Using appropriate dtypes in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of memory usage with different dtypes

What is the output of the following code snippet that compares memory usage of integer columns with default and optimized dtypes?

Pandas

import pandas as pd
import numpy as np

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

mem_default = df.memory_usage(deep=True, index=False).sum()
df['A'] = df['A'].astype('int8')
df['B'] = df['B'].astype('int8')
mem_optimized = df.memory_usage(deep=True, index=False).sum()
print(mem_default, mem_optimized)

A 16000 2000

B 16000 4000

C 8000 4000

D 8000 2000

💡 Hint

Think about how changing from default int64 to int8 affects memory size.

The default integer dtype in pandas is int64, which uses 8 bytes per value. Changing to int8 reduces each value to 1 byte. For 1000 rows and 2 columns, default memory is 8*1000*2=16000 bytes. After conversion, 1*1000*2=2000 bytes. So output is '16000 2000'.

❓ data_output

intermediate

Resulting dtypes after conversion

Given the DataFrame below, what are the dtypes of columns after applying the conversion code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'col1': [1, 2, 3],
    'col2': [0.1, 0.2, 0.3],
    'col3': ['a', 'b', 'c']
})

df['col1'] = df['col1'].astype('int16')
df['col2'] = df['col2'].astype('float32')

A col1: int16, col2: float32, col3: object

B col1: int64, col2: float64, col3: object

C col1: int16, col2: float64, col3: string

D col1: int32, col2: float32, col3: object

💡 Hint

Check the astype conversions applied to col1 and col2.

The code explicitly converts col1 to int16 and col2 to float32. col3 remains unchanged as object dtype because it contains strings.

🔧 Debug

advanced

Identify the error in dtype conversion

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

Pandas

import pandas as pd

df = pd.DataFrame({'age': [25, 300, 45]})
df['age'] = df['age'].astype('int8')

A TypeError

B ValueError

C OverflowError

D No error, conversion succeeds

💡 Hint

Check if values fit in int8 range (-128 to 127).

Although 300 is outside int8 range, pandas silently wraps around values when converting to int8, so no error is raised. The value 300 becomes -56 due to overflow.

🧠 Conceptual

advanced

Best dtype for categorical data

Which dtype is most memory efficient and appropriate for a column with repeated string categories like 'red', 'blue', 'green'?

A object

B category

C string

D int64

💡 Hint

Think about how pandas stores repeated categories internally.

The 'category' dtype stores repeated string values as integer codes internally, saving memory and improving performance for repeated categories.

🚀 Application

expert

Optimize memory usage for mixed dtype DataFrame

Given a DataFrame with columns: 'id' (integers 0-100000), 'score' (floats 0-1), 'grade' (strings 'A', 'B', 'C'), which dtype conversions will minimize memory usage without losing data?

A id: int8, score: float32, grade: category

B id: int16, score: float64, grade: object

C id: int32, score: float32, grade: category

D id: int64, score: float16, grade: string

💡 Hint

Consider the range of 'id' and precision needed for 'score'.

'id' values up to 100000 fit in int32 but not int16 or int8. 'score' between 0 and 1 can use float32 safely. 'grade' with few repeated strings is best as category dtype.

Practice

1. Why is it important to use appropriate data types in a pandas DataFrame?

easy

A. It automatically fixes missing data values.

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

C. It makes the DataFrame look colorful in output.

D. It changes the order of rows in the DataFrame.

Solution

1. Step 1: Understand data types impact on memory

   Using smaller or correct data types reduces the memory needed to store data.

2. Step 2: Understand data types impact on speed

   Operations on smaller data types run faster, improving performance.

