Using a seed of 123, Generate a random sample of length 10,000 from a standard normal distribution. Report the mean and variance of the sample. Draw the histogram of the sample. Make sure to adjust the plotting parameters/arguments for the graph to be visually appealing. Overlay the density estimate of the sample on the histogram. Without changing the seed, add the above sample to another sample drawn from a uniform distribution between 1 and 2. Report the correlation between the original and the newly formed sample. Compare the boxplots of the original and the newly formed samples side by side (on the same plot, do not produce a separate plot).
Using R.
and style of Matplotlib have begun to show their age. Newer tools like ggplot and ggvis in the R language, along with web visualization toolkits based on D3js and HTML5 canvas, often make Matplotlib feel clunky and old-fashioned. Still, I’m of the opinion that we cannot ignore Matplotlib’s strength as a well-tested, cross-platform graphics engine. Recent Matplotlib versions make it relatively easy to set new global plotting styles (see “Customizing Matplotlib: Configurations and Stylesheets”), and people have been developing new packages that build on its powerful internals to drive Matplotlib via cleaner, more modern APIs—for example, Seaborn (discussed in “Visualization with Seaborn”), ggplot, HoloViews, Altair, and even Pandas itself can be used as wrappers around Matplotlib’s API. Even with wrappers like these, it is still often useful to dive into Matplotlib’s syntax to adjust the final plot output. For this reason, I believe that Matplotlib itself will remain a vital piece of the data visualization stack, even if new tools mean the community gradually moves away from using the Matplotlib API directly.
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