diff --git a/.gitignore b/.gitignore
index 176af7e..267d6b1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 .ipynb_checkpoints/
-
+.python-version
+.venv/
diff --git a/1_column_headers_are_values.ipynb b/1_column_headers_are_values.ipynb
index c5d50ab..b01a4b5 100644
--- a/1_column_headers_are_values.ipynb
+++ b/1_column_headers_are_values.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# Column Headers are Values, not Variable Names\n",
     "\n",
-    "This notebook shows two examples of how column headers display values. These type of messy datasets have practical use in two types of settings:\n",
+    "This notebook shows two examples of how column headers display values. These type of messy datasets have practical use in two kinds of settings:\n",
     "\n",
     "1. Presentations\n",
     "2. Recordings of regularly spaced observations over time"
@@ -23,24 +23,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2018-08-26 14:39:56 CEST\n",
-      "\n",
-      "CPython 3.6.5\n",
-      "IPython 6.5.0\n",
-      "\n",
-      "numpy 1.15.1\n",
-      "pandas 0.23.4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "% load_ext watermark\n",
-    "% watermark -d -t -v -z -p numpy,pandas"
+    "%load_ext lab_black"
    ]
   },
   {
@@ -90,32 +75,35 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "columns = ['q16', 'reltrad', 'income']\n",
+    "columns = [\"q16\", \"reltrad\", \"income\"]\n",
     "encodings = {}\n",
     "\n",
-    "# For sake of simplicity all data cleaning operations\n",
+    "# For the sake of simplicity, all data cleaning operations\n",
     "# are done within the for-loop for all columns.\n",
-    "with spss.SavHeaderReader('data/pew.sav') as pew:\n",
-    "    for c in columns:\n",
-    "        encodings[c] = {\n",
-    "            int(k): (\n",
-    "                re.sub(r'\\(.*\\)', '', (\n",
-    "                        v.decode('iso-8859-1')\n",
-    "                        .replace('\\x92', \"'\")\n",
-    "                        .replace(' Churches', '')\n",
-    "                        .replace('Less than $10,000', '<$10k')\n",
-    "                        .replace('10 to under $20,000', '$10-20k')\n",
-    "                        .replace('20 to under $30,000', '$20-30k')\n",
-    "                        .replace('30 to under $40,000', '$30-40k')\n",
-    "                        .replace('40 to under $50,000', '$40-50k')\n",
-    "                        .replace('50 to under $75,000', '$50-75k')\n",
-    "                        .replace('75 to under $100,000', '$75-100k')\n",
-    "                        .replace('100 to under $150,000', '$100-150k')\n",
-    "                        .replace('$150,000 or more', '>150k')\n",
+    "with spss.SavHeaderReader(\"data/pew.sav\") as pew:\n",
+    "    for column in columns:\n",
+    "        encodings[column] = {\n",
+    "            int(key): (\n",
+    "                re.sub(\n",
+    "                    r\"\\(.*\\)\",\n",
+    "                    \"\",\n",
+    "                    (\n",
+    "                        value.decode(\"iso-8859-1\")\n",
+    "                        .replace(\"\\x92\", \"'\")\n",
+    "                        .replace(\" Churches\", \"\")\n",
+    "                        .replace(\"Less than $10,000\", \"<$10k\")\n",
+    "                        .replace(\"10 to under $20,000\", \"$10-20k\")\n",
+    "                        .replace(\"20 to under $30,000\", \"$20-30k\")\n",
+    "                        .replace(\"30 to under $40,000\", \"$30-40k\")\n",
+    "                        .replace(\"40 to under $50,000\", \"$40-50k\")\n",
+    "                        .replace(\"50 to under $75,000\", \"$50-75k\")\n",
+    "                        .replace(\"75 to under $100,000\", \"$75-100k\")\n",
+    "                        .replace(\"100 to under $150,000\", \"$100-150k\")\n",
+    "                        .replace(\"$150,000 or more\", \">150k\")\n",
     "                    ),\n",
     "                ).strip()\n",
     "            )\n",
-    "            for (k, v) in pew.all().valueLabels[c.encode()].items()\n",
+    "            for (key, value) in pew.all().valueLabels[column.encode()].items()\n",
     "        }"
    ]
   },
@@ -132,25 +120,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with spss.SavReader('data/pew.sav', selectVars=[c.encode() for c in columns]) as pew:\n",
+    "with spss.SavReader(\n",
+    "    \"data/pew.sav\", selectVars=[column.encode() for column in columns]\n",
+    ") as pew:\n",
     "    pew = list(pew)\n",
     "\n",
     "# Use the above encodings to map the numeric data\n",
     "# to the actual labels.\n",
     "pew = pd.DataFrame(pew, columns=columns, dtype=int)\n",
-    "for c in columns:\n",
-    "    pew[c] = pew[c].map(encodings[c])\n",
+    "for column in columns:\n",
+    "    pew[column] = pew[column].map(encodings[column])\n",
     "\n",
-    "for v in ('Atheist', 'Agnostic'):\n",
-    "    pew.loc[(pew['q16'] == v), 'reltrad'] = v\n",
+    "for value in (\"Atheist\", \"Agnostic\"):\n",
+    "    pew.loc[(pew[\"q16\"] == value), \"reltrad\"] = value\n",
     "\n",
-    "income_columns = ['<$10k', '$10-20k', '$20-30k', '$30-40k', '$40-50k', '$50-75k',\n",
-    "                  '$75-100k', '$100-150k', '>150k', 'Don\\'t know/Refused']\n",
-    "\n",
-    "pew = pew.groupby(['reltrad', 'income']).size().unstack('income')\n",
+    "income_columns = [\n",
+    "    \"<$10k\",\n",
+    "    \"$10-20k\",\n",
+    "    \"$20-30k\",\n",
+    "    \"$30-40k\",\n",
+    "    \"$40-50k\",\n",
+    "    \"$50-75k\",\n",
+    "    \"$75-100k\",\n",
+    "    \"$100-150k\",\n",
+    "    \">150k\",\n",
+    "    \"Don't know/Refused\",\n",
+    "]\n",
     "\n",
+    "pew = pew.groupby([\"reltrad\", \"income\"]).size().unstack(\"income\")\n",
     "pew = pew[income_columns]\n",
-    "pew.index.name = 'religion'"
+    "pew.index.name = \"religion\""
    ]
   },
   {
@@ -426,9 +425,9 @@
     "\n",
     "> This dataset has **three** variables, **religion**, **income** and **frequency**. To tidy it, we need to **melt**, or stack it. In other words, we need to turn columns into rows.\n",
     "\n",
-    "pandas provides a [pd.melt](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html) function to un-pivot the dataset.\n",
+    "pandas provides a [pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) function to un-pivot the dataset.\n",
     "\n",
-    "**Notes:** *reset_index()* transforms the religion index column into a data column (*pd.melt()* needs that). Further, the resulting table is sorted implicitly by the *religion* column. To get to the same ordering as in the paper, the molten table is explicitly sorted."
+    "**Notes:** `.reset_index()` transforms the religion index column into a data column (`pd.melt()` needs that). Further, the resulting table is sorted implicitly by the `\"religion\"` column. To get to the same ordering as in the paper, the molten table is explicitly sorted."
    ]
   },
   {
@@ -437,7 +436,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "molten_pew = pd.melt(pew.reset_index(), id_vars=['religion'], value_name='frequency')"
+    "molten_pew = pd.melt(pew.reset_index(), id_vars=[\"religion\"], value_name=\"frequency\")"
    ]
   },
   {
@@ -448,8 +447,8 @@
    "source": [
     "# Create a ordered column for the income labels.\n",
     "income_dtype = pd.api.types.CategoricalDtype(income_columns, ordered=True)\n",
-    "molten_pew['income'] = molten_pew['income'].astype(income_dtype)\n",
-    "molten_pew = molten_pew.sort_values(['religion', 'income']).reset_index(drop=True)"
+    "molten_pew[\"income\"] = molten_pew[\"income\"].astype(income_dtype)\n",
+    "molten_pew = molten_pew.sort_values([\"religion\", \"income\"]).reset_index(drop=True)"
    ]
   },
   {
@@ -616,37 +615,40 @@
    "outputs": [],
    "source": [
     "# Usage of \"1st\", \"2nd\", \"3rd\" should be forbidden by law :)\n",
-    "usecols = ['artist.inverted', 'track', 'time', 'date.entered'] + (\n",
-    "    [f'x{i}st.week' for i in range(1, 76, 10) if i != 11]\n",
-    "    + [f'x{i}nd.week' for i in range(2, 76, 10) if i != 12]\n",
-    "    + [f'x{i}rd.week' for i in range(3, 76, 10) if i != 13]\n",
-    "    + [f'x{i}th.week' for i in range(1, 76) if (i % 10) not in (1, 2, 3)]\n",
-    "    + [f'x11th.week', f'x12th.week', f'x13th.week']\n",
+    "usecols = [\"artist.inverted\", \"track\", \"time\", \"date.entered\"] + (\n",
+    "    [f\"x{i}st.week\" for i in range(1, 76, 10) if i != 11]\n",
+    "    + [f\"x{i}nd.week\" for i in range(2, 76, 10) if i != 12]\n",
+    "    + [f\"x{i}rd.week\" for i in range(3, 76, 10) if i != 13]\n",
+    "    + [f\"x{i}th.week\" for i in range(1, 76) if (i % 10) not in (1, 2, 3)]\n",
+    "    + [f\"x11th.week\", f\"x12th.week\", f\"x13th.week\"]\n",
+    ")\n",
+    "billboard = pd.read_csv(\n",
+    "    \"data/billboard.csv\",\n",
+    "    encoding=\"iso-8859-1\",\n",
+    "    parse_dates=[\"date.entered\"],\n",
+    "    usecols=usecols,\n",
     ")\n",
     "\n",
-    "billboard = pd.read_csv('data/billboard.csv', encoding='iso-8859-1',\n",
-    "                        parse_dates=['date.entered'], usecols=usecols)\n",
-    "\n",
-    "billboard = billboard.assign(year=lambda x: x['date.entered'].dt.year)\n",
+    "billboard = billboard.assign(year=lambda x: x[\"date.entered\"].dt.year)\n",
     "\n",
     "# Rename the week columns.\n",
     "week_columns = {\n",
-    "    c: ('wk' + re.sub(r'[^\\d]+', '', c))\n",
-    "    for c in billboard.columns\n",
-    "    if c.endswith('.week')\n",
+    "    column: (\"wk\" + re.sub(r\"[^\\d]+\", \"\", column))\n",
+    "    for column in billboard.columns\n",
+    "    if column.endswith(\".week\")\n",
     "}\n",
-    "billboard = billboard.rename(columns={'artist.inverted': 'artist', **week_columns})\n",
+    "billboard = billboard.rename(columns={\"artist.inverted\": \"artist\", **week_columns})\n",
     "\n",
     "# Ensure the columns' order is the same as in the paper.\n",
-    "columns = ['year', 'artist', 'track', 'time', 'date.entered'] + [\n",
-    "    f'wk{i}' for i in range(1, 76)\n",
+    "columns = [\"year\", \"artist\", \"track\", \"time\", \"date.entered\"] + [\n",
+    "    f\"wk{i}\" for i in range(1, 76)\n",
     "]\n",
     "billboard = billboard[columns]\n",
     "\n",
     "# Ensure the rows' order is similar as in the paper.\n",
     "# For unknown reasons the exact ordering as in the paper cannot be reconstructed.\n",
-    "billboard = billboard[billboard['year'] == 2000]\n",
-    "billboard = billboard.sort_values(['artist', 'track'])"
+    "billboard = billboard[billboard[\"year\"] == 2000]\n",
+    "billboard = billboard.sort_values([\"artist\", \"track\"])"
    ]
   },
   {
@@ -986,17 +988,17 @@
        "14   2000         Aaliyah                                          Try Again   \n",
        "200  2000  Adams, Yolanda                                      Open My Heart   \n",
        "\n",
-       "     time date.entered  wk1   wk2   wk3   wk4    wk5  ...   wk66  wk67  wk68  \\\n",
-       "246  4:22   2000-02-26   87  82.0  72.0  77.0   87.0  ...    NaN   NaN   NaN   \n",
-       "287  3:15   2000-09-02   91  87.0  92.0   NaN    NaN  ...    NaN   NaN   NaN   \n",
-       "24   3:53   2000-04-08   81  70.0  68.0  67.0   66.0  ...    NaN   NaN   NaN   \n",
-       "193  4:24   2000-10-21   76  76.0  72.0  69.0   67.0  ...    NaN   NaN   NaN   \n",
-       "69   3:35   2000-04-15   57  34.0  25.0  17.0   17.0  ...    NaN   NaN   NaN   \n",
-       "22   3:24   2000-08-19   51  39.0  34.0  26.0   26.0  ...    NaN   NaN   NaN   \n",
-       "304  3:44   2000-07-08   97  97.0  96.0  95.0  100.0  ...    NaN   NaN   NaN   \n",
-       "135  4:15   2000-01-29   84  62.0  51.0  41.0   38.0  ...    NaN   NaN   NaN   \n",
-       "14   4:03   2000-03-18   59  53.0  38.0  28.0   21.0  ...    NaN   NaN   NaN   \n",
-       "200  5:30   2000-08-26   76  76.0  74.0  69.0   68.0  ...    NaN   NaN   NaN   \n",
+       "     time date.entered  wk1   wk2   wk3   wk4    wk5  ...  wk66  wk67  wk68  \\\n",
+       "246  4:22   2000-02-26   87  82.0  72.0  77.0   87.0  ...   NaN   NaN   NaN   \n",
+       "287  3:15   2000-09-02   91  87.0  92.0   NaN    NaN  ...   NaN   NaN   NaN   \n",
+       "24   3:53   2000-04-08   81  70.0  68.0  67.0   66.0  ...   NaN   NaN   NaN   \n",
+       "193  4:24   2000-10-21   76  76.0  72.0  69.0   67.0  ...   NaN   NaN   NaN   \n",
+       "69   3:35   2000-04-15   57  34.0  25.0  17.0   17.0  ...   NaN   NaN   NaN   \n",
+       "22   3:24   2000-08-19   51  39.0  34.0  26.0   26.0  ...   NaN   NaN   NaN   \n",
+       "304  3:44   2000-07-08   97  97.0  96.0  95.0  100.0  ...   NaN   NaN   NaN   \n",
+       "135  4:15   2000-01-29   84  62.0  51.0  41.0   38.0  ...   NaN   NaN   NaN   \n",
+       "14   4:03   2000-03-18   59  53.0  38.0  28.0   21.0  ...   NaN   NaN   NaN   \n",
+       "200  5:30   2000-08-26   76  76.0  74.0  69.0   68.0  ...   NaN   NaN   NaN   \n",
        "\n",
        "     wk69  wk70  wk71  wk72  wk73  wk74  wk75  \n",
        "246   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
@@ -1028,7 +1030,7 @@
    "source": [
     "### \"Tidy\" Data\n",
     "\n",
-    "As before the *pd.melt* function is used to transform the data from \"wide\" to \"long\" form."
+    "As before the `pd.melt()` function is used to transform the data from \"wide\" to \"long\" form."
    ]
   },
   {
@@ -1039,9 +1041,9 @@
    "source": [
     "molten_billboard = pd.melt(\n",
     "    billboard,\n",
-    "    id_vars=['year', 'artist', 'track', 'time', 'date.entered'],\n",
-    "    var_name='week',\n",
-    "    value_name='rank',\n",
+    "    id_vars=[\"year\", \"artist\", \"track\", \"time\", \"date.entered\"],\n",
+    "    var_name=\"week\",\n",
+    "    value_name=\"rank\",\n",
     ")"
    ]
   },
@@ -1049,7 +1051,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In contrast to R, pandas keeps (unneccesary) rows for weeks where the song was already out of the charts. These are discarded. Also, a new column *date* indicating when exactly a particular song was at a certain rank in the charts is added."
+    "In contrast to R, pandas keeps (unneccesary) rows for weeks where the song was already out of the charts. These are discarded. Also, a new column`\"date\"` indicating when exactly a particular song was at a certain rank in the charts is added."
    ]
   },
   {
@@ -1059,24 +1061,23 @@
    "outputs": [],
    "source": [
     "# pandas keeps \"wide\" variables that had missing values as rows.\n",
-    "molten_billboard = molten_billboard[molten_billboard['rank'].notnull()]\n",
+    "molten_billboard = molten_billboard[molten_billboard[\"rank\"].notnull()]\n",
     "\n",
     "# Cast as integer after missing values are removed.\n",
-    "molten_billboard['week'] = molten_billboard['week'].map(lambda x: int(x[2:]))\n",
-    "molten_billboard['rank'] = molten_billboard['rank'].map(int)\n",
+    "molten_billboard[\"week\"] = molten_billboard[\"week\"].map(lambda x: int(x[2:]))\n",
+    "molten_billboard[\"rank\"] = molten_billboard[\"rank\"].map(int)\n",
     "\n",
     "# Calculate the actual week from the date of first entering the list.\n",
     "molten_billboard = molten_billboard.assign(\n",
-    "    date=lambda x: x['date.entered'] + (x['week'] - 1) * datetime.timedelta(weeks=1)\n",
+    "    date=lambda x: x[\"date.entered\"] + (x[\"week\"] - 1) * datetime.timedelta(weeks=1)\n",
     ")\n",
     "\n",
     "# Sort rows and columns as in the paper.\n",
     "molten_billboard = molten_billboard[\n",
-    "    ['year', 'artist', 'time', 'track', 'date', 'week', 'rank']\n",
+    "    [\"year\", \"artist\", \"time\", \"track\", \"date\", \"week\", \"rank\"]\n",
     "]\n",
-    "molten_billboard = (\n",
-    "    molten_billboard.sort_values(['artist', 'track', 'week']).reset_index(drop=True)\n",
-    ")"
+    "molten_billboard = molten_billboard.sort_values([\"artist\", \"track\", \"week\"])\n",
+    "molten_billboard = molten_billboard.reset_index(drop=True)"
    ]
   },
   {
@@ -1336,7 +1337,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "molten_billboard.to_csv('data/billboard_cleaned.csv', index=False)"
+    "molten_billboard.to_csv(\"data/billboard_cleaned.csv\", index=False)"
    ]
   }
  ],
@@ -1356,9 +1357,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/2_multiple_variables_stored_in_one_column.ipynb b/2_multiple_variables_stored_in_one_column.ipynb
index d4bd4a8..535b8c0 100644
--- a/2_multiple_variables_stored_in_one_column.ipynb
+++ b/2_multiple_variables_stored_in_one_column.ipynb
@@ -20,24 +20,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2018-08-26 11:50:39 CEST\n",
-      "\n",
-      "CPython 3.6.5\n",
-      "IPython 6.5.0\n",
-      "\n",
-      "numpy 1.15.1\n",
-      "pandas 0.23.4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "% load_ext watermark\n",
-    "% watermark -d -t -v -z -p numpy,pandas"
+    "%load_ext lab_black"
    ]
   },
   {
@@ -71,15 +56,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "columns = ['iso2', 'year',\n",
-    "           'new_sp_m014', 'new_sp_m1524', 'new_sp_m2534', 'new_sp_m3544',\n",
-    "           'new_sp_m4554', 'new_sp_m5564', 'new_sp_m65', 'new_sp_mu',\n",
-    "           'new_sp_f014', 'new_sp_f1524', 'new_sp_f2534', 'new_sp_f3544',\n",
-    "           'new_sp_f4554', 'new_sp_f5564', 'new_sp_f65', 'new_sp_fu']\n",
-    "tb = pd.read_csv('data/tb.csv', usecols=columns)\n",
+    "columns = [\n",
+    "    \"iso2\",\n",
+    "    \"year\",\n",
+    "    \"new_sp_m014\",\n",
+    "    \"new_sp_m1524\",\n",
+    "    \"new_sp_m2534\",\n",
+    "    \"new_sp_m3544\",\n",
+    "    \"new_sp_m4554\",\n",
+    "    \"new_sp_m5564\",\n",
+    "    \"new_sp_m65\",\n",
+    "    \"new_sp_mu\",\n",
+    "    \"new_sp_f014\",\n",
+    "    \"new_sp_f1524\",\n",
+    "    \"new_sp_f2534\",\n",
+    "    \"new_sp_f3544\",\n",
+    "    \"new_sp_f4554\",\n",
+    "    \"new_sp_f5564\",\n",
+    "    \"new_sp_f65\",\n",
+    "    \"new_sp_fu\",\n",
+    "]\n",
+    "tb = pd.read_csv(\"data/tb.csv\", usecols=columns)\n",
     "\n",
-    "rename = {c: c[7:] for c in columns if c.startswith('new_sp_')}\n",
-    "rename = {'iso2': 'country', **rename}\n",
+    "rename = {column: column[7:] for column in columns if column.startswith(\"new_sp_\")}\n",
+    "rename = {\"iso2\": \"country\", **rename}\n",
     "tb = tb.rename(columns=rename)"
    ]
   },
@@ -89,7 +89,7 @@
    "source": [
     "### Messy Data\n",
     "\n",
-    "The data are assumed to be provided as below. Except for the *country* and *year* columns, the remaining columns are actually joint realizations of two variables **sex** and **age**."
+    "The data are assumed to be provided as below. Except for the `\"country\"` and `\"year\"` columns, the remaining columns are actually joint realizations of two variables `\"sex\"` and `\"age\"`."
    ]
   },
   {
@@ -385,7 +385,7 @@
     }
    ],
    "source": [
-    "tb[(tb['year'] == 2000)].head(10)"
+    "tb[(tb[\"year\"] == 2000)].head(10)"
    ]
   },
   {
@@ -394,7 +394,7 @@
    "source": [
     "### Molten Data\n",
     "\n",
-    "As in the previous notebook the [*pd.melt*](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html) function can be used to un-pivot the columns. As before, pandas keeps rows for columns with missing data that are then discarded (then, without any more missing values, the column's data type is casted as integer). Furthermore, the resulting *molten* dataset is sorted as in the paper."
+    "As in the previous notebook the [pd.melt()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html) function can be used to un-pivot the columns. As before, pandas keeps rows for columns with missing data that are discarded. Then, without any more missing values, the column's data type is casted as `int`. Furthermore, the resulting *molten* dataset is sorted as in the paper."
    ]
   },
   {
@@ -403,10 +403,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "molten_tb = pd.melt(tb, id_vars=['country', 'year'], var_name='column', value_name='cases')\n",
