# 安裝 Plotly（如果尚未安裝）
# pip install plotly pandas

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np

import plotly.express as px
import numpy as np
import pandas as pd

# 生成模擬數(shù)據(jù)
np.random.seed(42)
n = 150
df = pd.DataFrame({
    '廣告投入': np.random.uniform(10, 100, n),
    '銷售額': 0,
    '產(chǎn)品類別': np.random.choice(['電子產(chǎn)品', '服裝', '食品', '家居'], n),
    '區(qū)域': np.random.choice(['華東', '華南', '華北', '西部'], n)
})

df['銷售額'] = (2.5 * df['廣告投入'] + 
                np.where(df['產(chǎn)品類別'] == '電子產(chǎn)品', 50, 0) +
                np.where(df['區(qū)域'] == '華東', 30, 0) +
                np.random.normal(0, 15, n))

# 一行代碼創(chuàng)建交互式散點(diǎn)圖
fig = px.scatter(df, x='廣告投入', y='銷售額', 
                 color='產(chǎn)品類別',        # 顏色按類別區(qū)分
                 size='銷售額',           # 氣泡大小按銷售額
                 hover_data=['區(qū)域'],     # 懸停時(shí)顯示區(qū)域信息
                 title='廣告投入與銷售額關(guān)系（交互式）',
                 labels={'廣告投入': '廣告投入（萬(wàn)元）', '銷售額': '銷售額（萬(wàn)元）'},
                 color_discrete_map={'電子產(chǎn)品': '#1E88E5', '服裝': '#E53935', 
                                    '食品': '#43A047', '家居': '#FB8C00'})

# 設(shè)置圖表布局
fig.update_layout(
    font=dict(size=13),
    title=dict(font=dict(size=18), x=0.5),
    hovermode='closest'  # 懸停時(shí)最近的數(shù)據(jù)點(diǎn)都高亮
)

# 顯示圖表（在 Jupyter 中直接顯示，腳本中會(huì)打開(kāi)瀏覽器）
fig.show()

import plotly.express as px
import pandas as pd
import numpy as np

# 模擬數(shù)據(jù)：各部門(mén)季度營(yíng)收
df = pd.DataFrame({
    '部門(mén)': ['銷售部', '市場(chǎng)部', '技術(shù)部', '運(yùn)營(yíng)部', '人事部', '財(cái)務(wù)部'],
    'Q1': [450, 320, 580, 290, 150, 200],
    'Q2': [480, 350, 620, 310, 160, 210],
    'Q3': [520, 380, 650, 330, 155, 220],
    'Q4': [550, 400, 700, 350, 165, 230]
})

# 需要將寬格式轉(zhuǎn)為長(zhǎng)格式
df_melted = df.melt(id_vars='部門(mén)', var_name='季度', value_name='營(yíng)收')

# 創(chuàng)建分組柱狀圖
fig = px.bar(df_melted, x='部門(mén)', y='營(yíng)收', color='季度',
             barmode='group',  # 分組模式（還有 'stack' 堆疊模式）
             title='各部門(mén)季度營(yíng)收對(duì)比',
             labels={'部門(mén)': '部門(mén)', '營(yíng)收': '營(yíng)收（萬(wàn)元）', '季度': '季度'},
             color_discrete_sequence=['#4CAF50', '#FF9800', '#2196F3', '#9C27B0'])

fig.update_layout(
    title=dict(font=dict(size=18), x=0.5),
    font=dict(size=13),
    xaxis_tickangle=-30,  # X軸標(biāo)簽旋轉(zhuǎn)
    hovermode='x unified'  # 懸停時(shí)同一X軸的所有值一起顯示
)

fig.show()

import plotly.express as px
import pandas as pd

# 模擬數(shù)據(jù)：各產(chǎn)品線年度銷售排名
df = pd.DataFrame({
    '產(chǎn)品線': ['智能手機(jī)Pro Max', '筆記本電腦Ultra', '智能手表S9', 
              '無(wú)線耳機(jī)AirPods', '平板電腦Mini', '智能音箱Home'],
    '銷售額': [2850, 2100, 1650, 1420, 980, 750]
})

# 按銷售額排序
df = df.sort_values('銷售額', ascending=True)

fig = px.bar(df, x='銷售額', y='產(chǎn)品線', orientation='h',
             title='各產(chǎn)品線年度銷售額排行（萬(wàn)元）',
             color='銷售額',
             color_continuous_scale='Blues',
             text='銷售額')  # 在柱子上顯示數(shù)值

fig.update_layout(
    title=dict(font=dict(size=16), x=0.5),
    font=dict(size=13),
    yaxis={'categoryorder': 'total ascending'}
)

# 添加數(shù)據(jù)標(biāo)簽
fig.update_traces(texttemplate='%{text:,}', textposition='outside')

fig.show()

import plotly.express as px
import pandas as pd
import numpy as np

# 模擬數(shù)據(jù)：兩年的日銷售數(shù)據(jù)
np.random.seed(42)
dates = pd.date_range('2023-01-01', '2024-12-31', freq='D')
base_sales = 1000

df = pd.DataFrame({
    '日期': dates,
    '基礎(chǔ)銷量': base_sales + 200 * np.sin(np.arange(len(dates)) * 2 * np.pi / 365),  # 季節(jié)性
    '隨機(jī)波動(dòng)': np.random.normal(0, 100, len(dates))
})
df['銷售額'] = df['基礎(chǔ)銷量'] + df['隨機(jī)波動(dòng)']
df['年份'] = df['日期'].dt.year.astype(str)
df['月份'] = df['日期'].dt.month

# 按月聚合
monthly = df.groupby(['年份', '月份'])['銷售額'].mean().reset_index()
monthly.columns = ['年份', '月份', '平均日銷售額']

fig = px.line(monthly, x='月份', y='平均日銷售額', color='年份',
              title='月度平均日銷售額趨勢(shì)對(duì)比',
              labels={'月份': '月份', '平均日銷售額': '平均日銷售額（元）', '年份': '年份'},
              markers=True,
              line_shape='linear',
              color_discrete_map={'2023': '#1E88E5', '2024': '#E53935'})

fig.update_layout(
    title=dict(font=dict(size=16), x=0.5),
    font=dict(size=13),
    hovermode='x unified',
    xaxis=dict(tickmode='linear', tick0=1, dtick=1, title='月份')
)

# 添加標(biāo)記和填充
fig.update_traces(mode='lines+markers', marker=dict(size=8))

fig.show()

import plotly.graph_objects as go
import numpy as np
import pandas as pd

# 模擬A/B測(cè)試數(shù)據(jù)
df = pd.DataFrame({
    '周次': range(1, 9),
    'A組轉(zhuǎn)化率': [3.2, 3.5, 3.8, 4.0, 4.2, 4.1, 4.3, 4.5],
    'A組誤差': [0.3, 0.3, 0.4, 0.3, 0.3, 0.4, 0.3, 0.3],
    'B組轉(zhuǎn)化率': [3.1, 3.3, 3.6, 3.8, 4.0, 4.3, 4.6, 4.9],
    'B組誤差': [0.3, 0.3, 0.3, 0.4, 0.3, 0.3, 0.4, 0.4]
})

fig = go.Figure()

# A組
fig.add_trace(go.Scatter(
    x=df['周次'], y=df['A組轉(zhuǎn)化率'],
    name='A組（對(duì)照組）',
    mode='lines+markers',
    line=dict(color='#1E88E5', width=3),
    marker=dict(size=10),
    error_y=dict(type='data', array=df['A組誤差'], visible=True, thickness=2, width=5)
))

