import geopandas

path_to_data = geopandas.datasets.get_path("nybb")
gdf = geopandas.read_file(path_to_data)

gdf

"""
Explanation: Introduction to GeoPandas
This quick tutorial introduces the key concepts and basic features of GeoPandas to help you get started with your projects.
Concepts
GeoPandas, as the name suggests, extends the popular data science library pandas by adding support for geospatial data. If you are not familiar with pandas, we recommend taking a quick look at its Getting started documentation before proceeding.
The core data structure in GeoPandas is the geopandas.GeoDataFrame, a subclass of pandas.DataFrame, that can store geometry columns and perform spatial operations. The geopandas.GeoSeries, a subclass of pandas.Series, handles the geometries. Therefore, your GeoDataFrame is a combination of pandas.Series, with traditional data (numerical, boolean, text etc.), and geopandas.GeoSeries, with geometries (points, polygons etc.). You can have as many columns with geometries as you wish; there's no limit typical for desktop GIS software.

Each GeoSeries can contain any geometry type (you can even mix them within a single array) and has a GeoSeries.crs attribute, which stores information about the projection (CRS stands for Coordinate Reference System). Therefore, each GeoSeries in a GeoDataFrame can be in a different projection, allowing you to have, for example, multiple versions (different projections) of the same geometry.
Only one GeoSeries in a GeoDataFrame is considered the active geometry, which means that all geometric operations applied to a GeoDataFrame operate on this active column.
<div class="alert alert-info">
User Guide

See more on [data structures in the User Guide](../docs/user_guide/data_structures.rst).
</div>

Let's see how some of these concepts work in practice.
Reading and writing files
First, we need to read some data.
Reading files
Assuming you have a file containing both data and geometry (e.g. GeoPackage, GeoJSON, Shapefile), you can read it using geopandas.read_file(), which automatically detects the filetype and creates a GeoDataFrame. This tutorial uses the "nybb" dataset, a map of New York boroughs, which is part of the GeoPandas installation. Therefore, we use geopandas.datasets.get_path() to retrieve the path to the dataset.
End of explanation
"""


gdf.to_file("my_file.geojson", driver="GeoJSON")

"""
Explanation: Writing files
To write a GeoDataFrame back to file use GeoDataFrame.to_file(). The default file format is Shapefile, but you can specify your own with the driver keyword.
End of explanation
"""


gdf = gdf.set_index("BoroName")

gdf["area"] = gdf.area
gdf["area"]

"""
Explanation: <div class="alert alert-info">
User Guide

See more on [reading and writing data in the User Guide](../docs/user_guide/io.rst).
</div>

Simple accessors and methods
Now we have our GeoDataFrame and can start working with its geometry. 
Since there was only one geometry column in the New York Boroughs dataset, this column automatically becomes the active geometry and spatial methods used on the GeoDataFrame will be applied to the "geometry" column.
Measuring area
To measure the area of each polygon (or MultiPolygon in this specific case), access the GeoDataFrame.area attribute, which returns a pandas.Series. Note that GeoDataFrame.area is just GeoSeries.area applied to the active geometry column.
But first, to make the results easier to read, set the names of the boroughs as the index:
End of explanation
"""


gdf['boundary'] = gdf.boundary
gdf['boundary']

"""
Explanation: Getting polygon boundary and centroid
To get the boundary of each polygon (LineString), access the GeoDataFrame.boundary:
End of explanation
"""


gdf['centroid'] = gdf.centroid
gdf['centroid']

"""
Explanation: Since we have saved boundary as a new column, we now have two geometry columns in the same GeoDataFrame.
We can also create new geometries, which could be, for example, a buffered version of the original one (i.e., GeoDataFrame.buffer(10)) or its centroid:
End of explanation
"""


first_point = gdf['centroid'].iloc[0]
gdf['distance'] = gdf['centroid'].distance(first_point)
gdf['distance']

"""
Explanation: Measuring distance
We can also measure how far each centroid is from the first centroid location.
End of explanation
"""


gdf['distance'].mean()

"""
Explanation: Note that geopandas.GeoDataFrame is a subclass of pandas.DataFrame, so we have all the pandas functionality available to use on the geospatial dataset — we can even perform data manipulations with the attributes and geometry information together.
For example, to calculate the average of the distances measured above, access the 'distance' column and call the mean() method on it:
End of explanation
"""


gdf.plot("area", legend=True)

"""
Explanation: Making maps
GeoPandas can also plot maps, so we can check how the geometries appear in space. To plot the active geometry, call GeoDataFrame.plot(). To color code by another column, pass in that column as the first argument. In the example below, we plot the active geometry column and color code by the "area" column. We also want to show a legend (legend=True).
End of explanation
"""


gdf.explore("area", legend=False)

