Python実務で必ず使うデータ可視化レシピ106選

pip install numpy pandas matplotlib seaborn plotly scipy networkx \
    japanize-matplotlib folium wordcloud janome squarify circlify matplotlib-venn kaleido

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

HIGHLIGHT = '#1E56A0'

df_sales = pd.DataFrame({
    '商品カテゴリ': ['ノートPC', '周辺機器', 'ソフトウェア', '保守サービス', 'クラウド', '研修'],
    '売上': [1280, 760, 540, 920, 1110, 430],
})

sales_by_category = (
    df_sales.groupby('商品カテゴリ')['売上']
    .sum()
    .sort_values(ascending=False)
)

plt.figure(figsize=(8, 5))
plt.bar(sales_by_category.index, sales_by_category.values, color=HIGHLIGHT)
plt.title('商品カテゴリ別 売上合計')
plt.xlabel('商品カテゴリ')
plt.ylabel('売上（万円）')
plt.ylim(0, sales_by_category.max() * 1.15)
plt.xticks(rotation=30, ha='right')
plt.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

departments = np.array(['法人営業第一部', '法人営業第二部', 'カスタマーサクセス部', 'マーケティング部', 'プロダクト企画部'])
scores = np.array([82, 76, 91, 69, 88])

order = np.argsort(scores)
departments_sorted = departments[order]
scores_sorted = scores[order]

plt.figure(figsize=(8, 5))
plt.barh(departments_sorted, scores_sorted, color='#1E56A0')
plt.title('部門別 顧客満足度スコア')
plt.xlabel('スコア')
plt.xlim(0, 100)
plt.grid(axis='x', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

df_sales = pd.DataFrame({
    '地域': ['関東', '関東', '関東', '関西', '関西', '関西', '中部', '中部', '中部', '九州', '九州', '九州'],
    'チャネル': ['オンライン', '店舗', '電話'] * 4,
    '売上': [520, 430, 210, 410, 390, 180, 360, 310, 160, 300, 260, 140],
})

pivot = df_sales.groupby(['地域', 'チャネル'])['売上'].sum().unstack()

x = np.arange(len(pivot.index))
width = 0.24
colors = ['#1f77b4', '#ff7f0e', '#2ca02c']

plt.figure(figsize=(8, 5))
for i, channel in enumerate(pivot.columns):
    plt.bar(x + (i - 1) * width, pivot[channel], width, label=channel, color=colors[i])

plt.title('地域別・チャネル別 売上')
plt.xlabel('地域')
plt.ylabel('売上（万円）')
plt.xticks(x, pivot.index)
plt.ylim(0, pivot.max().max() * 1.2)
plt.legend(title='チャネル')
plt.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

categories = [
    'エンタープライズ向け保守契約',
    '中堅企業向けクラウド移行',
    '小売向け分析ダッシュボード',
    '製造業向けIoT導入支援',
]
last_year = np.array([82, 64, 48, 55])
this_year = np.array([96, 71, 62, 68])

y = np.arange(len(categories))
bar_height = 0.34

fig, ax = plt.subplots(figsize=(9, 5))
ax.barh(y - bar_height / 2, last_year, height=bar_height,
        color=colors_professional[0], label='前年売上')
ax.barh(y + bar_height / 2, this_year, height=bar_height,
        color=colors_professional[1], label='当年売上')

for values, offset in [(last_year, -bar_height / 2), (this_year, bar_height / 2)]:
    for index, value in enumerate(values):
        ax.text(value + 1.5, index + offset, f'{value}百万円',
                va='center', fontsize=9)

ax.set_yticks(y)
ax.set_yticklabels(categories)
ax.invert_yaxis()
ax.set_xlabel('売上')
ax.set_title('サービス別売上の前年比較')
ax.legend(loc='lower right')
ax.grid(axis='x', linestyle=':', alpha=0.4)
ax.set_xlim(0, 110)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

age_groups = ['20代', '30代', '40代', '50代', '60代']
male = np.array([42, 58, 64, 49, 33])
female = np.array([39, 55, 61, 52, 36])

y = np.arange(len(age_groups))

plt.figure(figsize=(8, 5))
plt.barh(y, -male, color='#1E56A0', label='男性')
plt.barh(y, female, color='#EA580C', label='女性')

max_value = max(male.max(), female.max())
ticks = np.arange(-70, 71, 20)
plt.xticks(ticks, [abs(tick) for tick in ticks])
plt.yticks(y, age_groups)
plt.axvline(0, color='#555555', linewidth=1)
plt.title('年齢層別 顧客構成')
plt.xlabel('人数')
plt.ylabel('年齢層')
plt.xlim(-max_value * 1.25, max_value * 1.25)
plt.legend()
plt.grid(axis='x', alpha=0.25)

plt.tight_layout()
plt.show()

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

df_sales = pd.DataFrame({
    '商品': ['CRM Pro', '分析ダッシュボード', '勤怠クラウド', '請求管理', 'SFA Lite', '契約管理', 'FAQ Bot', '経費精算'],
    '売上': [920, 860, 780, 690, 640, 590, 510, 460],
})

sales_by_product = df_sales.sort_values('売上', ascending=True)

plt.figure(figsize=(8, 5))
plt.hlines(
    y=sales_by_product['商品'],
    xmin=0,
    xmax=sales_by_product['売上'],
    color='#BBBBBB',
    linewidth=2,
)
plt.plot(
    sales_by_product['売上'],
    sales_by_product['商品'],
    'o',
    color='#1E56A0',
    markersize=9,
)

plt.title('商品別 売上ランキング')
plt.xlabel('売上（万円）')
plt.ylabel('商品')
plt.xlim(0, sales_by_product['売上'].max() * 1.15)
plt.grid(axis='x', alpha=0.3)

plt.tight_layout()
plt.show()

import pandas as pd
import plotly.graph_objects as go

df_sales = pd.DataFrame({
    '年': [2022, 2022, 2022, 2022, 2023, 2023, 2023, 2023, 2024, 2024, 2024, 2024, 2025, 2025, 2025, 2025],
    '製品': ['CRM', 'SFA', 'MA', 'BI'] * 4,
    '売上': [420, 380, 300, 260, 460, 410, 360, 320, 510, 430, 420, 390, 560, 470, 455, 430],
})

years = sorted(df_sales['年'].unique())
colors = {
    'CRM': '#1f77b4',
    'SFA': '#ff7f0e',
    'MA': '#2ca02c',
    'BI': '#9467bd',
}

frames = []
for year in years:
    yearly = df_sales[df_sales['年'] == year].sort_values('売上', ascending=True)
    frames.append(
        go.Frame(
            data=[
                go.Bar(
                    x=yearly['売上'],
                    y=yearly['製品'],
                    orientation='h',
                    marker_color=[colors[product] for product in yearly['製品']],
                    text=yearly['売上'],
                    textposition='outside',
                )
            ],
            name=str(year),
        )
    )

initial = df_sales[df_sales['年'] == years[0]].sort_values('売上', ascending=True)

fig = go.Figure(
    data=[
        go.Bar(
            x=initial['売上'],
            y=initial['製品'],
            orientation='h',
            marker_color=[colors[product] for product in initial['製品']],
            text=initial['売上'],
            textposition='outside',
        )
    ],
    frames=frames,
)

fig.update_layout(
    title=dict(text='製品別売上ランキングの推移', font=dict(size=20)),
    xaxis=dict(title='売上（万円）', range=[0, df_sales['売上'].max() * 1.2]),
    yaxis=dict(title='製品'),
    updatemenus=[
        dict(
            type='buttons',
            buttons=[
                dict(
                    label='再生',
                    method='animate',
                    args=[None, dict(frame=dict(duration=900, redraw=True), fromcurrent=True)],
                )
            ],
        )
    ],
    sliders=[
        dict(
            steps=[
                dict(
                    label=str(year),
                    method='animate',
                    args=[[str(year)], dict(frame=dict(duration=0, redraw=True), mode='immediate')],
                )
                for year in years
            ],
            currentvalue=dict(prefix='年: '),
        )
    ],
)

fig.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

df_scores = pd.DataFrame({
    '評価軸': ['品質', '価格', '機能性', 'サポート', '導入しやすさ', '拡張性'],
    '自社製品': [4.5, 3.8, 4.2, 4.6, 4.0, 4.1],
    '競合製品': [4.1, 4.4, 3.9, 3.7, 4.2, 3.8],
})

labels = df_scores['評価軸'].tolist()
angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist()
angles += angles[:1]

plt.figure(figsize=(6, 6))
ax = plt.subplot(111, polar=True)

for column, color in [('自社製品', '#1E56A0'), ('競合製品', '#EA580C')]:
    values = df_scores[column].tolist()
    values += values[:1]
    ax.plot(angles, values, color=color, linewidth=2, label=column)
    ax.fill(angles, values, color=color, alpha=0.15)

ax.set_xticks(angles[:-1])
ax.set_xticklabels(labels)
ax.set_ylim(0, 5)
ax.set_yticks([1, 2, 3, 4, 5])
ax.set_title('製品評価の比較', pad=20)
ax.legend(loc='upper right', bbox_to_anchor=(1.2, 1.1))

plt.tight_layout()
plt.show()

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

df_scores = pd.DataFrame({
    '部門': ['営業部', '開発部', 'サポート部', '管理部', 'マーケティング部'],
    '施策前': [64, 58, 71, 62, 67],
    '施策後': [76, 69, 78, 70, 74],
})

df_scores = df_scores.sort_values('施策後')
y_positions = range(len(df_scores))

plt.figure(figsize=(8, 5))
for y, (_, row) in zip(y_positions, df_scores.iterrows()):
    plt.plot([row['施策前'], row['施策後']], [y, y], color='#BBBBBB', linewidth=2)

plt.scatter(df_scores['施策前'], y_positions, color='#999999', s=80, label='施策前', zorder=3)
plt.scatter(df_scores['施策後'], y_positions, color='#1E56A0', s=80, label='施策後', zorder=3)

plt.yticks(y_positions, df_scores['部門'])
plt.title('部門別 満足度スコアの施策前後比較')
plt.xlabel('満足度スコア')
plt.xlim(50, 85)
plt.legend()
plt.grid(axis='x', alpha=0.3)

plt.tight_layout()
plt.show()

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

df_share = pd.DataFrame({
    '製品': ['CRM', 'SFA', 'MA', 'BI', 'FAQ'],
    '2024年': [28, 24, 19, 17, 12],
    '2025年': [31, 21, 23, 15, 10],
})

plt.figure(figsize=(8, 5))

for _, row in df_share.iterrows():
    x = [0, 1]
    y = [row['2024年'], row['2025年']]
    color = '#1E56A0' if row['2025年'] >= row['2024年'] else '#EA580C'
    plt.plot(x, y, marker='o', color=color, linewidth=2)
    plt.text(-0.04, row['2024年'], f"{row['製品']} {row['2024年']}%", ha='right', va='center')
    plt.text(1.04, row['2025年'], f"{row['製品']} {row['2025年']}%", ha='left', va='center')

plt.xticks([0, 1], ['2024年', '2025年'])
plt.xlim(-0.35, 1.35)
plt.ylim(5, 35)
plt.title('製品別 市場シェアの変化')
plt.ylabel('市場シェア（%）')
plt.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

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

df_rank = pd.DataFrame({
    '四半期': ['Q1', 'Q2', 'Q3', 'Q4'],
    'CRM': [1, 1, 2, 1],
    'SFA': [2, 3, 3, 4],
    'MA': [4, 2, 1, 2],
    'BI': [3, 4, 4, 3],
})

quarters = df_rank['四半期']
products = ['CRM', 'SFA', 'MA', 'BI']
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#9467bd']

plt.figure(figsize=(8, 5))
for product, color in zip(products, colors):
    plt.plot(quarters, df_rank[product], marker='o', linewidth=2, color=color, label=product)

plt.gca().invert_yaxis()
plt.yticks([1, 2, 3, 4], ['1位', '2位', '3位', '4位'])
plt.title('四半期別 製品ランキング推移')
plt.xlabel('四半期')
plt.ylabel('順位')
plt.legend(title='製品', bbox_to_anchor=(1.02, 1), loc='upper left')
plt.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

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

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']

df = pd.DataFrame({
    "商品": ["クラウドCRM", "分析ダッシュボード", "保守サポート", "研修サービス", "導入コンサル"],
    "売上": [3200000, 2500000, 1800000, 1200000, 900000],
}).sort_values("売上", ascending=False)

fig, ax = plt.subplots(figsize=(7, 5))
ax.pie(
    df["売上"],
    labels=df["商品"],
    autopct="%1.1f%%",
    startangle=90,
    counterclock=False,
    colors=colors_professional,
    wedgeprops={"edgecolor": "white", "linewidth": 1.5},
)
ax.set_title("商品別売上構成比")
ax.axis("equal")

plt.tight_layout()
plt.show()

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

df = pd.DataFrame({
    "会員区分": ["法人", "法人", "個人", "個人", "学生", "学生"],
    "満足度": ["満足", "要フォロー", "満足", "要フォロー", "満足", "要フォロー"],
    "会員数": [420, 80, 310, 90, 140, 60],
})

segments = ["法人", "個人", "学生"]
inner_values = [df.loc[df["会員区分"] == s, "会員数"].sum() for s in segments]

outer_labels = [f"{r.会員区分}\n{r.満足度}" for r in df.itertuples()]
outer_values = df["会員数"]

inner_colors = ['#1f77b4', '#ff7f0e', '#2ca02c']
outer_colors = ['#6baed6', '#2171b5', '#fdbf6f', '#e6550d', '#74c476', '#238b45']

fig, ax = plt.subplots(figsize=(7, 6))
ax.pie(
    outer_values,
    radius=1.0,
    labels=outer_labels,
    colors=outer_colors,
    startangle=90,
    counterclock=False,
    wedgeprops={"width": 0.28, "edgecolor": "white"},
)
ax.pie(
    inner_values,
    radius=0.68,
    labels=segments,
    colors=inner_colors,
    startangle=90,
    counterclock=False,
    labeldistance=0.45,
    wedgeprops={"width": 0.28, "edgecolor": "white"},
)
ax.text(0, 0, f"総会員数\n{outer_values.sum():,}人", ha="center", va="center", fontsize=12, fontweight="bold")
ax.set_title("会員区分と満足度の二重ドーナツチャート")
ax.axis("equal")

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import japanize_matplotlib

HIGHLIGHT = '#1E56A0'
GRAYS = ['#999999', '#BBBBBB', '#DDDDDD']

achievement_rate = 72
remaining_rate = 100 - achievement_rate

values = [achievement_rate, remaining_rate, 100]
colors = [HIGHLIGHT, GRAYS[2], 'none']

fig, ax = plt.subplots(figsize=(7, 4.5))
wedges, _ = ax.pie(
    values,
    startangle=180,
    counterclock=False,
    colors=colors,
    wedgeprops=dict(width=0.24, edgecolor='white', linewidth=2)
)

wedges[-1].set_alpha(0)

ax.text(0, 0.10, f'{achievement_rate}%',
        ha='center', va='center', fontsize=34,
        fontweight='bold', color=HIGHLIGHT)
ax.text(0, -0.10, '四半期売上目標 達成率',
        ha='center', va='center', fontsize=12, color='#444444')

ax.text(-1.05, -0.04, '0%', ha='center', va='center',
        fontsize=10, color='#666666')
ax.text(1.05, -0.04, '100%', ha='center', va='center',
        fontsize=10, color='#666666')

ax.set_aspect('equal')
ax.set_xlim(-1.2, 1.2)
ax.set_ylim(-0.18, 1.15)
ax.axis('off')
ax.set_title('売上目標の進捗', pad=12)

plt.tight_layout()
plt.show()

import math
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Wedge
import japanize_matplotlib

HIGHLIGHT = '#1E56A0'
ACCENT = '#EA580C'

score = 78
ranges = [
    (0, 60, '#D9534F', '要注意'),
    (60, 85, '#F0AD4E', '順調'),
    (85, 100, '#3A9D5D', '目標達成'),
]

def value_to_angle(value):
    return 180 - 180 * value / 100

fig, ax = plt.subplots(figsize=(8, 4.8))

for start, end, color, label in ranges:
    theta_start = value_to_angle(start)
    theta_end = value_to_angle(end)
    band = Wedge(
        (0, 0), 1.0, theta_end, theta_start,
        width=0.22, facecolor=color, edgecolor='white', linewidth=2
    )
    ax.add_patch(band)

    label_angle = math.radians(value_to_angle((start + end) / 2))
    ax.text(0.78 * math.cos(label_angle), 0.78 * math.sin(label_angle),
            label, ha='center', va='center', fontsize=10, color='white',
            fontweight='bold')

needle_angle = math.radians(value_to_angle(score))
needle_x = 0.70 * math.cos(needle_angle)
needle_y = 0.70 * math.sin(needle_angle)
ax.plot([0, needle_x], [0, needle_y], color='#222222', linewidth=4,
        solid_capstyle='round')
ax.add_patch(Circle((0, 0), 0.055, color='#222222'))

for tick in [0, 20, 40, 60, 80, 100]:
    angle = math.radians(value_to_angle(tick))
    ax.text(1.12 * math.cos(angle), 1.12 * math.sin(angle),
            str(tick), ha='center', va='center', fontsize=9, color='#555555')

ax.text(0, -0.22, f'顧客満足度スコア {score}',
        ha='center', va='center', fontsize=16,
        fontweight='bold', color=HIGHLIGHT)
ax.set_title('顧客満足度KPIゲージ', pad=14)
ax.set_aspect('equal')
ax.set_xlim(-1.25, 1.25)
ax.set_ylim(-0.35, 1.15)
ax.axis('off')

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(42)
df = pd.DataFrame({
    "月": [f"{m}月" for m in range(1, 13)],
    "問い合わせ件数": rng.integers(80, 180, size=12),
})

angles = np.linspace(0, 2 * np.pi, len(df), endpoint=False)
width = 2 * np.pi / len(df)
colors = plt.cm.Blues(np.linspace(0.35, 0.9, len(df)))

fig, ax = plt.subplots(figsize=(7, 7), subplot_kw={"projection": "polar"})
ax.bar(
    angles,
    df["問い合わせ件数"],
    width=width * 0.92,
    color=colors,
    edgecolor="white",
    linewidth=1,
)

ax.set_theta_offset(np.pi / 2)
ax.set_theta_direction(-1)
ax.set_xticks(angles)
ax.set_xticklabels(df["月"])
ax.set_ylim(0, df["問い合わせ件数"].max() * 1.15)
ax.set_title("月別問い合わせ件数の鶏頭図", pad=20)
ax.grid(alpha=0.3)

plt.tight_layout()
plt.show()

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

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

df = pd.DataFrame({
    "月": ["4月", "5月", "6月", "7月", "8月", "9月"],
    "商品A": [120, 135, 150, 160, 175, 190],
    "商品B": [90, 105, 115, 130, 125, 140],
    "商品C": [70, 80, 85, 95, 110, 120],
    "商品D": [45, 50, 55, 65, 70, 80],
})

products = ["商品A", "商品B", "商品C", "商品D"]
bottom = pd.Series(0, index=df.index)

fig, ax = plt.subplots(figsize=(8, 5))
for product, color in zip(products, colors_professional):
    ax.bar(df["月"], df[product], bottom=bottom, label=product, color=color)
    bottom = bottom + df[product]

ax.set_title("月別・商品別売上高")
ax.set_xlabel("月")
ax.set_ylabel("売上高（万円）")
ax.legend(title="製品")
ax.grid(axis="y", alpha=0.3)

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import PercentFormatter
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

pivot = pd.DataFrame({
    "月": ["4月", "5月", "6月", "7月", "8月", "9月"],
    "商品A": [120, 135, 150, 160, 175, 190],
    "商品B": [90, 105, 115, 130, 125, 140],
    "商品C": [70, 80, 85, 95, 110, 120],
    "商品D": [45, 50, 55, 65, 70, 80],
}).set_index("月")

ratio = pivot.div(pivot.sum(axis=1), axis=0)
bottom = pd.Series(0, index=ratio.index)

fig, ax = plt.subplots(figsize=(8, 5))
for product, color in zip(ratio.columns, colors_professional):
    ax.bar(ratio.index, ratio[product], bottom=bottom, label=product, color=color)
    bottom = bottom + ratio[product]

ax.set_title("月別・商品別売上構成比")
ax.set_xlabel("月")
ax.set_ylabel("構成比")
ax.set_ylim(0, 1)
ax.yaxis.set_major_formatter(PercentFormatter(1.0))
ax.legend(title="製品", bbox_to_anchor=(1.02, 1), loc="upper left")
ax.grid(axis="y", alpha=0.3)

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.ticker import PercentFormatter
import japanize_matplotlib

df = pd.DataFrame({
    "業界": ["製造", "小売", "金融", "医療"],
    "市場規模": [420, 300, 180, 100],
    "自社": [0.34, 0.22, 0.18, 0.15],
    "競合A": [0.28, 0.31, 0.40, 0.26],
    "競合B": [0.38, 0.47, 0.42, 0.59],
})

segments = ["自社", "競合A", "競合B"]
colors = ['#1f77b4', '#ff7f0e', '#999999']
total_market = df["市場規模"].sum()

fig, ax = plt.subplots(figsize=(9, 5))
x = 0
centers = []

for _, row in df.iterrows():
    width = row["市場規模"] / total_market
    y = 0
    centers.append(x + width / 2)

    for segment, color in zip(segments, colors):
        height = row[segment]
        ax.add_patch(Rectangle((x, y), width, height, facecolor=color, edgecolor="white", linewidth=1.2, label=segment if x == 0 else None))
        if width > 0.12 and height > 0.12:
            ax.text(x + width / 2, y + height / 2, f"{segment}\n{height:.0%}", ha="center", va="center", color="white", fontsize=10)
        y += height

    x += width

ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.set_xticks(centers)
ax.set_xticklabels([f"{r.業界}\n{r.市場規模}億円" for r in df.itertuples()])
ax.yaxis.set_major_formatter(PercentFormatter(1.0))
ax.set_title("業界別市場規模と競合シェア")
ax.set_ylabel("業界内シェア")
ax.legend(title="区分", bbox_to_anchor=(1.02, 1), loc="upper left")

