Class 12 IP Worksheet: DataFrame Attributes AND CSV FILE WORKSHEET NO 2

Class 12 IP Worksheet: DataFrame Attributes

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Section A: Multiple Choice Questions (MCQs)

1. Which attribute of a DataFrame returns the number of rows and columns?

   • a) df.shape
   • b) df.size
   • c) df.ndim
   • d) df.columns
     

2. Which attribute gives the data types of the columns in a DataFrame?

   • a) df.dtypes
   • b) df.types
   • c) df.columns
   • d) df.info()
     

3. What does the df.index attribute return?

   • a) The names of the columns
   • b) The labels of the rows (index labels)
   • c) The data type of the DataFrame
   • d) The shape of the DataFrame
     

4. Which attribute would you use to get a summary of non-null values for each column in a DataFrame?

   • a) df.describe()
   • b) df.columns
   • c) df.notnull()
   • d) df.count()
     

Section B: Short Answer Questions

1. What is the use of the df.columns attribute in a DataFrame?
   
   

2. Explain the difference between df.size and df.shape.
   
   

3. How can you use the df.T attribute? Provide an example.
   
   

4. Describe what the df.ndim attribute returns and its significance.
   

Section C: Long Answer Questions

1. Given the following DataFrame, write code to display and explain the use of each of the following attributes: df.shape, df.columns, df.index, df.dtypes, and df.size.

   data = {
       'Product': ['Laptop', 'Tablet', 'Smartphone'],
       'Price': [1000, 500, 800],
       'Stock': [50, 100, 75]
   }
   df = pd.DataFrame(data)

   
   

2. Create a DataFrame with the following data and perform the following tasks using DataFrame attributes:

   data = {
       'Name': ['Alice', 'Bob', 'Charlie'],
       'Math': [90, 85, 78],
       'Science': [95, 80, 88],
       'English': [85, 87, 92]
   }
   df = pd.DataFrame(data)

   • a)Use df.T to transpose the DataFrame and display it.
   • b) Find the data type of each column using df.dtypes.
   • c) Display the number of dimensions of the DataFrame using df.ndim.

   
   

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Section D: Practical Application

1. Load a DataFrame from a CSV file and use the following attributes to understand its structure and data:

   • a) df.info()
   • b) df.head()
   • c) df.columns
   • d) df.shape

2. Create a DataFrame to record student attendance with columns: 'Name', 'Present_Days', 'Total_Days'. Using the attributes df.columns, df.index, and df.dtypes, analyze the DataFrame and perform the following tasks:

   • a) Rename the columns to 'Student_Name', 'Days_Present', and 'Total_Attendance'.
   • b) Reset the index to start from 1 instead of 0.
   • c) Display the new data types of the columns.

   3. Load a dataset from a CSV file into a DataFrame. Using the following attributes, perform an in-depth analysis:

   • A) df.columns
   • B) df.dtypes
   • C) df.index

   Instructions: Analyze the output to determine the structure, data types, and memory usage of the dataset. Propose changes to optimize memory and enhance data handling.

4. Create a DataFrame with at least 5 rows and 4 columns. Use the following attributes to perform operations and explain the output:

• a) df.columns
• b) df.index
• c) df.dtypes
• d) df.T

5. Create a DataFrame to record student attendance with columns: 'Name', 'Present_Days', 'Total_Days'. Using the attributes df.columns, df.index, and df.dtypes, analyze the DataFrame and perform the following tasks:

• a) Rename the columns to 'Student_Name', 'Days_Present', and 'Total_Attendance'.
• b) Reset the index to start from 1 instead of 0.
• c) Display the new data types of the columns.

6. Load a DataFrame from a CSV file and use the following attributes to understand its structure and data:

• a) df.head()
• b) df.columns
• c) df.shape

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