diff --git a/conda/meta.yaml b/conda/meta.yaml
index 319c7e6c80577d197daa9e6bb23869b86fe3e7ea..159eebb583c95fba23cfeeb5fdd21265e37148ce 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -56,7 +56,6 @@ test:
     - sphinx {{ sphinx }}
     - sphinx_rtd_theme {{ sphinx_rtd_theme }}
     - matplotlib {{ matplotlib }}
-    - bob.db.iris
 
 about:
   home: https://www.idiap.ch/software/bob/
diff --git a/doc/plot/plot_ML.py b/doc/plot/plot_ML.py
index 4717249139eef6dce906d41341768fd0fe41ab80..32f664338343883bb27540f255d3bac433ba971d 100644
--- a/doc/plot/plot_ML.py
+++ b/doc/plot/plot_ML.py
@@ -5,26 +5,18 @@ import numpy
 
 from matplotlib.lines import Line2D
 from matplotlib.patches import Ellipse
-
-import bob.db.iris
+from sklearn.datasets import load_iris
 
 from bob.learn.em import GMMMachine
 
 logger = logging.getLogger("bob.learn.em")
 logger.setLevel("DEBUG")
 
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
-    (data_per_class["setosa"][:, 0], data_per_class["setosa"][:, 3])
-)
-versicolor = numpy.column_stack(
-    (data_per_class["versicolor"][:, 0], data_per_class["versicolor"][:, 3])
-)
-virginica = numpy.column_stack(
-    (data_per_class["virginica"][:, 0], data_per_class["virginica"][:, 3])
-)
-
-data = numpy.vstack((setosa, versicolor, virginica))
+iris_data = load_iris()
+data = iris_data.data
+setosa = data[iris_data.target == 0]
+versicolor = data[iris_data.target == 1]
+virginica = data[iris_data.target == 2]
 
 # Two clusters with a feature dimensionality of 3
 machine = GMMMachine(
diff --git a/doc/plot/plot_kmeans.py b/doc/plot/plot_kmeans.py
index af565c119fb164cc22cf2f57e32f5390ac3276cc..1deeb0b007b044249ac7d9809f3d7c64a6c4d907 100644
--- a/doc/plot/plot_kmeans.py
+++ b/doc/plot/plot_kmeans.py
@@ -1,22 +1,14 @@
 import matplotlib.pyplot as plt
-import numpy
 
-import bob.db.iris
+from sklearn.datasets import load_iris
 
 from bob.learn.em import KMeansMachine
 
-data_per_class = bob.db.iris.data()
-setosa = numpy.column_stack(
-    (data_per_class["setosa"][:, 0], data_per_class["setosa"][:, 3])
-)
-versicolor = numpy.column_stack(
-    (data_per_class["versicolor"][:, 0], data_per_class["versicolor"][:, 3])
-)
-virginica = numpy.column_stack(
-    (data_per_class["virginica"][:, 0], data_per_class["virginica"][:, 3])
-)
-
-data = numpy.vstack((setosa, versicolor, virginica))
+iris_data = load_iris()
+data = iris_data.data
+setosa = data[iris_data.target == 0]
+versicolor = data[iris_data.target == 1]
+virginica = data[iris_data.target == 2]
 
 # Training KMeans
 # 3 clusters with a feature dimensionality of 2
diff --git a/test-requirements.txt b/test-requirements.txt
deleted file mode 100644
index 6f36688509fc95b8754520e56b4920804355c3d1..0000000000000000000000000000000000000000
--- a/test-requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-bob.db.iris