Effective Strategies for Plotting Cumulative Graphs with Python Datetimes in Matplotlib

💡 Problem Formulation: You have a dataset with timestamps and values that you wish to analyze over time. The goal is to produce a cumulative graph that clearly displays the sum of values up to each point in time within the dataset. For instance, if your input is a list of datetime objects and their corresponding values, the desired output would be a graph where the x-axis represents time and the y-axis shows the running total.

Method 1: Using cumsum() with plot_date()

This method involves calculating the cumulative sum of values using Pandas‘ cumsum() function and then plotting them against dates with Matplotlib’s plot_date(). This method is convenient when working with Pandas DataFrames.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

# Create a sample DataFrame
df = pd.DataFrame({
    'dates': pd.date_range('2021-01-01', periods=5),
    'values': [2, 3, 5, 7, 11]
})

# Calculate cumulative sum
df['cumulative'] = df['values'].cumsum()

# Plot
plt.figure(figsize=(10, 5))
plt.plot_date(df['dates'], df['cumulative'], linestyle='solid')
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
plt.gcf().autofmt_xdate()
plt.show()

The output is a line graph with dates on the x-axis and the running total of values on the y-axis.

The provided code snippet creates a sample dataframe with a sequence of dates and integer values. Then it calculates the cumulative sum of these values using df['values'].cumsum(). Finally, using Matplotlib’s plot_date(), it creates a line graph, formats the date axis for better readability, and displays the cumulative graph.

Method 2: Using stackplot() for a Filled Cumulative Plot

Matplotlib’s stackplot() function can be used to generate a filled cumulative plot, providing a visual weight to the cumulative data. It is especially useful for representing cumulative data in an area plot format.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample DataFrame
df = pd.DataFrame({
    'dates': pd.date_range('2021-01-01', periods=5),
    'values': [2, 3, 5, 7, 11]
})

# Plot
plt.figure(figsize=(10, 5))
plt.stackplot(df['dates'], df['values'].cumsum())
plt.show()

The output is a filled cumulative plot with time on the x-axis and the cumulative values on the y-axis.

This code snippet constructs a filled cumulative plot by first calculating the cumulative sum of a series of values, and then using plt.stackplot() to generate the area under the curve. The resulting graph highlights the cumulative values with a solid fill coloring, which can be more visually impactful.

Method 3: Using bar() for a Cumulative Bar Chart

A bar chart can be an effective visual representation for displaying the cumulative sum, especially if the data points are discrete events in time. Matplotlib’s bar() function creates a cumulative bar chart, facilitating easy comparison between different points in time.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample DataFrame
df = pd.DataFrame({
    'dates': pd.date_range('2021-01-01', periods=5),
    'values': [2, 3, 5, 7, 11]
})

# Calculate cumulative sum
df['cumulative'] = df['values'].cumsum()

# Plot
plt.figure(figsize=(10, 5))
plt.bar(df['dates'], df['cumulative'])
plt.show()

The output is a bar chart with time on the x-axis and the cumulative values on the y-axis.

In this snippet, a dataframe with dates and values is used to calculate the cumulative sum, and then a bar chart is plotted with these cumulative sums. By using the bar chart format, each bar represents the cumulative total up to that date, allowing for an intuitive understanding of how the total value builds up over time.

Method 4: Custom Accumulation Function with plot()

If you need more control over the accumulation process, for example, to handle missing dates or apply custom business logic, defining a custom accumulation function and plotting it with plt.plot() can be quite flexible.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt

# Custom accumulation function
def custom_accumulate(values):
    total, cumulative = 0, []
    for value in values:
        total += value
        cumulative.append(total)
    return cumulative

# Create a sample DataFrame
df = pd.DataFrame({
    'dates': pd.date_range('2021-01-01', periods=5),
    'values': [2, 3, 5, 7, 11]
})

# Apply custom accumulation
df['cumulative'] = custom_accumulate(df['values'])

# Plot
plt.figure(figsize=(10, 5))
plt.plot(df['dates'], df['cumulative'])
plt.show()

The output is a line graph similar to Method 1, but with accumulation logic tailored to specific requirements.

This code leverages a custom function to calculate the cumulative sum, providing flexibility to include any special conditions or treatment of the data. After applying this function to the dataframe’s values, the result is plotted using Matplotlib’s plt.plot(), giving a clear visualization of the custom cumulative data.

Bonus One-Liner Method 5: Cumulative Plot with Resampling

When working with time-series data that spans long periods, resampling can simplify the data before plotting the cumulative sum. Pandas’ resample method combined with sum() and Matplotlib’s plot function makes for a concise one-liner to achieve this.

Here’s an example:

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample DataFrame with frequent data points
df = pd.DataFrame({
    'dates': pd.date_range('2021-01-01', periods=100, freq='H'),
    'values': range(100)
})

# Plot resampled cumulative data
(df.set_index('dates').resample('D')['values'].sum().cumsum().plot(figsize=(10, 5)))
plt.show()

The output is a simplified line graph with dates resampled to the specified frequency and cumulative values.

This one-liner packs a lot of functionality: it sets the dates as the index, resamples the data to daily frequency, then calculates the cumulative sum and plots it, all in one go. It is especially useful for handling very granular time-series data and creating a more readable cumulative graph.

Summary/Discussion

Method 1: Using cumsum() with plot_date(). Excellent for basic time-series data. Limited customization of the cumulative logic.
Method 2: Using stackplot() for a Filled Cumulative Plot. Provides a visually heavy representation of data. Not as clear as line plots for precise values at specific points in time.
Method 3: Using bar() for a Cumulative Bar Chart. Good visual distinction for discrete events over time. Potentially less useful for continuous data streams.
Method 4: Custom Accumulation Function with plot(). Highly customizable. Can be complex and may require additional debugging.
Bonus Method 5: Cumulative Plot with Resampling. Great for simplifying complex or high-frequency data. The fixed resampling rate might hide details of data fluctuation within the resampling period.