-    "molten_tb = molten_tb[molten_tb['cases'].notnull()]\n",
-    "molten_tb['cases'] = molten_tb['cases'].astype(int)\n",
-    "molten_tb = molten_tb.sort_values(['country', 'year', 'column']).reset_index(drop=True)"
+    "molten_tb = pd.melt(\n",
+    "    tb, id_vars=[\"country\", \"year\"], var_name=\"column\", value_name=\"cases\"\n",
+    ")\n",
+    "molten_tb = molten_tb[molten_tb[\"cases\"].notnull()]\n",
+    "molten_tb[\"cases\"] = molten_tb[\"cases\"].astype(int)\n",
+    "molten_tb = molten_tb.sort_values([\"country\", \"year\", \"column\"]).reset_index(drop=True)"
    ]
   },
   {
@@ -536,7 +538,7 @@
     }
    ],
    "source": [
-    "molten_tb[(molten_tb['year'] == 2000)].head(10)"
+    "molten_tb[(molten_tb[\"year\"] == 2000)].head(10)"
    ]
   },
   {
@@ -545,7 +547,7 @@
    "source": [
     "### Tidy Data\n",
     "\n",
-    "Using the [*pd.Series.str.extract*](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.extract.html) method the two variables are isolated. The age labels are renamed as in the paper."
+    "Using the [pd.Series.str.extract()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.extract.html) method the two variables are isolated. The age labels are renamed as in the paper."
    ]
   },
   {
@@ -554,14 +556,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tidy_tb = molten_tb[['country', 'year', 'cases']]\n",
-    "tidy_tb[['sex', 'age']] = molten_tb['column'].str.extract(r'(f|m)(.*)')\n",
-    "tidy_tb['age'] = tidy_tb['age'].map({\n",
-    "    '014': '0-14', '1524': '15-24', '2534': '25-34',\n",
-    "    '3544': '35-44', '4554': '45-54', '5564': '55-64',\n",
-    "    '65': '65+', 'u': 'unknown'\n",
-    "})\n",
-    "tidy_tb = tidy_tb[['country', 'year', 'sex', 'age', 'cases']]"
+    "tidy_tb = molten_tb[[\"country\", \"year\", \"cases\"]]\n",
+    "tidy_tb[[\"sex\", \"age\"]] = molten_tb[\"column\"].str.extract(r\"(f|m)(.*)\")\n",
+    "tidy_tb[\"age\"] = tidy_tb[\"age\"].map(\n",
+    "    {\n",
+    "        \"014\": \"0-14\",\n",
+    "        \"1524\": \"15-24\",\n",
+    "        \"2534\": \"25-34\",\n",
+    "        \"3544\": \"35-44\",\n",
+    "        \"4554\": \"45-54\",\n",
+    "        \"5564\": \"55-64\",\n",
+    "        \"65\": \"65+\",\n",
+    "        \"u\": \"unknown\",\n",
+    "    }\n",
+    ")\n",
+    "tidy_tb = tidy_tb[[\"country\", \"year\", \"sex\", \"age\", \"cases\"]]"
    ]
   },
   {
@@ -702,7 +711,7 @@
     }
    ],
    "source": [
-    "tidy_tb[(tidy_tb['year'] == 2000)].head(10)"
+    "tidy_tb[(tidy_tb[\"year\"] == 2000)].head(10)"
    ]
   }
  ],
@@ -722,9 +731,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/3_variables_are_stored_in_both_rows_and_columns.ipynb b/3_variables_are_stored_in_both_rows_and_columns.ipynb
index 7d0f2f5..1c1d3d5 100644
--- a/3_variables_are_stored_in_both_rows_and_columns.ipynb
+++ b/3_variables_are_stored_in_both_rows_and_columns.ipynb
@@ -18,24 +18,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2018-08-26 12:56:31 CEST\n",
-      "\n",
-      "CPython 3.6.5\n",
-      "IPython 6.5.0\n",
-      "\n",
-      "numpy 1.15.1\n",
-      "pandas 0.23.4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "% load_ext watermark\n",
-    "% watermark -d -t -v -z -p numpy,pandas"
+    "%load_ext lab_black"
    ]
   },
   {
@@ -54,7 +39,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pd.set_option('display.max_columns', 40)"
+    "pd.set_option(\"display.max_columns\", 40)"
    ]
   },
   {
@@ -83,38 +68,46 @@
    "source": [
     "# Extract the data as one column and\n",
     "# use string slicing to obtain groups of columns.\n",
-    "weather = pd.read_csv('data/weather.txt', header=None, sep='^')\n",
+    "weather = pd.read_csv(\"data/weather.txt\", header=None, sep=\"^\")\n",
     "\n",
     "# First, remove the weird character seperators,\n",
     "# then split the columns by whitespace, and\n",
     "# finally name them appropriately.\n",
     "days = (\n",
     "    weather[0]\n",
-    "    .map(lambda x: x[21:]).str.replace('OI', '  ')\n",
-    "    .str.replace('OS', '  ').str.replace('SI', '  ').str.replace('I', ' ')\n",
-    "    .str.replace('S', ' ').str.replace('B', ' ').str.replace('D', ' ')\n",
-    "    .map(str.lstrip).str.split(r'\\s+', expand=True)\n",
-    ")[list(range(31))].rename(columns={i: f'd{i+1}' for i in range(31)})\n",
+    "    .map(lambda x: x[21:])\n",
+    "    .str.replace(\"OI\", \"  \")\n",
+    "    .str.replace(\"OS\", \"  \")\n",
+    "    .str.replace(\"SI\", \"  \")\n",
+    "    .str.replace(\"I\", \" \")\n",
+    "    .str.replace(\"S\", \" \")\n",
+    "    .str.replace(\"B\", \" \")\n",
+    "    .str.replace(\"D\", \" \")\n",
+    "    .map(str.lstrip)\n",
+    "    .str.split(r\"\\s+\", expand=True)\n",
+    ")[list(range(31))].rename(columns={i: f\"d{i+1}\" for i in range(31)})\n",
     "\n",
     "# The non-temperature columns can be extracted as simple slices.\n",
-    "weather = pd.DataFrame(data={\n",
-    "    'id': weather[0].map(lambda x: x[:11]),\n",
-    "    'year': weather[0].map(lambda x: x[11:15]).astype(int),\n",
-    "    'month': weather[0].map(lambda x: x[15:17]).astype(int),\n",
-    "    'element': weather[0].map(lambda x: x[17:21]).str.lower(),\n",
-    "})\n",
+    "weather = pd.DataFrame(\n",
+    "    data={\n",
+    "        \"id\": weather[0].map(lambda x: x[:11]),\n",
+    "        \"year\": weather[0].map(lambda x: x[11:15]).astype(int),\n",
+    "        \"month\": weather[0].map(lambda x: x[15:17]).astype(int),\n",
+    "        \"element\": weather[0].map(lambda x: x[17:21]).str.lower(),\n",
+    "    }\n",
+    ")\n",
     "\n",
     "# The temperatures were stored as whole integers\n",
     "# with -9999 indicating missing values.\n",
     "for i in range(1, 32):\n",
-    "    weather[f'd{i}'] = days[f'd{i}'].astype(float) / 10\n",
+    "    weather[f\"d{i}\"] = days[f\"d{i}\"].astype(float) / 10\n",
     "weather = weather.replace(-999.9, np.NaN)\n",
     "\n",
     "# Discard the non-temperature observations and\n",
     "# sort the dataset as in the paper.\n",
     "weather = (\n",
-    "    weather[weather['element'].isin(['tmax', 'tmin'])]\n",
-    "    .sort_values(['id', 'year', 'month', 'element'])\n",
+    "    weather[weather[\"element\"].isin([\"tmax\", \"tmin\"])]\n",
+    "    .sort_values([\"id\", \"year\", \"month\", \"element\"])\n",
     "    .reset_index(drop=True)\n",
     ")"
    ]
@@ -128,8 +121,7 @@
     "Below is a dataset assumed to have been provided like this as \"raw\", i.e., the data analyst did not do the above parsing work but some third party instead.\n",
     "\n",
     "> The most complicated form of messy data occurs when variables are stored in both rows and columns. Table 11 shows daily weather data from the Global Historical Climatology Network for one weather station (MX17004) in Mexico for five months in 2010. It has variables in\n",
-    "individual columns (*id*, *year*, *month*), spread across columns (day, d1–d31) and across rows (*tmin*, *tmax*) (minimum and maximum temperature). Months with less than 31 days have\n",
-    "structural missing values for the last day(s) of the month. The *element* column is not a variable; it stores the names of variables."
+    "individual columns (`\"id\"`, `\"year\"`, `\"month\"`), spread across columns (day, `\"d1\"`–`\"d31\"`) and across rows (`\"tmin\"` and `\"tmax\"` for the minimum and maximum temperatures). Months with less than 31 days have missing values for the last day(s) of the month. The `\"element\"` column is not a variable: it stores the *names* of variables."
    ]
   },
   {
@@ -624,7 +616,7 @@
     }
    ],
    "source": [
-    "weather[(weather['year'] == 2010)].head(10)"
+    "weather[(weather[\"year\"] == 2010)].head(10)"
    ]
   },
   {
@@ -638,7 +630,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> To tidy this dataset we first melt it with colvars *id*, *year*, *month* and the column that contains variable names, *element* [...]. For presentation, we have dropped the missing values, making them implicit rather than explicit. This is permissible because we know how many days are in each month and can easily reconstruct the explicit missing values."
+    "> To tidy this dataset we first melt it with colvars `\"id\"`, `\"year\"`, `\"month\"`, and the column that contains the actual variable names, `\"element\"` [...]. For presentation, we have dropped the missing values, making them implicit rather than explicit. This is permissible because we know how many days are in each month and can easily reconstruct the explicit missing values."
    ]
   },
   {
@@ -649,27 +641,25 @@
    "source": [
     "# Melt the dataset and extract a date column.\n",
     "molten_weather = (\n",
-    "    pd.melt(weather, id_vars=['id', 'year', 'month', 'element'], var_name='day')\n",
-    "    .assign(day=lambda x: x['day'].str.extract('(\\d+)').astype(int))\n",
-    "    .assign(date=lambda x: pd.to_datetime(x[['year', 'month', 'day']], errors='coerce'))\n",
-    ")[['id', 'date', 'element', 'value']]\n",
+    "    pd.melt(weather, id_vars=[\"id\", \"year\", \"month\", \"element\"], var_name=\"day\")\n",
+    "    .assign(day=lambda x: x[\"day\"].str.extract(\"(\\d+)\").astype(int))\n",
+    "    .assign(date=lambda x: pd.to_datetime(x[[\"year\", \"month\", \"day\"]], errors=\"coerce\"))\n",
+    ")\n",
+    "molten_weather = molten_weather[[\"id\", \"date\", \"element\", \"value\"]]\n",
     "\n",
     "# Make the missing values implicit.\n",
-    "molten_weather = molten_weather[molten_weather['value'].notnull()]\n",
+    "molten_weather = molten_weather[molten_weather[\"value\"].notnull()]\n",
     "\n",
     "# Sort the data as in the paper.\n",
-    "molten_weather = (\n",
-    "    molten_weather\n",
-    "    .sort_values(['id', 'date', 'element'])\n",
-    "    .reset_index(drop=True)\n",
-    ")"
+    "molten_weather = molten_weather.sort_values([\"id\", \"date\", \"element\"])\n",
+    "molten_weather = molten_weather.reset_index(drop=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "> This dataset is mostly tidy, but we have two variables stored in rows: *tmin* and *tmax*, the type of observation."
+    "> This dataset is mostly tidy, but we have two variables stored in rows: `\"tmin\"` and `\"tmax\"`, the type of observation."
    ]
   },
   {
@@ -799,7 +789,7 @@
     }
    ],
    "source": [
-    "molten_weather[(molten_weather['date'].dt.year == 2010)].head(10)"
+    "molten_weather[(molten_weather[\"date\"].dt.year == 2010)].head(10)"
    ]
   },
   {
@@ -815,7 +805,7 @@
    "source": [
     "> Fixing this requires the cast, or unstack, operation. This performs the inverse of melting by rotating the element variable back out into the columns\n",
     "\n",
-    "Note that [pd.DataFrame.unstack](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) method uses a DataFrame's index as columns to unstack over."
+    "Below, [pd.DataFrame.unstack()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.unstack.html) uses a DataFrame's index as columns to unstack over."
    ]
   },
   {
@@ -824,7 +814,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tidy_weather = molten_weather.set_index(['id', 'date', 'element']).unstack()\n",
+    "tidy_weather = molten_weather.set_index([\"id\", \"date\", \"element\"]).unstack()\n",
     "\n",
     "# Make the column headers look as in the paper.\n",
     "tidy_weather.columns = tidy_weather.columns.droplevel(0)\n",
@@ -966,7 +956,7 @@
     }
    ],
    "source": [
-    "tidy_weather[(tidy_weather['date'].dt.year == 2010)].head(10)"
+    "tidy_weather[(tidy_weather[\"date\"].dt.year == 2010)].head(10)"
    ]
   }
  ],
@@ -986,9 +976,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/4_multiple_types_in_one_table.ipynb b/4_multiple_types_in_one_table.ipynb
index 7a70fac..11c23ab 100644
--- a/4_multiple_types_in_one_table.ipynb
+++ b/4_multiple_types_in_one_table.ipynb
@@ -20,24 +20,9 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2018-08-26 15:32:47 CEST\n",
-      "\n",
-      "CPython 3.6.5\n",
-      "IPython 6.5.0\n",
-      "\n",
-      "numpy 1.15.1\n",
-      "pandas 0.23.4\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "% load_ext watermark\n",
-    "% watermark -d -t -v -z -p numpy,pandas"
+    "%load_ext lab_black"
    ]
   },
   {
@@ -71,7 +56,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "billboard = pd.read_csv('data/billboard_cleaned.csv')"
+    "billboard = pd.read_csv(\"data/billboard_cleaned.csv\")"
    ]
   },
   {
@@ -81,7 +66,7 @@
     "### Messy Data\n",
     "\n",
     "> The Billboard dataset described in Table 8 actually contains observations on two types of\n",
-    "observational units: the *song* and its *rank* in each week. This manifests itself through the duplication of facts about the song: *artist* and *time* are repeated for every song in each *week*."
+    "observational units: the **song** and its **rank** in each week. This manifests itself through the duplication of facts about the song: `\"artist\"` and `\"time\"` are repeated for every song in each `\"week\"`."
    ]
   },
   {
@@ -325,9 +310,9 @@
    "source": [
     "### Tidy Data\n",
     "\n",
-    "> The billboard dataset needs to be broken down into two datasets: a **song** dataset which stores *artist*, *song name* and *time*, and a **ranking** dataset which gives the *rank* of the song in each *week*.\n",
+    "> The billboard dataset needs to be broken down into two datasets: a **song** dataset which stores `\"artist\"`, `\"song name\"` and `\"time\"`, and a **ranking** dataset which gives the `\"rank\"` of the song in each `\"week\".\n",
     "\n",
-    "Transforming data columns into index columns is enough in pandas to obtain unique tuples from several columns. So no real \"function\" is needed to tidy up the dataset."
+    "Transforming data columns into index columns is enough in pandas to obtain unique `tuple`s from several columns. So, no real \"function\" is needed to tidy up the dataset."
    ]
   },
   {
@@ -338,26 +323,25 @@
    "source": [
     "# Get the unique combinations for the song DataFrame and\n",
     "# \"store\" them in the original dataset for reuse.\n",
-    "billboard = billboard.set_index(['artist', 'track', 'time'])\n",
+    "billboard = billboard.set_index([\"artist\", \"track\", \"time\"])\n",
     "\n",
     "# Create the song DataFrame.\n",
     "songs = pd.DataFrame.from_records(\n",
-    "    columns=['id', 'artist', 'track', 'time'],\n",
+    "    columns=[\"id\", \"artist\", \"track\", \"time\"],\n",
     "    data=[  # Combine enumerate with tuple unpacking\n",
     "        (a + 1, b, c, d)  # to create the ID column.\n",
-    "        for (a, (b, c, d))\n",
-    "        in enumerate(billboard.index.unique())\n",
+    "        for (a, (b, c, d)) in enumerate(billboard.index.unique())\n",
     "    ],\n",
     ")\n",
     "\n",
     "# Take the date and rank columns from the original dataset\n",
     "# and use the implicit index alignment to assign the songs' IDs.\n",
-    "ranking = billboard[['date', 'rank']].copy()\n",
-    "ranking['id'] = songs.set_index(['artist', 'track', 'time'])\n",
+    "ranking = billboard[[\"date\", \"rank\"]].copy()\n",
+    "ranking[\"id\"] = songs.set_index([\"artist\", \"track\", \"time\"])\n",
     "\n",
     "# Use the song ID as the index as in the paper.\n",
-    "ranking = ranking.reset_index(drop=True).set_index('id')\n",
-    "songs = songs.set_index('id')"
+    "ranking = ranking.reset_index(drop=True).set_index(\"id\")\n",
+    "songs = songs.set_index(\"id\")"
    ]
   },
   {
@@ -700,9 +684,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/5_one_type_in_multiple_tables.ipynb b/5_one_type_in_multiple_tables.ipynb
index 0ccc8b7..b6812ea 100644
--- a/5_one_type_in_multiple_tables.ipynb
+++ b/5_one_type_in_multiple_tables.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# One Type in multiple Tables\n",
     "\n",
-    "The repository with the original R code does not provide code for this case but only refers to other projects that cannot be replicated any more (source website not available any more)."
+    "The repository with the original R code does not provide code for this case but only refers to other projects that cannot be replicated any more (because the source website is *not* available any more)."
    ]
   },
   {
@@ -47,9 +47,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/6_case_study.ipynb b/6_case_study.ipynb
index 27f6c5f..fb2b3b9 100644
--- a/6_case_study.ipynb
+++ b/6_case_study.ipynb
@@ -22,44 +22,35 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2018-09-06 11:17:29 CEST\n",
-      "\n",
-      "CPython 3.6.5\n",
-      "IPython 6.5.0\n",
-      "\n",
-      "matplotlib 2.2.3\n",
-      "numpy 1.15.1\n",
-      "pandas 0.23.4\n",
-      "seaborn 0.9.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "% load_ext watermark\n",
-    "% watermark -d -t -v -z -p matplotlib,numpy,pandas,seaborn"
+    "%load_ext lab_black"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/webartifex/repos/tidy-data/.venv/lib/python3.7/site-packages/rpy2/robjects/pandas2ri.py:11: FutureWarning: pandas.core.index is deprecated and will be removed in a future version.  The public classes are available in the top-level namespace.\n",
+      "  from pandas.core.index import Index as PandasIndex\n"
+     ]
+    }
+   ],
    "source": [
     "import math\n",
     "import textwrap\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import rpy2.robjects as robjects\n",
-    "import rpy2.robjects.pandas2ri as pandas2ri\n",
     "import seaborn as sns\n",
-    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from rpy2 import robjects  # leads to a FutureWarning that can be safely ignored\n",
+    "from rpy2.robjects import pandas2ri\n",
     "from sklearn.linear_model import HuberRegressor"
    ]
   },
@@ -69,7 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "% matplotlib inline"
+    "%matplotlib inline"
    ]
   },
   {
@@ -94,13 +85,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "deaths = pandas2ri.ri2py(robjects.r['readRDS']('data/deaths.rds'))\n",
-    "deaths = deaths[(deaths['yod'] == 2008) & (deaths['mod'] != 0)\n",
-    "                & (deaths['dod'] != 0)]\n",
-    "#deaths.loc[(deaths['hod'] < 0), 'hod'] = 0\n",
-    "deaths = deaths[~(deaths['hod'] < 0)]\n",
+    "deaths = pandas2ri.ri2py(robjects.r[\"readRDS\"](\"data/deaths.rds\"))\n",
+    "deaths = deaths[(deaths[\"yod\"] == 2008) & (deaths[\"mod\"] != 0) & (deaths[\"dod\"] != 0)]\n",
+    "deaths = deaths[~(deaths[\"hod\"] < 0)]\n",
     "deaths = deaths.reset_index(drop=True)\n",
-    "assert set(deaths['hod'].unique()) <= set(range(24))"
+    "\n",
+    "assert set(deaths[\"hod\"].unique()) <= set(range(24))"
    ]
   },
   {
@@ -225,9 +215,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Note that this file contains 7 duplicates that are removed.\n",
-    "codes = pd.read_csv('data/icd-main.csv')\n",
-    "codes = codes[(codes['code'] != codes['code'].shift())].set_index('code')"
+    "# The file contains 7 duplicates that are discarded.\n",
+    "codes = pd.read_csv(\"data/icd-main.csv\")\n",
+    "codes = codes[(codes[\"code\"] != codes[\"code\"].shift())].set_index(\"code\")"
    ]
   },
   {
@@ -333,7 +323,7 @@
    "source": [
     "## Counts\n",
     "\n",
-    "Count the number of deaths by *hour of the day* and *cause of death* (and also join in the more descriptive labels for the various causes)."
+    "Count the number of deaths by `\"hod\"` (=\"hour of the day\") and `\"cod\"` (=\"cause of death\"), and also join in the more descriptive labels for the various causes."
    ]
   },
   {
@@ -424,16 +414,16 @@
    ],
    "source": [
     "counts = (\n",
-    "    pd.DataFrame(deaths.groupby(['hod', 'cod']).size(), columns=['freq'])\n",
+    "    pd.DataFrame(deaths.groupby([\"hod\", \"cod\"]).size(), columns=[\"freq\"])\n",
     "    .reset_index()\n",
-    "    .join(codes, on='cod')\n",
+    "    .join(codes, on=\"cod\")\n",
     ")\n",
