4 (continued from previous page) In [18]: stations_coord.head() Out[18]: location coordinates.latitude coordinates.longitude 0 BELAL01 51.23619 4.38522 1 BELHB23 51.17030 4.34100 2 BELLD01 51.10998 ....: In [21]: air_quality.head() Out[21]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude 0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51. ˓→49467 -0.13193 ....: In [25]: air_quality.head() Out[25]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude id ˓→description name 0 2019-05-07 01:00:00+00:00 London Westminster no2

0 (continued from previous page) In [18]: stations_coord.head() Out[18]: location coordinates.latitude coordinates.longitude 0 BELAL01 51.23619 4.38522 1 BELHB23 51.17030 4.34100 2 BELLD01 51.10998 ....: In [21]: air_quality.head() Out[21]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude 0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51. ˓→49467 -0.13193 ....: In [25]: air_quality.head() Out[25]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude id ˓→description name 0 2019-05-07 01:00:00+00:00 London Westminster no2

3 (continued from previous page) In [18]: stations_coord.head() Out[18]: location coordinates.latitude coordinates.longitude 0 BELAL01 51.23619 4.38522 1 BELHB23 51.17030 4.34100 2 BELLD01 51.10998 ....: In [21]: air_quality.head() Out[21]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude 0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51. ˓→49467 -0.13193 ....: In [25]: air_quality.head() Out[25]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude id ˓→description name 0 2019-05-07 01:00:00+00:00 London Westminster no2

5 (continued from previous page) In [18]: stations_coord.head() Out[18]: location coordinates.latitude coordinates.longitude 0 BELAL01 51.23619 4.38522 1 BELHB23 51.17030 4.34100 2 BELLD01 51.10998 ....: In [21]: air_quality.head() Out[21]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude 0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51. ˓→49467 -0.13193 correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters

1 (continued from previous page) In [18]: stations_coord.head() Out[18]: location coordinates.latitude coordinates.longitude 0 BELAL01 51.23619 4.38522 1 BELHB23 51.17030 4.34100 2 BELLD01 51.10998 ....: In [21]: air_quality.head() Out[21]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude 0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51. ˓→49467 -0.13193 correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters

correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters @property def center(self): # return the geographic center point of this DataFrame lat = self._obj.latitude lon = self._obj.longitude return (float(lon.mean()), float(lat.mean())) def plot(self): # plot in an interactive IPython session: >>> ds = pd.DataFrame({'longitude': np.linspace(0, 10), ... 'latitude': np.linspace(0, 20)}) >>> ds.geo.center (5.0, 10.0) >>> ds.geo.plot() # plots data on a map 4

correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters page) def center(self): # return the geographic center point of this DataFrame lat = self._obj.latitude lon = self._obj.longitude return (float(lon.mean()), float(lat.mean())) def plot(self): # plot in an interactive IPython session: >>> ds = pd.DataFrame({'longitude': np.linspace(0, 10), ... 'latitude': np.linspace(0, 20)}) >>> ds.geo.center (5.0, 10.0) >>> ds.geo.plot() # plots data on a map 6

correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters page) def center(self): # return the geographic center point of this DataFrame lat = self._obj.latitude lon = self._obj.longitude return (float(lon.mean()), float(lat.mean())) def plot(self): # plot in an interactive IPython session: >>> ds = pd.DataFrame({'longitude': np.linspace(0, 10), ... 'latitude': np.linspace(0, 20)}) >>> ds.geo.center (5.0, 10.0) >>> ds.geo.plot() # plots data on a map 6

read_csv("data/air_quality_stations.csv") In [18]: stations_coord.head() Out[18]: location coordinates.latitude coordinates.longitude 0 BELAL01 51.23619 4.38522 1 BELHB23 51.17030 4.34100 2 BELLD01 51.10998 on="location ˓→") In [21]: air_quality.head() Out[21]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude 0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51. ˓→49467 -0.13193 correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters

read_csv("data/air_quality_stations.csv") In [18]: stations_coord.head() Out[18]: location coordinates.latitude coordinates.longitude 0 BELAL01 51.23619 4.38522 1 BELHB23 51.17030 4.34100 2 BELLD01 51.10998 on="location ˓→") In [21]: air_quality.head() Out[21]: date.utc location parameter value coordinates. ˓→latitude coordinates.longitude 0 2019-05-07 01:00:00+00:00 London Westminster no2 23.0 51. ˓→49467 -0.13193 correlations between two variables. Points could be for instance natural 2D coordinates like longitude and latitude in a map or, in general, any pair of metrics that can be plotted against each other. Parameters