Regenerate notebooks and solutions

Author: jorisvandenbossche

notebooks/01-introduction-tabular-data.ipynb

@@ -428,7 +428,7 @@
     "editable": true
    },
    "source": [
-    "Pandas provides a large set of **summary** functions that operate on different kinds of pandas objects (DataFrames, Series, Index) and produce single value. When applied to a DataFrame, the result is returned as a pandas Series (one value for each column). "
+    "Pandas provides a large set of **summary** functions that operate on different kinds of pandas objects (DataFrames, Series, Index) and produce a single value. When applied to a DataFrame, the result is returned as a pandas Series (one value for each column). "
    ]
   },
   {
@@ -832,7 +832,7 @@
     "\\>       | Greater than\n",
     "\\>=       | Greater than or equal\n",
     "<       | Lesser than\n",
-    "!=       | Lesser than or equal\n",
+    "<=       | Lesser than or equal\n",
     "\n",
     "and to combine multiple conditions:\n",
     "\n",
@@ -958,7 +958,7 @@
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* What is the average popolation number of the districts?\n",
+    "* What is the average population number of the districts?\n",
     "* And what is the median?\n",
     "\n",
     "<details>\n",
@@ -1241,7 +1241,7 @@
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* Select all rows for the districts of the 1st arrondissement.\n",
+    "* Select all rows for the districts of the 3rd arrondissement.\n",
     "* For this subset, what is the total population of the arrondissement?\n",
     "    \n",
     "</div>"
@@ -1669,7 +1669,7 @@
     "editable": true
    },
    "source": [
-    "This is only an ultra short intro to matplotlib. In the course materials, you can find another notebook with some more details ([visualization_01_matplotlib.ipynb](visualization_01_matplotlib.ipynb#An-small-cheat-sheet-reference-for-some-common-elements))."
+    "This is only an ultra short intro to matplotlib. In the course materials, you can find another notebook with some more details ([visualization-01-matplotlib.ipynb](visualization_01_matplotlib.ipynb#An-small-cheat-sheet-reference-for-some-common-elements))."
    ]
   },
   {
@@ -1685,7 +1685,7 @@
     "\n",
     "Galleries are great to get inspiration, see the plot you want, and check the code how it is created:\n",
     "    \n",
-    "* [matplotlib gallery](http://matplotlib.org/gallery.html\") is an important resource to start from\n",
+    "* [matplotlib gallery](http://matplotlib.org/gallery.html) is an important resource to start from\n",
     "* [seaborn gallery](https://seaborn.pydata.org/examples/index.html)\n",
     "* The Python Graph Gallery (https://python-graph-gallery.com/)\n",
     "\n",

notebooks/02-introduction-geospatial-data.ipynb

@@ -63,7 +63,7 @@
    },
    "outputs": [],
    "source": [
-    "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n",
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
     "# or if the archive is unpacked:\n",
     "# countries = geopandas.read_file(\"data/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp\")"
    ]
@@ -351,7 +351,7 @@
    },
    "outputs": [],
    "source": [
-    "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")"
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")"
    ]
   },
   {
@@ -388,7 +388,7 @@
    },
    "outputs": [],
    "source": [
-    "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")"
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
    ]
   },
   {
@@ -625,7 +625,7 @@
     "<details><summary>Hints</summary>\n",
     "\n",
     "* Use `type(..)` to check any Python object type\n",
-    "* The geopandas.read_file() function can read different geospatial file formats. You pass the file name as first argument.\n",
+    "* The `geopandas.read_file()` function can read different geospatial file formats. You pass the file name as first argument.\n",
     "* Use the `.shape` attribute to get the number of features\n",
     "\n",
     "</details>\n",
@@ -1235,7 +1235,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

notebooks/03-coordinate-reference-systems.ipynb

@@ -42,9 +42,9 @@
    },
    "outputs": [],
    "source": [
-    "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n",
-    "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")\n",
-    "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")"
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
    ]
   },
   {
@@ -259,7 +259,7 @@
    },
    "outputs": [],
    "source": [
-    "countries_mercator = countries.to_crs(epsg=3395)  # or .to_crs({'init': 'epsg:3395'})"
+    "countries_mercator = countries.to_crs(epsg=3395)  # or .to_crs(\"EPSG:3395\")"
    ]
   },
   {
@@ -315,7 +315,7 @@
     "\n",
     "**ATTENTION:**\n",
     "\n",
-    "All the calculations that happen in geopandas and shapely assume that your data is in a 2D cartesian plane, and thus the result of those calculations will only be correct if your data is properly projected.\n",
+    "All the calculations that happen in GeoPandas and Shapely assume that your data is in a 2D cartesian plane, and thus the result of those calculations will only be correct if your data is properly projected.\n",
     "\n",
     "</div>"
    ]
@@ -590,7 +590,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

