Matplotlib中3种图的绘制代码总结

Mtscoptor

已于 2025-03-12 16:28:33 修改

阅读量832

点赞数 13

分类专栏： # Matplotlib 文章标签： matplotlib python pycharm

于 2025-03-08 18:40:37 首次发布

本文链接：https://blog.youkuaiyun.com/2401_84561760/article/details/146119162

版权

Matplotlib 专栏收录该内容

2 篇文章

订阅专栏

一、点线图

核心部分：

import matplotlib.pyplot as plt

#fig为整张图像，ax为曲线
fig, ax = plt.subplots(figsize = (16,12), dpi = 50)

#输入曲线参数,a1为x轴（可忽略），a2和a3为y轴，alpha为透明度
ax.plot(a1, a2, c = 'red', alpha = 0.5)
ax.plot(a1, a3, c = 'blue', alpha = 0.5)

#在两条线之间填充颜色
ax.fill_between(dates, highs, lows, facecolor = 'blue', alpha = 0.1)

#设置两条线共用的参数
ax.set_title("...", fontsize = 20)
ax.set_xlabel('', fontsize = 16)
ax.set_ylabel("...", fontsize = 16)

#X轴刻度标签字体斜体化
fig.autofmt_xdate()

#设置图的参数
ax.tick_params(axis = 'both', which = 'major', labelsize = 16)

#绘制图像
plt.show()

完整示例：

import csv
from datetime import datetime

import matplotlib.pyplot as plt

filename = 'data/death_valley_2018_simple.csv'
with open(filename) as f:
    reader = csv.reader(f)
    header_row = next(reader)

    #从文件中获取日期和最高温度
    dates, highs, lows = [], [], []
    for row in reader:
        current_date = datetime.strptime(row[2], '%Y-%m-%d')
        try:
            high = int(row[4])
            low = int(row[5])
        except ValueError:
            print(f"Missing data for {current_date}")
        else:
            dates.append(current_date)
            highs.append(high)
            lows.append(low)

#根据最高温度和最低温度绘制图形
fig, ax = plt.subplots(figsize = (16,12), dpi = 50)
ax.plot(dates, highs, c = 'red', alpha = 0.5)
ax.plot(dates, lows, c = 'blue', alpha = 0.5)
ax.fill_between(dates, highs, lows, facecolor = 'blue', alpha = 0.1)

#设置图形的格式
ax.set_title("2018年每日最高温度和最低温度\n美国加利福尼亚州死亡谷", fontsize = 20)
ax.set_xlabel('', fontsize = 16)
fig.autofmt_xdate()
ax.set_ylabel("温度 (F)", fontsize = 16)
ax.tick_params(axis = 'both', which = 'major', labelsize = 16)

plt.show()

二、散点图

方法1：只用plotly.express模块（可定制内容少，不推荐）

核心部分：

import plotly.express as px

fig = px.scatter(
    #a,b均为列表
    x = a,
    y = b,

    #两个坐标轴的名称
    labels = {'x': '...', 'y': '...'},

    #坐标轴范围
    range_x = [-200, 200],
    range_y = [-90, 90],

    #图的大小
    width = 800,
    height = 800,

    #图的标题
    title = '...',
)

#产出html文件
fig.write_html('....html')

#绘制图像
fig.show()

完整示例：

import json
import plotly.express as px

filename = 'data/eq_data_1_day_m1.json'
with open(filename) as f:
    all_eq_data = json.load(f)

all_eq_dicts = all_eq_data['features']

mags, titles, lons, lats = [],[],[],[]
for eq_dict in all_eq_dicts:
    mag = eq_dict['properties']['mag']
    title = eq_dict['properties']['title']
    lon = eq_dict['geometry']['coordinates'][0]
    lat = eq_dict['geometry']['coordinates'][1]
    mags.append(mag)
    titles.append(title)
    lons.append(lon)
    lats.append(lat)

fig = px.scatter(
    x = lons,
    y = lats,
    labels = {'x': '经度', 'y': '纬度'},
    range_x = [-200, 200],
    range_y = [-90, 90],
    width = 800,
    height = 800,
    title = '全球地震散点图',
)
fig.write_html('graph/global_earthquakes.html')
fig.show()