     "# This is to ensure that no duplicates are created\n",
     "# because of duplicate entries in the codes DataFrame.\n",
-    "assert counts['cod'].value_counts().max() <= 24\n",
+    "assert counts[\"cod\"].value_counts().max() <= 24\n",
     "\n",
     "# Keep only causes where a death happened in every hour.\n",
-    "counts = counts[counts['cod'].isin(list((counts['cod'].value_counts() == 24).index))]\n",
+    "counts = counts[counts[\"cod\"].isin(list((counts[\"cod\"].value_counts() == 24).index))]\n",
     "\n",
     "counts.head()"
    ]
@@ -442,7 +432,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Add a *prop* column indicating the relative frequency of a given *cause of death* on an hourly basis."
+    "Add a `\"prop\"` (=\"proportion\") column indicating the relative frequency of a given cause of death on an hourly basis."
    ]
   },
   {
@@ -538,14 +528,11 @@
     }
    ],
    "source": [
-    "counts = counts.set_index('cod')\n",
-    "counts['prop'] = (\n",
-    "    counts['freq']\n",
-    "    / deaths.groupby(['cod']).size().reindex(counts.index)\n",
-    ")\n",
+    "counts = counts.set_index(\"cod\")\n",
+    "counts[\"prop\"] = counts[\"freq\"] / deaths.groupby([\"cod\"]).size().reindex(counts.index)\n",
     "counts = counts.reset_index()\n",
     "# Re-order the columns as in the paper.\n",
-    "counts = counts[['hod', 'cod', 'freq', 'disease', 'prop']]\n",
+    "counts = counts[[\"hod\", \"cod\", \"freq\", \"disease\", \"prop\"]]\n",
     "\n",
     "counts.head()"
    ]
@@ -554,7 +541,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Add *freq_all* and *prop_all* columns that show the absolute number of deaths for a given *hour of day* (disregarding *cause of death*) and the proportion of deaths for a certain *hour of day* with respect to the whole day."
+    "Add `\"freq_all\"` and `\"prop_all\"` columns that show the absolute number of deaths for a given hour of day (disregarding cause of death) and the proportion of deaths for a certain hour of day with respect to the whole day."
    ]
   },
   {
@@ -669,9 +656,9 @@
     }
    ],
    "source": [
-    "counts = counts.set_index('hod')\n",
-    "counts['freq_all'] = deaths.groupby('hod').size()\n",
-    "counts['prop_all'] = counts['freq_all'] / deaths.shape[0]\n",
+    "counts = counts.set_index(\"hod\")\n",
+    "counts[\"freq_all\"] = deaths.groupby(\"hod\").size()\n",
+    "counts[\"prop_all\"] = counts[\"freq_all\"] / deaths.shape[0]\n",
     "counts = counts.reset_index()\n",
     "\n",
     "counts.head()"
@@ -768,22 +755,16 @@
    ],
    "source": [
     "devi = (\n",
-    "    codes\n",
+    "    codes.join(deaths.groupby(\"cod\").count()[\"yod\"].to_frame(), how=\"inner\")\n",
     "    .join(\n",
-    "        deaths.groupby('cod')\n",
-    "        .count()['yod']\n",
+    "        counts.groupby(\"cod\")\n",
+    "        .apply(lambda x: ((x[\"prop\"] - x[\"prop_all\"]) ** 2).mean())\n",
     "        .to_frame(),\n",
-    "        how='inner',\n",
+    "        how=\"inner\",\n",
     "    )\n",
-    "    .join(\n",
-    "        counts.groupby('cod')\n",
-    "        .apply(lambda x: ((x['prop'] - x['prop_all']) ** 2).mean())\n",
-    "        .to_frame(),\n",
-    "        how='inner',\n",
-    "    )\n",
-    "    .rename(columns={'yod': 'n', 0: 'dist'})\n",
+    "    .rename(columns={\"yod\": \"n\", 0: \"dist\"})\n",
     ")\n",
-    "devi = devi[(devi['n'] > 50)]\n",
+    "devi = devi[(devi[\"n\"] > 50)]\n",
     "\n",
     "devi.head()"
    ]
@@ -792,7 +773,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Plot *dist* vs. *n*. Not a whole lot can be seen here."
+    "Plot `\"dist\"` vs. `\"n\"`. Not a whole lot can be seen here."
    ]
   },
   {
@@ -802,7 +783,17 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "<AxesSubplot:xlabel='n', ylabel='dist'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEQCAYAAAAkgGgxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcHklEQVR4nO3dfWxT59038G9s3tJCSu0lxh5pgbVlZiTtSp8+Yi3dCE6cghOnjNRVRtSJEjQRFo1qW9NNJQSo2iCtWseIdE90TJEqhqyu0LhpyFJuNpLnKYWVhjIDpWlCgDgv2GThLTQ5vu4/anzHhMZO8OvF9yNViv27bP/OqfvtdR0fHycJIQSIiCSkinUDRESRwoAjImkx4IhIWgw4IpIWA46IpMWAIyJpRS3g2traYLPZYDabYbPZ0N7ePmKMoiiorKyEyWRCdnY27HZ7SDUAqKurQ15eHiwWC/Ly8nDhwoVIbxIRxTsRJcXFxWLPnj1CCCH27NkjiouLR4x59913xapVq4SiKMLtdotFixaJs2fPBq0dO3ZMPP3006Knp0cIIUR/f78YGBiI0pYRUbyKygzO7XbD6XTCYrEAACwWC5xOJzweT8C4uro6FBYWQqVSQaPRwGQyob6+PmjtL3/5C1atWoXU1FQAwLRp0zB58uRobBoRxbGoBJzL5YJOp4NarQYAqNVqpKWlweVyjRhnMBj8t/V6Pbq6uoLWWltbcfbsWfzkJz/BM888g+rqagh+QYPojjch1g2Eg6IoOHXqFHbu3ImvvvoKq1evhsFgQEFBQaxbI6IYikrA6fV6dHd3Q1EUqNVqKIqCnp4e6PX6EeM6OzuRmZkJIHDWNlrNYDAgNzcXkyZNwqRJk7BkyRIcO3ZsTAF38eIVeL2JM+vTaqfC7b4c6zZClmj9Auw50lSqJNx7792RfY2IPruPVquF0WiEw+EAADgcDhiNRmg0moBxubm5sNvt8Hq98Hg8aGxshNlsDlqzWCxoamqCEAKDg4P46KOP8N3vfndMPXq9IqH+SbSeE61f9hy9fiMpakvUjRs3ory8HNXV1UhJSUFVVRUAoKSkBGVlZcjIyIDVakVLSwtycnIAAKWlpUhPTweAUWvLli3D8ePHsXTpUqhUKjz55JNYsWJFtDaNiOJUkuDReACA2305Kv9HCZfU1Gno7b0U6zZClmj9Auw50lSqJGi1UyP7GhF9diKiGGLAEZG0GHBEJC0GHBFJiwHnc+36UKxbIKIwY8D5ONs9wQcRUUJhwPnMm6UJPoiIEgoDzid5shRfyyWiYRhwRCQtBhwRSYsBR0TSYsARkbQYcEQkLQYcEUmLAUdE0mLAEZG0GHBEJC0GHBFJiwFHRNJiwBGRtBhwRCQtBhwRSYsBR0TSYsARkbQYcEQkLQacD390hkg+DDgf/ugMkXyiFnBtbW2w2Wwwm82w2Wxob28fMUZRFFRWVsJkMiE7Oxt2uz2k2rZt27Bw4UJYrVZYrVZUVlaOuT/+6AyRfKL2SysVFRUoKiqC1WrF3r17sWHDBtTU1ASMqa2tRUdHBxoaGtDX14eCggIsXLgQM2fOHLUGAAUFBXjppZfG3V/y5AnwesVtbSMRxZeozODcbjecTicsFgsAwGKxwOl0wuMJXBbW1dWhsLAQKpUKGo0GJpMJ9fX1QWtERLcSlYBzuVzQ6XRQq9UAALVajbS0NLhcrhHjDAaD/7Zer0dXV1fQGgC8//77yMvLw6pVq3D06NFIbg4RJQgpfgz0ueeew89+9jNMnDgRzc3NWLt2Lerq6nDvvfeG/Bxa7dQIdhgZqanTYt3CmCRavwB7TnRRCTi9Xo/u7m4oigK1Wg1FUdDT0wO9Xj9iXGdnJzIzMwEEztpGq6Wmpvqf44knnoBer8fp06fx+OOPh9yj2305oY7BpaZOQ2/vpVi3EbJE6xdgz5GmUiVFfGIRlSWqVquF0WiEw+EAADgcDhiNRmg0gZ9c5ubmwm63w+v1wuPxoLGxEWazOWitu7vb/xwnTpzA+fPnMXv27GhsGhHFsagtUTdu3Ijy8nJUV1cjJSUFVVVVAICSkhKUlZUhIyMDVqsVLS0tyMnJAQCUlpYiPT0dAEatvfHGG/j3v/8NlUqFiRMnYuvWrQGzOiK6MyUJIRJnXRZBXKJGVqL1C7DnSJNmiUpEFAsMOCKSFgOOiKTFgCMiaTHgiEhaDDgikhYDjoikxYAjImkx4IhIWgw4IpIWA46IpMWAIyJpMeCISFoMOCKSFgOOiKTFgCMiaTHgiEhaDDgikhYDjoikxYAjImkx4IhIWgw4IpIWA46IpMWA87l2fSjWLRBRmDHgfJztnli3QERhxoDzmTdLE+sWiCjMGHA+yZMnxLoFIgozBhwRSStqAdfW1gabzQaz2QybzYb29vYRYxRFQWVlJUwmE7Kzs2G320Oq3fDll1/i4YcfRlVVVSQ3hYgSRNQCrqKiAkVFRdi3bx+KioqwYcOGEWNqa2vR0dGBhoYG7N69G9u2bcO5c+eC1oCvA7CiogImkylam0REcS4qAed2u+F0OmGxWAAAFosFTqcTHk/gJ5d1dXUoLCyESqWCRqOByWRCfX190BoA/OlPf8KPfvQjzJo1KxqbREQJICpH1l0uF3Q6HdRqNQBArVYjLS0NLpcLGo0mYJzBYPDf1uv16OrqClo7efIkmpqaUFNTg+rq6nH1qNVOHdfjYik1dVqsWxiTROsXYM+JLuE/OhwcHMQrr7yC1157zR+g4+F2X4bXK8LYWWSlpk5Db++lWLcRskTrF2DPkaZSJUV8YhGVgNPr9eju7oaiKFCr1VAUBT09PdDr9SPGdXZ2IjMzE0DgrO2bar29vejo6MCaNWsAAP39/RBC4PLly9i8eXM0No+I4lRUjsFptVoYjUY4HA4AgMPhgNFoDFieAkBubi7sdju8Xi88Hg8aGxthNptHrRkMBhw6dAj79+/H/v378fzzz+PZZ59luBFR9JaoGzduRHl5Oaqrq5GSkuI/laOkpARlZWXIyMiA1WpFS0sLcnJyAAClpaVIT08HgFFrRES3kiSESJwDTxHEY3CRlWj9Auw50qJxDI7fZCAiaTHgiEhaDDgikhYDjoikxYAjImkx4IhIWgw4IpIWA46IpMWAIyJpMeCISFoMOCKSFgOOiKTFgCMiaTHgiEhaDDgikhYDjoikxYAjImkx4IhIWgw4IpIWA46IpMWAIyJpMeCISFohB1xBQcEt71++fHm4eiEiCquQA+7MmTMj7hNC4Ny5c2FtiIgoXIL+sv2vf/1rAMDg4KD/7xvOnz+PBx54IDKdERHdpqABd999993ybwB49NFHkZubG/6uiIjCIGjArVu3DgDw8MMPY9GiRRFviIgoXEI+Bjdx4kScPXsWANDb24uXXnoJL7/8Mnp7e0N6fFtbG2w2G8xmM2w2G9rb20eMURQFlZWVMJlMyM7Oht1uD6n2zjvvIC8vD1arFXl5eaipqQl1s4hIYiEHXGVlJdRqNQDg9ddfx9DQEJKSkvDKK6+E9PiKigoUFRVh3759KCoqwoYNG0aMqa2tRUdHBxoaGrB7925s27bN/yHGaDWz2Yz33nsPe/fuxa5du7Bz506cPHky1E0jIkmFHHDd3d0wGAwYGhpCU1MTNm3ahI0bN+Lo0aNBH+t2u+F0OmGxWAAAFosFTqcTHo8nYFxdXR0KCwuhUqmg0WhgMplQX18ftDZ16lQkJSUBAAYGBjA4OOi/TUR3rpADburUqbhw4QIOHz6M73znO7j77rsBAENDQ0Ef63K5oNPp/DNAtVqNtLQ0uFyuEeMMBoP/tl6vR1dXV9AaAHz44YdYtmwZFi9ejNWrV2Pu3LmhbhoRSSrohww3rFy5EitWrMDg4CB+85vfAAA++eQTzJkzJ2LNjcWSJUuwZMkSdHZ2orS0FE899dSYetNqp0awu8hITZ0W6xbGJNH6Bdhzogs54NasWYPs7Gyo1Wr/6SI6nQ5btmwJ+li9Xo/u7m4oigK1Wg1FUdDT0wO9Xj9iXGdnJzIzMwEEztpGqw1nMBiQkZGBAwcOjCng3O7L8HpFyONjLTV1Gnp7L8W6jZAlWr8Ae440lSop4hOLMX0Xdfbs2QHnws2ePTukpaBWq4XRaITD4QAAOBwOGI1GaDSagHG5ubmw2+3wer3weDxobGyE2WwOWmttbfU/h8fjwaFDh/DQQw+NZdOISEKjzuCefvppfPDBBwCAH/7wh9944P7AgQNBX2jjxo0oLy9HdXU1UlJSUFVVBQAoKSlBWVkZMjIyYLVa0dLSgpycHABAaWkp0tPTAWDU2u7du9Hc3IwJEyZACIGVK1fiySefDGHziUhmSUKIb1yXHTlyBI899hgA4OOPP/7GJ3n88cfD31mUcYkaWYnWL8CeIy0aS9RRZ3DNzc1obm4O+iQyBBwRyWfUgBt+Gsb169fR0NCA+fPn49vf/jY6Ozvx2Wef+ZeMRETxZtSAe+211/x/r1+/Hr/73e/8B/YBoKGhwX+yLRFRvAn5U9R//vOfMJlMAfdlZWXhH//4R9ibIiIKh5AD7v7778fbb78dcN+uXbtGXEKJiChehHyi75YtW7Bu3Trs2LEDOp0O3d3dmDBhArZt2xbJ/oiIxi3kgJs3bx727duHlpYW9PT0IDU1FY888ggmTpwYyf6IiMYt5IADvr4m3I3z4oiI4h1/NpCIpMWAIyJpMeCISFoMOCKSFgOOiKTFgCMiaTHgiEhaDDgikhYDjoikxYAjImkx4IhIWgw4IpIWA46IpMWAIyJpMeCISFoMOCKSFgOOiKTFgCMiaTHgiEhaUQu4trY22Gw2mM1m2Gw2tLe3jxijKAoqKythMpmQnZ0Nu90eUm379u1YtmwZ8vLysHz5chw8eDAam0REcW5MPzpzOyoqKlBUVASr1Yq9e/diw4YNqKmpCRhTW1uLjo4ONDQ0oK+vDwUFBVi4cCFmzpw5ai0zMxOrVq1CcnIyTp48iZUrV6KpqQlTpkyJ1uYRURyKygzO7XbD6XTCYrEAACwWC5xOJzweT8C4uro6FBYWQqVSQaPRwGQyob6+Pmht0aJFSE5OBgDMnTsXQgj09fVFY9OIKI5FJeBcLhd0Oh3UajUAQK1WIy0tDS6Xa8Q4g8Hgv63X69HV1RW0NtyePXtw3333YcaMGZHYFCJKIFFbokbDxx9/jDfffBN//vOfx/xYrXZqBDqKrNTUabFuYUwSrV+APSe6qAScXq9Hd3c3FEWBWq2Goijo6emBXq8fMa6zsxOZmZkAAmdto9UA4OjRo/jVr36F6upqzJkzZ8w9ut2X4fWK8W5i1KWmTkNv76VYtxGyROsXYM+RplIlRXxiEZUlqlarhdFohMPhAAA4HA4YjUZoNJqAcbm5ubDb7fB6vfB4PGhsbITZbA5aO3bsGNavX48//OEP+N73vheNTSKiBJAkhIjKtKW1tRXl5eXo7+9HSkoKqqqqMGfOHJSUlKCsrAwZGRlQFAWbNm1Cc3MzAKCkpAQ2mw0ARq39+Mc/xvnz56HT6fyvt3XrVsydOzfk/jiDi6xE6xdgz5EWjRlc1AIu3jHgIivR+gXYc6RJs0QlIooFBhwRSYsBR0TSYsARkbQYcEQkLQYcEUmLAUdE0mLAEZG0GHA+164PxboFIgozBpyPs90TfBARJRQGnM+8WZrgg4gooTDgfJInS3VpPCICA46IJMaA8/nXqR5cHRiMdRtEFEYMOJ/a5nYcPX0h1m0QURjxwJNP3hOzMDd9eqzbIKIw4gzOZ94sDY6evsBlKpFEGHA+znYP/vrhaS5TiSTCgPOZN0uD55Y8iO8/+K1Yt0JEYcJjcD7JkyfgiQx98IFElDA4gyMiaTHgfPhleyL5MOB8+GV7Ivkw4Hz4ZXsi+TDgiEhaDDgfLlGJ5MOA85mjT4l1C0QUZlELuLa2NthsNpjNZthsNrS3t48YoygKKisrYTKZkJ2dDbvdHlKtqakJy5cvx/z581FVVTWu/k509I3rcUQUv6J2om9FRQWKiopgtVqxd+9ebNiwATU1NQFjamtr0dHRgYaGBvT19aGgoAALFy7EzJkzR62lp6fj1VdfRX19Pb766qtx9Tc05A3HZhJRHInKDM7tdsPpdMJisQAALBYLnE4nPJ7A4151dXUoLCyESqWCRqOByWRCfX190Nr9998Po9GICRPGn9cTJnC1TiSbqPxX7XK5oNPpoFarAQBqtRppaWlwuVwjxhkMBv9tvV6Prq6uoLVw4HdQieTD76L6zDRMj3ULY5aaOi3WLYxJovULsOdEF5WA0+v16O7uhqIoUKvVUBQFPT090Ov1I8Z1dnYiMzMTQOCsbbRaOPzXO5/iqUwDvjU9OWzPGUmpqdPQ23sp1m2ELNH6BdhzpKlUSdBqp0b2NSL67D5arRZGoxEOhwMA4HA4YDQaodEEfnsgNzcXdrsdXq8XHo8HjY2NMJvNQWvh8PGJHjj+f3vYno+IYi9qS9SNGzeivLwc1dXVSElJ8Z/OUVJSgrKyMmRkZMBqtaKlpQU5OTkAgNLSUqSnpwPAqLUjR47gxRdfxOXLlyGEwPvvv49XX30VixYtCrm/1OlTsOTRmeHcZCKKsSQhhIh1E/Fg3db9MD02M2GuCZdISxEg8foF2HOkSbNETQSalMkBPzpzdWAQzZ+5+BsNRAmMAefjcl/BqbN9/ttHT1/gbzQQJTieJuJz77TJAefC3fib58cRJS4GnM/VgSHcNWWi//ZdUyYmzPE4Iro1LlF9kidP4PE2Iskw4Hw8l67jI2d3rNsgojBiwPkkJQFJsW6CiMKKAeejUgEPfPueWLdBRGHEgPNRFGDf4Y5Yt0FEYcSAG+bKtUF+0EAkEQbcMMfbLvLEXiKJMOCGuV83lSf2EkmEATfMs4sfCDjZl4gSGwNuGPt/fxHrFogojBhww3zZlRiXmSGi0DDghrlrcqw7IKJwYsANc+164G1eE44osTHghrn5q1q8JhxRYuPlkoZRbrp4O68JR5TYOIO7yfDl6I1rwvHUEaLExIC7CZejRPJgwN3E859r/FCBSBIMuJu89//OcBZHJAkG3E1maJIDfj4w0ngqClHkMOBucv7CVRz70j2mx9xOSPFUFKLI4WkitzA4qNzy/qsDgzh6+gK+/+C3Aj5ZvRFSAMb8S1w8FYUochhwt9DpvoLmz1whB9n3H/wWrg8q+GpQwdWBwTGdVsKfJySKHC5Rb+HgsS7U1J/w/8rWjSXo3PTpeG7JgyNmW3dNmYjJE9X42z+/5FKTKI5EbQbX1taG8vJy9PX1Yfr06aiqqsKsWbMCxiiKgi1btuDgwYNISkrCmjVrUFhYeFu18RpUgEP/7sbgoBdney+j5YsLeG7Jg3giQ+8PvOEzPC41ieJP1AKuoqICRUVFsFqt2Lt3LzZs2ICampqAMbW1tejo6EBDQwP6+vpQUFCAhQsXYubMmeOu3Y7T5/+D0+f/AwBIu2cyjp7uhfs/19DTN4BPT1/A6XN9sCychWNfupEE4P/O0/FbD0RxJCoB53a74XQ6sXPnTgCAxWLB5s2b4fF4oNFo/OPq6upQWFgIlUoFjUYDk8mE+vp6rF69ety1UM2frcFxeEYdc673Cs71XoFaBTyYfg9OdvThTPdnuH5dQZI6CRf6B/BUpgGNR86it+8aklRJyJijwRMZBiRP/npXX+wfwIefnMOSR2fi3pQpuHZ9CM52D+boU/Clqx9z9Ck42dEHga9ngzced8ON8U9MmwKVavRfcr3YP4CGI2eh19yF/2PUjXiu0dx4nXmzNGN63GiC9RsJt7sdsej5dt2q53D9+wzn+yIa+zYqAedyuaDT6aBWqwEAarUaaWlpcLlcAQHncrlgMBj8t/V6Pbq6um6rFqr1P3ls7Bv2DebP1X1jTaudigdmBy5jZxqmA4D//pvrN7sx/u4gs8VbvdZY3HidcNFqp4b1+UJ1O9sRq55vxzf1HK5/n+F+X0QSP2QgImlFJeD0ej26u7uhKF+fX6YoCnp6eqDX60eM6+zs9N92uVyYMWPGbdWI6M4VlYDTarUwGo1wOBwAAIfDAaPRGLA8BYDc3FzY7XZ4vV54PB40NjbCbDbfVo2I7lxJQggRfNjta21tRXl5Ofr7+5GSkoKqqirMmTMHJSUlKCsrQ0ZGBhRFwaZNm9Dc3AwAKCkpgc1mA4Bx14jozhW1gCMiijZ+yEBE0mLAEZG0GHBEJC0GHBFJ644OuLa2NthsNpjNZthsNrS3t0fldauqqpCVlYW5c+fi888/D6mfSNTG4uLFiygpKYHZbEZeXh7WrVsHj+frr7Z9+umnyM/Ph9lsxqpVq+B2/+8FQyNRC9XatWuRn5+PgoICFBUV4cSJE0H3Saz38w1//OMfA94f8bqPASArKwu5ubmwWq2wWq04ePBg/PQs7mDFxcViz549Qggh9uzZI4qLi6PyuocPHxadnZ1i8eLF4tSpUyH1E4naWFy8eFF89NFH/tuvv/66ePnll4WiKMJkMonDhw8LIYTYvn27KC8vF0KIiNTGor+/3//33//+d1FQUCCEiO/9LIQQx48fFy+88IL//RHP+1gIMeJ9HKm+xtPzHRtwFy5cEAsWLBBDQ0NCCCGGhobEggULhNvtjloPw98Yo/UTidrtqq+vF88//7xoaWkRy5Yt89/vdrvFI488IoQQEamN17vvviueeeaZuN/P169fF88++6w4e/as//0R7/v4VgEXLz3fsVf0DfUCAPHQjxAi7LXb2Uav14tdu3YhKytrxIUONBoNvF4v+vr6IlKbPn36mHr97W9/i+bmZgghsGPHjrjfz2+++Sby8/MDLvUV7/sYAH75y19CCIEFCxbgxRdfjJue7+hjcDQ+mzdvxl133YWVK1fGupWgXn31VRw4cADr16/H1q1bY93OqI4ePYrjx4+jqKgo1q2Mydtvv4333nsP77zzDoQQ2LRpU6xb8rtjAy7UCwDEQz+RqI1XVVUVzpw5g9///vdQqVQjLnTg8XigUqkwffr0iNTGq6CgAIcOHcKMGTPidj8fPnwYra2tWLJkCbKystDV1YUXXngBZ86ciet9fGM7J02ahKKiInzyySdx8764YwMu1AsAxEM/kaiNxxtvvIHjx49j+/btmDRpEgBg/vz5GBgYwJEjRwAAf/3rX5GbmxuxWqiuXLkCl8vlv71//37cc889cb2f16xZg6amJuzfvx/79+/HjBkz8NZbb2H16tVxuY8B4OrVq7h06RIAQAiBuro6GI3G+HlfjOloomS++OILsWLFCpGTkyNWrFghWltbo/K6mzdvFosWLRJGo1H84Ac/EEuXLg3aTyRqY/H555+Lhx56SOTk5Ij8/HyRn58v1q5dK4QQ4l//+pewWCwiOztb/PSnPxW9vb3+x0WiFore3l5RWFgoLBaLyM/PF8XFxeL48eO3tb+isZ+HG37wPh73sRBCdHR0CKvVKiwWi1i6dKn4+c9/Lrq7u+OmZ37ZnoikdccuUYlIfgw4IpIWA46IpMWAIyJpMeCISFoMOCKSFgOOiKTFgCMiaTHgKCFlZWXhrbfeQl5eHhYsWIBf/OIXuH79eqzbojjDgKOE9cEHH2DHjh348MMPcerUKfztb3+LdUsUZ+7Y68FR4isuLoZOpwMALF682H9JcqIbOIOjhJWamur/Ozk5GVevXo1hNxSPGHBEJC0GHBFJiwFHRNLi9eCISFqcwRGRtBhwRCQtBhwRSYsBR0TSYsARkbQYcEQkLQYcEUmLAUdE0mLAEZG0/gfuJben+iOcjQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 288x288 with 1 Axes>"
       ]
@@ -815,16 +806,14 @@
     "_, ax = plt.subplots(figsize=(4, 4))\n",
     "ax.set_xlim(0, 50000)\n",
     "ax.set_ylim(0, 0.006)\n",
-    "sns.regplot(\n",
-    "    x='n', y='dist', data=devi, ax=ax, fit_reg=False, scatter_kws={'s': 1}\n",
-    ");"
+    "sns.regplot(x=\"n\", y=\"dist\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The relationship becomes more obvious if one plots the same points on a log-log scale."
+    "The relationship becomes more obvious if one plots the same points on a `\"log\"`-`\"log\"` scale."
    ]
   },
   {
@@ -834,7 +823,17 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "<AxesSubplot:xlabel='n', ylabel='dist'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x288 with 1 Axes>"
       ]
@@ -845,13 +844,11 @@
    ],
    "source": [
     "_, ax = plt.subplots(figsize=(4, 4))\n",
-    "ax.set_xscale('log')\n",
-    "ax.set_yscale('log')\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_yscale(\"log\")\n",
     "ax.set_xlim(30, 150000)\n",
     "ax.set_ylim(0.00001, 0.1)\n",
-    "sns.regplot(\n",
-    "    'n', 'dist', data=devi, ax=ax, fit_reg=False, scatter_kws={'s': 1}\n",
-    ");"
+    "sns.regplot(\"n\", \"dist\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})"
    ]
   },
   {
@@ -869,11 +866,11 @@
    "source": [
     "# Note that the HuberRegressor is not the exact\n",
     "# same method as in the paper but close.\n",
-    "X = np.log(devi['n']).values[:, np.newaxis]\n",
-    "y = np.log(devi['dist']).values\n",
+    "X = np.log(devi[\"n\"]).values[:, np.newaxis]\n",
+    "y = np.log(devi[\"dist\"]).values\n",
     "rlm = HuberRegressor()\n",
     "rlm.fit(X, y)\n",
-    "devi['residuals'] = y - rlm.predict(X)"
+    "devi[\"residuals\"] = y - rlm.predict(X)"
    ]
   },
   {
@@ -890,7 +887,17 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.collections.LineCollection at 0x7f5528c90550>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 288x288 with 1 Axes>"
       ]
@@ -901,13 +908,11 @@
    ],
    "source": [
     "_, ax = plt.subplots(figsize=(4, 4))\n",
-    "ax.set_xscale('log')\n",
+    "ax.set_xscale(\"log\")\n",
     "ax.set_xlim(50, 200000)\n",
     "ax.set_ylim(-1, 3)\n",
-    "sns.regplot(\n",
-    "    'n', 'residuals', data=devi, ax=ax, fit_reg=False, scatter_kws={'s': 1}\n",
-    ")\n",
-    "ax.hlines(1.5, 0, 200000);"
+    "sns.regplot(\"n\", \"residuals\", data=devi, ax=ax, fit_reg=False, scatter_kws={\"s\": 1})\n",
+    "ax.hlines(1.5, 0, 200000)"
    ]
   },
   {
@@ -924,7 +929,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x864 with 8 Axes>"
       ]
@@ -934,7 +939,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x864 with 5 Axes>"
       ]
@@ -945,26 +950,23 @@
    ],
    "source": [
     "# Get all temporarily \"unusual\" deaths.\n",
-    "unusual = (\n",
-    "    devi.loc[(devi['residuals'] > 1.5), ['disease', 'n']]\n",
-    "    .sort_values('disease')\n",
-    ")\n",
+    "unusual = devi.loc[(devi[\"residuals\"] > 1.5), [\"disease\", \"n\"]].sort_values(\"disease\")\n",
     "# Helper dataset for easy indexing / value retrieval.\n",
-    "plot_data = counts[['cod', 'hod', 'prop', 'prop_all']].set_index('cod')\n",
+    "plot_data = counts[[\"cod\", \"hod\", \"prop\", \"prop_all\"]].set_index(\"cod\")\n",
     "# Divide the plots in two big categories.\n",
     "for header, cond, ylim in [\n",
-    "    ('> 350 Deaths / Year', (unusual['n'] > 350), 0.125),\n",
-    "    ('< 350 Deaths / Year', (unusual['n'] <= 350), 0.3),\n",
+    "    (\"> 350 Deaths / Year\", (unusual[\"n\"] > 350), 0.125),\n",
+    "    (\"< 350 Deaths / Year\", (unusual[\"n\"] <= 350), 0.3),\n",
     "]:\n",
     "    nrows = math.ceil(len(unusual[cond]) / 3)\n",
-    "    fig = plt.figure(figsize=(16, 12), )\n",
+    "    fig = plt.figure(figsize=(16, 12),)\n",
     "    for i, (cod, (disease, _)) in enumerate(unusual[cond].iterrows(), 1):\n",
     "        ax = fig.add_subplot(nrows, 3, i)\n",
-    "        ax.set_title('\\n'.join(textwrap.wrap(disease, 40)))\n",
+    "        ax.set_title(\"\\n\".join(textwrap.wrap(disease, 40)))\n",
     "        ax.set_xlim(0, 24)\n",
     "        ax.set_ylim(0, ylim)\n",
-    "        ax.plot(plot_data.loc[cod, 'hod'], plot_data.loc[cod, 'prop'])\n",
-    "        ax.plot(plot_data.loc[cod, 'hod'], plot_data.loc[cod, 'prop_all'])\n",
+    "        ax.plot(plot_data.loc[cod, \"hod\"], plot_data.loc[cod, \"prop\"])\n",
+    "        ax.plot(plot_data.loc[cod, \"hod\"], plot_data.loc[cod, \"prop_all\"])\n",
     "        # Show only lower and left axes.\n",
     "        if i not in (3 * nrows - 2, 3 * nrows - 1, 3 * nrows):\n",
     "            plt.setp(ax.get_xticklabels(), visible=False)\n",
@@ -990,9 +992,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..68007e6
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,19 @@
+Copyright (c) 2018-2020 Alexander Hess [alexander@webartifex.biz]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Pipfile b/Pipfile
deleted file mode 100644
index e0fd7c0..0000000
--- a/Pipfile
+++ /dev/null
@@ -1,24 +0,0 @@
-[[source]]
-url = "https://pypi.org/simple"
-verify_ssl = true
-name = "pypi"
-
-[packages]
-pandas = "*"
-jupyter = "*"
-watermark = "*"
-savreaderwriter = "*"
-"rpy2" = "==2.8.*"
-matplotlib = "*"
-seaborn = "*"
-sklearn = "*"
-
-[dev-packages]
-black = "*"
-blackcellmagic = "*"
-
-[requires]
-python_version = "3.6"
-
-[pipenv]
-allow_prereleases = true
diff --git a/Pipfile.lock b/Pipfile.lock
deleted file mode 100644
index a47851f..0000000
--- a/Pipfile.lock
+++ /dev/null
@@ -1,758 +0,0 @@
-{
-    "_meta": {
-        "hash": {
-            "sha256": "9fc4c60d75aac99be98f4bd18fa6b1bf507d093c96a4c639901a1d0746a83ace"
-        },
-        "pipfile-spec": 6,
-        "requires": {
-            "python_version": "3.6"
-        },
-        "sources": [
-            {
-                "name": "pypi",
-                "url": "https://pypi.org/simple",
-                "verify_ssl": true
-            }
-        ]
-    },
-    "default": {
-        "attrs": {
-            "hashes": [
-                "sha256:4b90b09eeeb9b88c35bc642cbac057e45a5fd85367b985bd2809c62b7b939265",
-                "sha256:e0d0eb91441a3b53dab4d9b743eafc1ac44476296a2053b6ca3af0b139faf87b"
-            ],
-            "version": "==18.1.0"
-        },
-        "backcall": {
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diff --git a/README.md b/README.md
index ac80dce..a336f9f 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,18 @@
 # Tidy Data
 
