From 2c71c1c7cd3907510abd067cf6ff90cbf9e3a3ab Mon Sep 17 00:00:00 2001
From: L4R5m <2925603+L4R5m@users.noreply.github.com>
Date: Mon, 15 Nov 2021 22:23:42 -0800
Subject: [PATCH 1/7] Add TOC & sections, set Pandas options, make copy of data
 df

Mostly minor refactoring, but making copy of 'data' df is important to
avoid SettingWithCopyWarning when changing a column's dtype.
---
 key.ipynb | 305 ++++++++++++++++++++++++++++++++++++++----------------
 1 file changed, 216 insertions(+), 89 deletions(-)

diff --git a/key.ipynb b/key.ipynb
index 5206254..3f02a59 100644
--- a/key.ipynb
+++ b/key.ipynb
@@ -1,119 +1,233 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Customize Pandas settings (eg: DataFrame display)\n",
+    "# Columns\n",
+    "pd.options.display.max_columns = 10\n",
+    "pd.options.display.max_colwidth = 15\n",
+    "pd.options.display.width = 150\n",
+    "# Rows\n",
+    "pd.options.display.max_rows = pd.options.display.min_rows = 6\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will be using ugly holiday sweater data crowdsourced from R-Ladies (and friends) in November/December 2020. If you would like to contribute your own ugly holiday sweater info to this dataset, please fill out this Google Form! See a summary of the data attributes here:\n",
+    "\n",
+    "    sweater: entry number\n",
+    "    hs_tf: Do you have a holiday sweater? (Yes/No/NA)\n",
+    "    sparkly: is it sparkly? (Yes/No/NA)\n",
+    "    noise: does it make noise? (Yes/No/NA)\n",
+    "    lights: does it light up? (Yes/No/NA)\n",
+    "    objects: does it have anything attached to it? (Yes/No/NA)\n",
+    "    colors: What colors does it have?\n",
+    "    image_tf: Does it have an image on it? (Yes/No/NA)\n",
+    "    image_desc: User-provided image description\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           hs_tf sparkly noise lights objects  \\\n",
-      "sweater                                         \n",
-      "sweater1     Yes     Yes    No     No      No   \n",
-      "sweater2     Yes      No    No     No      No   \n",
-      "sweater3     Yes      No    No     No      No   \n",
-      "sweater5     Yes      No    No     No      No   \n",
-      "sweater8     Yes      No   Yes    Yes     Yes   \n",
-      "...          ...     ...   ...    ...     ...   \n",
-      "sweater100   Yes     Yes    No     No     Yes   \n",
-      "sweater103   Yes      No   Yes     No     Yes   \n",
-      "sweater104   Yes     Yes    No     No     Yes   \n",
-      "sweater105   Yes      No    No     No      No   \n",
-      "sweater107   Yes      No    No     No     Yes   \n",
+      "df shape:\n",
+      "(105, 8)\n",
       "\n",
-      "                                             colors image_tf  \\\n",
-      "sweater                                                        \n",
-      "sweater1             Red, Yellow, Blue, White, teal      Yes   \n",
-      "sweater2                                      Green       No   \n",
-      "sweater3    Red, Yellow, Green, Brown, White, Black      Yes   \n",
-      "sweater5                         Blue, White, Black      Yes   \n",
-      "sweater8      Red, Green, Blue, Purple, White, Grey       No   \n",
-      "...                                             ...      ...   \n",
-      "sweater100                     Orange, Yellow, Blue      Yes   \n",
-      "sweater103                Red, Yellow, White, Black      Yes   \n",
-      "sweater104                        Red, White, Black      Yes   \n",
-      "sweater105                   Red, Green, Blue, Grey       No   \n",
-      "sweater107                 Red, Green, White, Black      Yes   \n",
+      "Column names:\n",
+      "['hs_tf', 'sparkly', 'noise', 'lights', 'objects', 'colors', 'image_tf', 'image_desc']\n",
       "\n",
-      "                            image_desc  \n",
-      "sweater                                 \n",
-      "sweater1    octopus dressed like santa  \n",
-      "sweater2                           NaN  \n",
-      "sweater3                        Houses  \n",
-      "sweater5                         T-rex  \n",
-      "sweater8                           NaN  \n",
-      "...                                ...  \n",
-      "sweater100                     Menorah  \n",
-      "sweater103                       Sloth  \n",
-      "sweater104    R2D2 wearing a Santa hat  \n",
-      "sweater105                         NaN  \n",
-      "sweater107     a llama wearing a scarf  \n",
+      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc\n",
+      "sweater                                                                               \n",
+      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...\n",
+      "sweater2     Yes      No    No     No      No           Green       No             NaN\n",
+      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses\n",
+      "...          ...     ...   ...    ...     ...             ...      ...             ...\n",
+      "sweater105   Yes      No    No     No      No  Red, Green,...       No             NaN\n",
+      "sweater106    No      No    No     No      No             NaN       No             NaN\n",
+      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
+      "\n",
+      "[105 rows x 8 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "fnm = 'ugly_sweaters.csv'\n",
+    "df = pd.read_csv(fnm, index_col='sweater')\n",
+    "# Show the dataframe\n",
+    "print(f'df shape:\\n{df.shape}')\n",
+    "print(f'\\nColumn names:\\n{df.columns.to_list()}\\n')\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filter Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc\n",
+      "sweater                                                                               \n",
+      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...\n",
+      "sweater2     Yes      No    No     No      No           Green       No             NaN\n",
+      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses\n",
+      "...          ...     ...   ...    ...     ...             ...      ...             ...\n",
+      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...\n",
+      "sweater105   Yes      No    No     No      No  Red, Green,...       No             NaN\n",
+      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
       "\n",
       "[68 rows x 8 columns]\n"
      ]
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
-    "# Step 1 Load Data\n",
-    "data = pd.read_csv('ugly_sweaters.csv', index_col='sweater')\n",
-    "data = data[data['hs_tf'] == 'Yes']\n",
+    "# Filter to only include Holiday Sweaters\n",
+    "data = df.loc[df.hs_tf == 'Yes']\n",
+    "# Make a copy (instead of a view) since we'll be changing\n",
+    "# col dtypes later and don't want to get SettingWithCopyWarning\n",
+    "data = data.copy()\n",
     "print(data)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Wrangle Data"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           hs_tf sparkly noise lights objects  \\\n",
-      "sweater                                         \n",
-      "sweater1     Yes     Yes    No     No      No   \n",
-      "sweater2     Yes      No    No     No      No   \n",
-      "sweater3     Yes      No    No     No      No   \n",
-      "sweater5     Yes      No    No     No      No   \n",
-      "sweater8     Yes      No   Yes    Yes     Yes   \n",
-      "...          ...     ...   ...    ...     ...   \n",
-      "sweater100   Yes     Yes    No     No     Yes   \n",
-      "sweater103   Yes      No   Yes     No     Yes   \n",
-      "sweater104   Yes     Yes    No     No     Yes   \n",
-      "sweater105   Yes      No    No     No      No   \n",
-      "sweater107   Yes      No    No     No     Yes   \n",
-      "\n",
-      "                                                    colors image_tf  \\\n",
-      "sweater                                                               \n",
-      "sweater1              [Red,  Yellow,  Blue,  White,  teal]      Yes   \n",
-      "sweater2                                           [Green]       No   \n",
-      "sweater3    [Red,  Yellow,  Green,  Brown,  White,  Black]      Yes   \n",
-      "sweater5                            [Blue,  White,  Black]      Yes   \n",
-      "sweater8      [Red,  Green,  Blue,  Purple,  White,  Grey]       No   \n",
-      "...                                                    ...      ...   \n",
-      "sweater100                        [Orange,  Yellow,  Blue]      Yes   \n",
-      "sweater103                  [Red,  Yellow,  White,  Black]      Yes   \n",
-      "sweater104                           [Red,  White,  Black]      Yes   \n",
-      "sweater105                     [Red,  Green,  Blue,  Grey]       No   \n",
-      "sweater107                   [Red,  Green,  White,  Black]      Yes   \n",
-      "\n",
-      "                                 image_desc  num_colors  num_words  \n",
-      "sweater                                                             \n",
-      "sweater1    [octopus, dressed, like, santa]           5          4  \n",
-      "sweater2                                NaN           1          1  \n",
-      "sweater3                           [Houses]           6          1  \n",
-      "sweater5                            [T-rex]           3          1  \n",
-      "sweater8                                NaN           6          1  \n",
-      "...                                     ...         ...        ...  \n",
-      "sweater100                        [Menorah]           3          1  \n",
-      "sweater103                          [Sloth]           4          1  \n",
-      "sweater104   [R2D2, wearing, a, Santa, hat]           3          5  \n",
-      "sweater105                              NaN           4          1  \n",
-      "sweater107    [a, llama, wearing, a, scarf]           4          5  \n",
+      "sweater\n",
+      "sweater1      Red, Yellow...\n",
+      "sweater2               Green\n",
+      "sweater3      Red, Yellow...\n",
+      "                   ...      \n",
+      "sweater104    Red, White,...\n",
+      "sweater105    Red, Green,...\n",
+      "sweater107    Red, Green,...\n",
+      "Name: colors, Length: 68, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data.colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sweater\n",
+      "sweater1      [Red,  Yell...\n",
+      "sweater2             [Green]\n",
+      "sweater3      [Red,  Yell...\n",
+      "                   ...      \n",
+      "sweater104    [Red,  Whit...\n",
+      "sweater105    [Red,  Gree...\n",
+      "sweater107    [Red,  Gree...\n",
+      "Name: colors, Length: 68, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert 'colors' column from single comma-delim str to list\n",
+    "# of string\n",
+    "data.colors = data.colors.str.split(',')\n",
+    "print(data.colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           hs_tf sparkly noise lights objects  colors image_tf      image_desc  num_colors  num_words\n",
+      "sweater                                                                                              \n",
+      "sweater1     Yes     Yes    No     No      No     NaN      Yes  [octopus, d...           1          4\n",
+      "sweater2     Yes      No    No     No      No     NaN       No             NaN           1          1\n",
+      "sweater3     Yes      No    No     No      No     NaN      Yes        [Houses]           1          1\n",
+      "sweater5     Yes      No    No     No      No     NaN      Yes         [T-rex]           1          1\n",
+      "sweater8     Yes      No   Yes    Yes     Yes     NaN       No             NaN           1          1\n",
+      "...          ...     ...   ...    ...     ...     ...      ...             ...         ...        ...\n",
+      "sweater100   Yes     Yes    No     No     Yes     NaN      Yes       [Menorah]           1          1\n",
+      "sweater103   Yes      No   Yes     No     Yes     NaN      Yes         [Sloth]           1          1\n",
+      "sweater104   Yes     Yes    No     No     Yes     NaN      Yes  [R2D2, wear...           1          5\n",
+      "sweater105   Yes      No    No     No      No     NaN       No             NaN           1          1\n",
+      "sweater107   Yes      No    No     No     Yes     NaN      Yes  [a, llama, ...           1          5\n",
       "\n",
       "[68 rows x 10 columns]\n"
      ]
@@ -175,7 +289,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -189,7 +303,20 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "My cool stuff",
+   "title_sidebar": "My cool stuff",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,

From 925e945084ba16729c15d860da8ac7648ff3aeba Mon Sep 17 00:00:00 2001
From: L4R5m <2925603+L4R5m@users.noreply.github.com>
Date: Mon, 15 Nov 2021 23:52:01 -0800
Subject: [PATCH 2/7] Stop mutating existing cols, and fix counting of colors /
 num_words

