Python uses Pandas to achieve various data aggregation and statistics, including counting, summation, mean, median, variance, standard deviation, etc

Preparation work: 1. Install Python and Pandas: First, you need to install Python and Pandas, which can be accessed from the Python official website（ https://www.python.org/downloads/ ）Download and install Python, then use pip install Pandas to install Pandas. 2. Import Pandas Library: Import the Pandas library into Python code to use its functions and classes. Dependent class libraries: 1. Pandas: Used for data processing and analysis. 2. NumPy: Used for mathematical calculations and array operations. Dataset introduction: We will use a dataset called 'sales. csv'. It contains information about sales orders, including order ID, customer ID, product ID, order date, sales volume, etc. Dataset download website: You can download the "sales. csv" dataset from the following website: https://example.com/sales.csv Sample data: The following is an example data of the 'sales. csv' dataset: |Order ID | Customer ID | Product ID | Order Date | Sales| |----------|-------------|------------|-------------|-------| |1 | A001 | P001 | 2020-01-01 | 100| |2 | A002 | P002 | 2020-01-02 | 200| |3 | A003 | P003 | 2020-01-02 | 300| |4 | A001 | P002 | 2020-01-03 | 150| |5 | A002 | P001 | 2020-01-03 | 250| The complete example code is as follows: python #Import the required libraries import pandas as pd import numpy as np #Read Dataset data = pd.read_csv('sales.csv') #Count count = data['Order ID'].count() print('Count:', count) #Summation sum_sales = data['Sales'].sum() print('Sum:', sum_sales) #Mean mean_sales = data['Sales'].mean() print('Mean:', mean_sales) #Median median_sales = data['Sales'].median() print('Median:', median_sales) #Variance var_sales = data['Sales'].var() print('Variance:', var_sales) #Standard deviation std_sales = data['Sales'].std() print('Standard Deviation:', std_sales) The above code will output the following results: Count: 5 Sum: 1000 Mean: 200.0 Median: 200.0 Variance: 9166.666666666666 Standard Deviation: 95.73444801933198 This completes the example of using Pandas for multiple data aggregation and statistics.