Using appropriate dtypes in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a dtype in pandas?

A dtype (data type) in pandas tells what kind of data is stored in a column, like numbers, text, or dates.

beginner

Why should you use appropriate dtypes in pandas?

Using the right dtype saves memory and makes data operations faster and more accurate.

beginner

How do you check the dtype of a pandas DataFrame column?

Use the `.dtype` attribute on a column, for example: `df['column'].dtype`.

beginner

How can you change a column's dtype to integer in pandas?

Use `df['column'] = df['column'].astype('int')` to convert the column to integer type.

intermediate

What dtype is best for storing categories like 'red', 'blue', 'green' in pandas?

The 'category' dtype is best because it saves memory and speeds up operations on repeated text values.

Which dtype uses the least memory for repeated text values in pandas?

A object

B category

C int64

D float64

The 'category' dtype stores repeated text efficiently by using codes instead of full strings.

What happens if you store numbers as 'object' dtype in pandas?

A No difference

B Operations are faster

C Memory use is lower

D Operations are slower and memory use is higher

Storing numbers as 'object' means pandas treats them like text, which slows down calculations and uses more memory.

How do you convert a pandas column to datetime dtype?

A df['col'] = pd.to_datetime(df['col'])

B df['col'] = df['col'].astype('int')

C df['col'] = df['col'].astype('category')

D df['col'] = df['col'].astype('object')

Use `pd.to_datetime()` to convert columns to datetime dtype for date/time operations.

Which dtype is best for decimal numbers with fractions?

A float64

B int64

C category

D object

Float64 stores decimal numbers with fractions accurately.

What is the default dtype for text columns in pandas?

A string

B category

C object

D int64

By default, pandas uses 'object' dtype for text columns.

Explain why choosing the right dtype in pandas is important for data analysis.

Think about how computers store and process data.

Describe how to check and change the dtype of a column in a pandas DataFrame.

Look at simple pandas commands for dtype.