使用Python Plotly绘制堆叠图求助：时间轴与动态系列问题

Got it, let's solve this problem step by step. The key challenge here is handling the variable series (s1, s2, etc.) across different time points—we need to first restructure your data into a format Plotly can work with, then create the stacked chart.

Step 1: Restructure the Raw Data #

First, we'll convert your tuple-based data into a pandas DataFrame where each row represents a time point, and each column is a series (with 0 values for series that aren't present at that time). This ensures all time points have the same set of series, which is required for stacked charts.

Here's the code to do that:

import pandas as pd
import plotly.express as px

# Your raw data
raw_data = [
    ('2018-04-09', '10:18:11',['s1',10],['s2',15],['s3',5]),
    ('2018-04-09', '10:20:11',['s4',8],['s2',20],['s1',10]),
    ('2018-04-10', '10:30:11',['s4',10],['s5',6],['s6',3])
]

# Process each entry into a dictionary with datetime and series values
processed_entries = []
for entry in raw_data:
    # Combine date and time into a single datetime object
    datetime_obj = pd.to_datetime(f"{entry[0]} {entry[1]}")
    entry_dict = {"datetime": datetime_obj}
    
    # Add each series and its value to the dictionary
    for series in entry[2:]:
        series_name, value = series
        entry_dict[series_name] = value
    
    processed_entries.append(entry_dict)

# Convert to DataFrame
df = pd.DataFrame(processed_entries)

# Get all unique series names across all time points
all_series = list({key for entry in processed_entries for key in entry if key != "datetime"})

# Reindex to include all series, filling missing values with 0
df = df.set_index("datetime").reindex(columns=all_series, fill_value=0).reset_index()

Step 2: Create the Stacked Chart #

Now that we have a clean DataFrame, we can use Plotly Express to create either a stacked bar chart or a stacked area chart.

Option 1: Stacked Bar Chart

fig = px.bar(
    df,
    x="datetime",
    y=all_series,
    barmode="stack",
    labels={"datetime": "Time", "value": "Value"},
    title="Stacked Series Over Time (Bar Chart)"
)

# Format the x-axis to show your desired time format
fig.update_layout(
    xaxis=dict(
        tickformat="%H:%M, %Y年%m月%d日"  # Example: "10:18, 2018年04月09日"
    )
)

fig.show()

Option 2: Stacked Area Chart

If you prefer an area chart instead, use this code:

fig = px.area(
    df,
    x="datetime",
    y=all_series,
    labels={"datetime": "Time", "value": "Value"},
    title="Stacked Series Over Time (Area Chart)"
)

# Format x-axis
fig.update_layout(
    xaxis=dict(
        tickformat="%H:%M, %Y年%m月%d日"
    )
)

fig.show()

Key Notes: #

• The reindex step ensures every time point has all possible series, filling missing values with 0. This is critical for Plotly to render the stacked chart correctly.
• You can adjust the tickformat string to match your preferred date/time format. Check pandas' strftime documentation for more format options.
• If you need to sort the series in a specific order, just reorder the all_series list before creating the chart.

内容的提问来源于stack exchange，提问作者somerandomguy