M&A - Tech/mergers and acquisitions by tech companies.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "c661d9f1",
   "metadata": {},
   "source": [
    "### Goal: Understand the M&A market since 1988 to identify trends in the companies bought by Big Tech\n",
    "\n",
    "Step 1: Download dataset and upload it to Jupyter. \n",
    "\n",
    "Step 2: Create a dataframe and begin exploring and processing the dataset (include steps here)\n",
    "\n",
    "Step n: Ask questions (and generate hypotheses) about the data and conduct online research to answer questions (or confirm hypotheses)\n",
    "\n",
    "Ex: I see FB did 11 social media acquisitions in 2012, I research what their corporate strategy was in 2012 and figure out they were trying to grow users in Asia and all 11 acqusitions were from Asia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11d28162",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "# new - understand\n",
    "plt.style.use('ggplot')\n",
    "import numpy as np\n",
    "# new - understand\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22c14035",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    }\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>ID</th>\n",
       "      <th>Parent Company</th>\n",
       "      <th>Acquisition Year</th>\n",
       "      <th>Acquisition Month</th>\n",
       "      <th>Acquired Company</th>\n",
       "      <th>Business</th>\n",
       "      <th>Country</th>\n",
       "      <th>Acquisition Price</th>\n",
       "      <th>Category</th>\n",
       "      <th>Derived Products</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Network Innovations</td>\n",
       "      <td>Software</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Orion Network Systems</td>\n",
       "      <td>Computer Software</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Styleware</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Nashoba Systems</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Apple</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Coral Software</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>1451</td>\n",
       "      <td>Citrix</td>\n",
       "      <td>2015</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Grasshopper</td>\n",
       "      <td>Telephone services</td>\n",
       "      <td>United States</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>GoToMeeting, GoToTraining, GoToWebinar, ShareF...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>1452</td>\n",
       "      <td>Citrix</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Norskale</td>\n",
       "      <td>UEM and application performance optimization</td>\n",
       "      <td>France</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>XenApp, XenDesktop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>1453</td>\n",
       "      <td>Citrix</td>\n",
       "      <td>2017</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Unidesk</td>\n",
       "      <td>-</td>\n",
       "      <td>United States</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>XenApp, XenDesktop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>1454</td>\n",
       "      <td>Citrix</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>Cedexis</td>\n",
       "      <td>Intelligent traffic management</td>\n",
       "      <td>France</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Citrix ITM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>1455</td>\n",
       "      <td>Citrix</td>\n",
       "      <td>2021</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Wrike</td>\n",
       "      <td>Project management</td>\n",
       "      <td>United States</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Citrix Workspace</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1455 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID Parent Company Acquisition Year Acquisition Month  \\\n",
       "0        1          Apple             1988               Mar   \n",
       "1        2          Apple             1988               Jun   \n",
       "2        3          Apple             1988               Jun   \n",
       "3        4          Apple             1988               Jul   \n",
       "4        5          Apple             1989               Jan   \n",
       "...    ...            ...              ...               ...   \n",
       "1450  1451         Citrix             2015               Apr   \n",
       "1451  1452         Citrix             2016               Sep   \n",
       "1452  1453         Citrix             2017               Jan   \n",
       "1453  1454         Citrix             2018               Feb   \n",
       "1454  1455         Citrix             2021               Mar   \n",
       "\n",
       "           Acquired Company                                      Business  \\\n",
       "0       Network Innovations                                      Software   \n",
       "1     Orion Network Systems                             Computer Software   \n",
       "2                 Styleware                             Computer software   \n",
       "3           Nashoba Systems                             Computer software   \n",
       "4            Coral Software                             Computer software   \n",
       "...                     ...                                           ...   \n",
       "1450            Grasshopper                            Telephone services   \n",
       "1451               Norskale  UEM and application performance optimization   \n",
       "1452                Unidesk                                             -   \n",
       "1453                Cedexis                Intelligent traffic management   \n",
       "1454                  Wrike                            Project management   \n",
       "\n",
       "            Country Acquisition Price Category  \\\n",
       "0                 -                 -        -   \n",
       "1                 -                 -        -   \n",
       "2                 -                 -        -   \n",
       "3                 -                 -        -   \n",
       "4                 -                 -        -   \n",
       "...             ...               ...      ...   \n",
       "1450  United States                 -        -   \n",
       "1451         France                 -        -   \n",
       "1452  United States                 -        -   \n",
       "1453         France                 -        -   \n",
       "1454  United States                 -        -   \n",
       "\n",
       "                                       Derived Products  \n",
       "0                                                     -  \n",
       "1                                                     -  \n",
