How to plot heatmap for high-dimensional dataset?

Question

I would greatly appreciate if you could let me know how to plot high-resolution heatmap for a large dataset with approximately 150 features.

My code is as follows:

Answer 1

If I understand your problem correctly, I think all you have to do is increase you figure size:

f, ax = plt.subplots(figsize=(20, 20))

instead of

f, ax = plt.subplots(figsize=(9, 9))

Answer 2

Adjusting the figsize and dpi worked for me.

I adapted your code and doubled the size of the heatmap to 165 x 165. The rendering takes a while, but the png looks fine. My backend is "module://ipykernel.pylab.backend_inline."

As noted in my original answer, I'm pretty sure you forgot close the figure object before creating a new one. Try plt.close("all") before fig, ax = plt.subplots() if you get wierd effects.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

print(plt.get_backend())

# close any existing plots
plt.close("all")

df = pd.read_csv("Financial Distress.csv")
# select out the desired columns
df = df.iloc[:, 3:].select_dtypes(include=['float64','int64'])

# copy columns to double size of dataframe
df2 = df.copy()
df2.columns = "c_" + df2.columns
df3 = pd.concat([df, df2], axis=1)

# get the correlation coefficient between the different columns
corr = df3.iloc[:, 1:].corr()
arr_corr = corr.as_matrix()
# mask out the top triangle
arr_corr[np.triu_indices_from(arr_corr)] = np.nan

fig, ax = plt.subplots(figsize=(24, 18))

hm = sns.heatmap(arr_corr, cbar=True, vmin=-0.5, vmax=0.5,
                 fmt='.2f', annot_kws={'size': 3}, annot=True, 
                 square=True, cmap=plt.cm.Blues)

ticks = np.arange(corr.shape[0]) + 0.5
ax.set_xticks(ticks)
ax.set_xticklabels(corr.columns, rotation=90, fontsize=8)
ax.set_yticks(ticks)
ax.set_yticklabels(corr.index, rotation=360, fontsize=8)

ax.set_title('correlation matrix')
plt.tight_layout()
plt.savefig("corr_matrix_incl_anno_double.png", dpi=300)

full figure: zoom of top left section: