Essential Numpy and Pandas
Chapter 1: Introduction to Data Handling in Python
What are Numpy and Pandas?
Importance for AI and Data Science
Setting Up Your Environment
Running Your First Code Snippets
Introduction to Jupyter Notebooks
Hands-on practical: Setup and Verification
Chapter 2: Getting Started with NumPy Arrays
Understanding NumPy N-dimensional Arrays
Creating Arrays from Python Lists
Built-in Array Creation Functions
Understanding Array Data Types
Basic Array Attributes
Hands-on practical: Creating and Inspecting Arrays
Chapter 3: NumPy Array Indexing and Slicing
Accessing Single Elements
Slicing 1D Arrays
Slicing 2D Arrays
Boolean Indexing
Fancy Indexing
Modifying Array Subsets
Hands-on practical: Selecting Data from Arrays
Chapter 4: Fundamental NumPy Operations
Basic Arithmetic Operations
Introduction to Universal Functions (ufuncs)
Mathematical and Statistical Functions
Logical Operations on Arrays
Introduction to Broadcasting
Hands-on practical: Performing Calculations on Arrays
Chapter 5: Introduction to Pandas
What is Pandas?
Pandas Data Structure: Series
Creating Series
Pandas Data Structure: DataFrame
Creating DataFrames
Inspecting DataFrames
Hands-on practical: Creating and Examining Series/DataFrames
Chapter 6: Loading and Saving Data with Pandas
Reading Data from CSV Files
Reading Data from Excel Files
Reading Data from Other Formats
Writing Data to CSV Files
Writing Data to Excel Files
Hands-on practical: Importing and Exporting Datasets
Chapter 7: Data Selection and Indexing in Pandas
Selecting Columns
Selecting Rows Using Labels (.loc)
Selecting Rows Using Integer Position (.iloc)
Mixing Label and Position Based Indexing
Conditional Selection (Boolean Indexing)
Setting DataFrame Index
Resetting DataFrame Index
Hands-on practical: Accessing Specific Data Subsets
Chapter 8: Basic Data Manipulation with Pandas
Detecting Missing Data
Handling Missing Data: Dropping
Handling Missing Data: Filling
Dropping Columns and Rows
Adding New Columns
Modifying Existing Columns
Renaming Columns
Sorting Data
Hands-on practical: Cleaning and Modifying DataFrames
Chapter 9: Grouping and Aggregating Data
The Split-Apply-Combine Concept
Grouping Data with groupby()
Applying Aggregation Functions
Applying Multiple Aggregations
Grouping by Multiple Columns
Iterating Through Groups
Hands-on practical: Summarizing Data with GroupBy
Chapter 10: Combining DataFrames
Introduction to Combining Data
Concatenating DataFrames (pd.concat)
Database-Style Merging (pd.merge)
Understanding Merge Types (Joins)
Merging on Index
Index-Based Joining (.join)
Hands-on practical: Combining Datasets
Fundamental NumPy Operations
Having established how to create NumPy arrays and access their elements, the next step is to perform computations using them. This chapter introduces the core operations that make NumPy a foundation for numerical computing in Python.
You will learn how to:
- Apply basic arithmetic operations (like addition, subtraction, multiplication) element by element across arrays.
- Utilize Universal Functions (ufuncs) for optimized, vectorized operations such as sqrt(x) or sin(x).
- Calculate common mathematical and statistical metrics like sum, mean, and standard deviation for entire arrays or along specific axes.
- Perform logical comparisons between arrays or between an array and a scalar value.
- Understand broadcasting, the mechanism NumPy uses to implicitly handle operations on arrays with different but compatible shapes.
These operations are fundamental for data analysis and form the building blocks for more complex algorithms.
Sections
4.1 Basic Arithmetic Operations
4.2 Introduction to Universal Functions (ufuncs)
4.3 Mathematical and Statistical Functions
4.4 Logical Operations on Arrays
4.5 Introduction to Broadcasting
4.6 Hands-on practical: Performing Calculations on Arrays
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