diff --git a/tools-appendix/modules/python/images/histograms-seaborn-aa.png b/tools-appendix/modules/python/images/histograms-seaborn-aa.png
deleted file mode 100644
index 8bdb6dce0..000000000
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diff --git a/tools-appendix/modules/python/pages/seaborn-examples.adoc b/tools-appendix/modules/python/pages/seaborn-examples.adoc
index 7d2c251e1..187dba395 100644
--- a/tools-appendix/modules/python/pages/seaborn-examples.adoc
+++ b/tools-appendix/modules/python/pages/seaborn-examples.adoc
@@ -208,7 +208,7 @@ plt.tight_layout()
 plt.show()
 ----
 
-image::histograms-seaborn-aa.png[Histograms using Seaborn, width=792, height=500, loading=lazy, title="Histogram using Seaborn"]
+image::histogram-seaborn-aa.png[Histograms using Seaborn, width=792, height=500, loading=lazy, title="Histogram using Seaborn"]
 
 Similar to the plotly example, the histogram helps us understand the distribution of the number of days the houses are on zillow. It seems that some houses genuinely stay on Zillow for much longer. It may be due to location, pricing, or other factors.