This question already has answers here:
Matplotlib histogram with multiple legend entries
(2 answers)
Closed 4 years ago.
I want to colour different bars in a histogram based on which bin they belong to. e.g. in the below example, I want the first 3 bars to be blue, the next 2 to be red, and the rest black (the actual bars and colour is determined by other parts of the code).
I can change the colour of all the bars using the color option, but I would like to be able to give a list of colours that are used.
import numpy as np
import matplotlib.pyplot as plt
data = np.random.rand(1000)
plt.hist(data,color = 'r')
One way may be similar to approach in other answer:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
data = np.random.rand(1000)
N, bins, patches = ax.hist(data, edgecolor='white', linewidth=1)
for i in range(0,3):
patches[i].set_facecolor('b')
for i in range(3,5):
patches[i].set_facecolor('r')
for i in range(5, len(patches)):
patches[i].set_facecolor('black')
plt.show()
Result:
Related
By using this code I'm able to generate 20 data points on y-axis corresponding to x-axis, but I want to mark the 25 data points on the line as downward pointed triangles without changing arr_x=np.linspace(0.0,5.0,20) to arr_x=np.linspace(0.0,5.0,25).
will it possible to mark additional data points on y-axis without changing x-axis ?
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
def multi_curve_plot():
# Write your functionality below
fig=plt.figure(figsize=(13,4))
ax=fig.add_subplot(111)
arr_x=np.linspace(0.0,5.0,20)
arr_y1=np.array(arr_x)
arr_y2=np.array(arr_x**2)
arr_y3=np.array(arr_x**3)
ax.set(title="Linear, Quadratic, & Cubic Equations", xlabel="arr_X",
ylabel="f(arr_X)")
ax.plot(arr_x, arr_y1, label="y = arr_x", color="green", marker="v")
ax.plot(arr_x, arr_y2, label ="y = arr_x**2", color ="blue", marker="s")
ax.plot(arr_x, arr_y3, label="y = arr_x**3", color="red", marker="o")
plt.legend()
return fig
return None
multi_curve_plot()
I tried changing arr_x=np.linspace(0.0,5.0,20) to arr_x=np.linspace(0.0,5.0,25). But I want to show 25 data points on y axis without changing x-axis attributes.
This question already has answers here:
Matplotlib: get and set axes position
(1 answer)
Matplotlib different size subplots
(6 answers)
How to fully customize subplot size in matplotlib
(2 answers)
Closed 8 months ago.
This post was edited and submitted for review 8 months ago and failed to reopen the post:
Original close reason(s) were not resolved
This code creates following PNG file though, This wasn't what I want.
import seaborn as sns
import matplotlib.pyplot as plt
fig, ax = plt.subplots(2,figsize=(20, 6),gridspec_kw={'height_ratios': [2,1]})
fmri = sns.load_dataset("fmri")
flights = sns.load_dataset("flights")
sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event", ax=ax[0])
ax[0].legend(bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0.)
sns.lineplot(data=flights, x="year", y="passengers", ax=ax[1])
fig.savefig("test.png")
How can I make the width of second plot longer like this?
It looks easy, but I'm stuck on it..
Edit
The method I came up with was to use GridSpec like a following code, but it is complicated and not intuitive. There is another method that uses ax[0].get_position(), like Redox san taught me, but it is not good enough. I just want to increase the width of second plot a bit, however, Increasing the width of second plot doesn't work. I am still looking for another way.
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(20, 10))
gs = GridSpec(2, 2, width_ratios=[100,1], height_ratios=[2,1])
ax = []
ax.append(plt.subplot(gs.new_subplotspec((0, 0))))
plt.subplot(gs[0,1]).axis('off')
ax.append(plt.subplot(gs.new_subplotspec((1, 0), colspan=2)))
fmri = sns.load_dataset("fmri")
flights = sns.load_dataset("flights")
sns.lineplot(data=fmri, x="timepoint", y="signal", hue="event", ax=ax[0])
ax[0].legend(bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0.)
sns.lineplot(data=flights, x="year", y="passengers", ax=ax[1])
fig.savefig("test.png")
you can do this by adjusting the widths of the subplots. After plotting (just before save), add these lines. This will get the width information and you can adjust the ratio to what you want it to be
gPos = ax[0].get_position()
gPos.x1 = 0.83 # I have used 83% to set the first plot to be of 83% of original width
ax[0].set_position(gPos)
The plot
This question already has answers here:
How to combine gridspec with plt.subplots() to eliminate space between rows of subplots
(1 answer)
How to remove the space between subplots in matplotlib.pyplot?
(5 answers)
Closed 3 years ago.
When creating subplots with matplotlib i cannot get tight layout where there would not be any spaces between subplot items.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(3, 3, figsize=(10,10),gridspec_kw = {'wspace':0, 'hspace':0})
for i, ax in enumerate(axes.ravel()):
im = ax.imshow(np.random.normal(size=200).reshape([10,20]))
ax.axis('off')
plt.tight_layout()
Subplots would consist of images. Seems like there is way to do this when you are not using images. So i assume, there is some configuration about imshow().
I would like to keep aspect ratio of images, but make subplots compact as possible.
this is what i get now, but as you can see, there is a lot of row padding
https://imgur.com/a/u4IntRV
I have plotted a bar chart using the code below:
dffinal['CI-noCI']='Cognitive Impairement'
nocidffinal['CI-noCI']='Non Cognitive Impairement'
res=pd.concat([dffinal,nocidffinal])
sns.barplot(x='6month',y='final-formula',data=res,hue='CI-noCI')
plt.xticks(fontsize=8, rotation=45)
plt.show()
the result is as below:
I want to change the color of them to red and green.
How can I do?
just as information, this plot is reading two different data frame.
the links I have gone through were with the case the dataframe was only one data frame so did not apply to my case.
Thanks :)
You can use matplotlib to overwrite Seaborn's default color cycling to ensure the hues it uses are red and green.
import matplotlib.pyplot as plt
plt.rcParams['axes.prop_cycle'] = ("cycler('color', 'rg')")
Example:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame({'date': [1,2,3,4,4,5],
'value': [10,15,35,14,18,4],
'hue_v': [1,1,2,1,2,2]})
# The normal seaborn coloring is blue and orange
sns.barplot(x='date', y='value', data=df, hue='hue_v')
# Now change the color cycling and re-make the same plot:
plt.rcParams['axes.prop_cycle'] = ("cycler('color', 'rg')")
sns.barplot(x='date', y='value', data=df, hue='hue_v')
This will now impact all of the other figures you make, so if you want to restore the seaborn defaults for all other plots you need to then do:
sns.reset_orig()
This question already has an answer here:
how to plot gradient fill on the 3d bars in matplotlib
(1 answer)
Closed 5 years ago.
The standard way in which I create bar plots in matplotlib is using ax.bar3d. This has the disadvantage of returning block of solid color. Does anyone know how to attach a gradient color to every bar? I am think in reproducing Fig. 1 from https://arxiv.org/pdf/1706.09289.pdf.
Try this:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(18,12))
ax = fig.add_subplot(111, projection='3d')
x_data, y_data = np.meshgrid(np.arange(5),np.arange(3))
z_data = np.random.rand(3,5)
colors = ['r','g','b'] # colors for every line of y
# plot colored 3d bars
for i in xrange(3): # cycle though y
# I multiply one color by len of x (it is 5) to set one color for y line
ax.bar3d(x_data[i], y_data[i], z_data[i], 1, 1, z_data[i], alpha=0.1, color=colors[i]*5)
# or use random colors
# ax.bar3d(x_data[i], y_data[i], z_data[i], 1, 1, z_data[i], alpha=0.1, color=[np.random.rand(3,1),]*5)
plt.show()
Result: