columns and index attributes in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of accessing the columns attribute?

Given the following pandas DataFrame:

import pandas as pd

df = pd.DataFrame({
  'Name': ['Alice', 'Bob'],
  'Age': [25, 30]
})

What is the output of df.columns?

Pandas

import pandas as pd
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [25, 30]})
print(df.columns)

A RangeIndex(start=0, stop=2, step=1)

B ['Name', 'Age']

C ['Alice', 'Bob']

D Index(['Name', 'Age'], dtype='object')

💡 Hint

The columns attribute returns the labels of the columns, not the data itself.

The columns attribute returns an Index object containing the column labels of the DataFrame.

❓ query_result

intermediate

What does the index attribute represent?

Consider this DataFrame:

import pandas as pd

df = pd.DataFrame({
  'City': ['Paris', 'London'],
  'Population': [2148000, 8982000]
})

What is the output of df.index?

Pandas

import pandas as pd
df = pd.DataFrame({'City': ['Paris', 'London'], 'Population': [2148000, 8982000]})
print(df.index)

A RangeIndex(start=0, stop=2, step=1)

B Index(['City', 'Population'], dtype='object')

C ['Paris', 'London']

D ['City', 'Population']

💡 Hint

The index attribute shows the row labels, not the column names or data.

By default, pandas assigns a RangeIndex starting at 0 for rows if no index is specified.

📝 Syntax

advanced

Which option correctly sets a new index for a DataFrame?

Given a DataFrame df with columns 'A' and 'B', which code correctly sets column 'A' as the index?

Pandas

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

A df.set_index('A')

B df.set_index('A', inplace=True)

C df.index('A')

D df.index = df['A']

💡 Hint

To change the DataFrame index permanently, you need to use the inplace parameter.

Option B correctly sets column 'A' as the index and modifies df in place. Option B returns a new DataFrame but does not change df unless assigned. Option B assigns the index but does not remove column 'A'. Option B is invalid syntax.

❓ query_result

advanced

What is the output of resetting the index?

Given this DataFrame with a custom index:

import pandas as pd

df = pd.DataFrame({'X': [10, 20]}, index=['a', 'b'])
reset_df = df.reset_index()
print(reset_df)

Pandas

import pandas as pd
df = pd.DataFrame({'X': [10, 20]}, index=['a', 'b'])
reset_df = df.reset_index()
print(reset_df)

A

  level_0   X
0       a  10
1       b  20

B

     X
0  10
1  20

C

  index   X
0     a  10
1     b  20

D

  index  X
a    10
b    20

💡 Hint

Resetting index moves the index into a column named 'index' by default.

reset_index() moves the current index into a new column named 'index' and creates a new default integer index.

🧠 Conceptual

expert

Why is it important to understand the difference between columns and index in pandas?

Which of the following best explains why knowing the difference between columns and index attributes is crucial when working with pandas DataFrames?

A Because columns hold the data values and index defines the row labels, affecting data selection and alignment.

B Because columns represent row labels and index represents column labels, mixing them causes errors.

C Because both columns and index store data values, but index is faster to access.

D Because index is only used for sorting, while columns are used for filtering data.

💡 Hint

Think about how pandas uses index and columns to organize data.

Columns contain the data fields, while index labels the rows. This distinction affects how you select, join, and align data in pandas.