Changing data types (astype) in Data Analysis Python - Mini Project: Build & Apply

Changing Data Types with astype

📖 Scenario: You work in a small store and keep track of sales data. Sometimes the numbers are saved as text, but you need them as numbers to do math.

🎯 Goal: You will change the data types of sales data from text to numbers using astype so you can analyze it easily.

📋 What You'll Learn

Create a pandas DataFrame with sales data as strings

Create a list of columns to convert

Use astype to change the data types of those columns to integers

Print the DataFrame to see the changes

💡 Why This Matters

🌍 Real World

Stores and businesses often get data as text but need numbers to calculate totals, averages, or do other analysis.

💼 Career

Data analysts and scientists frequently convert data types to prepare data for calculations and visualizations.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales with these columns and string values: 'Product' with values 'Pen', 'Notebook', 'Eraser'; 'Quantity' with values '10', '5', '8'; and 'Price' with values '1.5', '3.0', '0.5'.

Data Analysis Python

import pandas as pd
# Create the sales DataFrame with string values
# Your code here

Hint

Use pd.DataFrame with a dictionary where each key is a column name and each value is a list of strings.

2

Create a list of columns to convert

Create a list called cols_to_convert containing the column names 'Quantity' and 'Price' that you want to change from strings to numbers.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
# Create a list called cols_to_convert with 'Quantity' and 'Price'
# Your code here

Hint

Make a list with the exact column names as strings.

3

Convert the columns to numeric types

Use astype on the sales DataFrame to convert the columns in cols_to_convert to numeric types. Convert 'Quantity' to integers and 'Price' to floats. Assign the result back to sales[cols_to_convert].

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
cols_to_convert = ['Quantity', 'Price']
# Convert 'Quantity' to int and 'Price' to float using astype
# Your code here

Hint

Use sales['Quantity'].astype(int) and sales['Price'].astype(float) to convert each column separately.

4

Print the updated DataFrame

Print the sales DataFrame to see the columns 'Quantity' and 'Price' as numbers.

Data Analysis Python

import pandas as pd
sales = pd.DataFrame({
    'Product': ['Pen', 'Notebook', 'Eraser'],
    'Quantity': ['10', '5', '8'],
    'Price': ['1.5', '3.0', '0.5']
})
cols_to_convert = ['Quantity', 'Price']
sales['Quantity'] = sales['Quantity'].astype(int)
sales['Price'] = sales['Price'].astype(float)
# Print the sales DataFrame
# Your code here

Hint

Use print(sales) 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.

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

   Filtering, sorting, and merging are different pandas operations and not related to changing data types.

3. Final Answer:

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

4. Quick Check:

   astype() changes data types [OK]

Hint: Remember: astype() changes column types directly [OK]

Common Mistakes:

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

2. Which of the following is the correct syntax to convert a pandas column named 'age' to integer type?

easy

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

B. df.astype['age'](int)

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

D. df['age'].astype(int)

Solution

1. Step 1: Recall correct method call syntax

   The astype() method is called on the Series object like df['age'].astype(int).

2. Step 2: Check other options for syntax errors

   df.astype['age'](int) uses wrong bracket placement, C uses a non-existent method, and D uses wrong function call style.

3. Final Answer:

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

4. Quick Check:

   Correct syntax is Series.astype(type) [OK]

Hint: Use dot notation on column, then astype(type) [OK]

Common Mistakes:

• Using wrong brackets or parentheses
• Calling astype on DataFrame instead of Series
• Using non-existent methods like convert()

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'score': ['10', '20', '30']})
df['score'] = df['score'].astype(int)
print(df.dtypes)

What will be the output?

medium

A. score int64
dtype: int64

B. score object
dtype: object

C. score int32
dtype: int32

D. score int64
dtype: object

Solution

1. Step 1: Understand the initial data type

   The 'score' column starts as strings (object type) because values are in quotes.

2. Step 2: Apply astype(int) conversion

   Using astype(int) converts the column to integer type, which pandas shows as int64.

3. Step 3: Check the printed dtypes output

   The output shows the column name and its type, followed by dtype info. The correct output is:
   score int64
   dtype: int64

4. Final Answer:

   score int64 dtype: int64 -> Option A

5. Quick Check:

   astype(int) converts to int64 dtype [OK]

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

Common Mistakes:

• Expecting dtype to be 'object' after conversion
• Confusing dtype display format
• Thinking astype changes values, not type

4. What error will occur if you try to convert a pandas column with non-numeric strings to integer using astype(int)?

medium

A. KeyError: column not found

B. TypeError: unsupported operand type(s)

C. ValueError: invalid literal for int() with base 10

D. No error, conversion succeeds

Solution

1. Step 1: Understand what astype(int) expects

   The method expects values that can be converted to integers, like numeric strings or numbers.

2. Step 2: Consider non-numeric strings

   If the column contains strings like 'abc', conversion to int is impossible and raises a ValueError.

3. Step 3: Identify the error message

   The error message is: ValueError: invalid literal for int() with base 10.

4. Final Answer:

   ValueError: invalid literal for int() with base 10 -> Option C

5. Quick Check:

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

Hint: Non-numeric strings cause ValueError on int conversion [OK]

Common Mistakes:

• Expecting silent failure or automatic conversion
• Confusing ValueError with TypeError
• Thinking KeyError relates to conversion

5. You have a DataFrame with a column 'price' containing numbers as strings with dollar signs, like '$100', '$250'. How do you convert this column to float type for calculations?

hard

A. df['price'] = df['price'].str.replace('$', '').astype(float)

B. df['price'] = df['price'].astype(float)

C. df['price'] = df['price'].replace('$', '').astype(float)

D. df['price'] = df['price'].astype(str).astype(float)

Solution

1. Step 1: Remove dollar signs from strings

   Dollar signs prevent direct conversion to float. Use str.replace('$', '') to remove them.

2. Step 2: Convert cleaned strings to float

   After removing '$', use astype(float) to convert the column to float type.

3. Step 3: Check other options

   df['price'] = df['price'].astype(float) tries to convert without removing '$' causing error. df['price'] = df['price'].replace('$', '').astype(float) uses replace without str accessor, which won't work on strings. df['price'] = df['price'].astype(str).astype(float) converts to string again unnecessarily.

4. Final Answer:

   df['price'] = df['price'].str.replace('$', '').astype(float) -> Option A

5. Quick Check:

   Remove symbols first, then astype(float) [OK]

Hint: Remove symbols with str.replace before astype(float) [OK]

Common Mistakes:

• Trying to convert strings with symbols directly
• Using replace without str accessor on strings
• Double converting types unnecessarily