Data type optimization in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of memory usage after optimization

Given a pandas DataFrame with integer columns, what is the output of the memory usage after converting the columns to int8?

Data Analysis Python

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': np.random.randint(0, 100, 1000),
    'B': np.random.randint(0, 100, 1000)
})

before = df.memory_usage(deep=True).sum()
df['A'] = df['A'].astype('int8')
df['B'] = df['B'].astype('int8')
after = df.memory_usage(deep=True).sum()
print(after < before)

A False

B True

C TypeError

D MemoryError

💡 Hint

Think about how changing data types to smaller ones affects memory.

Converting integer columns from default int64 to int8 reduces memory usage, so after < before is True.

❓ data_output

intermediate

Resulting data types after optimization

What are the data types of the columns after applying the following optimization?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': [1000, 2000, 3000],
    'col3': ['a', 'b', 'c']
})

df['col1'] = df['col1'].astype('int8')
df['col2'] = df['col2'].astype('int16')
result = df.dtypes.to_dict()

A {'col1': 'int8', 'col2': 'int16', 'col3': 'object'}

B {'col1': 'int64', 'col2': 'int64', 'col3': 'object'}

C {'col1': 'float64', 'col2': 'float64', 'col3': 'object'}

D {'col1': 'int8', 'col2': 'int8', 'col3': 'object'}

💡 Hint

Check the explicit astype conversions.

The code converts col1 to int8 and col2 to int16. col3 remains object.

🔧 Debug

advanced

Identify the error in data type conversion

What error will this code raise when trying to convert the column to int8?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'values': [0, 127, 128, 255]})
df['values'] = df['values'].astype('int8')

A OverflowError

B No error, conversion succeeds

C ValueError

D TypeError

💡 Hint

Check the range of int8 and the values in the column.

Values like 128 and 255 exceed the range of int8 (-128 to 127), causing a ValueError.

🚀 Application

advanced

Choosing optimal data types for mixed data

You have a DataFrame with columns: age (0-120), income (0-1,000,000), and gender ('M' or 'F'). Which data types optimize memory without losing information?

A {'age': 'int8', 'income': 'int32', 'gender': 'category'}

B {'age': 'int16', 'income': 'int16', 'gender': 'object'}

C {'age': 'int8', 'income': 'float64', 'gender': 'object'}

D {'age': 'int32', 'income': 'int64', 'gender': 'category'}

💡 Hint

Consider the value ranges and best types for categorical data.

age fits in int8 (0-127 covers 0-120), income fits in int32 (max ~2 billion), and gender is best as category for two values.

🧠 Conceptual

expert

Impact of data type optimization on performance

Which statement best describes the impact of data type optimization on data processing performance?

A Data type optimization has no effect on performance, only on memory usage.

B Smaller data types always speed up computations because less memory is used.

C Using larger data types improves performance by avoiding overflow errors.

D Optimizing data types reduces memory usage but can sometimes slow down computations due to type conversions.

💡 Hint

Think about trade-offs between memory and CPU operations.

While smaller data types reduce memory, some operations may require upcasting or conversions, which can slow down processing.

Practice

1. Why is data type optimization important when working with large datasets in pandas?

easy

A. It sorts the data in ascending order.

B. It changes the data values to strings.

C. It deletes unnecessary columns automatically.

D. It reduces memory usage and speeds up data processing.

Solution

1. Step 1: Understand data type optimization purpose

   Data type optimization aims to reduce the memory used by data and improve processing speed.

2. Step 2: Identify the correct benefit

   Reducing memory and speeding up processing matches the purpose, unlike changing values or sorting.

