notebook-stats.ipynb

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   "source": [
    "Similarly, if you want to see view counts on your Kaggle notebooks - you have to increase the view count by one... *until now!* We can spy on the latest view counts (and other stats) by requesting a listing from the Kaggle API...\n",
    "\n",
    "To use this notebook:\n",
    "\n",
    " 1.   Fork this notebook by clicking on Copy and Edit.\n",
    " 2.   Set the username variable to your own username."
   ]
  },
  {
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   "source": [
    "username = 'azminetoushikwasi'"
   ]
  },
  {
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   "id": "5c4d09c3",
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   "source": [
    "from IPython.display import Markdown, display\n",
    "import datetime, requests, json, os, re, sys, time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('display.max_rows', 500)\n",
    "\n",
    "session = requests.session()\n",
    "profile = session.get('https://www.kaggle.com/' + username)\n",
    "session.headers['x-xsrf-token'] = session.cookies['XSRF-TOKEN']\n",
    "\n",
    "uid = int(re.findall('Kaggle\\.State\\.push\\({.*?\"userId\":(\\d+),', profile.text)[0])\n",
    "count = int(re.findall('\"scriptsSummary\":{.*?\"totalResults\":(\\d+),', profile.text)[0])\n",
    "pages = int(np.ceil(count / 20))"
   ]
  },
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   "id": "642c6299",
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    "tags": []
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   "outputs": [],
   "source": [
    "url = 'https://www.kaggle.com/api/i/kernels.KernelsService/ListKernels'\n",
    "request = {\n",
    "    'kernelFilterCriteria': {\n",
    "        'search': '',\n",
    "        'listRequest': {\n",
    "            'userId': 0,\n",
    "            'sortBy': 'DATE_RUN',\n",
    "            'pageSize': 20,\n",
    "            'group': 'PROFILE',\n",
    "            'page': 1,\n",
    "            'tagIds': '',\n",
    "            'excludeResultsFilesOutputs': False,\n",
    "            'wantOutputFiles': False\n",
    "        }\n",
    "    },\n",
    "    'detailFilterCriteria': {\n",
    "        'deletedAccessBehavior': 'RETURN_NOTHING',\n",
    "        'unauthorizedAccessBehavior': 'RETURN_NOTHING',\n",
    "        'excludeResultsFilesOutputs': False,\n",
    "        'wantOutputFiles': False,\n",
    "        'kernelIds': [],\n",
    "        'outputFileTypes': []\n",
    "    }\n",
    "}"
   ]
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   "execution_count": 4,
   "id": "45d80ce2",
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    {
     "data": {
      "text/markdown": [
       "\n",
       " - Notebook count: 10\n",
       " - Total views: 7,681\n",
       " - Total comments: 154\n",
       " - Total medals: 10\n",
       " - Total votes: 344\n",
       " - Mean views per vote: 22\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "request['kernelFilterCriteria']['listRequest']['userId'] = uid\n",
    "\n",
    "dfs = []\n",
    "for page in range(1, pages + 1):\n",
    "    request['kernelFilterCriteria']['listRequest']['page'] = page\n",
    "    data = json.dumps(request, separators=(',', ':'))\n",
    "    res = session.post(url, data=data)\n",
    "    #print(page, res.status_code, len(res.text))\n",
    "    if res.status_code != 200:\n",
    "        break\n",
    "    df = pd.DataFrame(json.loads(res.text)['kernels'])\n",
    "    dfs.append(df)\n",
    "\n",
    "def split_columns(df, col):\n",
    "    values = df.pop(col)\n",
    "    return df.join(values.apply(pd.Series).add_prefix(col + \"_\"))\n",
    "\n",
    "df = pd.concat(dfs, ignore_index=True)\n",
    "df = split_columns(df, 'author')\n",
    "df = split_columns(df, 'dataSources')\n",
    "df = split_columns(df, 'categories')\n",
    "\n",
    "filename = f'{username}-{datetime.datetime.now().strftime(\"%Y-%m-%d\")}.csv'\n",
    "df.to_csv(filename, index=False)\n",
    "\n",
    "display(Markdown(f\"\"\"\n",
    " - Notebook count: {df.shape[0]:,.0f}\n",
    " - Total views: {df.totalViews.sum():,.0f}\n",
    " - Total comments: {df.totalComments.sum():,.0f}\n",
    " - Total medals: {df.medal.count():,.0f}\n",
    " - Total votes: {df.totalVotes.sum():,.0f}\n",
    " - Mean views per vote: {df.totalViews.sum() / df.totalVotes.sum():,.0f}\n",
    "\"\"\"))"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Notebooks by Azmine Toushik Wasi"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"col_heading level0 col0\" >title</th>\n",
       "      <th class=\"col_heading level0 col1\" >totalViews</th>\n",
       "      <th class=\"col_heading level0 col2\" >totalVotes</th>\n",
       "      <th class=\"col_heading level0 col3\" >totalLines</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row0_col0\" class=\"data row0 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/intro-to-imputation-different-techniques\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Tue Jun 14 09:44:41 2022\n",
       "Runtime: 88s\n",
       "Views per vote: 20\">➡️ Intro to Imputation 🔍 Different Techniques</a></td>\n",
       "      <td id=\"T_7da7b_row0_col1\" class=\"data row0 col1\" >1,232</td>\n",
       "      <td id=\"T_7da7b_row0_col2\" class=\"data row0 col2\" >60</td>\n",
       "      <td id=\"T_7da7b_row0_col3\" class=\"data row0 col3\" >357</td>\n",
       "      <td id=\"T_7da7b_row0_col4\" class=\"data row0 col4\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row1_col0\" class=\"data row1 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/07-cross-validation-methods-tutorial\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Tue Jun 14 09:41:59 2022\n",
       "Runtime: 68s\n",
       "Views per vote: 17\">✅ 07 Cross Validation Methods ➡️ Tutorial 📊</a></td>\n",
       "      <td id=\"T_7da7b_row1_col1\" class=\"data row1 col1\" >622</td>\n",
       "      <td id=\"T_7da7b_row1_col2\" class=\"data row1 col2\" >35</td>\n",
       "      <td id=\"T_7da7b_row1_col3\" class=\"data row1 col3\" >344</td>\n",
       "      <td id=\"T_7da7b_row1_col4\" class=\"data row1 col4\" >8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row2_col0\" class=\"data row2 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/ucl-eda-viz-2021-22-players-teams\"  title=\"Language: Python\n",
       "Medal: BRONZE\n",
       "Last run: Sat Jun 11 17:01:43 2022\n",
       "Runtime: 50s\n",
       "Views per vote: 27\">UCL ⚽ EDA & Viz ⭐ 2021-22 ⭐ Players & Teams</a></td>\n",
       "      <td id=\"T_7da7b_row2_col1\" class=\"data row2 col1\" >490</td>\n",
       "      <td id=\"T_7da7b_row2_col2\" class=\"data row2 col2\" >17</td>\n",
       "      <td id=\"T_7da7b_row2_col3\" class=\"data row2 col3\" >536</td>\n",
       "      <td id=\"T_7da7b_row2_col4\" class=\"data row2 col4\" >7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row3_col0\" class=\"data row3 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/divorce-xgboost-pca-analysis-with-shap-tsne\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Wed Jun  8 10:16:51 2022\n",
       "Runtime: 110s\n",
       "Views per vote: 15\">Divorce 💥 XGBoost 📊 PCA Analysis with SHAP,TSNE</a></td>\n",
       "      <td id=\"T_7da7b_row3_col1\" class=\"data row3 col1\" >488</td>\n",
       "      <td id=\"T_7da7b_row3_col2\" class=\"data row3 col2\" >31</td>\n",
       "      <td id=\"T_7da7b_row3_col3\" class=\"data row3 col3\" >488</td>\n",
       "      <td id=\"T_7da7b_row3_col4\" class=\"data row3 col4\" >12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row4_col0\" class=\"data row4 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/feature-engineering-tutorial-housing-data\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Mon Jun  6 09:14:09 2022\n",
       "Runtime: 97s\n",
       "Views per vote: 18\">🛠 Feature Engineering ➡️ Tutorial  ⚙ Housing Data</a></td>\n",
       "      <td id=\"T_7da7b_row4_col1\" class=\"data row4 col1\" >655</td>\n",
       "      <td id=\"T_7da7b_row4_col2\" class=\"data row4 col2\" >36</td>\n",
       "      <td id=\"T_7da7b_row4_col3\" class=\"data row4 col3\" >354</td>\n",
       "      <td id=\"T_7da7b_row4_col4\" class=\"data row4 col4\" >16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row5_col0\" class=\"data row5 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/bias-variance-tradeoff-with-numpy-seaborn\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Wed Jun  1 19:00:46 2022\n",
       "Runtime: 47s\n",
       "Views per vote: 18\">📋 Bias-Variance Tradeoff ➡️ with NumPy & Seaborn</a></td>\n",
       "      <td id=\"T_7da7b_row5_col1\" class=\"data row5 col1\" >497</td>\n",
       "      <td id=\"T_7da7b_row5_col2\" class=\"data row5 col2\" >27</td>\n",
       "      <td id=\"T_7da7b_row5_col3\" class=\"data row5 col3\" >596</td>\n",
       "      <td id=\"T_7da7b_row5_col4\" class=\"data row5 col4\" >17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row6_col0\" class=\"data row6 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/lionel-messi-extended-eda-goals\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Wed Jun  1 04:28:42 2022\n",
       "Runtime: 51s\n",
       "Views per vote: 24\">▶️ Lionel Messi 📊 Extended EDA ⚽ Goals </a></td>\n",
       "      <td id=\"T_7da7b_row6_col1\" class=\"data row6 col1\" >1,076</td>\n",
       "      <td id=\"T_7da7b_row6_col2\" class=\"data row6 col2\" >44</td>\n",
       "      <td id=\"T_7da7b_row6_col3\" class=\"data row6 col3\" >259</td>\n",
       "      <td id=\"T_7da7b_row6_col4\" class=\"data row6 col4\" >20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row7_col0\" class=\"data row7 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/cristiano-ronaldo-extended-eda-goals\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Tue May 31 07:51:02 2022\n",
       "Runtime: 74s\n",
       "Views per vote: 39\">▶️ Cristiano Ronaldo 📊 Extended EDA ⚽ Goals </a></td>\n",
       "      <td id=\"T_7da7b_row7_col1\" class=\"data row7 col1\" >1,563</td>\n",
       "      <td id=\"T_7da7b_row7_col2\" class=\"data row7 col2\" >39</td>\n",
       "      <td id=\"T_7da7b_row7_col3\" class=\"data row7 col3\" >273</td>\n",
       "      <td id=\"T_7da7b_row7_col4\" class=\"data row7 col4\" >30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row8_col0\" class=\"data row8 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/coursera-eda-fe-analysis-insights\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Sun May 29 07:50:50 2022\n",
       "Runtime: 40s\n",
       "Views per vote: 16\"> 📘 Coursera 📉 EDA, FE 📈 Analysis + Insights 📊</a></td>\n",
       "      <td id=\"T_7da7b_row8_col1\" class=\"data row8 col1\" >438</td>\n",
       "      <td id=\"T_7da7b_row8_col2\" class=\"data row8 col2\" >26</td>\n",
       "      <td id=\"T_7da7b_row8_col3\" class=\"data row8 col3\" >279</td>\n",
       "      <td id=\"T_7da7b_row8_col4\" class=\"data row8 col4\" >15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_7da7b_row9_col0\" class=\"data row9 col0\" ><a href=\"https://www.kaggle.com/code/azminetoushikwasi/export-processing-zones-eda-statistical-analysis\"  title=\"Language: Python\n",
       "Medal: SILVER\n",
       "Last run: Fri May 27 02:34:12 2022\n",
       "Runtime: 79s\n",
       "Views per vote: 21\">Export Processing Zones - EDA+Statistical Analysis</a></td>\n",
       "      <td id=\"T_7da7b_row9_col1\" class=\"data row9 col1\" >620</td>\n",
       "      <td id=\"T_7da7b_row9_col2\" class=\"data row9 col2\" >29</td>\n",
       "      <td id=\"T_7da7b_row9_col3\" class=\"data row9 col3\" >461</td>\n",
       "      <td id=\"T_7da7b_row9_col4\" class=\"data row9 col4\" >15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1cdc069550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(Markdown(f'# Notebooks by {df.author_displayName.get(0)}'))\n",
    "df['viewsPerVote'] = df.eval('(totalViews+1)/(totalVotes+1)')\n",
    "df['lastRun'] = pd.to_datetime(df.lastRunTime).dt.strftime('%c')\n",
    "stats = ['totalViews', 'totalVotes', 'totalLines', 'totalComments']\n",
    "links = ('<a href=\"https://www.kaggle.com' + df.scriptUrl + '\" '\n",
    "         + ' title=\"'\n",
    "         + 'Language: ' + df.languageName.fillna('N/A') + '\\n'\n",
    "         + 'Medal: ' + df.medal.fillna('N/A') + '\\n'\n",
    "         + 'Last run: ' + df.lastRun + '\\n'\n",
    "         + 'Runtime: ' + df.lastRunExecutionTimeSeconds.map(str) + 's' + '\\n'\n",
    "         + 'Views per vote: ' + df.eval('(totalViews+1)/(totalVotes+1)').apply(lambda x: f'{x:,.0f}')\n",
    "         + '\">' # end title=\"...\"\n",
    "         + df.title\n",
    "         + '</a>')\n",
    "show = df[['title'] + stats].copy()\n",
    "show['title'] = links\n",
    "show.style.format({k:'{:,.0f}' for k in stats}, na_rep=\"\").hide_index().bar(width=50, color='#4cf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa0aa8af",
   "metadata": {
    "papermill": {
     "duration": 0.006114,
     "end_time": "2022-06-14T15:12:37.765672",
     "exception": false,
     "start_time": "2022-06-14T15:12:37.759558",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Plotly Scatter Plot\n",
    "\n",
    "You may want to tweak these settings depending on how many notebooks you have...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f3e9f348",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-06-14T15:12:37.780684Z",
     "iopub.status.busy": "2022-06-14T15:12:37.779323Z",
     "iopub.status.idle": "2022-06-14T15:12:40.672332Z",
     "shell.execute_reply": "2022-06-14T15:12:40.671466Z"
    },
    "papermill": {
     "duration": 2.902524,
     "end_time": "2022-06-14T15:12:40.674495",
     "exception": false,
     "start_time": "2022-06-14T15:12:37.771971",
     "status": "completed"
    },
    "tags": []
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   "outputs": [
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       "                'plotly': ['https://cdn.plot.ly/plotly-2.12.1.min']\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "df['totalComments'].fillna(0, inplace=True)\n",
    "df['medal'].fillna('N/A', inplace=True)\n",
    "df['size'] = df['totalComments'].clip(lower=1)\n",
    "color_map = {\n",
    "    'GOLD': 'gold',\n",
    "    'SILVER': 'silver',\n",
    "    'BRONZE': 'chocolate',\n",
    "    'N/A': '#333'\n",
    "}\n",
    "px.scatter(\n",
    "    df,\n",
    "    'totalViews',\n",
    "    'totalVotes',\n",
    "    title=f'Notebooks by {df.author_displayName.get(0)}',\n",
    "    log_x=True,\n",
    "    log_y=True,\n",
    "    color='medal',\n",
    "    color_discrete_map=color_map,\n",
    "    size='size',\n",
    "    trendline='ols',\n",
    "    trendline_options=dict(log_x=True, log_y=True, add_constant=False),\n",
    "    #trendline='lowess', # Use for Locally Weighted Scatterplot Smoothing line\n",
    "    #trendline_scope='overall', # Use this for one trendline\n",
    "    hover_name='title',\n",
    "    hover_data={\n",
    "        'size': False,\n",
    "        'totalViews': ':,.0f',\n",
    "        'totalVotes': ':,.0f',\n",
    "        'totalLines': ':,.0f',\n",
    "        'totalComments': ':,.0f',\n",
    "        'viewsPerVote': ':,.0f',\n",
    "        'languageName': True,\n",
    "        'versionNumber': True,\n",
    "        'lastRun': True,\n",
    "        'lastRunExecutionTimeSeconds': ':,.0f',\n",
    "        'medal': True,\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bec5259",
   "metadata": {
    "papermill": {
     "duration": 0.006625,
     "end_time": "2022-06-14T15:12:40.688463",
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     "start_time": "2022-06-14T15:12:40.681838",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9b469b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T15:12:40.704990Z",
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     "exception": false,
     "start_time": "2022-06-14T15:12:40.695502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "links=[]\n",
    "for dirname, _, filenames in os.walk('/kaggle/working'):\n",
    "    for filename in filenames:\n",
    "        link=os.path.join(dirname, filename)\n",
    "        links.append(link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50c732fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T15:12:40.721151Z",
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    },
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     "duration": 0.029734,
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     "exception": false,
     "start_time": "2022-06-14T15:12:40.715335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>2022-06-14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>➡️ Intro to Imputation 🔍 Different Techniques</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>✅ 07 Cross Validation Methods ➡️ Tutorial 📊</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UCL ⚽ EDA &amp; Viz ⭐ 2021-22 ⭐ Players &amp; Teams</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Divorce 💥 XGBoost 📊 PCA Analysis with SHAP,TSNE</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>🛠 Feature Engineering ➡️ Tutorial  ⚙ Housing Data</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>📋 Bias-Variance Tradeoff ➡️ with NumPy &amp; Seaborn</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>▶️ Lionel Messi 📊 Extended EDA ⚽ Goals</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>▶️ Cristiano Ronaldo 📊 Extended EDA ⚽ Goals</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>📘 Coursera 📉 EDA, FE 📈 Analysis + Insights 📊</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Export Processing Zones - EDA+Statistical Anal...</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  2022-06-14\n",
       "0      ➡️ Intro to Imputation 🔍 Different Techniques          60\n",
       "1        ✅ 07 Cross Validation Methods ➡️ Tutorial 📊          35\n",
       "2        UCL ⚽ EDA & Viz ⭐ 2021-22 ⭐ Players & Teams          17\n",
       "3    Divorce 💥 XGBoost 📊 PCA Analysis with SHAP,TSNE          31\n",
       "4  🛠 Feature Engineering ➡️ Tutorial  ⚙ Housing Data          36\n",
       "5   📋 Bias-Variance Tradeoff ➡️ with NumPy & Seaborn          27\n",
       "6            ▶️ Lionel Messi 📊 Extended EDA ⚽ Goals           44\n",
       "7       ▶️ Cristiano Ronaldo 📊 Extended EDA ⚽ Goals           39\n",
       "8       📘 Coursera 📉 EDA, FE 📈 Analysis + Insights 📊          26\n",
       "9  Export Processing Zones - EDA+Statistical Anal...          29"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx=pd.DataFrame()\n",
    "date_list=[]\n",
    "for link in links:\n",
    "    if link.split(\".\")[len(links[0].split(\".\"))-1]!=\"csv\":\n",
    "        continue\n",
    "    date=link[34:44]\n",
    "    date_list.append(date)\n",
    "    temp_df=pd.read_csv(link)\n",
    "    if 'title' not in dfx.columns:\n",
    "        dfx['title']=temp_df['title']\n",
    "    dfx[date]=temp_df['totalVotes']\n",
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30f21430",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T15:12:40.755949Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dfx=pd.DataFrame()\n",
    "date_list=[]\n",
    "titles_dft=[]\n",
    "date_dft=[]\n",
    "count_dft=[]\n",
    "\n",
    "gTotalUpvotes=[]\n",
    "gTotalViews=[]\n",
    "gTotalComments=[]\n",
    "\n",
    "\n",
    "for link in links:\n",
    "    if link.split(\".\")[len(links[0].split(\".\"))-1]!=\"csv\":\n",
    "        continue\n",
    "        \n",
    "    date=link[34:44]\n",
    "    date_list.append(date)\n",
    "    temp_df=pd.read_csv(link)\n",
    "    \n",
    "    gTotalUpvotes.append(temp_df.totalVotes.sum())\n",
    "    gTotalViews.append(temp_df.totalViews.sum())\n",
    "    gTotalComments.append(temp_df.totalComments.sum())\n",
    "    \n",
    "    temp_titles=list(temp_df.title)\n",
    "    temp_totalVotes=list(temp_df.totalVotes)\n",
    "    \n",
    "    for name in temp_titles:\n",
    "        titles_dft.append(name)\n",
    "        date_dft.append(date)\n",
    "        count_dft.append(temp_totalVotes[temp_titles.index(name)])\n",
    "\n",
    "\n",
    "df_d={\"title\":titles_dft, \"date\":date_dft, \"count\":count_dft}\n",
    "df_d=pd.DataFrame(df_d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41728a11",
   "metadata": {
    "execution": {
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    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w=5\n",
    "import math\n",
    "h=math.ceil(len(list(df_d.title.unique()))/w)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(25,h*5)})\n",
    "for i, column in enumerate(list(df_d.title.unique()), 1):\n",
    "\n",
    "    plt.subplot(h,w,i)\n",
    "    plt.subplots_adjust(left=0.2,\n",
    "                    bottom=0.4, \n",
    "                    right=0.9, \n",
    "                    top=0.9, \n",
    "                    wspace=0.6, \n",
    "                    hspace=0.6)\n",
    "    tdx=df_d[df_d['title']==column]\n",
    "    p=sns.barplot(x='date',data=tdx,y='count',palette='Wistia')\n",
    "    p.axes.set_title(\"\\n\"+str(column)+\"\\n\",fontsize=10);\n",
    "    for container in p.containers:\n",
    "        p.bar_label(container,label_type='edge',padding=-20)\n",
    "    p=sns.lineplot(x='date',data=tdx,y='count',color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3cb9527",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-14T15:12:41.831183Z",
     "iopub.status.busy": "2022-06-14T15:12:41.830809Z",
     "iopub.status.idle": "2022-06-14T15:12:42.157654Z",
     "shell.execute_reply": "2022-06-14T15:12:42.156936Z"
    },
    "papermill": {
     "duration": 0.337278,
     "end_time": "2022-06-14T15:12:42.159202",
     "exception": false,
     "start_time": "2022-06-14T15:12:41.821924",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x576 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_g={\"date\":date_list, \"views\":gTotalViews, \"upvotes\":gTotalUpvotes, \"comments\":gTotalComments}\n",
    "df_g=pd.DataFrame(df_g)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(25,8)})\n",
    "for i, column in enumerate([\"views\",\"upvotes\",\"comments\"], 1):\n",
    "    plt.subplot(1,3,i)\n",
    "    plt.subplots_adjust(left=0.2,\n",
    "                    bottom=0.4, \n",
    "                    right=0.9, \n",
    "                    top=0.9, \n",
    "                    wspace=0.6, \n",
    "                    hspace=0.6)\n",
    "    p=sns.lineplot(x='date',data=df_g,y=column,palette='Wistia')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 14.7814,
   "end_time": "2022-06-14T15:12:43.087993",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-06-14T15:12:28.306593",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}