Plotting Stacked Event Durations in Python with Pandas

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💡 Problem Formulation: Data analysts often need to visualize the durations of sequential or overlapping events. This article solves the problem of creating a visual stacked representation of events using Python’s Pandas library. For example, you may have a DataFrame with start and end times for several events and you want to plot a stacked bar chart to represent the durations and overlaps of these events clearly.

Method 1: Using Pandas TimeGrouper and Plot Function

Pandas provides a built-in functionality called TimeGrouper, which allows grouping time-series data based on a specified frequency. Combined with the plot function’s ‘bar’ kind, you can create stacked bar charts to visually represent durations of events.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt

# Create sample DataFrame
df = pd.DataFrame({
    'Start': pd.to_datetime(['2023-01-01 10:00', '2023-01-01 10:30']),
    'End': pd.to_datetime(['2023-01-01 11:00', '2023-01-01 11:30']),
    'Event': ['A', 'B']
})

# Calculate durations
df['Duration'] = (df['End'] - df['Start']).dt.total_seconds() / 3600

# Plot
df.set_index('Start').groupby('Event').resample('15T').sum().unstack('Event').plot(kind='bar', stacked=True)
plt.show()

Output: This code will generate a stacked bar chart that visualizes the durations of events A and B in 15-minute intervals.

This code snippet creates a sample DataFrame with start and end times of events, calculates their durations, groups the data into 15-minute intervals, and then plots the stacked bar chart. Matplotlib is used for actual plotting. This stack visualization helps identify overlap and gaps among multiple events.

Method 2: Using Pandas Pivot Table

A pivot table in Pandas can reorganize and aggregate your data, which can then be used to plot a stacked bar chart. This method is particularly useful when dealing with categorical data that corresponds to different durations.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt

# Sample data
df = pd.DataFrame({
    'Start': pd.to_datetime(['2023-01-01', '2023-01-02']),
    'End': pd.to_datetime(['2023-01-01', '2023-01-02']),
    'Event': ['A', 'B'],
    'Duration': [4, 3]
})

# Create a pivot table
pivot = df.pivot_table(index='Start', columns='Event', values='Duration', aggfunc='sum')

# Plot a stacked bar chart
pivot.plot(kind='bar', stacked=True)
plt.show()

Output: This will generate a stacked bar chart with each bar representing a day. The segments of each bar show the duration of events A and B for that day.

This snippet creates a DataFrame, then generates a pivot table to reorganize the data so that the index (or x-axis) represents the start of the events, columns represent different events, and the values represent the duration of these events. This pivot table easily converts to a stacked bar chart where durations are summed up across events for each day.

Method 3: Using Bokeh for Interactive Visualization

Bokeh is a Python library for interactive visualization that enables beautiful and meaningful visual presentation of data in an easy-to-use manner. It is particularly effective for creating stacked bar charts when you want interactive capabilities.

Here’s an example:

from bokeh.plotting import figure, show, output_file
from bokeh.models import ColumnDataSource
import pandas as pd

# Sample data
df = pd.DataFrame({
    'Start': ['2023-01-01', '2023-01-02'],
    'Event': ['A', 'B'],
    'Duration': [4, 3]
})

# Prepare data for Bokeh
source = ColumnDataSource(df)

# Create figure and plot
p = figure(x_range=df['Start'], title="Event Durations")
p.vbar_stack(stackers=['Duration'], x='Start', width=0.9, color=['blue', 'red'], source=source)

# Show the plot
output_file("stacked_bar.html")
show(p)

Output: An interactive HTML file (“stacked_bar.html”) with a stacked bar chart that represents the duration of events A and B.

This code creates a Bokeh plot, which is interactive and great for a web-based dashboard. Data is first prepared for Bokeh and then a figure is created with a vertical stacked bar chart. The x_range is set to the dates on which the events started, and vbar_stack is used to stack durations of events on those dates.

Method 4: Using Seaborn for Advanced Styling

Seaborn is a statistical data visualization library in Python that’s built on top of Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics, including stacked bar charts with advanced styling options.

Here’s an example:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# Sample data
df = pd.DataFrame({
    'Date': ['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-02'],
    'Event': ['A', 'B', 'A', 'B'],
    'Duration': [4, 2, 5, 3]
})

# Pivot for Seaborn
pivot_df = df.pivot('Date', 'Event', 'Duration')

# Use seaborn to plot
sns.set_style('whitegrid')
sns.barplot(data=pivot_df, stacked=True)
plt.show()

Output: This will produce a stacked bar chart with advanced styling, where bars are split by event types A and B for each date.

In this method, we use Seaborn to leverage the barplot function together with a pivot table from Pandas. Seaborn’s sns.set_style() allows for advanced styling of the chart, and the actual visualization is created with the sns.barplot() function which stacks the durations for each event type plotted by date.

Bonus One-Liner Method 5: Quick and Dirty with Pandas Plotting

If you’re pressed for time and need a quick visualization without much fuss, Pandas plotting utilities have got you covered with a one-liner for a basic stacked bar chart.

Here’s an example:

df.pivot(index='Start', columns='Event', values='Duration').plot(kind='bar', stacked=True)

Output: A fundamental stacked bar chart plotting the events’ durations against their start times.

The one-liner code pivots the DataFrame to organize the data by event start times and then directly calls the plot method to produce a stacked bar chart. While this doesn’t give you much control over styling or interactive capabilities, it’s perfect for a quick data check or simple reports.

Summary/Discussion

  • Method 1: TimeGrouper and Plot. Great for time-series data. May require additional preprocessing to work with non-standard time intervals.
  • Method 2: Pivot Table Method. Good for handling categorical data and simple aggregation. Not as flexible for detailed customization of the plot.
  • Method 3: Bokeh for Interactivity. Excellent for interactive web presentations. Higher learning curve and more code required than simpler methods.
  • Method 4: Seaborn for Styling. Offers advanced visual styles. Requires understanding of Seaborn’s API and data reshaping with pivot tables.
  • Bonus Method 5: Quick Pandas Plotting. Fast and easy with minimal code. Limited functionality and customization options.