Creating DataFrame from dictionary in Pandas - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of DataFrame from dictionary with lists

What is the output of this code?

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

Pandas

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

A

   A  B
0  1  3
1  2  4

B

   A  B
1  1  3
2  2  4

C Error: Length mismatch between columns and data

D

   0  1
A  1  2
B  3  4

💡 Hint

Think about how pandas uses dictionary keys as column names and list values as column data.

When creating a DataFrame from a dictionary of lists, keys become column names and lists become column data. Index defaults to 0,1,...

❓ data_output

intermediate

Number of rows in DataFrame from dictionary with unequal list lengths

Given this code, how many rows will the DataFrame have?

import pandas as pd
data = {'X': [10, 20, 30], 'Y': [5, 15]}
df = pd.DataFrame(data)
print(len(df))

Pandas

import pandas as pd
data = {'X': [10, 20, 30], 'Y': [5, 15]}
df = pd.DataFrame(data)
print(len(df))

A Error: All arrays must be of the same length

B 2

C 3

D 1

💡 Hint

Check if pandas allows columns with different lengths when creating DataFrame.

Pandas requires all columns to have the same length when creating a DataFrame from a dictionary of lists. Otherwise, it raises a ValueError.

❓ visualization

advanced

Visualizing DataFrame created from nested dictionary

What does the DataFrame look like after running this code?

import pandas as pd
data = {'one': {'a': 1, 'b': 2}, 'two': {'a': 3, 'b': 4}}
df = pd.DataFrame(data)
print(df)

Pandas

import pandas as pd
data = {'one': {'a': 1, 'b': 2}, 'two': {'a': 3, 'b': 4}}
df = pd.DataFrame(data)
print(df)

A Error: Cannot create DataFrame from nested dictionary

B

   a  b
one 1  2
two 3  4

C

   one  two
0    1    3
1    2    4

D

   one  two
 a    1    3
 b    2    4

💡 Hint

Nested dictionaries create DataFrame with outer keys as columns and inner keys as index.

When creating a DataFrame from a nested dictionary, outer keys become columns and inner keys become row indices.

🧠 Conceptual

advanced

Effect of dictionary keys on DataFrame columns

If you create a DataFrame from this dictionary:

{0: ['x', 'y'], 1: ['a', 'b']}

What will be the column names of the DataFrame?

A ['a', 'b']

B ['x', 'y']

C [0, 1]

D ['0', '1']

💡 Hint

Dictionary keys become column names directly, preserving their type.

The keys 0 and 1 become column names as integers, not strings or values.

🔧 Debug

expert

DataFrame from dictionary with mixed list and scalar values

What happens when running this code?

import pandas as pd
data = {'col1': [1, 2], 'col2': 3}
df = pd.DataFrame(data)

A TypeError: DataFrame constructor not supported for this input

B No error, DataFrame created with col2 filled with 3

C ValueError: If using all scalar values, you must pass index

D KeyError: 'col2'

💡 Hint

Pandas broadcasts scalar values to match the length of list columns.

When dictionary values mix lists and scalars, pandas broadcasts the scalar value to fill the column, matching the list length.