astype() for type conversion in Pandas - Mini Project: Build & Apply

Using astype() for Type Conversion in pandas

📖 Scenario: You work in a small store and have a list of products with their prices and quantities. The data was entered as text, but you need to convert the prices and quantities to numbers to do calculations.

🎯 Goal: You will create a pandas DataFrame with product data, then convert the price and quantity columns from text to the correct number types using astype(). Finally, you will print the updated DataFrame.

📋 What You'll Learn

Create a pandas DataFrame with product names, prices, and quantities as strings

Create a variable to hold the list of columns to convert

Use astype() to convert the price column to float and quantity column to integer

Print the final DataFrame to see the changes

💡 Why This Matters

🌍 Real World

Stores and businesses often get data as text but need to convert it to numbers for calculations like totals and averages.

💼 Career

Data analysts and scientists frequently convert data types to prepare data for analysis and visualization.

1

Create the initial DataFrame with product data

Import pandas as pd. Create a DataFrame called products with these exact columns and values:
'Product': ['Apple', 'Banana', 'Carrot'],
'Price': ['0.50', '0.30', '0.20'],
'Quantity': ['10', '20', '30']. All values should be strings.

Pandas

import pandas as pd
# Create the products 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 strings.

2

Create a list of columns to convert

Create a list called cols_to_convert that contains the exact column names 'Price' and 'Quantity'.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': ['0.50', '0.30', '0.20'],
    'Quantity': ['10', '20', '30']
})
# Create the list cols_to_convert with 'Price' and 'Quantity'
# Your code here

Hint

Just assign the list ['Price', 'Quantity'] to the variable cols_to_convert.

3

Convert the columns to correct types using astype()

Use astype() to convert the 'Price' column to float and the 'Quantity' column to int. Update the products DataFrame with these conversions.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': ['0.50', '0.30', '0.20'],
    'Quantity': ['10', '20', '30']
})
cols_to_convert = ['Price', 'Quantity']
# Convert 'Price' to float and 'Quantity' to int using astype()
# Your code here

Hint

Use products['Price'] = products['Price'].astype(float) and similarly for 'Quantity' with int.

4

Print the updated DataFrame

Print the products DataFrame to see the updated data types and values.

Pandas

import pandas as pd
products = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot'],
    'Price': ['0.50', '0.30', '0.20'],
    'Quantity': ['10', '20', '30']
})
cols_to_convert = ['Price', 'Quantity']
products['Price'] = products['Price'].astype(float)
products['Quantity'] = products['Quantity'].astype(int)
# Print the products DataFrame
# Your code here

Hint

Use print(products) to show the DataFrame.

Practice

1. What does the astype() method do in pandas?

easy

A. It filters rows based on a condition.

B. It changes the data type of a DataFrame column or Series.

C. It sorts the DataFrame by column values.

D. It merges two DataFrames together.

Solution

1. Step 1: Understand the purpose of astype()

   The astype() method is used to convert the data type of a pandas Series or DataFrame column.

2. Step 2: Compare with other options

   Sorting, filtering, and merging are different pandas operations unrelated to astype().

3. Final Answer:

   It changes the data type of a DataFrame column or Series. -> Option B

4. Quick Check:

   astype() changes types = C [OK]

Hint: Remember: astype() changes data types only [OK]

Common Mistakes:

• Confusing astype() with sorting or filtering methods
• Thinking astype() modifies data values instead of types
• Assuming astype() merges DataFrames

2. Which of the following is the correct syntax to convert a pandas Series col to integer type?

easy

A. col.convert(int)

B. astype(col, int)

C. col.astype(int)

D. col.to_int()

Solution

1. Step 1: Recall the method syntax

   The correct syntax to convert a Series is Series.astype(type), so col.astype(int) is correct.

2. Step 2: Check other options

   Options B, C, and D are not valid pandas syntax for type conversion.

3. Final Answer:

   col.astype(int) -> Option C

4. Quick Check:

   Use Series.astype(type) = A [OK]

Hint: Use series.astype(type) to convert types [OK]

Common Mistakes:

• Using astype as a standalone function
• Trying non-existent methods like to_int()
• Passing arguments in wrong order

3. What will be the output of this code?

import pandas as pd
df = pd.DataFrame({'A': ['1', '2', '3']})
df['A'] = df['A'].astype(int)
print(df.dtypes['A'])

medium

A. int64

B. string

C. float64

D. object

Solution

1. Step 1: Understand the initial data type

   Column 'A' starts as strings (object type) because values are in quotes.

2. Step 2: Apply astype(int)

   Converting with astype(int) changes the type to integer, which pandas shows as int64.

3. Final Answer:

   int64 -> Option A

4. Quick Check:

   astype(int) converts to int64 = D [OK]

Hint: astype(int) converts string numbers to int64 type [OK]

Common Mistakes:

• Expecting type to remain object after conversion
• Confusing int64 with float64
• Thinking astype changes values but not type

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'B': ['10', '20', 'thirty']})
df['B'] = df['B'].astype(int)

medium

A. ValueError due to non-numeric string 'thirty'

B. No error, code runs fine

C. SyntaxError because of missing parentheses

D. TypeError because astype() cannot convert strings

Solution

1. Step 1: Identify data in column 'B'

   Column 'B' contains strings '10', '20', and 'thirty'. 'thirty' is not numeric.

2. Step 2: Understand astype(int) behavior

   Trying to convert 'thirty' to int causes a ValueError because it cannot be parsed as a number.

3. Final Answer:

   ValueError due to non-numeric string 'thirty' -> Option A

4. Quick Check:

   Non-numeric string causes ValueError = B [OK]

Hint: Non-numeric strings cause ValueError on astype(int) [OK]

Common Mistakes:

• Assuming astype silently converts all strings
• Confusing ValueError with SyntaxError
• Thinking astype() can convert any string

5. You have a DataFrame df with a column 'score' containing float values as strings like '85.5'. You want to convert this column to numeric floats but also handle any non-numeric values by replacing them with 0.0. Which code correctly does this?

hard

A. df['score'] = df['score'].convert(float, fill=0.0)

B. df['score'] = df['score'].astype(float).fillna(0.0)

C. df['score'] = df['score'].astype(int).fillna(0)

D. df['score'] = pd.to_numeric(df['score'], errors='coerce').fillna(0.0)

Solution

1. Step 1: Understand the problem with astype()

   Using astype(float) fails if there are non-numeric strings, causing errors.

2. Step 2: Use pd.to_numeric() with error handling

   pd.to_numeric() with errors='coerce' converts invalid values to NaN, which can be replaced by fillna(0.0).

3. Step 3: Check other options

   df['score'] = df['score'].astype(float).fillna(0.0) does not handle errors properly; df['score'] = df['score'].astype(int).fillna(0) converts to int, losing decimals; df['score'] = df['score'].convert(float, fill=0.0) uses a non-existent method.

4. Final Answer:

   df['score'] = pd.to_numeric(df['score'], errors='coerce').fillna(0.0) -> Option D

5. Quick Check:

   Use pd.to_numeric with errors='coerce' + fillna = A [OK]

Hint: Use pd.to_numeric with errors='coerce' to handle bad data [OK]

Common Mistakes:

• Using astype(float) without error handling
• Trying to convert to int losing decimals
• Using non-existent methods like convert()