Q.What would happen if we pass 3 columns or 5 columns instead of 4 in the above code? What is the reason?
[Context: The referenced textbook code is:

array1 = np.array([10,20,30])
array2 = np.array([100,200,300])
array3 = np.array([-10,-20,-30,-40])
dFrame5 = pd.DataFrame([array1, array3, array2], columns=['A', 'B', 'C', 'D'])]

Shape: Plain theory question about DataFrame construction with mismatched dimensions.

When you construct a DataFrame from a list of arrays using pd.DataFrame([array1, array3, array2], columns=['A', 'B', 'C', 'D']), pandas expects every array in the list to have the same length as the number of column labels you provide.

In the given code, columns=['A', 'B', 'C', 'D'] specifies 4 columns. The arrays have lengths:

• array1: 3 elements
• array2: 3 elements
• array3: 4 elements

Because array3 has 4 elements and the others have 3, pandas can successfully create the DataFrame — it treats each array as a row, and array3 fills all four columns while array1 and array2 leave the fourth column as NaN.

If you pass 3 columns instead of 4:

dFrame5 = pd.DataFrame([array1, array3, array2], columns=['A', 'B', 'C'])

pandas will raise a ValueError:

ValueError: Shape of passed values is (3, 4), indices imply (3, 3)

The reason: array3 has 4 elements, but you are asking for only 3 columns. The data shape (3 rows × 4 actual elements per row for at least one array) does not match the column specification (3 columns). pandas cannot reconcile this mismatch.

If you pass 5 columns instead of 4:

dFrame5 = pd.DataFrame([array1, array3, array2], columns=['A', 'B', 'C', 'D', 'E'])

pandas will again raise a ValueError:

ValueError: Shape of passed values is (3, 4), indices imply (3, 5)
``` …

The question asks what happens when the number of column labels in the columns parameter doesn't match the actual width of the data arrays being passed to pd.DataFrame().

In the given code, three arrays are passed as rows:

• array1 has 3 elements
• array2 has 3 elements
• array3 has 4 elements

The columns parameter specifies 4 labels: ['A', 'B', 'C', 'D'].

Pandas must reconcile this mismatch between the data shape and the requested column structure. Let's examine both scenarios.

Case 1: Passing 3 columns instead of 4

import numpy as np
import pandas as pd

array1 = np.array([10, 20, 30])
array2 = np.array([100, 200, 300])
array3 = np.array([-10, -20, -30, -40])

dFrame5 = pd.DataFrame([array1, array3, array2], columns=['A', 'B', 'C'])
print(dFrame5)

Output:

When you specify fewer columns than the data contains, Pandas silently truncates the extra data. The first row (array1) fits perfectly with 3 elements. The second row (array3) has 4 elements, but only the first 3 are used — the value -40 is discarded. The third row (array2) also fits with 3 elements.

Case 2: Passing 5 columns instead of 4

import numpy as np
import pandas as pd

array1 = np.array([10, 20, 30])
array2 = np.array([100, 200, 300])
array3 = np.array([-10, -20, -30, -40])

dFrame5 = pd.DataFrame([array1, array3, array2], columns=['A', 'B', 'C', 'D', 'E'])
print(dFrame5)

Output: