Double axes does not share labels properly

[lwq-star]

I made the following settings, but share=1 only shares the bottom and left labels. When sharing axes, the right label becomes redundant. How can I keep only the left, bottom, and right labels?

array = [  # the "picture" (1 == subplot A, 2 == subplot B, etc.)
    [1, 2, 3],
    [4, 5, 6],
    [7, 0, 0],
]
fig, axs = uplt.subplots(array, span=True, share=1, journal='nat2')

[cvanelteren]

Could you provide a minimal and verifiable example? The code you list does not contain what I see in the image.

[lwq-star]

Could you provide a minimal and verifiable example? The code you list does not contain what I see in the image.您能提供一个最小且可验证的示例吗？您列出的代码不包含我在图片中看到的内容。

This is my code

import ultraplot as uplt
import pandas as pd
import numpy as np
import seaborn as sns

array = [  # the "picture" (1 == subplot A, 2 == subplot B, etc.)
    [1, 2, 3],
    [4, 5, 6],
    [7, 0, 0],
]
fig, axs = uplt.subplots(array, span=True, share=1, journal='nat2')
axs.format(abc=True, abcloc="ul")

df = pd.read_csv(r"C:\Users\A1827\Desktop\data.csv")

class_names = [
    'TrMBF', 'TeBF', 'TeCF', 
    'TeG', 'FIG', 'MoG', 'DXS'
]  
for cls, name in zip(range(0, 7), class_names):
    ax = axs[cls]
    sub_df = df[df['veg_type'] == cls]
    
    ax.plot(x=sub_df['year'], y=sub_df['lambda_xt'], lw=2)
    ax.plot(x=sub_df['year'], y=sub_df['lambda_var'], lw=2)
    ox = ax.alty()
    ox.plot(x=sub_df['year'], y=sub_df['gpp'], lw=2)

[cvanelteren]

A minimal verifiable example is an snippet that is self-contained and does not contain complexities not required to understand the problem. Put simply: I cannot run your code because it refers to data do not have. I cannot discern if the problem is due to the input or something else.

Here is a minimal and verifiable example for example:

import ultraplot as uplt, numpy as np, pandas as pd

df = pd.DataFrame(np.random.rand(10, 2), columns=["a", "b"])
array = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 0, 0],
]
fig, axs = uplt.subplots(array, span=True, share=1, journal="nat2")
for axi in axs:
    axi.plot(x="a", y="b", data=df)
uplt.show(block=1)

which produces

[cvanelteren]

Same goes for #231

[lwq-star]

Same goes for #231

A minimal verifiable example is an snippet that is self-contained and does not contain complexities not required to understand the problem. Put simply: I cannot run your code because it refers to data do not have. I cannot discern if the problem is due to the input or something else.

Here is a minimal and verifiable example for example:

import ultraplot as uplt, numpy as np, pandas as pd

df = pd.DataFrame(np.random.rand(10, 2), columns=["a", "b"])
array = [
[1, 2, 3],
[4, 5, 6],
[7, 0, 0],
]
fig, axs = uplt.subplots(array, span=True, share=1, journal="nat2")
for axi in axs:
axi.plot(x="a", y="b", data=df)
uplt.show(block=1)
which produces

I mean share=1 cannot merge the labels of the right y-axis in a dual y-axis plot.

[cvanelteren]

I see -- need to look into this.

[cvanelteren]

For future reference this would be the minimal problem:

import ultraplot as uplt, numpy as np, pandas as pd

df = pd.DataFrame(np.random.rand(10, 2), columns=["a", "b"])
array = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 0, 0],
]
fig, axs = uplt.subplots(array, share=1)
for axi in axs:
    axi.plot(x="a", y="b", data=df)
    alt = axi.alty()
    alt.plot(x="a", y="b", data=df)
uplt.show(block=1)

[cvanelteren]

The array could even be removed.

[lwq-star]

The array could even be removed.该阵列甚至可以被移除。

Thank you for your work. To be honest, I really like arrays—they are incredibly intuitive when dealing with complex graphical combinations.

[cvanelteren]

No worries not intending to remove that.

Closing this in favor of #233.