diff --git a/examples/collocates.py b/examples/collocates.py
new file mode 100644
index 0000000..168d53b
--- /dev/null
+++ b/examples/collocates.py
@@ -0,0 +1,45 @@
+#!/usr/bin/env python3
+
+from KorAPClient import KorAPConnection
+import plotly.express as px
+import pandas as pd
+
+startYear = 1991
+endYear = 2020
+span = 5
+
+NODE = "Ei"
+COLLOCATES = ["pellen", "schälen"]
+COUNTRIES = ["DE", "AT", "CH"]
+
+TITLE = f"Collocation strength of <i>{NODE} + {' / '.join(COLLOCATES)} </i> in {', '.join(COUNTRIES)} {startYear}-{endYear}"
+
+YEARS = [y for y in range(startYear, endYear, span)]
+
+# build all combinations of all variables
+df = pd.DataFrame(YEARS, columns=["year"]) \
+    .merge(pd.DataFrame(COUNTRIES, columns=["Country"]), how='cross') \
+    .merge(pd.DataFrame(COLLOCATES, columns=["Collocate"]), how='cross')
+
+# add column with virtual corpus specifications based on Country and year variables
+df['vc'] = [
+    f"textType=/Zeit.*/ & pubPlaceKey={df['Country'][i]} & pubDate since {df['year'][i]} & pubDate until {df['year'][i] + span - 1} "
+    for i in range(0, len(df.index))]
+
+# add column with label for x axis
+df['Period'] = [f"{df['year'][i]}-{df['year'][i] + span - 1}" for i in range(0, len(df.index))]
+
+# connect to KorAP API server
+kcon = KorAPConnection(verbose=True)
+
+# perform the actual KorAP query
+results = kcon.collocationScoreQuery(NODE, df['Collocate'], df['vc'], lemmatizeNodeQuery=True,
+                                     lemmatizeCollocateQuery=True)
+
+# join query result columns (axis=1 ...) with condition information columns
+# (why is reset_index needed?)
+df = pd.concat([df.reset_index(drop=True), results.reset_index(drop=True)], axis=1)
+
+fig = px.line(df, title=TITLE, x="Period", y="logDice", color="Country", line_dash="Collocate")
+fig.show()
+# fig.write_image(f"{NODE}_collocates_{startYear}-{endYear}_in_{'_'.join(COUNTRIES)}.png")