[python] 基于matplotlib实现树形图的绘制
树形图Tree diagram (代码下载)
本文旨在描述如何使用Python实现基本的树形图。要实现这样的树形图，首先需要有一个数值矩阵。每一行代表一个实体（这里是一辆汽车）。每列都是描述汽车的变量。目标是将实体聚类以了解谁与谁有共同点。python下通过scipy中hierarchy.linkage进行聚类，hierarchy.dendrogram画树形图。参考文档：https://python-graph-gallery.com/dendrogram/
该章节主要内容有：
- 数据处理 data processing
- 基础树形图 basic dendrogram
- 自定义树形图 customised dendrogram
- 彩色树形图标签 color dendrogram labels
1. 数据处理 data processing
画树形图，往往第一列是数据实体名字，即物体种类。其他列分别为物体变量。
# 导入库
import pandas as pd
from matplotlib import pyplot as plt
from scipy.cluster import hierarchy
import numpy as np
# Import the mtcars dataset from the web + keep only numeric variables
url = 'https://python-graph-gallery.com/wp-content/uploads/mtcars.csv'
df = pd.read_csv(url)
df
.dataframe tbody tr th:only-of-type { vertical-align: middle }
\3cpre>\3ccode>.dataframe tbody tr th { vertical-align: top }
.dataframe thead th { text-align: right }
\3cpre>\3ccode>.dataframe tbody tr th { vertical-align: top }
.dataframe thead th { text-align: right }
| model | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| 1 | Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| 2 | Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| 3 | Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| 4 | Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| 5 | Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| 6 | Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| 7 | Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| 8 | Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| 9 | Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| 10 | Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| 11 | Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| 12 | Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| 13 | Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| 14 | Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| 15 | Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| 16 | Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| 17 | Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| 18 | Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| 19 | Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| 20 | Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| 21 | Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| 22 | AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| 23 | Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| 24 | Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| 25 | Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| 26 | Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| 27 | Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| 28 | Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| 29 | Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| 30 | Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| 31 | Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
# 通常获得数据表格需要将车名设置行标题，这里model代表车的类型
df = df.set_index('model')
df
.dataframe tbody tr th:only-of-type { vertical-align: middle }
\3cpre>\3ccode>.dataframe tbody tr th { vertical-align: top }
.dataframe thead th { text-align: right }
\3cpre>\3ccode>.dataframe tbody tr th { vertical-align: top }
.dataframe thead th { text-align: right }
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| model | |||||||||||
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
# 同时需要删除行标索引的标题名
del df.index.name
df
.dataframe tbody tr th:only-of-type { vertical-align: middle }
\3cpre>\3ccode>.dataframe tbody tr th { vertical-align: top }
.dataframe thead th { text-align: right }
\3cpre>\3ccode>.dataframe tbody tr th { vertical-align: top }
.dataframe thead th { text-align: right }
| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
2. 基础树形图 basic dendrogram
# 执行分层聚类
Z = hierarchy.linkage(df, 'ward')
# 函数原型如下：
# scipy.cluster.hierarchy.linkage(y, method='single', metric='euclidean', optimal_ordering=False)
# y输入矩阵，method聚类方法，metric距离计算方法。通常ward比较靠谱
# optimal_ordering重新排序链接矩阵，以使连续叶之间的距离最小，这样树形结构更为直观，但是计算速度变慢。
# 参数选择见：https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
# Make the dendrogram
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('sample index')
plt.ylabel('distance (Ward)')
# 画聚类图，常用参数labels设定横坐标下标，leaf_rotation标题旋转
# 详细使用见：https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.cluster.hierarchy.dendrogram.html
hierarchy.dendrogram(Z, labels=df.index, leaf_rotation=90);
3. 自定义树形图 customised dendrogram
- 叶标签 leaf label
- 聚类簇数 number of clusters
- 颜色 color
- 截减 truncate
- 方向 orientation
# 叶标签 leaf label
# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')
# Plot with Custom leaves
# 常用参数labels设定横坐标下标，leaf_rotation标题旋转，leaf_font_size设置字号
hierarchy.dendrogram(Z, leaf_rotation=90, leaf_font_size=8, labels=df.index);
# 聚类簇数 number of clusters
# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')
# Control number of clusters in the plot + add horizontal line.
# color_threshold设定颜色阈值，小于olor_threshold根据簇节点为一簇
hierarchy.dendrogram(Z, color_threshold=240)
# 画水平线，y纵坐标，c颜色，lw线条粗细，linestyle线形
plt.axhline(y=240, c='grey', lw=1, linestyle='dashed');
# 颜色 color
# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')
# Set the colour of the cluster here: 设置聚类颜色
hierarchy.set_link_color_palette(['#b30000','#996600', '#b30086'])
# Make the dendrogram and give the colour above threshold
# above_threshold_color设置color_threshold上方链接的颜色
hierarchy.dendrogram(Z, color_threshold=240, above_threshold_color='grey')
# Add horizontal line.
plt.axhline(y=240, c='grey', lw=1, linestyle='dashed');
# 截减 truncate
# 原始观察矩阵很大时，树形图很难读取。截断用于压缩树形图。有几种模式：
# 1 None 不执行截断
# 2 lastp lastp设置叶子节点数，最底层节点数
# 3 level 根据level设置图中层最大数
# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')
# method 1: lastp
# you will have 4 leaf at the bottom of the plot
hierarchy.dendrogram(Z, truncate_mode = 'lastp', p=4);
# method 2: level
# No more than ``p`` levels of the dendrogram tree are displayed.
hierarchy.dendrogram(Z, truncate_mode = 'level', p=2);
# 方向 orientation
# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')
# Orientation of the dendrogram
# 设置层次树的朝向，orientation可选"top", "left", "bottom", "right"，默认top
hierarchy.dendrogram(Z, orientation="right", labels=df.index);
# Orientation of the dendrogram
hierarchy.dendrogram(Z, orientation="bottom", labels=df.index);
4. 彩色树形图标签 color dendrogram labels
# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')
# Make the dendro
# 画树状图
hierarchy.dendrogram(Z, labels=df.index, leaf_rotation=0, orientation="left", color_threshold=240, above_threshold_color='grey')
# Create a color palette with 3 color for the 3 cyl possibilities
# 设置渐变颜色，共三种颜色
my_palette = plt.cm.get_cmap("Accent", 3)
# transforme the 'cyl' column in a categorical variable. It will allow to put one color on each level.
# 根据cyl设置颜色参数，对参数进行分类
df['cyl']=pd.Categorical(df['cyl'])
# 获得每种汽车cyl对应的颜色
my_color=df['cyl'].cat.codes
# Apply the right color to each label
ax = plt.gca()
# 获得y轴坐标标签
xlbls = ax.get_ymajorticklabels()
num=-1
for lbl in xlbls:
num+=1
val=my_color[num]
# 设置颜色
lbl.set_color(my_palette(val))
[python] 基于matplotlib实现树形图的绘制的更多相关文章
- 用python的matplotlib和numpy库绘制股票K线均线和成交量的整合效果（含量化验证交易策略代码）
在用python的matplotlib和numpy库绘制股票K线均线的整合效果(含从网络接口爬取数据和验证交易策略代码)一文里,我讲述了通过爬虫接口得到股票数据并绘制出K线均线图形的方式,在本文里,将 ...
- [python] 基于matplotlib实现圆环图的绘制
圆环图本质上是一个中间切出一块区域的饼状图.可以使用python和matplotlib库来实现.本文主要介绍基于matplotlib实现圆环图.本文所有代码见:Python-Study-Notes # ...
- [python] 基于matplotlib实现雷达图的绘制
雷达图(也称为蜘蛛图或星形图)是一种可视化视图,用于使用一致的比例尺显示三个或更多维度上的多元数据.并非每个人都是雷达图的忠实拥护者,但我认为雷达图能够以视觉上吸引人的方式比较不同类别各个特征的值.本 ...
- 用python的matplotlib和numpy库绘制股票K线均线的整合效果（含从网络接口爬取数据和验证交易策略代码）
本人最近在尝试着发表“以股票案例入门Python编程语言”系列的文章,在这些文章里,将用Python工具绘制各种股票指标,在讲述各股票指标的含义以及计算方式的同时,验证基于各种指标的交易策略,本文是第 ...
- [python] 基于NetworkX实现网络图的绘制
NETWORK CHART(网络图) 代码下载地址 网络图 (或图表或图形)显示了一组实体之间的互连.每个实体由一个或多个节点表示.节点之间的连接通过链接(或边)表示.网络的理论与实现是一个广阔的研究 ...
- python基于matplotlib绘图
import math import numpy as np import matplotlib.pyplot as plt from matplotlib.font_manager import F ...
- 【Python】matplotlib 双y轴绘制及合并图例
1.双y轴绘制 关键函数:twinx() 问题在于此时图例会有两个. # -*- coding: utf-8 -*- import numpy as np import matplotlib.pypl ...
- python使用matplotlib在一个图形中绘制多个子图以及一个子图中绘制多条动态折线问题
在讲解绘制多个子图之前先简单了解一下使用matplotlib绘制一个图,导入绘图所需库matplotlib并创建一个等间隔的列表x,将[0,2*pi]等分为50等份,绘制函数sin(x).当没有给定x ...
- [R语言] 基于R语言实现树形图的绘制
树状图(或树形图)是一种网络结构.它由一个根节点组成,根节点产生由边或分支连接的多个节点.层次结构的最后一个节点称为叶.本文主要基于R语言实现树形图的绘制.关于python实现树形图的绘制见:基于ma ...
随机推荐
- [ZJOI2012] 灾难 题解
爵士好提 Solution 定义\(u\)控制\(v\)当且仅当\(u\)死后\(v\)也会死 把图建出来,从食物向消费者连边 我们不难想到只能先处理食物,再处理消费者,所以先上个拓扑排序 想一想暴力 ...
- 跨平台客户端Blazor方案尝试
一.方案选择 Electron/MAUI + Blazor(AntDesgin blazor) BlazorApp:Blazor Razor页面层,抽象独立层,被BlazorAppElectron/B ...
- 齐博x1模块安装文件讲解
频道模块存放的目录是/application/频道目录/ 插件存放的目录是/plugins/插件目录/ 他的安装目录都是/install/ 推荐参考默认的/application/cms/instal ...
- Windows10 + Eclipse C/C++开发环境配置极简教程
下载安装Eclipse 访问下载Eclipse IDE for C/C++ Developers https://www.eclipse.org/downloads/packages/ 将下载下来的压 ...
- 题解 P6355 [COCI2007-2008#3] DEJAVU
kcm的原题.. 貌似是个组合数(? \(\sf {Solution}\) 对于每一个点,我们需要统计与它同一行的点数\(a\) 和同一列的点数\(b\) ,则该点对结果\(ans\) 的贡献为\(( ...
- webpack -- element-ui 的按需引入
简单说明原理: 使用babel-plugin-component实现按需引入.打包.将webpack配置成多入口,保证最终打包的目录结构符合babel-plugin-component插件的要求,实现 ...
- zk系列二：zookeeper实战之分布式统一配置获取
前面介绍了zk的一些基础知识,这篇文章主要介绍下如何在java环境下获取zk的配置信息:主要基于zk的监听器以及回调函数通过响应式编程的思想将核心代码糅合成一个工具类,几乎做到了拿来即用: 在分布式集 ...
- CC3
cc_link_three 0x00前言 这里要单独学cc链子三是因为它的调用方式不是执行命令而是代码执行,是一种动态类加载机制来执行代码,然后类加载的时候要用类加载器 0x01开整 首先明白调用机制 ...
- 【翻译】Spring Security - 如何解决WebSecurityConfigurerAdapter类已被弃用的问题？
原文链接:Spring Security - How to Fix WebSecurityConfigurerAdapter Deprecated 原文作者:Nam Ha Minh 原文发表日期:20 ...
- Xtrabackup使用帮助
目录 1.安装工具 2.下载后上传到需要备份的服务器 全备 1.安装完成后我们进行数据库备份执行以下命令 2.查看备份的数据 3.进入数据库,删除一个测试库 4.删除school库 5.备份数据目录 ...