Selecting columns in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the simplest way to select a single column from a DataFrame in pandas?

You can select a single column by using the column name in square brackets, like df['column_name']. This returns a Series with the data from that column.

beginner

How do you select multiple columns from a DataFrame?

Use a list of column names inside double square brackets, like df[['col1', 'col2']]. This returns a new DataFrame with only those columns.

beginner

What happens if you try to select a column that does not exist in the DataFrame?

Pandas will raise a KeyError because the column name is not found in the DataFrame's columns.

intermediate

Can you select columns using attribute-style access like df.column_name? When is it recommended?

Yes, you can use df.column_name if the column name is a valid Python identifier and does not conflict with DataFrame methods. However, it is safer to use df['column_name'] to avoid errors.

intermediate

How can you select columns based on a condition, for example, all columns with names starting with 'A'?

You can use list comprehension or the filter method. For example, df[[col for col in df.columns if col.startswith('A')]] selects all columns starting with 'A'.

Which syntax selects a single column named 'age' from a DataFrame df?

A df['age']

B df[['age']]

C df.age()

D df.get('age')

Using df['age'] selects the 'age' column as a Series.

How do you select multiple columns 'name' and 'salary' from a DataFrame df?

A df['name', 'salary']

B df[['name', 'salary']]

C df.name.salary

D df.get(['name', 'salary'])

Use double square brackets with a list of column names: df[['name', 'salary']].

What error occurs if you try to select a non-existent column 'height' from df?

A KeyError

B ValueError

C TypeError

D IndexError

Pandas raises a KeyError when the column name is not found.

Which method is safest to select a column named 'class' that conflicts with DataFrame methods?

A df.class()

B df.class

C df['class']

D df.get_class()

Using df['class'] avoids conflicts with DataFrame methods.

How can you select all columns starting with 'A' in a DataFrame df?

A df.filter(regex='^A', axis=1)

B df[[col for col in df.columns if col.startswith('A')]]

C df.select('A*')

D Both A and B

Both df.filter(regex='^A', axis=1) and list comprehension work to select columns starting with 'A'.

Explain how to select a single column and multiple columns from a pandas DataFrame.

Think about the difference between single and double square brackets.

Describe how to safely select columns when column names might conflict with DataFrame methods or are not valid Python identifiers.

Consider what happens if a column name is a Python keyword.