Introduction to Matplotlib

False (B)

Line plots, bar charts, scatter plots, and pie charts

<p>Using the Python Package Installer</p> Signup and view all the answers

<p>import matplotlib.pyplot as plt</p> Signup and view all the answers

<p>1.5 points</p> Signup and view all the answers

<p>It's the overall window or Page containing the plot.</p> Signup and view all the answers

<p>It's the area within the figure where the data is plotted.</p> Signup and view all the answers

<p>Creates a basic line plot of y versus x</p> Signup and view all the answers

<p>To display the plot.</p> Signup and view all the answers

<p>Saves the plot as a PNG file</p> Signup and view all the answers

<p>plt.xticks([1, 2, 3, 4])</p> Signup and view all the answers

<p>plt.xticks([1, 2, 3, 4], labels=['One', 'Two', 'Three', 'Four'])</p> Signup and view all the answers

<p>plt.xticks(range(0, 50, 5))</p> Signup and view all the answers

<p>To make the plot informative by identifying data values and units</p> Signup and view all the answers

<p>plt.title(&quot;My Plot&quot;) (B)</p> Signup and view all the answers

<p>plt.xlabel(&quot;X values&quot;)</p> Signup and view all the answers

<p>Size, color, style</p> Signup and view all the answers

<p>The <code>fontdict</code> parameter or by passing properties directly</p> Signup and view all the answers

<p>plt.barh(categories, values)</p> Signup and view all the answers

<p>To specify the marker type</p> Signup and view all the answers

<p>Circle ('o'), Star ('*'), Plus ('+'), Cross ('x')</p> Signup and view all the answers

<p>6 points</p> Signup and view all the answers

<p>Add annotations to highlight specific points using an arrow</p> Signup and view all the answers

<p>To add a legend to the plot for clarity</p> Signup and view all the answers

<p>To enable grid lines on the plot, making it easier to read and interpret values</p> Signup and view all the answers

<p>Using the color argument in the <code>plt.grid</code> function</p> Signup and view all the answers

<p>Light gray</p> Signup and view all the answers

<p>plt.grid(linewidth = 2) (B)</p> Signup and view all the answers

A powerful and versatile Python library for creating static, animated, and interactive visualizations in plots and charts.

The overall window or page that contains a plot. It acts as a canvas for our visualization.

The specific area within a figure where the data is plotted. It defines the coordinate system and contains plot elements.

A Matplotlib module used for basic plotting, providing convenient functions for creating various plots.

A function in Pyplot that generates a basic line plot. It takes two arrays (x and y) as input.

A function in Pyplot that displays the created plot. It renders the plot so you can see it.

A function in Pyplot that saves the current plot as an image file. This lets you store your work.

They are used to customize the positions of ticks along an axis. This makes the plot more readable.

A function in Pyplot that adds a title to the plot. It makes the plot more informative by describing the data.

Functions in Pyplot that add labels to the x-axis and y-axis. They tell you what the axes represent.

A way to change the font properties (size, weight, color, style) for titles and labels. It makes the plot more visually appealing.

Used to adjust the horizontal position of the title. It controls how centered the title is.

A function in Pyplot that adds text annotations to the plot. This highlights specific points or adds extra information.

A function in Pyplot that adds annotated text with an arrow. This helps connect text with specific points on the plot.

They are symbols used to highlight individual data points in a plot. They can be circles, stars, plus signs, or crosses.

The "marker" keyword argument in plt.plot() sets the type of marker to use. You can change the marker with a single character.

The "markersize" or "ms" keyword argument in plt.plot() controls the size of the markers. A larger number means a bigger marker.

The "markeredgecolor" or "mec" argument sets the color of the marker's border. "markerfacecolor" or "mfc" sets the color inside the marker.

They define the style, width, and color of lines used to connect data points in a plot.

The "linestyle" or "ls" keyword argument in plt.plot() determines the type of line (solid, dashed, dotted, etc.).

The "linewidth" or "lw" keyword argument in plt.plot() controls the thickness of the line.

A single parameter that combines the marker, line type, and color in one string. It provides a shorthand for customizing plots.

A function in Pyplot that adds a legend to the plot. It helps identify the different lines or data sets represented in the plot.

They are horizontal or vertical lines drawn on the plot to visually guide the reader. They make it easier to interpret data values.

The "grid()" function in Pyplot enables grid lines. You can customize their appearance and behavior using keyword arguments.

They create multiple plots within a single figure. It's useful for comparing different datasets or viewing different aspects of the same data.

A scatter plot displays data points without connecting them. It helps visualize the relationship between two variables.

The "scatter()" function in Pyplot generates a scatter plot. It takes two arrays (x and y) as input.

A bar chart visually represents data as bars, making it easy to compare categories. Each bar represents a category and its corresponding value.

The "bar()" function in Pyplot creates a vertical bar chart. The "barh()" function creates a horizontal bar chart. Both take two arrays (x and y) as input.

A pie chart represents data as slices of a circle. Each slice represents a category, and the size of the slice corresponds to the proportion of the whole.

The "pie()" function in Pyplot generates a pie chart. It takes a list of values representing proportions, and optionally labels for each slice.

A Pandas method that provides a convenient way to create plots directly from DataFrames or Series. It uses Matplotlib behind the scenes.

A Pandas method that allows you to specify the type of plot to create. It can be used for various plot types like bar charts, histograms, etc.

Pandas plotting can be further customized using Matplotlib functions, providing greater control over plot appearance and details.