"""
Explanation: You can also explore your data interactively using GeoDataFrame.explore(), which behaves in the same way plot() does but returns an interactive map instead.
End of explanation
"""


gdf = gdf.set_geometry("centroid")
gdf.plot("area", legend=True)

"""
Explanation: Switching the active geometry (GeoDataFrame.set_geometry) to centroids, we can plot the same data using point geometry.
End of explanation
"""


ax = gdf["geometry"].plot()
gdf["centroid"].plot(ax=ax, color="black")

"""
Explanation: And we can also layer both GeoSeries on top of each other. We just need to use one plot as an axis for the other.
End of explanation
"""


gdf = gdf.set_geometry("geometry")

"""
Explanation: Now we set the active geometry back to the original GeoSeries.
End of explanation
"""


gdf["convex_hull"] = gdf.convex_hull

ax = gdf["convex_hull"].plot(alpha=.5)  # saving the first plot as an axis and setting alpha (transparency) to 0.5
gdf["boundary"].plot(ax=ax, color="white", linewidth=.5)  # passing the first plot and setting linewitdth to 0.5

"""
Explanation: <div class="alert alert-info">
User Guide

See more on [mapping in the User Guide](../docs/user_guide/mapping.rst).
</div>

Geometry creation
We can further work with the geometry and create new shapes based on those we already have. 
Convex hull
If we are interested in the convex hull of our polygons, we can access GeoDataFrame.convex_hull.
End of explanation
"""


# buffering the active geometry by 10 000 feet (geometry is already in feet)
gdf["buffered"] = gdf.buffer(10000)

# buffering the centroid geometry by 10 000 feet (geometry is already in feet)
gdf["buffered_centroid"] = gdf["centroid"].buffer(10000)

ax = gdf["buffered"].plot(alpha=.5)  # saving the first plot as an axis and setting alpha (transparency) to 0.5
gdf["buffered_centroid"].plot(ax=ax, color="red", alpha=.5)  # passing the first plot as an axis to the second
gdf["boundary"].plot(ax=ax, color="white", linewidth=.5)  # passing the first plot and setting linewitdth to 0.5

"""
Explanation: Buffer
In other cases, we may need to buffer the geometry using GeoDataFrame.buffer(). Geometry methods are automatically applied to the active geometry, but we can apply them directly to any GeoSeries as well. Let's buffer the boroughs and their centroids and plot both on top of each other.
End of explanation
"""


brooklyn = gdf.loc["Brooklyn", "geometry"]
brooklyn

"""
Explanation: <div class="alert alert-info">
User Guide

See more on [geometry creation and manipulation in the User Guide](../docs/user_guide/geometric_manipulations.rst).
</div>

Geometry relations
We can also ask about the spatial relations of different geometries. Using the geometries above, we can check which of the buffered boroughs intersect the original geometry of Brooklyn, i.e., is within 10 000 feet from Brooklyn.
First, we get a polygon of Brooklyn.
End of explanation
"""


type(brooklyn)

"""
Explanation: The polygon is a shapely geometry object, as any other geometry used in GeoPandas.
End of explanation
"""


gdf["buffered"].intersects(brooklyn)

"""
Explanation: Then we can check which of the geometries in gdf["buffered"] intersects it.
End of explanation
"""


gdf["within"] = gdf["buffered_centroid"].within(gdf)
gdf["within"]

"""
Explanation: Only Bronx (on the north) is more than 10 000 feet away from Brooklyn. All the others are closer and intersect our polygon.
Alternatively, we can check which buffered centroids are entirely within the original boroughs polygons. In this case, both GeoSeries are aligned, and the check is performed for each row.
End of explanation
"""


gdf = gdf.set_geometry("buffered_centroid")
ax = gdf.plot("within", legend=True, categorical=True, legend_kwds={'loc': "upper left"})  # using categorical plot and setting the position of the legend
gdf["boundary"].plot(ax=ax, color="black", linewidth=.5)  # passing the first plot and setting linewitdth to 0.5

"""
Explanation: We can plot the results on the map to confirm the finding.
End of explanation
"""


gdf.crs

"""
Explanation: Projections
Each GeoSeries has its Coordinate Reference System (CRS) accessible at GeoSeries.crs. The CRS tells GeoPandas where the coordinates of the geometries are located on the earth's surface. In some cases, the CRS is geographic, which means that the coordinates are in latitude and longitude. In those cases, its CRS is WGS84, with the authority code EPSG:4326. Let's see the projection of our NY boroughs GeoDataFrame.
End of explanation
"""


gdf = gdf.set_geometry("geometry")
boroughs_4326 = gdf.to_crs("EPSG:4326")
boroughs_4326.plot()

boroughs_4326.crs

"""
Explanation: Geometries are in EPSG:2263 with coordinates in feet. We can easily re-project a GeoSeries to another CRS, like EPSG:4326 using GeoSeries.to_crs().
End of explanation
"""


