Plotting Combined Bar and Line Plot with Secondary Y-Axis in Python

In this article, we will explore how to create a combined bar and line plot with a secondary y-axis using Python. We’ll discuss two approaches: one where we use a matplotlib workaround and another where we neglect the fact that the points are dates.

Introduction

When working with data from CSV files, it’s often necessary to visualize the data to gain insights or understand patterns. Matplotlib is a popular library for creating static, animated, and interactive visualizations in Python. In this article, we’ll focus on plotting combined bar and line plots using matplotlib.

We’re given an example of how to plot individual bar and line plots separately, but we want to know if it’s possible to combine these plots into one graph with a secondary y-axis.

Approach 1: Using Matplotlib Workaround

Unfortunately, it seems impossible to plot a bar plot and a line plot on the same axes in pandas if the x-axis is a dates axis. However, we can use a matplotlib workaround to achieve this.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("data.csv", index_col="DateTime", parse_dates=True, delim_whitespace=True)

fig, ax = plt.subplots()

ax.plot_date(df.index, df.iloc[:, 11], '-')
for i in range(10):
    diff = df.index[1] - df.index[0]
    spacing = diff / (1.3 * len(df.columns))
    ax.bar(df.index + (-5 + i) * spacing, df.iloc[:, i],
           width=spacing/diff, label=df.columns[i])
plt.legend()
plt.gcf().autofmt_xdate()
plt.show()

In this code:

• We create a figure and axis using plt.subplots().
• We plot the line data using ax.plot_date() with the date index as the x-axis.
• We use a loop to create bar plots for each column except the 11th one, which is used for the line plot. The bars are spaced evenly along the x-axis by adjusting the width of each bar.

Approach 2: Neglecting Date Information

If we neglect the fact that the points are dates, we can read the first column as a regular index and create a combined bar and line plot using the iloc method to access columns 0 through 11.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("data.csv", parse_dates=True, delim_whitespace=True)

ax = df.iloc[:, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]].plot(kind='bar')
df.iloc[:, 12].plot(linestyle='-', marker='o', ax=ax)

ax.set_xticklabels(df.DateTime, rotation=40)
plt.show()

In this code:

• We create a combined bar and line plot using the iloc method to access columns 0 through 11.
• We use the plot() method with the kind='bar' argument to create a bar plot for each column except the 12th one, which is used for the line plot.
• We set the x-axis tick labels using ax.set_xticklabels().

Comparison of Approach 1 and Approach 2

Both approaches produce acceptable results. However:

• In Approach 1, we use a workaround to create evenly spaced bars along the x-axis. This approach is more flexible because it doesn’t rely on equal spacing between dates.
• In Approach 2, we neglect the date information and assume that the dates are equally spaced. If this assumption is incorrect, the resulting plot may not accurately represent the data.

Conclusion

In conclusion, creating a combined bar and line plot with a secondary y-axis in Python can be achieved using two different approaches. The choice of approach depends on whether you want to rely on equal spacing between dates or use a more flexible workaround to create evenly spaced bars along the x-axis.

By following these examples, you should be able to create high-quality visualizations for your data using matplotlib.

Last modified on 2024-09-28