{"id":2149,"date":"2025-10-03T03:29:25","date_gmt":"2025-10-03T03:29:25","guid":{"rendered":"https:\/\/itxperts.co.in\/blog\/?p=2149"},"modified":"2026-01-11T08:57:50","modified_gmt":"2026-01-11T08:57:50","slug":"python-pandas-questions-cbse-class-12-board-exam","status":"publish","type":"post","link":"https:\/\/itxperts.co.in\/blog\/python-pandas-questions-cbse-class-12-board-exam\/","title":{"rendered":"100+ Python Pandas Questions for CBSE Class 12 Board Exam 2026 | IP & CS"},"content":{"rendered":"\n

Part 1: Foundational Concepts<\/strong><\/h3>\n\n\n\n

1. What is Pandas?<\/strong> An open-source Python library used for high-performance data manipulation and analysis<\/strong>. \ud83d\udc3c<\/p>\n\n\n\n

2. What are the two primary data structures in Pandas?<\/strong> Series<\/strong> (1-dimensional) and DataFrame<\/strong> (2-dimensional).<\/p>\n\n\n\n

3. What is the standard alias for importing pandas?<\/strong> pd<\/code> (as in import pandas as pd<\/code>).<\/p>\n\n\n\n

4. What is Matplotlib?<\/strong> A comprehensive library for creating static, animated, and interactive visualizations (plots and graphs)<\/strong> in Python. \ud83d\udcca<\/p>\n\n\n\n

5. What is the standard alias for importing matplotlib.pyplot<\/code>?<\/strong> plt<\/code> (as in import matplotlib.pyplot as plt<\/code>).<\/p>\n\n\n\n

\n\n\n\n

Part 2: Pandas Series<\/strong><\/h3>\n\n\n\n

6. What is a Series?<\/strong> A one-dimensional labeled array<\/strong> capable of holding data of any type. Think of it as a single column of a spreadsheet.<\/p>\n\n\n\n

7. What is an Index in a Series?<\/strong> The labels<\/strong> associated with the data values. If not specified, a default integer index (0, 1, 2, ...)<\/code> is created.<\/p>\n\n\n\n

8. What does S.values<\/code> return?<\/strong> It returns the data of the Series S<\/code> as a NumPy array<\/strong>.<\/p>\n\n\n\n

9. What does S.index<\/code> return?<\/strong> It returns the index (labels)<\/strong> of the Series S<\/code>.<\/p>\n\n\n\n

10. What is the use of the S.head()<\/code> method?<\/strong> To view the first few rows<\/strong> of the Series (5 by default).<\/p>\n\n\n\n

11. What is the use of the S.tail()<\/code> method?<\/strong> To view the last few rows<\/strong> of the Series (5 by default).<\/p>\n\n\n\n

12. What does the S.size<\/code> attribute give?<\/strong> The total number of elements<\/strong> in the Series.<\/p>\n\n\n\n

13. What is Vectorization in Pandas?<\/strong> Applying an operation or function to an entire Series at once<\/strong>, without using explicit loops.<\/p>\n\n\n\n

\n\n\n\n

Part 3: Pandas DataFrame<\/strong><\/h3>\n\n\n\n

14. What is a DataFrame?<\/strong> A two-dimensional labeled data structure<\/strong> with columns of potentially different types. It’s like a table, spreadsheet, or SQL table.<\/p>\n\n\n\n

15. What are the two axes in a DataFrame?<\/strong> axis=0<\/code> for rows<\/strong> (index) and axis=1<\/code> for columns<\/strong>.<\/p>\n\n\n\n

16. What does df.shape<\/code> return?<\/strong> A tuple representing the dimensions of the DataFrame: (number of rows, number of columns)<\/strong>.<\/p>\n\n\n\n

17. How do you access the column labels of a DataFrame df<\/code>?<\/strong> Using the df.columns<\/code> attribute.<\/p>\n\n\n\n

18. What does df.T<\/code> do?<\/strong> It transposes<\/strong> the DataFrame, swapping its rows and columns.<\/p>\n\n\n\n

19. How do you select a single column named ‘Name’ from df<\/code>?<\/strong> Using df['Name']<\/code>.<\/p>\n\n\n\n

20. What is the purpose of df.info()<\/code>?<\/strong> To get a concise summary<\/strong> of the DataFrame, including data types and non-null counts.<\/p>\n\n\n\n

21. What does df.describe()<\/code> do?<\/strong> It generates descriptive statistics<\/strong> (mean, std, min, max, etc.) for the numerical columns.<\/p>\n\n\n\n

\n\n\n\n

Part 4: Data Accessing & Selection<\/strong><\/h3>\n\n\n\n

22. What is the difference between .loc<\/code> and .iloc<\/code>?<\/strong><\/p>\n\n\n\n

    \n
  • .loc<\/code><\/strong> is used for label-based<\/strong> indexing.<\/li>\n\n\n\n
  • .iloc<\/code><\/strong> is used for integer position-based<\/strong> indexing.<\/li>\n<\/ul>\n\n\n\n

    23. How do you select the first 5 rows of a DataFrame df<\/code>?<\/strong> df.iloc[0:5]<\/code> or simply df.head()<\/code>.<\/p>\n\n\n\n

    24. What is Boolean Indexing?<\/strong> Using a boolean condition to filter data<\/strong>. For example, df[df['Age'] > 18]<\/code> selects rows where the ‘Age’ is greater than 18.<\/p>\n\n\n\n

    \n\n\n\n

    Part 5: File Operations (CSV)<\/strong><\/h3>\n\n\n\n

    25. Which function is used to read a CSV file into a DataFrame?<\/strong> pd.read_csv()<\/code>.<\/p>\n\n\n\n

    26. Which function is used to write a DataFrame to a CSV file?<\/strong> df.to_csv()<\/code>.<\/p>\n\n\n\n

    27. How do you prevent the index from being written to the CSV file?<\/strong> Use the parameter index=False<\/code> in df.to_csv()<\/code>.<\/p>\n\n\n\n

    28. What does the index_col<\/code> parameter do in read_csv<\/code>?<\/strong> It sets one of the columns from the CSV file as the DataFrame’s index<\/strong>.<\/p>\n\n\n\n

    29. What does the sep<\/code> parameter specify in read_csv<\/code>?<\/strong> The delimiter<\/strong> or separator used in the file (e.g., sep=';'<\/code>).<\/p>\n\n\n\n

    30. What does the usecols<\/code> parameter do?<\/strong> It allows you to read only a specific subset of columns<\/strong> from the CSV file.<\/p>\n\n\n\n

    \n\n\n\n

    Part 6: Data Manipulation & Cleaning<\/strong><\/h3>\n\n\n\n

    31. How do you add a new column named ‘Total’ to a DataFrame df<\/code>?<\/strong> By simple assignment: df['Total'] = ...<\/code><\/p>\n\n\n\n

    32. How do you delete a column?<\/strong> Using the df.drop()<\/code> method with axis=1<\/code>. Example: df.drop('ColumnName', axis=1)<\/code>.<\/p>\n\n\n\n

    33. How do you delete a row by its label?<\/strong> Using the df.drop()<\/code> method with axis=0<\/code>. Example: df.drop('RowLabel', axis=0)<\/code>.<\/p>\n\n\n\n

    34. What is NaN<\/code>?<\/strong> It stands for “Not a Number”<\/strong> and is the standard representation for missing data<\/strong> in Pandas.<\/p>\n\n\n\n

    35. How do you check for missing values in a DataFrame?<\/strong> Using the df.isnull()<\/code> or df.notnull()<\/code> methods.<\/p>\n\n\n\n

    36. What does df.isnull().sum()<\/code> return?<\/strong> A Series containing the count of NaN<\/code> values in each column<\/strong>.<\/p>\n\n\n\n

    37. What does df.dropna()<\/code> do?<\/strong> It removes rows<\/strong> (or columns) that contain missing values (NaN<\/code>).<\/p>\n\n\n\n

    38. What does df.fillna(0)<\/code> do?<\/strong> It replaces all NaN<\/code> values<\/strong> in the DataFrame with 0<\/code>.<\/p>\n\n\n\n

    39. How do you sort a DataFrame by the values in the ‘Score’ column?<\/strong> df.sort_values(by='Score')<\/code>.<\/p>\n\n\n\n

    40. What is the purpose of the ascending=False<\/code> parameter in sorting?<\/strong> To sort the data in descending order<\/strong>.<\/p>\n\n\n\n

    41. What is the purpose of the df.groupby()<\/code> function?<\/strong> To split the data into groups based on some criteria to perform aggregate operations<\/strong> (like sum, mean, count).<\/p>\n\n\n\n

    42. What does df['Category'].value_counts()<\/code> do?<\/strong> It returns a Series containing the counts of unique values<\/strong> in the ‘Category’ column.<\/p>\n\n\n\n

    43. How do you rename a column ‘OldName’ to ‘NewName’?<\/strong> df.rename(columns={'OldName': 'NewName'})<\/code>.<\/p>\n\n\n\n

    \n\n\n\n

    Part 7: Plotting<\/strong><\/h3>\n\n\n\n

    44. Which function displays the plot window?<\/strong> plt.show()<\/code>.<\/p>\n\n\n\n

    45. What is the easiest way to plot data from a DataFrame?<\/strong> Using the DataFrame’s built-in .plot()<\/code> method (e.g., df.plot()<\/code>).<\/p>\n\n\n\n

    46. How do you specify the type of plot (e.g., bar, line, pie)?<\/strong> Using the kind<\/code> parameter in the .plot()<\/code> method. E.g., df.plot(kind='bar')<\/code>.<\/p>\n\n\n\n

    47. What is a line plot (kind='line'<\/code>) used for?<\/strong> Typically for showing trends over a continuous interval<\/strong> like time.<\/p>\n\n\n\n

    48. What is a bar plot (kind='bar'<\/code>) used for?<\/strong> For comparing quantities<\/strong> across different categories.<\/p>\n\n\n\n

    49. What is a histogram (kind='hist'<\/code>) used for?<\/strong> To show the frequency distribution<\/strong> of a set of continuous numerical data.<\/p>\n\n\n\n

    50. What function sets the title of a plot?<\/strong> plt.title('My Plot Title')<\/code>.<\/p>\n\n\n\n

    51. What functions are used to label the X and Y axes?<\/strong> plt.xlabel('X-axis Label')<\/code> and plt.ylabel('Y-axis Label')<\/code>.<\/p>\n\n\n\n

    52. What is a legend on a plot?<\/strong> A small box or area that identifies the different data series<\/strong> in a plot. It can be controlled with plt.legend()<\/code>.<\/p>\n\n\n\n

    53. How do you make a horizontal bar chart?<\/strong> By setting kind='barh'<\/code>.<\/p>\n\n\n\n

    \n\n\n\n

    Long Question\/Ans<\/h2>\n\n\n\n
    \n\n\n\n

    Part 1: Core Pandas – Series (1-Mark Questions)<\/strong><\/h3>\n\n\n\n

    Q1: What is a Pandas Series?<\/strong> A Series is a one-dimensional labeled array, similar to a column in a spreadsheet. It can hold data of any type (integer, string, float, python objects, etc.).<\/p>\n\n\n\n

    Q2: Write the command to import the pandas library with the alias pd<\/code>.<\/strong> import pandas as pd<\/code><\/p>\n\n\n\n

    Q3: How do you create a Series from a Python list L = [10, 20, 30]<\/code>?<\/strong><\/p>\n\n\n\n

    Python<\/p>\n\n\n\n

    import pandas as pd\nL = [10, 20, 30]\nS = pd.Series(L)\n<\/code><\/pre>\n\n\n\n
    Q4: What is the primary difference between a NumPy array and a Pandas Series?<\/strong> The main difference is that a Series has an explicitly defined index<\/strong> associated with its values, whereas a NumPy array has an implicit integer index.<\/p>\n\n\n\n
    Q5: How do you create a Series from a dictionary D = {'a': 1, 'b': 2, 'c': 3}<\/code>?<\/strong><\/p>\n\n\n\n
    Python<\/p>\n\n\n\n
    import pandas as pd\nD = {'a': 1, 'b': 2, 'c': 3}\nS = pd.Series(D)\n# The dictionary keys become the Series index.\n<\/code><\/pre>\n\n\n\n
    Q6: Consider the Series S<\/code>. What will be the output of S.values<\/code> and S.index<\/code>?<\/strong> S.values<\/code> will return a NumPy array containing the data [1, 2, 3]<\/code>. S.index<\/code> will return an Index object ['a', 'b', 'c']<\/code>.<\/p>\n\n\n\n
    Q7: How can you access the element with the label ‘b’ in the Series S<\/code> from Q5?<\/strong> You can use S['b']<\/code> or S.loc['b']<\/code>.<\/p>\n\n\n\n
    Q8: What do the .head(2)<\/code> and .tail(2)<\/code> functions do on a Series?<\/strong> .head(2)<\/code> returns a new Series containing the first 2<\/strong> elements. .tail(2)<\/code> returns a new Series containing the last 2<\/strong> elements.<\/p>\n\n\n\n
    Q9: Write the code to create a Series with the data [5, 10, 15, 20]<\/code> and a custom index ['w', 'x', 'y', 'z']<\/code>.<\/strong><\/p>\n\n\n\n
    Python<\/p>\n\n\n\n
    import pandas as pd\ndata = [5, 10, 15, 20]\nindex = ['w', 'x', 'y', 'z']\nS = pd.Series(data, index=index)\n<\/code><\/pre>\n\n\n\n
    Q10: What does the size<\/code> attribute of a Series return?<\/strong> The size<\/code> attribute returns the total number of elements in the Series.<\/p>\n\n\n\n
    \n\n\n\n
    Part 2: Core Pandas – DataFrame (1 & 2-Mark Questions)<\/strong><\/h3>\n\n\n\n
    Q11: What is a Pandas DataFrame?<\/strong> A DataFrame is a two-dimensional<\/strong>, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns). It’s like a spreadsheet or a SQL table.<\/p>\n\n\n\n
    Q12: How do you create a DataFrame from the dictionary data = {'Name': ['Anu', 'Bob'], 'Age': [21, 25]}<\/code>?<\/strong><\/p>\n\n\n\n
    Python<\/p>\n\n\n\n
    import pandas as pd\ndata = {'Name': ['Anu', 'Bob'], 'Age': [21, 25]}\ndf = pd.DataFrame(data)\n<\/code><\/pre>\n\n\n\n
    Q13: What does the df.shape<\/code> attribute return for the DataFrame created in Q12?<\/strong> It will return a tuple (2, 2)<\/code>, representing (number of rows, number of columns).<\/p>\n\n\n\n
    Q14: What is the command to display the first 3 rows of a DataFrame df<\/code>?<\/strong> df.head(3)<\/code><\/p>\n\n\n\n
    Q15: What is the command to display the column labels of a DataFrame df<\/code>?<\/strong> df.columns<\/code><\/p>\n\n\n\n
    Q16: Explain the difference between loc<\/code> and iloc<\/code> for selecting data from a DataFrame.<\/strong><\/p>\n\n\n\n
      \n
    • .loc<\/code><\/strong> is label-based, meaning you have to specify the names of the rows and columns.<\/li>\n\n\n\n
    • .iloc<\/code><\/strong> is integer-position based, meaning you have to specify rows and columns by their integer position (starting from 0).<\/li>\n<\/ul>\n\n\n\n
      Q17: Given a DataFrame df<\/code>, how would you select the column named ‘Score’?<\/strong> df['Score']<\/code> or df.Score<\/code> (if the column name is a valid Python identifier).<\/p>\n\n\n\n
      Q18: How do you add a new column ‘Grade’ with values ['A', 'B']<\/code> to the DataFrame from Q12?<\/strong> df['Grade'] = ['A', 'B']<\/code><\/p>\n\n\n\n
      Q19: What is the command to transpose a DataFrame df<\/code>?<\/strong> df.T<\/code><\/p>\n\n\n\n
      Q20: How can you delete the column named ‘Age’ from a DataFrame df<\/code>?<\/strong> df.drop('Age', axis=1)<\/code> (Note: axis=1<\/code> specifies that we are dropping a column. To make the change permanent, use inplace=True<\/code> or reassign: df = df.drop(...)<\/code>)<\/em><\/p>\n\n\n\n
      Q21: What does the df.info()<\/code> method do?<\/strong> It prints a concise summary of the DataFrame, including the index dtype, column dtypes, non-null values, and memory usage.<\/p>\n\n\n\n
      Q22: What does the df.describe()<\/code> method show?<\/strong> It generates descriptive statistics for the numerical columns, such as count, mean, standard deviation, min, max, and quartiles.<\/p>\n\n\n\n
      \n\n\n\n
      Part 3: CSV File Handling (2-Mark Questions)<\/strong><\/h3>\n\n\n\n
      Q23: Write the Python statement to read a file named student.csv<\/code> into a pandas DataFrame df<\/code>.<\/strong> df = pd.read_csv('student.csv')<\/code><\/p>\n\n\n\n
      Q24: How would you write the contents of a DataFrame df<\/code> to a new CSV file named output.csv<\/code>?<\/strong> df.to_csv('output.csv')<\/code><\/p>\n\n\n\n
      Q25: When writing a DataFrame to a CSV, an extra unnamed column is often created. How do you prevent this?<\/strong> Use the index=False<\/code> parameter. df.to_csv('output.csv', index=False)<\/code><\/p>\n\n\n\n
      Q26: Your CSV file data.csv<\/code> uses a semicolon (;<\/code>) as a separator instead of a comma. How do you read it correctly?<\/strong> df = pd.read_csv('data.csv', sep=';')<\/code><\/p>\n\n\n\n
      Q27: How can you read a CSV file and specify the ‘RollNo’ column as the index for the DataFrame?<\/strong> df = pd.read_csv('student.csv', index_col='RollNo')<\/code><\/p>\n\n\n\n
      Q28: How do you read only specific columns, ‘Name’ and ‘Marks’, from student.csv<\/code>?<\/strong> df = pd.read_csv('student.csv', usecols=['Name', 'Marks'])<\/code><\/p>\n\n\n\n
      Q29: What parameter in pd.read_csv()<\/code> would you use if your CSV file has no header row?<\/strong> header=None<\/code>. Pandas will automatically assign integer column names: 0, 1, 2, … df = pd.read_csv('data.csv', header=None)<\/code><\/p>\n\n\n\n
      \n\n\n\n
      Part 4: Data Manipulation & Analysis (2 & 3-Mark Questions)<\/strong><\/h3>\n\n\n\n
      Q30: Given a DataFrame df<\/code> with a ‘Marks’ column, how do you find the average marks?<\/strong> df['Marks'].mean()<\/code><\/p>\n\n\n\n
      Q31: How do you sort the DataFrame df<\/code> based on the ‘Marks’ column in descending order?<\/strong> df.sort_values(by='Marks', ascending=False)<\/code><\/p>\n\n\n\n
      Q32: How can you check for missing\/null values in a DataFrame df<\/code>?<\/strong> df.isnull()<\/code> or df.isnull().sum()<\/code> to get a count of nulls per column.<\/p>\n\n\n\n
      Q33: Write the command to drop all rows that contain any missing values from a DataFrame df<\/code>.<\/strong> df.dropna()<\/code><\/p>\n\n\n\n
      Q34: How do you fill all NaN<\/code> values in a DataFrame df<\/code> with the value 0?<\/strong> df.fillna(0)<\/code><\/p>\n\n\n\n
      Q35: Consider a DataFrame sales<\/code> with columns ‘Region’ and ‘Revenue’. How do you calculate the total revenue for each region?<\/strong> sales.groupby('Region')['Revenue'].sum()<\/code><\/p>\n\n\n\n
      Q36: How do you select all rows from a DataFrame df<\/code> where the value in the ‘Age’ column is greater than 25?<\/strong> df[df['Age'] > 25]<\/code><\/p>\n\n\n\n
      Q37: What is the command to rename the column ‘Name’ to ‘Student_Name’ in DataFrame df<\/code>?<\/strong> df.rename(columns={'Name': 'Student_Name'})<\/code> (Note: Use inplace=True<\/code> to modify the original DataFrame.)<\/em><\/p>\n\n\n\n
      Q38: How do you count the occurrences of each unique value in the ‘City’ column of a DataFrame df<\/code>?<\/strong> df['City'].value_counts()<\/code><\/p>\n\n\n\n
      Q39: How would you add a new row to a DataFrame df<\/code>?<\/strong> While direct appending is possible (df.loc[new_index] = [values]<\/code>), the modern and recommended approach is to create a new DataFrame for the row and use pd.concat()<\/code>:<\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      new_row = pd.DataFrame([{'Name': 'Ram', 'Age': 23}])\ndf = pd.concat([df, new_row], ignore_index=True)\n<\/code><\/pre>\n\n\n\n
      \n\n\n\n
      Part 5: Plotting with Matplotlib (2 & 3-Mark Questions)<\/strong><\/h3>\n\n\n\n
      Q40: Write the command to import the pyplot<\/code> module from matplotlib<\/code> with the alias plt<\/code>.<\/strong> import matplotlib.pyplot as plt<\/code><\/p>\n\n\n\n
      Q41: Given a DataFrame df<\/code> with columns ‘Year’ and ‘Sales’, write the code to create a simple line plot.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      import matplotlib.pyplot as plt\ndf.plot(x='Year', y='Sales', kind='line')\nplt.title('Yearly Sales')\nplt.xlabel('Year')\nplt.ylabel('Sales')\nplt.show()\n<\/code><\/pre>\n\n\n\n
      Q42: How do you create a vertical bar chart from the data in Q41?<\/strong> Change kind='line'<\/code> to kind='bar'<\/code>. df.plot(x='Year', y='Sales', kind='bar')<\/code><\/p>\n\n\n\n
      Q43: How do you create a horizontal bar chart?<\/strong> Change kind='bar'<\/code> to kind='barh'<\/code>. df.plot(x='Year', y='Sales', kind='barh')<\/code><\/p>\n\n\n\n
      Q44: What is a histogram and what does it represent? How do you create one for a ‘Marks’ column in a DataFrame df<\/code>?<\/strong> A histogram is a graphical representation of the distribution of numerical data. It groups numbers into ranges (bins) and shows the frequency of data in each range. df['Marks'].plot(kind='hist')<\/code> plt.show()<\/code><\/p>\n\n\n\n
      Q45: How can you create a pie chart to show the proportion of students in different sections (‘A’, ‘B’, ‘C’) stored in a ‘Section’ column of a DataFrame df<\/code>?<\/strong> First, get the counts of each section, then plot.<\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      section_counts = df['Section'].value_counts()\nsection_counts.plot(kind='pie', autopct='%1.1f%%')\nplt.ylabel('') # Hides the 'Section' label on the side\nplt.title('Student Distribution by Section')\nplt.show()\n<\/code><\/pre>\n\n\n\n
      Q46: How do you add a legend to your plot?<\/strong> The legend is often added automatically when plotting from a DataFrame. You can customize it using plt.legend()<\/code>. For example: plt.legend(['Sales Data'])<\/code>.<\/p>\n\n\n\n
      \n\n\n\n
      Part 6: Applied\/Case-Study Questions (3-5 Marks)<\/strong><\/h3>\n\n\n\n
      Assume you have a file named Product.csv<\/code><\/strong> with the following content:<\/p>\n\n\n\n
      Code snippet<\/p>\n\n\n\n
      PID,PName,Price,Category\n101,Laptop,75000,Electronics\n102,Keyboard,2500,Electronics\n103,Book,800,Stationery\n104,Pen,50,Stationery\n105,Smartphone,45000,Electronics\n<\/code><\/pre>\n\n\n\n
      Q47: Write a Python program to read the Product.csv<\/code> file into a DataFrame and display its first 3 rows.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      import pandas as pd\ndf = pd.read_csv(\"Product.csv\")\nprint(df.head(3))\n<\/code><\/pre>\n\n\n\n
      Output:<\/strong><\/p>\n\n\n\n
         PID     PName   Price     Category\n0  101    Laptop   75000  Electronics\n1  102  Keyboard    2500  Electronics\n2  103      Book     800   Stationery\n<\/code><\/pre>\n\n\n\n
      Q48: Using the DataFrame from Q47, write the code to display the PName<\/code> and Price<\/code> of all products where the Category<\/code> is ‘Electronics’.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      electronics_df = df[df['Category'] == 'Electronics']\nprint(electronics_df[['PName', 'Price']])\n<\/code><\/pre>\n\n\n\n
      Output:<\/strong><\/p>\n\n\n\n
              PName  Price\n0      Laptop  75000\n1    Keyboard   2500\n4  Smartphone  45000\n<\/code><\/pre>\n\n\n\n
