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'keywords_matched','lemmatized_body'])\n","df.info()"],"metadata":{"id":"AWerUJrKWLde","executionInfo":{"status":"ok","timestamp":1684859730583,"user_tz":-330,"elapsed":4399,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c35d7a96-5357-4348-e8bf-e27b31738f83"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 128759 entries, 0 to 128758\n","Data columns (total 5 columns):\n"," #   Column            Non-Null Count   Dtype \n","---  ------            --------------   ----- \n"," 0   post_id           128759 non-null  int64 \n"," 1   date              128759 non-null  object\n"," 2   lemmatized_body   128759 non-null  object\n"," 3   keywords_matched  128759 non-null  object\n"," 4   group_name        128759 non-null  object\n","dtypes: int64(1), object(4)\n","memory usage: 4.9+ MB\n"]}]},{"cell_type":"code","source":["df.head(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"cFXRcC5VYkwe","executionInfo":{"status":"ok","timestamp":1684859745550,"user_tz":-330,"elapsed":823,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"b4a100b7-5fcd-4d91-ddaf-239404c3f607"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   post_id          date                                    lemmatized_body  \\\n","0  1641475  Jun 16, 2022  one relate symmetry huge hang one taking pictu...   \n","1  1709493   Sep 4, 2022  pm bit detailed question hamvil said get back ...   \n","\n","  keywords_matched   group_name  \n","0         ['hang']  ['hanging']  \n","1         ['hang']  ['hanging']  "],"text/html":["\n","  <div id=\"df-a91e744c-8a18-4432-9377-86edfcf4d8d8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr 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<td>['hanging']</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a91e744c-8a18-4432-9377-86edfcf4d8d8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  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group\n","\n","df.group_name = df.group_name.apply(literal_eval)\n","\n","for i in range(len(df)):\n","  curr_lis = df.loc[i,'group_name']\n","  for name in curr_lis:\n","    df.loc[i,name] = df.loc[i,name] + 1"],"metadata":{"id":"sMcTCLM3nglV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T6fWg-5-oUFZ","executionInfo":{"status":"ok","timestamp":1684859791576,"user_tz":-330,"elapsed":38,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"ff42abca-615b-404d-bd38-11f03fcf99f5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 128759 entries, 0 to 128758\n","Data columns (total 19 columns):\n"," #   Column               Non-Null Count   Dtype \n","---  ------               --------------   ----- \n"," 0   post_id              128759 non-null  int64 \n"," 1   date                 128759 non-null  object\n"," 2   lemmatized_body      128759 non-null  object\n"," 3   keywords_matched     128759 non-null  object\n"," 4   group_name           128759 non-null  object\n"," 5   sn                   128759 non-null  int64 \n"," 6   hanging              128759 non-null  int64 \n"," 7   firearm              128759 non-null  int64 \n"," 8   benzodiazepines      128759 non-null  int64 \n"," 9   opioids              128759 non-null  int64 \n"," 10  antiemetic           128759 non-null  int64 \n"," 11  barbiturates         128759 non-null  int64 \n"," 12  acid regulator       128759 non-null  int64 \n"," 13  cyanides             128759 non-null  int64 \n"," 14  other preservatives  128759 non-null  int64 \n"," 15  plant-based poisons  128759 non-null  int64 \n"," 16  household chemicals  128759 non-null  int64 \n"," 17  ricin                128759 non-null  int64 \n"," 18  nitric oxide         128759 non-null  int64 \n","dtypes: int64(15), object(4)\n","memory usage: 18.7+ MB\n"]}]},{"cell_type":"code","source":["df2 = df.drop(['post_id'], axis=1)\n","df2 = df2.groupby(by='monthyear').sum()\n","df2.to_csv(my_path+'KEYWORDS_FILTERED_TEXT_MONTHYEAR.csv',index=False)\n","df2"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Ky8JCv19GI7l","executionInfo":{"status":"ok","timestamp":1678764625753,"user_tz":-330,"elapsed":19,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"cb7145fe-19e5-458a-9be0-002a528b3626"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["             sn  hanging  firearm  benzodiazepines  opioids  antiemetic  \\\n","monthyear                                                                 \n","2018-03       0       15        7                1        2           1   \n","2018-04      20       39       44               32       24          20   \n","2018-05      56       62       43               28       18          57   \n","2018-06     186      225      110               69       55          77   \n","2018-07     225      316      245              147       80         108   \n","2018-08     357      722      247              185      113         141   \n","2018-09     421      672      424              295      212         255   \n","2018-10     359      695      366              267      139         190   \n","2018-11     504      547      375              186      224         189   \n","2018-12     581      520      421              184      201         216   \n","2019-01     490      582      431              181      159         214   \n","2019-02     729      498      312              158      122         188   \n","2019-03     899      576      321              297      101         285   \n","2019-04     779      550      305              275      154         251   \n","2019-05     809      622      414              277      168         266   \n","2019-06     694      667      409              367      221         276   \n","2019-07     717      513      362              286      175         234   \n","2019-08     831      507      274              265      225         206   \n","2019-09    1017      519      340              297      202         194   \n","2019-10    1593      594      328              288      200         284   \n","2019-11    2228      705      329              311      187         321   \n","2019-12    1804      580      265              251      198         242   \n","2020-01    2043      691      387              420      244         258   \n","2020-02    2183      791      473              327      217         302   \n","2020-03    1858      665      401              264      190         247   \n","2020-04    1319      639      304              334      198         143   \n","2020-05    1647      697      369              378      193         208   \n","2020-06    1665      565      300              196      154         206   \n","2020-07    1635      721      323              246      167         208   \n","2020-08    1844      815      319              323      166         184   \n","2020-09    2048      683      329              257      169         191   \n","2020-10    1832      768      382              241      222         150   \n","2020-11    2004      714      413              240      148         117   \n","2020-12    1778      591      370              240      123         133   \n","2021-01    1320      585      363              254      117         127   \n","2021-02     953      446      251              120       54          96   \n","2021-03    1101      504      303              191      106          81   \n","2021-04    1042      576      303              207       62          74   \n","2021-05    1063      376      276              120       65         100   \n","2021-06     988      330      210              181       66         109   \n","2021-07     877      360      248              190      112         108   \n","2021-08     815      347      202              171      130         136   \n","2021-09     875      390      189              165      119         135   \n","2021-10     931      431      223              178       76         109   \n","2021-11    1335      667      272              198      110         147   \n","2021-12    1098      735      393              225      157         106   \n","2022-01     737      481      244              227       95          93   \n","2022-02     887      455      338              146      115          96   \n","2022-03    1214      567      334              189      187         118   \n","2022-04    1672      616      283              255      202         162   \n","2022-05    1379      394      221              255      136          98   \n","2022-06    1507      419      294              231      145         109   \n","2022-07    1327      541      245              197      143         168   \n","2022-08    1519      588      302              240      155         150   \n","2022-09    1830      621      338              372      244         181   \n","2022-10     553      128       62               65       50          27   \n","\n","           barbiturates  acid regulator  cyanides  other preservatives  \\\n","monthyear                                                                \n","2018-03               0               0         0                    0   \n","2018-04              23               0        10                    4   \n","2018-05              25               0         6                   12   \n","2018-06              47              15        10                   20   \n","2018-07              84              30        29                   34   \n","2018-08             121              64        35                   34   \n","2018-09             142              81        48                   42   \n","2018-10             133              55        37                   57   \n","2018-11             126              81        52                   36   \n","2018-12              60             157        41                   19   \n","2019-01             111              72        25                   30   \n","2019-02              92              97        26                   32   \n","2019-03             131             124        38                   39   \n","2019-04              89             112        31                   58   \n","2019-05             131              96        24                   20   \n","2019-06             142              84        60                   11   \n","2019-07             103             102        31                   10   \n","2019-08              66              70        41                   12   \n","2019-09              75             109        62                   14   \n","2019-10             109             206        65                   28   \n","2019-11              63             227        52                   37   \n","2019-12              82             113        52                   18   \n","2020-01              86             169        47                   20   \n","2020-02             108             142        67                   35   \n","2020-03              69             112        25                   23   \n","2020-04              81              55        25                   16   \n","2020-05              96              60        78                   26   \n","2020-06              58             105        53                   18   \n","2020-07              54             125        39                   36   \n","2020-08              67              96        83                   30   \n","2020-09              67             120        63                   25   \n","2020-10              65             121        66                   32   \n","2020-11              38              81       111                   39   \n","2020-12              69              88        39                   36   \n","2021-01              58              69        33                   23   \n","2021-02              37              58        18                   10   \n","2021-03              42              37        17                   21   \n","2021-04              43              39        21                    7   \n","2021-05              51              45        12                    4   \n","2021-06              75              27        18                   14   \n","2021-07              62              34        12                    3   \n","2021-08              66              39        13                   11   \n","2021-09              58              36        14                    6   \n","2021-10              40              23        10                   14   \n","2021-11              62              75         9                   17   \n","2021-12              46              52        17                   23   \n","2022-01              46              40        17                    8   \n","2022-02              53              40        13                   16   \n","2022-03             100              62        22                   13   \n","2022-04             113              73        32                   33   \n","2022-05              46             124        15                   25   \n","2022-06              69              59        16                   16   \n","2022-07              88              56        13                   18   \n","2022-08              81              73        57                   19   \n","2022-09              83              81        21                   26   \n","2022-10              25              10         3                   15   \n","\n","           plant-based poisons  household chemicals  ricin  nitric oxide  \n","monthyear                                                                 \n","2018-03                      0                    0      0             0  \n","2018-04                      0                    3      0             0  \n","2018-05                      0                    1      0             1  \n","2018-06                      1                    3      0             0  \n","2018-07                      6                    7      0             0  \n","2018-08                      3                    7      0             0  \n","2018-09                     19                    7      3             0  \n","2018-10                     36                    8      5             1  \n","2018-11                      8                    7      2             2  \n","2018-12                     17                   36      1             1  \n","2019-01                     19                   51      3             0  \n","2019-02                     17                    7      2             0  \n","2019-03                     28                   10      2             0  \n","2019-04                      9                    7      1             2  \n","2019-05                     18                    9      0             1  \n","2019-06                     12                    7      0             0  \n","2019-07                      4                   12      0             0  \n","2019-08                      5                    3      4             0  \n","2019-09                     11                   18      4             0  \n","2019-10                      8                   15      9             3  \n","2019-11                      8                   14      2             6  \n","2019-12                      5                   19      0             1  \n","2020-01                     14                   10      2            11  \n","2020-02                      4                   11      3             2  \n","2020-03                      5                    9      1             1  \n","2020-04                      6                    6      1             2  \n","2020-05                      8                   11      3             1  \n","2020-06                      1                   11      2             0  \n","2020-07                     10                   22      3             0  \n","2020-08                     16                   12      1             1  \n","2020-09                     15                   10     11             2  \n","2020-10                      6                   14      1             5  \n","2020-11                     26                   19      1            17  \n","2020-12                     32                    4     26             6  \n","2021-01                      2                   13      2             0  \n","2021-02                     10                   10      0             0  \n","2021-03                     17                   13      0             1  \n","2021-04                      5                    5      1             0  \n","2021-05                      4                    5      1             1  \n","2021-06                      1                    5      0             1  \n","2021-07                      4                    9      0             1  \n","2021-08                      5                    4      0             0  \n","2021-09                      1                    6      6             0  \n","2021-10                      1                    6      4             0  \n","2021-11                     21                   17      3             5  \n","2021-12                      8                    7      3             1  \n","2022-01                      5                    7      0             1  \n","2022-02                     13                    4      0             0  \n","2022-03                     13                    9      0             0  \n","2022-04                     11                   10      1             4  \n","2022-05                     14                    8      3            17  \n","2022-06                      7                    3      0             3  \n","2022-07                      4                   12      0             3  \n","2022-08                     12                    3      0             4  \n","2022-09                     10                    6      0             4  \n","2022-10                      1                    0      0             1  "],"text/html":["\n","  <div id=\"df-654ba141-d37a-4b90-bf3b-a20aabc95b10\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sn</th>\n","      <th>hanging</th>\n","      <th>firearm</th>\n","      <th>benzodiazepines</th>\n","      <th>opioids</th>\n","      <th>antiemetic</th>\n","      <th>barbiturates</th>\n","      <th>acid regulator</th>\n","      <th>cyanides</th>\n","      <th>other preservatives</th>\n","      <th>plant-based poisons</th>\n","      <th>household chemicals</th>\n","      <th>ricin</th>\n","      <th>nitric oxide</th>\n","    </tr>\n","    <tr>\n","      <th>monthyear</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2018-03</th>\n","      <td>0</td>\n","      <td>15</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-04</th>\n","      <td>20</td>\n","      <td>39</td>\n","      <td>44</td>\n","      <td>32</td>\n","      <td>24</td>\n","      <td>20</td>\n","      <td>23</td>\n","      <td>0</td>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-05</th>\n","      <td>56</td>\n","      <td>62</td>\n","      <td>43</td>\n","      <td>28</td>\n","      <td>18</td>\n","      <td>57</td>\n","      <td>25</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>12</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2018-06</th>\n","      <td>186</td>\n","      <td>225</td>\n","      <td>110</td>\n","      <td>69</td>\n","      <td>55</td>\n","      <td>77</td>\n","      <td>47</td>\n","      <td>15</td>\n","      <td>10</td>\n","      <td>20</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-07</th>\n","      <td>225</td>\n","      <td>316</td>\n","      <td>245</td>\n","      <td>147</td>\n","      <td>80</td>\n","      <td>108</td>\n","      <td>84</td>\n","      <td>30</td>\n","      <td>29</td>\n","      <td>34</td>\n","      <td>6</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-08</th>\n","      <td>357</td>\n","      <td>722</td>\n","      <td>247</td>\n","      <td>185</td>\n","      <td>113</td>\n","      <td>141</td>\n","      <td>121</td>\n","      <td>64</td>\n","      <td>35</td>\n","      <td>34</td>\n","      <td>3</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-09</th>\n","      <td>421</td>\n","      <td>672</td>\n","      <td>424</td>\n","      <td>295</td>\n","      <td>212</td>\n","      <td>255</td>\n","      <td>142</td>\n","      <td>81</td>\n","      <td>48</td>\n","      <td>42</td>\n","      <td>19</td>\n","      <td>7</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2018-10</th>\n","      <td>359</td>\n","      <td>695</td>\n","      <td>366</td>\n","      <td>267</td>\n","      <td>139</td>\n","      <td>190</td>\n","      <td>133</td>\n","      <td>55</td>\n","      <td>37</td>\n","      <td>57</td>\n","      <td>36</td>\n","      <td>8</td>\n","      <td>5</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2018-11</th>\n","      <td>504</td>\n","      <td>547</td>\n","      <td>375</td>\n","      <td>186</td>\n","      <td>224</td>\n","      <td>189</td>\n","      <td>126</td>\n","      <td>81</td>\n","      <td>52</td>\n","      <td>36</td>\n","      <td>8</td>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2018-12</th>\n","      <td>581</td>\n","      <td>520</td>\n","      <td>421</td>\n","      <td>184</td>\n","      <td>201</td>\n","      <td>216</td>\n","      <td>60</td>\n","      <td>157</td>\n","      <td>41</td>\n","      <td>19</td>\n","      <td>17</td>\n","      <td>36</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2019-01</th>\n","      <td>490</td>\n","      <td>582</td>\n","      <td>431</td>\n","      <td>181</td>\n","      <td>159</td>\n","      <td>214</td>\n","      <td>111</td>\n","      <td>72</td>\n","      <td>25</td>\n","      <td>30</td>\n","      <td>19</td>\n","      <td>51</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2019-02</th>\n","      <td>729</td>\n","      <td>498</td>\n","      <td>312</td>\n","      <td>158</td>\n","      <td>122</td>\n","      <td>188</td>\n","      <td>92</td>\n","      <td>97</td>\n","      <td>26</td>\n","      <td>32</td>\n","      <td>17</td>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2019-03</th>\n","      <td>899</td>\n","      <td>576</td>\n","      <td>321</td>\n","      <td>297</td>\n","      <td>101</td>\n","      <td>285</td>\n","      <td>131</td>\n","      <td>124</td>\n","      <td>38</td>\n","      <td>39</td>\n","      <td>28</td>\n","      <td>10</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2019-04</th>\n","      <td>779</td>\n","      <td>550</td>\n","      <td>305</td>\n","      <td>275</td>\n","      <td>154</td>\n","      <td>251</td>\n","      <td>89</td>\n","      <td>112</td>\n","      <td>31</td>\n","      <td>58</td>\n","      <td>9</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2019-05</th>\n","      <td>809</td>\n","      <td>622</td>\n","      <td>414</td>\n","      <td>277</td>\n","      <td>168</td>\n","      <td>266</td>\n","      <td>131</td>\n","      <td>96</td>\n","      <td>24</td>\n","      <td>20</td>\n","      <td>18</td>\n","      <td>9</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2019-06</th>\n","      <td>694</td>\n","      <td>667</td>\n","      <td>409</td>\n","      <td>367</td>\n","      <td>221</td>\n","      <td>276</td>\n","      <td>142</td>\n","      <td>84</td>\n","      <td>60</td>\n","      <td>11</td>\n","      <td>12</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2019-07</th>\n","      <td>717</td>\n","      <td>513</td>\n","      <td>362</td>\n","      <td>286</td>\n","      <td>175</td>\n","      <td>234</td>\n","      <td>103</td>\n","      <td>102</td>\n","      <td>31</td>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>12</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2019-08</th>\n","      <td>831</td>\n","      <td>507</td>\n","      <td>274</td>\n","      <td>265</td>\n","      <td>225</td>\n","      <td>206</td>\n","      <td>66</td>\n","      <td>70</td>\n","      <td>41</td>\n","      <td>12</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2019-09</th>\n","      <td>1017</td>\n","      <td>519</td>\n","      <td>340</td>\n","      <td>297</td>\n","      <td>202</td>\n","      <td>194</td>\n","      <td>75</td>\n","      <td>109</td>\n","      <td>62</td>\n","      <td>14</td>\n","      <td>11</td>\n","      <td>18</td>\n","      <td>4</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2019-10</th>\n","      <td>1593</td>\n","      <td>594</td>\n","      <td>328</td>\n","      <td>288</td>\n","      <td>200</td>\n","      <td>284</td>\n","      <td>109</td>\n","      <td>206</td>\n","      <td>65</td>\n","      <td>28</td>\n","      <td>8</td>\n","      <td>15</td>\n","      <td>9</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2019-11</th>\n","      <td>2228</td>\n","      <td>705</td>\n","      <td>329</td>\n","      <td>311</td>\n","      <td>187</td>\n","      <td>321</td>\n","      <td>63</td>\n","      <td>227</td>\n","      <td>52</td>\n","      <td>37</td>\n","      <td>8</td>\n","      <td>14</td>\n","      <td>2</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2019-12</th>\n","      <td>1804</td>\n","      <td>580</td>\n","      <td>265</td>\n","      <td>251</td>\n","      <td>198</td>\n","      <td>242</td>\n","      <td>82</td>\n","      <td>113</td>\n","      <td>52</td>\n","      <td>18</td>\n","      <td>5</td>\n","      <td>19</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2020-01</th>\n","      <td>2043</td>\n","      <td>691</td>\n","      <td>387</td>\n","      <td>420</td>\n","      <td>244</td>\n","      <td>258</td>\n","      <td>86</td>\n","      <td>169</td>\n","      <td>47</td>\n","      <td>20</td>\n","      <td>14</td>\n","      <td>10</td>\n","      <td>2</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>2020-02</th>\n","      <td>2183</td>\n","      <td>791</td>\n","      <td>473</td>\n","      <td>327</td>\n","      <td>217</td>\n","      <td>302</td>\n","      <td>108</td>\n","      <td>142</td>\n","      <td>67</td>\n","      <td>35</td>\n","      <td>4</td>\n","      <td>11</td>\n","      <td>3</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2020-03</th>\n","      <td>1858</td>\n","      <td>665</td>\n","      <td>401</td>\n","      <td>264</td>\n","      <td>190</td>\n","      <td>247</td>\n","      <td>69</td>\n","      <td>112</td>\n","      <td>25</td>\n","      <td>23</td>\n","      <td>5</td>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2020-04</th>\n","      <td>1319</td>\n","      <td>639</td>\n","      <td>304</td>\n","      <td>334</td>\n","      <td>198</td>\n","      <td>143</td>\n","      <td>81</td>\n","      <td>55</td>\n","      <td>25</td>\n","      <td>16</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2020-05</th>\n","      <td>1647</td>\n","      <td>697</td>\n","      <td>369</td>\n","      <td>378</td>\n","      <td>193</td>\n","      <td>208</td>\n","      <td>96</td>\n","      <td>60</td>\n","      <td>78</td>\n","      <td>26</td>\n","      <td>8</td>\n","      <td>11</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2020-06</th>\n","      <td>1665</td>\n","      <td>565</td>\n","      <td>300</td>\n","      <td>196</td>\n","      <td>154</td>\n","      <td>206</td>\n","      <td>58</td>\n","      <td>105</td>\n","      <td>53</td>\n","      <td>18</td>\n","      <td>1</td>\n","      <td>11</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2020-07</th>\n","      <td>1635</td>\n","      <td>721</td>\n","      <td>323</td>\n","      <td>246</td>\n","      <td>167</td>\n","      <td>208</td>\n","      <td>54</td>\n","      <td>125</td>\n","      <td>39</td>\n","      <td>36</td>\n","      <td>10</td>\n","      <td>22</td>\n","      <td>3</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2020-08</th>\n","      <td>1844</td>\n","      <td>815</td>\n","      <td>319</td>\n","      <td>323</td>\n","      <td>166</td>\n","      <td>184</td>\n","      <td>67</td>\n","      <td>96</td>\n","      <td>83</td>\n","      <td>30</td>\n","      <td>16</td>\n","      <td>12</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2020-09</th>\n","      <td>2048</td>\n","      <td>683</td>\n","      <td>329</td>\n","      <td>257</td>\n","      <td>169</td>\n","      <td>191</td>\n","      <td>67</td>\n","      <td>120</td>\n","      <td>63</td>\n","      <td>25</td>\n","      <td>15</td>\n","      <td>10</td>\n","      <td>11</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2020-10</th>\n","      <td>1832</td>\n","      <td>768</td>\n","      <td>382</td>\n","      <td>241</td>\n","      <td>222</td>\n","      <td>150</td>\n","      <td>65</td>\n","      <td>121</td>\n","      <td>66</td>\n","      <td>32</td>\n","      <td>6</td>\n","      <td>14</td>\n","      <td>1</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2020-11</th>\n","      <td>2004</td>\n","      <td>714</td>\n","      <td>413</td>\n","      <td>240</td>\n","      <td>148</td>\n","      <td>117</td>\n","      <td>38</td>\n","      <td>81</td>\n","      <td>111</td>\n","      <td>39</td>\n","      <td>26</td>\n","      <td>19</td>\n","      <td>1</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12</th>\n","      <td>1778</td>\n","      <td>591</td>\n","      <td>370</td>\n","      <td>240</td>\n","      <td>123</td>\n","      <td>133</td>\n","      <td>69</td>\n","      <td>88</td>\n","      <td>39</td>\n","      <td>36</td>\n","      <td>32</td>\n","      <td>4</td>\n","      <td>26</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2021-01</th>\n","      <td>1320</td>\n","      <td>585</td>\n","      <td>363</td>\n","      <td>254</td>\n","      <td>117</td>\n","      <td>127</td>\n","      <td>58</td>\n","      <td>69</td>\n","      <td>33</td>\n","      <td>23</td>\n","      <td>2</td>\n","      <td>13</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2021-02</th>\n","      <td>953</td>\n","      <td>446</td>\n","      <td>251</td>\n","      <td>120</td>\n","      <td>54</td>\n","      <td>96</td>\n","      <td>37</td>\n","      <td>58</td>\n","      <td>18</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2021-03</th>\n","      <td>1101</td>\n","      <td>504</td>\n","      <td>303</td>\n","      <td>191</td>\n","      <td>106</td>\n","      <td>81</td>\n","      <td>42</td>\n","      <td>37</td>\n","      <td>17</td>\n","      <td>21</td>\n","      <td>17</td>\n","      <td>13</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2021-04</th>\n","      <td>1042</td>\n","      <td>576</td>\n","      <td>303</td>\n","      <td>207</td>\n","      <td>62</td>\n","      <td>74</td>\n","      <td>43</td>\n","      <td>39</td>\n","      <td>21</td>\n","      <td>7</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2021-05</th>\n","      <td>1063</td>\n","      <td>376</td>\n","      <td>276</td>\n","      <td>120</td>\n","      <td>65</td>\n","      <td>100</td>\n","      <td>51</td>\n","      <td>45</td>\n","      <td>12</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2021-06</th>\n","      <td>988</td>\n","      <td>330</td>\n","      <td>210</td>\n","      <td>181</td>\n","      <td>66</td>\n","      <td>109</td>\n","      <td>75</td>\n","      <td>27</td>\n","      <td>18</td>\n","      <td>14</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2021-07</th>\n","      <td>877</td>\n","      <td>360</td>\n","      <td>248</td>\n","      <td>190</td>\n","      <td>112</td>\n","      <td>108</td>\n","      <td>62</td>\n","      <td>34</td>\n","      <td>12</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>9</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2021-08</th>\n","      <td>815</td>\n","      <td>347</td>\n","      <td>202</td>\n","      <td>171</td>\n","      <td>130</td>\n","      <td>136</td>\n","      <td>66</td>\n","      <td>39</td>\n","      <td>13</td>\n","      <td>11</td>\n","      <td>5</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2021-09</th>\n","      <td>875</td>\n","      <td>390</td>\n","      <td>189</td>\n","      <td>165</td>\n","      <td>119</td>\n","      <td>135</td>\n","      <td>58</td>\n","      <td>36</td>\n","      <td>14</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>6</td>\n","      <td>6</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2021-10</th>\n","      <td>931</td>\n","      <td>431</td>\n","      <td>223</td>\n","      <td>178</td>\n","      <td>76</td>\n","      <td>109</td>\n","      <td>40</td>\n","      <td>23</td>\n","      <td>10</td>\n","      <td>14</td>\n","      <td>1</td>\n","      <td>6</td>\n","      <td>4</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2021-11</th>\n","      <td>1335</td>\n","      <td>667</td>\n","      <td>272</td>\n","      <td>198</td>\n","      <td>110</td>\n","      <td>147</td>\n","      <td>62</td>\n","      <td>75</td>\n","      <td>9</td>\n","      <td>17</td>\n","      <td>21</td>\n","      <td>17</td>\n","      <td>3</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2021-12</th>\n","      <td>1098</td>\n","      <td>735</td>\n","      <td>393</td>\n","      <td>225</td>\n","      <td>157</td>\n","      <td>106</td>\n","      <td>46</td>\n","      <td>52</td>\n","      <td>17</td>\n","      <td>23</td>\n","      <td>8</td>\n","      <td>7</td>\n","      <td>3</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2022-01</th>\n","      <td>737</td>\n","      <td>481</td>\n","      <td>244</td>\n","      <td>227</td>\n","      <td>95</td>\n","      <td>93</td>\n","      <td>46</td>\n","      <td>40</td>\n","      <td>17</td>\n","      <td>8</td>\n","      <td>5</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2022-02</th>\n","      <td>887</td>\n","      <td>455</td>\n","      <td>338</td>\n","      <td>146</td>\n","      <td>115</td>\n","      <td>96</td>\n","      <td>53</td>\n","      <td>40</td>\n","      <td>13</td>\n","      <td>16</td>\n","      <td>13</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2022-03</th>\n","      <td>1214</td>\n","      <td>567</td>\n","      <td>334</td>\n","      <td>189</td>\n","      <td>187</td>\n","      <td>118</td>\n","      <td>100</td>\n","      <td>62</td>\n","      <td>22</td>\n","      <td>13</td>\n","      <td>13</td>\n","      <td>9</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2022-04</th>\n","      <td>1672</td>\n","      <td>616</td>\n","      <td>283</td>\n","      <td>255</td>\n","      <td>202</td>\n","      <td>162</td>\n","      <td>113</td>\n","      <td>73</td>\n","      <td>32</td>\n","      <td>33</td>\n","      <td>11</td>\n","      <td>10</td>\n","      <td>1</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2022-05</th>\n","      <td>1379</td>\n","      <td>394</td>\n","      <td>221</td>\n","      <td>255</td>\n","      <td>136</td>\n","      <td>98</td>\n","      <td>46</td>\n","      <td>124</td>\n","      <td>15</td>\n","      <td>25</td>\n","      <td>14</td>\n","      <td>8</td>\n","      <td>3</td>\n","      <td>17</td>\n","    </tr>\n","    <tr>\n","      <th>2022-06</th>\n","      <td>1507</td>\n","      <td>419</td>\n","      <td>294</td>\n","      <td>231</td>\n","      <td>145</td>\n","      <td>109</td>\n","      <td>69</td>\n","      <td>59</td>\n","      <td>16</td>\n","      <td>16</td>\n","      <td>7</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2022-07</th>\n","      <td>1327</td>\n","      <td>541</td>\n","      <td>245</td>\n","      <td>197</td>\n","      <td>143</td>\n","      <td>168</td>\n","      <td>88</td>\n","      <td>56</td>\n","      <td>13</td>\n","      <td>18</td>\n","      <td>4</td>\n","      <td>12</td>\n","      <td>0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>2022-08</th>\n","      <td>1519</td>\n","      <td>588</td>\n","      <td>302</td>\n","      <td>240</td>\n","      <td>155</td>\n","      <td>150</td>\n","      <td>81</td>\n","      <td>73</td>\n","      <td>57</td>\n","      <td>19</td>\n","      <td>12</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2022-09</th>\n","      <td>1830</td>\n","      <td>621</td>\n","      <td>338</td>\n","      <td>372</td>\n","      <td>244</td>\n","      <td>181</td>\n","      <td>83</td>\n","      <td>81</td>\n","      <td>21</td>\n","      <td>26</td>\n","      <td>10</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2022-10</th>\n","      <td>553</td>\n","      <td>128</td>\n","      <td>62</td>\n","      <td>65</td>\n","      <td>50</td>\n","      <td>27</td>\n","      <td>25</td>\n","      <td>10</td>\n","      <td>3</td>\n","      <td>15</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-654ba141-d37a-4b90-bf3b-a20aabc95b10')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 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Das","userId":"08332010396883382094"}},"outputId":"2665b34a-290c-4ef7-edcd-32159c29aa59"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting bertopic\n","  Downloading bertopic-0.14.1-py2.py3-none-any.whl (120 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/120.7 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m112.6/120.7 KB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.7/120.7 KB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting hdbscan>=0.8.29\n","  Downloading hdbscan-0.8.29.tar.gz (5.2 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sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 umap-learn-0.5.3\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","from copy import deepcopy\n","from bertopic import BERTopic"],"metadata":{"id":"Jl8knq4ei8pL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["docs = list(df.loc[:, \"lemmatized_body\"].values)\n","\n","model = BERTopic(language=\"english\")\n","topics, probs = 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literal_eval})\n","df_temp.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n19HnerHF4n2","executionInfo":{"status":"ok","timestamp":1684859869381,"user_tz":-330,"elapsed":5236,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"e8d0ccec-1f1c-41d5-b5fb-7ea30ea58c13"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 128759 entries, 0 to 128758\n","Data columns (total 2 columns):\n"," #   Column      Non-Null Count   Dtype \n","---  ------      --------------   ----- \n"," 0   post_id     128759 non-null  int64 \n"," 1   group_name  128759 non-null  object\n","dtypes: int64(1), object(1)\n","memory usage: 2.0+ MB\n"]}]},{"cell_type":"code","source":["def list_count(lis):\n","  return len(lis)\n","\n","df_temp['no_of_groups'] = df_temp.apply(lambda x: list_count(x['group_name']),axis=1)"],"metadata":{"id":"5TlUWpRgGjnP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_temp['no_of_groups'].value_counts()  # How many posts contain how many groups"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"i5tkYpUnGmKc","executionInfo":{"status":"ok","timestamp":1684859880251,"user_tz":-330,"elapsed":14,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"b4aba367-358e-41dd-ab28-e99d9292c86a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    110230\n","2     15414\n","3      2630\n","4       422\n","5        53\n","6        10\n","Name: no_of_groups, dtype: int64"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["# Posts with two groups occurring together\n","'''\n","We know by now that there are 15414 posts which consists of two groups together.