t-test compares the means of two groups (two-sample t-test) to determine whether they are significantly different from one another.
In Python, the NumPy library does not have a built-in function to perform the two-sample t-test. But you can calculate a two-sample t-test manually using the NumPy.
Two-sample t-test formula:
Where, x̄1 and x̄2 are sample means, s1 and s2 are standard deviations of the samples, and n1 and n2 are the sample sizes.

You can use the boxplot function from seaborn python package to plot the boxplot.
You can show the mean on the seaborn boxplot using the showmeans=True parameter. By default, it shows the mean by a green triangle marker.
You can adjust the shape, size, and color of the mean marker on seaborn boxplot using the meanprops parameter.
The following example explains how to customize the shape and color of the mean marker on seaborn boxplot.

In pandas, you can use the hist() function for plotting the histogram.
When you plot multiple histograms in a single plot, you may notice that the default spacing between multiple histograms can make your histograms appear cluttered.
You can use figsize and tight_layout functions from the matplotlib to adjust the space between histograms and avoid cluttering.
The following example explains how to adjust the spacing between pandas histograms.
Create a sample DataFrame,

In pandas, you can use the hist() function for plotting the histogram.
Sometimes, it could be tricky to add the global title at the top of the collection of histograms when you plot them in a single plot.
You can use the suptitle() function from matplotlib to add the centered global title to the collection of pandas histogram.
The following example explains how to plot multiple histograms using pandas hist() function and add a global title at the top of these histograms using the suptitle() function.

When you fit a random forest model in Python, it is essential to save the fitted model for future use for predicting the new dataset.
If you save the random forest model (or any other machine learning model) to a file, it will save your time for future use, especially when the model takes significant time or resources to train.
In Python, you can use the dump function from pickle and joblib packages to save the random forest model to file.

A confidence interval provides an estimated range of interval which is likely to include the unknown parameter (such as mean) of a population when you draw samples many times from the population.
In R, you can use the ggplot function from the ggplot2 library to plot the confidence interval.
The following examples explain plotting confidence Intervals using the ggplot2 library.
Plot 95% confidence interval Let’s use an example of built-in mtcars data for plotting a 95% confidence interval,