# B組
fig.add_trace(go.Scatter(
    x=df['周次'], y=df['B組轉(zhuǎn)化率'],
    name='B組（實(shí)驗(yàn)組）',
    mode='lines+markers',
    line=dict(color='#E53935', width=3, dash='dash'),
    marker=dict(size=10, symbol='square'),
    error_y=dict(type='data', array=df['B組誤差'], visible=True, thickness=2, width=5)
))

fig.update_layout(
    title='A/B測(cè)試：轉(zhuǎn)化率對(duì)比（含置信區(qū)間）',
    title=dict(font=dict(size=16), x=0.5),
    xaxis=dict(title='周次', tickmode='linear', dtick=1),
    yaxis=dict(title='轉(zhuǎn)化率（%）'),
    font=dict(size=13),
    hovermode='x unified',
    legend=dict(yanchor='top', y=0.99, xanchor='left', x=0.01)
)

fig.show()

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

# 創(chuàng)建 2x2 子圖
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('銷售額趨勢(shì)', '產(chǎn)品占比', '區(qū)域分布', '客戶評(píng)分'),
    specs=[[{'type': 'xy'}, {'type': 'domain'}],  # domain 用于餅圖
           [{'type': 'xy'}, {'type': 'xy'}]]
)

# 子圖(1,1)：折線圖
months = ['1月', '2月', '3月', '4月', '5月', '6月', 
          '7月', '8月', '9月', '10月', '11月', '12月']
fig.add_trace(go.Scatter(
    x=months, y=[120, 135, 148, 162, 175, 190, 210, 225, 240, 230, 215, 250],
    mode='lines+markers', name='銷售額', line=dict(color='#1E88E5', width=2),
    marker=dict(size=8)
), row=1, col=1)

# 子圖(1,2)：餅圖
fig.add_trace(go.Pie(
    labels=['電子產(chǎn)品', '服裝', '食品', '家居'],
    values=[450, 320, 280, 180],
    marker=dict(colors=['#1E88E5', '#E53935', '#43A047', '#FB8C00']),
    textinfo='label+percent',
    hole=0.4  # 空心圓環(huán)圖
), row=1, col=2)

# 子圖(2,1)：柱狀圖
fig.add_trace(go.Bar(
    x=['華東', '華南', '華北', '西南', '華中', '東北'],
    y=[580, 420, 380, 250, 310, 190],
    name='區(qū)域',
    marker=dict(color=['#1E88E5', '#E53935', '#43A047', '#FB8C00', '#9C27B0', '#00BCD4'])
), row=2, col=1)

# 子圖(2,2)：面積圖
fig.add_trace(go.Scatter(
    x=['Q1', 'Q2', 'Q3', 'Q4'], y=[60, 79, 101, 104],
    fill='tozeroy', name='利潤(rùn)',
    line=dict(color='#E53935', width=2),
    fillcolor='rgba(229, 57, 53, 0.3)'
), row=2, col=2)

fig.update_layout(
    title=dict(text='年度經(jīng)營(yíng)數(shù)據(jù)看板', x=0.5, font=dict(size=20)),
    showlegend=False,
    height=700,
    font=dict(size=12)
)

fig.show()

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

# 創(chuàng)建不規(guī)則布局：上方大圖 + 下方兩個(gè)小圖
fig = make_subplots(
    rows=3, cols=2,
    row_heights=[0.5, 0.25, 0.25],
    specs=[[{'colspan': 2}, None],  # 第一行橫跨兩列
           [{'type': 'xy'}, {'type': 'xy'}],  # 第二行兩個(gè)子圖
           [{'colspan': 2}, None]]  # 第三行橫跨兩列
)

x = np.linspace(0, 10, 100)

# 上方大圖：多條曲線
fig.add_trace(go.Scatter(x=x, y=np.sin(x), name='sin(x)', line=dict(color='#1E88E5')), row=1, col=1)
fig.add_trace(go.Scatter(x=x, y=np.cos(x), name='cos(x)', line=dict(color='#E53935')), row=1, col=1)

# 左下：散點(diǎn)
np.random.seed(42)
fig.add_trace(go.Scatter(
    x=np.random.randn(100), y=np.random.randn(100),
    mode='markers', name='隨機(jī)數(shù)據(jù)',
    marker=dict(color=np.random.randn(100), colorscale='Viridis', 
                size=8, showscale=True)
), row=2, col=1)

