Is there a way to clear matplotlib labels inside a graph's legend? This post explains how to remove the legend itself, but the labels themselves still remain, and appear again if you plot a new figure. I tried the following code, but it does not work:
handles, labels = ax.get_legend_handles_labels()
labels = []
EDIT: Here is an example
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca()
ax.scatter([1,2,3], [4,5,6], label = "a")
legend = ax.legend()
plt.show()
legend.remove()
handles, labels = ax.get_legend_handles_labels()
print(labels)
Output: ["a"]
Use set_visible() method:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca()
ax.scatter([1,2,3], [4,5,6], label = "a")
legend = ax.legend()
for text in legend.texts:
if (text.get_text() == 'a'): text.set_text('b') # change label text
text.set_visible(False) # disable label
plt.show()
Related
I'm using the following dummy code for generating a 3D plot.
import random
from matplotlib import pyplot as plt
random.seed(0)
D = [[random.random() for x in range(3)] for y in range(1000)]
df = pd.DataFrame(D,columns=['Feature_1','Feature_2','Feature_3'])
predictions = [random.randint(0,4) for x in range(1000)]
df['predictions'] = predictions
plt.rcParams["figure.figsize"]=(10,10)
plt.rcParams['legend.fontsize'] = 10
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(df['Feature_1'],df['Feature_2'],df['Feature_3'], c=df['predictions'], s =150,cmap='rainbow')
ax.legend(loc = 'upper left')
ax.set_xlabel('Feature_1',fontsize=20,labelpad=10)
ax.set_ylabel('Feature_2', fontsize=20, rotation=150,labelpad=10)
ax.set_zlabel('Feature_3', fontsize=20, rotation=60,labelpad=15)
plt.show()
I'm using as marker color the column predictions, and i would like for each element of that column to appear in the legend but it does not.
Here's a screenshot of the resulting plot
You forgot to put a label handle into the scatter function. If you replace your scatter call with the following line, a legend will show up:
ax.scatter(
df['Feature_1'], df['Feature_2'], df['Feature_3'],
c=df['predictions'], s=150, cmap='rainbow', label='Dummy data'
)
Or to show the predictions classes as labels:
scatter = ax.scatter(df['Feature_1'], df['Feature_2'], df['Feature_3'],
c=df['predictions'], s=150, cmap='rainbow')
legend1 = ax.legend(*scatter.legend_elements(),
loc="upper left", title="Classes")
ax.add_artist(legend1)
I have the next scatterplot
But i want to change the dots on the legend by continuos color map like this:
This is my code:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("whitegrid")
gene_list = pd.read_csv('interseccion.csv', header=None)
glist = gene_list.squeeze().str.strip().tolist()
names = gp.get_library_name()
enr = gp.enrichr(gene_list= glist,
gene_sets=['KEGG_2019_Human'],
organism='Human', # don't forget to set organism to the one you desired! e.g. Yeast
description='KEGG',
# no_plot=True,
cutoff=0.5 # test dataset, use lower value from range(0,1)
)
resultados = enr.results.head(15)
resultados['-log10(FDR)'] = -np.log10(resultados['Adjusted P-value'])
resultados['Genes'] = resultados['Genes'].str.split(';')
resultados['Genes'] = resultados['Genes'].apply(lambda x: len(x))
g = sns.scatterplot(data=resultados, x="-log10(FDR)", y="Term", hue='-log10(FDR)', palette="seismic"
, size="Genes", sizes=(30, 300), legend=True)
g.legend(loc=6, bbox_to_anchor=(1, 0.5), ncol=1)
g.fig.colorbar()
plt.ylabel('')
plt.xlabel('-log10(FDR)')
When i try to put a color bar with the funcion plt.colorbar() is not possible
I customized the code in the official sample with the understanding that I wanted to add a legend and color bars to the Seaborn scatterplot. A colormap has been created to match the colors of the sample graph, but it can be drawn without problems by specifying the colormap name. The color bar is customized by getting its position and adjusting it manually in the legend. The height of the color bar is halved to match the legend.