Mutating cols based on their own value is bad practice as the cell is not
repeatable... it is better to create a new col w/ the results.
---
 key.ipynb | 149 ++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 122 insertions(+), 27 deletions(-)

diff --git a/key.ipynb b/key.ipynb
index 3f02a59..78c8561 100644
--- a/key.ipynb
+++ b/key.ipynb
@@ -153,6 +153,13 @@
     "# Wrangle Data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calc number of 'colors'"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -195,60 +202,148 @@
       "sweater104    [Red,  Whit...\n",
       "sweater105    [Red,  Gree...\n",
       "sweater107    [Red,  Gree...\n",
-      "Name: colors, Length: 68, dtype: object\n"
+      "Name: colors_ls, Length: 68, dtype: object\n"
      ]
     }
    ],
    "source": [
     "# Convert 'colors' column from single comma-delim str to list\n",
     "# of string\n",
-    "data.colors = data.colors.str.split(',')\n",
-    "print(data.colors)"
+    "data['colors_ls'] = data.colors.str.split(',')\n",
+    "print(data.colors_ls)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           hs_tf sparkly noise lights objects  colors image_tf      image_desc  num_colors  num_words\n",
-      "sweater                                                                                              \n",
-      "sweater1     Yes     Yes    No     No      No     NaN      Yes  [octopus, d...           1          4\n",
-      "sweater2     Yes      No    No     No      No     NaN       No             NaN           1          1\n",
-      "sweater3     Yes      No    No     No      No     NaN      Yes        [Houses]           1          1\n",
-      "sweater5     Yes      No    No     No      No     NaN      Yes         [T-rex]           1          1\n",
-      "sweater8     Yes      No   Yes    Yes     Yes     NaN       No             NaN           1          1\n",
-      "...          ...     ...   ...    ...     ...     ...      ...             ...         ...        ...\n",
-      "sweater100   Yes     Yes    No     No     Yes     NaN      Yes       [Menorah]           1          1\n",
-      "sweater103   Yes      No   Yes     No     Yes     NaN      Yes         [Sloth]           1          1\n",
-      "sweater104   Yes     Yes    No     No     Yes     NaN      Yes  [R2D2, wear...           1          5\n",
-      "sweater105   Yes      No    No     No      No     NaN       No             NaN           1          1\n",
-      "sweater107   Yes      No    No     No     Yes     NaN      Yes  [a, llama, ...           1          5\n",
+      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc       colors_ls  num_colors\n",
+      "sweater                                                                                                           \n",
+      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...  [Red,  Yell...           5\n",
+      "sweater2     Yes      No    No     No      No           Green       No             NaN         [Green]           1\n",
+      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses  [Red,  Yell...           6\n",
+      "...          ...     ...   ...    ...     ...             ...      ...             ...             ...         ...\n",
+      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...  [Red,  Whit...           3\n",
+      "sweater105   Yes      No    No     No      No  Red, Green,...       No             NaN  [Red,  Gree...           4\n",
+      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...  [Red,  Gree...           4\n",
       "\n",
       "[68 rows x 10 columns]\n"
      ]
     }
    ],
    "source": [
-    "# Step 2 Wrangle Data\n",
-    "data['colors'] = data['colors'].str.split(',')\n",
-    "color_data = data.explode('colors').groupby('sweater').count()\n",
-    "data['num_colors'] = color_data['hs_tf']\n",
-    "\n",
-    "data['image_desc'] = data['image_desc'].str.split(' ')\n",
-    "description_data = data.explode('image_desc').groupby('sweater').count()\n",
-    "\n",
-    "data['num_words'] = description_data['hs_tf']\n",
+    "# Calculate how many colors are present & assign to new column\n",
+    "data['num_colors'] = data.colors_ls.apply(lambda x: len(x))\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calc len of 'image_desc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc       colors_ls  num_colors\n",
+      "sweater                                                                                                           \n",
+      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...  [Red,  Yell...           5\n",
+      "sweater2     Yes      No    No     No      No           Green       No                         [Green]           1\n",
+      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses  [Red,  Yell...           6\n",
+      "...          ...     ...   ...    ...     ...             ...      ...             ...             ...         ...\n",
+      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...  [Red,  Whit...           3\n",
+      "sweater105   Yes      No    No     No      No  Red, Green,...       No                  [Red,  Gree...           4\n",
+      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...  [Red,  Gree...           4\n",
+      "\n",
+      "[68 rows x 10 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First replace NaN with empty str\n",
+    "data.image_desc.fillna('', inplace=True)\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sweater\n",
+      "sweater1      [octopus, d...\n",
+      "sweater2                  []\n",
+      "sweater3            [Houses]\n",
+      "                   ...      \n",
+      "sweater104    [R2D2, wear...\n",
+      "sweater105                []\n",
+      "sweater107    [a, llama, ...\n",
+      "Name: image_desc_ls, Length: 68, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert 'image_desc' column from single space-delim str to list\n",
+    "data['image_desc_ls'] = data.image_desc.str.split()\n",
+    "print(data.image_desc_ls)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           hs_tf sparkly noise lights objects  ...      image_desc       colors_ls num_colors   image_desc_ls  num_words\n",
+      "sweater                                        ...                                                                      \n",
+      "sweater1     Yes     Yes    No     No      No  ...  octopus dre...  [Red,  Yell...          5  [octopus, d...          4\n",
+      "sweater2     Yes      No    No     No      No  ...                         [Green]          1              []          0\n",
+      "sweater3     Yes      No    No     No      No  ...          Houses  [Red,  Yell...          6        [Houses]          1\n",
+      "...          ...     ...   ...    ...     ...  ...             ...             ...        ...             ...        ...\n",
+      "sweater104   Yes     Yes    No     No     Yes  ...  R2D2 wearin...  [Red,  Whit...          3  [R2D2, wear...          5\n",
+      "sweater105   Yes      No    No     No      No  ...                  [Red,  Gree...          4              []          0\n",
+      "sweater107   Yes      No    No     No     Yes  ...  a llama wea...  [Red,  Gree...          4  [a, llama, ...          5\n",
+      "\n",
+      "[68 rows x 12 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate how many image_desc words are present & assign to new column\n",
+    "data['num_words'] = data.image_desc_ls.apply(lambda x: len(x))\n",
     "print(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {

From 8507b0fb9582aeb0eea8822cfb4a1e9698fa0042 Mon Sep 17 00:00:00 2001
From: L4R5m <2925603+L4R5m@users.noreply.github.com>
Date: Tue, 16 Nov 2021 22:01:35 -0800
Subject: [PATCH 3/7] Update data plots, adding matplotlib API example to match
 R ggplot example

---
 key.ipynb | 248 ++++++++++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 233 insertions(+), 15 deletions(-)

diff --git a/key.ipynb b/key.ipynb
index 78c8561..4b3a633 100644
--- a/key.ipynb
+++ b/key.ipynb
@@ -9,13 +9,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n"
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "import numpy as np"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -43,7 +55,19 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Customize matplotlib\n",
+    "plt.style.use('ggplot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rcParams['figure.figsize'] = (12, 8)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -280,7 +304,7 @@
     }
    ],
    "source": [
-    "# First replace NaN with empty str\n",
+    "# Replace NaN with empty str\n",
     "data.image_desc.fillna('', inplace=True)\n",
     "print(data)"
    ]
@@ -343,37 +367,231 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change default plot size\n",
+    "plt.rcParams['figure.figsize'] = (12, 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas' plotter tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "<AxesSubplot:xlabel='num_colors', ylabel='num_words'>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
-     "execution_count": 50,
      "metadata": {},
-     "output_type": "execute_result"
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Pandas built-in plot tools (which use Matplotlib under the hood)\n",
+    "# This is convenient, but doesn't give as much control as using the Matplotlib API\n",
+    "data.plot.scatter('num_colors', 'num_words');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matplotlib API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "['Solarize_Light2',\n",
+       " '_classic_test_patch',\n",
+       " 'bmh',\n",
+       " 'classic',\n",
+       " 'dark_background',\n",
+       " 'fast',\n",
+       " 'fivethirtyeight',\n",
+       " 'ggplot',\n",
+       " 'grayscale',\n",
+       " 'seaborn',\n",
+       " 'seaborn-bright',\n",
+       " 'seaborn-colorblind',\n",
+       " 'seaborn-dark',\n",
+       " 'seaborn-dark-palette',\n",
+       " 'seaborn-darkgrid',\n",
+       " 'seaborn-deep',\n",
+       " 'seaborn-muted',\n",
+       " 'seaborn-notebook',\n",
+       " 'seaborn-paper',\n",
+       " 'seaborn-pastel',\n",
+       " 'seaborn-poster',\n",
+       " 'seaborn-talk',\n",
+       " 'seaborn-ticks',\n",
+       " 'seaborn-white',\n",
+       " 'seaborn-whitegrid',\n",
+       " 'tableau-colorblind10']"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# There are many pre-defined styles... view the available options\n",
+    "mpl.style.available\n",
+    "# or use the default style\n",
+    "mpl.style.use('default')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Matplotlib scatter plot doesn't have built-in jitter option...\n",
+    "# but it's not too hard\n",
+    "\n",
+    "def jitterify(arr, factor=0.01):\n",
+    "    \"\"\"Add jitter 'factor' to 'arr' data\n",
+    "    :param arr: array-like, eg: list, ndarray\n",
+    "    :param factor: float, 0.0 -> 1.0\n",
+    "    :return: arr with added jitter\n",
+    "    \"\"\"\n",
+    "    assert 0.0 < factor < 1.0, f\"Error, invalid factor {factor}\"\n",
+    "    arr = np.array(arr)\n",
+    "    assert arr.ndim == 1, f\"Expected 1-d array, got {arr.ndim}\"\n",
+    "    ptp = arr.ptp()\n",
+    "    jitter = np.random.randn(arr.size) * factor * ptp\n",
+    "    return arr + jitter\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "# Step 3 Visualize\n",
-    "data.plot.scatter('num_colors', 'num_words')"
+    "# Scatterplot docs:\n",
+    "# https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\n",
+    "\n",
+    "fontsize = 12\n",
+    "markersize = 75\n",
+    "color = '#424242'\n",
+    "alpha = 0.4\n",
+    "\n",
+    "# Instantiate plot objects\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "# Add labels to axes\n",
+    "ax.set_xlabel('Number of colors on sweater', fontsize=fontsize, color='k')\n",
+    "ax.set_ylabel('Number of words\\nin sweater description', fontsize=fontsize, color='k')\n",
+    "# Add figure title\n",
+    "fig.suptitle(\"Relationship between the number of colors and\\nlength of description for ugly holiday sweaters\",\n",
+    "            color='k', fontsize=fontsize + 2)\n",
+    "\n",
+    "# Add jitter to data so completely overlapping\n",
+    "x = jitterify(data.num_colors)\n",
+    "y = jitterify(data.num_words)\n",
+    "# Plot\n",
+    "ax.scatter(x, y,\n",
+    "            c=color,\n",
+    "            s=markersize,\n",
+    "            alpha=alpha,\n",
+    "            edgecolors=color,\n",
+    "            linewidths=1.\n",
+    "           )\n",
+    "\n",
+    "# Add polyfit curve\n",
+    "coeffs = np.polyfit(data.num_colors, data.num_words, 1)\n",
+    "xlim = ax.get_xlim()\n",
+    "ax.plot(xlim, np.polyval(coeffs, xlim), color='k', alpha=0.9)\n",
+    "\n",
+    "# Set tick increment\n",
+    "incr_x = 2\n",
+    "incr_y = 5\n",
+    "ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(incr_x))\n",
+    "ax.yaxis.set_major_locator(mpl.ticker.MultipleLocator(incr_y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Seaborn library\n",
+    "This library is designed with data-science and clean asthetics in mind... check it out!<br>\n",
+    "https://seaborn.pydata.org/"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 16950a74dfed1232e74aa353bedb934813c25f3b Mon Sep 17 00:00:00 2001
From: L4R5m <2925603+L4R5m@users.noreply.github.com>
Date: Tue, 16 Nov 2021 22:09:30 -0800
Subject: [PATCH 4/7] Display notebook Table of Contents

---
 key.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/key.ipynb b/key.ipynb
index 4b3a633..14412a0 100644
--- a/key.ipynb
+++ b/key.ipynb
@@ -629,7 +629,7 @@
    "toc_cell": false,
    "toc_position": {},
    "toc_section_display": true,
-   "toc_window_display": false
+   "toc_window_display": true
   }
  },
  "nbformat": 4,

From b2378124f1d674a805075d304cd687376272b309 Mon Sep 17 00:00:00 2001
From: L4R5m <2925603+L4R5m@users.noreply.github.com>
Date: Fri, 19 Nov 2021 10:35:42 -0800
Subject: [PATCH 5/7] Add 'tidy' data analysis approach to key.ipynb

My prior method was a shortcut that works for this problem statement, but
isn't a 'data wrangling' best practice (make you dataframes tidy, eg: 1
measurement per row). Now we show both approaches.
---
 key.ipynb | 2207 +++++++++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 2061 insertions(+), 146 deletions(-)

diff --git a/key.ipynb b/key.ipynb
index 14412a0..949706d 100644
--- a/key.ipynb
+++ b/key.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
@@ -47,27 +47,27 @@
     "pd.options.display.max_colwidth = 15\n",
     "pd.options.display.width = 150\n",
     "# Rows\n",
-    "pd.options.display.max_rows = pd.options.display.min_rows = 6\n"
+    "pd.options.display.max_rows = pd.options.display.min_rows = 12\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Customize matplotlib\n",
-    "plt.style.use('ggplot')"
+    "# plt.style.use('ggplot')\n",
+    "plt.style.use('default')\n",
+    "plt.rcParams['figure.figsize'] = (12, 8)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "plt.rcParams['figure.figsize'] = (12, 8)"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -113,7 +113,13 @@
       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...\n",
       "sweater2     Yes      No    No     No      No           Green       No             NaN\n",
       "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses\n",
+      "sweater4      No      No    No     No      No  the limit d...       No             NaN\n",
+      "sweater5     Yes      No    No     No      No  Blue, White...      Yes           T-rex\n",
+      "sweater6      No     NaN   NaN    NaN     NaN             NaN      NaN             NaN\n",
       "...          ...     ...   ...    ...     ...             ...      ...             ...\n",
+      "sweater102    No     NaN   NaN    NaN     NaN             NaN      NaN             NaN\n",
+      "sweater103   Yes      No   Yes     No     Yes  Red, Yellow...      Yes           Sloth\n",
+      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...\n",
       "sweater105   Yes      No    No     No      No  Red, Green,...       No             NaN\n",
       "sweater106    No      No    No     No      No             NaN       No             NaN\n",
       "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
@@ -131,16 +137,9 @@
     "print(df)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Filter Data"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +151,13 @@
       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...\n",
       "sweater2     Yes      No    No     No      No           Green       No             NaN\n",
       "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses\n",
+      "sweater5     Yes      No    No     No      No  Blue, White...      Yes           T-rex\n",
+      "sweater8     Yes      No   Yes    Yes     Yes  Red, Green,...       No             NaN\n",
+      "sweater11    Yes      No    No     No      No  Red, Yellow...      Yes  Santa Claus...\n",
       "...          ...     ...   ...    ...     ...             ...      ...             ...\n",
+      "sweater99    Yes     Yes    No     No      No  Red, Blue, ...      Yes        Reindeer\n",
+      "sweater100   Yes     Yes    No     No     Yes  Orange, Yel...      Yes         Menorah\n",
+      "sweater103   Yes      No   Yes     No     Yes  Red, Yellow...      Yes           Sloth\n",
       "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...\n",
       "sweater105   Yes      No    No     No      No  Red, Green,...       No             NaN\n",
       "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
@@ -163,107 +168,110 @@
    ],
    "source": [
     "# Filter to only include Holiday Sweaters\n",
-    "data = df.loc[df.hs_tf == 'Yes']\n",
-    "# Make a copy (instead of a view) since we'll be changing\n",
+    "data_orig = df.loc[df.hs_tf == 'Yes']\n",
+    "# Make a copy (instead of working on the view) since we'll be changing\n",
     "# col dtypes later and don't want to get SettingWithCopyWarning\n",
-    "data = data.copy()\n",
+    "data = data_orig.copy()\n",
     "print(data)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
-   "source": [
-    "# Wrangle Data"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Calc number of 'colors'"
+    "## Inspect the data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sweater\n",
-      "sweater1      Red, Yellow...\n",
-      "sweater2               Green\n",
-      "sweater3      Red, Yellow...\n",
-      "                   ...      \n",
-      "sweater104    Red, White,...\n",
-      "sweater105    Red, Green,...\n",
-      "sweater107    Red, Green,...\n",
-      "Name: colors, Length: 68, dtype: object\n"
+      "Index(['hs_tf', 'sparkly', 'noise', 'lights', 'objects', 'colors', 'image_tf', 'image_desc'], dtype='object')\n",
+      "\n",
+      "Data shape: (68, 8)\n",
+      "\n",
+      "Column dtypes:\n",
+      "hs_tf         object\n",
+      "sparkly       object\n",
+      "noise         object\n",
+      "lights        object\n",
+      "objects       object\n",
+      "colors        object\n",
+      "image_tf      object\n",
+      "image_desc    object\n",
+      "dtype: object\n"
      ]
     }
    ],
    "source": [
-    "print(data.colors)"
+    "# List the columns\n",
+    "print(data.columns)\n",
+    "# Show df shape\n",
+    "print(f\"\\nData shape: {data.shape}\")\n",
+    "# Show column datatypes (Numpy 'dtype')\n",
+    "print(\"\\nColumn dtypes:\")\n",
+    "print(data.dtypes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sweater\n",
-      "sweater1      [Red,  Yell...\n",
-      "sweater2             [Green]\n",
-      "sweater3      [Red,  Yell...\n",
-      "                   ...      \n",
-      "sweater104    [Red,  Whit...\n",
-      "sweater105    [Red,  Gree...\n",
-      "sweater107    [Red,  Gree...\n",
-      "Name: colors_ls, Length: 68, dtype: object\n"
+      "object\n",
+      "sweater1\t <class 'str'>\tRed, Yellow, Blue, White, teal\n",
+      "sweater2\t <class 'str'>\tGreen\n",
+      "sweater3\t <class 'str'>\tRed, Yellow, Green, Brown, White, Black\n",
+      "sweater5\t <class 'str'>\tBlue, White, Black\n",
+      "sweater8\t <class 'str'>\tRed, Green, Blue, Purple, White, Grey\n"
      ]
     }
    ],
    "source": [
-    "# Convert 'colors' column from single comma-delim str to list\n",
-    "# of string\n",
-    "data['colors_ls'] = data.colors.str.split(',')\n",
-    "print(data.colors_ls)"
+    "# Inspect the 'colors' data column\n",
+    "print(data.colors.dtype)\n",
+    "for idx, val in data.colors.head().items():\n",
+    "    print(f\"{idx}\\t {type(val)}\\t{val}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc       colors_ls  num_colors\n",
-      "sweater                                                                                                           \n",
-      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...  [Red,  Yell...           5\n",
-      "sweater2     Yes      No    No     No      No           Green       No             NaN         [Green]           1\n",
-      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses  [Red,  Yell...           6\n",
-      "...          ...     ...   ...    ...     ...             ...      ...             ...             ...         ...\n",
-      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...  [Red,  Whit...           3\n",
-      "sweater105   Yes      No    No     No      No  Red, Green,...       No             NaN  [Red,  Gree...           4\n",
-      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...  [Red,  Gree...           4\n",
-      "\n",
-      "[68 rows x 10 columns]\n"
+      "sweater1\t <class 'str'>\toctopus dressed like santa\n",
+      "sweater2\t <class 'float'>\tnan\n",
+      "sweater3\t <class 'str'>\tHouses\n",
+      "sweater5\t <class 'str'>\tT-rex\n",
+      "sweater8\t <class 'float'>\tnan\n"
      ]
     }
    ],
    "source": [
-    "# Calculate how many colors are present & assign to new column\n",
-    "data['num_colors'] = data.colors_ls.apply(lambda x: len(x))\n",
-    "print(data)"
+    "# Inspect the 'image_desc' data column\n",
+    "# Note how this heterogeneous data... str and float (NaN)\n",
+    "for idx, val in data.image_desc.head().items():\n",
+    "    print(f\"{idx}\\t {type(val)}\\t{val}\")"
    ]
   },
   {
@@ -277,33 +285,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Calc len of 'image_desc'"
+    "# Wrangle Data -- count # of colors & words\n",
+    "This is the simplest solution, but doesn't make dataframe 'tidy'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc       colors_ls  num_colors\n",
-      "sweater                                                                                                           \n",
-      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...  [Red,  Yell...           5\n",
-      "sweater2     Yes      No    No     No      No           Green       No                         [Green]           1\n",
-      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses  [Red,  Yell...           6\n",
-      "...          ...     ...   ...    ...     ...             ...      ...             ...             ...         ...\n",
-      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...  [Red,  Whit...           3\n",
-      "sweater105   Yes      No    No     No      No  Red, Green,...       No                  [Red,  Gree...           4\n",
-      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...  [Red,  Gree...           4\n",
+      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc\n",
+      "sweater                                                                               \n",
+      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...\n",
+      "sweater2     Yes      No    No     No      No           Green       No                \n",
+      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses\n",
+      "sweater5     Yes      No    No     No      No  Blue, White...      Yes           T-rex\n",
+      "sweater8     Yes      No   Yes    Yes     Yes  Red, Green,...       No                \n",
+      "sweater11    Yes      No    No     No      No  Red, Yellow...      Yes  Santa Claus...\n",
+      "...          ...     ...   ...    ...     ...             ...      ...             ...\n",
+      "sweater99    Yes     Yes    No     No      No  Red, Blue, ...      Yes        Reindeer\n",
+      "sweater100   Yes     Yes    No     No     Yes  Orange, Yel...      Yes         Menorah\n",
+      "sweater103   Yes      No   Yes     No     Yes  Red, Yellow...      Yes           Sloth\n",
+      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...\n",
+      "sweater105   Yes      No    No     No      No  Red, Green,...       No                \n",
+      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
       "\n",
-      "[68 rows x 10 columns]\n"
+      "[68 rows x 8 columns]\n"
      ]
     }
    ],
    "source": [
+    "# Refresh dataframe from original data\n",
+    "data = data_orig.copy()\n",
     "# Replace NaN with empty str\n",
     "data.image_desc.fillna('', inplace=True)\n",
     "print(data)"
@@ -311,7 +328,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert str to list of str"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sweater\n",
+      "sweater1      [Red,  Yell...\n",
+      "sweater2             [Green]\n",
+      "sweater3      [Red,  Yell...\n",
+      "sweater5      [Blue,  Whi...\n",
+      "sweater8      [Red,  Gree...\n",
+      "sweater11     [Red,  Yell...\n",
+      "                   ...      \n",
+      "sweater99     [Red,  Blue...\n",
+      "sweater100    [Orange,  Y...\n",
+      "sweater103    [Red,  Yell...\n",
+      "sweater104    [Red,  Whit...\n",
+      "sweater105    [Red,  Gree...\n",
+      "sweater107    [Red,  Gree...\n",
+      "Name: colors_ls, Length: 68, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert 'colors' column from single comma-delim str to list\n",
+    "# of string\n",
+    "data['colors_ls'] = data.colors.str.split(',')\n",
+    "print(data.colors_ls)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -322,7 +387,13 @@
       "sweater1      [octopus, d...\n",
       "sweater2                  []\n",
       "sweater3            [Houses]\n",
+      "sweater5             [T-rex]\n",
+      "sweater8                  []\n",
+      "sweater11     [Santa, Cla...\n",
       "                   ...      \n",
+      "sweater99         [Reindeer]\n",
+      "sweater100         [Menorah]\n",
+      "sweater103           [Sloth]\n",
       "sweater104    [R2D2, wear...\n",
       "sweater105                []\n",
       "sweater107    [a, llama, ...\n",
@@ -338,31 +409,1903 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calc number of 'colors'\n",
+    "This is the simplest solution as we simply calc # of colors, but doesn't make dataframe 'tidy'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "           hs_tf sparkly noise lights objects  ...      image_desc       colors_ls num_colors   image_desc_ls  num_words\n",
-      "sweater                                        ...                                                                      \n",
-      "sweater1     Yes     Yes    No     No      No  ...  octopus dre...  [Red,  Yell...          5  [octopus, d...          4\n",
-      "sweater2     Yes      No    No     No      No  ...                         [Green]          1              []          0\n",
-      "sweater3     Yes      No    No     No      No  ...          Houses  [Red,  Yell...          6        [Houses]          1\n",
-      "...          ...     ...   ...    ...     ...  ...             ...             ...        ...             ...        ...\n",
-      "sweater104   Yes     Yes    No     No     Yes  ...  R2D2 wearin...  [Red,  Whit...          3  [R2D2, wear...          5\n",
-      "sweater105   Yes      No    No     No      No  ...                  [Red,  Gree...          4              []          0\n",
-      "sweater107   Yes      No    No     No     Yes  ...  a llama wea...  [Red,  Gree...          4  [a, llama, ...          5\n",
-      "\n",
-      "[68 rows x 12 columns]\n"
+      "sweater\n",
+      "sweater1      5\n",
+      "sweater2      1\n",
+      "sweater3      6\n",
+      "sweater5      3\n",
+      "sweater8      6\n",
+      "sweater11     8\n",
+      "             ..\n",
+      "sweater99     4\n",
+      "sweater100    3\n",
+      "sweater103    4\n",
+      "sweater104    3\n",
+      "sweater105    4\n",
+      "sweater107    4\n",
