Changing data types (astype) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code after changing data types?

Given the DataFrame below, what will be the data type of column 'A' after applying astype('float')?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': ['1', '2', '3'], 'B': [4, 5, 6]})
df['A'] = df['A'].astype('float')
print(df['A'].dtype)

A string

B int64

C object

D float64

💡 Hint

Think about what astype('float') does to string numbers.

The astype('float') converts the string numbers in column 'A' to floating point numbers, so the dtype becomes float64.

❓ data_output

intermediate

What is the resulting DataFrame after type conversion?

What will be the output DataFrame after converting column 'C' to integers?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'C': [1.1, 2.5, 3.9]})
df['C'] = df['C'].astype('int')
print(df)

A

   C
0  2
1  3
2  4

B

   C
0  1
1  2
2  3

C

   C
0  1.1
1  2.5
2  3.9

D

   C
0  1
1  3
2  4

💡 Hint

Remember that converting float to int truncates the decimal part.

When converting floats to integers using astype('int'), the decimal part is dropped (not rounded). So 1.1 becomes 1, 2.5 becomes 2, and 3.9 becomes 3.

🔧 Debug

advanced

Why does this code raise an error?

This code tries to convert a column with mixed types to integer. What error does it raise?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'D': ['10', 'twenty', '30']})
df['D'] = df['D'].astype('int')

A ValueError: invalid literal for int() with base 10: 'twenty'

B TypeError: cannot convert float to int

C KeyError: 'D'

D SyntaxError: invalid syntax

💡 Hint

Look at the string 'twenty' and think about converting it to int.

The string 'twenty' cannot be converted to an integer, so Python raises a ValueError indicating the invalid literal.

🧠 Conceptual

advanced

Which data type conversion is NOT possible with astype()?

Which of the following conversions using astype() will raise an error?

A Converting a string column with non-numeric text to float

B Converting an integer column to boolean

C Converting a float column to string

D Converting a boolean column to integer

💡 Hint

Think about converting text that is not a number to a numeric type.

Converting a string column that contains non-numeric text (like words) to float will raise a ValueError because the text cannot be parsed as a number.

🚀 Application

expert

How many unique data types are in this DataFrame after conversions?

After running the code below, how many unique data types are present in the DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'X': ['1', '2', '3'],
    'Y': [1.0, 2.0, 3.0],
    'Z': [True, False, True]
})
df['X'] = df['X'].astype('int')
df['Y'] = df['Y'].astype('int')
df['Z'] = df['Z'].astype('int')
print(df.dtypes.nunique())

A 4

B 3

C 1

D 2

💡 Hint

Check the data types after conversion carefully.

After converting all columns to int, all columns have dtype int64, so there is only 1 unique data type.

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