Complete Guide to NumPy, Pandas, SciPy, Matplotlib & Seaborn

Explore advanced array manipulation with numpy, including reshaping and stacking arrays, perform matrix multiplication, and solve linear equations for practical linear algebra in scientific computing.

Compute mean, median, and standard deviation with NumPy, using a sample data set and printed results to explore central tendency and data dispersion.

Explore how numpy detects missing data and replaces missing values with the array mean. Remove incomplete rows to preserve data integrity for analysis.

Explore performance optimization with NumPy through vectorized operations for element-wise computations on large datasets, and avoid loop-based computation. Learn how views, not copies, boost memory efficiency when modifying arrays.

Learn how to load data with pandas using read_csv, inspect with head, and save to excel or work with sql databases via to_excel and read_sql.

Explore three essential pandas operations in dataframes: selecting by column names, indexing with label-based and position-based methods, and filtering rows by conditions such as score > 85.

Learn data cleaning in pandas by handling missing data, removing duplicates, and detecting outliers with z-scores, using numpy for basic operations.

Transform data with pandas by merging, joining, and concatenating data frames, aligning by index and common columns, and exploring inner, outer, left, and right joins.

Explore pivot tables and cross tabulations in pandas to summarize sales by city and month, and analyze relationships between categorical values like male/female and yes/no purchases.

Learn to handle time series data with Pandas in Python by setting date-time index, converting date columns, and grouping by weekly frequency to compute total sales.

Explore how to use apply, map, and lambda functions in pandas to transform dataframe data, perform row- or column-wise operations, and element-wise transformations for data analysis.

Learn to create basic plots with matplotlib, including line, bar, and scatter plots, by defining data, customizing color and markers, and adding titles and axis labels.

Learn to customize plots in Matplotlib by adding titles, x and y labels, legends, and grids to improve readability and convey growth over time.

Explore creating and arranging multiple plots in a single matplotlib figure using subplots, grids, and custom layouts to prevent overlap and showcase line, bar, and scatter visuals.

Explore how to plot statistical data using histograms, box plots, and pie charts to understand data distribution, visualize frequencies, and show category proportions.

Learn to customize Matplotlib plots by adding titles, axis labels, legends, and grid lines, and adjust color, line style, and width to create clearer, more professional visualizations.

Explore multivariate relationships with seaborn pairplots, heat maps, and regression plots, using the iris dataset to visualize scatter matrices, correlations, and regression trends.

Explore Seaborn customization with styles like white grid, pastel palettes, and tailored plots, including scatter plots, box plots, and violin plots, plus figure size and aspect ratio for professional visuals.

Explore SciPy modules to perform numerical integration, optimization, and solving equations with concise Python code. See examples of computing integrals, minimizing functions, and finding roots using integrate, optimize, and root.

Explore linear algebra with SciPy and NumPy, performing matrix inversion, transposition, multiplication, and solving systems, while computing determinants, eigenvalues, singular value decomposition (svd), and pca-based dimensionality reduction.

Explore hypothesis testing and descriptive statistics using SciPy, NumPy, and SciPy's statistics tools to compute mean, median, standard deviation, and t-test with p-values.

Learn how to use SciPy for signal processing by filtering a noisy sine wave and visualize results with Matplotlib, then apply edge detection in images using SciPy's image processing workflow.

Apply Pandas and NumPy to clean messy data, identify missing values, create and inspect data frames, and perform feature scaling with min-max normalization to prepare data for analysis or modeling.

Explore statistical analysis with SciPy, performing descriptive statistics (mean, median, standard deviation) and hypothesis testing, including t-tests and p-values, using NumPy and SciPy functions.