BasicEDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataCsv = pd.read_csv(\"data.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Publication number</th>\n",
       "      <th>Category</th>\n",
       "      <th>Publication number.1</th>\n",
       "      <th>Category.1</th>\n",
       "      <th>Publication number.2</th>\n",
       "      <th>Category.2</th>\n",
       "      <th>Publication number.3</th>\n",
       "      <th>Category.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US9040302B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US9012694B2</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9277674B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>US9646356B1</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US9040109B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US9012188B2</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9277385B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>US9645576B2</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US9040107B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US9010952B2</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9277311B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>US9622452B2</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US9040036B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US8987530B2</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9277191B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>US9599482B2</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US9038991B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US8981165B2</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9276431B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>US9598078B2</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Publication number     Category Publication number.1 Category.1  \\\n",
       "0        US9040302B2  Non-Alcohol          US9012694B2    Alcohol   \n",
       "1        US9040109B2  Non-Alcohol          US9012188B2    Alcohol   \n",
       "2        US9040107B2  Non-Alcohol          US9010952B2    Alcohol   \n",
       "3        US9040036B2  Non-Alcohol          US8987530B2    Alcohol   \n",
       "4        US9038991B2  Non-Alcohol          US8981165B2    Alcohol   \n",
       "\n",
       "  Publication number.2               Category.2 Publication number.3  \\\n",
       "0          US9277674B2  Non-Autonomous Vehicles          US9646356B1   \n",
       "1          US9277385B2  Non-Autonomous Vehicles          US9645576B2   \n",
       "2          US9277311B2  Non-Autonomous Vehicles          US9622452B2   \n",
       "3          US9277191B2  Non-Autonomous Vehicles          US9599482B2   \n",
       "4          US9276431B2  Non-Autonomous Vehicles          US9598078B2   \n",
       "\n",
       "            Category.3  \n",
       "0  Autonomous Vehicles  \n",
       "1  Autonomous Vehicles  \n",
       "2  Autonomous Vehicles  \n",
       "3  Autonomous Vehicles  \n",
       "4  Autonomous Vehicles  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataCsv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataCsv.iloc[:,[1,3,5,7]]=[\"Non-Alcohol\",\"Alcohol\", \"Non-Autonomous Vehicles\",\"Autonomous Vehicles\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nnew_header = dataCsv.iloc[0] #grab the first row for the header\\ndataCsv = dataCsv[1:] #take the data less the header row\\ndataCsv.columns = new_header\\n'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# remove unnammed columns\n",
    "dataCsv = dataCsv.loc[:, ~dataCsv.columns.str.contains('^Unnamed')]\n",
    "\"\"\"\n",
    "new_header = dataCsv.iloc[0] #grab the first row for the header\n",
    "dataCsv = dataCsv[1:] #take the data less the header row\n",
    "dataCsv.columns = new_header\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Publication number</th>\n",
       "      <th>Category</th>\n",
       "      <th>Publication number.1</th>\n",
       "      <th>Category.1</th>\n",
       "      <th>Publication number.2</th>\n",
       "      <th>Category.2</th>\n",
       "      <th>Publication number.3</th>\n",
       "      <th>Category.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>WO2015032008A1</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US20160176589A1</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>CN103354991B</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>CN103281742B</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>WO2015031940A1</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US20160175783A1</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9257926B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>CN102806912B</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>WO2015003995A3</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US20160175782A1</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9257918B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>CN206091477U</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>WO2014201037A3</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US20160175781A1</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9257867B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>EP2544928B1</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>WO2015031637A1</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>US20160145551A1</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>US9257832B2</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>EP2758295B1</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Publication number     Category Publication number.1 Category.1  \\\n",
       "295     WO2015032008A1  Non-Alcohol      US20160176589A1    Alcohol   \n",
       "296     WO2015031940A1  Non-Alcohol      US20160175783A1    Alcohol   \n",
       "297     WO2015003995A3  Non-Alcohol      US20160175782A1    Alcohol   \n",
       "298     WO2014201037A3  Non-Alcohol      US20160175781A1    Alcohol   \n",
       "299     WO2015031637A1  Non-Alcohol      US20160145551A1    Alcohol   \n",
       "\n",
       "    Publication number.2               Category.2 Publication number.3  \\\n",
       "295         CN103354991B  Non-Autonomous Vehicles         CN103281742B   \n",
       "296          US9257926B2  Non-Autonomous Vehicles         CN102806912B   \n",
       "297          US9257918B2  Non-Autonomous Vehicles         CN206091477U   \n",
       "298          US9257867B2  Non-Autonomous Vehicles          EP2544928B1   \n",
       "299          US9257832B2  Non-Autonomous Vehicles          EP2758295B1   \n",
       "\n",
       "              Category.3  \n",
       "295  Autonomous Vehicles  \n",
       "296  Autonomous Vehicles  \n",
       "297  Autonomous Vehicles  \n",
       "298  Autonomous Vehicles  \n",
       "299  Autonomous Vehicles  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataCsv.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataCsv.to_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###############################\n",
    "############  EDA  ############\n",
    "###############################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_csv = pd.read_csv(\"test.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Publication number</th>\n",
       "      <th>Category</th>\n",
       "      <th>Publication number.1</th>\n",
       "      <th>Category.1</th>\n",
       "      <th>Publication number.2</th>\n",
       "      <th>Category.2</th>\n",
       "      <th>Publication number.3</th>\n",
       "      <th>Category.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9040302</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>9012694</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>9277674</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>9646356</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9040109</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>9012188</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>9277385</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>9645576</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9040107</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>9010952</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>9277311</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>9622452</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9040036</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>8987530</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>9277191</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>9599482</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9038991</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "      <td>8981165</td>\n",
       "      <td>Alcohol</td>\n",
       "      <td>9276431</td>\n",
       "      <td>Non-Autonomous Vehicles</td>\n",
       "      <td>9598078</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Publication number     Category  Publication number.1 Category.1  \\\n",
       "0             9040302  Non-Alcohol               9012694    Alcohol   \n",
       "1             9040109  Non-Alcohol               9012188    Alcohol   \n",
       "2             9040107  Non-Alcohol               9010952    Alcohol   \n",
       "3             9040036  Non-Alcohol               8987530    Alcohol   \n",
       "4             9038991  Non-Alcohol               8981165    Alcohol   \n",
       "\n",
       "   Publication number.2               Category.2  Publication number.3  \\\n",
       "0               9277674  Non-Autonomous Vehicles               9646356   \n",
       "1               9277385  Non-Autonomous Vehicles               9645576   \n",
       "2               9277311  Non-Autonomous Vehicles               9622452   \n",
       "3               9277191  Non-Autonomous Vehicles               9599482   \n",
       "4               9276431  Non-Autonomous Vehicles               9598078   \n",
       "\n",
       "            Category.3  \n",
       "0  Autonomous Vehicles  \n",
       "1  Autonomous Vehicles  \n",
       "2  Autonomous Vehicles  \n",
       "3  Autonomous Vehicles  \n",
       "4  Autonomous Vehicles  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerintData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Publication number</th>\n",
       "      <th>Publication number.1</th>\n",
       "      <th>Publication number.2</th>\n",
       "      <th>Publication number.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>3.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.516569e+09</td>\n",
       "      <td>9.533417e+09</td>\n",
       "      <td>5.257887e+08</td>\n",
       "      <td>5.287028e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.873167e+09</td>\n",
       "      <td>9.973553e+09</td>\n",
       "      <td>8.625534e+08</td>\n",
       "      <td>8.625964e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.432321e+06</td>\n",
       "      <td>2.783900e+04</td>\n",
       "      <td>1.421460e+06</td>\n",
       "      <td>2.436620e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.859976e+06</td>\n",
       "      <td>9.666260e+06</td>\n",
       "      <td>2.827759e+06</td>\n",
       "      <td>9.534319e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.012510e+06</td>\n",
       "      <td>2.015034e+09</td>\n",
       "      <td>9.273610e+06</td>\n",
       "      <td>1.066921e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.015063e+09</td>\n",
