What is Descriptive Statistical Analysis? What are the main contents? Python
Descriptive statistics is an important concept in statistics, which is a method of describing and displaying data characteristics by summarizing, inducing and explaining data. Descriptive statistical analysis can help us conduct preliminary analysis and understanding of data, thus providing a basis for subsequent data analysis and decision-making.
The main contents are as follows:
- Analysis of the basic characteristics of the data set
Describe the basic characteristics of the data set, including the type of data, the number of data, missing values, etc., and descriptive statistics such as mean, standard deviation, maximum, minimum, and quartile for continuous variables.
- univariate analysis
Univariate analysis is mainly for the study of a single variable, including discrete variables and continuous variables. Discrete variables can be represented by frequency tables and frequency distribution tables, and continuous variables can be analyzed by histograms, box plots and other graphics or descriptive statistics.
- bivariate analysis
Bivariate analysis refers to the study between two variables, mainly to understand the correlation between the two variables. You can use scatter plots, regression analysis, etc. to show the relationship between two variables.
In Python, we can use the Pandas library and Matplotlib library for descriptive statistical analysis.
The following is a simple sample code that shows how to use the describe() function in the Pandas library to analyze the basic characteristics of the data set. It also shows how to use the hist() function in the Matplotlib library to draw histograms.
import pandas as pd
import matplotlib