// choose your dataset
//////////////////////////////
// csv_string = FileAttachment("10K_random.csv").text()
// csv_string = FileAttachment("1M_random.csv").text()
csv_string = FileAttachment("ccle_data_observable.csv").text()
run_clustergrammer = {
py`
import micropip
micropip.install(['clustergrammer2_core==0.3.0'])
`
var run_clustergrammer = py`
import micropip
import pandas as pd
import scipy
import sklearn
import statsmodels
import matplotlib
import json
from io import StringIO
from clustergrammer2_core import net
from sklearn.manifold import TSNE
def run_clustergrammer(csv_string):
df = pd.read_csv(StringIO(csv_string), sep=",", index_col=0)
X_embedded = TSNE(n_components=2).fit_transform(df.transpose())
rows = ['row-' + str(x) for x in range(X_embedded.shape[0])]
cols = ['col-' + str(x) for x in range(X_embedded.shape[1])]
df_tsne = pd.DataFrame(X_embedded, index=rows, columns=cols)
# net.load_df(df)
net.load_df(df_tsne)
# Z-Score Data
#########################
# net.normalize(axis='row', norm_type='zscore')
net.cluster()
return json.dumps(net.viz)
run_clustergrammer
`
return run_clustergrammer
}
network = JSON.parse(run_clustergrammer(csv_string))
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
