Binning continuous variables in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create bins using pandas cut function.

Data Analysis Python

import pandas as pd

ages = [22, 45, 18, 34, 65, 27]
bins = [0, 18, 35, 60, 100]
categories = pd.cut(ages, [1])
print(categories)

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

Abins

Bages

Crange

Dcategories

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the data itself instead of the bins list.

Using a variable that is not defined as bins.

The pd.cut function requires the bins argument to define the intervals. Here, bins is the list of bin edges.

2fill in blank

medium

Complete the code to assign labels to the bins.

Data Analysis Python

import pandas as pd

ages = [22, 45, 18, 34, 65, 27]
bins = [0, 18, 35, 60, 100]
labels = ['Child', 'Young Adult', 'Adult', 'Senior']
categories = pd.cut(ages, bins, [1]=labels)
print(categories)

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Abins

Bnames

Ccategories

Dlabels

Attempts:

3 left

💡 Hint

Common Mistakes

Using names instead of labels.

Passing the labels list as the first argument.

The labels parameter in pd.cut assigns names to each bin interval.

3fill in blank

hard

Fix the error in the code to create equal-width bins.

Data Analysis Python

import pandas as pd

ages = [22, 45, 18, 34, 65, 27]
categories = pd.cut(ages, bins=4, [1]=True)
print(categories)

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Ainclude_lowest

Bright

Clabels

Dretbins

Attempts:

3 left

💡 Hint

Common Mistakes

Using right=True which controls bin edge inclusion on the right side.

Using labels when not assigning labels.

The include_lowest=True parameter ensures the lowest value is included in the first bin.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for the value.

Using < instead of > in the condition.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 2 characters.

Data Analysis Python

words = ['data', 'science', 'is', 'fun']
result = { [1]: [2] for word in words if len(word) [3] 2 }
print(result)

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Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of word.upper() for keys.

Using < instead of > in the condition.

The dictionary comprehension creates keys as uppercase words and values as their lengths, filtering words longer than 2 characters.

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