1  {
   2   "cells": [
   3    {
   4     "cell_type": "code",
   5     "execution_count": 3,
   6     "id": "e40e7a4d",
   7     "metadata": {},
   8     "outputs": [],
   9     "source": [
  10      "from pytrends.request import TrendReq"
  11     ]
  12    },
  13    {
  14     "cell_type": "code",
  15     "execution_count": 4,
  16     "id": "0817481a",
  17     "metadata": {},
  18     "outputs": [],
  19     "source": [
  20      "t= TrendReq()"
  21     ]
  22    },
  23    {
  24     "cell_type": "code",
  25     "execution_count": 28,
  26     "id": "7941e2aa",
  27     "metadata": {},
  28     "outputs": [],
  29     "source": [
  30      "keywords=['rahul gandhi', 'narendra modi']\n",
  31      "t.build_payload(keywords, cat=0, timeframe='today 5-y', geo='IN', gprop='')"
  32     ]
  33    },
  34    {
  35     "cell_type": "code",
  36     "execution_count": 31,
  37     "id": "727a6628",
  38     "metadata": {},
  39     "outputs": [],
  40     "source": [
  41      "df=t.interest_over_time()[:-1]"
  42     ]
  43    },
  44    {
  45     "cell_type": "code",
  46     "execution_count": 32,
  47     "id": "15d9e364",
  48     "metadata": {},
  49     "outputs": [
  50      {
  51       "data": {
  52        "text/plain": [
  53         "<AxesSubplot:xlabel='date'>"
  54        ]
  55       },
  56       "execution_count": 32,
  57       "metadata": {},
  58       "output_type": "execute_result"
  59      },
  60      {
  61       "data": {
  62        "image/png": 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\n",
  63        "text/plain": [
  64         "<Figure size 1080x360 with 1 Axes>"
  65        ]
  66       },
  67       "metadata": {
  68        "needs_background": "light"
  69       },
  70       "output_type": "display_data"
  71      }
  72     ],
  73     "source": [
  74      "df=df.rolling(10).mean()\n",
  75      "df.dropna(axis=0, inplace= True)\n",
  76      "df=df.div(df.iloc[0,:])\n",
  77      "df.plot(figsize=(15,5))"
  78     ]
  79    },
  80    {
  81     "cell_type": "code",
  82     "execution_count": null,
  83     "id": "a65f7f60",
  84     "metadata": {},
  85     "outputs": [],
  86     "source": []
  87    },
  88    {
  89     "cell_type": "code",
  90     "execution_count": 40,
  91     "id": "a592f0ef",
  92     "metadata": {},
  93     "outputs": [
  94      {
  95       "name": "stdout",
  96       "output_type": "stream",
  97       "text": [
  98        "The request failed: Google returned a response with code 500.\n"
  99       ]
 100      }
 101     ],
 102     "source": [
 103      "hourly=t.get_historical_interest(keywords, year_start=2021, month_start=6, day_start=1, hour_start=0, year_end=2021, month_end=6, day_end=30, hour_end=23, cat=0, geo='IN', gprop='', sleep=0)"
 104     ]
 105    },
 106    {
 107     "cell_type": "code",
 108     "execution_count": 43,
 109     "id": "c1b403bd",
 110     "metadata": {},
 111     "outputs": [
 112      {
 113       "data": {
 114        "text/plain": [
 115         "<AxesSubplot:xlabel='date'>"
 116        ]
 117       },
 118       "execution_count": 43,
 119       "metadata": {},
 120       "output_type": "execute_result"
 121      },
 122      {
 123       "data": {
 124        "image/png": 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\n",
 125        "text/plain": [
 126         "<Figure size 1080x360 with 1 Axes>"
 127        ]
 128       },
 129       "metadata": {
 130        "needs_background": "light"
 131       },
 132       "output_type": "display_data"
 133      }
 134     ],
 135     "source": [
 136      "df=hourly[:-1].copy()\n",
 137      "df=df.rolling(10).mean()\n",
 138      "df.dropna(axis=0, inplace= True)\n",
 139      "df=df.div(df.iloc[0,:])\n",
 140      "df.plot(figsize=(15,5))"
 141     ]
 142    },
 143    {
 144     "cell_type": "code",
 145     "execution_count": null,
 146     "id": "3dcee745",
 147     "metadata": {},
 148     "outputs": [],
 149     "source": []
 150    },
 151    {
 152     "cell_type": "code",
 153     "execution_count": null,
 154     "id": "b65da314",
 155     "metadata": {},
 156     "outputs": [],
 157     "source": []
 158    },
 159    {
 160     "cell_type": "code",
 161     "execution_count": 48,
 162     "id": "868abbb5",
 163     "metadata": {},
 164     "outputs": [],
 165     "source": [
 166      "keywords=['rahul gandhi', 'narendra modi']\n",
 167      "t.build_payload(keywords, cat=0, timeframe='today 3-m', geo='IN', gprop='')\n",
 168      "region= t.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=False)"
 169     ]
 170    },
 171    {
 172     "cell_type": "code",
 173     "execution_count": 49,
 174     "id": "a42a2e7d",
 175     "metadata": {},
 176     "outputs": [
 177      {
 178       "data": {
 179        "text/html": [
 180         "<div>\n",
 181         "<style scoped>\n",
 182         "    .dataframe tbody tr th:only-of-type {\n",
 183         "        vertical-align: middle;\n",
 184         "    }\n",
 185         "\n",
 186         "    .dataframe tbody tr th {\n",
 187         "        vertical-align: top;\n",
 188         "    }\n",
 189         "\n",
 190         "    .dataframe thead th {\n",
 191         "        text-align: right;\n",
 192         "    }\n",
 193         "</style>\n",
 194         "<table border=\"1\" class=\"dataframe\">\n",
 195         "  <thead>\n",
 196         "    <tr style=\"text-align: right;\">\n",
 197         "      <th></th>\n",
 198         "      <th>rahul gandhi</th>\n",
 199         "      <th>narendra modi</th>\n",
 200         "    </tr>\n",
 201         "    <tr>\n",
 202         "      <th>geoName</th>\n",
 203         "      <th></th>\n",
 204         "      <th></th>\n",
 205         "    </tr>\n",
 206         "  </thead>\n",
 207         "  <tbody>\n",
 208         "    <tr>\n",
 209         "      <th>Andaman and Nicobar Islands</th>\n",
 210         "      <td>50</td>\n",
 211         "      <td>50</td>\n",
 212         "    </tr>\n",
 213         "    <tr>\n",
 214         "      <th>Andhra Pradesh</th>\n",
 215         "      <td>23</td>\n",
 216         "      <td>77</td>\n",
 217         "    </tr>\n",
 218         "    <tr>\n",
 219         "      <th>Arunachal Pradesh</th>\n",
 220         "      <td>42</td>\n",
 221         "      <td>58</td>\n",
 222         "    </tr>\n",
 223         "    <tr>\n",
 224         "      <th>Assam</th>\n",
 225         "      <td>21</td>\n",
 226         "      <td>79</td>\n",
 227         "    </tr>\n",
 228         "    <tr>\n",
 229         "      <th>Bihar</th>\n",
 230         "      <td>26</td>\n",
 231         "      <td>74</td>\n",
 232         "    </tr>\n",
 233         "    <tr>\n",
 234         "      <th>Chandigarh</th>\n",
 235         "      <td>31</td>\n",
 236         "      <td>69</td>\n",
 237         "    </tr>\n",
 238         "    <tr>\n",
 239         "      <th>Chhattisgarh</th>\n",
 240         "      <td>33</td>\n",
 241         "      <td>67</td>\n",
 242         "    </tr>\n",
 243         "    <tr>\n",
 244         "      <th>Dadra and Nagar Haveli</th>\n",
 245         "      <td>33</td>\n",
 246         "      <td>67</td>\n",
 247         "    </tr>\n",
 248         "    <tr>\n",
 249         "      <th>Daman and Diu</th>\n",
 250         "      <td>14</td>\n",
 251         "      <td>86</td>\n",
 252         "    </tr>\n",
 253         "    <tr>\n",
 254         "      <th>Delhi</th>\n",
 255         "      <td>41</td>\n",
 256         "      <td>59</td>\n",
 257         "    </tr>\n",
 258         "    <tr>\n",
 259         "      <th>Goa</th>\n",
 260         "      <td>30</td>\n",
 261         "      <td>70</td>\n",
 262         "    </tr>\n",
 263         "    <tr>\n",
 264         "      <th>Gujarat</th>\n",
 265         "      <td>17</td>\n",
 266         "      <td>83</td>\n",
 267         "    </tr>\n",
 268         "    <tr>\n",
 269         "      <th>Haryana</th>\n",
 270         "      <td>29</td>\n",
 271         "      <td>71</td>\n",
 272         "    </tr>\n",
 273         "    <tr>\n",
 274         "      <th>Himachal Pradesh</th>\n",
 275         "      <td>32</td>\n",
 276         "      <td>68</td>\n",
 277         "    </tr>\n",
 278         "    <tr>\n",
 279         "      <th>Jammu and Kashmir</th>\n",
 280         "      <td>31</td>\n",
 281         "      <td>69</td>\n",
 282         "    </tr>\n",
 283         "    <tr>\n",
 284         "      <th>Jharkhand</th>\n",
 285         "      <td>29</td>\n",
 286         "      <td>71</td>\n",
 287         "    </tr>\n",
 288         "    <tr>\n",
 289         "      <th>Karnataka</th>\n",
 290         "      <td>30</td>\n",
 291         "      <td>70</td>\n",
 292         "    </tr>\n",
 293         "    <tr>\n",
 294         "      <th>Kerala</th>\n",
 295         "      <td>32</td>\n",
 296         "      <td>68</td>\n",
 297         "    </tr>\n",
 298         "    <tr>\n",
 299         "      <th>Lakshadweep</th>\n",
 300         "      <td>0</td>\n",
 301         "      <td>0</td>\n",
 302         "    </tr>\n",
 303         "    <tr>\n",
 304         "      <th>Madhya Pradesh</th>\n",
 305         "      <td>32</td>\n",
 306         "      <td>68</td>\n",
 307         "    </tr>\n",
 308         "    <tr>\n",
 309         "      <th>Maharashtra</th>\n",
 310         "      <td>30</td>\n",
 311         "      <td>70</td>\n",
 312         "    </tr>\n",
 313         "    <tr>\n",
 314         "      <th>Manipur</th>\n",
 315         "      <td>34</td>\n",
 316         "      <td>66</td>\n",
 317         "    </tr>\n",
 318         "    <tr>\n",
 319         "      <th>Meghalaya</th>\n",
 320         "      <td>27</td>\n",
 321         "      <td>73</td>\n",
 322         "    </tr>\n",
 323         "    <tr>\n",
 324         "      <th>Mizoram</th>\n",
 325         "      <td>16</td>\n",
 326         "      <td>84</td>\n",
 327         "    </tr>\n",
 328         "    <tr>\n",
 329         "      <th>Nagaland</th>\n",
 330         "      <td>54</td>\n",
 331         "      <td>46</td>\n",
 332         "    </tr>\n",
 333         "    <tr>\n",
 334         "      <th>Odisha</th>\n",
 335         "      <td>25</td>\n",
 336         "      <td>75</td>\n",
 337         "    </tr>\n",
 338         "    <tr>\n",
 339         "      <th>Puducherry</th>\n",
 340         "      <td>40</td>\n",
 341         "      <td>60</td>\n",
 342         "    </tr>\n",
 343         "    <tr>\n",
 344         "      <th>Punjab</th>\n",
 345         "      <td>32</td>\n",
 346         "      <td>68</td>\n",
 347         "    </tr>\n",
 348         "    <tr>\n",
 349         "      <th>Rajasthan</th>\n",
 350         "      <td>30</td>\n",
 351         "      <td>70</td>\n",
 352         "    </tr>\n",
 353         "    <tr>\n",
 354         "      <th>Sikkim</th>\n",
 355         "      <td>0</td>\n",
 356         "      <td>100</td>\n",
 357         "    </tr>\n",
 358         "    <tr>\n",
 359         "      <th>Tamil Nadu</th>\n",
 360         "      <td>37</td>\n",
 361         "      <td>63</td>\n",
 362         "    </tr>\n",
 363         "    <tr>\n",
 364         "      <th>Telangana</th>\n",
 365         "      <td>33</td>\n",
 366         "      <td>67</td>\n",
 367         "    </tr>\n",
 368         "    <tr>\n",
 369         "      <th>Tripura</th>\n",
 370         "      <td>22</td>\n",
 371         "      <td>78</td>\n",
 372         "    </tr>\n",
 373         "    <tr>\n",
 374         "      <th>Uttar Pradesh</th>\n",
 375         "      <td>33</td>\n",
 376         "      <td>67</td>\n",
 377         "    </tr>\n",
 378         "    <tr>\n",
 379         "      <th>Uttarakhand</th>\n",
 380         "      <td>38</td>\n",
 381         "      <td>62</td>\n",
 382         "    </tr>\n",
 383         "    <tr>\n",
 384         "      <th>West Bengal</th>\n",
 385         "      <td>20</td>\n",
 386         "      <td>80</td>\n",
 387         "    </tr>\n",
 388         "  </tbody>\n",
 389         "</table>\n",
 390         "</div>"
 391        ],
 392        "text/plain": [
 393         "                             rahul gandhi  narendra modi\n",
 394         "geoName                                                 \n",
 395         "Andaman and Nicobar Islands            50             50\n",
 396         "Andhra Pradesh                         23             77\n",
 397         "Arunachal Pradesh                      42             58\n",
 398         "Assam                                  21             79\n",
 399         "Bihar                                  26             74\n",
 400         "Chandigarh                             31             69\n",
 401         "Chhattisgarh                           33             67\n",
 402         "Dadra and Nagar Haveli                 33             67\n",
 403         "Daman and Diu                          14             86\n",
 404         "Delhi                                  41             59\n",
 405         "Goa                                    30             70\n",
 406         "Gujarat                                17             83\n",
 407         "Haryana                                29             71\n",
 408         "Himachal Pradesh                       32             68\n",
 409         "Jammu and Kashmir                      31             69\n",
 410         "Jharkhand                              29             71\n",
 411         "Karnataka                              30             70\n",
 412         "Kerala                                 32             68\n",
 413         "Lakshadweep                             0              0\n",
 414         "Madhya Pradesh                         32             68\n",
 415         "Maharashtra                            30             70\n",
 416         "Manipur                                34             66\n",
 417         "Meghalaya                              27             73\n",
 418         "Mizoram                                16             84\n",
 419         "Nagaland                               54             46\n",
 420         "Odisha                                 25             75\n",
 421         "Puducherry                             40             60\n",
 422         "Punjab                                 32             68\n",
 423         "Rajasthan                              30             70\n",
 424         "Sikkim                                  0            100\n",
 425         "Tamil Nadu                             37             63\n",
 426         "Telangana                              33             67\n",
 427         "Tripura                                22             78\n",
 428         "Uttar Pradesh                          33             67\n",
 429         "Uttarakhand                            38             62\n",
 430         "West Bengal                            20             80"
 431        ]
 432       },
 433       "execution_count": 49,
 434       "metadata": {},
 435       "output_type": "execute_result"
 436      }
 437     ],
 438     "source": [
 439      "region"
 440     ]
 441    },
 442    {
 443     "cell_type": "code",
 444     "execution_count": 55,
 445     "id": "85a9eb78",
 446     "metadata": {},
 447     "outputs": [
 448      {
 449       "data": {
 450        "text/html": [
 451         "<div>\n",
 452         "<style scoped>\n",
 453         "    .dataframe tbody tr th:only-of-type {\n",
 454         "        vertical-align: middle;\n",
 455         "    }\n",
 456         "\n",
 457         "    .dataframe tbody tr th {\n",
