时间点归入不同时间段分类，统计不同时间段数据量，matplotlib绘图排名，Python

https://zhangphil.blog.csdn.net/article/details/125912667

https://zhangphil.blog.csdn.net/article/details/125912667

把不同时间点（时刻）划入不同时间段，然后统计每个时间段的时间点（时刻）总量，最终把数据放入到pandas数据帧，然后用matplotlib对pandas数据绘图。

import datetime
import random
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt

# 生成随机测试时间数量
from pprint import pprint

SAMPLE_COUNT = 2000
SECTION = 'section'
SUM = 'sum'

def my_time():
    times = []
    for i in range(24):
        times.append({SECTION: (i, i + 1), SUM: 0})

    cnt = 0
    while cnt < SAMPLE_COUNT:
        h = random.randint(0, 23)
        m = random.randint(0, 59)
        t = datetime.time(hour=h, minute=m)
        for tx in times:
            if tx[SECTION][0] <= t.hour < tx[SECTION][1]:
                tx[SUM] = tx[SUM] + 1
                # pprint(f'{t.strftime("%H:%M")} @ {tx[SECTION]}')
                break

        cnt = cnt + 1

    return times

def drawchart(df):
    myfont = matplotlib.font_manager.FontProperties(fname='C:\Windows\Fonts\msyh.ttc')
    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
    plt.rc('font', family='YaHei', weight='bold')

    order = []
    name = []
    mem = []
    for d, i in zip(df.values, df.index):
        order.append(i)
        name.append(d[0])
        mem.append(int(d[1]))

    FONT_SIZE = 12

    fig, ax = plt.subplots(figsize=(15, 13))

    b = ax.barh(y=range(len(name)), width=mem, align='center', color='red')

    # 为横向水平的柱图右侧添加数据标签。
    i = 0
    for rect in b:
        w = rect.get_width()
        ax.text(x=w, y=rect.get_y() + rect.get_height() / 2, s='%d' % (int(w)),
                horizontalalignment='left', verticalalignment='center',
                fontproperties=myfont, fontsize=FONT_SIZE - 2, color='green')
        ax.text(x=w / 2, y=rect.get_y() + rect.get_height() / 2, s=str(order[i]),
                horizontalalignment='center', verticalalignment='center',
                fontproperties=myfont, fontsize=FONT_SIZE - 3, color='white')
        i = i + 1

    ax.set_yticks(range(len(name)))
    ax.set_yticklabels(name, fontsize=FONT_SIZE - 1, fontproperties=myfont)

    ax.invert_yaxis()

    ax.set_xlabel('数据', fontsize=FONT_SIZE + 2, fontproperties=myfont)
    ax.set_title('不同时间段的数据点总量排名', fontsize=FONT_SIZE + 5, fontproperties=myfont)

    # 不要横坐标上的label标签。
    plt.xticks(())

    # 清除四周的边框线
    ax.get_yaxis().set_visible(True)
    for spine in ["left", "top", "right", "bottom"]:
        ax.spines[spine].set_visible(False)

    plt.subplots_adjust(left=0.15)  # 调整左侧边距

    # ax.margins(y=0.01) #缩放 zoom in

    ax.set_aspect('auto')

    plt.show()

if __name__ == '__main__':
    times = my_time()
    # pprint(times)

    # 数据组装成pandas数据帧。
    pd_data = []
    for t in times:
        l = [t[SECTION], t[SUM]]
        pd_data.append(l)

    col = ['时间段', '时间点次数']
    df = pd.DataFrame(data=pd_data, columns=col)
    df = df.sort_values(by=col[1], axis=0, ascending=False)  # 降序

    # 重置索引
    df = df.reset_index(drop=True)
    df.index = df.index + 1

    # 前10名
    pprint(df.head(10))
    # pprint(df.values)

    drawchart(df)

输出：

时间段  时间点次数
1   (19, 20)    103
2   (21, 22)     99
3   (18, 19)     95
4     (3, 4)     90
5   (17, 18)     89
6     (1, 2)     88
7   (13, 14)     88
8   (12, 13)     86
9   (22, 23)     85
10  (16, 17)     83

绘制的图：