# Python可视化

**Repository Path**: mengliyilei/python-visualization

## Basic Information

- **Project Name**: Python可视化
- **Description**: 可视化项目
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-09-18
- **Last Updated**: 2024-12-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Python可视化

#### 介绍
Python实现基础的可视化项目

#### 安装教程

##### 一、Python环境安装

1.下载环境

官网：https://www.python.org/

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20172442.png)

左侧为稳定版本，选择自己需要的版本下载即可。

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20172651.png)

2.环境变量配置

在电脑设置中搜索“环境变量”

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20172936.png)

点击环境变量

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20172951.png)

选中 Path 进行编辑

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20173010.png)

选择新建，将之前下载的 Python 的文件路径添加进去，再将 Python 文件中 Bin 的文件路径添加进去，最后一路“确定”保存设置。

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20173105.png)

##### 二、安装 PyCharm

官网：https://www.jetbrains.com/pycharm/download/?section=windows

选择“社区版”安装（社区版免费）。

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20192713.png)

安装后，点击左下角设置。

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20192815.png)

在设置中找到“Python解释器”

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20192859.png)

将下载的 Python 的文件路径添加进去，点击应用

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-09-28%20192917.png)

##### 三、安装必要的库

###### 1.安装基础画图库 matplotlib

```
pip install matplotlib
```

查询版本号，是否安装成功

```
import matplotlib
print(matplotlib.__version__)
```

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-10-06%20193229.png)

###### 2.安装有交互功能的图库 pyecharts

```
pip install pyecharts
```

查询版本号，是否安装成功

```
import pyecharts
print(pyecharts.__version__)
```

![输入图片说明](%E5%9B%BE%E7%89%87/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-10-06%20193513.png)

#### 可视化项目

##### 一、柱形图

###### 1.渐变柱形图

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx',sheet_name='1 渐变柱形图',usecols='B:C',skiprows=1)

# 设置渐变函数
def gradient_bar(x, y, ax, colormap='Blues'):   # 选用蓝色色卡
    n_bars = len(x)
    bar_colors = plt.get_cmap(colormap)(np.linspace(0.5, 0.9, 100))  # 设置渐变起始点与终止点

    # 创建柱子
    for i in range(n_bars):
        left = i
        bottom = 0
        width = 0.6
        height = y[i]

        # 创建渐变效果
        polys = []
        for j in range(100):
            h = height * (j / 100)
            verts_j = [
                (left, bottom + h),
                (left, bottom + height),
                (left + width, bottom + height),
                (left + width, bottom + h),
                (left, bottom + h)
            ]
            polys.append(verts_j)

        poly = PolyCollection(polys, facecolors=bar_colors)
        ax.add_collection(poly)

    ax.autoscale_view()

# 创建图形和坐标轴
fig, ax = plt.subplots(figsize=(8, 6))

# 设置背景颜色
fig.patch.set_facecolor('#00002d')
ax.set_facecolor('#00002d')

# 设置X轴和Y轴数据
x = np.arange(len(data['区域']))  # X轴位置
y = data['销售量']  # Y轴高度

# 绘制渐变柱形图
gradient_bar(x, y, ax)

# 设置X轴标签
ax.set_xticks(x+0.3)
ax.set_xticklabels(data['区域'], color='w')

# 设置Y轴标签颜色为白色
ax.set_yticklabels(ax.get_yticks(), color='w')

# 添加网格线
ax.grid(True, axis='y' ,linestyle='--', linewidth=0.5, color='w')

# 隐藏边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 输出每个柱子的值
for i in range(len(x)):
    ax.text(x[i] + 0.3, y[i] + 0.1, f'{int(y[i])}', ha='center', va='bottom', color='w', fontsize=10)

# 调整图形位置
plt.subplots_adjust(top=0.8)  # 留出空间给标题

# 添加标题
plt.text(0, 1.15, '3月各区域销量分布', color='w', fontsize=24, transform=ax.transAxes)
plt.text(0, 1.05, '东北销量最多占比总销量的22%，华南销量最低', color='w', fontsize=16, transform=ax.transAxes)

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章前15/图片/渐变柱形图.png', dpi=300, bbox_inches='tight', facecolor='#00002d')

# 显示图形
plt.show()
```

![输入图片说明](实验二/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%B8%90%E5%8F%98%E6%9F%B1%E5%BD%A2%E5%9B%BE.png)

###### 2.带均值柱形图

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx',sheet_name='1 渐变柱形图',usecols='B:C',skiprows=1)

# 设置渐变函数
def gradient_bar(x, y, ax, colormap='Blues'):   # 选用蓝色色卡
    n_bars = len(x)
    bar_colors = plt.get_cmap(colormap)(np.linspace(0.5, 0.9, 100))  # 设置渐变起始点与终止点

    # 创建柱子
    for i in range(n_bars):
        left = i
        bottom = 0
        width = 0.6
        height = y[i]

        # 创建渐变效果
        polys = []
        for j in range(100):
            h = height * (j / 100)
            verts_j = [
                (left, bottom + h),
                (left, bottom + height),
                (left + width, bottom + height),
                (left + width, bottom + h),
                (left, bottom + h)
            ]
            polys.append(verts_j)

        poly = PolyCollection(polys, facecolors=bar_colors)
        ax.add_collection(poly)

    ax.autoscale_view()

# 创建图形和坐标轴
fig, ax = plt.subplots(figsize=(8, 6))

# 设置背景颜色
fig.patch.set_facecolor('#00002d')
ax.set_facecolor('#00002d')

# 设置X轴和Y轴数据
x = np.arange(len(data['区域']))  # X轴位置
y = data['销售量']  # Y轴高度

# 计算y的均值
y_mean = y.mean()

# 绘制渐变柱形图
gradient_bar(x, y, ax)

# 设置X轴标签
ax.set_xticks(x+0.3)
ax.set_xticklabels(data['区域'], color='w')

# 设置Y轴标签颜色为白色
ax.set_yticklabels(ax.get_yticks(), color='w')

# 保留底部边框，隐藏其他边框
for spine in ax.spines.values():
    if spine.spine_type == 'bottom':
        spine.set_color('white')  # 设置底部边框颜色为白色
    else:
        spine.set_visible(False)

# 输出每个柱子的值
for i in range(len(x)):
    ax.text(x[i] + 0.3, y[i] + 0.1, f'{int(y[i])}', ha='center', va='bottom', color='w', fontsize=10)

# 绘制y的均值水平线
ax.axhline(y=y_mean, color='red', linestyle='--', linewidth=2, label='平均值')

# 添加均值的文本标注
ax.text(0.9, 0.75, f'平均值: {y_mean:.2f}', color='red', fontsize=12, transform=ax.transAxes)

# 调整图形位置
plt.subplots_adjust(top=0.8)  # 留出空间给标题

# 添加标题
plt.text(0, 1.15, '3月各区域销量分布', color='w', fontsize=24, transform=ax.transAxes)
plt.text(0, 1.05, '东北销量最多占比总销量的22%，华南销量最低', color='w', fontsize=16, transform=ax.transAxes)

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章前15/图片/带均值柱形图.png', dpi=300, bbox_inches='tight', facecolor='#00002d')

# 显示图形
plt.show()
```

![输入图片说明](实验二/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%B8%A6%E5%9D%87%E5%80%BC%E6%9F%B1%E5%BD%A2%E5%9B%BE.png)

###### 3.渐变圆角柱形图

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx',sheet_name='3 渐变圆角柱形图',usecols='B:C',skiprows=1)

# 设置渐变函数
def gradient_bar(x, y, ax, colormap='Blues'):   # 选用蓝色色卡
    n_bars = len(x)
    bar_colors = plt.get_cmap(colormap)(np.linspace(0.5, 0.9, 100))  # 设置渐变起始点与终止点

    # 创建柱子
    for i in range(n_bars):
        left = i
        bottom = 0
        width = 0.6
        height = y[i]

        # 创建渐变效果
        polys = []
        for j in range(100):
            h = height * (j / 100)
            verts_j = [
                (left, bottom + h),
                (left, bottom + height),
                (left + width, bottom + height),
                (left + width, bottom + h),
                (left, bottom + h)
            ]
            polys.append(verts_j)

        poly = PolyCollection(polys, facecolors=bar_colors)
        ax.add_collection(poly)

    ax.autoscale_view()

# 创建图形和坐标轴
fig, ax = plt.subplots(figsize=(8, 6))

# 设置背景颜色
fig.patch.set_facecolor('#00002d')
ax.set_facecolor('#00002d')

# 设置X轴和Y轴数据
x = np.arange(len(data['商品']))  # X轴位置
y = data['销量']  # Y轴高度

# 绘制渐变柱形图
gradient_bar(x, y, ax)

# 设置X轴标签
ax.set_xticks(x+0.3)
ax.set_xticklabels(data['商品'], color='w')

# 设置Y轴标签颜色为白色
ax.set_yticklabels(ax.get_yticks(), color='w')

# 添加网格线
ax.grid(True, axis='y' ,linestyle='--', linewidth=0.5, color='w')

# 隐藏边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 输出每个柱子的值
for i in range(len(x)):
    ax.text(x[i] + 0.3, y[i] + 0.1, f'{int(y[i])}', ha='center', va='bottom', color='w', fontsize=10)