       "2                                                     -  \n",
       "3                                                     -  \n",
       "4                                                     -  \n",
       "...                                                 ...  \n",
       "1450  GoToMeeting, GoToTraining, GoToWebinar, ShareF...  \n",
       "1451                                 XenApp, XenDesktop  \n",
       "1452                                 XenApp, XenDesktop  \n",
       "1453                                         Citrix ITM  \n",
       "1454                                   Citrix Workspace  \n",
       "\n",
       "[1455 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('~/Downloads/acquisitions_update_2021.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "632721fd",
   "metadata": {},
   "source": [
    "### Step 1: Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "82367ad3",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Acquisition Year</th>\n",
       "      <th>Acquisition Month</th>\n",
       "      <th>Acquired Company</th>\n",
       "      <th>Business</th>\n",
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       "      <th>Acquisition Price</th>\n",
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       "      <th>Derived Products</th>\n",
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       "      <td>1451</td>\n",
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       "      <td>Apr</td>\n",
       "      <td>Grasshopper</td>\n",
       "      <td>Telephone services</td>\n",
       "      <td>United States</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>GoToMeeting, GoToTraining, GoToWebinar, ShareF...</td>\n",
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       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>XenApp, XenDesktop</td>\n",
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       "      <td>Jan</td>\n",
       "      <td>Unidesk</td>\n",
       "      <td>-</td>\n",
       "      <td>United States</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>XenApp, XenDesktop</td>\n",
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       "      <th>1453</th>\n",
       "      <td>1454</td>\n",
       "      <td>Citrix</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>Cedexis</td>\n",
       "      <td>Intelligent traffic management</td>\n",
       "      <td>France</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Citrix ITM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>1455</td>\n",
       "      <td>Citrix</td>\n",
       "      <td>2021</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Wrike</td>\n",
       "      <td>Project management</td>\n",
       "      <td>United States</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Citrix Workspace</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        ID Parent Company Acquisition Year Acquisition Month Acquired Company  \\\n",
       "1450  1451         Citrix             2015               Apr      Grasshopper   \n",
       "1451  1452         Citrix             2016               Sep         Norskale   \n",
       "1452  1453         Citrix             2017               Jan          Unidesk   \n",
       "1453  1454         Citrix             2018               Feb          Cedexis   \n",
       "1454  1455         Citrix             2021               Mar            Wrike   \n",
       "\n",
       "                                          Business        Country  \\\n",
       "1450                            Telephone services  United States   \n",
       "1451  UEM and application performance optimization         France   \n",
       "1452                                             -  United States   \n",
       "1453                Intelligent traffic management         France   \n",
       "1454                            Project management  United States   \n",
       "\n",
       "     Acquisition Price Category  \\\n",
       "1450                 -        -   \n",
       "1451                 -        -   \n",
       "1452                 -        -   \n",
       "1453                 -        -   \n",
       "1454                 -        -   \n",
       "\n",
       "                                       Derived Products  \n",
       "1450  GoToMeeting, GoToTraining, GoToWebinar, ShareF...  \n",
       "1451                                 XenApp, XenDesktop  \n",
       "1452                                 XenApp, XenDesktop  \n",
       "1453                                         Citrix ITM  \n",
       "1454                                   Citrix Workspace  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understand total rows by total columns\n",
    "df.shape # (1455, 10)\n",
    "df.head(5) # country, acqusition price, category, derived products are empty\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f41ff8e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                    int64\n",
       "Parent Company       object\n",
       "Acquisition Year     object\n",
       "Acquisition Month    object\n",
       "Acquired Company     object\n",
       "Business             object\n",
       "Country              object\n",
       "Acquisition Price    object\n",
       "Category             object\n",
       "Derived Products     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# object type means that the DataFrame has columns with string or mixed datatypes. \n",
    "# I can use astype() pandas method to convert object into best data type for column, ex: string, int, categorial (similar to factor in R)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23039b70",
   "metadata": {},
   "source": [
    "### Change dtypes to appropriate\n",
    "\n",
    "Best practice in data science is to wait until the data is clean to remove types but my goal, as stated in block 1 of this notebook, doesn't need any of these columns. \n",
    "\n",
    "Acquisition Price is important but because of the large # of NaN values I've removed it and will evaluate the price on a case-by-case basis as the analysis continues. \n",
    "\n",
    "*Notes on why acquisition price is important:*\n",
    "\n",
    "* Market value: Fundamentally the market value is what buyers/sellers are willing to pay for a company. The analysis of market value is limited to a balance sheet/income statement instead of a user base or IP which are analyzed separately. \n",
    "* Growth potential: Fundamentally growth is about how many new customers a company can get, especially at a low CAC value, to continue generating high returns for the company and consequently the acquirer. Because once the parent company purchases the acquired company the acquired companies' growth turns into parent company growth, this is an obvious metric to look at. It's especially important if growth of the acquired company is in an adjacent market to the parent company. \n",
