I'm plotting some data using sns.jointplot and I want the data inside the scatterplot to remain as points and the histograms on the side to be kde plots instead. I've tried using the kind='kde' argument, but this changes the data inside to not look like points in a scatterplot anymore. I've searched around for a bit and can't find how.
Here's my code for the plot:
plota = sns.jointplot( data = Hub_all_data, y = "Within module degree", x= "Participation coefficient", s=100, joint_kws=({'color':'green'}), marginal_kws=({'color': 'green'}))
plota.ax_joint.axvline(x=np.quantile(Pall,.25), color = "black", linestyle = "--")
plota.ax_joint.axvline(x=np.quantile(Pall,.75), color = "black", linestyle = "--")
plota.ax_joint.axhline(y=np.quantile(within_module_degree,.25), color = "black", linestyle = "--")
plota.ax_joint.axhline(y=np.quantile(within_module_degree,.75), color = "black", linestyle = "--")
plota.ax_marg_x.set_xlim(0, .6)
plota.ax_marg_y.set_ylim(-3, 2)
plota.set_axis_labels('P', 'Z', fontsize=16)
You could create a JointGrid and then plot the central and the marginal plots separately:
import seaborn as sns
import numpy as np
iris = sns.load_dataset('iris')
g = sns.JointGrid(data=iris, x="sepal_length", y="petal_length")
g.plot_joint(sns.scatterplot, s=100, color='green')
g.plot_marginals(sns.kdeplot, color='green', fill=True)
for q in np.quantile(iris['sepal_length'], [0.25, 0.75]):
for ax in (g.ax_joint, g.ax_marg_x):
ax.axvline(q, color="black", linestyle="--")
for q in np.quantile(iris['petal_length'], [0.25, 0.75]):
for ax in (g.ax_joint, g.ax_marg_y):
ax.axhline(q, color="black", linestyle="--")
Related
I have the following issue.
I have a graph of which has colored segments. The problem is in relating those segments to the color bar (which also contains text), so that each color segment is aligned with the color bar.
The code is the following:
from matplotlib.colorbar import colorbar_factory
x_v = datosg["Hour"]+div
y_v = datosg["UV Index"]
fig, ax= plt.subplots(figsize = (7,7))
ax.plot(x_v, y_v, color = "green")
ax.set_xlim(7, 19)
ax.grid()
ax.axhspan(0, 2.5, facecolor='green', alpha=0.8)
ax.axhspan(2.5, 5.5, facecolor='blue', alpha=0.7)
ax.axhspan(5.5, 7.5, facecolor='red', alpha=0.7)
ax.axhspan(7.5, 10.5, facecolor='yellow', alpha=0.7)
ax.axhspan(10.5, 16, facecolor='pink', alpha=0.7)
ax.margins(0)
from matplotlib.colors import ListedColormap
#discrete color scheme
cMap = ListedColormap(['green', 'blue','red', 'yellow', 'pink'])
#data
np.random.seed(42)
data = np.random.rand(5, 5)
heatmap = ax.pcolor(data, cmap=cMap)
#legend
cbar_ay = fig.add_axes([0.93, 0.125, 0.2, 0.755])
cbar = plt.colorbar(heatmap, cax=cbar_ay, orientation="vertical")
cbar.ax.get_yaxis().set_ticks([])
for j, lab in enumerate(['$Bajo$','$Medio$','$Alto$','$Muy Alto$','$Extremo$']):
cbar.ax.text(.5, (2 * j + 1) / 10.0, lab, ha='center', va='center')
plt.show()
The graph that results from this code is as follows:
Result_code
I have tried everything, the result I expect is very similar to this graph:
resulting image
But I can't change the range of the colors in the color bar.
Also note that I created random values in order to create the colorbar, I couldn't think of any other way, however so far it has worked. I only have to modify the range, so that it is similar to the last graph.
Any help would be appreciated.