       "      <td>2.017011e+10</td>\n",
       "      <td>5.823487e+08</td>\n",
       "      <td>6.552852e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.015015e+10</td>\n",
       "      <td>2.018015e+10</td>\n",
       "      <td>2.016023e+09</td>\n",
       "      <td>2.017015e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Publication number  Publication number.1  Publication number.2  \\\n",
       "count        3.000000e+02          3.000000e+02          3.000000e+02   \n",
       "mean         4.516569e+09          9.533417e+09          5.257887e+08   \n",
       "std          7.873167e+09          9.973553e+09          8.625534e+08   \n",
       "min          1.432321e+06          2.783900e+04          1.421460e+06   \n",
       "25%          2.859976e+06          9.666260e+06          2.827759e+06   \n",
       "50%          9.012510e+06          2.015034e+09          9.273610e+06   \n",
       "75%          2.015063e+09          2.017011e+10          5.823487e+08   \n",
       "max          2.015015e+10          2.018015e+10          2.016023e+09   \n",
       "\n",
       "       Publication number.3  \n",
       "count          3.000000e+02  \n",
       "mean           5.287028e+09  \n",
       "std            8.625964e+09  \n",
       "min            2.436620e+06  \n",
       "25%            9.534319e+06  \n",
       "50%            1.066921e+08  \n",
       "75%            6.552852e+09  \n",
       "max            2.017015e+10  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centerintData.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Side-by-Side Histogram with Multiple Airlines')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a separate list for each airline\n",
    "x1 = list(centerintData.iloc[:,0])\n",
    "x2 = list(centerintData.iloc[:,2])\n",
    "x3 = list(centerintData.iloc[:,4])\n",
    "x4 = list(centerintData.iloc[:,6])\n",
    "# Assign colors for each airline and the names\n",
    "colors = ['#E69F00', '#56B4E9', '#F0E442', '#009E73']\n",
    "names = ['United Air Lines Inc.', 'JetBlue Airways', 'ExpressJet Airlines Inc',\n",
    "         'Delta Air Lines Inc.']\n",
    "         \n",
    "# Make the histogram using a list of lists\n",
    "# Normalize the flights and assign colors and names\n",
    "plt.rcParams[\"figure.figsize\"] = [16,9]\n",
    "plt.hist([x1, x2, x3, x4], bins = 2, density=True,\n",
    "         color = colors, label=names)\n",
    "\n",
    "# Plot formatting\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel('Delay (min)')\n",
    "plt.ylabel('Normalized Flights')\n",
    "plt.title('Side-by-Side Histogram with Multiple Airlines')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f94f025b9e8>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import fastai\n",
    "\n",
    "\n",
    "centerintData.plot(kind = \"density\",figsize = (20,10),logx = True,logy = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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</th>\n",
       "      <th>cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US9040302B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US9040109B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US9040107B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US9040036B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US9038991B2</td>\n",
       "      <td>Non-Alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>CN103281742B</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>CN102806912B</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>CN206091477U</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>EP2544928B1</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>EP2758295B1</td>\n",
       "      <td>Autonomous Vehicles</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              num                  cat\n",
       "0     US9040302B2          Non-Alcohol\n",
       "1     US9040109B2          Non-Alcohol\n",
       "2     US9040107B2          Non-Alcohol\n",
       "3     US9040036B2          Non-Alcohol\n",
       "4     US9038991B2          Non-Alcohol\n",
       "..            ...                  ...\n",
       "595  CN103281742B  Autonomous Vehicles\n",
       "596  CN102806912B  Autonomous Vehicles\n",
       "597  CN206091477U  Autonomous Vehicles\n",
       "598   EP2544928B1  Autonomous Vehicles\n",
       "599   EP2758295B1  Autonomous Vehicles\n",
       "\n",
       "[600 rows x 2 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combinedCsv = pd.DataFrame(dataCsv.iloc[:,[0,1]]).rename(columns={\"Publication number\":\"num\",\"Category\":\"cat\"})\n",
    "new_cols = [\"num\",\"cat\"]\n",
    "#combinedCsv.columns= new_cols\n",
    "combinedCsv\n",
    "combinedCsv.append(pd.DataFrame(dataCsv.iloc[:,[2,3]]).rename(columns={\"Publication number.1\":\"num\",\"Category.1\":\"cat\"}),ignore_index = True)\n",
    "combinedCsv.append(pd.DataFrame(dataCsv.iloc[:,[4,5]]).rename(columns={\"Publication number.2\":\"num\",\"Category.2\":\"cat\"}),ignore_index = True)\n",
    "combinedCsv.append(pd.DataFrame(dataCsv.iloc[:,[6,7]]).rename(columns={\"Publication number.3\":\"num\",\"Category.3\":\"cat\"}),ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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</th>\n",
       "      <th>cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [num, cat]\n",
       "Index: []"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combinedCsv.iloc[399:502]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "combinedCsv.columns= new_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
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}