3. Final Answer:

   It reduces memory usage and speeds up data processing. -> Option D

4. Quick Check:

   Data type optimization = reduce memory and speed up [OK]

Hint: Think about saving memory and faster work [OK]

Common Mistakes:

• Confusing optimization with sorting or deleting data
• Thinking it changes data values instead of types
• Assuming it automatically cleans data

2. Which pandas method is used to change the data type of a DataFrame column?

easy

A. astype()

B. convert()

C. change_type()

D. modify_dtype()

Solution

1. Step 1: Recall pandas method for type conversion

   The pandas method to change a column's data type is astype().

2. Step 2: Verify other options

   convert(), change_type(), and modify_dtype() are not valid pandas methods.

3. Final Answer:

   astype() -> Option A

4. Quick Check:

   pandas type change = astype() [OK]

Hint: Remember pandas astype() for type changes [OK]

Common Mistakes:

• Using non-existent methods like convert()
• Confusing with methods for data cleaning
• Trying to assign types directly without astype()

3. Given the code below, what will be the memory usage of the DataFrame after optimization?

import pandas as pd
import numpy as np

df = pd.DataFrame({
  'A': np.arange(1000, dtype='int64'),
  'B': ['cat', 'dog', 'mouse', 'cat'] * 250
})

memory_before = df.memory_usage(deep=True).sum()
df['B'] = df['B'].astype('category')
memory_after = df.memory_usage(deep=True).sum()

print(memory_after < memory_before)

medium

A. True, memory usage decreases after converting to category.

B. False, memory usage increases after converting to category.

C. True, memory usage stays the same after converting to category.

D. Error, astype('category') is invalid.

Solution

1. Step 1: Understand memory usage before optimization

   Column 'B' is object type with repeated strings, using more memory.

2. Step 2: Effect of converting to category

   Converting 'B' to category stores unique values once, reducing memory.

3. Final Answer:

   True, memory usage decreases after converting to category. -> Option A

4. Quick Check:

   Category type reduces memory = True [OK]

Hint: Categorical reduces repeated string memory [OK]

Common Mistakes:

• Thinking category increases memory
• Assuming memory stays same after astype
• Believing astype('category') causes error

4. Identify the error in the following code snippet that tries to optimize data types:

import pandas as pd

df = pd.DataFrame({'col': [1, 2, 3, 4]})
df['col'] = df['col'].astype('int8')
df['col'] = df['col'].astype('category')
df['col'] = df['col'].astype('int16')

medium

A. int8 is not a valid data type in pandas.

B. Cannot convert from category back to int16 directly.

C. astype() cannot be used multiple times on the same column.

D. DataFrame creation syntax is incorrect.

Solution

1. Step 1: Check data type conversions

   Converting from int8 to category is valid, but category stores codes, not original ints.

2. Step 2: Conversion from category to int16

   Directly converting category to int16 causes error because category is not numeric type.

3. Final Answer:

   Cannot convert from category back to int16 directly. -> Option B

4. Quick Check:

   Category to int conversion invalid [OK]

Hint: Category to int needs .cat.codes first [OK]

Common Mistakes:

• Assuming int8 is invalid
• Thinking astype can chain without issues
• Believing DataFrame creation is wrong

5. You have a DataFrame with a column 'status' containing only three unique string values repeated many times. Which is the best way to optimize memory usage for this column while keeping it easy to analyze?

hard

A. Leave 'status' as object type because conversion wastes time.

B. Convert 'status' to integer type directly with astype('int32').

C. Convert 'status' to a categorical type using astype('category').

D. Convert 'status' to float type with astype('float64').

Solution

1. Step 1: Analyze the column data

   'status' has few unique strings repeated many times, ideal for categorical type.

2. Step 2: Choose the best optimization

   astype('category') reduces memory by storing unique values once and codes for repeats.

3. Final Answer:

   Convert 'status' to a categorical type using astype('category'). -> Option C

4. Quick Check:

   Few unique strings = use category [OK]

Hint: Few unique strings? Use category type [OK]

Common Mistakes:

• Trying to convert strings directly to int or float
• Ignoring memory benefits of categorical
• Thinking object type is always best