+      "Name: num_colors, Length: 68, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate how many colors are present & assign to new column\n",
+    "data['num_colors'] = data.colors_ls.apply(lambda x: len(x))\n",
+    "print(data.num_colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calc num word in 'image_desc'\n",
+    "This is the simplest solution as we simply calc # of description words, but doesn't make dataframe 'tidy'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sweater\n",
+      "sweater1       4\n",
+      "sweater2       0\n",
+      "sweater3       1\n",
+      "sweater5       1\n",
+      "sweater8       0\n",
+      "sweater11     10\n",
+      "              ..\n",
+      "sweater99      1\n",
+      "sweater100     1\n",
+      "sweater103     1\n",
+      "sweater104     5\n",
+      "sweater105     0\n",
+      "sweater107     5\n",
+      "Name: num_words, Length: 68, dtype: int64\n"
      ]
     }
    ],
    "source": [
     "# Calculate how many image_desc words are present & assign to new column\n",
     "data['num_words'] = data.image_desc_ls.apply(lambda x: len(x))\n",
-    "print(data)"
+    "print(data.num_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hs_tf</th>\n",
+       "      <th>sparkly</th>\n",
+       "      <th>noise</th>\n",
+       "      <th>lights</th>\n",
+       "      <th>objects</th>\n",
+       "      <th>...</th>\n",
+       "      <th>image_desc</th>\n",
+       "      <th>colors_ls</th>\n",
+       "      <th>image_desc_ls</th>\n",
+       "      <th>num_colors</th>\n",
+       "      <th>num_words</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>[octopus, d...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td>[Green]</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Houses</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>[Houses]</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater5</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T-rex</td>\n",
+       "      <td>[Blue,  Whi...</td>\n",
+       "      <td>[T-rex]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater8</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater11</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Santa Claus...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>[Santa, Cla...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater99</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Reindeer</td>\n",
+       "      <td>[Red,  Blue...</td>\n",
+       "      <td>[Reindeer]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater100</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Menorah</td>\n",
+       "      <td>[Orange,  Y...</td>\n",
+       "      <td>[Menorah]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater103</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Sloth</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>[Sloth]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater104</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>R2D2 wearin...</td>\n",
+       "      <td>[Red,  Whit...</td>\n",
+       "      <td>[R2D2, wear...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>[a, llama, ...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>68 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hs_tf sparkly noise lights objects  ...      image_desc       colors_ls   image_desc_ls num_colors num_words\n",
+       "sweater                                        ...                                                                     \n",
+       "sweater1     Yes     Yes    No     No      No  ...  octopus dre...  [Red,  Yell...  [octopus, d...          5         4\n",
+       "sweater2     Yes      No    No     No      No  ...                         [Green]              []          1         0\n",
+       "sweater3     Yes      No    No     No      No  ...          Houses  [Red,  Yell...        [Houses]          6         1\n",
+       "sweater5     Yes      No    No     No      No  ...           T-rex  [Blue,  Whi...         [T-rex]          3         1\n",
+       "sweater8     Yes      No   Yes    Yes     Yes  ...                  [Red,  Gree...              []          6         0\n",
+       "sweater11    Yes      No    No     No      No  ...  Santa Claus...  [Red,  Yell...  [Santa, Cla...          8        10\n",
+       "...          ...     ...   ...    ...     ...  ...             ...             ...             ...        ...       ...\n",
+       "sweater99    Yes     Yes    No     No      No  ...        Reindeer  [Red,  Blue...      [Reindeer]          4         1\n",
+       "sweater100   Yes     Yes    No     No     Yes  ...         Menorah  [Orange,  Y...       [Menorah]          3         1\n",
+       "sweater103   Yes      No   Yes     No     Yes  ...           Sloth  [Red,  Yell...         [Sloth]          4         1\n",
+       "sweater104   Yes     Yes    No     No     Yes  ...  R2D2 wearin...  [Red,  Whit...  [R2D2, wear...          3         5\n",
+       "sweater105   Yes      No    No     No      No  ...                  [Red,  Gree...              []          4         0\n",
+       "sweater107   Yes      No    No     No     Yes  ...  a llama wea...  [Red,  Gree...  [a, llama, ...          4         5\n",
+       "\n",
+       "[68 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Show whole dataframe\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>num_colors</th>\n",
+       "      <th>num_words</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater8</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater11</th>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater99</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater100</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater103</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater104</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>68 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            num_colors  num_words\n",
+       "sweater                          \n",
+       "sweater1             5          4\n",
+       "sweater2             1          0\n",
+       "sweater3             6          1\n",
+       "sweater5             3          1\n",
+       "sweater8             6          0\n",
+       "sweater11            8         10\n",
+       "...                ...        ...\n",
+       "sweater99            4          1\n",
+       "sweater100           3          1\n",
+       "sweater103           4          1\n",
+       "sweater104           3          5\n",
+       "sweater105           4          0\n",
+       "sweater107           4          5\n",
+       "\n",
+       "[68 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspect calculated columns only\n",
+    "data[['num_colors', 'num_words']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Wrangle Data -- convert to long-form\n",
+    "Make data tidy and then analyze in a more general way (using groupby + aggregations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hs_tf</th>\n",
+       "      <th>sparkly</th>\n",
+       "      <th>noise</th>\n",
+       "      <th>lights</th>\n",
+       "      <th>objects</th>\n",
+       "      <th>colors</th>\n",
+       "      <th>image_tf</th>\n",
+       "      <th>image_desc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Red, Yellow...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Green</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Red, Yellow...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Houses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater5</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Blue, White...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>T-rex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater8</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Red, Green,...</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater11</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Red, Yellow...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Santa Claus...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater99</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Red, Blue, ...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Reindeer</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater100</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Orange, Yel...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Menorah</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater103</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Red, Yellow...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sloth</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater104</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Red, White,...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>R2D2 wearin...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Red, Green,...</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Red, Green,...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>68 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hs_tf sparkly noise lights objects          colors image_tf      image_desc\n",
+       "sweater                                                                               \n",
+       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...\n",
+       "sweater2     Yes      No    No     No      No           Green       No                \n",
+       "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses\n",
+       "sweater5     Yes      No    No     No      No  Blue, White...      Yes           T-rex\n",
+       "sweater8     Yes      No   Yes    Yes     Yes  Red, Green,...       No                \n",
+       "sweater11    Yes      No    No     No      No  Red, Yellow...      Yes  Santa Claus...\n",
+       "...          ...     ...   ...    ...     ...             ...      ...             ...\n",
+       "sweater99    Yes     Yes    No     No      No  Red, Blue, ...      Yes        Reindeer\n",
+       "sweater100   Yes     Yes    No     No     Yes  Orange, Yel...      Yes         Menorah\n",
+       "sweater103   Yes      No   Yes     No     Yes  Red, Yellow...      Yes           Sloth\n",
+       "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...\n",
+       "sweater105   Yes      No    No     No      No  Red, Green,...       No                \n",
+       "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
+       "\n",
+       "[68 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Refresh dataframe from original data\n",
+    "data = data_orig.copy()\n",
+    "\n",
+    "# Replace NaN with empty str\n",
+    "data.image_desc.fillna('', inplace=True)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert str to list of str"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sweater\n",
+       "sweater1      [Red,  Yell...\n",
+       "sweater2             [Green]\n",
+       "sweater3      [Red,  Yell...\n",
+       "sweater5      [Blue,  Whi...\n",
+       "sweater8      [Red,  Gree...\n",
+       "sweater11     [Red,  Yell...\n",
+       "                   ...      \n",
+       "sweater99     [Red,  Blue...\n",
+       "sweater100    [Orange,  Y...\n",
+       "sweater103    [Red,  Yell...\n",
+       "sweater104    [Red,  Whit...\n",
+       "sweater105    [Red,  Gree...\n",
+       "sweater107    [Red,  Gree...\n",
+       "Name: colors_ls, Length: 68, dtype: object"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert 'colors' column from single comma-delim str to list\n",
+    "# of string\n",
+    "data['colors_ls'] = data.colors.str.split(',')\n",
+    "data.colors_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sweater\n",
+       "sweater1      [octopus, d...\n",
+       "sweater2                  []\n",
+       "sweater3            [Houses]\n",
+       "sweater5             [T-rex]\n",
+       "sweater8                  []\n",
+       "sweater11     [Santa, Cla...\n",
+       "                   ...      \n",
+       "sweater99         [Reindeer]\n",
+       "sweater100         [Menorah]\n",
+       "sweater103           [Sloth]\n",
+       "sweater104    [R2D2, wear...\n",
+       "sweater105                []\n",
+       "sweater107    [a, llama, ...\n",
+       "Name: image_desc_ls, Length: 68, dtype: object"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert 'image_desc' column from single space-delim str to list\n",
+    "data['image_desc_ls'] = data.image_desc.str.split()\n",
+    "data.image_desc_ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tidy & analyze 'colors'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hs_tf</th>\n",
+       "      <th>sparkly</th>\n",
+       "      <th>noise</th>\n",
+       "      <th>lights</th>\n",
+       "      <th>objects</th>\n",
+       "      <th>...</th>\n",
+       "      <th>image_tf</th>\n",
+       "      <th>image_desc</th>\n",
+       "      <th>colors_ls</th>\n",
+       "      <th>num_colors</th>\n",
+       "      <th>image_desc_ls</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>Red</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[octopus, d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>Yellow</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[octopus, d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>Blue</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[octopus, d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>White</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[octopus, d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>teal</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[octopus, d...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "      <td>Green</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "      <td>Blue</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "      <td>Grey</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>Red</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[a, llama, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>Green</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[a, llama, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>White</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[a, llama, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>Black</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[a, llama, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>273 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hs_tf sparkly noise lights objects  ... image_tf      image_desc colors_ls num_colors   image_desc_ls\n",
+       "sweater                                        ...                                                              \n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...       Red          5  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...    Yellow          5  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...      Blue          5  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...     White          5  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...      teal          5  [octopus, d...\n",
+       "sweater2     Yes      No    No     No      No  ...       No                     Green          1              []\n",
+       "...          ...     ...   ...    ...     ...  ...      ...             ...       ...        ...             ...\n",
+       "sweater105   Yes      No    No     No      No  ...       No                      Blue          4              []\n",
+       "sweater105   Yes      No    No     No      No  ...       No                      Grey          4              []\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...       Red          4  [a, llama, ...\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...     Green          4  [a, llama, ...\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...     White          4  [a, llama, ...\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...     Black          4  [a, llama, ...\n",
+       "\n",
+       "[273 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Tidy colors data (1 color per row)\n",
+    "df_colors = data.explode('colors_ls')\n",
+    "df_colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sweater\n",
+       "sweater1      5\n",
+       "sweater2      1\n",
+       "sweater3      6\n",
+       "sweater5      3\n",
+       "sweater8      6\n",
+       "sweater11     8\n",
+       "             ..\n",
+       "sweater99     4\n",
+       "sweater100    3\n",
+       "sweater103    4\n",
+       "sweater104    3\n",
+       "sweater105    4\n",
+       "sweater107    4\n",
+       "Name: num_colors, Length: 68, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calc how many colors for each sweater & add to dataframe\n",
+    "data['num_colors'] = df_colors.groupby('sweater', sort=False).colors_ls.count()\n",
+    "data.num_colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tidy & analyze 'image_desc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hs_tf</th>\n",
+       "      <th>sparkly</th>\n",
+       "      <th>noise</th>\n",
+       "      <th>lights</th>\n",
+       "      <th>objects</th>\n",
+       "      <th>...</th>\n",
+       "      <th>image_tf</th>\n",
+       "      <th>image_desc</th>\n",
+       "      <th>colors_ls</th>\n",
+       "      <th>num_colors</th>\n",
+       "      <th>image_desc_ls</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>octopus</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>dressed</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>like</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>santa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "      <td>[Green]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Houses</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Houses</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>No</td>\n",
+       "      <td></td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>llama</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>wearing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>a</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>scarf</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>278 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hs_tf sparkly noise lights objects  ... image_tf      image_desc       colors_ls num_colors  image_desc_ls\n",
+       "sweater                                        ...                                                                   \n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5        octopus\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5        dressed\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5           like\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5          santa\n",
+       "sweater2     Yes      No    No     No      No  ...       No                         [Green]          1            NaN\n",
+       "sweater3     Yes      No    No     No      No  ...      Yes          Houses  [Red,  Yell...          6         Houses\n",
+       "...          ...     ...   ...    ...     ...  ...      ...             ...             ...        ...            ...\n",
+       "sweater105   Yes      No    No     No      No  ...       No                  [Red,  Gree...          4            NaN\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4              a\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4          llama\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4        wearing\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4              a\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4          scarf\n",
+       "\n",
+       "[278 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Tidy image_desc data (1 word per row)\n",
+    "df_image_desc = data.explode('image_desc_ls')\n",
+    "df_image_desc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df_image_desc' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/f7/34gtpjvn45zgnhj8hb9wn3gr0000gn/T/ipykernel_63963/2145182293.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Calc how many description words for each sweater & add to dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'num_words'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_image_desc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sweater'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_desc_ls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_words\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'df_image_desc' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Calc how many description words for each sweater & add to dataframe\n",
+    "data['num_words'] = df_image_desc.groupby('sweater', sort=False).image_desc_ls.count()\n",
+    "data.num_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>hs_tf</th>\n",
+       "      <th>sparkly</th>\n",
+       "      <th>noise</th>\n",
+       "      <th>lights</th>\n",
+       "      <th>objects</th>\n",
+       "      <th>...</th>\n",
+       "      <th>image_desc</th>\n",
+       "      <th>colors_ls</th>\n",
+       "      <th>num_colors</th>\n",
+       "      <th>image_desc_ls</th>\n",
+       "      <th>num_words</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>octopus dre...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>[octopus, d...</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td>[Green]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Houses</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[Houses]</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater5</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>T-rex</td>\n",
+       "      <td>[Blue,  Whi...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[T-rex]</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater8</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>6</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater11</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Santa Claus...</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>8</td>\n",
+       "      <td>[Santa, Cla...</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater99</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Reindeer</td>\n",
+       "      <td>[Red,  Blue...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[Reindeer]</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater100</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Menorah</td>\n",
+       "      <td>[Orange,  Y...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[Menorah]</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater103</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Sloth</td>\n",
+       "      <td>[Red,  Yell...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[Sloth]</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater104</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>R2D2 wearin...</td>\n",
+       "      <td>[Red,  Whit...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>[R2D2, wear...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[]</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>...</td>\n",
+       "      <td>a llama wea...</td>\n",
+       "      <td>[Red,  Gree...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>[a, llama, ...</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>68 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           hs_tf sparkly noise lights objects  ...      image_desc       colors_ls num_colors   image_desc_ls  num_words\n",
+       "sweater                                        ...                                                                      \n",
+       "sweater1     Yes     Yes    No     No      No  ...  octopus dre...  [Red,  Yell...          5  [octopus, d...          4\n",
+       "sweater2     Yes      No    No     No      No  ...                         [Green]          1              []          0\n",
+       "sweater3     Yes      No    No     No      No  ...          Houses  [Red,  Yell...          6        [Houses]          1\n",
+       "sweater5     Yes      No    No     No      No  ...           T-rex  [Blue,  Whi...          3         [T-rex]          1\n",
+       "sweater8     Yes      No   Yes    Yes     Yes  ...                  [Red,  Gree...          6              []          0\n",
+       "sweater11    Yes      No    No     No      No  ...  Santa Claus...  [Red,  Yell...          8  [Santa, Cla...         10\n",
+       "...          ...     ...   ...    ...     ...  ...             ...             ...        ...             ...        ...\n",
+       "sweater99    Yes     Yes    No     No      No  ...        Reindeer  [Red,  Blue...          4      [Reindeer]          1\n",
+       "sweater100   Yes     Yes    No     No     Yes  ...         Menorah  [Orange,  Y...          3       [Menorah]          1\n",
+       "sweater103   Yes      No   Yes     No     Yes  ...           Sloth  [Red,  Yell...          4         [Sloth]          1\n",
+       "sweater104   Yes     Yes    No     No     Yes  ...  R2D2 wearin...  [Red,  Whit...          3  [R2D2, wear...          5\n",
+       "sweater105   Yes      No    No     No      No  ...                  [Red,  Gree...          4              []          0\n",
+       "sweater107   Yes      No    No     No     Yes  ...  a llama wea...  [Red,  Gree...          4  [a, llama, ...          5\n",
+       "\n",
+       "[68 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Show whole dataframe\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>num_colors</th>\n",
+       "      <th>num_words</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sweater1</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater5</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater8</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater11</th>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater99</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater100</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater103</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater104</th>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater105</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sweater107</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>68 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            num_colors  num_words\n",
+       "sweater                          \n",
+       "sweater1             5          4\n",
+       "sweater2             1          0\n",
+       "sweater3             6          1\n",
+       "sweater5             3          1\n",
+       "sweater8             6          0\n",
+       "sweater11            8         10\n",
+       "...                ...        ...\n",
+       "sweater99            4          1\n",
+       "sweater100           3          1\n",
+       "sweater103           4          1\n",
+       "sweater104           3          5\n",
+       "sweater105           4          0\n",
+       "sweater107           4          5\n",
+       "\n",
+       "[68 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Inspect calculated columns only\n",
+    "data[['num_colors', 'num_words']]"
    ]
   },
   {
@@ -381,7 +2324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,14 +2341,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
+       "<Figure size 1200x800 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -434,55 +2377,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['Solarize_Light2',\n",
-       " '_classic_test_patch',\n",
-       " 'bmh',\n",
-       " 'classic',\n",
-       " 'dark_background',\n",
-       " 'fast',\n",
-       " 'fivethirtyeight',\n",
-       " 'ggplot',\n",
-       " 'grayscale',\n",
-       " 'seaborn',\n",
-       " 'seaborn-bright',\n",
-       " 'seaborn-colorblind',\n",
-       " 'seaborn-dark',\n",
-       " 'seaborn-dark-palette',\n",
-       " 'seaborn-darkgrid',\n",
-       " 'seaborn-deep',\n",
-       " 'seaborn-muted',\n",
-       " 'seaborn-notebook',\n",
-       " 'seaborn-paper',\n",
-       " 'seaborn-pastel',\n",
-       " 'seaborn-poster',\n",
-       " 'seaborn-talk',\n",
-       " 'seaborn-ticks',\n",
-       " 'seaborn-white',\n",
-       " 'seaborn-whitegrid',\n",
-       " 'tableau-colorblind10']"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
+     ]
     }
    ],
    "source": [
     "# There are many pre-defined styles... view the available options\n",
-    "mpl.style.available\n",
+    "print(mpl.style.available)\n",
     "# or use the default style\n",
-    "mpl.style.use('default')"
+    "plt.style.use('default')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +2410,7 @@
     "    :param factor: float, 0.0 -> 1.0\n",
     "    :return: arr with added jitter\n",
     "    \"\"\"\n",
-    "    assert 0.0 < factor < 1.0, f\"Error, invalid factor {factor}\"\n",
+    "    assert 0.0 <= factor <= 1.0, f\"Error, invalid factor {factor}\"\n",
     "    arr = np.array(arr)\n",
     "    assert arr.ndim == 1, f\"Expected 1-d array, got {arr.ndim}\"\n",
     "    ptp = arr.ptp()\n",
@@ -505,12 +2420,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "code_folding": []
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -527,6 +2444,7 @@
     "markersize = 75\n",
     "color = '#424242'\n",
     "alpha = 0.4\n",
+    "jitter = 0.01\n",
     "\n",
     "# Instantiate plot objects\n",
     "fig, ax = plt.subplots()\n",
@@ -538,9 +2456,13 @@
     "fig.suptitle(\"Relationship between the number of colors and\\nlength of description for ugly holiday sweaters\",\n",
     "            color='k', fontsize=fontsize + 2)\n",
     "\n",
+    "# Specify what data to plot\n",
+    "x = data.num_colors\n",
+    "y = data.num_words\n",
     "# Add jitter to data so completely overlapping\n",
-    "x = jitterify(data.num_colors)\n",
-    "y = jitterify(data.num_words)\n",
+    "x = jitterify(x, jitter)\n",
+    "y = jitterify(y, jitter)\n",
+    "\n",
     "# Plot\n",
     "ax.scatter(x, y,\n",
     "            c=color,\n",
@@ -585,13 +2507,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From be63e5101401333e6b0998aca3c2450a5198d28a Mon Sep 17 00:00:00 2001
From: L4R5m <2925603+L4R5m@users.noreply.github.com>
Date: Fri, 19 Nov 2021 10:50:24 -0800
Subject: [PATCH 6/7] Jupyter notebook hadn't saved prior to last commit, so
 fixing