 458         "        vertical-align: top;\n",
 459         "    }\n",
 460         "\n",
 461         "    .dataframe thead th {\n",
 462         "        text-align: right;\n",
 463         "    }\n",
 464         "</style>\n",
 465         "<table border=\"1\" class=\"dataframe\">\n",
 466         "  <thead>\n",
 467         "    <tr style=\"text-align: right;\">\n",
 468         "      <th></th>\n",
 469         "      <th>rahul gandhi</th>\n",
 470         "      <th>narendra modi</th>\n",
 471         "    </tr>\n",
 472         "    <tr>\n",
 473         "      <th>geoName</th>\n",
 474         "      <th></th>\n",
 475         "      <th></th>\n",
 476         "    </tr>\n",
 477         "  </thead>\n",
 478         "  <tbody>\n",
 479         "    <tr>\n",
 480         "      <th>Andaman and Nicobar Islands</th>\n",
 481         "      <td>50</td>\n",
 482         "      <td>50</td>\n",
 483         "    </tr>\n",
 484         "    <tr>\n",
 485         "      <th>Andhra Pradesh</th>\n",
 486         "      <td>23</td>\n",
 487         "      <td>77</td>\n",
 488         "    </tr>\n",
 489         "    <tr>\n",
 490         "      <th>Arunachal Pradesh</th>\n",
 491         "      <td>42</td>\n",
 492         "      <td>58</td>\n",
 493         "    </tr>\n",
 494         "    <tr>\n",
 495         "      <th>Assam</th>\n",
 496         "      <td>21</td>\n",
 497         "      <td>79</td>\n",
 498         "    </tr>\n",
 499         "    <tr>\n",
 500         "      <th>Bihar</th>\n",
 501         "      <td>26</td>\n",
 502         "      <td>74</td>\n",
 503         "    </tr>\n",
 504         "    <tr>\n",
 505         "      <th>Chandigarh</th>\n",
 506         "      <td>31</td>\n",
 507         "      <td>69</td>\n",
 508         "    </tr>\n",
 509         "    <tr>\n",
 510         "      <th>Chhattisgarh</th>\n",
 511         "      <td>33</td>\n",
 512         "      <td>67</td>\n",
 513         "    </tr>\n",
 514         "    <tr>\n",
 515         "      <th>Dadra and Nagar Haveli</th>\n",
 516         "      <td>33</td>\n",
 517         "      <td>67</td>\n",
 518         "    </tr>\n",
 519         "    <tr>\n",
 520         "      <th>Daman and Diu</th>\n",
 521         "      <td>14</td>\n",
 522         "      <td>86</td>\n",
 523         "    </tr>\n",
 524         "    <tr>\n",
 525         "      <th>Delhi</th>\n",
 526         "      <td>41</td>\n",
 527         "      <td>59</td>\n",
 528         "    </tr>\n",
 529         "    <tr>\n",
 530         "      <th>Goa</th>\n",
 531         "      <td>30</td>\n",
 532         "      <td>70</td>\n",
 533         "    </tr>\n",
 534         "    <tr>\n",
 535         "      <th>Gujarat</th>\n",
 536         "      <td>17</td>\n",
 537         "      <td>83</td>\n",
 538         "    </tr>\n",
 539         "    <tr>\n",
 540         "      <th>Haryana</th>\n",
 541         "      <td>29</td>\n",
 542         "      <td>71</td>\n",
 543         "    </tr>\n",
 544         "    <tr>\n",
 545         "      <th>Himachal Pradesh</th>\n",
 546         "      <td>32</td>\n",
 547         "      <td>68</td>\n",
 548         "    </tr>\n",
 549         "    <tr>\n",
 550         "      <th>Jammu and Kashmir</th>\n",
 551         "      <td>31</td>\n",
 552         "      <td>69</td>\n",
 553         "    </tr>\n",
 554         "    <tr>\n",
 555         "      <th>Jharkhand</th>\n",
 556         "      <td>29</td>\n",
 557         "      <td>71</td>\n",
 558         "    </tr>\n",
 559         "    <tr>\n",
 560         "      <th>Karnataka</th>\n",
 561         "      <td>30</td>\n",
 562         "      <td>70</td>\n",
 563         "    </tr>\n",
 564         "    <tr>\n",
 565         "      <th>Kerala</th>\n",
 566         "      <td>32</td>\n",
 567         "      <td>68</td>\n",
 568         "    </tr>\n",
 569         "    <tr>\n",
 570         "      <th>Lakshadweep</th>\n",
 571         "      <td>0</td>\n",
 572         "      <td>0</td>\n",
 573         "    </tr>\n",
 574         "    <tr>\n",
 575         "      <th>Madhya Pradesh</th>\n",
 576         "      <td>32</td>\n",
 577         "      <td>68</td>\n",
 578         "    </tr>\n",
 579         "    <tr>\n",
 580         "      <th>Maharashtra</th>\n",
 581         "      <td>30</td>\n",
 582         "      <td>70</td>\n",
 583         "    </tr>\n",
 584         "    <tr>\n",
 585         "      <th>Manipur</th>\n",
 586         "      <td>34</td>\n",
 587         "      <td>66</td>\n",
 588         "    </tr>\n",
 589         "    <tr>\n",
 590         "      <th>Meghalaya</th>\n",
 591         "      <td>27</td>\n",
 592         "      <td>73</td>\n",
 593         "    </tr>\n",
 594         "    <tr>\n",
 595         "      <th>Mizoram</th>\n",
 596         "      <td>16</td>\n",
 597         "      <td>84</td>\n",
 598         "    </tr>\n",
 599         "    <tr>\n",
 600         "      <th>Nagaland</th>\n",
 601         "      <td>54</td>\n",
 602         "      <td>46</td>\n",
 603         "    </tr>\n",
 604         "    <tr>\n",
 605         "      <th>Odisha</th>\n",
 606         "      <td>25</td>\n",
 607         "      <td>75</td>\n",
 608         "    </tr>\n",
 609         "    <tr>\n",
 610         "      <th>Puducherry</th>\n",
 611         "      <td>40</td>\n",
 612         "      <td>60</td>\n",
 613         "    </tr>\n",
 614         "    <tr>\n",
 615         "      <th>Punjab</th>\n",
 616         "      <td>32</td>\n",
 617         "      <td>68</td>\n",
 618         "    </tr>\n",
 619         "    <tr>\n",
 620         "      <th>Rajasthan</th>\n",
 621         "      <td>30</td>\n",
 622         "      <td>70</td>\n",
 623         "    </tr>\n",
 624         "    <tr>\n",
 625         "      <th>Sikkim</th>\n",
 626         "      <td>0</td>\n",
 627         "      <td>100</td>\n",
 628         "    </tr>\n",
 629         "    <tr>\n",
 630         "      <th>Tamil Nadu</th>\n",
 631         "      <td>37</td>\n",
 632         "      <td>63</td>\n",
 633         "    </tr>\n",
 634         "    <tr>\n",
 635         "      <th>Telangana</th>\n",
 636         "      <td>33</td>\n",
 637         "      <td>67</td>\n",
 638         "    </tr>\n",
 639         "    <tr>\n",
 640         "      <th>Tripura</th>\n",
 641         "      <td>22</td>\n",
 642         "      <td>78</td>\n",
 643         "    </tr>\n",
 644         "    <tr>\n",
 645         "      <th>Uttar Pradesh</th>\n",
 646         "      <td>33</td>\n",
 647         "      <td>67</td>\n",
 648         "    </tr>\n",
 649         "    <tr>\n",
 650         "      <th>Uttarakhand</th>\n",
 651         "      <td>38</td>\n",
 652         "      <td>62</td>\n",
 653         "    </tr>\n",
 654         "    <tr>\n",
 655         "      <th>West Bengal</th>\n",
 656         "      <td>20</td>\n",
 657         "      <td>80</td>\n",
 658         "    </tr>\n",
 659         "  </tbody>\n",
 660         "</table>\n",
 661         "</div>"
 662        ],
 663        "text/plain": [
 664         "                             rahul gandhi  narendra modi\n",
 665         "geoName                                                 \n",
 666         "Andaman and Nicobar Islands            50             50\n",
 667         "Andhra Pradesh                         23             77\n",
 668         "Arunachal Pradesh                      42             58\n",
 669         "Assam                                  21             79\n",
 670         "Bihar                                  26             74\n",
 671         "Chandigarh                             31             69\n",
 672         "Chhattisgarh                           33             67\n",
 673         "Dadra and Nagar Haveli                 33             67\n",
 674         "Daman and Diu                          14             86\n",
 675         "Delhi                                  41             59\n",
 676         "Goa                                    30             70\n",
 677         "Gujarat                                17             83\n",
 678         "Haryana                                29             71\n",
 679         "Himachal Pradesh                       32             68\n",
 680         "Jammu and Kashmir                      31             69\n",
 681         "Jharkhand                              29             71\n",
 682         "Karnataka                              30             70\n",
 683         "Kerala                                 32             68\n",
 684         "Lakshadweep                             0              0\n",
 685         "Madhya Pradesh                         32             68\n",
 686         "Maharashtra                            30             70\n",
 687         "Manipur                                34             66\n",
 688         "Meghalaya                              27             73\n",
 689         "Mizoram                                16             84\n",
 690         "Nagaland                               54             46\n",
 691         "Odisha                                 25             75\n",
 692         "Puducherry                             40             60\n",
 693         "Punjab                                 32             68\n",
 694         "Rajasthan                              30             70\n",
 695         "Sikkim                                  0            100\n",
 696         "Tamil Nadu                             37             63\n",
 697         "Telangana                              33             67\n",
 698         "Tripura                                22             78\n",
 699         "Uttar Pradesh                          33             67\n",
 700         "Uttarakhand                            38             62\n",
 701         "West Bengal                            20             80"
 702        ]
 703       },
 704       "execution_count": 55,
 705       "metadata": {},
 706       "output_type": "execute_result"
 707      }
 708     ],
 709     "source": [
 710      "region"
 711     ]
 712    },
 713    {
 714     "cell_type": "code",
 715     "execution_count": 58,
 716     "id": "a5bcce9e",
 717     "metadata": {},
 718     "outputs": [
 719      {
 720       "name": "stdout",
 721       "output_type": "stream",
 722       "text": [
 723        "{}\n"
 724       ]
 725      }
 726     ],
 727     "source": [
 728      "keywords=['rahul gandhi', 'narendra modi']\n",
 729      "t.build_payload(keywords, cat=0, timeframe='today 3-m', geo='IN', gprop='')\n",
 730      "print(t.related_topics())"
 731     ]
 732    },
 733    {
 734     "cell_type": "code",
 735     "execution_count": 60,
 736     "id": "6bfa2aa0",
 737     "metadata": {},
 738     "outputs": [],
 739     "source": [
 740      "related= t.related_queries()"
 741     ]
 742    },
 743    {
 744     "cell_type": "code",
 745     "execution_count": 63,
 746     "id": "6204d68a",
 747     "metadata": {},
 748     "outputs": [
 749      {
 750       "data": {
 751        "text/html": [
 752         "<div>\n",
 753         "<style scoped>\n",
 754         "    .dataframe tbody tr th:only-of-type {\n",
 755         "        vertical-align: middle;\n",
 756         "    }\n",
 757         "\n",
 758         "    .dataframe tbody tr th {\n",
 759         "        vertical-align: top;\n",
 760         "    }\n",
 761         "\n",
 762         "    .dataframe thead th {\n",
 763         "        text-align: right;\n",
 764         "    }\n",
 765         "</style>\n",
 766         "<table border=\"1\" class=\"dataframe\">\n",
 767         "  <thead>\n",
 768         "    <tr style=\"text-align: right;\">\n",
 769         "      <th></th>\n",
 770         "      <th>query</th>\n",
 771         "      <th>value</th>\n",
 772         "    </tr>\n",
 773         "  </thead>\n",
 774         "  <tbody>\n",
 775         "    <tr>\n",
 776         "      <th>0</th>\n",
 777         "      <td>modi</td>\n",
 778         "      <td>100</td>\n",
 779         "    </tr>\n",
 780         "    <tr>\n",
 781         "      <th>1</th>\n",
 782         "      <td>rahul gandhi news</td>\n",
 783         "      <td>79</td>\n",
 784         "    </tr>\n",
 785         "    <tr>\n",
 786         "      <th>2</th>\n",
 787         "      <td>rahul gandhi twitter</td>\n",
 788         "      <td>76</td>\n",
 789         "    </tr>\n",
 790         "    <tr>\n",
 791         "      <th>3</th>\n",
 792         "      <td>rahul gandhi age</td>\n",
 793         "      <td>71</td>\n",
 794         "    </tr>\n",
 795         "    <tr>\n",
 796         "      <th>4</th>\n",
 797         "      <td>congress</td>\n",
 798         "      <td>45</td>\n",
 799         "    </tr>\n",
 800         "    <tr>\n",
 801         "      <th>5</th>\n",
 802         "      <td>narendra modi</td>\n",
 803         "      <td>42</td>\n",
 804         "    </tr>\n",
 805         "    <tr>\n",
 806         "      <th>6</th>\n",
 807         "      <td>rahul gandhi wife</td>\n",
 808         "      <td>38</td>\n",
 809         "    </tr>\n",
 810         "    <tr>\n",
 811         "      <th>7</th>\n",
 812         "      <td>sonia gandhi</td>\n",
 813         "      <td>34</td>\n",
 814         "    </tr>\n",
 815         "    <tr>\n",
 816         "      <th>8</th>\n",
 817         "      <td>rahul gandhi corona</td>\n",
 818         "      <td>34</td>\n",
 819         "    </tr>\n",
 820         "    <tr>\n",
 821         "      <th>9</th>\n",
 822         "      <td>priyanka gandhi</td>\n",
 823         "      <td>32</td>\n",
 824         "    </tr>\n",
 825         "    <tr>\n",
 826         "      <th>10</th>\n",
 827         "      <td>rahul gandhi tweet</td>\n",
 828         "      <td>28</td>\n",
 829         "    </tr>\n",
 830         "    <tr>\n",
 831         "      <th>11</th>\n",
 832         "      <td>bjp</td>\n",
 833         "      <td>26</td>\n",
 834         "    </tr>\n",
 835         "    <tr>\n",
 836         "      <th>12</th>\n",
 837         "      <td>rahul gandhi birthday</td>\n",
 838         "      <td>22</td>\n",
 839         "    </tr>\n",
 840         "    <tr>\n",
 841         "      <th>13</th>\n",
 842         "      <td>rajiv gandhi</td>\n",
 843         "      <td>19</td>\n",
 844         "    </tr>\n",
 845         "    <tr>\n",
 846         "      <th>14</th>\n",
 847         "      <td>rahul gandhi education</td>\n",
 848         "      <td>18</td>\n",
 849         "    </tr>\n",
 850         "    <tr>\n",
 851         "      <th>15</th>\n",
 852         "      <td>indira gandhi</td>\n",
 853         "      <td>15</td>\n",
 854         "    </tr>\n",
 855         "    <tr>\n",
 856         "      <th>16</th>\n",
 857         "      <td>age of rahul gandhi</td>\n",
 858         "      <td>12</td>\n",
 859         "    </tr>\n",
 860         "    <tr>\n",
 861         "      <th>17</th>\n",
 862         "      <td>manmohan singh</td>\n",
 863         "      <td>11</td>\n",
 864         "    </tr>\n",
 865         "    <tr>\n",
 866         "      <th>18</th>\n",
 867         "      <td>amit shah</td>\n",
 868         "      <td>11</td>\n",
 869         "    </tr>\n",
 870         "    <tr>\n",
 871         "      <th>19</th>\n",
 872         "      <td>maza aaya rahul gandhi</td>\n",
 873         "      <td>10</td>\n",
 874         "    </tr>\n",
 875         "    <tr>\n",
 876         "      <th>20</th>\n",
 877         "      <td>salman khan</td>\n",
 878         "      <td>10</td>\n",
 879         "    </tr>\n",
 880         "    <tr>\n",