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import japanize_matplotlib

df = pd.DataFrame({
    "会員セグメント": ["ロイヤル", "通常", "休眠", "新規"],
    "会員数": [386, 274, 203, 137],
})

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#9467bd']
grid_counts = (df["会員数"] / df["会員数"].sum() * 100).round().astype(int)
grid_counts.iloc[-1] += 100 - grid_counts.sum()

cell_colors = []
for count, color in zip(grid_counts, colors_professional):
    cell_colors.extend([color] * count)

fig, ax = plt.subplots(figsize=(7, 6))
for i, color in enumerate(cell_colors):
    col = i % 10
    row = 9 - i // 10
    ax.add_patch(Rectangle((col, row), 0.92, 0.92, facecolor=color, edgecolor="white", linewidth=1))

handles = [
    Rectangle((0, 0), 1, 1, facecolor=color, label=f"{segment}: {count}%")
    for segment, count, color in zip(df["会員セグメント"], grid_counts, colors_professional)
]
ax.legend(handles=handles, bbox_to_anchor=(1.02, 1), loc="upper left")
ax.set_title("会員セグメント構成比のワッフルチャート")
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
ax.set_aspect("equal")
ax.axis("off")

plt.tight_layout()
plt.show()

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

df = pd.DataFrame({
    "都市": ["東京", "大阪", "名古屋", "福岡"],
    "顧客数": [1230, 940, 720, 480],
})

unit = 100
df["マーク数"] = (df["顧客数"] / unit).round().astype(int)

fig, ax = plt.subplots(figsize=(8, 4.8))
for y, row in enumerate(df.itertuples()):
    xs = list(range(row.マーク数))
    ys = [y] * row.マーク数
    ax.scatter(xs, ys, s=260, color="#1E56A0")
    ax.text(row.マーク数 + 0.4, y, f"{row.顧客数:,}人", va="center", fontsize=10)

ax.set_yticks(range(len(df)))
ax.set_yticklabels(df["都市"])
ax.set_xlim(-0.8, df["マーク数"].max() + 2)
ax.set_ylim(-0.7, len(df) - 0.3)
ax.invert_yaxis()
ax.set_xlabel(f"丸1個 = {unit:,}人（端数は四捨五入）")
ax.set_title("都市別顧客数のピクトグラム")
ax.tick_params(axis="x", bottom=False, labelbottom=False)
for spine in ax.spines.values():
    spine.set_visible(False)

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib_venn import venn2, venn3
import japanize_matplotlib

df = pd.DataFrame({
    "顧客ID": [f"C{i:03d}" for i in range(1, 15)],
    "動画プラン": [True, True, True, True, True, False, False, True, False, True, False, True, False, True],
    "音楽プラン": [True, True, False, False, True, True, True, False, False, True, True, False, True, False],
    "電子書籍": [True, False, True, False, False, True, False, True, True, True, False, False, True, False],
})

video = set(df.loc[df["動画プラン"], "顧客ID"])
music = set(df.loc[df["音楽プラン"], "顧客ID"])
ebook = set(df.loc[df["電子書籍"], "顧客ID"])

fig, axes = plt.subplots(1, 2, figsize=(10, 4.5))

v2 = venn2([video, music], set_labels=("動画プラン", "音楽プラン"), ax=axes[0])
axes[0].set_title("2サービスの重複")

v3 = venn3([video, music, ebook], set_labels=("動画", "音楽", "電子書籍"), ax=axes[1])
axes[1].set_title("3サービスの重複")

for diagram in [v2, v3]:
    for label in diagram.subset_labels:
        if label:
            label.set_fontsize(10)
    for label in diagram.set_labels:
        if label:
            label.set_fontsize(10)

plt.tight_layout()
plt.show()

import pandas as pd
import plotly.graph_objects as go

df = pd.DataFrame({
    "カテゴリ": ["開始", "商品A", "商品B", "商品C", "返品・値引き", "総売上"],
    "金額": [0, 2500000, 1800000, 2200000, -500000, 0],
    "区分": ["absolute", "relative", "relative", "relative", "relative", "total"],
})

running_total = 0
text = []
for amount, measure in zip(df["金額"], df["区分"]):
    if measure == "absolute":
        running_total = amount
        text.append("" if amount == 0 else f"¥{amount:,.0f}")
    elif measure == "relative":
        running_total += amount
        text.append(f"{amount:+,.0f}円")
    else:
        text.append(f"¥{running_total:,.0f}")

fig = go.Figure()
fig.add_trace(go.Waterfall(
    name="売上構成",
    orientation="v",
    measure=df["区分"],
    x=df["カテゴリ"],
    y=df["金額"],
    text=text,
    textposition="outside",
    connector={"line": {"color": "rgb(80, 80, 80)"}},
    increasing={"marker": {"color": "#2ca02c"}},
    decreasing={"marker": {"color": "#d62728"}},
    totals={"marker": {"color": "#1f77b4"}},
))

fig.update_layout(
    title=dict(text="商品別売上構成分析（ウォーターフォール）", font=dict(size=20)),
    xaxis_title="カテゴリ",
    yaxis_title="売上金額（円）",
    template="plotly_white",
    height=600,
    showlegend=False,
)

fig.show()

import plotly.graph_objects as go

stages = ['サイト訪問', '商品一覧閲覧', '商品詳細閲覧', 'カート投入', '購入完了']
values = [12000, 7600, 4200, 1800, 950]

fig = go.Figure(
    go.Funnel(
        y=stages,
        x=values,
        textinfo='value+percent initial',
        marker=dict(color=['#1E56A0', '#2878C8', '#5AA0E6', '#F59E0B', '#EA580C']),
        connector=dict(line=dict(color='#BBBBBB', width=1))
    )
)

fig.update_layout(
    title=dict(text='ECサイト購入ファネル', font=dict(size=22)),
    template='plotly_white',
    height=520
)

fig.show()

import pandas as pd
import plotly.express as px

df_sales_daily = pd.DataFrame({
    'region': ['関東', '関東', '関東', '関西', '関西', '関西', '中部', '中部', '中部', '九州', '九州', '九州'],
    'product': ['ノートPC', 'モニター', '保守サービス'] * 4,
    'sales': [5200000, 3100000, 1800000, 3900000, 2700000, 1600000, 3400000, 2100000, 1300000, 2800000, 1900000, 1100000]
})

region_product_sales = df_sales_daily.groupby(['region', 'product'], as_index=False)['sales'].sum()

fig = px.treemap(
    region_product_sales,
    path=['region', 'product'],
    values='sales',
    title='地域・商品別売上構成（ツリーマップ）',
    color='sales',
    color_continuous_scale='RdYlGn',
    labels={'sales': '売上金額（円）'}
)

fig.update_layout(
    template='plotly_white',
    height=600
)

fig.show()

import pandas as pd
import plotly.express as px

df_sales = pd.DataFrame({
    'region': ['関東', '関東', '関東', '関西', '関西', '関西', '中部', '中部', '九州', '九州'],
    'category': ['ハードウェア', 'ハードウェア', 'サービス', 'ハードウェア', 'ソフトウェア', 'サービス', 'ソフトウェア', 'サービス', 'ハードウェア', 'サービス'],
    'sales': [6200000, 3500000, 2400000, 4300000, 2600000, 1800000, 3100000, 1500000, 2900000, 1200000]
})

fig = px.sunburst(
    df_sales,
    path=['region', 'category'],
    values='sales',
    color='region',
    title='地域・商品カテゴリ別売上構成（サンバースト）',
    labels={'sales': '売上金額（円）'}
)

fig.update_layout(
    template='plotly_white',
    height=600
)

fig.show()

import pandas as pd
import plotly.express as px

df_sales = pd.DataFrame({
    'region': ['関東', '関東', '関東', '関西', '関西', '関西', '中部', '中部', '九州', '九州'],
    'category': ['ハードウェア', 'ハードウェア', 'サービス', 'ハードウェア', 'ソフトウェア', 'サービス', 'ソフトウェア', 'サービス', 'ハードウェア', 'サービス'],
    'sales': [6200000, 3500000, 2400000, 4300000, 2600000, 1800000, 3100000, 1500000, 2900000, 1200000]
})

fig = px.icicle(
    df_sales,
    path=['region', 'category'],
    values='sales',
    color='region',
    title='地域・商品カテゴリ別売上構成（アイシクル）',
    labels={'sales': '売上金額（円）'}
)

fig.update_layout(
    template='plotly_white',
    height=600
)

fig.show()

import matplotlib.pyplot as plt
import pandas as pd
import circlify
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

df_sales = pd.DataFrame({
    'region': ['関東', '関東', '関東', '関西', '関西', '中部', '中部', '九州'],
    'category': ['PC', '周辺機器', '保守', 'PC', 'ソフトウェア', 'PC', '保守', 'ソフトウェア'],
    'sales': [6200000, 3100000, 1800000, 4200000, 2600000, 3300000, 1500000, 2100000]
})

hierarchy = []
for region, group in df_sales.groupby('region'):
    hierarchy.append({
        'id': region,
        'datum': group['sales'].sum(),
        'children': [
            {'id': row['category'], 'datum': row['sales'], 'parent': region}
            for _, row in group.iterrows()
        ]
    })

circles = circlify.circlify(
    hierarchy,
    show_enclosure=False,
    target_enclosure=circlify.Circle(x=0, y=0, r=1)
)

fig, ax = plt.subplots(figsize=(8, 8))
ax.set_title('地域・商品カテゴリ別売上構成（サークルパッキング）', fontsize=16, pad=16)
ax.axis('off')
ax.set_xlim(-1.05, 1.05)
ax.set_ylim(-1.05, 1.05)
ax.set_aspect('equal')

region_color = {item['id']: colors_professional[i] for i, item in enumerate(hierarchy)}

for circle in circles:
    if circle.level == 1:
        color = region_color[circle.ex['id']]
        ax.add_patch(plt.Circle((circle.x, circle.y), circle.r, color=color, alpha=0.18, linewidth=2))
        ax.text(circle.x, circle.y + circle.r * 0.75, circle.ex['id'], ha='center', va='center', fontsize=13, weight='bold')
    elif circle.level == 2:
        color = region_color[circle.ex['parent']]
        ax.add_patch(plt.Circle((circle.x, circle.y), circle.r, color=color, alpha=0.65, linewidth=1))
        ax.text(circle.x, circle.y, circle.ex['id'], ha='center', va='center', fontsize=10, color='white', weight='bold')

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import pandas as pd
import japanize_matplotlib
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.stats import zscore

df_customers = pd.DataFrame({
    'segment': ['法人高単価', '法人高単価', '法人標準', '法人標準', '個人優良', '個人優良', '個人一般', '個人一般'],
    'city': ['東京', '大阪', '東京', '名古屋', '東京', '福岡', '大阪', '福岡'],
    'annual_income': [950, 880, 720, 690, 610, 570, 430, 390],
    'monthly_spend': [48, 44, 32, 29, 27, 24, 16, 14],
    'purchase_count': [32, 29, 24, 21, 20, 18, 12, 10]
})

profile = df_customers.groupby(['segment', 'city']).mean(numeric_only=True)
scaled_profile = profile.apply(zscore)

linked = linkage(scaled_profile, method='ward')

fig, ax = plt.subplots(figsize=(10, 5))
dendrogram(
    linked,
    labels=[f'{segment}・{city}' for segment, city in profile.index],
    leaf_rotation=35,
    leaf_font_size=10,
    ax=ax
)

ax.set_title('顧客グループの類似度（デンドログラム）', fontsize=15)
ax.set_ylabel('距離')
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style='white')
import japanize_matplotlib

df_sales_daily = pd.DataFrame({
    'region': ['関東', '関東', '関東', '関西', '関西', '関西', '中部', '中部', '中部', '九州', '九州', '九州'],
    'category': ['PC', '周辺機器', '保守'] * 4,
    'sales': [5200000, 3100000, 1800000, 3900000, 2700000, 1600000, 3400000, 2100000, 1300000, 2800000, 1900000, 1100000]
})

sales_matrix = df_sales_daily.pivot_table(
    index='region',
    columns='category',
    values='sales',
    aggfunc='mean'
)

g = sns.clustermap(
    sales_matrix,
    cmap='YlGnBu',
    annot=True,
    fmt='.0f',
    linewidths=0.5,
    figsize=(8, 6),
    cbar_kws={'label': '平均売上（円）'}
)

g.fig.suptitle('地域×商品カテゴリの平均売上（クラスターマップ）', y=1.02, fontsize=15)
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import japanize_matplotlib

np.random.seed(42)

df_customers = pd.DataFrame({
    'annual_income': np.random.normal(loc=620, scale=120, size=120).clip(320, 980)
})

fig, ax = plt.subplots(figsize=(8, 5))
ax.hist(
    df_customers['annual_income'],
    bins=14,
    color='#1E56A0',
    edgecolor='white',
    alpha=0.85
)

ax.set_title('顧客年収の分布', fontsize=15)
ax.set_xlabel('年収（万円）')
ax.set_ylabel('顧客数')
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style='whitegrid')
import japanize_matplotlib

rng = np.random.default_rng(42)

regular = rng.normal(loc=5400, scale=850, size=220)
new = rng.normal(loc=4300, scale=700, size=180)
vip = rng.normal(loc=7200, scale=950, size=120)

df = pd.DataFrame({
    '購入単価': np.concatenate([regular, new, vip]).clip(1800, 9800),
    '顧客区分': (
        ['既存顧客'] * len(regular)
        + ['新規顧客'] * len(new)
        + ['VIP顧客'] * len(vip)
    )
})

palette = {
    '既存顧客': '#1f77b4',
    '新規顧客': '#ff7f0e',
    'VIP顧客': '#2ca02c',
}

fig, axes = plt.subplots(1, 2, figsize=(11, 4.8), sharex=True, sharey=True)

for ax, bw in zip(axes, [0.55, 1.35]):
    sns.kdeplot(
        data=df, x='購入単価', hue='顧客区分',
        bw_adjust=bw, common_norm=False, fill=True,
        alpha=0.24, linewidth=2, palette=palette, ax=ax
    )
    ax.set_title(f'bw_adjust = {bw}')
    ax.set_xlabel('購入単価（円）')
    ax.set_ylabel('密度')
    ax.grid(axis='y', linestyle=':', alpha=0.45)

fig.suptitle('顧客区分別の購入単価分布', fontsize=14, fontweight='bold')

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

sns.set_theme(style='whitegrid')
import japanize_matplotlib

np.random.seed(42)

df_customers = pd.DataFrame({
    'annual_income': np.random.normal(loc=620, scale=120, size=140).clip(320, 980)
})
df_customers['monthly_spend'] = (
    df_customers['annual_income'] * 0.045
    + np.random.normal(loc=0, scale=5, size=len(df_customers))
).clip(8, 60)

fig, ax = plt.subplots(figsize=(8, 6))
sns.kdeplot(
    data=df_customers,
    x='annual_income',
    y='monthly_spend',
    fill=True,
    levels=8,
    thresh=0.05,
    cmap='Blues',
    ax=ax
)
sns.scatterplot(
    data=df_customers,
    x='annual_income',
    y='monthly_spend',
    color='#EA580C',
    alpha=0.35,
    s=28,
    ax=ax
)

ax.set_title('年収と月間支出額の2次元KDE', fontsize=15)
ax.set_xlabel('年収（万円）')
ax.set_ylabel('月間支出額（万円）')

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

sns.set_theme(style='white')
import japanize_matplotlib

np.random.seed(42)

cities = ['東京', '大阪', '名古屋', '福岡']
means = {'東京': 690, '大阪': 610, '名古屋': 570, '福岡': 520}

df_customers = pd.DataFrame([
    {'city': city, 'annual_income': income}
    for city in cities
    for income in np.random.normal(loc=means[city], scale=75, size=60).clip(320, 980)
])

g = sns.FacetGrid(
    df_customers,
    row='city',
    hue='city',
    aspect=4,
    height=1.1,
    palette='Set2',
    sharex=True
)

g.map_dataframe(sns.kdeplot, x='annual_income', fill=True, alpha=0.75, linewidth=0)
g.map_dataframe(sns.kdeplot, x='annual_income', color='white', linewidth=1.2)
g.set_titles('{row_name}')
g.set_axis_labels('年収（万円）', '')
g.set(yticks=[], ylabel='')
g.despine(left=True)

g.fig.suptitle('都市別の年収分布（リッジラインプロット）', y=1.03, fontsize=15)
g.fig.subplots_adjust(hspace=-0.25)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

rng = np.random.default_rng(42)
departments = ['営業', '企画', '開発', '管理']
overtime_hours = [
    np.concatenate([rng.normal(28, 8, 60), [62, 70]]),
    rng.normal(18, 5, 45),
    np.concatenate([rng.normal(35, 10, 80), [75]]),
    rng.normal(10, 4, 30),
]
overtime_hours = [np.clip(x, 0, None) for x in overtime_hours]

fig, ax = plt.subplots(figsize=(10, 6))
bp = ax.boxplot(
    overtime_hours,
    tick_labels=departments,
    patch_artist=True,
    flierprops=dict(marker='o', markerfacecolor='#d62728', markersize=7, alpha=0.7),
)
for patch, color in zip(bp['boxes'], colors_professional):
    patch.set_facecolor(color)
    patch.set_alpha(0.7)

ax.set_ylabel('月間残業時間（時間）')
ax.set_title('部署別の月間残業時間の分布', fontsize=15)
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style='whitegrid')
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

rng = np.random.default_rng(42)
segments = ['法人', '個人', '代理店', 'EC']
averages = [82000, 54000, 68000, 47000]
spreads = [12000, 9000, 15000, 8000]

df_spending = pd.DataFrame({
    'セグメント': np.repeat(segments, 80),
    '月間支出': np.concatenate([
        np.clip(rng.normal(avg, spread, 80), 10000, None)
        for avg, spread in zip(averages, spreads)
    ]),
})

fig, ax = plt.subplots(figsize=(10, 6))
sns.violinplot(
    data=df_spending,
    x='セグメント',
    y='月間支出',
    hue='セグメント',
    palette=colors_professional,
    inner='box',
    cut=0,
    linewidth=1.2,
    legend=False,
    ax=ax,
)

ax.set_title('セグメント別月間支出の分布', fontsize=14, fontweight='bold')
ax.set_xlabel('顧客セグメント')
ax.set_ylabel('月間支出（円）')
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style='whitegrid')
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

rng = np.random.default_rng(42)
segments = ['法人', '個人', '代理店', 'EC']
centers = [7.6, 6.8, 7.2, 6.4]

df_satisfaction = pd.DataFrame({
    'セグメント': np.repeat(segments, 70),
    '満足度': np.concatenate([
        np.clip(np.round(rng.normal(center, 1.0, 70), 1), 1, 10)
        for center in centers
    ]),
})

fig, ax = plt.subplots(figsize=(10, 6))
sns.stripplot(
    data=df_satisfaction,
    x='セグメント',
    y='満足度',
    hue='セグメント',
    palette=colors_professional,
    jitter=0.25,
    size=5,
    alpha=0.75,
    edgecolor='white',
    linewidth=0.4,
    legend=False,
    ax=ax,
)

ax.set_title('顧客セグメント別満足度の個票分布', fontsize=14, fontweight='bold')
ax.set_xlabel('顧客セグメント')
ax.set_ylabel('満足度（10点満点）')
ax.set_ylim(0, 10.5)
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style='whitegrid')
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

rng = np.random.default_rng(42)
segments = ['法人', '個人', '代理店', 'EC']
centers = [78, 70, 74, 66]

df_survey = pd.DataFrame({
    'セグメント': np.repeat(segments, 90),
    '推奨度': np.concatenate([
        np.clip(rng.normal(center, 8, 90), 30, 100)
        for center in centers
    ]),
})

df_sample = df_survey.sample(n=200, random_state=42)

fig, ax = plt.subplots(figsize=(10, 6))
sns.swarmplot(
    data=df_sample,
    x='セグメント',
    y='推奨度',
    hue='セグメント',
    palette=colors_professional,
    size=4.5,
    alpha=0.85,
    legend=False,
    ax=ax,
)

ax.set_title('顧客セグメント別推奨度のスウォームプロット', fontsize=14, fontweight='bold')
ax.set_xlabel('顧客セグメント')
ax.set_ylabel('推奨度スコア')
ax.set_ylim(25, 105)
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection

sns.set_theme(style='whitegrid')
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c']

rng = np.random.default_rng(42)
segments = ['法人', '個人', 'EC']
centers = [7.5, 6.8, 7.9]

df_score = pd.DataFrame({
    'セグメント': np.repeat(segments, 70),
    '満足度': np.concatenate([
        np.clip(rng.normal(center, 0.9, 70), 1, 10)
        for center in centers
    ]),
})

fig, ax = plt.subplots(figsize=(10, 6))
sns.violinplot(
    data=df_score,
    x='セグメント',
    y='満足度',
    hue='セグメント',
    palette=colors_professional,
    inner=None,
    cut=0,
    linewidth=1.1,
    density_norm='width',
    legend=False,
    ax=ax,
)

for collection in ax.collections:
    if isinstance(collection, PolyCollection):
        for path in collection.get_paths():
            vertices = path.vertices
            center = (vertices[:, 0].min() + vertices[:, 0].max()) / 2
            vertices[:, 0] = np.maximum(vertices[:, 0], center)

positions = np.arange(len(segments))
box_data = [
    df_score.loc[df_score['セグメント'] == segment, '満足度'].to_numpy()
    for segment in segments
]

ax.boxplot(
    box_data,
    positions=positions - 0.18,
    widths=0.12,
    patch_artist=True,
    showfliers=False,
    manage_ticks=False,
    boxprops=dict(facecolor='white', edgecolor='#333333', linewidth=1.1),
    medianprops=dict(color='#333333', linewidth=1.6),
    whiskerprops=dict(color='#333333', linewidth=1.1),
    capprops=dict(color='#333333', linewidth=1.1),
)

for i, (segment, color) in enumerate(zip(segments, colors_professional)):
    values = df_score.loc[df_score['セグメント'] == segment, '満足度'].to_numpy()
    x = rng.normal(i - 0.35, 0.035, size=len(values))
    ax.scatter(
        x,
        values,
        s=24,
        color=color,
        alpha=0.55,
        edgecolors='white',
        linewidths=0.4,
        zorder=3,
    )

ax.set_xticks(positions)
ax.set_xticklabels(segments)
ax.set_title('セグメント別満足度のレインクラウドプロット', fontsize=14, fontweight='bold')
ax.set_xlabel('顧客セグメント')
ax.set_ylabel('満足度（10点満点）')
ax.set_ylim(1, 10)
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import japanize_matplotlib

rng = np.random.default_rng(42)
dates = pd.date_range('2026-01-01', periods=180, freq='D')
trend = np.linspace(0, 70, len(dates))
weekly = 18 * np.sin(np.arange(len(dates)) * 2 * np.pi / 7)
sales = 240 + trend + weekly + rng.normal(0, 14, len(dates))

df_sales_daily = pd.DataFrame({
    '日付': dates,
    '売上': np.round(sales, 1),
})

fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(
    df_sales_daily['日付'],
    df_sales_daily['売上'],
    color='#1E56A0',
    linewidth=2.2,
    marker='o',
    markersize=3,
    markevery=7,
)

ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m'))
ax.set_title('日次売上の推移', fontsize=14, fontweight='bold')
ax.set_xlabel('日付')
ax.set_ylabel('売上（万円）')
ax.grid(True, alpha=0.3)
ax.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

HIGHLIGHT = '#1E56A0'

rng = np.random.default_rng(42)
weeks = pd.date_range('2026-01-05', periods=24, freq='W-MON')
inventory = 420 + np.cumsum(rng.normal(4, 18, len(weeks)))
inventory = np.clip(inventory, 260, 620)

df_inventory = pd.DataFrame({
    '週': weeks,
    '在庫数': np.round(inventory),
})

fig, ax = plt.subplots(figsize=(12, 6))
ax.plot(
    df_inventory['週'],
    df_inventory['在庫数'],
    color=HIGHLIGHT,
    linewidth=2.4,
)
ax.fill_between(
    df_inventory['週'],
    df_inventory['在庫数'],
    0,
    color=HIGHLIGHT,
    alpha=0.2,
)

ax.set_title('週次在庫水準の面グラフ', fontsize=14, fontweight='bold')
ax.set_xlabel('週')
ax.set_ylabel('在庫数（件）')
ax.grid(True, alpha=0.3)
ax.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#9467bd']

rng = np.random.default_rng(42)
months = pd.date_range('2025-01-01', periods=12, freq='MS')
products = ['クラウド', '保守', '研修', 'ライセンス']

sales_by_product = pd.DataFrame({
    '月': months,
    'クラウド': np.linspace(80, 150, 12) + rng.normal(0, 6, 12),
    '保守': np.linspace(60, 72, 12) + rng.normal(0, 4, 12),
    '研修': np.linspace(30, 48, 12) + rng.normal(0, 5, 12),
    'ライセンス': np.linspace(45, 40, 12) + rng.normal(0, 4, 12),
})

fig, ax = plt.subplots(figsize=(12, 6))
ax.stackplot(
    sales_by_product['月'],
    sales_by_product[products].to_numpy().T,
    labels=products,
    colors=colors_professional,
    alpha=0.85,
)

ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m'))
ax.set_title('製品カテゴリ別月次売上の積み上げ面グラフ', fontsize=14, fontweight='bold')
ax.set_xlabel('月')
ax.set_ylabel('売上（百万円）')
ax.legend(loc='upper left')
ax.grid(axis='y', alpha=0.3)
ax.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import japanize_matplotlib

HIGHLIGHT = '#1E56A0'

rng = np.random.default_rng(0)
n_days = 60
biz_dates = pd.bdate_range('2023-01-02', periods=n_days)

close = 1000 + np.cumsum(rng.normal(0, 15, n_days))
open_ = np.r_[1000, close[:-1]] + rng.normal(0, 8, n_days)
high = np.maximum(open_, close) + np.abs(rng.normal(5, 4, n_days))
low = np.minimum(open_, close) - np.abs(rng.normal(5, 4, n_days))

ohlc = pd.DataFrame({
    'date': biz_dates,
    'open': open_,
    'high': high,
    'low': low,
    'close': close,
})

fig, ax = plt.subplots(figsize=(12, 6))
body_width = 0.6

for i, row in ohlc.iterrows():
    color = HIGHLIGHT if row['close'] >= row['open'] else '#C0392B'
    ax.vlines(i, row['low'], row['high'], color=color, linewidth=1.2, zorder=2)

    body_bottom = min(row['open'], row['close'])
    body_height = abs(row['close'] - row['open'])
    if body_height < 0.8:
        ax.hlines(
            row['close'],
            i - body_width / 2,
            i + body_width / 2,
            color=color,
            linewidth=1.6,
            zorder=3,
        )
    else:
        ax.add_patch(Rectangle(
            (i - body_width / 2, body_bottom),
            body_width,
            body_height,
            facecolor=color,
            edgecolor=color,
            alpha=0.85,
            zorder=3,
        ))

tick_positions = np.arange(0, n_days, 10)
ax.set_xticks(tick_positions)
ax.set_xticklabels(
    ohlc.loc[tick_positions, 'date'].dt.strftime('%m/%d'),
    rotation=45,
    ha='right',
)
ax.set_xlim(-1, n_days)
ax.set_title('株価のローソク足チャート（模擬データ）', fontsize=14, fontweight='bold')
ax.set_xlabel('日付')
ax.set_ylabel('価格（円）')
ax.grid(axis='y', alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

rng = np.random.default_rng(42)
weeks = pd.date_range('2026-01-05', periods=26, freq='W-MON')
products = ['クラウド', '広告', '保守', '研修']

weekly_sales = pd.DataFrame({
    '週': weeks,
    'クラウド': 55 + 18 * np.sin(np.linspace(0, 2.6 * np.pi, 26)) + rng.normal(0, 4, 26),
    '広告': 42 + 12 * np.cos(np.linspace(0, 2.2 * np.pi, 26)) + rng.normal(0, 4, 26),
    '保守': 35 + np.linspace(0, 12, 26) + rng.normal(0, 3, 26),
    '研修': 25 + 10 * np.sin(np.linspace(0.8, 3.4 * np.pi, 26)) + rng.normal(0, 3, 26),
})

weekly_sales[products] = weekly_sales[products].clip(lower=5)

fig, ax = plt.subplots(figsize=(12, 6))
ax.stackplot(
    weekly_sales['週'],
    weekly_sales[products].to_numpy().T,
    labels=products,
    colors=colors_professional,
    baseline='wiggle',
    alpha=0.85,
)

ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m'))
ax.set_title('製品別週次売上のストリームグラフ', fontsize=14, fontweight='bold')
ax.set_xlabel('週')
ax.set_ylabel('週次売上（中心化表示）')
ax.legend(loc='upper left', ncol=2)
ax.grid(axis='x', alpha=0.2)
ax.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="white", font_scale=1.0)
import japanize_matplotlib

df_customers = pd.DataFrame({
    "都市": ["東京", "東京", "東京", "大阪", "大阪", "大阪", "名古屋", "名古屋", "名古屋", "福岡", "福岡", "福岡"],
    "セグメント": ["プレミアム", "スタンダード", "ベーシック"] * 4,
    "購入金額": [16800, 11200, 7800, 14200, 9800, 6900, 13100, 9200, 6400, 12500, 8800, 6100],
})

heatmap_data = df_customers.pivot_table(
    index="都市",
    columns="セグメント",
    values="購入金額",
    aggfunc="mean",
).reindex(
    index=["東京", "大阪", "名古屋", "福岡"],
    columns=["プレミアム", "スタンダード", "ベーシック"],
)

plt.figure(figsize=(8, 5))
ax = sns.heatmap(
    heatmap_data,
    annot=True,
    fmt=",.0f",
    cmap="Blues",
    linewidths=0.5,
    cbar_kws={"label": "平均購入金額（円）"},
)

ax.set_title("都市・セグメント別の平均購入金額")
ax.set_xlabel("セグメント")
ax.set_ylabel("都市")

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import japanize_matplotlib

rng = np.random.default_rng(42)
dates = pd.date_range("2026-04-06", periods=14, freq="D")
weekday_bonus = np.array([18, 6, 0, 10, 32, -12, -20])[dates.weekday]

df_sales_daily = pd.DataFrame({
    "日付": dates,
    "売上（万円）": rng.integers(80, 130, size=len(dates)) + weekday_bonus,
})

df_sales_daily["曜日番号"] = df_sales_daily["日付"].dt.weekday
df_sales_daily["週番号"] = ((df_sales_daily["日付"] - df_sales_daily["日付"].min()).dt.days // 7) + 1

calendar_data = df_sales_daily.pivot_table(
    index="曜日番号",
    columns="週番号",
    values="売上（万円）",
    aggfunc="sum",
).reindex(index=range(7))

cmap = LinearSegmentedColormap.from_list("sales_blue", ["#ffffff", "#1E56A0"])

fig, ax = plt.subplots(figsize=(7, 4.5))
im = ax.imshow(calendar_data.to_numpy(), cmap=cmap, aspect="auto")

ax.set_xticks(np.arange(calendar_data.shape[1]))
ax.set_xticklabels([f"第{week}週" for week in calendar_data.columns])
ax.set_yticks(np.arange(7))
ax.set_yticklabels(["月", "火", "水", "木", "金", "土", "日"])
ax.set_title("日次売上のカレンダーヒートマップ")
ax.set_xlabel("週")
ax.set_ylabel("曜日")

threshold = calendar_data.to_numpy().mean()
for y in range(calendar_data.shape[0]):
    for x in range(calendar_data.shape[1]):
        value = calendar_data.iloc[y, x]
        text_color = "white" if value >= threshold else "#333333"
        ax.text(x, y, f"{value:.0f}", ha="center", va="center", color=text_color, fontsize=10)

plt.colorbar(im, ax=ax, label="売上（万円）")

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="whitegrid")
import japanize_matplotlib

df_customers = pd.DataFrame({
    "年齢": [24, 28, 31, 35, 38, 42, 45, 49, 52, 55, 59, 63],
    "年収（万円）": [360, 410, 470, 520, 580, 640, 690, 730, 780, 820, 880, 930],
})

plt.figure(figsize=(7, 5))
ax = sns.scatterplot(
    data=df_customers,
    x="年齢",
    y="年収（万円）",
    s=90,
    alpha=0.65,
    color="#1E56A0",
    edgecolor="white",
    linewidth=0.8,
)

ax.set_title("顧客の年齢と年収の関係")
ax.set_xlabel("年齢（歳）")
ax.set_ylabel("年収（万円）")

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="whitegrid")
import japanize_matplotlib

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

df_customers = pd.DataFrame({
    "age": [26, 31, 37, 42, 48, 54, 29, 35, 44, 51, 57, 62],
    "income": [420, 560, 740, 810, 920, 980, 390, 510, 650, 700, 760, 830],
    "segment": [
        "ベーシック", "スタンダード", "プレミアム", "プレミアム",
        "プレミアム", "プレミアム", "ベーシック", "スタンダード",
        "スタンダード", "スタンダード", "ベーシック", "ベーシック",
    ],
})

palette = {
    "プレミアム": colors_professional[0],
    "スタンダード": colors_professional[1],
    "ベーシック": colors_professional[2],
}

plt.figure(figsize=(7, 5))
ax = sns.scatterplot(
    data=df_customers,
    x="age",
    y="income",
    hue="segment",
    style="segment",
    palette=palette,
    s=95,
    alpha=0.8,
    edgecolor="white",
    linewidth=0.7,
)

ax.set_title("セグメント別の年齢と年収の関係")
ax.set_xlabel("年齢（歳）")
ax.set_ylabel("年収（万円）")
ax.legend(title="セグメント", frameon=True)

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="whitegrid")
import japanize_matplotlib

HIGHLIGHT = "#1E56A0"

df_customers = pd.DataFrame({
    "年収（万円）": [360, 410, 470, 520, 580, 640, 690, 730, 780, 820, 880, 930, 980, 1040],
    "購入金額（円）": [7200, 8400, 9600, 11200, 12800, 13500, 15100, 16400, 17000, 18800, 20400, 21300, 22600, 24100],
})

g = sns.jointplot(
    data=df_customers,
    x="年収（万円）",
    y="購入金額（円）",
    kind="scatter",
    height=6,
    color=HIGHLIGHT,
    joint_kws={"alpha": 0.65, "s": 70, "edgecolor": "white", "linewidth": 0.6},
    marginal_kws={"bins": 6, "fill": True, "alpha": 0.8},
)

g.ax_joint.set_xlabel("年収（万円）")
g.ax_joint.set_ylabel("購入金額（円）")
g.fig.suptitle("年収と購入金額の関係", y=1.02)

plt.tight_layout()
plt.show()

import pandas as pd
import plotly.graph_objects as go

df_customers_geo = pd.DataFrame({
    "customer_id": [f"C{i:03d}" for i in range(1, 16)],
    "city": ["東京", "東京", "東京", "大阪", "大阪", "大阪", "名古屋", "名古屋", "名古屋", "福岡", "福岡", "福岡", "札幌", "札幌", "仙台"],
    "age": [34, 41, 29, 38, 47, 52, 31, 44, 50, 28, 36, 43, 39, 55, 46],
    "income": [7200000, 8400000, 6100000, 6500000, 7600000, 8100000, 5600000, 6900000, 7300000, 5000000, 6200000, 6600000, 5400000, 6000000, 5800000],
    "spending_score": [82, 88, 74, 76, 80, 72, 68, 73, 70, 64, 71, 69, 58, 63, 66],
    "satisfaction": [4.7, 4.8, 4.3, 4.2, 4.4, 4.0, 3.9, 4.1, 4.0, 3.7, 4.0, 3.8, 3.5, 3.6, 3.9],
})

city_analysis = df_customers_geo.groupby("city").agg({
    "age": "mean",
    "income": "mean",
    "spending_score": "mean",
    "satisfaction": "mean",
    "customer_id": "count",
}).round(1)

city_analysis.columns = ["平均年齢", "平均年収", "平均支出スコア", "平均満足度", "顧客数"]
city_analysis = city_analysis.reset_index()

max_bubble_size = 48
sizeref = city_analysis["顧客数"].max() / max_bubble_size

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=city_analysis["平均年収"],
    y=city_analysis["平均支出スコア"],
    mode="markers+text",
    marker=dict(
        size=city_analysis["顧客数"],
        sizemode="diameter",
        sizeref=sizeref,
        sizemin=14,
        color=city_analysis["平均満足度"],
        colorscale="RdYlGn",
        colorbar=dict(title=dict(text="平均満足度")),
        line=dict(width=2, color="black"),
    ),
    text=city_analysis["city"],
    textposition="middle center",
    customdata=city_analysis[["平均年齢", "顧客数"]],
    hovertemplate=(
        "都市: %{text}<br>"
        "平均年齢: %{customdata[0]}歳<br>"
        "平均年収: ¥%{x:,.0f}<br>"
        "平均支出スコア: %{y}<br>"
        "顧客数: %{customdata[1]}名<br>"
        "平均満足度: %{marker.color}<extra></extra>"
    ),
))

fig.update_layout(
    title=dict(text="都市別顧客分析（バブルチャート）", font=dict(size=20)),
    xaxis=dict(title=dict(text="平均年収（円）"), tickprefix="¥", separatethousands=True),
    yaxis=dict(title=dict(text="平均支出スコア")),
    template="plotly_white",
    height=600,
    showlegend=False,
)

fig.show()

import pandas as pd
import plotly.graph_objects as go

df_customers_geo = pd.DataFrame({
    "customer_id": [f"C{i:03d}" for i in range(1, 13)],
    "age": [26, 31, 37, 42, 48, 54, 29, 35, 44, 51, 57, 62],
    "income": [4200000, 5600000, 7400000, 8100000, 9200000, 9800000, 3900000, 5100000, 6500000, 7000000, 7600000, 8300000],
    "spending_score": [62, 70, 88, 91, 84, 79, 55, 66, 73, 77, 69, 72],
    "satisfaction": [3.4, 3.8, 4.7, 4.8, 4.4, 4.1, 3.2, 3.7, 4.0, 4.2, 3.9, 4.0],
})

numeric_features = ["age", "income", "spending_score", "satisfaction"]
feature_min = df_customers_geo[numeric_features].min()
feature_range = df_customers_geo[numeric_features].max() - feature_min
normalized_data = (df_customers_geo[numeric_features] - feature_min) / feature_range

fig = go.Figure(data=go.Parcoords(
    line=dict(
        color=df_customers_geo["spending_score"],
        colorscale="Viridis",
        showscale=True,
        colorbar=dict(title=dict(text="支出スコア")),
    ),
    dimensions=[
        dict(range=[0, 1], label="年齢", values=normalized_data["age"]),
        dict(range=[0, 1], label="年収", values=normalized_data["income"]),
        dict(range=[0, 1], label="支出スコア", values=normalized_data["spending_score"]),
        dict(range=[0, 1], label="満足度", values=normalized_data["satisfaction"]),
    ],
))

fig.update_layout(
    title=dict(text="顧客特性の多次元分析（平行座標プロット）", font=dict(size=20)),
    template="plotly_white",
    height=600,
)

fig.show()

import pandas as pd
import plotly.graph_objects as go

df_customers_geo = pd.DataFrame({
    "customer_id": [f"C{i:03d}" for i in range(1, 13)],
    "age": [26, 31, 37, 42, 48, 54, 29, 35, 44, 51, 57, 62],
    "income": [4200000, 5600000, 7400000, 8100000, 9200000, 9800000, 3900000, 5100000, 6500000, 7000000, 7600000, 8300000],
    "spending_score": [62, 70, 88, 91, 84, 79, 55, 66, 73, 77, 69, 72],
    "satisfaction": [3.4, 3.8, 4.7, 4.8, 4.4, 4.1, 3.2, 3.7, 4.0, 4.2, 3.9, 4.0],
})

fig = go.Figure(data=[go.Scatter3d(
    x=df_customers_geo["age"],
    y=df_customers_geo["income"],
    z=df_customers_geo["spending_score"],
    mode="markers",
    marker=dict(
        size=df_customers_geo["satisfaction"] * 3,
        color=df_customers_geo["satisfaction"],
        colorscale="Viridis",
        opacity=0.82,
        colorbar=dict(title=dict(text="満足度")),
    ),
    text=df_customers_geo["customer_id"],
    hovertemplate=(
        "顧客ID: %{text}<br>"
        "年齢: %{x}歳<br>"
        "年収: ¥%{y:,.0f}<br>"
        "支出スコア: %{z}<br>"
        "満足度: %{marker.color}<extra></extra>"
    ),
)])

fig.update_layout(
    title=dict(text="3D顧客分析：年齢 × 年収 × 支出スコア", font=dict(size=20)),
    scene=dict(
        xaxis=dict(title=dict(text="年齢（歳）")),
        yaxis=dict(title=dict(text="年収（円）"), tickprefix="¥"),
        zaxis=dict(title=dict(text="支出スコア")),
        camera=dict(eye=dict(x=1.2, y=1.2, z=1.2)),
    ),
    template="plotly_white",
    height=700,
)

fig.show()

