![]() You will see these often displayed in a boxplot. To achieve this we sort the values and cut at Q1 (25% below), Q2 (50% or the median), and Q3 (75%). A very common way is to split a distribution into 4 groups (quartiles) so that each group has 25% of the values. the 75th percentile is 75% or 3/4 of the way up the sorted list of values. You might be more familiar with the term percentiles, which are just quantiles relative to 100, e.g. Quantiles divide a distribution into equal-sized groups, giving the rank order of values. Therefore, I’ll also show the equivelant functions for NumPy and Pandas as well.Ĭv = sample_std_dev / mean_estimate signal_noise_ratio = 1 / cv Quantiles, quatiles, and percentiles: In reality, once you understand how they work, it’s better to use packages that are optimised and maintained. Now we have an idea of when to use them, I’ll show you the mechanics of how to calculate descriptive statistics with base Python functions. However, if the distribution is normal, we can use the mean and standard deviation which are more commonly understood, and easier to work with for things like financial calculations. This will become more clear once we work through the formulas below. income data), the median and percentiles will give a better representation of the center and spread as they’re less affected. If the distribution is skewed and/or has outliers (e.g. These statistics sum the values in some way, so extremes this will impact them. Notice in the image below how the mean is pulled to the right in the skewed distribution, well the same is true for the standard deviation and variance. We generally want one of each, but which ones will depend on the distribution of the data. percentiles, variance, and standard deviation). 2) Measures of spread which describe how far apart values are (e.g.1) Measures of central tendency which describe a ‘typical’ or common value (e.g.How to select appropriate statistics.ĭescriptive statistics fall into two general categories: My approach is to first use just the base functions (so you understand the mechanics), and then show the equivelant functions for two common packages: NumPy (for lists/arrays etc.) and Pandas (for dataframes). In this post I’ll briefly cover when to use which statistics, and then focus on how to calculate them in Python. They allow us to summarise data sets quickly with just a couple of numbers, and are in general easy to explain to others. Descriptive statistics might seem simple, but they are a daily essential for anlaysts and data scientists. ![]()
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