# 调整图形位置
plt.subplots_adjust(top=0.8)  # 留出空间给标题

# 添加标题
plt.text(0, 1.15, '3月商品销量对比', color='w', fontsize=24, transform=ax.transAxes)
plt.text(0, 1.05, '防晒销量最多，3月销量856；面膜最少，3月销量523', color='w', fontsize=16, transform=ax.transAxes)

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章前15/图片/渐变圆角柱形图.png', dpi=300, bbox_inches='tight', facecolor='#00002d')

# 显示图形
plt.show()
```

###### 4.标注柱形图

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

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx',sheet_name='4 标注柱形图',usecols='B:C',skiprows=1)

# 创建图形和坐标轴
fig, ax = plt.subplots(figsize=(8, 6))

# 设置背景颜色
fig.patch.set_facecolor('#00002d')
ax.set_facecolor('#00002d')

# 设置X轴和Y轴数据
x = np.arange(len(data['月份']))  # X轴位置
y = data['销量']  # Y轴高度

# 绘制不同颜色的柱形图
bars = ax.bar(x, y, color=['red','navy','royalblue','green','teal','blue','purple','slateblue'])

# 设置X轴标签
ax.set_xticks(x)
ax.set_xticklabels(data['月份'], color='w')

# 设置Y轴标签颜色为白色
ax.set_yticks(ax.get_yticks())
ax.set_yticklabels(ax.get_yticks(), color='w')

# 添加网格线
ax.grid(True, axis='y' ,linestyle='--', linewidth=0.5, color='w')

# 隐藏边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 输出每个柱子的值
colors=['red','navy','royalblue','green','teal','blue','purple','slateblue']
for i in range(len(x)):
    facecolor = colors[i]
    ax.text(x[i], y[i]+300, f'{int(y[i])}', ha='center', color='w', fontsize=10, bbox=dict(facecolor=facecolor, boxstyle='round'))

# 调整图形位置
plt.subplots_adjust(top=0.8)  # 留出空间给标题

# 添加标题
plt.text(0, 1.15, '2021年商品销量情况', color='w', fontsize=24, transform=ax.transAxes)
plt.text(0, 1.05, '口红销量最好达9221，是眼影最低值2645近3.5倍', color='w', fontsize=16, transform=ax.transAxes)

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章前15/图片/标注柱形图.png', dpi=300, bbox_inches='tight', facecolor='#00002d')

# 显示图形
plt.show()
```

![输入图片说明](实验二/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%A0%87%E6%B3%A8%E6%9F%B1%E5%BD%A2%E5%9B%BE.png)

###### 5.层叠柱形图

```
import pandas as pd
from pyecharts.charts import Bar
from pyecharts import options as opts

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx',sheet_name='5 层叠柱形图',usecols='B:D',skiprows=1)

# 数据准备
categories = data['季度'].tolist()
values1 = data['销售额'].tolist()
values2 = data['利润额'].tolist()

# 创建柱状图
bar = (
    Bar(init_opts=opts.InitOpts(bg_color='#00002d'))
    .add_xaxis(categories)  # 设置 x 轴数据
    .add_yaxis("销售额", values1, gap='-30%', color='blue')
    .add_yaxis("利润额", values2, gap='-30%', color='red')
    .set_global_opts(
        # 设置标题
        title_opts=opts.TitleOpts(
            title="2021年至今季度销售额（万）和利润额（万）",
            title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=24),
            subtitle="2022年第二季度销售额首次出现下降，降幅达到15%",
            subtitle_textstyle_opts=opts.TextStyleOpts(color="white", font_size=16)
        ),
        # 设置交互按钮
        legend_opts=opts.LegendOpts(
            is_show=True,
            textstyle_opts=opts.TextStyleOpts(color="white"),
            pos_right="0%",
            pos_top="5%"
        ),
        tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
        xaxis_opts=opts.AxisOpts(
            axisline_opts=opts.AxisLineOpts(is_show=True),  #  X 轴线
            axislabel_opts=opts.LabelOpts(is_show=True, color='white'),    #  X 轴标签
            splitline_opts=opts.SplitLineOpts(is_show=False) #  X 轴网格线
        ),
        yaxis_opts=opts.AxisOpts(
            axisline_opts=opts.AxisLineOpts(is_show=False),  #  Y 轴线
            axislabel_opts=opts.LabelOpts(is_show=False),    #  Y 轴标签
            splitline_opts=opts.SplitLineOpts(is_show=False) #  Y 轴网格线
        )
    )
    .set_series_opts(
        label_opts=opts.LabelOpts(
            is_show=True,
            position='top',
            color='white')  # 显示数据
    )
)

# 导出交互的 HTML 文件
bar.render("D:/python charm/01/可视化/第二章前15/图片/层叠柱形图.html")
```

运行结果在“实验二”文件夹的“运行结果”文件夹中。

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%BA%8C/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-10-06%20195916.png)

##### 二、折线图

###### 1.平滑折线图

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import make_interp_spline

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据与数据处理
data = pd.read_excel('第二章 图表(前15).xlsx',sheet_name='11 平滑折线图',usecols='B:D',skiprows=1)
data['月份'] = data['月份'].str.extract(r'^(.*)月').astype(int)
x1 = data['月份'].values[0:8]
x2 = data['月份'].values[8:] +12
x = [*x1, *x2]
y = data['销量'].values.tolist()

# 插值使得折线平滑
spl = make_interp_spline(x, y, k=3)
x_smooth = np.linspace(np.min(x), np.max(x), 500)
y_smooth = spl(x_smooth)

# 创建图形和坐标轴
fig, ax = plt.subplots(figsize=(10, 6))

# 设置背景颜色
fig.patch.set_facecolor('#00002d')
ax.set_facecolor('#00002d')

# 设置X轴标签
ax.set_xticks(x)
ax.set_xticklabels(data['月份'], color='w')

# 设置Y轴标签颜色为白色
ax.set_ylim(0, 4000)
ax.set_yticks(ax.get_yticks())
ax.set_yticklabels(ax.get_yticks(), color='w')

# 保留底部边框，隐藏其他边框
for spine in ax.spines.values():
    if spine.spine_type == 'bottom':
        spine.set_color('white')  # 设置底部边框颜色为白色
    else:
        spine.set_visible(False)

# 调整图形位置
plt.subplots_adjust(top=0.8)  # 留出空间给标题

# 添加标题
plt.text(0, 1.15, '化妆品品类月度销量走势', color='w', fontsize=24, transform=ax.transAxes)
plt.text(0, 1.05, '2022年销量迅速增加，1月最高，销量达到3782', color='w', fontsize=16, transform=ax.transAxes)

# 绘制从最大y值到x轴的垂线
ax.axvline(x[y.index(np.max(y))], ymin=0, ymax=np.max(y) / 4000, color='white', linestyle='--')
ax.annotate(f'{np.max(y)}', xy=(x[y.index(np.max(y))], np.max(y)),
            xytext=(x[y.index(np.max(y))], np.max(y) + 300),
            color='white', fontsize=12)

# 绘制图
plt.plot(x_smooth, y_smooth, color='red', linewidth=2)

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/平滑折线图.png', dpi=300, bbox_inches='tight', facecolor='#00002d')
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%B9%B3%E6%BB%91%E6%8A%98%E7%BA%BF%E5%9B%BE.png)

###### 2.对比折线图

```
import pandas as pd
import matplotlib.pyplot as plt
from qtawesome.iconic_font import text_color

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据与数据处理
data = pd.read_excel('第二章 图表(前15).xlsx',sheet_name='13 对比折线图',usecols='B:D',skiprows=1)
x = data['月份']
y = data['2021年']
z = data['2022年']

# 创建图形和坐标轴
fig, ax = plt.subplots(figsize=(10, 6))

# 设置背景颜色
fig.patch.set_facecolor('#00002d')
ax.set_facecolor('#00002d')

# 设置X轴标签
ax.set_xticklabels(data['月份'], color='w')

# 设置Y轴标签颜色为白色
ax.set_ylim(0, 3000)
ax.set_yticklabels(ax.get_yticks(), color='w')

# 添加网格线
ax.grid(True, axis='y' ,linestyle='--', linewidth=0.5, color='w')

# 隐藏边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 调整图形位置
plt.subplots_adjust(top=0.8)  # 留出空间给标题

# 添加标题
plt.text(0, 1.15, '2022年上半年各月同比去年销量', color='w', fontsize=24, transform=ax.transAxes)
plt.text(0, 1.05, '上半年同比去年增长明显，5月份同比增长最多，增长近40%', color='w', fontsize=16, transform=ax.transAxes)

# 绘制图
plt.plot(x, y,'o-', color='tomato', linewidth=2, markeredgecolor='#00002d', label='2021年')
plt.plot(x, z,'o-', color='dodgerblue', linewidth=2, markeredgecolor='#00002d', label='2022年')
ax.legend(facecolor='#00002d', labelcolor='w')

# 添加每个点的数值
for i in range(len(x)):
    plt.text(x[i], y[i] + 30, f'{int(y[i])}', color='w', fontsize=12, ha='center')
    plt.text(x[i], z[i] + 30, f'{int(z[i])}', color='w', fontsize=12, ha='center')

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/对比折线图.png', dpi=300, bbox_inches='tight', facecolor='#00002d')
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%AF%B9%E6%AF%94%E6%8A%98%E7%BA%BF%E5%9B%BE.png)

###### 3.折线柱形图