# 右下：柱狀圖
fig.add_trace(go.Bar(
    x=['A', 'B', 'C', 'D'], y=[45, 32, 58, 29],
    marker_color=['#4CAF50', '#FF9800', '#2196F3', '#9C27B0']
), row=2, col=2)

# 底部：面積圖
fig.add_trace(go.Scatter(
    x=list(range(30)), y=np.cumsum(np.random.randn(30)),
    fill='tozeroy', fillcolor='rgba(229, 57, 53, 0.3)',
    line=dict(color='#E53935')
), row=3, col=1)

fig.update_layout(
    title=dict(text='綜合數(shù)據(jù)看板', x=0.5, font=dict(size=18)),
    height=900,
    font=dict(size=12)
)

fig.show()

import plotly.graph_objects as go
import pandas as pd
import numpy as np

# 模擬產(chǎn)品數(shù)據(jù)
np.random.seed(42)
df = pd.DataFrame({
    '產(chǎn)品': [f'產(chǎn)品{i}' for i in range(1, 16)],
    '銷售額': np.random.randint(100, 500, 15),
    '利潤(rùn)率': np.random.uniform(10, 40, 15),
    '增長(zhǎng)率': np.random.uniform(-10, 30, 15),
    '庫(kù)存天數(shù)': np.random.randint(5, 60, 15)
})

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=df['銷售額'], y=df['利潤(rùn)率'],
    mode='markers',
    marker=dict(
        size=df['庫(kù)存天數(shù)'] / 2,  # 氣泡大小
        color=df['增長(zhǎng)率'],        # 顏色
        colorscale='RdYlGn',       # 紅黃綠漸變
        showscale=True,
        colorbar=dict(title='增長(zhǎng)率(%)')
    ),
    # 自定義懸停模板
    hovertemplate=
    '<b>%{text}</b><br><br>' +
    '銷售額: %{x:,.0f} 萬(wàn)元<br>' +
    '利潤(rùn)率: %{y:.1f}%<br>' +
    '庫(kù)存天數(shù): %{marker.size:.0f} 天<br>' +
    '<extra></extra>',  # 隱藏 trace name
    text=df['產(chǎn)品']
))

fig.update_layout(
    title='產(chǎn)品矩陣分析（氣泡=庫(kù)存，顏色=增長(zhǎng)率）',
    title=dict(x=0.5, font=dict(size=16)),
    xaxis=dict(title='銷售額（萬(wàn)元）', gridcolor='lightgray'),
    yaxis=dict(title='利潤(rùn)率（%）', gridcolor='lightgray'),
    font=dict(size=13)
)

fig.show()

import plotly.graph_objects as go
import numpy as np

# 生成數(shù)據(jù)：不同產(chǎn)品的銷售曲線
np.random.seed(42)
months = [f'{i}月' for i in range(1, 13)]

fig = go.Figure()

products = {
    '產(chǎn)品A': np.cumsum(np.random.normal(20, 10, 12)) + 100,
    '產(chǎn)品B': np.cumsum(np.random.normal(15, 8, 12)) + 80,
    '產(chǎn)品C': np.cumsum(np.random.normal(25, 12, 12)) + 120
}

for name, data in products.items():
    fig.add_trace(go.Scatter(
        x=months, y=data, name=name,
        mode='lines+markers', visible=True
    ))

# 創(chuàng)建按鈕
buttons = []
for i, name in enumerate(products.keys()):
    visibility = [False] * len(products)
    visibility[i] = True
    buttons.append(dict(
        label=name,
        method='update',
        args=[{'visible': visibility},
              {'title': f'{name} 月度銷售趨勢(shì)'}]
    ))