方法2：利用plotly.express + pandas 分析工具（推荐，可定制 + 图像中丰富的工具提示）

核心部分：

import plotly.express as px
import pandas as pd
import webbrowser
import os

data = pd.DataFrame(
    #columns中为数据名称，顺序与zip()中的对应
    data = zip(lons, lats, titles, mags), columns = ['...', '...', '...', '...']
)
data.head()

#散点图参数
fig = px.scatter(
    data,
    x = '...',
    y = '...',

    #两个坐标轴的名称
    labels = {'x': '...', 'y': '...'},

    #坐标轴范围
    range_x = [-200, 200],
    range_y = [-90, 90],

    #图的大小
    width = 800,
    height = 800,

    #图的标题
    title = '...',
    
    #每个点的大小
    size = '...',
    size_max = 10,

    #每个点的颜色
    color = '...',

    #用的颜色渐变类型
    color_continuous_scale = px.colors.sequential.Magma[::-1],

    #每个点的详细信息的标题
    hover_name = '...',
)

#产出html文件
fig.write_html('graph/global_earthquakes.html')

#在浏览器打开
edge_path = r"C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe"
webbrowser.register('edge', None, webbrowser.BackgroundBrowser(edge_path))

html_file = os.path.abspath('graph/global_earthquakes.html')
url = f"file:///{html_file}"

webbrowser.get('edge').open(url)

import json
import plotly.express as px
import pandas as pd
import webbrowser
import os

filename = 'data/eq_data_1_day_m1.json'
with open(filename) as f:
    all_eq_data = json.load(f)

all_eq_dicts = all_eq_data['features']

mags, titles, lons, lats = [],[],[],[]
for eq_dict in all_eq_dicts:
    mag = eq_dict['properties']['mag']
    title = eq_dict['properties']['title']
    lon = eq_dict['geometry']['coordinates'][0]
    lat = eq_dict['geometry']['coordinates'][1]
    mags.append(mag)
    titles.append(title)
    lons.append(lon)
    lats.append(lat)

data = pd.DataFrame(
    data = zip(lons, lats, titles, mags), columns = ['经度', '纬度', '位置', '震级']
)
data.head()

fig = px.scatter(
    # x = lons,
    # y = lats,
    data,
    x = '经度',
    y = '纬度',
    labels = {'x': '经度', 'y': '纬度'},
    range_x = [-200, 200],
    range_y = [-90, 90],
    width = 800,
    height = 800,
    title = '全球地震散点图',
    size = '震级',
    size_max = 10,
    color = '震级',
    color_continuous_scale = px.colors.sequential.Magma[::-1],
    hover_name = '位置',
)
fig.write_html('graph/global_earthquakes.html')

edge_path = r"C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe"
webbrowser.register('edge', None, webbrowser.BackgroundBrowser(edge_path))

html_file = os.path.abspath('graph/global_earthquakes.html')
url = f"file:///{html_file}"

webbrowser.get('edge').open(url)

注：这里最后用浏览器打开而不是fig.show()的原因参考这里

生成global_earthquakes.html时无反应的解决方法-优快云博客文章浏览阅读102次。方法1：在点击运行后即可点击停止，随后找到生成的html文件，右键在浏览器打开即可。方法2（推荐）：用代码在浏览器打开。https://blog.youkuaiyun.com/2401_84561760/article/details/146114672?spm=1001.2014.3001.5501

三、条形图

方法：利用offline模块

写法1：

核心部分：

from plotly.graph_objs import Bar, Layout
from plotly import offline

#将列表转化为条形图数据（a和b均为列表）
data = [Bar(x = a , y = b )]

x_axis_config = {'title': '...'}
y_axis_config ={'title': '...'}

#配置layout参数
my_layout = Layout(title = '...', xaxis = x_axis_config, yaxis = y_axis_config)

