columns and index attributes in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the column names of the DataFrame.

Pandas

column_names = df.[1]

Drag options to blanks, or click blank then click option'

Akeys

Bindex

Cvalues

Dcolumns

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'index' instead of 'columns' to get column names.

Trying to use a method instead of an attribute.

The columns attribute returns the column labels of the DataFrame.

2fill in blank

medium

Complete the code to get the index labels of the DataFrame.

Pandas

row_labels = df.[1]

Drag options to blanks, or click blank then click option'

Aindex

Baxes

Ccolumns

Dkeys

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'columns' when trying to get row labels.

Using methods like keys() which do not return index labels.

The index attribute returns the row labels of the DataFrame.

3fill in blank

hard

Fix the error in the code to access the DataFrame's columns attribute correctly.

Pandas

cols = df.[1]()

Drag options to blanks, or click blank then click option'

Avalues

Bindex

Ccolumns

Dkeys

Attempts:

3 left

💡 Hint

Common Mistakes

Adding parentheses after columns causing a TypeError.

Confusing attributes with methods.

The columns attribute is not a method, so it should not have parentheses.

4fill in blank

hard

Fill both blanks to get the index and columns of the DataFrame as a tuple.

Pandas

info = (df.[1], df.[2])

Drag options to blanks, or click blank then click option'

Aindex

Bvalues

Ccolumns

Dkeys

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping index and columns in the tuple.

Using values or keys which do not return labels.

The index attribute gives row labels, and columns gives column names.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as column names and values as index labels.

Pandas

mapping = {col: df.[1][[2]] for col in df.[3]

Drag options to blanks, or click blank then click option'

Aindex

B0

Ccolumns

Dvalues

Attempts:

3 left

💡 Hint

Common Mistakes

Using values instead of index for row labels.

Using string '0' instead of integer 0 for indexing.

Swapping columns and index.

This creates a dictionary where each column name maps to the first index label (position 0).

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()