This reading introduces learners to Pandas descriptive statistics methods, covering how to compute summary measures such as mean, median, standard deviation, and aggregations on DataFrames, enabling learners to quickly explore and summarize engineering and scientific datasets without writing statistical formulas from scratch.
Topics
This module covers 8 topics. Open a topic to see its subtopics.
Module Content
9 items.
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This reading introduces learners to the Matplotlib library and its pyplot interface, covering what Matplotlib is, how pyplot works, and the distinction between Figure and Axes objects, giving learners the foundational conceptual framework needed before creating any plots in Python.
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This reading teaches learners how to create and customize line plots and scatter plots using Matplotlib, explaining when each plot type is appropriate, how to control markers and line styles, and how to use them to visualize trends or relationships in scientific and engineering data.
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This reading explains the differences between bar charts and histograms and teaches learners how to create, annotate, and interpret both using Matplotlib, illustrating when to use each for comparing categorical quantities versus visualizing the distribution of continuous data.
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This reading teaches learners how to arrange multiple plots within a single Matplotlib figure using plt.subplots(), covering how to create subplot grids, plot on individual Axes objects, and control figure layout to enable side-by-side comparison of related visualizations.
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This reading guides learners through the techniques required to style Matplotlib figures to publication or professional standards, covering typography, color choices, line weights, tick formatting, and other visual elements needed for journal articles, reports, or conference posters.
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This reading introduces learners to the two core Pandas data structures—Series and DataFrames—explaining their properties, how they are created, and how they relate to NumPy, giving learners the foundational knowledge needed for all subsequent data manipulation and analysis tasks.
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This reading teaches learners how to load structured data into Pandas DataFrames from files, and how to filter rows and sort data using Pandas' expressive querying and indexing tools, enabling learners to prepare and slice real datasets for analysis.
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This reading introduces learners to Pandas descriptive statistics methods, covering how to compute summary measures such as mean, median, standard deviation, and aggregations on DataFrames, enabling learners to quickly explore and summarize engineering and scientific datasets without writing statistical formulas from scratch.