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

rng = np.random.default_rng(42)

age_axis = np.linspace(25, 65, 8)
income_axis = np.linspace(3500000, 9500000, 8)
age_grid, income_grid = np.meshgrid(age_axis, income_axis)

purchase_grid = (
    18000
    + (age_grid - 25) * 650
    + (income_grid - 3500000) / 1000000 * 9000
    + 4000 * np.sin((age_grid - 25) / 40 * np.pi)
    + rng.normal(0, 1800, size=age_grid.shape)
)

df_customers = pd.DataFrame({
    "age": age_grid.ravel(),
    "income": income_grid.ravel(),
    "purchase_amount": np.round(purchase_grid.ravel(), -2),
})

df_customers["age_bin"] = pd.cut(df_customers["age"], bins=8, include_lowest=True)
df_customers["income_bin"] = pd.cut(df_customers["income"], bins=8, include_lowest=True)

surface_data = df_customers.pivot_table(
    index="income_bin",
    columns="age_bin",
    values="purchase_amount",
    aggfunc="mean",
    observed=True,
).sort_index().sort_index(axis=1)

age_centers = [interval.mid for interval in surface_data.columns]
income_centers = [interval.mid for interval in surface_data.index]

fig = go.Figure(data=[go.Surface(
    x=age_centers,
    y=income_centers,
    z=surface_data.to_numpy(),
    colorscale="Viridis",
    colorbar=dict(title=dict(text="平均購入金額")),
    hovertemplate=(
        "年齢: %{x:.0f}歳<br>"
        "年収: ¥%{y:,.0f}<br>"
        "平均購入金額: ¥%{z:,.0f}<extra></extra>"
    ),
)])

fig.update_layout(
    title=dict(text="年齢・年収別の平均購入金額（3Dサーフェス）", font=dict(size=20)),
    scene=dict(
        xaxis=dict(title=dict(text="年齢（歳）")),
        yaxis=dict(title=dict(text="年収（円）"), tickprefix="¥"),
        zaxis=dict(title=dict(text="平均購入金額（円）"), tickprefix="¥"),
        camera=dict(eye=dict(x=1.4, y=1.4, z=0.9)),
    ),
    template="plotly_white",
    height=700,
)

fig.show()

import pandas as pd
import folium

df_geo = pd.DataFrame({
    "拠点名": ["東京本社", "大阪支店", "名古屋支店", "福岡営業所", "札幌営業所", "仙台営業所"],
    "都道府県": ["東京都", "大阪府", "愛知県", "福岡県", "北海道", "宮城県"],
    "lat": [35.6812, 34.7025, 35.1709, 33.5902, 43.0687, 38.2602],
    "lon": [139.7671, 135.4959, 136.8815, 130.4017, 141.3508, 140.8824],
    "月間売上（万円）": [1280, 920, 760, 610, 430, 520],
})

m = folium.Map(location=[36.2, 138.2], zoom_start=5, tiles="OpenStreetMap")

for _, row in df_geo.iterrows():
    popup_html = (
        f"{row['拠点名']}<br>"
        f"{row['都道府県']}<br>"
        f"月間売上: {row['月間売上（万円）']:,}万円"
    )
    folium.Marker(
        location=[row["lat"], row["lon"]],
        popup=folium.Popup(popup_html, max_width=240),
        tooltip=row["拠点名"],
        icon=folium.Icon(color="blue", icon="info-sign"),
    ).add_to(m)

m.save('map_marker.html')

import pandas as pd
import folium

df_geo = pd.DataFrame({
    'pref': ['東京都', '大阪府', '愛知県', '福岡県', '宮城県'],
    'lat': [35.6812, 34.6937, 35.0116, 33.5902, 38.2682],
    'lon': [139.7671, 135.7681, 135.7681, 130.4017, 140.8694],
    'sales': [12800, 9600, 8200, 6100, 4300],
    'stores': [42, 35, 28, 22, 16],
})

m = folium.Map(location=[36.5, 138.0], zoom_start=5, tiles='OpenStreetMap')

for _, row in df_geo.iterrows():
    popup_html = (
        f"<b>{row['pref']}</b><br>"
        f"売上: {row['sales']:,}万円<br>"
        f"店舗数: {row['stores']}店"
    )
    folium.Marker(
        location=[row['lat'], row['lon']],
        popup=folium.Popup(popup_html, max_width=220),
        tooltip=f"{row['pref']}（クリックで詳細）",
    ).add_to(m)

m.save('map_popup.html')

import numpy as np
import pandas as pd
import folium
from folium.plugins import MarkerCluster

rng = np.random.default_rng(42)

centers = pd.DataFrame({
    'area': ['新宿', '渋谷', '横浜', '大宮'],
    'center_lat': [35.6909, 35.6580, 35.4658, 35.9062],
    'center_lon': [139.7003, 139.7016, 139.6223, 139.6236],
    'count': [8, 7, 6, 5],
})

records = []
for _, center in centers.iterrows():
    for _ in range(int(center['count'])):
        records.append({
            'customer_id': f"C{len(records) + 1:03d}",
            'area': center['area'],
            'latitude': center['center_lat'] + rng.normal(0, 0.025),
            'longitude': center['center_lon'] + rng.normal(0, 0.025),
            'satisfaction': round(rng.uniform(3.2, 5.0), 1),
        })

df_customers_geo = pd.DataFrame(records)

m = folium.Map(location=[35.68, 139.65], zoom_start=8, tiles='OpenStreetMap')
cluster = MarkerCluster(name='顧客プロット').add_to(m)

for _, row in df_customers_geo.iterrows():
    folium.Marker(
        location=[row['latitude'], row['longitude']],
        popup=f"顧客ID: {row['customer_id']} / 満足度: {row['satisfaction']:.1f}",
        tooltip=row['area'],
    ).add_to(cluster)

m.save('map_cluster.html')

import pandas as pd
import folium

geojson_url = 'https://raw.githubusercontent.com/dataofjapan/land/master/japan.geojson'

df_geo = pd.DataFrame({
    'pref': ['北海道', '宮城県', '東京都', '神奈川県', '愛知県', '大阪府', '京都府', '広島県', '福岡県', '沖縄県'],
    'sales': [7200, 4300, 12800, 9000, 8200, 9600, 5100, 4700, 6100, 2900],
})

m = folium.Map(location=[36.5, 138.0], zoom_start=5, tiles='cartodbpositron')

folium.Choropleth(
    geo_data=geojson_url,
    data=df_geo,
    columns=['pref', 'sales'],
    key_on='feature.properties.nam_ja',
    fill_color='YlGnBu',
    fill_opacity=0.75,
    line_opacity=0.4,
    nan_fill_color='white',
    legend_name='売上（万円）',
).add_to(m)

m.save('map_choropleth.html')

import numpy as np
import pandas as pd
import folium
from folium.plugins import HeatMap

rng = np.random.default_rng(42)

centers = pd.DataFrame({
    'area': ['丸の内', '新宿', '品川'],
    'center_lat': [35.6812, 35.6909, 35.6285],
    'center_lon': [139.7671, 139.7003, 139.7388],
    'count': [8, 10, 6],
})

records = []
for _, center in centers.iterrows():
    for _ in range(int(center['count'])):
        records.append({
            'area': center['area'],
            'latitude': center['center_lat'] + rng.normal(0, 0.018),
            'longitude': center['center_lon'] + rng.normal(0, 0.018),
            'satisfaction': round(rng.uniform(0.4, 1.0), 2),
        })

df_customers_geo = pd.DataFrame(records)

m = folium.Map(location=[35.68, 139.65], zoom_start=8, tiles='cartodbpositron')

heat_data = (
    df_customers_geo[['latitude', 'longitude', 'satisfaction']]
    .dropna()
    .values
    .tolist()
)
HeatMap(heat_data, radius=15, blur=18, max_zoom=10, min_opacity=0.25).add_to(m)

m.save('map_heat.html')

import numpy as np
import pandas as pd
import folium
import branca.colormap as cm

ACCENT = '#EA580C'

df_geo = pd.DataFrame({
    'pref': ['北海道', '宮城県', '東京都', '神奈川県', '愛知県', '大阪府', '福岡県', '沖縄県'],
    'lat': [43.0642, 38.2682, 35.6812, 35.4478, 35.1709, 34.6937, 33.5902, 26.2124],
    'lon': [141.3469, 140.8694, 139.7671, 139.6425, 136.8815, 135.5023, 130.4017, 127.6792],
    'sales': [7200, 4300, 12800, 9000, 8200, 9600, 6100, 2900],
    'growth': [5.2, 6.8, 8.4, 7.1, 4.9, 6.2, 9.3, 11.5],
})

m = folium.Map(location=[36.5, 138.0], zoom_start=5, tiles='cartodbpositron')

growth_min, growth_max = df_geo['growth'].min(), df_geo['growth'].max()
growth_colormap = cm.LinearColormap(
    colors=['#FFEDA0', ACCENT],
    vmin=growth_min,
    vmax=growth_max,
    caption='成長率（%）',
)
growth_colormap.add_to(m)

sqrt_sales = np.sqrt(df_geo['sales'].astype(float))
sales_min, sales_max = sqrt_sales.min(), sqrt_sales.max()
sales_span = sales_max - sales_min
min_radius, max_radius = 5, 24

for _, row in df_geo.iterrows():
    if sales_span == 0:
        radius = (min_radius + max_radius) / 2
    else:
        sales_ratio = (np.sqrt(row['sales']) - sales_min) / sales_span
        radius = min_radius + sales_ratio * (max_radius - min_radius)

    color = growth_colormap(row['growth'])

    folium.CircleMarker(
        location=[row['lat'], row['lon']],
        radius=radius,
        color='#555555',
        weight=1,
        fill=True,
        fill_color=color,
        fill_opacity=0.75,
        popup=f"{row['pref']}: 売上{row['sales']:,.0f}万円 / 成長率{row['growth']:.1f}%",
        tooltip=f"{row['pref']}（売上{row['sales']:,.0f}万円）",
    ).add_to(m)

m.save('map_bubble.html')

import pandas as pd
import plotly.graph_objects as go

flow_data = pd.DataFrame({
    'source': ['認知', '認知', '興味', '興味', '検討', '検討', '購入', '購入'],
    'target': ['興味', '離脱', '検討', '離脱', '購入', '離脱', '継続', '離脱'],
    'value': [1000, 500, 700, 300, 400, 300, 300, 100],
})

nodes = list(dict.fromkeys(flow_data['source'].tolist() + flow_data['target'].tolist()))
node_indices = {node: i for i, node in enumerate(nodes)}

fig = go.Figure(data=[go.Sankey(
    node=dict(
        pad=15,
        thickness=20,
        line=dict(color='black', width=0.5),
        label=nodes,
        color='#1f77b4',
    ),
    link=dict(
        source=[node_indices[src] for src in flow_data['source']],
        target=[node_indices[tgt] for tgt in flow_data['target']],
        value=flow_data['value'],
        color='rgba(0,116,217,0.4)',
    ),
)])

fig.update_layout(
    title=dict(text='顧客行動フロー分析', font=dict(size=18)),
    font=dict(size=12),
    template='plotly_white',
    height=600,
)

fig.show()

import pandas as pd
import plotly.express as px

projects = pd.DataFrame([
    dict(Task='データ収集', Start='2024-01-01', Finish='2024-01-15', Resource='チームA'),
    dict(Task='データクリーニング', Start='2024-01-10', Finish='2024-01-25', Resource='チームB'),
    dict(Task='探索的分析', Start='2024-01-20', Finish='2024-02-05', Resource='チームA'),
    dict(Task='モデル構築', Start='2024-02-01', Finish='2024-02-20', Resource='チームC'),
    dict(Task='可視化作成', Start='2024-02-15', Finish='2024-03-01', Resource='チームB'),
    dict(Task='レポート作成', Start='2024-02-25', Finish='2024-03-10', Resource='チームA'),
])

fig = px.timeline(
    projects,
    x_start='Start',
    x_end='Finish',
    y='Task',
    color='Resource',
    color_discrete_sequence=['#1f77b4', '#ff7f0e', '#2ca02c'],
)

fig.update_yaxes(autorange='reversed')
fig.update_layout(
    title=dict(text='データサイエンスプロジェクト スケジュール', font=dict(size=18)),
    template='plotly_white',
    height=500,
)

fig.show()

import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib
import networkx as nx

nodes = pd.DataFrame({
    'department': ['営業', 'マーケティング', '商品企画', 'データ分析', 'カスタマーサポート', '物流'],
    'members': [18, 12, 10, 8, 15, 11],
})

edges = pd.DataFrame({
    'source': ['営業', '営業', 'マーケティング', '商品企画', 'データ分析', 'カスタマーサポート', '物流'],
    'target': ['マーケティング', 'カスタマーサポート', '商品企画', 'データ分析', '営業', '商品企画', '営業'],
    'projects': [5, 4, 3, 4, 2, 3, 2],
})

G = nx.Graph()
for _, row in nodes.iterrows():
    G.add_node(row['department'], members=row['members'])

for _, row in edges.iterrows():
    G.add_edge(row['source'], row['target'], projects=row['projects'])

pos = nx.spring_layout(G, seed=42, k=0.8)
node_sizes = [G.nodes[node]['members'] * 120 for node in G.nodes]
edge_widths = [G.edges[edge]['projects'] * 0.8 for edge in G.edges]

fig, ax = plt.subplots(figsize=(8, 6))

nx.draw_networkx_edges(
    G,
    pos,
    ax=ax,
    width=edge_widths,
    edge_color='#999999',
    alpha=0.7,
)

nx.draw_networkx_nodes(
    G,
    pos,
    ax=ax,
    node_size=node_sizes,
    node_color='#1E56A0',
    edgecolors='white',
    linewidths=1.5,
    alpha=0.9,
)

nx.draw_networkx_labels(
    G,
    pos,
    ax=ax,
    font_size=11,
    font_weight='bold',
    font_color='white',
)

edge_labels = nx.get_edge_attributes(G, 'projects')
nx.draw_networkx_edge_labels(
    G,
    pos,
    edge_labels=edge_labels,
    ax=ax,
    font_size=9,
    font_color='#555555',
)

ax.set_title('部署間の連携ネットワーク', fontweight='bold')
ax.axis('off')

plt.tight_layout()
plt.show()

import math
import matplotlib.pyplot as plt
from matplotlib.patches import PathPatch, Wedge
from matplotlib.path import Path
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

departments = ['営業', '開発', 'マーケ', 'CS', '管理', '海外']
flows = [
    ('営業', 'CS', 18),
    ('営業', 'マーケ', 12),
    ('開発', '海外', 16),
    ('マーケ', '営業', 10),
    ('CS', '開発', 8),
    ('海外', '営業', 14),
    ('管理', '開発', 6),
    ('CS', '管理', 5),
]

node_count = len(departments)
angles = {
    name: 90 - index * 360 / node_count
    for index, name in enumerate(departments)
}
color_map = {
    name: colors_professional[index]
    for index, name in enumerate(departments)
}

fig, ax = plt.subplots(figsize=(7.5, 7.5))

for name in departments:
    angle = angles[name]
    node = Wedge(
        (0, 0), 1.0, angle - 22, angle + 22,
        width=0.10, facecolor=color_map[name],
        edgecolor='white', linewidth=2
    )
    ax.add_patch(node)

    rad = math.radians(angle)
    label_x = 1.18 * math.cos(rad)
    label_y = 1.18 * math.sin(rad)
    ha = 'left' if label_x > 0.05 else 'right' if label_x < -0.05 else 'center'
    ax.text(label_x, label_y, name, ha=ha, va='center',
            fontsize=12, fontweight='bold')

for source, target, value in flows:
    start_angle = math.radians(angles[source])
    end_angle = math.radians(angles[target])
    start = (0.88 * math.cos(start_angle), 0.88 * math.sin(start_angle))
    end = (0.88 * math.cos(end_angle), 0.88 * math.sin(end_angle))
    control1 = (0.28 * math.cos(start_angle), 0.28 * math.sin(start_angle))
    control2 = (0.28 * math.cos(end_angle), 0.28 * math.sin(end_angle))

    path = Path(
        [start, control1, control2, end],
        [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4]
    )
    ribbon = PathPatch(
        path, facecolor='none', edgecolor=color_map[source],
        linewidth=1.2 + value * 0.35, alpha=0.42,
        capstyle='round'
    )
    ax.add_patch(ribbon)

ax.set_title('部門間の人材異動フロー', pad=18)
ax.set_aspect('equal')
ax.set_xlim(-1.35, 1.35)
ax.set_ylim(-1.25, 1.30)
ax.axis('off')

plt.tight_layout()
plt.show()

from pathlib import Path

import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib
from janome.tokenizer import Tokenizer
from wordcloud import WordCloud

df_reviews = pd.DataFrame({
    'review': [
        '管理画面が見やすく、売上レポートの確認がとても簡単になりました。',
        '検索機能の反応が速く、顧客対応の時間を短縮できています。',
        'スマートフォンでも操作しやすい一方で、通知設定は少し分かりにくいです。',
        '請求データの連携が安定しており、月次作業のミスが減りました。',
        'ダッシュボードのグラフが分かりやすく、部門会議で共有しやすいです。',
        '初期設定に時間はかかりましたが、サポート対応が丁寧でした。',
        '顧客情報の更新が簡単で、営業チーム全体の確認作業が楽になりました。',
        'レポート出力の種類が多く、経営層への報告資料を作りやすいです。',
    ],
})

text = '。'.join(df_reviews['review'])
tokenizer = Tokenizer()
stopwords = {'こと', 'ため', 'よう', 'ところ', 'こちら', 'それ', 'これ', 'さん', 'です', 'ます'}

words = [
    token.surface
    for token in tokenizer.tokenize(text)
    if token.part_of_speech.split(',')[0] in {'名詞', '形容詞'}
    and len(token.surface) > 1
    and token.surface not in stopwords
]

font_candidates = [
    Path(japanize_matplotlib.__file__).with_name('fonts') / 'ipaexg.ttf',
    Path('C:/Windows/Fonts/meiryo.ttc'),
    Path('C:/Windows/Fonts/YuGothM.ttc'),
    Path('C:/Windows/Fonts/msgothic.ttc'),
    Path('/System/Library/Fonts/ヒラギノ角ゴシック W3.ttc'),
    Path('/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc'),
    Path('/usr/share/fonts/truetype/noto/NotoSansCJK-Regular.ttc'),
]

font_path = next((str(path) for path in font_candidates if path.exists()), None)
if font_path is None:
    raise FileNotFoundError('日本語表示に使えるフォントが見つかりません。font_candidatesに利用可能なフォントパスを追加してください。')

wordcloud = WordCloud(
    font_path=font_path,
    width=900,
    height=500,
    background_color='white',
    colormap='tab10',
    max_words=80,
    random_state=42,
).generate(' '.join(words))

fig, ax = plt.subplots(figsize=(9, 5))
ax.imshow(wordcloud, interpolation='bilinear')
ax.set_title('顧客レビュー頻出語ワードクラウド', fontweight='bold')
ax.axis('off')

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

x = np.linspace(0, 10, 100)
y = np.sin(x) * np.exp(-x / 5)

fig, ax = plt.subplots(figsize=(8, 5))

ax.plot(x, y, linewidth=2, color=colors_professional[0])
ax.axhline(0, color='#999999', linewidth=1, linestyle='--')
ax.set_xlim(0, 10)
ax.set_ylim(-0.5, 1)
ax.set_xlabel('経過時間（月）')
ax.set_ylabel('反応指数')
ax.set_title('基本範囲設定', fontweight='bold')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

weeks = np.arange(0, 11, 1)
sales_index = weeks**2

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, ax = plt.subplots(figsize=(8, 5))

ax.plot(weeks, sales_index, 'o-', linewidth=2, color=colors_professional[0])
ax.set_xticks(weeks[::2])
ax.set_yticks(np.arange(0, 101, 20))
ax.set_xlabel('キャンペーン開始後の週数')
ax.set_ylabel('売上指数')
ax.set_title('基本ティック間隔', fontweight='bold')
ax.grid(True)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

days = np.linspace(0, 10, 16)
conversion_rate = 8 + 1.2 * np.sin(days)

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, ax = plt.subplots(figsize=(8, 5))

ax.plot(days, conversion_rate, linewidth=2, color=colors_professional[0])
ax.tick_params(
    axis='both',
    which='major',
    labelsize=12,
    length=8,
    width=2,
    colors='red'
)
ax.set_xlabel('集計日')
ax.set_ylabel('購入率（%）')
ax.set_title('ティックサイズ・カラー', fontweight='bold')
ax.grid(True)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(68)
weeks = np.arange(1, 17)
weekly_sales = 120 + 8 * np.sin(weeks / 2) + rng.normal(0, 3, len(weeks))

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, ax = plt.subplots(figsize=(8, 5))

ax.plot(weeks, weekly_sales, 'o-', linewidth=2, color=colors_professional[0])
ax.grid(True)
ax.set_xlabel('週')
ax.set_ylabel('売上（万円）')
ax.set_title('基本グリッド', fontweight='bold')

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

weeks = np.linspace(0, 10, 16)
response_index = 100 + 18 * np.sin(weeks) * np.exp(-weeks / 8)

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, ax = plt.subplots(figsize=(8, 5))

ax.plot(weeks, response_index, linewidth=3, color=colors_professional[0])
ax.grid(True, linestyle='--', linewidth=1, alpha=0.7, color='gray')
ax.set_xlabel('施策開始後の週数')
ax.set_ylabel('問い合わせ指数')
ax.set_title('カスタムグリッドスタイル', fontweight='bold')

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(70)
sample_size = 18
datasets = [
    (rng.normal(520, 45, sample_size), '店舗別月商（正規分布）'),
    (rng.exponential(1.8, sample_size) * 10, '初回購入までの日数（指数分布）'),
    (rng.uniform(80, 160, sample_size), '支店別客単価（一様分布）'),
    (rng.gamma(2, 15, sample_size), '案件単価（ガンマ分布）'),
    (rng.beta(2, 5, sample_size) * 100, '満足度スコア（ベータ分布）'),
    (rng.lognormal(3.5, 0.35, sample_size), '広告反応数（対数正規分布）')
]

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(2, 3, figsize=(18, 12))
fig.suptitle('サブプロット配置システム', fontsize=16, fontweight='bold')

for i, (data, title) in enumerate(datasets):
    row, col = i // 3, i % 3
    ax = axes[row, col]

    ax.hist(data, bins=8, alpha=0.7, color=colors_professional[i % len(colors_professional)])
    ax.set_title(title, fontweight='bold')
    ax.set_xlabel('値')
    ax.set_ylabel('件数')
    ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(71)
months = np.arange(1, 13)
main_sales = 100 + np.linspace(0, 14, len(months)) + 8 * np.sin(months / 2)
departments = np.array(['営業', '開発', 'CS', '管理', 'マーケ'])
department_values = np.array([23, 45, 56, 78, 32])
detail_series = [
    80 + rng.normal(1.0, 2.0, len(months)).cumsum(),
    60 + rng.normal(0.7, 1.8, len(months)).cumsum(),
    90 + rng.normal(0.5, 2.5, len(months)).cumsum(),
    70 + rng.normal(0.9, 1.6, len(months)).cumsum()
]

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig = plt.figure(figsize=(16, 12))
gs = fig.add_gridspec(4, 4, hspace=0.3, wspace=0.3)

ax_main = fig.add_subplot(gs[0:2, 0:2])
ax_main.plot(months, main_sales, linewidth=3, color=colors_professional[0])
ax_main.set_title('メイン分析', fontsize=14, fontweight='bold')
ax_main.set_xlabel('月')
ax_main.set_ylabel('売上指数')
ax_main.grid(True, alpha=0.3)

ax_side = fig.add_subplot(gs[0:2, 2:4])
ax_side.barh(departments, department_values, color=colors_professional[1])
ax_side.set_title('サイド分析', fontsize=14, fontweight='bold')
ax_side.set_xlabel('対応件数')

for i in range(2):
    for j in range(2):
        ax = fig.add_subplot(gs[2 + i, j * 2:(j + 1) * 2])
        data = detail_series[i * 2 + j]
        ax.plot(months, data, linewidth=2, color=colors_professional[2 + i * 2 + j])
        ax.set_title(f'詳細分析 {i + 1}-{j + 1}', fontweight='bold')
        ax.set_xlabel('月')
        ax.grid(True, alpha=0.3)

plt.suptitle('GridSpec高度レイアウトシステム', fontsize=16, fontweight='bold')
plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(72)
dates = pd.date_range('2025-01-01', periods=12, freq='MS')
base_trend = np.linspace(0, 18, len(dates)) + rng.normal(0, 1.5, len(dates)).cumsum()

indicators = {
    '売上高': 100 + base_trend + rng.normal(0, 3, len(dates)),
    '利益': 80 + base_trend * 0.8 + rng.normal(0, 2, len(dates)),
    '顧客数': 90 + base_trend * 1.1 + rng.normal(0, 4, len(dates)),
    '商品数': 60 + base_trend * 0.6 + rng.normal(0, 2, len(dates)),
    '満足度': 75 + 5 * np.sin(np.arange(len(dates)) * 0.6) + rng.normal(0, 1, len(dates)),
    '継続率': 82 + 4 * np.sin(np.arange(len(dates)) * 0.4) + rng.normal(0, 1, len(dates))
}

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(3, 2, figsize=(14, 12), sharex=True, sharey='col')
fig.suptitle('軸共有システム', fontsize=16, fontweight='bold')

left_indicators = ['売上高', '利益', '顧客数']
for i, indicator in enumerate(left_indicators):
    ax = axes[i, 0]
    ax.plot(dates, indicators[indicator], linewidth=2, color=colors_professional[i], label=indicator)
    ax.set_title(indicator, fontweight='bold')
    ax.grid(True, alpha=0.3)
    if i == len(left_indicators) - 1:
        ax.set_xlabel('日付')

right_indicators = ['商品数', '満足度', '継続率']
for i, indicator in enumerate(right_indicators):
    ax = axes[i, 1]
    ax.plot(dates, indicators[indicator], linewidth=2, color=colors_professional[i + 3], label=indicator)
    ax.set_title(indicator, fontweight='bold')
    ax.grid(True, alpha=0.3)
    if i == len(right_indicators) - 1:
        ax.set_xlabel('日付')

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(73)
months = np.arange(1, 13)
series = [
    100 + rng.normal(1.0, 2.0, len(months)).cumsum(),
    120 + rng.normal(0.8, 2.5, len(months)).cumsum(),
    90 + rng.normal(1.2, 1.8, len(months)).cumsum(),
    110 + rng.normal(0.6, 2.2, len(months)).cumsum()
]

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig = plt.figure(figsize=(16, 10))

plt.subplot(2, 2, 1)
plt.plot(months, series[0], linewidth=2, color=colors_professional[0])
plt.title('調整前：重複するタイトルとラベル', fontweight='bold')
plt.xlabel('非常に長いX軸ラベルの例：月次集計期間')
plt.ylabel('非常に長いY軸ラベルの例：売上指数（基準月=100）')

plt.subplot(2, 2, 2)
plt.plot(months, series[1], linewidth=2, color=colors_professional[1])
plt.title('tight_layout適用後', fontweight='bold')
plt.xlabel('月')
plt.ylabel('売上指数')

plt.subplot(2, 2, 3)
plt.plot(months, series[2], linewidth=2, color=colors_professional[2])
plt.title('手動調整：カスタム余白', fontweight='bold')
plt.xlabel('月')
plt.ylabel('利益指数')

plt.subplot(2, 2, 4)
plt.plot(months, series[3], linewidth=2, color=colors_professional[3])
plt.title('最適化完了', fontweight='bold')
plt.xlabel('月')
plt.ylabel('顧客指数')

plt.suptitle('余白調整システム比較', fontsize=16, fontweight='bold')
plt.subplots_adjust(top=0.93, hspace=0.3, wspace=0.3)
plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

months = np.arange(1, 13)
series = {
    '売上指数': 100 + 12 * np.sin(months / 2),
    '利益指数': 90 + 10 * np.cos(months / 2),
    '広告反応': 80 + 8 * np.sin(months),
    '解約抑制': 70 + 14 * np.exp(-months / 8) * np.sin(months / 2)
}

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(3, 3, figsize=(18, 14))
fig.suptitle('凡例制御システム', fontsize=16, fontweight='bold')

legend_positions = [
    ('upper right', '右上'),
    ('upper left', '左上'),
    ('lower right', '右下'),
    ('lower left', '左下'),
    ('center', '中央'),
    ('upper center', '上部中央'),
    ('lower center', '下部中央'),
    ('center left', '左中央'),
    ('center right', '右中央')
]

for i, (pos, pos_jp) in enumerate(legend_positions):
    row, col = i // 3, i % 3
    ax = axes[row, col]

    for j, (series_name, values) in enumerate(series.items()):
        ax.plot(
            months,
            values,
            linewidth=2,
            color=colors_professional[j % len(colors_professional)],
            label=series_name
        )

    ax.legend(loc=pos, fontsize=8)
    ax.set_title(f'凡例位置: {pos_jp}', fontweight='bold')
    ax.set_xlabel('月')
    ax.set_ylabel('指数')
    ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

dates = pd.date_range('2025-01-01', periods=12, freq='MS')
sales = np.array([100, 120, 115, 140, 160, 180, 200, 190, 220, 240, 260, 280])
profit_rate = np.array([15, 18, 12, 20, 22, 25, 23, 19, 28, 30, 32, 35])
inventory = np.array([500, 480, 520, 490, 460, 440, 420, 450, 430, 410, 400, 380])
orders = np.array([45, 52, 48, 58, 62, 68, 72, 69, 76, 82, 85, 88])

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(1, 2, figsize=(16, 6))
fig.suptitle('双軸グラフシステム', fontsize=16, fontweight='bold')

ax1 = axes[0]
ax2 = ax1.twinx()

ax1.bar(dates, sales, alpha=0.7, color=colors_professional[0], label='売上（百万円）')
ax1.set_ylabel('売上（百万円）', color=colors_professional[0], fontweight='bold')
ax1.tick_params(axis='y', labelcolor=colors_professional[0])

ax2.plot(dates, profit_rate, color=colors_professional[1], linewidth=3, marker='o', markersize=6, label='利益率（%）')
ax2.set_ylabel('利益率（%）', color=colors_professional[1], fontweight='bold')
ax2.tick_params(axis='y', labelcolor=colors_professional[1])

ax1.set_title('売上と利益率の推移', fontweight='bold')
ax1.grid(True, alpha=0.3)
ax1.tick_params(axis='x', rotation=45)

handles1, labels1 = ax1.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(handles1 + handles2, labels1 + labels2, loc='upper left')

ax3 = axes[1]
ax4 = ax3.twinx()

ax3.plot(dates, inventory, color=colors_professional[2], linewidth=3, marker='s', markersize=6, label='在庫数')
ax3.set_ylabel('在庫数（個）', color=colors_professional[2], fontweight='bold')
ax3.tick_params(axis='y', labelcolor=colors_professional[2])

ax4.plot(dates, orders, color=colors_professional[3], linewidth=3, marker='^', markersize=6, label='受注数')
ax4.set_ylabel('受注数（件）', color=colors_professional[3], fontweight='bold')
ax4.tick_params(axis='y', labelcolor=colors_professional[3])

ax3.set_title('在庫と受注のバランス', fontweight='bold')
ax3.grid(True, alpha=0.3)
ax3.tick_params(axis='x', rotation=45)

handles3, labels3 = ax3.get_legend_handles_labels()
handles4, labels4 = ax4.get_legend_handles_labels()
ax3.legend(handles3 + handles4, labels3 + labels4, loc='upper right')

plt.tight_layout()
plt.show()

import pandas as pd
import plotly.graph_objects as go

df_sales_daily = pd.DataFrame({
    "date": [
        "2026-04-01", "2026-04-01", "2026-04-02", "2026-04-02",
        "2026-04-03", "2026-04-03", "2026-04-04", "2026-04-04",
        "2026-04-05", "2026-04-05", "2026-04-06", "2026-04-06",
        "2026-04-07", "2026-04-07", "2026-04-08", "2026-04-08",
    ],
    "customer_id": [
        "C001", "C002", "C001", "C003", "C004", "C005", "C002", "C002",
        "C006", "C007", "C003", "C008", "C009", "C010", "C001", "C011",
    ],
    "sales": [
        180000, 220000, 260000, 190000, 310000, 240000, 210000, 95000,
        330000, 280000, 250000, 360000, 410000, 270000, 300000, 390000,
    ],
})
df_sales_daily["date"] = pd.to_datetime(df_sales_daily["date"])

daily_metrics = df_sales_daily.groupby("date").agg({
    "sales": "sum",
    "customer_id": "nunique",
}).reset_index()

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=daily_metrics["date"],
    y=daily_metrics["sales"],
    mode="lines",
    name="売上金額",
    line=dict(color="blue", width=2),
    yaxis="y",
))

fig.add_trace(go.Scatter(
    x=daily_metrics["date"],
    y=daily_metrics["customer_id"],
    mode="lines",
    name="ユニーク顧客数",
    line=dict(color="red", width=2, dash="dash"),
    yaxis="y2",
))

fig.update_layout(
    title=dict(text="売上とユニーク顧客数の推移"),
    xaxis_title="日付",
    yaxis=dict(
        title=dict(text="売上金額（円）", font=dict(color="blue")),
        tickfont=dict(color="blue"),
        side="left",
    ),
    yaxis2=dict(
        title=dict(text="ユニーク顧客数", font=dict(color="red")),
        tickfont=dict(color="red"),
        anchor="x",
        overlaying="y",
        side="right",
    ),
    template="plotly_white",
    height=600,
)

print("実務POINT: 異なるスケールの指標を一つのグラフで表示できます")
fig.show()

import pandas as pd
import plotly.graph_objects as go

df_sales_daily = pd.DataFrame({
    "date": [
        "2026-04-01", "2026-04-01", "2026-04-01",
        "2026-04-02", "2026-04-02", "2026-04-02",
        "2026-04-03", "2026-04-03", "2026-04-03",
        "2026-04-04", "2026-04-04", "2026-04-04",
        "2026-04-05", "2026-04-05", "2026-04-05",
    ],
    "customer_id": [
        "C001", "C002", "C003", "C001", "C004", "C005", "C002", "C006",
        "C007", "C003", "C008", "C009", "C010", "C011", "C010",
    ],
    "sales": [
        180000, 220000, 150000, 260000, 190000, 240000, 310000, 280000,
        210000, 250000, 360000, 320000, 410000, 390000, 120000,
    ],
})
df_sales_daily["date"] = pd.to_datetime(df_sales_daily["date"])

daily_metrics = df_sales_daily.groupby("date").agg(
    total_sales=("sales", "sum"),
    customers=("customer_id", "nunique"),
    avg_sales=("sales", "mean"),
).reset_index()

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=daily_metrics["date"],
    y=daily_metrics["total_sales"],
    mode="lines",
    name="総売上",
    line=dict(color="blue", width=3),
    yaxis="y",
))

fig.add_trace(go.Scatter(
    x=daily_metrics["date"],
    y=daily_metrics["customers"],
    mode="lines",
    name="顧客数",
    line=dict(color="red", width=2),
    yaxis="y2",
))

fig.add_trace(go.Scatter(
    x=daily_metrics["date"],
    y=daily_metrics["avg_sales"],
    mode="lines",
    name="平均売上",
    line=dict(color="green", width=2, dash="dash"),
    yaxis="y3",
))

fig.update_layout(
    title=dict(text="売上・顧客・効率性の統合分析（3軸表示）"),
    xaxis=dict(title="日付", domain=[0.12, 0.88]),
    yaxis=dict(
        title=dict(text="総売上（円）", font=dict(color="blue")),
        tickfont=dict(color="blue"),
        side="left",
    ),
    yaxis2=dict(
        title=dict(text="顧客数", font=dict(color="red")),
        tickfont=dict(color="red"),
        anchor="free",
        overlaying="y",
        side="right",
        position=0.94,
    ),
    yaxis3=dict(
        title=dict(text="平均売上（円）", font=dict(color="green")),
        tickfont=dict(color="green"),
        anchor="free",
        overlaying="y",
        side="left",
        position=0.06,
    ),
    template="plotly_white",
    height=600,
    margin=dict(l=110, r=110),
)

print("実務POINT: 3つの異なる指標を同時に比較できます")
fig.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

fig, axes = plt.subplots(3, 4, figsize=(20, 14))
fig.suptitle("カラーマップ制御システム", fontsize=16, fontweight="bold")

rng = np.random.default_rng(42)
sales_gap_matrix = rng.normal(loc=0, scale=1, size=(20, 20))

colormaps = [
    ("viridis", "デフォルト（知覚均等）"),
    ("plasma", "プラズマ（高輝度）"),
    ("RdYlBu_r", "温冷（発散型）"),
    ("coolwarm", "寒暖（発散型）"),
    ("Blues", "単色グラデーション"),
    ("RdBu_r", "赤青（対称）"),
    ("seismic", "地震波（対称）"),
    ("hot", "温度（知覚的）"),
    ("gray", "グレースケール"),
    ("Set3", "カテゴリ用"),
    ("tab10", "タブ形式"),
    ("rainbow", "レインボー"),
]

for i, (cmap_name, description) in enumerate(colormaps):
    row, col = i // 4, i % 4
    ax = axes[row, col]

    im = ax.imshow(sales_gap_matrix, cmap=cmap_name, aspect="auto")
    ax.set_title(f"{cmap_name}\n{description}", fontsize=10, fontweight="bold")
    ax.set_xticks([])
    ax.set_yticks([])

    cbar = plt.colorbar(im, ax=ax, shrink=0.8)
    cbar.ax.tick_params(labelsize=8)

print("ビジネス用途別カラーマップ推奨:")
print("・財務データ: 'RdYlGn'（赤から黄から緑）")
print("・品質管理: 'RdBu_r'（赤から青）")
print("・売上分析: 'viridis'（紫から青から緑から黄）")
print("・リスク分析: 'coolwarm'（青から白から赤）")
print("・一般レポート: 'Blues' または 'Greys'")

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import japanize_matplotlib

rng = np.random.default_rng(42)

fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle("カスタムカラーパレット作成システム", fontsize=16, fontweight="bold")

ax = axes[0, 0]
brand_colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7"]
categories = ["広告", "展示会", "Web", "紹介", "店舗"]
values = [25, 30, 35, 20, 40]

bars = ax.bar(categories, values, color=brand_colors)
ax.set_title("企業ブランドカラー", fontweight="bold")
ax.set_ylabel("売上構成比（%）")

for bar, color in zip(bars, brand_colors):
    height = bar.get_height()
    ax.text(
        bar.get_x() + bar.get_width() / 2,
        height + 1,
        color,
        ha="center",
        va="bottom",
        fontsize=8,
        rotation=45,
    )

ax = axes[0, 1]
colors_gradient = ["#FF6B6B", "#FFE66D", "#4ECDC4", "#45B7D1"]
custom_cmap = LinearSegmentedColormap.from_list("custom", colors_gradient, N=10)

data_heatmap = rng.normal(loc=100, scale=12, size=(8, 8))
im = ax.imshow(data_heatmap, cmap=custom_cmap, aspect="auto")
ax.set_title("カスタムグラデーション", fontweight="bold")
ax.set_xlabel("週")
ax.set_ylabel("店舗")
plt.colorbar(im, ax=ax, shrink=0.8)

ax = axes[1, 0]
finance_colors = {
    "株式": "#2E8B57",
    "債券": "#4682B4",
    "不動産": "#CD853F",
    "商品": "#DAA520",
    "現金": "#708090",
}

asset_classes = list(finance_colors.keys())
allocations = [40, 30, 15, 10, 5]
colors_finance = list(finance_colors.values())

ax.pie(
    allocations,
    labels=asset_classes,
    colors=colors_finance,
    autopct="%1.1f%%",
    startangle=90,
)
ax.set_title("金融業界標準カラー", fontweight="bold")

ax = axes[1, 1]
barrier_free_colors = [
    "#E69F00", "#56B4E9", "#009E73", "#F0E442",
    "#0072B2", "#D55E00", "#CC79A7", "#999999",
]

months = np.arange(1, 11)
performance_data = rng.normal(loc=1.0, scale=0.4, size=(8, 10)).cumsum(axis=1)
for i in range(8):
    ax.plot(
        months,
        performance_data[i],
        linewidth=2,
        color=barrier_free_colors[i],
        label=f"部門{i + 1}",
    )

ax.set_title("色覚バリアフリー対応", fontweight="bold")
ax.set_xlabel("月")
ax.set_ylabel("累積成長率")
ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
ax.grid(True, alpha=0.3)

print("推奨カラーパレット:")
print("企業ブランド:", brand_colors)
print("金融業界:", list(finance_colors.values()))
print("バリアフリー:", barrier_free_colors[:5])

plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="whitegrid")
import japanize_matplotlib

df_customers = pd.DataFrame({
    "city": [
        "東京", "東京", "大阪", "大阪", "名古屋", "名古屋",
        "福岡", "福岡", "東京", "大阪", "名古屋", "福岡",
    ],
    "spending": [
        128000, 142000, 98000, 105000, 87000, 92000,
        76000, 83000, 136000, 101000, 95000, 79000,
    ],
})

custom_palette = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#FFA07A"]
sns.set_palette(custom_palette)

plt.figure(figsize=(10, 6))
sns.barplot(
    data=df_customers,
    x="city",
    y="spending",
    hue="city",
    palette=custom_palette,
    errorbar=None,
    legend=False,
)
plt.title("カスタムブランドカラー適用", fontweight="bold")
plt.xlabel("都市")
plt.ylabel("平均支出（円）")
plt.xticks(rotation=45)

print("実務POINT: クライアントのブランドカラーに合わせた可視化")

plt.tight_layout()
plt.show()

