spore234 - 1 year ago 227

Python Question

I have a grouped pandas boxplot, arrange in a (2,2) grid:

`import pandas as pd`

import numpy as np

import matplotlib.pyplot as plt

df = pd.DataFrame(np.random.rand(140, 4), columns=['A', 'B', 'C', 'D'])

df['models'] = pd.Series(np.repeat(['model1','model2', 'model3', 'model4', 'model5', 'model6', 'model7'], 20))

bp = df.boxplot(by="models",layout=(2,2),figsize=(6,8))

plt.show()

I now want to change the

`ylim`

My idea was to add:

`[ax_tmp.set_ylim(-10,10) for ax_tmp in np.asarray(bp).reshape(-1)[2:4]]`

or

`[ax_tmp.set_ylim(-10,10) for ax_tmp in np.asarray(bp)[1,:]]`

but they both change the ylim of all subplots.

This may be because of the sharedy. But I have no idea to get rid of it.

my problem is somewhat related to this one: pandas boxplot, groupby different ylim in each subplot but not a duplicate in my opinion. Also the solution is not easily applicable here.

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Answer Source

The solution is to pass a `fig,axes`

to pandas's `boxplot`

that are customised with `sharey=False`

:

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.rand(140, 4), columns=['A', 'B', 'C', 'D'])
df['models'] = pd.Series(np.repeat(['model1','model2', 'model3', 'model4', 'model5', 'model6', 'model7'], 20))
fig, ax_new = plt.subplots(2,2, sharey=False)
bp = df.boxplot(by="models",ax=ax_new,layout=(2,2),figsize=(6,8))
[ax_tmp.set_xlabel('') for ax_tmp in ax_new.reshape(-1)]
[ax_tmp.set_ylim(-2, 2) for ax_tmp in ax_new[1]]
fig.suptitle('New title here')
plt.show()
```

result:

If you want to sharey row-wise. This code works for you :

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.rand(140, 4), columns=['A', 'B', 'C', 'D'])
df['models'] = pd.Series(np.repeat(['model1','model2', 'model3', 'model4', 'model5', 'model6', 'model7'], 20))
layout = [2,2]
fig = plt.figure()
all_axes = []
counter = 1
for i in range(layout[0]):
tmp_row_axes = []
for j in range(layout[1]):
if j!=0 :
exec "tmp_row_axes.append(fig.add_subplot(%d%d%d, sharey=tmp_row_axes[0]))"%(layout[0],layout[1],counter)
else:
exec "tmp_row_axes.append(fig.add_subplot(%d%d%d))" % (layout[0], layout[1], counter)
counter+=1
all_axes.append(tmp_row_axes)
all_axes = np.array(all_axes)
bp = df.boxplot(by="models",ax=np.array(all_axes),layout=(2,2),figsize=(6,8))
[ax_tmp.set_xlabel('') for ax_tmp in all_axes.reshape(-1)]
all_axes[1][0].set_ylim(-2,2)
fig.suptitle('New title here')
plt.show()
```

As you see by only changing the ylim of 1st axes in the 2nd row using `all_axes[1][0].set_ylim(-2,2)`

the whole row is changed. `all_axes[1][1].set_ylim(-2,2)`

would do the same since they have a shared y axis.

If you want the x-axis only in the last row and the y-axis label only in the first column, just change the loop to this:

```
for i in range(layout[0]):
tmp_row_axes = []
for j in range(layout[1]):
if j!=0 :
exec "tmp_ax = fig.add_subplot(%d%d%d, sharey=tmp_row_axes[0])"%(layout[0],layout[1],counter)
tmp_ax.get_yaxis().set_visible(False)
else:
exec "tmp_ax=fig.add_subplot(%d%d%d)" % (layout[0], layout[1], counter)
if i!=layout[1]-1 :
tmp_ax.get_xaxis().set_visible(False)
tmp_row_axes.append(tmp_ax)
counter+=1
all_axes.append(tmp_row_axes)
```

result:

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