Outlier detection with IQR in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the first quartile (Q1) of the 'data' column using pandas.

Pandas

Q1 = df['data'].quantile([1])

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

A0.25

B0.5

C0.75

D1.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0.5 which is the median instead of Q1.

Using 0.75 which is the third quartile.

The first quartile (Q1) is the 25th percentile, so we use 0.25 in the quantile function.

2fill in blank

medium

Complete the code to calculate the interquartile range (IQR) from Q1 and Q3.

Pandas

IQR = Q3 [1] Q1

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A+

B-

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Adding Q1 and Q3 instead of subtracting.

Multiplying or dividing Q3 and Q1.

IQR is the difference between the third quartile (Q3) and the first quartile (Q1), so we subtract Q1 from Q3.

3fill in blank

hard

Fix the error in the code to calculate the lower bound for outliers using IQR.

Pandas

lower_bound = Q1 [1] 1.5 * IQR

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A-

B/

C+

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Adding 1.5 * IQR instead of subtracting.

Using multiplication or division incorrectly.

The lower bound is Q1 minus 1.5 times the IQR to detect outliers below the normal range.

4fill in blank

hard

Fill both blanks to create a dictionary of outliers below the lower bound and above the upper bound.

Pandas

outliers = {x: x for x in df['data'] if x [1] lower_bound or x [2] upper_bound}

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A<

B>

C<=

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using <= or >= which includes boundary values.

Using wrong comparison operators that do not detect outliers.

Outliers are values less than the lower bound or greater than the upper bound.

5fill in blank

hard

Fill all three blanks to create a dictionary of non-outlier values within the IQR bounds.

Pandas

non_outliers = {x: x for x in df['data'] if x [1] lower_bound and x [2] upper_bound and x [3] None}

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

A>

B<

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators that include outliers.

Not checking for None values.

Non-outliers are values greater than the lower bound and less than the upper bound, and not None.