对给定数据集pandas_data.csv，完成如下题目(除pe成绩）： （1）计算成绩的平均值，作为新的一列加入到原数据中。 （1）统计每个科目75分以上的人数。Python

对于给定的pandas_data.csv数据集，你可以按照以下步骤操作：

**步骤 1: 导入所需的库和数据**

import pandas as pd

# 读取CSV数据
pandas_data = pd.read_csv('pandas_data.csv')

**步骤 2: 计算成绩的平均值并创建新列**

# 假设成绩列名为'Scores'，如果不是，请替换为实际列名
average_scores = pandas_data['Scores'].mean()
pandas_data['Average_Score'] = average_scores

**步骤 3: 统计每个科目75分以上的人数**
由于题目未明确指定科目列名，这里假设科目列名为'results'（如果不是，请替换为实际列名），并且成绩列包含具体科目分数，比如 'Math', 'English', etc.。我们可以遍历这些科目：

subject_counts = {}
for subject in pandas_data.columns[-1]:  # 遍历除了姓名和平均分之外的所有列
    if subject != 'Scores' and subject != 'Average_Score':
        high_scores_mask = pandas_data[f'{subject}'].astype(int) >= 75
        high_score_count = high_scores_mask.sum()
        subject_counts[subject] = high_score_count

subject_counts_dict = pd.DataFrame.from_dict(subject_counts, orient='index').reset_index()
subject_counts_dict.columns = ['Subject', 'High_Score_Count']

现在`subject_counts_dict`包含了每个科目的75分以上人数。

完整的代码示例如下：

import pandas as pd

# 读取CSV数据
pandas_data = pd.read_csv('pandas_data.csv')

# 计算平均成绩并添加到数据框
average_scores = pandas_data['Scores'].mean()
pandas_data['Average_Score'] = average_scores

# 统计各科目75分以上的人数
subject_counts = {}
for subject in pandas_data.columns[-1]:  # 假设最后一列是科目
    if subject != 'Scores' and subject != 'Average_Score':
        high_scores_mask = pandas_data[subject].astype(int) >= 75
        high_score_count = high_scores_mask.sum()
        subject_counts[subject] = high_score_count

# 将结果转换为 DataFrame 并展示
subject_counts_dict = pd.DataFrame.from_dict(subject_counts, orient='index').reset_index()
subject_counts_dict.columns = ['Subject', 'High_Score_Count']
print(subject_counts_dict)