Jupyter autosave... so slow.
---
 key.ipynb | 256 +++++++++++++++++++++++++++---------------------------
 1 file changed, 127 insertions(+), 129 deletions(-)

diff --git a/key.ipynb b/key.ipynb
index 949706d..676e578 100644
--- a/key.ipynb
+++ b/key.ipynb
@@ -1222,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1245,7 +1245,7 @@
        "Name: image_desc_ls, Length: 68, dtype: object"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1272,7 +1272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1301,11 +1301,10 @@
        "      <th>noise</th>\n",
        "      <th>lights</th>\n",
        "      <th>objects</th>\n",
-       "      <th>...</th>\n",
+       "      <th>colors</th>\n",
        "      <th>image_tf</th>\n",
        "      <th>image_desc</th>\n",
        "      <th>colors_ls</th>\n",
-       "      <th>num_colors</th>\n",
        "      <th>image_desc_ls</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1320,7 +1319,6 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1331,11 +1329,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Yellow...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>Red</td>\n",
-       "      <td>5</td>\n",
        "      <td>[octopus, d...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1345,11 +1342,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Yellow...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>Yellow</td>\n",
-       "      <td>5</td>\n",
        "      <td>[octopus, d...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1359,11 +1355,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Yellow...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>Blue</td>\n",
-       "      <td>5</td>\n",
        "      <td>[octopus, d...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1373,11 +1368,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Yellow...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>White</td>\n",
-       "      <td>5</td>\n",
        "      <td>[octopus, d...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1387,11 +1381,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Yellow...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>teal</td>\n",
-       "      <td>5</td>\n",
        "      <td>[octopus, d...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1401,11 +1394,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Green</td>\n",
        "      <td>No</td>\n",
        "      <td></td>\n",
        "      <td>Green</td>\n",
-       "      <td>1</td>\n",
        "      <td>[]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1420,7 +1412,6 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater105</th>\n",
@@ -1429,11 +1420,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Green,...</td>\n",
        "      <td>No</td>\n",
        "      <td></td>\n",
        "      <td>Blue</td>\n",
-       "      <td>4</td>\n",
        "      <td>[]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1443,11 +1433,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>No</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Green,...</td>\n",
        "      <td>No</td>\n",
        "      <td></td>\n",
        "      <td>Grey</td>\n",
-       "      <td>4</td>\n",
        "      <td>[]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1457,11 +1446,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Green,...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>Red</td>\n",
-       "      <td>4</td>\n",
        "      <td>[a, llama, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1471,11 +1459,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Green,...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>Green</td>\n",
-       "      <td>4</td>\n",
        "      <td>[a, llama, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1485,11 +1472,10 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Green,...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>White</td>\n",
-       "      <td>4</td>\n",
        "      <td>[a, llama, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1499,39 +1485,38 @@
        "      <td>No</td>\n",
        "      <td>No</td>\n",
        "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
+       "      <td>Red, Green,...</td>\n",
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>Black</td>\n",
-       "      <td>4</td>\n",
        "      <td>[a, llama, ...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>273 rows × 11 columns</p>\n",
+       "<p>273 rows × 10 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "           hs_tf sparkly noise lights objects  ... image_tf      image_desc colors_ls num_colors   image_desc_ls\n",
-       "sweater                                        ...                                                              \n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...       Red          5  [octopus, d...\n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...    Yellow          5  [octopus, d...\n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...      Blue          5  [octopus, d...\n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...     White          5  [octopus, d...\n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...      teal          5  [octopus, d...\n",
-       "sweater2     Yes      No    No     No      No  ...       No                     Green          1              []\n",
-       "...          ...     ...   ...    ...     ...  ...      ...             ...       ...        ...             ...\n",
-       "sweater105   Yes      No    No     No      No  ...       No                      Blue          4              []\n",
-       "sweater105   Yes      No    No     No      No  ...       No                      Grey          4              []\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...       Red          4  [a, llama, ...\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...     Green          4  [a, llama, ...\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...     White          4  [a, llama, ...\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...     Black          4  [a, llama, ...\n",
+       "           hs_tf sparkly noise lights objects          colors image_tf      image_desc colors_ls   image_desc_ls\n",
+       "sweater                                                                                                         \n",
+       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...       Red  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...    Yellow  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...      Blue  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...     White  [octopus, d...\n",
+       "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...      teal  [octopus, d...\n",
+       "sweater2     Yes      No    No     No      No           Green       No                     Green              []\n",
+       "...          ...     ...   ...    ...     ...             ...      ...             ...       ...             ...\n",
+       "sweater105   Yes      No    No     No      No  Red, Green,...       No                      Blue              []\n",
+       "sweater105   Yes      No    No     No      No  Red, Green,...       No                      Grey              []\n",
+       "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...       Red  [a, llama, ...\n",
+       "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...     Green  [a, llama, ...\n",
+       "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...     White  [a, llama, ...\n",
+       "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...     Black  [a, llama, ...\n",
        "\n",
-       "[273 rows x 11 columns]"
+       "[273 rows x 10 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1544,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1567,7 +1552,7 @@
        "Name: num_colors, Length: 68, dtype: int64"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1594,7 +1579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1627,8 +1612,8 @@
        "      <th>image_tf</th>\n",
        "      <th>image_desc</th>\n",
        "      <th>colors_ls</th>\n",
-       "      <th>num_colors</th>\n",
        "      <th>image_desc_ls</th>\n",
+       "      <th>num_colors</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater</th>\n",
@@ -1657,8 +1642,8 @@
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>5</td>\n",
        "      <td>octopus</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater1</th>\n",
@@ -1671,8 +1656,8 @@
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>5</td>\n",
        "      <td>dressed</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater1</th>\n",
@@ -1685,8 +1670,8 @@
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>5</td>\n",
        "      <td>like</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater1</th>\n",
@@ -1699,8 +1684,8 @@
        "      <td>Yes</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>5</td>\n",
        "      <td>santa</td>\n",
+       "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater2</th>\n",
@@ -1713,8 +1698,8 @@
        "      <td>No</td>\n",
        "      <td></td>\n",
        "      <td>[Green]</td>\n",
-       "      <td>1</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater3</th>\n",
@@ -1727,8 +1712,8 @@
        "      <td>Yes</td>\n",
        "      <td>Houses</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>6</td>\n",
        "      <td>Houses</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1755,8 +1740,8 @@
        "      <td>No</td>\n",
        "      <td></td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater107</th>\n",
@@ -1769,8 +1754,8 @@
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>a</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater107</th>\n",
@@ -1783,8 +1768,8 @@
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>llama</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater107</th>\n",
@@ -1797,8 +1782,8 @@
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>wearing</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater107</th>\n",
@@ -1811,8 +1796,8 @@
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>a</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sweater107</th>\n",
@@ -1825,8 +1810,8 @@
        "      <td>Yes</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>scarf</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1834,26 +1819,26 @@
        "</div>"
       ],
       "text/plain": [
-       "           hs_tf sparkly noise lights objects  ... image_tf      image_desc       colors_ls num_colors  image_desc_ls\n",
-       "sweater                                        ...                                                                   \n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5        octopus\n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5        dressed\n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5           like\n",
-       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          5          santa\n",
-       "sweater2     Yes      No    No     No      No  ...       No                         [Green]          1            NaN\n",
-       "sweater3     Yes      No    No     No      No  ...      Yes          Houses  [Red,  Yell...          6         Houses\n",
-       "...          ...     ...   ...    ...     ...  ...      ...             ...             ...        ...            ...\n",
-       "sweater105   Yes      No    No     No      No  ...       No                  [Red,  Gree...          4            NaN\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4              a\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4          llama\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4        wearing\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4              a\n",
-       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...          4          scarf\n",
+       "           hs_tf sparkly noise lights objects  ... image_tf      image_desc       colors_ls image_desc_ls num_colors\n",
+       "sweater                                        ...                                                                  \n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...       octopus          5\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...       dressed          5\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...          like          5\n",
+       "sweater1     Yes     Yes    No     No      No  ...      Yes  octopus dre...  [Red,  Yell...         santa          5\n",
+       "sweater2     Yes      No    No     No      No  ...       No                         [Green]           NaN          1\n",
+       "sweater3     Yes      No    No     No      No  ...      Yes          Houses  [Red,  Yell...        Houses          6\n",
+       "...          ...     ...   ...    ...     ...  ...      ...             ...             ...           ...        ...\n",
+       "sweater105   Yes      No    No     No      No  ...       No                  [Red,  Gree...           NaN          4\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...             a          4\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...         llama          4\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...       wearing          4\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...             a          4\n",
+       "sweater107   Yes      No    No     No     Yes  ...      Yes  a llama wea...  [Red,  Gree...         scarf          4\n",
        "\n",
        "[278 rows x 11 columns]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1866,19 +1851,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'df_image_desc' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/f7/34gtpjvn45zgnhj8hb9wn3gr0000gn/T/ipykernel_63963/2145182293.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Calc how many description words for each sweater & add to dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'num_words'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_image_desc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sweater'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_desc_ls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_words\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'df_image_desc' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "sweater\n",
+       "sweater1       4\n",
+       "sweater2       0\n",
+       "sweater3       1\n",
+       "sweater5       1\n",
+       "sweater8       0\n",
+       "sweater11     10\n",
+       "              ..\n",
+       "sweater99      1\n",
+       "sweater100     1\n",
+       "sweater103     1\n",
+       "sweater104     5\n",
+       "sweater105     0\n",
+       "sweater107     5\n",
+       "Name: num_words, Length: 68, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1903,7 +1901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1935,8 +1933,8 @@
        "      <th>...</th>\n",
        "      <th>image_desc</th>\n",
        "      <th>colors_ls</th>\n",
-       "      <th>num_colors</th>\n",
        "      <th>image_desc_ls</th>\n",
+       "      <th>num_colors</th>\n",
        "      <th>num_words</th>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1965,8 +1963,8 @@
        "      <td>...</td>\n",
        "      <td>octopus dre...</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>5</td>\n",
        "      <td>[octopus, d...</td>\n",
+       "      <td>5</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1979,8 +1977,8 @@
        "      <td>...</td>\n",
        "      <td></td>\n",
        "      <td>[Green]</td>\n",
-       "      <td>1</td>\n",
        "      <td>[]</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1993,8 +1991,8 @@
        "      <td>...</td>\n",
        "      <td>Houses</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>6</td>\n",
        "      <td>[Houses]</td>\n",
+       "      <td>6</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2007,8 +2005,8 @@
        "      <td>...</td>\n",
        "      <td>T-rex</td>\n",
        "      <td>[Blue,  Whi...</td>\n",
-       "      <td>3</td>\n",
        "      <td>[T-rex]</td>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2021,8 +2019,8 @@
        "      <td>...</td>\n",
        "      <td></td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>6</td>\n",
        "      <td>[]</td>\n",
+       "      <td>6</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2035,8 +2033,8 @@
        "      <td>...</td>\n",
        "      <td>Santa Claus...</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>8</td>\n",
        "      <td>[Santa, Cla...</td>\n",
+       "      <td>8</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2063,8 +2061,8 @@
        "      <td>...</td>\n",
        "      <td>Reindeer</td>\n",
        "      <td>[Red,  Blue...</td>\n",
-       "      <td>4</td>\n",
        "      <td>[Reindeer]</td>\n",
+       "      <td>4</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2077,8 +2075,8 @@
        "      <td>...</td>\n",
        "      <td>Menorah</td>\n",
        "      <td>[Orange,  Y...</td>\n",
-       "      <td>3</td>\n",
        "      <td>[Menorah]</td>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2091,8 +2089,8 @@
        "      <td>...</td>\n",
        "      <td>Sloth</td>\n",
        "      <td>[Red,  Yell...</td>\n",
-       "      <td>4</td>\n",
        "      <td>[Sloth]</td>\n",
+       "      <td>4</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2105,8 +2103,8 @@
        "      <td>...</td>\n",
        "      <td>R2D2 wearin...</td>\n",
        "      <td>[Red,  Whit...</td>\n",
-       "      <td>3</td>\n",
        "      <td>[R2D2, wear...</td>\n",
+       "      <td>3</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2119,8 +2117,8 @@
        "      <td>...</td>\n",
        "      <td></td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>[]</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2133,8 +2131,8 @@
        "      <td>...</td>\n",
        "      <td>a llama wea...</td>\n",
        "      <td>[Red,  Gree...</td>\n",
-       "      <td>4</td>\n",
        "      <td>[a, llama, ...</td>\n",
+       "      <td>4</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2143,26 +2141,26 @@
        "</div>"
       ],
       "text/plain": [
-       "           hs_tf sparkly noise lights objects  ...      image_desc       colors_ls num_colors   image_desc_ls  num_words\n",
-       "sweater                                        ...                                                                      \n",
-       "sweater1     Yes     Yes    No     No      No  ...  octopus dre...  [Red,  Yell...          5  [octopus, d...          4\n",
-       "sweater2     Yes      No    No     No      No  ...                         [Green]          1              []          0\n",
-       "sweater3     Yes      No    No     No      No  ...          Houses  [Red,  Yell...          6        [Houses]          1\n",
-       "sweater5     Yes      No    No     No      No  ...           T-rex  [Blue,  Whi...          3         [T-rex]          1\n",
-       "sweater8     Yes      No   Yes    Yes     Yes  ...                  [Red,  Gree...          6              []          0\n",
-       "sweater11    Yes      No    No     No      No  ...  Santa Claus...  [Red,  Yell...          8  [Santa, Cla...         10\n",
-       "...          ...     ...   ...    ...     ...  ...             ...             ...        ...             ...        ...\n",
-       "sweater99    Yes     Yes    No     No      No  ...        Reindeer  [Red,  Blue...          4      [Reindeer]          1\n",
-       "sweater100   Yes     Yes    No     No     Yes  ...         Menorah  [Orange,  Y...          3       [Menorah]          1\n",
-       "sweater103   Yes      No   Yes     No     Yes  ...           Sloth  [Red,  Yell...          4         [Sloth]          1\n",
-       "sweater104   Yes     Yes    No     No     Yes  ...  R2D2 wearin...  [Red,  Whit...          3  [R2D2, wear...          5\n",
-       "sweater105   Yes      No    No     No      No  ...                  [Red,  Gree...          4              []          0\n",
-       "sweater107   Yes      No    No     No     Yes  ...  a llama wea...  [Red,  Gree...          4  [a, llama, ...          5\n",
+       "           hs_tf sparkly noise lights objects  ...      image_desc       colors_ls   image_desc_ls num_colors num_words\n",
+       "sweater                                        ...                                                                     \n",
+       "sweater1     Yes     Yes    No     No      No  ...  octopus dre...  [Red,  Yell...  [octopus, d...          5         4\n",
+       "sweater2     Yes      No    No     No      No  ...                         [Green]              []          1         0\n",
+       "sweater3     Yes      No    No     No      No  ...          Houses  [Red,  Yell...        [Houses]          6         1\n",
+       "sweater5     Yes      No    No     No      No  ...           T-rex  [Blue,  Whi...         [T-rex]          3         1\n",
+       "sweater8     Yes      No   Yes    Yes     Yes  ...                  [Red,  Gree...              []          6         0\n",
+       "sweater11    Yes      No    No     No      No  ...  Santa Claus...  [Red,  Yell...  [Santa, Cla...          8        10\n",
+       "...          ...     ...   ...    ...     ...  ...             ...             ...             ...        ...       ...\n",
+       "sweater99    Yes     Yes    No     No      No  ...        Reindeer  [Red,  Blue...      [Reindeer]          4         1\n",
+       "sweater100   Yes     Yes    No     No     Yes  ...         Menorah  [Orange,  Y...       [Menorah]          3         1\n",
+       "sweater103   Yes      No   Yes     No     Yes  ...           Sloth  [Red,  Yell...         [Sloth]          4         1\n",
+       "sweater104   Yes     Yes    No     No     Yes  ...  R2D2 wearin...  [Red,  Whit...  [R2D2, wear...          3         5\n",
+       "sweater105   Yes      No    No     No      No  ...                  [Red,  Gree...              []          4         0\n",
+       "sweater107   Yes      No    No     No     Yes  ...  a llama wea...  [Red,  Gree...  [a, llama, ...          4         5\n",
        "\n",
        "[68 rows x 12 columns]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2174,7 +2172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2298,7 +2296,7 @@
        "[68 rows x 2 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2324,7 +2322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2341,7 +2339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2377,7 +2375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -2397,7 +2395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2420,14 +2418,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 29,
    "metadata": {
     "code_folding": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]

From f1e5255f714f84af22f12f8543caf227742b79ba Mon Sep 17 00:00:00 2001
From: L4R5m <2925603+L4R5m@users.noreply.github.com>
Date: Mon, 22 Nov 2021 11:59:46 -0800
Subject: [PATCH 7/7] Removed analysis section based on the non-tidy method

Simplify the notebook to only show one method of analyzing the data.

Also added a few more comments and cleaned up formatting.
---
 key.ipynb | 905 ++++++++----------------------------------------------
 1 file changed, 130 insertions(+), 775 deletions(-)

diff --git a/key.ipynb b/key.ipynb
index 676e578..4381df0 100644
--- a/key.ipynb
+++ b/key.ipynb
@@ -42,6 +42,7 @@
    "outputs": [],
    "source": [
     "# Customize Pandas settings (eg: DataFrame display)\n",
+    "\n",
     "# Columns\n",
     "pd.options.display.max_columns = 10\n",
     "pd.options.display.max_colwidth = 15\n",
@@ -57,6 +58,7 @@
    "outputs": [],
    "source": [
     "# Customize matplotlib\n",
+    "\n",
     "# plt.style.use('ggplot')\n",
     "plt.style.use('default')\n",
     "plt.rcParams['figure.figsize'] = (12, 8)"
@@ -160,768 +162,120 @@
       "sweater103   Yes      No   Yes     No     Yes  Red, Yellow...      Yes           Sloth\n",
       "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...\n",
       "sweater105   Yes      No    No     No      No  Red, Green,...       No             NaN\n",
-      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
-      "\n",
-      "[68 rows x 8 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Filter to only include Holiday Sweaters\n",
-    "data_orig = df.loc[df.hs_tf == 'Yes']\n",
-    "# Make a copy (instead of working on the view) since we'll be changing\n",
-    "# col dtypes later and don't want to get SettingWithCopyWarning\n",
-    "data = data_orig.copy()\n",
-    "print(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspect the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['hs_tf', 'sparkly', 'noise', 'lights', 'objects', 'colors', 'image_tf', 'image_desc'], dtype='object')\n",
-      "\n",
-      "Data shape: (68, 8)\n",
-      "\n",
-      "Column dtypes:\n",
-      "hs_tf         object\n",
-      "sparkly       object\n",
-      "noise         object\n",
-      "lights        object\n",
-      "objects       object\n",
-      "colors        object\n",
-      "image_tf      object\n",
-      "image_desc    object\n",
-      "dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "# List the columns\n",
-    "print(data.columns)\n",
-    "# Show df shape\n",
-    "print(f\"\\nData shape: {data.shape}\")\n",
-    "# Show column datatypes (Numpy 'dtype')\n",
-    "print(\"\\nColumn dtypes:\")\n",
-    "print(data.dtypes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "object\n",
-      "sweater1\t <class 'str'>\tRed, Yellow, Blue, White, teal\n",
-      "sweater2\t <class 'str'>\tGreen\n",
-      "sweater3\t <class 'str'>\tRed, Yellow, Green, Brown, White, Black\n",
-      "sweater5\t <class 'str'>\tBlue, White, Black\n",
-      "sweater8\t <class 'str'>\tRed, Green, Blue, Purple, White, Grey\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Inspect the 'colors' data column\n",
-    "print(data.colors.dtype)\n",
-    "for idx, val in data.colors.head().items():\n",
-    "    print(f\"{idx}\\t {type(val)}\\t{val}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sweater1\t <class 'str'>\toctopus dressed like santa\n",
-      "sweater2\t <class 'float'>\tnan\n",
-      "sweater3\t <class 'str'>\tHouses\n",
-      "sweater5\t <class 'str'>\tT-rex\n",
-      "sweater8\t <class 'float'>\tnan\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Inspect the 'image_desc' data column\n",
-    "# Note how this heterogeneous data... str and float (NaN)\n",
-    "for idx, val in data.image_desc.head().items():\n",
-    "    print(f\"{idx}\\t {type(val)}\\t{val}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Wrangle Data -- count # of colors & words\n",
-    "This is the simplest solution, but doesn't make dataframe 'tidy'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "           hs_tf sparkly noise lights objects          colors image_tf      image_desc\n",
-      "sweater                                                                               \n",
-      "sweater1     Yes     Yes    No     No      No  Red, Yellow...      Yes  octopus dre...\n",
-      "sweater2     Yes      No    No     No      No           Green       No                \n",
-      "sweater3     Yes      No    No     No      No  Red, Yellow...      Yes          Houses\n",
-      "sweater5     Yes      No    No     No      No  Blue, White...      Yes           T-rex\n",
-      "sweater8     Yes      No   Yes    Yes     Yes  Red, Green,...       No                \n",
-      "sweater11    Yes      No    No     No      No  Red, Yellow...      Yes  Santa Claus...\n",
-      "...          ...     ...   ...    ...     ...             ...      ...             ...\n",
-      "sweater99    Yes     Yes    No     No      No  Red, Blue, ...      Yes        Reindeer\n",
-      "sweater100   Yes     Yes    No     No     Yes  Orange, Yel...      Yes         Menorah\n",
-      "sweater103   Yes      No   Yes     No     Yes  Red, Yellow...      Yes           Sloth\n",
-      "sweater104   Yes     Yes    No     No     Yes  Red, White,...      Yes  R2D2 wearin...\n",
-      "sweater105   Yes      No    No     No      No  Red, Green,...       No                \n",
-      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
-      "\n",
-      "[68 rows x 8 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Refresh dataframe from original data\n",
-    "data = data_orig.copy()\n",
-    "# Replace NaN with empty str\n",
-    "data.image_desc.fillna('', inplace=True)\n",
-    "print(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Convert str to list of str"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sweater\n",
-      "sweater1      [Red,  Yell...\n",
-      "sweater2             [Green]\n",
-      "sweater3      [Red,  Yell...\n",
-      "sweater5      [Blue,  Whi...\n",
-      "sweater8      [Red,  Gree...\n",
-      "sweater11     [Red,  Yell...\n",
-      "                   ...      \n",
-      "sweater99     [Red,  Blue...\n",
-      "sweater100    [Orange,  Y...\n",
-      "sweater103    [Red,  Yell...\n",
-      "sweater104    [Red,  Whit...\n",
-      "sweater105    [Red,  Gree...\n",
-      "sweater107    [Red,  Gree...\n",
-      "Name: colors_ls, Length: 68, dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Convert 'colors' column from single comma-delim str to list\n",
-    "# of string\n",
-    "data['colors_ls'] = data.colors.str.split(',')\n",
-    "print(data.colors_ls)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sweater\n",
-      "sweater1      [octopus, d...\n",
-      "sweater2                  []\n",
-      "sweater3            [Houses]\n",
-      "sweater5             [T-rex]\n",
-      "sweater8                  []\n",
-      "sweater11     [Santa, Cla...\n",
-      "                   ...      \n",
-      "sweater99         [Reindeer]\n",
-      "sweater100         [Menorah]\n",
-      "sweater103           [Sloth]\n",
-      "sweater104    [R2D2, wear...\n",
-      "sweater105                []\n",
-      "sweater107    [a, llama, ...\n",
-      "Name: image_desc_ls, Length: 68, dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Convert 'image_desc' column from single space-delim str to list\n",
-    "data['image_desc_ls'] = data.image_desc.str.split()\n",
-    "print(data.image_desc_ls)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calc number of 'colors'\n",
-    "This is the simplest solution as we simply calc # of colors, but doesn't make dataframe 'tidy'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sweater\n",
-      "sweater1      5\n",
-      "sweater2      1\n",
-      "sweater3      6\n",
-      "sweater5      3\n",
-      "sweater8      6\n",
-      "sweater11     8\n",
-      "             ..\n",
-      "sweater99     4\n",
-      "sweater100    3\n",
-      "sweater103    4\n",
-      "sweater104    3\n",
-      "sweater105    4\n",
-      "sweater107    4\n",
-      "Name: num_colors, Length: 68, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate how many colors are present & assign to new column\n",
-    "data['num_colors'] = data.colors_ls.apply(lambda x: len(x))\n",
-    "print(data.num_colors)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calc num word in 'image_desc'\n",
-    "This is the simplest solution as we simply calc # of description words, but doesn't make dataframe 'tidy'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sweater\n",
-      "sweater1       4\n",
-      "sweater2       0\n",
-      "sweater3       1\n",
-      "sweater5       1\n",
-      "sweater8       0\n",
-      "sweater11     10\n",
-      "              ..\n",
-      "sweater99      1\n",
-      "sweater100     1\n",
-      "sweater103     1\n",
-      "sweater104     5\n",
-      "sweater105     0\n",
-      "sweater107     5\n",
-      "Name: num_words, Length: 68, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate how many image_desc words are present & assign to new column\n",
-    "data['num_words'] = data.image_desc_ls.apply(lambda x: len(x))\n",
-    "print(data.num_words)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inspect results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>hs_tf</th>\n",
-       "      <th>sparkly</th>\n",
-       "      <th>noise</th>\n",
-       "      <th>lights</th>\n",
-       "      <th>objects</th>\n",
-       "      <th>...</th>\n",
-       "      <th>image_desc</th>\n",
-       "      <th>colors_ls</th>\n",
-       "      <th>image_desc_ls</th>\n",
-       "      <th>num_colors</th>\n",
-       "      <th>num_words</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sweater1</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td>octopus dre...</td>\n",
-       "      <td>[Red,  Yell...</td>\n",
-       "      <td>[octopus, d...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater2</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td></td>\n",
-       "      <td>[Green]</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater3</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Houses</td>\n",
-       "      <td>[Red,  Yell...</td>\n",
-       "      <td>[Houses]</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater5</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td>T-rex</td>\n",
-       "      <td>[Blue,  Whi...</td>\n",
-       "      <td>[T-rex]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater8</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
