How to modify xtick label of plt in Matplotlib

Question

The objective is to modify the xticklabel upon plotting pcolormesh and scatter.

However, I am having difficulties accessing the existing xtick labels.

Simply

ax = plt.axes()
labels_x = [item.get_text() for item in ax.get_xticklabels()]

which produced:

['', '', '', '', '', '']

or

fig.canvas.draw()
xticks = ax.get_xticklabels()

which produced:

['', '', '', '', '', '']

does not return the corresponding label.

May I know how to properly access axis tick labels for a plt cases.

For readability, I split the code into two section.

The first section to generate the data used for plotting
Second section deal the plotting

Section 1: Generate data used for plotting

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math

np.random.seed(0)
increment=120
max_val=172800

aran=np.arange(0,max_val,increment).astype(int)
arr=np.concatenate((aran.reshape(-1,1), np.random.random((aran.shape[0],4))), axis=1)
df=pd.DataFrame(arr,columns=[('lapse',''),('a','i'),('a','j'),('b','k'),('c','')])

ridx=df.index[df[('lapse','')] == 3600].tolist()[0]+1 # minus 1 so to allow 3600 start at new row


df[('event','')]=0
df.loc[[1,2,3,10,20,30],[('event','')]]=1

arr=df[[('a','i'),('event','')]].to_numpy()
col_len=ridx
v=arr[:,0].view()

nrow_size=math.ceil(v.shape[0]/col_len)
X=np.pad(arr[:,0].astype(float), (0, nrow_size*col_len - arr[:,0].size),
       mode='constant', constant_values=np.nan).reshape(nrow_size,col_len)

mask_append_val=0  # This value must equal to 1 for masking
arrshape=np.pad(arr[:,1].astype(float), (0, nrow_size*col_len - arr[:,1].size),
       mode='constant', constant_values=mask_append_val).reshape(nrow_size,col_len)

Section 2 Plotting

fig = plt.figure(figsize=(8,6))
plt.pcolormesh(X,cmap="plasma")

x,y = X.shape
xs,ys = np.ogrid[:x,:y]
# the non-zero coordinates
u = np.argwhere(arrshape)

plt.scatter(ys[:,u[:,1]].ravel()+.5,xs[u[:,0]].ravel()+0.5,marker='*', color='r', s=55)

plt.gca().invert_yaxis()

xlabels_to_use_this=df.loc[:30,[('lapse','')]].values.tolist()

# ax = plt.axes()
# labels_x = [item.get_text() for item in ax.get_xticklabels()]
# labels_y = [item.get_text() for item in ax.get_yticklabels()]

plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title("Plot 2D array")
plt.colorbar()
plt.tight_layout()
plt.show()

Expected output

Please describe what the specific problem is: what tick-values are you getting returned? It is also useful to mention what you're trying to achieve that you need the tick-values for. — mand, Dec 16 '21 at 16:08
Well, the xtick positions can still change a lot as more data is added to the plot. You can force a draw, but the recommended solution is that you yourself decide where you wnat the ticks, and then call `ax.set_xticks(your_ticks)`. Note that "the `plt` case" is exactly the same as the `ax = plt.gca()` case. Internally both do the same. For example, you could do `plt.xticks(range(len(xlabels_to_use_this)), xlabels_to_use_this)`. Note that both setting the ticks as trying to receive the ticks should happen as late as possible in your code, as many plot commands change the ticks. — JohanC, Dec 16 '21 at 16:32
Also note that `ax = plt.axes()` creates a new dummy plot on top of your existing plot. You should really avoid that. Use `ax = plt.gca()` instead to access the existing subplot. The preferred way to get correct ticks, is to use `plt.pcolormesh(x-values, y-values, color-values, ...)` — JohanC, Dec 16 '21 at 16:36
Thanks for the detail explaination @JohanC, Maybe I understanding something, but use ax = plt.gca() followed by labels_x = [item.get_text() for item in ax.get_xticklabels()] still yield `['', '', '', '', '', '', '', '']` — mpx, Dec 16 '21 at 16:46
There aren't any xtick labels set in your code. They are only shown at "show" time, and use something like a ticker formatter ([docs](https://matplotlib.org/stable/api/ticker_api.html)). You can get `plt.gca().get_xticks()` instead. But better, you use `ax.set_xticks(...)` together with `ax.set_xticklabels(...)`. Your code is extremely hard to understand with all the reshaping going on. Also, the colorbar needs an argument. E.g. `mesh = plt.pcolormesh(...)` and `plt.colorbar(mesh, ....)` — JohanC, Dec 16 '21 at 17:11
Thanks for advice @JohanC. But, I end up with Seaborn for this issue. — mpx, Dec 16 '21 at 17:46
The [docs](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.get_xticklabels.html) mention the following: > Notes The tick label strings are not populated until a draw method has > been called. So, place `ax.get_xticklabels()` after `plt.show()`. Does that work? (also see [32700935](https://stackoverflow.com/questions/32700935/get-xticklabels-contains-empty-text-instances)) — mand, Dec 16 '21 at 19:49

JohanC · Accepted Answer · 2021-12-17T22:39:38.057

This is how the plot could be generated using matplotlib's pcolormesh and scatter:

import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import pandas as pd
import numpy as np

np.random.seed(0)
increment = 120
max_val = 172800
aran = np.arange(0, max_val, increment).astype(int)
arr_df = np.concatenate((aran.reshape(-1, 1), np.random.random((aran.shape[0], 4))), axis=1)
df = pd.DataFrame(arr_df, columns=[('lapse', ''), ('a', 'i'), ('a', 'j'), ('b', 'k'), ('c', '')])
df[('event', '')] = 0
df.loc[[1, 2, 3, 10, 20, 30], [('event', '')]] = 1

col_len_lapse = 3600
col_len = df[df[('lapse', '')] == col_len_lapse].index[0]
nrow_size = int(np.ceil(v.shape[0] / col_len))

a_i_values = df[('a', 'i')].values
a_i_values_meshed = np.pad(a_i_values.astype(float), (0, nrow_size * col_len - len(a_i_values)),
                           mode='constant', constant_values=np.nan).reshape(nrow_size, col_len)

fig, ax = plt.subplots(figsize=(8, 6))
# the x_values indicate the mesh borders, subtract one half so the ticks can be at the centers
x_values = df[('lapse', '')][:col_len + 1].values - increment / 2
# divide lapses for y by col_len_lapse to get hours
y_values = df[('lapse', '')][::col_len].values / col_len_lapse - 0.5
y_values = np.append(y_values, 2 * y_values[-1] - y_values[-2])  # add the bottommost border (linear extension)

mesh = ax.pcolormesh(x_values, y_values, a_i_values_meshed, cmap="plasma")

event_lapses = df[('lapse', '')][df[('event', '')] == 1]
ax.scatter(event_lapses % col_len_lapse,
           np.floor(event_lapses / col_len_lapse),
           marker='*', color='red', edgecolor='white', s=55)

ax.xaxis.set_major_locator(MultipleLocator(increment * 5))
ax.yaxis.set_major_locator(MultipleLocator(5))
ax.invert_yaxis()
ax.set_xlabel('X-axis (s)')
ax.set_ylabel('Y-axis (hours)')
ax.set_title("Plot 2D array")
plt.colorbar(mesh)

plt.tight_layout()  # fit the labels nicely into the plot
plt.show()

With Seaborn things can be simplified, adding new columns for hours and seconds, and using pandas' pivot (which automatically fills unavailable data with NaNs). Adding xtick_labels=5 sets the labels every 5 positions. (The star for lapse=3600 is at 1 hour, 0 seconds).

