Point out the wrong statement.

A DataFrame is like a fixed-size dict in that you can get and set values by index label
Series can be be passed into most NumPy methods expecting an ndarray
A key difference between Series and ndarray is that operations between Series automatically align the data based on label
None of the mentioned

The correct answer is: A. A DataFrame is like a fixed-size dict in that you can get and set values by index label.

A DataFrame is a two-dimensional data structure with columns and rows. It is similar to a table in a spreadsheet. A Series is a one-dimensional data structure with values and labels. It is similar to a column in a spreadsheet.

You can get and set values in a DataFrame by index label. For example, to get the value in the first row and second column of a DataFrame, you would use the following code:

df.iloc[0, 1]

To set the value in the first row and second column of a DataFrame, you would use the following code:

df.iloc[0, 1] = 10

However, you cannot get or set values in a Series by index label. For example, the following code will raise an error:

s.iloc[0, 1]

The correct way to get or set values in a Series is by using the index or the name of the column. For example, to get the value in the first row and second column of a Series, you would use the following code:

s[1]

To set the value in the first row and second column of a Series, you would use the following code:

s[1] = 10

Therefore, the statement “A DataFrame is like a fixed-size dict in that you can get and set values by index label” is incorrect.