'''
I did a clustermap with thousands of genes, using seaborn. Because, I'm interested in only few genes, I'd like to display those genes of interest on the ytick. I'm trying to figure it out using the iris dataset. Please find below my code. I'm not sure how to get the samples of interest at their right indexes. Thank you in advance for helpful assistance.
'''
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
iris = sns.load_dataset('iris')
samples = ['sample_'+str(x) for x in list(iris.index)] #creating sample ID lining up with the internal index.[![enter image description here][1]][1]
iris.insert(0,'Sample_ID',samples)
samples_of_interest = ['sample_41','sample_34','sample_114','sample_55'] #samples to be visible on ytick
sns.clustermap(iris.iloc[:,1:-1],yticklabels=samples_of_interest) #Not giving the expected result as all of thesmples of interest are not at their right index
plt.show()
plt.close()
Here's why your answer wasn't working:
See this about the yticklabels argument in the documentation:
If list-like, plot these alternate labels as the xticklabels.
So basically when you only pass a few tick labels, it is just setting those names as the tick labels, without knowledge of the tick positions. One way to get around this is to do the following, adding sample_labels which makes a label for all ticks, but sets non-interesting ones to None. You then follow this answer to rotate the ticks):
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
iris = sns.load_dataset('iris')
samples = ['sample_'+str(x) for x in list(iris.index)]
iris.insert(0,'Sample_ID',samples)
samples_of_interest = ['sample_41','sample_34','sample_114','sample_55']
sample_labels = [i if i in samples_of_interest else None
for i in iris['Sample_ID'] ]
cm=sns.clustermap(iris.iloc[:,1:-1], yticklabels=sample_labels)
plt.setp(cm.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
But this is still not ideal b/c there are ticks for all the positions I'm sure there is a way to edit this but instead..
Here's a method I like more:
Get the new order of the samples from the clustergrid (object returned by clustermap, then manually set the y-tick labels and positions (with credit to this post):
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
iris = sns.load_dataset('iris')
samples_of_interest = [41, 34, 114, 55]
sample_names = ['Sample ' + str(i) for i in samples_of_interest]
cm=sns.clustermap(iris.iloc[:,:-1]) #note the loc has changed!
reorder = cm.dendrogram_row.reordered_ind
new_positions = [reorder.index(i) for i in samples_of_interest]
plt.setp(cm.ax_heatmap.yaxis.set_ticks(new_positions))
plt.setp(cm.ax_heatmap.yaxis.set_ticklabels(sample_names))
Oddly the cm.ax_heatmap.yaxis.set... commands print out the get versions (it seems), but this doesn't affect outcome
Related
I am attempting to create a histogram using seaborn and census data that displays 3 subplots for age composition, and I have the data grouped the way that I would like it, but I am struggling to turn that into a histogram.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
filename = "/scratch/%s_class_root/%s_class/materials/data/pums_short.csv.gz"
acs = pd.read_csv(filename)
R65_agg = acs.groupby(["R65", "PUMA"])["HINCP"]
R65_meds = R65_agg.agg(np.median).unstack()
R65_f = R65_meds.dropna()
R65_f = R65_meds.reset_index(drop = True)
I was expecting this code to give me data that I could plug into a histogram but instead of being distinct subplots, the "0.0, 1.0, 2,0" in the final variable just get added together when I apply the .describe() function. Any advice for how I can convert this into a form that's readable with the sns.histplot() function?
I have a dataframe and I'm using seaborn pairplot to plot one target column vs the rest of the columns.
Code is below,
import seaborn as sns
import matplotlib.pyplot as plt
tgt_var = 'AB'
var_lst = ['A','GH','DL','GT','MS']
pp = sns.pairplot(data=df,
y_vars=[tgt_var],
x_vars=var_lst)
pp.fig.set_figheight(6)
pp.fig.set_figwidth(20)
The var_lst is not a static list, I just provided an example.
What I need is to plot tgt_var on Y axis and each var_lst on x axis.
I'm able to do this with above code, but I also want to use log scale on X axis only if the var_lst item is 'GH' or 'MS', for the rest normal scale. Is there any way to achieve this?
Iterate pp.axes.flat and set xscale="log" if the xlabel matches "GH" or "MS":
log_columns = ["GH", "MS"]
for ax in pp.axes.flat:
if ax.get_xlabel() in log_columns:
ax.set(xscale="log")
Full example with the iris dataset where the petal columns are xscale="log":
import seaborn as sns
df = sns.load_dataset("iris")
pp = sns.pairplot(df)
log_columns = ["petal_length", "petal_width"]
for ax in pp.axes.flat:
if ax.get_xlabel() in log_columns:
ax.set(xscale="log")
I'm using seaborn to make a violinplot, which uses hues to identify who survived and who didn't. This is given by the column 'DEATH_EVENT', where 0 means the person survived and 1 means they didn't. The only issue I'm having is that I can't figure out how to set labels for this hue legend. As seen below, 'DEATH_EVENT' presents 0 and 1, but I want to change this into 'Survived' and 'Not survived'.
Current code:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
sns.set()
plt.style.use('seaborn')
data = pd.read_csv('heart_failure_clinical_records_dataset.csv')
g = sns.violinplot(data=data, x='smoking', y='age', hue='DEATH_EVENT')
g.set_xticklabels(['No smoking', 'Smoking'])
I tried to use: g.legend(labels=['Survived', 'Not survived']), but it returns it without the colors, instead a thin and thick line for some reason.
