astype() for type conversion in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

Aconvert

Bchange_type

Cto_int

Dastype

Attempts:

3 left

💡 Hint

Common Mistakes

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

Trying to assign a string directly without conversion.

The astype() method is used to convert 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.

Pandas

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

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

Ato_float

Bchange_type

Castype

Dconvert

Attempts:

3 left

💡 Hint

Common Mistakes

Using methods that do not exist in pandas.

Passing the wrong type string.

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

3fill in blank

hard

Complete the code to convert the 'score' column to string type.

Pandas

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

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

Achange_type

Bastype

Cto_str

Dconvert

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the Python type str instead of the string 'str'.

Using a non-existent method.

The astype() method requires the type as a string like 'str', not the Python str type directly.

4fill in blank

hard

Fill both blanks to create a new column 'is_adult' with boolean type based on 'age' >= 18.

Pandas

df['is_adult'] = (df['age'] [1] 18).[2]('bool')

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

A>=

Bastype

C<

Dconvert

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators like '<'.

Using a non-existent method for type conversion.

Use the comparison operator >= to check if age is at least 18, then convert the result to boolean with astype('bool').

5fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

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

A:

Blen(word)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong dictionary syntax.

Using '<' instead of '>' for filtering.

The dictionary comprehension uses : to separate keys and values, len(word) to get the length, and > to filter words longer than 3 characters.

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()