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Python for Data Science
1. Introduction to Python for Data Science
2. Python Programming Fundamentals
3. Numerical Computing with NumPy
4. Data Manipulation with Pandas
5. Data Visualization
6. Introduction to Machine Learning with Scikit-learn
7. Advanced and Specialized Topics
Numerical Computing with NumPy
NumPy Fundamentals
Introduction to NumPy
NumPy's Role in Scientific Computing
Performance Benefits
Integration with Other Libraries
The ndarray Object
Array Concept
Array Properties
shape Attribute
dtype Attribute
ndim Attribute
size Attribute
itemsize Attribute
Memory Layout
Contiguous Arrays
Strides
Views vs Copies
NumPy vs Python Lists
Performance Comparison
Memory Efficiency
Vectorization Benefits
Type Homogeneity
Array Creation and Initialization
Creating Arrays from Existing Data
From Python Lists
From Tuples
From Other Arrays
array() Function Parameters
Intrinsic Array Creation
Zeros and Ones
zeros() Function
ones() Function
empty() Function
full() Function
Range-based Creation
arange() Function
linspace() Function
logspace() Function
Identity and Diagonal Arrays
eye() Function
identity() Function
diag() Function
Random Array Generation
Random Module Overview
Random Number Generation
rand() Function
randn() Function
randint() Function
random() Function
Random Sampling
choice() Function
shuffle() Function
Random Seed Management
Reproducible Results
seed() Function
Array Data Types
NumPy Data Types
Integer Types
Float Types
Complex Types
Boolean Type
String Types
Data Type Specification
dtype Parameter
Type Conversion
astype() Method
Array Indexing and Selection
Basic Indexing
Single Element Access
One-Dimensional Indexing
Multi-Dimensional Indexing
Row and Column Selection
Element Access in 2D Arrays
Negative Indexing
Array Slicing
One-Dimensional Slicing
Multi-Dimensional Slicing
Row Slicing
Column Slicing
Combined Slicing
Step Parameter in Slicing
Slice Assignment
Advanced Indexing
Boolean Indexing
Creating Boolean Masks
Conditional Selection
Multiple Conditions
Boolean Array Operations
Fancy Indexing
Integer Array Indexing
Multi-Dimensional Fancy Indexing
Combining Boolean and Fancy Indexing
Array Views and Copies
Understanding Views
Creating Copies
copy() Method
Memory Implications
Array Operations and Computation
Universal Functions (ufuncs)
Element-wise Operations
Vectorization Concept
Broadcasting Rules
ufunc Methods
reduce()
accumulate()
outer()
Arithmetic Operations
Basic Arithmetic
Addition and Subtraction
Multiplication and Division
Power and Modulo
Array-Scalar Operations
Array-Array Operations
In-place Operations
Mathematical Functions
Trigonometric Functions
sin(), cos(), tan()
Inverse Trigonometric Functions
Exponential and Logarithmic
exp(), log(), log10()
Power Functions
Rounding Functions
round(), floor(), ceil()
Truncation Functions
Comparison Operations
Element-wise Comparisons
Logical Operations
logical_and()
logical_or()
logical_not()
Array Equality
allclose() Function
array_equal() Function
Aggregation Functions
Statistical Functions
sum() and nansum()
mean() and nanmean()
std() and var()
min() and max()
median() and percentile()
Axis Parameter
Aggregation Along Axes
Keepdims Parameter
Conditional Aggregations
where() Function
Masked Arrays
Array Manipulation and Reshaping
Shape Manipulation
Reshaping Arrays
reshape() Method
Reshape Parameters
Automatic Dimension Calculation
Flattening Arrays
flatten() Method
ravel() Function
Differences and Use Cases
Transposition
transpose() Method
T Attribute
Multi-dimensional Transposition
Array Combination
Stacking Arrays
Vertical Stacking (vstack)
Horizontal Stacking (hstack)
Depth Stacking (dstack)
General Stacking (stack)
Concatenation
concatenate() Function
Axis Parameter
Multiple Array Concatenation
Array Splitting
Splitting Functions
split() Function
vsplit() Function
hsplit() Function
dsplit() Function
Array Sections
Equal vs Unequal Splits
Split Indices
Array Repetition
repeat() Function
tile() Function
Broadcasting for Repetition
Linear Algebra Operations
Matrix Operations
Matrix Multiplication
dot() Function
matmul() Operator (@)
Differences and Use Cases
Matrix Properties
Transpose Operations
Matrix Trace
Matrix Rank
Linear Algebra Functions
Matrix Decomposition
Eigenvalues and Eigenvectors
Singular Value Decomposition
QR Decomposition
Cholesky Decomposition
Matrix Inverse and Determinant
inv() Function
det() Function
Pseudo-inverse
Solving Linear Systems
solve() Function
Least Squares Solutions
Vector Operations
Vector Norms
Dot Products
Cross Products
Vector Projections
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4. Data Manipulation with Pandas