```
import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(后15).xlsx',sheet_name='23 柱形折线图',usecols='B:D',skiprows=1)
print(data)

# 数据准备
x = data['年份'].tolist()
y = data['销售量'].tolist()
z = (data['同比']).tolist()

# 创建图形和坐标轴
fig, ax = plt.subplots(figsize=(8, 6))

# 设置背景颜色
fig.patch.set_facecolor('#00002d')
ax.set_facecolor('#00002d')

# 隐藏边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 设置X轴标签
ax.set_xticks(x)
ax.set_xticklabels(data['年份'], color='w')
# 设置Y轴标签颜色为白色
ax.set_yticks([])
ax.set_ylim(0, 7000)
ax.set_yticklabels(ax.get_yticks(), color='w')
# 绘制柱形图
bars = ax.bar(x,y, color='dodgerblue', width=0.3, label='销售量')
# 输出每个柱子的值
for i in range(len(x)):
    ax.text(x[i], y[i], f'{int(y[i])}', ha='center', va='bottom', color='w', fontsize=10)

# 创建第二个坐标轴
ax2 = ax.twinx()
# 设置范围
ax2.set_ylim(min(z) * (-10), max(z) * 1.2)
# 绘制折线图
ax2.plot(x, z,'o-', color='tomato', linewidth=2, markeredgecolor='#00002d', label='同比')
# 输出每个点的值
for i in range(len(x)):
    ax2.text(x[i], z[i] + 0.02, f'{int(z[i] * 100)}%', ha='center', color='w', fontsize=10)

# 调整图形位置
plt.subplots_adjust(top=0.8)  # 留出空间给标题

# 添加标题
plt.text(0, 1.15, '近六年销售量及增长率', color='w', fontsize=24, transform=ax.transAxes)
plt.text(0, 1.05, '平台销量近6年持续增长，但近两年增长率有所放缓', color='w', fontsize=16, transform=ax.transAxes)

# 添加图例
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax2.legend(lines + lines2, labels + labels2, loc='best', facecolor='#00002d', labelcolor='w')

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/柱形折线图.png', dpi=300, bbox_inches='tight', facecolor='#00002d')
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%9F%B1%E5%BD%A2%E6%8A%98%E7%BA%BF%E5%9B%BE.png)

##### 三、饼图

###### 1.南丁格尔圆饼图

```
import pandas as pd
from pyecharts.charts import Pie
from pyecharts import options as opts

# 导入数据
data = pd.read_excel('第二章 图表(后15).xlsx',sheet_name='19 南丁格尔圆饼图',usecols='B:C',skiprows=1)

# 准备数据
x = data['部门'].tolist()
y = data['人数占比'].tolist()

# 创建南丁格尔圆饼图
pie = (
    Pie(init_opts=opts.InitOpts(bg_color='#00002d'))
    .add("", [list(z) for z in zip(x, y)])
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="2021年各部门人数分布",
            title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=24),
        ),
        legend_opts=opts.LegendOpts(
            pos_top="5%",
            pos_right="5%",
            textstyle_opts=opts.TextStyleOpts(color="white")
        )
    )
    .set_series_opts(
        label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)"),
        radius=["0%", "75%"],
        rosetype="radius"
    )
)

# 渲染图表
pie.render("D:/python charm/01/可视化/第二章/图片/南丁格尔圆饼图.html")
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-10-10%20162659.png)

###### 2.南丁格尔玫瑰图

```
import pandas as pd
from pyecharts.charts import Pie
from pyecharts import options as opts

# 导入数据
data = pd.read_excel('第二章 图表(后15).xlsx',sheet_name='20 南丁格尔（PPT）',usecols='B:C',skiprows=1)

# 准备数据
x = data['部门'].tolist()
y = data['人数占比'].tolist()

# 创建南丁格尔玫瑰图
rose_chart = (
    Pie(init_opts=opts.InitOpts(bg_color='#00002d'))
    .add("",
         [list(z) for z in zip(x, y)],
         rosetype="area",
         )
    .set_global_opts(
        title_opts=opts.TitleOpts(
            title="2021年各部门人数分布",
            title_textstyle_opts=opts.TextStyleOpts(color="white", font_size=24),
        ),
        legend_opts=opts.LegendOpts(
            pos_top="5%",
            pos_right="5%",
            textstyle_opts=opts.TextStyleOpts(color="white")
        )
    )
    .set_series_opts(
        label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)"),
    )
    .set_colors(
        ['rgb({r},0,{b})'.format(r=255-20*(len(y)-x+1), b=255-15*x) for x in range(len(y))]
    )
)

# 渲染图表
rose_chart.render("D:/python charm/01/可视化/第二章/图片/南丁格尔不等半径圆饼图.html")
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-10-10%20162718.png)

##### 四、蝴蝶图、数值百分比、对比柱形图、甘特图

###### 1.蝴蝶图

```
import pandas as pd
import matplotlib.pyplot as plt

# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx', sheet_name='6 蝴蝶图', usecols='B:F', skiprows=1)

# 创建图形和轴
fig, ax = plt.subplots(figsize=(10, 6))

# 设置条形图的宽度
bar_width = 0.4
index = range(len(data))

# 计算条形图的偏移量
offset = bar_width * 500  # 增加偏移量，使条形图之间有更大的间隔

# 绘制2022年的条形图
bars1 = ax.barh(index, data['2022年销量']+offset, bar_width, color='skyblue', label='2022', left=offset)

# 绘制2021年的条形图
bars2 = ax.barh(index, -(data['2021年销量']+offset), bar_width, color='lightgreen', label='2021', left=-offset)

# 添加标题和标签
ax.set_title('2022年与2021年数据对比')

# 设置Y轴标签
ax.set_yticks(index)
ax.set_yticklabels([])  # 清空Y轴标签

# 在条形图中间添加Y轴标签
for i, region in enumerate(data['区域']):
    ax.text(0, i, region, ha='center', va='center', color='black')

# 在条形图中输出2021年和2022年的销量值
for i, (value2021, value2022) in enumerate(zip(data['2021年销量'], data['2022年销量'])):
    # 2021年销量值
    ax.text(-value2021 - offset - 0.1, i, f'{value2021}', ha='right', va='center', color='black')
    # 2022年销量值
    ax.text(value2022 + offset + 0.1, i, f'{value2022}', ha='left', va='center', color='black')

# 隐藏边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 隐藏X轴和Y轴
ax.axis('off')

# 反转X轴，使2021年的条形图向左显示
ax.invert_xaxis()

# 将图例输出在条形图的上方
ax.legend(loc='upper center', bbox_to_anchor=(1, 1.1), ncol=2)

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/对称条形图.png', dpi=300, bbox_inches='tight')

# 显示图形
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%9B%9B/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%AF%B9%E7%A7%B0%E6%9D%A1%E5%BD%A2%E5%9B%BE.png)

###### 2.数值百分比

```
import pandas as pd
import matplotlib.pyplot as plt

# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx', sheet_name='8 数值百分比', usecols='B:F', skiprows=1)

# 创建图形和轴
fig, ax = plt.subplots(figsize=(10, 6))

# 设置条形图的宽度
bar_width = 0.4
index = range(len(data))

bars1 = ax.barh(index, data['销量'], bar_width, color='skyblue', label='销量')
bars2 = ax.barh(index, 500, bar_width, color='lightgreen', label='同比去年', left=max(data['销量']))

# 添加标题和标签
ax.set_title('2021年各区域销量及同比情况')

# 设置Y轴标签
ax.set_yticks(index)
ax.set_yticklabels(data['区域'])

# 隐藏边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 隐藏X轴
ax.xaxis.set_visible(False)

for i, bar in enumerate(bars1):
    ax.text(bar.get_width(), bar.get_y() + bar.get_height() / 2, f"{data['销量'][i]}", va='center', ha='right', color='black')

for i, bar in enumerate(bars2):
    ax.text(max(data['销量']) + bar.get_width(), bar.get_y() + bar.get_height() / 2, f"{data['同比去年'][i] * 100:.1f}%", va='center', ha='right', color='black')

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/数值百分比图.png', dpi=300, bbox_inches='tight')

# 显示图形
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%9B%9B/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%95%B0%E5%80%BC%E7%99%BE%E5%88%86%E6%AF%94%E5%9B%BE.png)

