Python uses Pandas to implement time series analysis, including date and timestamp processing, sliding window analysis, moving average, etc

In order to use Pandas for time series analysis, we need to first establish a corresponding working environment. Firstly, ensure that you have installed Python and Pandas libraries. You can install these libraries through Anaconda and create and activate a new environment through the following command line operations: Conda create - n time series analysis environment Python=3.8 Conda Activate Time Series Analysis Environment pip install pandas Next, we will introduce some commonly used class libraries, which are important components of Pandas' time series analysis: 1. Pandas: Pandas is a powerful data processing library that provides many functions for processing and analyzing time series data. 2. NumPy: NumPy is a Python library for scientific computing, providing efficient multidimensional array objects and related operation functions. 3. Matplotlib: Matplotlib is a library for drawing graphics that can be used to visualize time series data. For the sample data, we used a classic time series dataset called "AirPassengers". This is a dataset that describes the number of international air passengers per month. You can download it to your environment using the following code: python from statsmodels.datasets import get_rdataset data = get_rdataset('AirPassengers').data This dataset contains two columns: "time" and "AirPassengers". 'time 'is a date timestamp, and' AirPassengers' is the number of passengers per month. The following is a complete example that includes date and timestamp processing, sliding window analysis, and moving average: python import pandas as pd from statsmodels.datasets import get_rdataset #Obtain AirPassengers dataset data = get_rdataset('AirPassengers').data #Convert the 'time' column to date time format data['time'] = pd.to_datetime(data['time']) #Set the 'time' column as an index data.set_index('time', inplace=True) #Sliding Window Analysis window = 12 data['rolling_mean'] = data['AirPassengers'].rolling(window=window).mean() data['rolling_std'] = data['AirPassengers'].rolling(window=window).std() #Moving Average data['moving_average'] = data['AirPassengers'].expanding().mean() #Print Results print(data.head()) This code first imports the required libraries, and then uses' get '_ The rdataset() function retrieves the 'AirPassengers' dataset from the statsmoodels. datasets module. Next, we will convert the 'time' column to date time format and set it as an index. Then, we use the rolling function to calculate the average and standard deviation of the sliding window, and the expanding function to calculate the moving average. Finally, we print the processed dataset. In this way, we have completed an example of using Pandas for basic time series analysis.