Specifying column names and index in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of this code when specifying column names?

Given the following code, what will be the column names of the resulting DataFrame?

import pandas as pd

data = [[1, 2], [3, 4]]
df = pd.DataFrame(data, columns=['A', 'B'])
print(df.columns.tolist())

Pandas

import pandas as pd

data = [[1, 2], [3, 4]]
df = pd.DataFrame(data, columns=['A', 'B'])
print(df.columns.tolist())

A ['1', '2']

B ['0', '1']

C ['A', 'B']

D ['a', 'b']

💡 Hint

Look at the columns parameter in the DataFrame constructor.

The columns parameter explicitly sets the column names. Here, it is set to ['A', 'B'], so the DataFrame columns will be named 'A' and 'B'.

❓ query_result

intermediate

What is the index of the DataFrame after specifying it explicitly?

Consider this code snippet. What will be the index of the DataFrame?

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data, index=['x', 'y'])
print(df.index.tolist())

Pandas

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data, index=['x', 'y'])
print(df.index.tolist())

A ['x', 'y']

B [0, 1]

C ['Name', 'Age']

D ['Alice', 'Bob']

💡 Hint

Check the index parameter in the DataFrame constructor.

The index parameter sets the row labels. Here, it is set to ['x', 'y'], so the DataFrame index will be ['x', 'y'].

📝 Syntax

advanced

Which option causes an error when specifying columns and index?

Which of the following DataFrame constructions will cause an error?

import pandas as pd

data = [[1, 2], [3, 4]]

A pd.DataFrame(data, columns=['A', 'B'], index=['row1', 'row2'])

B pd.DataFrame(data, columns='A,B', index=['row1', 'row2'])

C )]'2wor' ,'1wor'[=xedni ,]'B' ,'A'[=snmuloc ,atad(emarFataD.dp

D d.DataFrame(data, columns=['A', 'B'], index=['row1', 'row2'])

💡 Hint

Check the type expected for the columns parameter.

The columns parameter expects a list of strings, not a single string with commas. Option B passes a single string 'A,B' which causes a ValueError in pandas.

❓ query_result

advanced

What is the output of this code specifying columns and index with mismatched lengths?

What happens when the number of columns and data columns do not match?

import pandas as pd

data = [[1, 2], [3, 4]]
df = pd.DataFrame(data, columns=['A', 'B', 'C'])
print(df)

Pandas

import pandas as pd

data = [[1, 2], [3, 4]]
df = pd.DataFrame(data, columns=['A', 'B', 'C'])
print(df)

A Raises a ValueError about columns length mismatch

B Creates DataFrame with NaN in column 'C'

C Ignores extra column name 'C' and creates DataFrame with columns 'A' and 'B'

D Creates DataFrame with only one column 'A'

💡 Hint

Check if pandas allows columns list length to differ from data width.

Pandas raises a ValueError if the number of columns specified does not match the number of data columns.

🧠 Conceptual

expert

Why specify both columns and index explicitly in a DataFrame?

Which of the following is the best reason to specify both columns and index explicitly when creating a DataFrame?

A To convert all data to strings

B To reduce the memory usage of the DataFrame

C To automatically sort the data by the index and columns

D To ensure the DataFrame has meaningful row and column labels for easier data access and readability

💡 Hint

Think about how labels help when working with data.

Specifying columns and index explicitly gives clear labels to rows and columns, making data easier to understand and access.