###### 3.对比柱形图

```
import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx', sheet_name='9 对比柱形图', usecols='B:E', skiprows=1)

# 设置图形大小
plt.figure(figsize=(10, 6))

# 定义柱状图的宽度
bar_width = 0.35

# 计算每个商品的索引位置
index = range(len(data['商品']))

# 绘制2021年销量柱状图
plt.bar(index, data['2021销量'], bar_width, label='2021年销量', alpha=0.7)

# 绘制2022年销量柱状图
plt.bar([i + bar_width for i in index], data['2022销量'], bar_width, label='2022年销量', alpha=0.7)

# 添加数值标签
for i, v in enumerate(data['2021销量']):
    plt.text(i, v + 10, str(v), ha='center', va='bottom', fontsize=10)

for i, v in enumerate(data['2022销量']):
    plt.text(i + bar_width, v + 10, str(v), ha='center', va='top', fontsize=10)

# 计算2022年销量与2021年销量的差值
differences = data['2021销量'] - data['2022销量']

# 在2022年销量柱子上绘制向上的箭头，并输出差值
for i, diff in enumerate(differences):
    if diff > 0:
        plt.annotate(f'{diff}',
                     xy=(i + bar_width, data['2021销量'][i]),  # 箭头的终点位置
                     xytext=(i + bar_width, data['2022销量'][i] + 100),  # 注释文本的位置
                     textcoords='data',
                     arrowprops=dict(arrowstyle="<-", color='red'),
                     ha='center', va='bottom', fontsize=10)

# 添加标题和标签
plt.title('2021年与2022年销量对比')
plt.xlabel('商品')
plt.ylabel('销量')

# 设置x轴刻度和标签
plt.xticks([i + bar_width / 2 for i in index], data['商品'])

# 显示图例
plt.legend()

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/对比柱形图.png', dpi=300, bbox_inches='tight')

# 显示图形
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%9B%9B/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%AF%B9%E6%AF%94%E6%9F%B1%E5%BD%A2%E5%9B%BE.png)

###### 4.甘特图

```
import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx', sheet_name='10 甘特图', usecols='B:G', skiprows=2)

# 绘制甘特图
fig, ax = plt.subplots(figsize=(10, 6))

# 将日期字符串转换为日期对象
data['开始日期'] = pd.to_datetime(data['开始日期'])
data['结束日期'] = pd.to_datetime(data['结束日期'])
data['项目天数'] = data['项目天数']

# 绘制甘特图
ax.barh(data['项目名称'], data['项目天数'], left=data['开始日期'])
ax.barh(data['项目名称'], data['进行天数'], left=data['开始日期'], color='skyblue')

# 在柱子中输出完成度的值
for index, row in data.iterrows():
    x = row['开始日期'] + pd.Timedelta(days=row['进行天数'])
    y = index
    ax.text(x, y, f"{row['完成度'] * 100:.0f}%", ha='center', va='center', color='white')

# 设置图表标题和标签
ax.set_title('项目甘特图')
ax.set_xlabel('日期')
ax.set_ylabel('项目名称')

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/甘特图.png', dpi=300, bbox_inches='tight')

# 显示图表
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%9B%9B/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E7%94%98%E7%89%B9%E5%9B%BE.png)

##### 五、菱形走势图、单值圆环图、水球图

###### 1.菱形走势图

```
import pandas as pd
import matplotlib.pyplot as plt

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# 导入数据
data = pd.read_excel('第二章 图表(前15).xlsx', sheet_name='12 菱形走势图', usecols='B:C', skiprows=1)

# 设置图形大小
plt.figure(figsize=(10, 6))

# 设置Y轴范围
plt.ylim(0, 1)

# 绘制菱形走势图
plt.scatter(data.iloc[:, 0], data.iloc[:, 1], marker='d', s=100, facecolors='none', edgecolors='red')

# 绘制从菱形到X轴的连线
for x, y in zip(data.iloc[:, 0], data.iloc[:, 1]):
    plt.plot([x, x], [0, y - 0.02], color='red', linestyle='-')
    plt.text(x, y + 0.02, f'{y * 100:.2f}%', ha='center', va='bottom', fontsize=10, color='green')

# 隐藏除X轴以外的边框
ax = plt.gca()  # 获取当前的坐标轴
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)

# 隐藏Y轴标签
plt.yticks([])

# 导出图片
#plt.savefig('D:/python charm/01/可视化/第二章/图片/菱形走势图.png', dpi=300, bbox_inches='tight')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%BA%94/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E8%8F%B1%E5%BD%A2%E8%B5%B0%E5%8A%BF%E5%9B%BE.png)

###### 2.单值圆环图

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

plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

data = {
    '类别': ['完成率', '占位'],
    '数值': [85, 15]
}
df = pd.DataFrame(data)

plt.figure(figsize=(6, 6))
colors = ['lightgreen', 'red']

#绘制饼图
plt.pie(df['数值'], autopct=None, startangle=-160, colors=colors)

#绘制中心圆覆盖饼图中心
centre_circle = plt.Circle((0, 0), 0.8, fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)

total = np.sum(df['数值'])
completed_percentage = df['数值'][0] / total * 100

#添加文字
plt.text(0, 0, f'{completed_percentage:.0f}%\n目标完成率', ha='center', va='center', fontsize=30, weight='bold')

plt.axis('equal')
plt.tight_layout()

#plt.savefig('D:/python charm/01/可视化/第二章/图片/单值圆环图.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%BA%94/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%8D%95%E5%80%BC%E5%9C%86%E7%8E%AF%E5%9B%BE.png)

###### 3.水球图

```
from pyecharts import options as opts
from pyecharts.charts import Liquid

#数据准备
completed_percentage = 65
auxiliary_percentage = 100

#绘制水球图
liquid = (
    Liquid()
    .add("完成率", [completed_percentage / 100])
    .set_global_opts(title_opts=opts.TitleOpts(title="完成率水球图"))
)

#输出图表
#iquid.render("D:/python charm/01/可视化/第二章/图片/水球图.html")
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%BA%94/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-11-21%20205423.png)

##### 六、波浪水球图、玉玦图、跑道图

###### 1.波浪水球图

```
from pyecharts import options as opts
from pyecharts.charts import Liquid

completed_percentage = 65

colors = ["#599CB4"]

liquid = (
    Liquid()
    .add("完成率", [completed_percentage / 100], color=colors)  # 设置颜色
    .set_global_opts(title_opts=opts.TitleOpts(title="波浪水球图"))
)

#显示图表
#liquid.render("D:/python charm/01/可视化/第二章/图片/波浪水球图.html")
```

###### 2.玉玦图

```
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches

ages = ['>=50', '[40,50)', '[30,40)', '[20,30)']
percentages = [12.5, 20.8, 29.2, 37.5]

colors = ['#F7C1CF', '#FFD47F', '#7B92C7', '#ADD9EE']

fig, ax = plt.subplots(figsize=(8, 8))

ring_width = 0.3

directions = [(-70, 90), (-110, 90), (-140, 90), (-180, 90)]

for i, (age, percent, direction, color) in enumerate(zip(ages[::-1], percentages[::-1], directions[::-1], colors[::-1])):
    # 计算内外半径
    inner_radius = (len(ages) - i - 1) * ring_width
    outer_radius = inner_radius + ring_width

    # 获取当前圆环的方向
    theta1, theta2 = direction

    # 创建环形
    arc = mpatches.Wedge(center=(0, 0), r=outer_radius, width=ring_width,
                         theta1=theta1, theta2=theta2,
                         facecolor=color, edgecolor='black')

    # 添加环形到坐标轴
    ax.add_patch(arc)

    # 添加年龄标签
    age_angle = -1
    age_x = outer_radius * np.cos(np.radians(90 - age_angle))-0.05
    age_y = (outer_radius + inner_radius) / 2 * np.sin(np.radians(90 - age_angle))
    ax.text(age_x, age_y, age, ha='center', va='center', rotation=age_angle - 90)

    # 添加占比标签
    percent_angle = theta2
    percent_x = outer_radius * np.cos(np.radians(90 - percent_angle))
    percent_y = outer_radius * np.sin(np.radians(90 - percent_angle))
    ax.text(percent_x, percent_y, f'{percent}%', ha='center', va='center', rotation=percent_angle - 90)

#在内部画一个比内圈圆半径小的空白圆
inner_circle_radius = (len(ages) - 1) * ring_width
smaller_inner_circle = mpatches.Circle((0, 0), inner_circle_radius * 0.15,
                                       facecolor='white', edgecolor='none')
ax.add_patch(smaller_inner_circle)

ax.set_xlim(-1 - outer_radius, 1 + outer_radius)
ax.set_ylim(-1 - outer_radius, 1 + outer_radius)

# 设置坐标轴属性
ax.set_aspect('equal')
ax.axis('off')

#plt.savefig('D:/python charm/01/可视化/第二章/图片/玉块图.png')

# 显示图形
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%85%AD/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E7%8E%89%E5%9D%97%E5%9B%BE.png)

###### 3.跑道图

```
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

#年龄分布数据
ages = ['人力部 130', '行政部 226', '财务部 238', '工程部 293', '采购部 326', '销售部 451']
percentages = [12.5, 20.8, 29.2, 37.5, 100, 100]  # 假设新增的两个圆环占比为100%

#自定义颜色
colors = ['#F7C1CF', '#FFD47F', '#7B92C7', '#ADD9EE', '#E47373', '#73E4C7']

#创建图形和坐标轴
fig, ax = plt.subplots(figsize=(10, 10))