 881         "      <th>21</th>\n",
 882         "      <td>congress news</td>\n",
 883         "      <td>10</td>\n",
 884         "    </tr>\n",
 885         "    <tr>\n",
 886         "      <th>22</th>\n",
 887         "      <td>milkha singh</td>\n",
 888         "      <td>8</td>\n",
 889         "    </tr>\n",
 890         "    <tr>\n",
 891         "      <th>23</th>\n",
 892         "      <td>rahul gandhi net worth</td>\n",
 893         "      <td>8</td>\n",
 894         "    </tr>\n",
 895         "    <tr>\n",
 896         "      <th>24</th>\n",
 897         "      <td>rahul gandhi date of birth</td>\n",
 898         "      <td>5</td>\n",
 899         "    </tr>\n",
 900         "  </tbody>\n",
 901         "</table>\n",
 902         "</div>"
 903        ],
 904        "text/plain": [
 905         "                         query  value\n",
 906         "0                         modi    100\n",
 907         "1            rahul gandhi news     79\n",
 908         "2         rahul gandhi twitter     76\n",
 909         "3             rahul gandhi age     71\n",
 910         "4                     congress     45\n",
 911         "5                narendra modi     42\n",
 912         "6            rahul gandhi wife     38\n",
 913         "7                 sonia gandhi     34\n",
 914         "8          rahul gandhi corona     34\n",
 915         "9              priyanka gandhi     32\n",
 916         "10          rahul gandhi tweet     28\n",
 917         "11                         bjp     26\n",
 918         "12       rahul gandhi birthday     22\n",
 919         "13                rajiv gandhi     19\n",
 920         "14      rahul gandhi education     18\n",
 921         "15               indira gandhi     15\n",
 922         "16         age of rahul gandhi     12\n",
 923         "17              manmohan singh     11\n",
 924         "18                   amit shah     11\n",
 925         "19      maza aaya rahul gandhi     10\n",
 926         "20                 salman khan     10\n",
 927         "21               congress news     10\n",
 928         "22                milkha singh      8\n",
 929         "23      rahul gandhi net worth      8\n",
 930         "24  rahul gandhi date of birth      5"
 931        ]
 932       },
 933       "execution_count": 63,
 934       "metadata": {},
 935       "output_type": "execute_result"
 936      }
 937     ],
 938     "source": [
 939      "related['rahul gandhi']['top']"
 940     ]
 941    },
 942    {
 943     "cell_type": "code",
 944     "execution_count": 64,
 945     "id": "40299896",
 946     "metadata": {},
 947     "outputs": [
 948      {
 949       "data": {
 950        "text/html": [
 951         "<div>\n",
 952         "<style scoped>\n",
 953         "    .dataframe tbody tr th:only-of-type {\n",
 954         "        vertical-align: middle;\n",
 955         "    }\n",
 956         "\n",
 957         "    .dataframe tbody tr th {\n",
 958         "        vertical-align: top;\n",
 959         "    }\n",
 960         "\n",
 961         "    .dataframe thead th {\n",
 962         "        text-align: right;\n",
 963         "    }\n",
 964         "</style>\n",
 965         "<table border=\"1\" class=\"dataframe\">\n",
 966         "  <thead>\n",
 967         "    <tr style=\"text-align: right;\">\n",
 968         "      <th></th>\n",
 969         "      <th>query</th>\n",
 970         "      <th>value</th>\n",
 971         "    </tr>\n",
 972         "  </thead>\n",
 973         "  <tbody>\n",
 974         "    <tr>\n",
 975         "      <th>0</th>\n",
 976         "      <td>milkha singh</td>\n",
 977         "      <td>40500</td>\n",
 978         "    </tr>\n",
 979         "    <tr>\n",
 980         "      <th>1</th>\n",
 981         "      <td>rahul gandhi covid positive</td>\n",
 982         "      <td>20300</td>\n",
 983         "    </tr>\n",
 984         "    <tr>\n",
 985         "      <th>2</th>\n",
 986         "      <td>rahul gandhi birthday wishes</td>\n",
 987         "      <td>20100</td>\n",
 988         "    </tr>\n",
 989         "    <tr>\n",
 990         "      <th>3</th>\n",
 991         "      <td>leeds united vs liverpool</td>\n",
 992         "      <td>13250</td>\n",
 993         "    </tr>\n",
 994         "    <tr>\n",
 995         "      <th>4</th>\n",
 996         "      <td>liverpool fc</td>\n",
 997         "      <td>6600</td>\n",
 998         "    </tr>\n",
 999         "    <tr>\n",
1000         "      <th>5</th>\n",
1001         "      <td>rahul gandhi corona positive</td>\n",
1002         "      <td>6550</td>\n",
1003         "    </tr>\n",
1004         "    <tr>\n",
1005         "      <th>6</th>\n",
1006         "      <td>rahul gandhi corona</td>\n",
1007         "      <td>1100</td>\n",
1008         "    </tr>\n",
1009         "    <tr>\n",
1010         "      <th>7</th>\n",
1011         "      <td>manmohan singh</td>\n",
1012         "      <td>300</td>\n",
1013         "    </tr>\n",
1014         "    <tr>\n",
1015         "      <th>8</th>\n",
1016         "      <td>rahul gandhi education</td>\n",
1017         "      <td>150</td>\n",
1018         "    </tr>\n",
1019         "    <tr>\n",
1020         "      <th>9</th>\n",
1021         "      <td>maza aaya rahul gandhi</td>\n",
1022         "      <td>130</td>\n",
1023         "    </tr>\n",
1024         "    <tr>\n",
1025         "      <th>10</th>\n",
1026         "      <td>rahul gandhi birthday</td>\n",
1027         "      <td>120</td>\n",
1028         "    </tr>\n",
1029         "    <tr>\n",
1030         "      <th>11</th>\n",
1031         "      <td>bjp</td>\n",
1032         "      <td>110</td>\n",
1033         "    </tr>\n",
1034         "    <tr>\n",
1035         "      <th>12</th>\n",
1036         "      <td>rahul gandhi news</td>\n",
1037         "      <td>100</td>\n",
1038         "    </tr>\n",
1039         "    <tr>\n",
1040         "      <th>13</th>\n",
1041         "      <td>amit shah</td>\n",
1042         "      <td>100</td>\n",
1043         "    </tr>\n",
1044         "    <tr>\n",
1045         "      <th>14</th>\n",
1046         "      <td>rahul gandhi tweet</td>\n",
1047         "      <td>80</td>\n",
1048         "    </tr>\n",
1049         "    <tr>\n",
1050         "      <th>15</th>\n",
1051         "      <td>narendra modi</td>\n",
1052         "      <td>60</td>\n",
1053         "    </tr>\n",
1054         "    <tr>\n",
1055         "      <th>16</th>\n",
1056         "      <td>modi</td>\n",
1057         "      <td>50</td>\n",
1058         "    </tr>\n",
1059         "    <tr>\n",
1060         "      <th>17</th>\n",
1061         "      <td>rahul gandhi education qualification</td>\n",
1062         "      <td>50</td>\n",
1063         "    </tr>\n",
1064         "    <tr>\n",
1065         "      <th>18</th>\n",
1066         "      <td>rahul gandhi twitter</td>\n",
1067         "      <td>40</td>\n",
1068         "    </tr>\n",
1069         "  </tbody>\n",
1070         "</table>\n",
1071         "</div>"
1072        ],
1073        "text/plain": [
1074         "                                   query  value\n",
1075         "0                           milkha singh  40500\n",
1076         "1            rahul gandhi covid positive  20300\n",
1077         "2           rahul gandhi birthday wishes  20100\n",
1078         "3              leeds united vs liverpool  13250\n",
1079         "4                           liverpool fc   6600\n",
1080         "5           rahul gandhi corona positive   6550\n",
1081         "6                    rahul gandhi corona   1100\n",
1082         "7                         manmohan singh    300\n",
1083         "8                 rahul gandhi education    150\n",
1084         "9                 maza aaya rahul gandhi    130\n",
1085         "10                 rahul gandhi birthday    120\n",
1086         "11                                   bjp    110\n",
1087         "12                     rahul gandhi news    100\n",
1088         "13                             amit shah    100\n",
1089         "14                    rahul gandhi tweet     80\n",
1090         "15                         narendra modi     60\n",
1091         "16                                  modi     50\n",
1092         "17  rahul gandhi education qualification     50\n",
1093         "18                  rahul gandhi twitter     40"
1094        ]
1095       },
1096       "execution_count": 64,
1097       "metadata": {},
1098       "output_type": "execute_result"
1099      }
1100     ],
1101     "source": [
1102      "related['rahul gandhi']['rising']"
1103     ]
1104    },
1105    {
1106     "cell_type": "code",
1107     "execution_count": null,
1108     "id": "33e90ed9",
1109     "metadata": {},
1110     "outputs": [],
1111     "source": []
1112    },
1113    {
1114     "cell_type": "code",
1115     "execution_count": null,
1116     "id": "00fd1c53",
1117     "metadata": {},
1118     "outputs": [],
1119     "source": []
1120    },
1121    {
1122     "cell_type": "code",
1123     "execution_count": 65,
1124     "id": "16f8936c",
1125     "metadata": {},
1126     "outputs": [
1127      {
1128       "data": {
1129        "text/html": [
1130         "<div>\n",
1131         "<style scoped>\n",
1132         "    .dataframe tbody tr th:only-of-type {\n",
1133         "        vertical-align: middle;\n",
1134         "    }\n",
1135         "\n",
1136         "    .dataframe tbody tr th {\n",
1137         "        vertical-align: top;\n",
1138         "    }\n",
1139         "\n",
1140         "    .dataframe thead th {\n",
1141         "        text-align: right;\n",
1142         "    }\n",
1143         "</style>\n",
1144         "<table border=\"1\" class=\"dataframe\">\n",
1145         "  <thead>\n",
1146         "    <tr style=\"text-align: right;\">\n",
1147         "      <th></th>\n",
1148         "      <th>query</th>\n",
1149         "      <th>value</th>\n",
1150         "    </tr>\n",
1151         "  </thead>\n",
1152         "  <tbody>\n",
1153         "    <tr>\n",
1154         "      <th>0</th>\n",
1155         "      <td>narendra modi stadium</td>\n",
1156         "      <td>100</td>\n",
1157         "    </tr>\n",
1158         "    <tr>\n",
1159         "      <th>1</th>\n",
1160         "      <td>narendra modi india</td>\n",
1161         "      <td>72</td>\n",
1162         "    </tr>\n",
1163         "    <tr>\n",
1164         "      <th>2</th>\n",
1165         "      <td>india</td>\n",
1166         "      <td>70</td>\n",
1167         "    </tr>\n",
1168         "    <tr>\n",
1169         "      <th>3</th>\n",
1170         "      <td>prime minister narendra modi</td>\n",
1171         "      <td>63</td>\n",
1172         "    </tr>\n",
1173         "    <tr>\n",
1174         "      <th>4</th>\n",
1175         "      <td>pm narendra modi</td>\n",
1176         "      <td>54</td>\n",
1177         "    </tr>\n",
1178         "    <tr>\n",
1179         "      <th>5</th>\n",
1180         "      <td>twitter narendra modi</td>\n",
1181         "      <td>39</td>\n",
1182         "    </tr>\n",
1183         "    <tr>\n",
1184         "      <th>6</th>\n",
1185         "      <td>narendra modi news</td>\n",
1186         "      <td>35</td>\n",
1187         "    </tr>\n",
1188         "    <tr>\n",
1189         "      <th>7</th>\n",
1190         "      <td>narendra modi age</td>\n",
1191         "      <td>32</td>\n",
1192         "    </tr>\n",
1193         "    <tr>\n",
1194         "      <th>8</th>\n",
1195         "      <td>narendra modi live</td>\n",
1196         "      <td>30</td>\n",
1197         "    </tr>\n",
1198         "    <tr>\n",
1199         "      <th>9</th>\n",
1200         "      <td>narendra modi movie</td>\n",
1201         "      <td>25</td>\n",
1202         "    </tr>\n",
1203         "    <tr>\n",
1204         "      <th>10</th>\n",
1205         "      <td>narendra modi vaccine</td>\n",
1206         "      <td>16</td>\n",
1207         "    </tr>\n",
1208         "    <tr>\n",
1209         "      <th>11</th>\n",
1210         "      <td>narendra modi stadium pitch report</td>\n",
1211         "      <td>16</td>\n",
1212         "    </tr>\n",
1213         "    <tr>\n",
1214         "      <th>12</th>\n",
1215         "      <td>rahul gandhi</td>\n",
1216         "      <td>15</td>\n",
1217         "    </tr>\n",
1218         "    <tr>\n",
1219         "      <th>13</th>\n",
1220         "      <td>amit shah</td>\n",
1221         "      <td>15</td>\n",
1222         "    </tr>\n",
1223         "    <tr>\n",
1224         "      <th>14</th>\n",
1225         "      <td>narendra modi photo</td>\n",
1226         "      <td>13</td>\n",
1227         "    </tr>\n",
1228         "    <tr>\n",
1229         "      <th>15</th>\n",
1230         "      <td>pm narendra modi movie</td>\n",
1231         "      <td>13</td>\n",
1232         "    </tr>\n",
1233         "    <tr>\n",
1234         "      <th>16</th>\n",
1235         "      <td>narendra modi tweet</td>\n",
1236         "      <td>11</td>\n",
1237         "    </tr>\n",
1238         "    <tr>\n",
1239         "      <th>17</th>\n",
1240         "      <td>narendra modi education</td>\n",
1241         "      <td>10</td>\n",
1242         "    </tr>\n",
1243         "    <tr>\n",
1244         "      <th>18</th>\n",
1245         "      <td>bjp</td>\n",
1246         "      <td>10</td>\n",
1247         "    </tr>\n",
1248         "    <tr>\n",
1249         "      <th>19</th>\n",
1250         "      <td>narendra modi wife</td>\n",
1251         "      <td>9</td>\n",
1252         "    </tr>\n",
1253         "    <tr>\n",
1254         "      <th>20</th>\n",
1255         "      <td>age of narendra modi</td>\n",
1256         "      <td>9</td>\n",
1257         "    </tr>\n",
1258         "    <tr>\n",
1259         "      <th>21</th>\n",
1260         "      <td>narendra modi net worth</td>\n",
1261         "      <td>9</td>\n",
1262         "    </tr>\n",
1263         "    <tr>\n",
1264         "      <th>22</th>\n",
1265         "      <td>prime minister of india</td>\n",
1266         "      <td>9</td>\n",
1267         "    </tr>\n",
1268         "    <tr>\n",
1269         "      <th>23</th>\n",
1270         "      <td>narendra modi live today</td>\n",
1271         "      <td>8</td>\n",
1272         "    </tr>\n",
1273         "    <tr>\n",
1274         "      <th>24</th>\n",
1275         "      <td>narendra modi mother</td>\n",
1276         "      <td>8</td>\n",
1277         "    </tr>\n",
1278         "  </tbody>\n",
1279         "</table>\n",
1280         "</div>"
1281        ],
1282        "text/plain": [
1283         "                                 query  value\n",
1284         "0                narendra modi stadium    100\n",
1285         "1                  narendra modi india     72\n",
1286         "2                                india     70\n",
1287         "3         prime minister narendra modi     63\n",
1288         "4                     pm narendra modi     54\n",
1289         "5                twitter narendra modi     39\n",
1290         "6                   narendra modi news     35\n",
1291         "7                    narendra modi age     32\n",
1292         "8                   narendra modi live     30\n",
1293         "9                  narendra modi movie     25\n",
1294         "10               narendra modi vaccine     16\n",
1295         "11  narendra modi stadium pitch report     16\n",
1296         "12                        rahul gandhi     15\n",
1297         "13                           amit shah     15\n",
