Fix typos discovered by codespell (#225)

Author: mplough-kobold

.github/workflows/ci-repo2docker.yaml

@@ -17,9 +17,9 @@ jobs:
       - name: Install repo2docker
         run: |
           python -m pip install --upgrade pip
-          # Explicity adding `six` as a workaround for https://github.com/docker/docker-py/pull/2844
-          # Explicity adding `chardet` as a workaround for https://github.com/jupyterhub/repo2docker/issues/1065
+          # Explicitly adding `six` as a workaround for https://github.com/docker/docker-py/pull/2844
+          # Explicitly adding `chardet` as a workaround for https://github.com/jupyterhub/repo2docker/issues/1065
           python -m pip install jupyter-repo2docker six chardet
 
       - name: Build dask-tutorial Docker image
-        run: jupyter-repo2docker --no-run --debug .
+        run: jupyter-repo2docker --no-run --debug .

03_array.ipynb

@@ -509,7 +509,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There is 2GB of somewhat artifical weather data in HDF5 files in `data/weather-big/*.hdf5`.  We'll use the `h5py` library to interact with this data and `dask.array` to compute on it.\n",
+    "There is 2GB of somewhat artificial weather data in HDF5 files in `data/weather-big/*.hdf5`.  We'll use the `h5py` library to interact with this data and `dask.array` to compute on it.\n",
     "\n",
     "Our goal is to visualize the average temperature on the surface of the Earth for this month.  This will require a mean over all of this data.  We'll do this in the following steps\n",
     "\n",
@@ -814,7 +814,7 @@
     "    mat = (diff*diff).sum(-1)\n",
     "    return mat\n",
     "\n",
-    "# the lj function is evaluated over the upper traingle\n",
+    "# the lj function is evaluated over the upper triangle\n",
     "# after removing distances near zero\n",
     "def potential(cluster):\n",
     "    d2 = distances(cluster)\n",

04_dataframe.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "# Dask DataFrames\n",
     "\n",
-    "We finished Chapter 1 by building a parallel dataframe computation over a directory of CSV files using `dask.delayed`.  In this section we use `dask.dataframe` to automatically build similiar computations, for the common case of tabular computations.  Dask dataframes look and feel like Pandas dataframes but they run on the same infrastructure that powers `dask.delayed`.\n",
+    "We finished Chapter 1 by building a parallel dataframe computation over a directory of CSV files using `dask.delayed`.  In this section we use `dask.dataframe` to automatically build similar computations, for the common case of tabular computations.  Dask dataframes look and feel like Pandas dataframes but they run on the same infrastructure that powers `dask.delayed`.\n",
     "\n",
     "In this notebook we use the same airline data as before, but now rather than write for-loops we let `dask.dataframe` construct our computations for us.  The `dask.dataframe.read_csv` function can take a globstring like `\"data/nycflights/*.csv\"` and build parallel computations on all of our data at once.\n",
     "\n",
@@ -81,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We create artifical data."
+    "We create artificial data."
    ]
   },
   {

06_distributed_advanced.ipynb

@@ -474,7 +474,7 @@
     "from dask.distributed import as_completed\n",
     "from random import uniform\n",
     "\n",
-    "scale = 5                  # Intial random perturbation scale\n",
+    "scale = 5                  # Initial random perturbation scale\n",
     "best_point = (0, 0)        # Initial guess\n",
     "best_score = float('inf')  # Best score so far\n",
     "startx = [uniform(-scale, scale) for _ in range(10)]\n",

Homework.ipynb

@@ -60,7 +60,7 @@
     "**Try the following:**\n",
     "\n",
     "*  Use `dask.bag` to inspect the data\n",
-    "*  Combine `dask.bag` with `nltk` or `gensim` to perform textual analyis on the data\n",
+    "*  Combine `dask.bag` with `nltk` or `gensim` to perform textual analysis on the data\n",
     "*  Reproduce the work of [Daniel Rodriguez](https://extrapolations.dev/blog/2015/07/reproduceit-reddit-word-count-dask/) and see if you can improve upon his speeds when analyzing this data."
    ]
   },