Python数据科学作业：补全新冠数据30-40岁组误差棒图函数

需求：绘制30-40岁年龄组新冠阳性结果相对频率随时间变化的误差棒图，包含各点上下1个标准差，补全以下函数的关键代码段：

def create_bar_plot_for_derek(covid19_data):
  # first we subset the data by the appropriate age bracket and do a bit of cleaning
  prof_age_data = covid19_data[covid19_data.age_range=="30-40"]
  prof_age_data=prof_age_data.replace(to_replace='25.02.2020 - 26.02.2020',value='25.02.2020')

  # and we convert the column to a date-time
  prof_age_data['date_confirmation']=pd.to_datetime(prof_age_data['date_confirmation'],dayfirst=True)

  outcomes_over_time =  **# Problem 6) fill in here**

  outcomes_over_time = outcomes_over_time.dropna() # we should drop the rows with missing values

  x =  **# Problem 6) fill in here**
  y =  **# Problem 6) fill in here**
  error =  **# Problem 6) fill in here**

  fig, ax = plt.subplots(figsize=(20, 10))
  ax.errorbar(x, y, yerr=error, fmt='-o')
  plt.ylabel('Relative Frequency', fontsize=14)
  plt.xlabel('Date', fontsize=14)
  return x, y, error

补全后的完整函数 #

import pandas as pd
import matplotlib.pyplot as plt

def create_bar_plot_for_derek(covid19_data):
    # first we subset the data by the appropriate age bracket and do a bit of cleaning
    prof_age_data = covid19_data[covid19_data.age_range=="30-40"]
    prof_age_data=prof_age_data.replace(to_replace='25.02.2020 - 26.02.2020',value='25.02.2020')

    # and we convert the column to a date-time
    prof_age_data['date_confirmation']=pd.to_datetime(prof_age_data['date_confirmation'],dayfirst=True)

    # Problem 6) fill in here: 按日期分组计算阳性相对频率和标准差
    outcomes_over_time = prof_age_data.groupby('date_confirmation').apply(
        lambda group: pd.Series({
            'relative_freq': (group['outcome'] == 'positive').mean(),
            'std_dev': (group['outcome'] == 'positive').std()
        })
    ).reset_index()

    outcomes_over_time = outcomes_over_time.dropna() # we should drop the rows with missing values

    # Problem 6) fill in here: 提取绘图所需的X/Y轴及误差数据
    x = outcomes_over_time['date_confirmation']
    y = outcomes_over_time['relative_freq']
    error = outcomes_over_time['std_dev']

    fig, ax = plt.subplots(figsize=(20, 10))
    ax.errorbar(x, y, yerr=error, fmt='-o')
    plt.ylabel('Relative Frequency', fontsize=14)
    plt.xlabel('Date', fontsize=14)
    return x, y, error

关键补全逻辑说明 #

• outcomes_over_time 代码段：
  按date_confirmation分组，对每组计算：

  • 阳性结果相对频率：通过(group['outcome'] == 'positive').mean()得到（阳性样本数/组内总样本数）
  • 标准差：通过(group['outcome'] == 'positive').std()得到组内阳性结果的标准差
    注意：如果你的数据中记录检测结果的列名不是outcome，阳性标记不是'positive'，请替换为实际的列名和取值

• x/y/error 代码段：
  直接从分组统计后的outcomes_over_time数据框中提取对应字段：

  • x为日期序列，作为绘图的X轴
  • y为每日阳性相对频率，作为绘图的Y轴
  • error为每日阳性结果的标准差，用于绘制误差棒

内容的提问来源于stack exchange，提问作者Grace Mcpadden