Binning continuous variables in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pandas cut with custom bins

What is the output of the following code snippet?

Data Analysis Python

import pandas as pd
import numpy as np

values = np.array([1, 5, 10, 15, 20])
bins = [0, 5, 10, 15]
categories = pd.cut(values, bins)
print(categories)

A

[(0, 5], (0, 5], (5, 10], (10, 15], NaN]
Categories (3, interval[int64, right]): [(0, 5] < (5, 10] < (10, 15]]

B

[(0, 5], NaN, (5, 10], (10, 15], NaN]
Categories (3, interval[int64, right]): [(0, 5] < (5, 10] < (10, 15]]

C

[NaN, (0, 5], (5, 10], (10, 15], NaN]
Categories (3, interval[int64, right]): [(0, 5] < (5, 10] < (10, 15]]

D

[NaN, (0, 5], (5, 10], (10, 15], (15, 20]]
Categories (4, interval[int64, right]): [(0, 5] < (5, 10] < (10, 15] < (15, 20]]

💡 Hint

Remember that pd.cut assigns values to bins based on intervals defined by the bins list. Values equal to the left edge are excluded, right edge included by default.

The bins are (0,5], (5,10], and (10,15]. The value 1 falls into (0,5], 5 also falls into (0,5] because the right edge is included, 10 falls into (5,10], 15 falls into (10,15], and 20 is outside the bins so it is NaN.

❓ data_output

intermediate

Number of bins created by qcut

Given the following code, how many unique bins will be created?

Data Analysis Python

import pandas as pd
import numpy as np

values = np.array([1, 2, 2, 3, 4, 5, 6, 7, 8, 9, 10])
categories = pd.qcut(values, 4)
unique_bins = categories.unique()
print(len(unique_bins))

A 3

B 6

C 5

D 4

💡 Hint

qcut tries to create equal-sized bins but duplicates in data can reduce the number of bins.

Because the value 2 appears twice, qcut merges bins to avoid empty bins, resulting in 3 unique bins instead of 4.

❓ visualization

advanced

Visualizing binning effect on data distribution

Which option shows the correct histogram with bins created by pd.cut for the data below?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

np.random.seed(0)
data = np.random.normal(loc=50, scale=10, size=1000)
bins = [20, 40, 60, 80]
categories = pd.cut(data, bins)
plt.hist(data, bins=bins, edgecolor='black')
plt.title('Histogram with bins [20, 40, 60, 80]')
plt.show()

A Histogram with 4 bars showing counts of data in intervals [20,40), [40,60), [60,80), [80,100)

B Histogram with 3 bars showing counts of data in intervals (20,40], (40,60], (60,80]

C Histogram with 3 bars showing counts of data in intervals [20,40), [40,60), [60,80)

D Histogram with 4 bars showing counts of data in intervals (20,40], (40,60], (60,80], (80,100]

💡 Hint

pd.cut by default includes the right edge of intervals and excludes the left edge.

The bins define intervals (20,40], (40,60], and (60,80]. The histogram shows 3 bars corresponding to these intervals.

🧠 Conceptual

advanced

Effect of right parameter in pd.cut

What is the effect of setting right=False in pd.cut when binning data?

A Bins include the right edge and exclude the left edge of intervals.

B Bins include both edges of intervals.

C Bins include the left edge and exclude the right edge of intervals.

D Bins exclude both edges of intervals.

💡 Hint

By default, pd.cut includes the right edge. Changing right changes which edge is included.

Setting right=False means intervals are left-inclusive and right-exclusive, so the left edge is included in the bin.

🔧 Debug

expert

Identify the error in binning code

What error will the following code raise?

Data Analysis Python

import pandas as pd
values = [1, 2, 3, 4, 5]
bins = [0, 2, 4]
categories = pd.cut(values, bins, labels=['Low', 'Medium', 'High'])

A No error, code runs successfully

B TypeError: 'list' object is not callable

C IndexError: list index out of range

D ValueError: Bin labels must be one fewer than the number of bin edges

💡 Hint

Check the length of labels compared to bins.

The labels list has 3 elements but bins define only 2 intervals, so labels length must be 2, causing a ValueError.

Practice

1. What is the main purpose of binning continuous variables in data analysis?

easy

A. To group continuous data into categories for easier analysis

B. To convert categorical data into numbers

C. To remove missing values from the dataset

D. To sort data in ascending order

Solution

1. Step 1: Understand the concept of binning

   Binning means dividing continuous data into intervals or groups.

2. Step 2: Identify the purpose of binning

   This grouping helps simplify data and makes it easier to analyze patterns.

