13.3: Matplotlib basics, 2
- Page ID
- 23997
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)In Matplotlib, the functions xlim and ylim are used to set the range of values that will be displayed on the x and y-axes, respectively. By default, Matplotlib automatically determines these ranges based on the data provided, aiming to showcase the entirety of the data points. However, there are scenarios where a specific focus or range is necessary, either to emphasize a particular segment of the data or to maintain consistent axis scales across multiple plots for comparison. The xlim function allows you to set the lower and upper bounds of the x-axis, while ylim does the same for the y-axis. By using these functions, you have granular control over the visible area of your plot, ensuring that your visualizations convey the intended message or highlight specific aspects of your data.
You use the commands by calling the function with the lower and upper ranges: plt.xlim(5,10) sets the limit of the x-axis to be 5 to 10. ylim is used in the same way.
The functions xlabel and ylabel are used to add descriptive labels to the x and y-axes of a plot, respectively. These labels provide context to the data being visualized, making it easier for viewers to understand what each axis represents. For instance, if you're plotting a graph of time against revenue, the x-axis might represent time (e.g., months or years), and the y-axis might represent the revenue amount (e.g., dollars or thousands of dollars). By using xlabel and ylabel, you can clearly annotate these axes with labels like "Months" for the x-axis and "Revenue (in thousands)" for the y-axis. Properly labeled axes are essential for ensuring the clarity and comprehensibility of a chart or graph, as they guide the viewer in interpreting the presented data correctly.
When working with data visualizations, there often arises the need to share or present your findings outside the immediate environment in which you created them. This is especially true when crafting reports, presentations, or even when collaborating with team members who might not have access to your exact development setup. In such instances, Matplotlib offers the function savefig for this.
Thesavefigcommand allows users to export and save their plots in a variety of common file formats suitable for diverse applications. Here's the basic syntax and some commonly used parameters:
plt.savefig(fname, dpi=None, format=None, bbox_inches=None, **kwargs)
- fname: This is the filename (including path). The output format can often be deduced from the file extension (e.g., `.png`, `.pdf`, `.jpeg`, etc.). If not provided, the `format` parameter will be necessary.
- dpi: The resolution of the saved figure in dots per inch. If not specified, it uses the default value from the `savefig.dpi` configuration parameter.
- format: This explicitly determines the format of the output file. Common values include 'png', 'pdf', 'jpeg', etc. If not provided, Matplotlib will use the extension of the `fname` to determine the format.
- bbox_inches: Determines which part of the figure is saved. Typical values include 'tight' (which tries to eliminate any unnecessary padding around the figure) or None (which uses the default setting).
- **kwargs: Additional keyword arguments that are passed to the backend being used.