#在浏览器中生成图像
offline.plot({'data': data, 'layout': my_layout}, filename ='....html')

完整示例：

from die import Die
from plotly.graph_objs import Bar, Layout
from plotly import offline

die = Die()

results = []
for roll_num in range(10000):
    result = die.roll()
    results.append(result)

frequencies = []
for value in range(1, die.num_sides + 1):
    frequency = results.count(value)
    frequencies.append(frequency)

#对结果进行可视化
x_values = list(range(1, die.num_sides + 1))
data = [Bar(x = x_values, y = frequencies)]

x_axis_config = {'title': '结果'}
y_axis_config ={'title': '结果的频率'}
my_layout = Layout(title = '掷一个D6 1000次的结果', xaxis = x_axis_config, yaxis = y_axis_config)
offline.plot({'data': data, 'layout': my_layout}, filename ='graph/d6.html')

写法2：

核心部分：

from plotly import offline

#用字典来配置参数
data = [{
    #设置类型为条形
    'type': 'bar',

    #这里a,b,c均为列表
    'x': a,
    'y': b,

    #当鼠标指向条形的时候显示的信息
    'hovertext' : c,

    #条形参数
    'marker': {
        #颜色
        'color': 'rgb(60, 100, 150)',
        
        #轮廓线
        'line': {'width': 1.5, 'color': 'rgb(25, 25, 25)'}
    },
    
    #条形不透明度
    'opacity': 0.6,
}]

my_layout = {
    #图的标题
    'title':{
        'text': '...',
        'font': {'size': 28},
    },
    
    'xaxis': {
        #坐标轴名称参数
        'title': {
            'text' : '...',
            'font': {'size': 24}
        },
        
        #坐标轴刻度标签字号
        'tickfont': {'size': 14},
    },
    'yaxis': {
        'title': {
            'text' : '...',
            'font': {'size': 24}
        },
        'tickfont': {'size': 14},
    },
}

#配置图像数据
fig = {'data': data, 'layout': my_layout}

#在浏览器中绘制图像
offline.plot(fig, filename = '....html')

完整示例：

import requests

from plotly.graph_objs import Bar
from plotly import offline

#执行API调用并存储响应
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
headers = {'Accept': 'application/vnd.github.v3+json'}
r = requests.get(url, headers = headers)
print(f"Status code: {r.status_code}")

#处理结果
response_dict = r.json()
repo_dicts = response_dict['items']
repo_links, stars, labels = [], [], []
for repo_dict in repo_dicts:
    repo_name = repo_dict['name']
    repo_url = repo_dict['html_url']
    repo_link = f"<a href = '{repo_url}'>{repo_name}</a>"
    repo_links.append(repo_link)

    stars.append(repo_dict['stargazers_count'])

    owner = repo_dict['owner']['login']
    description = repo_dict['description']
    label = f"{owner}<br />{description}"
    labels.append(label)

#可视化
data = [{
    'type': 'bar',
    'x': repo_links,
    'y': stars,
    'hovertext' : labels,
    'marker': {
        'color': 'rgb(60, 100, 150)',
        'line': {'width': 1.5, 'color': 'rgb(25, 25, 25)'}
    },
    'opacity': 0.6,
}]
my_layout = {
    'title':{
        'text': 'GitHub上最受欢迎的Python项目',
        'font': {'size': 28},
    },
    'xaxis': {
        'title': {
            'text' : 'Repository',
            'font': {'size': 24}
        },
        'tickfont': {'size': 14},
    },
    'yaxis': {
        'title': {
            'text' : 'Repository',
            'font': {'size': 24}
        },
        'tickfont': {'size': 14},
    },
}

fig = {'data': data, 'layout': my_layout}
offline.plot(fig, filename = 'graph/python_repos.html')