columns and index attributes in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the columns attribute in a pandas DataFrame represent?

The columns attribute shows the labels of all the columns in the DataFrame. It is like the names on top of each column.

beginner

What is the purpose of the index attribute in a pandas DataFrame?

The index attribute holds the labels for the rows. It helps to identify each row uniquely, like row numbers or custom labels.

beginner

How can you access the column names of a DataFrame named df?

You can get the column names by using df.columns. It returns an Index object with all column labels.

beginner

How do you get the row labels of a DataFrame named df?

Use df.index to get the row labels. It returns an Index object with all row labels.

intermediate

Can the index and columns attributes be changed after creating a DataFrame?

Yes, both index and columns can be changed by assigning new labels. For example, df.columns = ['A', 'B'] changes column names.

What does df.columns return in pandas?

A The data types of columns

B The list of row labels

C The list of column labels

D The number of rows

df.columns returns the labels of the columns in the DataFrame.

Which attribute gives you the row labels in a pandas DataFrame?

A df.index

B df.shape

C df.values

D df.columns

df.index holds the row labels of the DataFrame.

If you want to rename the columns of a DataFrame, which attribute would you assign new values to?

A df.columns

B df.index

C df.values

D df.rename

Assigning a new list to df.columns changes the column names.

What type of object do df.columns and df.index return?

A List

B Index object

C Dictionary

D Tuple

Both df.columns and df.index return a pandas Index object.

Can the index attribute contain non-numeric labels?

A Only boolean values are allowed

B No, only numbers are allowed

C Only dates are allowed

D Yes, it can have strings or other labels

The index can have any hashable labels like strings, dates, or numbers.

Explain what the columns and index attributes represent in a pandas DataFrame.

Think about how you name rows and columns in a table.

How can you change the column names and row labels of a pandas DataFrame?

Consider assigning new values directly to attributes.

Practice

1. What does the columns attribute in a pandas DataFrame represent?

easy

A. The number of rows in the DataFrame

B. The data types of each column

C. The names of the columns in the DataFrame

D. The values of the first row

Solution

1. Step 1: Understand the role of columns in pandas

   The columns attribute stores the labels for each column in the DataFrame, like the headers in a table.

2. Step 2: Compare with other attributes

   Other attributes like index store row labels, not column names.

3. Final Answer:

   The names of the columns in the DataFrame -> Option C

4. Quick Check:

   columns = column names [OK]

Hint: Columns attribute holds column names, like table headers [OK]

Common Mistakes:

• Confusing columns with index
• Thinking columns store data types
• Assuming columns count rows

2. Which of the following is the correct way to get the row labels of a pandas DataFrame df?

easy

A. df.index

B. df.columns

C. df.labels

D. df.rows

Solution

1. Step 1: Recall the attribute for row labels

   The index attribute in pandas stores the row labels or indices.

2. Step 2: Verify other options

   columns is for column names; labels and rows are not valid pandas attributes.

3. Final Answer:

   df.index -> Option A

4. Quick Check:

   index = row labels [OK]

Hint: Use df.index to get row labels, not df.columns [OK]

Common Mistakes:

• Using df.columns to get row labels
• Trying non-existent attributes like df.labels
• Confusing rows with index attribute

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({
  'A': [1, 2],
  'B': [3, 4]
}, index=['x', 'y'])
print(df.columns)

What will be the output?

medium

A. Index(['A', 'B'], dtype='object')

B. Index(['x', 'y'], dtype='object')

C. ['x', 'y']

D. ['A', 'B']

Solution

1. Step 1: Understand the DataFrame creation

   The DataFrame has columns 'A' and 'B' and row indices 'x' and 'y'.

2. Step 2: Check what df.columns returns

   df.columns returns an Index object listing the column names, which are 'A' and 'B'.

3. Final Answer:

   Index(['A', 'B'], dtype='object') -> Option A

4. Quick Check:

   df.columns = Index of column names [OK]

Hint: df.columns shows column names as Index object [OK]

Common Mistakes:

• Confusing columns with index values
• Expecting a list instead of Index object
• Mixing up row labels with columns

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
df.index = ['a', 'b', 'c']

medium

A. The DataFrame must have numeric index only

B. DataFrame columns cannot be renamed this way

C. The code is correct and runs without error

D. The index list length does not match the number of rows

Solution

1. Step 1: Check DataFrame shape

   The DataFrame has 2 rows but the new index list has 3 labels.

2. Step 2: Understand index assignment rules

   When assigning to df.index, the length must match the number of rows exactly.

3. Final Answer:

   The index list length does not match the number of rows -> Option D

4. Quick Check:

   Index length must equal row count [OK]

Hint: Index length must match rows count exactly [OK]

Common Mistakes:

• Assigning index list with wrong length
• Thinking columns rename affects index
• Assuming any list can be index regardless of length

5. You have a DataFrame df with columns ['name', 'age', 'city'] and default numeric index. How do you rename the columns to ['Name', 'Age', 'City'] and set the index to the 'name' column in one step?

hard

A. df.columns = ['Name', 'Age', 'City']; df.set_index('name', inplace=True)

B. df.rename(columns={'name':'Name', 'age':'Age', 'city':'City'}, inplace=True); df.set_index('Name', inplace=True)

C. df.columns = ['Name', 'Age', 'City']; df.index = df['Name']

D. df.rename(index={'name':'Name'}, inplace=True); df.columns = ['Name', 'Age', 'City']

Solution

1. Step 1: Rename columns properly

   Use df.rename(columns=..., inplace=True) to rename columns safely without losing data.

2. Step 2: Set the index to the renamed 'Name' column

   Use df.set_index('Name', inplace=True) to set the index to the 'Name' column after renaming.

3. Final Answer:

   df.rename(columns={'name':'Name', 'age':'Age', 'city':'City'}, inplace=True); df.set_index('Name', inplace=True) -> Option B

4. Quick Check:

   Rename columns then set index by column name [OK]

Hint: Rename columns with rename(), then set_index() by new name [OK]

Common Mistakes:

• Assigning columns list directly without rename()
• Setting index before renaming columns
• Assigning index to a Series without set_index()