I guess it's much easier to just draw a second Axes and fill it with axhspans the same way you did it with the main Axes, but if you want to use a colorbar, you can do it as follows:
import itertools
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
colors = ['green', 'blue','red', 'yellow', 'pink']
labels = ['$Bajo$','$Medio$','$Alto$','$Muy Alto$','$Extremo$']
bounds = np.array([0, 2.5, 5.5, 7.5, 10.5, 16 ])
fig, ax= plt.subplots()
for span, color in zip(itertools.pairwise(bounds), colors):
ax.axhspan(*span, facecolor=color, alpha=0.8)
ax.margins(0)
cmap = mpl.colors.ListedColormap(colors)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
ax_pos = ax.get_position().bounds
cbar_ay = fig.add_axes([0.93, ax_pos[1], 0.2, ax_pos[3]])
cbar = plt.colorbar(mpl.cm.ScalarMappable(cmap=cmap, norm=norm), cax=cbar_ay, orientation="vertical", spacing='proportional')
cbar.ax.set_axis_off()
for y, lab in zip(bounds[:-1] + np.diff(bounds) / 2, labels):
cbar.ax.text(.5, y, lab, ha='center', va='center')
I'm trying to make a heatmap a heatmap with extensive y axis descriptions.
I would like to know if there is anyways to have a second and a third layer on the y tick labels.
fig, ax = plt.subplots(figsize=(20,25))
sns.set(style="darkgrid")
colName = [r'A', r'B', r'C', r'D', r'E']
colTitile = 'Test'
rowName = [r'a', r'b', r'c', r'd']
rowsName = [r'Vegetables', r'Fruits', r'Meats', r'Cheese',
r'Candy', r'Other']
rowTitile = 'Groups'
heatmapdata= np.arange(100).reshape(24,5)
sns.heatmap(heatmapdata,
cmap = 'turbo',
cbar = True,
vmin=0,
vmax=100,
ax=ax,
xticklabels = colName,
yticklabels = rowName)
for x in np.arange(0,len(ax.get_yticks()),4):
ax.axhline(x, color = 'white', lw=2)
Is there any way to do this? Which function should I use?
Thanks!
The labels for the rows can be set up in the graph settings, but other than that, I think the annotation function is the only way to handle this. the second level group names are set using the annotation function, and the coordinate criteria are set using the axis criteria. Axis labels are added using the text function with axis criteria.
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(figsize=(10,10))
sns.set(style="darkgrid")
colName = [r'A', r'B', r'C', r'D', r'E']
colTitile = 'Test'
rowName = [r'a', r'b', r'c', r'd']
rowsName = [r'Vegetables', r'Fruits', r'Meats', r'Cheese',
r'Candy', r'Other']
rowTitle = 'Groups'
heatmapdata= np.arange(120).reshape(24,5)
sns.heatmap(heatmapdata,
cmap='turbo',
cbar=True,
vmin=0,
vmax=100,
ax=ax,
xticklabels=colName,
yticklabels=np.tile(rowName, 6))
for x in np.arange(0,ax.get_ylim()[0],4):
ax.axhline(x, color = 'white', lw=2)
for idx,g in enumerate(rowsName[::-1]):
ax.annotate(g, xy=(-100, idx*90+45), xycoords='axes points', size=14)
ax.text(x=-0.3, y=0.5, s=rowTitle, ha='center', transform=ax.transAxes, rotation=90, font=dict(size=16))
plt.show()
pls I need to add a area color to my code to show a plot similar to this one bellow:
My code is here:
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
from matplotlib import pyplot as plt
df = pd.DataFrame({'Time': [1,2,3,4,5],
'T=0': [0.5,0.16,0,0.25,0],
'T=2': [0.5,0.5,1,1,1],
'T=10': [0.75,0.8,0.85,0.9,0.8]
})
plt.plot( 'Time', 'T=10', data=df, marker='d', color='black', markersize=5, linewidth=1.5, linestyle=':')
plt.plot( 'Time', 'T=2', data=df, marker='^', color='black', markersize=4, linewidth=1.5,linestyle='--')
plt.plot( 'Time', 'T=0', data=df, marker='o', color='black', markersize=4, linewidth=1.5,linestyle='-')
plt.legend()
plt.xlabel("Time")
plt.xticks([1,2,3,4,5])
plt.xlim(0.9, 5.02)
plt.ylabel("Average")
plt.ylim(0, 1.02)
plt.show()
The actual result:
Many thanks.
All you need to do is add the following 3 lines to your code:
plt.fill_between(df['Time'], df['T=0'], alpha = 0.3, color = 'steelblue')
plt.fill_between(df['Time'], df['T=0'], df['T=2'], alpha = 0.3, color = 'yellow')
plt.fill_between(df['Time'], df['T=2'], df['T=10'], alpha = 0.3, color = 'red')
You can also create a legend corresponding to the colors. However, in the case of your graph, since two plot lines cross, it is best to leave the legend assigned to the plot lines rather than the colors (as you have).
