InstaStats - Unveiling Instagram Insights_dataanalystduo_batch2.ipynb

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  {
   "cell_type": "markdown",
   "id": "702ccae7",
   "metadata": {},
   "source": [
    "# InstaStats: Unveiling Instagram Insights (EDA)\n",
    "\n",
    "**Domain: Social Media**\n",
    "\n",
    "**Context:**\n",
    "\n",
    "Data Analyst Duo is an instagram community (@𝒅𝒂𝒕𝒂𝒂𝒏𝒂𝒍𝒚𝒔𝒕𝒅𝒖𝒐) of ~𝟕9𝐤 data enthusiasts founded by two individuals Aditi & Kalpesh. They share content around statistics, data science & analytics with budding data aspirants. \n",
    "\n",
    "**Data:** \n",
    "dataanalystduo instagram analytics.xlsx- : The data set contains stats about the channel (@𝒅𝒂𝒕𝒂𝒂𝒏𝒂𝒍𝒚𝒔𝒕𝒅𝒖𝒐)\n",
    "\n",
    "**Data Dictionary:**\n",
    "\n",
    "* Date: Date \n",
    "* Instagram reach: The number of unique accounts that saw any of your posts or stories at least once. Reach is different from impressions, which may include multiple views of your posts by the same accounts. This metric is estimated.\n",
    "* Instagram profile visits: The number of times your profile was visited.\n",
    "* New Instagram followers: The number of new accounts that started following your Instagram account.\n",
    "* Title: Title of the post\n",
    "* Post type: Type of post - IG video, IG carousel, IG Image\n",
    "* Impressions: The number of times your post was viewed\n",
    "* Reach: The number of accounts your post was reached\n",
    "* Shares: The number of times you post was shared\n",
    "* Follows: The number of new accounts that started following your Instagram account.\n",
    "* Likes: The number of likes on the post\n",
    "* Comments: The number of comments on the post\n",
    "* Saves: The number of saves on the post\n",
    "\n",
    "**Project Objective**\n",
    "\n",
    "The goal is to analyse the data and provide recommendations on what type of post are working for the page. \n",
    "\n",
    "*Note*\n",
    "\n",
    "*- Unauthorised use or distribution of this project prohibited @dataanalystduo\n",
    "\n",
    "*- Dataset is owned by @dataanalystduo page & instagram.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c06e1513",
   "metadata": {},
   "source": [
    "**Key learning after this project:**\n",
    "\n",
    "Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset.\n",
    "Observation writing involves examining the data and noting any notable findings, anomalies, or areas of interest.\n",
    "Exploratory Data Analysis (EDA) is the process of examining and visualizing a dataset to understand its main characteristics, such as the distribution of data, the relationships between variables, and any anomalies or patterns that may exist. The goal of EDA is to uncover insights and trends that can help inform further analysis or decision-making. It is often the first step in any data analysis project, as it provides a foundation for more advanced statistical methods and models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11fef095",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries for data cleaning & data analysis & data visualisation\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aad87f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the xlsx file into python\n",
    "\n",
    "df = pd.read_excel(r'D:\\Data_Analyst_duo\\batch2day2\\dataanalystduo_instagram_analytics_batch2.xlsx', sheet_name='data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb94282",
   "metadata": {},
   "source": [
    "### Section A: Explore the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9eba6a95",
   "metadata": {},
   "outputs": [
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       "<div><div id=67c1faf6-7fc5-47fe-952d-7660a5d0ec72 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('67c1faf6-7fc5-47fe-952d-7660a5d0ec72').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Instagram reach</th>\n",
       "      <th>Instagram profile visits</th>\n",
       "      <th>New Instagram followers</th>\n",
       "      <th>Title</th>\n",
       "      <th>Post type</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Follows</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Saves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01T00:00:00</td>\n",
       "      <td>348</td>\n",
       "      <td>70</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-02T00:00:00</td>\n",
       "      <td>497</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-03T00:00:00</td>\n",
       "      <td>2745</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-04T00:00:00</td>\n",
       "      <td>1619</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-05T00:00:00</td>\n",
       "      <td>2434</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table></div>"
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      "text/plain": [
       "                  Date  Instagram reach  Instagram profile visits  \\\n",
       "0  2022-01-01T00:00:00              348                        70   \n",
       "1  2022-01-02T00:00:00              497                       100   \n",
       "2  2022-01-03T00:00:00             2745                       249   \n",
       "3  2022-01-04T00:00:00             1619                       154   \n",
       "4  2022-01-05T00:00:00             2434                       195   \n",
       "\n",
       "   New Instagram followers Title Post type  Impressions  Reach  Shares  \\\n",
       "0                        0     0         0          0.0    0.0       0   \n",
       "1                        0     0         0          0.0    0.0       0   \n",
       "2                        0     0         0          0.0    0.0       0   \n",
       "3                        0     0         0          0.0    0.0       0   \n",
       "4                        0     0         0          0.0    0.0       0   \n",
       "\n",
       "   Follows  Likes  Comments  Saves  \n",
       "0      0.0      0         0      0  \n",
       "1      0.0      0         0      0  \n",
       "2      0.0      0         0      0  \n",
       "3      0.0      0         0      0  \n",
       "4      0.0      0         0      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View to Snapshot of data - first 5 rows\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f702ae17",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Instagram reach</th>\n",
       "      <th>Instagram profile visits</th>\n",
       "      <th>New Instagram followers</th>\n",
       "      <th>Title</th>\n",
       "      <th>Post type</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Follows</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Saves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>32378</td>\n",
       "      <td>447</td>\n",
       "      <td>89</td>\n",
       "      <td>ChatGPT with Jupyter notebook</td>\n",
       "      <td>IG video</td>\n",
       "      <td>64045.0</td>\n",
       "      <td>62424.0</td>\n",
       "      <td>1239</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2648</td>\n",
       "      <td>41</td>\n",
       "      <td>3368</td>\n",
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       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>2023-04-27T00:00:00</td>\n",
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       "      <td>475</td>\n",
       "      <td>128</td>\n",
       "      <td>why documentation is important</td>\n",
       "      <td>IG video</td>\n",
       "      <td>18247.0</td>\n",
       "      <td>14951.0</td>\n",
       "      <td>47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>958</td>\n",
       "      <td>17</td>\n",
       "      <td>276</td>\n",
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       "    <tr>\n",
       "      <th>482</th>\n",
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       "      <td>89</td>\n",
       "      <td>Udemy courses meme</td>\n",
       "      <td>IG video</td>\n",
       "      <td>13282.0</td>\n",
       "      <td>10231.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>281</td>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>2023-04-29T00:00:00</td>\n",
       "      <td>13472</td>\n",
       "      <td>345</td>\n",
       "      <td>78</td>\n",
       "      <td>1:1 consultation</td>\n",
       "      <td>IG carousel</td>\n",
       "      <td>12847.0</td>\n",
       "      <td>8938.0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>296</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
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       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>2023-04-30T00:00:00</td>\n",
       "      <td>27194</td>\n",
       "      <td>671</td>\n",
       "      <td>59</td>\n",
       "      <td>Statistics interview question</td>\n",
       "      <td>IG video</td>\n",
       "      <td>36419.0</td>\n",
       "      <td>31734.0</td>\n",
       "      <td>76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1151</td>\n",
       "      <td>201</td>\n",
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      "text/plain": [
       "                    Date  Instagram reach  Instagram profile visits  \\\n",
       "480  2023-04-26T00:00:00            32378                       447   \n",
       "481  2023-04-27T00:00:00            31429                       475   \n",
       "482  2023-04-28T00:00:00            20506                       363   \n",
       "483  2023-04-29T00:00:00            13472                       345   \n",
       "484  2023-04-30T00:00:00            27194                       671   \n",
       "\n",
       "     New Instagram followers                           Title    Post type  \\\n",
       "480                       89   ChatGPT with Jupyter notebook     IG video   \n",
       "481                      128  why documentation is important     IG video   \n",
       "482                       89              Udemy courses meme     IG video   \n",
       "483                       78                1:1 consultation  IG carousel   \n",
       "484                       59   Statistics interview question     IG video   \n",
       "\n",
       "     Impressions    Reach  Shares  Follows  Likes  Comments  Saves  \n",
       "480      64045.0  62424.0    1239     10.0   2648        41   3368  \n",
       "481      18247.0  14951.0      47      0.0    958        17    276  \n",
       "482      13282.0  10231.0       5      0.0    281         7     34  \n",
       "483      12847.0   8938.0      14      0.0    296         0    130  \n",
       "484      36419.0  31734.0      76      NaN   1151       201    455  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "# View to Snapshot of data - Last 5 rows\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2b9544",
   "metadata": {},
   "source": [
    "By looking at the data, we can see the issue with the date column. We will fix it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2e8be35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(485, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Size of data\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f46a59",
   "metadata": {},
   "source": [
    "There are 485 rows and 13 columns in the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a8d9f27",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 485 entries, 0 to 484\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Date                      485 non-null    object \n",
      " 1   Instagram reach           485 non-null    int64  \n",
      " 2   Instagram profile visits  485 non-null    int64  \n",
      " 3   New Instagram followers   485 non-null    int64  \n",
      " 4   Title                     485 non-null    object \n",
      " 5   Post type                 485 non-null    object \n",
      " 6   Impressions               485 non-null    float64\n",
      " 7   Reach                     485 non-null    float64\n",
      " 8   Shares                    485 non-null    int64  \n",
      " 9   Follows                   484 non-null    float64\n",
      " 10  Likes                     485 non-null    int64  \n",
      " 11  Comments                  485 non-null    int64  \n",
      " 12  Saves                     485 non-null    int64  \n",
      "dtypes: float64(3), int64(7), object(3)\n",
