cleaning data.ipynb

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   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exploratory Data Analysis"
   ]
  },
  {
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   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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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>year</th>\n",
       "      <th>state</th>\n",
       "      <th>state_po</th>\n",
       "      <th>county</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>office</th>\n",
       "      <th>candidate</th>\n",
       "      <th>party</th>\n",
       "      <th>candidatevotes</th>\n",
       "      <th>totalvotes</th>\n",
       "      <th>version</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>President</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>democrat</td>\n",
       "      <td>5936.0</td>\n",
       "      <td>24973.0</td>\n",
       "      <td>20190722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>President</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>republican</td>\n",
       "      <td>18172.0</td>\n",
       "      <td>24973.0</td>\n",
       "      <td>20190722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>President</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>865.0</td>\n",
       "      <td>24973.0</td>\n",
       "      <td>20190722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>President</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>democrat</td>\n",
       "      <td>18458.0</td>\n",
       "      <td>95215.0</td>\n",
       "      <td>20190722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>President</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>republican</td>\n",
       "      <td>72883.0</td>\n",
       "      <td>95215.0</td>\n",
       "      <td>20190722.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     year    state state_po  ... candidatevotes  totalvotes     version\n",
       "0  2016.0  Alabama       AL  ...         5936.0     24973.0  20190722.0\n",
       "1  2016.0  Alabama       AL  ...        18172.0     24973.0  20190722.0\n",
       "2  2016.0  Alabama       AL  ...          865.0     24973.0  20190722.0\n",
       "3  2016.0  Alabama       AL  ...        18458.0     95215.0  20190722.0\n",
       "4  2016.0  Alabama       AL  ...        72883.0     95215.0  20190722.0\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "### Getting an overview of the data\n",
    "dask_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9474 entries, 0 to 9473\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   year            9474 non-null   float64\n",
      " 1   state           9474 non-null   object \n",
      " 2   state_po        9462 non-null   object \n",
      " 3   county          9474 non-null   object \n",
      " 4   FIPS            9462 non-null   float64\n",
      " 5   office          9474 non-null   object \n",
      " 6   candidate       9474 non-null   object \n",
      " 7   party           6316 non-null   object \n",
      " 8   candidatevotes  9468 non-null   float64\n",
      " 9   totalvotes      9474 non-null   float64\n",
      " 10  version         9474 non-null   float64\n",
      "dtypes: float64(5), object(6)\n",
      "memory usage: 814.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Getting overview of the the data type (dtype) of all the features and get an overview of features with missing values via the 'Non-Null count'\n",
    "dask_df.compute().info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an attempt to manage memory, Dask takes all the numeric values as float and non-numeric values as objects"
   ]
  }
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