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

# df created as before

df['hours'] = (df[('lapse', '')].astype(int) // 3600)
df['seconds'] = (df[('lapse', '')].astype(int) % 3600)

df_heatmap = df.pivot(index='hours', columns='seconds', values=('a', 'i'))
df_heatmap_markers = df.pivot(index='hours', columns='seconds', values=('event', '')).replace(
    {0: '', 1: '★', np.nan: ''})

fig, ax = plt.subplots(figsize=(8, 6))

sns.heatmap(df_heatmap, xticklabels=5, yticklabels=5,
            annot=df_heatmap_markers, fmt='s', annot_kws={'color': 'lime'}, ax=ax)
ax.tick_params(rotation=0)

plt.tight_layout()
plt.show()

Instead of a 'seconds' column, a 'minutes' column also might be interesting.

Here is an attempt to add time information as suggested in the comments:

from matplotlib import patheffects # to add some outline effect

# df prepared as the other seaborn example

fig, ax = plt.subplots(figsize=(8, 6))

path_effect = patheffects.withStroke(linewidth=2, foreground='yellow')
sns.heatmap(df_heatmap, xticklabels=5, yticklabels=5,
            annot=df_heatmap_markers, fmt='s',
            annot_kws={'color': 'red', 'path_effects': [path_effect]},
            cbar=True, cbar_kws={'pad': 0.16}, ax=ax)
ax.tick_params(rotation=0)

ax2 = ax.twinx()
ax2.set_ylim(ax.get_ylim())
yticks = ax.get_yticks()
ax2.set_yticks(yticks)
ax2.set_yticklabels([str(pd.to_datetime('2019-01-15 7:00:00') + pd.to_timedelta(h, unit='h')).replace(' ', '\n')
                     for h in yticks])

This is way better and compact than the originally propose. – mpx Dec 17 '21 at 07:27 — mpx, Dec 17 '21 at 07:27

score 0 · Answer 2 · answered Dec 16 '21 at 17:45

I end up using Seaborn to address this issue.

Specifically, the following lines able to easily tweak the xticklabel

fig.canvas.draw()
new_ticks = [i.get_text() for i in g.get_xticklabels()]

i=[int(idx) for idx in new_ticks]
newlabel=xlabels_to_use_this[i]
newlabel=[np.array2string(x, precision=0) for x in newlabel]

The full code for plotting is as below

import seaborn as sns
fig, ax = plt.subplots()

sns.heatmap(X,ax=ax)
x,y = X.shape
xs,ys = np.ogrid[:x,:y]
# the non-zero coordinates
u = np.argwhere(arrshape)
g=sns.scatterplot(ys[:,u[:,1]].ravel()+.5,xs[u[:,0]].ravel()+0.5,marker='*', color='r', s=55)


fig.canvas.draw()
new_ticks = [i.get_text() for i in g.get_xticklabels()]

i=[int(idx) for idx in new_ticks]
newlabel=xlabels_to_use_this[i]
newlabel=[np.array2string(x, precision=0) for x in newlabel]

ax.set_xticklabels(newlabel)

ax.set_xticklabels(ax.get_xticklabels(),rotation = 90)

for ind, label in enumerate(g.get_xticklabels()):
    if ind % 2 == 0:  # every 10th label is kept
        label.set_visible(True)
    else:
        label.set_visible(False)


for ind, label in enumerate(g.get_yticklabels()):
    if ind % 4 == 0:  # every 10th label is kept
        label.set_visible(True)
    else:
        label.set_visible(False)
      
plt.xlabel('Elapsed (s)')
plt.ylabel('Hour (h)')
plt.title("Rastar Plot")
plt.tight_layout()
plt.show()

Note that you are grouping 31 times 120 seconds into your hours (as 172800 is a multiple of 3600, you shouldn't have any empty space at the end). — JohanC, Dec 16 '21 at 18:16
With seaborn you should use `sns.heatmap(...., xticklabels=..., yticklabels=...)`, leaving empty strings for the unlabeled ticks. — JohanC, Dec 16 '21 at 18:17

How to modify xtick label of plt in Matplotlib

Section 1: Generate data used for plotting

Section 2 Plotting

2 Answers2