Python画图常用代码总结，这20个画图代码现拿现用_python画图代码大全

目录

前言

1、散点图

2、带边界的气泡图

3、带线性回归最佳拟合线的散点图

4、抖动图

5、计数图

6、边缘直方图

7、边缘箱形图

9、矩阵图

10、发散型条形图

11、发散型文本

12、发散型包点图

13、带标记的发散型棒棒糖图

14、面积图

15、有序条形图

16、棒棒糖图

17、包点图

18、坡度图

19、哑铃图

20、连续变量的直方图

前言

Python画图常用代码总结，现拿现用！Python画图常用代码总结，现拿现用！

大家好，今天分享给大家20个Matplotlib图的汇总，在数据分析与可视化中非常有用，大家可以收藏下来慢慢练手。

1、散点图

Scatteplot是用于研究两个变量之间关系的经典和基本图。如果数据中有多个组，则可能需要以不同颜色可视化每个组。在Matplotlib，你可以方便地使用。

 
# Import dataset 
midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv")
 
# Prepare Data 
# Create as many colors as there are unique midwest['category']
categories = np.unique(midwest['category'])
colors = [plt.cm.tab10(i/float(len(categories)-1)) for i in range(len(categories))]
 
# Draw Plot for Each Category
plt.figure(figsize=(16, 10), dpi= 80, 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),
              xlabel='Area', ylabel='Population')
 
plt.xticks(fontsize=12); plt.yticks(fontsize=12)
plt.title("Scatterplot of Midwest Area vs Population", fontsize=22)
plt.legend(fontsize=12)    
plt.show()    

 展示图：

2、带边界的气泡图

有时，你希望在边界内显示一组点以强调其重要性。在此示例中，你将从应该被环绕的数据帧中获取记录，并将其传递给下面的代码中描述的记录。encircle()

from matplotlib import patches
from scipy.spatial import ConvexHull
import warnings; warnings.simplefilter('ignore')
sns.set_style("white")
 
# Step 1: Prepare Data
midwest = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/midwest_filter.csv")
 
# As many colors as there are unique midwest['category']
categories = np.unique(midwest['category'])
colors = [plt.cm.tab10(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=(16, 10), 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):
    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_data = midwest.loc[midwest.state=='IN', :]                         
 
# Draw polygon surrounding verti