5 Basic Pandas Assignments for Beginner Tutorial

[Pankaj-Str]

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Tasks

1. Create a markdown file named pandas_assignments.md in the repository (e.g., under docs/assignments/ or a similar directory).
2. Include 5 assignments as described below, ensuring each has:
   • A clear task description.
   • Example data (e.g., dictionary or CSV content).
   • A code template with placeholders for learners to fill in.
   • Expected output (console output and, where applicable, file output).
3. Ensure clarity and simplicity for beginners, avoiding any topics not covered in the tutorial.
4. Test the assignments by running the code in a Python environment (e.g., Jupyter Notebook) to verify the expected outputs.
5. Commit the file to the repository and link it in this issue for review.

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Assignment Details

Assignment 1: Creating and Exploring a DataFrame

• Task: Create a DataFrame from a dictionary, display the first 2 rows, and show basic information about the DataFrame (e.g., data types and non-null counts).
• Example Data:

  data = {
      'Product': ['Laptop', 'Phone', 'Tablet'],
      'Price': [1000, 500, 300],
      'Stock': [50, 100, 75]
  }

• Code Template:

  import pandas as pd
  
  # Create DataFrame
  data = {'Product': ['Laptop', 'Phone', 'Tablet'], 'Price': [1000, 500, 300], 'Stock': [50, 100, 75]}
  df = pd.DataFrame(data)
  
  # Display first 2 rows
  print("First 2 rows:")
  # Your code here
  
  # Display DataFrame info
  print("\nDataFrame Info:")
  # Your code here

• Expected Output:

  First 2 rows:
    Product  Price  Stock
  0  Laptop   1000     50
  1   Phone    500    100
  
  DataFrame Info:
  <class 'pandas.core.frame.DataFrame'>
  RangeIndex: 3 entries, 0 to 2
  Data columns (total 3 columns):
   #   Column   Non-Null Count  Dtype 
  ---  ------   --------------  ----- 
   0   Product  3 non-null      object
   1   Price    3 non-null      int64 
   2   Stock    3 non-null      int64 
  dtypes: int64(2), object(1)
  memory usage: 200.0+ bytes
  None
  

Assignment 2: Selecting and Filtering Data

• Task: From a given DataFrame, select specific columns and filter rows based on a condition.
• Example Data:

  data = {
      'Name': ['John', 'Emma', 'Sam'],
      'Grade': [85, 78, 92],
      'Subject': ['Math', 'Science', 'English']
  }

• Code Template:

  import pandas as pd
  
  # Create DataFrame
  data = {'Name': ['John', 'Emma', 'Sam'], 'Grade': [85, 78, 92], 'Subject': ['Math', 'Science', 'English']}
  df = pd.DataFrame(data)
  
  # Select Name and Grade columns
  print("Name and Grade columns:")
  # Your code here
  
  # Filter rows where Grade > 80
  print("\nStudents with Grade > 80:")
  # Your code here

• Expected Output:

  Name and Grade columns:
     Name  Grade
  0  John     85
  1  Emma     78
  2   Sam     92
  
  Students with Grade > 80:
     Name  Grade Subject
  0  John     85    Math
  2   Sam     92 English
  

Assignment 3: Modifying a DataFrame

• Task: Add a new column to a DataFrame, update an existing column, and drop a column.
• Example Data:

  data = {
      'Item': ['Pen', 'Notebook', 'Bag'],
      'Price': [5, 10, 50]
  }

• Code Template:

  import pandas as pd
  
  # Create DataFrame
  data = {'Item': ['Pen', 'Notebook', 'Bag'], 'Price': [5, 10, 50]}
  df = pd.DataFrame(data)
  
  # Add Discount column
  # Your code here
  print("DataFrame with Discount:")
  print(df)
  
  # Update Price by adding 50
  # Your code here
  print("\nDataFrame with Updated Price:")
  print(df)
  
  # Drop Discount column
  # Your code here
  print("\nDataFrame after dropping Discount:")
  print(df)

• Expected Output:

  DataFrame with Discount:
         Item  Price  Discount
  0       Pen      5        10
  1  Notebook     10        15
  2       Bag     50        20
  