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
fig, ax = plt.subplots()
g = sns.scatterplot(
data=tips, x="total_bill", y="tip", hue="size", size="size",
sizes=(20, 200), legend="full", ax=ax)
g.legend(loc='upper right', bbox_to_anchor=(1.2, 1.0), ncol=1)
norm = plt.Normalize(tips['size'].min(), tips['size'].max())
cmap = sns.cubehelix_palette(light=1, as_cmap=True)
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array([])
cax = fig.add_axes([ax.get_position().x1+0.05, ax.get_position().y0, 0.06, ax.get_position().height / 2])
ax.figure.colorbar(sm, cax=cax)
plt.show()
I have an issue where adding tick labels interferes with my given padding preference between subplots. What I want, is a tight_layout with no padding at all in between, but with some custom ticks along the x-axis. This snippet and resulting figures shows the issue:
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
fig_names = ['fig1']
gs = gridspec.GridSpec(1, len(fig_names))
gs.update(hspace=0.0)
figs = dict()
for fig_name in fig_names:
figs[fig_name] = plt.figure(figsize=(3*len(fig_names),6))
for i in range(0,len(fig_names)):
ax = figs[fig_name].add_subplot(gs[i])
ax.plot([0,1],[0,1], 'r-')
if i != 0:
ax.set_yticks(list())
ax.set_yticklabels(list())
ax.set_xticks(list())
ax.set_xticklabels(list())
for name,fig in figs.items():
fig.text(0.5, 0.03, 'Common xlabel', ha='center', va='center')
gs.tight_layout(fig, h_pad=0.0, w_pad=0.0)
ax = fig.add_subplot(gs[len(fig_names)-1])
ax.legend(('Some plot'), loc=2)
plt.show()
By changing the corresponding lines into:
ax.set_xticks([0.5,1.0])
ax.set_xticklabels(['0.5','1.0'])
...unwanted padding is added to the graphs.
How can I customize the tick text so that the graph plots has no padding, regardless of what tick text I enter? The text may "overlap" with the next subplot.
Perhaps you could simply create the axes with plt.subplots:
import numpy as np
import matplotlib.pyplot as plt
fig, axs = plt.subplots(ncols=2, sharey=True)
for ax in axs:
ax.plot([0,1],[0,1], 'r-')
ax.set_xticks([0.5,1.0])
ax.set_xticklabels(['0.5','1.0'])
axs[-1].legend(('Some plot'), loc=2)
for ax in axs[1:]:
ax.yaxis.set_visible(False)
fig.subplots_adjust(wspace=0)
plt.show()
Here is an example that reproduces my problem:
import matplotlib.pyplot as plt
import numpy as np
data1,data2,data3,data4 = np.random.random(100),np.random.random(100),np.random.random(100),np.random.random(100)
fig,ax = plt.subplots()
ax.plot(data1)
ax.plot(data2)
ax.plot(data3)
ax2 = ax.twinx()
ax2.plot(data4)
plt.grid('on')
ax.legend(['1','2','3'], loc='center')
ax2.legend(['4'], loc=1)
How can I get the legend in the center to plot on top of the lines?
To get exactly what you have asked for, try the following. Note I have modified your code to define the labels when you generate the plot and also the colors so you don't get a repeated blue line.
import matplotlib.pyplot as plt
import numpy as np
data1,data2,data3,data4 = (np.random.random(100),
np.random.random(100),
np.random.random(100),
np.random.random(100))
fig,ax = plt.subplots()
ax.plot(data1, label="1", color="k")
ax.plot(data2, label="2", color="r")
ax.plot(data3, label="3", color="g")
ax2 = ax.twinx()
ax2.plot(data4, label="4", color="b")
# First get the handles and labels from the axes
handles1, labels1 = ax.get_legend_handles_labels()
handles2, labels2 = ax2.get_legend_handles_labels()
# Add the first legend to the second axis so it displaysys 'on top'
first_legend = plt.legend(handles1, labels1, loc='center')
ax2.add_artist(first_legend)
# Add the second legend as usual
ax2.legend(handles2, labels2)
plt.show()
Now I will add that it would be clearer if you just use a single legend adding all the lines to that. This is described in this SO post and in the code above can easily be achieved with
ax2.legend(handles1+handles2, labels1+labels2)
But obviously you may have your own reasons for wanting two legends.
Is it possible to have a different color for certain labels on an axis?
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_yticks([0,1,2])
ax1.set_yticklabels(['red','red', 'blue'], color='blue')
#What I would like to do
ax1.set_yticklabels(['red','red', 'blue'], colors=['red','red','blue']) <-- doesn't work
plt.show()
Is there a way to accomplish what I want?
You can acces ALL the properties of a Tick object using this approach:
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_yticks([0,1,2])
ax1.set_yticklabels(['red','red', 'blue'], color='blue')
colors=['red','red','blue']
for color,tick in zip(colors,ax1.yaxis.get_major_ticks()):
tick.label1.set_color(color) #set the color property
plt.show()
The last cycle can be used to change other properties as well, for example, the size of the labels:
colors=['red','red','blue']
sizes=[10,20,30]
for color,size,tick in zip(colors,sizes,ax1.yaxis.get_major_ticks()):
tick.label1.set_color(color) #set the color
tick.label1.set_size(size) #set the size
The output of the last example would be something like: