Binning continuous variables in Data Analysis Python - Mini Project: Build & Apply

Binning continuous variables

📖 Scenario: You work as a data analyst for a retail company. You have a list of customer ages and want to group these ages into categories like 'Young', 'Middle-aged', and 'Senior' to better understand customer segments.

🎯 Goal: Build a Python program that bins a list of customer ages into three categories using defined age ranges.

📋 What You'll Learn

Create a list of customer ages with exact values

Define bin edges for age groups

Use a list comprehension to assign each age to a bin label

Print the list of age group labels

💡 Why This Matters

🌍 Real World

Binning continuous variables helps simplify data analysis by grouping continuous data into meaningful categories, like age groups for marketing.

💼 Career

Data analysts and scientists often bin continuous data to prepare it for reports, visualizations, or machine learning models.

1

Create the list of customer ages

Create a list called ages with these exact values: 22, 35, 58, 45, 18, 67, 30, 50.

Data Analysis Python

# Create the list of ages
# Your code here

Hint

Use square brackets to create a list and separate values with commas.

2

Define the bin edges and labels

Create a list called bins with values 0, 30, 60, and 100. Then create a list called labels with strings 'Young', 'Middle-aged', and 'Senior'.

Data Analysis Python

ages = [22, 35, 58, 45, 18, 67, 30, 50]
# Define bins and labels
# Your code here

Hint

Bins define the edges of age groups. Labels are the names for each group.

3

Assign each age to a bin label using list comprehension

Create a list called age_groups using a list comprehension. For each age in ages, assign the label 'Young' if age < 30, 'Middle-aged' if 30 <= age < 60, and 'Senior' if age >= 60.

Data Analysis Python

ages = [22, 35, 58, 45, 18, 67, 30, 50]
bins = [0, 30, 60, 100]
labels = ['Young', 'Middle-aged', 'Senior']
# Create age_groups list using list comprehension
# Your code here

Hint

Use nested if-else inside the list comprehension to assign labels based on age.

4

Print the list of age group labels

Write a print statement to display the age_groups list.

Data Analysis Python

ages = [22, 35, 58, 45, 18, 67, 30, 50]
bins = [0, 30, 60, 100]
labels = ['Young', 'Middle-aged', 'Senior']
age_groups = [
    'Young' if age < 30 else 'Middle-aged' if age < 60 else 'Senior'
    for age in ages
]
# Print the age_groups list
# Your code here

Hint

Use print(age_groups) to show the result.

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