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

custom_colors = [
    "#003f5c", "#2f4b7c", "#665191", "#a05195",
    "#d45087", "#f95d6a", "#ff7c43", "#ffa600",
]

df_sales_daily = pd.DataFrame({
    "region": [
        "東日本", "東日本", "東日本", "東日本",
        "西日本", "西日本", "西日本", "西日本",
        "九州", "九州", "九州", "九州",
    ],
    "product": [
        "ノートPC", "モニター", "周辺機器", "保守契約",
        "ノートPC", "モニター", "周辺機器", "保守契約",
        "ノートPC", "モニター", "周辺機器", "保守契約",
    ],
    "sales": [
        520000, 310000, 180000, 260000,
        460000, 280000, 220000, 240000,
        390000, 210000, 160000, 190000,
    ],
})

region_product_matrix = df_sales_daily.pivot_table(
    values="sales",
    index="region",
    columns="product",
    aggfunc="sum",
).fillna(0)

product_sales = df_sales_daily.groupby("product")["sales"].sum().sort_values(ascending=True)

fig = make_subplots(
    rows=1,
    cols=2,
    subplot_titles=("地域×商品売上マトリックス（企業カラー）", "商品別売上（企業ブランドカラー）"),
    horizontal_spacing=0.16,
)

fig.add_trace(
    go.Heatmap(
        z=region_product_matrix.values,
        x=region_product_matrix.columns,
        y=region_product_matrix.index,
        colorscale=[
            [0.0, "#003f5c"],
            [0.2, "#2f4b7c"],
            [0.4, "#665191"],
            [0.6, "#a05195"],
            [0.8, "#d45087"],
            [1.0, "#f95d6a"],
        ],
        hovertemplate="地域: %{y}<br>商品: %{x}<br>売上: %{z:,.0f}円<extra></extra>",
        colorbar=dict(title=dict(text="売上金額（円）"), x=0.46),
    ),
    row=1,
    col=1,
)

fig.add_trace(
    go.Bar(
        y=product_sales.index,
        x=product_sales.values,
        orientation="h",
        marker=dict(
            color=custom_colors[:len(product_sales)],
            line=dict(color="white", width=1),
        ),
        text=[f"{x:,.0f}円" for x in product_sales.values],
        textposition="outside",
    ),
    row=1,
    col=2,
)

fig.update_layout(
    title=dict(text="企業カラーで統一した売上可視化"),
    template="plotly_white",
    height=520,
    showlegend=False,
)

fig.update_xaxes(title_text="商品", row=1, col=1)
fig.update_yaxes(title_text="地域", row=1, col=1)
fig.update_xaxes(title_text="売上金額（円）", row=1, col=2)
fig.update_yaxes(title_text="商品", row=1, col=2)

print("実務POINT: 企業ブランドに合わせたカスタムカラーで統一感を演出できます")
fig.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle("線種・マーカー制御システム", fontsize=16, fontweight="bold")

months = np.arange(1, 13)
base_y = 100 + np.linspace(0, 18, len(months)) + 8 * np.sin(months / 2)

ax = axes[0, 0]
line_styles = ["-", "--", "-.", ":"]
line_names = ["実線", "破線", "一点鎖線", "点線"]

for i, (style, name) in enumerate(zip(line_styles, line_names)):
    y = base_y + i * 12
    ax.plot(months, y, linestyle=style, linewidth=3, color=colors_professional[i], label=name)

ax.legend()
ax.set_title("基本線種パターン", fontweight="bold")
ax.set_xlabel("月")
ax.set_ylabel("売上指数")
ax.grid(True, alpha=0.3)

ax = axes[0, 1]
marker_styles = ["o", "s", "^", "D", "v", "<"]
marker_names = ["丸", "四角", "上三角", "ダイヤ", "下三角", "左三角"]

for i, (style, name) in enumerate(zip(marker_styles, marker_names)):
    y = base_y * (1 + i * 0.03)
    ax.plot(
        months[::2],
        y[::2],
        marker=style,
        markersize=8,
        linewidth=2,
        color=colors_professional[i % len(colors_professional)],
        label=name,
    )

ax.legend(fontsize=9)
ax.set_title("マーカースタイル", fontweight="bold")
ax.set_xlabel("月")
ax.set_ylabel("売上指数")
ax.grid(True, alpha=0.3)

ax = axes[1, 0]
widths = [1, 2, 3, 4]
sizes = [4, 6, 8, 10]

for i, (width, size) in enumerate(zip(widths, sizes)):
    y = base_y + i * 10
    ax.plot(
        months,
        y,
        linewidth=width,
        marker="o",
        markersize=size,
        color=colors_professional[i],
        label=f"幅{width}, サイズ{size}",
    )

ax.legend()
ax.set_title("線幅・マーカーサイズ", fontweight="bold")
ax.set_xlabel("月")
ax.set_ylabel("売上指数")
ax.grid(True, alpha=0.3)

ax = axes[1, 1]
business_styles = [
    ("実績", "-", "o", 3, 8, colors_professional[0]),
    ("予測", "--", "s", 2, 6, colors_professional[1]),
    ("目標", "-.", "^", 3, 8, colors_professional[2]),
    ("ベンチマーク", ":", "D", 2, 6, colors_professional[3]),
]

for i, (label, linestyle, marker, linewidth, markersize, color) in enumerate(business_styles):
    y = base_y + i * 8 + 4 * np.sin(months / 3 + i)
    ax.plot(
        months,
        y,
        linestyle=linestyle,
        marker=marker,
        linewidth=linewidth,
        markersize=markersize,
        color=color,
        label=label,
        alpha=0.8,
    )

ax.legend()
ax.set_title("ビジネス用途別スタイル", fontweight="bold")
ax.set_xlabel("月")
ax.set_ylabel("売上指数")
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]
rng = np.random.default_rng(42)

fig, axes = plt.subplots(1, 3, figsize=(18, 6))
fig.suptitle("透明度制御システム", fontsize=16, fontweight="bold")

ax = axes[0]
n_groups = 5
n_points = 120

for i in range(n_groups):
    x = rng.normal(80 + i * 18, 7, n_points)
    y = rng.normal(100 + i * 14, 10, n_points)
    ax.scatter(
        x,
        y,
        alpha=0.6,
        s=50,
        color=colors_professional[i % len(colors_professional)],
        label=f"店舗群{i + 1}",
    )

ax.legend()
ax.set_title("重複データの可視化", fontweight="bold")
ax.set_xlabel("広告費（万円）")
ax.set_ylabel("売上（万円）")
ax.grid(True, alpha=0.3)

ax = axes[1]
x_conf = np.linspace(1, 12, 100)
y_main = 100 + 4 * x_conf + 8 * np.sin(x_conf / 2)
y_upper = y_main + 8
y_lower = y_main - 8

ax.plot(x_conf, y_main, linewidth=3, color=colors_professional[0], label="主要トレンド")
ax.fill_between(x_conf, y_lower, y_upper, alpha=0.3, color=colors_professional[0], label="信頼区間")

x_obs = np.arange(1, 13)
y_obs = 100 + 4 * x_obs + 8 * np.sin(x_obs / 2) + rng.normal(0, 5, len(x_obs))
ax.scatter(x_obs, y_obs, alpha=0.7, s=50, color=colors_professional[1], label="観測値")

ax.legend()
ax.set_title("信頼区間付きトレンド", fontweight="bold")
ax.set_xlabel("月")
ax.set_ylabel("売上指数")
ax.grid(True, alpha=0.3)

ax = axes[2]
categories = ["A", "B", "C", "D", "E"]
values_primary = [100, 80, 60, 40, 20]
values_secondary = [80, 70, 50, 35, 15]
alphas = [1.0, 0.8, 0.6, 0.4, 0.2]

bars1 = ax.bar(categories, values_primary, color=colors_professional[0], label="主要指標")
bars2 = ax.bar(categories, values_secondary, color=colors_professional[1], label="副次指標")

for bar, alpha in zip(bars1, alphas):
    bar.set_alpha(alpha)

for bar, alpha in zip(bars2, alphas):
    bar.set_alpha(alpha)

ax.legend()
ax.set_title("重要度別透明度設定", fontweight="bold")
ax.set_xlabel("施策")
ax.set_ylabel("値")

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

fig, ax = plt.subplots(figsize=(8, 5))

months = np.arange(1, 13)
sales_index = 100 + np.linspace(0, 20, len(months)) + 10 * np.sin(months / 2)

fig.patch.set_facecolor("#0B1220")
ax.set_facecolor("#0B1220")

ax.plot(months, sales_index, linewidth=3, color="white", marker="o")
ax.fill_between(months, sales_index - 6, sales_index + 6, color="white", alpha=0.18)

ax.set_title("ダーク背景", fontweight="bold", color="white")
ax.set_xlabel("月", color="white")
ax.set_ylabel("売上指数", color="white")
ax.tick_params(colors="white")
ax.grid(True, color="white", alpha=0.18)

for spine in ax.spines.values():
    spine.set_color("white")
    spine.set_alpha(0.5)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

available_styles = plt.style.available
print("利用可能なスタイル:")
for style in available_styles[:10]:
    print(f"  - {style}")

styles_comparison = ["default", "seaborn-v0_8", "ggplot", "bmh", "classic"]

months = np.arange(1, 13)
sales_index = 100 + np.linspace(0, 18, len(months)) + 8 * np.sin(months / 2)
customer_index = 92 + np.linspace(0, 14, len(months)) + 6 * np.cos(months / 2)
campaign_points = sales_index - customer_index

fig = plt.figure(figsize=(20, 10))
fig.suptitle("スタイルテーマ比較システム", fontsize=16, fontweight="bold")

for i, style_name in enumerate(styles_comparison):
    style_to_use = style_name if style_name in available_styles else "default"
    with plt.style.context(style_to_use):
        ax = fig.add_subplot(2, 4, i + 1)
        ax.plot(months, sales_index, linewidth=2, label="売上指数")
        ax.plot(months, customer_index, linewidth=2, label="顧客指数")
        ax.scatter(months[::2], campaign_points[::2] + 100, s=50, alpha=0.7, label="施策反応")
        ax.set_title(f"スタイル: {style_name}", fontweight="bold")
        ax.set_xlabel("月")
        ax.legend()
        ax.grid(True, alpha=0.3)

business_data = {
    "Q1": 100,
    "Q2": 120,
    "Q3": 95,
    "Q4": 140,
}
quarters = list(business_data.keys())
values = list(business_data.values())

with plt.style.context("classic"):
    ax = fig.add_subplot(2, 4, 6)
    ax.bar(quarters, values, color=["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"], alpha=0.8)
    ax.set_title("保守的・フォーマル\n（金融・官公庁）", fontweight="bold")
    ax.set_ylabel("売上（百万円）")
    ax.grid(True, alpha=0.3)

with plt.style.context("seaborn-v0_8" if "seaborn-v0_8" in available_styles else "default"):
    ax = fig.add_subplot(2, 4, 7)
    ax.bar(quarters, values, color=colors_professional[:4], alpha=0.8)
    ax.set_title("モダン・洗練\n（IT・コンサル）", fontweight="bold")
    ax.set_ylabel("売上（百万円）")

with plt.style.context("ggplot" if "ggplot" in available_styles else "default"):
    ax = fig.add_subplot(2, 4, 8)
    ax.bar(quarters, values, color=["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4"], alpha=0.8)
    ax.set_title("カジュアル・親しみやすい\n（小売・サービス）", fontweight="bold")
    ax.set_ylabel("売上（百万円）")

print("ビジネス用途別スタイル推奨:")
print("・金融・官公庁: 'classic' - 保守的で信頼感")
print("・IT・コンサル: 'seaborn-v0_8' - モダンで洗練")
print("・小売・サービス: 'ggplot' - 親しみやすく明るい")
print("・学術・研究: 'bmh' - 清潔で読みやすい")

plt.tight_layout()
plt.show()

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

df_customers = pd.DataFrame({
    'カテゴリ': ['小規模顧客', '中規模顧客', '大規模顧客'],
    '売上指数': [20, 35, 25]
})

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, ax = plt.subplots(figsize=(8, 5))

bars = ax.bar(
    df_customers['カテゴリ'],
    df_customers['売上指数'],
    color=colors_professional[:len(df_customers)],
    alpha=0.8
)

ax.set_title('顧客規模別の売上指数', fontsize=16, fontweight='bold')
ax.set_xlabel('カテゴリ', fontsize=14, fontweight='bold')
ax.set_ylabel('売上指数', fontsize=14, fontweight='bold')
ax.tick_params(labelsize=12)

for bar in bars:
    height = bar.get_height()
    ax.text(
        bar.get_x() + bar.get_width() / 2,
        height + 1,
        f'{height:.0f}',
        ha='center',
        va='bottom',
        fontsize=12
    )

plt.tight_layout()
plt.show()

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

df_fonts = pd.DataFrame({
    'font_family': ['serif', 'sans-serif', 'monospace', 'cursive', 'fantasy'],
    '説明': [
        'Times New Roman風（セリフ）',
        'Arial風（サンセリフ）',
        'Courier風（等幅）',
        '筆記体風',
        '装飾フォント風'
    ],
    '表示例': [
        '営業レポート 123',
        '売上ダッシュボード 123',
        '在庫コード A-123',
        'ブランド見出し 123',
        'キャンペーン告知 123'
    ]
})

fig, ax = plt.subplots(figsize=(8, 5))
ax.set_axis_off()

for i, row in df_fonts.iterrows():
    ax.text(
        0.05,
        0.9 - i * 0.18,
        f"{row['説明']}: {row['表示例']}",
        fontfamily=row['font_family'],
        fontsize=16
    )

ax.set_title('フォントファミリーの比較', fontweight='bold')

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

df_sales = pd.DataFrame({
    '月': pd.date_range('2026-01-01', periods=12, freq='MS'),
    '売上': np.array([82, 86, 91, 88, 95, 101, 108, 106, 112, 118, 123, 130])
})

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

aspect_ratios = [
    (4, 3, 'Traditional (4:3)'),
    (16, 9, 'Wide (16:9)'),
    (1, 1, 'Square (1:1)')
]

fig, axes = plt.subplots(1, 3, figsize=(15, 4))
fig.suptitle('アスペクト比による見え方の違い', fontsize=14, fontweight='bold')

for ax, (w, h, label) in zip(axes, aspect_ratios):
    ax.plot(
        df_sales['月'],
        df_sales['売上'],
        color=colors_professional[0],
        linewidth=2,
        marker='o'
    )
    ax.set_box_aspect(h / w)
    ax.set_title(label, fontweight='bold')
    ax.set_xlabel('月')
    ax.set_ylabel('売上（百万円）')
    ax.grid(True, alpha=0.3)
    ax.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(42)
df_quality = pd.DataFrame({
    '日': pd.date_range('2026-04-01', periods=60, freq='D')
})
df_quality['売上指数'] = 100 + np.cumsum(rng.normal(0.2, 1.2, len(df_quality)))

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(1, 3, figsize=(18, 5), dpi=150)
fig.suptitle('DPI・解像度制御システム', fontsize=16, fontweight='bold')

dpi_settings = [72, 150, 300]
dpi_names = ['Web用 (72 DPI)', '標準 (150 DPI)', '印刷用 (300 DPI)']

for i, (dpi, name) in enumerate(zip(dpi_settings, dpi_names)):
    ax = axes[i]
    ax.plot(
        df_quality['日'],
        df_quality['売上指数'],
        linewidth=1.5,
        color=colors_professional[i]
    )
    ax.set_title(f'{name}\n保存時 dpi={dpi}', fontweight='bold')
    ax.set_xlabel('日付')
    ax.set_ylabel('売上指数')
    ax.grid(True, alpha=0.3)
    ax.tick_params(axis='x', rotation=45)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(42)

df_curve = pd.DataFrame({
    '期間': np.linspace(0, 10, 100)
})
df_curve['需要指数'] = np.sin(df_curve['期間']) * np.exp(-df_curve['期間'] / 5)

df_sales = pd.DataFrame({
    '月': ['1月', '2月', '3月', '4月', '5月', '6月'],
    '売上': [100, 85, 120, 95, 140, 160]
})
target = 120

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(1, 2, figsize=(14, 6))
fig.suptitle('注釈・テキストシステム', fontsize=16, fontweight='bold')

ax = axes[0]
ax.plot(
    df_curve['期間'],
    df_curve['需要指数'],
    linewidth=2,
    color=colors_professional[0]
)

peak_idx = df_curve['需要指数'].idxmax()
peak_x = df_curve.loc[peak_idx, '期間']
peak_y = df_curve.loc[peak_idx, '需要指数']

ax.annotate(
    f'最高点\n({peak_x:.1f}, {peak_y:.2f})',
    xy=(peak_x, peak_y),
    xytext=(peak_x + 1, peak_y + 0.2),
    arrowprops=dict(arrowstyle='->', color='red', lw=2),
    fontsize=12,
    fontweight='bold',
    bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7)
)

ax.set_title('重要ポイント強調', fontweight='bold')
ax.set_xlabel('期間')
ax.set_ylabel('需要指数')
ax.grid(True, alpha=0.3)

ax = axes[1]
bar_colors = [
    colors_professional[0] if sale >= target else colors_professional[3]
    for sale in df_sales['売上']
]
bars = ax.bar(df_sales['月'], df_sales['売上'], color=bar_colors, alpha=0.8)

ax.axhline(
    y=target,
    color='red',
    linestyle='--',
    linewidth=2,
    label=f'目標値: {target}'
)

for i, row in df_sales.iterrows():
    sale = row['売上']
    if sale >= target:
        ax.annotate(
            '達成!',
            xy=(i, sale),
            xytext=(i, sale + 10),
            ha='center',
            fontweight='bold',
            color='green',
            arrowprops=dict(arrowstyle='->', color='green')
        )
    else:
        shortage = target - sale
        ax.annotate(
            f'-{shortage}',
            xy=(i, sale),
            xytext=(i, sale + 10),
            ha='center',
            fontweight='bold',
            color='red'
        )

ax.set_title('売上目標達成状況', fontweight='bold')
ax.set_ylabel('売上（百万円）')
ax.legend()

plt.tight_layout()
plt.show()

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

rng = np.random.default_rng(42)

df_sales_daily = pd.DataFrame({
    'date': pd.date_range('2026-06-01', periods=30, freq='D')
})
df_sales_daily['sales'] = (
    850000
    + np.linspace(0, 180000, len(df_sales_daily))
    + rng.normal(0, 90000, len(df_sales_daily))
).round()

df_sales_daily.loc[10, 'sales'] = 1350000
df_sales_daily.loc[21, 'sales'] = 620000

daily_sales = df_sales_daily.groupby('date', as_index=False)['sales'].sum()

max_sales_idx = daily_sales['sales'].idxmax()
min_sales_idx = daily_sales['sales'].idxmin()
max_sales_date = daily_sales.loc[max_sales_idx, 'date']
min_sales_date = daily_sales.loc[min_sales_idx, 'date']
max_sales_value = daily_sales.loc[max_sales_idx, 'sales']
min_sales_value = daily_sales.loc[min_sales_idx, 'sales']

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=daily_sales['date'],
    y=daily_sales['sales'],
    mode='lines',
    name='日別売上',
    line=dict(color='blue', width=2)
))

fig.add_trace(go.Scatter(
    x=[max_sales_date, min_sales_date],
    y=[max_sales_value, min_sales_value],
    mode='markers',
    marker=dict(
        size=12,
        color=['red', 'orange'],
        symbol=['star', 'diamond']
    ),
    name='極値',
    showlegend=False
))

fig.add_annotation(
    x=max_sales_date,
    y=max_sales_value,
    text=f"最高売上<br>¥{max_sales_value:,.0f}",
    showarrow=True,
    arrowhead=2,
    arrowcolor='red',
    arrowwidth=2,
    bgcolor='white',
    bordercolor='red',
    borderwidth=2
)

fig.add_annotation(
    x=min_sales_date,
    y=min_sales_value,
    text=f"最低売上<br>¥{min_sales_value:,.0f}",
    showarrow=True,
    arrowhead=2,
    arrowcolor='orange',
    arrowwidth=2,
    bgcolor='white',
    bordercolor='orange',
    borderwidth=2
)

fig.update_layout(
    title=dict(
        text='売上推移（重要ポイント注釈付き）',
        font=dict(size=20)
    ),
    xaxis_title='日付',
    yaxis_title='売上金額（円）',
    template='plotly_white',
    height=600
)

fig.show()

print(f"最高売上日: {max_sales_date.strftime('%Y-%m-%d')} (¥{max_sales_value:,.0f})")
print(f"最低売上日: {min_sales_date.strftime('%Y-%m-%d')} (¥{min_sales_value:,.0f})")

import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib
from matplotlib.patches import Rectangle, Circle, Polygon, FancyBboxPatch, FancyArrow

df_processes = pd.DataFrame({
    '工程': ['企画', '開発', 'テスト', 'リリース'],
    'x': [1, 4, 7, 10],
    'y': [6, 6, 6, 6],
    '幅': [2, 2, 2, 2],
    '高さ': [1, 1, 1, 1]
})

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(1, 2, figsize=(14, 6))
fig.suptitle('図形描画システム', fontsize=16, fontweight='bold')

ax = axes[0]
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)

rect = Rectangle(
    (2, 2),
    3,
    2,
    linewidth=2,
    edgecolor=colors_professional[0],
    facecolor=colors_professional[0],
    alpha=0.3
)
ax.add_patch(rect)
ax.text(3.5, 3, '商品A', ha='center', va='center', fontweight='bold')

circle = Circle(
    (7, 7),
    1.5,
    linewidth=2,
    edgecolor=colors_professional[1],
    facecolor=colors_professional[1],
    alpha=0.3
)
ax.add_patch(circle)
ax.text(7, 7, '顧客層', ha='center', va='center', fontweight='bold')

triangle = Polygon(
    [(1, 7), (3, 9), (5, 7)],
    closed=True,
    linewidth=2,
    edgecolor=colors_professional[2],
    facecolor=colors_professional[2],
    alpha=0.3
)
ax.add_patch(triangle)
ax.text(3, 7.6, '施策', ha='center', va='center', fontweight='bold')

ax.set_title('基本図形の組み合わせ', fontweight='bold')
ax.grid(True, alpha=0.3)

ax = axes[1]
ax.set_xlim(0, 12)
ax.set_ylim(0, 8)

for i, row in df_processes.iterrows():
    box = FancyBboxPatch(
        (row['x'], row['y']),
        row['幅'],
        row['高さ'],
        boxstyle='round,pad=0.1',
        facecolor=colors_professional[i % len(colors_professional)],
        alpha=0.7,
        edgecolor='black'
    )
    ax.add_patch(box)
    ax.text(
        row['x'] + row['幅'] / 2,
        row['y'] + row['高さ'] / 2,
        row['工程'],
        ha='center',
        va='center',
        fontweight='bold',
        fontsize=12
    )

    if i < len(df_processes) - 1:
        arrow = FancyArrow(
            row['x'] + row['幅'],
            row['y'] + row['高さ'] / 2,
            0.8,
            0,
            width=0.2,
            head_width=0.5,
            head_length=0.3,
            color='black',
            alpha=0.7
        )
        ax.add_patch(arrow)

ax.set_title('ビジネスプロセスフロー', fontweight='bold')
ax.axis('off')