    "* Competitive advantage: Fundamentally this is about whether the acquired company has an economic moat (can sustain long-term competitive advantage because of price, value from product, market share, etc.) \n",
    "* Customer base: If a company has a large and loyal customer base they are valued higher than customers without one. In some cases the parent company wants to acquire users and paying for an acquired company makes more economic sense than the CAC to adopt those users. **Plug:** A company I worked for in 2020/2021 was acquired by a Series A startup because of our customer base. Read [Tech Pod.](https://blog.acquire.com/when-selling-a-newsletter-business-focus-on-the-intangibles/)\n",
    "* Intellectual property: When an acquired company has proprietary technology with strong patents, trademarks a parent company can see this value and opt to buy because of the fit or the parent company has another part of the tech stack understood and the acquired company can help increase the success of that stack. **Example:** Oculus and FB in 2014. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1588d81",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df.drop(['Acquisition Price', 'Category', 'Derived Products', 'ID'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5b762f2",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parent Company</th>\n",
       "      <th>Acquisition Year</th>\n",
       "      <th>Acquisition Month</th>\n",
       "      <th>Acquired Company</th>\n",
       "      <th>Business</th>\n",
       "      <th>Country</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Network Innovations</td>\n",
       "      <td>Software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Orion Network Systems</td>\n",
       "      <td>Computer Software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Styleware</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Nashoba Systems</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Coral Software</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2015</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Grasshopper</td>\n",
       "      <td>Telephone services</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Norskale</td>\n",
       "      <td>UEM and application performance optimization</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2017</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Unidesk</td>\n",
       "      <td>-</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>Cedexis</td>\n",
       "      <td>Intelligent traffic management</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2021</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Wrike</td>\n",
       "      <td>Project management</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1455 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Parent Company Acquisition Year Acquisition Month       Acquired Company  \\\n",
       "0             Apple             1988               Mar    Network Innovations   \n",
       "1             Apple             1988               Jun  Orion Network Systems   \n",
       "2             Apple             1988               Jun              Styleware   \n",
       "3             Apple             1988               Jul        Nashoba Systems   \n",
       "4             Apple             1989               Jan         Coral Software   \n",
       "...             ...              ...               ...                    ...   \n",
       "1450         Citrix             2015               Apr            Grasshopper   \n",
       "1451         Citrix             2016               Sep               Norskale   \n",
       "1452         Citrix             2017               Jan                Unidesk   \n",
       "1453         Citrix             2018               Feb                Cedexis   \n",
       "1454         Citrix             2021               Mar                  Wrike   \n",
       "\n",
       "                                          Business        Country  \n",
       "0                                         Software              -  \n",
       "1                                Computer Software              -  \n",
       "2                                Computer software              -  \n",
       "3                                Computer software              -  \n",
       "4                                Computer software              -  \n",
       "...                                            ...            ...  \n",
       "1450                            Telephone services  United States  \n",
       "1451  UEM and application performance optimization         France  \n",
       "1452                                             -  United States  \n",
       "1453                Intelligent traffic management         France  \n",
       "1454                            Project management  United States  \n",
       "\n",
       "[1455 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16960a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df['Parent Company'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b65246ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df['Acquisition Year'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5f647f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coerce Types\n",
    "# Convert the column Acquisition Year into a numeric type and and for values not numeric coerce them to be NaN then convert them to type Int64\n",
    "df['Acquisition Year'] = pd.to_numeric(df['Acquisition Year'], errors='coerce').astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "402198e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.int64'>\n"
     ]
    }
   ],
   "source": [
    "# can view type change from str to np.int64 in three code blocks\n",
    "print(type(df['Acquisition Year'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34075d15",
   "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>Parent Company</th>\n",
       "      <th>Acquisition Year</th>\n",
       "      <th>Acquisition Month</th>\n",
       "      <th>Acquired Company</th>\n",
       "      <th>Business</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Network Innovations</td>\n",
       "      <td>Software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Orion Network Systems</td>\n",
       "      <td>Computer Software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Styleware</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Nashoba Systems</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Coral Software</td>\n",
