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：
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：