      "memory usage: 49.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Get more information about datatypes and null values in the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a3e60dc",
   "metadata": {},
   "source": [
    "### Section B: Clean the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36c4ce8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us first start with Club column\n",
    "\n",
    "df['Date']= df['Date'].str.split('T').str[0]\n",
    "\n",
    "#In the above example, str.split('T') splits the string in the \"date_column\" on the \"T\" character\n",
    "#str[0] selects the first part of the resulting split, which is the date part before the \"T\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b1d2c2d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th></th>\n",
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       "      <th>Instagram reach</th>\n",
       "      <th>Instagram profile visits</th>\n",
       "      <th>New Instagram followers</th>\n",
       "      <th>Title</th>\n",
       "      <th>Post type</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Follows</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>2745</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>1619</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>2434</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "         Date  Instagram reach  Instagram profile visits  \\\n",
       "0  2022-01-01              348                        70   \n",
       "1  2022-01-02              497                       100   \n",
       "2  2022-01-03             2745                       249   \n",
       "3  2022-01-04             1619                       154   \n",
       "4  2022-01-05             2434                       195   \n",
       "\n",
       "   New Instagram followers Title Post type  Impressions  Reach  Shares  \\\n",
       "0                        0     0         0          0.0    0.0       0   \n",
       "1                        0     0         0          0.0    0.0       0   \n",
       "2                        0     0         0          0.0    0.0       0   \n",
       "3                        0     0         0          0.0    0.0       0   \n",
       "4                        0     0         0          0.0    0.0       0   \n",
       "\n",
       "   Follows  Likes  Comments  Saves  \n",
       "0      0.0      0         0      0  \n",
       "1      0.0      0         0      0  \n",
       "2      0.0      0         0      0  \n",
       "3      0.0      0         0      0  \n",
       "4      0.0      0         0      0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "417add7d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 485 entries, 0 to 484\n",
      "Data columns (total 13 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   Date                      485 non-null    datetime64[ns]\n",
      " 1   Instagram reach           485 non-null    int64         \n",
      " 2   Instagram profile visits  485 non-null    int64         \n",
      " 3   New Instagram followers   485 non-null    int64         \n",
      " 4   Title                     485 non-null    object        \n",
      " 5   Post type                 485 non-null    object        \n",
      " 6   Impressions               485 non-null    float64       \n",
      " 7   Reach                     485 non-null    float64       \n",
      " 8   Shares                    485 non-null    int64         \n",
      " 9   Follows                   484 non-null    float64       \n",
      " 10  Likes                     485 non-null    int64         \n",
      " 11  Comments                  485 non-null    int64         \n",
      " 12  Saves                     485 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(3), int64(7), object(2)\n",
      "memory usage: 49.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Convert the 'date' column to datetime type\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "00f10783",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows without zero values in column 'Date': 485\n",
      "Number of rows without zero values in column 'Instagram reach': 485\n",
      "Number of rows without zero values in column 'Instagram profile visits': 485\n",
      "Number of rows without zero values in column 'New Instagram followers': 287\n",
      "Number of rows without zero values in column 'Title': 89\n",
      "Number of rows without zero values in column 'Post type': 89\n",
      "Number of rows without zero values in column 'Impressions': 89\n",
      "Number of rows without zero values in column 'Reach': 89\n",
      "Number of rows without zero values in column 'Shares': 88\n",
      "Number of rows without zero values in column 'Follows': 68\n",
      "Number of rows without zero values in column 'Likes': 88\n",
      "Number of rows without zero values in column 'Comments': 85\n",
      "Number of rows without zero values in column 'Saves': 88\n"
     ]
    }
   ],
   "source": [
    "#Check total number of rows in all the columns without zero values\n",
    "\n",
    "columns_to_check = ['Date', 'Instagram reach', 'Instagram profile visits', 'New Instagram followers', 'Title', 'Post type', 'Impressions', 'Reach', 'Shares', 'Follows', 'Likes', 'Comments', 'Saves']\n",
    "\n",
    "for column in columns_to_check:\n",
    "    non_zero_count = df[column].value_counts().drop(0, errors='ignore').sum()\n",
    "    print(\"Number of rows without zero values in column '{}': {}\".format(column, non_zero_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "159d9002",
   "metadata": {},
   "source": [
    "We will loop through each column in the list and use the value_counts() method to get the count of each unique value, and the drop(0) method to remove the count of zero values. Finally, we use the sum() method to get the total count of non-zero values in each column, and print the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e818eb",
   "metadata": {},
   "source": [
    "### Section C: Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1c58bc9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the trend for the following columns 'Instagram reach, Instagram profile visits, New Instagram followers'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e77d9a3c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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AUkNpXHPXBgBgAY9hGIZpaoLMc8cwZbOqf9AQ7CTpTA6r+gcDahHDMAzDhAMW7pi6ZOtQuqT1DMMwDNMssHDH1CXzOuMlrWcYhmGYZoGFO6Yu6e3pQjwWNa2Lx6Lo7ekKqEUMwzAMEw5YuGPqkmR3AjdeutRYntLWghsvXcrBFAzDMEzTw8IdU7eogtwVZx7Bgh3DMAzDgIU7hmEYhmGYhoKFO6Yh4EIrDMMwDKPBwh3DMAzDMEwDwcId0xCw4o5hGIZhNFi4YxiGYRiGaSBYuGMYhmEYhmkgWLhjGIZhGIZpIFi4YxoCjpZlGIZhGA0W7piGQHBIBcMwDMMAYOGOYRiGYRimoWDhjmEYhmEYpoFg4Y5pDNgqyzAMwzAAWLhjGIZhGIZpKFi4YxoCVtwxDMMwjAYLdwzDMAzDMA0EC3cMwzAMwzANBAt3TEMgOIsxwzAMwwBg4Y5hGIZhGKahYOGOYRiGYRimgWDhjmkI2CrLMAzDMBos3DENAct2DMMwDKPBwh3DMAzDMEwDwcIdwzAMwzBMA8HCHcMwDMMwTAPBwh3TEHBABcMwDMNosHDHNASCQyoYhmEYBgALdwzDMAzDMA0FC3cMwzAMwzANBAt3TEPAPncMwzAMo9ESdAMYxom+gRRW9Q9i61Aa8zrj6O3pQrI7EXSzGIZhGCbUsHDHhJK+gRSuuWsD0pkcACA1lMY1d20AABbwGIZhGMYFNssyoWRV/6Ah2EnSmRxW9Q8ay4JtsQzDMAxTBAt3TCjZOpT2XK/Kdrk8C3oMwzAMA7Bwx4SUeZ1xz/V5RbrLsxaPYRiGYQBUUbgjoh8R0U4ielpZN4OIHiCi5/X/0/X1RETfJqLNRPQUEb1e+c6V+v7PE9GVyvpTiGiD/p1vExG5/QZTX/T2dKE1au6e8VgUvT1dxrKqrGPFHcMwDMNoVFNz92MA51vWrQDwoBDiGAAP6ssAcAGAY/S/jwH4HqAJagCuBXA6gNMAXKsIa98D8A/K9873+A2mjkh2J/DRsxYby4nOOG68dKkpmELV1rH/HcMwDMNoVE24E0L8EcBey+pLANyif74FQFJZ/xOh8RiATiKaC6AHwANCiL1CiH0AHgBwvr5tqhDiMaGN6j+xHMvuN5g6481HzzY+r1mxvChKVpg0dyzcMQzDMAxQe5+7OUKIbfrn7QDm6J8TAF5T9tuir3Nbv8VmvdtvFEFEHyOiJ4joiV27dpVxOkw10QztzqgCXS5f5cYwDMMwTJ0QWECFrnGrqrrF6zeEED8UQpwqhDh19uzZTrsxIUW9sWyWZRiGYRiNWgt3O3STKvT/O/X1KQALlP3m6+vc1s+3We/2G0yd4aG442hZhmEYhrGh1sLdPQBkxOuVAO5W1l+hR82eAWC/blrtB3AeEU3XAynOA9CvbztARGfoUbJXWI5l9xtMgyEUUyxHyzIMwzCMRtXKjxHRLwCcDWAWEW2BFvW6EsAviegjAF4B8B599/sAXAhgM4ARAB8GACHEXiL6CoC/6ftdL4SQQRofhxaRGwfwO/0PLr/B1Bnk4XRn8rljzR3DMAzDAKiicCeEeL/DpnNs9hUAPuFwnB8B+JHN+icAnGCzfo/dbzD1RykBFexzxzAMwzAaXKGCqQvshDdTEmOOlmUYhmEYACzcMSHGnMfOZjs4oIJhGIZhrLBwx4SWXF7YfpZwEmOGYRiGKYaFOya0CI9UJ+ZUKDVpEsMwDMOEHhbumNCiCmx2mrs8a+4YhmEYpggW7pjQ4pXqJJ9nzR3DMAzDWGHhjgktJrOrl88dS3cMwzAMA4CFOybEePnUcbQswzAMwxTDwh0TWtTcdexzxzAMwzD+qFqFCoaZKHmOlmVCRt9ACqv6B7F1KI15nXH09nQh2Z0IulkMwzAmWLhjQotXtKzg8mNMDekbSOGauzYgnckBAFJDaVxz1wYAYAGPYZhQwWZZJrSoApuXWdZuO8NUklX9g4ZgJ0lncljVPxhQixiGYexh4Y4JLV4+dWyWZWrJ1qF0SesZhmGCgoU7JrTkPDR3qrzHZlmm2szrjJe0nmEYJihYuGNCi/DQzLHmjqklvT1diMeipnXxWBS9PV0BtYhhGMYeFu6Y0OIVLSvY546pIcnuBG68dCk6WjUBb3JbFDdeupSDKRiGCR0cLcuEFu88d5zEmKktye4E/vjcLtw1kMJlpyxgwY4x4DQ5TJhgzR0TWvIlRMuybMfUivFc3vSfYWSanNRQGgJampyrb1+HL/RtCLppTJPCwh0TWkRJ0bIs3TG1IaMLdZksC3eMhl2aHAHg1sdeRd9AKphGMU0NC3dMaCklWjbHwh1TIzI5of9n4Y7RcEqHIwDOg8gEAgt3TGjxDqhQK1TUpEkMU9Dc5bjTMRpu6XA4DyITBCzcMaHFnMTYazsPtExtyLDPXVPSN5DCspWrsXjFvVi2crXJ3OqWDofzIDJBwMIdE1q8y4+xzx1Te9gs23zYBUxcc9cGQ8BLdicwqbV4OOU8iExQsHDHhJa8ItDlvYQ7HmeZGlEwy3Knaxa86gpncnmMZs3vqERnnPMgMoHBee6Y0JL3CJjwiqZlmGpgaO6y3OeaBa+6wrf85WWTdWHB9Dj+9LnlNWkbw9jBmjsmtHjluWPhjgkC9rlrPtzqCvcNpPD135sjYrfsS3MKFCZQWLhjQouXTx3XlmWCQAp3WfYFaBrc6gqv6h8sEvQ5BQoTNCzcMaHFFA1rM46afe5YumNqg0xezGbZ5kHWFZbEomT403mZbBkmCFi4Y0KLySzLPndMSMjkOVq2GVEDIxbNnGQsu5lsGSYoWLhjQotJePNMhVKLFjEM+9wxwMHRrPG5t6cLUaKifTgFChMkLNwxoUUNorDT3HESYyYIDLMsC3dNy8HRjPE52Z3AcXOnmLa3RYlToDCBwsIdE1q8o2W5/BhTewpJjLnTNSvD4+acdx2tWlaxH3/4DQAAgWJNHsPUEhbumNDipZkz5cFjuyxTA4QQyORlQAVr7pqNWLQgtKUVAW9/WtPkzZ7SBkAz2QuecTIBwsIdE1rM5cfct7NZlqkFubwwtMTsc9dc5PPCpK09+xsPGbnshtLjAIDpHa1oiWgCIGt2mSDhChVMaPHOc1f4zLIdUwvUAZt97pqLO5/cYlrecWAM19y1AUBBczctHkMsGkE2n0Mml0drC+tPmGDgnseElnxJ0bIs3THVR9XW5QW7AzQT33zguaJ16UwOX//9Joxm8ohFCR2tUUOgG2ezPRMgLNwxoSXvES2rrrHbzjCVJmvR1rH2rnnYvn/Udv02ff20eAxEhFhUG1a5bzBBwsIdE1q8KlBYo2XZgZmpNlY/Kva7ax4Om9pmu14GUUyLxwAAbbrmbow1d0yAsHDHhBavaFirKZZlO6baWLUxHDHbPPz9ssVF6+KxKN532gIABeFOmmVZc8cECQdUMKHFXH7MZrvl3ZkXAhHOL9Uw9A2ksKp/EFuH0pjXGUdvT1fgiWGtmjqOiGwe3nTMLOB3heWp7S24/pITsCG1HwDw5KtDWLZyNXJ5rmDCBA8Ld0xoEaZoWG/NXU4I7tANQt9ACtfctQHpjJZLLDWUNiITgxTwshZhjrUzzYPVzHrFGxcBAH7y6MvGutRQGrISGQdUMEHCZlkmtHhXqHBfZuqXVf2DhmAnSWdyWNU/GFCLNKzCHGtnmoexjPlej+r90aq9le8hFvyZIAlEuCOiq4loIxE9TUS/IKJ2IlpMRI8T0WYiup2IWvV92/Tlzfr2RcpxrtHXDxJRj7L+fH3dZiJaEcApMhXAq7asgHkdp0NpHLYOpUtaXyuswpxVk8c0LmPZ4smGW3/8x5+uxeIV92LZytVGsmOGqRU1F+6IKAHgXwGcKoQ4AUAUwPsAfA3ATUKIowHsA/AR/SsfAbBPX3+Tvh+I6Hj9e0sAnA/gu0QUJaIogO8AuADA8QDer+/L1Bneee7cl5n6ZV5nvKT1tcIaQPGBmx/nAbxJsJpl05mca3/cfWgcAgWXAu4fTC0JyizbAiBORC0AOgBsA7AcwB369lsAJPXPl+jL0LefQ0Skr79NCDEmhHgJwGYAp+l/m4UQLwohxgHcpu/L1Ble5ceKfO5YumsYenu6jDJOkngsit6eroBapJG19LFdB8d4AG8SpHAnferu27ANKZ+a5DC4FDDNRc2FOyFECsA3ALwKTajbD2AtgCEhRFbfbQsA6TWdAPCa/t2svv9Mdb3lO07riyCijxHRE0T0xK5duyZ+ckxFMUfLemvuOM9d45DsTuBNR880lhOdcdx46dLQRcuq8ADe2IzpPqBxPdXJaKa4LyRcNHlBuxQwzUUQZtnp0DRpiwHMAzAJmlm15gghfiiEOFUIcers2bODaALjgpdZ1irMseKusTjqsCnG5zUrlgcu2AHeee14AG9cpOZuzMHPsjMew5oVyxGPRW23B+1SwDQXQZhlzwXwkhBilxAiA+AuAMsAdOpmWgCYD0DaN1IAFgCAvn0agD3qest3nNYzdYYq0NkFS1gFPg6oaCzCeDu98trxAN64SOHOyf1jKJ0BACxNTC3aFgaXAqa5CEK4exXAGUTUofvOnQPgGQAPAbhM3+dKAHfrn+/Rl6FvXy00lc09AN6nR9MuBnAMgL8C+BuAY/To21ZoQRf31OC8mArjZZa1rrHT7jH1SxiF9aw1c7YCD+CNjYyWleXFrMyYpFWoUDXOQHhcCpjmouY5X4UQjxPRHQCeBJAFMADghwDuBXAbEd2gr7tZ/8rNAH5KRJsB7IUmrEEIsZGIfglNMMwC+IQQIgcARPRJAP3QInF/JITYWKvzYyoHR8s2N8JSWzgSCb76iFNi2kRIKmgw1UPmuTth3lSsfXWoaPtlr58PoFj4W7NiedXbxjBWAknoL4S4FsC1ltUvQot0te47CuDdDsf5KoCv2qy/D8B9E28pEyR5j2jZYp87lu4aCTWJ8Vg2j3irvS9TLbEzyx592GT84d/eEkBrmFoizbJL53faCndnH3sYACAWDX4SwjBcoYIJLaqsZutzx8JdQ5NWohFHLdUqgsKu6sCUdi561wxIs+zUeMx2e1uLNvlodTDbMkwt4V7IhBav8mPFqVCq3SKmlqTHs8bn0Wx4hTvW0zQHUnPX6SjcacNpLMrDKhM83AuZ0OJVfoyTGDc2I+MFgS49HhbhrriPWSsXMI2J9Lmb7KCpbY9pwylr7pgwwL2QCS2qrGaXoNi6is2yjYXqc2eXMDYI7DR3g9sPcmWKJsArWtYwy7LmjgkB3AuZ0CI8zLKcxLixUbV1YTbLZvOCS481OH0DKfzhmR0AgC//5hnbfdpYc8eECO6FTGjxipbl8mONjVlzF7xw1zeQwv+tedl2G5cea1z6BlK45q4NGNXN73uHx233k5q7CBW8MJ2qVTBMteEwLya05EuMlrXzy2PqF9XnLmjhTg7waZd2cOmxxmRV/6DrfZdIc63qg8muIkxQsOaOCS2lRsu6FA9g6hCTWTZgnzs/AzyXHmtM/ArtUrhb+/JeY91YNs/meiYQWLhjQotn+THOc9ewCCFCZZb1GuC59Fjj4kdob2uJgIjQN5DC/bpvnuSau55iAY+pOSzcMaFF1cT5iZZl2a5xGM/lTdraoDV3XgP8V5NLuPRYg9Lb0+XpOye1dqv6B5G1mBTSmTz7YzI1h4U7JrR4m2XZ565Rsea1C1pzZzfAx2NRyMDI85fODaBVTC1Ididw46VL0eJS21hOPp00vOyPydQaT+GOiC4loueJaD8RHSCig0R0oBaNY5obVVbzEy3LZtnGwerf5sehvZrIAV4KeNM7Yrjx0qWY1KZVKxgLSR4+pjokuxM4YmaH4/ZhfTLipOFlf0ym1vjR3H0dwDuEENOEEFOFEFOEEFOr3TCGUYU1O8HNaqrlVCiNw4hFczcWglQoye4Ezj1+DgDgundoZlhpjhu3m30wDYVbBRy5yU7D2x6LsD8mU3P8pELZIYR4tuotYRgLuRLNspzEuHG4b8M20/KG1P6AWmImpzuCRnUTnUxYy5q7xsfqS6ciLbbS73JV/yBSuin2ixcdz/6YTM1x1Nzp5thLATxBRLcT0fvlOn09w1QV7zx35mWuLdsY9A2k8P9Wbzat++Pzu0MRcSj7mPS/KuQ2C16zyFQXee/tyo91tBa0dcnuBNasWI6Zk1oBAOctObw2DWQYBTez7MX631QAIwDOU9a9vfpNY5od7/Jj5mX2uWsMVvUPYjxr1oTl8iIUEYeyH0Yj2qtTViUYy7LmrtGRmrt/v/BYJDrjUMMrWluKo2mldpcnnUwQOJplhRAfrmVDGMaKl8+ddR3Ldo1BmCMOsxbNXatNVQKmMZFC2kUnzsMVZy4GACxacS8A+/eT7CNZzq7OBICfaNlbiKhTWZ5ORD+qaqsYBuY8d3bvR05i3Jg4RRZG9CSxQSIH+AibZZuOjB400xIpHjbtXj3RqNZHWLZjgsBPtOyJQoghuSCE2Aegu2otYhgdrwoVVmvHVbetC3zwZyZOb08XYtHinGI5IXDNXRsCvcdFPnd6ZKTVjMw0HoZJ3qZv2kXqSyGQNXdMEPgR7iJENF0uENEM+IuyZZgJ4ZXE+IVdB03Le4bHAx/8mYmT7E7g/acttN2WzuQC9b3LGj53ulk2ymbZZsFqklexsxmwzx0TJH6Eu/8E8CgRfYWIbgDwF2i57ximqnhFy657rTg9RtCDP1MZTjliuuO2IH3vijV3LNw1C9Z7r2Jnli343LFwx9QeT+FOCPETAO8CsAPAdgCXCiF+Wu2GMYyX5s6a6FYSBsd7ZmK4+U8Gme3fyeeOzbKNjRBCiZTW7r1qIRgeyxZZDFhzxwSJL/OqEGIjEe0C0A4ARLRQCPFqVVvGND3CpLkr3t7RGrUV8LjUT/0j3ZQIZpNXPBYNNNs/57lrTlRzPOmBPdfctcHYLgBjWSYsZs0dEyR+omXfQUTPA3gJwCMAXgbwuyq3i2HMqVBsXpAnzCuughf04M9UBnnv588oCOqJzjhuvHRpoNn+rT53Rp47rlDR0Fi1dqv6B23rH6suIQXNHfcNpvb48bn7CoAzADwnhFgM4BwAj1W1VQwD72jZBTMmmZZlMXcu9VP/yNs9d6om3B0xswNrViwP/N7KgbrFSGLMtWWbAWswhZ9cjEa0bI41d0zt8WOWzQgh9hBRhIgiQoiHiOhb1W4Yw5jz3NlVqDCvu/biJYEP/kxlkMK8TBIclgGyoMHRlrm2bH3SN5DCqv5BbB1KY15nHL09Xa7vjlzOLNzN64wbtWNVVJcQ9rljgsSP5m6IiCYD+BOAW4novwAMV7dZDOMnz515Hb9EG4e8VbgLiWmruPwY+9zVG9JfLjWUhgCQGkp7plCS/a9Fl+p7e7oQj5lLjlldQlqi7HPHBIcf4e4SaLVlrwLwewAvQKsvyzBVxStaVq6Rs2k7AZCpT+TtlsmMw6K5s5rnnt9xCADw3YdfwLKVqznHYh3gx1/OitXnLtmdwI2XLjVqzNr5g7LmjgkST7OsEGKYiI4AcIwQ4hYi6gBQXCWZYSqMKc+dzQtSrmqJErJ5YbsPU5/IeykLsodF+6EO8n0DKdz39DZjm9QAAWD3gBBTTu3ijE2Ou2R3wvU+c7QsEyR+omX/AcAdAH6gr0oA6KtimxgGgNmnbuv+0SLNiNTsxXQTGWvuGgfDLBuVPndhM8sSVvUPImPRKHIS7fDjlCrJLYWS9LmL2iQwdoKjZZkg8WOW/QSAZQAOAIAQ4nkAh1WzUQwDACNjWdOy1TdGCn8tRoFuFu4aBXkrpc9dJiT3VhXuytEAMcHT29MFq4jmlUJJ+tzFon6GTI1Cbdlw9F2mufDTU8eEEONygYhaYF9Kj2EqStom67+qGZETYunkzL4tjYMU1GXAQljurepzV44GiAmeZHcCk9oKnkV+8idafe78wD53TJD4Ee4eIaLPA4gT0dsA/ArAb6rbLIaxr9cIFDQjBbOsDKioSbOYGmDc22hhgLSmvgkCdZDv7ekyzMYSTqJdH6i1gP3kT7QG0vjB8LnjFxMTAH6Eu88B2AVgA4B/BHAfgC9Us1EMA6DIdCKRmhH5yoyyWbbhkLcyEiFDAxIG85Yq3CW7E/ins480toWhggbjD+krKTXDXmTL8LmLsOaOCRDXaFkiigLYKIQ4FsD/1KZJDKPREqUih3VVMyI4oKJhkZq7CBFaIoRcXiCbE4gFHKdvNc+dd/zh+PaDm3H83Km471NvDrJpTBlMi8d87WfkuStHc8fCHRMArtMWIUQOwCARLaxRexjGwDpLtmpG1FQoAM+QGwmphY3qwh0QjkTGWUv5sXirJm2OZjiJcb2QHi/cq3afswX5bmkpIaCCo2WZIPFTfmw6gI1E9FcolSmEEO+oWqsYBubyY7EoYc2K5ebtMlpWH2jZLNs4GGZZkgNqLnDfJSGEqV0AjCoFI+Ms3NULP//rK8bnLftG0DeQ8u1zV4pZljV3TJD4Ee6+WPVWMIwNXhUqrFUM2CzbOMh7SUSFKhUBD5KqSZZIa5MU7qwVD5hw0jeQwtd/X8hDmBfwlXg6V0ZAhSxRxxYFJgj8VKh4pBYNYRgrqnCXF5rmRA6qQMHnTs6mWXPXOKj3NhoSs6yd9kaaZVm4qw9W9Q+aImWBQnolN+GuLM1dSCYlTHPi34GAYWqM1QRmnQELw+eOAyoajUJAhZIMNmCzrJ32RkZbjmfzrKGpA8pNPC0rpJSmuWNfYCY4WLhjQoma06zFIRq2OBdajRrHVB15L4koNBoQO+0NERmmWQ6qCD/lJp4u3PtSKlSwcMcEBwt3TChRtXbyfWq1yhUFVLDmrmFQzbItIYk6zDuY5jrYNFs39PZ0GZNBSXss4pl4Wgpo1u+6Eab8jEzz4SncEdHbiWiAiPYS0QEiOkhEBybyo0TUSUR3ENEmInqWiN5IRDOI6AEiel7/P13fl4jo20S0mYieIqLXK8e5Ut//eSK6Ull/ChFt0L/zbVIdtZi6QL5MI0SIkn3ARFFABb9EGwY7s6w152GtcapSINNppDliNvQkuxO48IS5pnXXXbykqtGyQU9KmObEj+buWwCuBDBTCDFVCDFFCDF1gr/7XwB+rydHPgnAswBWAHhQCHEMgAf1ZQC4AMAx+t/HAHwPAIhoBoBrAZwO4DQA10qBUN/nH5TvnT/B9jI1xhjcFYf6nGVwFxbNHQt3jUNBc6uYZUPic2cd4DnXXX1x7Fzz8HXeksM9v5MrI4mxNOGy5o4JAj/C3WsAnhYVKuxIRNMAnAXgZgAQQowLIYYAXALgFn23WwAk9c+XAPiJ0HgMQCcRzQXQA+ABIcReIcQ+AA8AOF/fNlUI8Zje5p8ox2LqBGEM7opjsoPmzig/xmbZhkHV3IYliXHOMpmQcK67+iJrcc4dz3r3q4xRfqwMnzuuLcsEgJ88d58FcB8RPQJgTK4UQnyzzN9cDK1W7f8R0UkA1gL4FIA5Qoht+j7bAczRPyegCZiSLfo6t/VbbNYXQUQfg6YNxMKFXIQjTKjlp+yizvoGUnhqyxAAYPWzO4u2M/WNUM2y0XBoQOQgbR3fOdddfWHtR36Eu/Ly3LHPHRMcfqYhXwUwAqAdwBTlr1xaALwewPeEEN3Qql6sUHfQNW5VfyKEED8UQpwqhDh19uzZ1f45pgRU4S5CZs1c30AK19y1wZhNy0H1xV2HAmgpUw0Ms6ya5y5wnztz6TFJOwdU1BVWDfB4zvu+Gf6WJQRUcFlEJkj8aO7mCSFOqOBvbgGwRQjxuL58BzThbgcRzRVCbNNNqzv17SkAC5Tvz9fXpQCcbVn/sL5+vs3+TB2RN1JhFOeLWtU/aDuQrt+yv2btY6pLThHuZcDMJ3/+JPYOj2NeZxy9PV2eTvAVb5OTz11ME/ZG2SxbF1gnCdakxnbkJpDnjjV3TBD40dzdR0TnVeoHhRDbAbxGRDL2/BwAzwC4B1rgBvT/d+uf7wFwhR41ewaA/br5th/AeUQ0XQ+kOA9Av77tABGdoUfJXqEci6kT3MyyTglH2eepcRDK/d9zaBwAsGd4HAJAaiiNa+7agL6B2s7ZCj53VuGONXf1RDlm2YnlueNoWab2+NHc/TOAzxDRGIAMAIJmOZ1IxOy/ALiViFoBvAjgw9AEzV8S0UcAvALgPfq+9wG4EMBmaObhD0NrwF4i+gqAv+n7XS+E2Kt//jiAHwOIA/id/sfUEXmb8lNSuJvXGUfKRsCTgyxT/8jxMELAy7uHi7b7KRlVaaTGJ2LJrBRvbTHaJOkbSGFV/yC2DqUD0zQy9pQTUJErwyzL0bJMkPipLTsR/zqnY64DcKrNpnNs9hUAPuFwnB8B+JHN+icAVNKUzNQYNYmxNc9db08XrrlrQ5Gm5LjDK95VmYBQzbKjDoOvV8moSuM0wMctee6kT6jsn1LTCLgXp2dqQ5Hmzkdpm4nluWPhjqk9fjR30M2ex0ALqgAACCH+WK1GMYw0yxERIvpL8oGN23HFY69i61Aa0+IxjGVzyAtgSnsLDo5mMXe6ewkhpn5Q8xzGYxGkM8UDsFfJqErjNMDHW3WfO12Ys/MJDULTyNhj9bnzZZbNcbQsU1/4qVDxUQB/hObj9mX9/3XVbRbT7KiaO/lCvekPzyM1lIYAMJTOGLnwPn720dp3+CXaMKh5Do+bW+wBEo9FPUtGVZq8T5+7covTM7UhY42W9WWWtY+UdoPz3DFB4qenfgrAGwC8IoR4K4BuAEPVbBTD5GxSoVij2uQrs62FK1Q0GmoS40WzJpm2JTrjuPHSpTXXgjn53G3eqaXg+c5DL2DZytXo7IjZfr/WmkbGHut7ohSzbGk+d6y5Y4LDj1l2VAgxSkQgojYhxCYl0pVhqkJeGdy9/FxadeGOK1Q0DqpZNmbRlqxZsTyIJtn63PUNpHDvhm3GcmoojZgeBKQKEZXUNHKwxsSQQnpbSwRj2by/VChl+NxFOVqWCRA/wt0WIuoE0AfgASLaBy2alWGqhmGWi8DwuXOilTV3DYep/FwJ2pJqIpPfqukwVvUPGsm0JZm8QFsLQSqEZk9pw79feFxFBDAO1pg4Gf3GdLRGMZbNl1R+jH3umHrB0ywrhHinEGJICHEdgC9CqwmbrHK7mCbHlOdOf59K86sVwyzL79CGQTXLxkoYUKuJnc+dkx/dWLbQGb/+rhMrJni5BWsw/pB9q0NPYZPxYZbNGYJ9KdGyPOlkgsNVuCOiKBFtkstCiEeEEPcIIcar3zSmmVGFO/mS/Ic3Lza2HzalzahcIIU7DqhoHMxJrP07sVcTO587Jz86dSKy69CY7T7lwMEaEydjCHdaIEwpSYxlnWM/sOaOCRLXniqEyAEYJKKFNWoPwwAAHnhmBwDgpd3DWLdlCABw5lGzjO03X/kGEKRwp72keYbcOKjR0rGQmGXtisf39nQVaZRjETJpg/743E5UCidhkoM1/CO1cB1tmuaupCTGpWjuuLYsEyB+piHTAWwkogeJ6B75V+2GMc1L30AK33zgOWNZvnwfVgbJ8VzO8IEyfO44oKJhEDbl54LGyHOnCJvJ7gS+cskSY7kz3gJQQTgFgN89vaNipdLshMkg0sLUM9J/bpLU3FUpifGazbsBAGtf2YdlK1fXvFwe09z4Caj4YtVbwTAKq/oHbSPY7lhbeDmOjOeMATQWZbNso5FTys9ZTWH5vPAMsqkGTnnu3vOGhbj2nmeQzuQQb23BUDpr2p7Li4olME52J/Di7kP49oObAQDzOtvx2Z5jOZiiBLJKQAXgN4mxzHPnr9/1DaTwvYdfMJY58KW2cES5v/Jjj9SiIQwjcfIf2jtccPUcHtOcymNRghz7WXPXOEg5nQhFARU5IRBB7YU76XMXpeLfnjm5FVv2pbF9/6jtd1NDafQNpCoywLx+4XTj88OfeauhuWb8YQ2o8JMKpVTNnd0ElauU1AaOKNfwU6HiIBEdsPy9RkS/JqIja9FIprlw8h9Sk8MOj2nakWikkOSYNXeNg8ksa/G5C8qHyS3X2czJbQCAWfp/O665a0NFTHNyYqO2ifGPNMt2lGCWLfjc+ROkOfAlODiiXMNPT/0WgF4ACQDzAXwGwM8B3AbgR1VrGdO09PZ0odUmKu1tx88xPg+Pa8JdLBIpJAtlzV3DoApS1iTGQSWrlv3LrkrBrEmtAIB3nDTXUZNWqQFG9n21TYx/rJq70qJl/WnuOPAlOFiw1vAj3L1DCPEDIcRBIcQBIcQPAfQIIW6HFmzBMBUl2Z3AB994hLHcHtO66RKlxughqbmLFjR3PibgTJ0gBTiiYk1ZpbRVfQMpLFu5GotX3OvL4d3NNDdzsibcHT1nCj6ybHHRdkklBpiRMUW44+SOJSNry27ZNwwAuGPtFs/7X2q0bG9Pl/HeknDgS21gwVrDj3A3QkTvIaKI/vceANKxhN8sTFU4eUEnAOCipXNxznGaxk71YZFm2RZFc8dm2cahkAqFilKhVKKak/TLSQ2lIVDwy3Ed4PXZg53P3R7dH/Sauzbgl0+85niMSgwww+OKWZY1dyUjfSdXb9plrPO6/6X63CW7E1h56YnG8txp7YHUQ25Genu6EI9FTeuaUbD2I9xdDuCDAHYC2KF//gARxQF8soptY5qYUd1noi0WMWbLZuFO294SITbLNiB5ZTC1JjGuxH0uxy+nMMCb29M3kMIfnysIClLQs8qAlRpghlXNHU9oSkZeM2tyYbf7X4iW9R+8kuxOYPYUzQfz1x9fxoJdjUh2J3DjpUuN5c54rCkFaz/lx14UQlwshJglhJitf94shEgLIf5ci0YyzceoLsi1x6KGpkT1jTnEARUNTaFCRbGfUyUEmnL8cvIOPnd29WUBoMVUyaJympuRcQ6omAhu5cac7n85ee4AYEq75td3cDRT0veYiaE+ZysuaM5UQZ6pUIioHcBHACwB0C7XCyH+vortYpqcMV2r0t4SNYQ6NaptRAZURFlz14gUUqFQkZ9TJQIq5nXGkbIZyN3Mpk4DvJNAkMkLtLVEMJbN48F/Oxvx1qjtfqVyaIwDKiaCm0Bsd//7BlJY9+oQAOBffvEkvnDR8b6FhSntWoT/gdGsx55MtWjWJ8SPjvmnAA4H0APgEWgRswer2SiGUc2y9po7bXs0QsZ21mI0DlKAi1JxEuNK3Ofenq6iqFYvs2nOIc+dk0A4qS1qCKaZSjgK6owo0bKsrS4dqbmzRuTb3X/pmyknlrsPjZeU0mYqa+6YgPAj3B0thPgigGEhxC0ALgJwenWbxTQ70r+uvSVqVCOwC6iIRSOQbjA80DUOeSXPnVVzVwnhLtmdwEffXIhqTXTGPc2mTpo7O0ERAE5fNMMQTLMVjGpV89xxUfrSkdfsn88+ylhnvf8ykvqq29dNKGdawSzLmrugaFblth/hTk45hojoBADTABxWvSYxTEFz124KqCi8ZNUkxmyWbTykoouoOP1EpfLcnb54JgBNg7NmxXJPU5tT+bFkdwIfVwQFOaCfML/TiPTNVlBzxwEVE0MKdxcunQsAWDAjbrr/aiS1E35T2kxp08yyqimdqS2iSQ2zfoS7HxLRdABfAHAPgGcAfK2qrWKantGMElARcQ6oaIlGFLNsjRvJVI28Uls2VgWzLACTL6fwITAamjubRLbn6ul6lsybir87fSEAOTGpguZOCagIKqFzPSMjXye1aT6QYxnzi8MuktqK35Q2UtB/9IXdJeVUZCpHsz4ingEVAB4UQuwD8EcARwIAETln6WSYCqBq7iI2PneFPHdkmG15oGscVLOs1QxaqfusRk1mcgKtLe6RkEbVDJs8d1IAzeTyhrDQ1hI1Imsra5YtaIEqedxmIJ8XRrDOJL1CxahFkPPSypWS0mayLtzdt2G7MTlo1lqnTG3xo7m702bdHZVuCMOomFKh6L1UjZY15bnjgIqGo5DEuNgMWikNrSrc+akvKgUpu3QYqhAn3QfaY5GC0FetgAqe0JSEUUYsQmjXE92OWcqPuWnlIoSSUtrIaNlScuqVQqlVVpqRZn1CHDV3RHQstPQn04joUmXTVCgpURimGhjRskpAhaq5k4NxS1TR3LFw1zAUyo9R0cu5UkK8OqiPZ/NAm7822ZWgkpGX41bNXaSymru+gRT2HBo3lldv2oETEtN8fW9V/yC2DqUxrzOO3p6uptQaSd/HliihTQ+CGctqZnnSJ4m9PV246vZ1tt8XojRtmzTL2jHRUnTSN1CakFkjyKi4ae66ALwdQCeAi5W/1wP4h6q3jGlq7AMqirUfavkxDqhoHNQKFY88t9O07cFNOyryG2azrLtmrW8ghV/pZcVu+sPzRRoS1Sw7qmjuWpT1E0UbzJ8yCbvfeegFT21NOaXWGpWC5i6CSIQMoVx9tyS7E+iMx2y/X2r5uKkuwt1ES9GVU2WlKWnSccFRuBNC3C2E+DCAtwshPqz8/asQ4i81bCPThIzZVKhQo2UlbJZtTOStfPDZHfjxmldM2/579eaKCCYZq+bOASkcyUCG/elMkXAko2IzOVEIBlI0d5Xom9pgbm7nWDbvOZg7CQGf/uX6phPwpAZVmtHbYrpwZ7mun7vA3qeu1PJxT23Zb7u+EqXoyqmywjQPfnzu3klEU4koRkQPEtEuIvpA1VvGNDVjhlk2YmuWlUQjVMhz16QztEZE3sv/+dOLRf5wfgQaP6glw+y0whI/GhJVQycnIW2xSMEXrwI+d+UO5k7bc0I0nQbPMMvq75S2Ful3Z76/PUvmFn23M95SkrmzbyCFm//8UtH6qe0tFSlF56T5m6hGsNFo1lHBj3B3nhDiADQT7csAjgbQW81GMYwpFYpNtKwkZkqF0qyPceMhzbI7D4zZbq+EdkIVGt00d36EqlbVLKv03VhErq9MybRS1vvZ3mxmPENzp9+Xdl1zN2rR3NmZ0WdO9nDKtLCqf9B20tDaEqmIT1xvT5fRfkklNIJMY+BHuJPOBxcB+JUQwl7PzDBlYhfxVfBbsq9QIVGTGOcFfOUrY8KPlNPnTLUfUCuhnVAFOjefOD9CldksW9A6VzIVSm9PlxEEIGmNRjwH896eLrgleWkmM16RWdYIqjBr7uz6Q0ern8xhBZyu624lIGYiJLsT+MJFxxnL8zrbK6IRrARhiuJt1iHBj3D3GyLaBOAUAA8S0WwAo9VtFlMKYXqQSsXJ2XvfsKaxUQMq7LQrLVECEUGmHmPlXWMgzbL/fPZRiOspKyStLd4CjR/8pkLp7ekqaoNVQxKNaH0wlxdIjxcmJi0VTIWS7E7g0+e9zrTuQ2ce4TmYJ7sTmDvNOcFBM5nxrGZZmQ7FqrmzE8bjrdGidW64XddKvaPPW3K48fnBfzs7NIIdB/AEj6dwJ4RYAeBMAKcKITIAhgFcUu2GMf6o9wfJyZ/poJ7Hrr3FvkKFRL6k2TTbWEjh7sKl83DjpUuRUAbKjyxbXJFBbNxnQEWyO4EbL12Kdl3LM2NSa5GGhIgME6ysntLeEkWswqlQ3tplrvx4ml5CzYvDHYS7ZjPjGdGyutAtNXejFs2dnY+kVcD3wk1jWilTuPq+q2SJu4kQhihe1YLTrL7YfjR3AHAsgPcS0RUALgNwXvWaxJRCGB6kieBkupDPY3ssalSoGLPRrry2bwQAuEpFg6EmMU52J7BmxXK8+ZhZAIAzjvIn0HhRShLjZHcCy47Wfn/VZSfaCpfSNCuFO1NARYUyL1t99/ym/2m3EUwSnfHQmPFqRcHnzqy5s0bLjmeLr2tHiZq7ZHfC0Zm/UqZwddIQloltGKJ41WsRlutSazydCIjopwCOArAOgJQiBICfVK9ZjF/C8CBNhHmdcdcC3W0tEVfN3V9f2oe+gRRr7hoMtfyYRH6uVLLqcWVgdNPcSTJK7j07Yi0RYDyHkfGC1tmoLVuhNlv7t9/+bifcrVmxvCJtqifUJMaAapb1obkrUbgDNAHa7v1WKVO4OpmtVB+bKE7v9Fqa/9VL0axjgh/N3akAlgkhPi6E+Bf971+r3TDGH/UeDt/b01U0WMoIsFY9DYrTYApoD+6q/kFOZNxgyBdyRLn30QrmjAP8m2ULbdL2kQmLrVjXVzoVClDsu+dfuPNrpGlsMpZoWbVKhd1+KqWaZQF7f80oUcVM4dkQaqjs3um1Nv+rQm+zjgl+nvinARzuuRcTCH6cvcNMsjuBs3RzG6D5Bn3p7ccDgOHj5CbcAZqWUu7CJcgaA/k+Vm+91NxV6mWd8ZkKpbC/h+bOsr6tJWIIEZVIhQIU++75dUMg13jZ5kEKQMUBFX6iZUsX7qS/puozevScyRUzhavvu0pUQakEye4EzlRcJ4Iw/6vPRbOOCX5iu2cBeIaI/grASDolhHhH1VrF+CbZnUAml0fvHU8B0GoZfuWSE+rKj2bhjA7j83cvf732Ivz102jTX7xRch+Y5nXGjWLqYZm9MhPDziwrFWOVelmXUn4MKPQt6VtnJaakKWlriWhBFhVMhQIUawD9HtfqU+jxSDUs2ZzZLOukubOPli0tFYok2Z1AsjuBgVf34Z3f/Ysxaa0EYdTcAcDr5kzBn57fDSAY87/ZLFvznw8FfnrrddVuBDMxuhdONz5/9E1H1pVgB5hfrJd+9y+Y2q4JdbsOjmHZytU463WznL6Klohm4rjh3mcANK8KvtHI2fi3Vdr0XkpABVAQDKIRb7OsFBoqbZa1Ch1+r4VVeG1vKV0L1QhkDAFdJjF20Nzp9yseixoBa+Vo7lT+9vJeAMD6LfuxbOVq9PZ0lf2u7htIYVX/oMm3Oiw+d4BZ4x4EpoCKJh0T/KRCecTurxaNY7zpG0jhvT941FjeuHUouMaUyfM7DpqWD4wWXrSpoTTuWLvF8bvnHjcHye6E4mxfnTYytUW+j8nOLFuhQWysRJ+7rMWkZ0UV7g6MZrFs5Wq8skeL5q6UWbbcgAqrUNhaQe1RPSH9JuVEwUtz19kRM9aV43Mn6RtI4ZsPPGcsTyRllTX9leT+jdvLbl+liQQs3Qk2yzoLd0R0kIgO2PwdJKIDtWwkY498yPcMFzKer960q25y3Ek2bnPvTm4D49L50wBUXqvDBIu9Wbay6W5UbZZbbVmJtbqBlUOjGdNyaiiNR1/YA6AgVEwUqwbOr3Bn1UyGyYRXS4oCKjx87jo7Wo115UTLSlb1DxYlSi43ZZVd+isA+NGal8ttXsXxcqWpNqy5czHLCiGm1LIhTOnYPeRZPXq0nkyz1pdeKUgtil2aDNV0Ma8zPiEzCFNbpACnDhKFdDeV+Q110uDH565Q3cB+TrzDpg6u1PZVS3PnV9C1np8fTWUjYg2ocI6W1ZanxQtD5ETMspVMWeVY1uygfR3mIIgELNypjwlr7pi6o95z3Ems9TJLQWaat6bJqPfKHc2MEMJ4OZvMspFiAX4ilJoKxcss6+a3V6mAiozl3P0et0i4y+WbctDLWAIqHPPcSbNsXNHcTcAsW8mUVU7fmTXZvg5zEATtc2dKhdKE/RwIULgjoigRDRDRb/XlxUT0OBFtJqLbiahVX9+mL2/Wty9SjnGNvn6QiHqU9efr6zYT0Yqan1yNqPccd5LDHYrDS1otZjB1wDfKj1nMsvVeuaOZUf3tyE5zF1AqlKxHKhS3SUqlAiqs5l2/mjs7IfDOJ519WRsVeR1iUa88d9ry3uGCNuyzdzxV9uSwkimrenu60GqTa/H9py8oq23VIGifO85zF6zm7lMAnlWWvwbgJiHE0QD2AfiIvv4jAPbp62/S9wMRHQ/gfQCWADgfwHd1gTEK4DsALgBwPID36/s2HLYJMiOVS5BZKya3x0zLkxTzR6IzjivOXGTaPi1e2F/OwK157hpFq9mM2JlkgcKAUbEkxqVGy3okMT5q9qSidXLyUSmzbFH5MZ/XYs9wscnui3c/3XSabBmx+uuBFJatXI2NW/cD0DT9i1fci2UrV6NvIGVoSNe+OmR8d8/weNnaf5nvTqbGmT25rezcb8nuBD607Iii9W880jmrQK0p1yzbp98X9V6Ug/pcNKOGGghIuCOi+QAuAvC/+jIBWA7gDn2XWwAk9c+X6MvQt5+j738JgNuEEGNCiJcAbAZwmv63WQjxohBiHMBt+r4Nh3xhqBqDU4/orDu/MtUk8vqFnfjaZScCAC5aOhdrVizHm442v7SmKsKgk+auUbSazUjOJpgCKNzrSgVUqNq6UvLcOWnuFij5GgFtYnLhUi3/e6Vqy1o1cH7TX+wdzhStG83km0qT3TeQMmkrU0Np/OKvrwHQtPqq+8bAK5oQaBWeJ6L9T3Yn8IZFMwAAN7335Am9p+0EuUpphytBOYq7SrrSqK+IMKWIqSVBae6+BeCzAGRvnAlgSAiR1Ze3AJA9PwHgNQDQt+/X9zfWW77jtL4IIvoYET1BRE/s2rVrgqcUDMnuBE5a0GksJzo7nHcOKapJZCybNwbdVocKFVMVJ2fp3G5Nk9Hb01X0gqmnyh3NjF0aFKDy5cdKjZbNGCY971Qo7z11AdasWI6TF2g5KCtXW9ZilvV5XKdr1kya7FX9g740qOlMDn94dqfj9olcs0lt2rvr0FjWY0937O5nmISYcsyylXSlMWnu2CxbG4jo7QB2CiHW1vq3rQghfiiEOFUIcers2bODbk7ZqBqIMD3gflGjZccV4U4OolbznElzFzVr7uTYl+xO4MTENGO/IErgMOVhmGUtA0Sl89ypA72/2rLumjvVD6qjTXMtiFW6tmyZSYydxloBTMj8VU+UIpQdGHUWviai/Z9SIeHOTmDJVcj0X2n8TkAq6UrDARXBaO6WAXgHEb0MzWS6HMB/AegkIqmSmQ9Avm1SABYAgL59GoA96nrLd5zWNyxjJuEuPKp5v4xlc8rnvKFRkZo76yzQbJa1RMsqD3VCMZP1X30WC3Z1gnwZW82yRvmxSgVUlGiWlfs4+dyp6yfppapkNHelomXLTWJsDUpSaZZI8lKEsint+v2zvHsmqv03NHejxWbyUrB7BsI0sTfVvPU5JlXSlcYs3JX89Yag5sKdEOIaIcR8IcQiaAERq4UQlwN4CMBl+m5XArhb/3yPvgx9+2qhpZ++B8D79GjaxQCOAfBXAH8DcIwefduq/8Y9NTi1wBhXhKNKDSK1ZMyiuRvLmgdRN7Ns1JLnTh3shpXZ8Y4DoxVuNVMt7NKgAGpARWV+Z6zEaFkvzZ2a3Fhq7uS+d6/bOmEncaB4oPQr3BFpz9Lcae2225shkry3p8sxjY2VI2ZoAsW5x81BojMOQmW0/5N1oXF4vDgJcSnYmZfDpKFSn1G/wUSVjCg25blrUrNseZWQq8PnANxGRDcAGABws77+ZgA/JaLNAPZCE9YghNhIRL8E8AyALIBPCCFyAEBEnwTQDyAK4EdCiI01PZMao0b6hWn25od8XhRFLcqXgaPPnUtAhfogH1JMKzsPjOGo2ZMr3HqmGggHs2whifHEpTshhGNtWbvk15ecPK+k8mNSc7d+yz4AMHyJpJYMQFlCQi5XaEM2L3wP6PJcH+l9K173hd/Z7tPo/nfJ7gTuWPsa/rxZqxrSGY9heDxrK3xs2n4IAHDigmn4/gdPqVgbJuuau4MuZl8/2E1GwmS1US0ofoOJ5PNw1e3rAGjPf7nCtKlCRZ2NiZUiUOFOCPEwgIf1zy9Ci3S17jMK4N0O3/8qgK/arL8PwH0VbGqoUTVf9SbcWR3ZxzK5QkCF1NxZfe5sUqFEbTR3ql/L+//nMSS4SkVd4GyWrZzmLpcXpog62edkxJ5VGMsrNUnJIc2DWq9VVjO476niep9SS1ZOP5QpOtpaIsiO53wNXEII470QixJaWyK2wkEzRJLPmaqd49cvOxHvOXUB+gZS+PQv1xf5LhrXy6EaSblMNnzuJmaWtUvdEyarjcksW0K7kt0JQ7ibPbmt7Hc157njChUNgfqgV6qGZa2Q/nbtMa0rapo7i3Bn0ZSY8tzJaFnpj6W8VKym2GbxLap35C20KsgiFUxibB0cx3Puya+/cb9W9N3NrKdG0Xbomrt9I/aDeLlaMvl8y5qofq6FGuVLRDhyVnE+vmaJJB8Z1yZ8UrOa7E64mu2c6giXixTuhscmapYtfs+rgn6l8sWVi9ov/fiz2qH6YpeKOgw2a567MJllmTIx5+uqr44sI2WntMcwmhlDJieMvHdGQEWR5k5JhWKJllVfKkPp4oF1IloTpjZIs6xVQ1aIiJ54H89kzcdY/9oQlq1cjZSD0LVtvzZRcBPu1Jqz0uduxqRW7B0eL9rXj5bMzjwstTMyt6UfzZ01EGTBjA5s2n7Q2D57chv+/aLjmuKZkL5u8v4A2r1wuu9OwTPlMqmqZlmtLzhpn4HyXAHKQX1Gy9Uo+klP5Pj7HC3LmrtGQH0I6q0jq5o7KcwN67Nr+WK1zp7tfO6sARVCmM1uKo3uW1TvSAHdao63E+DLxc6slRpKw0l0O1wPRGhxGexVs6zUDF12SvFg6kdLpg3QTxUldN207QCA8oQ7+axYy6Rd944lTSHYAcDImFlzBzhU+tH7nlNOw3KRUbgTNcvaCT5SqxuG0ouqcOU3WtbKRIQ79R3RrAEVLNzVOdlc3vSCr1Qm/FohNXftLVG06QOnnNU6ae46WosVztaACrcXQzP4FtUzXmbZSmjunMqNCaBIwIvHovj4W48CUIpZVhMW3vK6w0z7zJnqr+yUNkCb25jO5PDEK1qARluLdnw/A9e4JbVQq0W4+8TPn2zIXHd2pklDc6eUOJSVfuS9PWxKG7oXajkyWyrsczepQmZZO82dtNqEofSiOVq2tDFJPmITUVQI1tyxcFfvWAep+guo0F5ybYrmTgZCOPncPfribuPzP/1sLfoGUkpABUzHsNIsvkX1jBTeis2y2v9K+Bbd99Q2x23qEzR7iiaMve04rYyYmw+WKVq2zT5P2k8/crovLZnTQCyFk7aY//x5BZ877TtWzR3QeP6oTqWsdh7Qrqu8P5JkdwInztcEuv/+u9fj8GnaBLBaPnelJjG29nNZE1dFPhdhKL2YN0XLljYmWScf5f1+4XOdeSpVDBbu6hzrDC5MEVN+MGnupFl2zKy5s5rnfvDIi8bn3YfG0fur9fjj81r5uM/d+RT6BlKmNCgSrlJRH3hWqBBm36JSa1H2DaTwjfudTVRqLrgffvAUJLsTRpoJN01OiykVSlRfZz4HP/n0AOeBWGqc5MTHj4k6a/G5k1o/K42U687JNCkDXCa1Fl+DGZNaAQD7RsaLgroqxZrN2nvqpd3Dvicjdib6hwaLy2XKiX0l88WVizoBc9KSO+HUP8v9/WYNqGDhrs4pEu7qNFpW1dxJs6wcjKzjqdXkmskLQzuxd3gc19y1Ab95aqtpnzcfMwtrVixnwa4OcDLLWgMqyvUtWtU/6Gi2j8ei+Ke3HGksywFTTpqcEhgD5ioQHYbmztx5/foR9fZ0Ff1WPBbF8XOnACho7vwMXIWACu14bpqRRvFHdToPebk62opdO6Z36MLd8Lhxv918LEulbyCFG3+3yVj2OxmxM9HbmRqlz500M0umtLfUfFKbm4DmTmZOmAgmzWGdjYmVgoW7Osc6WNSbf4FZc6fN2A5ZNHel+r2kMznc8peXTevqTaPZzMgXs2OeOzEx3yK37TdeuhRnKX5yUjAyEhi7mOmiSj+VmpNyNXfJ7gROWzTdWJZa58R0raSefFb8uGGMZ81mWTdtVKP4o3qdh1WzBQDTDc1dxsgnWEmz7Kr+QVMdbcDfZMSvwK1mSlAFub9ftrjmk9r8BPzAK2KWzdt/biZYuKtzCqW6tJdQvaVCcfO5k+dUjk/z7kPm9BPbDzSGRqIZkM7Q1prCEYtfZbm+RU7bD5/ajmR3wsiFBhQmBQWzrPNgr+aYlIKoNZVGKSaqY+ZMMT5LrbM1FYqfgAprKhQ7nzugsfxR7UyTUiMUj0VtNbCG5m5k3Kg7XEmzbLmTEb8Ct9PE3iHndlWplFlWlBnpykmMWbgLPV4O41ITICNI601zt2azFhxx34btGNRzbx0adfe5cxqc3Hh5z0jDOIs3KrKvv+2bfwRQXFzdapYt17eot6fLdtD+yJsWA4CRZxEoPE+Gmc5lpmGnlbMKg341d4C9CVgKmfIZ8KORlt+RkyW7bzSaP6rVNBkh4PMXHgsAmNRm79M1vUNLsbRveNyXMF8q5U5Genu6ilKy2CkUnbS45Jjgp3pMxCyrYtV0lvP79TYmVgoW7kKMH4dxqfmSDsL15F/QN5DCHWu3GMvjFhOYHID7N5pLOF1wwuFGMe/OeKzoxWf3KhMCDeMs3ohY+zoA7DgwZurrUUtARZFvUZs/36JkdwJXvPEIY1kO4G88aiYAYEQp6l6KWXatnqYEgDERK9LclSDc2QkWBc1dCalQLGbZUYufYjwWbUh/VPV84rEozjpGM7fbpVICgOd2aJPLX63dgnWvDQGorM9duZORZHcCl5xcOJdEZxzHzZ1atJ9TdaIgNHfm8mOljUmqMKZq0UtBcJ47Fu7CjB+HcUNzpzsI11MqlFX9g65m5NaWCPoGUrj+t8+Y1v/u6e3o7enCSysvwrprz8Oqy04yUgxMaW+x1UwAjeMs3ojY9XUBs0AesalQoQ7g733DAt8CyuuP0PzZzl9yOLoXdgIoRGmrwp18nnIempy+gRR+q6RXkROxBzftMO03nvOf3yxqoyU0Jj4TqFBhTaORzuQaPqJweDxnJEfvsImU7RtI4WePv2osy/fSXzbvLtq3XORkRH1X+dWWnjBPE+a6F3ZizYrlmD2lrWifn//11cDKjVlRX+uZEvuWqqBQn8WSfj+vfq5e3w66zJsbLNyFGD8+GlLbJV9YuTryufMStmLRiK0T8lg2bxr0k90JI8LxA2ccgUQI8jwxpeGnrxt57hxm4sMlDARyUtTaEjE0OXIgSdto7jIeZtlV/YNFE6t0JofvP/yiaV0pmju76ghFZtkSo2X7BlL48+Y9RfvcsfY13+2qJ1QXjrd/+88AijWXgHb/7O7NTx97paLtSXYn8KW3Hw8AOOfYw3xPRmT/k5pbOz+24bGcycIjqaBl2Td+AiqcBCNVATlcpuauFuXHyk3FVCtYuAsxfnw0xjJm4a7cUi9B4CVstbZEfDshywE6PZ6zTSNBQMM4izcifvq6tcSclbTHQKAOJtfdsxGANvhbE8umbXzuch5mWad+uuPAqGl5oj53cpBvj/k3y6qau1X9g7bX75t/eN53u+oJ9XbJs351b7H/rdP923VwrOJtenXvMACgb91WLFpxL7qvv99TIBg3Jhnaf69+pPZhazLwWpDzMMu6CUaV0NypwmW1zLJOljX5bgkaFu5CTG9PV1HwgNVHQz70k+owoMJOCFNpjUZ8OyFL4XZkPItkdwJLE2aflGnxlobzKWok7ARvIvN6a4k5K26aO+tgMpTWgjVSQyOGg72tWVYXpuQA5dRfnfrp1LjZv0v1y/NCNQFLHyL5fJdWW1b3uXOZLO3YP2q7vt6x5ocDtFx3Vv9bp/t32NRi8+dE6BtI4fsPv2Bat28kg9471rsKeNZ+WMokIQhU7bqd642by5HJ567MMm2mChVVGhNTDs/SUDoTCu0dC3chJtmdwKfOPcZYnjutvchHQwZUxKXmLifKDh+vNdZcXlZfmNaWiK0TMgC89djZpuW4Idxp12NKuxb59vXLTgQAxCqQ9ZypHnaC98LpcdP6qKfmznkgsBtMAGD9lv2G1nfYMMsWNIBSEy5/0xogIbHrp7EIFWkefvvUNt8vfvUxLpjldLNsrAyfuwg5CjF2PlyNgNPVSQ2lTffB6T3zT285qqLtWdU/iKxNozI54RrwZQ3sGS/B/SaIHJ9eZlk3i8yIUprtX28bKEtQypkCKkr+uid9AynXGOQwBO+xcBdyTl880/j8648vKxoE5QwuHosavhV1pLzDYVO1Uk83vfckvOfUBaZtsWgEye4E3nVK8cB/59qU6aFXzbIAcEDXzMzUE5OWmkiTCZ7ZU9pNyzKgQr2V6iDiVq/TsVbrWM4wy7pr7twrVEhneRnFneiMY3J7S5HWIpt3H8St+0rkJM4aSV5qQIWTEHP5GQt9tame8IrSVP2j5P2zTjAvWjq3om1y8zN22yYnGYbPXdZdo6VWeXCKoq0mqnD1zQeeK/Krc5pkTIvHMKJoW2XFoVIFPFFln7tV/YOOEwcgHMF7LNyFHDUU3E4VrzqGS2fvUkPPg2RYV7vHYy1FJmgZEfjQpuI6itao4Q6L5m6/FO4maxoJrlBRf1iTGEvNnbm0UOHzQUtePBWnwWRKe4tRRH7Yxucua/G5swtykCS7E1izYjleWnkR1qxYjqER+/b4ffGr5ymfcyMVii6g+enW44pZ1iqESiHxDYtm+GpTPXGnR5CI9R2S7E5g+bGHmfa5/5kd1q9NCDc/Y7tt0k9U1tM+kNaSs7slBj5sShu+dPHxxnKp0aqVQP3JA6PZIr86p7Qwdu6B5dQ8VgW6agh3lUo8XU1YuAs5w4rPgV0aBVmhoq2lkHW9nvzu0hltQJ3UFi0S7uRA6ieoIq743AHaCwUoaO7qKdCkGbFzJXCqLev04pb33I7enq6iZNgAcNYxswo+d3rfSZs0d9IcJn3u/L8yy01aK8mZNHfmdhR87rz7tTwHKcipQuiyozXLgJtJu1751h82e+6jvkP6BlK435JT8wt9T+MLfRusXyub3p4uxGy0v7EoFfmdqn6ikoNjOfQNpFx97n70oTfgvOMPN5aDsFo4pdaRgpo1R+WMSa248dKlE54QGb9fZZ87t2c4LJVeWLgLOarmzq7o+JiqudOFoXrKdSc1bR2t0aKagnIw8jNIqpo7IYShuZthmGXr55o0I3aCmbW2bMQmoEIV2qUp3o5kdwKnHNFpLEuB7uQF041gJDmRsstzJ/uP3cDshJ12IhopHsSdyNlp7oyACl1z5zFu9w2kcNMDzwHQ0p1YzVvWNDCNhDVS2Q71HbKqf9DWl+3Wx16tmIN8sjuBVe8+CZ3xmLGuozWKVZedVORy4+Qn6pUfdDyXN73vgihJ6SZQSUHtkpPnGeu+cskJSHYnXN/1peSUU4XLapQfc3JvaGuJhKbSCwt3IUeNAPQ2y+rCXR2ZZaXGQDPLFh6WWJSMEH4/md0nKYPUyHgOubxAPBY1hL5svn4CTZqRvcPjReus/m12ARVqXsexbN42h5nkqMMKtVov7Z4PQHtuJllSodhXqHCPlrVDNYFK3nDEdN8vfuu5ASiqLeumuZOaHyk4HxrLFfkvyZQqjai58woSsb5DnLRD1mTaEyXZncC6a8/Dx87ScnP+y/JjbPuEm8XCTXM3ns2bXHOCsOS4CVRSgLPzKbWb+MRjUbz12NlYcddTvnPKqRPAaiTols+2fA6lj+O7TpkfCsEOYOEu9KiRQ3YPdMEsGzFK5dSTWVaawia1mTV3au1PO2d16+xImmXTmZyhtZsabwERGUJvEDNYxh92wp01P5e0iJpyaFmEm7NXPez4wlf95caV50Zq8aSW3La2rJHnrrRXpjSBrtKjtkvxxVEHyEJAhf9oWT8Vbjos7gyNxAdsgkRkD7B7h7jdm2o4yE/TtXf7HTTOblosL+FO9ckLoiSlk0ClCtTqOcgJ1TtOmle0/42XLsVDm3YVJbN388Uz1Zat0qReS7k1Tfusl4cbDdEkyb7IHhMYfQMprOofxNahNOZ1xo2yM4C9cKIOUoYQU0fCnaG5a42ayiKNZDTfEvnyTXYnXGdEcpDaOzyOZStXAyi8yFuihGxeIJvPo5XnM6HDrsQcYONzZxNQca9S8gsAth8YNbLzW/uLmsZECktmzZ00yyqpUCyVAcotJC8nLmMlaNXVAdIaUCEnP26Puh9fVUO4c9F41itapoHn0RqNIJPLY15nHL09XY7vkd6eLlx9+zrbKMhqOMh7CXe9PV34zK/WF7nZfOa81+HqX653PG4mDGZZG4EqYbn+YybhTnvmrOf6xqNmItmdwNW3r7P9Hac+Xos8d0AhsGWqfi/tzOhBwcJdiJBmFNlBUkNpbFf8RtSACikESmfbZ7cdMHzu6qkEmZyxrX52J36taFyEgOMgbUf/hoIjtDz7nQe1wvOxSASjyLPmLoRY+7yKNQDCLqDi+4+YE8ICZqdtFVW4ky/l1pYIHn9RK8e1/rUhnPzl+3FAibrdtE2bcMhBpxSzrIo035SSfDZrG1BhjpZ108rM64zbJlpVBRWp8Q6TxqFSyD51xlEz8ZO/P81z/2R3Ak+8she3PvaqScCrloN8Qbgr1lrL9tz71FY88OxO0/qeEw4HXIQ7q1k2LAEVf/7cW03aeDvNnVUQk360fvqy0+9Xs26yPIep7Zoo5eYWUmtYjREi7MwoOZvZu10U1Z1PprBPN23VS2RoPi+M8/326ueLhK9SQuC/oTuNm46vZ6I3Ak3qyBexWXByGgfszLK6cKd0k50H7MtD2c3oVa2bNPEMvLIP31JKbw2lM6ZZ/58279FKIin1WcuhtQzhzj4Vijla1u1R9+Orak0h1EjIgba9xf8wd0NyKW5678muLiCVwktzBwCLZk0qWud1r8ZzFuEuJD53VrOq+ixIC471ewd1f9Henq6ipMFuQne+BmZZoDBJnKZo7koJ/KgmrLkLEV5+HXL27jQgSrNSvfjcjWZzEEJzRt02ZB/Z5tfXxWm/1FDacKyupyjiZsHt/jqaZZX7OHtKG3ba1P+MEGHxintNpjj1/kuNQN+6rbZR6JKcnnT4707X/LdKSYWi0hrVhKhShDtzKhRzEmM/5cekQLLirqcwmsljRkcMX7p4icVXVQ9ECpHGoVJIYSLeWhzV6IaXC0il6OzQBAKn9B+AfX/xKsl17T0bsV85ZhCZAuzm0cPjWdO9GFMSMRuaO0tbpRY92Z3A5+5cjzG9vIfVxGtFfSyqqeswNHe6cLdVD/RQrW+lWKAqCWvuQoSXX4fsSF4CT72k/SikQWmZcE4wp/0IQEZ/idRTcudmwe3+FkXL2phlrzxzke13c0IURdWpkaWytuwem0AOK1uH0r6SGLthaO5K6IP+zLLuz3qyO2EkKL7pfd1FA0xHA0fLygHWLmVFGPCjubPrL795aqvrcYdGMiaz8mv7hstq30SwM4VahVJ1UjVs+NyZNeRqeqMxpW7bmhXLXYWlaqdCkRQLd6OeQUy1goW7ENHb01XksK0uygfdS+AJIjqqHAppUKK2JiRCcQ1ZJ5zU8wJa4k+gfoTeZsIpXxQA/O7p7SazRsQmoOKsY8z9wy3DvWr2l9qSWZO9a6rO64wb5tByfe7KMstahDshhCFkFgIqvPu0TPEyyUaDZU3+3UgYZtmQCnedcS0Hp5twN5Yp7i/ffvB5mz2d2byz9sKdnUA1bOljquBqNctObY8hQloqMKs7jSwX6Ea+yuXHJEZAhV7L3GmyFUQ5MhbuQkSyO4Fzj5tjLCc646YcWXJgsPM/UKkX86PU3E1qi9rWkBUoriHrhNssrpDOoj6E3mZCprlRa2GqqJo3O82d1b/USdbZOpQ23X+pEbjyzCNcExNHSUs6nDU0d+WaZUsX7tS5yFg2bwrqkJoNPwOX1JhMshkU1722DwDQv3FHoP5B1SAdcuFu9bNaabODo1mcufJB22tvF13t5kZgR6n7VwI7zd3vnzZHtquCqzWgIhaNGELcwdGsKUfpNCUBtBNW4bJaQRXyeZ4W19rq9CoJohwZC3ch4+jDJhuf16xYbphfgEJHSnYnbAt9y45VLxoqOZOTfj9+asi64fRgSW3oeLY+rkuzoZoO7ZB9QMpV6ovb2tedZvWa9k0JUNAHzfOXHI7J7c6agKWJqSZ/vQlr7iaQCkUOfC0RKgSX+Bi0pObOem36BlL48V9eMZa9EsPWG6Pj4TXL9g2k8Pm+p43lrUOjtte+lMmAE61lTkgmgl0E8PceftF0fuqzYKRCyRWeM2nqPDiaxUEl36ufZ9A6yauWaTZjSYXSGqUi61tQ5chYuAsZql+YEMIxifENyaXG7F1GdS3WI6vqRUMlVfHS78dPXi4n+gZStjm/4rEoDp8qAyrq47o0I4fG3M2CW4fSRjCDKvRY7+nS+VMdI0TtNNqtLRFXh/Y509q135lgnrvyUqEU9h3L5ox3Q0uEbKt1OCEnUR0Ws+yq/sGi9pTqHxSWyEA7RvVzc9IKB4mfBNNAZYS7eZ3tjtuqdf+G0sXP83gubzo/u1Qo0pwajZBh6jwwmsFtf33V2HfLvhHPdlqfi2qYZvN5Ybh6yLbmBeGtXQVXkWpGW3sRvl7f5Khh7neu3YJtSp67p1NDxuec3rGIgBf+40KsWbEcc6fpZV3qRHOnmmWB8guty9QwVqZ3xHDjpUsxa4r2cuM8d+FFmg6dAhbmdcYLAo2L5m725HZTQXKZ4T7ZnbBNhdPaEnHtX/L4UtCacBLjkqJlC5/Hs4XEtC3RCO7boJm40pmc56DsZJadyGQKMKdksgavBE3fQAq3PqZpJb/78AuhaJOK32tvJ9xZ8z964WTGrOb9cxKm1PNTo2XlRD+raKen6qbO+zZswzfuL6S6yus5UF1ry4rqC3dGrsxoBG0tERBp6xbO1JQs11xwrGfgRzVh4S5kHFQSqH7h7qdN6uUHN+0yOvSh0YKpRZpo7HySwsyIxSzrJy+XHU6pYTpaW5DsThgCA+e5Cy9Sc3fU7MlF22QfkFlIVGWdta8fGsuaXqaXKbUe7Sq3tEYjrkEdGcNfsyBYlUPB585/VKo6QP3vn15C91ceAKA54H/2jqeMbW6DsixFFY2QoT2UTDRC3a/2qdZIoUXW5d6fzoRG6JT4vfZ2ZvwFM0rz33Lywa7m/fPjezZuEy0rn+dDY1mse3UIgJaovFQNs9XHrhpmWTUROhGhXa+NLgNkWkvIr1gNWLgLGTJpI1Cc9DGr59wCgINjWgeaoszGDSGmDoS7voEUrr17IwDgwWd3GKXGvGrI2uE1C27RpYJ6uC7Niny5n6DXagRQ1AdsAyr0F6x8Dg6NZk1Z4tUXrF3llrZYtKgIOKBpfYHChCCbq4zmrtxUKFaztfVMnAY7OYGa1BotSgrd29NVZLIsxT9oopq/ahFWoVPF70TWTnMn+8KFSw/39VtOlpxq3j+7yOyWCJnOz84sK9u66+CYYVZ3em27tdP6nWoEVMj2y3FXRp5L4a6tJVhfT05iHDJU4c4O2aENJ2nFGVwOfmHXUFlLTo2M50yJHktVY3uVppEVKjjPXXiRpsPUvhFj3dzOdlOi0gc2aiXmth8YxaIV92J6RwzJk7VC452TYjg4pjleD5kSuLoXUJcatWR3An96fjfufHILAGDhjA7sG9mPbF6gbyCF36zXzKArf78J7bpAWAoTTYXiB7vBzimYAtDOeTybw2fv1J49r8SwVkotCVUL+gZStm0Cghc6VeQ1vvaep7E/nUVHaxT/8c7iiaxdf9l9SAtWWDijuHqFHU4Vi6p5/1pbosBYDodPbTdKaC4/drbp/FQXhbTF585Pz3drp1VTVw1rVkbR3AGFSiisuWNsUeta2iE79MHR4pe2NBmFXUNV6Zm11yxYpq+oF1/EZkOaDgnA4y/tNdarEYR9Aylc/9tnTN/bN5LBTx7THK1lzrBDYxnsVRITq5oyO59L1cdv5uRW4/OMSdrnHfvNGeeHRsoz8f12vZZ4Ni/gmPbCSqkDkt1gZyQKd4givuyUBcbnP332rSUJraWWhKo2Tr63kiCFTjuS3Ql8490nA9Du9dW3ryvyn3TT9KpBBm44vfd6e7qKIk8rdf+kcHXfp96ML1x0HABg/nSzMKoKrtm80PxKffb5eCzi2s4in7tqmGWzFuFOau5GpOaOhTtGQdXcWZ3LZc4toOBzN6W94CwrTUZhjwqttDnAy5xbMFeH+7o0K8OKydH6bpdC/6r+QdgpvaQAJEs5HRrNYt+IItwp6W+s91/6ykhmTlKFOy3CeksFMs73DaTw+V97p72wUopw5zQoGwmMHYS7iOKLV2o+tGR3AnOmFpJABxkZCLjXKQ5S6HTjuR0HAehJqlHsP+mm6R1ySX6s4tSPkt0JvOGI6cZyJe+f/M0okdH3hseckxgDwFtWPYSHNu3wdfzrLznBd4UKbdnXYUvCEO505YFUMBTMssGKV2yWDRlqQMWZR87EI8/vNpaXJKYYHfqgq1k23BqqapgD3My5UqM5HvLr0qxIAcTp7vgR+js7NMFs30gGl//v48Z6UwF1y/23vnxnKMKd1OL5ifrzwk1T7TZA7TxoX2/ZypypbbjmguNsjzXsUp1C0h6LYiybx2gmV3Id1sOmtmP7Aa2275oVy0v6bqVxuydBCp1u/M8fXyxap/aNSiQgdnNHOWLmJDyma8sref+kcBWJFFLwWCtUPKUn0JZs2z+K7z2iXY8ImSd6LREyafXOW+Lub2h9bKuhuRszfO50zV2MAyoYB4QQJsdpaUqRGoXZkwv5igzNnRpQUSeBA+VGxZZLrE58EZsVaTp0ClaY1xn3FPx3HbAXhAZeKZh5rc+FVbizM8s6UcpEpFxNtZPvGKBpWWbr7b31o2c4Ci7DLtUpJPJZdNJ6uRG0dkLF6Z7MmdoWSsGubyDlqH2TfaOU6Gon3MaDErOq+Can5KvbsGU/AOC3T20zmZ3XvLC36HtSG7Z45iRTdSbrOXj5T1snZdUIqJBtkM+A9TkKOqAiPE8mg+HxnGnGIR/ww/VEqqoaW2r4pqiauzqJlk12J/DFi48zlud1tld1Zt3i0+cuzAlZGxk5oZnb2e4o9Pf2dNmWCZM5v55K7bc99mtDo8Z9tAr3+4bHTfd4g3KM7z/ygmubS5mIlJtyxC0v45oVy438jTI62K7/DrsEVEiktq484S481R+cUtr8y/KjA2iNN26mfdk3SomudsJNEKqWcCfNoPc+tQ0/ecxcBeXq29fhC30bXBOXH64HUzkFp3sJd6IWee6sPneWvtcWcPJsFu5CxB1rXzMtv7jrEABgri7cqSr6QhSc5mvUN5DC3fpA9Y3+TaEWTPoGUvimnpQyFiV8tufYqs6spc+dU9SYbFNYE7I2OlIAWTijw9F3MtmdwKp3n4ROJSHrlPYWvOOkuQDMyb+tyEF018Ex0/qckgy1byCF7z5UEOjcqlZ0xmMlBx6Uo6l20mTKOpYyjclYNufYfx99QXPrsFanUJGDUtrlGjqhmp6sA2qtkb63Vt71+gU2ewePm+a2t6cLQoiKVKhwF2yqI91Jzd1Nf3iu6BwEgFsfe9XVbBmNRLCqf9AxDYrXdSmKlq1ynjuguBJKEGXfVNjnLiT0DaRw432bTOsO6iaVOVNlhQVVc1fwubOmFtmfzppSi4QJa1szOVH1thp57lw0IeX6RTETp5CLrcXVd1Jue98PH8VjL+7F9y4/Bc9uO4Bfr9vqenw5iO60CHeAdo+vu2cjJrW1+PJviseiuO4dSzz3s7YbgNHvOztiuO7iJZ79avbkVmw7UNzmd5ykpX+RSVNHM3nH/nvnk9rk5O51W/GGRTNsfzOuD0qjZWju1KjEdCaHjtZgh5RkdwJX3b7OWCYKZ/kxwNn3GNDeR7l83lG4AYDZk9uw61Bx/7Di9t4Tpmov+bKTdFuRAuW2IXt3CQH7yUBrNKIl3SZ34ddLc+cnz13fQAqr+gexdSiNeSWmAQLMARV9Ayms3rTTtD3oflfzXyeiBUT0EBE9Q0QbiehT+voZRPQAET2v/5+uryci+jYRbSaip4jo9cqxrtT3f56IrlTWn0JEG/TvfJus2TtDyKr+QcfBRWruZGfqG0jh9r9pWr7/9+Dz+PJvNoY+aackiASjfvLchTUhazNwyIdfmIosp7Q/nXHVxkqkicvJXWEonXH1b5PIcnblCPvJ7gQuP30hAOAfzzrK1zEm65Hwh01pM60//wRNW9muCGVO/VSe8aGxrKMmWpplrUnT/TCmfOeATT3RiTJRV4mOWHHy5rDgVhklNZTGv/c9bbsNAD76psX4vw+/wVie19nuqINze0ZUgX60AlpCwCxIubkeWN0OZk5qxQfO0J6RaMS9LKDXRMyrQkUlLDVyPN51cAzX3LWhyHrwyHO7fB+rGgQhWmYBfFoIcTyAMwB8goiOB7ACwINCiGMAPKgvA8AFAI7R/z4G4HuAJgwCuBbA6QBOA3CtFAj1ff5B+d75NTivCeEmREjN3Xg2X6T5GkpnsM/BhBRGwSQIIarVR/6/iZZiYsqjbyCFr+j56+7fuN3Xy1UV7rz8KIkK/nHWnF4qfup1liP8qMzWhbTdPrQtQEH78fN/OAP/eNaRxnoZ+CHNqaOZvK9+6jSJkhrAcnzu1PqgXjk6S6FvIIWTv3w/rrp9nWkAvur2dei+/n7fg3A8YE2iG2oKJzvc+ltHW4upP6/53HI88G9vsd1XCGfTrF0i4YmiBlPY5UKUWDVbNyRPwBsWzQBQqGbhVGvaq064Nc+d9T1RCYWINMu+sOuQ7bPzP398yfexqkHNhTshxDYhxJP654MAngWQAHAJgFv03W4BkNQ/XwLgJ0LjMQCdRDQXQA+AB4QQe4UQ+wA8AOB8fdtUIcRjQtP7/kQ5Vmhxezmv/J1mrh3P2ZtfyjlmUAQhRLX4qC1b6whepjB7lqkDhvVKJV4Dt0m483CUnjOlECk5pc3Z7ywnhKMWRTJRDfOsySUKd8ogOWNScSRvQbjLuWqBVOwmUe0TCKhQhYMDPvOueSH7hVMk6T6bRNKqhk9lkss9DwPJ7kRZKUgmt0VNwRBExbWDVZxyfJo0d2XcfzvUHHfJ7gQuP2OhrYAnBS4ZFPj5X2/AP9/6JABg+4G0SdttRbXC2Gl3i8yyQhj7LVpxb0UUIlJzl3YQwnc4RPDXikCNwkS0CEA3gMcBzBFCbNM3bQcwR/+cAKBGGmzR17mt32KzPtT09nSh1WGWskfPuD80Mu6784VVMAlCiJI+d26zPasz9pT2ltDmxmoUyjXRmzV39jVfjfQEiuam1SWyUwZvyGAOJyaiYZ6la+6sgR1OyEGyJUJ4ec+wsT75nTXoG0gVzLLZnGMwgRW7SZR8HkfL0NyoAoFX6US/+JnAqv3EamJT8SPwBk3fQKrksIaO1haQ5VtuAQpOGm5VO1gp4U5qzfTXLm5ILsVN7z0Z1uFNTszkM6wKXBu2HEDfQApv6TrM9B2pyVNdlOzMqy/vPmT63uX/8xh671jv6X5RipJBau6cgpWkO1VQBCbcEdFkAHcCuEoIcUDdpmvcqh56RUQfI6IniOiJXbuCtY8nuxN432n2sxTJ/nTW8aJ0xmNGsfN4LBpawUQOQtKkMGdqW9XbGvNZW1ZtwwfOOCKU16+RKNdErwp3UgA6/4TDTVG2nz1fmyxYSxwBxeYgObmQWpSXVl6EeQ4v5olomGeXqrnT2/uHZ3fgzrUFLZWscLFln3ad5ADtp7/aTaImkufOpLmrkFnWrwAt93MTBt2ihMPCqv7Bkge7yW0tWK1Uc1i2cjUeeMa5usMJ1/bb+iyOKmb1cu6/HarmzrTe4STtNF85IbCqf7BIOJcpfaRg5TRBXP+aOTXSwbGcpykXKC3FkXy3nLKw03YS8ZnzglWuBCLcEVEMmmB3qxDiLn31Dt2kCv2/DD1JAVBj2efr69zWz7dZX4QQ4odCiFOFEKfOnj17YidVAU5e0FnW92QE39fedSIAYNnRM0MtmCS7E8bDcP/Vb6l6W/3W3FWjt0ZccjAxlaFcE/1UXbg7kM4YL+yTF3QagtmaFcvx9hO1iFJzbVnt85fefrxjqTpJr82LeaIa5ide2QMAeG7HIV/BAXKQ/MEjLxblO0tnchh4dQiAf42LUwqXQkDFxIS7T91WXBu1HPwK0HI/N2Ew6OhdP5SjDd6wZQjf+sPzxnJqKI0b7n3GcX+noIExk+auQgEV+ms0omjTy3Fn2DqULhKaZLnNjN7vnK5dpoy8dqWmOJLCXdfhU3HjpUsxo6OQpokAXHrKfIdv1oYgomUJwM0AnhVCfFPZdA8AGfF6JYC7lfVX6FGzZwDYr5tv+wGcR0TT9UCK8wD069sOENEZ+m9doRwr1JST0yihJAAu1PCrzAys0qi+EYfGsiAAU9ur//KVJjsvzZ06gO6vkP8Q40y5Jnqz5s7eLCuDaOzKj11ycsIkCNq90N9peTFPtO5m30AK/6GkOlKTuTohzVtOvjsy1+WYLpSpEYLW2BG3FC7tE9DcHUyPm5YrkR/SzQlfovYTN2GwHjR35WiD716/tShi1I9wZnV7qIbmzig9pmjuyhFg53XGi8rhWTV3lfLVLifFUUbJc5fsTuC/3t9tbAtDvwtCc7cMwAcBLCeidfrfhQBWAngbET0P4Fx9GQDuA/AigM0A/gfAxwFACLEXwFcA/E3/u15fB32f/9W/8wKA39XixCaK7CzxEvLjrFlxjjHgGMLdePi0TnZ+MQJa/q1qE/NZoUIVrn/39HauVFFlpIleCnh+U42YU6HoJiBLfq6Y7n+k3lPDh83Bt9WJRGfcUQj0y6r+waLBVyZzdepfUtN8uIOJWE6MZAoLdbBfNHOS4fpw2BR31wdppi5ncB/NFj9TEw08SXYncPjUNsftVkHbLZgkDIOsF36DYVT2HBr33skBVdBSNXfViJaVuAlhdo+jjLQtMsvqfV6Olb09XUWBJPFY1DHK1o5YlMqauFkrVEitorouSIKIlv2zEIKEECcKIU7W/+4TQuwRQpwjhDhGCHGuFNT0KNlPCCGOEkIsFUI8oRzrR0KIo/W//1PWPyGEOEH/zidF0KnTfSIL28+a4vxiU7FqK2Rx8OEQmhSd/GJqkYvPEO48cqKpg+NYNs+VKmpAsjuB5cdqTtPXX3KCrxfs317W5nAbUvuN+2ItTWbnZynzfckazH6ZqlTFKBe3PHROz4AURv9l+dG2Gs5zj9OumzSnqs9Xa0vEEP7u+9SbXa9ruQEVbq/ViaY2kqXV7LAK2snuBP7jnSfY7hvmVCgSOcnpLKGfzfY5RtihClqqKX6sAnVsAXvNnZsAazfnPvNILeG2taSX7NMZfVKR7E7gX885xtg+W5/IHHv4FN/tXTRzkuvz4ZRrcSxnFe4KfS0MZfmCFy8ZAzkQ+S1yvHhWh2m5QzHLhq1OapBJggtJjN2vq1NizLAmhG4UxiwzYDf6BlL45gPPGcsycej6LUOm/QpmWQEhBHJ5ASG0vHcRl3x3dshyXxPBTXPh9AzI98DFJ82zLct22uKZAOyFu73D44ZrwTQPoaHcgAq3uqcTNZeVonkBgAtPnGu7flIdaO4ATUhZd+15+IBD2hArH3/rUUXCkl3tZStWt4fq5rkrrPPK6Wfl6DmacOZklh1T+t4ZR840Pv/X+05GsjuBOVO13/HTj9z6vVuyY7VCBWAR7kJQFSX4FjAGsrNs2+8vP441o/9kfZa66+AorrYk/wxa+xRkkuD1W/YBAO5Yu8VV0B1zecjDmBC6UTB8V3yUPrIzbwLA75/eblomokLahFze0NqWqrUDvIUjP7j5kU2Lx2wnYnKQbIlETJG8UnOlJjEGzIPzzoNjyAttMIx5XNe4keeuNJ9fp8lQJVIbubXZznfWyd8sFgLzWCk8tGmXr8jZd3bPNwn8nfGYrzKxcqIq+5iquat2tGwpOf2kVardcv+sARWA+b29X0+nIrXKquDnxGjGWRnilq6pyCzbpphlA64rC7BwFyoMzZ1PI/KG1AGToNKhJ+zMieI8MkFrn5zU8m89trpRyn0DKfzyb4W0h26CbjU1EYwz1pekG05Ctl1SUlV7J/0tS/W3Ayoj3Dklc41FCMPjWduJmBwkneTRdktNWLtoVz9tV5Mhl4L015rcFjWdVyVqaroJd3btdGp7fTjkFPA7iXzwme0mgX9SW4uvVB9AIZjn3+96ypRBoGLRsvphnDTkfoLoonqnb4lGTMFBciKivqtV4Utqq+XEaHpHIfm3E/vTGUftnJvFyTopVft9GCresXAXItyECztyeWES2Lxm6EFqn5LdCbzrlGK/hjvXpqqqUVzVP1j00nMSdO/fuL1oHaCp9sOYELpRkP3eq/8CzkL2pNbi76pBFYZw59Mkq/bJ+zZsq0gflclcO5V8lJPbiwdl2T+dNCCSNimUGZnyiwUcP+7GZQt3uo9WSyRi8q+yqyBRKm6R7XbmQ2fhrr6kO7+TyP984HnTcqnvdgHg1r++ZlpXMc2dTUCFykUOJnQV9VWg9i2pjf/OQ5sNLdsflRqusqqJlFllNRe7psyYpGsBc8JRO+d0P9pjEfxmvVZz4Wu/36QlolbaWU7mi0rDwl2IyNhEnnlRykMdtPbpoU3FiaKrrVEsxdfvJ4++YrvvpNaWmuUNDJuvZC3I5Pxr7np7umx9i0azouhaqelQZDBFiw8BUvrZSA6N+SuL5odkdwI//OCpAICuw6dgyKUMkhygnAZJWRNWCjYPPlucxHb7gVHPdj+hB6j86fndJfU5aZY9NJYtKsw+0efaTdC0E0KcBJNbH3eORg4j5ZaRq8S7vRrlx+w4b8nhpuWZk4q1a1FFXa0Kdy/s1Cq1HBzNGlq2n//1VWP7f6/WhL7HX9RySkrNXWJ6vMj/7shZk13Np1uH0o6WpXQmb/Q5u8kMC3eMiYyhwTB3wngsalSfsGI1uzgNBITSsm9XgyCCKkrx9dvtkF6gVjnv3Jx3GxmrY7Ibye6EkQ5BxarFBgqawPFs3lTKy4tyy6L55ZltWvb8da8NmQYulbmdWrRohGDSCKhIM5D0OfrlE1uK9skL94j0voEUfvjHF43lUvqcNMs6JQefyHPtZiK0E+Sc9j80lq2rZ8hv5GyEyHROTjkjS6EU4c5tElooP2bfbzcowU/T4i1472kLivZRn1PVLeEvL+wu2lfVfB8a04Q+OfF4be+wfryI8c6Xh346tR9ur5xp8ZipMowb1veDkz9qLWHhLkTIQe6d3YmiyLhrL15iq7EYHs+aHqzZk4tnQQTg8jMWBl61Ioigit6eLsdyU1bsZpBA7TSe1RYqwsq4obnzZzJ103apSE3geC6vTJzK9+urxCSkbyCFr/2ukMzYqvECtP75b+e+DoDzZA1AUUDF3mH7yYlbu1f1DxYNRH77nDTLOkUkTuS5GXVJyzFiY5Z1i/Sst2dITUjvRE4Ik9CqRqPKccPODcYN6zV0EuDsJqG9v1qP7uvvx+IV9+ID//s4AHvNXd9ACt956AVjeX86i5v/9FLRfmq/VydAB0qsX/zgJq3Q1dDIOHbrNZ3lXGQ0mzeCiKyTvngsCqLSTNXqc8aaO8aEHIBOWzyzKDLOSWORyZk1FnNtXqhfvfQE3JD0LipebT79tmOK1lUiss6NZHcCN76zcO7zprU7Jqy8+CR7X5DPvO11VWufSpDpYoKkoLnzp2nwO0lQzbKlBFRUcxKiCfDuL/4bL12Ki/TyaU6aPaAg3O0+NIplK1c77ucWVOHUt1JDaU9tlxQKF0zvsE0kO5Hn2k2LZJePz00YBOrvGfLTXqvQqgZY9PZ0+dY6SX6lZBNwsyLYTUIzeYF9IxkIaJHaAHDQptaw3WTCTsvlJNyVGtwkA632pzMYdpkAnLxgmvFZJv12mkQ6ob4fxkr0n68GLNyFiHEHs6zEj8ZikpK0Uya6PG3RjEo1cUKcq/takP430ZJOfnnn6+cb+ZVu/8c3Ov7eSXptX6s542tK6oBqEmS6mCAxtGo+NXd2JqjWlkiRMCGPN54tpEJx04S5Hb9SkxA/g/aq/kEc/6XfA3APCJAa6T3DGaRcjmvV7qu49a3eO9a79nspgM2f0YHPnl+4NupzXa4PqZcmrqgtHjna6u0Z8ttep/7klDTeCynEffk3Gx2tCH4F5V2Hxny314qqSVPnNz1L5vj6vmTW5FZMao16ZqBoU573r75T67ul9Bnr+2E8mw/cZ5qFuxDhle/Lz+CvVmFYPGsSAGDngeKHLAhk5YzDpra51vWsBrLqh5xV2iF9iE6aP800uG8/MFoTv53eni5bf8ugfSWrTSk+d0DBBDVZMV19/OyjivqSSXOnv9395LmzM3FVahLi9Ayrg5laom88VxwoIln97E5fv2nV7qu45d/L5ASuu2ej43GlxqWtJYLLXq/5TU1pbzGe64n4kI66mLVshTsXzV09PkN+Ayuc+tNENJXpTM42tZA8rl+hxy41i9/vqto69bNM3O2XUxZO95XiZYcyRv7DT57AspWr8dZjZxdppAEtd+QHzlho+O4dPlWzBlkJ2meahbsQ4ZXvy49G4bW9I8Zn6bjqJtDUEincefmTVIPZkzXhbpfNtZDahRV6hOTAa0OB+L4luxO4aGnBNFwrzWbQlJLnTpLsTuCaC481ls89rnhGL/3rxrKlmWXl8a2uEZXA6Rme61A/FnAOiCilPzoN9snuhGvS3KF0Bl/o22C7TRXupMvIobGsUVmjXB/SfF64+iz9eXOxU3163Lx/R2u0ptaBSmOdYHTGYyVN/KqlqZzXGS+pFq5VsLH7rjVRsRVV2b7utX3+Gqrz8HO7bP1arceWgReS1FAad65N4axjZhXaqWvKV112Im5ILjXeVw9++i1Idids+3WQ/p7hL7zXRMiZjpPTt3xBSdW4fNDkei3pYqG6hfTtuer2dbjuno0g0ky71u/ViuEx7UU/OQjhbooWLGE1FUjtgjoIOUU61cJv5xi97M5J86fh7k++qeq/FwTSb0f2YWne8xPsoDJ/eqH8np3QJl++jzy3C3fp/kfP7TiIvoFUYIO9/N0v3v00Do5mMaWtBV9JnoDvP/KC43fs+l3fQMrI6eUHt8E+0Rl3Neve+tirOPWIGUXXTEbptrVEEY0QprS14OBYFgfHspgWj5XtQ+rlP3fn2i04Y/FMU3usPnqXnJyw1abUE9LXWmJ9btze4b09XUXvNQmhOMm9H6QwKX/zqtvXeX5nVf9gUR1guV6exyeXH4Vr7nra9D31Haxq7m6z5Obzwtafjwh5ITCvM47OjhZs3HoQYzZpyNKZHNa+OgQAOOfYwzAtHsNdAymMjOeQyeUxmskjQtpEAgifzzQLdyHCTzJX6wOvsqp/0PGhVQcCqS6Wx6sVUnPXEUC9x726UPfFvqfx/YdfMF5SpfimVKJSgRdywAxDKH0lkQNTaihtGlxUoaLUkj2bth0wPl9x81/x+QuPM/Vnebz/+/PLxrOVyYlA+r5KsjuBA6MZfOnujXjHyfOQ7E7g2w8+77i/nWBWijbAyyzZ29OF3l+tR8bBMUmgeJAGFM2drtGYGo/h4FgWB9IZTIvHMM9BaPTSKnmZ0aSZWW2P9Rm2M6fVO27vfrt9ARjPXJQIOSE8BXk3yqk8Yvdb1vMYz+aLhDtVWFdTATml3SmFvBB4aeVFAIBP/vxJbNx60HFfGYE+NR4zKkCNjGeNsWxyW4vRvnL7e7Vg4S5EZEpMCWGllBmCVBfXcoA7pDwQtaRvIIXVg4UEyrL8zhOv7C3pmknH9GpeMzlgNpJwZ9WOOr2enfJiOR3zpj88ZyzvPDhWJLSpqVBUguj7VmTm/H0j2uDhlMYEsM9P6dZvj549CelM3peGByhcr6t/uc6xXFdqKI1lK1cbx3zrsbPxm/VbAQB3r0vhtEUzjMLpB/Qoyd6eLnz2jvUYt/hejXg8R37yrVnP31oX2k/B+EbHSRhctnJ1WQKeTNYLAF/v3+SxtwYBnu9Mu3ul9oHxbGnpT7xQhS0v8/K0eAv2pzVNtJwwDI/ncFBPySJr3QL22tIg/T1ZuAsRhYCK8jRbTjMHJ2qlLla1NoD7QFYN7EqQCWjmps6OmK3zMFFxXUo7jUGlkUJdGPIkVYpyI/e8jmnV8FiFNjcNeNCpMWbomfP3Do+7mlind8Rs+5vbs37127p8lXhSMQS829fZCt+EghYmNZTGzx4rVAUY1it4SL/BA+msccw1L+zGryzJlfeNZHD17etw1e3rkLARPv0Id1ZtiLV/lWribyZ6e7oc77MX8hnbprj/uOGk9VWxS9L9s8dfwXFzpwIADoz6e3dItwCVlgiZtH1RMpeSdLMiEbQ8fACwbWgEx83T0qWMjGUN4U5VVHi5TdUafgJChBzQ/aaEsFKKoytQG3WxGjEnWb9lf00jiJwGcgH7gSQWJUcNRrWFglHDLFtZYShI/FyzUgtt+/FvcRvgg06NMX1SQbhzM7FOdXAFcHvWv3T302U9X8nuBC4/Y6Ft9KyXIJDO5Ixrf0DJb5ZwuM6qWf7q29eZgjakoObUJaKR4lrPVkGfhTtn3O6zn8dw61Aah01t8/175bwz5YThy79xjta28rGzjiwyHWfzwhSwccaR003CVruDcGf1S1w9uAsv7dKCLobHc4YVaool92y1ArHKgZ+AEOEVUOGFGmHlRa3UxXZaG7tSUdXEbSC3SyjbvbDT8RpWWygwzLI+wvfrBT/XbHKJfpheaYH6BlL43dPbbPcJQ2qMGYZwl3Ed/Jzqc1qfdXWvPcPjZadguCG5FJefsbDk7wGF9CUPD+40ctuppc2ckFp02V4pqC2YETfOT70OpyzsLBo0re+YUiKvm5Ebkktx03tPNqJxJX60efM64/jAGUf4/q1y35luKVkAbeJw6hGdxvJPH3vF1l9TTatz4oLppm2v7DZHyU7W/eqs1yGTE3jkOc21Z2Q8ayRotissEBb4CQgRpeb7skPOHF5eeRE+4DA7mxZvqVl6gDBEELnl8rLjuR2HqprI1g2psaulz125iWb94qZlihvO+Pal30o5prw/UltsV6aqrSUSitQYnXqt6H0j40YdWTvc/BDls57ojBcNRhNJwfDQpl3eO9kwSR8Y71i7xchtZ3cP7BDQTMKLVtyLd33vLwC0Sa58l71w44X49vu7AQCzpxZfL6sGfiLv0GZB1TL5dXeVz9jrF0633V5OpZJy3jeJzjjWrFiOkxRhzS7NlZWfPfqKqYzaHyy5ImVGBzuk68SIorkLIvODX/gJCBGl1L/0g5ydWbPy/+hDp1VkcPMjFISh6oI0Q/hlaCRjaEYO05Mft0SoJkKB1NiN5/JGvrBqMpFEs35Jdifw1eQJxrLq53LKEVr1lFI1LW6Jht18/MayeayqUcURN363YTsImhb7kEu9TGvNSzsqPYEq53vxWBRvOkpLMGuXvNYP1m+9uGvYdJ/WvzYEALj3qW046pr7sEh571iFOw6oKA0/r5rpHTHjGduvCzrW7vmVS5YYn6M+3pny/eNEZzzmOsmeodQDn+OSK1JycCxrKqNmjb51uwzyt4bH7AMqwgYLdyGiUEC9crcl2Z3Am46eZVq366A/Z1g3vtC3AVffvs5TKHDS2rz12NkTbkMp3JBciukd/h5E+RAnuxN4uPdsAJpP2MUnzatW8wxUjZ01yrMalJtotlTOO+Fw4/Pr9Fx+gGZCBMrTtDj5t3gJJ0FnjpcDmhxI1GLoBLP2w622rKTSEyg/3/vAGQuNds6c1IobL12K3YcqGyglnfEB7Zr99NFXjG0yMW1qKI2rbl+HhwfN2sYYm2VLws8k4qxjZhvPmBTuFswo5JokAi47ZYGxLN1v3J4zt4lYPBbFde9Y4lotRhXu/u3cY3xZaPyUUbNejraWCN73Bu3cNLOsvc9dmOAnIERkZEBFhWedubxZSLj/mR0TOl7fQAq3PvaqL1OQFo5fLBTduTZV88HVbyHod5863/h8/8YdiJCmjThz5YNVb7MaSFELv7tamc1HlCi2we2FvFJ7hzVTSrlBRHb4EU6CzBzvNKBFCHhp5UVYMm+qse75nQc9+1ylXQj8BGbdkFyKrsM1If3mD70BAIyEr5VE9sNV/YOukx2rBobNsqUxf7r3M/PgpoIJU75LFyiJxDtiUdyzfqtJwPKaSLm9Z6QQ5xak8NyOQq7Lbz34PM48aobvoBC394T1GO2xiKF00QIqdJ87NssyfphoQIUdfQMpPPbiXtO6e9ZtnZCQ4pYs2e5hfWhTcQ3MIAZXv5qMs193GICChkWOGzsOjFVd46Nq7sZy1Y+YrZXZfFjxvVIFm70T0Nw54TdqPKh0KE6/mxdan1u/Zb+xTiZddutzla6FK4/nGK2qb1C1F9V6liNEeuWd0u4VB1SUhqqBc0L6mfUNpPCdhzYDAJ58tVAOrKOtxXZscHvXO71nEp1xz/6rKRkKFSu2Do3iyVf34/IzFhrPglNAkkxT4tTHrZ4F+9NZfO9hrYrMyFjWcKVgzR3jiRCiYJat4EBn51eQzQt8+pfryxZS3F60dg+rWpTZ73Gqgd2gL80RFy4tmA1lxv1amSxVVG1dLTR3vT1dZTlBl8rwmL1fmZzQVNoV4cZLlzq+2CVBpUNx+t2WCGFV/yBylufVT5+rRgoGp9dQTgCLV9yLV/ZokYZT2luq9iznhCbcdvp0qZBwKpTSmNRaEFLcTLSLVtyLq25fZwh6asDMpNZoyZaAiWid7bS56UwOD23aZTwL//mekxyP71VX2YqceG/eeQi36C4CYfDfdYKfgJAgBbCWCJWUqd8Lp4dKvjTL6ZhOgxPBPpu+6hfh5zjVQg76rYrZW/o0qZf8H3+61lVbUE2hVK2raY2YrUZUa7I7gb9ftthYntIWRXssgqtvX1fRyNn7n9nuur3Sg3GyO4G8S8HwINOhOGkW50xtC0V0OSAnhc7bBQpO+I8M7ir5We6Mx3xHsKczOQjhXU1A5W8v7/XeiTFQg5wOLyGHnUp7LFqyJWAiWmc/z4rX8f2kDbOivlVGxnPovaN8RUk1YeEuJBgJjCs8yLnnePOvhVIFi+GxLKxugQTg8jMW2j6UF9pkyw9ycFUfTjnz++1TBeFjl17KyklbUCmh1E5YM2nuFEGvmlGtSxIFH6+xrMC+kUzFf+NWpaKBHdWoBep0n6JUm8hnJ+SAIwN85MRiztT2UESXA6UJk9/6w3MlJVCXjvKlaE32pzN41yn+79fdIRxsw4ysmwoA2x0sLV7khShLE1eu1tnvs+J2/FIT/9shKxeFDRbuQkIhDUplgym8Oq+fl7hVsBhKZ4pC52+89ATckFxq+/2jZ08GoKntK+ETNBHsSpHZ4aQtmIhQqgpzJ3/5fvTesb5IWDuQLkQcqpq7SpqIrUKlGmnoVId1ouzxKDlXDTOa00Dzn+85KfA8d8nuBH54xakAChqwtpZozczkXpQiTG4dGi3SkHTGY+iIqVG/2n/12S9FazKvM15S/r29PoOnGA3VLGt14/FLNi8q7v/pRiUCiUpJ/O9G0OUM7QivN2CTUY00KECh3t2nf7neSB+gMs2hvJGKnWBhPdKb9SAEO+TA/pE3H4l/e9vrPH+vmpTyEO5PZ3DTe0/GDfc+g92HxtEaLT8BrhSQ5XW0qyWazuRMpipVi1cpc521HamhtKdmrhIvrs6OmGu0cjUc4MNW69GKVYBqj0WQ7E7gwOg4vnT3MwCCS7psVwTdCXkeToXq3X6j91frkfEQJuSAffXt63wfO0LeBeuZAvESK8TYIX1FS+0H5VKp51u2d/GKex21ydZyZFaCLmdoBwt3IaEakbIS2dntXqTD41nPl6Cfwf2d31mDXQfHih6wvoEUbv6TVoLolr+8hCNnTQr0hetWcN1u32R3AickpuHcbz6ChJ4uYNnK1SW/TNzyOanYmYzd2l3qS8WuHV4zdaffkIlAtw6lMS0eA5GWIsHuuiw/9jDc9aSzEFmt1BW1GmjK4fEXdpuW9xzSzGGXn77IEO5OWzwjkPZ7TQpVytUqyt+47p6NxmRnekcMF504Fw9t2lX0jK3qH/T97OYFjOS4Yb3/YULV3JWCKvRky0xePREq+Xw7vWMJ2uTUqRRaLFpc6zgMsFk2JBilx6oUwp/sTtjWwcvkBK7ycJ73I0DsPDhW5KclEx3LNBj709lAk8cC/n0sVPX+7Mmag/HWfSNl+72Vo/0aU4Sw3p6uor5RjrnOqx3WaiZOv6FpAJ8ymerdfPWOOWxK0TFUmi3pbN9ACv/eZy6KvnGrltNOvQd7PczZ1cQrKAUADpvSOqHBNdmdwLprz8PLKy/CyysvwsCXzsMNyaW2PlKl+kcFmcuw3lB97tpj/p5FAnDmUTOM5W37va0AYcauf0lfcjerw6rLgnfzsKO53qghptKlx+xw66BugkpvT5fv2oOA9lK97p6NvhMd1xLpYxGzOSG5xuonMjXegliUMJYTZfu9+RGQrS9V1ecu2Z3A351WyP4uqwKU+lLxasdxhxeEsKntzjWINQ2gczhlOpMzpdsZGddSJzilWXht74hn2xsJOw1qThQ7Zu+pcNWHUvHqLyfO76xNQ1Ac+eiV6gYIpy9UGGlvKQg1/5E8wdc1FgD+8kIhKllqS+tVwLPzF7zpvSfjhuTSCeXjCwoW7kJCtaJlVbxe1Kqgojrdr+ofxKQSfTKG0pmSEh3XkmR3Av989lGmdfJBftkmooqIMHOSc3oAP+fT29NVJLxZBcwvXHS8admaCuUoRfu14oJjy3qp9PZ0FQXtqM1QTbSXn3GE42/4OWc13Y4syP32E+ei08bP89EX9tTtoFAOfn0o9wyXF7lYKey0GWq//dPzu2t639TIRy+tIhBOX6gwoqaO+c8HnkdvT5djnjiVsE3eJ4pTZG2lq8DUAhbuQkLGSGBcvYLXfswaqaE0Tv7y/bjKUjf24FjlqiWE4YW7+1Bh0Jw5qdXVd65vIOU6yPo5n2R3Ap9+W+FFEI9FsOrdJ5n2WX6sOShFTYUCAHsVLc6+kfI0OsnuBN6pnOecqW2YM6UguKoatH0uJkG/91C+7KXm7rTFMzHJpmRPNh/OdALVwiuNgxTAMzlR0XyDpWIXBatG/Yxl84Fpa7z6YNgH37DQN5DCnU9uMZZVK47fhOAqQU/eq0Eto4ArBQt3IWG1XqJr/Zb9VXuZF5L4Ot92gn0kp9v+ftap24J+4Wovs8L13TM87jhAyehSp/QppQwgx84taN6OmzsVF59krrmrZnsHiitU7FUETCfnXj8cpaemAYD3nLoAOw4WjquWCXPz9+rt6Sryz3Ni61DaOO6kttKz2DciTtVSenu60DeQMjmnVzLfYDmo2oxJbS1Fz0JQ2honHymgPgbfsGCXHkq9p8nuhK0Gz+npD8PkvRpUowpMNWHhLmD6BlI4+cv34/+t3mysq+bLPNmdwAV6qS3rbMwr3NuOt9skKHYLJ3dKdFxLVvUPFpk8nQYotyjX6R2xkgaQLfsKwsvWodEi4ckaqWVto5orzk2r5sV+RXj//iMvFOUsNH7DRTuY7E5gqZL82E3Mm9cZx4herijuksXeT1qeRkFOtA5TtKbnHT/HiAoNq7krTIK5k4+UnWsF40y5lR4uP2Nh3ZkqmwlOhRIgMuLQzjFdvsyr8YKaO00bXKd1tGDvsDbQd7RGizRHfvCbH2lSaxRffWc4ZtKlDFBug1ba5nqp6UHUNA59Ayl89d5njf22HxjFhd/+k+m7//qLAdPyeNaquRu3/VwqB0YLwp1bQmev31DNq05HkWkCbv/ba8Z3nPKb+UnL00gkuxPI5fP49K+eAgD8afPuwMre+aVSKXkqRZhT3dQLfu+p3bU+9YgZoc0j2eyw5i5AvCIOq/Uyn9fZDgCGYAdoJsES3CoMfrt+q6/9yhEcq0UpJZ7cBq3RbN6kTXEqEfaFvg245q4NRrFtya6DZj++/RZz+IbUkOnYf32p4PT8/M6Dju3y4kA6670TvE2/L+0a9jyGzJ/1xCta26+6bR0AOKblCYN2qlZo6YIK6VAOjmZrUvZuItSjYznjzkTuab2ZKpsJ1tzVEGvSVy/ftmq9zF/ZY592wkfwWREjLsKp6dhA1TSRpWKXfd/pZeaVqT81lEb39fdjaCSDCFFRwtd0JodfPP6aZyJYOx59cQ+AgtCoRrK+tHsE3dff75jw1Q2rEOnE3uFxnLnyQXy2xz4yd/uBUc9jDKUzJp/FXYfGXK9nGLRTtcKppFxbSwTxWNRX/6w1Ya/6wZQO39PGhIW7GuGn/JRKtV7mfQMp/OyxVyp+XD+EZeAu5WXmJ1O/1HA5bS9HsAOA4bEc+gZSjr+9bySDnz32qrEsNYVqu+14efch323YOjSKa+7agCde2VskRPopQRklshVgojaCMBAO7VStcHoeZNm7sA62bAptPPieNh4kyhx4Go1TTz1VPPHEE1U7/rKVq32Xzqmmf1op7ag0ic441qxYHshvTxSrcB52pnfEcO3FS2z70NGfv6/s4uCVxE471UwRjk7PYj0/JwzD1A4iWiuEONVuG2vuakQpWqtqjrtBac/CYlYqFylwXFVC8fIg2TeSQe8d641lVQsUBsEuoWujwqqdqgWluAcwDMOUAgt3NaKUgvXVjJQtpR0SmSIl0RnHyHi25BxriQYZuEstXh40mZzANXc9BaBgGg1D26UA0+ymIPZ1YhimWrBwVyPeeuxsk3+UF9XSsPX2dOHq29f5zmdnFcxKNU++vPKiMlsaTkq9fkHjFo0dBATgXac0t1Cn0uwCLsMw1aFhU6EQ0flENEhEm4loRZBt+ULfBtxagmAHVM+xPNmdwOVnLHRNOgtoZYbskoHaJbOc7pC6IdGAzvHJ7kTFBTsCsOyoGZ6l4RoBAeChTbuCbgbDMExD05CaOyKKAvgOgLcB2ALgb0R0jxDimVq3pW8ghVsfe7UkgaDafjc3JJcaySdTQ+miyhTxWBTXvWOJ4/et2gY7bV4j+w4lyjBtW5neEcPQSKYo0fF192wsqfxbpUh0xvHWY2fjoU27JnRunT5S/IQlapphGKZRaUjhDsBpADYLIV4EACK6DcAlAGou3NmVEnLDLcqxkqgCmlNVhVKOBTSP71AlTLMdrS0Y+NJ5pnXynvQNpHD1L9fZ5h2MEuH9py8w0pJUQotoF525aMW9JR+HAKy79jx0X3+/q19mM6U7YRiGCYJGFe4SAF5TlrcAOD2IhrhpKQhAp40Gp9ZUwu+nmXyHkt0JPPHK3pI1sipu/UJeRzttqDVVSCVS29i1pRztpBTarr14iaPwS0DDanQZhmHCQsP63PmBiD5GRE8Q0RO7dlXHD8hJS0EAbnrvyRj40nlcuqUOuSG5FDe992R0uhS7j5D2Z4eX9srOt9EuB5xd6aBSsWtLb0+Xq19mLGreqprhnfw6CcDlZyzkfs4wDFNlGlVzlwKwQFmer68zIYT4IYAfAloS42o0xC6XFQ9yjYFqRrX6yknzOmCvgfNbt9Grj1hN4tPiMQyPZ41yX144tcVJOyn7rlfBcNWvsxlM9QzDMGGiIStUEFELgOcAnANNqPsbgL8TQmx0+k41K1RM1KeNqW9qff/l76WG0kaZL5nSBijNN5L7LsMwTDhxq1DRkMIdABDRhQC+BSAK4EdCiK+67V/t8mMMwzAMwzCVoinLjwkh7gNwX9DtYBiGYRiGqSVNHVDBMAzDMAzTaLBwxzAMwzAM00CwcMcwDMMwDNNAsHDHMAzDMAzTQLBwxzAMwzAM00CwcMcwDMMwDNNAsHDHMAzDMAzTQLBwxzAMwzAM00CwcMcwDMMwDNNANGz5sVIhol0AXqnyz8wCsLvKvxFGmvW8JXz+zX3+AF8DPv/mPP9mPW9Jtc//CCHEbLsNLNzVECJ6wqkOXCPTrOct4fNv7vMH+Brw+Tfn+TfreUuCPH82yzIMwzAMwzQQLNwxDMMwDMM0ECzc1ZYfBt2AgGjW85bw+TPNfg34/JuTZj1vSWDnzz53DMMwDMMwDQRr7hiGYRiGYRoIFu5cIKIFRPQQET1DRBuJ6FP6+hlE9AARPa//n66vv5yIniKiDUT0FyI6ye04Dr95PhENEtFmIlqhrP+kvk4Q0awmOu+biWi9fvw7iGhyNc89hOf/YyJ6iYjW6X8nV/n0w3b+f1LOfSsR9VX59OXvhukaLCeiJ4noaSK6hYhaGvT8f0REO4noacv6d+vfzRNRTSIPK3j+7UT0V9LeYRuJ6Msuv3mlftzniehKZf1Xieg1IjrUZOf9e+X73yeiaJOd/8P6+0C+/w4r6WSEEPzn8AdgLoDX65+nAHgOwPEAvg5ghb5+BYCv6Z/PBDBd/3wBgMfdjmPze1EALwA4EkArgPVyPwDdABYBeBnArCY676nKft+Uv99E5/9jAJc1a7+37HcngCua6RpAm4C/BuB1+n7XA/hIo52/vv0sAK8H8LRl/XEAugA8DODUOrv/BGCy/jkG4HEAZ9j83gwAL+r/p+uf5fHO0NtzqMnOe6pyrDsBvK/Jzn9C/b3qD0kj/QG4G8DbAAwCmKt0hkGbfacDSLkdx2b9GwH0K8vXALjGss/LqLJwF9LzJgDfA/C5ZrrvCEC4C9P5K+umAtgHRdhvhmsAYDaAF5T1bwZwX6Odv7J9ESzCnbLtYdRIuKvG+QPoAPAkgNNttr0fwA+U5R8AeL9ln6oLdyE97xiA3wB4bzOd/0T7O5tlfUJEi6Bpzx4HMEcIsU3ftB3AHJuvfATA7zyOYyUBbZYu2aKvC4wwnDcR/Z/+e8cC+H+lnsNECMP5A/iqrvq/iYjaSj2HiRCS8weAJIAHhRAH/Le+MgR8DXYDaFHMkZcBWFDySUyAGp1/aJno+RNRlIjWAdgJ4AEhRFO8+ytx3kTUr3//IIA7yj2XcgjD+QP4P90k+0UiolLaz8KdD0jz87oTwFXWwUVoIraw7P9WaDf6c36PE0bCct5CiA8DmAfgWQDvLfX75RKS878GmlD7Bmiq+8+57145QnL+kvcD+EWZ3y2boK+B/hvvA3ATEf0V2iCXK+NUyiLo8w+aSpy/ECInhDgZwHwApxHRCdVu90QJy3kLIXqgacraACwv9fvlEpLzv1wIsRSatv7NAD5YypdZuPOAiGLQbvKtQoi79NU7iGiuvn0uNMlc7n8igP8FcIkQYo/bcXTnTeks+U8AUjDPyufr62pO2M5bCJEDcBuAd1X2TO0Jy/kLIbYJjTEA/wfgtOqcsZmwnL++/yxo531v5c/UmbBcAyHEo0KINwshTgPwR2h+QFWnxucfOip1/hIhxBCAhwCcT0SnK+f/DjTgu18y0fMWQoxCM49eUqFTdCUs5y+EkP8PAvg5Sn33l2vPbYY/aH5ePwHwLcv6VTA7V35d/7wQwGYAZ/o5js3vtUBzqFyMglP1Ess+L6P6ARWhOG/9+0crx/oGgG80031Hwc+DAHwLwMpmOn99+z8BuKXa5x3WawDgMP1/G4AHASxvtPNX9l+EEPjcVfD8ZwPo1D/HAfwJwNttfm8GgJeg+W1N1z/PsOxTi4CKUJw3gMkovPtaANwO4JNNdP4t0Md5aD6HdwD4p5LOpRYPSr3+AXgTNPXrUwDW6X8XApgJ7SX7PIA/yIcQmvS+T9n3CbfjOPzmhdBm5i8A+Hdl/b9Cs8dnAWwF8L+Nft7QNMtrAGwA8DSAW1EDh/qwnL++frVy/j+DHoHVLOevb3sYwPnVPu+wXgNoA8uz0Jy6r2rg8/8FgG0AMtDedR/R179TXx4DsANK4EkdnP+JAAb04zwN4Esuv/n30ASFzQA+rKz/un7+ef3/dY1+3tB82v6mfP//AWhplvsOYBKAtfr3NwL4LwDRUs6FK1QwDMMwDMM0EOxzxzAMwzAM00CwcMcwDMMwDNNAsHDHMAzDMAzTQLBwxzAMwzAM00CwcMcwDMMwDNNAsHDHMAxTIkSU0xORbiSi9UT0aSJyfZ8S0SIi+rtatZFhmOaFhTuGYZjSSQshThZCLIFWWPwCANd6fGcRABbuGIapOpznjmEYpkSI6JAQYrKyfCS0pKuzABwB4KfQEpECWmb9vxDRYwCOg5aF/hYA3wawEsDZ0KpPfEcI8YOanQTDMA0LC3cMwzAlYhXu9HVDALoAHASQF0KMEtExAH4hhDiViM4G8BkhxNv1/T8GrbTYDUTUBq0ay7uFEC/V8FQYhmlAWoJuAMMwTIMRA/DfRHQygByA1znsdx6AE4noMn15GoBjoGn2GIZhyoaFO4ZhmAmim2VzAHZC873bAeAkaH7No05fA/AvQoj+mjSSYZimgQMqGIZhJgARzQbwfQD/LTQ/l2kAtgkh8gA+CCCq73oQwBTlq/0A/pmIYvpxXkdEk8AwDDNBWHPHMAxTOnEiWgfNBJuFFkDxTX3bdwHcSURXAPg9gGF9/VMAckS0HsCPAfwXtAjaJ4mIAOwCkKxN8xmGaWQ4oIJhGIZhGKaBYLMswzAMwzBMA8HCHcMwDMMwTAPBwh3DMAzDMEwDwcIdwzAMwzBMA8HCHcMwDMMwTAPBwh3DMAzDMEwDwcIdwzAMwzBMA8HCHcMwDMMwTAPx/wFkwci/VeVw1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instagram reach\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create the line chart\n",
    "plt.plot(df['Date'], df['Instagram reach'], marker='o', markersize=6, linewidth=2)\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Date')\n",
    "\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Instagram reach')\n",
    "\n",
    "# Set the title\n",
    "plt.title('Instagram reach by day: Jan 2022- Apr 2023')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aac8c0e",
   "metadata": {},
   "source": [
    "**The reach trend exhibits a highly volatile pattern, with three notable instances of significant increases in reach observed around March/April 2022, August/September 2022, and December/January 2023.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "597d0162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instagram profile visits\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create the line chart\n",
    "plt.plot(df['Date'], df['Instagram profile visits'], marker='o', markersize=6, linewidth=2)\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Date')\n",
    "\n",
    "# Set the y-axis label\n",
    "plt.ylabel('Instagram profile visits')\n",
    "\n",
    "# Set the title\n",
    "plt.title('Instagram profile visits by day: Jan 2022- Apr 2023')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b39f115",
   "metadata": {},
   "source": [
    "**We can see similar trend in number of profile visits as well.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98d61b05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#New Instagram followers\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "df_followers = df[df['New Instagram followers']!=0]\n",
    "\n",
    "# Create the line chart\n",
    "plt.plot(df_followers['Date'], df_followers['New Instagram followers'], marker='o', markersize=6, linewidth=2)\n",
    "\n",
    "# Set the x-axis label\n",
    "plt.xlabel('Date')\n",
    "\n",
    "# Set the y-axis label\n",
    "plt.ylabel('New followers')\n",
    "\n",
    "# Set the title\n",
    "plt.title('New Instagram followers gained by day: Jan 2022- Apr 2023')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1659c249",
   "metadata": {},
   "source": [
    "**On a daily basis, the number of new followers gained hovers around 200. However, a remarkable surge in the count of new followers was witnessed during the period from December 2022 to January 2023.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4acbee95",
   "metadata": {},
   "source": [
    "\n",
    "Let's try to understand what exactly happenend during this period. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d086cd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Date:  2022-09-12 00:00:00\n",
      "Maximum Date:  2023-04-30 00:00:00\n"
     ]
    }
   ],
   "source": [
    "#We will begin by determining the start date for which the posts' insights are available.\n",
    "df_Title = df[df['Title']!=0]\n",
    "\n",
    "# Check the minimum and maximum date in the filtered dataframe\n",
    "min_date = df_Title['Date'].min()\n",
    "max_date = df_Title['Date'].max()\n",
    "\n",
    "print(\"Minimum Date: \", min_date)\n",
    "print(\"Maximum Date: \", max_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b7e73e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=2b1578cd-3145-47f1-a543-c32099c93584 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('2b1578cd-3145-47f1-a543-c32099c93584').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Instagram reach</th>\n",
       "      <th>Instagram profile visits</th>\n",
       "      <th>New Instagram followers</th>\n",
       "      <th>Title</th>\n",
       "      <th>Post type</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Follows</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Saves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2022-09-12</td>\n",
       "      <td>13732</td>\n",
       "      <td>563</td>\n",
       "      <td>76</td>\n",
       "      <td>how to reach out to people of linkedin</td>\n",
       "      <td>IG video</td>\n",
       "      <td>18762.0</td>\n",
       "      <td>17879.0</td>\n",
       "      <td>51</td>\n",
       "      <td>0.0</td>\n",
       "      <td>630</td>\n",
       "      <td>11</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2022-09-15</td>\n",
       "      <td>36445</td>\n",
       "      <td>832</td>\n",
       "      <td>153</td>\n",
       "      <td>time allocation to study</td>\n",
       "      <td>IG image</td>\n",
       "      <td>169062.0</td>\n",
       "      <td>164673.0</td>\n",
       "      <td>438</td>\n",
       "      <td>481.0</td>\n",
       "      <td>2450</td>\n",
       "      <td>46</td>\n",
       "      <td>3181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2022-09-17</td>\n",
       "      <td>24188</td>\n",
       "      <td>373</td>\n",
       "      <td>77</td>\n",
       "      <td>responding to dms</td>\n",
       "      <td>IG video</td>\n",
       "      <td>15674.0</td>\n",
       "      <td>14551.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>312</td>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>2022-09-20</td>\n",