       "      <td>Computer software</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2015</td>\n",
       "      <td>Apr</td>\n",
       "      <td>Grasshopper</td>\n",
       "      <td>Telephone services</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2016</td>\n",
       "      <td>Sep</td>\n",
       "      <td>Norskale</td>\n",
       "      <td>UEM and application performance optimization</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2017</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Unidesk</td>\n",
       "      <td>-</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2018</td>\n",
       "      <td>Feb</td>\n",
       "      <td>Cedexis</td>\n",
       "      <td>Intelligent traffic management</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>Citrix</td>\n",
       "      <td>2021</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Wrike</td>\n",
       "      <td>Project management</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1455 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Parent Company  Acquisition Year Acquisition Month  \\\n",
       "0             Apple              1988               Mar   \n",
       "1             Apple              1988               Jun   \n",
       "2             Apple              1988               Jun   \n",
       "3             Apple              1988               Jul   \n",
       "4             Apple              1989               Jan   \n",
       "...             ...               ...               ...   \n",
       "1450         Citrix              2015               Apr   \n",
       "1451         Citrix              2016               Sep   \n",
       "1452         Citrix              2017               Jan   \n",
       "1453         Citrix              2018               Feb   \n",
       "1454         Citrix              2021               Mar   \n",
       "\n",
       "           Acquired Company                                      Business  \\\n",
       "0       Network Innovations                                      Software   \n",
       "1     Orion Network Systems                             Computer Software   \n",
       "2                 Styleware                             Computer software   \n",
       "3           Nashoba Systems                             Computer software   \n",
       "4            Coral Software                             Computer software   \n",
       "...                     ...                                           ...   \n",
       "1450            Grasshopper                            Telephone services   \n",
       "1451               Norskale  UEM and application performance optimization   \n",
       "1452                Unidesk                                             -   \n",
       "1453                Cedexis                Intelligent traffic management   \n",
       "1454                  Wrike                            Project management   \n",
       "\n",
       "            Country  \n",
       "0                 -  \n",
       "1                 -  \n",
       "2                 -  \n",
       "3                 -  \n",
       "4                 -  \n",
       "...             ...  \n",
       "1450  United States  \n",
       "1451         France  \n",
       "1452  United States  \n",
       "1453         France  \n",
       "1454  United States  \n",
       "\n",
       "[1455 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca379110",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Parent Company to string\n",
    "df['Parent Company'] = df['Parent Company'].replace('-', np.nan).fillna('Unknown').astype(str)\n",
    "df['Parent Company'] = df['Parent Company'].astype(str)\n",
    "\n",
    "# Convert Acquired Company to string\n",
    "df['Acquired Company'] = df['Acquired Company'].replace('-', np.nan).fillna('Unknown').astype(str)\n",
    "df['Acquired Company'] = df['Acquired Company'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c52b984e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df['Parent Company'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cd5d33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parent Company       object\n",
       "Acquisition Year      Int64\n",
       "Acquisition Month    object\n",
       "Acquired Company     object\n",
       "Business             object\n",
       "Country              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# says objects because it's a pandas series.Series but if I check the index of a row like Parent Company, \n",
    "# it appears to be a string\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "773ae8b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Apple Acquisitions by Category')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter for Apple acquisitions\n",
    "apple_df = df[df['Parent Company'] == 'Apple']\n",
    "\n",
    "apple_categories = apple_df['Business'].unique()\n",
    "# print number of unique businesses (answer: 102)\n",
    "num_categories = apple_df['Business'].nunique()\n",
    "#print(\"Number of categories: \", num_categories)\n",
    "\n",
    "\n",
    "# Group by category and year and count the number of acquisitions\n",
    "acquisitions_by_category = apple_df.groupby(['Business', 'Acquisition Year']).size().reset_index(name='count')\n",
    "\n",
    "# Filter for categories that Apple has acquired companies in\n",
    "acquisitions_by_category = acquisitions_by_category[acquisitions_by_category['Business'].isin(apple_categories)]\n",
    "\n",
    "# Plot a bar chart of the number of acquisitions by category\n",
    "sns.barplot(x='Business', y='count', hue='Acquisition Year', data=acquisitions_by_category)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Apple Acquisitions by Category')\n",
    "#plt.show() - chart looks too congested. Likely going to use PCA to reduce categories and replot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b44020e0",
   "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>Parent Company</th>\n",
       "      <th>Acquisition Year</th>\n",
       "      <th>Acquisition Month</th>\n",
       "      <th>Acquired Company</th>\n",
       "      <th>Business</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Network Innovations</td>\n",
       "      <td>Software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Orion Network Systems</td>\n",
       "      <td>Computer Software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Styleware</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Nashoba Systems</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Coral Software</td>\n",
       "      <td>Computer software</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Mobeewave</td>\n",
       "      <td>Payments startup</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Camerai</td>\n",
