Updated On : Mar-03,2023 Time Investment : ~20 mins

Matplotlib - Secondary X and Y Axis | Python¶

Matplotlib is a widely used plotting library in Python that provides a comprehensive range of tools for creating visualizations. It offers a variety of options to customize plots such as setting axis limits, adding titles and labels, and creating legends. One of the key features of Matplotlib is its ability to create multiple axes on a single plot.

In some cases, it may be necessary to display two sets of data on a single plot with different scales. This is where secondary axes come in handy. Matplotlib allows users to create both secondary X and Y axes, which can be useful in various scenarios such as comparing two data sets with different units or visualizing data from two different domains.

What Can You Learn From This Article?¶

In this tutorial, we have explained how to create secondary axes (Secondary X axis and Secondary Y axis) in "Matplotlib" and demonstrated how they can be used to enhance your visualizations. We have covered the basic concepts of creating a plot with multiple axes, including the steps to create a secondary axis, customize its appearance, and plot data on it. Additionally, we have discussed some common use cases where secondary axes can be useful and demonstrate how to implement them in Python.

Video Tutorial¶

Please feel free to check below video tutorial if feel comfortable learning through videos.

Below, we have imported matplotlib and printed the version that we'll use for our purpose.

import matplotlib

print("Matplotlib Version : {}".format(matplotlib.__version__))

Matplotlib Version : 3.5.3

Load Wine Dataset¶

In this section, we have loaded a wine dataset available from Python ML library scikit-learn. The dataset has measurements of various ingredients used in preparation of 3 different types of wine. We have loaded dataset as pandas dataframe.

After loading dataset as a dataframe, in the next cell, we have grouped entries by wine type and calculated average values of ingredients per wine type. It's another dataframe that we'll use to create charts.

import pandas as pd
from sklearn.datasets import load_wine

wine  = load_wine()

print("Dataset Size : ", wine.data.shape)

wine_df = pd.DataFrame(data=wine.data, columns=wine.feature_names)
wine_df["WineType"] = [wine.target_names[typ] for typ in wine.target]

wine_df.head()

Dataset Size :  (178, 13)

	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline	WineType
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	class_0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0	class_0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0	class_0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0	class_0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0	class_0

avg_wine_df = wine_df.groupby(by="WineType").mean().reset_index()

avg_wine_df

	WineType	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline
0	class_0	13.744746	2.010678	2.455593	17.037288	106.338983	2.840169	2.982373	0.290000	1.899322	5.528305	1.062034	3.157797	1115.711864
1	class_1	12.278732	1.932676	2.244789	20.238028	94.549296	2.258873	2.080845	0.363662	1.630282	3.086620	1.056282	2.785352	519.507042
2	class_2	13.153750	3.333750	2.437083	21.416667	99.312500	1.678750	0.781458	0.447500	1.153542	7.396250	0.682708	1.683542	629.895833

Example 1: Secondary Y-Axis¶

The below code imports the Matplotlib library and creates a new figure object with a size of 10x6 inches. It then creates three bar charts with heights corresponding to the "flavanoids", "magnesium", and "proline" columns of average wine dataframe.

Each bar chart is assigned a different color and label. This code creates a bar plot that compares the average levels of flavanoids, magnesium, and proline across different types of wine.

As we can see from bar chart, all ingredients are on quite different scales. The values of ingredient "proline" are quite high compared to ingredients "magnesium" and "flavanoids". Due to this, it seems to be suppressing those two bars.

In order to properly compare those three ingredients, we can add extra Y axes on right for "magnesium" and "flavanoids".

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10,6))

plt.bar(x=[0,4,8], height=avg_wine_df["flavanoids"], color="tomato", label="Flavanoids");
plt.bar(x=[1,5,9], height=avg_wine_df["magnesium"], color="dodgerblue", label="Magnesium");
plt.bar(x=[2,6,10], height=avg_wine_df["proline"], color="orange", label="proline");

plt.xticks([1,5,9], avg_wine_df.WineType);

plt.xlabel("Wine Type")
plt.ylabel("Avg Ingredients")
plt.title("Avg. Ingredients per Wine Type")
plt.legend(loc="best");

Below, we have created our first example explaining how to create a secondary Y axis. We have represented "magnesium" using a secondary Y-axis.