I'm aware I could just use:
data['DEATH_EVENT'].replace({0:'Survived', 1:'Not survived'}, inplace=True)
but I wanted to see if there was another way. I'm still a rookie, so I'm guessing that there's a reason why the CSV's author made it so that it uses integers to describe plenty of things. Ex: if someone smokes or not, sex, diabetic or not, etc. Maybe it runs faster?
Controlling Seaborn legends is still somewhat tricky (some extensions to matplotlib's API would be helpful). In this case, you could grab the handles from the just-created legend and reuse them for a new legend:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.DataFrame({"smoking": np.random.randint(0, 2, 200),
"survived": np.random.randint(0, 2, 200),
"age": np.random.normal(60, 10, 200),
"DEATH_EVENT": np.random.randint(0, 2, 200)})
ax = sns.violinplot(data=data, x='smoking', y='age', hue='DEATH_EVENT')
ax.set_xticklabels(['No smoking', 'Smoking'])
ax.legend(handles=ax.legend_.legendHandles, labels=['Survived', 'Not survived'])
Here is an approach to make the change via the dataframe without changing the original dataframe. To avoid accessing ax.legend_ alltogether (to remove the legend title), a trick is to rename the column to a blank string (and use that blank string for hue). If the dataframe isn't super long (i.e. not having millions of rows), the speed and memory overhead are quite modest.
names = {0: 'Survived', 1: 'Not survived'}
ax = sns.violinplot(data=data.replace({'DEATH_EVENT': names}).rename(columns={'DEATH_EVENT': ''}),
x='smoking', y='age', hue='')
Here I am trying to separate the data with the factor male or not by plotting Age on x-axis and Fare on y-axis and I want to display two labels in the legend differentiating male and female with respective colors.Can anyone help me do this.
Code:
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('https://sololearn.com/uploads/files/titanic.csv')
df['male']=df['Sex']=='male'
sc1= plt.scatter(df['Age'],df['Fare'],c=df['male'])
plt.legend()
plt.show()
You could use the seaborn library which builds on top of matplotlib to perform the exact task you require. You can scatterplot 'Age' vs 'Fare' and colour code it by 'Sex' by just passing the hue parameter in sns.scatterplot, as follows:
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure()
# No need to call plt.legend, seaborn will generate the labels and legend
# automatically.
sns.scatterplot(df['Age'], df['Fare'], hue=df['Sex'])
plt.show()
Seaborn generates nicer plots with less code and more functionality.
You can install seaborn from PyPI using pip install seaborn.
Refer: Seaborn docs
PathCollection.legend_elements method
can be used to steer how many legend entries are to be created and how they
should be labeled.
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('https://sololearn.com/uploads/files/titanic.csv')
df['male'] = df['Sex']=='male'
sc1= plt.scatter(df['Age'], df['Fare'], c=df['male'])
plt.legend(handles=sc1.legend_elements()[0], labels=['male', 'female'])
plt.show()
Legend guide and Scatter plots with a legend for reference.
This can be achieved by segregating the data in two separate dataframe and then, label can be set for these dataframe.
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('https://sololearn.com/uploads/files/titanic.csv')
subset1 = df[(df['Sex'] == 'male')]
subset2 = df[(df['Sex'] != 'male')]
plt.scatter(subset1['Age'], subset1['Fare'], label = 'Male')
plt.scatter(subset2['Age'], subset2['Fare'], label = 'Female')
plt.legend()
plt.show()
enter image description here
I am trying to create a heatmap with dendrograms on Python using Seaborn and I have a csv file with about 900 rows. I'm importing the file as a pandas dataframe and attempting to plot that but a large number of the rows are not being represented in the heatmap. What am I doing wrong?
This is the code I have right now. But the heatmap only represents about 49 rows.
Here is an image of the clustermap I've obtained but it is not displaying all of my data.
import seaborn as sns
import pandas as pd
from matplotlib import pyplot as plt
# Data set
df = pd.read_csv('diff_exp_gene.csv', index_col = 0)
# Default plot
sns.clustermap(df, cmap = 'RdBu', row_cluster=True, col_cluster=True)
plt.show()
Thank you.
An alternative approach would be to use imshow in matpltlib. I'm not exactly sure what your question is but I demonstrate a way to graph points on a plane from csv file
import numpy as np
import matplotlib.pyplot as plt
import csv
infile = open('diff_exp_gene.csv')
df = csv.DictReader(in_file)
temp = np.zeros((128,128), dtype = int)
for row in data:
if row['TYPE'] == types:
temp[int(row['Y'])][int(row['X'])] = temp[int(row['Y'])][int(row['X'])] + 1
plt.imshow(temp, cmap = 'hot', origin = 'lower')
plt.show()
As far as I know, keywords that apply to seaborn heatmaps also apply to clustermap, as the sns.clustermap passes to the sns.heatmap. In that case, all you need to do in your example is to set yticklabels=True as a keyword argument in sns.clustermap(). That will make all of the 900 rows appear.
By default, it is set as "auto" to avoid overlap. The same applies to the xticklabels. See more here: https://seaborn.pydata.org/generated/seaborn.heatmap.html