1298         "14                 narendra modi photo     13\n",
1299         "15              pm narendra modi movie     13\n",
1300         "16                 narendra modi tweet     11\n",
1301         "17             narendra modi education     10\n",
1302         "18                                 bjp     10\n",
1303         "19                  narendra modi wife      9\n",
1304         "20                age of narendra modi      9\n",
1305         "21             narendra modi net worth      9\n",
1306         "22             prime minister of india      9\n",
1307         "23            narendra modi live today      8\n",
1308         "24                narendra modi mother      8"
1309        ]
1310       },
1311       "execution_count": 65,
1312       "metadata": {},
1313       "output_type": "execute_result"
1314      }
1315     ],
1316     "source": [
1317      "related['narendra modi']['top']"
1318     ]
1319    },
1320    {
1321     "cell_type": "code",
1322     "execution_count": null,
1323     "id": "dff372cf",
1324     "metadata": {},
1325     "outputs": [],
1326     "source": []
1327    },
1328    {
1329     "cell_type": "code",
1330     "execution_count": null,
1331     "id": "a195abd3",
1332     "metadata": {},
1333     "outputs": [],
1334     "source": []
1335    },
1336    {
1337     "cell_type": "code",
1338     "execution_count": 66,
1339     "id": "40738a32",
1340     "metadata": {},
1341     "outputs": [
1342      {
1343       "data": {
1344        "text/html": [
1345         "<div>\n",
1346         "<style scoped>\n",
1347         "    .dataframe tbody tr th:only-of-type {\n",
1348         "        vertical-align: middle;\n",
1349         "    }\n",
1350         "\n",
1351         "    .dataframe tbody tr th {\n",
1352         "        vertical-align: top;\n",
1353         "    }\n",
1354         "\n",
1355         "    .dataframe thead th {\n",
1356         "        text-align: right;\n",
1357         "    }\n",
1358         "</style>\n",
1359         "<table border=\"1\" class=\"dataframe\">\n",
1360         "  <thead>\n",
1361         "    <tr style=\"text-align: right;\">\n",
1362         "      <th></th>\n",
1363         "      <th>query</th>\n",
1364         "      <th>value</th>\n",
1365         "    </tr>\n",
1366         "  </thead>\n",
1367         "  <tbody>\n",
1368         "    <tr>\n",
1369         "      <th>0</th>\n",
1370         "      <td>pbks vs kkr</td>\n",
1371         "      <td>40850</td>\n",
1372         "    </tr>\n",
1373         "    <tr>\n",
1374         "      <th>1</th>\n",
1375         "      <td>kkr vs rcb</td>\n",
1376         "      <td>20300</td>\n",
1377         "    </tr>\n",
1378         "    <tr>\n",
1379         "      <th>2</th>\n",
1380         "      <td>narendra modi crying</td>\n",
1381         "      <td>16300</td>\n",
1382         "    </tr>\n",
1383         "    <tr>\n",
1384         "      <th>3</th>\n",
1385         "      <td>narendra modi address to nation today</td>\n",
1386         "      <td>16250</td>\n",
1387         "    </tr>\n",
1388         "    <tr>\n",
1389         "      <th>4</th>\n",
1390         "      <td>narendra modi stadium pitch report</td>\n",
1391         "      <td>1850</td>\n",
1392         "    </tr>\n",
1393         "    <tr>\n",
1394         "      <th>5</th>\n",
1395         "      <td>pinarayi vijayan</td>\n",
1396         "      <td>800</td>\n",
1397         "    </tr>\n",
1398         "    <tr>\n",
1399         "      <th>6</th>\n",
1400         "      <td>narendra modi new house</td>\n",
1401         "      <td>400</td>\n",
1402         "    </tr>\n",
1403         "    <tr>\n",
1404         "      <th>7</th>\n",
1405         "      <td>nitin gadkari</td>\n",
1406         "      <td>300</td>\n",
1407         "    </tr>\n",
1408         "    <tr>\n",
1409         "      <th>8</th>\n",
1410         "      <td>narendra modi speech today</td>\n",
1411         "      <td>300</td>\n",
1412         "    </tr>\n",
1413         "    <tr>\n",
1414         "      <th>9</th>\n",
1415         "      <td>narendra modi tweet</td>\n",
1416         "      <td>300</td>\n",
1417         "    </tr>\n",
1418         "    <tr>\n",
1419         "      <th>10</th>\n",
1420         "      <td>narendra modi son</td>\n",
1421         "      <td>250</td>\n",
1422         "    </tr>\n",
1423         "    <tr>\n",
1424         "      <th>11</th>\n",
1425         "      <td>kangana ranaut</td>\n",
1426         "      <td>250</td>\n",
1427         "    </tr>\n",
1428         "    <tr>\n",
1429         "      <th>12</th>\n",
1430         "      <td>narendra modi live today</td>\n",
1431         "      <td>250</td>\n",
1432         "    </tr>\n",
1433         "    <tr>\n",
1434         "      <th>13</th>\n",
1435         "      <td>narendra modi vaccine</td>\n",
1436         "      <td>250</td>\n",
1437         "    </tr>\n",
1438         "    <tr>\n",
1439         "      <th>14</th>\n",
1440         "      <td>educational qualification of narendra modi</td>\n",
1441         "      <td>200</td>\n",
1442         "    </tr>\n",
1443         "    <tr>\n",
1444         "      <th>15</th>\n",
1445         "      <td>twitter narendra modi</td>\n",
1446         "      <td>180</td>\n",
1447         "    </tr>\n",
1448         "    <tr>\n",
1449         "      <th>16</th>\n",
1450         "      <td>narendra modi live</td>\n",
1451         "      <td>140</td>\n",
1452         "    </tr>\n",
1453         "    <tr>\n",
1454         "      <th>17</th>\n",
1455         "      <td>narendra modi news</td>\n",
1456         "      <td>70</td>\n",
1457         "    </tr>\n",
1458         "    <tr>\n",
1459         "      <th>18</th>\n",
1460         "      <td>narendra modi net worth</td>\n",
1461         "      <td>60</td>\n",
1462         "    </tr>\n",
1463         "  </tbody>\n",
1464         "</table>\n",
1465         "</div>"
1466        ],
1467        "text/plain": [
1468         "                                         query  value\n",
1469         "0                                  pbks vs kkr  40850\n",
1470         "1                                   kkr vs rcb  20300\n",
1471         "2                         narendra modi crying  16300\n",
1472         "3        narendra modi address to nation today  16250\n",
1473         "4           narendra modi stadium pitch report   1850\n",
1474         "5                             pinarayi vijayan    800\n",
1475         "6                      narendra modi new house    400\n",
1476         "7                                nitin gadkari    300\n",
1477         "8                   narendra modi speech today    300\n",
1478         "9                          narendra modi tweet    300\n",
1479         "10                           narendra modi son    250\n",
1480         "11                              kangana ranaut    250\n",
1481         "12                    narendra modi live today    250\n",
1482         "13                       narendra modi vaccine    250\n",
1483         "14  educational qualification of narendra modi    200\n",
1484         "15                       twitter narendra modi    180\n",
1485         "16                          narendra modi live    140\n",
1486         "17                          narendra modi news     70\n",
1487         "18                     narendra modi net worth     60"
1488        ]
1489       },
1490       "execution_count": 66,
1491       "metadata": {},
1492       "output_type": "execute_result"
1493      }
1494     ],
1495     "source": [
1496      "related['narendra modi']['rising']"
1497     ]
1498    },
1499    {
1500     "cell_type": "code",
1501     "execution_count": null,
1502     "id": "5a9dfa39",
1503     "metadata": {},
1504     "outputs": [],
1505     "source": []
1506    },
1507    {
1508     "cell_type": "code",
1509     "execution_count": null,
1510     "id": "e2b71663",
1511     "metadata": {},
1512     "outputs": [],
1513     "source": []
1514    },
1515    {
1516     "cell_type": "code",
1517     "execution_count": 78,
1518     "id": "062ea210",
1519     "metadata": {},
1520     "outputs": [],
1521     "source": [
1522      "kw_list = ['narendra modi']\n",
1523      "t.build_payload(kw_list, cat=0, timeframe='today 3-m', geo='IN')\n",
1524      "topic_modi=t.related_topics()"
1525     ]
1526    },
1527    {
1528     "cell_type": "code",
1529     "execution_count": 79,
1530     "id": "5872cb34",
1531     "metadata": {},
1532     "outputs": [
1533      {
1534       "data": {
1535        "text/html": [
1536         "<div>\n",
1537         "<style scoped>\n",
1538         "    .dataframe tbody tr th:only-of-type {\n",
1539         "        vertical-align: middle;\n",
1540         "    }\n",
1541         "\n",
1542         "    .dataframe tbody tr th {\n",
1543         "        vertical-align: top;\n",
1544         "    }\n",
1545         "\n",
1546         "    .dataframe thead th {\n",
1547         "        text-align: right;\n",
1548         "    }\n",
1549         "</style>\n",
1550         "<table border=\"1\" class=\"dataframe\">\n",
1551         "  <thead>\n",
1552         "    <tr style=\"text-align: right;\">\n",
1553         "      <th></th>\n",
1554         "      <th>value</th>\n",
1555         "      <th>formattedValue</th>\n",
1556         "      <th>link</th>\n",
1557         "      <th>topic_mid</th>\n",
1558         "      <th>topic_title</th>\n",
1559         "      <th>topic_type</th>\n",
1560         "    </tr>\n",
1561         "  </thead>\n",
1562         "  <tbody>\n",
1563         "    <tr>\n",
1564         "      <th>0</th>\n",
1565         "      <td>43200</td>\n",
1566         "      <td>Breakout</td>\n",
1567         "      <td>/trends/explore?q=/m/0127gk6w&amp;date=today+3-m&amp;g...</td>\n",
1568         "      <td>/m/0127gk6w</td>\n",
1569         "      <td>International Day of Yoga</td>\n",
1570         "      <td>Celebration</td>\n",
1571         "    </tr>\n",
1572         "    <tr>\n",
1573         "      <th>1</th>\n",
1574         "      <td>32950</td>\n",
1575         "      <td>Breakout</td>\n",
1576         "      <td>/trends/explore?q=/m/06m6d5&amp;date=today+3-m&amp;geo=IN</td>\n",
1577         "      <td>/m/06m6d5</td>\n",
1578         "      <td>M. A. Chidambaram Stadium</td>\n",
1579         "      <td>Cricket stadium</td>\n",
1580         "    </tr>\n",
1581         "    <tr>\n",
1582         "      <th>2</th>\n",
1583         "      <td>29050</td>\n",
1584         "      <td>Breakout</td>\n",
1585         "      <td>/trends/explore?q=/m/05f74n&amp;date=today+3-m&amp;geo=IN</td>\n",
1586         "      <td>/m/05f74n</td>\n",
1587         "      <td>Wankhede Stadium Mumbai</td>\n",
1588         "      <td>Cricket stadium in Mumbai, India</td>\n",
1589         "    </tr>\n",
1590         "    <tr>\n",
1591         "      <th>3</th>\n",
1592         "      <td>25450</td>\n",
1593         "      <td>Breakout</td>\n",
1594         "      <td>/trends/explore?q=/m/0f7gy&amp;date=today+3-m&amp;geo=IN</td>\n",
1595         "      <td>/m/0f7gy</td>\n",
1596         "      <td>Cabinet</td>\n",
1597         "      <td>Government</td>\n",
1598         "    </tr>\n",
1599         "    <tr>\n",
1600         "      <th>4</th>\n",
1601         "      <td>20700</td>\n",
1602         "      <td>Breakout</td>\n",
1603         "      <td>/trends/explore?q=/m/034x37&amp;date=today+3-m&amp;geo=IN</td>\n",
1604         "      <td>/m/034x37</td>\n",
1605         "      <td>Boundary</td>\n",
1606         "      <td>Topic</td>\n",
1607         "    </tr>\n",
1608         "    <tr>\n",
1609         "      <th>5</th>\n",
1610         "      <td>900</td>\n",
1611         "      <td>+900%</td>\n",
1612         "      <td>/trends/explore?q=/m/02clxk&amp;date=today+3-m&amp;geo=IN</td>\n",
1613         "      <td>/m/02clxk</td>\n",
1614         "      <td>Cricket pitch</td>\n",
1615         "      <td>Topic</td>\n",
1616         "    </tr>\n",
1617         "    <tr>\n",
1618         "      <th>6</th>\n",
1619         "      <td>900</td>\n",
1620         "      <td>+900%</td>\n",
1621         "      <td>/trends/explore?q=/m/0j42v4r&amp;date=today+3-m&amp;ge...</td>\n",
1622         "      <td>/m/0j42v4r</td>\n",
1623         "      <td>Arun Jaitley Stadium</td>\n",
1624         "      <td>Cricket stadium in Delhi, India</td>\n",
1625         "    </tr>\n",
1626         "    <tr>\n",
1627         "      <th>7</th>\n",
1628         "      <td>500</td>\n",
1629         "      <td>+500%</td>\n",
1630         "      <td>/trends/explore?q=/m/0279wtw&amp;date=today+3-m&amp;ge...</td>\n",
1631         "      <td>/m/0279wtw</td>\n",
1632         "      <td>Rajnath Singh</td>\n",
1633         "      <td>Minister of Defence of India</td>\n",
1634         "    </tr>\n",
1635         "    <tr>\n",
1636         "      <th>8</th>\n",
1637         "      <td>500</td>\n",
1638         "      <td>+500%</td>\n",
1639         "      <td>/trends/explore?q=/m/0c62hf&amp;date=today+3-m&amp;geo=IN</td>\n",
1640         "      <td>/m/0c62hf</td>\n",
1641         "      <td>M. A. Chidambaram</td>\n",
1642         "      <td>Indian industrialist</td>\n",
1643         "    </tr>\n",
1644         "    <tr>\n",
1645         "      <th>9</th>\n",
1646         "      <td>500</td>\n",
1647         "      <td>+500%</td>\n",
1648         "      <td>/trends/explore?q=/m/0dnnl&amp;date=today+3-m&amp;geo=IN</td>\n",
1649         "      <td>/m/0dnnl</td>\n",
1650         "      <td>Bowling (cricket)</td>\n",
1651         "      <td>Cricket</td>\n",
1652         "    </tr>\n",
1653         "    <tr>\n",
1654         "      <th>10</th>\n",
1655         "      <td>400</td>\n",
1656         "      <td>+400%</td>\n",
1657         "      <td>/trends/explore?q=/m/0fww52&amp;date=today+3-m&amp;geo=IN</td>\n",
1658         "      <td>/m/0fww52</td>\n",
1659         "      <td>Arvind Kejriwal</td>\n",
1660         "      <td>Chief Minister of Delhi</td>\n",
1661         "    </tr>\n",
1662         "    <tr>\n",
1663         "      <th>11</th>\n",
1664         "      <td>400</td>\n",
1665         "      <td>+400%</td>\n",
1666         "      <td>/trends/explore?q=/m/0fdnns&amp;date=today+3-m&amp;geo=IN</td>\n",
1667         "      <td>/m/0fdnns</td>\n",
1668         "      <td>Pitch</td>\n",
1669         "      <td>Sports field</td>\n",
1670         "    </tr>\n",
1671         "  </tbody>\n",
1672         "</table>\n",
1673         "</div>"
1674        ],
1675        "text/plain": [
1676         "    value formattedValue                                               link  \\\n",
1677         "0   43200       Breakout  /trends/explore?q=/m/0127gk6w&date=today+3-m&g...   \n",
1678         "1   32950       Breakout  /trends/explore?q=/m/06m6d5&date=today+3-m&geo=IN   \n",
1679         "2   29050       Breakout  /trends/explore?q=/m/05f74n&date=today+3-m&geo=IN   \n",
1680         "3   25450       Breakout   /trends/explore?q=/m/0f7gy&date=today+3-m&geo=IN   \n",
1681         "4   20700       Breakout  /trends/explore?q=/m/034x37&date=today+3-m&geo=IN   \n",
1682         "5     900          +900%  /trends/explore?q=/m/02clxk&date=today+3-m&geo=IN   \n",
1683         "6     900          +900%  /trends/explore?q=/m/0j42v4r&date=today+3-m&ge...   \n",
1684         "7     500          +500%  /trends/explore?q=/m/0279wtw&date=today+3-m&ge...   \n",
1685         "8     500          +500%  /trends/explore?q=/m/0c62hf&date=today+3-m&geo=IN   \n",
1686         "9     500          +500%   /trends/explore?q=/m/0dnnl&date=today+3-m&geo=IN   \n",