The code imports the Matplotlib library and creates a figure object with a size of 10x6 inches, with a single axis called "ax_main".

It creates two bar charts, one for "flavanoids" and one for "magnesium", with different colors and labels. It starts by creating a bar chart for "flavanoids".

Then, a second y-axis is added to the plot using the "twinx()" function and another bar chart is created for "magnesium" with a different color and label. As we can see, in order to create a secondary y-axis, we need to use "twinx()" function which creates another axes object whose X-axis is same as first but has new Y-axis on right side.

Finally, a legend is added to the plot using the "legend()" function, and a title is set using the "title()" function. This code creates a plot that compares the average levels of flavanoids and magnesium across different types of wine, with two y-axes for each bar chart.

import matplotlib.pyplot as plt

fig, ax_main = plt.subplots(figsize=(10,6))

bar1 = ax_main.bar(x=[0,4,8], height=avg_wine_df["flavanoids"], color="tomato", label="Flavanoids");
ax_main.set_xlabel("WineType")
ax_main.set_ylabel("Avg Flavanoids");
ax_main.set_ylim(0,5);
ax_main.set_xticks([0.5, 4.5, 8.5],avg_wine_df.WineType)

ax2 = ax_main.twinx()
bar2 = ax2.bar(x=[1,5,9], height=avg_wine_df["magnesium"], color="dodgerblue", label="Magnesium");
ax2.set_ylabel("Avg Magnesium");
ax2.set_ylim(0,120);


plt.legend(loc="best", handles=[bar1, bar2])
plt.title("Avg Ingredients per Wine Type");

Below, we have created another example demonstrating how to create secondary Y-axis using matplotlib. This time, we have created two secondary Y axes, one to represent "magnesium" and another for "proline".

import matplotlib.pyplot as plt

fig, ax_main = plt.subplots(figsize=(10,6))

bar1 = ax_main.bar(x=[0,4,8], height=avg_wine_df["flavanoids"], color="tomato", label="Flavanoids");
ax_main.set_xlabel("WineType")
ax_main.set_ylabel("Avg Flavanoids");
ax_main.set_ylim(0,5);
ax_main.set_xticks([1, 5, 9],avg_wine_df.WineType)

ax2 = ax_main.twinx()
bar2 = ax2.bar(x=[1,5,9], height=avg_wine_df["magnesium"], color="dodgerblue", label="Magnesium");
ax2.set_ylabel("Avg Magnesium");
ax2.set_ylim(0,120);

ax3 = ax_main.twinx()
bar3 = ax3.bar(x=[2,6,10], height=avg_wine_df["proline"], color="orange", label="proline");
ax3.spines.right.set_position(("axes", 1.1));
ax3.set_ylabel("Avg Proline");
ax3.set_ylim(0,1200);

plt.legend(loc="best", handles=[bar1, bar2, bar3])
plt.title("Avg Ingredients per Wine Type");

Below, we have recreated our previous example but tried to improve look of the chart by introducing theme to the chart. We also modified tick labels to align ticks of all 3 Y axes.

import matplotlib.pyplot as plt
import numpy as np

with plt.style.context("fivethirtyeight"):
    fig, ax_main = plt.subplots(figsize=(10,6))

    bar1 = ax_main.bar(x=[0,4,8], height=avg_wine_df["flavanoids"], color="tomato", label="Flavanoids");
    ax_main.set_xlabel("WineType")
    ax_main.set_ylabel("Avg Flavanoids");
    ax_main.set_ylim(0,5);
    ax_main.set_xticks([1, 5, 9],avg_wine_df.WineType);
    ax_main.set_yticks(np.arange(0,5.1,0.5), np.arange(0,5.1,0.5));

    ax2 = ax_main.twinx()
    bar2 = ax2.bar(x=[1,5,9], height=avg_wine_df["magnesium"], color="dodgerblue", label="Magnesium");
    ax2.set_ylabel("Avg Magnesium");
    ax2.set_ylim(0,120);
    ax2.grid(visible=False);
    ax2.set_yticks(np.arange(0,121,12), np.arange(0,121,12));