3. Final Answer:

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

4. Quick Check:

   Appropriate dtypes = less memory and faster [OK]

Hint: Smaller data types save memory and speed up work [OK]

Common Mistakes:

• Thinking data types change data values automatically
• Believing data types affect DataFrame colors
• Assuming data types reorder rows

2. Which of the following is the correct way to change a column named age to integer type in pandas?

easy

A. df['age'] = df['age'].astype('int')

B. df.astype('age', int)

C. df['age'].convert(int)

D. df.change_type('age', 'int')

Solution

1. Step 1: Recall pandas syntax for changing dtype

   The correct method is to use astype() on the column.

2. Step 2: Check method usage

   Use df['age'] = df['age'].astype('int') to convert the 'age' column to integer.

3. Final Answer:

   df['age'] = df['age'].astype('int') -> Option A

4. Quick Check:

   astype() method changes dtype [OK]

Hint: df[col] = df[col].astype(dtype) to change dtype [OK]

Common Mistakes:

• Forgetting to assign the result of astype() back to the column
• Using non-existent methods like convert() or change_type()
• Passing arguments in wrong order or syntax

3. Given the code below, what will be the output of df.dtypes?

import pandas as pd
data = {'score': [90, 85, 88], 'passed': ['True', 'False', 'True']}
df = pd.DataFrame(data)
df['passed'] = df['passed'].astype('bool')
print(df.dtypes)

medium

A. score object
passed bool
dtype: object

B. score int64
passed object
dtype: object

C. score int64
passed bool
dtype: object

D. score float64
passed bool
dtype: object

Solution

1. Step 1: Check initial data types

   'score' is a list of integers, so pandas infers int64. 'passed' is string initially.

2. Step 2: Convert 'passed' to boolean

   Using astype('bool') converts 'passed' column to boolean type.

3. Final Answer:

   score int64 passed bool dtype: object -> Option C

4. Quick Check:

   astype('bool') converts string to bool [OK]

Hint: astype('bool') converts strings like 'True' to boolean [OK]

Common Mistakes:

• Assuming 'passed' stays as object after astype('bool')
• Thinking 'score' becomes float automatically
• Confusing bool with string type

4. What is wrong with this code snippet?

df['price'] = df['price'].astype('int64')
df['price'] = df['price'].astype('category')

Why might this cause an error or unexpected behavior?

medium

A. Converting numeric to category without checking unique values may increase memory usage or cause unexpected behavior.

B. You cannot convert a column twice in pandas.

C. astype('category') only works on string columns.

D. int64 is not a valid dtype in pandas.

Solution

1. Step 1: Understand dtype conversion to category

   Converting numeric to category is allowed but categories are based on unique values.

2. Step 2: Consider memory and behavior risks

   If the numeric column has many unique values, category conversion may increase memory or cause unexpected grouping.

3. Final Answer:

   Converting numeric to category without checking unique values may increase memory usage or cause unexpected behavior. -> Option A

4. Quick Check:

   Category dtype needs careful unique value check [OK]

Hint: Check unique values before converting to category [OK]

Common Mistakes:

• Thinking multiple astype() calls are invalid
• Believing astype('category') only works on strings
• Assuming int64 is invalid dtype

5. You have a large DataFrame with a column status containing only three unique string values: 'new', 'in_progress', 'done'. Which dtype change will save memory and keep data efficient?

hard

A. Keep status as string without changes.

B. Convert status to object dtype.

C. Convert status to int64 dtype directly.

D. Convert status to category dtype.

Solution

1. Step 1: Identify unique values in 'status'

   Only three unique strings exist, ideal for categorical dtype.

2. Step 2: Understand category dtype benefits

   Category stores unique values once and uses integer codes internally, saving memory.

3. Step 3: Compare other options

   Object dtype stores full strings repeatedly; int64 conversion is invalid without mapping; no change wastes memory.

4. Final Answer:

   Convert status to category dtype. -> Option D

5. Quick Check:

   Few unique strings? Use category dtype [OK]

Hint: Few unique strings? Use category dtype for memory [OK]

Common Mistakes:

• Trying to convert strings directly to int64 without mapping
• Keeping object dtype wastes memory
• Ignoring category dtype benefits