1687         "10    400          +400%  /trends/explore?q=/m/0fww52&date=today+3-m&geo=IN   \n",
1688         "11    400          +400%  /trends/explore?q=/m/0fdnns&date=today+3-m&geo=IN   \n",
1689         "\n",
1690         "      topic_mid                topic_title                        topic_type  \n",
1691         "0   /m/0127gk6w  International Day of Yoga                       Celebration  \n",
1692         "1     /m/06m6d5  M. A. Chidambaram Stadium                   Cricket stadium  \n",
1693         "2     /m/05f74n    Wankhede Stadium Mumbai  Cricket stadium in Mumbai, India  \n",
1694         "3      /m/0f7gy                    Cabinet                        Government  \n",
1695         "4     /m/034x37                   Boundary                             Topic  \n",
1696         "5     /m/02clxk              Cricket pitch                             Topic  \n",
1697         "6    /m/0j42v4r       Arun Jaitley Stadium   Cricket stadium in Delhi, India  \n",
1698         "7    /m/0279wtw              Rajnath Singh      Minister of Defence of India  \n",
1699         "8     /m/0c62hf          M. A. Chidambaram              Indian industrialist  \n",
1700         "9      /m/0dnnl          Bowling (cricket)                           Cricket  \n",
1701         "10    /m/0fww52            Arvind Kejriwal           Chief Minister of Delhi  \n",
1702         "11    /m/0fdnns                      Pitch                      Sports field  "
1703        ]
1704       },
1705       "execution_count": 79,
1706       "metadata": {},
1707       "output_type": "execute_result"
1708      }
1709     ],
1710     "source": [
1711      "topic_modi['narendra modi']['rising']"
1712     ]
1713    },
1714    {
1715     "cell_type": "code",
1716     "execution_count": null,
1717     "id": "e7678595",
1718     "metadata": {},
1719     "outputs": [],
1720     "source": []
1721    },
1722    {
1723     "cell_type": "code",
1724     "execution_count": null,
1725     "id": "4b2d062c",
1726     "metadata": {},
1727     "outputs": [],
1728     "source": []
1729    },
1730    {
1731     "cell_type": "code",
1732     "execution_count": 80,
1733     "id": "742a6460",
1734     "metadata": {},
1735     "outputs": [],
1736     "source": [
1737      "kw_list = ['rahul gandhi']\n",
1738      "t.build_payload(kw_list, cat=0, timeframe='today 3-m', geo='IN')\n",
1739      "topic_rahul=t.related_topics()"
1740     ]
1741    },
1742    {
1743     "cell_type": "code",
1744     "execution_count": 82,
1745     "id": "12f85d64",
1746     "metadata": {},
1747     "outputs": [
1748      {
1749       "data": {
1750        "text/html": [
1751         "<div>\n",
1752         "<style scoped>\n",
1753         "    .dataframe tbody tr th:only-of-type {\n",
1754         "        vertical-align: middle;\n",
1755         "    }\n",
1756         "\n",
1757         "    .dataframe tbody tr th {\n",
1758         "        vertical-align: top;\n",
1759         "    }\n",
1760         "\n",
1761         "    .dataframe thead th {\n",
1762         "        text-align: right;\n",
1763         "    }\n",
1764         "</style>\n",
1765         "<table border=\"1\" class=\"dataframe\">\n",
1766         "  <thead>\n",
1767         "    <tr style=\"text-align: right;\">\n",
1768         "      <th></th>\n",
1769         "      <th>value</th>\n",
1770         "      <th>formattedValue</th>\n",
1771         "      <th>link</th>\n",
1772         "      <th>topic_mid</th>\n",
1773         "      <th>topic_title</th>\n",
1774         "      <th>topic_type</th>\n",
1775         "    </tr>\n",
1776         "  </thead>\n",
1777         "  <tbody>\n",
1778         "    <tr>\n",
1779         "      <th>0</th>\n",
1780         "      <td>20100</td>\n",
1781         "      <td>Breakout</td>\n",
1782         "      <td>/trends/explore?q=/m/05mcgb&amp;date=today+3-m&amp;geo=IN</td>\n",
1783         "      <td>/m/05mcgb</td>\n",
1784         "      <td>Rajdeep Sardesai</td>\n",
1785         "      <td>Indian news anchor</td>\n",
1786         "    </tr>\n",
1787         "    <tr>\n",
1788         "      <th>1</th>\n",
1789         "      <td>19950</td>\n",
1790         "      <td>Breakout</td>\n",
1791         "      <td>/trends/explore?q=/m/07ty80&amp;date=today+3-m&amp;geo=IN</td>\n",
1792         "      <td>/m/07ty80</td>\n",
1793         "      <td>Ramdev</td>\n",
1794         "      <td>Indian businessman</td>\n",
1795         "    </tr>\n",
1796         "    <tr>\n",
1797         "      <th>2</th>\n",
1798         "      <td>13450</td>\n",
1799         "      <td>Breakout</td>\n",
1800         "      <td>/trends/explore?q=/m/0204sn&amp;date=today+3-m&amp;geo=IN</td>\n",
1801         "      <td>/m/0204sn</td>\n",
1802         "      <td>Donation</td>\n",
1803         "      <td>Topic</td>\n",
1804         "    </tr>\n",
1805         "    <tr>\n",
1806         "      <th>3</th>\n",
1807         "      <td>13150</td>\n",
1808         "      <td>Breakout</td>\n",
1809         "      <td>/trends/explore?q=/m/0246lq&amp;date=today+3-m&amp;geo=IN</td>\n",
1810         "      <td>/m/0246lq</td>\n",
1811         "      <td>Sharad Pawar</td>\n",
1812         "      <td>Member of Rajya Sabha</td>\n",
1813         "    </tr>\n",
1814         "    <tr>\n",
1815         "      <th>4</th>\n",
1816         "      <td>13100</td>\n",
1817         "      <td>Breakout</td>\n",
1818         "      <td>/trends/explore?q=/g/11b77dhw02&amp;date=today+3-m...</td>\n",
1819         "      <td>/g/11b77dhw02</td>\n",
1820         "      <td>2024</td>\n",
1821         "      <td>Topic</td>\n",
1822         "    </tr>\n",
1823         "    <tr>\n",
1824         "      <th>5</th>\n",
1825         "      <td>6750</td>\n",
1826         "      <td>Breakout</td>\n",
1827         "      <td>/trends/explore?q=/m/01kc36&amp;date=today+3-m&amp;geo=IN</td>\n",
1828         "      <td>/m/01kc36</td>\n",
1829         "      <td>Dassault Rafale</td>\n",
1830         "      <td>Multirole fighter aircraft</td>\n",
1831         "    </tr>\n",
1832         "    <tr>\n",
1833         "      <th>6</th>\n",
1834         "      <td>6600</td>\n",
1835         "      <td>Breakout</td>\n",
1836         "      <td>/trends/explore?q=/m/0n5v_6t&amp;date=today+3-m&amp;ge...</td>\n",
1837         "      <td>/m/0n5v_6t</td>\n",
1838         "      <td>Aam Aadmi Party</td>\n",
1839         "      <td>Political party</td>\n",
1840         "    </tr>\n",
1841         "    <tr>\n",
1842         "      <th>7</th>\n",
1843         "      <td>6600</td>\n",
1844         "      <td>Breakout</td>\n",
1845         "      <td>/trends/explore?q=/m/04g2rrp&amp;date=today+3-m&amp;ge...</td>\n",
1846         "      <td>/m/04g2rrp</td>\n",
1847         "      <td>Randeep Surjewala</td>\n",
1848         "      <td>Indian Politician</td>\n",
1849         "    </tr>\n",
1850         "    <tr>\n",
1851         "      <th>8</th>\n",
1852         "      <td>130</td>\n",
1853         "      <td>+130%</td>\n",
1854         "      <td>/trends/explore?q=/m/063gq&amp;date=today+3-m&amp;geo=IN</td>\n",
1855         "      <td>/m/063gq</td>\n",
1856         "      <td>Prime Minister of India</td>\n",
1857         "      <td>Topic</td>\n",
1858         "    </tr>\n",
1859         "    <tr>\n",
1860         "      <th>9</th>\n",
1861         "      <td>120</td>\n",
1862         "      <td>+120%</td>\n",
1863         "      <td>/trends/explore?q=/m/04wpw&amp;date=today+3-m&amp;geo=IN</td>\n",
1864         "      <td>/m/04wpw</td>\n",
1865         "      <td>Meme</td>\n",
1866         "      <td>Topic</td>\n",
1867         "    </tr>\n",
1868         "    <tr>\n",
1869         "      <th>10</th>\n",
1870         "      <td>90</td>\n",
1871         "      <td>+90%</td>\n",
1872         "      <td>/trends/explore?q=/m/0289n8t&amp;date=today+3-m&amp;ge...</td>\n",
1873         "      <td>/m/0289n8t</td>\n",
1874         "      <td>Twitter</td>\n",
1875         "      <td>Social networking service</td>\n",
1876         "    </tr>\n",
1877         "    <tr>\n",
1878         "      <th>11</th>\n",
1879         "      <td>70</td>\n",
1880         "      <td>+70%</td>\n",
1881         "      <td>/trends/explore?q=/m/0hn1vcg&amp;date=today+3-m&amp;ge...</td>\n",
1882         "      <td>/m/0hn1vcg</td>\n",
1883         "      <td>Twitter</td>\n",
1884         "      <td>Social network company</td>\n",
1885         "    </tr>\n",
1886         "    <tr>\n",
1887         "      <th>12</th>\n",
1888         "      <td>60</td>\n",
1889         "      <td>+60%</td>\n",
1890         "      <td>/trends/explore?q=/m/060bp&amp;date=today+3-m&amp;geo=IN</td>\n",
1891         "      <td>/m/060bp</td>\n",
1892         "      <td>Prime minister</td>\n",
1893         "      <td>Topic</td>\n",
1894         "    </tr>\n",
1895         "    <tr>\n",
1896         "      <th>13</th>\n",
1897         "      <td>60</td>\n",
1898         "      <td>+60%</td>\n",
1899         "      <td>/trends/explore?q=/m/02jfc&amp;date=today+3-m&amp;geo=IN</td>\n",
1900         "      <td>/m/02jfc</td>\n",
1901         "      <td>Education</td>\n",
1902         "      <td>Topic</td>\n",
1903         "    </tr>\n",
1904         "    <tr>\n",
1905         "      <th>14</th>\n",
1906         "      <td>60</td>\n",
1907         "      <td>+60%</td>\n",
1908         "      <td>/trends/explore?q=/m/0135cw&amp;date=today+3-m&amp;geo=IN</td>\n",
1909         "      <td>/m/0135cw</td>\n",
1910         "      <td>Bharatiya Janata Party</td>\n",
1911         "      <td>Political party</td>\n",
1912         "    </tr>\n",
1913         "    <tr>\n",
1914         "      <th>15</th>\n",
1915         "      <td>40</td>\n",
1916         "      <td>+40%</td>\n",
1917         "      <td>/trends/explore?q=/m/0d3sk3&amp;date=today+3-m&amp;geo=IN</td>\n",
1918         "      <td>/m/0d3sk3</td>\n",
1919         "      <td>Amit Shah</td>\n",
1920         "      <td>Topic</td>\n",
1921         "    </tr>\n",
1922         "  </tbody>\n",
1923         "</table>\n",
1924         "</div>"
1925        ],
1926        "text/plain": [
1927         "    value formattedValue                                               link  \\\n",
1928         "0   20100       Breakout  /trends/explore?q=/m/05mcgb&date=today+3-m&geo=IN   \n",
1929         "1   19950       Breakout  /trends/explore?q=/m/07ty80&date=today+3-m&geo=IN   \n",
1930         "2   13450       Breakout  /trends/explore?q=/m/0204sn&date=today+3-m&geo=IN   \n",
1931         "3   13150       Breakout  /trends/explore?q=/m/0246lq&date=today+3-m&geo=IN   \n",
1932         "4   13100       Breakout  /trends/explore?q=/g/11b77dhw02&date=today+3-m...   \n",
1933         "5    6750       Breakout  /trends/explore?q=/m/01kc36&date=today+3-m&geo=IN   \n",
1934         "6    6600       Breakout  /trends/explore?q=/m/0n5v_6t&date=today+3-m&ge...   \n",
1935         "7    6600       Breakout  /trends/explore?q=/m/04g2rrp&date=today+3-m&ge...   \n",
1936         "8     130          +130%   /trends/explore?q=/m/063gq&date=today+3-m&geo=IN   \n",
1937         "9     120          +120%   /trends/explore?q=/m/04wpw&date=today+3-m&geo=IN   \n",
1938         "10     90           +90%  /trends/explore?q=/m/0289n8t&date=today+3-m&ge...   \n",
1939         "11     70           +70%  /trends/explore?q=/m/0hn1vcg&date=today+3-m&ge...   \n",
1940         "12     60           +60%   /trends/explore?q=/m/060bp&date=today+3-m&geo=IN   \n",
1941         "13     60           +60%   /trends/explore?q=/m/02jfc&date=today+3-m&geo=IN   \n",
1942         "14     60           +60%  /trends/explore?q=/m/0135cw&date=today+3-m&geo=IN   \n",
1943         "15     40           +40%  /trends/explore?q=/m/0d3sk3&date=today+3-m&geo=IN   \n",
1944         "\n",
1945         "        topic_mid              topic_title                  topic_type  \n",
1946         "0       /m/05mcgb         Rajdeep Sardesai          Indian news anchor  \n",
1947         "1       /m/07ty80                   Ramdev          Indian businessman  \n",
1948         "2       /m/0204sn                 Donation                       Topic  \n",
1949         "3       /m/0246lq             Sharad Pawar       Member of Rajya Sabha  \n",
1950         "4   /g/11b77dhw02                     2024                       Topic  \n",
1951         "5       /m/01kc36          Dassault Rafale  Multirole fighter aircraft  \n",
1952         "6      /m/0n5v_6t          Aam Aadmi Party             Political party  \n",
1953         "7      /m/04g2rrp        Randeep Surjewala           Indian Politician  \n",
1954         "8        /m/063gq  Prime Minister of India                       Topic  \n",
1955         "9        /m/04wpw                     Meme                       Topic  \n",
1956         "10     /m/0289n8t                  Twitter   Social networking service  \n",
1957         "11     /m/0hn1vcg                  Twitter      Social network company  \n",
1958         "12       /m/060bp           Prime minister                       Topic  \n",
1959         "13       /m/02jfc                Education                       Topic  \n",
1960         "14      /m/0135cw   Bharatiya Janata Party             Political party  \n",
1961         "15      /m/0d3sk3                Amit Shah                       Topic  "
1962        ]
1963       },
1964       "execution_count": 82,
1965       "metadata": {},
1966       "output_type": "execute_result"
1967      }
1968     ],
1969     "source": [
1970      "topic_rahul['rahul gandhi']['rising']"
1971     ]
1972    },
1973    {
1974     "cell_type": "code",
1975     "execution_count": null,
1976     "id": "31cf4f98",
1977     "metadata": {},
1978     "outputs": [],
1979     "source": []
1980    },
1981    {
1982     "cell_type": "code",
1983     "execution_count": 83,
1984     "id": "e6d6a4b6",
1985     "metadata": {},
1986     "outputs": [],
1987     "source": [
1988      "import pandas as pd"
1989     ]
1990    },
1991    {
1992     "cell_type": "code",
1993     "execution_count": 149,
1994     "id": "f9ae8447",
1995     "metadata": {},
1996     "outputs": [
1997      {
1998       "data": {
1999        "text/plain": [
2000         "[{'mid': '/m/0296q2',\n",
2001         "  'title': 'Narendra Modi',\n",
2002         "  'type': 'Prime Minister of India'},\n",
2003         " {'mid': '/m/06m73y',\n",
2004         "  'title': 'Narendra Modi Stadium',\n",
2005         "  'type': 'Stadium in Ahmedabad, India'},\n",
2006         " {'mid': '/m/0g59_72', 'title': 'Jashodaben Modi', 'type': 'Indian teacher'},\n",
2007         " {'mid': '/g/11gyb0n813', 'title': 'PM Narendra Modi', 'type': '2019 film'}]"
2008        ]
2009       },
2010       "execution_count": 149,
2011       "metadata": {},
2012       "output_type": "execute_result"
2013      }
2014     ],
2015     "source": [
2016      "df = t.suggestions('narendra modi')\n",
2017      "df"
2018     ]
2019    },
2020    {
2021     "cell_type": "code",
2022     "execution_count": 150,
2023     "id": "650c6bca",
2024     "metadata": {},
2025     "outputs": [
2026      {
2027       "name": "stdout",
2028       "output_type": "stream",
2029       "text": [
2030        "Narendra Modi\n",
2031        "Narendra Modi Stadium\n",
2032        "Jashodaben Modi\n",
2033        "PM Narendra Modi\n"
2034       ]
2035      }
2036     ],
2037     "source": [
2038      "for i in df:\n",
2039      "    print(i['title'])"
2040     ]
2041    },
2042    {
2043     "cell_type": "code",
2044     "execution_count": 87,
2045     "id": "9f3f03eb",
2046     "metadata": {},
2047     "outputs": [
2048      {
2049       "data": {
2050        "text/plain": [
