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A method for quickly generating and visualizing data distribution in Python
In data science and machine learning, understanding the distribution of data is an important step in data analysis and modeling. The data distribution reveals the frequency and characteristics of data in different value ranges, helping to better understand the features of the data. By analyzing data distribution, trends, biases, and outliers in the data can be identified, enabling feature engineering, data cleaning, and model optimization.
Normal distribution
Overview of Normal Distribution
Normal distribution is the most common type of distribution, also known as “Gaussian distribution” or “bell shaped distribution”. Its characteristic is that the data gathers around the average value and gradually decreases towards both sides to form a symmetrical bell shaped curve. Normal distribution has applications in many natural phenomena, such as height, weight, exam scores, etc.
In a normal distribution, the mean determines the center position of the distribution, and the standard deviation determines the width of the distribution. Normal distribution is very common in machine learning and statistical analysis, and many models assume that the data follows a normal distribution.