-The purpose of this repository is to re-do the work described in the paper
-[Tidy Data](tidy-data.pdf) by Hadley Wickham (member of the RStudio team) in
-Python.
+The purpose of this repository is to illustrate how the data cleaning process described
+    in the paper "[Tidy Data](tidy-data.pdf)" by Hadley Wickham, a member of the
+    [RStudio](https://rstudio.com/) team, can be done in
+    [Python](https://www.python.org/).
 
-The paper was published in 2014 in the Journal of
-[Statistical Software](https://www.jstatsoft.org/article/view/v059i10). The
-author offers it for free download
-[here](http://vita.had.co.nz/papers/tidy-data.html). Furthermore, the original
-R code is available in a Github
-[repository](https://github.com/hadley/tidy-data)
+The paper was published in 2014 in the [Journal of Statistical Software](https://www.jstatsoft.org/article/view/v059i10).
+The author offers it for free [here](http://vita.had.co.nz/papers/tidy-data.html).
+Furthermore, the original [R](https://www.r-project.org/) code is available [here](https://github.com/hadley/tidy-data).
 
-After installing this project, it is recommended to first read the paper to get
-the big picture and then work through the six Jupyter notebooks (listed further
-below).
+After installing the dependencies for this project (cf., the [installation notes](https://github.com/webartifex/tidy-data#installation)
+    below), it is recommended to first read the paper to get the big picture and
+    then work through the six Jupyter notebooks listed below.
 
