实现数据可视化

"""数据可视化，歌单介绍词云图"""
from wordcloud import WordCloud, ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
import jieba
import time

def data_visualization_of_music_wordcloud():
    """歌单介绍词云图"""
    df = pd.read_csv('music_data/music_detail.csv', header=None)
    text = ''

    print("正在生成歌单介绍词云图片...")

    # 输出进度条
    t = 60
    start = time.perf_counter()

    for i in range(t + 1):
        finsh = "■" * i
        need_do = "□" * (t - i)
        progress = (i / t) * 100
        dur = time.perf_counter() - start

        print("\r{:^3.0f}% |{}{}| {:.2f}s".format(progress, finsh, need_do, dur), end="")

        time.sleep(0.02)

    for line in df:
        text += ' '.join(jieba.cut(line, cut_all=False))

    background_image = plt.imread('music_image/img/background_image.jpg')

    stopwords = set('')
    stopwords.update([])

    wc = WordCloud(
        background_color='white',
        mask=background_image,
        font_path='./font_resources/STZHONGS.ttf',
        max_words=2000,
        max_font_size=150,
        random_state=30,
        stopwords=stopwords
    )
    wc.generate_from_text(text)

    # 看看词频高的有哪些,把无用信息去除
    process_word = WordCloud.process_text(wc, text)
    sort = sorted(process_word.items(), key=lambda e: e[1], reverse=True)
    # print(sort)

    img_colors = ImageColorGenerator(background_image)
    wc.recolor(color_func=img_colors)
    plt.imshow(wc)
    plt.axis('off')

    # 保存图片
    wc.to_file("./music_image/music_wordcloud.png")

    # 显示图片
    plt.show()

    print("\n已生成歌单介绍词云图片，保存至 music_image/music_wordcloud.png")

pip install pandas matplotlib

import pandas as pd
    import matplotlib.pyplot as plt
    import sqlite3

conn = sqlite3.connect('your_database.db')

query = "SELECT column1, column2 FROM your_table"
    df = pd.read_sql_query(query, conn)

df.plot(kind='bar', x='column1', y='column2')
    plt.xlabel('X Label')
    plt.ylabel('Y Label')
    plt.title('Database Data Visualization')
    plt.show()

conn.close()

pip install pandas matplotlib mysql-connector-python

import pandas as pd
   import matplotlib.pyplot as plt
   import mysql.connector
   import matplotlib.animation as animation

conn = mysql.connector.connect(
       user='your_username',
       password='your_password',
       host='your_host',
       database='year'
   )

query = "SELECT column1, column2 FROM your_table"
   df = pd.read_sql_query(query, conn)

fig, ax = plt.subplots()

def update_data(i):
       # 在此处更新数据
       pass

ani = animation.FuncAnimation(fig, update_data, interval=1000)  # 每隔1秒更新一次数据
   plt.show()

conn.close()

df = pd.read_sql_query(query, conn, header=0)

def update_data(i):
    x = df.iloc[:, 0]  # 提取第一列作为x轴数据
    y = df.iloc[:, 1]  # 提取第二列作为y轴数据

    # 更新图表数据
    ax.clear()  # 清空原有图表
    ax.plot(x, y)  # 重新绘制图表

# 使用FuncAnimation创建动态图表
ani = animation.FuncAnimation(fig, update_data, interval=1000)

df = pd.read_sql_query(query, conn)

df = pd.read_sql_query(query, conn, names=['column1', 'column2'])

import pandas as pd
import matplotlib.pyplot as plt
import mysql.connector
import matplotlib.animation as animation

# 连接到MySQL数据库
conn = mysql.connector.connect(
    user='your_username',
    password='your_password',
    host='your_host',
    database='year'
)

# 从数据库中读取数据
query = "SELECT * FROM your_table"
df = pd.read_sql_query(query, conn)

# 创建一个画布和子图
fig, ax = plt.subplots()

def update_data(i):
    # 提取x和y的值
    x = df.iloc[:, 0]  # 提取第一列作为x轴数据
    y = df.iloc[:, 1]  # 提取第二列作为y轴数据

    # 更新图表数据
    ax.clear()  # 清空原有图表
    ax.plot(x, y)  # 重新绘制图表

# 使用FuncAnimation创建动态图表
ani = animation.FuncAnimation(fig, update_data, interval=1000)

# 显示图表
plt.show()

# 关闭数据库连接
conn.close()

import pandas as pd
import matplotlib.pyplot as plt
import mysql.connector

# 连接到MySQL数据库
conn = mysql.connector.connect(
    user='your_username',
    password='your_password',
    host='your_host',
    database='year'
)

# 从数据库中读取数据
query = "SELECT id, name FROM your_table"
df = pd.read_sql_query(query, conn)

# 绘制柱状图
plt.bar(df['id'], df['name'])

# 设置x轴和y轴标签
plt.xlabel('id')
plt.ylabel('name')

# 设置图表标题
plt.title('Data Visualization')

# 显示图表
plt.show()

# 关闭数据库连接
conn.close()

from PIL import Image
import matplotlib.pyplot as plt

# 打开并加载图像
image_path = r'C:\Users\Administrator\Pictures\4k\1.png'
image = Image.open(image_path)

# 将图像转换为RGB模式
image_rgb = image.convert("RGB")

# 获取图像的像素值
pixels = image_rgb.getdata()

# 获取每个像素点的R、G、B值
red_values = []
green_values = []
blue_values = []
for pixel in pixels:
    red, green, blue = pixel
    red_values.append(red)
    green_values.append(green)
    blue_values.append(blue)

# 绘制柱状图
fig, ax = plt.subplots()
ax.bar(range(len(red_values)), red_values, color='red', alpha=0.5, label='Red')
ax.bar(range(len(green_values)), green_values, color='green', alpha=0.5, label='Green')
ax.bar(range(len(blue_values)), blue_values, color='blue', alpha=0.5, label='Blue')

# 设置x轴和y轴标签
ax.set_xlabel('Pixel Index')
ax.set_ylabel('RGB Value')
ax.set_title('Pixel RGB Values')

# 显示图例
ax.legend()

# 显示图表
plt.show()