       "      <td>AR</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Spaces</td>\n",
       "      <td>VR startup</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2021</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Curious AI</td>\n",
       "      <td>Core AI startup</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2021</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Primephonic</td>\n",
       "      <td>classical music streaming service</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Parent Company  Acquisition Year Acquisition Month       Acquired Company  \\\n",
       "0            Apple              1988               Mar    Network Innovations   \n",
       "1            Apple              1988               Jun  Orion Network Systems   \n",
       "2            Apple              1988               Jun              Styleware   \n",
       "3            Apple              1988               Jul        Nashoba Systems   \n",
       "4            Apple              1989               Jan         Coral Software   \n",
       "..             ...               ...               ...                    ...   \n",
       "118          Apple              2020               Jul              Mobeewave   \n",
       "119          Apple              2020               Aug                Camerai   \n",
       "120          Apple              2020               Aug                 Spaces   \n",
       "121          Apple              2021               Jan             Curious AI   \n",
       "122          Apple              2021               Aug            Primephonic   \n",
       "\n",
       "                              Business Country  \n",
       "0                             Software       -  \n",
       "1                    Computer Software       -  \n",
       "2                    Computer software       -  \n",
       "3                    Computer software       -  \n",
       "4                    Computer software       -  \n",
       "..                                 ...     ...  \n",
       "118                   Payments startup       -  \n",
       "119                                 AR       -  \n",
       "120                         VR startup       -  \n",
       "121                    Core AI startup       -  \n",
       "122  classical music streaming service       -  \n",
       "\n",
       "[123 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_df = pd.DataFrame(apple_df)\n",
    "apple_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7cdfec79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Software', 'Computer Software', 'Computer software',\n",
       "       'Unix-like hardware and software platform', 'Macintosh clones',\n",
       "       'Computer graphic chips', 'Internet software',\n",
       "       'Productivity software', 'Internet service provider (ISP)',\n",
       "       'Graphics software', 'Online info systems services',\n",
       "       'Special effects software', 'FireWire chips and software',\n",
       "       'Digital effects software',\n",
       "       'internet and network optimization for wireless carriers',\n",
       "       'Special-effects titling software for film and video',\n",
       "       'Music production software', 'Gesture recognition company',\n",
       "       'Semiconductors', 'Maps', 'Music streaming', 'Mobile advertising',\n",
       "       'Voice control software', 'Application Regionalization Firm',\n",
       "       'Web-based mapping', 'Facial recognition',\n",
       "       'High-dynamic-range (HDR) photography', '3D mapping',\n",
       "       'Flash memory', 'App search engine', 'Audio',\n",
       "       'PC and mobile security products', 'HTML5 Web app firm',\n",
       "       'Speech recognition', 'Search engine', 'Indoor location',\n",
       "       'Media discovery app', 'Mobile data compression[citation needed]',\n",
       "       'Personal assistant', 'Structured-light 3D scanners', 'Analytics',\n",
       "       'Database analytics', 'Photography software', 'Software testing',\n",
       "       'microLED displays', 'Social search engine', 'Book analytics',\n",
       "       'Headphones, music streaming (Beats Music)', 'Digital magazine',\n",
       "       'On-Screen Keyboard', 'Audio plug-ins and sound libraries',\n",
       "       'Music analytics', 'Database', 'Camera', 'GPS',\n",
       "       'Augmented reality', 'Mapping visualization and data collection',\n",
       "       'Speech technology', 'Machine learning, Image recognition',\n",
       "       'Realtime Motion Capture', 'Emotion recognition',\n",
       "       'Education technology', 'Platform security', 'Machine learning',\n",
       "       'Personal health info collection company',\n",
       "       'Indoor mapping and navigation', 'Automation and scripting app',\n",
       "       'Sleep tracking hardware', 'Artificial intelligence',\n",
       "       'Eye tracking hardware and software',\n",
       "       'Augmented reality head-mounted display', 'Computer vision',\n",
       "       'Messaging assistant', 'Wireless charging',\n",
       "       'Quantum dot-based image sensors',\n",
       "       'tools for searching digital spoken words',\n",
       "       'Computer vision, real-time editing',\n",
       "       'Artificial Intelligence, home monitoring', 'Asthma monitoring',\n",
       "       'Data science, data engineering, analytics',\n",
       "       'Continuous integration, debugging, and testing for mobile apps.',\n",
       "       'Digital magazine subscription service',\n",
       "       'Lenses for augmented reality glasses',\n",
       "       'Music and Image recognition', 'Chip development',\n",
       "       'Artist development', 'Backend workflow development',\n",
       "       'Autonomous vehicles', 'Smartphone modems', 'Motion capture',\n",
       "       'Low-light photography', 'Edge computing, artificial intelligence',\n",
       "       'Podcast A.I.', 'Weather forecasting and app',\n",
       "       'Artificial intelligence/voice assistant',\n",
       "       'Virtual reality events', 'Mobile device management',\n",
       "       'Payments startup', 'AR', 'VR startup', 'Core AI startup',\n",
       "       'classical music streaming service'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_categories = apple_df['Business'].unique()\n",
    "apple_categories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e5c794",
   "metadata": {},
   "source": [
    "### K-means review & why use\n",
    "\n",
    "I [wrote an article](https://medium.com/python-in-plain-english/using-k-means-algorithm-to-create-customer-segmentation-tools-7712132e7ff6) on kMeans clustering in 2021 but needed a review before deciding this was best to implement. \n",