3. Final Answer:

   To group continuous data into categories for easier analysis -> Option A

4. Quick Check:

   Binning = Grouping continuous data [OK]

Hint: Binning groups numbers into categories for simpler analysis [OK]

Common Mistakes:

• Confusing binning with sorting
• Thinking binning changes data type
• Assuming binning removes missing values

2. Which of the following is the correct syntax to bin a pandas Series data into 3 equal-width bins?

easy

A. pd.bins(data, 3)

B. pd.cut(data, bin=3)

C. pd.bin(data, bins=3)

D. pd.cut(data, bins=3)

Solution

1. Step 1: Recall pandas binning function syntax

   The correct function is pd.cut() with parameters: data and bins.

2. Step 2: Identify correct parameter usage

   Using bins=3 specifies 3 equal-width bins correctly.

3. Final Answer:

   pd.cut(data, bins=3) -> Option D

4. Quick Check:

   pd.cut with bins=3 is correct syntax [OK]

Hint: Use pd.cut(data, bins=number) to bin data [OK]

Common Mistakes:

• Using pd.bins or pd.bin which do not exist
• Using 'bin=3' instead of 'bins=3'
• Confusing pd.cut with other pandas functions

3. Given the code below, what will be the output of print(binned.value_counts())?

import pandas as pd
values = [1, 2, 3, 4, 5, 6]
binned = pd.cut(values, bins=[0, 2, 4, 6])
print(binned.value_counts())

medium

A. (0, 2] 3
(2, 4] 2
(4, 6] 1
dtype: int64

B. (0, 2] 1
(2, 4] 2
(4, 6] 3
dtype: int64

C. (0, 2] 2
(2, 4] 2
(4, 6] 2
dtype: int64

D. (0, 2] 2
(2, 4] 3
(4, 6] 1
dtype: int64

Solution

1. Step 1: Understand bin intervals and data assignment

   Bins are (0,2], (2,4], (4,6]. Values 1 and 2 fall in (0,2], 3 and 4 in (2,4], 5 and 6 in (4,6].

2. Step 2: Count values in each bin

   Each bin has exactly 2 values, so counts are 2, 2, and 2 respectively.

3. Final Answer:

   (0, 2] 2 (2, 4] 2 (4, 6] 2 dtype: int64 -> Option C

4. Quick Check:

   Each bin has 2 values [OK]

Hint: Count how many values fall into each bin interval [OK]

Common Mistakes:

• Miscounting values on bin edges
• Confusing bin intervals as inclusive on left side
• Assuming bins are equal count instead of equal width

4. What is wrong with this code snippet for binning a list data into 4 bins?

import pandas as pd
bins = [0, 10, 20, 30]
binned = pd.cut(data, bins=4)
print(binned)

medium

A. The bins list should have 4 edges for 4 bins

B. The data variable is not defined

C. pd.cut does not accept bins as a parameter

D. The bins parameter should be a list of bin edges, not an integer

Solution

1. Step 1: Check for syntax and runtime errors

   The code references data which is not defined, causing a NameError.

2. Step 2: Confirm other aspects

   pd.cut(data, bins=4) is valid syntax if data exists; the primary issue is the missing definition.

3. Final Answer:

   The data variable is not defined -> Option B

4. Quick Check:

   Undefined data causes NameError [OK]

Hint: Always define data variables before using them [OK]

Common Mistakes:

• Passing bins list but using bins=integer in function
• Not defining data variable
• Confusing pd.cut parameters

5. You have a dataset with ages ranging from 0 to 100. You want to create 5 age groups labeled 'Child' (0-12], 'Teen' (12-19], 'Adult' (19-35], 'Senior' (35-60], 'Elder' (60-100]. Which code correctly bins the ages using pandas with these labels?

hard

A. pd.cut(ages, bins=[0, 12, 19, 35, 60, 100], labels=['Child', 'Teen', 'Adult', 'Senior', 'Elder'])

B. pd.cut(ages, bins=5, labels=['Child', 'Teen', 'Adult', 'Senior', 'Elder'])

C. pd.cut(ages, bins=[0, 12, 19, 35, 60], labels=['Child', 'Teen', 'Adult', 'Senior', 'Elder'])

D. pd.cut(ages, bins=[0, 12, 19, 35, 60, 100], labels=['Child', 'Teen', 'Adult', 'Senior'])

Solution

1. Step 1: Check bins and labels length match

   For 5 labels, bins must have 6 edges to create 5 intervals.

2. Step 2: Verify bins cover full age range

   Bins [0, 12, 19, 35, 60, 100] cover ages 0 to 100 correctly.

3. Step 3: Confirm labels match bins count

   Labels list has 5 names matching 5 bins intervals.

4. Final Answer:

   pd.cut(ages, bins=[0, 12, 19, 35, 60, 100], labels=['Child', 'Teen', 'Adult', 'Senior', 'Elder']) -> Option A

5. Quick Check:

   Bins edges = labels + 1 [OK]

Hint: Bins edges count = labels count + 1 for correct binning [OK]

Common Mistakes:

• Mismatch between number of bins and labels
• Bins not covering full data range
• Using bins=integer with custom labels