\n","'''\n","import numpy as np\n","matrix = np.array([[0]*14]*14)\n","\n","groups = ['sn','hanging','firearm', 'benzodiazepines','opioids','antiemetic','barbiturates','acid regulator','cyanides',\n","          'other preservatives','plant-based poisons','household chemicals', 'ricin','nitric oxide']\n","\n","df2 = df_temp[df_temp['no_of_groups']<3]\n","df2.reset_index(inplace = True)\n","\n","for i in range(len(df2)):\n","  curr_list = df2.loc[i,'group_name']\n","  k = groups.index(curr_list[0])\n","\n","  if(len(curr_list)==1):\n","    matrix[k][k] = matrix[k][k]+1\n","\n","  else:\n","    j = groups.index(curr_list[1])\n","    matrix[k][j] = matrix[k][j]+1\n","    matrix[j][k] = matrix[j][k]+1\n","\n","groups_full = ['sodium nitrite','hanging','firearm', 'benzodiazepines','opioids','antiemetic','barbiturates','acid regulator','cyanides',\n","               'other preservatives','plant-based poisons','household chemicals', 'ricin','nitric oxide']\n","\n","matrix = pd.DataFrame(matrix, columns=groups_full, index=groups_full)\n","matrix"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":585},"id":"oRXTlG8ZG7nn","executionInfo":{"status":"ok","timestamp":1684860175328,"user_tz":-330,"elapsed":2162,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"3f833d34-7aa3-4e99-f62b-133e695dfae2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                     sodium nitrite  hanging  firearm  benzodiazepines  \\\n","sodium nitrite                48624     2780      969             1444   \n","hanging                        2780    23749     1179              277   \n","firearm                         969     1179    13172               52   \n","benzodiazepines                1444      277       52             8330   \n","opioids                         471      210      217              712   \n","antiemetic                     2464       60       25              358   \n","barbiturates                    424       92       77               77   \n","acid regulator                 1404        6        1              124   \n","cyanides                        271       64       46               12   \n","other preservatives             537        5       12                2   \n","plant-based poisons              40       12        5                5   \n","household chemicals              51       15       10               28   \n","ricin                            13        2        1                0   \n","nitric oxide                     43        1        0                2   \n","\n","                     opioids  antiemetic  barbiturates  acid regulator  \\\n","sodium nitrite           471        2464           424            1404   \n","hanging                  210          60            92               6   \n","firearm                  217          25            77               1   \n","benzodiazepines          712         358            77             124   \n","opioids                 5570          92           127              10   \n","antiemetic                92        4170            92             282   \n","barbiturates             127          92          2851               3   \n","acid regulator            10         282             3            1307   \n","cyanides                  12          11            26               0   \n","other preservatives        4          18             5               2   \n","plant-based poisons        1          34             3               1   \n","household chemicals        7           9             3               1   \n","ricin                      0           2             0               0   \n","nitric oxide               0           0             0               5   \n","\n","                     cyanides  other preservatives  plant-based poisons  \\\n","sodium nitrite            271                  537                   40   \n","hanging                    64                    5                   12   \n","firearm                    46                   12                    5   \n","benzodiazepines            12                    2                    5   \n","opioids                    12                    4                    1   \n","antiemetic                 11                   18                   34   \n","barbiturates               26                    5                    3   \n","acid regulator              0                    2                    1   \n","cyanides                 1155                   28                    9   \n","other preservatives        28                  463                    0   \n","plant-based poisons         9                    0                  376   \n","household chemicals        10                    0                    7   \n","ricin                       7                    1                    2   \n","nitric oxide                0                    0                    0   \n","\n","                     household chemicals  ricin  nitric oxide  \n","sodium nitrite                        51     13            43  \n","hanging                               15      2             1  \n","firearm                               10      1             0  \n","benzodiazepines                       28      0             2  \n","opioids                                7      0             0  \n","antiemetic                             9      2             0  \n","barbiturates                           3      0             0  \n","acid regulator                         1      0             5  \n","cyanides                              10      7             0  \n","other preservatives                    0      1             0  \n","plant-based poisons                    7      2             0  \n","household chemicals                  361      0             0  \n","ricin                                  0     81             0  \n","nitric oxide                           0      0            21  "],"text/html":["\n","  <div id=\"df-d97f1900-23c7-43fb-b608-3679b80eaf0d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sodium nitrite</th>\n","      <th>hanging</th>\n","      <th>firearm</th>\n","      <th>benzodiazepines</th>\n","      <th>opioids</th>\n","      <th>antiemetic</th>\n","      <th>barbiturates</th>\n","      <th>acid regulator</th>\n","      <th>cyanides</th>\n","      <th>other preservatives</th>\n","      <th>plant-based poisons</th>\n","      <th>household chemicals</th>\n","      <th>ricin</th>\n","      <th>nitric oxide</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>sodium nitrite</th>\n","      <td>48624</td>\n","      <td>2780</td>\n","      <td>969</td>\n","      <td>1444</td>\n","      <td>471</td>\n","      <td>2464</td>\n","      <td>424</td>\n","      <td>1404</td>\n","      <td>271</td>\n","      <td>537</td>\n","      <td>40</td>\n","      <td>51</td>\n","      <td>13</td>\n","      <td>43</td>\n","    </tr>\n","    <tr>\n","      <th>hanging</th>\n","      <td>2780</td>\n","      <td>23749</td>\n","      <td>1179</td>\n","      <td>277</td>\n","      <td>210</td>\n","      <td>60</td>\n","      <td>92</td>\n","      <td>6</td>\n","      <td>64</td>\n","      <td>5</td>\n","      <td>12</td>\n","      <td>15</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>firearm</th>\n","      <td>969</td>\n","      <td>1179</td>\n","      <td>13172</td>\n","      <td>52</td>\n","      <td>217</td>\n","      <td>25</td>\n","      <td>77</td>\n","      <td>1</td>\n","      <td>46</td>\n","      <td>12</td>\n","      <td>5</td>\n","      <td>10</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>benzodiazepines</th>\n","      <td>1444</td>\n","      <td>277</td>\n","      <td>52</td>\n","      <td>8330</td>\n","      <td>712</td>\n","      <td>358</td>\n","      <td>77</td>\n","      <td>124</td>\n","      <td>12</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>28</td>\n","      <td>0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>opioids</th>\n","      <td>471</td>\n","      <td>210</td>\n","      <td>217</td>\n","      <td>712</td>\n","      <td>5570</td>\n","      <td>92</td>\n","      <td>127</td>\n","      <td>10</td>\n","      <td>12</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>antiemetic</th>\n","      <td>2464</td>\n","      <td>60</td>\n","      <td>25</td>\n","      <td>358</td>\n","      <td>92</td>\n","      <td>4170</td>\n","      <td>92</td>\n","      <td>282</td>\n","      <td>11</td>\n","      <td>18</td>\n","      <td>34</td>\n","      <td>9</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>barbiturates</th>\n","      <td>424</td>\n","      <td>92</td>\n","      <td>77</td>\n","      <td>77</td>\n","      <td>127</td>\n","      <td>92</td>\n","      <td>2851</td>\n","      <td>3</td>\n","      <td>26</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>acid regulator</th>\n","      <td>1404</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>124</td>\n","      <td>10</td>\n","      <td>282</td>\n","      <td>3</td>\n","      <td>1307</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>cyanides</th>\n","      <td>271</td>\n","      <td>64</td>\n","      <td>46</td>\n","      <td>12</td>\n","      <td>12</td>\n","      <td>11</td>\n","      <td>26</td>\n","      <td>0</td>\n","      <td>1155</td>\n","      <td>28</td>\n","      <td>9</td>\n","      <td>10</td>\n","      <td>7</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>other preservatives</th>\n","      <td>537</td>\n","      <td>5</td>\n","      <td>12</td>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>18</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>28</td>\n","      <td>463</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>plant-based poisons</th>\n","      <td>40</td>\n","      <td>12</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>34</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>9</td>\n","      <td>0</td>\n","      <td>376</td>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>household chemicals</th>\n","      <td>51</td>\n","      <td>15</td>\n","      <td>10</td>\n","      <td>28</td>\n","      <td>7</td>\n","      <td>9</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>361</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>ricin</th>\n","      <td>13</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>7</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>81</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>nitric oxide</th>\n","      <td>43</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>21</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d97f1900-23c7-43fb-b608-3679b80eaf0d')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 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Das","userId":"08332010396883382094"}},"outputId":"4facf712-0ef8-4392-b8ed-ee56ea12b13f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Index: 14 entries, sodium nitrite to nitric oxide\n","Data columns (total 14 columns):\n"," #   Column               Non-Null Count  Dtype\n","---  ------               --------------  -----\n"," 0   sodium nitrite       14 non-null     int64\n"," 1   hanging              14 non-null     int64\n"," 2   firearm              14 non-null     int64\n"," 3   benzodiazepines      14 non-null     int64\n"," 4   opioids              14 non-null     int64\n"," 5   antiemetic           14 non-null     int64\n"," 6   barbiturates         14 non-null     int64\n"," 7   acid regulator       14 non-null     int64\n"," 8   cyanides             14 non-null     int64\n"," 9   other preservatives  14 non-null     int64\n"," 10  plant-based poisons  14 non-null     int64\n"," 11  household chemicals  14 non-null     int64\n"," 12  ricin                14 non-null     int64\n"," 13  nitric oxide         14 non-null     int64\n","dtypes: int64(14)\n","memory usage: 2.2+ KB\n"]}]},{"cell_type":"code","source":["# co-occurrence heatmap\n","import seaborn as sns\n","\n","import matplotlib.pyplot as plt\n","\n","fig, ax = plt.subplots(figsize=(10,10))\n","# create mask dataframe\n","mask = np.full((14, 14), False)\n","mask = pd.DataFrame(mask, columns = groups_full, index=groups_full)\n","mask.values[np.fill_diagonal(mask.values, True)]\n","\n","sns.heatmap(matrix, mask = mask, annot=True, fmt='d', cmap=\"crest\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":979},"id":"R_RKeilQxPZs","executionInfo":{"status":"ok","timestamp":1684860330840,"user_tz":-330,"elapsed":1913,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"e302a19c-215f-45bb-f090-2792afd12f98"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":18},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x1000 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["df_temp.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"27Esi0V1LMLS","executionInfo":{"status":"ok","timestamp":1678764728936,"user_tz":-330,"elapsed":16,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"ad059f19-71e0-4d43-b886-7b92642b58d9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 128759 entries, 0 to 128758\n","Data columns (total 3 columns):\n"," #   Column        Non-Null Count   Dtype \n","---  ------        --------------   ----- \n"," 0   post_id       128759 non-null  int64 \n"," 1   group_name    128759 non-null  object\n"," 2   no_of_groups  128759 non-null  int64 \n","dtypes: int64(2), object(1)\n","memory usage: 2.9+ MB\n"]}]},{"cell_type":"code","source":["# BERTopic on only SN dictionary group\n","\n","df_temp2 = df[df['post_id'].isin(df_temp['post_id'])]\n","df_temp2 = df_temp\n","del df_temp2"],"metadata":{"id":"VfwYmwi6HG-f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_temp.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"mv4zWX8jKM0D","executionInfo":{"status":"ok","timestamp":1678764744556,"user_tz":-330,"elapsed":14,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"6dbf12fe-a12e-4128-cf5d-bbe0c075d2d7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   post_id         group_name  no_of_groups\n","0  1641475          [hanging]             1\n","1  1709493          [hanging]             1\n","2   233329          [hanging]             1\n","3   241976               [sn]             1\n","4   409855  [benzodiazepines]             1"],"text/html":["\n","  <div id=\"df-8c1cd2a7-79b4-4592-8a9c-6cb937787e25\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type 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<th>4</th>\n","      <td>409855</td>\n","      <td>[benzodiazepines]</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c1cd2a7-79b4-4592-8a9c-6cb937787e25')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-8c1cd2a7-79b4-4592-8a9c-6cb937787e25');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":18}]},{"cell_type":"code","source":["df_temp = df_temp.explode('group_name')\n","df_temp.reset_index(drop = True,inplace = True)\n","len(df_temp)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o7XyxSWHIdFb","executionInfo":{"status":"ok","timestamp":1678764760619,"user_tz":-330,"elapsed":16,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"80f0983e-8795-4d53-9741-4928e4415c2f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["150961"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["df_sn = df_temp[df_temp['group_name']=='sn']\n","len(df_sn)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ap6oa4PuIgbw","executionInfo":{"status":"ok","timestamp":1678764770501,"user_tz":-330,"elapsed":5,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"f78b1f72-d624-4457-ce73-a572a64fe048"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["62178"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["df_sn.reset_index(drop = True,inplace = True)"],"metadata":{"id":"tRU9tM7NIouD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#del df_temp"],"metadata":{"id":"8W1TbXbpJzsW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_sn.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QnkZDlXGJ5_s","executionInfo":{"status":"ok","timestamp":1678764786915,"user_tz":-330,"elapsed":14,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"684156b6-5fd7-4f3a-8c80-e2501144dfd9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 62178 entries, 0 to 62177\n","Data columns (total 3 columns):\n"," #   Column        Non-Null Count  Dtype \n","---  ------        --------------  ----- \n"," 0   post_id       62178 non-null  int64 \n"," 1   group_name    62178 non-null  object\n"," 2   no_of_groups  62178 non-null  int64 \n","dtypes: int64(2), object(1)\n","memory usage: 1.4+ MB\n"]}]},{"cell_type":"code","source":["df_temp = df[df['post_id'].isin(df_sn['post_id'])]"],"metadata":{"id":"E7dX-VrHJ9Ml"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_temp.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"v4RcVdHPLvz9","executionInfo":{"status":"ok","timestamp":1678764874505,"user_tz":-330,"elapsed":13,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"7f40695e-e272-4819-91ba-c41d30a1d082"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 62178 entries, 3 to 128758\n","Data columns (total 19 columns):\n"," #   Column               Non-Null Count  Dtype \n","---  ------               --------------  ----- \n"," 0   post_id              62178 non-null  int64 \n"," 1   lemmatized_body      62178 non-null  object\n"," 2   keywords_matched     62178 non-null  object\n"," 3   group_name           62178 non-null  object\n"," 4   monthyear            62178 non-null  object\n"," 5   sn                   62178 non-null  int64 \n"," 6   hanging              62178 non-null  int64 \n"," 7   firearm              62178 non-null  int64 \n"," 8   benzodiazepines      62178 non-null  int64 \n"," 9   opioids              62178 non-null  int64 \n"," 10  antiemetic           62178 non-null  int64 \n"," 11  barbiturates         62178 non-null  int64 \n"," 12  acid regulator       62178 non-null  int64 \n"," 13  cyanides             62178 non-null  int64 \n"," 14  other preservatives  62178 non-null  int64 \n"," 15  plant-based poisons  62178 non-null  int64 \n"," 16  household chemicals  62178 non-null  int64 \n"," 17  ricin                62178 non-null  int64 \n"," 18  nitric oxide         62178 non-null  int64 \n","dtypes: int64(15), object(4)\n","memory usage: 9.5+ MB\n"]}]},{"cell_type":"code","source":["sn_keywords = ['sn', 'sni', 'sodium nitrite', 'sodium nitrate', 'nano3','nano2', 'snit','nitrite', 'nitrate','nitrit','nitrites','snwhat']\n","\n","pat = r'\\b(?:{})\\b'.format('|'.join(sn_keywords))\n","\n","df_sn['new'] = df_temp['lemmatized_body'].str.replace(pat, '')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nD70UcidIrsh","executionInfo":{"status":"ok","timestamp":1678764928299,"user_tz":-330,"elapsed":793,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"117adf31-1d03-4555-9c27-d46a00759191"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-26-39eabe1226a2>:5: FutureWarning: The default value of regex will change from True to False in a future version.\n","  df_sn['new'] = df_temp['lemmatized_body'].str.replace(pat, '')\n"]}]},{"cell_type":"code","source":["len(df_sn)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lnSNF1pGMC1a","executionInfo":{"status":"ok","timestamp":1678764948495,"user_tz":-330,"elapsed":13,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"48d4a902-d121-4e44-8a01-463d9345c945"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["62178"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["df_sn.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mU6oQ6OeMZvN","executionInfo":{"status":"ok","timestamp":1678765044712,"user_tz":-330,"elapsed":3,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"a70fc8de-fb8b-40c9-fed3-d8af0ea14635"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 62178 entries, 0 to 62177\n","Data columns (total 4 columns):\n"," #   Column        Non-Null Count  Dtype \n","---  ------        --------------  ----- \n"," 0   post_id       62178 non-null  int64 \n"," 1   group_name    62178 non-null  object\n"," 2   no_of_groups  62178 non-null  int64 \n"," 3   new           28997 non-null  object\n","dtypes: int64(2), object(2)\n","memory usage: 1.9+ MB\n"]}]},{"cell_type":"code","source":["df_temp.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bu2FkFYaMbg1","executionInfo":{"status":"ok","timestamp":1678765053689,"user_tz":-330,"elapsed":14,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"1c8bec12-9c8e-4687-c999-4a7f11c6051c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 62178 entries, 3 to 128758\n","Data columns (total 19 columns):\n"," #   Column               Non-Null Count  Dtype \n","---  ------               --------------  ----- \n"," 0   post_id              62178 non-null  int64 \n"," 1   lemmatized_body      62178 non-null  object\n"," 2   keywords_matched     62178 non-null  object\n"," 3   group_name           62178 non-null  object\n"," 4   monthyear            62178 non-null  object\n"," 5   sn                   62178 non-null  int64 \n"," 6   hanging              62178 non-null  int64 \n"," 7   firearm              62178 non-null  int64 \n"," 8   benzodiazepines      62178 non-null  int64 \n"," 9   opioids              62178 non-null  int64 \n"," 10  antiemetic           62178 non-null  int64 \n"," 11  barbiturates         62178 non-null  int64 \n"," 12  acid regulator       62178 non-null  int64 \n"," 13  cyanides             62178 non-null  int64 \n"," 14  other preservatives  62178 non-null  int64 \n"," 15  plant-based poisons  62178 non-null  int64 \n"," 16  household chemicals  62178 non-null  int64 \n"," 17  ricin                62178 non-null  int64 \n"," 18  nitric oxide         62178 non-null  int64 \n","dtypes: int64(15), object(4)\n","memory usage: 11.5+ MB\n"]}]},{"cell_type":"code","source":["df_sn.reset_index(drop = True,inplace = True)"],"metadata":{"id":"dPRhQSO6Mq_W"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df_sn.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4MwxpbVqMJkp","executionInfo":{"status":"ok","timestamp":1678765132199,"user_tz":-330,"elapsed":13,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"e4c3a286-8703-467d-8581-7f3304fcd608"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 62178 entries, 0 to 62177\n","Data columns (total 4 columns):\n"," #   Column        Non-Null Count  Dtype \n","---  ------        --------------  ----- \n"," 0   post_id       62178 non-null  int64 \n"," 1   group_name    62178 non-null  object\n"," 2   no_of_groups  62178 non-null  int64 \n"," 3   new           28997 non-null  object\n","dtypes: int64(2), object(2)\n","memory usage: 1.9+ MB\n"]}]},{"cell_type":"code","source":["def list_count(lis):\n","  return len(lis)\n","\n","df_temp['no_of_groups'] = df_temp.apply(lambda x: list_count(x['group_name']),axis=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rvjxN6kEFXs6","executionInfo":{"status":"ok","timestamp":1678763297067,"user_tz":-330,"elapsed":38,"user":{"displayName":"Sudeshna Das","userId":"08332010396883382094"}},"outputId":"3a26a161-3564-45e8-afad-9f6b12ee6456"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 128759 entries, 0 to 128758\n","Data columns (total 19 columns):\n"," #   Column               Non-Null Count   Dtype \n","---  ------               --------------   ----- \n"," 0   post_id              128759 non-null  int64 \n"," 1   lemmatized_body      128759 non-null  object\n"," 2   keywords_matched     128759 non-null  object\n"," 3   group_name           128759 non-null  object\n"," 4   monthyear            128759 non-null  object\n"," 5   sn                   128759 non-null  int64 \n"," 6   hanging              128759 non-null  int64 \n"," 7   firearm              128759 non-null  int64 \n"," 8   benzodiazepines      128759 non-null  int64 \n"," 9   opioids              128759 non-null  int64 \n"," 10  antiemetic           128759 non-null  int64 \n"," 11  barbiturates         128759 non-null  int64 \n"," 12  acid regulator       128759 non-null  int64 \n"," 13  cyanides             128759 non-null  int64 \n"," 14  other preservatives  128759 non-null  int64 \n"," 15  plant-based poisons  128759 non-null  int64 \n"," 16  household chemicals  128759 non-null  int64 \n"," 17  ricin                128759 non-null  int64 \n"," 18  nitric oxide         128759 non-null  int64 \n","dtypes: int64(15), object(4)\n","memory usage: 18.7+ MB\n"]}]}]}