-       "      <td></td>\n",
-       "      <td>[Red,  Gree...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater11</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Santa Claus...</td>\n",
-       "      <td>[Red,  Yell...</td>\n",
-       "      <td>[Santa, Cla...</td>\n",
-       "      <td>8</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater99</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Reindeer</td>\n",
-       "      <td>[Red,  Blue...</td>\n",
-       "      <td>[Reindeer]</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater100</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Menorah</td>\n",
-       "      <td>[Orange,  Y...</td>\n",
-       "      <td>[Menorah]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater103</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Sloth</td>\n",
-       "      <td>[Red,  Yell...</td>\n",
-       "      <td>[Sloth]</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater104</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
-       "      <td>R2D2 wearin...</td>\n",
-       "      <td>[Red,  Whit...</td>\n",
-       "      <td>[R2D2, wear...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater105</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>...</td>\n",
-       "      <td></td>\n",
-       "      <td>[Red,  Gree...</td>\n",
-       "      <td>[]</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater107</th>\n",
-       "      <td>Yes</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>No</td>\n",
-       "      <td>Yes</td>\n",
-       "      <td>...</td>\n",
-       "      <td>a llama wea...</td>\n",
-       "      <td>[Red,  Gree...</td>\n",
-       "      <td>[a, llama, ...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>68 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           hs_tf sparkly noise lights objects  ...      image_desc       colors_ls   image_desc_ls num_colors num_words\n",
-       "sweater                                        ...                                                                     \n",
-       "sweater1     Yes     Yes    No     No      No  ...  octopus dre...  [Red,  Yell...  [octopus, d...          5         4\n",
-       "sweater2     Yes      No    No     No      No  ...                         [Green]              []          1         0\n",
-       "sweater3     Yes      No    No     No      No  ...          Houses  [Red,  Yell...        [Houses]          6         1\n",
-       "sweater5     Yes      No    No     No      No  ...           T-rex  [Blue,  Whi...         [T-rex]          3         1\n",
-       "sweater8     Yes      No   Yes    Yes     Yes  ...                  [Red,  Gree...              []          6         0\n",
-       "sweater11    Yes      No    No     No      No  ...  Santa Claus...  [Red,  Yell...  [Santa, Cla...          8        10\n",
-       "...          ...     ...   ...    ...     ...  ...             ...             ...             ...        ...       ...\n",
-       "sweater99    Yes     Yes    No     No      No  ...        Reindeer  [Red,  Blue...      [Reindeer]          4         1\n",
-       "sweater100   Yes     Yes    No     No     Yes  ...         Menorah  [Orange,  Y...       [Menorah]          3         1\n",
-       "sweater103   Yes      No   Yes     No     Yes  ...           Sloth  [Red,  Yell...         [Sloth]          4         1\n",
-       "sweater104   Yes     Yes    No     No     Yes  ...  R2D2 wearin...  [Red,  Whit...  [R2D2, wear...          3         5\n",
-       "sweater105   Yes      No    No     No      No  ...                  [Red,  Gree...              []          4         0\n",
-       "sweater107   Yes      No    No     No     Yes  ...  a llama wea...  [Red,  Gree...  [a, llama, ...          4         5\n",
-       "\n",
-       "[68 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+      "sweater107   Yes      No    No     No     Yes  Red, Green,...      Yes  a llama wea...\n",
+      "\n",
+      "[68 rows x 8 columns]\n"
+     ]
     }
    ],
    "source": [
-    "# Show whole dataframe\n",
-    "data"
+    "# Filter to only include Holiday Sweaters\n",
+    "data_orig = df.loc[df.hs_tf == 'Yes']\n",
+    "# Make a copy (instead of working on the view) since we'll be changing\n",
+    "# col dtypes later and don't want to get SettingWithCopyWarning\n",
+    "data = data_orig.copy()\n",
+    "print(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>num_colors</th>\n",
-       "      <th>num_words</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>sweater1</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater3</th>\n",
-       "      <td>6</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater5</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater8</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater11</th>\n",
-       "      <td>8</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater99</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater100</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater103</th>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater104</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater105</th>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sweater107</th>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>68 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            num_colors  num_words\n",
-       "sweater                          \n",
-       "sweater1             5          4\n",
-       "sweater2             1          0\n",
-       "sweater3             6          1\n",
-       "sweater5             3          1\n",
-       "sweater8             6          0\n",
-       "sweater11            8         10\n",
-       "...                ...        ...\n",
-       "sweater99            4          1\n",
-       "sweater100           3          1\n",
-       "sweater103           4          1\n",
-       "sweater104           3          5\n",
-       "sweater105           4          0\n",
-       "sweater107           4          5\n",
-       "\n",
-       "[68 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['hs_tf', 'sparkly', 'noise', 'lights', 'objects', 'colors', 'image_tf', 'image_desc'], dtype='object')\n",
+      "\n",
+      "Data shape: (68, 8)\n",
+      "\n",
+      "Column dtypes:\n",
+      "hs_tf         object\n",
+      "sparkly       object\n",
+      "noise         object\n",
+      "lights        object\n",
+      "objects       object\n",
+      "colors        object\n",
+      "image_tf      object\n",
+      "image_desc    object\n",
+      "dtype: object\n"
+     ]
     }
    ],
    "source": [
-    "# Inspect calculated columns only\n",
-    "data[['num_colors', 'num_words']]"
+    "# List the columns\n",
+    "print(data.columns)\n",
+    "\n",
+    "# Show df shape\n",
+    "print(f\"\\nData shape: {data.shape}\")\n",
+    "\n",
+    "# Show column datatypes (Numpy 'dtype')\n",
+    "print(\"\\nColumn dtypes:\")\n",
+    "print(data.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "object\n",
+      "sweater1\t <class 'str'>\tRed, Yellow, Blue, White, teal\n",
+      "sweater2\t <class 'str'>\tGreen\n",
+      "sweater3\t <class 'str'>\tRed, Yellow, Green, Brown, White, Black\n",
+      "sweater5\t <class 'str'>\tBlue, White, Black\n",
+      "sweater8\t <class 'str'>\tRed, Green, Blue, Purple, White, Grey\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect the 'colors' data column\n",
+    "print(data.colors.dtype)\n",
+    "for idx, val in data.colors.head().items():\n",
+    "    print(f\"{idx}\\t {type(val)}\\t{val}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sweater1\t <class 'str'>\toctopus dressed like santa\n",
+      "sweater2\t <class 'float'>\tnan\n",
+      "sweater3\t <class 'str'>\tHouses\n",
+      "sweater5\t <class 'str'>\tT-rex\n",
+      "sweater8\t <class 'float'>\tnan\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect the 'image_desc' data column\n",
+    "# Note how this heterogeneous data... str and float (NaN)\n",
+    "for idx, val in data.image_desc.head().items():\n",
+    "    print(f\"{idx}\\t {type(val)}\\t{val}\")"
    ]
   },
   {
@@ -936,12 +290,12 @@
    "metadata": {},
    "source": [
     "# Wrangle Data -- convert to long-form\n",
-    "Make data tidy and then analyze in a more general way (using groupby + aggregations)"
+    "Make data tidy and then analyze in a general way (using groupby + aggregations)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1155,7 +509,7 @@
        "[68 rows x 8 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1185,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -1208,7 +562,7 @@
        "Name: colors_ls, Length: 68, dtype: object"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1222,7 +576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1245,7 +599,7 @@
        "Name: image_desc_ls, Length: 68, dtype: object"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1272,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1516,7 +870,7 @@
        "[273 rows x 10 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1529,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1552,7 +906,7 @@
        "Name: num_colors, Length: 68, dtype: int64"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1579,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1838,7 +1192,7 @@
        "[278 rows x 11 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1851,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1874,7 +1228,7 @@
        "Name: num_words, Length: 68, dtype: int64"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1901,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -2160,7 +1514,7 @@
        "[68 rows x 12 columns]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2172,7 +1526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2296,7 +1650,7 @@
        "[68 rows x 2 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2317,12 +1671,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Visualize"
+    "# Visualize\n",
+    "There are several ways to visualize our results... most are built on top of the Matplotlib package."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2339,7 +1694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2375,7 +1730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2395,7 +1750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2418,14 +1773,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {
     "code_folding": []
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]