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 Python, you can use the interval function from SciPy to calculate various confidence intervals based on Student’s t-distribution and Z-distributions (standard normal distribution).
The following examples explain calculating confidence Intervals using the scipy library.
Calculate 95% confidence interval based on t-distribution Create a sample dataset,

ppf (Percent Point Function) and cdf (Cumulative Distribution Function) are probability distribution functions available in the scipy library in Python.
The cdf function is used for getting a probability (p) value from a specific value, whereas the ppf function is used for getting a specific value from the probability (p) value.
The ppf function is the inverse of the cdf function.
The following examples explain the differences in cdf and ppf functions and how to calculate them.

Calculating a confidence interval helps determine the estimated range of values in which the true parameter value such as the population mean, is likely to fall, with a certain level of confidence (e.g., a 95% confidence interval).
In Python, you can use the groupby function from pandas to calculate the mean and confidence interval for various groups in the DataFrame.
Sample dataset In this article, we will use the flights dataset from the seaborn package to calculate the confidence interval.

In Python, you can use the fill_between function from matplotlib to shade the desired regions under the curve.
The basic syntax for the fill_between function is:
# impoat package import matplotlib.pyplot as plt plt.fill_between(x, y) The fill_between function requires values for the x and y coordinates to define the area for shading.
The following examples explain how to use the fill_between function to shade the desired regions under the curve.

You can use bedtools merge tools to filter out the overlapping regions (i.e. keep regions that are not overlapping) from the BED file.
For example, if you have the following BED file and you want to keep only non-overlapping regions from the BED file.
cat file.bed # output chr1 1 100 exon1 chr1 80 300 exon1 chr1 400 700 exon1 chr1 900 1000 exon1 This BED file (file.bed) has four regions among which two regions overlap.

In bioinformatics, the analysis of BED files involves merging genomic intervals (regions) into contiguous regions that share the common feature names (fourth column of BED file).
In this case, you can use the bedtools groupby function to merge genomic intervals into contiguous regions based on the name column.
For example, you have the following BED file with their feature names in the fourth column
cat file1.bed chr1 10 100 exon1 chr1 60 200 exon1 chr2 200 500 exon2 chr2 350 450 exon2 chr2 600 700 exon2 Now, merge regions in BED file using bedtools groupby function based on feature names,