notebooks/04-spatial-relationships-joins.ipynb

@@ -33,9 +33,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n",
-    "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")\n",
-    "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")"
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
    ]
   },
   {
@@ -555,10 +555,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Make a plot of the close-by restaurants\n",
-    "ax = stations_eiffel.to_crs(epsg=3857).plot()\n",
-    "geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').to_crs(epsg=3857).plot(ax=ax, color='red')\n",
+    "# Make a plot of the close-by bike stations\n",
+    "import matplotlib.pyplot as plt\n",
     "import contextily\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "stations_eiffel.to_crs(epsg=3857).plot(ax=ax)\n",
+    "geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').to_crs(epsg=3857).plot(ax=ax, color='red')\n",
     "contextily.add_basemap(ax)\n",
     "ax.set_axis_off()"
    ]
@@ -733,7 +736,7 @@
     "## Spatial join operation\n",
     "\n",
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "    \n",
+    "\n",
     "**SPATIAL JOIN** = *transferring attributes from one layer to another based on their spatial relationship* <br>\n",
     "\n",
     "\n",
@@ -781,6 +784,15 @@
     "joined"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined[joined[\"name_right\"] == \"France\"]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1055,7 +1067,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

notebooks/05-spatial-operations-overlays.ipynb

@@ -420,7 +420,7 @@
     "\n",
     "GeoPandas (and Shapely for the individual objects) provide a whole lot of basic methods to analyze the geospatial data (distance, length, centroid, boundary, convex_hull, simplify, transform, ....), much more than what we can touch in this tutorial.\n",
     "\n",
-    "* An overview of all methods provided by GeoPandas can be found here: https://geopandas.readthedocs.io/en/latest/docs/reference.html\n",
+    "An overview of all methods provided by GeoPandas can be found here: https://geopandas.readthedocs.io/en/latest/docs/reference.html\n",
     "\n",
     "\n",
     "</div>\n",
@@ -713,7 +713,7 @@
    },
    "outputs": [],
    "source": [
-    "land_use = geopandas.read_file(\"zip://./data/paris_land_use.zip\")\n",
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
     "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)"
    ]
   },
@@ -833,7 +833,7 @@
    },
    "outputs": [],
    "source": [
-    "land_use = geopandas.read_file(\"zip://./data/paris_land_use.zip\")\n",
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
     "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)\n",
     "muette = districts[districts.district_name == 'Muette'].geometry.item()"
    ]
@@ -1290,7 +1290,7 @@
    },
    "outputs": [],
    "source": [
-    "land_use = geopandas.read_file(\"zip://./data/paris_land_use.zip\")\n",
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
     "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)"
    ]
   },
@@ -1575,7 +1575,14 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,

notebooks/_solutions/01-introduction-tabular-data0.py

@@ -0,0 +1 @@
+print("I am a part of the solution!!")

notebooks/_solutions/03-coordinate-reference-systems10.py

@@ -1,3 +1,3 @@
 # Convert to the Web Mercator projection
-stations_webmercator = stations.to_crs(epsg=3857)
+stations_webmercator = stations.to_crs("EPSG:3857")
 stations.head()

notebooks/_solutions/04-spatial-relationships-joins10.py

@@ -1,2 +1,2 @@
-# Filter the restaurants for closer than 1 km
+# Filter the bike stations closer than 1 km
 stations_eiffel = stations[dist_eiffel < 1000]

notebooks/_solutions/05-spatial-operations-overlays4.py

@@ -1,3 +1,3 @@
 # Import the land use dataset
-land_use = geopandas.read_file("zip://./data/paris_land_use.zip")
+land_use = geopandas.read_file("data/paris_land_use.zip")
 land_use.head()

notebooks/_solutions/case-curieuzeneuzen-air-quality42.py

@@ -5,6 +5,6 @@
 frequent_categories = counts[counts > 50].index
 classes = [value for value in classes if value in frequent_categories]
 
-fig, ax = plt.subplots(figsize=(10, 10))
+fig, ax = plt.subplots(figsize=(10, 8))
 seaborn.boxplot(y="land_use_class", x="no2", data=subset, ax=ax, color="C0", order=classes)
 ax.set(xlabel="NO$_2$ concentration (µg/m³)", ylabel="CORINE Land Cover classes");