Python可視化ライブラリAltairをMatplotlibやPlotly、Bokehと比較してみた

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import pandas as pd

# サンプルデータ
df = pd.DataFrame({
    'fruit': ['Apple', 'Apple', 'Banana', 'Banana', 'Orange', 'Orange'],
    'type': ['A', 'B', 'A', 'B', 'A', 'B'],
    'sales': [30, 45, 10, 25, 40, 35]
})

df

fruit    type    sales
Apple    A       30
Apple    B       45
Banana   A       10
Banana   B       25
Orange   A       40
Orange   B       35

今回の目的は、fruit×type の売上 (sales) を棒グラフで表現することです。

## Matplotlibでの実装

```python
import matplotlib.pyplot as plt
import numpy as np

fruits = df['fruit'].unique()
types = df['type'].unique()

x_indices = np.arange(len(fruits))
bar_width = 0.35

# Type A と Type B の sales を抽出
sales_A = df[df['type'] == 'A'].sort_values('fruit')['sales'].values
sales_B = df[df['type'] == 'B'].sort_values('fruit')['sales'].values

fig, ax = plt.subplots(figsize=(6, 4))

# Type A のバー
ax.bar(x_indices, sales_A, width=bar_width, label='Type A')
# Type B のバー（少し右にずらす）
ax.bar(x_indices + bar_width, sales_B, width=bar_width, label='Type B')

ax.set_xticks(x_indices + bar_width/2)
ax.set_xticklabels(fruits)

ax.set_title("Matplotlib Bar Chart")
ax.set_xlabel("Fruit")
ax.set_ylabel("Sales")
ax.legend()

plt.show()

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import altair as alt

chart = alt.Chart(df).mark_bar().encode(
    x=alt.X('fruit:N', title='Fruit'),  # 名義データ
    y=alt.Y('sales:Q', title='Sales'),  # 数値データ
    color='type:N',                     # type を色分けに使う
    column='type:N'                     # typeごとに列で分割表示
).properties(
    title='Altair Bar Chart'
)

chart

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import plotly.express as px

fig = px.bar(
    df,
    x='fruit',
    y='sales',
    color='type',
    barmode='group',    # 並行バー表示
    title='Plotly Bar Chart'
)
fig.show()

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from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource
from bokeh.transform import dodge
from bokeh.io import output_notebook

output_notebook()

source = ColumnDataSource(df)

fruits = list(df['fruit'].unique())
p = figure(
    x_range=fruits,
    plot_height=300,
    title="Bokeh Bar Chart",
    toolbar_location=None
)

# Type A のバー
p.vbar(
    x=dodge('fruit', -0.15, range=p.x_range),
    top='sales',
    width=0.3,
    source=source[df['type'] == 'A'],
    color="blue",
    legend_label="Type A"
)

# Type B のバー
p.vbar(
    x=dodge('fruit', 0.15, range=p.x_range),
    top='sales',
    width=0.3,
    source=source[df['type'] == 'B'],
    color="orange",
    legend_label="Type B"
)

p.x_range.range_padding = 0.1
p.legend.location = "top_left"
p.xgrid.grid_line_color = None

show(p)