Using appropriate dtypes in Pandas - Mini Project: Build & Apply

Using appropriate dtypes

📖 Scenario: You work in a small store that tracks sales data. You have a table with product names, quantities sold, and prices. You want to make sure the data uses the best types to save memory and make calculations faster.

🎯 Goal: You will create a pandas DataFrame with sales data, set a configuration for the data type of the quantity column, convert the quantity column to that type, and then print the DataFrame info to see the changes.

📋 What You'll Learn

Create a pandas DataFrame with product names, quantities sold, and prices.

Create a variable to hold the desired data type for the quantity column.

Convert the quantity column to the desired data type using the variable.

Print the DataFrame info to show the data types and memory usage.

💡 Why This Matters

🌍 Real World

Using the right data types helps save memory and speeds up data processing in real-world data science projects.

💼 Career

Data scientists and analysts often optimize data storage by choosing appropriate dtypes to handle large datasets efficiently.

1

Create the sales DataFrame

Create a pandas DataFrame called sales with these exact columns and values:
'Product': ['Apple', 'Banana', 'Cherry', 'Date']
'Quantity': [10, 20, 15, 5]
'Price': [0.5, 0.3, 0.2, 1.0]

Pandas

# Create the sales DataFrame with the exact data
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Set the desired dtype for Quantity

Create a variable called quantity_dtype and set it to the string 'int8' to specify the data type for the Quantity column.

Pandas

import pandas as pd

sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry', 'Date'],
    'Quantity': [10, 20, 15, 5],
    'Price': [0.5, 0.3, 0.2, 1.0]
})

# Create quantity_dtype variable and set it to 'int8'
# Your code here

Hint

Just assign the string 'int8' to the variable quantity_dtype.

3

Convert Quantity column to the desired dtype

Use the astype() method on the Quantity column of sales to convert it to the data type stored in quantity_dtype. Assign the result back to the Quantity column.

Pandas

import pandas as pd

sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry', 'Date'],
    'Quantity': [10, 20, 15, 5],
    'Price': [0.5, 0.3, 0.2, 1.0]
})

quantity_dtype = 'int8'

# Convert the Quantity column to the dtype in quantity_dtype
# Your code here

Hint

Use sales['Quantity'].astype(quantity_dtype) and assign it back to sales['Quantity'].

4

Print DataFrame info to see dtypes and memory usage

Use print() to display the output of sales.info() so you can see the data types and memory usage of the DataFrame.

Pandas

import pandas as pd

sales = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Cherry', 'Date'],
    'Quantity': [10, 20, 15, 5],
    'Price': [0.5, 0.3, 0.2, 1.0]
})

quantity_dtype = 'int8'

sales['Quantity'] = sales['Quantity'].astype(quantity_dtype)

# Print the DataFrame info
# Your code here

Hint

Call sales.info() inside print() to show the DataFrame details.

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