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(42)

regions = ['東日本', '西日本', '中部', '九州', '北海道']
products = ['製品A', '製品B', '製品C', '製品D', '製品E', '製品F']
sales_matrix = rng.integers(60, 180, size=(len(products), len(regions)))

df_metrics = pd.DataFrame({
    'サイト訪問': rng.normal(1000, 120, 80),
    '資料請求': rng.normal(220, 40, 80),
    '商談数': rng.normal(80, 18, 80),
    '受注数': rng.normal(32, 8, 80),
    '売上': rng.normal(120, 25, 80)
})

months = [f'{i}月' for i in range(1, 7)]
hours = list(range(8, 20))
traffic_matrix = rng.normal(100, 18, size=(len(months), len(hours)))
traffic_matrix[:, 4:8] += 35

fig, axes = plt.subplots(1, 3, figsize=(18, 5))
fig.suptitle('画像埋め込みシステム', fontsize=16, fontweight='bold')

ax = axes[0]
im1 = ax.imshow(sales_matrix, cmap='viridis', aspect='auto')
ax.set_title('製品・地域別売上ヒートマップ', fontweight='bold')
ax.set_xticks(range(len(regions)))
ax.set_xticklabels(regions, rotation=45)
ax.set_yticks(range(len(products)))
ax.set_yticklabels(products)
plt.colorbar(im1, ax=ax)

ax = axes[1]
correlation_matrix = np.corrcoef(df_metrics.to_numpy().T)
im2 = ax.imshow(correlation_matrix, cmap='RdBu_r', aspect='auto', vmin=-1, vmax=1)
ax.set_title('営業指標の相関行列', fontweight='bold')
ax.set_xticks(range(len(df_metrics.columns)))
ax.set_yticks(range(len(df_metrics.columns)))
ax.set_xticklabels(df_metrics.columns, rotation=45, ha='right')
ax.set_yticklabels(df_metrics.columns)
plt.colorbar(im2, ax=ax)

ax = axes[2]
im3 = ax.imshow(traffic_matrix, cmap='coolwarm', aspect='auto')
ax.set_title('時間別問い合わせ傾向', fontweight='bold')
ax.set_xlabel('時間')
ax.set_ylabel('月')
ax.set_xticks(range(0, len(hours), 2))
ax.set_xticklabels([f'{hours[i]}:00' for i in range(0, len(hours), 2)])
ax.set_yticks(range(len(months)))
ax.set_yticklabels(months)
plt.colorbar(im3, ax=ax)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib

rng = np.random.default_rng(42)

df_plan = pd.DataFrame({
    '月': np.arange(1, 13),
    '計画売上': np.array([82, 86, 90, 94, 98, 103, 108, 112, 116, 120, 124, 130])
})
df_plan['実績売上'] = df_plan['計画売上'] + rng.normal(0, 6, len(df_plan)).round(1)

df_stock = pd.DataFrame({
    '日付': pd.date_range('2026-01-01', periods=100, freq='D')
})
df_stock['株価指数'] = 100 + np.cumsum(rng.normal(0.1, 1.5, len(df_stock)))

df_revenue = pd.DataFrame({
    '月': np.arange(1, 13),
    'サービスA': [50, 55, 60, 58, 65, 70, 75, 72, 80, 85, 90, 95],
    'サービスB': [30, 32, 35, 33, 38, 40, 42, 39, 45, 48, 50, 52],
    'サービスC': [20, 22, 25, 23, 28, 30, 32, 29, 35, 38, 40, 42]
})

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, axes = plt.subplots(1, 3, figsize=(18, 5))
fig.suptitle('塗りつぶしシステム', fontsize=16, fontweight='bold')

ax = axes[0]
ax.plot(df_plan['月'], df_plan['実績売上'], linewidth=2, color=colors_professional[0], label='実績売上')
ax.plot(df_plan['月'], df_plan['計画売上'], linewidth=2, color=colors_professional[1], label='計画売上')
ax.fill_between(
    df_plan['月'],
    df_plan['実績売上'],
    df_plan['計画売上'],
    alpha=0.3,
    color=colors_professional[2]
)
ax.legend()
ax.set_title('計画と実績の差分', fontweight='bold')
ax.set_xlabel('月')
ax.set_ylabel('売上（百万円）')
ax.grid(True, alpha=0.3)

ax = axes[1]
benchmark = 105
ax.plot(df_stock['日付'], df_stock['株価指数'], linewidth=2, color='blue', label='株価指数')
ax.axhline(y=benchmark, color='red', linestyle='--', label='ベンチマーク')

above_benchmark = df_stock['株価指数'] >= benchmark
ax.fill_between(
    df_stock['日付'],
    df_stock['株価指数'],
    benchmark,
    where=above_benchmark,
    alpha=0.3,
    color='green',
    label='アウトパフォーム'
)

below_benchmark = df_stock['株価指数'] < benchmark
ax.fill_between(
    df_stock['日付'],
    df_stock['株価指数'],
    benchmark,
    where=below_benchmark,
    alpha=0.3,
    color='red',
    label='アンダーパフォーム'
)

ax.legend()
ax.set_title('条件付き塗りつぶし', fontweight='bold')
ax.set_xlabel('日付')
ax.set_ylabel('株価指数')
ax.grid(True, alpha=0.3)
ax.tick_params(axis='x', rotation=45)

ax = axes[2]
months = df_revenue['月']
service_a = df_revenue['サービスA']
service_b = df_revenue['サービスB']
service_c = df_revenue['サービスC']

ax.fill_between(months, 0, service_a, alpha=0.7, color=colors_professional[0], label='サービスA')
ax.fill_between(months, service_a, service_a + service_b, alpha=0.7, color=colors_professional[1], label='サービスB')
ax.fill_between(
    months,
    service_a + service_b,
    service_a + service_b + service_c,
    alpha=0.7,
    color=colors_professional[2],
    label='サービスC'
)

ax.set_xlabel('月')
ax.set_ylabel('売上（百万円）')
ax.set_title('収益源別積み上げ', fontweight='bold')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import japanize_matplotlib
from matplotlib.animation import FuncAnimation

rng = np.random.default_rng(42)

df_stream = pd.DataFrame({
    '時点': np.arange(200) * 0.1
})
df_stream['売上指数'] = np.sin(df_stream['時点']) + rng.normal(0, 0.1, len(df_stream))

colors_professional = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']

fig, ax = plt.subplots(figsize=(10, 6))
ax.set_xlim(0, 10)
ax.set_ylim(-2, 2)
ax.set_title('リアルタイムデータ可視化シミュレーション', fontweight='bold')
ax.set_xlabel('経過時間')
ax.set_ylabel('売上指数')
ax.grid(True, alpha=0.3)

x_data = []
y_data = []
line, = ax.plot([], [], linewidth=3, color=colors_professional[0], label='リアルタイムデータ')
ax.legend()

def animate(frame):
    row = df_stream.iloc[frame]
    x_data.append(row['時点'])
    y_data.append(row['売上指数'])

    if len(x_data) > 100:
        x_data.pop(0)
        y_data.pop(0)

    line.set_data(x_data, y_data)

    if x_data:
        ax.set_xlim(max(0, max(x_data) - 10), max(x_data) + 1)

    return line,

anim = FuncAnimation(
    fig,
    animate,
    frames=len(df_stream),
    interval=50,
    blit=False,
    repeat=False
)

plt.tight_layout()
plt.show()

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

rng = np.random.default_rng(42)
regions = ["北海道", "関東", "関西", "九州"]
products = {
    "北海道": "保守サービス",
    "関東": "クラウド基盤",
    "関西": "分析ダッシュボード",
    "九州": "店舗システム",
}
base_sales = {"北海道": 750_000, "関東": 1_600_000, "関西": 1_150_000, "九州": 900_000}

rows = []
for month_index, month_start in enumerate(pd.date_range("2026-01-01", periods=5, freq="MS")):
    for region_index, region in enumerate(regions):
        transaction_count = 2 + (month_index + region_index) % 3
        for transaction in range(transaction_count):
            rows.append({
                "date": month_start + pd.Timedelta(days=transaction * 8 + int(rng.integers(0, 4))),
                "region": region,
                "product": products[region],
                "sales": int(base_sales[region] * (1 + month_index * 0.08) * rng.uniform(0.82, 1.18)),
                "customer_id": f"C{region_index + 1}{month_index + 1:02d}{transaction + 1:02d}",
            })

df_sales_daily = pd.DataFrame(rows)
colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]
color_map = dict(zip(regions, colors_professional))

df_sales_daily["year_month"] = df_sales_daily["date"].dt.to_period("M")
monthly_data = df_sales_daily.groupby(["year_month", "region", "product"], as_index=False).agg(
    sales=("sales", "sum"),
    customer_id=("customer_id", "nunique"),
)
monthly_data["year_month_str"] = monthly_data["year_month"].astype(str)

fig = px.scatter(
    monthly_data,
    x="sales",
    y="customer_id",
    size="sales",
    color="region",
    color_discrete_map=color_map,
    hover_name="product",
    animation_frame="year_month_str",
    animation_group="product",
    labels={
        "sales": "売上金額（円）",
        "customer_id": "ユニーク顧客数",
        "region": "地域",
    },
    template="plotly_white",
    range_x=[0, monthly_data["sales"].max() * 1.15],
    range_y=[0, monthly_data["customer_id"].max() * 1.25],
)

for region in regions:
    region_data = monthly_data[monthly_data["region"] == region].sort_values("year_month_str")
    fig.add_trace(go.Scatter(
        x=region_data["sales"],
        y=region_data["customer_id"],
        mode="lines+markers",
        name=f"{region} 軌跡",
        line=dict(color=color_map[region], width=2, dash="dot"),
        marker=dict(size=7, symbol="circle-open"),
        hovertemplate=(
            f"{region}<br>"
            "売上: %{x:,.0f}円<br>"
            "顧客数: %{y}<extra></extra>"
        ),
    ))

fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 1000
fig.layout.updatemenus[0].buttons[0].args[1]["transition"]["duration"] = 500
fig.layout.sliders[0].currentvalue.prefix = "期間: "

fig.update_layout(
    title=dict(text="月次推移アニメーション: 売上 × 顧客数", font=dict(size=20)),
    height=650,
    legend_title_text="地域",
)

fig.show()

print("アニメーションで時系列変化を動的に表現し、軌跡で全体の変遷も確認できます。")

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import CheckButtons, Slider
import japanize_matplotlib

rng = np.random.default_rng(42)
days = np.linspace(0, 30, 400)
baseline = 120
amp0, freq0, phase0 = 25, 0.7, 0.0

months = np.arange(1, 13)
month_labels = [f"{month}月" for month in months]
base_sales = np.array([100, 118, 105, 132, 148, 165, 178, 171, 190, 205, 218, 235], dtype=float)
regions = ["北海道", "関東", "関西", "九州"]
regional_weights = {"北海道": 0.72, "関東": 1.35, "関西": 1.05, "九州": 0.86}
regional_data = {
    region: base_sales * regional_weights[region] * rng.uniform(0.94, 1.08, len(months))
    for region in regions
}

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]
colors_regions = dict(zip(regions, colors_professional))

fig = plt.figure(figsize=(16, 9))
gs = fig.add_gridspec(4, 4, height_ratios=[1, 1, 0.15, 0.15], width_ratios=[1, 1, 1, 0.45])

ax_wave = fig.add_subplot(gs[0:2, 0:2])
ax_region = fig.add_subplot(gs[0:2, 2:4])
ax_freq = fig.add_subplot(gs[2, 0])
ax_amp = fig.add_subplot(gs[3, 0])
ax_phase = fig.add_subplot(gs[2, 1])
ax_check = fig.add_subplot(gs[3, 2])

demand = baseline + amp0 * np.sin(freq0 * days + phase0)
line, = ax_wave.plot(days, demand, linewidth=2.5, color=colors_professional[0])

ax_wave.set_xlim(0, 30)
ax_wave.set_ylim(60, 180)
ax_wave.set_title("需要変動シミュレーション", fontweight="bold", fontsize=14)
ax_wave.set_xlabel("日数")
ax_wave.set_ylabel("予測需要（件）")
ax_wave.grid(True, alpha=0.3)

slider_freq = Slider(ax_freq, "周波数", 0.2, 1.5, valinit=freq0)
slider_amp = Slider(ax_amp, "振幅", 5, 60, valinit=amp0)
slider_phase = Slider(ax_phase, "位相", 0, 2 * np.pi, valinit=phase0)

def update_wave(_):
    demand = baseline + slider_amp.val * np.sin(slider_freq.val * days + slider_phase.val)
    line.set_ydata(demand)
    ax_wave.set_ylim(demand.min() - 15, demand.max() + 15)
    fig.canvas.draw_idle()

slider_freq.on_changed(update_wave)
slider_amp.on_changed(update_wave)
slider_phase.on_changed(update_wave)

check = CheckButtons(ax_check, regions, [True, True, True, True])
ax_check.set_title("地域", fontweight="bold")

def draw_regions():
    ax_region.clear()
    active_regions = [region for region, active in zip(regions, check.get_status()) if active]

    for region in active_regions:
        ax_region.plot(
            months,
            regional_data[region],
            "o-",
            linewidth=2,
            color=colors_regions[region],
            label=region,
        )

    ax_region.set_title("地域別売上比較", fontweight="bold", fontsize=14)
    ax_region.set_xlabel("月")
    ax_region.set_ylabel("売上（百万円）")
    ax_region.set_xticks(months)
    ax_region.set_xticklabels(month_labels, rotation=45)
    ax_region.grid(True, alpha=0.3)
    if active_regions:
        ax_region.legend()

def toggle_region(_):
    draw_regions()
    fig.canvas.draw_idle()

check.on_clicked(toggle_region)
draw_regions()

print("インタラクティブ要素の活用例:")
print("- パラメータ調整: リアルタイム結果確認")
print("- データフィルタリング: 動的表示切替")
print("- プレゼンテーション: 観客との対話型説明")
print("- 教育・トレーニング: 体験型学習支援")

plt.tight_layout()
plt.show()

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

df_customers_geo = pd.DataFrame({
    "customer_id": ["C001", "C002", "C003", "C004", "C005", "C006", "C007", "C008", "C009", "C010", "C011", "C012"],
    "age": [24, 31, 38, 45, 52, 29, 34, 41, 57, 63, 48, 36],
    "income": [2_800_000, 4_200_000, 5_600_000, 7_400_000, 9_200_000, 3_300_000, 4_800_000, 6_300_000, 8_600_000, 10_500_000, 7_900_000, 5_100_000],
    "spending_score": [62, 71, 58, 84, 77, 69, 73, 81, 66, 74, 88, 79],
    "satisfaction": [3.8, 4.2, 3.5, 4.6, 4.1, 3.9, 4.3, 4.7, 3.6, 4.0, 4.8, 4.4],
    "city": ["札幌市", "東京都", "大阪市", "福岡市", "横浜市", "仙台市", "名古屋市", "京都市", "神戸市", "広島市", "さいたま市", "千葉市"],
})

df_customers_geo["income_bracket"] = pd.cut(
    df_customers_geo["income"],
    bins=[0, 3_000_000, 5_000_000, 8_000_000, float("inf")],
    labels=["低所得", "中所得", "高所得", "超高所得"],
)

colors = {
    "低所得": "#d62728",
    "中所得": "#ff7f0e",
    "高所得": "#1f77b4",
    "超高所得": "#2ca02c",
}

fig = go.Figure()

for bracket in df_customers_geo["income_bracket"].cat.categories:
    subset = df_customers_geo[df_customers_geo["income_bracket"] == bracket]

    fig.add_trace(go.Scatter(
        x=subset["age"],
        y=subset["spending_score"],
        mode="markers",
        name=str(bracket),
        marker=dict(size=10, opacity=0.75, color=colors[str(bracket)]),
        customdata=np.column_stack((
            subset["customer_id"],
            subset["income"],
            subset["satisfaction"],
            subset["city"],
        )),
        hovertemplate=(
            "<b>顧客詳細情報</b><br>"
            "ID: %{customdata[0]}<br>"
            "年齢: %{x}歳<br>"
            "年収: ¥%{customdata[1]:,.0f}<br>"
            "支出スコア: %{y}<br>"
            "満足度: %{customdata[2]:.1f}/5.0<br>"
            "都市: %{customdata[3]}<br>"
            "所得層: %{fullData.name}<extra></extra>"
        ),
    ))

fig.update_layout(
    title=dict(text="顧客分析: 所得層別セグメンテーション", font=dict(size=20)),
    xaxis_title="年齢（歳）",
    yaxis_title="支出スコア",
    template="plotly_white",
    height=600,
)

fig.show()

print("ホバーで詳細な顧客情報を表示し、データ探索を効率化できます。")

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

df_customers_geo = pd.DataFrame({
    "customer_id": ["C101", "C102", "C103", "C104", "C105", "C106", "C107", "C108", "C109", "C110", "C111", "C112"],
    "age": [26, 33, 39, 44, 51, 28, 35, 42, 55, 61, 47, 37],
    "income": [3_100_000, 4_500_000, 5_800_000, 6_900_000, 9_500_000, 2_700_000, 4_200_000, 6_400_000, 8_100_000, 11_200_000, 7_600_000, 5_300_000],
    "spending_score": [68, 74, 61, 86, 79, 64, 72, 83, 69, 75, 90, 80],
    "satisfaction": [3.9, 4.2, 3.6, 4.7, 4.3, 3.5, 4.1, 4.8, 3.7, 4.0, 4.9, 4.5],
    "city": ["札幌市", "東京都", "大阪市", "福岡市", "横浜市", "仙台市", "名古屋市", "京都市", "神戸市", "広島市", "さいたま市", "千葉市"],
})

income_brackets = pd.cut(
    df_customers_geo["income"],
    bins=4,
    labels=["低所得", "中低所得", "中高所得", "高所得"],
)
colors = {
    "低所得": "red",
    "中低所得": "orange",
    "中高所得": "lightblue",
    "高所得": "blue",
}

fig = go.Figure()

for bracket in income_brackets.cat.categories:
    subset = df_customers_geo[income_brackets == bracket]
    bracket_label = str(bracket)

    fig.add_trace(go.Scatter(
        x=subset["age"],
        y=subset["spending_score"],
        mode="markers",
        name=bracket_label,
        marker=dict(
            size=subset["satisfaction"] * 3,
            color=colors[bracket_label],
            opacity=0.7,
            line=dict(width=1, color="white"),
        ),
        customdata=np.column_stack((
            subset["customer_id"],
            subset["income"],
            subset["satisfaction"],
            subset["city"],
            ["⭐" * int(round(value)) for value in subset["satisfaction"]],
        )),
        hovertemplate=(
            "<b style='color:#2E86AB;'>📊 顧客詳細プロファイル</b><br><br>"
            "<b>🆔 顧客ID:</b> %{customdata[0]}<br>"
            "<b>👤 年齢:</b> %{x}歳<br>"
            "<b>💰 年収:</b> ¥%{customdata[1]:,.0f}<br>"
            "<b>🛒 支出スコア:</b> %{y}<br>"
            "<b>😊 満足度:</b> %{customdata[2]:.1f}/5.0 %{customdata[4]}<br>"
            "<b>🏙️ 居住地:</b> %{customdata[3]}<br>"
            "<b>💳 所得層:</b> %{fullData.name}<br>"
            "<extra></extra>"
        ),
        hoverlabel=dict(
            bgcolor="white",
            bordercolor="gray",
            font_size=12,
            font_family="Arial",
        ),
    ))

fig.update_layout(
    title=dict(text="顧客分析: 詳細ツールチップ付きセグメンテーション", font=dict(size=20)),
    xaxis_title="年齢（歳）",
    yaxis_title="支出スコア",
    template="plotly_white",
    height=600,
    hovermode="closest",
)

fig.show()

print("リッチなツールチップで詳細情報を見やすく表示できます。")

import pandas as pd
import plotly.graph_objects as go

df_sales_daily = pd.DataFrame({
    "date": pd.to_datetime([
        "2026-04-01", "2026-04-01", "2026-04-02", "2026-04-02",
        "2026-04-03", "2026-04-04", "2026-04-04", "2026-04-05",
        "2026-04-05", "2026-04-06", "2026-04-07", "2026-04-07",
        "2026-04-08", "2026-04-08", "2026-04-09", "2026-04-10",
    ]),
    "customer_id": [
        "C001", "C002", "C003", "C004", "C002", "C005", "C006", "C007",
        "C008", "C009", "C010", "C011", "C003", "C012", "C013", "C014",
    ],
    "sales": [
        18000, 26000, 22000, 31000, 27000, 34000, 18000, 29000,
        37000, 33000, 41000, 24000, 36000, 42000, 39000, 45000,
    ],
})

daily_sales = df_sales_daily.groupby("date")["sales"].sum()
daily_customers = df_sales_daily.groupby("date")["customer_id"].nunique()
daily_avg = df_sales_daily.groupby("date")["sales"].mean()

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=daily_sales.index,
    y=daily_sales.values,
    mode="lines+markers",
    name="売上推移",
    visible=True,
))

fig.add_trace(go.Scatter(
    x=daily_customers.index,
    y=daily_customers.values,
    mode="lines+markers",
    name="顧客数推移",
    visible=False,
))

fig.add_trace(go.Scatter(
    x=daily_avg.index,
    y=daily_avg.values,
    mode="lines+markers",
    name="平均売上推移",
    visible=False,
))

updatemenus = [dict(
    type="buttons",
    direction="left",
    buttons=[
        dict(
            args=[
                {"visible": [True, False, False]},
                {"title": {"text": "日別売上推移"}, "yaxis": {"title": {"text": "売上金額（円）"}}},
            ],
            label="売上",
            method="update",
        ),
        dict(
            args=[
                {"visible": [False, True, False]},
                {"title": {"text": "日別顧客数推移"}, "yaxis": {"title": {"text": "ユニーク顧客数"}}},
            ],
            label="顧客数",
            method="update",
        ),
        dict(
            args=[
                {"visible": [False, False, True]},
                {"title": {"text": "日別平均売上推移"}, "yaxis": {"title": {"text": "平均売上（円）"}}},
            ],
            label="平均売上",
            method="update",
        ),
    ],
    pad={"r": 10, "t": 10},
    showactive=True,
    x=0.01,
    xanchor="left",
    y=1.15,
    yanchor="top",
)]

fig.update_layout(
    updatemenus=updatemenus,
    title=dict(text="日別売上推移", font=dict(size=20)),
    xaxis=dict(title=dict(text="日付")),
    yaxis=dict(title=dict(text="売上金額（円）")),
    template="plotly_white",
    height=600,
)

fig.show()

print("ボタンで指標を切り替え、1つのグラフで複数の視点を確認できます。")

import pandas as pd
import plotly.graph_objects as go

df_sales_daily = pd.DataFrame({
    "date": pd.date_range("2026-05-01", periods=14, freq="D"),
    "sales": [9500, 12800, 16400, 19800, 23500, 27600, 31200, 18500, 22100, 34700, 28900, 40100, 36800, 43000],
})

initial_threshold = 20_000
high_sales = df_sales_daily[df_sales_daily["sales"] >= initial_threshold]
low_sales = df_sales_daily[df_sales_daily["sales"] < initial_threshold]

