Creating DataFrame from NumPy array in Pandas - Practice Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code creating a DataFrame?

Consider the following code that creates a DataFrame from a NumPy array. What will be the output DataFrame?

Pandas

import numpy as np
import pandas as pd

arr = np.array([[1, 2], [3, 4]])
df = pd.DataFrame(arr, columns=['A', 'B'])
print(df)

A

   A  B
1  1  2
2  3  4

B

   A  B
0  1  2
1  3  4

C

   A  B
0  2  1
1  4  3

D

   0  1
0  1  2
1  3  4

💡 Hint

Look at how the columns parameter names the columns and the default index.

The DataFrame is created with columns named 'A' and 'B'. The default index is 0 and 1. The values match the array rows.

❓ data_output

intermediate

How many rows and columns does this DataFrame have?

Given this code creating a DataFrame from a NumPy array, how many rows and columns does the resulting DataFrame have?

Pandas

import numpy as np
import pandas as pd

arr = np.arange(12).reshape(3, 4)
df = pd.DataFrame(arr)
print(df.shape)

A (3, 4)

B (4, 3)

C (12, 1)

D (1, 12)

💡 Hint

The reshape method sets rows and columns as (3, 4).

The array is reshaped to 3 rows and 4 columns, so the DataFrame shape is (3, 4).

🔧 Debug

advanced

What error does this code raise?

This code tries to create a DataFrame from a NumPy array with mismatched columns. What error will it raise?

Pandas

import numpy as np
import pandas as pd

arr = np.array([[1, 2], [3, 4]])
df = pd.DataFrame(arr, columns=['A', 'B', 'C'])

A No error, DataFrame created with NaN in missing column

B TypeError: columns must be a list of integers

C IndexError: list index out of range

D ValueError: 3 columns passed, passed data had 2 columns

💡 Hint

Check if the number of columns matches the array shape.

The columns list has 3 names but the array has only 2 columns, causing a ValueError.

🚀 Application

advanced

Which option produces a DataFrame with custom row labels?

You want to create a DataFrame from a NumPy array with columns 'X', 'Y' and row labels 'a', 'b'. Which code does this correctly?

Pandas

import numpy as np
import pandas as pd

arr = np.array([[10, 20], [30, 40]])

A pd.DataFrame(arr, index=['X', 'Y'], columns=['a', 'b'])

B pd.DataFrame(arr, columns=['a', 'b'], index=['X', 'Y'])

C pd.DataFrame(arr, columns=['X', 'Y'], index=['a', 'b'])

D pd.DataFrame(arr, index=['a', 'b'])

💡 Hint

Columns are the names of the data columns, index is for row labels.

Option C correctly assigns columns and index labels matching the data shape.

🧠 Conceptual

expert

What happens if you create a DataFrame from a 3D NumPy array?

You try to create a DataFrame from a 3-dimensional NumPy array. What will happen?

Pandas

import numpy as np
import pandas as pd

arr = np.ones((2, 2, 2))
df = pd.DataFrame(arr)

A Raises ValueError: Must pass 2-d input

B Creates a DataFrame flattening the array automatically

C Creates a DataFrame with nested arrays in cells

D Creates a DataFrame with default integer columns and rows

💡 Hint

DataFrames require 2D data structures.

Pandas DataFrame constructor only accepts 1D or 2D arrays. 3D arrays cause a ValueError.