使用Python绘制三类论文级图表(分组柱状图、双轴折线图与多面板图)

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Times New Roman']
plt.rcParams['axes.unicode_minus'] = False

plt.rcParams['figure.dpi'] = 300
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['axes.linewidth'] = 1.2
plt.rcParams['grid.linewidth'] = 0.8

plt.rcParams['font.size'] = 10
plt.rcParams['axes.labelsize'] = 12

df = pd.DataFrame(data)
years = ['2007年', '2015年', '2024年']
indicators = ['Agdp', 'urban', 'country', 'urb']

df[(df['年份']==y) & (df['指标']==ind)]

for i, ind in enumerate(indicators):
    cv_vals = [...]
    ax.bar(...)

    wcv_vals = [...]
    ax.bar(..., hatch='///')

ax1 = plt.subplots()[1]
ax2 = ax1.twinx()

ax1.plot(...)  # 左轴：绝对离差
ax2.plot(...)  # 右轴：变异系数

fig, axes = plt.subplots(2, 2)

for coef in coefs:
    ax.plot(...)

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

ax.grid(axis='y', linestyle='--', alpha=0.6)

ax.text(..., f'{value:.3f}')

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')

# ================= 中文 + 期刊风格 =================
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Times New Roman']
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['figure.dpi'] = 300
plt.rcParams['savefig.dpi'] = 300

colors = {
    'Agdp': '#2E86AB',
    'urban': '#A23B72',
    'country': '#F18F01',
    'urb': '#C73E1D'
}

coef_colors = {
    '相对平均离差': '#2E86AB',
    '加权平均离差': '#4A90E2',
    '变异系数': '#F18F01',
    '加权变异系数(威廉姆森系数)': '#C73E1D'
}

# ================= 数据 =================
data = {
    '年份': ['2007年','2007年','2007年','2007年',
           '2015年','2015年','2015年','2015年',
           '2024年','2024年','2024年','2024年'],
    '指标': ['Agdp','urban','country','urb']*3,
    '相对平均离差': [22480.4,6234.2,2933.7,0.624,5.9,10181.1,9323,0.87,12.99,15031.3,17943.5,0.81],
    '加权平均离差': [23643.5,5540.9,3768.7,0.523,6.78,11957.6,10605.7,0.98,13.01,14938.9,19725.8,0.86],
    '变异系数': [0.733,0.411,0.526,0.500,0.586,0.264,0.805,0.814,0.856,0.222,0.808,0.81],
    '加权变异系数(威廉姆森系数)': [0.732,0.390,0.576,0.442,0.635,0.298,0.877,0.879,0.802,0.219,0.847,0.83]
}

df = pd.DataFrame(data)
years = ['2007年','2015年','2024年']
indicators = ['Agdp','urban','country','urb']

# ================= 图1：分组柱状图 =================
fig, ax = plt.subplots(figsize=(10,6))
bar_width = 0.18
x = np.arange(len(years))

for i, ind in enumerate(indicators):
    cv_vals = [df[(df['年份']==y)&(df['指标']==ind)]['变异系数'].values[0] for y in years]
    ax.bar(x+(i-1.5)*bar_width, cv_vals, width=bar_width,
           label=f'{ind}(CV)', color=colors[ind])

ax.set_title('变异系数分组柱状图')
ax.set_xticks(x)
ax.set_xticklabels(years)
ax.legend()
ax.grid(axis='y', linestyle='--', alpha=0.6)

plt.tight_layout()
plt.savefig('图1_柱状图.png')
plt.close()

# ================= 图2：双轴折线图 =================
fig, ax1 = plt.subplots(figsize=(10,6))
ax2 = ax1.twinx()

country_data = df[df['指标']=='country']

# 左轴
ax1.plot(years, country_data['相对平均离差'],
         marker='o', label='相对平均离差',
         color=coef_colors['相对平均离差'])

ax1.plot(years, country_data['加权平均离差'],
         marker='s', label='加权平均离差',
         color=coef_colors['加权平均离差'])

# 右轴
ax2.plot(years, country_data['变异系数'],
         marker='^', linestyle='--',
         label='变异系数',
         color=coef_colors['变异系数'])

ax2.plot(years, country_data['加权变异系数(威廉姆森系数)'],
         marker='*', linestyle='--',
         label='加权变异系数',
         color=coef_colors['加权变异系数(威廉姆森系数)'])

# 图例合并
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1+lines2, labels1+labels2)

ax1.set_title('农村收入差异双轴折线图')
ax1.set_xlabel('年份')

plt.tight_layout()
plt.savefig('图2_双轴折线图.png')
plt.close()

# ================= 图3：多面板图 =================
fig, axes = plt.subplots(2, 2, figsize=(12,8))
axes = axes.flatten()

coefs = ['相对平均离差','加权平均离差','变异系数','加权变异系数(威廉姆森系数)']

for idx, coef in enumerate(coefs):
    ax = axes[idx]
    for ind in indicators:
        vals = [df[(df['年份']==y)&(df['指标']==ind)][coef].values[0] for y in years]
        ax.plot(years, vals, marker='o', label=ind, color=colors[ind])

    ax.set_title(coef)
    ax.grid(axis='y', linestyle='--', alpha=0.6)

axes[-1].legend()
plt.tight_layout()
plt.savefig('图3_面板图.png')
plt.close()

print("图已生成：柱状图 / 双轴图 / 面板图")