2051         "[{'mid': '/m/01sd6z',\n",
2052         "  'title': 'Rahul Gandhi',\n",
2053         "  'type': 'Member of the Lok Sabha'},\n",
2054         " {'mid': '/g/11h6yry_xr',\n",
2055         "  'title': 'Rohit Gandhi + Rahul Khanna',\n",
2056         "  'type': 'Topic'}]"
2057        ]
2058       },
2059       "execution_count": 87,
2060       "metadata": {},
2061       "output_type": "execute_result"
2062      }
2063     ],
2064     "source": [
2065      "df = t.suggestions('rahul gandhi')\n",
2066      "df"
2067     ]
2068    },
2069    {
2070     "cell_type": "code",
2071     "execution_count": null,
2072     "id": "a0fbfbb6",
2073     "metadata": {},
2074     "outputs": [],
2075     "source": []
2076    },
2077    {
2078     "cell_type": "code",
2079     "execution_count": null,
2080     "id": "b225df90",
2081     "metadata": {},
2082     "outputs": [],
2083     "source": []
2084    },
2085    {
2086     "cell_type": "code",
2087     "execution_count": null,
2088     "id": "82ea9a3d",
2089     "metadata": {},
2090     "outputs": [],
2091     "source": []
2092    },
2093    {
2094     "cell_type": "code",
2095     "execution_count": null,
2096     "id": "61f5ace5",
2097     "metadata": {},
2098     "outputs": [],
2099     "source": []
2100    },
2101    {
2102     "cell_type": "code",
2103     "execution_count": 126,
2104     "id": "a7de982f",
2105     "metadata": {},
2106     "outputs": [
2107      {
2108       "data": {
2109        "text/html": [
2110         "<div>\n",
2111         "<style scoped>\n",
2112         "    .dataframe tbody tr th:only-of-type {\n",
2113         "        vertical-align: middle;\n",
2114         "    }\n",
2115         "\n",
2116         "    .dataframe tbody tr th {\n",
2117         "        vertical-align: top;\n",
2118         "    }\n",
2119         "\n",
2120         "    .dataframe thead th {\n",
2121         "        text-align: right;\n",
2122         "    }\n",
2123         "</style>\n",
2124         "<table border=\"1\" class=\"dataframe\">\n",
2125         "  <thead>\n",
2126         "    <tr style=\"text-align: right;\">\n",
2127         "      <th></th>\n",
2128         "      <th>title</th>\n",
2129         "      <th>exploreQuery</th>\n",
2130         "    </tr>\n",
2131         "  </thead>\n",
2132         "  <tbody>\n",
2133         "    <tr>\n",
2134         "      <th>0</th>\n",
2135         "      <td>Budget 2009</td>\n",
2136         "      <td></td>\n",
2137         "    </tr>\n",
2138         "    <tr>\n",
2139         "      <th>1</th>\n",
2140         "      <td>Kambakth Ishq</td>\n",
2141         "      <td></td>\n",
2142         "    </tr>\n",
2143         "    <tr>\n",
2144         "      <th>2</th>\n",
2145         "      <td>IRCTC</td>\n",
2146         "      <td></td>\n",
2147         "    </tr>\n",
2148         "    <tr>\n",
2149         "      <th>3</th>\n",
2150         "      <td>Satyam Share Price</td>\n",
2151         "      <td></td>\n",
2152         "    </tr>\n",
2153         "    <tr>\n",
2154         "      <th>4</th>\n",
2155         "      <td>Bhuvan</td>\n",
2156         "      <td></td>\n",
2157         "    </tr>\n",
2158         "    <tr>\n",
2159         "      <th>5</th>\n",
2160         "      <td>Michael Jackson</td>\n",
2161         "      <td></td>\n",
2162         "    </tr>\n",
2163         "    <tr>\n",
2164         "      <th>6</th>\n",
2165         "      <td>Twitter</td>\n",
2166         "      <td></td>\n",
2167         "    </tr>\n",
2168         "    <tr>\n",
2169         "      <th>7</th>\n",
2170         "      <td>Election Results</td>\n",
2171         "      <td></td>\n",
2172         "    </tr>\n",
2173         "    <tr>\n",
2174         "      <th>8</th>\n",
2175         "      <td>Windows 7</td>\n",
2176         "      <td></td>\n",
2177         "    </tr>\n",
2178         "    <tr>\n",
2179         "      <th>9</th>\n",
2180         "      <td>Nokia 5800</td>\n",
2181         "      <td></td>\n",
2182         "    </tr>\n",
2183         "  </tbody>\n",
2184         "</table>\n",
2185         "</div>"
2186        ],
2187        "text/plain": [
2188         "                title exploreQuery\n",
2189         "0         Budget 2009             \n",
2190         "1       Kambakth Ishq             \n",
2191         "2               IRCTC             \n",
2192         "3  Satyam Share Price             \n",
2193         "4              Bhuvan             \n",
2194         "5     Michael Jackson             \n",
2195         "6             Twitter             \n",
2196         "7    Election Results             \n",
2197         "8           Windows 7             \n",
2198         "9          Nokia 5800             "
2199        ]
2200       },
2201       "execution_count": 126,
2202       "metadata": {},
2203       "output_type": "execute_result"
2204      }
2205     ],
2206     "source": [
2207      "t.top_charts(2021-6-6, hl='en-US', tz=360, geo='IN')"
2208     ]
2209    },
2210    {
2211     "cell_type": "code",
2212     "execution_count": null,
2213     "id": "a2f0314a",
2214     "metadata": {},
2215     "outputs": [],
2216     "source": []
2217    },
2218    {
2219     "cell_type": "code",
2220     "execution_count": null,
2221     "id": "4b4a138f",
2222     "metadata": {},
2223     "outputs": [],
2224     "source": []
2225    },
2226    {
2227     "cell_type": "code",
2228     "execution_count": null,
2229     "id": "d9c28eee",
2230     "metadata": {},
2231     "outputs": [],
2232     "source": []
2233    },
2234    {
2235     "cell_type": "code",
2236     "execution_count": 160,
2237     "id": "e2b14379",
2238     "metadata": {},
2239     "outputs": [],
2240     "source": [
2241      "terms = ['bjp', 'congress', 'aap','muslim','hindu',\"debt\", \"color\", \"stocks\",'hindusim', \"restaurant\", \"portfolio\", \"inflation\", \n",
2242      "         \"housing\", \"dow jones\", \"revenue\",'muslim', \"economics\", \"credit\", \"markets\", \n",
2243      "         \"return\", \"unemployment\", \"money\", \"religion\", \"cancer\", \"growth\", \n",
2244      "         \"investment\", \"hedge\", \"marriage\", \"bonds\", \"derivatives\", \"headlines\", \n",
2245      "         \"profit\" , \"society\", \"leverage\", \"loss\", \"cash\", \"office\", \"fine\", \"stock market\", \n",
2246      "         \"banking\", \"crisis\", \"car\", \"gains\", \"finance\", \"sell\", \"invest\", \n",
2247      "         \"fed\", \"house\", \"metals\", \"travel\", \"returns\", \"gain\", \"default\", \"present\", \"holiday\", \n",
2248      "         \"water\", \"rich\", \"risk\", \"gold\", \"success\", \"oil\", \"war\", \"economy\", \"chance\", \n",
2249      "         \"short selling\", \"lifestyle\", \"earnings\", \"arts\", \"culture\", \"bubble\", \"buy\", \"trader\", \n",
2250      "         \"tourism\", \"politics\", \"energy\", \"consume\", \"consumption\", \"freedom\", \n",
2251      "         \"dividend\", \"world\", \"conflict\", \"kitchen\", \"forex\", \"home\", \"crash\", \"transaction\", \n",
2252      "         \"garden\", \"fond\", \"train\", \"labor\", \"fun\", \"environment\", \"ring\"]"
2253     ]
2254    },
2255    {
2256     "cell_type": "code",
2257     "execution_count": null,
2258     "id": "322c1961",
2259     "metadata": {},
2260     "outputs": [],
2261     "source": []
2262    },
2263    {
2264     "cell_type": "code",
2265     "execution_count": 209,
2266     "id": "4fc725ea",
2267     "metadata": {},
2268     "outputs": [
2269      {
2270       "name": "stdout",
2271       "output_type": "stream",
2272       "text": [
2273        "Bharatiya Janata Party\n",
2274        "1\n",
2275        "BJP\n",
2276        "2\n",
2277        "Bence Jones protein\n",
2278        "3\n",
2279        "BJP\n",
2280        "BJP Party Office\n",
2281        "4\n",
2282        "bjp\n",
2283        "5\n",
2284        "United States Congress\n",
2285        "6\n",
2286        "Congress\n",
2287        "7\n",
2288        "Indian National Congress\n",
2289        "8\n",
2290        "The Congress\n",
2291        "9\n",
2292        "United States Senate\n",
2293        "10\n",
2294        "congress\n",
2295        "11\n",
2296        "Aam Aadmi Party\n",
2297        "12\n",
2298        "NASDAQ:AAPL\n",
2299        "13\n",
2300        "Monkey\n",
2301        "14\n",
2302        "Advance Auto Parts\n",
2303        "15\n",
2304        "Reynolds Metals Company\n",
2305        "16\n",
2306        "aap\n",
2307        "17\n",
2308        "Muslim\n",
2309        "18\n",
2310        "Sunni Islam\n",
2311        "19\n",
2312        "Muslim\n",
2313        "Muslims\n",
2314        "20\n",
2315        "Muslim ibn al-Hajjaj\n",
2316        "21\n",
2317        "muslim\n",
2318        "22\n",
2319        "India\n",
2320        "23\n",
2321        "Hinduism\n",
2322        "24\n",
2323        "Hindus\n",
2324        "25\n",
2325        "Hindi\n",
2326        "26\n",
2327        "Bali\n",
2328        "27\n",
2329        "hindu\n",
2330        "28\n",
2331        "Debt\n",
2332        "29\n",
2333        "Debtor\n",
2334        "30\n",
2335        "The Debt\n",
2336        "31\n",
2337        "Debt consolidation\n",
2338        "32\n",
2339        "Debt collection\n",
2340        "33\n",
2341        "debt\n",
2342        "34\n",
2343        "Color\n",
2344        "35\n",
2345        "Person of color\n",
2346        "36\n",
2347        "Timbre\n",
2348        "37\n",
2349        "DEEP\n",
2350        "38\n",
2351        "Colorectal cancer\n",
2352        "39\n",
2353        "color\n",
2354        "40\n",
2355        "Stocks\n",
2356        "41\n",
2357        "Stocks\n",
2358        "Stock\n",
2359        "42\n",
2360        "Stocks\n",
2361        "Stocks House\n",
2362        "43\n",
2363        "stocks\n",
2364        "44\n",
2365        "hindusim\n",
2366        "45\n",
2367        "Restaurant\n",
2368        "46\n",
2369        "Izakaya\n",
2370        "47\n",
2371        "A&W Restaurants\n",
2372        "48\n",
2373        "Fast food restaurant\n",
2374        "49\n",
2375        "McDonald's\n",
2376        "50\n",
2377        "restaurant\n",
2378        "51\n",
2379        "Portfolio\n",
2380        "52\n",
2381        "Portfolio.hu\n",
2382        "53\n",
2383        "Portfolio\n",
2384        "Career portfolio\n",
2385        "54\n",
2386        "Electronic portfolio\n",
2387        "55\n",
2388        "portfolio\n",
2389        "56\n",
2390        "Inflation\n",
2391        "57\n",
2392        "Inflation\n",
2393        "Inflation rate\n",
2394        "58\n",
2395        "Demand-pull inflation\n",
2396        "59\n",
2397        "Variance inflation factor\n",
2398        "60\n",
2399        "inflation\n",
2400        "61\n",
2401        "Housing\n",
2402        "62\n",
2403        "HDFC Bank\n",
2404        "63\n",
2405        "Housing\n",
2406        "Public housing\n",
2407        "64\n",
2408        "Caparison\n",
2409        "65\n",
2410        "housing\n",
2411        "66\n",
2412        "Dow Jones Industrial Average\n",
2413        "67\n",
2414        "Dow Jones & Company\n",
2415        "68\n",
2416        "STOXX Europe 50\n",
2417        "69\n",
2418        "Dow Jones\n",
2419        "70\n",
2420        "dow jones\n",
2421        "71\n",
2422        "Revenue\n",
2423        "72\n",
2424        "Gross income\n",
2425        "73\n",
2426        "Receita Federal do Brasil\n",
2427        "74\n",
2428        "Tax Administration\n",
2429        "75\n",
2430        "Revenue Commissioners\n",
2431        "76\n",
2432        "revenue\n",
2433        "77\n",
2434        "Muslim\n",
2435        "Sunni Islam\n",
2436        "Muslim\n",
2437        "Muslims\n",
2438        "Muslim ibn al-Hajjaj\n",
2439        "muslim\n",
2440        "Economics\n",
2441        "78\n",
2442        "Labour economics\n",
2443        "79\n",
2444        "Economics\n",
2445        "economics\n",
2446        "80\n",
2447        "Property rights\n",
2448        "81\n",
2449        "economics\n",
2450        "Credit\n",
2451        "82\n",
2452        "Credit card\n",
2453        "83\n",
2454        "Creditor\n",
2455        "84\n",
2456        "Credit\n",
2457        "Alpha Bank\n",
2458        "85\n",
2459        "credit\n",
2460        "86\n",
2461        "Credit\n",
2462        "markets\n",
2463        "87\n",
2464        "Return on investment\n",
2465        "88\n",
2466        "Volver\n",
2467        "89\n",
2468        "Tax report\n",
2469        "90\n",
2470        "Enter key\n",
2471        "91\n",
2472        "The Return\n",
2473        "92\n",
2474        "return\n",
2475        "93\n",
2476        "Unemployment\n",
2477        "94\n",
2478        "Unemployment benefits\n",
2479        "95\n",
2480        "Federal Unemployment Tax Act\n",
2481        "96\n",
2482        "Unemployment insurance in Japan\n",
2483        "97\n",
2484        "cyclical unemployment\n",
2485        "98\n",
2486        "unemployment\n",
2487        "99\n",
2488        "Floyd Mayweather\n",
2489        "100\n",
2490        "Money\n",
2491        "101\n",
2492        "Currency\n",
2493        "102\n",
2494        "Wealth\n",
2495        "103\n",
2496        "Euro\n",
2497        "104\n",
2498        "money\n",
2499        "105\n",
2500        "Religion\n",
2501        "106\n",
2502        "Dharm\n",
2503        "107\n",
2504        "Socinianism\n",
2505        "108\n",
2506        "Re-Legion\n",
2507        "109\n",
2508        "Religious education\n",
2509        "110\n",
2510        "religion\n",
2511        "111\n",
2512        "Cancer\n",
2513        "112\n",
2514        "Cancer\n",
2515        "Cancer\n",
2516        "Colorectal cancer\n",
2517        "Breast cancer\n",
2518        "113\n",
2519        "cancer\n",
2520        "114\n",
2521        "growth\n",
2522        "115\n",
2523        "Economic growth\n",
2524        "116\n",
2525        "Growth\n",
2526        "117\n",
2527        "growth\n",
2528        "Investment\n",
2529        "118\n",
2530        "Bank Melli Iran\n",
2531        "119\n",
2532        "Investment fund\n",
2533        "120\n",
2534        "Return on investment\n",
2535        "Real estate investment trust\n",
2536        "121\n",
2537        "investment\n",
2538        "122\n",
2539        "Hedge\n",
2540        "123\n",
2541        "Hedgehog\n",
2542        "124\n",
2543        "Hedge\n",
2544        "hedgehog\n",
2545        "125\n",
2546        "European hedgehog\n",
2547        "126\n",
2548        "hedge\n",
2549        "127\n",
2550        "Marriage\n",
2551        "128\n",
2552        "Vivah\n",
2553        "129\n",
2554        "Marriage, Not Dating\n",
2555        "130\n",
2556        "Marriage\n",
2557        "Same-sex marriage\n"
2558       ]
2559      },
2560      {
2561       "ename": "KeyboardInterrupt",
2562       "evalue": "",
2563       "output_type": "error",
2564       "traceback": [
2565        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
2566        "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
