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

table_values = [[10, 20, 30, 40], [34, 15, 27, 8]]
table_labels = ["Label1", "Label2"]
table_fig = make_subplots(
    rows=2,
    cols=2,
    specs=[[{"type": "domain"}, {"type": "domain"}], [{"colspan": 2}, None]],
)
table_fig.add_trace(
    go.Table(header={"values": table_labels}, cells={"values": table_values}),
    row=1,
    col=1,
)
table_fig.add_trace(
    go.Table(
        cells={
            "values": pd.DataFrame(table_values),
            "line": {"width": 2, "color": "black"},  # 罫線のスタイル
            "fill": {"color": "white"},  # 塗りつぶし色
            "align": "right",  # 配置
        },
        header={
            "values": table_labels,
            "height": 18,  # セルの高さ
            "line": {"width": 2, "color": "black"},
            "fill": {"color": "white"},
            "font": {"size": 10},  # フォントサイズ
        },
    ),
    row=1,
    col=2,
)
table_fig.add_trace(
    go.Scatter(x=table_values[0], y=table_values[1]), row=2, col=1
)
table_fig.show()

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

asset_labels  = ["資産", "債権", "A社",  "B社",  "株式", "C社",  "D社",  "預金","Z社"]
asset_parents = ["",     "資産", "債権", "債権", "資産", "株式", "株式", "資産","B社"]
asset_values  = [1000,    400,    300,    100,    200,    160,    40,     400,   50]

go.Figure(
    go.Treemap(
        labels=asset_labels,    # セクタごとのラベル
        parents=asset_parents,  # 親セクタのラベル
        values=asset_values,    # セクタごとの値
        branchvalues="total"    # 親が子の階層すべての合計値
    )
).show()

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

asset_labels  = ["資産", "債権", "A社",  "B社",  "株式", "C社",  "D社",  "預金","Z社"]
asset_parents = ["",     "資産", "債権", "債権", "資産", "株式", "株式", "資産","B社"]
asset_values  = [1000,    400,    300,    100,    200,    160,    40,     400,   50]

sunburst_fig = make_subplots(
    1, 2, specs=[[{"type": "domain"}, {"type": "domain"}]]
)

sunburst_fig.add_trace(
    go.Sunburst(
        labels=asset_labels,        # セクタごとのラベル
        parents=asset_parents,      # 親セクタのラベル
        values=asset_values,        # セクタごとの値
        branchvalues="total",       # 親が子の階層すべての合計値
    ),
    row=1,
    col=1,
)

sunburst_fig.add_trace(
    go.Sunburst(
        labels=asset_labels,
        parents=asset_parents,
        values=asset_values,
        branchvalues="remainder",   # 子が親とは別の値
    ),
    row=1,
    col=2,
)

sunburst_fig.show()

import plotly.graph_objects as go

companies = ["A社", "B社", "C社", "D社"]
sales_2020 = [900, 700, 200, 100]
sales_2021 = [1400, 980, 450, 380]

go.Figure(
    go.Pie(labels=companies, values=sales_2020),
    layout=go.Layout(title="売上"),  # figureのタイトル
).show()

from plotly.subplots import make_subplots

sales_pie_fig = make_subplots(
    rows=1,
    cols=2,
    specs=[[{"type": "domain"}, {"type": "domain"}]],
    subplot_titles=["2020年の売上", "2021年の売上"],  # サブプロットのタイトル
)
sales_pie_fig.add_trace(
    go.Pie(
        labels=companies,
        values=sales_2020,      # 同じscalegroupでサイズが調整される
        scalegroup="sales",
    ),
    row=1,
    col=1,
)
sales_pie_fig.add_trace(
    go.Pie(
        labels=companies,
        values=sales_2021,
        scalegroup="sales"),    # 同じscalegroupでサイズが調整される
    row=1,
    col=2,
)
sales_pie_fig.show()

import numpy as np
import plotly.graph_objects as go

np.random.seed(7)
area_x = np.arange(15)
area_y1, area_y2 = np.random.rand(2, 15)  # 一様乱数を生成
area_trace = go.Scatter(x=area_x,
                        y=area_y1,
                        name="area1",
                        fill="tozeroy",   # 0からY値まで塗りつぶし,
                        mode="none",      # 線とマーカーを描画しない
                        fillcolor="mediumslateblue"  # 塗りつぶし色
)
area_fig = go.Figure([area_trace])
area_fig.show()

next_area_trace = go.Scatter(x=area_x,
                             y=area_y1 + area_y2,
                             name="area2",
                             fill="tonexty",     # 前のtraceからY値までを塗りつぶし
                             mode="none",
                             fillcolor="lightpink"
)
stacked_area_fig = go.Figure([area_trace, next_area_trace])
stacked_area_fig.show()

