Finalization in wording

topic/timeseries/dask-weather-data-import.ipynb

@@ -7,10 +7,19 @@
    "source": [
     "# How to Build Time Series Applications in CrateDB\n",
     "\n",
-    "This notebook guides you through an example of how to import and work with\n",
+    "This notebook guides you through an example of how to batch import \n",
     "time series data in CrateDB. It uses Dask to import data into CrateDB.\n",
     "Dask is a framework to parallelize operations on pandas Dataframes.\n",
     "\n",
+    "## Important Note\n",
+    "If you are running this notebook on a (free) Google Colab environment, you \n",
+    "might not see the parallelized execution of Dask operations due to constrained\n",
+    "CPU availability.\n",
+    "\n",
+    "We therefore recommend to run this notebook either locally or on an environment\n",
+    "that provides sufficient CPU capacity to demonstrate the parallel execution of\n",
+    "dataframe operations as well as write operations to CrateDB.\n",
+    "\n",
     "## Dataset\n",
     "This notebook uses a daily weather data set provided on kaggle.com. This dataset\n",
     "offers a collection of **daily weather readings from major cities around the\n",
@@ -57,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "#!pip install dask pandas==2.0.0 'sqlalchemy[crate]'"
+    "!pip install dask 'pandas==2.0.0' 'crate[sqlalchemy]' 'cratedb-toolkit==0.0.10' 'pueblo>=0.0.7' kaggle"
    ]
   },
   {
@@ -75,6 +84,9 @@
     "- Countries (countries.csv)\n",
     "\n",
     "The subsequent code cell acquires the dataset directly from kaggle.com.\n",
+    "In order to import the data automatically, you need to create a (free)\n",
+    "API key in your kaggle.com user settings. \n",
+    "\n",
     "To properly configure the notebook to use corresponding credentials\n",
     "after signing up on Kaggle, define the `KAGGLE_USERNAME` and\n",
     "`KAGGLE_KEY` environment variables. Alternatively, put them into the\n",
@@ -85,55 +97,69 @@
     "  \"key\": \"2b1dac2af55caaf1f34df76236fada4a\"\n",
     "}\n",
     "```\n",
+    "\n",
     "Another variant is to acquire the dataset files manually, and extract\n",
     "them into a folder called `DOWNLOAD`. In this case, you can deactivate\n",
     "those two lines of code, in order to skip automatic dataset acquisition."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
+   "execution_count": 3,
+   "id": "8fcc014a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset URL: https://www.kaggle.com/datasets/guillemservera/global-daily-climate-data\n"
+     ]
+    }
+   ],
    "source": [
+    "from pueblo.util.environ import getenvpass\n",
     "from cratedb_toolkit.datasets import load_dataset\n",
     "\n",
+    "# Uncomment and execute the following lines to get prompted for kaggle user name and key\n",
+    "# getenvpass(\"KAGGLE_USERNAME\", prompt=\"Kaggle.com User Name:\")\n",
+    "# getenvpass(\"KAGGLE_KEY\", prompt=\"Kaggle.com Key:\")\n",
+    "\n",
     "dataset = load_dataset(\"kaggle://guillemservera/global-daily-climate-data/daily_weather.parquet\")\n",
     "dataset.acquire()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 6,
+   "id": "d9e2916d",
+   "metadata": {},
    "outputs": [],
    "source": [
     "from dask import dataframe as dd\n",
     "from dask.diagnostics import ProgressBar\n",
     "\n",
+    "# Use multiprocessing of dask\n",
+    "import dask.multiprocessing\n",
+    "dask.config.set(scheduler=dask.multiprocessing.get)\n",
+    "\n",
     "# Show a progress bar for dask activities\n",
     "pbar = ProgressBar()\n",
     "pbar.register()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 9,
    "id": "a506f7c9",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[########################################] | 100% Completed | 6.26 ss\n",
-      "[########################################] | 100% Completed | 6.37 s\n",
-      "[########################################] | 100% Completed | 6.47 s\n",
-      "[########################################] | 100% Completed | 6.47 s\n",
+      "[########################################] | 100% Completed | 127.49 s\n",
+      "[########################################] | 100% Completed | 127.49 s\n",
       "<class 'dask.dataframe.core.DataFrame'>\n",
       "Index: 27635763 entries, 0 to 24220\n",
       "Data columns (total 14 columns):\n",
@@ -155,10 +181,8 @@
       "13   sunshine_total_min      1021461 non-null      float64\n",
       "dtypes: category(3), datetime64[ns](1), float64(10)\n",
       "memory usage: 2.6 GB\n",
-      "[########################################] | 100% Completed | 5.37 ss\n",
-      "[########################################] | 100% Completed | 5.48 s\n",
-      "[########################################] | 100% Completed | 5.58 s\n",
-      "[########################################] | 100% Completed | 5.68 s\n"
+      "[########################################] | 100% Completed | 4.82 ss\n",
+      "[########################################] | 100% Completed | 4.89 s\n"
      ]
     },
     {
@@ -311,7 +335,7 @@
        "4                 NaN                     NaN                 NaN  "
       ]
      },
-     "execution_count": 56,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -490,14 +514,13 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ea1dfadc",
+   "metadata": {},
    "source": [
     "### Connect to CrateDB\n",
     "\n",
     "This code uses SQLAlchemy to connect to CrateDB."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",