2567        "\u001b[1;32m<ipython-input-209-c3a23cccc37f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m                 \u001b[0mtitle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msuggestion\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m                 \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msuggestion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'internet'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m                 \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild_payload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeframe\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'2010-1-1 2021-6-1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgeo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'IN'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgprop\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m                 \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterest_over_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m                 \u001b[1;31m#print(x.head())\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2568        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pytrends\\request.py\u001b[0m in \u001b[0;36mbuild_payload\u001b[1;34m(self, kw_list, cat, timeframe, geo, gprop)\u001b[0m\n\u001b[0;32m    167\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoken_payload\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'req'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoken_payload\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'req'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m         \u001b[1;31m# get tokens\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tokens\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m         \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
2569        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pytrends\\request.py\u001b[0m in \u001b[0;36m_tokens\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    173\u001b[0m         \u001b[1;34m\"\"\"Makes request to Google to get API tokens for interest over time, interest by region and related queries\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m         \u001b[1;31m# make the request and parse the returned json\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 175\u001b[1;33m         widget_dict = self._get_data(\n\u001b[0m\u001b[0;32m    176\u001b[0m             \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrendReq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGENERAL_URL\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrendReq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGET_METHOD\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2570        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pytrends\\request.py\u001b[0m in \u001b[0;36m_get_data\u001b[1;34m(self, url, method, trim_chars, **kwargs)\u001b[0m\n\u001b[0;32m    124\u001b[0m                               cookies=self.cookies, **kwargs, **self.requests_args)  # DO NOT USE retries or backoff_factor here\n\u001b[0;32m    125\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 126\u001b[1;33m             response = s.get(url, timeout=self.timeout, cookies=self.cookies,\n\u001b[0m\u001b[0;32m    127\u001b[0m                              **kwargs, **self.requests_args)   # DO NOT USE retries or backoff_factor here\n\u001b[0;32m    128\u001b[0m         \u001b[1;31m# check if the response contains json and throw an exception otherwise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2571        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, url, **kwargs)\u001b[0m\n\u001b[0;32m    553\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    554\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'allow_redirects'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 555\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'GET'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    556\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    557\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2572        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    540\u001b[0m         }\n\u001b[0;32m    541\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m         \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2573        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\sessions.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    653\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    654\u001b[0m         \u001b[1;31m# Send the request\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 655\u001b[1;33m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    656\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    657\u001b[0m         \u001b[1;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2574        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\requests\\adapters.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    437\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 439\u001b[1;33m                 resp = conn.urlopen(\n\u001b[0m\u001b[0;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2575        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    697\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    698\u001b[0m             \u001b[1;31m# Make the request on the httplib connection object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 699\u001b[1;33m             httplib_response = self._make_request(\n\u001b[0m\u001b[0;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2576        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    380\u001b[0m         \u001b[1;31m# Trigger any extra validation we need to do.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    381\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 382\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    383\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    384\u001b[0m             \u001b[1;31m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2577        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connectionpool.py\u001b[0m in \u001b[0;36m_validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m   1008\u001b[0m         \u001b[1;31m# Force connect early to allow us to validate the connection.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1009\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"sock\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# AppEngine might not have  `.sock`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1010\u001b[1;33m             \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1011\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1012\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mconn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_verified\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2578        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\connection.py\u001b[0m in \u001b[0;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    409\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_default_certs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 411\u001b[1;33m         self.sock = ssl_wrap_socket(\n\u001b[0m\u001b[0;32m    412\u001b[0m             \u001b[0msock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m             \u001b[0mkeyfile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkey_file\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2579        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\util\\ssl_.py\u001b[0m in \u001b[0;36mssl_wrap_socket\u001b[1;34m(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data, tls_in_tls)\u001b[0m\n\u001b[0;32m    426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    427\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0msend_sni\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 428\u001b[1;33m         ssl_sock = _ssl_wrap_socket_impl(\n\u001b[0m\u001b[0;32m    429\u001b[0m             \u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtls_in_tls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mserver_hostname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mserver_hostname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    430\u001b[0m         )\n",
2580        "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\urllib3\\util\\ssl_.py\u001b[0m in \u001b[0;36m_ssl_wrap_socket_impl\u001b[1;34m(sock, ssl_context, tls_in_tls, server_hostname)\u001b[0m\n\u001b[0;32m    470\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    471\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mserver_hostname\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 472\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mssl_context\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_socket\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mserver_hostname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mserver_hostname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    473\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    474\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mssl_context\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_socket\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2581        "\u001b[1;32m~\\anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[1;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[0;32m    498\u001b[0m         \u001b[1;31m# SSLSocket class handles server_hostname encoding before it calls\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    499\u001b[0m         \u001b[1;31m# ctx._wrap_socket()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 500\u001b[1;33m         return self.sslsocket_class._create(\n\u001b[0m\u001b[0;32m    501\u001b[0m             \u001b[0msock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    502\u001b[0m             \u001b[0mserver_side\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mserver_side\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2582        "\u001b[1;32m~\\anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36m_create\u001b[1;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[0;32m   1038\u001b[0m                         \u001b[1;31m# non-blocking\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1039\u001b[0m                         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"do_handshake_on_connect should not be specified for non-blocking sockets\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1041\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2583        "\u001b[1;32m~\\anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[1;34m(self, block)\u001b[0m\n\u001b[0;32m   1307\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1308\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1309\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1310\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1311\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
2584        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
2585       ]
2586      }
2587     ],
2588     "source": [
2589      "import time\n",
2590      "import random\n",
2591      "import requests\n",
2592      "i= []\n",
2593      "title=[]\n",
2594      "for term in terms:\n",
2595      "    try:\n",
2596      "        suggestions= t.suggestions(term)\n",
2597      "        suggestions.append({'title':term})\n",
2598      "        for suggestion in suggestions:\n",
2599      "            suggestion= suggestion['title']\n",
2600      "            print(suggestion)\n",
2601      "            if suggestion not in title:\n",
2602      "                t= TrendReq()\n",
2603      "                title.append(suggestion)\n",
2604      "                x=[suggestion, 'internet']\n",
2605      "                t.build_payload(x, cat=0, timeframe='2010-1-1 2021-6-1', geo='IN', gprop='')\n",
2606      "                x=t.interest_over_time().reset_index().iloc[:,:-2]\n",
2607      "                #print(x.head())\n",
2608      "                i.append(x)\n",
2609      "                print(len(i))\n",
2610      "                time.sleep((1+(random.randint(0,5)/10+ random.randint(0,5)/100)))\n",
2611      "    except Exception as e:\n",
2612      "        \n",
2613      "        print(e)"
2614     ]
2615    },
2616    {
2617     "cell_type": "code",
2618     "execution_count": 197,
2619     "id": "99ccca97",
2620     "metadata": {},
2621     "outputs": [],
2622     "source": [
2623      "final= a['date']\n",
2624      "for data in i:\n",
2625      "    final= pd.merge(final, data)"
2626     ]
2627    },
2628    {
2629     "cell_type": "code",
2630     "execution_count": 202,
2631     "id": "12d8e4ed",
2632     "metadata": {},
2633     "outputs": [
2634      {
2635       "name": "stdout",
2636       "output_type": "stream",
2637       "text": [
2638        "<class 'pandas.core.frame.DataFrame'>\n",
2639        "Int64Index: 138 entries, 0 to 137\n",
2640        "Columns: 357 entries, date to The Ring\n",
2641        "dtypes: datetime64[ns](1), int32(356)\n",
2642        "memory usage: 194.1 KB\n"
2643       ]
2644      }
2645     ],
2646     "source": [
2647      "final.info(show_counts=True)"
2648     ]
2649    },
2650    {
2651     "cell_type": "code",
2652     "execution_count": 203,
2653     "id": "e3db4fba",
2654     "metadata": {
2655      "scrolled": true
2656     },
2657     "outputs": [
2658      {
2659       "data": {
2660        "text/html": [
2661         "<div>\n",
2662         "<style scoped>\n",
2663         "    .dataframe tbody tr th:only-of-type {\n",
2664         "        vertical-align: middle;\n",
2665         "    }\n",
2666         "\n",
2667         "    .dataframe tbody tr th {\n",
2668         "        vertical-align: top;\n",
2669         "    }\n",
2670         "\n",
2671         "    .dataframe thead th {\n",
2672         "        text-align: right;\n",
2673         "    }\n",
2674         "</style>\n",
2675         "<table border=\"1\" class=\"dataframe\">\n",
2676         "  <thead>\n",
2677         "    <tr style=\"text-align: right;\">\n",
2678         "      <th></th>\n",
2679         "      <th>date</th>\n",
2680         "      <th>Bharatiya Janata Party</th>\n",
2681         "      <th>BJP</th>\n",
2682         "      <th>United States Congress</th>\n",
2683         "      <th>Congress</th>\n",
2684         "      <th>Indian National Congress</th>\n",
2685         "      <th>The Congress</th>\n",
2686         "      <th>United States Senate</th>\n",
2687         "      <th>Aam Aadmi Party</th>\n",
2688         "      <th>NASDAQ:AAPL</th>\n",
2689         "      <th>...</th>\n",
2690         "      <th>Funeral</th>\n",
2691         "      <th>Fungus</th>\n",
2692         "      <th>Function</th>\n",
2693         "      <th>fun.</th>\n",
2694         "      <th>Climate</th>\n",
2695         "      <th>Biophysical environment</th>\n",
2696         "      <th>Environment</th>\n",
2697         "      <th>Natural environment</th>\n",
2698         "      <th>Ring</th>\n",
2699         "      <th>The Ring</th>\n",
2700         "    </tr>\n",
2701         "  </thead>\n",
2702         "  <tbody>\n",
2703         "    <tr>\n",
2704         "      <th>0</th>\n",
2705         "      <td>2010-01-01</td>\n",
2706         "      <td>0</td>\n",
2707         "      <td>1</td>\n",
2708         "      <td>0</td>\n",
2709         "      <td>4</td>\n",
2710         "      <td>1</td>\n",
2711         "      <td>0</td>\n",
2712         "      <td>0</td>\n",
2713         "      <td>0</td>\n",
2714         "      <td>0</td>\n",
2715         "      <td>...</td>\n",
2716         "      <td>1</td>\n",
2717         "      <td>0</td>\n",
2718         "      <td>31</td>\n",
2719         "      <td>0</td>\n",
2720         "      <td>7</td>\n",
2721         "      <td>0</td>\n",
2722         "      <td>15</td>\n",
2723         "      <td>0</td>\n",
2724         "      <td>11</td>\n",
2725         "      <td>1</td>\n",
2726         "    </tr>\n",
2727         "    <tr>\n",
2728         "      <th>1</th>\n",
2729         "      <td>2010-02-01</td>\n",
2730         "      <td>0</td>\n",
2731         "      <td>2</td>\n",
2732         "      <td>0</td>\n",
2733         "      <td>4</td>\n",
2734         "      <td>1</td>\n",
2735         "      <td>0</td>\n",
2736         "      <td>0</td>\n",
2737         "      <td>0</td>\n",
2738         "      <td>0</td>\n",
2739         "      <td>...</td>\n",
2740         "      <td>1</td>\n",
2741         "      <td>0</td>\n",
2742         "      <td>36</td>\n",
2743         "      <td>0</td>\n",
2744         "      <td>6</td>\n",
2745         "      <td>0</td>\n",
2746         "      <td>16</td>\n",
2747         "      <td>0</td>\n",
2748         "      <td>10</td>\n",
2749         "      <td>1</td>\n",
2750         "    </tr>\n",
2751         "    <tr>\n",
2752         "      <th>2</th>\n",
2753         "      <td>2010-03-01</td>\n",
2754         "      <td>0</td>\n",
2755         "      <td>2</td>\n",
2756         "      <td>0</td>\n",
2757         "      <td>4</td>\n",
2758         "      <td>0</td>\n",
2759         "      <td>0</td>\n",
2760         "      <td>0</td>\n",
2761         "      <td>0</td>\n",
2762         "      <td>0</td>\n",
2763         "      <td>...</td>\n",
2764         "      <td>1</td>\n",
2765         "      <td>1</td>\n",
2766         "      <td>34</td>\n",
2767         "      <td>0</td>\n",
2768         "      <td>4</td>\n",
2769         "      <td>0</td>\n",
2770         "      <td>12</td>\n",
2771         "      <td>0</td>\n",
2772         "      <td>9</td>\n",
2773         "      <td>1</td>\n",
2774         "    </tr>\n",
2775         "    <tr>\n",
2776         "      <th>3</th>\n",
2777         "      <td>2010-04-01</td>\n",
2778         "      <td>0</td>\n",
2779         "      <td>2</td>\n",
2780         "      <td>0</td>\n",
2781         "      <td>3</td>\n",
2782         "      <td>0</td>\n",
2783         "      <td>0</td>\n",
2784         "      <td>0</td>\n",
2785         "      <td>0</td>\n",
2786         "      <td>0</td>\n",
2787         "      <td>...</td>\n",
2788         "      <td>1</td>\n",
2789         "      <td>0</td>\n",
2790         "      <td>35</td>\n",
2791         "      <td>0</td>\n",
2792         "      <td>5</td>\n",
2793         "      <td>0</td>\n",
2794         "      <td>15</td>\n",
2795         "      <td>0</td>\n",
2796         "      <td>8</td>\n",
2797         "      <td>1</td>\n",
2798         "    </tr>\n",
2799         "    <tr>\n",
2800         "      <th>4</th>\n",
2801         "      <td>2010-05-01</td>\n",
2802         "      <td>0</td>\n",
2803         "      <td>1</td>\n",
2804         "      <td>0</td>\n",
2805         "      <td>4</td>\n",
2806         "      <td>0</td>\n",
2807         "      <td>0</td>\n",
2808         "      <td>0</td>\n",
2809         "      <td>0</td>\n",
2810         "      <td>0</td>\n",
2811         "      <td>...</td>\n",
2812         "      <td>1</td>\n",
2813         "      <td>0</td>\n",
2814         "      <td>28</td>\n",
2815         "      <td>0</td>\n",
2816         "      <td>6</td>\n",
2817         "      <td>0</td>\n",
2818         "      <td>13</td>\n",
2819         "      <td>0</td>\n",
2820         "      <td>10</td>\n",
2821         "      <td>1</td>\n",
2822         "    </tr>\n",
2823         "    <tr>\n",
2824         "      <th>...</th>\n",
2825         "      <td>...</td>\n",
2826         "      <td>...</td>\n",
2827         "      <td>...</td>\n",
2828         "      <td>...</td>\n",
2829         "      <td>...</td>\n",
2830         "      <td>...</td>\n",
2831         "      <td>...</td>\n",
2832         "      <td>...</td>\n",
2833         "      <td>...</td>\n",
2834         "      <td>...</td>\n",
2835         "      <td>...</td>\n",
2836         "      <td>...</td>\n",
2837         "      <td>...</td>\n",
2838         "      <td>...</td>\n",
2839         "      <td>...</td>\n",
2840         "      <td>...</td>\n",
2841         "      <td>...</td>\n",
2842         "      <td>...</td>\n",
2843         "      <td>...</td>\n",
2844         "      <td>...</td>\n",
2845         "      <td>...</td>\n",
2846         "    </tr>\n",
2847         "    <tr>\n",
2848         "      <th>133</th>\n",
2849         "      <td>2021-02-01</td>\n",
2850         "      <td>0</td>\n",
2851         "      <td>5</td>\n",
2852         "      <td>0</td>\n",
2853         "      <td>4</td>\n",
2854         "      <td>1</td>\n",
2855         "      <td>1</td>\n",
2856         "      <td>0</td>\n",
2857         "      <td>0</td>\n",
2858         "      <td>0</td>\n",
2859         "      <td>...</td>\n",
2860         "      <td>1</td>\n",
2861         "      <td>1</td>\n",
2862         "      <td>21</td>\n",
2863         "      <td>0</td>\n",
2864         "      <td>4</td>\n",
2865         "      <td>0</td>\n",
2866         "      <td>6</td>\n",
2867         "      <td>0</td>\n",
2868         "      <td>16</td>\n",
2869         "      <td>1</td>\n",
2870         "    </tr>\n",
2871         "    <tr>\n",
2872         "      <th>134</th>\n",
2873         "      <td>2021-03-01</td>\n",
2874         "      <td>0</td>\n",
2875         "      <td>9</td>\n",
2876         "      <td>0</td>\n",
2877         "      <td>5</td>\n",
2878         "      <td>1</td>\n",
2879         "      <td>1</td>\n",
2880         "      <td>0</td>\n",
2881         "      <td>0</td>\n",
2882         "      <td>0</td>\n",
2883         "      <td>...</td>\n",
2884         "      <td>1</td>\n",
2885         "      <td>1</td>\n",
2886         "      <td>23</td>\n",
2887         "      <td>0</td>\n",
2888         "      <td>3</td>\n",
2889         "      <td>0</td>\n",
2890         "      <td>7</td>\n",
2891         "      <td>0</td>\n",
2892         "      <td>15</td>\n",
2893         "      <td>1</td>\n",
2894         "    </tr>\n",
2895         "    <tr>\n",
2896         "      <th>135</th>\n",
2897         "      <td>2021-04-01</td>\n",
2898         "      <td>0</td>\n",
2899         "      <td>6</td>\n",
2900         "      <td>0</td>\n",
2901         "      <td>3</td>\n",
2902         "      <td>0</td>\n",
2903         "      <td>1</td>\n",
2904         "      <td>0</td>\n",
2905         "      <td>0</td>\n",
2906         "      <td>0</td>\n",
2907         "      <td>...</td>\n",
2908         "      <td>1</td>\n",
2909         "      <td>0</td>\n",
2910         "      <td>19</td>\n",
2911         "      <td>0</td>\n",
2912         "      <td>2</td>\n",
2913         "      <td>0</td>\n",
2914         "      <td>6</td>\n",
2915         "      <td>0</td>\n",
2916         "      <td>13</td>\n",
2917         "      <td>1</td>\n",
2918         "    </tr>\n",
2919         "    <tr>\n",
2920         "      <th>136</th>\n",
2921         "      <td>2021-05-01</td>\n",
2922         "      <td>0</td>\n",
2923         "      <td>8</td>\n",
2924         "      <td>0</td>\n",
2925         "      <td>4</td>\n",
2926         "      <td>0</td>\n",
2927         "      <td>1</td>\n",
2928         "      <td>0</td>\n",
2929         "      <td>0</td>\n",
2930         "      <td>0</td>\n",
2931         "      <td>...</td>\n",
2932         "      <td>1</td>\n",
2933         "      <td>30</td>\n",
2934         "      <td>20</td>\n",
2935         "      <td>0</td>\n",
2936         "      <td>3</td>\n",
2937         "      <td>0</td>\n",
2938         "      <td>7</td>\n",
2939         "      <td>0</td>\n",
2940         "      <td>13</td>\n",
2941         "      <td>1</td>\n",
2942         "    </tr>\n",
2943         "    <tr>\n",
2944         "      <th>137</th>\n",
2945         "      <td>2021-06-01</td>\n",
2946         "      <td>0</td>\n",
2947         "      <td>4</td>\n",
2948         "      <td>0</td>\n",
2949         "      <td>3</td>\n",
2950         "      <td>0</td>\n",
2951         "      <td>1</td>\n",
2952         "      <td>0</td>\n",
2953         "      <td>0</td>\n",
2954         "      <td>0</td>\n",
2955         "      <td>...</td>\n",
2956         "      <td>1</td>\n",
2957         "      <td>6</td>\n",
2958         "      <td>22</td>\n",
2959         "      <td>0</td>\n",
2960         "      <td>3</td>\n",
2961         "      <td>0</td>\n",
2962         "      <td>19</td>\n",
2963         "      <td>0</td>\n",
2964         "      <td>14</td>\n",
2965         "      <td>1</td>\n",
2966         "    </tr>\n",
2967         "  </tbody>\n",
2968         "</table>\n",
2969         "<p>138 rows × 357 columns</p>\n",
2970         "</div>"
2971        ],
2972        "text/plain": [
2973         "          date  Bharatiya Janata Party  BJP  United States Congress  Congress  \\\n",
2974         "0   2010-01-01                       0    1                       0         4   \n",
2975         "1   2010-02-01                       0    2                       0         4   \n",
2976         "2   2010-03-01                       0    2                       0         4   \n",
2977         "3   2010-04-01                       0    2                       0         3   \n",
2978         "4   2010-05-01                       0    1                       0         4   \n",
2979         "..         ...                     ...  ...                     ...       ...   \n",
2980         "133 2021-02-01                       0    5                       0         4   \n",
2981         "134 2021-03-01                       0    9                       0         5   \n",
2982         "135 2021-04-01                       0    6                       0         3   \n",
2983         "136 2021-05-01                       0    8                       0         4   \n",
2984         "137 2021-06-01                       0    4                       0         3   \n",
2985         "\n",
2986         "     Indian National Congress  The Congress  United States Senate  \\\n",
2987         "0                           1             0                     0   \n",
2988         "1                           1             0                     0   \n",
2989         "2                           0             0                     0   \n",
2990         "3                           0             0                     0   \n",
2991         "4                           0             0                     0   \n",
2992         "..                        ...           ...                   ...   \n",
2993         "133                         1             1                     0   \n",
2994         "134                         1             1                     0   \n",
2995         "135                         0             1                     0   \n",
2996         "136                         0             1                     0   \n",
2997         "137                         0             1                     0   \n",
2998         "\n",
2999         "     Aam Aadmi Party  NASDAQ:AAPL  ...  Funeral  Fungus  Function  fun.  \\\n",
3000         "0                  0            0  ...        1       0        31     0   \n",
3001         "1                  0            0  ...        1       0        36     0   \n",
3002         "2                  0            0  ...        1       1        34     0   \n",
3003         "3                  0            0  ...        1       0        35     0   \n",
3004         "4                  0            0  ...        1       0        28     0   \n",
3005         "..               ...          ...  ...      ...     ...       ...   ...   \n",
3006         "133                0            0  ...        1       1        21     0   \n",
3007         "134                0            0  ...        1       1        23     0   \n",
3008         "135                0            0  ...        1       0        19     0   \n",
3009         "136                0            0  ...        1      30        20     0   \n",
3010         "137                0            0  ...        1       6        22     0   \n",
3011         "\n",
3012         "     Climate  Biophysical environment  Environment  Natural environment  Ring  \\\n",
3013         "0          7                        0           15                    0    11   \n",
3014         "1          6                        0           16                    0    10   \n",
3015         "2          4                        0           12                    0     9   \n",
3016         "3          5                        0           15                    0     8   \n",
3017         "4          6                        0           13                    0    10   \n",
3018         "..       ...                      ...          ...                  ...   ...   \n",
3019         "133        4                        0            6                    0    16   \n",
3020         "134        3                        0            7                    0    15   \n",
3021         "135        2                        0            6                    0    13   \n",
3022         "136        3                        0            7                    0    13   \n",
3023         "137        3                        0           19                    0    14   \n",
3024         "\n",
3025         "     The Ring  \n",
3026         "0           1  \n",
3027         "1           1  \n",
3028         "2           1  \n",
3029         "3           1  \n",
3030         "4           1  \n",
3031         "..        ...  \n",
3032         "133         1  \n",
3033         "134         1  \n",
3034         "135         1  \n",
3035         "136         1  \n",
3036         "137         1  \n",
3037         "\n",
3038         "[138 rows x 357 columns]"
3039        ]
3040       },
3041       "execution_count": 203,
3042       "metadata": {},
3043       "output_type": "execute_result"
3044      }
3045     ],
3046     "source": [
3047      "final"
3048     ]
3049    },
3050    {
3051     "cell_type": "code",
3052     "execution_count": 205,
3053     "id": "c62585ea",
3054     "metadata": {},
3055     "outputs": [],
3056     "source": [
3057      "import numpy as np"
3058     ]
3059    },
3060    {
3061     "cell_type": "code",
3062     "execution_count": null,
3063     "id": "6c4b2c72",
3064     "metadata": {},
3065     "outputs": [],
3066     "source": []
3067    },
3068    {
3069     "cell_type": "code",
3070     "execution_count": 254,
3071     "id": "1d94b288",
3072     "metadata": {},
3073     "outputs": [],
3074     "source": [
3075      "a=final.describe().T"
3076     ]
3077    },
3078    {
3079     "cell_type": "code",
3080     "execution_count": 255,
3081     "id": "4e5c3107",
3082     "metadata": {},
3083     "outputs": [],
3084     "source": [
3085      "a=a[(a['max']-a['min']>50) | (a['max']>80)]"
3086     ]
3087    },
3088    {
3089     "cell_type": "code",
3090     "execution_count": 256,
3091     "id": "4dd41e07",
3092     "metadata": {
3093      "scrolled": true
3094     },
3095     "outputs": [
3096      {
3097       "data": {
3098        "text/plain": [
3099         "16"
3100        ]
3101       },
3102       "execution_count": 256,
3103       "metadata": {},
3104       "output_type": "execute_result"
3105      }
3106     ],
3107     "source": [
3108      "len(a)"
3109     ]
3110    },
3111    {
3112     "cell_type": "code",
3113     "execution_count": 257,
3114     "id": "a89ebea7",
3115     "metadata": {},
3116     "outputs": [
3117      {
3118       "data": {
3119        "text/plain": [
3120         "<AxesSubplot:>"
3121        ]
3122       },
3123       "execution_count": 257,
3124       "metadata": {},
3125       "output_type": "execute_result"
3126      },
3127      {
3128       "data": {
3129        "image/png": 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