  DataFrame with Updated Price:
         Item  Price  Discount
  0       Pen     55        10
  1  Notebook     60        15
  2       Bag    100        20
  
  DataFrame after dropping Discount:
         Item  Price
  0       Pen     55
  1  Notebook     60
  2       Bag    100
  

Assignment 4: Handling Missing Data

• Task: Create a DataFrame with missing values, identify them, and fill them appropriately.
• Example Data:

  data = {
      'Player': ['Mike', 'Anna', None],
      'Score': [95, None, 88],
      'Team': ['Red', 'Blue', None]
  }

• Code Template:

  import pandas as pd
  
  # Create DataFrame
  data = {'Player': ['Mike', 'Anna', None], 'Score': [95, None, 88], 'Team': ['Red', 'Blue', None]}
  df = pd.DataFrame(data)
  
  # Print DataFrame
  print("DataFrame with missing values:")
  print(df)
  
  # Check for missing values
  print("\nMissing values:")
  # Your code here
  
  # Fill missing values
  # Your code here
  print("\nDataFrame after filling missing values:")
  print(df)

• Expected Output:

  DataFrame with missing values:
    Player  Score  Team
  0   Mike   95.0   Red
  1   Anna    NaN  Blue
  2   None   88.0  None
  
  Missing values:
     Player  Score  Team
  0  False  False  False
  1  False   True  False
  2   True  False   True
  
  DataFrame after filling missing values:
     Player  Score    Team
  0   Mike   95.0     Red
  1   Anna   91.5    Blue
  2  Unknown  88.0  Unknown
  

Assignment 5: Grouping and Saving Data

• Task: Group a DataFrame by a column, calculate an aggregate statistic, and save the result to a CSV file.
• Example Data:

  data = {
      'Employee': ['Alice', 'Bob', 'Charlie', 'David'],
      'Salary': [50000, 55000, 60000, 52000],
      'Department': ['HR', 'IT', 'HR', 'IT']
  }

• Code Template:

  import pandas as pd
  
  # Create DataFrame
  data = {'Employee': ['Alice', 'Bob', 'Charlie', 'David'], 'Salary': [50000, 55000, 60000, 52000], 'Department': ['HR', 'IT', 'HR', 'IT']}
  df = pd.DataFrame(data)
  
  # Group by Department and calculate mean Salary
  print("Mean Salary by Department:")
  # Your code here
  
  # Save the grouped result to a CSV file
  # Your code here
  print("\nGrouped data saved to dept_salary.csv")

• Expected Output:

  Mean Salary by Department:
  Department
  HR    55000.0
  IT    53500.0
  Name: Salary, dtype: float64
  
  Grouped data saved to dept_salary.csv
  

• Output File (dept_salary.csv):

  Department,Salary
  HR,55000.0
  IT,53500.0
  

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Acceptance Criteria

• The pandas_assignments.md file is created in the repository.
• All 5 assignments are included with clear task descriptions, example data, code templates, and expected outputs.
• The assignments use only concepts from the provided Pandas tutorial (no advanced topics).
• Code templates are beginner-friendly with placeholders for learners to complete.
• Expected outputs are accurate and match the results of running the completed code.
• The saved CSV file in Assignment 5 is verified to match the expected format.
• The markdown file is well-formatted, readable, and free of errors.

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Additional Notes

• Environment: Learners should use a Python environment with Pandas installed (pip install pandas). Recommend testing in Jupyter Notebook for interactive learning.
• Testing: Verify each assignment by running the completed code and checking outputs against the expected results.
• File Path: Ensure the CSV file for Assignment 5 is saved in the correct directory (e.g., the working directory of the script).
• Helpful Tips: Include a note in the markdown file encouraging learners to:
  • Compare their outputs with the expected outputs.
  • Check for common errors (e.g., incorrect column names or file paths).
  • Refer to the Pandas tutorial for guidance.

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References

• Link to the Pandas tutorial in the repository (if available, e.g., https://github.com/Pankaj-Str/Complete-Python-Mastery/blob/main/35%20Day%20Pandas%20Basic%2FPandas%20step%20by%20step.md
• Pandas official documentation: https://pandas.pydata.org/docs/

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