    ax3 = ax_main.twinx()
    bar3 = ax3.bar(x=[2,6,10], height=avg_wine_df["proline"], color="orange", label="proline");
    ax3.spines.right.set_position(("axes", 1.1));
    ax3.set_ylabel("Avg Proline");
    ax3.set_ylim(0,1200);
    ax3.grid(visible=False);
    ax3.set_yticks(np.arange(0,1201,120), np.arange(0,1201,120));

    plt.legend(loc="best", handles=[bar1, bar2, bar3])
    plt.title("Avg Ingredients per Wine Type");

Example 2: Secondary X Axis¶

In this section, we have explained how to create a secondary X-axis using matplotlib.

Below, we have again created our earlier bar chart example but have reversed X and Y axis data. As we can see from resulted bar chart, the wine type is represented on Y axis, and average values of ingredients are on X axis. We'll now try to solve same scale problem by introducing a secondary X axis for "magnesium" and "proline".

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10,6))

plt.barh(y=[0,4,8], width=avg_wine_df["flavanoids"], color="tomato", label="Flavanoids");
plt.barh(y=[1,5,9], width=avg_wine_df["magnesium"], color="dodgerblue", label="Magnesium");
plt.barh(y=[2,6,10], width=avg_wine_df["proline"], color="orange", label="proline");

plt.yticks([1,5,9], avg_wine_df.WineType);

plt.ylabel("Wine Type")
plt.xlabel("Avg Ingredients")
plt.title("Avg. Ingredients per Wine Type")
plt.legend(loc="best");

Below, we have created chart where we have added 2 secondary X axes. We have used the method named "twiny()" to add secondary X axis to represent values of ingredients "magnesium" and "proline". The majority of the code is same as earlier with only change that we are adding a new X axis using 'twiny()' function.

import matplotlib.pyplot as plt
import numpy as np

with plt.style.context("fivethirtyeight"):
    fig, ax_main = plt.subplots(figsize=(10,6))

    bar1 = ax_main.barh(y=[0,4,8], width=avg_wine_df["flavanoids"], color="tomato", label="Flavanoids");
    ax_main.set_ylabel("WineType")
    ax_main.set_xlabel("Avg Flavanoids");
    ax_main.set_xlim(0,5);
    ax_main.set_yticks([1, 5, 9],avg_wine_df.WineType);
    ax_main.set_xticks(np.arange(0,5.1,0.5), np.arange(0,5.1,0.5));

    ax2 = ax_main.twiny()
    bar2 = ax2.barh(y=[1,5,9], width=avg_wine_df["magnesium"], color="dodgerblue", label="Magnesium");
    ax2.set_xlabel("Avg Magnesium");
    ax2.set_xlim(0,120);
    ax2.grid(visible=False);
    ax2.set_xticks(np.arange(0,121,12), np.arange(0,121,12));

    ax3 = ax_main.twiny()
    bar3 = ax3.barh(y=[2,6,10], width=avg_wine_df["proline"], color="orange", label="proline");
    ax3.spines.top.set_position(("axes", 1.1));
    ax3.set_xlabel("Avg Proline");
    ax3.set_xlim(0,1200);
    ax3.grid(visible=False);
    ax3.set_xticks(np.arange(0,1201,120), np.arange(0,1201,120));

    plt.legend(loc="best", handles=[bar1, bar2, bar3])
    plt.title("Avg Ingredients per Wine Type");

This ends our small tutorial explaining how to add secondary X and Y axis to our matplotlib charts.

twinx() - Create a new Axes with an invisible x-axis and an independent y-axis positioned opposite to the original one (i.e. at right).
twiny() -Create a new Axes with an invisible y-axis and an independent x-axis positioned opposite to the original one (i.e. at top)

References¶

Matplotlib Pyplot

Sunny Solanki

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matplotlib, secondary-y-axis, secondary-x-axis

Sunny Solanki

Software Developer | Youtuber | Bonsai Enthusiast

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