Pythonでハイセンスなグラフ作成〜Plotlyスタイル一覧

最近ハマっているPythonライブラリ「Plotly」のスタイル一覧です。

Plotly Python Graphing Library

レポート作成時、グラフを「相手企業のコーポレートカラーに合わせたい」ときや「資料全体のトーン＆マナーをこだわりたい」ときがあり、よく調べるので、見返せるように整理しておこうと思います。

完全に自分用です。誰得か分からない酔狂noteですが、どなたかの参考になれば幸いです。

以前、matplotlibのスタイル一覧を整理しているので、コチラもよかったら。→Pythonでハイセンスなグラフ作成〜matplotlibスタイル一覧

やってみた①散布図

style_scatter = ["aggrnyl","agsunset","blackbody","bluered","blues","blugrn","bluyl","brwnyl","bugn","bupu","burg","burgyl"]

style_scatter = ["cividis","darkmint","electric","emrld","gnbu","greens","greys","hot","inferno","jet","magenta","magma"]

style_scatter = ["mint","orrd","oranges","oryel","peach","pinkyl","plasma","plotly3","pubu","pubugn","purd","purp"]

style_scatter = ["purples","purpor","rainbow","rdbu","rdpu","redor","reds","sunset","sunsetdark","teal","tealgrn","viridis"]

style_scatter = ["ylgn","ylgnbu","ylorbr","ylorrd"]

style_scatter = ["algae","amp","armyrose","balance","brbg","curl","deep","delta","dense","earth","edge","fall"]

style_scatter =["geyser","gray","haline","hsv","ice","icefire","matter","mrybm","mygbm","phase","picnic""piyg"]

style_scatter = ["portland","prgn","puor","rdgy","rdylbu","rdylgn","solar","spectral","speed","tealrose","tempo","temps"]

 style_scatter = ["thermal","tropic","turbid","twilight"]

草間弥生さんもびっくりの水玉です。データはpandasとnumpyを使って適当に作成してます。

#ライブラリのimport
import plotly
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
import pandas as pd
import numpy as np

#ランダムデータの作成
random = pd.DataFrame(columns=["x","y","z"])
random.x = np.random.rand(100)
random.y = random.x*(np.random.rand(100)*10)
random.z = np.random.f(3., 30., 100)

#描画
style_scatter = "amp"

fig = px.scatter(random, x="x", y="y",size='z',color="z",
                template="plotly_white",
                color_continuous_scale=style_bar,
                title="color_continuous_scale = " + str(style_bar)
               )
fig.show()

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やってみた②パラレルコーディネイト

一応、別のグラフでも。平行座標という分析手法です。

style_category = ["aggrnyl","agsunset","blackbody","bluered","blues","blugrn","bluyl","brwnyl","bugn","bupu","burg","burgyl"]

style_category = ["cividis","darkmint","electric","emrld","gnbu","greens","greys","hot","inferno","jet","magenta","magma"]

style_category = ["mint","orrd","oranges","oryel","peach","pinkyl","plasma","plotly3","pubu","pubugn","purd","purp"]

style_category = ["purples","purpor","rainbow","rdbu","rdpu","redor","reds","sunset","sunsetdark","teal","tealgrn","viridis"]

style_category = ["ylgn","ylgnbu","ylorbr","ylorrd"]

style_category = ["algae","amp","armyrose","balance","brbg","curl","deep","delta","dense","earth","edge","fall"]

style_category =["geyser","gray","haline","hsv","ice","icefire","matter","mrybm","mygbm","phase","picnic""piyg"]

style_category = ["portland","prgn","puor","rdgy","rdylbu","rdylgn","solar","spectral","speed","tealrose","tempo","temps"]

style_category  = ["thermal","tropic","turbid","twilight"]

データはコチラを適当に加工。

style_category = "emrld"

fig = px.parallel_categories(test.sort_values(by="medal_number"),
                            dimensions=["Sport","Gender","Country_Code","medal_number"],
                            color="medal_number",
                            color_continuous_scale= str(style_category)
                           )
fig.show()
#pio.write_image(fig, style_category+".png") 

この平行座標を使ってみて、以前、Spotifyの人気曲の条件を分析してみたりしました。

目がチカチカますね。

ほんと誰得か分かりませんが、楽しんでいただけたら幸いです。

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合わせてどうぞ

以前、「matplotlib」でも似たようなことやってます。

🐍Pythonをマーケティングの実務に活かす_その１｜マイキー_Mikey｜note