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=high_sales["date"],
    y=high_sales["sales"],
    mode="markers",
    name=f"高売上 (≥¥{initial_threshold:,})",
    marker=dict(color="red", size=9),
))

fig.add_trace(go.Scatter(
    x=low_sales["date"],
    y=low_sales["sales"],
    mode="markers",
    name=f"低売上 (<¥{initial_threshold:,})",
    marker=dict(color="blue", size=7, opacity=0.65),
))

steps = []
for threshold in range(10_000, 40_001, 5_000):
    high_sales = df_sales_daily[df_sales_daily["sales"] >= threshold]
    low_sales = df_sales_daily[df_sales_daily["sales"] < threshold]

    steps.append(dict(
        method="update",
        args=[
            {
                "x": [high_sales["date"].tolist(), low_sales["date"].tolist()],
                "y": [high_sales["sales"].tolist(), low_sales["sales"].tolist()],
                "name": [f"高売上 (≥¥{threshold:,})", f"低売上 (<¥{threshold:,})"],
            },
            {
                "title": {"text": f"売上分類閾値調整（閾値: ¥{threshold:,}）"},
            },
        ],
        label=f"¥{threshold:,}",
    ))

sliders = [dict(
    active=2,
    currentvalue={"prefix": "閾値: "},
    pad={"t": 50},
    steps=steps,
)]

fig.update_layout(
    sliders=sliders,
    title=dict(text="売上分類閾値調整（閾値: ¥20,000）", font=dict(size=20)),
    xaxis_title="日付",
    yaxis_title="売上金額（円）",
    template="plotly_white",
    height=600,
)

fig.show()

print("スライダーで閾値を動的に変更し、分類基準による見え方の違いを確認できます。")

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import japanize_matplotlib

categories_jp = ["売上高", "営業利益", "当期純利益", "総資産", "自己資本"]
values_jp = [1500, 200, 150, 5000, 2000]
months_jp = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"]
monthly_sales = [100, 95, 110, 120, 140, 160, 180, 170, 150, 130, 115, 125]
departments = ["営業部", "開発部", "企画部", "管理部", "マーケティング部"]
performance_scores = [85, 92, 78, 88, 90]

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

font_keywords = ("japan", "noto", "hiragino", "yu gothic", "meiryo", "ipa", "takao")
japanese_fonts = sorted({
    font.name
    for font in fm.fontManager.ttflist
    if any(keyword in font.name.lower() for keyword in font_keywords)
})

fig, axes = plt.subplots(2, 2, figsize=(16, 12))
fig.suptitle("日本語フォント制御システム", fontsize=16, fontweight="bold")

ax = axes[0, 0]
ax.axis("off")
ax.set_title("利用可能な日本語フォント", fontweight="bold")
font_text = "\n".join(japanese_fonts[:10]) if japanese_fonts else "japanize_matplotlib により日本語表示を補助"
ax.text(0.05, 0.95, font_text, transform=ax.transAxes, va="top", fontsize=11)

ax = axes[0, 1]
bars = ax.bar(categories_jp, values_jp, color=colors_professional[:len(categories_jp)])
ax.set_title("日本語ラベル基本表示", fontweight="bold")
ax.set_ylabel("金額（百万円）")
for bar, value in zip(bars, values_jp):
    ax.text(
        bar.get_x() + bar.get_width() / 2,
        bar.get_height() + 50,
        f"{value:,}",
        ha="center",
        va="bottom",
        fontweight="bold",
    )

ax = axes[1, 0]
ax.plot(months_jp, monthly_sales, marker="o", linewidth=3, markersize=8, color=colors_professional[0])
ax.set_title("月次売上推移", fontweight="bold")
ax.set_ylabel("売上（百万円）")
plt.setp(ax.get_xticklabels(), rotation=45)

max_idx = int(np.argmax(monthly_sales))
min_idx = int(np.argmin(monthly_sales))
ax.annotate(
    f"最高値\n{monthly_sales[max_idx]}百万円",
    xy=(max_idx, monthly_sales[max_idx]),
    xytext=(max_idx, monthly_sales[max_idx] + 20),
    ha="center",
    fontweight="bold",
    arrowprops=dict(arrowstyle="->", color="red"),
)
ax.annotate(
    f"最低値\n{monthly_sales[min_idx]}百万円",
    xy=(min_idx, monthly_sales[min_idx]),
    xytext=(min_idx, monthly_sales[min_idx] - 25),
    ha="center",
    fontweight="bold",
    arrowprops=dict(arrowstyle="->", color="blue"),
)
ax.grid(True, alpha=0.3)

ax = axes[1, 1]
colors_dept = plt.cm.Set3(np.linspace(0, 1, len(departments)))
bars = ax.barh(departments, performance_scores, color=colors_dept)
ax.set_title("部門別パフォーマンス評価", fontweight="bold")
ax.set_xlabel("評価スコア")
ax.set_xlim(0, 100)

for bar, score in zip(bars, performance_scores):
    ax.text(
        bar.get_width() + 1,
        bar.get_y() + bar.get_height() / 2,
        f"{score}点",
        ha="left",
        va="center",
        fontweight="bold",
    )

print("日本語表示のポイント:")
print("- japanize_matplotlib を読み込むと日本語フォント設定を補助できます。")
print("- 改行を含むラベルでは '\\n' を使うと注釈が読みやすくなります。")
print("- OSごとに利用可能な日本語フォントが異なるため、font_managerで確認できます。")

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib

months = np.arange(1, 13)
monthly_sales = np.array([100, 112, 108, 130, 145, 158, 176, 169, 184, 202, 215, 238])
quality_scores = [85, 95, 90, 98]
file_sizes = ["Web\n150KB", "印刷\n500KB", "プレゼン\n50KB", "データ\n200KB"]

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]

save_formats = {
    "Web用 (PNG)": {
        "format": "png",
        "dpi": 150,
        "bbox_inches": "tight",
        "facecolor": "white",
        "edgecolor": "none",
    },
    "印刷用 (PDF)": {
        "format": "pdf",
        "dpi": 300,
        "bbox_inches": "tight",
        "facecolor": "white",
        "edgecolor": "none",
    },
    "プレゼン用 (SVG)": {
        "format": "svg",
        "bbox_inches": "tight",
        "facecolor": "white",
        "edgecolor": "none",
    },
    "データ用 (EPS)": {
        "format": "eps",
        "dpi": 300,
        "bbox_inches": "tight",
    },
}

fig, axes = plt.subplots(2, 2, figsize=(16, 12))
fig.suptitle("保存形式最適化システム", fontsize=16, fontweight="bold")

ax = axes[0, 0]
ax.plot(months, monthly_sales, linewidth=3, marker="o", color=colors_professional[0], label="月次売上")
ax.fill_between(months, monthly_sales, monthly_sales.min(), alpha=0.25, color=colors_professional[0])
ax.set_title("高品質保存用グラフ", fontweight="bold")
ax.set_xlabel("月")
ax.set_ylabel("売上（百万円）")
ax.set_xticks(months)
ax.legend()
ax.grid(True, alpha=0.3)

ax = axes[0, 1]
ax.axis("off")
ax.set_title("保存形式別設定", fontweight="bold")

format_text = ""
for name, settings in save_formats.items():
    format_text += f"{name}:\n"
    for key, value in settings.items():
        format_text += f"  {key}: {value}\n"
    format_text += "\n"

ax.text(
    0.05,
    0.95,
    format_text,
    transform=ax.transAxes,
    fontsize=10,
    verticalalignment="top",
    fontfamily="monospace",
    bbox=dict(boxstyle="round,pad=0.5", facecolor="lightgray", alpha=0.8),
)

ax = axes[1, 0]
colors_format = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4"]
bars = ax.bar(file_sizes, quality_scores, color=colors_format, alpha=0.8)
ax.set_title("形式別品質・サイズバランス", fontweight="bold")
ax.set_ylabel("品質スコア")
ax.set_ylim(0, 100)

for bar, score in zip(bars, quality_scores):
    ax.text(
        bar.get_x() + bar.get_width() / 2,
        score + 1,
        f"{score}",
        ha="center",
        va="bottom",
        fontweight="bold",
    )

ax = axes[1, 1]
ax.axis("off")
ax.set_title("用途別推奨設定", fontweight="bold")

usage_text = """用途別推奨設定:

Webサイト・ブログ
- PNG, 72-150 DPI
- bbox_inches='tight'
- 透過背景対応

印刷・レポート
- PDF, 300 DPI
- facecolor='white'
- 高解像度維持

プレゼンテーション
- SVG または PNG
- 150 DPI, 軽量化
- 明瞭なフォント

データ解析・学術
- EPS または PDF
- 300-600 DPI
- ベクター形式推奨
"""

ax.text(
    0.05,
    0.95,
    usage_text,
    transform=ax.transAxes,
    fontsize=11,
    verticalalignment="top",
    bbox=dict(boxstyle="round,pad=0.5", facecolor="lightblue", alpha=0.3),
)

plt.tight_layout()

# 実際に3つの形式で保存する
plt.savefig('chart_web.png', dpi=150, bbox_inches='tight', facecolor='white')
plt.savefig('chart_print.pdf', dpi=300, bbox_inches='tight')
plt.savefig('chart_transparent.png', dpi=150, bbox_inches='tight', transparent=True)
print('保存しました: chart_web.png / chart_print.pdf / chart_transparent.png')

plt.show()

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

rng = np.random.default_rng(42)
cities = np.array(["札幌市", "東京都", "大阪市", "福岡市", "横浜市", "仙台市"])
df_customers_geo = pd.DataFrame({
    "customer_id": [f"C{i:03d}" for i in range(1, 13)],
    "age": rng.integers(24, 65, 12),
    "spending_score": rng.integers(45, 95, 12),
    "satisfaction": np.round(rng.uniform(2.8, 5.0, 12), 1),
    "city": cities[np.arange(12) % len(cities)],
})

fig = px.scatter(
    df_customers_geo,
    x="age",
    y="spending_score",
    size="satisfaction",
    color="city",
    hover_name="customer_id",
    labels={
        "age": "年齢（歳）",
        "spending_score": "支出スコア",
        "satisfaction": "満足度",
        "city": "都市",
    },
    template="plotly_white",
    size_max=18,
)
fig.update_layout(
    title=dict(text="顧客分析: エクスポート用サンプル", font=dict(size=20)),
    height=600,
)

html_str = pio.to_html(fig, include_plotlyjs="cdn", full_html=True)
with open("customer_analysis.html", "w", encoding="utf-8") as file:
    file.write(html_str)
print("HTMLファイル出力完了: customer_analysis.html")

try:
    fig.write_image("customer_analysis.png", width=1200, height=800, scale=2)
    print("PNG画像出力完了: customer_analysis.png")
except Exception as exc:
    print(f"画像出力エラー: {exc}")
    print("kaleido のインストールが必要です。v1系ではChromeも必要なため、未導入なら plotly_get_chrome を実行してください。")

try:
    fig.write_image("customer_analysis.pdf", width=1200, height=800)
    print("PDF出力完了: customer_analysis.pdf")
except Exception as exc:
    print(f"PDF出力エラー: {exc}")

fig_json = pio.to_json(fig)
with open("customer_analysis.json", "w", encoding="utf-8") as file:
    file.write(fig_json)
print("JSON出力完了: customer_analysis.json")

fig_ppt = pio.from_json(pio.to_json(fig))
fig_ppt.update_layout(
    width=1280,
    height=720,
    font=dict(size=16),
    title=dict(text="顧客分析: プレゼンテーション用", font=dict(size=24)),
    template="plotly_white",
)

try:
    fig_ppt.write_image("presentation_chart.png", width=1280, height=720, scale=2)
    print("プレゼンテーション用画像出力完了: presentation_chart.png")
except Exception as exc:
    print(f"プレゼンテーション用画像出力エラー: {exc}")

print("オンライン共有オプション:")
print("- Plotly Chart Studio: chart-studio.plotly.com")
print("- GitHub Pages: HTMLファイルをGitHubで公開")
print("- Streamlit/Dash: Webアプリとして公開")

config = {
    "displayModeBar": True,
    "displaylogo": False,
    "modeBarButtonsToRemove": ["pan2d", "lasso2d"],
}

fig.show(config=config)

print("カスタム設定でツールバーを調整できます。")

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import japanize_matplotlib

rng = np.random.default_rng(42)

n_points = 30_000
request_count = rng.gamma(shape=2.0, scale=120.0, size=n_points)
response_time = request_count * 0.35 + rng.normal(0, 45, n_points)
categories = rng.choice(["Webログ", "決済", "問い合わせ"], size=n_points, p=[0.55, 0.25, 0.20])

t = np.linspace(0, 1, 80)
segments = []
for _ in range(1000):
    phase = rng.uniform(0, 2 * np.pi)
    trend = rng.normal(0.25, 0.08) * t
    noise = rng.normal(0, 0.02, len(t))
    segments.append(np.column_stack([t, 0.4 + trend + 0.12 * np.sin(2 * np.pi * t + phase) + noise]))

large_matrix = rng.normal(120, 18, size=(450, 450))
sampled_matrix = large_matrix[::5, ::5]

time_axis = np.linspace(0, 24, 600)
daily_pattern = 100 + 18 * np.sin(2 * np.pi * time_axis / 24) + rng.normal(0, 2, len(time_axis))

colors_professional = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b"]
HIGHLIGHT = "#1E56A0"
ACCENT = "#EA580C"
GRAYS = ["#999999", "#BBBBBB", "#DDDDDD"]

fig, axes = plt.subplots(2, 3, figsize=(18, 10))
fig.suptitle("静的グラフのパフォーマンス最適化", fontsize=16, fontweight="bold")

ax = axes[0, 0]
sample_index = rng.choice(n_points, size=5000, replace=False)
ax.scatter(
    request_count[sample_index],
    response_time[sample_index],
    s=5,
    alpha=0.35,
    color=HIGHLIGHT,
    rasterized=True,
)
ax.set_title("1. rasterized=True とサンプリング", fontweight="bold")
ax.set_xlabel("リクエスト数")
ax.set_ylabel("応答時間（ms）")
ax.grid(True, alpha=0.3)

ax = axes[0, 1]
collection = LineCollection(segments, colors=colors_professional[0], linewidths=0.4, alpha=0.18, rasterized=True)
ax.add_collection(collection)
ax.autoscale()
ax.set_title("2. LineCollection による一括描画", fontweight="bold")
ax.set_xlabel("期間")
ax.set_ylabel("センサー値")
ax.grid(True, alpha=0.3)

ax = axes[0, 2]
image = ax.imshow(sampled_matrix, cmap="viridis", aspect="auto", interpolation="nearest")
ax.set_title("3. 行列の間引き表示", fontweight="bold")
ax.set_xlabel("設備列")
ax.set_ylabel("時間帯")
fig.colorbar(image, ax=ax, fraction=0.046, pad=0.04)

ax = axes[1, 0]
ax.plot(time_axis, daily_pattern, color=GRAYS[1], linewidth=1, label="静的背景")
for hour in [3, 8, 13, 18, 23]:
    center = np.argmin(np.abs(time_axis - hour))
    window = slice(max(center - 12, 0), min(center + 13, len(time_axis)))
    ax.plot(time_axis[window], daily_pattern[window], linewidth=3, color=ACCENT)
ax.set_title("4. 代表フレームだけを強調", fontweight="bold")
ax.set_xlabel("時刻")
ax.set_ylabel("処理件数")
ax.legend()
ax.grid(True, alpha=0.3)

ax = axes[1, 1]
category_colors = dict(zip(["Webログ", "決済", "問い合わせ"], colors_professional[:3]))
for category in ["Webログ", "決済", "問い合わせ"]:
    idx = np.where(categories == category)[0]
    step = max(1, len(idx) // 1200)
    sampled_idx = idx[::step][:1200]
    ax.scatter(
        request_count[sampled_idx],
        response_time[sampled_idx],
        s=5,
        alpha=0.35,
        color=category_colors[category],
        label=category,
        rasterized=True,
    )
ax.set_title("5. カテゴリ別フィルタリングとサンプリング", fontweight="bold")
ax.set_xlabel("リクエスト数")
ax.set_ylabel("応答時間（ms）")
ax.legend()
ax.grid(True, alpha=0.3)

ax = axes[1, 2]
ax.scatter(request_count, response_time, s=3, alpha=0.12, color=GRAYS[0], rasterized=True, label="明細点")
bins = np.linspace(request_count.min(), request_count.max(), 24)
centers = (bins[:-1] + bins[1:]) / 2
mean_response = []
for left, right in zip(bins[:-1], bins[1:]):
    mask = (request_count >= left) & (request_count < right)
    mean_response.append(np.mean(response_time[mask]) if np.any(mask) else np.nan)
ax.plot(centers, mean_response, linewidth=3, color=ACCENT, label="平均線")
ax.set_title("6. ラスター点とベクター線の混在", fontweight="bold")
ax.set_xlabel("リクエスト数")
ax.set_ylabel("応答時間（ms）")
ax.legend()
ax.grid(True, alpha=0.3)

print("静的グラフのパフォーマンス最適化:")
print("- 点群は rasterized=True を使うと、PDF/SVG出力時のファイルサイズを抑えやすくなります。")
print("- 大量の線は LineCollection で一括描画すると効率的です。")
print("- ヒートマップは必要な解像度まで間引いてから描画するとメモリ使用量を抑えられます。")
print("- 注目すべき代表フレームや平均線だけをベクター要素として重ねると、読みやすさを保てます。")

plt.tight_layout()
plt.show()

import sys
import time

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

np.random.seed(42)

data_size = 50000
large_df = pd.DataFrame({
    "x": np.random.randn(data_size),
    "y": np.random.randn(data_size),
    "部門": np.random.choice(["営業", "マーケティング", "開発", "サポート"], data_size),
    "売上規模": np.random.randint(1, 100, data_size)
})

print(f"大量データサイズ: {len(large_df):,}行")

sample_size = 5000

start_time = time.time()
sampled_df = large_df.sample(n=sample_size, random_state=42)
sampling_time = time.time() - start_time

start_time = time.time()
x_bins = pd.cut(large_df["x"], bins=50)
y_bins = pd.cut(large_df["y"], bins=50)

heatmap_data = (
    large_df
    .groupby([y_bins, x_bins], observed=True)
    .size()
    .reset_index(name="件数")
)

heatmap_matrix = heatmap_data.pivot_table(
    values="件数",
    index="y",
    columns="x",
    fill_value=0
)
aggregation_time = time.time() - start_time

start_time = time.time()
webgl_traces = []
for department in sampled_df["部門"].unique():
    department_data = sampled_df[sampled_df["部門"] == department]
    webgl_traces.append(
        go.Scattergl(
            x=department_data["x"],
            y=department_data["y"],
            mode="markers",
            name=f"{department}（WebGL）",
            marker=dict(size=5, opacity=0.7),
            showlegend=True
        )
    )
webgl_time = time.time() - start_time

fig = make_subplots(
    rows=1,
    cols=3,
    subplot_titles=[
        f"サンプリング（{sample_size:,}点）",
        "ビン集約ヒートマップ",
        "Scattergl（WebGL）"
    ],
    specs=[[{"type": "scatter"}, {"type": "heatmap"}, {"type": "scatter"}]]
)

for department in sampled_df["部門"].unique():
    department_data = sampled_df[sampled_df["部門"] == department]
    fig.add_trace(
        go.Scatter(
            x=department_data["x"],
            y=department_data["y"],
            mode="markers",
            name=f"{department}（通常）",
            marker=dict(
                size=department_data["売上規模"] / 12,
                opacity=0.65
            ),
            showlegend=True
        ),
        row=1,
        col=1
    )

fig.add_trace(
    go.Heatmap(
        z=heatmap_matrix.values,
        colorscale="Viridis",
        colorbar=dict(title="件数"),
        showscale=True
    ),
    row=1,
    col=2
)

for trace in webgl_traces:
    fig.add_trace(trace, row=1, col=3)

fig.update_layout(
    title=dict(
        text="大量データ可視化のパフォーマンス最適化",
        font=dict(size=20)
    ),
    template="plotly_white",
    height=520,
    width=1200
)

fig.update_xaxes(title_text="指標X")
fig.update_yaxes(title_text="指標Y")

fig.show()

print("\nパフォーマンス比較:")
print(f"サンプリング処理: {sampling_time:.3f}秒")
print(f"集約処理: {aggregation_time:.3f}秒")
print(f"WebGLトレース作成: {webgl_time:.3f}秒")

original_size = sys.getsizeof(large_df)
sampled_size = sys.getsizeof(sampled_df)

print("\nメモリ使用量:")
print(f"元データ: {original_size / 1024 / 1024:.1f} MB")
print(f"サンプリング後: {sampled_size / 1024 / 1024:.1f} MB")
print(f"削減率: {(1 - sampled_size / original_size) * 100:.1f}%")