Data Visualization Using Matplotlib in Google Colab

Vocabulary practice flashcards covering chart selection rules, interpretation principles, and Matplotlib syntax from the Google Colab data visualization lecture.

Data Visualization

The practice of making data easier to understand, allowing viewers to see the bigger picture faster, identify trends, simplify category comparisons, and highlight unusual values for decision-making.

Line Chart

A visualization that connects data points using a continuous line to display changes, trends, or movements across ordered values such as days, weeks, months, or years.

Bar Chart

A chart that uses rectangular bars where height or length corresponds to values, primarily used to compare separate, discrete categories.

Horizontal Bar Chart

A variation of the bar chart recommended when category names are long, improving readability along the vertical axis.

Pie Chart

A circular chart where each slice represents the proportional share of a category, used when all parts belong to a single whole totaling $100\%$.

Histogram

A chart that displays the distribution and frequency of a single continuous numerical variable by grouping values into ranges rather than discrete category labels.

Bins

The numerical intervals or range groupings in a histogram used to aggregate data points and reveal where values are most concentrated.

Scatter Plot

A visualization that plots paired observations as dots on an $x$-$y$ plane to examine the relationship or association between two numerical variables.

Box Plot

A chart that provides a compact summary of a numerical variable's distribution, variation, central tendency, and potential outliers across groups.

Area Chart

A variation of a line chart where the area beneath the line is filled, designed to show trends over time while placing strong visual emphasis on overall volume or magnitude.

Stacked Bar Chart

A chart where each bar is divided into colored sections, answering both the total magnitude of each group and the composition of its individual components.

Scatter Plot Patterns

The directional orientations of plotted points: positive (points slope upward), negative (points slope downward), or no clear relationship (points show no discernible direction).

Box Plot Anatomy

The structural elements of a box plot: the central box showing the middle $50\%$ ($Q_1$ to $Q_3$), a line inside indicating the median, whiskers extending to typical low and high values, and isolated dots representing possible outliers.

Median (Box Plot)

The middle value of a dataset, represented by the horizontal line drawn within the central box.

Whiskers (Box Plot)

The lines extending outward from the box that illustrate the range of typical low and typical high values in the dataset.

Outlier

An unusual data point that falls far outside the main distribution or beyond the whiskers of a box plot, signaling a value that requires investigation rather than automatic deletion.

Correlation vs. Causation Principle

The analytical rule stating that an observed association or trend between two variables on a scatter plot does not prove that one variable causes changes in the other.

plt.plot()

The Matplotlib command used to draw line plots and sequential line charts.

marker

A parameter in plt.plot() (such as marker="o") that renders distinct visual glyphs or symbols on individual data points.

plt.bar()

The Matplotlib function used to generate vertical bar charts given category names and corresponding numerical values.

plt.bar_label()

A helper function in Matplotlib that places value labels directly on top of bar chart containers for easier numeric reading.

plt.pie()

The Matplotlib function used to construct circular pie charts representing category shares of a total.

autopct

A parameter in plt.pie() (e.g., autopct="%1.1f%%") that calculates and formats string percentage values on each pie slice.

plt.hist()

The Matplotlib command used to compute and draw histograms by sorting a numerical array into a specified number of bins.

plt.scatter()

The Matplotlib function that plots independent paired coordinates to visualize relationships between two numerical variables.

plt.boxplot()

The Matplotlib command that generates box-and-whisker plots from numerical vectors or lists of lists.

plt.fill_between()

The Matplotlib function that fills the region between a line plot and the baseline, converting a line plot into an area chart.

bottom Parameter

A parameter in plt.bar() used to specify the starting vertical offset of a bar, enabling segments to stack directly on top of previous layers in a stacked bar chart.

figsize

A tuple parameter passed to plt.figure() (e.g., figsize=(10, 5)) that defines the width and height of the figure canvas.

plt.tight_layout()

A Matplotlib function that automatically adjusts subplot parameters and margins so that titles, tick labels, and axis names are not cut off.

plt.savefig()

The Matplotlib function used to export and save a rendered chart as an image file to local storage or the Google Colab environment.