diff --git a/examples/create_geopandas_from_pandas.py b/examples/create_geopandas_from_pandas.py
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+++ b/examples/create_geopandas_from_pandas.py
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+"""
+Creating a GeoDataFrame from a DataFrame with coordinates
+---------------------------------------------------------
+
+This example shows how to create a ``GeoDataFrame`` when starting from
+a *regular* ``DataFrame`` that has coordinates either WKT
+(`well-known text <https://en.wikipedia.org/wiki/Well-known_text>`_)
+format, or in
+two columns.
+
+"""
+import matplotlib.pyplot as plt
+import pandas as pd
+import geopandas as gpd
+from shapely.geometry import Point
+
+###############################################################################
+# From longitudes and latitudes
+# =============================
+#
+# First, let's consider a ``DataFrame`` containing cities and their respective
+# longitudes and latitudes.
+
+df = pd.DataFrame(
+    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
+     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
+     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
+     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})
+
+###############################################################################
+# A ``GeoDataFrame`` needs a ``shapely`` object, so we create a new column
+# **Coordinates** as a tuple of **Longitude** and **Latitude** :
+
+df['Coordinates']  = list(zip(df.Longitude, df.Latitude))
+
+###############################################################################
+# Then, we transform tuples to ``Point`` :
+
+df['Coordinates'] = df['Coordinates'].apply(Point)
+
+###############################################################################
+# Now, we can create the ``GeoDataFrame`` by setting ``geometry`` with the
+# coordinates created previously.
+
+gdf = gpd.GeoDataFrame(df, geometry='Coordinates')
+
+###############################################################################
+# ``gdf`` looks like this :
+
+print(gdf.head())
+
+###############################################################################
+# Finally, we plot the coordinates over a country-level map.
+
+world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
+
+# We restrict to South America.
+ax = world[world.continent == 'South America'].plot(
+    color='white', edgecolor='black')
+
+# We can now plot our GeoDataFrame.
+gdf.plot(ax=ax, color='red')
+
+plt.show()
+
+###############################################################################
+# From WKT format
+# ===============
+# Here, we consider a ``DataFrame`` having coordinates in WKT format.
+
+df = pd.DataFrame(
+    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
+     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
+     'Coordinates': ['POINT(-34.58 -58.66)', 'POINT(-15.78 -47.91)',
+                     'POINT(-33.45 -70.66)', 'POINT(4.60 -74.08)',
+                     'POINT(10.48 -66.86)']})
+
+###############################################################################
+# We use ``shapely.wkt`` sub-module to parse wkt format:
+from shapely import wkt
+
+df['Coordinates'] = df['Coordinates'].apply(wkt.loads)
+
+###############################################################################
+#  The ``GeoDataFrame`` is constructed as follows :
+
+gdf = gpd.GeoDataFrame(df, geometry='Coordinates')
+
+print(gdf.head())