CLASS 12 DATAFRAME WORKSHEET BASED ON DATAFRAME ATTRIBUTES WORKSHEET 4

Section A: Multiple Choice Questions (MCQs)

1. Which attribute is used to get the labels of the rows and columns in a DataFrame?

   • a) shape
   • b) index
   • c) columns
   • d) axes

2. The dtype attribute of a DataFrame is used to:

   • a) Find the number of dimensions
   • b) Get the data type of each column
   • c) Check for missing values
   • d) Get the shape of the DataFrame

3. What does the shape attribute return?

   • a) Number of rows
   • b) Number of columns
   • c) A tuple representing the number of rows and columns
   • d) The size of the DataFrame in memory

4. The ndim attribute of a DataFrame returns:

   • a) The number of rows
   • b) The number of columns
   • c) The number of dimensions
   • d) The total number of elements

5. Which attribute would you use to get the total number of elements in a DataFrame?

   • a) size
   • b) shape
   • c) ndim
   • d) axes

Section B: Short Answer Questions

1. Index and Columns: Given the following DataFrame:

   
   import pandas as pd
   data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
   df = pd.DataFrame(data)

   Write the Python code to get the index labels and column names of this DataFrame.

2. Shape and Size: Explain the difference between the shape and size attributes in a DataFrame. Provide a code example to demonstrate their usage.

3. Data Types: How can you find out the data type of each column in a DataFrame? Illustrate with an example.

4. Dimensionality: What does the ndim attribute tell you about a DataFrame? Provide an example using a DataFrame with more than two dimensions (e.g., a Panel).

Section C: Practical Task

1. Create a DataFrame with columns 'ProductID', 'ProductName', and 'Price' with at least 5 rows of data. Write the Python code to:

   • Get the shape of the DataFrame.
   • Retrieve the index and columns of the DataFrame.
   • Determine the size of the DataFrame.

2. Given a DataFrame with mixed data types (e.g., integers, floats, and strings), write a Python function check_dtype(df) that prints the data type of each column.

3. Create a DataFrame and demonstrate the use of the axes attribute. Explain how this attribute can be useful when manipulating data.

4. Consider a DataFrame df with a large number of columns. Write a Python snippet to list all column names along with their data types,