Creating DataFrame from list of dictionaries in Pandas - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this DataFrame creation?

Given the list of dictionaries, what will be the output DataFrame?

Pandas

import pandas as pd

data = [{'name': 'Alice', 'age': 25}, {'name': 'Bob', 'age': 30}]
df = pd.DataFrame(data)
print(df)

A

   name  age
0  Alice  25.0
1    Bob  30.0

B

   name  age
0  Alice   25
1    Bob   30

C

   name  age
0  Alice   25
1    Bob  NaN

D

   name  age
0  Alice  NaN
1    Bob   30

💡 Hint

Each dictionary becomes a row; keys become columns.

The DataFrame is created with columns 'name' and 'age'. Both dictionaries have these keys, so values fill the rows without missing data.

❓ data_output

intermediate

How many rows and columns does this DataFrame have?

What is the shape (rows, columns) of the DataFrame created from this list?

Pandas

import pandas as pd

data = [{'x': 1, 'y': 2}, {'x': 3, 'z': 4}, {'y': 5, 'z': 6}]
df = pd.DataFrame(data)
print(df.shape)

A (3, 3)

B (3, 2)

C (2, 3)

D (2, 2)

💡 Hint

Count unique keys across all dictionaries for columns; count dictionaries for rows.

There are 3 dictionaries (rows) and 3 unique keys ('x', 'y', 'z') making 3 columns.

🔧 Debug

advanced

What error does this code raise?

Identify the error raised by this code snippet:

Pandas

import pandas as pd

data = [{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]
df = pd.DataFrame(data, columns=['a', 'c'])
print(df)

A KeyError

B TypeError

C No error, prints DataFrame with NaN in column 'c'

D ValueError

💡 Hint

Check how pandas handles missing columns when specifying columns parameter.

Pandas creates the DataFrame with columns 'a' and 'c'. Since 'c' is missing in data, its values are NaN.

🚀 Application

advanced

Which option creates a DataFrame with only keys 'name' and 'score'?

Given this list of dictionaries, which code creates a DataFrame with only 'name' and 'score' columns?

Pandas

data = [{'name': 'Anna', 'score': 90, 'age': 20}, {'name': 'Ben', 'score': 85, 'age': 22}]

A All of the above

B pd.DataFrame(data).drop('age', axis=1)

C pd.DataFrame([{k: d[k] for k in ['name', 'score']} for d in data])

D pd.DataFrame(data, columns=['name', 'score'])

💡 Hint

Consider how each method selects or removes columns.

All options produce a DataFrame with only 'name' and 'score' columns.

🧠 Conceptual

expert

What happens when dictionaries have nested dictionaries as values?

If you create a DataFrame from a list of dictionaries where some values are nested dictionaries, what is the type of those nested values in the DataFrame?

Pandas

import pandas as pd

data = [{'id': 1, 'info': {'height': 170, 'weight': 65}}, {'id': 2, 'info': {'height': 180, 'weight': 75}}]
df = pd.DataFrame(data)
print(type(df.loc[0, 'info']))

A <class 'float'>

B <class 'str'>

C <class 'pandas.Series'>

D <class 'dict'>

💡 Hint

Think about how pandas stores complex objects inside cells.

Pandas stores nested dictionaries as objects in cells, so the type remains dict.