Plotting Errors on a Bar Plot from a Second Pandas DataFrame

Introduction

In this article, we will explore how to plot errors on a bar chart using two separate DataFrames in Python. We’ll cover the basics of creating and manipulating DataFrames with pandas and matplotlib, as well as strategies for visualizing uncertainty or error bars.

Background

When working with scientific data, it’s essential to visualize the uncertainty associated with each measurement. In this case, we have a DataFrame df containing the values for different variables across various runs, and another DataFrame edf storing the percent errors associated with each run-variable combination. Our objective is to plot these values on a bar chart with vertical error bars representing the errors from the edf DataFrame.

Prerequisites

• Python 3.x
• pandas library for data manipulation
• matplotlib library for plotting

Installing the necessary libraries, you can use pip:

pip install pandas matplotlib

Data Preparation

Let’s create our sample DataFrames and ensure they have the same structure.

Sample DataFrame df

We’ll start with df containing the values we want to plot:

import pandas as pd

# Creating a simple DataFrame
df = pd.DataFrame({
    'var1': [1.0, 2.0, 3.0],
    'run1': [10, 20, 30],
    'run2': [40, 50, 60],
    'run3': [70, 80, 90]
})

print(df)

Output:

    var1   run1   run2   run3
0   1.0     10     40     70
1   2.0     20     50     80
2   3.0     30     60     90

Sample DataFrame edf

Next, we’ll create edf containing the error values:

# Creating a DataFrame for errors
edf = pd.DataFrame({
    'var1': [1.0, 2.0, 3.0],
    'run1': [-0.5, -0.25, 0.25],
    'run2': [-0.25, -0.75, 0.75],
    'run3': [-0.25, 0.25, 0.75]
})

print(edf)

Output:

   var1   run1   run2   run3
0   1.0  -0.5  -0.25  0.25
1   2.0  -0.25 -0.75  0.75
2   3.0  0.25  0.75  0.75

Plotting Errors on a Bar Plot

Now that we have our DataFrames ready, let’s plot the values from df on a bar chart with vertical error bars representing the errors from edf. We’ll use matplotlib’s barplot() function to create the plot.

Using yerr for Vertical Error Bars

When plotting the data, you can specify the error values using the yerr parameter. This will add vertical error bars to the bar chart.

import matplotlib.pyplot as plt

# Plotting with yerr
fig, ax = plt.subplots(figsize=(8, 6))
ax.bar(df.columns, df.values[0], label='Values')
ax.errorbar(range(len(df.columns)), df.values[0], yerr=edf.values[0], fmt='none', ecolor='r')

# Customizing plot elements
plt.xlabel('Variables')
plt.ylabel('Values')
plt.title('Bar Plot with Error Bars')
plt.legend()
plt.xticks(rotation=90)
plt.show()

This code will create a bar chart with vertical error bars, where the y-error represents the percent errors.

Explanation of yerr

The yerr parameter is used to specify the error values when plotting. It takes two parameters:

• The first parameter specifies the value associated with each data point.
• The second parameter specifies the error values for each data point.

In our case, we use edf.values[0] as the y-error values, which means that each bar will have a vertical error bar representing the corresponding percent error.

Alternative Approaches

While using yerr is an effective way to add vertical error bars, there are alternative approaches you can take:

• Using a separate subplot: You can create a separate subplot for the error values and plot them on top of the main plot.
• Using a horizontal line: Instead of using vertical error bars, you can use horizontal lines to represent the errors.

These alternatives can be useful in specific situations or when working with different types of data.

Conclusion

Plotting errors on a bar chart is an essential skill for visualizing uncertainty in scientific data. By following these steps and understanding how to manipulate DataFrames with pandas and matplotlib, you can effectively add vertical error bars to your plots to provide more insights into the data.

Last modified on 2024-05-22