You can split a string with multiple delimiters in Python by using various functions such as split(), compile(), and translate().
Method 1: split() function # import package import re x = "ABC;DE:XY,Z" re.split(";|:|,", x) Method 2: compile() function # import package import re x = "ABC;DE:XY,Z" delims = re.compile(r";|:|,") delims.split(x) Method 3: translate() function x = "ABC;DE:XY,Z" x_rep = x.translate(str.maketrans({';': ',', ':': ','})) x_rep.split(",") The following examples demonstrate how to use split(), compile(), and translate() functions to split the string on multiple delimiters in Python.

You can use split(), join(), and RegEx functions to split a string on a second occurrence in Python.
The following examples explain how you can split the string based on a second occurrence of a character in Python.
Using split() and join() functions This example explains how to split a string on a second occurrence of a character using the split() and join() functions.
Suppose you have the following example string

Boxplots are a great way to visualize the distribution (min, max, quartiles, and median) of a dataset. However, they do not display the mean and standard error of the dataset by default.
In R, you can use the ggplot2 package to add mean and standard error on the boxplot.
The following example explains how to add mean and standard error on a boxplot using the ggplot2 package in R.
We will use the built-in mtcars data.

Boxplots are a great way to visualize the distribution (min, max, quartiles, and median) of a dataset. However, they do not display the mean of the dataset by default.
In Python matplotlib, you can use the showmeans parameter from the boxplot function to show the mean on the boxplot.
The following example explains how to plot a boxplot and show the mean on it using the matplotlib Python package.
Let’s create a random dataset for three groups using the rand function from NumPy,

Boxplots are a great way to visualize the distribution (min, max, quartiles, and median) of a dataset.
In Python, you can generate a boxplot using the boxplot function from matplotlib. By default, the boxplot displays the median line on the boxplot.
However, in some cases, we do not want to display the median line or want to add another metric line (e.g. mean) on the boxplot.
In matplotlib, you can remove the median line by passing the medianprops parameter with linewidth=0 value to the boxplot function.

The shading of confidence intervals on a line plot is useful to understand the range within which the true unknown parameter value lies with a certain level of confidence (e.g. 95% confidence interval).
In R, you can various functions such as ggplot2 and polygon to shade the confidence intervals with specific values given in the table.
The following example explains how to shade the confidence intervals using ggplot2 and polygon functions in R.