    "\n",
    "*How does it work:*\n",
    "\n",
    "* Choose the number of clusters (k) to create: I've done this with CrossValidation in the past. It's a more sophisticated approach because is creates a graph that shows the scientist what the optimal k value is. In this implementation I chose 10 somewhat randomly based on earlier EDA. \n",
    "* Initialize k centroid randomly (A centroid is the mean of a cluster and is set randomly. The centroid is updated to the mean of all data points in its cluster as the developer runs the code). \n",
    "* Assign each data point to nearest centroid. \n",
    "* Compute mean of cluster and update (as mentioned in bullet 2)\n",
    "* Repeat until max iterations reached or centroid stops moving "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "4e14c596",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6    66\n",
      "8    21\n",
      "1     7\n",
      "9     7\n",
      "4     5\n",
      "0     4\n",
      "2     4\n",
      "3     4\n",
      "5     4\n",
      "7     1\n",
      "Name: Cluster, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Create a CountVectorizer object to extract bag-of-words features from the \"Business\" column\n",
    "vectorizer = CountVectorizer()\n",
    "\n",
    "# Fit the vectorizer on the \"Business\" column to extract features\n",
    "features = vectorizer.fit_transform(apple_df['Business'])\n",
    "\n",
    "# Create a KMeans clustering object with k=10 clusters\n",
    "kmeans = KMeans(n_clusters=10, init='k-means++', random_state=42)\n",
    "\n",
    "# Fit the KMeans clustering model on the extracted features\n",
    "kmeans.fit(features)\n",
    "\n",
    "# Get the cluster labels for each data point\n",
    "cluster_labels = kmeans.labels_\n",
    "\n",
    "# Add the cluster labels as a new column in the DataFrame\n",
    "apple_df['Cluster'] = cluster_labels\n",
    "\n",
    "# Print the number of data points in each cluster\n",
    "print(apple_df['Cluster'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3b7aa932",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_counts = apple_df['Cluster'].value_counts()\n",
    "\n",
    "# Plot a histogram of the cluster counts\n",
    "plt.bar(cluster_counts.index, cluster_counts.values)\n",
    "plt.xlabel('Cluster')\n",
    "plt.ylabel('Number of Acquisitions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b7035b00",
   "metadata": {
    "scrolled": true
   },
   "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>Parent Company</th>\n",
       "      <th>Acquisition Year</th>\n",
       "      <th>Acquisition Month</th>\n",
       "      <th>Acquired Company</th>\n",
       "      <th>Business</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Mar</td>\n",
       "      <td>Network Innovations</td>\n",
       "      <td>Software</td>\n",
       "      <td>-</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Orion Network Systems</td>\n",
       "      <td>Computer Software</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Styleware</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Nashoba Systems</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Coral Software</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Mobeewave</td>\n",
       "      <td>Payments startup</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Camerai</td>\n",
       "      <td>AR</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Spaces</td>\n",
       "      <td>VR startup</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2021</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Curious AI</td>\n",
       "      <td>Core AI startup</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2021</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Primephonic</td>\n",
       "      <td>classical music streaming service</td>\n",
       "      <td>-</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Parent Company  Acquisition Year Acquisition Month       Acquired Company  \\\n",
       "0            Apple              1988               Mar    Network Innovations   \n",
       "1            Apple              1988               Jun  Orion Network Systems   \n",
       "2            Apple              1988               Jun              Styleware   \n",
       "3            Apple              1988               Jul        Nashoba Systems   \n",
       "4            Apple              1989               Jan         Coral Software   \n",
       "..             ...               ...               ...                    ...   \n",
       "118          Apple              2020               Jul              Mobeewave   \n",
       "119          Apple              2020               Aug                Camerai   \n",
       "120          Apple              2020               Aug                 Spaces   \n",
       "121          Apple              2021               Jan             Curious AI   \n",
       "122          Apple              2021               Aug            Primephonic   \n",
       "\n",
       "                              Business Country  Cluster  \n",
       "0                             Software       -        8  \n",
       "1                    Computer Software       -        1  \n",
       "2                    Computer software       -        1  \n",
       "3                    Computer software       -        1  \n",
       "4                    Computer software       -        1  \n",
       "..                                 ...     ...      ...  \n",
       "118                   Payments startup       -        6  \n",
       "119                                 AR       -        6  \n",
       "120                         VR startup       -        6  \n",
       "121                    Core AI startup       -        6  \n",
       "122  classical music streaming service       -        9  \n",
       "\n",
       "[123 rows x 7 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apple_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "919002f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.int32'>\n"
     ]
    }
   ],
   "source": [
    "# hence why I don't need to use '0' in the code block below\n",
    "print(type(apple_df['Cluster'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "e2bb6174",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Parent Company  Acquisition Year Acquisition Month  \\\n",
      "6            Apple              1997               Sep   \n",
      "13           Apple              2001               May   \n",
      "15           Apple              2001               Dec   \n",
      "19           Apple              2002               Jun   \n",
      "23           Apple              2005               Apr   \n",
      "..             ...               ...               ...   \n",
      "117          Apple              2020               Jun   \n",
      "118          Apple              2020               Jul   \n",
      "119          Apple              2020               Aug   \n",
      "120          Apple              2020               Aug   \n",
      "121          Apple              2021               Jan   \n",
      "\n",
      "                Acquired Company  \\\n",
      "6    Power Computing Corporation   \n",
      "13                      bluebuzz   \n",
      "15                   PowerSchool   \n",
      "19               Propel Software   \n",
      "23                   FingerWorks   \n",
      "..                           ...   \n",
      "117                   Fleetsmith   \n",
      "118                    Mobeewave   \n",
      "119                      Camerai   \n",
      "120                       Spaces   \n",
      "121                   Curious AI   \n",
      "\n",
      "                                              Business Country  Cluster  \\\n",
      "6                                     Macintosh clones       -        6   \n",
      "13                     Internet service provider (ISP)       -        6   \n",
      "15                        Online info systems services       -        6   \n",
      "19   internet and network optimization for wireless...       -        6   \n",
      "23                         Gesture recognition company       -        6   \n",
      "..                                                 ...     ...      ...   \n",
      "117                           Mobile device management       -        6   \n",
      "118                                   Payments startup       -        6   \n",
      "119                                                 AR       -        6   \n",
      "120                                         VR startup       -        6   \n",
      "121                                    Core AI startup       -        6   \n",
      "\n",
      "    manual_labels  \n",
      "6        Internet  \n",
      "13       Internet  \n",
      "15       Internet  \n",
      "19       Internet  \n",
      "23       Internet  \n",
      "..            ...  \n",
      "117      Internet  \n",
      "118      Internet  \n",
      "119      Internet  \n",
      "120      Internet  \n",
      "121      Internet  \n",
      "\n",
      "[66 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "cluster_6 = apple_df[apple_df['Cluster'] == 6]\n",
    "print(cluster_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "99958438",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "manual_labels       Business                                \n",
      "AI                  Artificial Intelligence, home monitoring    1\n",
      "                    Artificial intelligence                     1\n",
      "                    Artificial intelligence/voice assistant     1\n",
      "                    Edge computing, artificial intelligence     1\n",
      "Analytics           Analytics                                   1\n",
      "                                                               ..\n",
      "Software            Voice control software                      1\n",
      "Speech Recognition  Education technology                        1\n",
      "                    Speech recognition                          1\n",
      "                    Speech technology                           2\n",
      "Spoken Words        tools for searching digital spoken words    1\n",
      "Length: 102, dtype: int64\n"
     ]
    },
    {
     "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>Parent Company</th>\n",
       "      <th>Acquisition Year</th>\n",
       "      <th>Acquisition Month</th>\n",
       "      <th>Acquired Company</th>\n",
       "      <th>Business</th>\n",
       "      <th>Country</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>manual_labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1988</td>\n",
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       "      <td>Network Innovations</td>\n",
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       "      <td>Software</td>\n",
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       "      <td>Orion Network Systems</td>\n",
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       "      <td>Jun</td>\n",
       "      <td>Styleware</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>Computer Software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1988</td>\n",
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       "      <td>Nashoba Systems</td>\n",
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       "      <td>Computer Software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>1989</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Coral Software</td>\n",
       "      <td>Computer software</td>\n",
       "      <td>-</td>\n",
       "      <td>1</td>\n",
       "      <td>Computer Software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Mobeewave</td>\n",
       "      <td>Payments startup</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "      <td>Internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Camerai</td>\n",
       "      <td>AR</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "      <td>Internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2020</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Spaces</td>\n",
       "      <td>VR startup</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "      <td>Internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2021</td>\n",
       "      <td>Jan</td>\n",
       "      <td>Curious AI</td>\n",
       "      <td>Core AI startup</td>\n",
       "      <td>-</td>\n",
       "      <td>6</td>\n",
       "      <td>Internet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Apple</td>\n",
       "      <td>2021</td>\n",
       "      <td>Aug</td>\n",
       "      <td>Primephonic</td>\n",
       "      <td>classical music streaming service</td>\n",
       "      <td>-</td>\n",
       "      <td>9</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Parent Company  Acquisition Year Acquisition Month       Acquired Company  \\\n",
       "0            Apple              1988               Mar    Network Innovations   \n",
       "1            Apple              1988               Jun  Orion Network Systems   \n",
       "2            Apple              1988               Jun              Styleware   \n",
       "3            Apple              1988               Jul        Nashoba Systems   \n",
       "4            Apple              1989               Jan         Coral Software   \n",
       "..             ...               ...               ...                    ...   \n",
