代码收藏家技术教程 2025-01-07

趋势（七）利用python绘制日历热图

日历热图（Calendar Heatmap）简介

日历热图通过将事件聚合到日历网格中进行可视化分析，针对时序类数据特征较为直观，其中以github代码热图而知名。

快速绘制

基于plotly_calplot

import pandas as pd
import numpy as np
from plotly_calplot import calplot


# 自定义数据
dt = pd.date_range('2022-09-01', '2023-10-15')
values = np.random.randint(0, 100, len(dt))
df = pd.DataFrame({"dt": dt, "value": values})

# 绘制图形
fig = calplot(
         df,
         x="dt",
         y="value"
)

fig.show()

基于pyecharts

import pandas as pd
import numpy as np
from pyecharts import options as opts
from pyecharts.charts import Calendar
import datetime
import random

# 自定义数据
begin = datetime.date(2022, 1, 1)
end = datetime.date(2022, 12, 31)
data = [
    [str(begin + datetime.timedelta(days=i)), random.randint(1000, 25000)]
    for i in range((end - begin).days + 1)
]

c = (
    Calendar()
    .add("", data, calendar_opts=opts.CalendarOpts(range_="2022"))
    .set_global_opts(
        title_opts=opts.TitleOpts(title="2022年微信步数情况"),
        visualmap_opts=opts.VisualMapOpts(
            max_=30000,
            min_=500,
            orient="horizontal",
            is_piecewise=True,
            pos_top="230px",
            pos_left="100px",
        ),
    )
)

c.render_notebook()

基于calplot

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

# 自定义数据
np.random.seed(sum(map(ord, 'calplot')))
all_days = pd.date_range('1/1/2019', periods=730, freq='D')
days = np.random.choice(all_days, 500)
events = pd.Series(np.random.randn(len(days)), index=days)

calplot.calplot(events, cmap='YlGn', figsize = (16, 8), colorbar=False)

plt.show()

基于july

import numpy as np
import matplotlib.pyplot as plt
import july
from july.utils import date_range

# 自定义数据
dates_2022 = date_range("2022-01-01", "2022-12-31")
data_2022 = np.random.randint(0, 14, len(dates_2022))
dates_2023 = date_range("2023-01-01", "2023-12-31")
data_2023 = np.random.randint(0, 14, len(dates_2023))

july.heatmap(dates_2022, data_2022, title='Github Activity', cmap="github")
july.heatmap(dates_2023, data_2023, cmap="github")

plt.show()

定制多样化的日历热图

自定义日历热图一般是结合使用场景对相关参数进行修改，并辅以其他的绘图知识。参数信息可以通过官网进行查看，其他的绘图知识则更多来源于实战经验，大家不妨将接下来的绘图作为一种学习经验，以便于日后总结。

按月绘制

import numpy as np
import matplotlib.pyplot as plt
import july
from july.utils import date_range

# 自定义数据
dates = date_range("2022-01-01", "2022-12-31")
data = np.random.randint(0, 14, len(dates))

july.calendar_plot(dates, data)

plt.show()

指定月份绘制

import numpy as np
import matplotlib.pyplot as plt
import july
from july.utils import date_range

# 自定义数据
dates = date_range("2022-01-01", "2022-12-31")
data = np.random.randint(0, 14, len(dates))

# 初始化布局 
fig, axes = plt.subplots(1, 2)

# 带有日期标签
july.month_plot(dates, data, month=5, date_label=True, ax=axes[0])
axes[0].set_title('with date label') 

# 带有数值标签
july.month_plot(dates, data, month=5, value_label=True, ax=axes[1])
axes[1].set_title('with value label') 


plt.subplots_adjust(wspace=0.5)  # 增加子图之间的水平间隔
plt.show()