Have you ever found yourself working with a large dataset in Python and wondered how to effectively analyze and extract meaningful insights from it? One useful method is to use telling functions in Python. Telling functions allow you to extract statistical information about your data, such as its mean, median, and mode, as well as its distribution and variability. In this article, we’ll cover everything you need to know about telling functions in Python, from their basic syntax to more advanced techniques for using them effectively.
What are Telling Functions?
Telling functions, also known as descriptive statistics functions, are a set of functions in Python’s statistics module that allow you to calculate statistical measures of a dataset. These measures include central tendency (mean, median, and mode), measures of variability (variance and standard deviation), and measures of distribution (skewness and kurtosis). By using these functions, you can gain insights into the overall structure and characteristics of your data.
Basic Syntax of Telling Functions
The basic syntax for using telling functions in Python is as follows:
import statistics
data = [1, 2, 3, 4, 5]
mean = statistics.mean(data)
median = statistics.median(data)
mode = statistics.mode(data)
variance = statistics.variance(data)
stdev = statistics.stdev(data)
skewness = statistics.skew(data)
kurtosis = statistics.kurtosis(data)
In this example, we’ve imported the statistics module and created a list of data. We then use the various telling functions to calculate the mean, median, mode, variance, standard deviation, skewness, and kurtosis of the data. These functions take a single argument, which is the dataset you want to analyze.
Calculating Central Tendency
One of the most common uses of telling functions is to calculate central tendency. Central tendency refers to the typical or average value of a dataset. There are three measures of central tendency: mean, median, and mode.
The mean is calculated by adding up all the values in the dataset and dividing by the number of values. It’s a useful measure when the data is evenly distributed and doesn’t contain outliers. However, when the data is skewed or contains outliers, the mean can be misleading.
The median is the middle value of the dataset when it’s sorted in ascending or descending order. It’s a useful measure when the data is skewed or contains outliers, as it’s not affected by extreme values.
The mode is the most frequently occurring value in the dataset. It’s a useful measure when the data contains discrete values or when there are multiple peaks in the distribution.
Calculating Variability
Another important aspect of a dataset is its variability. Measures of variability tell you how spread out the data is. There are two measures of variability: variance and standard deviation.
The variance is calculated by taking the average of the squared differences between each value and the mean. It’s a useful measure when you want to know how far apart the values are from each other.
The standard deviation is the square root of the variance. It’s a more commonly used measure than variance because it’s in the same units as the data. It tells you how far the values are from the mean on average.
Calculating Distribution
Measures of distribution tell you how the data is spread out. There are two measures of distribution: skewness and kurtosis.
Skewness is a measure of the degree to which the data is asymmetrical. A positive skewness means that the data is skewed to the right, while a negative skewness means that the data is skewed to the left. A skewness of 0 means that the data is normally distributed.
Kurtosis is a measure of the degree to which the data is peaked or flat. A positive kurtosis means that the data is more peaked than a normal distribution, while a negative kurtosis means that the data is flatter than a normal distribution. A kurtosis of 0 means that the data is normally distributed.
Using Telling Functions Effectively
While telling functions are a useful tool for analyzing datasets, it’s important to use them effectively. Here are some tips for using telling functions effectively:

Always examine the distribution of your data before using telling functions. If your data is skewed or contains outliers, the results of telling functions can be misleading.

Use multiple telling functions to gain a more complete understanding of your data. For example, if the mean and median are very different, it may indicate that your data is skewed.

Consider the context of your data when interpreting the results of telling functions. For example, a kurtosis of 3 may be normal for a stock price dataset but abnormal for a height dataset.

Use telling functions in conjunction with visualization tools, such as histograms and box plots, to gain a more complete understanding of your data.

Practice using telling functions on datasets of different sizes and types to become more familiar with their capabilities.
Final Thoughts
Telling functions are a powerful tool for analyzing datasets in Python. By calculating measures of central tendency, variability, and distribution, you can gain insights into the overall structure and characteristics of your data. However, it’s important to use telling functions effectively and to interpret their results in the context of your data. With practice, you can become proficient at using telling functions to extract meaningful insights from your data.