#设置环形宽度
ring_width = 0.1

#定义每个圆环的方向
directions = [(10, 90), (-10, 90), (-30, 90), (-50, 90), (-70, 90), (-90, 90)]

#绘制每个年龄段的环形
for i, (age, percent, direction, color) in enumerate(zip(ages[::-1], percentages[::-1], directions[::-1], colors[::-1])):
    #计算内外半径
    inner_radius = (len(ages) - i - 1) * ring_width
    outer_radius = inner_radius + ring_width

    #获取当前圆环的方向
    theta1, theta2 = direction

    #创建环形
    arc = mpatches.Wedge(center=(0, 0), r=outer_radius, width=ring_width,
                         theta1=theta1, theta2=theta2,
                         facecolor=color, edgecolor='black')

    # 添加环形到坐标轴
    ax.add_patch(arc)

    # 添加年龄标签
    age_x = outer_radius * np.cos(np.radians(90))-0.15
    age_y = (outer_radius + inner_radius) / 2
    ax.text(age_x, age_y, age, ha='center', va='center', rotation=0)

#在内部画一个比内圈圆半径小的空白圆
inner_circle_radius = (len(ages) - 1) * ring_width
smaller_inner_circle = mpatches.Circle((0, 0), inner_circle_radius * 0.07,
                                       facecolor='white', edgecolor='none')
ax.add_patch(smaller_inner_circle)

# 设置坐标轴界限，使图形居中
ax.set_xlim(-1 - outer_radius, 1 + outer_radius)
ax.set_ylim(-1 - outer_radius, 1 + outer_radius)

# 设置坐标轴属性
ax.set_aspect('equal')
ax.axis('off')

#plt.savefig('D:/python charm/01/可视化/第二章/图片/跑道图.png')

# 显示图形
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%85%AD/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E8%B7%91%E9%81%93%E5%9B%BE.png)

##### 七、南丁格尔圆环图、仪表盘图、目标柱形图、子弹图

###### 1.南丁格尔圆环图

```
from pyecharts import options as opts
from pyecharts.charts import Pie

ages = ['>=50', '[40,50)', '[30,40)', '[20,30)']
percentages = [12.5, 20.8, 29.2, 37.5]

colors = ["#C3E2EC", "#CECCE5", "#F7C4C1", "#BCD1BC"]

pie = (
    Pie(init_opts=opts.InitOpts(width="800px", height="600px"))
    .add(
        series_name="年龄分布",
        data_pair=[(age, percentage) for age, percentage in zip(ages, percentages)],
        radius=["40%", "70%"],
        rosetype="radius",
        color=colors,
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="年龄分布图"),
        legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),
    )
    .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c} ({d}%)"))
)

# 显示图表
pie.render("D:/python charm/01/可视化/第二章/图片/南丁格尔圆环图.html")
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%83/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%B1%8F%E5%B9%95%E6%88%AA%E5%9B%BE%202024-11-21%20212902.png)

###### 2.仪表盘图

```
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

inner_radius = 0.9
outer_radius = 1
num_segments = 14

inner_colors = ['#F7C1D0', '#F7C1D0', '#ADD9EE', '#ADD9EE', '#ADD9EE', '#FFFFFF', '#FFFFFF', '#FFFFFF', '#FFFFFF', '#CAC0E1', '#CAC0E1', '#CAC0E1', '#F7C1DB', '#F7C1DC']

outer_colors = ['#FFD47E' for _ in range(num_segments)]

fig, ax = plt.subplots()

wedges1, texts1 = ax.pie([1] * num_segments, radius=inner_radius, colors=inner_colors, startangle=90, counterclock=False, wedgeprops=dict(width=0.3, edgecolor='w'))

wedges2, texts2 = ax.pie([1] * num_segments, radius=outer_radius, colors=outer_colors, startangle=90, counterclock=False, wedgeprops=dict(width=0.3, edgecolor='w'))

labels = []
label_value = 70
for color in inner_colors:
    if color != '#FFFFFF':
        labels.append(label_value)
        label_value += 10
    else:
        labels.append(None)

for i, wedge in enumerate(wedges1):
    if labels[i] is not None:
        angle = (wedge.theta2 - wedge.theta1) / 2 + wedge.theta1
        x_left = (inner_radius - 0.05) * np.cos(np.deg2rad(angle - 5))
        y_left = (inner_radius + 0.05) * np.sin(np.deg2rad(angle - 5))
        x_right = (inner_radius + 0.05) * np.cos(np.deg2rad(angle + 5))
        y_right = (inner_radius + 0.05) * np.sin(np.deg2rad(angle + 5))

        ax.text(x_left, y_left, str(labels[i]), ha='center', va='center', color='black')

        ax.text(x_right, y_right, str(labels[i]), ha='center', va='center', color='black')

ax.set_aspect('equal')
plt.title('仪表盘图')

#plt.savefig('D:/python charm/01/可视化/第二章/图片/仪表盘图.png')

# 显示图表
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%83/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E4%BB%AA%E8%A1%A8%E7%9B%98%E5%9B%BE.png)

###### 3.目标柱形图

```
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

products = ['口红', '面膜', '隔离', '防晒', '精华', '面霜']
target_sales = [700, 500, 600, 900, 600, 600]
actual_sales = [653, 523, 648, 856, 714, 785]

#创建柱状图
plt.figure(figsize=(12, 6))

#目标销量柱状图
plt.bar(products, target_sales, color='none', edgecolor='black', width=0.6, label='目标销量')

#实际销量柱状图，宽度小一些，蓝色填充，白色外框
actual_bars = plt.bar(products, actual_sales, color='skyblue', edgecolor='white', width=0.4, label='实际销量')

#在每个柱状图上方加上实际销量的标签
for bar in actual_bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2, yval, int(yval), va='bottom', ha='center', color='black')

# 添加标题和标签
plt.title('目标销量与实际销量柱状图')
plt.xlabel('商品')
plt.ylabel('销量')
plt.legend()

# 隐藏y轴的刻度线
plt.gca().yaxis.set_major_locator(plt.NullLocator())

#plt.savefig('D:/python charm/01/可视化/第二章/图片/目标柱形图.png')

# 显示图表
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%83/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E7%9B%AE%E6%A0%87%E6%9F%B1%E5%BD%A2%E5%9B%BE.png)

###### 4.子弹图

```
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

categories = ['口红', '面膜', '隔离', '防晒', '精华', '面霜']
targets = [700, 500, 600, 900, 600, 600]
passing_scores = [600] * len(categories)
good_scores = [200] * len(categories)
actual_scores = [653, 523, 648, 856, 714, 785]

#堆叠柱状图
bar_width = 0.5
index = np.arange(len(categories))

#目标、及格、良好分数的柱状图
plt.bar(index, targets, bar_width, label='目标', color='#F7C4C1')
plt.bar(index, passing_scores, bar_width, bottom=targets, label='及格', color='#DBEDC5')
plt.bar(index, good_scores, bar_width, bottom=[i+j for i,j in zip(targets, passing_scores)], label='良好', color='#C3E2EC')

#实际分数的柱状图
plt.bar(index, actual_scores, bar_width, label='实际', color='#7A10B5')

#目标的横线，颜色用红色
plt.hlines(targets, index - bar_width/2, index + bar_width/2, colors='red', linestyles='-', label='目标线')

plt.xlabel('商品')
plt.ylabel('分数')
plt.title('目标、及格、良好与实际分数柱状图')
plt.xticks(index, categories)
plt.legend()

#plt.savefig('D:/python charm/01/可视化/第二章/图片/子弹图.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B8%83/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%AD%90%E5%BC%B9%E5%9B%BE.png)

##### 八、柱形图、簇状柱形折线图、复合柱形图、滑珠图、对比滑珠图

###### 1.柱形图

```
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

regions = ['华北', '华南', '东北', '西北', '西南', '华东']
sales = [2354, 1902, 3524, 2698, 2896, 2563]
growth_rates = [12, 25, 16, 21, 18, 25]

plt.figure(figsize=(10, 6))

bars = plt.bar(regions, sales, color='skyblue')

for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width() / 2., height, f'{height}', ha='center', va='bottom', fontsize=10)

for i, rate in enumerate(growth_rates):
    plt.scatter(i, sales[i] + 500, color='red', s=400)
    plt.text(i, sales[i] + 500, f'{rate}%', ha='center', va='center', color='white', fontsize=10)

plt.title('各区域销量及同比去年增长率')
plt.xlabel('区域')
plt.ylabel('销量')

#plt.savefig('D:/python charm/01/可视化/第二章/图片/柱形图.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%85%AB/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%9F%B1%E5%BD%A2%E5%9B%BE.png)

###### 2.簇状柱形折线图

