Series vs DataFrame relationship in Pandas - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of accessing a DataFrame column

What is the output type of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
result = type(df['A'])
print(result)

A <class 'list'>

B <class 'pandas.core.frame.DataFrame'>

C <class 'pandas.core.series.Series'>

D <class 'dict'>

💡 Hint

Accessing a single column from a DataFrame returns a Series.

When you select one column from a DataFrame using df['A'], it returns a Series object representing that column.

❓ Predict Output

intermediate

Shape of a Series vs DataFrame

What is the output of the following code?

Pandas

import pandas as pd

s = pd.Series([10, 20, 30])
df = pd.DataFrame({'X': [10, 20, 30]})
print(s.shape, df.shape)

A (3,) (3, 1)

B (3, 1) (3,)

C (1, 3) (1, 3)

D (3,) (1, 3)

💡 Hint

Series shape is one-dimensional, DataFrame shape is two-dimensional.

A Series has a shape with one dimension (number of elements), while a DataFrame has two dimensions (rows, columns).

❓ data_output

advanced

Creating a DataFrame from multiple Series

What is the resulting DataFrame when combining these Series?

Pandas

import pandas as pd

s1 = pd.Series([1, 2, 3], name='A')
s2 = pd.Series([4, 5, 6], name='B')
df = pd.concat([s1, s2], axis=1)
print(df)

A {'A': [1, 2, 3], 'B': [4, 5, 6]}

B

   A  B
0  1  4
1  2  5
2  3  6

C

0    1
1    2
2    3
0    4
1    5
2    6

D

A    1
B    4
A    2
B    5
A    3
B    6

💡 Hint

Concatenating Series along axis=1 creates columns in a DataFrame.

Using pd.concat with axis=1 stacks Series side by side as columns, forming a DataFrame.

🧠 Conceptual

advanced

Relationship between Series and DataFrame

Which statement best describes the relationship between a pandas Series and a DataFrame?

A A Series is a single column of data, and a DataFrame is a collection of Series sharing the same index.

B A Series is a two-dimensional table, and a DataFrame is a one-dimensional array.

C A Series and a DataFrame are completely unrelated data structures in pandas.

D A DataFrame is a single column of data, and a Series is a collection of DataFrames sharing the same index.

💡 Hint

Think about how columns relate to the whole table.

A DataFrame is like a table made of multiple Series (columns) that share the same index (row labels).

🔧 Debug

expert

Error when converting DataFrame column to Series

What error does the following code produce?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]})
s = pd.Series(df['A'], index=[0, 1])

A TypeError: Cannot create a Series from a DataFrame column

B No error, s is a Series with length 3

C KeyError: 'A'

D ValueError: Length of passed values is 3, index implies 2

💡 Hint

Check if the index length matches the data length.

The error occurs because the Series constructor expects the index length to match the data length. Here, data has length 3 but index has length 2.