[Python+Altair (Vega-lite)]：来自 excel-like Pandas' Dataframe 的 Streamgraph

Question

我有一个 Pandas' 数据框，我想使用 Altair 进行可视化，但看起来库语法还超出了我的技能范围：

                 Time     WL1       WL2      WL3      WL4
0 2017-04-05 09:15:00  103448.0  100776.0  71581.1  81118.2
1 2017-04-05 09:30:00  114730.0  113206.0  68600.1  78322.2
2 2017-04-05 09:45:00  117252.0  114189.0  68208.5  77261.0
3 2017-04-05 10:00:00  115569.0  111300.0  67454.7  75347.4
4 2017-04-05 10:15:00  117504.0  114956.0  68709.9  76345.1  
5 2017-04-05 10:30:00  116123.0  112637.0  66210.9  74544.4
6 2017-04-05 10:45:00  111050.0  106342.0  61699.3  70165.2
7 2017-04-05 11:00:00  111221.0  108409.0  66124.6  74842.1
8 2017-04-05 11:15:00  111604.0  106853.0  66950.5  75782.9
9 2017-04-05 11:30:00  113026.0  108071.0  68222.2  77848.0

我找不到像这样创建 Streamgraph 的语法，摘自 Altair 的教程 (06-AreaCharts)：

我可以看到单行，但它不是我数据的最佳表示：

LayeredChart(data,
    layers=[
        Chart().mark_line().encode(
            x='Time:T',
            y='WL1:Q',
        ),

        Chart().mark_line().encode(
            x='Time:T',
            y='WL2:Q',
        ),
        Chart().mark_line().encode(
            x='Time:T',
            y='WL3:Q',
        ),

        Chart().mark_line().encode(
            x='Time:T',
            y='WL4:Q',
        ),
    ]
)

有没有办法直接从多列数据帧创建 Streamgraph 而无需通过 LayeredChart？

Answer 1

此 page 包含流图的代码。

顺便说一下，您可能希望将代码与 vega-lite json spec 进行比较，以便您更好地理解 Altair 语法。

Answer 2

可能已经很晚了，但我希望这个 post 对其他想要使用 altair 处理本地或远程 csv 数据，json 文件的人有用。

import altair as alt
import pandas as pd
import numpy as np

time = np.array([
    '2017-04-05 09:15:00',
    '2017-04-05 09:30:00',
    '2017-04-05 09:45:00',
    '2017-04-05 10:00:00',
    '2017-04-05 10:15:00',
    '2017-04-05 10:30:00',
    '2017-04-05 10:45:00',
    '2017-04-05 11:00:00',
    '2017-04-05 11:15:00',
    '2017-04-05 11:30:00',
])

wl1 = np.array([
    103448.0,
    114730.0,
    117252.0,
    115569.0,
    117504.0,
    116123.0,
    111050.0,
    111221.0,
    111604.0,
    113026.0,
])

wl2 = np.array([
    100776.0,
    113206.0,
    114189.0,
    111300.0,
    114956.0,
    112637.0,
    106342.0,
    108409.0,
    106853.0,
    108071.0,
])

wl3 = np.array([
    71581.1,
    68600.1,
    68208.5,
    67454.7,
    68709.9,
    66210.9,
    61699.3,
    66124.6,
    66950.5,
    68222.2,
])

wl4 = np.array([
    81118.2,
    78322.2,
    77261.0,
    75347.4,
    76345.1,
    74544.4,
    70165.2,
    74842.1,
    75782.9,
    77848.0,
])

df = pd.DataFrame({'time': time, "wl1": wl1, "wl2": wl2, "wl3": wl3, "wl4": wl4})
df = df.melt('time') # Unpivot df with 'time' as identifier

# Save the data into csv and json format and upload them to a public website
lcsv  = './timeseries.csv'
ljson = './timeseries.json'
rcsv  = 'https://raw.githubusercontent.com/yoonghm/data/master/timeseries.csv'
rjson = 'https://raw.githubusercontent.com/yoonghm/data/master/timeseries.json'
df.to_csv(lcsv, index=False)           # Remove index
df.to_json(ljson, orient='records')    # In records orietation

def plot_line(data, source=str(data)):
    return (
        alt.Chart(data, title='Source: ' + source).mark_line(point=True).encode(
            x=alt.X('time:T',  scale=alt.Scale(zero=False)),
            y=alt.Y('value:Q', scale=alt.Scale(zero=False)),
            color='variable:N',
            tooltip=['time:T', 'value:Q']
        ).properties(
            width=300, height=400
        )
    )

a = plot_line(df, source='DataFrame')
b = plot_line(lcsv, lcsv)
c = plot_line(ljson, ljson)
d = plot_line(rcsv, rcsv)
e = plot_line(rjson, rjson)

a & b & c & d & e

创建 vega-lite 图后，您可以使用 vega-lite 处理流式数据，如 this post and that post.

中所示

[Python+Altair (Vega-lite)]：来自 excel-like Pandas' Dataframe 的 Streamgraph

[Python+Altair (Vega-lite)]: Streamgraph from excel-like Pandas' Dataframe

python

stream-graph

vega-lite

altair