This example shows how to visualize a Pandas DataFrame using the Clustergrammer Jupyter Widget.
In [1]:
import numpy as np
import pandas as pd
from clustergrammer_widget import *
Generate a dataframe of random data with row and column labels
In [2]:
# generate random matrix
num_rows = 500
num_cols = 10
np.random.seed(seed=100)
mat = np.random.rand(num_rows, num_cols)
# make row and col labels
rows = range(num_rows)
cols = range(num_cols)
rows = [str(i) for i in rows]
cols = [str(i) for i in cols]
# make dataframe
df = pd.DataFrame(data=mat, columns=cols, index=rows)
Initialize the network object, load the dataframe, hierarchically cluster the rows and columns using default parameters, and finally visualize using clustergrammer_widget.
In [3]:
# initialize network object
net = Network(clustergrammer_widget)
# load dataframe
net.load_df(df)
# cluster using default parameters
net.cluster(enrichrgram=False)
# make the visualization
net.widget()
The network object can also be used to normalize and filter your data. Here we will Z-score normalize the columns and make an updated visualization.
In [4]:
net.normalize(axis='col', norm_type='zscore', keep_orig=True)
net.cluster(enrichrgram=False)
net.widget()
Here we will filter rows based on their sum. We will keep the top 20 rows based on their absolute value sum.
In [5]:
net.filter_N_top('row', 20, 'sum')
net.cluster(enrichrgram=False)
net.widget()
In [ ]: