python中数据可视化

1.掷一个D6和一个D10 50000次的结果

die.py

from random import randint


class Die:
    def __init__(self, num_sides=6):
        self.num_sides = num_sides

    def roll(self):
        return randint(1, self.num_sides)

die_visual.py

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

# 创建1个D6和1个D10
die_1 = Die()
die_2 = Die(10)

# 掷色子并将结果存储在一个列表中
results = []
for roll_num in range(50000):
    result = die_1.roll() + die_2.roll()
    results.append(result)

# 分析结果
frequencies = []
max_result = die_1.num_sides + die_2.num_sides
for value in range(2, max_result+1):
    frequency = results.count(value)
    frequencies.append(frequency)
# print(frequencies)

#对结果可视化
x_values = list(range(2, max_result+1))
data = [Bar(x=x_values, y=frequencies)]

x_axis_config = {'title': '结果', 'dtick': 1}
y_axis_config = {'title': '结果的频率'}
my_layout = Layout(title='掷一个D6和一个D10 50000次的结果', xaxis=x_axis_config, yaxis=y_axis_config)
offline.plot({'data': data, 'layout': my_layout}, filename='d6_d10.html')

可视化结果：

2.读取scv文件，绘制数据图，处理数据缺失错误

death_valley_highs_lows.py

import csv
import matplotlib.pyplot as plt
from datetime import datetime

filename = 'D:\python_project\Data_Visualization\source_code\chapter_16\\the_csv_file_format\data\death_valley_2018_simple.csv'
with open(filename) as f:
    reader = csv.reader(f)
    header_row = next(reader)

    # for index, column_header in enumerate(header_row):
    #     print(index, column_header)

    # 从文件中获取最高温度
    dates, highs, lows= [], [], []
    for row in reader:
        current_date = datetime.strptime(row[2], '%Y-%m-%d')
        # 处理缺失数据错误
        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)

# 根据最高温度绘制图形
plt.style.use('seaborn')
fig, ax = plt.subplots()
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
title = "2018年每日最高和最低温度\n 美国加利福尼亚州死亡谷"
ax.set_title(title, 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.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签（中文乱码问题）

plt.show()

数据结果图：

 3.绘制全球地震散点图：数据json格式

eq_world_map.py

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

filename = "D:\python_project\Data_Visualization\source_code\chapter_16\mapping_global_data_sets\data\eq_data_30_day_m1.json"
with open(filename) as f:
    all_eq_data = json.load(f)

all_eq_dicts = all_eq_data['features']
# print(len(all_eq_dicts))
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(
    data,
    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',
    hover_name='位置',
)
fig.write_html('global_earthquakes.html')
fig.show()

可视化结果：

4.使用Plotly可视化GitHub的API仓库

python_repos_visual.py

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}")

# 处理响应
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"{repo_name}"
    repo_links.append(repo_link)

    stars.append(repo_dict['stargazers_count'])

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

# 可视化
data = [{
    'type': 'bar',
    'x': repo_links,
    'y': stars,
    'hovertext': labels,
    # 条形设计
    'marker': {
        'color': 'rgb(60, 100, 150)',
        'line': {'width': 1.5, 'color': 'rgb(25, 25, 25)'}
    },
    'opacity': 0.6,  # 不透明度
}]
my_layout = {
    'title': 'GitHub上最受欢迎的Python项目',
    'titlefont': {'size': 28},
    'xaxis': {
        'title': 'Reposistory',
        'titlefont': {'size': 24},      # 图标名称字号
        'tickfont': {'size': 14},       # 刻度标签字号
    },
    'yaxis': {
        'title': 'Stars',
        'titlefont': {'size': 24},
        'tickfont': {'size': 14},
    },
}

fig = {'data': data, 'layout': my_layout}
offline.plot(fig, filename='python.repos.html')

可交互式图表：