The amount of data computation in the field of statistics is quite large. Therefore, using computers to solve statistical analysis problems has become an inevitable choice for people. They can use programming languages to design calculation formulas using basic functions in programming languages, input data to complete statistical analysis, or use statistical analysis software to input data to complete statistical analysis. The currently popular programming language is Python, and the popular data analysis software is SPSS. So what is the difference between SPSS and Python?

The following are the steps for descriptive statistical analysis using SPSS:

1. Enter data file

Taking the Chinese language scores of a class of students as an example, two variables, name and Chinese language scores, need to be set in the SPSS data table. The setting method is shown in Figure 1.

Figure 1: Setting Variable Attributes

After setting up, open the data view and input data into it, as shown in Figure 2.

Figure 2: Entering Student Grades

2. Analyze data files

After completing the data entry, as shown in Figure 3, click on "Analysis", "Description Statistics", "Description" in sequence, set the Chinese language score as a variable, then click on the options, select "Mean", "Maximum", "Minimum", "Variance", "Standard Deviation", "kurtosis" and "Skewness", and then click "Continue" to confirm.

Figure 3 Descriptive Statistical Analysis

The descriptive statistical analysis results of Chinese language scores are shown in Figure 4.

Figure 4 Analysis Results

If using Python for descriptive statistics, at least the following code needs to be run:

#Calculate the mean

mean(data)

#Calculate median

median(data)

#Calculate mode

mode(data)

#range

ptp(data)

#variance

var(data)

#standard deviation

std(data)

#coefficient of variation

mean(data) / std(data)

...

It can be seen that the Python language is very abstract and not beginner friendly, especially for users without a computer foundation. Many statistical analysis users are not professionals in the computer industry and have less time to learn languages, making it even more difficult to use Python.