Basic DataFrame info (shape, dtypes, describe) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the shape attribute of a DataFrame tell you?

The shape attribute shows the number of rows and columns in the DataFrame as a tuple (rows, columns).

beginner

How can you find the data types of each column in a DataFrame?

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

beginner

What information does the describe() method provide for a DataFrame?

The describe() method gives summary statistics like count, mean, std (standard deviation), min, max, and quartiles for numeric columns.

beginner

If you want to quickly check how many rows and columns your data has, which attribute do you use?

You use the shape attribute to quickly see the number of rows and columns.

beginner

Why is it useful to check dtypes before analysis?

Checking dtypes helps you understand what kind of data each column holds, so you can choose the right analysis or fix data issues early.

What does df.shape return for a DataFrame df?

A Summary statistics of numeric columns

B The data types of each column

C A tuple with (number of rows, number of columns)

D The first 5 rows of the DataFrame

df.shape returns a tuple showing the number of rows and columns.

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

A shape

B dtypes

C describe()

D info()

dtypes shows the data type of each column.

What kind of information does df.describe() provide?

A Summary statistics like mean, min, max for numeric columns

B Number of rows and columns

C Data types of columns

D The column names

describe() gives summary statistics for numeric columns.

If you want to know how many columns your DataFrame has, which is the easiest way?

A Use <code>df.dtypes</code>

B Use <code>df.head()</code>

C Use <code>df.describe()</code>

D Use <code>df.shape[1]</code>

df.shape[1] gives the number of columns.

Why is it important to check dtypes before starting data analysis?

A To understand the kind of data in each column for correct analysis

B To get summary statistics

C To see how many rows the data has

D To rename columns

Knowing data types helps choose the right analysis and catch errors early.

Explain how you would quickly check the size and data types of a DataFrame.

Think about attributes that give you quick info about rows, columns, and types.

Describe what kind of summary information the describe() method provides and why it is useful.

Focus on what describe() tells you about numeric columns.