利用panda解决以下问题:Firstly, input an integer N, which represents a total of N students. Then enter N lines, each containing the student's name, class, exam 1 score, and exam 2 score. Count the average and total scores of each exam for each class, and sort the results by class name before outputting them to the console. Output includes class, score1_mean, score1_sum, score2_mean, score2_sum.

时间: 2023-12-18 21:05:48 浏览: 55
以下是使用 Pandas 解决该问题的代码: ```python import pandas as pd # 输入学生人数 N = int(input("请输入学生人数:")) # 输入每个学生的信息 students = [] for i in range(N): name, cls, score1, score2 = input().split() students.append({ "name": name, "class": cls, "score1": int(score1), "score2": int(score2) }) # 将学生信息转换成 DataFrame df = pd.DataFrame(students) # 按班级分组,计算平均分和总分 grouped = df.groupby("class").agg({ "score1": ["mean", "sum"], "score2": ["mean", "sum"] }) # 对结果进行排序 grouped = grouped.sort_index() # 输出结果 print(grouped) ``` 运行该程序后,会先要求输入学生人数,然后再输入每个学生的信息。接着,程序会将学生信息转换成 Pandas DataFrame,并按班级分组,计算每个班级每门考试的平均分和总分。最后,程序对结果按班级名称进行排序,并输出到控制台上。
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利用pandas解决问题:Firstly, input an integer � N, which represents a total of � N students. Then enter � N lines, each containing the student's name, class, exam 1 score, and exam 2 score. Count the average and total scores of each exam for each class, and

以下是用pandas解决该问题的代码实现: ``` python import pandas as pd # 读取学生信息 n = int(input()) data = [] for i in range(n): line = input().split() data.append(line) # 创建DataFrame df = pd.DataFrame(data, columns=['name', 'class', 'exam1', 'exam2']) df[['exam1', 'exam2']] = df[['exam1', 'exam2']].astype(float) # 计算各班级的平均分和总分 result = df.groupby('class').agg({'exam1': ['mean', 'sum'], 'exam2': ['mean', 'sum']}) # 输出结果 print(result) ``` 上述代码首先读取学生信息,然后创建一个DataFrame。接着通过astype函数将考试成绩的数据类型转换为float类型。最后通过groupby函数对班级进行分组,并计算各班级的平均分和总分。最终输出结果。

Firstly, input an integer � N, which represents a total of � N students. Then enter � N lines, each containing the student's name, class, exam 1 score, and exam 2 score. Count the average and total scores of each exam for each class, and sort the results by class name before outputting them to the console. Output includes class, score1_mean, score1_sum, score2_mean, score2_sum.

Sure, I can help you with that. Here's an example Python code that solves the problem you stated: ``` # Input the total number of students N = int(input()) # Create a dictionary to store the results for each class class_results = {} # Loop through each student and store their results in the dictionary for i in range(N): name, class_name, exam1, exam2 = input().split() exam1 = int(exam1) exam2 = int(exam2) if class_name not in class_results: class_results[class_name] = {'exam1_total': 0, 'exam1_count': 0, 'exam2_total': 0, 'exam2_count': 0} class_results[class_name]['exam1_total'] += exam1 class_results[class_name]['exam1_count'] += 1 class_results[class_name]['exam2_total'] += exam2 class_results[class_name]['exam2_count'] += 1 # Sort the results by class name class_names = sorted(class_results.keys()) # Print the results for each class for class_name in class_names: exam1_mean = class_results[class_name]['exam1_total'] / class_results[class_name]['exam1_count'] exam2_mean = class_results[class_name]['exam2_total'] / class_results[class_name]['exam2_count'] exam1_sum = class_results[class_name]['exam1_total'] exam2_sum = class_results[class_name]['exam2_total'] print(f"{class_name} {exam1_mean:.2f} {exam1_sum} {exam2_mean:.2f} {exam2_sum}") ``` Here's how you can use this code: 1. Copy and paste the code into a Python file (e.g. `main.py`). 2. Run the Python file using the command `python main.py`. 3. Enter the total number of students when prompted. 4. Enter the details (name, class, exam 1 score, exam 2 score) for each student, one by one. 5. The program will print the results for each class, sorted by class name. I hope this helps! Let me know if you have any questions.
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Implementing the UAV waypoint planning algorithm in MATLAB can be achieved through a variety of methods to ensure precise and efficient results. Firstly, we can design a user-friendly interface using MATLAB's GUI function. This intuitive interface allows users to easily input flight mission parameters and flight environment models. Secondly, we can take advantage of MATLAB's matrix operations and graphic drawing functions. These tools enable us to calculate the waypoint planning algorithm and provide a comprehensive visualization of the results. Thirdly, we can use MATLAB's optimization toolbox, which includes powerful functions like fmincon. These tools allow us to optimize the results of the waypoint planning algorithm. By defining suitable optimization objectives, such as minimizing total distance or energy consumption, we can find the optimal set of waypoints. Finally, we can verify the accuracy and feasibility of the waypoint planning results by conducting realistic UAV flight simulations using MATLAB's simulation capabilities, such as Simulink. By inputting the calculated waypoints into the UAV flight model, we can observe and analyze the flight trajectory and the UAV's state to ensure the planning results are accurate and reliable. By carefully choosing the right algorithms, optimizing the planning process, and fully utilizing the capabilities of MATLAB, we can achieve high-quality waypoint planning results that meet the specific requirements of each flight mission. These methods, integrated within MATLAB, provide a human-like approach to UAV waypoint planning, ensuring accuracy, effectiveness, and detection avoidance.还能检测出来,这个基础上再修改

The LULC simulation data we utilized to create future EN maps was produced by X. Liu et al. (2017), which was conducted at the national level. The reason we apply national-level simulated data to a local area is as follows. Firstly, China has a top-down land use planning system (also known as spatial planning) with five levels. The quantitative objectives in national plans are handed down to county-level plans through provincial and prefectural level plans (Zhong et al., 2014). That means land use patterns of nine cities in WUA are required to reflect relevant upper-level plans, for example, to satisfy the land use quota made by Hubei provincial plans and the national plans. Secondly, there are interdependencies across places so what happens in one region produces effects not only on this location but on other regions (Overman et al., 2010). And the increase of construction land in one place will shift protection pressure on natural ecosystems elsewhere for a sustainable goal. The land use simulation at the national level allocated land resources from a top-down perspective and links land use changes in a region to events taking place in other locations through global simulation. However, the Kappa coefficient of the simulated data in WUA is 0.55 and the overall accuracy is 0.71, which is lower than the statistic value at the national-level data. Although the Kappa between 0.4~0.6 is moderate and at an acceptable level (Appiah et al., 2015; Ding et al., 2013; Ku, 2016), the simulated accuracy of the land use data needs to be improved. Future work on exploring the impact of LULC dynamics on EN will develop based on the high-accuracy simulated data and updating the initial simulated time to 2020, by integrating the impacts of socioeconomic factors, climate change, regional planning, land use policy, etc.

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