How does one plot a histogram with precomputed probability distribution? I have the following:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
dist = np.array([0.50416489, 0.1057769 , 0.08717909, 0.03758235, 0.02342604, 0.03694781, 0.04196706, 0.03448674, 0.04018618, 0.01171971])
sns.histplot(data=dist, discrete=True)
plt.show()
output:
How do I change the y-axis to percentile and make the x-axis discrete (with values [0,1,2,...,10]) ?
I am working with a long tail distribution but I can only see the first element, how do I visualize it in a meaningful way?
EDIT
plt.bar gives a more interpretable result:
plt.bar(np.arange(10), dist)
plt.show()
However, when I use it on my real-world data I get the following plot:
The first 10 elements are the same as in dist, is it possible to make the x axis logarithmic?
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.
I am creating shot plots for NHL games and I have succeeded in making the plot, but I would like to draw the lines that you see on a hockey rink on it. I basically just want to draw two circles and two lines on the plot like this.
Let me know if this is possible/how I could do it
Pandas plot is in fact matplotlib plot, you can assign it to variable and modify it according to your needs ( add horizontal and vertical lines or shapes, text, etc)
# plot your data, but instead diplaying it assing Figure and Axis to variables
fig, ax = df.plot()
ax.vlines(x, ymin, ymax, colors='k', linestyles='solid') # adjust to your needs
plt.show()
working code sample
import pandas as pd
import matplotlib.pyplot as plt
import seaborn
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
df = seaborn.load_dataset('tips')
ax = df.plot.scatter(x='total_bill', y='tip')
ax.vlines(x=40, ymin=0, ymax=20, colors='red')
patches = [Circle((50,10), radius=3)]
collection = PatchCollection(patches, alpha=0.4)
ax.add_collection(collection)
plt.show()
I'm trying to visualize what filters are learning in CNN text classification model. To do this, I extracted feature maps of text samples right after the convolutional layer, and for size 3 filter, I got an (filter_num)*(length_of_sentences) sized tensor.
df = pd.DataFrame(-np.random.randn(50,50), index = range(50), columns= range(50))
g= sns.clustermap(df,row_cluster=True,col_cluster=False)
plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), rotation=0) # ytick rotate
g.cax.remove() # remove colorbar
plt.show()
This code results in :
Where I can't see all the ticks in the y-axis. This is necessary
because I need to see which filters learn which information. Is there
any way to properly exhibit all the ticks in the y-axis?
kwargs from sns.clustermap get passed on to sns.heatmap, which has an option yticklabels, whose documentation states (emphasis mine):
If True, plot the column names of the dataframe. If False, don’t plot the column names. If list-like, plot these alternate labels as the xticklabels. If an integer, use the column names but plot only every n label. If “auto”, try to densely plot non-overlapping labels.
Here, the easiest option is to set it to an integer, so it will plot every n labels. We want every label, so we want to set it to 1, i.e.:
g = sns.clustermap(df, row_cluster=True, col_cluster=False, yticklabels=1)
In your complete example:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.DataFrame(-np.random.randn(50,50), index=range(50), columns=range(50))
g = sns.clustermap(df, row_cluster=True, col_cluster=False, yticklabels=1)
plt.setp(g.ax_heatmap.yaxis.get_majorticklabels(), rotation=0) # ytick rotate
g.cax.remove() # remove colorbar
plt.show()
I seem to have got stuck at a relatively simple problem but couldn't fix it after searching for last hour and after lot of experimenting.
I have two numpy arrays x and y and I am using seaborn's jointplot to plot them:
sns.jointplot(x, y)
Now I want to label the xaxis and yaxis as "X-axis label" and "Y-axis label" respectively. If I use plt.xlabel, the labels goes to the marginal distribution. How can I make them appear on the joint axes?
sns.jointplot returns a JointGrid object, which gives you access to the matplotlib axes and you can then manipulate from there.
import seaborn as sns
import numpy as np
# example data
X = np.random.randn(1000,)
Y = 0.2 * np.random.randn(1000) + 0.5
h = sns.jointplot(X, Y)
# JointGrid has a convenience function
h.set_axis_labels('x', 'y', fontsize=16)
# or set labels via the axes objects
h.ax_joint.set_xlabel('new x label', fontweight='bold')
# also possible to manipulate the histogram plots this way, e.g.
h.ax_marg_y.grid('on') # with ugly consequences...
# labels appear outside of plot area, so auto-adjust
h.figure.tight_layout()
(The problem with your attempt is that functions such as plt.xlabel("text") operate on the current axis, which is not the central one in sns.jointplot; but the object-oriented interface is more specific as to what it will operate on).
Note that the last command uses the figure attribute of the JointGrid. The initial version of this answer used the simpler - but not object-oriented - approach via the matplotlib.pyplot interface.
To use the pyplot interface:
import matplotlib.pyplot as plt
plt.tight_layout()
Alternatively, you can specify the axes labels in a pandas DataFrame in the call to jointplot.
import pandas as pd
import seaborn as sns
x = ...
y = ...
data = pd.DataFrame({
'X-axis label': x,
'Y-axis label': y,
})
sns.jointplot(x='X-axis label', y='Y-axis label', data=data)
The following code when graphed looks really messy at the moment. The reason is I have too many values for 'fare'. 'Fare' ranges from [0-500] with most of the values within the first 100.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
titanic = sns.load_dataset("titanic")
y =titanic.groupby([titanic.fare//1,'sex']).survived.mean().reset_index()
sns.set(style="whitegrid")
g = sns.factorplot(x='fare', y= 'survived', col = 'sex', kind ='bar' ,data= y,
size=4, aspect =2.5 , palette="muted")
g.despine(left=True)
g.set_ylabels("Survival Probability")
g.set_xlabels('Fare')
plt.show()
I would like to try slicing up the 'fare' of the plots into subsets but would like to see all the graphs at the same time on one screen. I was wondering it this is possible without having to resort to groupby.
I will have to play around with the values of 'fare' to see what I would want each graph to represent, but for a sample let's use break up the graph into these 'fare' values.
[0-18]
[18-35]
[35-70]
[70-300]
[300-500]
So the total would be 10 graphs on one page, because of the juxtaposition with the opposite sex.
Is it possible with Seaborn? Do I need to do a lot of configuring with matplotlib? Thanks.
Actually I wrote a little blog post about this a while ago. If you are plotting histograms you can use the by keyword:
import matplotlib.pyplot as plt
import seaborn.apionly as sns
sns.set() #rescue matplotlib's styles from the early '90s
data = sns.load_dataset('titanic')
data.hist(by='class', column = 'fare')
plt.show()
Otherwise if you're just plotting value-counts, you have to roll your own grid:
def categorical_hist(self,column,by,layout=None,legend=None,**params):
from math import sqrt, ceil
if layout==None:
s = ceil(sqrt(self[column].unique().size))
layout = (s,s)
return self.groupby(by)[column]\
.value_counts()\
.sort_index()\
.unstack()\
.plot.bar(subplots=True,layout=layout,legend=None,**params)
categorical_hist(data, by='class', column='embark_town')
Edit If you want survival rate by fare range, you could do something like this
data.groupby(pd.cut(data.fare,10)).apply(lambda x.survived.sum(): x./len(x))