astype() for type conversion in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of astype() converting float to int

What is the output DataFrame after converting the 'score' column from float to int using astype()?

Pandas

import pandas as pd

df = pd.DataFrame({'score': [95.7, 88.2, 76.9]})
df['score'] = df['score'].astype(int)
print(df)

A

   score
0     95
1     88
2     76

B

   score
0     96
1     88
2     77

C

   score
0    95.7
1    88.2
2    76.9

D

   score
0    95.0
1    88.0
2    76.0

💡 Hint

astype(int) truncates the decimal part without rounding.

When converting float to int with astype(), pandas truncates decimals, so 95.7 becomes 95, not rounded.

❓ data_output

intermediate

Resulting dtypes after astype() conversion

After running the code below, what are the data types of the columns in the DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3.5, 4.5]})
df = df.astype({'A': 'float64', 'B': 'int32'})
print(df.dtypes)

A

A    float64
B      int32
dtype: object

B

A      int64
B    float64
dtype: object

C

A      int32
B      int32
dtype: object

D

A    float64
B    float64
dtype: object

💡 Hint

Check the specified types in the astype dictionary.

Column 'A' is converted to float64 and 'B' to int32 as specified in astype().

🔧 Debug

advanced

Identify the error when converting string to numeric

What error will this code raise when trying to convert the 'age' column to int using astype()?

Pandas

import pandas as pd

df = pd.DataFrame({'age': ['25', 'thirty', '40']})
df['age'] = df['age'].astype(int)

A KeyError: 'age'

B TypeError: cannot convert string to int

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

D No error, conversion succeeds

💡 Hint

Check if all strings can be converted to int.

The string 'thirty' cannot be converted to int, causing a ValueError.

🚀 Application

advanced

Using astype() to optimize memory usage

Given a DataFrame with a column 'flag' containing only 0 and 1 as integers, which astype() conversion reduces memory usage most effectively?

Pandas

import pandas as pd

df = pd.DataFrame({'flag': [0, 1, 1, 0, 0]})

A df['flag'] = df['flag'].astype('object')

B df['flag'] = df['flag'].astype('int64')

C df['flag'] = df['flag'].astype('float32')

D df['flag'] = df['flag'].astype('bool')

💡 Hint

Boolean type uses less memory than integer types.

Converting to bool uses less memory than int64 or float32 for binary data.

🧠 Conceptual

expert

Effect of astype() on DataFrame copy behavior

Which statement about astype() behavior is TRUE?

A astype() returns a new DataFrame only if the dtype changes; otherwise, it returns the original.

B astype() always returns a new DataFrame and never modifies the original in place.

C astype() modifies the original DataFrame in place and returns the same object.

D astype() modifies the original DataFrame in place without returning anything.

💡 Hint

Check if astype() has an inplace parameter.

astype() returns a new DataFrame or Series with converted types and does not modify the original unless reassigned.

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()