Python for Data Mastery: Data Analytics with Top Libraries

Learn to create NumPy arrays from lists, ranges, and linspace, plus arrays from random values, zeros, ones, and identity matrices; reshape arrays and use shape for dimensions.

Read from numpy arrays by mastering indexing, slices, and negative indexing, then distinguish between a slice reference and a copy. Explore two dimensional arrays and boolean conditionals to filter data.

Explore numpy array operations in part three, from scalar arithmetic to elementwise operations on two arrays, including dot products, transform, sums, means, std, and universal functions like sin and cos.

practice numpy operations by creating rr_int 45 to 55, reading first four and last element; reshape rr_one from normal distribution to 3x4 rr_two, then add and compute mean of rr_three.

Explore NumPy basics through exercise solutions: create arrays, use ranges, index and reshape, generate random normal values, perform boolean filtering, combine arrays, and compute the mean.

Learn pandas fundamentals by creating series and data frames from lists, numpy arrays, and dictionaries, perform index-based operations, and read csv files with read_csv for analytics.

Explore pandas basics: load data with read_csv, access data frames and series, and filter rows with boolean indexing using loc and iloc; select columns by label or position.

Explore advanced pandas indexing by manipulating data frame indices, including range indexes, custom labels, and the set index and index name properties, with practical examples on iloc and dropping columns.

Apply pandas data frame operations to clean data: identify missing values with isna, fill with a fixed value or the mean, drop rows or columns, and reset the index.

Master pandas data frame operations, including handling nulls, one-hot encoding with get_dummies, merging with inner joins, grouping, describing statistics, and cleaning data for analysis.

Explore quick plotting with pandas, learn to create histograms, line, bar, area, scatter, and box plots, and diversify visuals using color, size, and matplotlib integration.

Learn practical pandas and numpy through hands-on exercises: create series and dataframes, read specific rows and columns, build a color dataframe, and analyze a CSV for volume statistics.

Explore pandas basics by building series and dataframes, indexing with iloc and loc, reading csv files, and calculating min, max, and mean on the volume column.

Import pandas and numpy, read the W.H.O. covid-19 Canada csv into data frame, drop Dumdum column, identify na columns, delete missing date reported row, and fill region and cumulative deaths.

Load the W.H.O. COVID-19 Canada dataset with pandas, drop the 'dumb' column, and handle missing values by filling the W.H.O. region and updating cumulative deaths.

Learn to use Matplotlib for data visualization by creating plots such as histograms and scatter plots, and customize figures with axes, labels, titles, and subplots using pyplot.

Master matplotlib figure creation and customization using plt.figure, subplots, and axes; control size and dpi, add legends with labels, and save figures with savefig.

Create a figure and axis in Matplotlib, plot x and y, and customize color, line width, alpha, line style, markers, marker sizes, set xlim and ylim, then save with savefig.

Practice data visualization with matplotlib by plotting India and Bangladesh COVID-19 data. Create cumulative and new case plots using pyplot and the object-oriented method, with subplots and legends.

Walks you through matplotlib exercise solutions, showing how to plot India and Bangladesh COVID-19 data with daily and cumulative cases using pandas, numpy, pyplot, and the object-oriented method.

Learn seaborn for statistical data visualization built on matplotlib and pandas. Load the penguins data and create distribution, kde, rug, joint, hex, and pair plots.

Explore seaborn categorical plots using penguins data, including bar plots, count plots, and box, violin, strip, swarm, and cat plots with hue by island, sex, and species.

Explore seaborn's matrix plots, from correlation matrices to heat maps and cluster maps, using the mpg dataset to build pivot tables with model year, origin, and mpg.

Learn to visualize data with Seaborn grids, including pair plots, pair grids with diagonal histograms, and facet grids comparing islands and sexes using the penguins data.

Learn how to create seaborn regression plots with lmplot, including scatter with a line of best fit, confidence intervals, and facet grids by sex and island using the penguins data.

Master Seaborn style and color parameters using the tips dataset. Set global styles, adjust spines, figure size, and context for clear visualizations.

Import pandas, numpy, and seaborn to explore the serial data frame: histograms with rug plots, joint and pair plots, by-manufacturer charts, and a correlation heatmap.

Learn seaborn exercise solutions by importing pandas, numpy, and seaborn, create histograms with KDE and rug plots, and use joint, pair, count, strip, and box plots with a correlation heatmap.

Install plotly and import pandas to build interactive plots using Gapminder and iris data, including line, scatter with species color, bar charts, and a trend line.

Explore advanced Plotly techniques with histograms, heatmaps, density heatmaps, and box plots using gapminder and iris data. Learn facets, marginal plots (rug, violin), and density contour to compare distributions interactively.

Import pandas, numpy, plotly express; load marathon results 2017 csv; plot overall rank against five k and ten k; show age distribution and density heatmap of age and official time.

Import pandas, numpy, and plotly express to explore Boston Marathon data, plotting overall rank against five k and ten k times, and visualizing age with a histogram and density heatmap.