Lesson plan

Objectives

  1. Understand the Basics: Familiarize participants with the fundamentals of the Pandas library and its relationship with NumPy.

  2. Data Handling: Equip participants with the skills to import and export various data formats using Pandas, facilitating easy data manipulation and analysis.

  3. Data Manipulation Skills: Provide practical techniques for data manipulation, including selection, addition, removal, and handling of missing values within DataFrames.

  4. Indexing Proficiency: Enable participants to effectively index and select data from DataFrames using various methods, enhancing their data exploration capabilities.

  5. Data Analysis Techniques: Introduce advanced data transformation, grouping, and aggregation techniques, enabling participants to perform in-depth data analysis and summarization.

Specific Objectives

  • Introduction to Pandas

    • Explain the relationship between Pandas and NumPy, highlighting when to use each library effectively.

    • Define and differentiate between Series and DataFrame data structures in Pandas.

    • Demonstrate the creation of Pandas objects from NumPy arrays, solidifying foundational knowledge.

  • Data Import and Export

    • Illustrate how to read data from various formats, including CSV, Excel, and JSON, into Pandas.

    • Show how to export DataFrames to different formats, ensuring participants can save their analyses.

    • Utilize data inspection methods (head, info, describe) to gain an understanding of data structure and content.

  • DataFrame Manipulation & Sorting

    • Demonstrate effective techniques for selecting specific rows and from DataFrames.

    • Equip participants with skills to add and remove columns within a DataFrame.

    • Implement sorting methods by values, by index, and perform multiple column sorting with custom orders.

  • Indexing, Selection & Slicing

    • Differentiate between label-based and position-based indexing and apply each method appropriately.

    • Use Boolean indexing to filter data based on specific conditions, connecting concepts from NumPy.

    • Apply .loc, .iloc, and .at selection methods to extract desired data, and employ multi-level slicing techniques.

  • Handling Missing Data

    • Identify missing values in DataFrames using isna() and notna() methods.

    • Strategize the filling of missing values with fillna() and interpolation methods tailored to scenario needs.

    • Demonstrate how to drop missing data with dropna() and discuss various strategies for handling missing data.

  • Merging DataFrames

    • Illustrate how to concatenate DataFrames using pd.concat() and understand its applications.

    • Explain database-style joins with the merge() function and illustrate the different join types (inner, outer, left, * right).

    • Address challenges faced with duplicate columns and indexes during merging operations.

  • Summary Statistics & Aggregations

    • Calculate basic statistics (mean, median, min, max) to summarize and analyze data.

    • Develop custom aggregation functions for specific needs in data analysis.

    • Apply GroupBy operations effectively, utilizing the Split-Apply-Combine pattern to derive insights from grouped data.

  • Advanced Data Transformation

    • Utilize apply() and map() functions for advanced data transformation on DataFrames.

    • Perform string and datetime operations effectively using Pandas functionality.

    • Construct pivot tables and crosstabs to summarize data visually and contextually.

  • Practical Exercises and Q&A

    • Execute practical exercises using real-world datasets to reinforce concepts learned during the workshop.

    • Connect concepts from both NumPy and Pandas to solidify understanding through application.

    • Engage in a Q&A session to clarify doubts, deepen understanding, and discuss challenges faced during practical sessions