Merge pull request #36 from fangohr/review-pandas-section

Review pandas section

Author: fangohr

.gitignore

@@ -22,3 +22,4 @@ stock.csv
 .venv
 .venv/
 book/_build
+.DS_Store

book/17-pandas.ipynb

@@ -124,7 +124,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In a way, the keys of the dictionary contain the stock labels and the values contain the actual values:"
+    "The keys of the dictionary contain the stock labels and the values contain the actual values:"
    ]
   },
   {
@@ -189,7 +189,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The Pandas Series object address the requriments above. It is similar to a dictionary, but with improvements for the given problem:\n",
+    "The Pandas Series object address the requirements above. It is similar to a dictionary, but with improvements for the given problem:\n",
     "\n",
     "* the order of the items is maintained\n",
     "* the values have to have the same type (higher execution performance)\n",
@@ -342,11 +342,11 @@
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
-    "# settings for jupyter book: svg for html version, high-resolution png for pdf\n",
-    "from IPython.display import set_matplotlib_formats\n",
-    "set_matplotlib_formats('svg', 'png')\n",
-    "import matplotlib as mpl\n",
-    "mpl.rcParams['figure.dpi'] = 400"
+    "# settings for Jupyter book: svg for html version, high-resolution png for pdf\n",
+    "import matplotlib\n",
+    "import matplotlib_inline\n",
+    "matplotlib_inline.backend_inline.set_matplotlib_formats('svg', 'png')\n",
+    "matplotlib.rcParams['figure.dpi'] = 400"
    ]
   },
   {
@@ -607,7 +607,7 @@
    "source": [
     "## Plotting data\n",
     "\n",
-    "Commonly used plots are easily accessible via the `plot()` method of the Series object. We have seen a bar plot above already. The `Series.plot()` method accepts an argument `kind` such as `kind=\"bar\"`, but there is an equivalent method `Series.plot.bar()` avaialble.\n",
+    "Commonly used plots are easily accessible via the `plot()` method of the Series object. We have seen a bar plot above already. The `Series.plot()` method accepts an argument `kind` such as `kind=\"bar\"`, but there is an equivalent method `Series.plot.bar()` available.\n",
     "\n",
     "Further examples:"
    ]
@@ -656,7 +656,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also fetch the data from the series and drive the plotting \"manually\" ourselves:"
+    "We can also retrieve the data from the series and drive the plotting \"manually\" ourselves:"
    ]
   },
   {
@@ -667,8 +667,8 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "names = list(stock.index)\n",
-    "values = list(stock.values)\n",
+    "names = list(stock.index)  # conversion to list not necessary \n",
+    "values = list(stock.values)  # conversion to list not necessary\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(9, 3))\n",
     "ax.bar(names, values)\n",
@@ -1788,7 +1788,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1802,9 +1802,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6-final"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

todo.org

@@ -1,12 +1,15 @@
 * TODO
 - [X] building natively on OSX works (make html, make pdf)
 - [X] Binder link is included
-- [ ] nbval shows some failures
-- [ ] building through docker fails for pdf
+- [X] nbval shows some failures
+- [X] building through docker fails for pdf
 - [ ] check github actions
 - [ ] make pdf available?
 - [ ] JupyterLite version
-- [ ] check CI
+- [X] check CI
 - [ ] run black over notebooks
-- [ ] review content
+- [ ] run spell checker over notebooks
+  - see https://stackoverflow.com/questions/39324039/highlight-typos-in-the-jupyter-notebook-markdown
+- [X] review content
+  - [X] pandas