python可视化分析绘制散点图和边界气泡图

目录

• 一、绘制散点图
• 二、绘制边界气泡图

一、绘制散点图

实现功能：

python绘制散点图，展现两个变量间的关系，当数据包含多组时，使用不同颜色和形状区分。

实现代码：

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings(action=\'once\')
plt.style.use(\'seaborn-whitegrid\')
sns.set_style(\"whitegrid\")
print(mpl.__version__)
print(sns.__version__)
def draw_scatter(file):
    # Import dataset
    midwest = pd.read_csv(file)
    # Prepare Data
    # Create as many colors as there are unique midwest[\'category\']
    categories = np.unique(midwest[\'category\'])
    colors = [plt.cm.Set1(i / float(len(categories) - 1)) for i in range(len(categories))]
    # Draw Plot for Each Category
    plt.figure(figsize=(10, 6), dpi=100, facecolor=\'w\', edgecolor=\'k\')

    for i, category in enumerate(categories):
        plt.scatter(\'area\', \'poptotal\', data=midwest.loc[midwest.category == category, :],s=20,c=colors[i],label=str(category))
    # Decorations
    plt.gca().set(xlim=(0.0, 0.1), ylim=(0, 90000),)
    plt.xticks(fontsize=10)
    plt.yticks(fontsize=10)
    plt.xlabel(\'Area\', fontdict={\'fontsize\': 10})
    plt.ylabel(\'Population\', fontdict={\'fontsize\': 10})
    plt.title(\"Scatterplot of Midwest Area vs Population\", fontsize=12)
    plt.legend(fontsize=10)
    plt.show()
draw_scatter(\"F:\\数据杂坛\\datasets\\midwest_filter.csv\")

实现效果：

二、绘制边界气泡图

实现功能：

气泡图是散点图中的一种类型，可以展现三个数值变量之间的关系，之前的文章介绍过一般的散点图都是反映两个数值型变量的关系，所以如果还想通过散点图添加第三个数值型变量的信息，一般可以使用气泡图。气泡图的实质就是通过第三个数值型变量控制每个散点的大小，点越大，代表的第三维数值越高，反之亦然。而边界气泡图则是在气泡图添加第四个类别型变量的信息，将一些重要的点选出来并连接。

实现代码：

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from scipy.spatial import ConvexHull
warnings.filterwarnings(action=\'once\')
plt.style.use(\'seaborn-whitegrid\')
sns.set_style(\"whitegrid\")
print(mpl.__version__)
print(sns.__version__)

def draw_scatter(file):
    # Step 1: Prepare Data
    midwest = pd.read_csv(file)

    # As many colors as there are unique midwest[\'category\']
    categories = np.unique(midwest[\'category\'])
    colors = [plt.cm.Set1(i / float(len(categories) - 1)) for i in range(len(categories))]

    # Step 2: Draw Scatterplot with unique color for each category
    fig = plt.figure(figsize=(10, 6), dpi=80, facecolor=\'w\', edgecolor=\'k\')

    for i, category in enumerate(categories):
        plt.scatter(\'area\',\'poptotal\',data=midwest.loc[midwest.category == category, :],s=\'dot_size\',c=colors[i],label=str(category),edgecolors=\'black\',linewidths=.5)
    # Step 3: Encircling
    # https://stackoverflow.com/questions/44575681/how-do-i-encircle-different-data-sets-in-scatter-plot
    def encircle(x, y, ax=None, **kw):  # 定义encircle函数，圈出重点关注的点
        if not ax: ax = plt.gca()
        p = np.c_[x, y]
        hull = ConvexHull(p)
        poly = plt.Polygon(p[hull.vertices, :], **kw)
        ax.add_patch(poly)
    # Select data to be encircled
    midwest_encircle_data1 = midwest.loc[midwest.state == \'IN\', :]
    encircle(midwest_encircle_data1.area,midwest_encircle_data1.poptotal,ec=\"pink\",fc=\"#74C476\",alpha=0.3)
    encircle(midwest_encircle_data1.area,midwest_encircle_data1.poptotal,ec=\"g\",fc=\"none\",linewidth=1.5)
    midwest_encircle_data6 = midwest.loc[midwest.state == \'WI\', :]
    encircle(midwest_encircle_data6.area,midwest_encircle_data6.poptotal,ec=\"pink\",fc=\"black\",alpha=0.3)
    encircle(midwest_encircle_data6.area,midwest_encircle_data6.poptotal,ec=\"black\",fc=\"none\",linewidth=1.5,linestyle=\'--\')
    # Step 4: Decorations
    plt.gca().set(xlim=(0.0, 0.1),ylim=(0, 90000),)
    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)
    plt.xlabel(\'Area\', fontdict={\'fontsize\': 14})
    plt.ylabel(\'Population\', fontdict={\'fontsize\': 14})
    plt.title(\"Bubble Plot with Encircling\", fontsize=14)
    plt.legend(fontsize=10)
    plt.show()
draw_scatter(\"F:\\数据杂坛\\datasets\\midwest_filter.csv\")

实现效果：