       "      <td>47147</td>\n",
       "      <td>764</td>\n",
       "      <td>117</td>\n",
       "      <td>print hello world</td>\n",
       "      <td>IG video</td>\n",
       "      <td>571748.0</td>\n",
       "      <td>554639.0</td>\n",
       "      <td>5030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17797</td>\n",
       "      <td>224</td>\n",
       "      <td>13544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>2022-09-27</td>\n",
       "      <td>21301</td>\n",
       "      <td>711</td>\n",
       "      <td>128</td>\n",
       "      <td>data lemur problem solving</td>\n",
       "      <td>IG video</td>\n",
       "      <td>22260.0</td>\n",
       "      <td>19708.0</td>\n",
       "      <td>102</td>\n",
       "      <td>0.0</td>\n",
       "      <td>791</td>\n",
       "      <td>94</td>\n",
       "      <td>572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "          Date  Instagram reach  Instagram profile visits  \\\n",
       "254 2022-09-12            13732                       563   \n",
       "257 2022-09-15            36445                       832   \n",
       "259 2022-09-17            24188                       373   \n",
       "262 2022-09-20            47147                       764   \n",
       "269 2022-09-27            21301                       711   \n",
       "\n",
       "     New Instagram followers                                   Title  \\\n",
       "254                       76  how to reach out to people of linkedin   \n",
       "257                      153                time allocation to study   \n",
       "259                       77                       responding to dms   \n",
       "262                      117                       print hello world   \n",
       "269                      128              data lemur problem solving   \n",
       "\n",
       "    Post type  Impressions     Reach  Shares  Follows  Likes  Comments  Saves  \n",
       "254  IG video      18762.0   17879.0      51      0.0    630        11    244  \n",
       "257  IG image     169062.0  164673.0     438    481.0   2450        46   3181  \n",
       "259  IG video      15674.0   14551.0      21      0.0    312         8     25  \n",
       "262  IG video     571748.0  554639.0    5030      0.0  17797       224  13544  \n",
       "269  IG video      22260.0   19708.0     102      0.0    791        94    572  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Title.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7477f72",
   "metadata": {},
   "source": [
    "**Considering Dec 2022 and Jan 2023 data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e9e9abdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dec_jan = df_Title[(df_Title['Date']>=pd.Timestamp('2022-12-01')) & (df_Title['Date']<=pd.Timestamp('2023-01-31'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9bf4685e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=e701df3c-7258-4c30-9fd3-9dc72f75e778 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('e701df3c-7258-4c30-9fd3-9dc72f75e778').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Instagram reach</th>\n",
       "      <th>Instagram profile visits</th>\n",
       "      <th>New Instagram followers</th>\n",
       "      <th>Title</th>\n",
       "      <th>Post type</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Follows</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Saves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2022-12-02</td>\n",
       "      <td>30583</td>\n",
       "      <td>1024</td>\n",
       "      <td>504</td>\n",
       "      <td>How to find a good project idea?</td>\n",
       "      <td>IG video</td>\n",
       "      <td>14050.0</td>\n",
       "      <td>14600.0</td>\n",
       "      <td>51</td>\n",
       "      <td>504.0</td>\n",
       "      <td>657</td>\n",
       "      <td>0</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2022-12-03</td>\n",
       "      <td>32112</td>\n",
       "      <td>875</td>\n",
       "      <td>421</td>\n",
       "      <td>What is Data Analytics?</td>\n",
       "      <td>IG video</td>\n",
       "      <td>67050.0</td>\n",
       "      <td>67600.0</td>\n",
       "      <td>742</td>\n",
       "      <td>421.0</td>\n",
       "      <td>2800</td>\n",
       "      <td>38</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2022-12-04</td>\n",
       "      <td>30851</td>\n",
       "      <td>829</td>\n",
       "      <td>475</td>\n",
       "      <td>Why data is considered as valuable resource?</td>\n",
       "      <td>IG video</td>\n",
       "      <td>10450.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>26</td>\n",
       "      <td>475.0</td>\n",
       "      <td>454</td>\n",
       "      <td>3</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2022-12-05</td>\n",
       "      <td>39611</td>\n",
       "      <td>1069</td>\n",
       "      <td>823</td>\n",
       "      <td>Coding Blocks</td>\n",
       "      <td>IG video</td>\n",
       "      <td>20650.0</td>\n",
       "      <td>21200.0</td>\n",
       "      <td>271</td>\n",
       "      <td>823.0</td>\n",
       "      <td>720</td>\n",
       "      <td>7</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2022-12-06</td>\n",
       "      <td>62370</td>\n",
       "      <td>1399</td>\n",
       "      <td>1418</td>\n",
       "      <td>Is MEDIAN better than MEAN?</td>\n",
       "      <td>IG video</td>\n",
       "      <td>16650.0</td>\n",
       "      <td>17200.0</td>\n",
       "      <td>50</td>\n",
       "      <td>1418.0</td>\n",
       "      <td>893</td>\n",
       "      <td>16</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "          Date  Instagram reach  Instagram profile visits  \\\n",
       "335 2022-12-02            30583                      1024   \n",
       "336 2022-12-03            32112                       875   \n",
       "337 2022-12-04            30851                       829   \n",
       "338 2022-12-05            39611                      1069   \n",
       "339 2022-12-06            62370                      1399   \n",
       "\n",
       "     New Instagram followers                                         Title  \\\n",
       "335                      504              How to find a good project idea?   \n",
       "336                      421                       What is Data Analytics?   \n",
       "337                      475  Why data is considered as valuable resource?   \n",
       "338                      823                                 Coding Blocks   \n",
       "339                     1418                   Is MEDIAN better than MEAN?   \n",
       "\n",
       "    Post type  Impressions    Reach  Shares  Follows  Likes  Comments  Saves  \n",
       "335  IG video      14050.0  14600.0      51    504.0    657         0    279  \n",
       "336  IG video      67050.0  67600.0     742    421.0   2800        38    335  \n",
       "337  IG video      10450.0  11000.0      26    475.0    454         3    388  \n",
       "338  IG video      20650.0  21200.0     271    823.0    720         7    334  \n",
       "339  IG video      16650.0  17200.0      50   1418.0    893        16    578  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dec_jan.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e0a569e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=7a496f1b-900c-421c-b91f-e25dfdec0158 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('7a496f1b-900c-421c-b91f-e25dfdec0158').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comments</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Saves</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post type</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>IG video</th>\n",
       "      <td>34.0</td>\n",
       "      <td>67288.0</td>\n",
       "      <td>2563.0</td>\n",
       "      <td>65636.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "           Comments  Impressions   Likes    Reach  Saves  Shares  Title\n",
       "Post type                                                              \n",
       "IG video       34.0      67288.0  2563.0  65636.0  697.0  1182.0     39"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define aggregation functions\n",
    "agg_funcs = {'Title': 'count', 'Impressions': 'mean', 'Reach': 'mean', 'Shares': 'mean', 'Likes': 'mean', 'Comments': 'mean', 'Saves': 'mean'}  # Example: 'sum', 'mean', 'median', 'min', 'max', etc.\n",
    "\n",
    "df_dec_jan_pivot = round(pd.pivot_table(df_dec_jan, index = 'Post type', values = ['Title', 'Impressions', 'Reach', 'Shares', 'Likes', 'Comments', 'Saves'], aggfunc=agg_funcs))\n",
    "df_dec_jan_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a7e15c5",
   "metadata": {},
   "source": [
    "**Comments**: On average, the IG videos received 34 comments per video. This metric indicates the average level of engagement and interaction with the videos in terms of comments.\n",
    "\n",
    "**Impressions**: On average, the IG videos garnered 67,288 impressions per video. Impressions refer to the average number of times the videos were viewed by users, whether or not they engaged with the content. This metric provides an indication of the average overall reach and visibility of the videos.\n",
    "\n",
    "**Likes**: On average, the IG videos received 2,563 likes per video. Likes are a common engagement metric on Instagram, reflecting the average number of users who showed appreciation for the videos by liking them.\n",
    "\n",
    "**Reach**: On average, the IG videos reached 65,636 users per video. Reach represents the average unique number of users who viewed the videos. It provides insights into the average audience reached by the videos, indicating the potential impact and exposure of the content.\n",
    "\n",
    "**Saves**: On average, the IG videos were saved by 697 users per video. Saves represent the average number of times users saved the videos to their collections for later viewing. This metric reflects the average level of interest and value users found in the videos.\n",
    "\n",
    "**Shares**: On average, the IG videos were shared by 1,182 users per video. Shares represent the average number of times users \n",
    "shared the videos with their own followers, extending the reach and potential exposure of the content."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5516def2",
   "metadata": {},
   "source": [
    "**Consistent upload of videos have performed good for Data Analyst Duo**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02b370e",
   "metadata": {},
   "source": [
    "**Let us look at that time of the data where Data Analyst Duo have released different types of post.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48bdba34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=207cb8f7-2c79-499f-b206-6fcbf8272f03 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('207cb8f7-2c79-499f-b206-6fcbf8272f03').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comments</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Saves</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post type</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>IG carousel</th>\n",
       "      <td>41.0</td>\n",
       "      <td>40784.0</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>31371.0</td>\n",
       "      <td>1654.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IG image</th>\n",
       "      <td>44.0</td>\n",
       "      <td>61590.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>58822.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IG video</th>\n",
       "      <td>34.0</td>\n",
       "      <td>58629.0</td>\n",
       "      <td>2149.0</td>\n",
       "      <td>55924.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "             Comments  Impressions   Likes    Reach   Saves  Shares  Title\n",
       "Post type                                                                 \n",
       "IG carousel      41.0      40784.0  1484.0  31371.0  1654.0   214.0     14\n",
       "IG image         44.0      61590.0  1356.0  58822.0   864.0   124.0      4\n",
