().max()

Solution

1. Step 1: Calculate correlation matrix

   Use df.corr() to get correlations between all numeric columns.

2. Step 2: Exclude self-correlation (which is 1)

   Use where(corr_matrix != 1) to ignore diagonal 1s, then find the maximum correlation value.

3. Step 3: Find the strongest positive correlation

   Use max().max() to get the highest correlation excluding 1.

4. Final Answer:

   corr_matrix = df.corr() max_corr = corr_matrix.where(corr_matrix != 1).max().max() -> Option D

5. Quick Check:

   Exclude 1, then find max correlation [OK]

Hint: Exclude 1s (self-correlation) before max() [OK]

Common Mistakes:

• Including diagonal 1s in max correlation
• Using min() instead of max()
• Not excluding self-correlation values