Pandas: Syllabus

Masterig Pandas for Data Manipulation & Analysis#

Pandas is a powerful open-source Python library designed for data manipulation, analysis, and preprocessing. It provides flexible and efficient data structures such as Series and DataFrames, making it a core tool for data science, engineering, and business analytics. This syllabus covers essential concepts, ensuring practical implementation with real-world datasets. By mastering Pandas, users can handle large-scale data operations, perform statistical analysis, and create meaningful visualizations.

Module 1: Introduction to Pandas#

• Overview of Pandas and its use cases
• Installing Pandas and setting up the environment
• Understanding Pandas Series and DataFrame
• Differences between Pandas, NumPy, and SQL

References:

• Pandas Official Documentation

Module 2: Working with Pandas Data Structures#

• Creating Series and DataFrames from lists, dictionaries, and NumPy arrays
• Importing data from CSV, Excel, JSON, and databases
• Viewing, summarizing, and exploring datasets (.head(), .info(), .describe())
• Selecting, indexing, and slicing data

References:

• Pandas Data Structures

Module 3: Data Cleaning and Preprocessing#

• Handling missing data (.dropna(), .fillna())
• Data type conversions (.astype(), .to_datetime())
• Renaming and reordering columns
• Removing duplicates and filtering data
• Handling outliers

References:

• Handling Missing Data

Module 4: Data Transformation and Manipulation#

• Applying functions (.apply(), .map(), lambda functions)
• Grouping and aggregating data (.groupby(), .agg())
• Merging, concatenating, and joining DataFrames
• Pivot tables and reshaping data (.pivot_table(), .melt(), .stack())

References:

• Data Aggregation and Grouping

Module 5: Time Series Analysis with Pandas#

• Working with datetime data (pd.to_datetime(), .dt accessor)
• Indexing, resampling, and rolling window calculations
• Handling missing dates in time series data
• Analyzing trends and seasonality

References:

• Time Series Analysis

Module 6: Data Visualization with Pandas#

• Plotting basic charts (.plot(), .hist(), .boxplot())
• Using Pandas with Matplotlib and Seaborn
• Customizing plots and adding labels
• Advanced visualization techniques

References:

• Pandas Visualization

Module 7: Performance Optimization in Pandas#

• Vectorization vs. loops in Pandas
• Using .apply() efficiently
• Memory optimization techniques
• Working with large datasets using Dask

References:

• Pandas Performance Optimization

Hands-on Projects (Recommended):#

• Data Cleaning: Process real-world datasets (e.g., Airbnb, COVID-19 data).
• Exploratory Data Analysis (EDA): Analyze customer behavior using Pandas.
• Time Series Forecasting: Work with financial or stock market data.
• Data Merging: Combine datasets from multiple sources (SQL, APIs).
• Data Visualization: Create dashboards using Pandas and Seaborn.

References:

• Pandas Official Tutorials