User:Niraj/Teaching-21

Teaching lesson plan 21 Subject: Python programming

Date: 6 Feb 2024

Time: 60 minutes

Period: 3rd

Teaching Item: Summary Statistics in Pandas

Class: Bachelor

Objective:

Students will learn how to compute summary statistics using pandas, understand the different statistical measures available, and apply them to analyze and summarize data effectively.

Materials Needed:

• Python interpreter with pandas installed
• Jupyter Notebook or IDE
• Sample dataset (e.g., CSV file)
• Projector

1. Introduction to Summary Statistics (10 mins)

• Brief overview of summary statistics:
  • Summary statistics provide a concise summary of the main characteristics of a dataset.
  • They include measures such as mean, median, standard deviation, minimum, maximum, and quartiles.
• Discuss the importance of summary statistics in data analysis and decision-making.

2. Computing Summary Statistics in pandas (15 mins)

• Introduce various methods for computing summary statistics in pandas:
  • describe(): Generates descriptive statistics for numeric columns in a DataFrame.
  • mean(), median(), std(), min(), max(): Compute individual statistics for specific columns or the entire DataFrame.
  • quantile(): Compute percentiles or quantiles of data.
• Demonstrate each method with examples and discuss their applications.

3. Grouped Summary Statistics (15 mins)

• Explain how to compute summary statistics for grouped data in pandas:
  • Using the groupby() method to group data by one or more columns.
  • Applying summary statistics functions to grouped data using agg() or apply() methods.
  • Computing group-wise statistics such as mean, median, and standard deviation.
• Show examples of computing grouped summary statistics and discuss their significance.

4. Handling Missing Data in Summary Statistics (10 mins)

• Discuss strategies for handling missing data when computing summary statistics:
  • Pandas automatically excludes missing values (NaN) when computing summary statistics.
  • Use of functions like dropna() to remove missing values before computing statistics.
  • Imputation techniques to replace missing values with meaningful estimates.
• Show examples of handling missing data in summary statistics calculations.

5. Visualizing Summary Statistics (10 mins)

• Introduce visualization techniques for summary statistics using pandas and matplotlib:
  • Creating bar plots, box plots, or histograms to visualize summary statistics.
  • Using seaborn or other visualization libraries for enhanced visualization.
• Show examples of visualizing summary statistics to gain insights into the data distribution.

6. Exercise (15 mins)

• Provide a programming exercise where students:
  • Load a sample dataset into a pandas DataFrame.
  • Compute summary statistics such as mean, median, standard deviation, and quartiles for numeric columns.
  • Compute summary statistics for grouped data based on specific categories.
  • Visualize summary statistics using appropriate plots.

7. Conclusion (5 mins)

• Recap the key points covered in the lesson:
  • Summary statistics provide a concise summary of the main characteristics of a dataset.
  • Pandas provides convenient methods for computing summary statistics for both individual columns and grouped data.
  • Handling missing data is essential when computing summary statistics to ensure accurate results.
  • Visualizing summary statistics can help in gaining insights and understanding the distribution of data.
• Encourage students to practice computing and visualizing summary statistics in their own projects and to explore additional functionalities offered by pandas and visualization libraries.