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.