Changing data types (astype) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'age' column to integer type.

Data Analysis Python

df['age'] = df['age'].[1](int)

Drag options to blanks, or click blank then click option'

Aastype

Bconvert

Cchange_type

Dto_int

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent method like 'convert' or 'to_int'.

Trying to assign the type directly without a method.

The astype method is used to change the data type of a pandas Series or DataFrame column.

2fill in blank

medium

Complete the code to convert the 'price' column to float type.

Data Analysis Python

df['price'] = df['price'].[1](float)

Drag options to blanks, or click blank then click option'

Aconvert

Bastype

Cto_float

Dchange_type

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'to_float' which is not a pandas method.

Trying to convert without using a method.

Use astype(float) to convert a column to float type in pandas.

3fill in blank

hard

Fix the error in the code to convert the 'date' column to string type.

Data Analysis Python

df['date'] = df['date'].[1](str)

Drag options to blanks, or click blank then click option'

Achange_type

Bconvert

Cto_string

Dastype

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'to_string' which is not a pandas method for type conversion.

Using 'convert' or 'change_type' which do not exist.

The correct method to change data types in pandas is astype. Other options are not valid pandas methods.

4fill in blank

hard

Fill both blanks to convert the 'score' column to float and then to string.

Data Analysis Python

df['score'] = df['score'].[1](float).[2](str)

Drag options to blanks, or click blank then click option'

Aastype

Bconvert

Cto_string

Dchange_type

Attempts:

3 left

💡 Hint

Common Mistakes

Using different methods for each conversion.

Using non-existent methods like 'convert' or 'to_string'.

You can chain astype calls to convert data types step by step.

5fill in blank

hard

Fill all three blanks to create a new column 'age_str' by converting 'age' to string after converting to float.

Data Analysis Python

df['age_str'] = df['age'].[1](int).[2](float).[3](str)

Drag options to blanks, or click blank then click option'

Aastype

Bconvert

Cto_string

Dchange_type

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing different methods for conversion.

Using methods that do not exist in pandas.

Use astype for each step of conversion. This chains conversions from int to float to string.

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