In Python, you can use the kdeplot function from seaborn to plot the distribution curve using kernel density estimation (KDE). The generated distribution plot (also called as KDE plot) is similar to a histogram plot but it has smooth line instead of discrete bins. Sometimes, you might want to fill the area under the KDE curve to highlight and compare the distribution more effectively. You can use the fill parameter to fill the area under curver in the distribution plot generated from the kdeplot function in seaborn.

You can use the histplot function from the seaborn library to create the histogram to see how the data is distributed. Sometimes, we need to compare the data distribution with the theoretical normal distribution. To overlay a normal distribution line (PDF line) on a histogram you can use the norm function from scipy. The following example explains how to create the histogram using seaborn and add a normal distribution line on the histogram plot.

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.