Python按性别和年龄区间绘制柱状图遇KeyError问题求助

核心原因 #

你的代码触发KeyError，本质是统计结果的索引和你定义的age_range_labels不匹配，可能是以下两种情况：

• 没有先将原始年龄数据分组到指定区间，直接对整行数据做value_counts()，统计结果的索引根本不是年龄区间；
• 即使有现成的age_range列，某些区间在数据中无对应记录，value_counts()不会生成这些索引，用[age_range_labels]直接索引就会报错。

针对性解决方法 #

情况1：原始数据是单个年龄数值（如age列存45、62这类数字）

需要先把年龄分到你指定的区间，再统计数量：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def create_bar_plot_by_sex(covid19_data, age_ranges):
    # 生成区间标签和分组边界
    age_range_labels = [f"{s}-{e}" for s, e in age_ranges]
    bins = [x for r in age_ranges for x in r]
    
    # 给数据新增年龄区间列
    covid19_data['age_range'] = pd.cut(
        covid19_data['age'], 
        bins=bins, 
        labels=age_range_labels,
        include_lowest=True  # 确保左边界包含在内
    )
    
    # 按性别+年龄区间分组统计，缺失的区间补0
    group_counts = covid19_data.groupby(['sex', 'age_range']).size().unstack(fill_value=0)
    
    counts_male = group_counts.loc['male']
    counts_female = group_counts.loc['female']
    
    # 绘图部分
    fig, ax = plt.subplots(figsize=(20, 10))
    index = np.arange(len(age_ranges))
    bar_width = 0.35
    opacity = 0.8

    rects1 = plt.bar(index, counts_male, bar_width, alpha=opacity, color='b', label='Male')
    rects2 = plt.bar(index + bar_width, counts_female, bar_width, alpha=opacity, color='g', label='Female')

    plt.xlabel('Age Range')
    plt.ylabel('Count')
    plt.title('Corona Cases per Age Group')
    plt.xticks(index + bar_width/2, [f"[{s},{e})" for s,e in age_ranges])
    plt.legend()

    plt.tight_layout()
    return counts_female, counts_male

cnts_f, cnts_m = create_bar_plot_by_sex(covid19_data, age_ranges)

情况2：数据已存在age_range列

只需修改统计逻辑，用reindex确保所有指定区间都被包含，缺失值补0：

import numpy as np
import matplotlib.pyplot as plt

def create_bar_plot_by_sex(covid19_data, age_ranges):
    age_range_labels = [f"{s}-{e}" for s, e in age_ranges]
    
    # 筛选女性数据并统计年龄区间，缺失区间补0
    female_ages = covid19_data[covid19_data['sex']=="female"]['age_range']
    counts_female = female_ages.value_counts().reindex(age_range_labels, fill_value=0)
    
    # 筛选男性数据并统计年龄区间，缺失区间补0
    male_ages = covid19_data[covid19_data['sex']=='male']['age_range']
    counts_male = male_ages.value_counts().reindex(age_range_labels, fill_value=0)
    
    # 绘图部分不变
    fig, ax = plt.subplots(figsize=(20, 10))
    index = np.arange(len(age_ranges))
    bar_width = 0.35
    opacity = 0.8

    rects1 = plt.bar(index, counts_male, bar_width, alpha=opacity, color='b', label='Male')
    rects2 = plt.bar(index + bar_width, counts_female, bar_width, alpha=opacity, color='g', label='Female')

    plt.xlabel('Age Range')
    plt.ylabel('Count')
    plt.title('Corona Cases per Age Group')
    plt.xticks(index + bar_width/2, [f"[{s},{e})" for s,e in age_ranges])
    plt.legend()

    plt.tight_layout()
    return counts_female, counts_male

cnts_f, cnts_m = create_bar_plot_by_sex(covid19_data, age_ranges)

额外注意点 #

• 检查sex列的取值大小写：如果数据中是Female/Male，而你代码里用的是female/male，会导致筛选不到数据，统计结果全为0；
• 确保age_range列的格式和你生成的age_range_labels完全一致（比如有没有空格、符号差异）。

内容的提问来源于stack exchange，提问作者kixx