Data Visualisation with Python

NumPy forms the numerical foundation of Python's scientific computing ecosystem. This module covers array operations, memory layout, and the tools that make large-scale numerical computation efficient.

• How Python manages data types internally, and how NumPy improves on built-in structures for numerical work
• Core properties of NumPy arrays — shape, dtype, indexing, slicing, and reshaping
• Universal functions (ufuncs) for fast element-wise operations across arrays
• Aggregation methods including sum, minimum, maximum, and statistical summaries
• Broadcasting rules that allow operations between arrays of different but compatible shapes
• Boolean masking and logical comparisons for conditional selection and filtering
• Advanced indexing with integer arrays for non-contiguous data access
• Techniques for sorting arrays along different axes
• Structured arrays for working with heterogeneous, record-style data

Pandas provides labelled, table-oriented data structures that make real-world data cleaning and analysis practical. This module moves from raw arrays to structured datasets.

• The Series and DataFrame objects — their design, construction, and relationship to NumPy
• Label-based and position-based selection using .loc, .iloc, and related indexers
• Arithmetic operations in Pandas and how index alignment works automatically
• Strategies for detecting, removing, and imputing missing values
• Multi-level (hierarchical) indexing for working with higher-dimensional data in a flat structure
• Stacking datasets vertically using concatenation
• Merging and joining tables on shared keys, similar to relational database operations
• Grouping data and applying aggregation functions with groupby
• Reshaping data into pivot table format for summary and cross-tabulation
• String method vectorization for efficient text processing on Series
• Time series functionality including resampling, rolling windows, and date-based indexing
• Performance-oriented querying with eval() and query() for large DataFrames

Matplotlib is Python's foundational plotting library. This module covers everything from basic charts to publication-quality figures and geospatial maps.

• Creating and formatting line plots for continuous data
• Building scatter plots and encoding additional dimensions through colour and size
• Adding error bars and uncertainty bands to represent measurement variability
• Drawing filled contour and density plots for two-dimensional distributions
• Plotting histograms and kernel density estimates for univariate distributions
• Controlling legend placement, content, and styling
• Selecting and configuring colormaps for effective data encoding
• Arranging multiple panels using subplots, GridSpec, and figure-level layout tools
• Adding annotations, labels, and arrows to highlight features in a figure
• Fine-grained control over axis tick locations and formatters
• Applying style sheets and rcParams for consistent visual themes
• Creating 3D line, scatter, and surface plots with mpl_toolkits.mplot3d
• Plotting geographic data using Cartopy — map projections, coastlines, and gridlines
• Statistical visualisation with Seaborn — distribution plots, categorical plots, pair plots, and heatmaps