Plotting Pandas Pivots with Different Scales Using Matplotlib

Plotting Pandas Pivots with Different Scales

Introduction

When working with dataframes in pandas, often we come across pivoted data where different variables have vastly different scales. Plotting such data can be challenging as most plotting libraries in Python, including matplotlib and seaborn, require that all variables have the same scale to ensure accurate and visually appealing representation.

In this article, we’ll explore how to plot a pandas pivot table with different scales using the popular plotting library matplotlib. We’ll delve into the secondary_y parameter of the plot function, which allows us to specify multiple y-values for each x-value.

Background

Before we dive into the solution, let’s first look at what happens when we try to plot a pivot table with different scales using the default plot function from pandas. When you call df.plot(), pandas will attempt to find a common scale for all variables and will rescale them accordingly. However, this can lead to misleading or distorted plots if the data has vastly different scales.

Using Secondary Y-axis

The secondary_y parameter is introduced in matplotlib 3.4 as a way to specify multiple y-values for each x-value. By using this parameter, we can ensure that our plot has separate axes for each variable with different scales.

Let’s look at an example code snippet that demonstrates how to use the secondary_y parameter:

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample dataframe
df = pd.DataFrame({'Time':['11:38:56', '11:39:46', '11:40:26', '11:43:18'],
                   'P_A': [500706., 501704., 501704., 502758.],
                   'P_B': [981098., 984751., 984737., 987173.]})

# Plot the dataframe with secondary y-axis
df.plot(x='Time', y=['P_A','P_B'], secondary_y=['P_B'])
plt.show()

In this example, we create a sample dataframe df and then plot it using the plot function. We specify x='Time' to plot the time variable on the x-axis, and y=['P_A', 'P_B'] to plot both P_A and P_B variables on the y-axis. The secondary_y=['P_B'] parameter specifies that we want to use a separate y-axis for the P_B variable.

Using Subplots

When dealing with multiple variables, it’s often more convenient to use subplots instead of a single plot with a secondary axis. We can create subplots using the subplots function from matplotlib and then plot our data on each subplot separately.

Let’s look at an example code snippet that demonstrates how to use subplots:

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample dataframe
df = pd.DataFrame({'Time':['11:38:56', '11:39:46', '11:40:26', '11:43:18'],
                   'P_A': [500706., 501704., 501704., 502758.],
                   'P_B': [981098., 984751., 984737., 987173.]})

# Create a figure with two subplots
fig, axs = plt.subplots(1, 2, figsize=(12, 6))

# Plot P_A on the first subplot
axs[0].plot(df['Time'], df['P_A'])
axs[0].set_title('P_A')
axs[0].set_xlabel('Time')

# Plot P_B on the second subplot
axs[1].plot(df['Time'], df['P_B'])
axs[1].set_title('P_B')
axs[1].set_xlabel('Time')

plt.tight_layout()
plt.show()

In this example, we create a sample dataframe df and then create a figure with two subplots using the subplots function. We plot P_A on the first subplot and P_B on the second subplot. We use the set_title, set_xlabel, and tight_layout functions to customize the appearance of each subplot.

Customizing the Plot

Once we have plotted our data, we can customize the appearance of the plot using various options available in matplotlib. Some common customization options include changing the colors, adding labels, modifying the axis limits, and adding a title.

Let’s look at an example code snippet that demonstrates how to customize the plot:

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample dataframe
df = pd.DataFrame({'Time':['11:38:56', '11:39:46', '11:40:26', '11:43:18'],
                   'P_A': [500706., 501704., 501704., 502758.],
                   'P_B': [981098., 984751., 984737., 987173.]})

# Create a figure with two subplots
fig, axs = plt.subplots(1, 2, figsize=(12, 6))

# Plot P_A on the first subplot
axs[0].plot(df['Time'], df['P_A'], color='blue')
axs[0].set_title('P_A', fontsize=18)
axs[0].set_xlabel('Time', fontsize=14)
axs[0].set_ylim(0, 100000)

# Plot P_B on the second subplot
axs[1].plot(df['Time'], df['P_B'], color='red')
axs[1].set_title('P_B', fontsize=18)
axs[1].set_xlabel('Time', fontsize=14)
axs[1].set_ylim(0, 100000)

plt.tight_layout()
plt.show()

In this example, we customize the appearance of each subplot by changing the colors, adding labels, modifying the axis limits, and adding a title. We use the color parameter to specify the color of the line, and we use the set_title, set_xlabel, and set_ylim functions to customize the appearance of each subplot.

Conclusion

Plotting pandas pivots with different scales can be challenging, but there are various ways to achieve this using matplotlib. By using the secondary_y parameter or subplots, we can ensure that our plot has separate axes for each variable with different scales. We can also customize the appearance of the plot using various options available in matplotlib.

Last modified on 2024-11-17