Overlaying Pandas Plot with Matplotlib is Sensitive to the Plotting Order

Introduction

When creating visualizations using both Pandas and Matplotlib, it’s common to encounter issues related to plotting order. In this article, we’ll explore a specific problem where overlaying a Pandas plot with Matplotlib results in unexpected behavior due to differences in plotting order.

Problem Description

The problem arises when trying to combine two plots: one created using Pandas plot.area() and the other created using Matplotlib’s pyplot.plot(). The issue is that the order of plotting affects the final result, with different values displayed on the x-axis depending on the plotting order.

Example Code

To illustrate this problem, let’s consider an example code snippet:

import pandas as pd
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 6))
# First -- Pandas area plot
revenue.plot.area(ax=ax)
# Second -- Matplotlib line plot 
ax.plot(revenue.index, projection, color='black', linewidth=3)
plt.tight_layout()
plt.show()

In this example, the Pandas plot.area() function creates an area plot with a DateTimeIndex on the x-axis. However, when we add the Matplotlib pyplot.plot() function to overlay the line plot, the results are inconsistent, with only the Pandas plot displayed.

Reversing the Plotting Order

If we reverse the plotting order by adding the line plot first and then the area plot, the results change significantly:

import pandas as pd
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 6))
# First -- Matplotlib line plot 
ax.plot(revenue.index, projection, color='black', linewidth=3)
# Second -- Pandas area plot
revenue.plot.area(ax=ax)
plt.tight_layout()
plt.show()

In this revised example, the overlaying works as expected, with both plots displayed on the same axes.

Understanding the Issue

The issue at hand is related to how Matplotlib and Pandas handle plotting orders. When using Pandas plot.area(), the DateTimeIndex is formatted in a specific way, which can conflict with the x-axis values used by Matplotlib’s pyplot.plot() function.

One possible explanation for this behavior is that Pandas’ formatting of the DateTimeIndex creates a unique set of x-axis values that are not compatible with Matplotlib’s default x-axis settings. This results in the inconsistent display of values on the x-axis when overlaying the two plots.

Solution: Mixing Plotting Libraries

To resolve this issue, we can either stick to using one plotting library (Pandas or Matplotlib) for both plots or mix and match different plotting functions from each library. Here are a few examples:

Option 1: Using only Pyplot

We can create both the line plot and area plot using Matplotlib’s pyplot functions:

import pandas as pd
from matplotlib import pyplot as plt

projection = [1000, 2000, 3000, 4000]

datetime_series = pd.to_datetime(["2021-12","2022-01", "2022-02", "2022-03"])
datetime_index = pd.DatetimeIndex(datetime_series.values)

revenue = pd.DataFrame({"value": [1200, 2200, 2800, 4100]})
revenue = revenue.set_index(datetime_index)

fig, ax = plt.subplots(1, 2, figsize=(10, 4))

# Option 1: only pyplot
ax[0].fill_between(revenue.index, revenue.value)
ax[0].plot(revenue.index, projection, color='black', linewidth=3)
ax[0].set_title("Pyplot")

# Option 2: only DataFrame.plot
revenue["projection"] = projection

revenue.plot.area(y='value', ax=ax[1])
revenue.plot.line(y='projection', ax=ax[1], color='black', linewidth=3)
ax[1].set_title("DataFrame.plot")

Option 2: Using DataFrame Plot

Alternatively, we can use Pandas’ plot() function to create both plots:

import pandas as pd
from matplotlib import pyplot as plt

projection = [1000, 2000, 3000, 4000]

datetime_series = pd.to_datetime(["2021-12","2022-01", "2022-02", "2022-03"])
datetime_index = pd.DatetimeIndex(datetime_series.values)

revenue = pd.DataFrame({"value": [1200, 2200, 2800, 4100]})
revenue["projection"] = projection

revenue.plot.area(y='projection', ax=plt.subplots(1, 2, figsize=(10, 4))[1])
revenue.plot.line(y='projection', ax=plt.subplots(1, 2, figsize=(10, 4))[1], color='black', linewidth=3)
plt.title("DataFrame.plot")

Conclusion

In conclusion, when overlaying a Pandas plot with Matplotlib, it’s essential to be aware of the differences in plotting order and formatting between the two libraries. By mixing and matching different plotting functions or using only one library for both plots, we can achieve consistent and high-quality visualizations.

Example Use Cases

Here are some example use cases that demonstrate how to mix and match plotting functions:

Using pyplot.fill_between() and ax.plot()
Using Pandas’ plot() function with different plot types (e.g., line, area)
Creating separate DataFrames for different plots

Advice

When working with both Pandas and Matplotlib, it’s crucial to:

Familiarize yourself with the plotting functions available in each library.
Understand how formatting affects x-axis values and plot appearance.
Experiment with different plotting combinations to achieve desired results.

Last modified on 2024-03-04