dtypes for column data types in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does dtypes represent in a pandas DataFrame?

dtypes shows the data type of each column in a pandas DataFrame, like numbers, text, or dates.

beginner

Name three common data types you might see in pandas dtypes output.

Common data types include int64 (integers), float64 (decimal numbers), and object (usually text).

beginner

How can you check the data types of columns in a pandas DataFrame?

Use DataFrame.dtypes to see the data type of each column.

intermediate

Why is it important to know the dtypes of your DataFrame columns?

Knowing dtypes helps you understand what kind of data you have and choose the right methods to analyze or clean it.

beginner

What does the object dtype usually mean in pandas?

object dtype usually means the column contains text (strings) or mixed types.

Which pandas attribute shows the data types of each column in a DataFrame?

A info

B shape

C head

D dtypes

dtypes shows the data types of columns. shape shows size, head shows first rows, and info gives summary including dtypes.

What dtype does pandas use for text data?

A int64

B float64

C object

D bool

Text data is usually stored as object dtype in pandas.

If a column has decimal numbers, what dtype will pandas assign?

A int64

B float64

C object

D datetime64

Decimal numbers are stored as float64 dtype.

Why might you want to check the dtypes of your DataFrame?

A To understand the kind of data in each column

B To sort the data

C To know the number of rows

D To rename columns

Checking dtypes helps you understand what kind of data each column holds.

Which dtype is NOT a common pandas column data type?

A string64

B int64

C object

D float64

string64 is not a standard pandas dtype; text is usually object or string.

Explain what dtypes tells you about a pandas DataFrame and why it matters.

Think about how knowing the kind of data helps you work with it.

Describe how you would check the data types of columns in a DataFrame and interpret the result.

Imagine you just loaded a table and want to know what kind of data each column holds.