I would like to make a scatter plot with unfilled squares. markerfacecolor is not an option recognized by scatter. I made a MarkerStyle but the fill style seems to be ignored by the scatter plot. Is there a way to make unfilled markers in the scatterplot?
import matplotlib.markers as markers
import matplotlib.pyplot as plt
import numpy as np
def main():
size = [595, 842] # in pixels
dpi = 72. # dots per inch
figsize = [i / dpi for i in size]
fig = plt.figure(figsize=figsize)
ax = fig.add_axes([0,0,1,1])
x_max = 52
y_max = 90
ax.set_xlim([0, x_max+1])
ax.set_ylim([0, y_max + 1])
x = np.arange(1, x_max+1)
y = [np.arange(1, y_max+1) for i in range(x_max)]
marker = markers.MarkerStyle(marker='s', fillstyle='none')
for temp in zip(*y):
plt.scatter(x, temp, color='green', marker=marker)
plt.show()
main()
It would appear that if you want to use plt.scatter() then you have to use facecolors = 'none' instead of setting fillstyle = 'none' in construction of the MarkerStyle, e.g.
marker = markers.MarkerStyle(marker='s')
for temp in zip(*y):
plt.scatter(x, temp, color='green', marker=marker, facecolors='none')
plt.show()
or, use plt.plot() with fillstyle = 'none' and linestyle = 'none' but since the marker keyword in plt.plot does not support MarkerStyle objects you have to specify the style inline, i.e.
for temp in zip(*y):
plt.plot(x, temp, color='green', marker='s', fillstyle='none')
plt.show()
either of which will give you something that looks like this
Refer to: How to do a scatter plot with empty circles in Python?
Try adding facecolors='none' to your plt.scatter
plt.scatter(x, temp, color='green', marker=marker, facecolors='none')
i wanted to know how to make a plot with two y-axis so that my plot that looks like this :
to something more like this by adding another y-axis :
i'm only using this line of code from my plot in order to get the top 10 EngineVersions from my data frame :
sns.countplot(x='EngineVersion', data=train, order=train.EngineVersion.value_counts().iloc[:10].index);
I think you are looking for something like:
import matplotlib.pyplot as plt
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.bar(x, y)
ax2.plot(x, y1, 'o-', color="red" )
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show()
Output:
#gdubs If you want to do this with Seaborn's library, this code set up worked for me. Instead of setting the ax assignment "outside" of the plot function in matplotlib, you do it "inside" of the plot function in Seaborn, where ax is the variable that stores the plot.
import seaborn as sns # Calls in seaborn
# These lines generate the data to be plotted
x = [1,2,3,4,5]
y = [1000,2000,500,8000,3000]
y1 = [1050,3000,2000,4000,6000]
fig, ax1 = plt.subplots() # initializes figure and plots
ax2 = ax1.twinx() # applies twinx to ax2, which is the second y axis.
sns.barplot(x = x, y = y, ax = ax1, color = 'blue') # plots the first set of data, and sets it to ax1.
sns.lineplot(x = x, y = y1, marker = 'o', color = 'red', ax = ax2) # plots the second set, and sets to ax2.
# these lines add the annotations for the plot.
ax1.set_xlabel('X data')
ax1.set_ylabel('Counts', color='g')
ax2.set_ylabel('Detection Rates', color='b')
plt.show(); # shows the plot.
Output:
Seaborn output example
You could try this code to obtain a very similar image to what you originally wanted.
import seaborn as sb
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
x = ['1.1','1.2','1.2.1','2.0','2.1(beta)']
y = [1000,2000,500,8000,3000]
y1 = [3,4,1,8,5]
g = sb.barplot(x=x, y=y, color='blue')
g2 = sb.lineplot(x=range(len(x)), y=y1, color='orange', marker='o', ax=g.axes.twinx())
g.set_xticklabels(g.get_xticklabels(), rotation=-30)
g.set_xlabel('EngineVersion')
g.set_ylabel('Counts')
g2.set_ylabel('Detections rate')
g.legend(handles=[Rectangle((0,0), 0, 0, color='blue', label='Nontouch device counts'), Line2D([], [], marker='o', color='orange', label='Detections rate for nontouch devices')], loc=(1.1,0.8))