# 添加"全部顯示"按鈕
buttons.append(dict(
    label='全部顯示',
    method='update',
    args=[{'visible': [True] * len(products)},
          {'title': '所有產(chǎn)品月度銷售趨勢(shì)'}]
))

fig.update_layout(
    updatemenus=[dict(
        buttons=buttons,
        direction='down',
        showactive=True,
        x=0.1, y=1.15,
        xanchor='left', yanchor='top',
        bgcolor='white',
        bordercolor='gray'
    )],
    title=dict(text='產(chǎn)品趨勢(shì)查看器', x=0.5, font=dict(size=16)),
    font=dict(size=13)
)

fig.show()

import plotly.express as px
import pandas as pd
import numpy as np

# 模擬數(shù)據(jù)：各區(qū)域月度銷售額變化（帶動(dòng)畫(huà)）
np.random.seed(42)
records = []
for month in range(1, 13):
    for region in ['華東', '華南', '華北', '西南', '華中']:
        base = {'華東': 500, '華南': 350, '華北': 300, '西南': 200, '華中': 250}[region]
        growth = month * 20
        records.append({
            '區(qū)域': region,
            '月份': f'{month}月',
            '月序號(hào)': month,
            '銷售額': base + growth + np.random.normal(0, 30),
            '利潤(rùn)率': np.random.uniform(15, 35)
        })

df = pd.DataFrame(records)

# 創(chuàng)建帶動(dòng)畫(huà)的散點(diǎn)圖
fig = px.scatter(df, x='銷售額', y='利潤(rùn)率',
                 animation_frame='月序號(hào)',  # 動(dòng)畫(huà)按月份推進(jìn)
                 animation_group='區(qū)域',
                 color='區(qū)域',
                 size='銷售額',
                 range_x=[100, 800],
                 range_y=[10, 40],
                 title='各區(qū)域銷售額與利潤(rùn)率月度演變',
                 labels={'銷售額': '銷售額（萬(wàn)元）', '利潤(rùn)率': '利潤(rùn)率（%）'},
                 color_discrete_map={'華東': '#1E88E5', '華南': '#E53935', 
                                    '華北': '#43A047', '西南': '#FB8C00', '華中': '#9C27B0'})

fig.update_layout(
    title=dict(x=0.5, font=dict(size=16)),
    font=dict(size=13)
)

# 設(shè)置動(dòng)畫(huà)速度
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 500

fig.show()

import plotly.express as px
import pandas as pd
import numpy as np

# 創(chuàng)建示例圖表
np.random.seed(42)
df = pd.DataFrame({
    '月份': [f'{i}月' for i in range(1, 13)],
    '銷售額': [120, 135, 148, 162, 175, 190, 210, 225, 240, 230, 215, 250]
})

fig = px.bar(df, x='月份', y='銷售額', 
             title='月度銷售額（可導(dǎo)出為HTML）',
             color='銷售額',
             color_continuous_scale='Blues')

# 導(dǎo)出為 HTML 文件
fig.write_html('月度銷售報(bào)告.html', 
               full_html=True,          # 包含完整的 HTML 頁(yè)面
               include_plotlyjs=True,   # 內(nèi)嵌 Plotly.js 庫(kù)
               auto_open=False)         # 不自動(dòng)打開(kāi)瀏覽器

print("HTML 文件已保存為: 月度銷售報(bào)告.html")
print("用瀏覽器打開(kāi)此文件即可查看交互式圖表")

# 導(dǎo)出為 PNG（需要安裝 kaleido: pip install kaleido）
# fig.write_image('chart.png', width=1200, height=600, scale=2)

# 導(dǎo)出為 PDF
# fig.write_image('chart.pdf')

# 導(dǎo)出為 SVG（矢量圖）
# fig.write_image('chart.svg')

print("提示：導(dǎo)出靜態(tài)圖片需要安裝 kaleido 庫(kù)")
print("安裝命令: pip install kaleido")

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np

np.random.seed(42)

# 創(chuàng)建儀表盤(pán)布局
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('月度銷售趨勢(shì)', '產(chǎn)品線占比', '區(qū)域銷售對(duì)比', '廣告投入-銷售額矩陣'),
    specs=[[{'type': 'xy'}, {'type': 'domain'}],
           [{'type': 'xy'}, {'type': 'xy'}]]
)