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

bar_fig = make_subplots(rows=2, cols=2, subplot_titles=["ラベル", "座標", "横"])

# X値が文字列型の場合
bar_fig.add_trace(go.Bar(x=["a", "b", "c", "d", "e"], y=[8, 3, 5, 4, 2]), row=1, col=1)

# X値が数値型の場合
bar_fig.add_trace(go.Bar(x=[0, 1, 2, 4, 5], y=[1, 4, 2, 3, 2]), row=1, col=2)

# 横向きの棒グラフの場合
bar_fig.add_trace(
    go.Bar(x=[3, 2, 1, 5], y=[1, 2, 3, 4], orientation="h"), row=2, col=1
)

bar_fig.show()

bar_trace1 = go.Bar(x=["a", "b", "c", "d", "e"], y=[3, 5, 2, 1, 6], name="group1")
bar_trace2 = go.Bar(x=["a", "b", "c", "d", "e"], y=[4, 3, 5, 2, 1], name="group2")
grouped_fig = go.Figure([bar_trace1, bar_trace2])
grouped_fig.show()

stacked_fig = go.Figure(
    [bar_trace1, bar_trace2],
    layout=go.Layout(barmode="stack")  # 積み上げ棒グラフにするためlayoutを設定
)
stacked_fig.show()

bar_trace3 = go.Bar(x=["a", "b", "c", "d", "e"], y=[-2, -3, 1, -4, 2], name="group3")
relative_fig = go.Figure(
    [bar_trace1, bar_trace2, bar_trace3],
    layout=go.Layout(barmode="relative")    # 0未満の値をした方向に積み上げるlayoutを作成
)
relative_fig.show()

import plotly.graph_objects as go
import numpy as np

np.random.seed(1)
scatter_x, scatter_y = np.random.randn(2, 100)  # 正規分布に従う乱数を生成
go.Figure(
    go.Scatter(x=scatter_x,
               y=scatter_y,
               name="standard normal distribution",
               mode="markers"                   # 描画モードを散布図に設定
    )
).show()

np.random.seed(1)
scatter_color = np.random.rand(100)
scatter_size = np.random.rand(100) * 30
go.Figure(
    go.Scatter(x=scatter_x,
               y=scatter_y,
               name="4d",
               mode="markers",
               marker={
                   "size": scatter_size,    # 要素ごとに大きさを指定
                   "color": scatter_color,  # 要素の大きさを色で表現
                   "sizemode": "diameter",  # 大きさを直径で指定
                   "opacity": 0.8,          # 要素の不透明度
                   "showscale": True        # カラースケールを表示
               }
    )
).show()

np.random.seed(1)
large_x, large_y = np.random.randn(2, 10000)
go.Figure([go.Scattergl(x=large_x, y=large_y, mode="markers")]).show()

import plotly

stocks = plotly.data.stocks()
stocks.head()

import plotly.graph_objects as go
go.Figure(go.Scatter(x=stocks["date"], y=stocks["GOOG"])).show()

ts_layout = go.Layout(
    # X軸のRange SliderとRange Selectorを表示
    xaxis={
        "rangeslider": {"visible": True},   # RangeSliderを表示
        "rangeselector": {                  # RangeSelectを設定
            "buttons": [
                {"label": "1m", "step": "month", "count": 1},   # 1ヵ月
                {"label": "7d", "step": "day", "count": 7},     # 7日間
                {"step": "all"},                                # 全期間
            ]
        },
    }
)

go.Figure(
    go.Scatter(x=stocks["date"], y=stocks["GOOG"]), layout=ts_layout
).show()

import plotly.express as px

gapminder = px.data.gapminder()
gapminder.head()

scatter_3d_fig = px.scatter_3d(gapminder,               # データセット
                               x="year",                # X軸のデータ
                               y="continent",           # Y軸のデータ
                               z="pop",                 # Z軸のデータ
                               size="gdpPercap",        # 要素の大きさ
                               color="lifeExp",         # lifeExp列で色分け
                               hover_data=["country"]   # ホバーツールに表示
)
scatter_3d_fig.layout.update(scene={"zaxis": {"type": "log"}})  # Z軸を対数にとる
scatter_3d_fig.show()

import plotly.express as px

gapminder = px.data.gapminder()
gapminder.head()

px.scatter_geo(gapminder,               # データセット
               locations="iso_alpha",   # 位置をISO 3166-1 alpha-3形式で指定
               size="gdpPercap",        # 要素のサイズ
               color="lifeExp",         # 要素の色
               hover_name="country",    # ホバーツールのタイトル
               animation_frame="year"   # アニメーション
).show()