       "118          Apple              2020               Jul              Mobeewave   \n",
       "119          Apple              2020               Aug                Camerai   \n",
       "120          Apple              2020               Aug                 Spaces   \n",
       "121          Apple              2021               Jan             Curious AI   \n",
       "122          Apple              2021               Aug            Primephonic   \n",
       "\n",
       "                              Business Country  Cluster      manual_labels  \n",
       "0                             Software       -        8           Software  \n",
       "1                    Computer Software       -        1  Computer Software  \n",
       "2                    Computer software       -        1  Computer Software  \n",
       "3                    Computer software       -        1  Computer Software  \n",
       "4                    Computer software       -        1  Computer Software  \n",
       "..                                 ...     ...      ...                ...  \n",
       "118                   Payments startup       -        6           Internet  \n",
       "119                                 AR       -        6           Internet  \n",
       "120                         VR startup       -        6           Internet  \n",
       "121                    Core AI startup       -        6           Internet  \n",
       "122  classical music streaming service       -        9              Music  \n",
       "\n",
       "[123 rows x 8 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# did these manually, need to decide if there's better way\n",
    "cluster_labels = {0: 'AI', 1:'Computer Software', 2:'Speech Recognition', 3:'Machine Learning', 4:'Mapping', 5:'Analytics', 6:'Internet', 7:'Spoken Words', 8:'Software', 9:'Music'}\n",
    "\n",
    "# add manual labels to dataframe\n",
    "apple_df['manual_labels'] = [cluster_labels[x] for x in apple_df['Cluster']]\n",
    "\n",
    "manual_counts = apple_df.groupby(['manual_labels', 'Business']).size()\n",
    "print(manual_counts)\n",
    "apple_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "64f50319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a countplot to get the frequency of each manual label\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.countplot(data=apple_df, y=\"manual_labels\")\n",
    "plt.ylabel(\"Manual Labels\")\n",
    "plt.xlabel(\"Count\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "1d6b5d9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of occurrences for each manual label\n",
    "label_counts = apple_df['manual_labels'].value_counts()\n",
    "\n",
    "# Get the top 4 manual labels and their counts\n",
    "top_labels = label_counts[:4]\n",
    "other_count = label_counts[4:].sum()\n",
    "\n",
    "# Combine the other labels into an \"Other\" category\n",
    "top_labels['Other'] = other_count\n",
    "\n",
    "# Create a pie chart with the top 4 labels and \"Other\"\n",
    "plt.pie(top_labels, labels=top_labels.index, autopct='%1.1f%%')\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "00acae74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group the data by acquisition year and manual label and get the count\n",
    "year_label_count = apple_df.groupby(['Acquisition Year', 'manual_labels'])['manual_labels'].count().reset_index(name='count')\n",
    "#print(year_label_count)\n",
    "# Create a pivot table with acquisition year as the index, manual labels as columns, and count as values\n",
    "pivot_table = year_label_count.pivot(index='Acquisition Year', columns='manual_labels', values='count')\n",
    "#print(pivot_table)\n",
    "\n",
    "# Create a stacked bar chart\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure().set_figwidth(25)\n",
    "pivot_table.plot(kind='bar', stacked=True, figsize=(12, 8))\n",
    "plt.xlabel('Acquisition Year')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Business Category Acquisitions by Acquisition Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "eb4c2df6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Acquisition Year  Cluster\n",
      "0              1988        1\n",
      "1              1988        1\n",
      "2              1988        1\n",
      "3              1988        1\n",
      "4              1989        1\n"
     ]
    }
   ],
   "source": [
    "year_cluster_df = pd.concat([apple_df['Acquisition Year'], apple_df['Cluster']], axis=1)\n",
    "\n",
    "# Print the first five rows of the new dataframe\n",
    "print(year_cluster_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28d95ae",
   "metadata": {},
   "source": [
    "### Error - including for learning\n",
    "\n",
    "The .isna() method works when there are values in the dataframe that say NaN. In this dataframe, NaN values are represented by a dash (-) so I need to write a replace method using numpy's nan library.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "45356061",
   "metadata": {},
   "outputs": [],
   "source": [
    "# count the number of non-missing values in 'Aquisition Price'\n",
    "# All Aquisition Price values are missing so we can drop this column\n",
    "#number_non_missing = df['Acquisition Price'].isna().sum()\n",
    "#number_non_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5ac43b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "#num_non_missing = df.isna().sum(axis=0)\n",
    "#num_non_missing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca765be",
   "metadata": {},
   "source": [
    "### Corrected NaN count\n",
    "\n",
    "In the first example `print(df['Category'].unique())` I can see values other than - which weren't showing up in `df.head()` or `df.tail()` commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "398c9980",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Category'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Category'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-7d36a6d6a64e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# prints unique values to see if there are other values besides '-'\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[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Category'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\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[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Category'"
     ]
    }
   ],
   "source": [
    "# prints unique values to see if there are other values besides '-'\n",
    "print(df['Category'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba5347a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Category'] = df['Category'].replace('-', np.nan)\n",
    "df['Category']"
   ]
  }
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