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Inline exercises for notebooks 1, 2, 3, and 4 #15

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Inline exercises for notebooks 1, 2, 3, and 4 #15

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61da92c
Adding exercises to notebook 1
darribas Jul 9, 2018
7ff6e22
Adding missing image
darribas Jul 9, 2018
ba74dd1
Inline exercises for notebook 2
darribas Jul 9, 2018
591beee
Exercises for notebook 3
darribas Jul 10, 2018
7a29420
Adding minor edits and exercises to notebook 4
darribas Jul 10, 2018
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120 changes: 119 additions & 1 deletion 01-introduction-geospatial-data.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -182,6 +182,40 @@
"africa.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: create a plot of South America\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"countries[countries['continent'] == 'South America'].plot()\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"countries.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -486,6 +520,31 @@
"Note the different scale of x and y."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: project the countries to [Web Mercator](http://epsg.io/3857), the CRS used by Google Maps, OpenStreetMap and most web providers.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"countries.to_crs(epsg=3857)\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -512,6 +571,34 @@
"See the [04-more-on-visualization.ipynb](04-more-on-visualization.ipynb) notebook for more details on visualizing geospatial datasets."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: replicate the figure above by coloring the countries in black and cities in yellow\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"ax = countries.plot(edgecolor='w', facecolor='k', figsize=(15, 10))\n",
"rivers.plot(ax=ax)\n",
"cities.plot(ax=ax, color='yellow')\n",
"ax.set(xlim=(-20, 60), ylim=(-40, 40))\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -629,6 +716,37 @@
"source": [
"See http://geopandas.readthedocs.io/en/latest/gallery/create_geopandas_from_pandas.html#sphx-glr-gallery-create-geopandas-from-pandas-py for full example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: use [geojson.io](http://geojson.io) to mark five points, and create a `GeoDataFrame` with it. Note that coordinates will be expressed in longitude and latitude, so you'll have to set the CRS accordingly.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"df = pd.DataFrame(\n",
" {'Name': ['Hotel', 'Capitol', 'Barton Springs'],\n",
" 'Latitude': [30.28195889019179, 30.274782936992608, 30.263728440902543],\n",
" 'Longitude': [-97.74006128311157, -97.74038314819336, -97.77013421058655]})\n",
"df['Coordinates'] = list(zip(df.Longitude, df.Latitude))\n",
"df['Coordinates'] = df['Coordinates'].apply(Point)\n",
"gdf = geopandas.GeoDataFrame(df, geometry='Coordinates', crs={'init': 'epsg:4326'})\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
}
],
"metadata": {
Expand All @@ -647,7 +765,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
"version": "3.6.5"
}
},
"nbformat": 4,
Expand Down
111 changes: 108 additions & 3 deletions 02-spatial-relationships-operations.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -125,8 +125,41 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"You can recognize the abstract shape of Belgium.\n",
"You can recognize the abstract shape of Belgium."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: produce a similar figure as above that includes France, Spain, Paris, Madrid, and a line that connects both capitals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"sp = countries.loc[countries['name'] == 'Spain', 'geometry'].squeeze()\n",
"mad = cities.loc[cities['name'] == 'Madrid', 'geometry'].squeeze()\n",
"fr = countries.loc[countries['name'] == 'France', 'geometry'].squeeze()\n",
"geopandas.GeoSeries([sp, fr, mad, paris, LineString([paris, mad])]).plot(cmap='tab10')\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Brussels, the capital of Belgium, is thus located within Belgium. This is a spatial relationship, and we can test this using the individual shapely geometry objects as follow:"
]
},
Expand Down Expand Up @@ -302,6 +335,32 @@
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: find all of the countries contiguous to Brazil"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"br = countries.query('name == \"Brazil\"')['geometry'].squeeze()\n",
"countries[countries.touches(br)] # or .intersects\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -326,7 +385,7 @@
"metadata": {},
"outputs": [],
"source": [
"geopandas.GeoSeries([belgium, brussels.buffer(1)]).plot(alpha=0.5, cmap='tab10')"
"geopandas.GeoSeries([belgium, brussels.buffer(1), brussels]).plot(alpha=0.5, cmap='tab10')"
]
},
{
Expand Down Expand Up @@ -399,6 +458,52 @@
"africa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: Create a single polygon for Europe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"eu = countries.query('continent == \"Africa\"')\n",
"eu.unary_union\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"**Exercise**: Create a single polygon with all of the countries that you would have to cross to travel directly (as the crow flies) from Beijing to Sydney"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"bei = cities.query('name == \"Beijing\"')['geometry'].squeeze()\n",
"syd = cities.query('name == \"Sydney\"')['geometry'].squeeze()\n",
"\n",
"countries[countries.crosses(LineString([bei, syd]))].unary_union\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -445,7 +550,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
"version": "3.6.5"
}
},
"nbformat": 4,
Expand Down