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.