      Q49: Using the DataFrame from Q47, calculate and display the average price of products for each category.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      avg_price = df.groupby('Category')['Price'].mean()\nprint(avg_price)\n<\/code><\/pre>\n\n\n\n
      Output:<\/strong><\/p>\n\n\n\n
      Category\nElectronics    40833.333333\nStationery       425.000000\nName: Price, dtype: float64\n<\/code><\/pre>\n\n\n\n
      Q50: Add a new column named Discount_Price<\/code> to the DataFrame which is 90% of the original Price<\/code>. Display the entire DataFrame after adding the column.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      df['Discount_Price'] = df['Price'] * 0.90\nprint(df)\n<\/code><\/pre>\n\n\n\n
      Output:<\/strong><\/p>\n\n\n\n
         PID       PName   Price     Category  Discount_Price\n0  101      Laptop   75000  Electronics         67500.0\n1  102    Keyboard    2500  Electronics          2250.0\n2  103        Book     800   Stationery           720.0\n3  104         Pen      50   Stationery            45.0\n4  105  Smartphone   45000  Electronics         40500.0\n<\/code><\/pre>\n\n\n\n
      Q51: Write a complete program to read Product.csv<\/code> and create a bar chart showing the price of each product. The product name should be on the x-axis and the price on the y-axis.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      import pandas as pd\nimport matplotlib.pyplot as plt\n\ndf = pd.read_csv(\"Product.csv\")\ndf.plot(x='PName', y='Price', kind='bar', figsize=(8, 5))\n\nplt.title('Product Prices')\nplt.xlabel('Product Name')\nplt.ylabel('Price (INR)')\nplt.xticks(rotation=45) # Rotate x-axis labels for better readability\nplt.tight_layout() # Adjust layout\nplt.show()\n<\/code><\/pre>\n\n\n\n
      Q52: Write the output of the following code, given the DataFrame df<\/code> from Q47.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      print(df.iloc[1:4, 1:3])\n<\/code><\/pre>\n\n\n\n
      Answer:<\/strong> iloc<\/code> is integer-based. It will select rows with index 1, 2, 3 (4 is exclusive) and columns with index 1, 2 (3 is exclusive). Output:<\/strong><\/p>\n\n\n\n
            PName  Price\n1  Keyboard   2500\n2      Book    800\n3       Pen     50\n<\/code><\/pre>\n\n\n\n
      Q53: Write the output of the following code, assuming the PID column is set as the index.<\/strong><\/p>\n\n\n\n
      Python<\/p>\n\n\n\n
      df = pd.read_csv(\"Product.csv\", index_col='PID')\nprint(df.loc[103])\n<\/code><\/pre>\n\n\n\n
      Answer:<\/strong> .loc<\/code> is label-based. It will select the row where the index label is 103<\/code>. Output:<\/strong><\/p>\n\n\n\n
      PName         Book\nPrice          800\nCategory    Stationery\nName: 103, dtype: object\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
      Part 1: Foundational Concepts 1. What is Pandas? An open-source Python library used for high-performance data manipulation and analysis. \ud83d\udc3c 2. What are the two primary data structures in Pandas? Series (1-dimensional) and DataFrame (2-dimensional). 3. What is the standard alias for importing pandas? pd (as in import pandas as pd). 4. What is Matplotlib? […]<\/p>\n","protected":false},"author":1,"featured_media":2150,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"googlesitekit_rrm_CAow44u0DA:productID":"","footnotes":""},"categories":[213,44],"tags":[24,34,33],"class_list":["post-2149","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-informatics-practices","category-python-tutorials","tag-cbse","tag-cs-coaching","tag-ip-coaching"],"acf":[],"yoast_head":"\n100+ Python Pandas Questions for CBSE Class 12 Board Exam 2026 | IP & CS<\/title>\n<meta name=\"description\" content=\"Ace your CBSE Class 12 Informatics Practices (IP) & CS exams! 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