       "IG video         34.0      58629.0  2149.0  55924.0   851.0   787.0     71"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define aggregation functions\n",
    "agg_funcs = {'Title': 'count', 'Impressions': 'mean', 'Reach': 'mean', 'Shares': 'mean', 'Likes': 'mean', 'Comments': 'mean', 'Saves': 'mean'}  # Example: 'sum', 'mean', 'median', 'min', 'max', etc.\n",
    "\n",
    "# Create pivot table\n",
    "df_Title_pivot = round(pd.pivot_table(df_Title, index = 'Post type', values = ['Title', 'Impressions', 'Reach', 'Shares', 'Likes', 'Comments', 'Saves'], aggfunc=agg_funcs))\n",
    "df_Title_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d931712",
   "metadata": {},
   "source": [
    "Based on the given pivot table, the following observations and insights can be inferred:\n",
    "\n",
    "The 'IG image' post type has the highest average values for most of the metrics including 'Comments', 'Impressions', 'Likes', 'Reach', 'Saves', and 'Shares', compared to 'IG carousel' and 'IG video' post types. This suggests that 'IG image' posts may be performing better in terms of engagement on average compared to the other two types of posts. But the sample of 'IG image' is 4 compared to 'IG carousel' 14 and 'IG video' 71. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fae73c8",
   "metadata": {},
   "source": [
    "Let us look at the 'IG image' data once before we make any conclusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10534f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=f4ce1b52-fbf0-4aec-9791-eaddcd4a9de4 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('f4ce1b52-fbf0-4aec-9791-eaddcd4a9de4').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Instagram reach</th>\n",
       "      <th>Instagram profile visits</th>\n",
       "      <th>New Instagram followers</th>\n",
       "      <th>Title</th>\n",
       "      <th>Post type</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Follows</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Saves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2022-09-15</td>\n",
       "      <td>36445</td>\n",
       "      <td>832</td>\n",
       "      <td>153</td>\n",
       "      <td>time allocation to study</td>\n",
       "      <td>IG image</td>\n",
       "      <td>169062.0</td>\n",
       "      <td>164673.0</td>\n",
       "      <td>438</td>\n",
       "      <td>481.0</td>\n",
       "      <td>2450</td>\n",
       "      <td>46</td>\n",
       "      <td>3181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>2022-10-02</td>\n",
       "      <td>29154</td>\n",
       "      <td>914</td>\n",
       "      <td>347</td>\n",
       "      <td>your favourite data couple</td>\n",
       "      <td>IG image</td>\n",
       "      <td>20346.0</td>\n",
       "      <td>19026.0</td>\n",
       "      <td>6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>817</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>19409</td>\n",
       "      <td>637</td>\n",
       "      <td>117</td>\n",
       "      <td>Introduction post</td>\n",
       "      <td>IG image</td>\n",
       "      <td>22446.0</td>\n",
       "      <td>20500.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1566</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>20627</td>\n",
       "      <td>695</td>\n",
       "      <td>160</td>\n",
       "      <td>Statistics for data analysis workshop</td>\n",
       "      <td>IG image</td>\n",
       "      <td>34504.0</td>\n",
       "      <td>31088.0</td>\n",
       "      <td>41</td>\n",
       "      <td>7.0</td>\n",
       "      <td>592</td>\n",
       "      <td>88</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "          Date  Instagram reach  Instagram profile visits  \\\n",
       "257 2022-09-15            36445                       832   \n",
       "274 2022-10-02            29154                       914   \n",
       "450 2023-03-27            19409                       637   \n",
       "463 2023-04-09            20627                       695   \n",
       "\n",
       "     New Instagram followers                                  Title Post type  \\\n",
       "257                      153               time allocation to study  IG image   \n",
       "274                      347             your favourite data couple  IG image   \n",
       "450                      117                      Introduction post  IG image   \n",
       "463                      160  Statistics for data analysis workshop  IG image   \n",
       "\n",
       "     Impressions     Reach  Shares  Follows  Likes  Comments  Saves  \n",
       "257     169062.0  164673.0     438    481.0   2450        46   3181  \n",
       "274      20346.0   19026.0       6     24.0    817         8     12  \n",
       "450      22446.0   20500.0      12      4.0   1566        34     47  \n",
       "463      34504.0   31088.0      41      7.0    592        88    216  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Title[(df_Title['Post type']=='IG image')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "050eee55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=000c8dfa-792b-4791-b60e-136d957b691e style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('000c8dfa-792b-4791-b60e-136d957b691e').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Instagram reach</th>\n",
       "      <th>Instagram profile visits</th>\n",
       "      <th>New Instagram followers</th>\n",
       "      <th>Title</th>\n",
       "      <th>Post type</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Follows</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Saves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>2022-10-02</td>\n",
       "      <td>29154</td>\n",
       "      <td>914</td>\n",
       "      <td>347</td>\n",
       "      <td>your favourite data couple</td>\n",
       "      <td>IG image</td>\n",
       "      <td>20346.0</td>\n",
       "      <td>19026.0</td>\n",
       "      <td>6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>817</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>19409</td>\n",
       "      <td>637</td>\n",
       "      <td>117</td>\n",
       "      <td>Introduction post</td>\n",
       "      <td>IG image</td>\n",
       "      <td>22446.0</td>\n",
       "      <td>20500.0</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1566</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>463</th>\n",
       "      <td>2023-04-09</td>\n",
       "      <td>20627</td>\n",
       "      <td>695</td>\n",
       "      <td>160</td>\n",
       "      <td>Statistics for data analysis workshop</td>\n",
       "      <td>IG image</td>\n",
       "      <td>34504.0</td>\n",
       "      <td>31088.0</td>\n",
       "      <td>41</td>\n",
       "      <td>7.0</td>\n",
       "      <td>592</td>\n",
       "      <td>88</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "          Date  Instagram reach  Instagram profile visits  \\\n",
       "274 2022-10-02            29154                       914   \n",
       "450 2023-03-27            19409                       637   \n",
       "463 2023-04-09            20627                       695   \n",
       "\n",
       "     New Instagram followers                                  Title Post type  \\\n",
       "274                      347             your favourite data couple  IG image   \n",
       "450                      117                      Introduction post  IG image   \n",
       "463                      160  Statistics for data analysis workshop  IG image   \n",
       "\n",
       "     Impressions    Reach  Shares  Follows  Likes  Comments  Saves  \n",
       "274      20346.0  19026.0       6     24.0    817         8     12  \n",
       "450      22446.0  20500.0      12      4.0   1566        34     47  \n",
       "463      34504.0  31088.0      41      7.0    592        88    216  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_IG_image = df_Title[(df_Title['Post type']=='IG image') & (df_Title['Title']!= 'time allocation to study')]\n",
    "df_IG_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99636af2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=4b3bad8a-c5bd-4b6f-b384-1ad148519415 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('4b3bad8a-c5bd-4b6f-b384-1ad148519415').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comments</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Saves</th>\n",
       "      <th>Shares</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post type</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>IG image</th>\n",
       "      <td>43.0</td>\n",
       "      <td>25765.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>23538.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "           Comments  Impressions  Likes    Reach  Saves  Shares\n",
       "Post type                                                      \n",
       "IG image       43.0      25765.0  992.0  23538.0   92.0    20.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_IG_image = round(pd.pivot_table(df_IG_image, index = 'Post type', values = ['Impressions', 'Reach', 'Shares', 'Likes', 'Comments', 'Saves'], aggfunc=np.mean))\n",
    "df_IG_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66814f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=84e06579-9803-438b-bca8-dbc2ff7b9853 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands?.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('84e06579-9803-438b-bca8-dbc2ff7b9853').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Comments</th>\n",
       "      <th>Impressions</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Reach</th>\n",
       "      <th>Saves</th>\n",
       "      <th>Shares</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Post type</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>IG carousel</th>\n",
       "      <td>41.0</td>\n",
       "      <td>40784.0</td>\n",
       "      <td>1484.0</td>\n",
       "      <td>31371.0</td>\n",
       "      <td>1654.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IG image</th>\n",
       "      <td>43.0</td>\n",
       "      <td>25765.0</td>\n",
       "      <td>992.0</td>\n",
       "      <td>23538.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IG video</th>\n",
       "      <td>34.0</td>\n",
       "      <td>58629.0</td>\n",
       "      <td>2149.0</td>\n",
       "      <td>55924.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "             Comments  Impressions   Likes    Reach   Saves  Shares  Title\n",
       "Post type                                                                 \n",
       "IG carousel      41.0      40784.0  1484.0  31371.0  1654.0   214.0     14\n",
       "IG image         43.0      25765.0   992.0  23538.0    92.0    20.0      3\n",
       "IG video         34.0      58629.0  2149.0  55924.0   851.0   787.0     71"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us exclude the post with title 'time allocation to study'\n",
    "\n",
    "df_Title = df_Title[(df_Title['Title']!= 'time allocation to study')]\n",
    "\n",
    "# Create pivot table\n",
    "df_Title_pivot = round(pd.pivot_table(df_Title, index = 'Post type', values = ['Title', 'Impressions', 'Reach', 'Shares', 'Likes', 'Comments', 'Saves'], aggfunc=agg_funcs))\n",
    "df_Title_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274ae82f",
   "metadata": {},
   "source": [
    "### Conclusion:\n",
    "\n",
    "Based on the observations, it appears that 'IG video' posts are performing better in terms of engagement compared to 'IG image' and 'IG carousel' posts for Data Analyst Duo. The comparative analysis showed that 'IG video' posts have the highest average values for most of the metrics, including 'Comments', 'Impressions', 'Likes', 'Reach', 'Saves', and 'Shares'. This suggests that 'IG videos' posts may be more effective in increasing engagement for Data Analyst Duo. They can leverage the higher performance of 'IG video' posts to create engaging content that resonates with their audience and consistently monitor the performance of their posts to further optimize their content strategy and continue to engage their followers effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0660f189",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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