```
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

regions = ['华北', '华南', '东北', '西北', '西南', '华东']
sales_2022 = [2354, 1902, 3524, 2698, 2896, 2563]
sales_2021 = [2021, 1563, 3213, 2531, 2631, 2361]
growth_rate = [16, 22, 10, 7, 10, 9]

bar_width = 0.35
index = np.arange(len(regions))

fig, ax1 = plt.subplots(figsize=(10, 6))
bar1 = ax1.bar(index - bar_width/2, sales_2022, bar_width, label='2022年销量', color='#CECCE5')
bar2 = ax1.bar(index + bar_width/2, sales_2021, bar_width, label='2021年销量', color='#C3E2EC')

for i in range(len(sales_2022)):
    ax1.text(i - bar_width/2, sales_2022[i] + 50, str(sales_2022[i]), ha='center', va='bottom', color='black')
    ax1.text(i + bar_width/2, sales_2021[i] + 50, str(sales_2021[i]), ha='center', va='bottom', color='black')

ax2 = ax1.twinx()

line3 = ax2.plot(index, growth_rate, color='#e74c3c', marker='o', linestyle='-', linewidth=2, label='同比增长率')

ax2.set_ylim(0, max(growth_rate) * 1.5)

for i in range(len(growth_rate)):
    ax2.text(i, growth_rate[i] + 0.005, f'{growth_rate[i]}%', ha='center', va='bottom', color='black')

ax1.set_xlabel('区域')
ax1.set_ylabel('销量（单位：件）')
ax2.set_ylabel('同比增长率 (%)')
ax1.set_title('2022 vs 2021 销量对比及同比增长率')
ax1.set_xticks(index)
ax1.set_xticklabels(regions)
ax1.legend(loc='upper left')
ax2.legend(loc='upper right')
plt.tight_layout()

#plt.savefig('D:/python charm/01/可视化/第二章/图片/簇状柱形折线图.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%85%AB/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E7%B0%87%E7%8A%B6%E6%9F%B1%E5%BD%A2%E6%8A%98%E7%BA%BF%E5%9B%BE.png)

###### 3.复合柱形图

```
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

months = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"]
monthly_sales = [2354, 1902, 3524, 2698, 2896, 2563, 3156, 2896, 3621, 2635, 2963, 2789]
quarterly_sales = [7780, 7780, 7780, 8157, 8157, 8157, 9673, 9673, 9673, 8387, 8387, 8387]

x_groups = [0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14]
group_labels = ["Q1", "Q2", "Q3", "Q4"]

quarter_colors = ['#4a4a8c', '#8c4a4a', '#4a8c4a', '#8c4a8c']

fig, ax = plt.subplots(figsize=(12, 7))
bar_width = 0.4
spacing = 1.5

for i, (x, sale) in enumerate(zip(x_groups, monthly_sales)):
    ax.bar(x, sale, width=bar_width, color=['#6a87ff', '#6a87ff', '#6a87ff', '#ff8b4b', '#ff8b4b', '#ff8b4b', '#a87332', '#a87332', '#a87332', '#b84d9b', '#b84d9b', '#b84d9b'][i])
    ax.text(x, sale + 100, str(sale), ha='center', va='bottom', color='white', fontsize=10)

for i in range(0, len(x_groups), 3):
    group_x = np.mean(x_groups[i:i+3])
    ax.bar(group_x, quarterly_sales[i], width=bar_width * 3 + spacing, color=quarter_colors[i // 3], alpha=0.6)
    ax.text(group_x, quarterly_sales[i] + 200, str(quarterly_sales[i]), ha='center', va='bottom', color='black', fontsize=12)

ax.set_xticks([np.mean(x_groups[i:i+3]) for i in range(0, len(x_groups), 3)])
ax.set_xticklabels(group_labels)
ax.set_xlabel('季度')
ax.set_ylabel('销量')
ax.set_title('2021年各月化妆品销量走势\n2021年第三季度销量最多9673，9月单月销量最大3621')

plt.figtext(0.1, -0.05, '*注：数据源于公司销售系统，统计日期截至2021.12.31', ha="left", fontsize=10, color="grey")

plt.tight_layout()

#plt.savefig('D:/python charm/01/可视化/第二章/图片/复合柱形图.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%85%AB/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%A4%8D%E5%90%88%E6%9F%B1%E5%BD%A2%E5%9B%BE.png)

###### 4.滑珠图

```
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

regions = ['华东', '西北', '东北', '华北', '华南']
completion_rates = [35, 51, 62, 74, 86]
not_completion_rates = [65, 49, 38, 26, 14]
colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99', '#c2c2f0']

fig, ax = plt.subplots(figsize=(10, 6))

for i in range(len(regions)):
    ax.barh(i, completion_rates[i], color=colors[i], edgecolor='white', height=0.4)
    ax.barh(i, not_completion_rates[i], left=completion_rates[i], color='lightgray', edgecolor='white', height=0.4)

    ax.plot(completion_rates[i], i, 'o', markersize=40, color=colors[i], markeredgewidth=2, markeredgecolor='white')

    ax.text(completion_rates[i], i, f"{completion_rates[i]}%", va='center', ha='center', color='white', fontsize=20,
            fontweight='bold')

ax.set_xlabel('百分比 (%)')
ax.set_title('2022年上半年产品销量目标达成率情况\n华南完成率最高达到86%，华东最低35%')
ax.set_yticks(range(len(regions)))
ax.set_yticklabels(regions)
ax.invert_yaxis()

plt.figtext(0.1, -0.05, '*注：数据源于公司销售系统，统计日期截至2022.06.30', ha="left", fontsize=10, color="grey")

plt.xlim(0, 100)
plt.grid(axis='x', linestyle='--', alpha=0.7)

#plt.savefig('D:/python charm/01/可视化/第二章/图片/滑珠图.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%85%AB/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%BB%91%E7%8F%A0%E5%9B%BE.png)

###### 5.对比滑珠图

```
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

regions = ["华南", "华北", "东北", "西北", "华东"]
completion_2022 = [86, 74, 62, 51, 35]
completion_2021 = [92, 69, 53, 39, 45]

fig, ax = plt.subplots(figsize=(10, 6))
ax.set_title("2022年上半年销量目标达成率与2021年对比")
ax.set_xlabel("完成率 (%)")
ax.set_yticks(range(len(regions)))
ax.set_yticklabels(regions)

ax.invert_yaxis()

bar_height = 0.2
for i in range(len(regions)):

    ax.barh(i, 100, height=bar_height, color='lightgreen', alpha=0.6)

    ax.barh(i, completion_2022[i], height=bar_height, color='skyblue', alpha=0.7)

ax.scatter(completion_2021, range(len(regions)), s=400, color='red', alpha=0.9, label="2021完成率")
ax.scatter(completion_2022, range(len(regions)), s=400, color='blue', alpha=0.9, label="2022完成率")

for i, comp_2022 in enumerate(completion_2022):
    ax.text(comp_2022, i + 0.3, f"{comp_2022}%", va='bottom', ha='center', color='skyblue', fontweight='bold')

for i, comp_2021 in enumerate(completion_2021):
    ax.text(comp_2021, i + 0.3, f"{comp_2021}%", va='bottom', ha='center', color='skyblue', fontweight='bold')

ax.legend()
plt.grid(axis='x', linestyle='--', alpha=0.6)

#plt.savefig('D:/python charm/01/可视化/第二章/图片/对比滑珠图.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%85%AB/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%AF%B9%E6%AF%94%E6%BB%91%E7%8F%A0%E5%9B%BE.png)

##### 九、人力资源数据静态大屏设计

```
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

#创建图形和子图
fig, axes = plt.subplots(2, 3, figsize=(18, 8), dpi=100)
fig.patch.set_facecolor('#2C3E50')

#年龄环形图
ax = axes[0, 0]
labels = ['18-24', '25-29', '30-34', '35-39', '40=以上']
sizes = [7, 16, 22, 20, 35]
colors = ['#5DADE2', '#F39C12', '#2ECC71', '#F4D03F', '#2874A6']
wedges, texts, autotexts = ax.pie(
    sizes, labels=None, colors=colors, autopct='%1.0f%%', startangle=90, pctdistance=0.75
)
centre_circle = plt.Circle((0, 0), 0.60, fc='#2C3E50')
ax.add_artist(centre_circle)
ax.set_title('年龄', fontsize=16, color='white', pad=10)
ax.set_facecolor('#2C3E50')
for autotext in autotexts:
    autotext.set_color('white')

legend = ax.legend(
    wedges, labels, loc="center left", bbox_to_anchor=(1, 0, 0.5, 1), fontsize=10
)
legend.get_frame().set_facecolor('#2C3E50')
legend.get_frame().set_edgecolor('white')
plt.setp(legend.get_texts(), color='white')

#性别环形图
ax = axes[0, 1]
labels = ['男', '女']
sizes = [60, 40]
colors = ['#F39C12', '#5DADE2']
wedges, texts, autotexts = ax.pie(
    sizes, labels=None, colors=colors, autopct='%1.0f%%', startangle=90, pctdistance=0.75
)
centre_circle = plt.Circle((0, 0), 0.60, fc='#2C3E50')
ax.add_artist(centre_circle)
ax.set_title('性别', fontsize=16, color='white', pad=10)
ax.set_facecolor('#2C3E50')
for autotext in autotexts:
    autotext.set_color('white')

legend = ax.legend(
    wedges, labels, loc="center left", bbox_to_anchor=(1, 0, 0.5, 1), fontsize=10
)
legend.get_frame().set_facecolor('#2C3E50')
legend.get_frame().set_edgecolor('white')
plt.setp(legend.get_texts(), color='white')

#婚姻状况环形图
ax = axes[0, 2]
labels = ['单身', '已婚', '离异']
sizes = [32, 46, 22]
colors = ['#F39C12', '#2874A6', '#5DADE2']
wedges, texts, autotexts = ax.pie(
    sizes, labels=None, colors=colors, autopct='%1.0f%%', startangle=90, pctdistance=0.75
)
centre_circle = plt.Circle((0, 0), 0.60, fc='#2C3E50')
ax.add_artist(centre_circle)
ax.set_title('婚姻状况', fontsize=16, color='white', pad=10)
ax.set_facecolor('#2C3E50')
for autotext in autotexts:
    autotext.set_color('white')

legend = ax.legend(
    wedges, labels, loc="center left", bbox_to_anchor=(1, 0, 0.5, 1), fontsize=10
)
legend.get_frame().set_facecolor('#2C3E50')
legend.get_frame().set_edgecolor('white')
plt.setp(legend.get_texts(), color='white')

#学历
ax = axes[1, 0]
labels = ['博士研究生', '硕士研究生', '本科', '专科', '专科以下']
values = [48, 398, 572, 170, 282]
bars = ax.barh(labels, values, color='#5DADE2')
ax.set_title('学历', fontsize=16, color='white', pad=10)
ax.tick_params(axis='y', colors='white')
ax.xaxis.set_visible(False)
for bar in bars:
    ax.text(bar.get_width() + 5, bar.get_y() + bar.get_height() / 2, f'{int(bar.get_width())}', va='center', color='white')
ax.set_facecolor('#2C3E50')

#入转调
ax = axes[1, 1]
labels = ['入职', '转入', '转出']
values = [40, 5, 5]
bars = ax.bar(labels, values, color='#5DADE2', width=0.5)
ax.text(1, max(values) + 10, '总人数: 147', fontsize=14, color='white', ha='center')
ax.set_title('入转调', fontsize=16, color='white', pad=10)
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', left=False, labelleft=False)
ax.set_facecolor('#2C3E50')
for bar in bars:
    ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 1, f'{int(bar.get_height())}', ha='center', color='white')

#部门
ax = axes[1, 2]
labels = ['财务部', '人力资源部', '行政部', '信息技术部', '研发部', '销售部']
values = [17, 46, 9, 162, 799, 437]
bars = ax.barh(labels, values, color='#5DADE2')
ax.set_title('部门', fontsize=16, color='white', pad=10)
ax.tick_params(axis='y', colors='white')
ax.tick_params(axis='x', length=0, labelbottom=False)
ax.invert_yaxis()
ax.set_facecolor('#2C3E50')
for bar in bars:
    ax.text(bar.get_width() + 10, bar.get_y() + bar.get_height() / 2, f'{int(bar.get_width())}', va='center', color='white')

plt.suptitle('公司人员结构看板', fontsize=20, color='white', y=0.95)
plt.tight_layout(rect=[0, 0, 1, 0.95])
plt.savefig('D:/python charm/01/可视化/人力资源静态数据大屏设计/图片.png', dpi=300, bbox_inches='tight')
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E4%B9%9D%EF%BC%88%E4%BA%BA%E5%8A%9B%E8%B5%84%E6%BA%90%E6%95%B0%E6%8D%AE%E9%9D%99%E6%80%81%E5%A4%A7%E5%B1%8F%E8%AE%BE%E8%AE%A1%EF%BC%89/%E5%9B%BE%E7%89%87.png)

##### 十、销售看板

###### 1.划分销售数据

```
import os
import pandas as pd

file_path = r"D:\python charm\01\可视化\销售看板参考\第四章 销售看板参考.xlsx"

excel_file = pd.ExcelFile(file_path)
print(f"文件包含的工作表：{excel_file.sheet_names}")

sheet_name = "销售明细"
if sheet_name not in excel_file.sheet_names:
    print(f"工作表 '{sheet_name}' 不存在，请选择以下之一：{excel_file.sheet_names}")
    raise ValueError("指定的工作表不存在")

data = pd.read_excel(file_path, sheet_name=sheet_name)

data['订单日期'] = pd.to_datetime(data['订单日期'], errors='coerce')

# 目标目录
output_dir = r"D:\python charm\01\可视化\销售看板参考"

for month in range(1, 13):
    df_month = data[data['订单日期'].dt.month == month]
    file_name = os.path.join(output_dir, f"销售明细_{month}.xlsx")
    df_month.to_excel(file_name, index=False)
    print(f"已导出 {file_name}")
```

###### 2.划分成本数据

```
import os
import pandas as pd

file_path = r"D:\python charm\01\可视化\销售看板参考\第四章 销售看板参考.xlsx"
sheet_name = "成本明细"

data = pd.read_excel(file_path, sheet_name=sheet_name)

monthly_data = {}

for month in range(1, 13):
    month_str = f"{month}月"
    monthly_data[f'dh{month}'] = data[data['月份_成本'] == month_str]

# 目标目录
output_dir = r"D:\python charm\01\可视化\销售看板参考"

for name, df in monthly_data.items():
    file_name = os.path.join(output_dir, f"成本明细_{name}.xlsx")
    df.to_excel(file_name, index=False)
    print(f"已导出 {file_name}")
```

###### 3.产品销量

```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

file_path = r"D:\python charm\01\可视化\销售看板参考\销售明细_12.xlsx"
data = pd.read_excel(file_path)
data = data['产品类别'].value_counts()

# 设置图表颜色和标签
labels = data.index
sizes = data.values
colors = ['#FF6F91', '#FF9671', '#FFC75F']

 # 自定义百分比显示函数
def custom_autopct(pct):
    value = pct * sum(sizes) / 100
    if round(value * 10) % 10 >= 6:
        return f"{round(pct)}%"
    else:
        return f"{np.floor(pct)}%"

# 绘制圆环图
plt.figure(figsize=(10, 10))
ax = plt.gca()
ax.set_facecolor('#2E4057')
plt.gcf().set_facecolor('#2E4057')

wedges, texts, autotexts = plt.pie(
    sizes,
    labels=labels,
    autopct=custom_autopct,
    startangle=90,
    colors=colors,
    wedgeprops={'width': 0.4, 'edgecolor': '#2E4057', 'linewidth': 1},
    textprops={'fontsize': 20, 'color': 'white'}
)

# 调整百分比的字体大小
for autotext in autotexts:
    autotext.set_fontsize(30)
    autotext.set_color('white')

# 添加中心的总销量信息
total_sales = sum(sizes)
plt.text(0, 0, f"{total_sales}\n总销", ha='center', va='center', fontsize=30, weight='bold', color='white')

# 添加图例
plt.legend(
    wedges,
    labels,
    loc="center right",
    fontsize=12,
    frameon=False,
    labelcolor='white',
    bbox_to_anchor=(1.2, 0.5)
)

# 设置标题
plt.title('产品类别销量', fontsize=20, color='white')

# 调整布局避免重叠
plt.tight_layout()

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\产品销量.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E4%BA%A7%E5%93%81%E9%94%80%E9%87%8F.png)

###### 4.利润率

```
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

sales = 146.87 # 销售额 (万)
profit = 14.27 # 利润额 (万)
profit_rate = (profit / sales) * 100  # 利润率计算

# 创建图表
plt.figure(figsize=(6, 6), facecolor='#2E4057')
ax = plt.gca()
ax.set_facecolor('#2E4057')

# 绘制圆环图
colors = ['#FF6F91', '#D9D9D9']
plt.pie(
    [profit_rate, 100 - profit_rate],
    radius=1,
    colors=colors,
    startangle=--47,
    wedgeprops={'width': 0.3, 'edgecolor': '#2E4057'}
)

# 中心文本
plt.text(0, 0.15, f"{profit_rate:.2f}%", ha='center', va='center', fontsize=24, color='white', weight='bold')
plt.text(0, -0.1, "利润率", ha='center', va='center', fontsize=20, color='white')

# 右侧文本信息
plt.text(1.2, 0.3, f"利润额(万)  {profit:.2f}", ha='left', va='center', fontsize=20, color='#FF6F91')
plt.text(1.2, 0.0, f"销售额(万)  {sales:.2f}", ha='left', va='center', fontsize=20, color='#FF6F91')
plt.text(1.2, 0.15, "/", ha='left', va='center', fontsize=20, color='white')

# 底部按钮样式文本
plt.text(0, -1.3, "利润额占比销售额", ha='center', va='center', fontsize=20, color='white',
        bbox=dict(facecolor='#2E4057', edgecolor='#4F81BD', boxstyle='round,pad=0.4', linewidth=2))

# 隐藏多余轴
ax.axis('equal')

# 调整布局
plt.tight_layout()

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\利润率.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%88%A9%E6%B6%A6%E7%8E%87.png)