# 圖1：趨勢(shì)
months = [f'{i}月' for i in range(1, 13)]
sales = [120, 135, 148, 162, 175, 190, 210, 225, 240, 230, 215, 250]
fig.add_trace(go.Scatter(x=months, y=sales, mode='lines+markers',
                         line=dict(color='#1E88E5', width=3),
                         marker=dict(size=8), fill='tozeroy',
                         fillcolor='rgba(30, 136, 229, 0.1)',
                         name='銷售額'),
              row=1, col=1)

# 圖2：餅圖
fig.add_trace(go.Pie(labels=['電子', '服裝', '食品', '家居'],
                     values=[450, 320, 280, 180],
                     marker=dict(colors=['#1E88E5', '#E53935', '#43A047', '#FB8C00']),
                     textinfo='label+percent+value', hole=0.4),
              row=1, col=2)

# 圖3：柱狀圖
regions = ['華東', '華南', '華北', '西南', '華中', '東北']
reg_sales = [580, 420, 380, 250, 310, 190]
fig.add_trace(go.Bar(x=regions, y=reg_sales,
                     marker=dict(color=['#1E88E5', '#E53935', '#43A047', 
                                       '#FB8C00', '#9C27B0', '#00BCD4'])),
              row=2, col=1)

# 圖4：散點(diǎn)
n = 50
ad = np.random.uniform(10, 80, n)
rev = 2.5 * ad + np.random.normal(0, 20, n)
profit = np.random.uniform(10, 60, n)
fig.add_trace(go.Scatter(x=ad, y=rev, mode='markers',
                         marker=dict(size=profit/3, color=profit, 
                                    colorscale='Viridis', showscale=True,
                                    colorbar=dict(title='利潤(rùn)'))),
              row=2, col=2)

fig.update_layout(
    title=dict(text='銷售數(shù)據(jù)交互式儀表盤(pán)', x=0.5, font=dict(size=20)),
    height=800, showlegend=False, font=dict(size=12)
)

# 導(dǎo)出
fig.write_html('銷售儀表盤(pán).html', full_html=True)
print("儀表盤(pán)已導(dǎo)出為: 銷售儀表盤(pán).html")

fig.show()

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

np.random.seed(42)

fig = make_subplots(
    rows=1, cols=2,
    subplot_titles=('轉(zhuǎn)化率趨勢(shì)對(duì)比', '累計(jì)轉(zhuǎn)化數(shù)對(duì)比')
)

weeks = [f'第{i}周' for i in range(1, 5)]
# A組（對(duì)照組）
conv_a = [3.2, 3.5, 3.8, 4.0]
cum_a = [320, 350, 380, 400]
# B組（實(shí)驗(yàn)組）
conv_b = [3.1, 3.4, 4.0, 4.5]
cum_b = [310, 340, 400, 450]

# 轉(zhuǎn)化率折線
fig.add_trace(go.Scatter(x=weeks, y=conv_a, name='A組（舊版）',
                         mode='lines+markers', line=dict(color='#1E88E5', width=3),
                         marker=dict(size=10)), row=1, col=1)
fig.add_trace(go.Scatter(x=weeks, y=conv_b, name='B組（新版）',
                         mode='lines+markers', line=dict(color='#E53935', width=3, dash='dash'),
                         marker=dict(size=10, symbol='square')), row=1, col=1)

# 累計(jì)轉(zhuǎn)化面積
fig.add_trace(go.Scatter(x=weeks, y=cum_a, name='A組累計(jì)',
                         fill='tozeroy', fillcolor='rgba(30,136,229,0.2)',
                         line=dict(color='#1E88E5')), row=1, col=2)
fig.add_trace(go.Scatter(x=weeks, y=cum_b, name='B組累計(jì)',
                         fill='tozeroy', fillcolor='rgba(229,57,53,0.2)',
                         line=dict(color='#E53935')), row=1, col=2)

fig.update_layout(
    title=dict(text='A/B測(cè)試：新舊版產(chǎn)品對(duì)比', x=0.5, font=dict(size=18)),
    height=450, font=dict(size=13)
)

fig.write_html('AB測(cè)試報(bào)告.html', full_html=True)
print("報(bào)告已導(dǎo)出為: AB測(cè)試報(bào)告.html")

fig.show()