astype() for type conversion in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the astype() method do in pandas?

The astype() method changes the data type of a pandas Series or DataFrame column to a specified type, like integer, float, or string.

beginner

How do you convert a column named 'age' in a DataFrame df to integers using astype()?

You use df['age'] = df['age'].astype(int) to convert the 'age' column to integer type.

beginner

Can astype() convert a column with numbers stored as strings to numeric types?

Yes, astype() can convert string numbers like '10' to numeric types like int or float, as long as the strings are valid numbers.

intermediate

What happens if you try to convert a column with non-numeric strings to a numeric type using astype()?

It will raise an error because astype() cannot convert non-numeric strings to numbers directly.

intermediate

How can you convert multiple columns in a DataFrame to different types using astype()?

You can pass a dictionary to astype() like df = df.astype({'col1': 'int', 'col2': 'float'}) to convert multiple columns at once.

What is the correct way to convert a pandas Series s to float type?

A s.astype(float)

B astype(s, float)

C s.convert(float)

D convert(s, float)

The correct syntax is s.astype(float) to convert the Series to float type.

If a DataFrame column has values ['1', '2', 'three'], what happens when you run df['col'].astype(int)?

A The column converts successfully to integers.

B It raises an error due to 'three' not being numeric.

C It converts 'three' to NaN automatically.

D It converts all values to strings.

Conversion fails because 'three' is not a numeric string, causing an error.

Which of these is a valid argument for astype() to convert to string type?

A All of the above

B 'string'

C str

D 'str'

You can use 'str', 'string', or the Python type str to convert to string type.

How do you convert multiple columns to different types in one step?

A df.type({'col1': 'int', 'col2': 'float'})

B df.convert({'col1': 'int', 'col2': 'float'})

C df.change_type({'col1': 'int', 'col2': 'float'})

D df.astype({'col1': 'int', 'col2': 'float'})

Passing a dictionary to astype() converts multiple columns at once.

What type of object can astype() be used on?

A pandas Series

B pandas DataFrame

C Both Series and DataFrame

D Only Python lists

astype() works on both pandas Series and DataFrame objects.

Explain how to use astype() to convert a DataFrame column from string to integer type. Include what happens if the strings are not numeric.

Think about what happens when conversion is impossible.

Describe how to convert multiple columns in a DataFrame to different data types using astype().

Use a dictionary inside astype()

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()