-See installation notes at the bottom.
 
 ## Summary
 
@@ -23,50 +20,51 @@ See installation notes at the bottom.
 ### Definition
 
 **Tidy** data is defined as data that comes in a table form adhering to the
-following requirements:
+    following requirements:
+1. each variable is a column,
+2. each observation a row, and
+3. each type of observational unit forms a table.
 
-1. Each variable forms a column.
-2. Each observation forms a row.
-3. Each type of observational unit forms a table.
-
-This is equivalent to Codd's 3rd normal form (in the context of relational
-databases). A dataset that does not satisfy these properties is called
-**messy**.
+This is equivalent to [Codd's 3rd normal form](https://en.wikipedia.org/wiki/Third_normal_form),
+    a concept from the theory on relational databases.
+A dataset that does *not* satisfy these properties is called **messy**.
 
 
-### Tidying messy Data
+### Tidying Data
 
-The five most common problems with messy data are as follows:
+The five most common problems with messy data are:
 
-- Column headers are values, not variable names
-[[notebook](1_column_headers_are_values.ipynb)]
-- Multiple variables are stored in one column
-[[notebook](2_multiple_variables_stored_in_one_column.ipynb)]
-- Variables are stored in both rows and columns
-[[notebook](3_variables_are_stored_in_both_rows_and_columns.ipynb)]
-- Multiple types of observational units are stored in the same table
-[[notebook](4_multiple_types_in_one_table.ipynb)]
-- A single observational unit is stored in multiple tables
-[[notebook](5_one_type_in_multiple_tables.ipynb)]
+- column headers are values, not variable names
+  (cf., [notebook 1](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/1_column_headers_are_values.ipynb))
+- multiple variables are stored in one column
+  (cf., [notebook 2](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/2_multiple_variables_stored_in_one_column.ipynb))
+- variables are stored in both rows and columns
+  (cf., [notebook 3](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/3_variables_are_stored_in_both_rows_and_columns.ipynb))
+- multiple types of observational units are stored in the same table
+  (cf., [notebook 4](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/4_multiple_types_in_one_table.ipynb))
+- a single observational unit is stored in multiple tables
+  (cf., [notebook 5](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/5_one_type_in_multiple_tables.ipynb))
 
-Further, a [case study](6_case_study.ipynb) shows the advantages of tidy data
-(as standardized input/output to statistical functions).
 
-## Download & Installation
+### Case Study
 
-Create a local copy of this repository with:
+A case study (cf., [notebook 6](https://nbviewer.jupyter.org/github/webartifex/tidy-data/blob/master/6_case_study.ipynb))
+    shows the advantages of tidy data as a standardized input to statistical functions.
+
+
+## Installation
+
+Get a local copy of this repository with [git](https://git-scm.com/).
 
 `git clone https://github.com/webartifex/tidy-data.git`
 
-This project uses [pipenv](https://docs.pipenv.org/) to manage its
-dependencies.
+If you are not familiar with [git](https://git-scm.com/), simply download the latest
+    version of the files in a zip archive [here](https://github.com/webartifex/tidy-data/archive/master.zip).
 
-To install all third-party Python packages in the most recent version into a
-project-local virtual environment, run:
+This project uses [poetry](https://python-poetry.org/docs/) to manage its dependencies.
+Install all third-party packages into a [virtual environment](https://docs.python.org/3/library/venv.html).
 
-`pipenv install`
+`poetry install`
 
-To install all packages with the same version as of the time of creating this
-project (for exact reproducability), run:
-
-`pipenv install --ignore-pipfile`
+Alternatively, use the [Anaconda Distribution](https://www.anaconda.com/products/individual)
+    that *should* also suffice to run the provided notebooks.
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..1c09045
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1558 @@
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..b04cc76
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,37 @@
+[build-system]
+build-backend = "poetry.masonry.api"
+requires = ["poetry>=0.12"]
+
+[tool.poetry]
+name = "tidy-data"
+version = "0.1.0"
+
+authors = ["Alexander Hess <alexander@webartifex.biz>"]
+description = "A Python implementation for Hadley Wickham's Tidy Data paper"
+keywords = [
+    "data-cleaning",
+    "data-science",
+    "messy-data",
+    "python",
+    "tidy-data",
+]
+license = "MIT"
+
+[tool.poetry.dependencies]
+python = "^3.7"
+
+# Data Science Tools
+jupyterlab = "^2.2.6"
+matplotlib = "^3.3.1"
+numpy = "^1.19.1"
+pandas = "^1.1.1"
+seaborn = "^0.10.1"
+sklearn = "^0.0"
+
+# Interfaces to other tools
+rpy2 = "==2.8.*"  # R support
+savreaderwriter = "^3.4.2"  # IBM SPSS support
+
+# Code Formatters
+black = "^19.10b0"
+nb_black = "^1.0.7"