Binning continuous variables in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create 3 equal-width bins for the data.

ML Python

import pandas as pd

values = [1, 5, 8, 12, 15, 18]
bins = pd.cut(values, [1])
print(bins)

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

A5

B[0, 10, 20]

C3

Dpd.qcut(values, 3)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list of bin edges instead of the number of bins.

Using pd.qcut instead of pd.cut for equal-width bins.

Using pd.cut with the number 3 creates 3 equal-width bins.

2fill in blank

medium

Complete the code to create 4 bins with equal number of data points.

ML Python

import pandas as pd

values = [2, 4, 6, 8, 10, 12, 14, 16]
bins = pd.qcut(values, [1])
print(bins)

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

A3

B4

C2

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using pd.cut instead of pd.qcut for equal-sized bins.

Passing the wrong number of bins.

Using pd.qcut with 4 creates 4 bins each having roughly equal number of points.

3fill in blank

hard

Fix the error in the code to create bins with custom edges.

ML Python

import pandas as pd

values = [3, 7, 11, 15, 19]
bins = pd.cut(values, bins=[1])
print(bins)

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A[0, 5, 10, 15, 20]

B(0, 5, 10, 15, 20)

C0, 5, 10, 15, 20

Drange(0, 21, 5)

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses instead of square brackets for bin edges.

Passing a range object instead of a list.

The bins argument must be a list or array of bin edges, so use square brackets.

4fill in blank

hard

Fill both blanks to create bins and label them as 'Low', 'Medium', 'High'.

ML Python

import pandas as pd

values = [2, 5, 8, 11, 14]
bins = pd.cut(values, [1], labels=[2])
print(bins)

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

A[0, 6, 12, 18]

B[1, 7, 13, 19]

C['Low', 'Medium', 'High']

D['Small', 'Medium', 'Large']

Attempts:

3 left

💡 Hint

Common Mistakes

Using labels that don't match the number of bins.

Using bin edges that don't cover all data points.

Use bin edges [0, 6, 12, 18] and labels ['Low', 'Medium', 'High'] to categorize values.

5fill in blank

hard

Fill all three blanks to create quantile bins, assign labels, and include the lowest value.

ML Python

import pandas as pd

values = [1, 3, 5, 7, 9, 11]
bins = pd.qcut(values, [1], labels=[2], [3]=True)
print(bins)

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

A3

B['Low', 'Medium', 'High']

Cinclude_lowest

Dduplicates='drop'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong parameter name instead of 'include_lowest'.

Mismatch between number of bins and labels.

Use 3 quantile bins with labels and include_lowest=True to cover the smallest value.