###### 5.各区域销量

```
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

file_path = r"D:\python charm\01\可视化\销售看板参考\销售明细_12.xlsx"
df12 = pd.read_excel(file_path)

# 统计各区域数量
region_counts = df12['区域'].value_counts()

# 指定区域的顺序
region_order = ['华北', '华南', '东北', '西北', '西南', '华东']
region_counts = region_counts.reindex(region_order)

# 创建图表
plt.figure(figsize=(8, 4), facecolor='#2E4057')
ax = plt.gca()
ax.set_facecolor('#2E4057')

# 绘制柱状图
bars = plt.bar(region_counts.index, region_counts.values, color='#6DCFF6', width=0.6)

# 添加数据标签
for bar in bars:
    plt.text(
        bar.get_x() + bar.get_width() / 2,
        bar.get_height() + 1,
        f"{int(bar.get_height())}",
        ha='center', va='bottom', fontsize=20, color='white'
    )

# 设置标题和标签
plt.title("区域销量", fontsize=20, color='white', pad=20)
plt.xticks(fontsize=22, color='white')
plt.yticks([])
plt.tick_params(axis='x', which='both', length=0)

# 隐藏图表边框
for spine in ax.spines.values():
    spine.set_visible(False)

# 调整布局
plt.tight_layout()

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\各区域销量.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%90%84%E5%8C%BA%E5%9F%9F%E9%94%80%E9%87%8F.png)

###### 6.各快递公司销量

```
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

file_path = r"D:\python charm\01\可视化\销售看板参考\销售明细_12.xlsx"
df12 = pd.read_excel(file_path)

dfkd1 = (
    df12.groupby(['快递公司'])
    .size()
    .reset_index(name='数量')
)

# 设置图表风格
plt.figure(figsize=(8, 5), facecolor='#2E4057')
ax = plt.gca()
ax.set_facecolor('#2E4057')

#反转数据顺序
dfkd1 = dfkd1.iloc[::-1]

# 绘制柱状图
bars = plt.bar(dfkd1['快递公司'], dfkd1['数量'], color='#FF6F91', width=0.5)

# 添加数据标签
for bar in bars:
    plt.text(
        bar.get_x() + bar.get_width() / 2,
        bar.get_height() + 5,
        f"{int(bar.get_height())}",
        ha='center', va='bottom', fontsize=20, color='white'
    )

# 设置标题和标签
plt.title("快递公司销量", fontsize=20, color='white', pad=20)
plt.xticks(fontsize=18, color='white')
plt.yticks([])
plt.tick_params(axis='x', which='both', length=0)

# 隐藏图表边框
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\各快递公司销量.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%90%84%E5%BF%AB%E9%80%92%E5%85%AC%E5%8F%B8%E9%94%80%E9%87%8F.png)

###### 7.各月销售额

```
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

months = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"]
sales = [145.22, 118.63, 131.27, 124.26, 116.51, 108.11, 121.62, 111.74, 134.29, 137.28, 119.55, 146.87]

plt.figure(figsize=(10, 6), facecolor='#2E4057')
ax = plt.gca()
ax.set_facecolor('#2E4057')

# 绘制折线图
plt.plot(months, sales, color='#4F81BD', marker='o', linewidth=2)

# 填充区域
plt.fill_between(months, sales, color='#4F81BD', alpha=0.5)

plt.title("各月销售额(万)", fontsize=22, color='white', pad=20)
plt.xticks(fontsize=20, color='white')
plt.yticks(fontsize=20, color='white')
plt.ylabel("销售额(万)", fontsize=20, color='white')
plt.xlabel("月份", fontsize=20, color='white')

# 隐藏边框
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

#调整 y 轴范围
plt.ylim(0, 200)

#添加网格线
plt.grid(axis='y', color='gray', linestyle='--', linewidth=0.5, alpha=0.5)

#调整布局
plt.tight_layout()

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\各月销售额.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E5%90%84%E6%9C%88%E9%94%80%E5%94%AE%E9%A2%9D.png)

###### 8.成本

```
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

file_path = r"D:\python charm\01\可视化\销售看板参考\成本明细_dh12.xlsx"
dh12 = pd.read_excel(file_path)

# 总成本
total_cost = dh12['成本'].sum()

# 准备数据
categories = dh12['类别']
costs = dh12['成本']
percentages = (costs / total_cost) * 100

# 设置图表风格
plt.figure(figsize=(10, 10), facecolor='#2E4057')

# 子图参数
colors = ['#4F81BD', '#C0504D', '#9BBB59', '#8064A2']
rows, cols = 2, 2  # 2x2 布局

# 绘制每个类别的圆环图
for i, (category, cost, percentage) in enumerate(zip(categories, costs, percentages)):
    ax = plt.subplot(rows, cols, i + 1)
    ax.set_facecolor('#2E4057')

    # 绘制圆环图
    wedges, texts = ax.pie(
        [percentage, 100 - percentage],
        radius=1,
        colors=[colors[i], '#D9D9D9'],  # 当前类别颜色和灰色背景
        startangle=90,  # 设置起始角度为 -45 度
        wedgeprops={'width': 0.3, 'edgecolor': 'white'}
    )

    # 中心文本
    ax.text(0, 0.1, f"{percentage:.2f}%", ha='center', va='center', fontsize=24, color='white')
    ax.text(0, -0.3, f"{category}\n用合计", ha='center', va='center', fontsize=20, color='white')
    ax.text(0, -0.6, f"{cost:.2f}", ha='center', va='center', fontsize=20, color='white')

    # 设置标题
    ax.set_title(category, fontsize=20, color='white', pad=10)

# 整体标题
plt.suptitle("成本费用", fontsize=20, color='white', y=0.95)

# 调整布局
plt.tight_layout(rect=[0, 0, 1, 0.93])

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\成本.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%88%90%E6%9C%AC.png)

###### 9.月同比增长

```
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

months = ["11月", "12月"]
sales = [664, 727]
change_rate = 9.49

# 颜色设置
colors = ['#4F81BD', '#6DCFF6']

# 创建图表
plt.figure(figsize=(8, 4), facecolor='#2E4057')
ax = plt.gca()
ax.set_facecolor('#2E4057')

# 绘制水平条形图
bars = plt.barh(months, sales, color=colors, height=0.4, edgecolor='none')

# 添加数值标签
for i, bar in enumerate(bars):
    # 条形图右侧显示销售值
    plt.text(bar.get_width() + 10, bar.get_y() + bar.get_height() / 2,
             f"{sales[i]}", va='center', ha='left', fontsize=22, color='white')

# 显示变化率文本
plt.text(max(sales) + 100, bars[1].get_y() + bars[1].get_height() / 2,
            f"{change_rate:.2f}%", ha='left', va='center', fontsize=22, color='white', weight='bold')
plt.text(max(sales) + 100, bars[0].get_y() + bars[0].get_height() / 2,
            "同比", ha='left', va='center', fontsize=22, color='white')

# 设置标题
plt.title("销量环比", fontsize=20, color='white', pad=20)

# 设置左侧月份字体大小
plt.yticks(fontsize=25, color='white')

# 隐藏坐标轴
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.tick_params(left=False, bottom=False, labelleft=True, labelbottom=False)

# 调整布局
plt.tight_layout()

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\月同比增长.png')

plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E6%9C%88%E5%90%8C%E6%AF%94%E5%A2%9E%E9%95%BF.png)

###### 10.看板

```
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

image_positions = {
    "part1": [
        {"path": r"D:\python charm\01\可视化\销售看板参考/图片/产品销量.png", "pos": [0.05, 0.55, 0.4, 0.3]},
        {"path": r"D:\python charm\01\可视化\销售看板参考/图片/成本.png", "pos": [0.05, 0.1, 0.49, 0.40]},
    ],
    "part2": [
        {"path": r"D:\python charm\01\可视化\销售看板参考/图片/月同比增长.png", "pos": [0.30, 0.60, 0.42, 0.22]},
        {"path": r"D:\python charm\01\可视化\销售看板参考/图片/利润率.png", "pos": [0.30, 0.10, 0.5, 0.40]},
    ],
    "part3": [
        {"path": r"D:\python charm\01\可视化\销售看板参考/图片/各月销售额.png", "pos": [0.65, 0.6, 0.30, 0.25]},
        {"path": r"D:\python charm\01\可视化\销售看板参考/图片/各区域销量.png", "pos": [0.65, 0.37, 0.25, 0.2]},
        {"path": r"D:\python charm\01\可视化\销售看板参考/图片/各快递公司销量.png", "pos": [0.65, 0.11, 0.25, 0.2]},
    ],
}

# 创建主布局
fig = plt.figure(figsize=(40, 15), facecolor="#2E4057")

# 绘制每张图片
for part, images in image_positions.items():
    for image_info in images:
        ax = fig.add_axes(image_info["pos"])
        img = mpimg.imread(image_info["path"])
        ax.imshow(img)
        ax.axis("off")

# 添加主标题
fig.suptitle("公司销售数据可视化看板", fontsize=40, color="white", y=0.95)

plt.savefig(r'D:\python charm\01\可视化\销售看板参考\图片\看板.png')

# 显示图表
plt.show()
```

![输入图片说明](%E5%AE%9E%E9%AA%8C%E5%8D%81%EF%BC%88%E9%94%80%E5%94%AE%E7%9C%8B%E6%9D%BF%E5%8F%82%E8%80%83%EF%BC%89/%E8%BF%90%E8%A1%8C%E7%BB%93%E6%9E%9C/%E7%9C%8B%E6%9D%BF.png)