Data type optimization in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'age' column to a smaller integer type.

Data Analysis Python

df['age'] = df['age'].astype([1])

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

A"int8"

B"int64"

C"float64"

D"object"

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'int64' which uses more memory than needed.

Using 'float64' which is unnecessary for integer data.

Using int8 reduces memory usage by storing integers in 8 bits, suitable for small integer ranges like age.

2fill in blank

medium

Complete the code to convert the 'category' column to a categorical data type.

Data Analysis Python

df['category'] = df['category'].[1]('category')

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

Aastype('category')

Bastype

Cto_numeric

Dastype('object')

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_numeric which converts to numbers, not categories.

Calling astype('category') as a method without arguments.

The astype method is used to convert data types. You need to pass the type as a string argument.

3fill in blank

hard

Fix the error in the code to convert the 'score' column to float32.

Data Analysis Python

df['score'] = df['score'].astype([1])

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

A"float32"

B"float"

Cfloat32

Dfloat

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the type without quotes causing a NameError.

Using just float which defaults to 64-bit float.

The astype method requires the data type as a string, so use "float32" to specify 32-bit float.

4fill in blank

hard

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

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using < which filters shorter words.

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

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than zero.

Data Analysis Python

result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using < instead of > in the condition.

The dictionary comprehension uses k.upper() to make keys uppercase, keeps values as v, and filters values greater than zero with >.

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